FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Dong, BB
Zhuang, MZ
Fang, E
Huang, MX
AF Dong, Beibei
Zhuang, Mengzhou
Fang, Eric (Er)
Huang, Minxue
TI Tales of Two Channels: Digital Advertising Performance Between AI
Recommendation and User Subscription Channels
SO JOURNAL OF MARKETING
LA English
DT Article
DE in-feed advertising; digital advertising; native advertising;
subscription; artificial intelligence; recommendation; click-through
rate; conversion rate
ID ONLINE; CONSUMERS; ADVERTISEMENTS; POSITION; SALES; MEDIA; MODEL; ME
AB Although in-feed advertising is popular on mainstream platforms, academic research on it is limited. Platforms typically deliver organic content through two methods: subscription by users or recommendation by artificial intelligence. However, little is known about the ad performance between these two channels. This research examines how the performance of in-feed ads, in terms of click-through rates and conversion rates, differs between subscription and recommendation channels and whether these effects are mediated by ad intrusiveness and moderated by ad attributes. Two ad attributes are investigated: ad appeal (informational vs. emotional) and ad link (direct vs. indirect). Study 1 finds that the recommendation channel generates higher click-through rates but lower conversion rates than the subscription channel, and these effects are amplified by informational ad appeal and direct ad links. Study 2 explores channel differences, revealing that the recommendation channel yields less source credibility and content control, reducing consumer engagement with organic content. Studies 3 and 4 validate the mediating role of ad intrusiveness and rule out ad recognition as an alternative explanation. Study 5 uses eye-tracking technology to show that the recommendation channel has lower content engagement, lower ad intrusiveness, and greater ad interest.
C1 [Dong, Beibei] Lehigh Univ, Coll Business, Mkt, Bethlehem, PA 18015 USA.
[Zhuang, Mengzhou] Univ Hong Kong, Fac Business & Econ, Mkt, Hong Kong, Peoples R China.
[Fang, Eric (Er)] Lehigh Univ, Coll Business, Iacocca Chair Business, Mkt, Bethlehem, PA USA.
[Fang, Eric (Er)] Lehigh Univ, Coll Business, Ctr Digital Mkt Strategy & Analyt, Bethlehem, PA USA.
[Huang, Minxue] Wuhan Univ, Econ & Management Sch, Mkt, Wuhan, Peoples R China.
C3 Lehigh University; University of Hong Kong; Lehigh University; Lehigh
University; Wuhan University
RP Dong, BB (autor correspondiente), Lehigh Univ, Coll Business, Mkt, Bethlehem, PA 18015 USA.
EM bdong@lehigh.edu; mzhuang@hku.hk; erf219@lehigh.edu;
huangminxue@whu.edu.cn
RI Zhuang, Mengzhou/AFD-9802-2022
OI Zhuang, Mengzhou/0000-0001-9970-4443
FU The authors thank the JM review team for their valuable
guidance in improving the article. Special thanks are extended to Lehigh
University colleagues Daniel Zane and Deirdre Malacrea for their
comments and assistance in shaping the initia
FX The authors thank the JM review team for their valuable
guidance in improving the article. Special thanks are extended to Lehigh
University colleagues Daniel Zane and Deirdre Malacrea for their
comments and assistance in shaping the initial draft of the manuscript.
CR Algesheimer R, 2005, J MARKETING, V69, P19, DOI 10.1509/jmkg.69.3.19.66363
ANA, 2021, MED KPIS MATT
Aribarg A, 2020, J MARKETING RES, V57, P20, DOI 10.1177/0022243719879711
Bass FM, 2007, MARKET SCI, V26, P179, DOI 10.1287/mksc.1060.0208
Becker M, 2023, J MARKETING, V87, P275, DOI 10.1177/00222429221105818
Ben Dickson, 2018, TECHTALKS
Bruce NI, 2017, J MARKETING RES, V54, P202, DOI 10.1509/jmr.14.0117
Campbell C, 2018, J INTERACT MARK, V43, P17, DOI 10.1016/j.intmar.2018.02.002
Campbell C, 2015, BUS HORIZONS, V58, P599, DOI 10.1016/j.bushor.2015.06.003
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Chandy RK, 2001, J MARKETING RES, V38, P399, DOI 10.1509/jmkr.38.4.399.18908
Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906
Chiou L, 2017, J ECON MANAGE STRAT, V26, P782, DOI 10.1111/jems.12207
Cho CH, 2004, J ADVERTISING, V33, P89, DOI 10.1080/00913367.2004.10639175
Cian L, 2014, J MARKETING RES, V51, P184, DOI 10.1509/jmr.13.0023
CodeFuel, 2021, WHAT AR IN FEED SOC
Colicev A, 2018, J MARKETING, V82, P37, DOI 10.1509/jm.16.0055
Dahl DW, 2007, J MARKETING RES, V44, P357, DOI 10.1509/jmkr.44.3.357
Danaher PJ, 2013, J MARKETING RES, V50, P517, DOI 10.1509/jmr.12.0241
Danaher PJ, 2003, J ADVERTISING RES, V43, P252, DOI 10.1017/S0021849903030319
Dinner IM, 2014, J MARKETING RES, V51, P527, DOI 10.1509/jmr.11.0466
eMarketer, 2018, NAT AD SPEND WILL MA
Ghose A, 2009, MANAGE SCI, V55, P1605, DOI 10.1287/mnsc.1090.1054
Goldfarb A, 2011, MARKET SCI, V30, P389, DOI 10.1287/mksc.1100.0583
GOODWIN S, 1980, J MARKETING RES, V17, P187, DOI 10.2307/3150929
Gordon BR, 2021, J MARKETING, V85, P7, DOI 10.1177/0022242920913236
Guitart IA, 2021, J MARKETING RES, V58, P299, DOI 10.1177/0022243720962505
Ha L, 1996, J ADVERTISING RES, V36, P76
Hayes AF., 2018, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, DOI DOI 10.1111/JEDM.12050
Hoffman DL, 2022, J MARKETING, V86, P1, DOI 10.1177/00222429211061636
Karmarkar UR, 2010, J CONSUM RES, V36, P1033, DOI 10.1086/648381
Lee D, 2018, MANAGE SCI, V64, P5105, DOI 10.1287/mnsc.2017.2902
Li HR, 2002, J ADVERTISING, V31, P37, DOI 10.1080/00913367.2002.10673665
Liu-Thompkins Y, 2019, J ADVERTISING, V48, P1, DOI 10.1080/00913367.2018.1556138
Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
MacInnis DJ, 2002, J MARKETING RES, V39, P391, DOI 10.1509/jmkr.39.4.391.19118
Mathwick C, 2004, J CONSUM RES, V31, P324, DOI 10.1086/422111
MILLAR MG, 1990, J PERS SOC PSYCHOL, V59, P217, DOI 10.1037/0022-3514.59.2.217
Moe WW, 2003, J CONSUM PSYCHOL, V13, P29, DOI 10.1207/153276603768344762
Muk Alexander., 2013, Journal of Marketing Analytics, V1, P127, DOI DOI 10.1057/JMA.2013.12
Petty R., 1986, Communication and persuasion: Central and peripheral routes to attitude change, DOI [DOI 10.1016/S0065-2601(08)60214-2, 10.1016/S0065-2601(08)60214-2]
PETTY RE, 1983, J CONSUM RES, V10, P135, DOI 10.1086/208954
Pieters R, 2007, J CONSUM RES, V34, P224, DOI 10.1086/519150
Pieters R, 2010, J MARKETING, V74, P48, DOI 10.1509/jmkg.74.5.48
Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
Rajavi K, 2019, J CONSUM RES, V46, P651, DOI 10.1093/jcr/ucz026
Rutz OJ, 2011, MARKET SCI, V30, P646, DOI 10.1287/mksc.1110.0635
Sahni NS, 2020, MARKET SCI, V39, P5, DOI 10.1287/mksc.2018.1125
Sahni NS, 2018, MARKET SCI, V37, P236, DOI 10.1287/mksc.2017.1066
SCHUMANN DW, 1990, J CONSUM RES, V17, P192, DOI 10.1086/208549
Seiler S, 2017, MARKET SCI, V36, P838, DOI 10.1287/mksc.2017.1045
TechXplore, 2018, GOOGL LAUNCH AI DRIV
Teixeira T, 2012, J MARKETING RES, V49, P144, DOI 10.1509/jmr.10.0207
Trendel O, 2018, J MARKETING RES, V55, P265, DOI 10.1509/jmr.14.0305
Trope Y., 2012, Handbook of Theories of Social Psychology, P118, DOI DOI 10.4135/9781446249215.N7
Vespe F, 1997, J PUBLIC POLICY MARK, V16, P176, DOI 10.1177/074391569701600121
Wang PY, 2019, J MARKETING, V83, P82, DOI 10.1177/0022242918817549
Wang QL, 2019, J ACAD MARKET SCI, V47, P1005, DOI 10.1007/s11747-019-00678-2
Wojdynski B.W., 2016, NATIVE ADVERTISING F
Wojdynski BW, 2016, J ADVERTISING, V45, P157, DOI 10.1080/00913367.2015.1115380
Xiang DD, 2019, J ACAD MARKET SCI, V47, P1046, DOI 10.1007/s11747-019-00638-w
Xu LZ, 2014, MANAGE SCI, V60, P1392, DOI 10.1287/mnsc.2014.1952
Zhuang MZ, 2021, INFORM SYST RES, V32, P1412, DOI 10.1287/isre.2021.1039
NR 63
TC 1
Z9 1
U1 214
U2 214
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2429
EI 1547-7185
J9 J MARKETING
JI J. Mark.
PD MAR
PY 2024
VL 88
IS 2
BP 141
EP 162
DI 10.1177/00222429231190021
EA SEP 2023
PG 22
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HB2B3
UT WOS:001093043000001
DA 2024-03-27
ER
PT J
AU Baek, TH
AF Baek, Tae Hyun
TI Digital Advertising in the Age of Generative AI
SO JOURNAL OF CURRENT ISSUES AND RESEARCH IN ADVERTISING
LA English
DT Article
ID IMPACT
AB Artificial intelligence (AI) is significantly reshaping branded content delivery and consumer engagement in the advertising industry. Generative AI, exemplified by ChatGPT, is anticipated to have a substantial impact on all digital advertising domains worldwide. This special issue delves into the exploration of future trends in global digital advertising in the era of generative AI. The research articles within this special issue encompass a diverse array of topics, ranging from consumer responses to AI-generated virtual influencers in the metaverse and livestreaming e-commerce to the influence of anthropomorphic virtual agents, privacy concerns in online behavioral advertising, understanding of AI-driven ethnic affinity targeting, and the role of relational bonds within online gaming communities.
C1 [Baek, Tae Hyun] Sungkyunkwan Univ, Dept Media & Commun, Seoul, South Korea.
C3 Sungkyunkwan University (SKKU)
RP Baek, TH (autor correspondiente), Sungkyunkwan Univ, Dept Media & Commun, Seoul, South Korea.
EM tbaek@skku.edu
RI Baek, Tae Hyun/JGD-1912-2023
CR Aalberts R.J., 2016, Journal of Current Issues Research in Advertising, V37, DOI DOI 10.1080/10641734.2016.1171177
Baek TH, 2023, J BUS RES, V164, DOI 10.1016/j.jbusres.2023.114039
Baek TH, 2022, INT J ADVERT, V41, P850, DOI 10.1080/02650487.2021.2011654
Baek TH, 2018, INT J ADVERT, V37, P421, DOI 10.1080/02650487.2016.1244887
Bakpayev M, 2022, AUSTRALAS MARK J, V30, P90, DOI 10.1016/j.ausmj.2020.04.002
Feng Y, 2019, J CURR ISS RES AD, V40, P36, DOI 10.1080/10641734.2018.1500321
Haikel-Elsabeh M, 2023, J CURR ISS RES AD, V44, P252, DOI 10.1080/10641734.2023.2218420
Kim M, 2023, J CURR ISS RES AD, V44, P274, DOI 10.1080/10641734.2023.2191117
Rodgers S, 2021, J ADVERTISING, V50, P1, DOI 10.1080/00913367.2020.1868233
Saad SB, 2023, J CURR ISS RES AD, V44, P295, DOI 10.1080/10641734.2023.2218432
Statista, 2023, Digital advertising spending worldwide from 2021 to 2026
Wan A, 2023, J CURR ISS RES AD, V44, P332, DOI 10.1080/10641734.2023.2224416
Yun J. T., 2023, J CURRENT ISSUES RES, V44, P24, DOI https://doi.org/10.1080/10641734.2022.2090464
Zhang D, 2023, J CURR ISS RES AD, V44, P392, DOI 10.1080/10641734.2023.2199812
Zhang WL, 2023, J CURR ISS RES AD, V44, P373, DOI 10.1080/10641734.2023.2212022
NR 15
TC 2
Z9 2
U1 155
U2 170
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1064-1734
EI 2164-7313
J9 J CURR ISS RES AD
JI J. Curr. Issues Res. Advert.
PD JUL 3
PY 2023
VL 44
IS 3
SI SI
BP 249
EP 251
DI 10.1080/10641734.2023.2243496
PG 3
WC Business; Communication
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics; Communication
GA Q0SG2
UT WOS:001054694000001
DA 2024-03-27
ER
PT J
AU Lambrecht, A
Tucker, C
AF Lambrecht, Anja
Tucker, Catherine
TI Algorithmic Bias? An Empirical Study of Apparent Gender-Based
Discrimination in the Display of STEM Career Ads
SO MANAGEMENT SCIENCE
LA English
DT Article
DE algorithmic bias; online advertising; algorithms; artificial
intelligence
ID FEMALE; WOMEN; TECHNOLOGY; MODELS
AB We explore data from a field test of how an algorithm delivered ads promoting job opportunities in the science, technology, engineering and math fields. This ad was explicitly intended to be gender neutral in its delivery. Empirically, however, fewer women saw the ad than men. This happened because younger women are a prized demographic and are more expensive to show ads to. An algorithm that simply optimizes cost-effectiveness in ad delivery will deliver ads that were intended to be gender neutral in an apparently discriminatory way, because of crowding out. We show that this empirical regularity extends to other major digital platforms.
C1 [Lambrecht, Anja] London Business Sch, Mkt, London NW1 4SA, England.
[Tucker, Catherine] MIT, Mkt, Cambridge, MA 02142 USA.
C3 University of London; London Business School; Massachusetts Institute of
Technology (MIT)
RP Lambrecht, A (autor correspondiente), London Business Sch, Mkt, London NW1 4SA, England.
EM alambrecht@london.edu; cetucker@mit.edu
RI Lambrecht, Anja/C-6300-2014; Tucker, Catherine E/H-4878-2011
OI /0000-0002-1847-4832
FU National Science Foundation Career Award [6923256]
FX Supported by a National Science Foundation Career Award [Grant 6923256].
CR Altonji J. G., 2015, 21655 NBER
[Anonymous], WORKING PAPER
[Anonymous], 2017, 23180 NBER
[Anonymous], 2016, 22776 NBER
Arrow K., 1973, Discrimination in Labour Markers
Athey S, 2010, AM ECON REV, V100, P608, DOI 10.1257/aer.100.2.608
Becker G.S., 2010, EC DISCRIMINATION
BECKER GS, 1993, J POLIT ECON, V101, P385, DOI 10.1086/261880
Bertrand M., 2017, Handbook of economic field experiments, P309, DOI 10.1016/bs.hefe.2016.08.004
Brown C, 1997, J LABOR ECON, V15, P431, DOI 10.1086/209867
Cheryan S, 2011, SOC PSYCHOL PERS SCI, V2, P656, DOI 10.1177/1948550611405218
Cowgill B, 2017, WORKING PAPER
Datta Amit, 2015, Proceedings on Privacy Enhancing Technologies, V1, P92, DOI 10.1515/popets-2015-0007
Diekman AB, 2010, PSYCHOL SCI, V21, P1051, DOI 10.1177/0956797610377342
Edelman B.G., 2014, DIGITAL DISCRIMINATI
Edelman B, 2017, AM ECON J-APPL ECON, V9, P1, DOI 10.1257/app.20160213
Facebook Business, 2015, SHOW REL SCOR ADS FA
FLATH D, 1979, J MARKETING RES, V16, P533, DOI 10.2307/3150813
Ghosh A, 2009, LECT NOTES COMPUT SC, V5929, P208, DOI 10.1007/978-3-642-10841-9_20
Goldfarb A, 2011, MANAGE SCI, V57, P458, DOI 10.1287/mnsc.1100.1287
Goldin C, 2014, AM ECON REV, V104, P1091, DOI 10.1257/aer.104.4.1091
GUNDERSON M, 1989, J ECON LIT, V27, P46
Lambrecht A, 2018, MARKET SCI, V37, P177, DOI 10.1287/mksc.2017.1062
Morton FS, 2003, QME-QUANT MARK ECON, V1, P65, DOI 10.1023/A:1023529910567
Mullainathan S, 2017, J ECON PERSPECT, V31, P87, DOI 10.1257/jep.31.2.87
Oaxaca R., 1973, INT ECON REV, V14, P693, DOI [DOI 10.2307/2525981, 10.2307/2525981]
ONeil Cathy, 2016, WEAPONS MATH DESTRUC
Pope DG, 2011, J HUM RESOUR, V46, P53, DOI 10.1353/jhr.2011.0025
Shapiro JR, 2012, SEX ROLES, V66, P175, DOI 10.1007/s11199-011-0051-0
Shaw M, 2012, OMI SOCIAL MEDIA SUM
Silverstein MJ, 2009, HARVARD BUS REV, V87, P46
Sweeney L., 2013, DISCRIMINATION ONLIN, V11, P10, DOI [DOI 10.1145/2447976.2447990, DOI 10.1145/2460276.2460278]
Swire P, 2014, CISC VIS NETW IND GL
Tucker C, 2014, SOCIAL ADVERTI UNPUB
Tucker CE, 2014, J MARKETING RES, V51, P546, DOI 10.1509/jmr.10.0355
Vermeren I., 2015, BRANDWATCH 0128
Williams WM, 2015, P NATL ACAD SCI USA, V112, P5360, DOI 10.1073/pnas.1418878112
NR 37
TC 233
Z9 266
U1 59
U2 345
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD JUL
PY 2019
VL 65
IS 7
BP 2966
EP 2981
DI 10.1287/mnsc.2018.3093
PG 16
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA IJ2CB
UT WOS:000475704700002
OA Green Submitted, Green Accepted
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Paskaramoorthy, AB
Gebbie, TJ
van Zyl, TL
AF Paskaramoorthy, Andrew B.
Gebbie, Tim J.
van Zyl, Terence L.
TI A framework for online investment decisions
SO INVESTMENT ANALYSTS JOURNAL
LA English
DT Article
DE online adaptive learning; portfolio management; machine learning
ID CROSS-SECTION; STOCK; MODEL
AB The artificial segmentation of the investment management process into silos of human operators can restrict silos from collectively and adaptively pursuing a unified investment goal. In this article, we argue that the investment process can be accelerated and be made more cohesive by replacing batch processing for component tasks of the investment process with online processing. We propose an integrated and online framework for investment workflows, where components produce outputs that are automatically and sequentially updated as new data arrives. The workflow can be further enhanced to refine signal generation and asset class evolution and definitions. Our results demonstrate that we use this framework in conjunction with resampling methods to optimise component decisions with direct reference to investment objectives while making clear the extent of backtest overfitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.
C1 [Paskaramoorthy, Andrew B.] Univ Witwatersrand, Comp Sci & Appl Maths, Johannesburg, South Africa.
[Gebbie, Tim J.] Univ Cape Town, Dept Stat Sci, Cape Town, South Africa.
[van Zyl, Terence L.] Univ Johannesburg, Inst Intelligent Syst, Johannesburg, South Africa.
C3 University of Witwatersrand; University of Cape Town; University of
Johannesburg
RP Paskaramoorthy, AB (autor correspondiente), Univ Witwatersrand, Comp Sci & Appl Maths, Johannesburg, South Africa.
EM andrew.paskaramoorthy@wits.ac.za
RI van Zyl, Terence L/B-9841-2008; Gebbie, Tim/AAH-7684-2019
OI van Zyl, Terence L/0000-0003-4281-630X; Gebbie, Tim/0000-0002-4061-2621;
Paskaramoorthy, Andrew/0000-0002-7812-5909
CR Bailey D., 2014, Notices of the AMS, V61, P458
Bailey DH, 2017, J COMPUT FINANC, V20, P39, DOI 10.21314/JCF.2016.322
Bailey DH, 2014, J PORTFOLIO MANAGE, V40, P94, DOI 10.3905/jpm.2014.40.5.094
Bailey DH, 2012, J RISK, V15, P3
Ban GY, 2018, MANAGE SCI, V64, P1136, DOI 10.1287/mnsc.2016.2644
Black F., 1992, Financial Analysts Journal, V48, P28, DOI DOI 10.2469/FAJ.V48.N5.28
Borodin A, 2004, J ARTIF INTELL RES, V21, P579, DOI 10.1613/jair.1336
Cover T., 1991, Mathematical finance, V1, P1, DOI [DOI 10.1111/J.1467-9965.1991.TB00002.X, https://doi.org/10.1111/j.1467-9965.1991.tb00002.x]
Cover TM, 1996, IEEE T INFORM THEORY, V42, P348, DOI 10.1109/18.485708
Daniel K, 1997, J FINANC, V52, P1, DOI 10.2307/2329554
De Prado M.L., 2018, Advances in Financial Machine Learning
FAMA EF, 1993, J FINANC ECON, V33, P3, DOI 10.1016/0304-405X(93)90023-5
Ferson WE, 1999, J FINANC, V54, P1325, DOI 10.1111/0022-1082.00148
Gelb A., 1989, Applied Optimal Estimation
Harvey CR, 2016, REV FINANC STUD, V29, P5, DOI [10.1093/rfs/hhv059, 10.18845/tm.v29i8.2980]
Harvey CR, 2015, J PORTFOLIO MANAGE, V42, P13, DOI 10.3905/jpm.2015.42.1.013
Haugen RA, 1996, J FINANC ECON, V41, P401, DOI 10.1016/0304-405X(95)00868-F
Hendricks D, 2014, IEEE C COMP INTEL FI, P457, DOI 10.1109/CIFEr.2014.6924109
Kolm PN, 2014, EUR J OPER RES, V234, P356, DOI 10.1016/j.ejor.2013.10.060
Lee W., 2000, Theory and Methodology of Tactical Asset Allocation
Li B, 2014, ACM COMPUT SURV, V46, DOI 10.1145/2512962
Ljung L., 1987, THEORY PRACTICE RECU
Lo AW, 2004, J PORTFOLIO MANAGE, P15, DOI 10.3905/jpm.2004.442611
Markowitz H, 1952, J FINANC, V7, P77, DOI 10.1111/j.1540-6261.1952.tb01525.x
Murphy N., 2019, ARXIV190302228
Noh JD, 2000, PHYS REV E, V61, P5981, DOI 10.1103/PhysRevE.61.5981
O'Toole R, 2017, J ASSET MANAG, V18, P580, DOI 10.1057/s41260-017-0055-6
ROLL R, 1977, J FINANC ECON, V4, P129, DOI 10.1016/0304-405X(77)90009-5
Snow D., 2020, The Journal of Financial Data Science, V1, P10
Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
THEIL H, 1961, INT ECON REV, V2, P65, DOI 10.2307/2525589
van Rensburg P., 2003, J ASSET MANAG, V4, P334, DOI DOI 10.1057/PALGRAVE.JAM.2240114
Wilcox D., 2014, HIERARCHICAL CAUSALI
Wilcox DL, 2015, INVEST ANAL J, V44, P1, DOI 10.1080/10293523.2014.994437
Zou Y, 2000, IEEE SIGNAL PROC LET, V7, P324, DOI 10.1109/97.873571
NR 35
TC 8
Z9 8
U1 0
U2 7
PU INVESTMENT ANALYSTS SOC SOUTHERN AFRICA
PI FERNDALE
PA PO BOX 131, FERNDALE, 2160, SOUTH AFRICA
SN 1029-3523
EI 2077-0227
J9 INVEST ANAL J
JI Invest. Anal. J.
PD JUL 2
PY 2020
VL 49
IS 3
SI SI
BP 215
EP 231
DI 10.1080/10293523.2020.1806460
EA SEP 2020
PG 17
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA OI6BK
UT WOS:000578874100001
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Sandrini, L
Somogyi, R
AF Sandrini, Luca
Somogyi, Robert
TI Generative AI and deceptive news consumption
SO ECONOMICS LETTERS
LA English
DT Article
DE Generative AI; News media market; Online advertising; Clickbait; Fake
news
AB In this paper, we analyze the effects of advancements in generative Artificial Intelligence (GenAI) on the news media market. We model a representative consumer who allocates their time between reading news and deceptive articles. We find that GenAI may induce consumers to inefficiently reallocate their time and increase the consumption of the lower value good, i.e. deceptive content (clickbait articles or fake news). Therefore, early-stage GenAI distorts the incentives of consumers and reduces their welfare. After GenAI technology reaches a certain threshold, however, consumers start benefiting from its advancements. Finally, we find that the negative effects of early-stage GenAI are exacerbated as they induce a lower level of investment in news production.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Sandrini, Luca] Budapest Univ Technol & Econ, QSMS Res Ctr, Muegyet rkp 3, H-1111 Budapest, Hungary.
[Somogyi, Robert] Budapest Univ Technol & Econ, Dept Finance, Muegyet Rkp 3, H-1111 Budapest, Hungary.
[Somogyi, Robert] Ctr Econ & Reg Studies, Toth Kalman Utca 4, H-1097 Budapest, Hungary.
C3 Budapest University of Technology & Economics; Budapest University of
Technology & Economics; Hungarian Academy of Sciences; Hungarian
Research Network; HUN-REN Centre for Economic & Regional Studies
RP Sandrini, L (autor correspondiente), Budapest Univ Technol & Econ, QSMS Res Ctr, Muegyet rkp 3, H-1111 Budapest, Hungary.
EM sandrini.luca@gtk.bme.hu
RI Somogyi, Robert/C-7476-2019
OI Somogyi, Robert/0000-0003-1033-1754
FU National Research Development and Innovation Office (NKFIH) [OTKA
FK-142492]
FX We thank Aniko Grad-Gyenge, Laszlo A. Koczy, Melika Liporace, Leonardo
Madio and seminar participants at the QSMS seminar for useful comments.
Robert Somogyi thanks the support of the National Research Development
and Innovation Office (NKFIH) under grant number OTKA FK-142492.
CR Acemoglu D., 2021, TASKS AUTOMATION, DOI DOI 10.3386/W28920
Ahmad W., 2023, SSRN
Anderson SP, 2020, J ECON THEORY, V186, DOI 10.1016/j.jet.2019.104990
Anderson SP, 2018, ECON J, V128, P34, DOI 10.1111/ecoj.12428
Anderson SP, 2005, REV ECON STUD, V72, P947, DOI 10.1111/0034-6527.00357
Angelucci C, 2019, AM ECON J-MICROECON, V11, P319, DOI 10.1257/mic.20170306
Athey S., 2021, WORKING PAPER SERIES, V28746
Calzada J, 2020, MARKET SCI, V39, P134, DOI 10.1287/mksc.2019.1150
Choné P, 2020, INT J IND ORGAN, V73, DOI 10.1016/j.ijindorg.2020.102663
D'Annunzio A, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.1435
Fergusson G., 2023, GENERATING HARMS GEN
Freimane M., 2023, SUBSTITUTING A UNPUB
Jones C.I., 2023, AI DILEMMA GROWTH VE
NAGLER MG, 1993, J PUBLIC ECON, V51, P359, DOI 10.1016/0047-2727(93)90071-Z
Ngo R, 2022, Arxiv, DOI [arXiv:2209.00626, DOI 10.48550/ARXIV.2209.00626]
Piccolo S, 2018, MANAGE SCI, V64, P1291, DOI 10.1287/mnsc.2016.2665
Sandrini L., 2023, SSRN
Simonov A., 2023, 17956 CEPR
SINGH N, 1984, RAND J ECON, V15, P546, DOI 10.2307/2555525
Zinman J, 2016, AM ECON J-APPL ECON, V8, P177, DOI 10.1257/app.20130346
NR 20
TC 0
Z9 0
U1 75
U2 75
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0165-1765
EI 1873-7374
J9 ECON LETT
JI Econ. Lett.
PD NOV
PY 2023
VL 232
AR 111317
DI 10.1016/j.econlet.2023.111317
EA SEP 2023
PG 4
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA S5JA1
UT WOS:001071515900001
OA hybrid
DA 2024-03-27
ER
PT J
AU Neumann, N
Tucker, CE
Whitfield, T
AF Neumann, Nico
Tucker, Catherine E.
Whitfield, Timothy
TI Frontiers: How Effective Is Third-Party Consumer Profiling? Evidence
from Field Studies
SO MARKETING SCIENCE
LA English
DT Article
DE digital advertising; data brokers; profiling; algorithms; machine
learning; big data
ID BROWSING BEHAVIOR; ONLINE
AB Data brokers often u,e (mime browsing records to create digital consumer profiles that they sell to marketers as predefined audiences for ad targeting. However, this process is a "black box"-little is known about the reliability of the digital profiles that are created or of the audience identification provided by buying platforms. In this paper, we investigate using three field tests the accuracy of a variety of demographic and audience-interest segments. We examine the accuracy of more than 90 third-party audiences across 19 data brokers. Audience segments vary greatly in quality and are often inaccurate across leading data brokers. In comparison with random audience selection, the use of black box data profiles, on average, increased identification of a user with a desired single attribute by 0%-77%. Audience identification can be improved, on average, by 123% when combined with optimization software. However, given the high extra costs of targeting solutions and the relative inaccuracy, we find that third-party audiences are often economically unattractive except for higher-priced media placements.
C1 [Neumann, Nico] Melbourne Business Sch, Ctr Business Analyt, Carlton, Vic 3053, Australia.
[Tucker, Catherine E.] MIT, Sloan Sch Management, Cambridge, MA 02142 USA.
[Tucker, Catherine E.] NBER, Cambridge, MA 02138 USA.
[Whitfield, Timothy] Burst SMS, Sydney, NSW 2000, Australia.
C3 Massachusetts Institute of Technology (MIT); National Bureau of Economic
Research
RP Neumann, N (autor correspondiente), Melbourne Business Sch, Ctr Business Analyt, Carlton, Vic 3053, Australia.
EM n.neumann@mbs.edu; cetucker@mit.edu; twhitfie@gmail.com
FU National Science Foundation CAREER Award [6923256]
FX This work was supported by a National Science Foundation CAREER Award
[6923256].
CR [Anonymous], 2017, ECONOMIST
Bergemann D, 2015, AM ECON J-MICROECON, V7, P259, DOI 10.1257/mic.20140155
Bucklin RE, 2003, J MARKETING RES, V40, P249, DOI 10.1509/jmkr.40.3.249.19241
Chen YX, 2001, MARKET SCI, V20, P23, DOI 10.1287/mksc.20.1.23.10201
Coey D, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P1103, DOI 10.1145/2872427.2882984
DeBruyn A, 2019, WORKING PAPER
Flosi S, 2013, J ADVERTISING RES, V53, P192, DOI 10.2501/JAR-53-2-192-199
Hof Robert, 2014, FORBES
IAB, 2018, Major advertising trade bodies unveil data transparency label
IAB and WinterberryGroup, 2018, STAT DAT
Johnson GA, 2017, J MARKETING RES, V54, P867, DOI 10.1509/jmr.15.0297
Kelly C, 2017, INACCURATE SEGMENTS
Kim Y, 2005, MANAGE SCI, V51, P264, DOI 10.1287/mnsc.1040.0296
Lambrecht A, 2013, J MARKETING RES, V50, P561, DOI 10.1509/jmr.11.0503
Lerner A, 2016, PROCEEDINGS OF THE 25TH USENIX SECURITY SYMPOSIUM, P997
Lin T.-C., 2018, GENDER DIFFERENCES R, DOI DOI 10.2139/SSRN.3045050
Lotame, 2018, NEW STAT AUD DAT ACC
Mallazzo M, 2018, DID FLAWED DATA BECO
Murthi BPS, 2003, MANAGE SCI, V49, P1344, DOI 10.1287/mnsc.49.10.1344.17313
Narayanan S, 2006, MARKET SCI, V28, P424
Pancras J, 2007, J MARKETING RES, V44, P560, DOI 10.1509/jmkr.44.4.560
Park YH, 2004, MARKET SCI, V23, P280, DOI 10.1287/mksc.1040.0050
Sahni NS, 2019, J MARKETING RES, V56, P401, DOI 10.1177/0022243718813987
Salesforce, 2018, DIG ADV 2020 INS NEW
Statista, 2015, AUSTR AG DISTR INT U
Trusov M, 2016, MARKET SCI, V35, P405, DOI 10.1287/mksc.2015.0956
NR 26
TC 35
Z9 39
U1 7
U2 56
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD NOV-DEC
PY 2019
VL 38
IS 6
BP 918
EP 926
DI 10.1287/mksc.2019.1188
PG 9
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JU3SK
UT WOS:000501598400002
DA 2024-03-27
ER
PT J
AU Li, LS
Chen, JQ
Raghunathan, S
AF Li, Lusi
Chen, Jianqing
Raghunathan, Srinivasan
TI INFORMATIVE ROLE OF RECOMMENDER SYSTEMS IN ELECTRONIC MARKETPLACES: A
BOON OR A BANE FOR COMPETING SELLERS
SO MIS QUARTERLY
LA English
DT Article
DE Recommender system; electronic commerce; online advertising; analytical
modeling; economics of IS
ID IMPACT; REVIEWS
AB Recommender systems have become the cornerstone of electronic marketplaces that sell products from competing sellers. Similar to traditional advertising, recommender systems can introduce consumers to new products and increase the market size which benefits sellers. This informative role of recommender systems in electronic marketplaces seems attractive to sellers because sellers do not pay the marketplaces for receiving recommendations. We show that in a marketplace that deploys a recommender system helping consumers discover the product that provides them the highest expected net utility, sellers do not necessarily benefit from the 'free" exposure provided by the recommender system. The impacts of the recommender system are the result of a subtle interaction between advertising effect and competition effect. The advertising effect causes sellers to advertise less on their own and the competition effect causes them to decrease prices in the presence of a recommender system. Essentially, sellers "pay" in the form of more intense price competition because of the recommender system. Furthermore, the competition effect is exacerbated by the advertising effect because the recommender system alters a seller's own strategies related to advertising intensity and price from being strategic substitutes in the absence of the recommender system to being strategic complements in its presence. As a result of these two effects, we find that sellers are more likely to benefit from the recommender system only when it has a high precision. The results do not change qualitatively whether sellers use targeted advertising or uniform advertising. However, we find that a recommender system that benefits sellers when they do not employ targeted advertising may actually hurt them when they adopt targeted advertising with a high precision. On the other hand, in the presence of the recommender system, an increase in sellers' targeting precision beyond a threshold softens price competition, increases seller profits, and reduces consumer surplus. Finally, we find that when the recommender system assigns a larger weight to product fit than price, the adverse impacts of the recommender system on sellers are mitigated, thereby expanding the region in the parameter space where the recommender system is beneficial to sellers.
C1 [Li, Lusi] Calif State Univ Los Angeles, Coll Business & Econ, 5151 State Univ Dr, Los Angeles, CA 90032 USA.
[Chen, Jianqing; Raghunathan, Srinivasan] Univ Texas Dallas, Jindal Sch Management, 800 West Campbell Rd, Richardson, TX 75080 USA.
C3 California State University System; California State University Los
Angeles; University of Texas System; University of Texas Dallas
RP Li, LS (autor correspondiente), Calif State Univ Los Angeles, Coll Business & Econ, 5151 State Univ Dr, Los Angeles, CA 90032 USA.
EM lli57@calstatela.edu; chenjq@utdallas.edu; sraghu@utdallas.edu
RI Raghunathan, Srinivasan/KFR-3471-2024
OI Raghunathan, Srinivasan/0000-0002-2782-3520
FU NSFC [71528004, 71431002]
FX We thank the detailed and constructive comments from the review team,
which have greatly improved the paper. We also thank participants at the
Workshop on Information Systems and Economics (2015), as well as seminar
participants at Dalian University of Technology and The University of
Texas at Dallas for their helpful feedback. Jianqing Chen acknowledges
the financial support from the NSFC [Grants No.71528004 and 71431002].
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Bergemann D., 2006, P ACM C EL COMM, P43, DOI DOI 10.1145/1134707.1134713
BESTER H, 1995, EUR ECON REV, V39, P1075, DOI 10.1016/0014-2921(94)00099-L
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Chen LS, 2008, INFORM SCIENCES, V178, P1032, DOI 10.1016/j.ins.2007.09.027
Choudhary V., 2019, WORKING PAPER, DOI [10.2139/ssrn.3398994, DOI 10.2139/SSRN.3398994]
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Gal-Or E, 2005, MARKET SCI, V24, P241, DOI 10.1287/mksc.1040.0092
Gal-Or E, 2006, MANAGE SCI, V52, P713, DOI 10.1287/mnsc.1050.0489
Ghoshal A, 2015, J MANAGE INFORM SYST, V31, P243, DOI 10.1080/07421222.2014.1001276
GROSSMAN GM, 1984, REV ECON STUD, V51, P63, DOI 10.2307/2297705
Hennig-Thurau T, 2012, J MARKETING, V76, P89, DOI 10.1509/jm.10.0537
Hervas-Drane A, 2015, INT J RES MARK, V32, P207, DOI 10.1016/j.ijresmar.2015.02.005
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
Iyer G, 2005, MARKET SCI, V24, P461, DOI 10.1287/mksc.1050.0117
Jabr W, 2014, MIS QUART, V38, P635, DOI 10.25300/MISQ/2014/38.3.01
Johnson JP, 2006, AM ECON REV, V96, P756, DOI 10.1257/aer.96.3.756
Kwark Y, 2014, INFORM SYST RES, V25, P93, DOI 10.1287/isre.2013.0511
Ni L, 2011, DESALIN WATER TREAT, V34, P32, DOI 10.5004/dwt.2011.2851
Oestreicher-Singer G, 2012, MANAGE SCI, V58, P1963, DOI 10.1287/mnsc.1120.1536
Oestreicher-Singer G, 2012, MIS QUART, V36, P65, DOI 10.2307/41410406
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Shani G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P257, DOI 10.1007/978-0-387-85820-3_8
Soberman DA, 2004, MANAGE SCI, V50, P1744, DOI 10.1287/mnsc.1040.0288
Tam KY, 2006, MIS QUART, V30, P865
Tirole J, 1988, THEORY IND ORG
VIVES X, 1984, J ECON THEORY, V34, P71, DOI 10.1016/0022-0531(84)90162-5
Zhang X., MIS Q, V45
NR 29
TC 13
Z9 15
U1 23
U2 150
PU SOC INFORM MANAGE-MIS RES CENT
PI MINNEAPOLIS
PA UNIV MINNESOTA-SCH MANAGEMENT 271 19TH AVE SOUTH, MINNEAPOLIS, MN 55455
USA
SN 0276-7783
J9 MIS QUART
JI MIS Q.
PD DEC
PY 2020
VL 44
IS 4
BP 1957
EP 1985
DI 10.25300/MISQ/2020/14614
PG 29
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA PB0TB
UT WOS:000596039600014
DA 2024-03-27
ER
PT J
AU Rathee, S
Banker, S
Mishra, A
Mishra, H
AF Rathee, Shelly
Banker, Sachin
Mishra, Arul
Mishra, Himanshu
TI Algorithms propagate gender bias in the marketplace-with consumers'
cooperation
SO JOURNAL OF CONSUMER PSYCHOLOGY
LA English
DT Article
DE customer segmentation; digital advertising; gender bias; natural
language processing; word embedding
ID PERSONALITY; STEREOTYPES; PREJUDICE; COGNITION
AB Recent research shows that algorithms learn societal biases from large text corpora. We examine the marketplace-relevant consequences of such bias for consumers. Based on billions of documents from online text corpora, we first demonstrate that from gender biases embedded in language, algorithms learn to associate women with more negative consumer psychographic attributes than men (e.g., associating women more closely with impulsive vs. planned investors). Second, in a series of field experiments, we show that such learning results in the delivery of gender-biased digital advertisements and product recommendations. Specifically, across multiple platforms, products, and attributes, we find that digital advertisements containing negative psychographic attributes (e.g., impulsive) are more likely to be delivered to women compared to men, and that search engine product recommendations are similarly biased, which influences consumer's consideration sets and choice. Finally, we empirically examine consumer's role in co-producing algorithmic gender bias in the marketplace and observe that consumers reinforce these biases by accepting gender stereotypes (i.e., clicking on biased ads). We conclude by discussing theoretical and practical implications.
C1 [Rathee, Shelly] Villanova Sch Business, Dept Mkt & Business Law, Villanova, PA USA.
[Banker, Sachin; Mishra, Arul; Mishra, Himanshu] Univ Utah, David Eccles Sch Business, Salt Lake City, UT USA.
[Rathee, Shelly] Villanova Sch Business, Dept Mkt & Business Law, 3044 Bartley Hall, Villanova, PA 19085 USA.
C3 Villanova University; Utah System of Higher Education; University of
Utah; Villanova University
RP Rathee, S (autor correspondiente), Villanova Sch Business, Dept Mkt & Business Law, 3044 Bartley Hall, Villanova, PA 19085 USA.
EM shelly.rathee@villanova.edu
OI Rathee, Shelly/0000-0002-4403-7775
CR Aaker JL, 1997, J MARKETING RES, V34, P347, DOI 10.2307/3151897
Agan A. Y., 2023, Automating Automaticity: How the Context of Human Choice Affects the Extent of Algorithmic Bias (No. w30981)
Ailawadi KL, 2001, J MARKETING, V65, P71, DOI 10.1509/jmkg.65.1.71.18132
Allport G. A., 1954, NATURE PREJUDICE
ANDERSON NH, 1968, J PERS SOC PSYCHOL, V9, P272, DOI 10.1037/h0025907
BELK RW, 1988, J CONSUM RES, V15, P139, DOI 10.1086/209154
Berger J, 2007, J CONSUM RES, V34, P121, DOI 10.1086/519142
BERRY DS, 1985, J PERS SOC PSYCHOL, V48, P312, DOI 10.1037/0022-3514.48.2.312
Boghrati R., 2022, QUANTIFYING GENDER B
Boghrati R., 2020, QUANTIFYING 50 YEARS
Bolukbasi Tolga, 2016, ADV NEURAL INFORM PR, P4356, DOI DOI 10.5555/3157382.3157584
Brehm JW., 1966, A theory of psychological reactance
BRIGGS SR, 1992, J PERS, V60, P253, DOI 10.1111/j.1467-6494.1992.tb00974.x
BRISTOR JM, 1993, J CONSUM RES, V19, P518, DOI 10.1086/209320
Caliskan A, 2017, SCIENCE, V356, DOI 10.1126/science.aal4230
Caselles-Dupré H, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P352, DOI 10.1145/3240323.3240377
Charlesworth TES, 2021, PSYCHOL SCI, V32, P218, DOI 10.1177/0956797620963619
Crawford K., 2017, TROUBLE BIAS
Defranza D, 2020, J PERS SOC PSYCHOL, V119, P7, DOI 10.1037/pspa0000188
DEVINE PG, 1989, J PERS SOC PSYCHOL, V56, P5, DOI 10.1037/0022-3514.56.1.5
Ellemers N, 2018, ANNU REV PSYCHOL, V69, P275, DOI 10.1146/annurev-psych-122216-011719
Epstein CynthiaFuchs., 1988, DECEPTIVE DISTINCTIO
EYSENCK HJ, 1982, PSYCHOL REP, V51, P531, DOI 10.2466/pr0.1982.51.2.531
Garg N, 2018, P NATL ACAD SCI USA, V115, pE3635, DOI 10.1073/pnas.1720347115
Grant Packard, 2024, Journal of Consumer Research, V51, DOI [10.1093/jcr/ucad013, DOI 10.1093/JCR/UCAD013]
Greenwald AG, 2000, J PERS SOC PSYCHOL, V79, P1022, DOI 10.1037//0022-3514.79.6.1022
HOFSTEE WKB, 1992, J PERS SOC PSYCHOL, V63, P146, DOI 10.1037/0022-3514.63.1.146
Hyde JS, 2016, CURR OPIN NEUROBIOL, V38, P53, DOI 10.1016/j.conb.2016.02.007
HYDE JS, 1995, AM PSYCHOL, V50, P159, DOI 10.1037/0003-066X.50.3.159
Kimball M.M., 1995, Feminist visions of gender similarities and differences
Kray LJ, 2001, J PERS SOC PSYCHOL, V80, P942, DOI 10.1037/0022-3514.80.6.942
Kronrod A., 2020, ADV CONSUM RES, V48, P1247
Lee K, 2011, J CONSUM RES, V38, P343, DOI 10.1086/659315
Liu Pengfei, 2015, P 2015 C EMP METH NA, P1433
MANIS M, 1988, J PERS SOC PSYCHOL, V55, P28, DOI 10.1037/0022-3514.55.1.28
May Chandler., 2019, On measuring social biases in sentence encoders
Mohr L.A., 1996, J CONSUM PSYCHOL, V5, P161, DOI DOI 10.1207/S15327663JCP0502_04
ONeil C., 2016, Weapons of math destruction: How big data increases inequality and threatens democracy
Ozsoy M.G., 2016, From Word Embeddings to Item Recommendation
Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI DOI 10.3115/V1/D14-1162
RAJU PS, 1980, J CONSUM RES, V7, P272, DOI 10.1086/208815
Reed A, 2012, INT J RES MARK, V29, P310, DOI 10.1016/j.ijresmar.2012.08.002
Roberts BW, 2005, J RES PERS, V39, P166, DOI 10.1016/j.jrp.2004.08.002
Spielmann N, 2021, J ASSOC CONSUM RES, V6, P211, DOI 10.1086/713188
Steenkamp JBEM, 2015, J MARKETING RES, V52, P287, DOI 10.1509/jmr.13.0592
Susser JA, 2016, J EXP PSYCHOL LEARN, V42, P657, DOI 10.1037/xlm0000189
World Bank, 2020, Women in Business Report 2020: Thailand
Zell E, 2015, AM PSYCHOL, V70, P10, DOI 10.1037/a0038208
Zheng L, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P425, DOI 10.1145/3018661.3018665
NR 49
TC 1
Z9 1
U1 46
U2 68
PU JOHN WILEY & SONS LTD
PI CHICHESTER
PA THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND
SN 1057-7408
EI 1532-7663
J9 J CONSUM PSYCHOL
JI J. Consum. Psychol.
PD OCT
PY 2023
VL 33
IS 4
SI SI
BP 621
EP 631
DI 10.1002/jcpy.1351
EA MAY 2023
PG 11
WC Business; Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA T1AG0
UT WOS:000991066000001
DA 2024-03-27
ER
PT J
AU Matz, SC
Segalin, C
Stillwell, D
Müller, SR
Bos, MW
AF Matz, Sandra C.
Segalin, Cristina
Stillwell, David
Muller, Sandrine R.
Bos, Maarten W.
TI Predicting the Personal Appeal of Marketing Images Using Computational
Methods
SO JOURNAL OF CONSUMER PSYCHOLOGY
LA English
DT Article
DE Personalization; Digital advertising; Personality; Image appeal; Machine
learning; Image processing; Computer vision
ID 5-FACTOR MODEL; COLOR PREFERENCE; SEX-DIFFERENCES; AESTHETICS;
JUDGMENTS; SEARCH; DESIGN; LEVEL; BRAND; LIFE
AB Images play a central role in digital marketing. They attract attention, trigger emotions, and shape consumers' first impressions of products and brands. We propose that the shift from one-to-many mass communication to highly personalized one-to-one communication requires an understanding of image appeal at a personal level. Instead of asking "How appealing is this image?" we ask "How appealing is this image to this particular consumer?" Using the well-established five-factor model of personality, we apply machine learning algorithms to predict an image's personality appeal-the personality of consumers to which the image appeals most-from a set of 89 automatically extracted image features (Study 1). We subsequently apply the same algorithm on new images to predict consequential outcomes from the fit between consumer and image personality. We show that image-person fit adds incremental predictive power over the images' general appeal when predicting (a) consumers' liking of new images (Study 2) and (b) consumers' attitudes and purchase intentions (Study 3).
C1 [Matz, Sandra C.] Columbia Business Sch, New York, NY 10027 USA.
[Segalin, Cristina] CALTECH, Pasadena, CA 91125 USA.
[Stillwell, David; Muller, Sandrine R.] Univ Cambridge, Cambridge, England.
[Bos, Maarten W.] Snap Inc, Santa Monica, CA USA.
C3 Columbia University; California Institute of Technology; University of
Cambridge
RP Matz, SC (autor correspondiente), Columbia Business Sch, New York, NY 10027 USA.
EM sm4409@gsb.columbia.edu
RI Müller, Sandrine Ruth/ABA-1888-2020
OI Müller, Sandrine Ruth/0000-0002-1226-6370; Matz, Sandra
C./0000-0002-0969-4403
CR Aaker JL, 1997, J MARKETING RES, V34, P347, DOI 10.2307/3151897
[Anonymous], COMPUTER VISION IMAG
[Anonymous], 2015, Deep learning, DOI 10.1038/nature14539
[Anonymous], PROC 18TH ACM INT CO
[Anonymous], LECT NOTES COMPUTER
Barrick MR, 2003, PERS PSYCHOL, V56, P45, DOI 10.1111/j.1744-6570.2003.tb00143.x
Bauerly M, 2008, INT J HUM-COMPUT INT, V24, P275, DOI 10.1080/10447310801920508
BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x
BIRREN F, 1973, ART PSYCHOTHERAPY, V1, P13, DOI 10.1016/0090-9092(73)90005-7
BRADLEY RA, 1952, BIOMETRIKA, V39, P324, DOI 10.2307/2334029
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Caprara GV, 2004, AM PSYCHOL, V59, P581, DOI 10.1037/0003-066X.59.7.581
Cerosaletti CD, 2009, INT WORK QUAL MULTIM, P47, DOI 10.1109/QOMEX.2009.5246977
Chamorro-Premuzic T, 2009, BRIT J PSYCHOL, V100, P501, DOI 10.1348/000712608X366867
CHILD IL, 1968, CHILD DEV, V39, P237, DOI 10.2307/1127374
CHILDERS TL, 1984, J CONSUM RES, V11, P643, DOI 10.1086/209001
Christoudias CM, 2002, INT C PATT RECOG, P150, DOI 10.1109/ICPR.2002.1047421
Comaniciu D, 2002, IEEE T PATTERN ANAL, V24, P603, DOI 10.1109/34.1000236
Cristani M., 2013, P 21 ACM INT C MULTI, P213
Cyr D, 2009, MIS QUART, V33, P539
Datta R, 2006, LECT NOTES COMPUT SC, V3953, P288, DOI 10.1007/11744078_23
de Montjoye Yves-Alexandre, 2013, Social Computing, Behavioral-Cultural Modeling and Prediction. 6th International Conference, SBP 2013. Proceedings, P48, DOI 10.1007/978-3-642-37210-0_6
Deza Michel Marie, 2009, Encyclopedia of Distances, P1, DOI [10.1007/978-3-642-35943-9_435-1, DOI 10.1007/978-3-642-35943-9_435-1, 10.1007/978-3-642-00234-2, DOI 10.1007/978-3-642-00234-2]
Dhar S, 2011, PROC CVPR IEEE, P1657, DOI 10.1109/CVPR.2011.5995467
Feist G.J., 2004, EMPIR STUD ARTS, V22, P77, DOI [10.2190/Y7CA-TBY6-V7LR-76GK, DOI 10.2190/Y7CA-TBY6-V7LR-76GK]
Felzenszwalb PF, 2010, IEEE T PATTERN ANAL, V32, P1627, DOI 10.1109/TPAMI.2009.167
Ferrari V, 2009, PROC CVPR IEEE, P1, DOI 10.1109/CVPRW.2009.5206495
Furnham A, 1997, PERS INDIV DIFFER, V23, P923, DOI 10.1016/S0191-8869(97)00131-1
Goldberg LR, 2006, J RES PERS, V40, P84, DOI 10.1016/j.jrp.2005.08.007
Gosling SD, 2003, J RES PERS, V37, P504, DOI 10.1016/S0092-6566(03)00046-1
Gujarati Damodar N., 2013, Basic Econometrics, Vfifth
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Hearst MA, 1998, IEEE INTELL SYST APP, V13, P18, DOI 10.1109/5254.708428
Hirsh JB, 2012, PSYCHOL SCI, V23, P578, DOI 10.1177/0956797611436349
Hurlbert AC, 2007, CURR BIOL, V17, pR623, DOI 10.1016/j.cub.2007.06.022
Jacobsen T, 2002, PERCEPT MOTOR SKILL, V95, P755, DOI 10.2466/PMS.95.7.755-766
Khosla A, 2014, WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P867, DOI 10.1145/2566486.2567996
Kosinski M, 2015, AM PSYCHOL, V70, P543, DOI 10.1037/a0039210
Kosinski M, 2013, P NATL ACAD SCI USA, V110, P5802, DOI 10.1073/pnas.1218772110
Kvalem Ingela Lundin, 2006, Body Image, V3, P263
Latto R, 2000, PERCEPTION, V29, P981, DOI 10.1068/p2352
Lecky Prescott., 1945, Self-Consistency: A Theory of Personality
Lee L, 2009, J CONSUM RES, V36, P173, DOI 10.1086/597160
Lindgaard G, 2006, BEHAV INFORM TECHNOL, V25, P115, DOI 10.1080/01449290500330448
Lovato P, 2014, IEEE T INF FOREN SEC, V9, P364, DOI 10.1109/TIFS.2014.2298370
Lu X, 2014, PROCEEDINGS OF THE 2014 ACM CONFERENCE ON MULTIMEDIA (MM'14), P457, DOI 10.1145/2647868.2654927
Marcus B, 2006, J PERS SOC PSYCHOL, V90, P1014, DOI 10.1037/0022-3514.90.6.1014
Mardia K.V., 2009, Directional Statistics, V494
Matz SC, 2017, P NATL ACAD SCI USA, V114, P12714, DOI 10.1073/pnas.1710966114
Matz SC, 2016, PSYCHOL SCI, V27, P715, DOI 10.1177/0956797616635200
McCrae RR, 2014, WILEY HANDBOOK OF GENIUS, P222
MCCRAE RR, 1992, J PERS, V60, P175, DOI 10.1111/j.1467-6494.1992.tb00970.x
MCCRAE RR, 1987, J PERS SOC PSYCHOL, V52, P81, DOI 10.1037/0022-3514.52.1.81
McCrae RR, 2002, INT CUL PSY, P105
Messaris P., 1997, VISUAL PERSUASION RO
Moon Y, 2002, J CONSUM PSYCHOL, V12, P313, DOI 10.1207/15327660260382351
Murray N, 2012, PROC CVPR IEEE, P2408, DOI 10.1109/CVPR.2012.6247954
Ng T.-T., 2004, Classifying photographic and photorealistic computer graphic images using natural image statistics
Oliva A, 2001, INT J COMPUT VISION, V42, P145, DOI 10.1023/A:1011139631724
Ozer DJ, 2006, ANNU REV PSYCHOL, V57, P401, DOI 10.1146/annurev.psych.57.102904.190127
Palmer SE, 2011, NEW DIRECTIONS IN COLOUR STUDIES, P361
Palmer SE, 2013, ANNU REV PSYCHOL, V64, P77, DOI 10.1146/annurev-psych-120710-100504
Park G, 2015, J PERS SOC PSYCHOL, V108, P934, DOI 10.1037/pspp0000020
Pieters R, 2004, J MARKETING, V68, P36, DOI 10.1509/jmkg.68.2.36.27794
Pieters R, 2012, MARKET SCI, V31, P59, DOI 10.1287/mksc.1110.0673
Pieters R, 2010, J MARKETING, V74, P48, DOI 10.1509/jmkg.74.5.48
Potter MC, 2014, ATTEN PERCEPT PSYCHO, V76, P270, DOI 10.3758/s13414-013-0605-z
Quercia D., 2011, Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and IEEE Third International Conference on Social Computing (PASSAT/SocialCom 2011), P180, DOI 10.1109/PASSAT/SocialCom.2011.26
Rawlings D., 1998, Empirical Studies of the Arts, V16, P153, DOI [10.2190/8GVJ-ERL3-HYUM-EH88, DOI 10.2190/8GVJ-ERL3-HYUM-EH88]
Redi J. A., 2013, VISUAL COMMUN-US, P1
Rentfrow PJ, 2003, J PERS SOC PSYCHOL, V84, P1236, DOI 10.1037/0022-3514.84.6.1236
ROBINSON C, 1975, PERCEPT MOTOR SKILL, V40, P702, DOI 10.2466/pms.1975.40.3.702
Rosenholtz R., 2005, P SIGCHI C HUMAN FAC, P761, DOI [10.1145/1054972.1055078, DOI 10.1145/1054972.1055078]
Schloss KB, 2011, ATTEN PERCEPT PSYCHO, V73, P551, DOI 10.3758/s13414-010-0027-0
Schwartz HA, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0073791
Segalin C, 2017, IEEE T AFFECT COMPUT, V8, P268, DOI 10.1109/TAFFC.2016.2516994
Shah D, 2006, J SERV RES-US, V9, P113, DOI 10.1177/1094670506294666
Sheth JN, 2000, J ACAD MARKET SCI, V28, P55, DOI 10.1177/0092070300281006
Specht J, 2011, J PERS SOC PSYCHOL, V101, P862, DOI 10.1037/a0024950
TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999
Tian-Tsong Ng, 2005, 13th Annual ACM International Conference on Multimedia, P239
Tibshirani R, 1996, J ROY STAT SOC B, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x
Turner H, 2012, J STAT SOFTW, V48, P1
VALDEZ P, 1994, J EXP PSYCHOL GEN, V123, P394, DOI 10.1037/0096-3445.123.4.394
Viola P, 2001, PROC CVPR IEEE, P511, DOI 10.1109/cvpr.2001.990517
Wedel M, 2015, MARKET SCI, V34, P134, DOI 10.1287/mksc.2014.0882
Wheeler SC, 2005, J CONSUM RES, V31, P787, DOI 10.1086/426613
Wu YY, 2015, P NATL ACAD SCI USA, V112, P1036, DOI 10.1073/pnas.1418680112
NR 88
TC 30
Z9 31
U1 17
U2 126
PU JOHN WILEY & SONS LTD
PI CHICHESTER
PA THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND
SN 1057-7408
EI 1532-7663
J9 J CONSUM PSYCHOL
JI J. Consum. Psychol.
PD JUL
PY 2019
VL 29
IS 3
BP 370
EP 390
DI 10.1002/jcpy.1092
PG 21
WC Business; Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA IG2SI
UT WOS:000473648500002
OA Green Accepted
DA 2024-03-27
ER
PT J
AU Yoldar, MT
Özcan, U
AF Yoldar, Mehmet Turkay
Ozcan, Ugur
TI Collaborative targeting: Biclustering-based online ad recommendation
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Behavioral targeting; Biclustering; Collaborative filtering;
Computational advertising; Online advertising; Ordered weighted
averaging; Recommender systems
ID AGGREGATION OPERATORS; USER PROFILES; PERSONALIZATION; FRAMEWORK;
SYSTEMS
AB In online advertising, it is essential to show appropriate ads to target users. However, this is a challenging process. Although conventional targeting methods yield successful results, they cannot effectively select different ads for all users. In this study, we explore collaborative filtering techniques on an online ad dataset. We propose a method of recommending different and effective ads to users. The proposed method, which is based on biclustering and ordered weighted average aggregation operators, can address situations such as the lack of implicit feedback on items. We present the results of an offline analysis of the proposed method together with those of collaborative filtering methods. It is shown that collaborative filtering methods are beneficial, and that the proposed method provides superior results, especially in systems where user navigation histories are well known.
C1 [Yoldar, Mehmet Turkay] Gazi Univ, Inst Informat, Dept Management Info Sys, TR-06680 Ankara, Turkey.
[Ozcan, Ugur] Gazi Univ, Fac Engn, Dept Ind Engn, TR-06570 Ankara, Turkey.
C3 Gazi University; Gazi University
RP Yoldar, MT (autor correspondiente), Gazi Univ, Inst Informat, Dept Management Info Sys, TR-06680 Ankara, Turkey.
EM mehmetturkay.yoldar@gazi.edu.tr
RI Özcan, Uğur/ABI-3999-2020
OI OZCAN, UGUR/0000-0001-8283-9579
CR Adeniyi D. A., 2016, Applied Computing and Informatics, V12, P90, DOI 10.1016/j.aci.2014.10.001
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Alqadah F, 2015, KNOWL INF SYST, V44, P475, DOI 10.1007/s10115-014-0771-x
Anand S., 2003, P 2003 INT C INT TEC
[Anonymous], 2000, P 2000 ACM C COMP SU
[Anonymous], 2001, P WWW10 MAY 1 5 HONG
[Anonymous], 2000, ACM SIGKDD Explor. Newsl, DOI [DOI 10.1145/846183.846188, 10.1145/846183.846188]
Beliakov G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P705, DOI 10.1007/978-0-387-85820-3_22
Ben-Dor A, 2003, J COMPUT BIOL, V10, P373, DOI 10.1089/10665270360688075
Bergmann S, 2003, PHYS REV E, V67, DOI 10.1103/PhysRevE.67.031902
Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
Broder A. Z., 2008, P 2008 ACM C REC SYS
Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
Chen G, 2016, IEEE COMMUN SURV TUT, V18, P2124, DOI 10.1109/COMST.2016.2519912
Cheng Y, 2000, INT SYST MOL BIOL C
Cho YH, 2002, EXPERT SYST APPL, V23, P329, DOI 10.1016/S0957-4174(02)00052-0
Dave K, 2014, FOUND TRENDS INF RET, V8, P265, DOI 10.1561/1500000045
Eirinaki M., 2003, ACM T INTERNET TECHN, V3, P1, DOI [DOI 10.1145/643477.643478, 10.1145/643477.643478]
Emrouznejad A, 2014, INT J INTELL SYST, V29, P994, DOI 10.1002/int.21673
Evans DS, 2009, J ECON PERSPECT, V23, P37, DOI 10.1257/jep.23.3.37
Fabricio O, 2007, 7 INT C INT SYST DES
Fang JY, 2009, IEEE CIRCUITS AND SYSTEMS INTERNATIONAL CONFERENCE ON TESTING AND DIAGNOSIS, P26, DOI 10.4248/IJOS.08017
Fernandez-Tobias I, 2012, SPAN C INF RETR ESC
Gagolewski M, 2014, AGOP AGGREGATION OPE
Goldfarb A, 2014, REV IND ORGAN, V44, P115, DOI 10.1007/s11151-013-9399-3
Golemati M., 2007, P 1 INT C RES CHALL
Guo G, 2015, UM WORKSH
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Herrera F, 1996, FUZZY SET SYST, V78, P73, DOI 10.1016/0165-0114(95)00107-7
Herrera F, 1997, IEEE T SYST MAN CY A, V27, P646, DOI 10.1109/3468.618263
Hoppe A, 2013, PROC VLDB ENDOW, V6, P1428, DOI 10.14778/2536274.2536330
Ignatov D. I., 2012, P 12 INT C DAT MIN W
Kant S, 2018, J COMPUT SCI-NETH, V25, P204, DOI 10.1016/j.jocs.2017.03.018
Katayama K, 2015, PHYSIOL REP, V3, DOI 10.14814/phy2.12421
Kazienko P, 2007, INFORM SCIENCES, V177, P2269, DOI 10.1016/j.ins.2007.01.002
Kluger Y, 2003, GENOME RES, V13, P703, DOI 10.1101/gr.648603
Kohavi R., 1995, P INT JOINT C ART IN, DOI 10.5555/1643031.1643047
Koren Y, 2011, RECOMMENDER SYSTEMS HANDBOOK, P145, DOI 10.1007/978-0-387-85820-3_5
Kosala Raymond., 2000, SIGKDD EXPLOR NEWSL, V2, P1, DOI [DOI 10.1145/360402.360406, 10.1145/360402.360406]
Lazzeroni L, 2002, STAT SINICA, V12, P61
Levene Mark., 2011, INTRO SEARCH ENGINES
Li B, 2011, P 2011 23 IEEE INT C
Li GJ, 2009, NUCLEIC ACIDS RES, V37, DOI 10.1093/nar/gkp491
Lü LY, 2012, PHYS REP, V519, P1, DOI 10.1016/j.physrep.2012.02.006
Luo Q, 2014, ASIA PAC CONF ANTEN, P104, DOI 10.1109/APCAP.2014.6992424
Markov Z., 2007, DATA MINING WEB UNCO
Mele I, 2013, P 6 ACM INT C WEB SE
Mobasher B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P90
Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169
Mobasher B, 2002, DATA MIN KNOWL DISC, V6, P61, DOI 10.1023/A:1013232803866
MOBASHER B, 2001, P IJCAI 2001 WORKSH
Mulvenna MD, 2000, COMMUN ACM, V43, P123, DOI 10.1145/345124.345165
Murali T, 2003, PAC S BIOC LIH HI
Nasraoui O., 2000, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V9, P509, DOI 10.1142/S021821300000032X
Nasraoui O, 2008, IEEE T KNOWL DATA EN, V20, P202, DOI 10.1109/TKDE.2007.190667
Ning X., 2011, 2011 IEEE 11 INT C D
Padilha VA, 2017, BMC BIOINFORMATICS, V18, DOI 10.1186/s12859-017-1487-1
Pan R., 2008, ONE CLASS COLLABORAT
Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159
Prelic A, 2006, BIOINFORMATICS, V22, P1122, DOI 10.1093/bioinformatics/btl060
Qiu F., 2006, P 15 INT C WORLD WID
Raad E., 2010, P 13 INT C NETW BAS
Rajan S., 2017, P ACM SIGIR INT C TH
Rendle S., 2009, UAI
Rosenkrans G, 2007, ONLINE ADVERTISING M, V15
Schafer J. B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P291
Schiaffino S, 2009, LECT NOTES COMPUT SC, V5640, P193, DOI 10.1007/978-3-642-03226-4_11
Serin A, 2011, ALGORITHM MOL BIOL, V6, DOI 10.1186/1748-7188-6-18
Shabalin AA, 2009, ANN APPL STAT, V3, P985, DOI 10.1214/09-AOAS239
Soltysiak SJ, 1998, BT TECHNOL J, V16, P110, DOI 10.1023/A:1009690117684
Sugiyama K., 2004, 13 INT WORLD WIDE WE
Symeonidis P., 2009, P 3 ACM C REC SYST
Symeonidis P, 2008, INFORM RETRIEVAL, V11, P51, DOI 10.1007/s10791-007-9038-4
Tanay Amos, 2002, Bioinformatics, V18 Suppl 1, pS136
Varnagar C. R., 2013, INT C INF COMM EMB S
YAGER RR, 1988, IEEE T SYST MAN CYB, V18, P183, DOI 10.1109/21.87068
YAGER RR, 1993, FUZZY SET SYST, V59, P125, DOI 10.1016/0165-0114(93)90194-M
Yager RR, 1999, IEEE T SYST MAN CY B, V29, P141, DOI 10.1109/3477.752789
Zhang DQ, 2014, IEEE T EMERG TOP COM, V2, P239, DOI 10.1109/TETC.2013.2283233
Zhang QG, 2009, 2009 INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN COMPUTER SCIENCE, ICRCCS 2009, P18, DOI 10.1109/ICRCCS.2009.14
Zhang Z, 2008, PROCEEDINGS OF THE 11TH JOINT CONFERENCE ON INFORMATION SCIENCES
Zhen Y., 2009, P 3 ACM C REC SYST
NR 82
TC 8
Z9 9
U1 0
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD MAY-JUN
PY 2019
VL 35
AR 100857
DI 10.1016/j.elerap.2019.100857
PG 17
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA IM0HP
UT WOS:000477668400019
DA 2024-03-27
ER
PT J
AU Nafizah, UY
Roper, S
Mole, K
AF Nafizah, Ully Y.
Roper, Stephen
Mole, Kevin
TI Estimating the innovation benefits of first-mover and second-mover
strategies when micro-businesses adopt artificial intelligence and
machine learning
SO SMALL BUSINESS ECONOMICS
LA English
DT Article
DE Advanced digital technology; Artificial Intelligence; Digital adoption;
Innovation; Machine Learning; Micro-Business; Timing Adoption
ID RESOURCE-BASED VIEW; RESEARCH-AND-DEVELOPMENT; INFORMATION-TECHNOLOGY;
MANAGEMENT-PRACTICES; EMPIRICAL-EVIDENCE; MOVER ADVANTAGE; FIRM
RESOURCES; CAPABILITY; ENTRY; PERSPECTIVE
AB Plain English SummaryDespite the powerful functions offered by advanced digital technologies, such as Artificial Intelligence and Machine Learning, it is unclear whether micro-businesses should adopt these technologies. In addition, micro-businesses are faced with two adoption strategy options: a first mover strategy by becoming an early adopter, or a second mover strategy by becoming a later adopter of the new technologies. Our study suggests that adopting Artificial Intelligence and Machine Learning enhances micro-businesses' innovation outcomes and innovation processes, highlighting the benefits of technology adoption on micro-businesses with limited financial and human resources. Interestingly, our study suggests the differential benefits of first mover and second mover strategies based on technology characteristics. The principal implication of this study is that micro-businesses should be encouraged to adopt Artificial Intelligence and Machine Learning to compensate for their resources and capabilities in the innovation process.
Abstract Digital technologies have the potential to transform all aspects of firms' operations. The emergence of advanced digital technologies such as Artificial Intelligence and Machine Learning raises questions about whether and when micro-businesses should adopt these technologies. In this paper we focus on how firms' adoption decisions on Artificial Intelligence and Machine Learning influence their innovation capabilities. Using survey data for over 6,000 micro-businesses in the UK, we identify two groups of adopters based on the timing of their adoption of Artificial Intelligence and Machine Learning. 'first movers' - early adopters of the new technologies - and 'second movers'- later adopters of the new technology. Probit models are used to investigate the innovation benefits of first and second mover adoption strategies. Our results suggest strong and positive impacts of adopting Artificial Intelligence and Machine Learning on micro-businesses' innovation outcomes and innovation processes. We highlight the differential benefits of first mover and second mover strategies and highlight the role of technology characteristics as the differentiating factor. Our results emphasize both the innovation enabling role of digital technologies and the importance of an appropriate strategic approach to adopting advanced digital technologies.
C1 [Nafizah, Ully Y.; Roper, Stephen; Mole, Kevin] Univ Warwick, Warwick Business Sch, Coventry, England.
C3 University of Warwick
RP Nafizah, UY (autor correspondiente), Univ Warwick, Warwick Business Sch, Coventry, England.
EM Ully-Yunita.Nafizah@warwick.ac.uk; Stephen.Roper@wbs.ac.uk;
Kevin.Mole@wbs.ac.uk
OI NAFIZAH, ULLY YUNITA/0000-0001-5444-4916
CR Agostini L, 2019, EUR J INNOV MANAG, V23, P1, DOI 10.1108/EJIM-11-2019-0330
Agyapong A., 2016, J SMALL BUSINESS ENT, V28, P449, DOI [10.1080/08276331.2016.1217727, DOI 10.1080/08276331.2016.1217727]
Ainin S, 2015, IND MANAGE DATA SYST, V115, P570, DOI 10.1108/IMDS-07-2014-0205
Angerrnann H, 2016, EXPERT SYST APPL, V66, P76, DOI 10.1016/j.eswa.2016.08.058
[Anonymous], 2020, OECD Digital Economy Outlook 2020, DOI DOI 10.1787/BB167041-EN
Antons D, 2018, J SERV RES-US, V21, P17, DOI 10.1177/1094670517738373
Audretsch DB, 2020, SMALL BUS ECON, V55, P997, DOI 10.1007/s11187-020-00366-4
Bag S, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120420
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Barney JB, 2001, ACAD MANAGE REV, V26, P41, DOI 10.2307/259393
Battisti G, 2015, IND CORP CHANGE, V24, P345, DOI 10.1093/icc/dtv003
Battisti G, 2010, BRIT J MANAGE, V21, P187, DOI 10.1111/j.1467-8551.2009.00629.x
Battisti G, 2009, RES POLICY, V38, P1326, DOI 10.1016/j.respol.2009.06.002
Bauer M, 2020, AMCIS 2020 PROCEEDINGS
Baumann J, 2016, RES POLICY, V45, P1263, DOI 10.1016/j.respol.2016.03.008
BEIS, 2020, BUSINESS POPULATION
Bharadwaj A, 2013, MIS QUART, V37, P471, DOI 10.25300/MISQ/2013/37:2.3
Boulding W, 2001, HARVARD BUS REV, V79, P20
Bourke J, 2016, TECHNOVATION, V55-56, P42, DOI 10.1016/j.technovation.2016.05.003
Brynjolfsson E., 2017, Harvard Bus Rev, V1, P1
Cassiman B, 2002, AM ECON REV, V92, P1169, DOI 10.1257/00028280260344704
Chen LJ, 2022, J BUS IND MARK, V37, P1025, DOI 10.1108/JBIM-09-2020-0448
Cockburn I. M., 2019, IMPACT ARTIFICIAL IN, P115, DOI DOI 10.7208/CHICAGO/9780226613475.001.0001
Loureiro SMC, 2021, J BUS RES, V129, P911, DOI 10.1016/j.jbusres.2020.11.001
Davenport TH, 2018, HARVARD BUS REV, V96, P108
DIXIT A, 1980, ECON J, V90, P95, DOI 10.2307/2231658
Dorrington P, 2016, SMART INNOV SYST TEC, V52, P215, DOI 10.1007/978-3-319-32098-4_19
Farazzmanesh F., 2017, J INFORM SYSTEMS TEL, V5, P162
Frank AG, 2019, INT J PROD ECON, V210, P15, DOI 10.1016/j.ijpe.2019.01.004
Ghobakhloo M, 2019, J IND INF INTEGR, V16, DOI 10.1016/j.jii.2019.100107
GILBERT RJ, 1982, AM ECON REV, V72, P514
Gimenez F.A.P., 2000, CREAT INNOV MANAG, V9, P235, DOI DOI 10.1111/1467-8691.00197
Giotopoulos I, 2017, J BUS RES, V81, P60, DOI 10.1016/j.jbusres.2017.08.007
GOLDER PN, 1993, J MARKETING RES, V30, P158, DOI 10.2307/3172825
Greene W.H., 2000, ECONOMETRIC ANAL
Haefner N, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120392
Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
Hartmann P, 2020, ACAD MANAG DISCOV, V6, P359, DOI 10.5465/amd.2019.0043
Hewitt-Dundas N, 2006, SMALL BUS ECON, V26, P257, DOI 10.1007/s11187-005-2140-3
Hoppe HC, 2000, INT J IND ORGAN, V18, P315, DOI 10.1016/S0167-7187(98)00020-4
Iftikhar N, 2021, PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INNOVATIVE INTELLIGENT INDUSTRIAL PRODUCTION AND LOGISTICS (IN4PL), P131, DOI 10.5220/0010691800003062
Jones P, 2014, INT SMALL BUS J, V32, P285, DOI 10.1177/0266242612461802
Kagermann H., 2013, Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group
Kelliher F, 2009, J SMALL BUS ENTERP D, V16, P521, DOI 10.1108/14626000910977206
Kianto A, 2017, J BUS RES, V81, P11, DOI 10.1016/j.jbusres.2017.07.018
Klepper S, 2000, STRATEGIC MANAGE J, V21, P997, DOI 10.1002/1097-0266(200010/11)21:10/11<997::AID-SMJ134>3.0.CO;2-O
Kopiec Daniel, 2019, Digital innovation: seizing policy opportunities, V52, P380, DOI [DOI 10.1787/A298DC87-EN, 10.1063/1.1731475, DOI 10.1063/1.1731475]
LAMBKIN M, 1988, STRATEGIC MANAGE J, V9, P127, DOI 10.1002/smj.4250090713
Liang Li, 2018, Information Systems Journal, V28, P1129, DOI 10.1111/isj.12153
Liberman-Yaconi L, 2010, J SMALL BUS MANAGE, V48, P70, DOI 10.1111/j.1540-627X.2009.00287.x
LIEBERMAN MB, 1988, STRATEGIC MANAGE J, V9, P41, DOI 10.1002/smj.4250090706
López-Mielgo N, 2009, TECHNOVATION, V29, P537, DOI 10.1016/j.technovation.2009.02.005
MacGregor RC, 2005, J SMALL BUS ENTERP D, V12, P510, DOI 10.1108/14626000510628199
Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
Mikalef P, 2018, INF SYST E-BUS MANAG, V16, P547, DOI 10.1007/s10257-017-0362-y
MILES RE, 1986, CALIF MANAGE REV, V28, P62, DOI 10.2307/41165202
Nambisan S, 2017, ENTREP THEORY PRACT, V41, P1029, DOI 10.1111/etap.12254
Netter J., 1989, Applied regression models
Niebel T, 2019, ECON INNOV NEW TECH, V28, P296, DOI 10.1080/10438599.2018.1493075
Parnell J.A., 2001, International Journal of Commerce Management, V11, P1, DOI 10.1108/eb047425
Paschen J, 2019, J BUS IND MARK, V34, P1410, DOI 10.1108/JBIM-10-2018-0295
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Raymond L, 2009, LECT NOTES BUS INF P, V24, P27
RIORDAN MH, 1992, RAND J ECON, V23, P334, DOI 10.2307/2555866
ROBINSON WT, 1992, STRATEGIC MANAGE J, V13, P609, DOI 10.1002/smj.4250130804
Robinson WT, 2002, J MARKETING RES, V39, P120, DOI 10.1509/jmkr.39.1.120.18938
Romijn H, 2002, RES POLICY, V31, P1053, DOI 10.1016/S0048-7333(01)00176-7
ROSENBLOOM RS, 1987, CALIF MANAGE REV, V29, P51, DOI 10.2307/41162131
Shepherd DA, 1999, MANAGE SCI, V45, P621, DOI 10.1287/mnsc.45.5.621
Simmons G, 2008, INT SMALL BUS J, V26, P351, DOI 10.1177/0266242608088743
Sirmon DG, 2007, ACAD MANAGE REV, V32, P273, DOI 10.2307/20159292
Srinivasan Vijay, 2014, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V23, DOI 10.1142/S0218213014600252
Steiber A, 2021, EUR J INNOV MANAG, V24, P799, DOI 10.1108/EJIM-01-2020-0018
Suarez FF, 2007, ACAD MANAGE REV, V32, P377, DOI 10.5465/AMR.2007.24349587
Tang Z, 2012, ASIA PAC J MANAG, V29, P409, DOI 10.1007/s10490-010-9200-1
Toms S, 2010, ACCOUNT AUDIT ACCOUN, V23, P647, DOI 10.1108/09513571011054927
Trabucchi D, 2019, EUR J INNOV MANAG, V22, P23, DOI 10.1108/EJIM-01-2018-0017
Trabucchi D, 2017, RES TECHNOL MANAGE, V60, P43, DOI 10.1080/08956308.2017.1276390
Tran VD, 2012, J IND ECON, V60, P517, DOI 10.1111/j.1467-6451.2012.00490.x
Wade M, 2004, MIS QUART, V28, P107
Wang CH, 2008, TECHNOVATION, V28, P349, DOI 10.1016/j.technovation.2007.10.007
Wu F, 2006, IND MARKET MANAG, V35, P493, DOI 10.1016/j.indmarman.2005.05.003
Yoo Y, 2012, ORGAN SCI, V23, P1398, DOI 10.1287/orsc.1120.0771
Yoo YJ, 2010, INFORM SYST RES, V21, P724, DOI 10.1287/isre.1100.0322
Yoon YR, 2009, INT J IND ORGAN, V27, P728, DOI 10.1016/j.ijindorg.2009.04.001
Zhou KZ, 2010, STRATEGIC MANAGE J, V31, P547, DOI 10.1002/smj.830
NR 86
TC 1
Z9 1
U1 66
U2 83
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0921-898X
EI 1573-0913
J9 SMALL BUS ECON
JI Small Bus. Econ. Group
PD JAN
PY 2024
VL 62
IS 1
BP 411
EP 434
DI 10.1007/s11187-023-00779-x
EA MAY 2023
PG 24
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA EL4T0
UT WOS:000996939400002
OA Green Published, hybrid
DA 2024-03-27
ER
PT J
AU Diwanji, VS
Lee, JJ
Cortese, J
AF Diwanji, Vaibhav Shwetangbhai
Lee, Jaejin
Cortese, Juliann
TI Deconstructing the role of artificial intelligence in programmatic
advertising: at the intersection of automation and transparency
SO JOURNAL OF STRATEGIC MARKETING
LA English
DT Article; Early Access
DE artificial intelligence; programmatic advertising; online advertising;
digital marketing; search engine marketing; social media marketing;
sponsorship disclosure
ID CONSUMER; SPONSORSHIP; ATTRIBUTIONS; CREDIBILITY; RELIABILITY;
CONGRUENCE
AB With the increasingly sophisticated improvements in artificial intelligence (AI) technologies in marketing communications, a gap prevails between heightened levels of excitement around it and high-level application. Consequently, this topic necessitates in-depth investigation to prepare for future changes. This study examines the role of AI in programmatic advertising through a quantitative content analysis of AI-enabled digital programmatic advertisements. Although the idea that sponsorship disclosure, appeals, and sentiments are important in persuasive messages has been documented, this study extends existing literature by examining these concepts within AI-enabled programmatic advertising. Sponsorship-linked marketing and congruence theory guided this study. The results showed that only about half of the ads revealed sponsorship information. Further, the brand-sponsor association was not clearly established in the ads. A sentiment analysis showed the prominence of positive sentiments in the ads. Additionally, the ads mostly used product- and consumer-focused appeals. Theoretical and research contributions and strategic marketing implications are discussed.
C1 [Diwanji, Vaibhav Shwetangbhai] Univ Kansas, William Allen White Sch Journalism & Mass Commun, Lawrence, KS 66045 USA.
[Lee, Jaejin; Cortese, Juliann] Informat Florida State Univ, Coll Commun, Sch Commun, Tallahassee, FL USA.
C3 University of Kansas; State University System of Florida; Florida State
University
RP Diwanji, VS (autor correspondiente), Univ Kansas, William Allen White Sch Journalism & Mass Commun, Lawrence, KS 66045 USA.
EM vdiwanji@ku.edu
RI Cortese, Juliann/ADS-4107-2022
CR Aackerman C.E., 2020, POSITIVEPSYCHOLOGY B
Abi K, 2021, INT J EC PERFORMANCE, V4, P322
Alaimo C, 2018, IFIP ADV INF COMM TE, V543, P110, DOI 10.1007/978-3-030-04091-8_9
[Anonymous], 2022, Federal Trade Commission." from
[Anonymous], 2019, FEDERAL TRADE COMMIS
Armstrong J. Scott., 2010, Persuasive advertising: evidencebased principles
Baker L., 2007, SEARCH ENGINE J BLOG
Bakpayev M, 2022, AUSTRALAS MARK J, V30, P90, DOI 10.1016/j.ausmj.2020.04.002
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Busch O., 2016, Programmatic Advertising, P3, DOI [10.1007/978-3-319-25023-6_1, DOI 10.1007/978-3-319-25023-6_1]
Cambria E, 2017, IEEE INTELL SYST, V32, P74, DOI 10.1109/MIS.2017.4531228
Campbell C, 2015, BUS HORIZONS, V58, P599, DOI 10.1016/j.bushor.2015.06.003
Carrillo-Durán MV, 2018, PROF INFORM, V27, P195, DOI 10.3145/epi.2018.ene.18
Chacon B., 2019, LATER BLOG
Chang HH, 2018, ONLINE INFORM REV, V42, P697, DOI 10.1108/OIR-08-2016-0214
Chen G, 2019, J ADVERTISING, V48, P347, DOI 10.1080/00913367.2019.1654421
Chen YB, 2021, LECT NOTE NETW SYST, V271, P148, DOI 10.1007/978-3-030-80624-8_19
Clemente S, 2014, MARKET LETT, V25, P373, DOI 10.1007/s11002-013-9257-y
CORNWELL B., 1995, SPORT MARKET Q, V4, P13
Cornwell T.B., 2001, J ADVERTISING RES, V41, P17, DOI [10.2501/JAR-41-1-17-31, DOI 10.2501/JAR-41-1-17-31]
Cornwell TB, 2005, J ADVERTISING, V34, P21, DOI 10.1080/00913367.2005.10639194
Dahlén M, 2008, J ADVERTISING, V37, P57, DOI 10.2753/JOA0091-3367370305
DeGeurin M., 2021, EMARKETER BLOG
Deng SS, 2019, J ADVERTISING, V48, P356, DOI 10.1080/00913367.2019.1652121
Diwanji V, 2022, J APPL MARKETING THE, V9, P5
Diwanji VS, 2021, J ELECTRON COMMER RE, V22, P22
Diwanji VS, 2020, J RETAIL CONSUM SERV, V54, DOI 10.1016/j.jretconser.2019.102024
Dix Steve., 2013, Journal of Promotion Management, V19, P393, DOI [10.1080/10496491.2013.817218, DOI 10.1080/10496491.2013.817218]
ebizmba, 2020, EBIZMBA BLOG
Enberg J., 2019, EMARKETER BLOG
Fang Z, 2019, IEEE DATA MINING, P190, DOI 10.1109/ICDM.2019.00029
Felzmann H, 2019, BIG DATA SOC, V6, DOI 10.1177/2053951719860542
Ferrell OC., 2021, Marketing strategy
Ferrer-Conill R, 2021, DIGIT JOURNAL, V9, P929, DOI 10.1080/21670811.2020.1836980
Fleck ND, 2007, PSYCHOL MARKET, V24, P975, DOI 10.1002/mar.20192
Forsey C., 2020, HUBSPOT BLOG
FRIESTAD M, 1994, J CONSUM RES, V21, P1, DOI 10.1086/209380
Frijda N.H., 1994, NATURE EMOTION, P197
Gibson D., 2018, DRUM BLOG
GroundTruth, 2019, GROUNDTRUTH BLOG
Hastie R., 1980, Person memory: The cognitive basis of social perception, P155
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Jiang H, 2018, J CREATIVE BEHAV, V52, P168, DOI 10.1002/jocb.141
KASSARJIAN HH, 1977, J CONSUM RES, V4, P8, DOI 10.1086/208674
Kellogg K., 2020, SEARCH ENGINE J BLOG
Keshari P., 2014, Journal of Marketing and Communication, V9, P37
Khanna P., 2016, INT J MANAGEMENT SOC, V4, P568, DOI DOI 10.21013/JMSS.V4.N3.P7
Kietzmann J, 2018, J ADVERTISING RES, V58, P263, DOI 10.2501/JAR-2018-035
Kotler P, 2021, J MACROMARKETING, V41, P194, DOI 10.1177/0276146721996433
Krippendorf K., 1980, CONTENT ANAL
Lacy S, 1996, JOURNALISM MASS COMM, V73, P963, DOI 10.1177/107769909607300414
Ledro C, 2022, J BUS IND MARK, V37, P48, DOI 10.1108/JBIM-07-2021-0332
Lee S, 2018, SPORT MARKET Q, V27, P82
Lennon R., 2017, THUNDER BLOG
Liao B., 2017, CONT COMMUNICATIONS, V7, P97
Liu L., 2018, MARKET SCI, V39(4), P669
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Machin D., 2015, Visual Journalism
Macnamara J, 2016, PUBLIC RELAT REV, V42, P377, DOI 10.1016/j.pubrev.2016.03.001
Malthouse EC, 2019, J ADVERTISING, V48, P366, DOI 10.1080/00913367.2019.1652123
Malthouse EC, 2017, J ADVERTISING, V46, P227, DOI 10.1080/00913367.2017.1299653
McGuigan L, 2019, NEW MEDIA SOC, V21, P2366, DOI 10.1177/1461444819846449
Mindaou G., 2019, LINKEDIN BLOG
Neuendorf K. A., 2002, CONTENT ANAL GUIDEBO
NEWCOMB T, 1958, AM SOCIOL REV, V23, P742, DOI 10.2307/2089062
Ng ICL, 2018, J SERV MANAGE, V29, P518, DOI 10.1108/JOSM-07-2018-412
Pizzi G, 2021, J BUS RES, V129, P878, DOI 10.1016/j.jbusres.2020.11.006
Potter WJ, 1999, J APPL COMMUN RES, V27, P258, DOI 10.1080/00909889909365539
Qin XB, 2019, J ADVERTISING, V48, P338, DOI 10.1080/00913367.2019.1652122
Rachul C, 2020, BMJ OPEN, V10, DOI 10.1136/bmjopen-2020-040989
Raghavan R., 2019, GOOGLE ADS BLOG
Rifon NJ, 2004, J ADVERTISING, V33, P29, DOI 10.1080/00913367.2004.10639151
Rodgers W, 2022, J BUS ETHICS, V178, P1043, DOI 10.1007/s10551-022-05048-7
Samuel A, 2021, COMPUT HUM BEHAV, V116, DOI 10.1016/j.chb.2020.106657
Schouten AP, 2020, INT J ADVERT, V39, P258, DOI 10.1080/02650487.2019.1634898
Sebastian M., 2014, ADVERTISINGAGE BLOG
Seigchrist G., 2019, LIFEWIRE BLOG
Shewan D., 2020, WORDSTREAM BLOG
Stone M, 2020, BOTTOM LINE, V33, P183, DOI 10.1108/BL-03-2020-0022
Taiminen K, 2015, PUBLIC RELAT REV, V41, P734, DOI 10.1016/j.pubrev.2015.06.016
Van Kleef GA, 2015, J APPL PSYCHOL, V100, P1124, DOI 10.1037/apl0000003
van Noort G, 2020, J ADVERTISING, V49, P411, DOI 10.1080/00913367.2020.1795954
Wojdynski BW, 2016, J ADVERTISING, V45, P157, DOI 10.1080/00913367.2015.1115380
Woolley JK, 2010, MASS COMMUN SOC, V13, P631, DOI 10.1080/15205436.2010.516864
Youngmann B, 2021, INFORM RETRIEVAL J, V24, P205, DOI 10.1007/s10791-021-09392-6
NR 86
TC 2
Z9 2
U1 37
U2 54
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0965-254X
EI 1466-4488
J9 J STRATEG MARK
JI J. Strateg. Mark.
PD 2022 NOV 24
PY 2022
DI 10.1080/0965254X.2022.2148269
EA NOV 2022
PG 18
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 6N6BJ
UT WOS:000889639300001
DA 2024-03-27
ER
PT J
AU Chan, JCF
Jiang, ZH
Tan, BCY
AF Chan, Jason C. F.
Jiang, Zhenhui
Tan, Bernard C. Y.
TI Understanding Online Interruption-Based Advertising: Impacts of Exposure
Timing, Advertising Intent, and Brand Image
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Advertising intent; brand image; experiment design; human-computer
interaction; interruption-based advertising; mindset; online
advertising; psychology of web users
ID IMPLEMENTAL MIND-SETS; INFORMATION SEARCH; IMPLICIT MEMORY; WEB SITES;
PERSUASION; CONSUMERS; CHOICE; KNOWLEDGE; INTERNET; BEHAVIOR
AB Interruption-based advertising has gained prominence in the online channel. Yet, little attention has been paid to deriving design principles and conceptualizations for online interruption-based advertising. This paper examines three novel design factors related to this phenomenon, namely, exposure timing, advertising intent, and brand image. Exposure timing pertains to the time by which the advertisement (ad) is launched within a website. Advertising intent refers to the explicitness of ad content in portraying the desire to induce purchase behavior. Brand image relates to consumers' overall perceptions of the advertised brand. In a laboratory experiment, participants were exposed to pop-up ads that were operationalized based on these three design considerations. Results reveal three two-way interactions among the study constructs. Online interruption-based ads shown in the predecisional shopping phase are more effective when their contents are designed with implicit advertising intent compared to explicit intent. Brand image is found to moderate the effects of advertising intent on consumer's purchase intention. Participants' responses also show that ads promoting weak brands with less favorable image tend to enjoy higher purchase intention when shown in the predecisional phase compared with the postdecisional phase. Theoretical and practical implications together with suggestions for future research are discussed.
C1 [Chan, Jason C. F.; Jiang, Zhenhui; Tan, Bernard C. Y.] Natl Univ Singapore, Dept Informat Syst, Singapore 119074, Singapore.
C3 National University of Singapore
RP Chan, JCF (autor correspondiente), NYU, Stern Sch Business, 550 1St Ave, New York, NY 10012 USA.
EM jason.chan@stern.nyu.edu; jiang@comp.nus.edu.sg; btan@comp.nus.edu.sg
CR Anand Punam., 1989, Cognitive and Affective Responses to Advertising, P135
[Anonymous], 1993, J CURR ISS RES AD
Bart Y, 2005, J MARKETING, V69, P133, DOI 10.1509/jmkg.2005.69.4.133
Bettman J.R., 1979, An Information Processing Theory of Consumer Choice
Bruner GC, 2000, J ADVERTISING RES, V40, P35, DOI 10.2501/JAR-40-1-2-35-42
Campbell MC, 2000, J CONSUM RES, V27, P69, DOI 10.1086/314309
Chang CC, 2007, J ADVERTISING, V36, P75, DOI 10.2753/JOA0091-3367360305
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Dahlen M, 2001, J ADVERTISING RES, V41, P23, DOI 10.2501/JAR-41-4-23-30
Danaher P., 1997, J INTERACT MARK, V11, P46, DOI DOI 10.1002/(SICI)1522-7138(199721)11:23.0.CO;2-Y
Danaher PJ, 2003, MARKET SCI, V22, P461, DOI 10.1287/mksc.22.4.461.24907
de Vries P, 2007, LECT NOTES COMPUT SC, V4744, P164
Degeratu AM, 2000, INT J RES MARK, V17, P55, DOI 10.1016/S0167-8116(00)00005-7
DIESBACH P, 2007, P PERS TECHN, P223
Edwards SM, 2002, J ADVERTISING, V31, P83, DOI 10.1080/00913367.2002.10673678
Engel J, 1990, Consumer Behavior
Fogg B.J., 2002, PERSUASIVE TECHNOLOG, DOI [10.1145/764008.763957, DOI 10.1145/764008.763957]
FRIED M, 1994, CRIT INQUIRY, V21, P1, DOI 10.1086/448738
GENSCH DH, 1970, J MARKETING RES, V7, P216, DOI 10.2307/3150112
Gibson JJ, 1941, PSYCHOL BULL, V38, P781, DOI 10.1037/h0055307
Gollwitzer P.M., 1996, PSYCHOL ACTION LINKI, P287, DOI DOI 10.1037/0033-2909.120.3.338
Gollwitzer P. M., 1990, Handbook of Motivation and Cognition: Foundations of Social Behavior, V2, P53
Gollwitzer PM, 1999, DUAL-PROCESS THEORIES IN SOCIAL PSYCHOLOGY, P403
GOLLWITZER PM, 1990, J PERS SOC PSYCHOL, V59, P1119, DOI 10.1037/0022-3514.59.6.1119
GOLLWITZER PM, 1989, J PERS SOC PSYCHOL, V56, P531, DOI 10.1037/0022-3514.56.4.531
Ha L, 1996, J ADVERTISING RES, V36, P76
Ha L, 2008, INT J ADVERT, V27, P569, DOI 10.2501/S0265048708080153
HAN CM, 1988, J ADVERTISING RES, V28, P25
Harris J, 2007, J SERV RES-US, V9, P297, DOI 10.1177/1094670507299379
HECKHAUSEN H, 1987, MOTIV EMOTION, V11, P101, DOI 10.1007/BF00992338
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
HOLBROOK MB, 1982, J CONSUM RES, V9, P132, DOI 10.1086/208906
Hong W, 2004, INFORM SYST RES, V15, P60, DOI 10.1287/isre.1040.0017
Howard J.A., 1989, Consumer Behavior in Marketing Strategy
*INT ADV BUR, 2008, IAB INT ADV REV REP
JACOBY LL, 1981, J EXP PSYCHOL GEN, V110, P306, DOI 10.1037/0096-3445.110.3.306
JIANG Z, J AIS IN PRESS
Jiang ZH, 2004, J MANAGE INFORM SYST, V21, P111, DOI 10.1080/07421222.2004.11045817
Jiang ZH, 2007, INFORM SYST RES, V18, P454, DOI 10.1287/isre.1070.0124
Kahneman D., 1973, ATTENTION EFFORT
Katz J, 1997, INTERNET RES, V7, P170, DOI 10.1108/10662249710171814
KELLER KL, 1993, J MARKETING, V57, P1, DOI 10.2307/1252054
Keppel G., 2004, DESIGN ANAL RES HDB
Khaled R, 2007, LECT NOTES COMPUT SC, V4744, P36
Kim B. H., 2001, Mass Communication and Society, V4, P265, DOI DOI 10.1207/S15327825MCS0403_02
Knight GA, 1999, J CONSUM MARK, V16, P151, DOI 10.1108/07363769910260524
Kotler P., 2005, PRINCIPLES MARKETING, V11th
Lee AY, 2004, J MARKETING RES, V41, P151, DOI 10.1509/jmkr.41.2.151.28665
Lee AY, 2001, PERS SOC PSYCHOL B, V27, P1255, DOI 10.1177/01461672012710002
Lee L, 2006, J CONSUM RES, V33, P60, DOI 10.1086/504136
Li HR, 2002, J ADVERTISING, V31, P43, DOI 10.1080/00913367.2002.10673675
Mithas S, 2006, J MANAGE INFORM SYST, V23, P97, DOI 10.2753/MIS0742-1222230305
Morimoto M., 2006, Journal of Interactive Advertising, V7, P8
NEDUNGADI P, 1990, J CONSUM RES, V17, P263, DOI 10.1086/208556
Ng S, 2006, J CONSUM RES, V32, P519, DOI 10.1086/500482
OLSEN H, 2003, SUPPORTING CUSTOMERS
Oxoby RJ, 2007, PSYCHOL MARKET, V24, P295, DOI 10.1002/mar.20162
Peracchio LA, 1997, J CONSUM RES, V24, P178, DOI 10.1086/209503
PETTY RE, 1983, J CONSUM RES, V10, P135, DOI 10.1086/208954
Pharr JM, 2005, J MARKET THEORY PRAC, V13, P34, DOI 10.1080/10696679.2005.11658557
Ratchford BT, 2003, J MARKETING RES, V40, P193, DOI 10.1509/jmkr.40.2.193.19221
Reinhard MA, 2006, J CONSUM PSYCHOL, V16, P249, DOI 10.1207/s15327663jcp1603_7
ROEDIGER HL, 1992, J EXP PSYCHOL LEARN, V18, P1251, DOI 10.1037/0278-7393.18.6.1251
SCHMITTLEIN DC, 1994, MARKET SCI, V13, P41, DOI 10.1287/mksc.13.1.41
SEAMON JG, 1995, J EXP PSYCHOL LEARN, V21, P711, DOI 10.1037/0278-7393.21.3.711
Shapiro S, 1997, J CONSUM RES, V24, P94, DOI 10.1086/209496
*SKYTR, 2008, OFF WORLD AIRL STAR
*SKYTR, 2008, IND PUBL SKYTR
*SKYTR, 2008, SKYTRAX CORP PROF
Speck PS, 1997, J ADVERTISING, V26, P61, DOI 10.1080/00913367.1997.10673529
SPOOL JM, 2002, CUSTOMER SIEVE
SRINIVAS K, 1993, J EXP PSYCHOL LEARN, V19, P582, DOI 10.1037/0278-7393.19.3.582
TAYLOR CP, 2001, ADVERTISING AGE, V72, P36
TAYLOR SE, 1995, J PERS SOC PSYCHOL, V69, P213, DOI 10.1037/0022-3514.69.2.213
Vakratsas D, 1999, J MARKETING, V63, P26, DOI 10.2307/1251999
Ward M.R., 2000, Journal of Product Brand Management, V9, P6, DOI DOI 10.1108/10610420010316302
WILLIAMS C, 2007, POP UP ADS TOLD POP
Williams P, 2004, J CONSUM RES, V31, P540, DOI 10.1086/425088
WINTER FW, 1973, J MARKETING RES, V10, P130, DOI 10.2307/3149817
Yoo B, 2001, J BUS RES, V52, P1, DOI 10.1016/S0148-2963(99)00098-3
ZHU JY, 2007, 28 INT C INF SYST MO
2007, MARKETING NEWS, V41, P28
NR 82
TC 23
Z9 25
U1 4
U2 78
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD AUG
PY 2010
VL 57
IS 3
BP 365
EP 379
DI 10.1109/TEM.2009.2034255
PG 15
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA 659CF
UT WOS:000282543900002
DA 2024-03-27
ER
PT J
AU Schwartz, EM
Bradlow, ET
Fader, PS
AF Schwartz, Eric M.
Bradlow, Eric T.
Fader, Peter S.
TI Customer Acquisition via Display Advertising Using Multi-Armed Bandit
Experiments
SO MARKETING SCIENCE
LA English
DT Article
DE multi-armed bandit; online advertising; field experiments; A/B testing;
adaptive experiments; sequential decision making; explore-exploit;
earning-and-learning; reinforcement learning; hierarchical models;
machine learning
ID 2-ARMED BANDIT; ALLOCATION; INDEX
AB Firms using online advertising regularly run experiments with multiple versions of their ads since they are uncertain about which ones are most effective. During a campaign, firms try to adapt to intermediate results of their tests, optimizing what they earn while learning about their ads. Yet how should they decide what percentage of impressions to allocate to each ad? This paper answers that question, resolving the well-known "learn-and-earn" trade-off using multi-armed bandit (MAB) methods. The online advertiser's MAB problem, however, contains particular challenges, such as a hierarchical structure (ads within a website), attributes of actions (creative elements of an ad), and batched decisions (millions of impressions at a time), that are not fully accommodated by existing MAB methods. Our approach captures how the impact of observable ad attributes on ad effectiveness differs by website in unobserved ways, and our policy generates allocations of impressions that can be used in practice. We implemented this policy in a live field experiment delivering over 750 million ad impressions in an online display campaign with a large retail bank. Over the course of two months, our policy achieved an 8% improvement in the customer acquisition rate, relative to a control policy, without any additional costs to the bank. Beyond the actual experiment, we performed counterfactual simulations to evaluate a range of alternative model specifications and allocation rules in MAB policies. Finally, we show that customer acquisition would decrease by about 10% if the firm were to optimize click-through rates instead of conversion directly, a finding that has implications for understanding the marketing funnel.
C1 [Schwartz, Eric M.] Univ Michigan, Stephen M Ross Sch Business, Ann Arbor, MI 48109 USA.
[Bradlow, Eric T.; Fader, Peter S.] Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA.
C3 University of Michigan System; University of Michigan; University of
Pennsylvania
RP Schwartz, EM (autor correspondiente), Univ Michigan, Stephen M Ross Sch Business, Ann Arbor, MI 48109 USA.
EM ericmsch@umich.edu; ebradlow@wharton.upenn.edu; faderp@wharton.upenn.edu
RI Fader, Peter/O-9757-2018
OI Fader, Peter/0000-0003-0272-2990
CR Agarwal Deepak., 2008, Yahoo Research paper series
AGRAWAL R, 1995, ADV APPL PROBAB, V27, P1054, DOI 10.2307/1427934
Agrawal S., 2012, JMLR P, P39
Anderson ET, 2011, HARVARD BUS REV, V89, P98
Auer P, 2003, J MACHINE LEARNING R, V3, P397, DOI DOI 10.4271/610369
Bates DM, 1988, NONLINEAR REGRESSION
BERRY D. A., 1985, BANDIT PROBLEMS
Berry DA, 2004, STAT SCI, V19, P175, DOI 10.1214/088342304000000044
BERRY DA, 1972, ANN MATH STAT, V43, P871, DOI 10.1214/aoms/1177692553
Bertsimas D, 2007, OPER RES, V55, P1120, DOI 10.1287/opre.1070.0427
BRADT RN, 1956, ANN MATH STAT, V27, P1060, DOI 10.1214/aoms/1177728073
Braun M, 2013, MARKET SCI, V32, P753, DOI 10.1287/mksc.2013.0802
Brezzi M, 2002, J ECON DYN CONTROL, V27, P87, DOI 10.1016/S0165-1889(01)00028-8
Chapelle O., 2011, P ADV NEUR INF PROC, V24, P1
Chick SE, 2001, OPER RES, V49, P732, DOI 10.1287/opre.49.5.732.10615
Chick SE, 2012, MANAGE SCI, V58, P550, DOI 10.1287/mnsc.1110.1425
Chick SE, 2010, INFORMS J COMPUT, V22, P71, DOI 10.1287/ijoc.1090.0327
Chick SE, 2009, MANAGE SCI, V55, P421, DOI 10.1287/mnsc.1080.0949
Dani V, 2008, COLT, P355
Davenport Thomas H., 2009, Harvard Business Review, V87, P1
Donahoe J, 2011, HARVARD BUS REV, V89, P92
Filippi S., 2010, ADV NEURAL INFORM PR, V23, P1
Frazier P, 2009, INFORMS J COMPUT, V21, P599, DOI 10.1287/ijoc.1080.0314
Gelman A, 2007, VOL ANAL METHODS SOC
Gelman A., 2004, BAYESIAN DATA ANAL
Gittins J, 2011, Multi-Armed Bandit Allocation Indices
GITTINS JC, 1979, J ROY STAT SOC B MET, V41, P148
Goldfarb A, 2011, MARKET SCI, V30, P389, DOI 10.1287/mksc.1100.0583
Granmo OC, 2010, INT J INTELL COMPUT, V3, P207, DOI 10.1108/17563781011049179
Hauser JR, 2014, MANAGE SCI, V60, P1594, DOI 10.1287/mnsc.2014.1961
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Hoban PR, 2015, J MARKETING RES, V52, P375, DOI 10.1509/jmr.13.0277
Kaufmann Emilie, 2012, Algorithmic Learning Theory. 23rd International Conference (ALT 2012). Proceedings, P199, DOI 10.1007/978-3-642-34106-9_18
Keller G, 2003, J ECON THEORY, V113, P302, DOI 10.1016/S0022-0531(03)00092-9
Krishnamurthy V, 2009, MATH OPER RES, V34, P287, DOI 10.1287/moor.1080.0371
LAI TL, 1987, ANN STAT, V15, P1091, DOI 10.1214/aos/1176350495
Lambrecht A, 2013, J MARKETING RES, V50, P561, DOI 10.1509/jmr.11.0503
Lin S, 2015, MARKET SCI, V34, P1, DOI 10.1287/mksc.2014.0868
Manchanda P, 2006, J MARKETING RES, V43, P98, DOI 10.1509/jmkr.43.1.98
May B. C., 2011, TECHNICAL REPORT
MEYER RJ, 1995, MANAGE SCI, V41, P817, DOI 10.1287/mnsc.41.5.817
Murphy SA, 2005, STAT MED, V24, P1455, DOI 10.1002/sim.2022
Ortega PA, 2014, COMPLEX ADAPT SYST M, V2, DOI 10.1186/2194-3206-2-2
Ortega PA, 2010, J ARTIF INTELL RES, V38, P475, DOI 10.1613/jair.3062
Osband I., 2013, P ADV NEUR INF PROC, P3003
Perchet V, 2016, ANN STAT, V44, P660, DOI 10.1214/15-AOS1381
Powell W. B., 2011, Approximate dynamic programming
Reiley D, 2011, P 17 ACM SIGKDD C KN, P1019
ROBBINS H, 1952, B AM MATH SOC, V58, P527, DOI 10.1090/S0002-9904-1952-09620-8
Rubin D, 1990, J EDUC PSYCHOL, V66, P688
Rusmevichientong P, 2010, MATH OPER RES, V35, P395, DOI 10.1287/moor.1100.0446
Russo D, 2014, MATH OPER RES, V39, P1221, DOI 10.1287/moor.2014.0650
Scott SL, 2010, APPL STOCH MODEL BUS, V26, P639, DOI 10.1002/asmb.874
Sutton R. S., 1998, INTRO REINFORCEMENT, V2
Thompson WR, 1933, BIOMETRIKA, V25, P285, DOI 10.1093/biomet/25.3-4.285
TSITSIKLIS JN, 1986, IEEE T AUTOMAT CONTR, V31, P576, DOI 10.1109/TAC.1986.1104332
Urban GL, 2014, MARKET SCI, V33, P27, DOI 10.1287/mksc.2013.0803
WAHRENBERGER DL, 1977, BIOMETRIKA, V64, P172, DOI 10.1093/biomet/64.1.172
White J., 2012, Bandit Algorithms for Website Optimization
WHITTLE P, 1980, J ROY STAT SOC B MET, V42, P143
NR 60
TC 109
Z9 126
U1 9
U2 120
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD JUL-AUG
PY 2017
VL 36
IS 4
BP 500
EP 522
DI 10.1287/mksc.2016.1023
PG 23
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA FD0JO
UT WOS:000407225800002
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Scholz, M
Dorner, V
Schryen, G
Benlian, A
AF Scholz, Michael
Dorner, Verena
Schryen, Guido
Benlian, Alexander
TI A configuration-based recommender system for supporting e-commerce
decisions
SO EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
LA English
DT Article
DE E-commerce; Recommender system; Attribute weights; Configuration system;
Decision support
ID MULTIATTRIBUTE UTILITY MEASUREMENT; PROSPECT-THEORY; CONJOINT-ANALYSIS;
MODELS; CHOICE; RANGE; CUSTOMIZATION; UNCERTAINTY; SENSITIVITY;
HEURISTICS
AB Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Scholz, Michael] Univ Passau, Fac Business Adm & Econ, Informat Syst Focus Elect Commerce, Innstr 43, D-94032 Passau, Germany.
[Dorner, Verena] Karlsruhe Inst Technol, Inst Informat Syst & Mkt, Chair Informat & Market Design, Karlsruhe, Germany.
[Schryen, Guido] Univ Regensburg, Dept Management Informat Syst, Management Informat Syst, Regensburg, Germany.
[Benlian, Alexander] Tech Univ Darmstadt, Chair Informat Syst & Elect Serv, Darmstadt, Germany.
C3 University of Passau; Helmholtz Association; Karlsruhe Institute of
Technology; University of Regensburg; Technical University of Darmstadt
RP Scholz, M (autor correspondiente), Univ Passau, Fac Business Adm & Econ, Informat Syst Focus Elect Commerce, Innstr 43, D-94032 Passau, Germany.
EM michael.scholz@uni-passau.de; verena.dorner@kit.edu;
guido.schryen@wiwi.uni-regensburg.de; benlian@ise.tu-darmstadt.de
RI ; Schryen, Guido/P-7623-2018; Benlian, Alexander/E-1075-2014
OI Scholz, Michael/0000-0003-0173-2900; Schryen, Guido/0000-0003-3794-1413;
Benlian, Alexander/0000-0002-7294-3097
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
AKAAH IP, 1983, J MARKETING RES, V20, P187, DOI 10.2307/3151685
[Anonymous], DECISION SUPPORT SYS
[Anonymous], RATIONAL DECISION MA
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
BETTMAN JR, 1993, J EXP PSYCHOL LEARN, V19, P931
BORCHERDING K, 1991, MANAGE SCI, V37, P1603, DOI 10.1287/mnsc.37.12.1603
Butler J, 1997, EUR J OPER RES, V103, P531, DOI 10.1016/S0377-2217(96)00307-4
DAWES RM, 1979, AM PSYCHOL, V34, P571, DOI 10.1037/0003-066X.34.7.571
De Bruyn A, 2008, MARKET SCI, V27, P443, DOI 10.1287/mksc.1070.0306
Dellaert BGC, 2012, J MARKETING RES, V49, P277, DOI 10.1509/jmr.09.0481
Dellaert BGC, 2009, INT J ELECTRON COMM, V13, P43, DOI 10.2753/JEC1086-4415130302
Ding M, 2005, J MARKETING RES, V42, P67, DOI 10.1509/jmkr.42.1.67.56890
Ding M, 2007, J MARKETING RES, V44, P214, DOI 10.1509/jmkr.44.2.214
EDWARDS W, 1994, ORGAN BEHAV HUM DEC, V60, P306, DOI 10.1006/obhd.1994.1087
Fan ZP, 2013, COMPUT IND ENG, V65, P341, DOI 10.1016/j.cie.2013.02.013
FISCHER GW, 1995, ORGAN BEHAV HUM DEC, V62, P252, DOI 10.1006/obhd.1995.1048
Fishburn PeterC., 1967, Management Science, V13, P435, DOI DOI 10.1287/MNSC.13.7.435
Forsati R, 2014, ACM T INFORM SYST, V32, DOI 10.1145/2641564
HART S G, 1988, P139
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Hauser JR, 2014, J BUS RES, V67, P1688, DOI 10.1016/j.jbusres.2014.02.015
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hoeffler S., 1999, Journal of Consumer Psychology, V8, P113, DOI DOI 10.1207/S15327663JCP0802_01
Huang SL, 2011, ELECTRON COMMER R A, V10, P398, DOI 10.1016/j.elerap.2010.11.003
Keeney RL, 2002, OPER RES, V50, P935, DOI 10.1287/opre.50.6.935.357
Keeney RL., 1976, DECISION ANAL MULTIP
Lahtinen TJ, 2016, EUR J OPER RES, V249, P890, DOI 10.1016/j.ejor.2015.09.056
Levi A., 2012, P ACM C REC SYST
Mustajoki J, 2005, DECISION SCI, V36, P317, DOI 10.1111/j.1540-5414.2005.00075.x
Pfeiffer J, 2013, BUS INFORM SYST ENG+, V5, P397, DOI 10.1007/s12599-013-0295-z
Pöyhönen M, 2001, EUR J OPER RES, V129, P569, DOI 10.1016/S0377-2217(99)00467-1
Pu P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P511, DOI 10.1007/978-0-387-85820-3_16
Scholz M, 2015, DECIS SUPPORT SYST, V72, P60, DOI 10.1016/j.dss.2015.02.006
Shi Y, 2014, ACM COMPUT SURV, V47, DOI 10.1145/2556270
SILVER NC, 1987, J APPL PSYCHOL, V72, P146, DOI 10.1037/0021-9010.72.1.146
Sun MH, 1996, EUR J OPER RES, V89, P462, DOI 10.1016/0377-2217(94)00228-2
TVERSKY A, 1992, J RISK UNCERTAINTY, V5, P297, DOI 10.1007/BF00122574
TVERSKY A, 1991, Q J ECON, V106, P1039, DOI 10.2307/2937956
TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
Valenzuela A, 2009, J MARKETING RES, V46, P754, DOI 10.1509/jmkr.46.6.754
Van Der Land S., 2013, COMPUT HUM BEHAV, V29, P1045
Van Ittersum K, 2007, J BUS RES, V60, P1177, DOI 10.1016/j.jbusres.2007.04.001
Von Winterfeldt Detlof., 1986, DECISION ANAL BEHAV, V604
VONNITZSCH R, 1993, MANAGE SCI, V39, P937, DOI 10.1287/mnsc.39.8.937
Wallenius J, 2008, MANAGE SCI, V54, P1336, DOI 10.1287/mnsc.1070.0838
Wang T, 2007, J MARKETING RES, V44, P200, DOI 10.1509/jmkr.44.2.200
Wansink B, 1998, J MARKETING RES, V35, P71, DOI 10.2307/3151931
WEBER M, 1993, EUR J OPER RES, V67, P1, DOI 10.1016/0377-2217(93)90318-H
Weng L.-T., 2008, P IEEE INT C TOOLS A
Wu G, 2008, MANAGE SCI, V54, P1322, DOI 10.1287/mnsc.1070.0846
Wyner G.A., 1992, Marketing Research, V4, P46
Xiao B, 2007, MIS QUART, V31, P137
Xu AJ, 2010, J CONSUM RES, V37, P329, DOI 10.1086/651204
Xu JJ, 2014, MIS QUART, V38, P379, DOI 10.25300/MISQ/2014/38.2.03
Yu X., 2014, P ACM INT C WEB SEAR
Zhao WNX, 2016, IEEE T KNOWL DATA EN, V28, P1147, DOI 10.1109/TKDE.2015.2508816
Zigoris P., 2006, P ACM INT C INF KNOW
NR 58
TC 35
Z9 37
U1 3
U2 106
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0377-2217
EI 1872-6860
J9 EUR J OPER RES
JI Eur. J. Oper. Res.
PD MAY 16
PY 2017
VL 259
IS 1
BP 205
EP 215
DI 10.1016/j.ejor.2016.09.057
PG 11
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA EJ9ES
UT WOS:000393530000016
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Li, LS
Chen, JQ
Raghunathan, S
AF Li, Lusi
Chen, Jianqing
Raghunathan, Srinivasan
TI Recommender System Rethink: Implications for an Electronic Marketplace
with Competing Manufacturers
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE analytical modeling; economics of IS; recommender system; electronic
commerce
ID ONLINE PRODUCT RECOMMENDATIONS; WEB PERSONALIZATION; IMPACT;
INFORMATION; SALES; STRATEGIES; PERSUASION; CONSUMERS; SEARCH; AGENT
AB Recommender systems that inform consumers about their likely ideal products have become the cornerstone of e-commerce platforms that sell products from competing manufacturers. Using a model of an electronic marketplace in which two competing manufacturers sell their products through a common retail platform, we study the effect of recommender systems on the retail platform, manufacturers, consumer surplus, and social welfare. In our setting, consumers are differentiated with respect to their preference for the two products (locational differentiation) and awareness about the two products (informational differentiation). A recommender system selects the recommendation based on a recommendation score, which is a weighted sum of expected retailer profit and expected consumer value. We find that the recommender system may benefit or hurt the retailer and the manufacturers depending on the signs and magnitudes of the substitution effect and demand effect of the recommender system. The substitution effect of the recommender system either intensifies or softens the price competition between two manufacturers through two forces-its direct influence alters the informational differentiation of consumers (which affects the markup that manufacturers can charge), and its strategic influence motivates the manufacturers to use price as a lever to attract more recommendations in their favor. The demand effect of the recommender system increases overall consumer awareness, but, depending on the substitution effect, may increase or decrease the demand. The recommendation strategy, namely, the relative weight assigned to retailer profit vis-a-vis consumer value in computing the recommendation score, along with recommender system precision and the relative sizes of segments of consumers with different awareness levels, determines whether the retailer benefits from the recommender system and by how much. We find that the retailer's optimal recommendation strategy is mildly profit oriented in the sense that it assigns a larger, but not too large, weight to retailer profit compared to consumer value, and that under the optimal strategy, the price competition is less intense and the retailer profit is higher compared to when there is no recommender system. Furthermore, an increase in either the recommender system precision or the fraction of consumers that are aware of at least one product induces the retailer to adopt a more profit-oriented recommendation strategy.
C1 [Li, Lusi] Calif State Univ Los Angeles, Coll Business & Econ, Los Angeles, CA 90032 USA.
[Chen, Jianqing; Raghunathan, Srinivasan] Univ Texas Dallas, Jindal Sch Management, Richardson, TX 75080 USA.
C3 California State University System; California State University Los
Angeles; University of Texas System; University of Texas Dallas
RP Li, LS (autor correspondiente), Calif State Univ Los Angeles, Coll Business & Econ, Los Angeles, CA 90032 USA.
EM lli57@calstatela.edu; chenjq@utdallas.edu; sraghu@utdallas.edu
RI Raghunathan, Srinivasan/KFR-3471-2024
OI Raghunathan, Srinivasan/0000-0002-2782-3520
FU National Natural Science Foundation of China [71528004, 71431002,
71671036]
FX Jianqing Chen acknowledges financial support from the National Natural
Science Foundation of China [Grants 71528004, 71431002, and 71671036].
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Bergemann D., 2006, P ACM C EL COMM, P43, DOI DOI 10.1145/1134707.1134713
BESTER H, 1995, EUR ECON REV, V39, P1075, DOI 10.1016/0014-2921(94)00099-L
Brynjolfsson E, 2011, MANAGE SCI, V57, P1373, DOI 10.1287/mnsc.1110.1371
Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
Chen LS, 2008, INFORM SCIENCES, V178, P1032, DOI 10.1016/j.ins.2007.09.027
Cooke ADJ, 2002, J MARKETING RES, V39, P488, DOI 10.1509/jmkr.39.4.488.19121
Das Aparna, 2009, ARXIV09083633
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Gal-Or E, 2005, MARKET SCI, V24, P241, DOI 10.1287/mksc.1040.0092
Gal-Or E, 2006, MANAGE SCI, V52, P713, DOI 10.1287/mnsc.1050.0489
Gretzel U, 2006, INT J ELECTRON COMM, V11, P81, DOI 10.2753/JEC1086-4415110204
GROSSMAN GM, 1984, REV ECON STUD, V51, P63, DOI 10.2307/2297705
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Häubl G, 2006, MIT SLOAN MANAGE REV, V47, P8
Hennig-Thurau T, 2012, J MARKETING, V76, P89, DOI 10.1509/jm.10.0537
Hervas-Drane A, 2015, INT J RES MARK, V32, P207, DOI 10.1016/j.ijresmar.2015.02.005
Hinz O, 2010, BUS INFORM SYST ENG+, V2, P67, DOI 10.1007/s12599-010-0092-x
Hosanagar K, 2008, P 29 INT C INF SYST, P31
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
Hostler RE, 2005, DECIS SUPPORT SYST, V41, P313, DOI 10.1016/j.dss.2004.07.002
Iyer G, 2005, MARKET SCI, V24, P461, DOI 10.1287/mksc.1050.0117
Jabr W, 2014, MIS QUART, V38, P635, DOI 10.25300/MISQ/2014/38.3.01
Jiang BJ, 2011, MARKET SCI, V30, P757, DOI 10.1287/mksc.1110.0656
Johnson JP, 2006, AM ECON REV, V96, P756, DOI 10.1257/aer.96.3.756
LEWIS TR, 1994, INT ECON REV, V35, P309, DOI 10.2307/2527055
McNee Sean M., 2006, CHI 06 HUM FACT COMP, P1097, DOI DOI 10.1145/1125451
Mooney R. J., 2000, ACM 2000. Digital Libraries. Proceedings of the Fifth ACM Conference on Digital Libraries, P195, DOI 10.1145/336597.336662
Oestreicher-Singer G, 2012, MANAGE SCI, V58, P1963, DOI 10.1287/mnsc.1120.1536
Oestreicher-Singer G, 2012, MIS QUART, V36, P65, DOI 10.2307/41410406
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Pu P, 2011, P 5 ACM C REC SYST, P157, DOI [10.1145/2043932.2043962, DOI 10.1145/2043932.2043962]
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Shani G, 2005, J MACH LEARN RES, V6, P1265
Soberman DA, 2004, MANAGE SCI, V50, P1744, DOI 10.1287/mnsc.1040.0288
Tam KY, 2006, MIS QUART, V30, P865
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Vargas S., 2011, P 5 ACM C REC SYST, P109, DOI DOI 10.1145/2043932.2043955
Wang JW, 2011, PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 16TH '11), P1003
Xiao B, 2007, MIS QUART, V31, P137
Xiao B, 2015, INFORM SYST RES, V26, P793, DOI 10.1287/isre.2015.0592
NR 41
TC 29
Z9 32
U1 18
U2 167
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD DEC
PY 2018
VL 29
IS 4
BP 1003
EP 1023
DI 10.1287/isre.2017.0765
PG 21
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA HL2FZ
UT WOS:000458521100013
DA 2024-03-27
ER
PT J
AU Palanivel, K
Sivakumar, R
AF Palanivel, K.
Sivakumar, R.
TI A STUDY ON IMPLICIT FEEDBACK IN MULTICRITERIA E-COMMERCE RECOMMENDER
SYSTEM
SO JOURNAL OF ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE implicit relevance feedback; evaluation; multicriteria E-commerce
recommender system
ID ALGORITHMS; ACCURACY; IMPROVE; AGENTS
AB Recommender systems are personalized intelligent systems capable of helping people to easily locate their relevant information through recommendations from a large repository of information. In order to provide personalized recommendations, the accurate modeling of user's preferences is required. Modeling of user's preferences needs their relevance feedback on the recommendations. The relevance feedback may be collected either explicitly or implicitly. The explicit relevance feedback introduces intrusiveness problem whereas the implicit feedback can be inferred from normal user-system interactions without disturbing the user. The users expect accuracy in recommendations and effortless assistance from the recommender systems. The multicriteria user preference ratings are useful to improve the accuracy of recommendations. However, collecting multiple ratings increases the cognitive load of the user. We believe that a combined, implicit relevance feedback and multicriteria user preference ratings, approach improve the accuracy in recommendations and eliminate the intrusiveness problem of recommender systems. In order to fulfill the above needs and to better understand the potential behind the implicit relevance feedback approach under multicriteria ratings context, this study focuses a new implicit-multicriteria combined recommendation approach. A Music recommender system is developed for this experiment to evaluate the recommendation accuracy of implicit and explicit feedback approaches under the user-based and item-based prediction algorithms against different data sparsity levels, training/test ratio and neighborhood sizes. Out of this experiment, the implicit ratings based prediction algorithms provide better performance than the explicit ratings based prediction algorithms with respect to all the three sensitive parameters. It is also observed that the proposed IB_PIR prediction algorithm computes better predictions than other prediction algorithms. Finally, we discuss the study's implications for theory and practice and conclude with many suggestions for future research on non-intrusive, multicriteria recommender systems.
C1 [Palanivel, K.; Sivakumar, R.] Bharathidasan Univ, AVVM Sri Pushpam Coll, Dept Comp Sci, Thanjavur, Tamil Nadu, India.
C3 Bharathidasan University
RP Palanivel, K (autor correspondiente), Bharathidasan Univ, AVVM Sri Pushpam Coll, Dept Comp Sci, Thanjavur, Tamil Nadu, India.
EM palani.avcc@hotmail.com; rskumar.avvmspc@gmail.com
CR Adoinavicius G, 2007, IEEE INTELL SYST, V22, P48, DOI 10.1109/MIS.2007.58
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
[Anonymous], 1999, P 22 ACM SIGIR C RES
[Anonymous], 1994, P 1994 ACM C COMP SU
[Anonymous], THESIS SCH ARCHITECT
Burke R., 2002, ENCY LIB INFORM SYST, V69
Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906
Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, P132, DOI 10.1145/280765.280789
Claypool M, 2001, IEEE INTERNET COMPUT, V5, P32, DOI 10.1109/4236.968829
COOLEY R, 1999, P 1999 KDD WORKSH WE
Fox S, 2005, ACM T INFORM SYST, V23, P147, DOI 10.1145/1059981.1059982
Frias-Martinez E, 2005, EXPERT SYST APPL, V29, P320, DOI 10.1016/j.eswa.2005.04.005
Fung TKF, 2008, J ELECTRON COMMER RE, V9, P296
Godoy D, 2005, KNOWL ENG REV, V20, P329, DOI 10.1017/S0269888906000397
GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867
HINGSTON M, 2006, THESIS U SYDNEY
Huberman B., 1996, BEEHIVE SYSTEM COOPE
Joachims T, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1229179.1229181
Jung K, 2001, THESIS FLORIDA I TEC
Jung S, 2007, INFORM PROCESS MANAG, V43, P791, DOI 10.1016/j.ipm.2006.07.021
Kellar M, 2004, P ASIST ANNU, V41, P168, DOI 10.1002/meet.1450410119
Kelly D., 2003, SIGIR Forum, V37, P18, DOI 10.1145/959258.959260
Kobsa A, 2001, KNOWL ENG REV, V16, P111, DOI 10.1017/S0269888901000108
Konstan JA, 2004, ACM T INFORM SYST, V22, P1, DOI 10.1145/963770.963771
Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126
Krulwich B, 1997, AI MAG, V18, P37
Lee HH, 2007, IRI 2007: PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, P273
Lee TQ, 2008, EXPERT SYST APPL, V34, P3055, DOI 10.1016/j.eswa.2007.06.031
Lieberman H, 1995, INT JOINT CONF ARTIF, P924
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
MARTINEZ L, 2009, INT J COMPUTATIONAL, V1, P225
MLADENIC D, 1996, IJSDP7472 J STEF I D
Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169
MOBASHER B, 2005, LNCS LNAI, V3169, P1
Montaner M, 2003, ARTIF INTELL REV, V19, P285, DOI 10.1023/A:1022850703159
Morita M., 1994, P 17 ANN INT ACM SIG, P272
Nah F., 2002, The Journal of Electronic Commerce Research, V3, P98
NIINIVAARA O, 2004, AGENT BASED RECOMMEN
Oard D., 1998, Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering, V83, P31
Papagelis M, 2005, ENG APPL ARTIF INTEL, V18, P781, DOI 10.1016/j.engappai.2005.06.010
Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54
Rao KN, 2008, DESIDOC J LIB INF TE, V28, P17
Sakagami H., 1997, P 6 INT WORLD WID WE, P291
SARWAR B, 2000, EC 00 OCT 17 20 MINN
Sarwar B. M., 1998, PROC
SHAHABI C, 2003, 3 INT WORKSH DAT NET
Shih TK, 2002, IND MANAGE DATA SYST, V102, P417, DOI 10.1108/02635570210445853
Shin N., 2001, Journal of Electronic Commerce Research, V2, P164, DOI [10.5195/emaj.2011.12, DOI 10.5195/EMAJ.2011.12]
Zenebe A, 2009, FUZZY SET SYST, V160, P76, DOI 10.1016/j.fss.2008.03.017
NR 49
TC 14
Z9 14
U1 1
U2 18
PU CALIFORNIA STATE UNIV
PI LONG BEACH
PA COLL BUSINESS, LONG BEACH, CA 90840 USA
SN 1526-6133
EI 1938-9027
J9 J ELECTRON COMMER RE
JI J. Electron. Commer. Res.
PY 2010
VL 11
IS 2
BP 140
EP 156
PG 17
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 760HP
UT WOS:000290315700004
DA 2024-03-27
ER
PT J
AU Baum, D
Spann, M
AF Baum, Daniela
Spann, Martin
TI The Interplay Between Online Consumer Reviews and Recommender Systems:
An Experimental Analysis
SO INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
LA English
DT Article
DE business-to-consumer (B2C) e-commerce; consumer behavior; decision
making; e-tail; electronic word of mouth (eWOM); online consumer
reviews; online experiments; recommender system
ID WORD-OF-MOUTH; E-COMMERCE; MODERATING ROLE; PRODUCT; INFORMATION;
CHOICE; ADVICE; IMPACT; OPINION; SEARCH
AB Online consumers face complex purchase decisions due to the huge selection of products and the vast amount of information available. Online retailers therefore try to support consumer decision making by providing recommendations generated by previous consumers as well as recommendations generated by recommender systems. The goal of this study is to analyze the interplay between online consumer reviews and recommender systems and its effect on consumers' decision making. We experimentally manipulate the provision of online consumer reviews and provider-generated recommendations for high-and low-involvement products, as well as search and experience products. Using a representative sample of online consumers, we find that providing online consumer reviews does not necessarily have to be beneficial for an online retailer, as inconsistent recommendations do negatively influence consumers' purchase decisions. In addition, by providing positive opinions of previous customers in addition to a recommender system's recommendation, online retailers may increase the effectiveness of their recommender system. However, if a previous customer's recommendation contradicts that of a recommender system, positive consumer reviews may even have negative consequences for online retailers.
C1 [Spann, Martin] Univ Munich, D-81377 Munich, Germany.
C3 University of Munich
EM baum@bwl.lmu.de; spann@spann.de
RI Spann, Martin/F-9636-2011
OI Spann, Martin/0000-0003-4645-3913
CR Ahluwalia R, 1997, ADV CONSUM RES, V24, P222
[Anonymous], P 31 INT C INF SYST
[Anonymous], J MANAGEMENT INFORM
[Anonymous], 2013, WORLDW B2C EC 2013 F
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
ARNDT J, 1967, J MARKETING RES, V4, P291, DOI 10.1177/002224376700400308
Baek H, 2012, INT J ELECTRON COMM, V17, P99, DOI 10.2753/JEC1086-4415170204
Ballinger GA, 2004, ORGAN RES METHODS, V7, P127, DOI 10.1177/1094428104263672
Benlian A, 2012, J MANAGE INFORM SYST, V29, P237, DOI 10.2753/MIS0742-1222290107
Bickart B., 2001, J INTERACT MARK, V15, P31, DOI [10.1002/dir.1014, DOI 10.1002/DIR.1014]
Chandrashekaran R, 2004, J RETAILING, V80, P53, DOI 10.1016/j.jretai.2004.01.004
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Clarke K., 1979, ADV CONSUM RES, V6, P313
Cui G, 2012, INT J ELECTRON COMM, V17, P39, DOI 10.2753/JEC1086-4415170102
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dellaert BGC, 2012, J MARKETING RES, V49, P277, DOI 10.1509/jmr.09.0481
Dellarocas C, 2003, MANAGE SCI, V49, P1407, DOI 10.1287/mnsc.49.10.1407.17308
Dias MB, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P291
Elsner H, 2013, BUS INFORM SYST ENG+, V5, P213, DOI 10.1007/s12599-013-0275-3
eMarketer, 2013, EC SAL TOPP 1 TRILL
Fitzsimons GJ, 2004, MARKET SCI, V23, P82, DOI 10.1287/mksc.1030.0033
Gupta P, 2010, J BUS RES, V63, P1041, DOI 10.1016/j.jbusres.2009.01.015
Harries C, 2004, J BEHAV DECIS MAKING, V17, P333, DOI 10.1002/bdm.474
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Häubl G, 2003, J CONSUM PSYCHOL, V13, P75
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
Hennig-Thurau T, 2003, INT J ELECTRON COMM, V8, P51, DOI 10.1080/10864415.2003.11044293
HOGARTH RM, 1989, INT J FORECASTING, V5, P593, DOI 10.1016/0169-2070(89)90015-0
Iyengar SS, 2000, J PERS SOC PSYCHOL, V79, P995, DOI 10.1037//0022-3514.79.6.995
Jiang YC, 2010, DECIS SUPPORT SYST, V48, P470, DOI 10.1016/j.dss.2009.06.006
Jiménez FR, 2013, J INTERACT MARK, V27, P226, DOI 10.1016/j.intmar.2013.04.004
Kumar N, 2006, INFORM SYST RES, V17, P425, DOI 10.1287/isre.1060.0107
Kwon O, 2012, INT J ELECTRON COMM, V17, P59, DOI 10.2753/JEC1086-4415170103
Lee J, 2008, ELECTRON COMMER R A, V7, P341, DOI 10.1016/j.elerap.2007.05.004
LIANG KY, 1986, BIOMETRIKA, V73, P13, DOI 10.2307/2336267
Litvin SW, 2008, TOURISM MANAGE, V29, P458, DOI 10.1016/j.tourman.2007.05.011
Ma X, 2013, J MANAGE INFORM SYST, V30, P279, DOI 10.2753/MIS0742-1222300310
MAHESWARAN D, 1990, J MARKETING RES, V27, P361, DOI 10.2307/3172593
Mudambi SM, 2010, MIS QUART, V34, P185
Park DH, 2007, INT J ELECTRON COMM, V11, P125, DOI 10.2753/JEC1086-4415110405
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
PAYNE JW, 1992, ANNU REV PSYCHOL, V43, P87, DOI 10.1146/annurev.ps.43.020192.000511
Pleger BebkoC., 2000, Journal of Services Marketing, V14, P9
Schindler RM, 2005, ADVERT CONS, P35
Sen S, 2007, J INTERACT MARK, V21, P76, DOI 10.1002/dir.20090
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Simon HA, 1955, Q J ECON, V69, P99, DOI 10.2307/1884852
Smith D, 2005, J INTERACT MARK, V19, P15, DOI 10.1002/dir.20041
SMITH DC, 1992, J MARKETING RES, V29, P296, DOI 10.2307/3172741
SNIEZEK JA, 1995, ORGAN BEHAV HUM DEC, V62, P159, DOI 10.1006/obhd.1995.1040
Srinivasan SS, 2002, J RETAILING, V78, P41, DOI 10.1016/S0022-4359(01)00065-3
Wang Alex, 2008, International Journal of Internet Marketing and Advertising, V4, P328, DOI 10.1504/IJIMA.2008.019152
Wang WQ, 2008, J MANAGE INFORM SYST, V24, P249, DOI 10.2753/MIS0742-1222240410
Xiao B, 2007, MIS QUART, V31, P137
Yaniv I, 2007, ORGAN BEHAV HUM DEC, V103, P104, DOI 10.1016/j.obhdp.2006.05.006
Zahedi F., 2003, P 2003 AM C INF SYST
ZEGER SL, 1986, BIOMETRICS, V42, P121, DOI 10.2307/2531248
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 58
TC 44
Z9 48
U1 9
U2 127
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1086-4415
EI 1557-9301
J9 INT J ELECTRON COMM
JI Int. J. Electron. Commer.
PD FAL
PY 2014
VL 19
IS 1
BP 129
EP 161
DI 10.2753/JEC1086-4415190104
PG 33
WC Business; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA CA1YK
UT WOS:000348705300005
DA 2024-03-27
ER
PT J
AU Lee, D
Nam, K
Han, I
Cho, K
AF Lee, Dongwon
Nam, Kihwan
Han, Ingoo
Cho, Kanghyun
TI From free to fee: Monetizing digital content through expected
utility-based recommender systems
SO INFORMATION & MANAGEMENT
LA English
DT Article
DE Utility-based business rule analytics; Digital content monetization;
Free-to-fee conversion; Recommendation system; Association rule mining
ID ASSOCIATION RULES; WEB PERSONALIZATION; BAYESIAN-ANALYSIS;
DECISION-MAKING; IMPACT; ONLINE; MODEL; RISK; ALGORITHM; DISCOVERY
AB This study proposes a novel framework for designing business rule analytics to assist businesses offering digital content in effectively converting free-only users (FOUs) into paying customers. Based on the theory of expected utility, we expand upon traditional frequency-driven rule analytics by integrating three business-relevant factors (target size, conversion profit, and conversion likelihood) into the process of generating recommendations for FOUs in digital content markets. The framework was tested using two different types of empirical analysis. We conducted a field experiment collaborating with a nationwide e-book store to determine how FOUs responded to the recommendations generated under the proposed framework. Furthermore, we analyzed over 5 million transactions collected from the e-book seller and a mobile application provider to examine the impact of customer segmentation on the effectiveness of our approach. Our findings suggest that business analytics derived from the utility-based mechanisms can significantly enhance digital content providers' business performance.
C1 [Lee, Dongwon] Hansung Univ, Sch Business Adm, 116 Samseongyoro 16gil, Seoul 02876, South Korea.
[Nam, Kihwan] Dongguk Univ, Business Sch, Management Informat Syst, 30 Pildong Ro,1 Gil, Seoul, South Korea.
[Han, Ingoo] Korea Adv Inst Sci & Technol, SUPEX, Coll Business Management Engn Dept, 85 Hoegiro, Seoul 130722, South Korea.
[Cho, Kanghyun] Temple Univ, Fox Sch Business, 1801 Liacouras Walk, Philadelphia, PA 19122 USA.
C3 Hansung University; Dongguk University; Korea Advanced Institute of
Science & Technology (KAIST); Pennsylvania Commonwealth System of Higher
Education (PCSHE); Temple University
RP Nam, K (autor correspondiente), Dongguk Univ, Business Sch, Management Informat Syst, 30 Pildong Ro,1 Gil, Seoul, South Korea.
EM namkh@dongguk.edu
OI Cho, Kanghyun/0000-0002-1140-3118; Nam, Kihwan/0000-0002-9138-7209
CR Adomavicius G, 2011, INFORM SYST RES, V22, P99, DOI 10.1287/isre.1100.0274
Adomavicius G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P217, DOI 10.1007/978-0-387-85820-3_7
Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072
ALBERT JH, 1993, J AM STAT ASSOC, V88, P669, DOI 10.2307/2290350
Albert TC, 2004, MIS QUART, V28, P161
Anand SS, 1998, KNOWL-BASED SYST, V10, P449, DOI 10.1016/S0950-7051(98)00035-5
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Aral S, 2021, MANAGE SCI, V67, P2381, DOI 10.1287/mnsc.2020.3650
Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124
Bernoulli D, 1954, ECONOMETRICA, V22, P23, DOI 10.2307/1909829
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Cao LB, 2010, IEEE T KNOWL DATA EN, V22, P755, DOI 10.1109/TKDE.2010.32
Chen LS, 2008, INFORM SCIENCES, V178, P1032, DOI 10.1016/j.ins.2007.09.027
Chen YL, 2006, DECIS SUPPORT SYST, V42, P1503, DOI 10.1016/j.dss.2005.12.004
Chiu MC, 2018, EUR J OPER RES, V271, P141, DOI 10.1016/j.ejor.2018.05.002
Churilov L, 2005, J MANAGE INFORM SYST, V21, P85, DOI 10.1080/07421222.2005.11045826
De Pessemier T, 2009, EUROITV'09: PROCEEDINGS OF THE SEVENTH EUROPEAN INTERACTIVE TELEVISION CONFERENCE, P133
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Foong Li Law, 2011, 2011 5th Malaysian Conference in Software Engineering (MySEC), P349, DOI 10.1109/MySEC.2011.6140696
Ghose A, 2009, MANAGE SCI, V55, P1605, DOI 10.1287/mnsc.1090.1054
Hu JY, 2007, PATTERN RECOGN, V40, P3317, DOI 10.1016/j.patcog.2007.02.003
Hu YH, 2006, DECIS SUPPORT SYST, V42, P1, DOI 10.1016/j.dss.2004.09.007
HUBER J, 1982, J CONSUM RES, V9, P90, DOI 10.1086/208899
Jain A, 2011, IEEE WIREL COMMUN, V18, P4, DOI 10.1109/MWC.2011.5999758
Jiang T, 2009, IEEE T KNOWL DATA EN, V21, P305, DOI 10.1109/TKDE.2008.163
Jiao WL, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103238
Kamakura WA, 2003, INT J RES MARK, V20, P45, DOI 10.1016/S0167-8116(02)00121-0
Kim Y, 2004, DECIS SUPPORT SYST, V37, P215, DOI 10.1016/S0167-9236(03)00008-3
Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126
Krulwich B., 1996, P AAAI SPRING S MACH
Kumar N, 2007, DECIS SUPPORT SYST, V43, P16, DOI 10.1016/j.dss.2005.05.004
Kwan ISY, 2005, DECIS SUPPORT SYST, V41, P189, DOI 10.1016/j.dss.2004.11.012
Lai TL, 2004, INFORM SYST FRONT, V6, P353, DOI 10.1023/B:ISFI.0000046377.32617.3d
Lee D, 2019, INFORM SYST RES, V30, P239, DOI 10.1287/isre.2018.0800
Lee D, 2013, EXPERT SYST APPL, V40, P2715, DOI 10.1016/j.eswa.2012.11.021
Li L., 2010, P 19 INT C WORLD WID, P661, DOI DOI 10.1145/1772690.1772758
Li XB, 2005, DECIS SUPPORT SYST, V41, P112, DOI 10.1016/j.dss.2004.06.016
Li XB, 2006, INFORM SYST RES, V17, P254, DOI 10.1287/isre.1060.0095
Lin QY, 2003, INFORM MANAGE-AMSTER, V40, P431, DOI 10.1016/S0378-7206(02)00062-9
Lin TC, 2013, J ELECTRON COMMER RE, V14, P315
Liu DR, 2009, INFORM SCIENCES, V179, P3505, DOI 10.1016/j.ins.2009.06.004
Liu JQ, 2016, IEEE T KNOWL DATA EN, V28, P1245, DOI 10.1109/TKDE.2015.2510012
Mäntymäki M, 2020, INFORM SYST J, V30, P295, DOI 10.1111/isj.12262
Misir M, 2017, ARTIF INTELL, V244, P291, DOI 10.1016/j.artint.2016.12.001
Niemand T, 2019, PSYCHOL MARKET, V36, P773, DOI 10.1002/mar.21211
Nobile A, 1998, STAT COMPUT, V8, P229, DOI 10.1023/A:1008905311214
Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P325
Rabin M, 2000, ECONOMETRICA, V68, P1281, DOI 10.1111/1468-0262.00158
Robbins SS, 2003, INFORM MANAGE-AMSTER, V40, P205, DOI 10.1016/S0378-7206(02)00002-2
Russell R, 2020, BUS INFORM SYST ENG+, V62, P253, DOI 10.1007/s12599-020-00632-5
Sarathy R, 2002, INFORM SYST RES, V13, P389, DOI 10.1287/isre.13.4.389.74
Schein A. I., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P253, DOI 10.1145/564376.564421
Schryen G, 2015, COMMUN ASSOC INF SYS, V37, P286
Shampanier K, 2007, MARKET SCI, V26, P742, DOI 10.1287/mksc.1060.0254
Swaminathan V, 2003, J CONSUM PSYCHOL, V13, P93, DOI 10.1207/S15327663JCP13-1&2_08
Tam KY, 2006, MIS QUART, V30, P865
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1, DOI 10.1017/CBO9780511805271
Tsai PSM, 2004, INFORM SYST, V29, P685, DOI 10.1016/S0306-4379(03)00061-9
Tseng VS, 2016, IEEE T KNOWL DATA EN, V28, P54, DOI 10.1109/TKDE.2015.2458860
Ulaga Wolfgang., 2018, MIT SLOAN MANAGE REV, V60, P1
Wang K, 2003, IEEE T KNOWL DATA EN, V15, P642, DOI 10.1109/TKDE.2003.1198396
Wang W, 2016, DECIS SUPPORT SYST, V87, P80, DOI 10.1016/j.dss.2016.05.002
Woerndl W, 2009, INT J INF TECHNOL WE, V4, P67, DOI 10.4018/jitwe.2009010105
Wooldridge JM, 2010, ECONOMETRIC ANALYSIS OF CROSS SECTION AND PANEL DATA, 2ND EDITION, P3
Xiao B, 2007, MIS QUART, V31, P137
Xue YF, 2006, DECIS SUPPORT SYST, V41, P560, DOI 10.1016/j.dss.2004.06.007
Yao H, 2006, DATA KNOWL ENG, V59, P603, DOI 10.1016/j.datak.2005.10.004
Zhang J, 2009, J MARKETING RES, V46, P190, DOI 10.1509/jmkr.46.2.190
Zhang JJ, 2011, MARKET SCI, V30, P170, DOI 10.1287/mksc.1100.0607
Zhang T, 2007, J BUS RES, V60, P912, DOI 10.1016/j.jbusres.2007.02.006
NR 72
TC 3
Z9 3
U1 11
U2 27
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-7206
EI 1872-7530
J9 INFORM MANAGE-AMSTER
JI Inf. Manage.
PD SEP
PY 2022
VL 59
IS 6
AR 103681
DI 10.1016/j.im.2022.103681
PG 14
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA 5A6TV
UT WOS:000863018900005
DA 2024-03-27
ER
PT J
AU Xu, LN
Roy, A
Niculescu, M
AF Xu, Lina
Roy, Abhijit
Niculescu, Mihai
TI A Dual Process Model of the Influence of Recommender Systems on Purchase
Intentions in Online Shopping Environments
SO JOURNAL OF INTERNET COMMERCE
LA English
DT Article
DE Low and high involvement; online purchase intention; recommendation
label; risk avoidance; social proof
ID WORD-OF-MOUTH; SOCIAL PROOF; MODERATING ROLE; DECISION; INFORMATION;
INVOLVEMENT; PERSONALIZATION; ABANDONMENT; CONSUMERS; QUALITY
AB Whereas much research has looked at how recommendation systems influence online purchase intentions, this article illustrates the dual process model by which they occur. Using two studies, we fill the research void in interactive marketing by demonstrating how the dual processes of social proof and risk avoidance mediate the impact of recommendation labels on consumer decision-making contingent upon their level of involvement. Study 1 (n = 73), used a mixed-subjects design with a college student sample to demonstrate that both types of recommendation labels will lead to higher purchase intentions in an online setting. Most importantly, it provides evidence for the main effect of our theoretical model across different product categories. Study 2 (n = 160) provides support for our remaining four hypotheses by demonstrating the underlying process through which recommendation labels have a two-fold effect on purchase intentions. Specifically, the recommendation label increased the risk avoidance effect for high-involvement consumers and enhanced the social proof effect for low-involvement consumers. In both cases, the recommendation labels increased purchase intentions. Implications of our findings for theoretical and practical contributions and future directions are also explored.
C1 [Xu, Lina; Niculescu, Mihai] New Mexico State Univ, Dept Mkt, Las Cruces, NM 88003 USA.
[Roy, Abhijit] Univ Scranton, Dept Mkt, 320 Madison Ave, Scranton, PA 18510 USA.
C3 New Mexico State University; University of Scranton
RP Roy, A (autor correspondiente), Univ Scranton, Dept Mkt, 320 Madison Ave, Scranton, PA 18510 USA.
EM roya2@scranton.edu
RI Xu, Lina/JXL-1543-2024; Roy, Abhijit/KDN-0263-2024
OI Roy, Abhijit/0000-0002-3312-3563
CR Amblee N, 2011, INT J ELECTRON COMM, V16, P91, DOI 10.2753/JEC1086-4415160205
Ashraf M, 2019, BEHAV INFORM TECHNOL, V38, P1259, DOI 10.1080/0144929X.2019.1583285
Barnes, 2020, P SOC CONS PSYCH 202
Bolin JH, 2014, J EDUC MEAS, V51, P335, DOI 10.1111/jedm.12050
Breugelmans E, 2012, J RETAILING, V88, P226, DOI 10.1016/j.jretai.2011.10.003
Carnoy, 2018, CNET
CELSI RL, 1988, J CONSUM RES, V15, P210, DOI 10.1086/209158
CHAIKEN S, 1980, J PERS SOC PSYCHOL, V39, P752, DOI 10.1037/0022-3514.39.5.752
Chen YB, 2008, MANAGE SCI, V54, P477, DOI 10.1287/mnsc.1070.0810
Choi J, 2011, INT J ELECTRON COMM, V16, P129, DOI 10.2753/JEC1086-4415160105
Cialdini RB, 2004, ANNU REV PSYCHOL, V55, P591, DOI 10.1146/annurev.psych.55.090902.142015
Dash G, 2021, J BUS RES, V122, P608, DOI 10.1016/j.jbusres.2020.10.016
Degeratu AM, 2000, INT J RES MARK, V17, P55, DOI 10.1016/S0167-8116(00)00005-7
Deldjoo Y, 2020, ACM COMPUT SURV, V53, DOI 10.1145/3407190
Dellarocas C, 2007, J INTERACT MARK, V21, P23, DOI 10.1002/dir.20087
Dickins TE, 2020, LEARN MOTIV, V72, DOI 10.1016/j.lmot.2020.101681
Dowling G.R., 1986, PSYCHOL MARKETING, V3, P193, DOI [10.1002/mar.4220030307, DOI 10.1002/MAR.4220030307]
Felfernig Alexander, 2017, Advances in Artificial Intelligence: from Theory to Practice. 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017. Proceedings: LNAI 10350, P335, DOI 10.1007/978-3-319-60042-0_39
Gai PJ, 2019, J MARKETING, V83, P61, DOI 10.1177/0022242919873901
Gass R.H., 2018, Persuasion, Social Influence, and Compliance Gaining, V6th
Goodman JK, 2013, J BEHAV DECIS MAKING, V26, P213, DOI 10.1002/bdm.1753
Guo JP, 2018, J MANAGE INFORM SYST, V35, P1121, DOI 10.1080/07421222.2018.1523564
Haas A, 2014, J RETAILING, V90, P428, DOI 10.1016/j.jretai.2014.05.003
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
HONG ST, 1989, J CONSUM RES, V16, P175, DOI 10.1086/209206
KAPLAN LB, 1974, J APPL PSYCHOL, V59, P287, DOI 10.1037/h0036657
Kees J, 2017, J ADVERTISING, V46, P141, DOI 10.1080/00913367.2016.1269304
Kohan S.E., 2021, FORBES
Kugler T, 2012, J BEHAV DECIS MAKING, V25, P123, DOI 10.1002/bdm.724
Landers RN, 2015, IND ORGAN PSYCHOL-US, V8, P142, DOI 10.1017/iop.2015.13
LAURENT G, 1985, J MARKETING RES, V22, P41, DOI 10.2307/3151549
Lee D, 2021, MANAGE SCI, V67, P524, DOI 10.1287/mnsc.2019.3546
Lenth RV, 2001, AM STAT, V55, P187, DOI 10.1198/000313001317098149
Lowe ML, 2019, J CONSUM RES, V46, P159, DOI 10.1093/jcr/ucy068
Murray KB, 2008, INT SER OPER RES MAN, V121, P55, DOI 10.1007/978-0-387-78213-3_3
Murray KB, 2009, J INTERACT MARK, V23, P138, DOI 10.1016/j.intmar.2009.02.009
Nolan JM, 2008, PERS SOC PSYCHOL B, V34, P913, DOI 10.1177/0146167208316691
Olson ErikL., 2002, Journal of Interactive Marketing, V16, P22, DOI DOI 10.1002/DIR.10007
PETTY RE, 1983, J CONSUM RES, V10, P135, DOI 10.1086/208954
PETTY RE, 1979, J PERS SOC PSYCHOL, V37, P1915, DOI 10.1037/0022-3514.37.10.1915
Rao H, 2001, ADMIN SCI QUART, V46, P502, DOI 10.2307/3094873
Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
Roethke K, 2020, DECIS SUPPORT SYST, V131, DOI 10.1016/j.dss.2020.113268
Salmon SJ, 2015, FOOD QUAL PREFER, V45, P113, DOI 10.1016/j.foodqual.2015.06.004
Shearman SM, 2007, COMMUN RES REP, V24, P271, DOI 10.1080/08824090701624148
Shi SW, 2014, MARKET SCI, V33, P871, DOI 10.1287/mksc.2014.0872
Smith B, 2017, IEEE INTERNET COMPUT, V21, P12, DOI 10.1109/MIC.2017.72
Sridhar S, 2012, J MARKETING, V76, P70, DOI 10.1509/jm.10.0377
Steelman ZR, 2014, MIS QUART, V38, P355, DOI 10.25300/MISQ/2014/38.2.02
Talib YYA, 2017, SHS WEB CONF, V34, DOI 10.1051/shsconf/20173402005
Tan X, 2019, INFORM SYST RES, V30, P1248, DOI 10.1287/isre.2019.0861
Tang H, 2019, ELECTRON MARK, V29, P533, DOI 10.1007/s12525-018-0313-6
Teas RK, 2000, J ACAD MARKET SCI, V28, P278, DOI 10.1177/0092070300282008
Van Der Heide B, 2016, COMMUN RES, V43, P672, DOI 10.1177/0093650214565915
Wang CL, 2021, J RES INTERACT MARK, V15, P1, DOI 10.1108/JRIM-03-2021-270
Wang WQ, 2013, INFORM SYST RES, V24, P861, DOI 10.1287/isre.1120.0445
Winter S, 2015, CYBERPSYCH BEH SOC N, V18, P431, DOI 10.1089/cyber.2015.0005
Yaniv I, 2009, J EXP PSYCHOL LEARN, V35, P558, DOI 10.1037/a0014589
NR 58
TC 1
Z9 1
U1 8
U2 30
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1533-2861
EI 1533-287X
J9 J INTERNET COMMER
JI J. Internet Commer.
PD JUL 3
PY 2023
VL 22
IS 3
BP 432
EP 453
DI 10.1080/15332861.2022.2049113
EA MAR 2022
PG 22
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA J6NI1
UT WOS:000769862700001
DA 2024-03-27
ER
PT J
AU Guo, GB
Zhang, J
Thalmann, D
Yorke-Smith, N
AF Guo, Guibing
Zhang, Jie
Thalmann, Daniel
Yorke-Smith, Neil
TI Leveraging prior ratings for recommender systems in e-commerce
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Prior ratings; Recommender systems; Rating confidence; Similarity
measure; Data sparsity; Cold start
ID PRICE; PERCEPTIONS; BRAND; INFORMATION; EXPERIENCE; ALLEVIATE; BEHAVIOR;
QUALITY; TRUST; MODEL
AB User ratings are the essence of recommender systems in e-commerce. Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of such systems and contribute to the well-known data sparsity and cold start problems. This article proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users' experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments. A method is proposed to show how to leverage prior ratings in collaborative filtering. Experimental results indicate the effectiveness of prior ratings in improving predictive performance. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Guo, Guibing; Zhang, Jie; Thalmann, Daniel] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore.
[Yorke-Smith, Neil] Amer Univ Beirut, Beirut, Lebanon.
[Yorke-Smith, Neil] Univ Cambridge, Cambridge, England.
C3 Nanyang Technological University; American University of Beirut;
University of Cambridge
RP Guo, GB (autor correspondiente), Inst Media Innovat, 50 Nanyang Dr,Res Techno Plaza,XFrontiers Block, Singapore 637553, Singapore.
EM gguo1@ntu.edu.sg; zhangj@ntu.edu.sg; danielthalmann@ntu.edu.sg;
nysmith@aub.edu.lb
RI Zhang, Jie/A-3737-2011; Thalmann, Daniel/AAL-1097-2020; Thalmann,
Daniel/A-4347-2008
OI Thalmann, Daniel/0000-0002-0451-7491;
FU St Edmunds College, Cambridge
FX We thank the reviewers for their constructive comments. Guibing Guo
acknowledges the Ph.D. Grant from the Institute for Media Innovation,
Nanyang Technological University, Singapore. Neil Yorke-Smith thanks the
Operations group at the Judge Business School and the fellowship at St
Edmunds College, Cambridge.
CR Ahmed A., 2013, P 6 ACM INT C WEB SE, P385
Ahn HJ, 2008, INFORM SCIENCES, V178, P37, DOI 10.1016/j.ins.2007.07.024
[Anonymous], 2012, P 2012 ACM INT C INT
[Anonymous], J COMPUTER MEDIATED
[Anonymous], 2009, ADV ARTIFICIAL INTEL
[Anonymous], P 2014 INT C ADV SOC
Arapakis I., 2009, P ACM INT C IM VID R, P1, DOI [10.1145/1646396.1646433, DOI 10.1145/1646396.1646433]
Batstone Cunningham, 2007, 2 LIFE OFFICIAL GUID
BEARDEN WO, 1982, J MARKETING RES, V19, P229, DOI 10.2307/3151623
Bobadilla J, 2012, INFORM PROCESS MANAG, V48, P204, DOI 10.1016/j.ipm.2011.03.007
Boyd d., 2006, First Monday, DOI DOI 10.5210/FM.V11I12.1418
Carenini G., 2003, IUI 03. 2003 International Conference on Intelligent User Interfaces, P12, DOI 10.1145/604045.604052
Crandall DavidJ., 2008, KDD, P160, DOI DOI 10.1145/1401890.1401914
DODDS WB, 1991, J MARKETING RES, V28, P307, DOI 10.2307/3172866
Eno J, 2011, LECT NOTES COMPUT SC, V6787, P87, DOI 10.1007/978-3-642-22362-4_8
Fang H., 2014, LEVERAGING DECOMPOSE
Gantner Z., 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P176, DOI 10.1109/ICDM.2010.129
GARDIAL SF, 1994, J CONSUM RES, V20, P548, DOI 10.1086/209369
GOERING PA, 1985, J CONSUM RES, V12, P74, DOI 10.1086/209036
Guibing Guo, 2012, User Modeling, Adaptation, and Personalization. Proceedings 20th International Conference, UMAP 2012, P114, DOI 10.1007/978-3-642-31454-4_10
Gunawardana A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P19
Guo G., 2013, P 7 ACM C REC SYST R, P383
Guo G., 2013, P IJCAI, V13, P2619
Guo G., 2013, Journal of Advanced Management Science, V1, P61, DOI [10.12720/joams.1.1.61-65, DOI 10.12720/JOAMS.1.1.61-65]
Guo G., 2014, P 29 ANN ACM S APPL, P248, DOI DOI 10.1145/2554850.2554878
Guo GB, 2014, KNOWL-BASED SYST, V57, P57, DOI 10.1016/j.knosys.2013.12.007
Guy I., 2009, P 14 INT C INTELLIGE, P77
Heeter C., 1992, Presence: Teleoperators & Virtual Environments, V1, P262, DOI [10.1162/pres.1992.1.2.262, DOI 10.1162/PRES.1992.1.2.262]
Hemp P, 2006, HARVARD BUS REV, V84, P48
Hu X., 2010, 2010 2nd International Workshop on Database Technology and Applications, P1
JACOBY J, 1977, J CONSUM RES, V3, P209, DOI 10.1086/208669
Jamali M., 2010, 4 ACM C REC SYST, P135, DOI [DOI 10.1145/1864708.1864736, 10.1145/1864708.1864736]
Jamali Mohsen., 2011, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI'11, P2644
Jiang ZH, 2004, J MANAGE INFORM SYST, V21, P111, DOI 10.1080/07421222.2004.11045817
Konstas I, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P195, DOI 10.1145/1571941.1571977
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Lathia N, 2008, APPLIED COMPUTING 2008, VOLS 1-3, P2000
Li H., 2001, Journal of Interactive Marketing, V15, P13, DOI [https://doi.org/10.1002/dir.1013, DOI 10.1002/DIR.1013, 10.1002/dir.1013]
Li HR, 2003, J CONSUM PSYCHOL, V13, P395, DOI 10.1207/S15327663JCP1304_07
LICHTENSTEIN DR, 1993, J MARKETING RES, V30, P234, DOI 10.2307/3172830
Liu NathanN., 2011, P 5 ACM C RECOMMENDE, P37, DOI [10.1145/2043932.2043943, DOI 10.1145/2043932.2043943]
Ma D., 2011, P 4 ACM INT C WEB SE, P287
Ma H., 2008, P 17 ACM C INF KNOWL, P931
Ma H, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P203, DOI 10.1145/1571941.1571978
Massa P, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P17
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
McNee SM, 2003, LECT NOTES ARTIF INT, V2702, P178
MILGROM P, 1986, J POLIT ECON, V94, P796, DOI 10.1086/261408
NELSON P, 1974, J POLIT ECON, V82, P729, DOI 10.1086/260231
Nguyen TienT., 2013, Proceedings of the 7th ACM Conference on Recommender Systems. RecSys '13, P149
Olson J., 1974, CUE PROPERTIES PRICE, P20
Phang C.W., 2009, P 30 INT C INF SYST, P167
Picciano A.G., 2002, J ASYNCHRONOUS LEARN, V6, P21, DOI [DOI 10.24059/OLJ.V6I1.1870, DOI 10.24059/olj.v6i1.1870]
RAO AR, 1989, J MARKETING RES, V26, P351, DOI 10.2307/3172907
Shah F., 2010, FLAIRS C, P475
Slater M, 2010, ACM T GRAPHIC, V29, DOI 10.1145/1778765.1778829
STEUER J, 1992, J COMMUN, V42, P73, DOI 10.1111/j.1460-2466.1992.tb00812.x
Stokes R., 1974, THESIS
Tang Jiliang, 2013, P 6 ACM INT C WEB SE, DOI DOI 10.1145/2433396.2433405
Xiao B, 2007, MIS QUART, V31, P137
Yang B., 2013, P 23 INT JOINT C ART, P2747
Yeasin M, 2006, IEEE T MULTIMEDIA, V8, P500, DOI 10.1109/TMM.2006.870737
ZEITHAML VA, 1988, J MARKETING, V52, P2, DOI 10.2307/1251446
Zhang J, 2012, COMPUT INTELL-US, V28, P549, DOI 10.1111/j.1467-8640.2012.00428.x
NR 64
TC 18
Z9 20
U1 0
U2 53
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD NOV-DEC
PY 2014
VL 13
IS 6
BP 440
EP 455
DI 10.1016/j.elerap.2014.10.003
PG 16
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA AW4FL
UT WOS:000346236500006
DA 2024-03-27
ER
PT J
AU Pu, P
Chen, L
Kumar, P
AF Pu, Pearl
Chen, Li
Kumar, Pratyush
TI Evaluating product search and recommender systems for E-commerce
environments
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Preference-based search; Product recommender systems; Example critiquing
interfaces; Decision technology; Electronic product catalog; Tradeoff
analysis; Fisheye view interfaces
AB Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper describes the design and implementation of two user studies in which a particular product search tool, known as example critiquing, was evaluated against a chosen baseline model. The results confirm that example critiquing significantly reduces users' task time and error rate while increasing decision accuracy. Additionally, the results of the second user study show that a particular implementation of example critiquing also made users more confident about their choices. The main contribution is that through these two user studies, an evaluation framework of three criteria was successfully identified, which can be used for evaluating general product search and recommender systems in E-commerce environments. These two experiments and the actual procedures also shed light on some of the most important issues which need to be considered for evaluating such tools, such as the preparation of materials for evaluation, user task design, the context of evaluation, the criteria, the measures and the methodology of result analyses.
C1 [Pu, Pearl; Chen, Li] Ecole Polytech Fed Lausanne, Swiss Fed Inst Technol Lausanne, Human Comp Interact Grp, Sch Comp & Commun Sci, CH-1015 Lausanne, Switzerland.
[Kumar, Pratyush] Univ Virginia, Business Adm, Darden Grad Sch Business, Charlottesville, VA USA.
[Kumar, Pratyush] Univ Virginia, Darden Grad Sch Business Adm, Charlottesville, VA USA.
C3 Swiss Federal Institutes of Technology Domain; Ecole Polytechnique
Federale de Lausanne; University of Virginia; University of Virginia
RP Pu, P (autor correspondiente), Ecole Polytech Fed Lausanne, Swiss Fed Inst Technol Lausanne, Human Comp Interact Grp, Sch Comp & Commun Sci, CH-1015 Lausanne, Switzerland.
EM pearl.pu@epfl.ch; li.chen@epfl.ch; KumarP08@darden.virginia.edu
CR [Anonymous], 1993, ADAPTIVE DECISION MA, DOI DOI 10.1017/CBO9781139173933
Bederson B. B., 2004, ACM Transactions on Computer-Human Interaction, V11, P90, DOI 10.1145/972648.972652
BEDERSON BB, 2000, P ACM C US INT SOFTW, P217
Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
Burke RD, 1997, IEEE EXPERT, V12, P32, DOI 10.1109/64.608186
Callahan E., 2000, EC '00, P197
CARENINI G, 2002, AAAI 02 WORKSH PREF
Faltings B, 2004, COMPUT INTELL, V20, P246, DOI 10.1111/j.0824-7935.2004.00237.x
FISCHER GW, 1993, J EXP PSYCHOL HUMAN, V19, P580
Furnas G. W., 1986, ACM SIGCHI Bulletin, V17, P16, DOI DOI 10.1145/22339.22342
HA V, 2003, ARTIF INTELL, V46, P9
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hogarth R.M., 1987, JUDGMENT CHOICE
Huan Pearl, 2004, P 5 ACM C EL COMM, P208
JANECEK P, 2002, P INT C ADV VIS INT
Jedetski J, 2002, IEEE INTERNET COMPUT, V6, P72, DOI 10.1109/4236.991446
Keeney RL., 1976, DECISION ANAL MULTIP
Lamping J, 1995, CHI 95, P401, DOI DOI 10.1145/223904.223956
Linden Greg., 1997, P 6 INT C USER MODEL, P67
Maes P, 1999, COMMUN ACM, V42, P81, DOI 10.1145/295685.295716
MCCARTHY K, 2004, P 3 INT C AD HYP AD
McCarthy KK, 2005, Proceedings of the Thirty-Eighth Annual Conference of the American Association of Bovine Practitioners, P175, DOI 10.1145/1040830.1040871
Paraschiv Corina., 2002, ELECTRON COMMER RES, V2, P255
Pirolli P., 2003, ACM Transactions on Computer-Human Interaction, V10, P20, DOI 10.1145/606658.606660
Pu P, 2004, CONSTRAINTS, V9, P289, DOI 10.1023/B:CONS.0000049205.05581.24
PU P, 2000, P SIGCHI C HUM FACT, P289, DOI DOI 10.1145/332040.332446
RAO R, 1994, HUMAN FACTORS IN COMPUTING SYSTEMS, CHI '94 CONFERENCE PROCEEDINGS - CELEBRATING INTERDEPENDENCE, P318, DOI 10.1145/191666.191776
REILLY J, 2004, P 7 EUR C CAS BAS RE
REILLY J, 2004, P 24 SGAI INT C INN
RESNICK P, 1994, P ACM C COMP SUPP CO, P130
Sarkar Manojit., 1992, Pro- ceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '92, P83, DOI DOI 10.1145/142750.142763
SMYTH B, 2004, P IEEE WIC ACM INT C
Spence R., 1982, Behaviour and Information Technology, V1, P43, DOI 10.1080/01449298208914435
Stolze M., 2000, International Journal on Digital Libraries, V3, P60, DOI 10.1007/s007990050010
Torrens M., 2002, Constraints, V7, P49, DOI 10.1023/A:1017940426216
TORRENS M, 1997, WORKSH NOT CONSTR AG
TORRENS M, 1999, WORKSH NOT ART INT E, P10
TVERSKY A, 1988, PSYCHOL REV, V95, P371, DOI 10.1037/0033-295X.95.3.371
Zhang J., 2004, IC200454 SWISS FED I IC200454 SWISS FED I
NR 40
TC 31
Z9 37
U1 0
U2 25
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD JUN
PY 2008
VL 8
IS 1-2
BP 1
EP 27
DI 10.1007/s10660-008-9015-z
PG 27
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA V15HZ
UT WOS:000207794400001
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Baczkiewicz, A
Kizielewicz, B
Shekhovtsov, A
Watróbski, J
Salabun, W
AF Baczkiewicz, Aleksandra
Kizielewicz, Bartlomiej
Shekhovtsov, Andrii
Watrobski, Jaroslaw
Salabun, Wojciech
TI Methodical Aspects of MCDM Based E-Commerce Recommender System
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE e-commerce; decision support system; MCDM; consumer multi-criteria
decision-making process; compromise ranking; TOPSIS-COMET; COCOSO; EDAS;
MAIRCA; MABAC
ID DECISION-SUPPORT-SYSTEM; OF-THE-ART; RANK REVERSAL; EDAS METHOD;
SELECTION; CRITERIA; TOPSIS; INTEGRATION; TRANSPORT; MODEL
AB The aim of this paper is to present the use of an innovative approach based on MCDM methods as the main component of a consumer Decision Support System (DSS) by recommending the most suitable products among a given set of alternatives. This system provides a reliable recommendation to the consumer in the form of a compromise ranking constructed from the five MCDM methods: the hybrid approach TOPSIS-COMET, COCOSO, EDAS, MAIRCA, and MABAC. Each of the methods used contributes significantly to the final compromise ranking built with the Copeland strategy. Chosen MCDM methods were combined with the objective CRITIC weighting method, and their performance was presented on the illustrative example of choosing the most suitable mobile phone. A sensitivity analysis involving the r(w) and WS correlation coefficients was performed to determine the match between the compromise ranking of the candidates and the rankings provided by each MCDM method. Sensitivity analysis demonstrated that all investigated compromise candidate rankings show high convergence with the rankings provided by the particular MCDM methods. Thus, the performed study proved that the proposed approach shows high potential to be successfully used as a central component of DSS for recommending the most suitable product. Such DSS could be a universal and future-proof solution for e-commerce sites and websites, providing advanced product comparison capabilities in delivering a recommendation to the user as a final ranking of alternatives.
C1 [Baczkiewicz, Aleksandra; Watrobski, Jaroslaw] Univ Szczecin, Inst Management, Ul Cukrowa 8, PL-71004 Szczecin, Poland.
[Baczkiewicz, Aleksandra] Univ Szczecin, Doctoral Sch, Ul Mickiewicza 16, PL-70383 Szczecin, Poland.
[Kizielewicz, Bartlomiej; Shekhovtsov, Andrii; Salabun, Wojciech] West Pomeranian Univ Technol Szczecin, Fac Comp Sci & Informat Technol, Dept Artificial Intelligence & Appl Math, Res Team Intelligent Decis Support Syst, Ul Zolnierska 49, PL-71210 Szczecin, Poland.
C3 University of Szczecin; University of Szczecin; West Pomeranian
University of Technology
RP Salabun, W (autor correspondiente), West Pomeranian Univ Technol Szczecin, Fac Comp Sci & Informat Technol, Dept Artificial Intelligence & Appl Math, Res Team Intelligent Decis Support Syst, Ul Zolnierska 49, PL-71210 Szczecin, Poland.
EM aleksandra.baczkiewicz@phd.usz.edu.pl;
bartlomiej-kizielewicz@zut.edu.pl; andrii-shekhovtsov@zut.edu.pl;
jaroslaw.watrobski@usz.edu.pl; wojciech.salabun@zut.edu.pl
RI Sałabun, Wojciech/H-2883-2016; Shekhovtsov, Andrii/AAM-6977-2021;
Bączkiewicz, Aleksandra/AAP-8553-2021; Wątróbski,
Jarosław/ABI-7844-2020; Kizielewicz, Bartłomiej/AAN-1295-2021
OI Sałabun, Wojciech/0000-0001-7076-2519; Shekhovtsov,
Andrii/0000-0002-0834-2019; Bączkiewicz, Aleksandra/0000-0003-4249-8364;
Wątróbski, Jarosław/0000-0002-4415-9414; Kizielewicz,
Bartłomiej/0000-0001-5736-4014
FU National Science Center [UMO-2018/29 /B/HS4/02725]; [001/RID/2018/19]
FX The work was supported by the National Science Center, Decision number
UMO-2018/29 /B/HS4/02725 (B.K., A.S. and W.S.) and by the project
financed within the framework of the program of the Minister of Science
and Higher Education under the name "Regional Excellence Initiative" in
the years 2019-2022, Project Number 001/RID/2018/19; the amount of
financing: PLN 10.684.00000 (A.B. and J.W.).
CR Acosta M, 2017, FORESTS, V8, DOI 10.3390/f8040116
Adali Esra Aytac., 2017, European Journal of Multidisciplinary Studies, V2, P88, DOI [DOI 10.26417/EJMS.V5I1.P93-101, 10.26417/ejms.v5i1.p93-101]
Aggarwal Ashish, 2018, Procedia Computer Science, V132, P236, DOI 10.1016/j.procs.2018.05.193
Aksoy E, 2021, GUMUHANE U SOSYAL BI, V12, P1
Alamdari PM, 2020, IEEE ACCESS, V8, P115694, DOI 10.1109/ACCESS.2020.3002803
Alyoubi BA, 2015, PROCEDIA COMPUT SCI, V65, P278, DOI 10.1016/j.procs.2015.09.079
Ansari N., 2016, SMARTPHONE SELECTION
Baczkiewicz A., 2021, MCDA BASED DECISION
Baidya J, 2021, COMPLEX INTELL SYST, V7, P2503, DOI 10.1007/s40747-021-00413-x
Baryannis G., 2019, Revisiting supply chain risk, P53, DOI DOI 10.1007/978-3-030-03813-7_4
BARZILAI J, 1994, INFOR, V32, P57
Behzadian M, 2012, EXPERT SYST APPL, V39, P13051, DOI 10.1016/j.eswa.2012.05.056
Brauers WKM, 2006, CONTROL CYBERN, V35, P445
Cánovas-Segura B, 2016, PROG ARTIF INTELL, V5, P181, DOI 10.1007/s13748-016-0089-x
Chen IF, 2018, INT J INF TECH DECIS, V17, P1915, DOI 10.1142/S0219622018500505
Deb Kaustuv, 2018, International Journal of Information Engineering and Electronic Business, V10, P52, DOI 10.5815/ijieeb.2018.06.06
Dezert J, 2020, PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020), P195
DIAKOULAKI D, 1995, COMPUT OPER RES, V22, P763, DOI 10.1016/0305-0548(94)00059-H
Ecer F, 2021, RENEW SUST ENERG REV, V143, DOI 10.1016/j.rser.2021.110916
Efe B, 2020, SOFT COMPUT, V24, P15447, DOI 10.1007/s00500-020-04876-x
Faizi S, 2018, INT J FUZZY SYST, V20, P93, DOI 10.1007/s40815-017-0313-2
Faizi S, 2017, SYMMETRY-BASEL, V9, DOI 10.3390/sym9080136
Gigovic LJ, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8040372
Goguen JA., 1973, J Symb Logic, V38, P656, DOI [10.2307/2272014, DOI 10.2307/2272014]
Goswami S., 2020, INT J DATA NETW SCI, V4, P27, DOI [10.5267/j.ijdns.2019.8.004, DOI 10.5267/J.IJDNS.2019.8.004]
Goswami S.S., 2021, International Journal of Service Science, Management, Engineering, and Technology, V12, P116, DOI [10.4018/IJSSMET.2021050107, DOI 10.4018/IJSSMET.2021050107]
Goswami SS, 2021, DECISION-INDIA, V48, P71, DOI 10.1007/s40622-020-00260-8
Zolfani SH, 2020, INT J STRATEG PROP M, V24, P102, DOI 10.3846/ijspm.2020.11543
Jahan A, 2012, INT J ADV MANUF TECH, V58, P411, DOI 10.1007/s00170-011-3366-7
Jahani A, 2016, J ENVIRON PLANN MAN, V59, P222, DOI 10.1080/09640568.2015.1005732
Jirí M, 2019, IFMBE PROC, V68, P83, DOI 10.1007/978-981-10-9038-7_15
Kazan H., 2014, International Journal of Management and Sustainability, V3, P203, DOI DOI 10.1109/ACCESS.2019.2942207
Keshavarz Ghorabaee M, 2017, TRANSPORT-VILNIUS, V32, P66, DOI 10.3846/16484142.2017.1282381
Keshavarz Ghorabaee M, 2016, INT J COMPUT COMMUN, V11, P358
Keshavarz Ghorabaee M, 2015, INFORMATICA-LITHUAN, V26, P435, DOI 10.15388/Informatica.2015.57
Khademolqorani S, 2013, PROCD SOC BEHV, V73, P388, DOI 10.1016/j.sbspro.2013.02.066
Khan S, 2021, INT J SUSTAIN ENG, V14, P357, DOI 10.1080/19397038.2020.1871442
Kizielewicz Bartlomiej, 2021, Computational Science - ICCS 2021. 21st International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12742), P338, DOI 10.1007/978-3-030-77961-0_29
Kizielewicz Bartlomiej, 2020, Procedia Computer Science, V176, P3581, DOI 10.1016/j.procs.2020.09.028
Kizielewicz B, 2020, Procedia Computer Science, V176, P3524
Kizielewicz B., 2020, Procedia Computer Science, V176, P2243, DOI [10.1016/j.procs.2020.09.274, DOI 10.1016/J.PROCS.2020.09.274]
Kizielewicz B., 2021, FACTA UNIV-SER MECH
Kumar G, 2020, INT J INF TECH DECIS, V19, P1037, DOI 10.1142/S021962202050025X
Kumar S., 2020, P INT C ADV RES INN, P147
Kuo T, 2017, EUR J OPER RES, V260, P152, DOI 10.1016/j.ejor.2016.11.052
Lahav O., 2018, ARXIV181110799
Lai H, 2020, INZ EKON, V31, P411, DOI 10.5755/j01.ee.31.4.24990
Lei NR, 2015, DECIS SUPPORT SYST, V69, P82, DOI 10.1016/j.dss.2014.11.010
Liu XL, 2021, OMEGA-INT J MANAGE S, V102, DOI 10.1016/j.omega.2020.102317
Madic Milos, 2015, University Politehnica of Bucharest, Scientific Bulletin, Series D (Mechanical Engineering), V77, P193
Mishra AR, 2021, INFORMATICA-LITHUAN, V32, P119, DOI 10.15388/20-INFOR430
Mishra AR, 2022, J AMB INTEL HUM COMP, V13, P295, DOI 10.1007/s12652-021-02902-w
Mishra AR, 2019, GRANULAR COMPUT, V4, P511, DOI 10.1007/s41066-018-0114-5
Mishra AR, 2020, NEURAL COMPUT APPL, V32, P3901, DOI 10.1007/s00521-018-3716-6
Mishra AR, 2019, NEURAL COMPUT APPL, V31, P1299, DOI 10.1007/s00521-017-3101-x
Musrini M., 2020, P 2 FAC IND TECH INT, P270
Natasya WAG, 2017, 2017 2ND INTERNATIONAL CONFERENCES ON INFORMATION TECHNOLOGY, INFORMATION SYSTEMS AND ELECTRICAL ENGINEERING (ICITISEE), P456, DOI 10.1109/ICITISEE.2017.8285550
Okfalisa O., 2021, SINERGI, V25, P101, DOI [10.22441/sinergi.2021.1.013, DOI 10.22441/SINERGI.2021.1.013]
Pamucar D., 2014, P 16 INT SCI EXPERT, P89
Pamucar D, 2017, EXPERT SYST APPL, V88, P58, DOI 10.1016/j.eswa.2017.06.037
Pamucar D, 2015, EXPERT SYST APPL, V42, P3016, DOI 10.1016/j.eswa.2014.11.057
Peng XD, 2020, TECHNOL ECON DEV ECO, V26, P695, DOI 10.3846/tede.2020.11920
Peng XD, 2020, ARTIF INTELL REV, V53, P3813, DOI 10.1007/s10462-019-09780-x
Piegat A, 2014, APPL COMPUT INTELL S, V2014, DOI 10.1155/2014/536492
Rani P, 2019, 2019 FIFTH INTERNATIONAL CONFERENCE ON IMAGE INFORMATION PROCESSING (ICIIP 2019), P509, DOI 10.1109/ICIIP47207.2019.8985985
Razmak J, 2015, J MULTI-CRITERIA DEC, V22, P101, DOI 10.1002/mcda.1530
Saabun W., 2015, STUD P POLISH ASS KN, V77, P54
Salabun W., 2019, COMET METHOD 1 MCDA, V3
Salabun W, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12091549
Salabun W, 2020, LECT NOTES COMPUT SC, V12138, P632, DOI 10.1007/978-3-030-50417-5_47
Salabun W, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11123314
Salabun W, 2018, 2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), P1485, DOI 10.1109/SSCI.2018.8628864
Salabun W, 2018, 2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), P1478, DOI 10.1109/SSCI.2018.8628934
Salabun W, 2018, PROCEDIA COMPUT SCI, V126, P2248, DOI 10.1016/j.procs.2018.07.224
Salabun W, 2016, SMART INNOV SYST TEC, V56, P181, DOI 10.1007/978-3-319-39630-9_15
Salabun W, 2014, MANAG PROD ENG REV, V5, P62, DOI 10.2478/mper-2014-0028
Salabun W, 2015, J MULTI-CRITERIA DEC, V22, P37, DOI 10.1002/mcda.1525
Sari F, 2020, J APICULT RES, V59, P546, DOI 10.1080/00218839.2020.1718341
Shekhovtsov Andrii, 2021, Computational Science - ICCS 2021. 21st International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12747), P458, DOI 10.1007/978-3-030-77980-1_35
Shekhovtsov Andrii, 2020, Procedia Computer Science, V176, P3730, DOI 10.1016/j.procs.2020.09.014
Shekhovtsov A., 2020, RELIABLE DECISION MA, DOI [10.22190/FUME210315056S, DOI 10.22190/FUME210315056S]
Shekhovtsov A, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12091541
Shekhovtsov A, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12197915
Shoja BM, 2019, IEEE ACCESS, V7, P119121, DOI 10.1109/ACCESS.2019.2937518
Singh R, 2020, INT J QUAL RELIAB MA, V37, P837, DOI 10.1108/IJQRM-01-2020-0022
García-Cascales MS, 2012, MATH COMPUT MODEL, V56, P123, DOI 10.1016/j.mcm.2011.12.022
Stanujkic D, 2017, STUD INFORM CONTROL, V26, P5
Sutton RT, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-0221-y
Tus A, 2019, OPSEARCH, V56, P528, DOI 10.1007/s12597-019-00371-6
Verly C., 2013, Intern. J. Multicriteria Decision Making, V3, P325, DOI 10.1504/IJMCDM.2013.056781
Vertakova Y., 2019, Journal of Applied Engineering Science, V17, P74
Watróbski J, 2019, OMEGA-INT J MANAGE S, V86, P107, DOI 10.1016/j.omega.2018.07.004
Watróbski J, 2017, FED CONF COMPUT SCI, P949, DOI 10.15439/2017F455
Wei GW, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16245082
Wen Z, 2020, ADV INTELL SYST COMP, V1001, P813, DOI 10.1007/978-3-030-21248-3_61
Yadegaridehkordi E, 2019, INT J INF TECH DECIS, V18, P1845, DOI 10.1142/S0219622019500366
Yalçin N, 2018, TECHNOL ECON DEV ECO, V24, P534, DOI 10.3846/20294913.2016.1213201
Yang M., 2009, PHARM CARE RES, V1
Yang WG, 2020, MATH PROBL ENG, V2020, DOI 10.1155/2020/9676518
Yazdani M, 2019, J CIV ENG MANAG, V25, P858, DOI 10.3846/jcem.2019.11309
Yazdani M, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133704
Yazdani M, 2018, MAT HORIZ, P113, DOI 10.1007/978-981-13-2417-8_5
Yazdani M, 2019, MANAGE DECIS, V57, P2501, DOI 10.1108/MD-05-2017-0458
Yusuff R.M., 2010, INT J INNOV MANAG TE, V1, P511, DOI [DOI 10.7763/IJIMT.2010.V1.89, 10.7763/IJIMT.2010.V1.89]
Zanakis SH, 1998, EUR J OPER RES, V107, P507, DOI 10.1016/S0377-2217(97)00147-1
Zandi I., 2021, GEOGR URBAN SPACE DE, V7, P41, DOI [10.22067/jgusd.2021.48121.0, DOI 10.22067/JGUSD.2021.48121.0]
Zavadskas EK, 2012, ELEKTRON ELEKTROTECH, V122, P3, DOI 10.5755/j01.eee.122.6.1810
Zhang XY, 2008, IEEE WCNC, P13
Zhang Y, 2019, ELECTRON MARK, V29, P219, DOI 10.1007/s12525-019-00346-x
Zizovic M, 2020, MATHEMATICS-BASEL, V8, DOI 10.3390/math8061015
Zolfani S. H., 2019, INT SCI C BUSINESS M, P797, DOI 10.3846/cibmee.2019.081
Zong K, 2021, J THEOR APPL EL COMM, V16, P1150, DOI 10.3390/jtaer16040065
NR 112
TC 38
Z9 37
U1 8
U2 48
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD SEP
PY 2021
VL 16
IS 6
BP 2192
EP 2229
DI 10.3390/jtaer16060122
PG 38
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA UV7RY
UT WOS:000699671700001
OA gold
DA 2024-03-27
ER
PT J
AU Sitar-Taut, DA
Mican, D
AF Sitar-Taut, Dan-Andrei
Mican, Daniel
TI MRS OZ: managerial recommender system for electronic commerce based on
Onicescu method and Zipf's law
SO INFORMATION TECHNOLOGY & MANAGEMENT
LA English
DT Article
DE Recommender systems; Onicescu method; Content based filtering;
Management decision making; Multi criteria decisions; Analytic hierarchy
process
ID SOCIAL NETWORKS; PERSONALIZATION; INFORMATION
AB User decision intuition is challenging and complex, even if the user and product are known. Thus, recommending products is a management decision with high degree of incertitude. What if we are facing also the cold-start problem, like new products or visitors? This is a hot topic in recommender systems, tackled in variously, successfully or not. This perspective adds more incertitude to the existing uncertain scenario. Our philosophy is the shift from a user-centric view, hit by uncertainty, to a company-centric one taken in certainty circumstances, later to apply win-win approaches. We propose a multi-criteria algorithm -MRS OZ- for an ecommerce site RS that tackles the cold-start differently. It uses Onicescu method, being adapted according to Zipf's Law, very popular in internet marketing. The paper opted for an exploratory research based on primary and secondary methods, consisting in literature review, 2-step survey addressed to 110 managers splat in 2 groups, and statistical analyses. The algorithm may substitute the human expertise on the given sample item list and criteria set. This work reveals that Onicescu method is suitable for recommender systems field, but relative inner category rankings and more domain related weight ratios strengthen the algorithm. Onicescu method has a wide applicability, but not for recommender systems. Also, the mixture with Zipf's Law is completely experimental in research area.
C1 [Sitar-Taut, Dan-Andrei; Mican, Daniel] Babes Bolyai Univ, Fac Econ & Business Adm, Dept Business Informat Syst, Cluj Napoca, Romania.
C3 Babes Bolyai University from Cluj
RP Mican, D (autor correspondiente), Babes Bolyai Univ, Fac Econ & Business Adm, Dept Business Informat Syst, Cluj Napoca, Romania.
EM daniel.mican@econ.ubbcluj.ro
RI Sitar-Taut, Dan-Andrei/U-4746-2017; Mican, Daniel/K-6927-2018
OI Sitar-Taut, Dan-Andrei/0000-0001-5360-3874; Mican,
Daniel/0000-0003-0277-9700
CR Alexandridis G, 2017, DATA MIN KNOWL DISC, V31, P1031, DOI 10.1007/s10618-017-0504-3
Amine Ait Younes RA, 2000, 118 BIS, V1-2
[Anonymous], 2013, CONSUMER BEHAV EUROP
[Anonymous], 2006, Hyperion
Baur AW, 2014, IFIP ADV INF COMM TE, V445, P1
Bouyahia T, 2017, FDN PRACTICE SECURIT, P51
Brandtner P, 2015, LECT NOTES COMPUT SC, V9073, P295, DOI 10.1007/978-3-319-18714-3_19
Broderick M, 2015, COMMUN ACM, V58, P21, DOI 10.1145/2732421
Chaffey D., 2016, Digital Marketing: Strategy, Implementation
Di Noia T, 2017, INFORM SCIENCES, V382, P234, DOI 10.1016/j.ins.2016.11.015
Dobre I, 2002, MODELAREA DECIZIILOR
Eirinaki M, 2018, FUTURE GENER COMP SY, V78, P413, DOI 10.1016/j.future.2017.09.015
Frémal S, 2017, EXPERT SYST APPL, V77, P105, DOI 10.1016/j.eswa.2017.01.031
Gan MX, 2018, FUTURE GENER COMP SY, V78, P462, DOI 10.1016/j.future.2017.02.027
Ghoshal A, 2015, J MANAGE INFORM SYST, V31, P243, DOI 10.1080/07421222.2014.1001276
GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867
Guo JP, 2018, FUTURE GENER COMP SY, V78, P451, DOI 10.1016/j.future.2017.02.031
Gurini DF, 2018, FUTURE GENER COMP SY, V78, P430, DOI 10.1016/j.future.2017.03.020
Huang SL, 2011, ELECTRON COMMER R A, V10, P398, DOI 10.1016/j.elerap.2010.11.003
Hwangbo H, 2017, EXPERT SYST APPL, V89, P254, DOI 10.1016/j.eswa.2017.07.041
Ilie L, 2006, MANAGEMENTUL FIRMEI
J Figueira, 2005, ELECTRE METHODS MULT
Karpus A, 2016, KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1, P419, DOI 10.5220/0006083904190424
Leskovec J, 2014, MINING OF MASSIVE DATASETS, 2ND EDITION, P1
Lika B, 2014, EXPERT SYST APPL, V41, P2065, DOI 10.1016/j.eswa.2013.09.005
Lin ZJ, 2015, J MANAGE INFORM SYST, V32, P246, DOI 10.1080/07421222.2015.1138572
Manber U, 2000, COMMUN ACM, V43, P35, DOI 10.1145/345124.345136
Margaris D, 2018, FUTURE GENER COMP SY, V78, P440, DOI 10.1016/j.future.2017.03.015
Marlinda L, 2017, 5 INT C CYB IT SERV
Martin A, 2017, MULT CRIT DECIS MAK, P83, DOI 10.1007/978-3-319-39292-9_5
Megyesi E, 2014, INT MULTI SCI GEOCO, P65
Morawski J, 2017, INT J INTELL SYST, V32, P1062, DOI 10.1002/int.21884
Nadolski RJ, 2009, JASSS-J ARTIF SOC S, V12
Nguyen PT, 2015, LECT NOTES COMPUT SC, V9366, P605, DOI 10.1007/978-3-319-25007-6_35
Nilashi M, 2015, ELECTRON COMMER R A, V14, P542, DOI 10.1016/j.elerap.2015.08.004
Onicescu O, 1970, REV STAT
Popescu V, 2009, MANAGEMENT OF TECHNOLOGICAL CHANGES, VOL 2, P769
Resteanu C, 2016, STUD INFORM CONTROL, V25, P433
Rezaeimehr F, 2018, FUTURE GENER COMP SY, V78, P419, DOI 10.1016/j.future.2017.04.003
ROY B, 1991, THEOR DECIS, V31, P49, DOI 10.1007/BF00134132
Roy B, 2001, PRESENTATION INTERPR
Roy B., 1973, METHODE ELECTRE 2 AP, P291
Roy B, 1968, REUNE FRANCAISE INFO, V8, P8
Roy B, 1978, CAHIERS CERO, V20, P22
Saaty T. L., 1980, The analytical hierarchy process, planning, priority
Saaty TL., 2008, INT J SERV SCI, V1, P83, DOI [10.1504/IJSSCI.2008.017590, DOI 10.1504/IJSSCI.2008.017590]
Shambour Q, 2016, INT J ADV COMPUT SC, V7, P274
Son J, 2017, EXPERT SYST APPL, V89, P404, DOI 10.1016/j.eswa.2017.08.008
VASTOTERRIENTES L, 2016, J INTELL INF SYST, V46, P313, DOI DOI 10.1007/s10844-015-0362-7
Yu S, 2018, J NETW COMPUT APPL, V104, P38, DOI 10.1016/j.jnca.2017.12.004
Zipf GK, 1950, J CLIN PSYCHOL, V6, P306
NR 51
TC 8
Z9 8
U1 1
U2 20
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1385-951X
EI 1573-7667
J9 INFORM TECHNOL MANAG
JI Inf. Technol. Manag.
PD JUN
PY 2020
VL 21
IS 2
BP 131
EP 143
DI 10.1007/s10799-019-00309-w
PG 13
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA LY4WN
UT WOS:000540530300004
DA 2024-03-27
ER
PT J
AU Huang, SL
AF Huang, Shiu-li
TI Designing utility-based recommender systems for e-commerce: Evaluation
of preference-elicitation methods
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Recommender systems; Multi-attribute utility theory; Radial basis
function network; SMARTER; Vector space model
ID ARTIFICIAL NEURAL-NETWORKS; CRITERIA DECISION-MAKING;
INDIVIDUAL-DIFFERENCES; SELECTION; AGENT; STRATEGIES
AB Recommender systems are useful in reducing information overload and improving decision making. Utility-based recommender systems provide recommendations based on the computation of the utility of each item for the user. Some utility-elicitation methods have been developed on the basis of multi-attribute utility theory (MAUT) to represent a decision maker's complete preference. This study investigates whether these utility-based techniques outperform the traditional content-based technique for online recommendations. A laboratory experiment was conducted in two e-commerce contexts to compare the decomposed and holistic utility-based methods, simple multi-attribute rating technique exploiting ranks (SMARTER) and radial basis function network (RBFN), with the content-based method vector space model (VSM) in terms of recommendation accuracy, time expense, and user perceptions. The results demonstrate that the performances of utility-based methods depend on recommendation contexts. Furthermore, this study proposes guidelines for choosing appropriate recommendation methods in different contexts. (C) 2010 Elsevier B. V. All rights reserved.
C1 [Huang, Shiu-li] Ming Chuan Univ, Dept Informat Management, Gueishan Township 333, Taoyuan County, Taiwan.
C3 Ming Chuan University
RP Huang, SL (autor correspondiente), Natl Taipei Univ, Dept Business Adm, 151 Univ Rd, New Taipei City 23741, Taiwan.
EM shiulihuang@gmail.com
RI Novoa, Kevin/J-2867-2014; Huang, Shiu-Li/CAH-5940-2022; Huang,
Shiu-Li/CAF-0381-2022
CR AG, 2005, ENGINEERING, V17, P734
Al-Aomar R, 2008, INT J INF TECH DECIS, V7, P35, DOI 10.1142/S0219622008002806
[Anonymous], 2005, NEURAL NETWORKS CLAS
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124
Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
Choi SH, 2004, EXPERT SYST APPL, V27, P549, DOI 10.1016/j.eswa.2004.05.017
DYER JS, 1992, MANAGE SCI, V38, P645, DOI 10.1287/mnsc.38.5.645
EDWARDS W, 1994, ORGAN BEHAV HUM DEC, V60, P306, DOI 10.1006/obhd.1994.1087
EDWARDS W, 1977, IEEE T SYST MAN CYB, V7, P326, DOI 10.1109/TSMC.1977.4309720
FISCHER GW, 1977, ORGAN BEHAV HUM PERF, V18, P295, DOI 10.1016/0030-5073(77)90032-0
Gretzel U, 2006, INT J ELECTRON COMM, V11, P81, DOI 10.2753/JEC1086-4415110204
Guan Steven., 2002, Electronic Commerce Research and Applications, V1, P314, DOI DOI 10.1016/S1567-4223(02)00023-6
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Haykin S., 1999, NEURAL NETWORKS COMP
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hostler RE, 2005, DECIS SUPPORT SYST, V41, P313, DOI 10.1016/j.dss.2004.07.002
Iijima J, 2007, ELECTRON COMMER R A, V6, P139, DOI 10.1016/j.elerap.2006.11.002
Kardes FR, 2006, J CONSUM PSYCHOL, V16, P135, DOI 10.1207/s15327663jcp1602_4
Keeney RL., 1976, DECISION ANAL MULTIP
LASKEY KB, 1987, MANAGE SCI, V33, P965, DOI 10.1287/mnsc.33.8.965
Lee BK, 2004, PSYCHOL MARKET, V21, P159, DOI 10.1002/mar.20000
Lee WP, 2004, EXPERT SYST APPL, V27, P665, DOI 10.1016/j.eswa.2004.07.001
LIN FR, 2005, P 4 WORKSH E BUS LAS
Liu DR, 2005, INFORM MANAGE-AMSTER, V42, P387, DOI 10.1016/j.im.2004.01.008
MALAKOOTI B, 1994, MANAGE SCI, V40, P1542, DOI 10.1287/mnsc.40.11.1542
Manouselis N, 2007, INT J PATTERN RECOGN, V21, P311, DOI 10.1142/S021800140700548X
Manouselis N, 2007, WORLD WIDE WEB, V10, P415, DOI 10.1007/s11280-007-0019-8
MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037/0033-295X.101.2.343
Morera OF, 1998, ORGAN BEHAV HUM DEC, V75, P187, DOI 10.1006/obhd.1998.2791
Nowlis SM, 1997, J MARKETING RES, V34, P205, DOI 10.2307/3151859
ONKEN J, 1985, J EXP PSYCHOL HUMAN, V11, P14, DOI 10.1037/0096-1523.11.1.14
PARK CW, 1976, J MARKETING RES, V13, P144, DOI 10.2307/3150848
Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159
Pomerol J-C., 2000, MULTICRITERION DECIS
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Saaty TL., 1980, ANAL HIERARCHY PROCE, DOI DOI 10.21236/ADA214804
Salton G., 1989, AUTOMATIC TEXT PROCE
SCHICKELZUBER V, 2005, P WORKSH KNOWL DISC
SCHMITT C, 2002, P ABIS WORKSH HANN G
SCHOEMAKER PJH, 1982, MANAGE SCI, V28, P182, DOI 10.1287/mnsc.28.2.182
Schunk D, 2006, J ECON PSYCHOL, V27, P386, DOI 10.1016/j.joep.2005.08.003
SRIVASTAVA J, 1995, ORGAN BEHAV HUM DEC, V63, P112, DOI 10.1006/obhd.1995.1066
STOLZE M, 2003, P 2 INT WORLD C MASS
Sun MH, 1996, MANAGE SCI, V42, P835, DOI 10.1287/mnsc.42.6.835
Tewari G, 2003, DECIS SUPPORT SYST, V34, P127, DOI 10.1016/S0167-9236(02)00076-3
Trope Y, 2003, PSYCHOL REV, V110, P403, DOI 10.1037/0033-295X.110.3.403
Viswanathan Madhubalan., 1994, Journal of Consumer Psychology, V3, P79, DOI DOI 10.1016/S1057-7408(08)80029-0
Wang M., 1998, J MULTI-CRITERIA DEC, V7, P340
Witten IH, 2005, DATA MINING PRACTICA, P525
WRIGHT P, 1975, J MARKETING RES, V12, P60, DOI 10.2307/3150659
Yamamoto N, 2005, CONSUM COMM NETWORK, P65
YAP CS, 1992, P 25 HAW INT C SYST, P578
Zeng C, 2004, INT J ELECTRON COMM, V8, P115, DOI 10.1080/10864415.2004.11044314
NR 54
TC 72
Z9 81
U1 2
U2 64
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD JUL-AUG
PY 2011
VL 10
IS 4
BP 398
EP 407
DI 10.1016/j.elerap.2010.11.003
PG 10
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 817MN
UT WOS:000294677800004
DA 2024-03-27
ER
PT J
AU Kim, HM
Ghiasi, B
Spear, M
Laskowski, M
Li, JY
AF Kim, Henry M.
Ghiasi, Bita
Spear, Max
Laskowski, Marek
Li, Jiye
TI Online serendipity: The case for curated recommender systems
SO BUSINESS HORIZONS
LA English
DT Article
DE Recommender systems; Curated recommender systems; E-commerce strategy;
E-business; Online retail
AB When used effectively, recommender systems provide users with suggestions based on their own preferences. These systems first showed their value with e commerce sites like Amazon and eBay, which provided recommendations algorithmically. A key drawback of these systems is that some items need personal touch recommendations to spur on purchase, use, or consumption. A recommender system that facilitates personal touch recommendations by enabling users to discover good recommenders as opposed to focusing on recommending items algorithmically addresses this drawback. In this article, we discuss such a system a curated recommender system. A curated recommender system is optimal for online retailers and service providers, especially those that sell books, stream content, or provide social networking platforms. (C) 2017 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.
C1 [Kim, Henry M.; Ghiasi, Bita; Laskowski, Marek; Li, Jiye] York Univ, Schulich Sch Business, 4700 Keele St, Toronto, ON, Canada.
[Spear, Max] Indigo Books & Mus Inc, Toronto, ON, Canada.
C3 York University - Canada
RP Kim, HM (autor correspondiente), York Univ, Schulich Sch Business, 4700 Keele St, Toronto, ON, Canada.
EM hmkim@yorku.ca; bghiasi13@schulich.yorku.ca; mspear@indigo.ca;
marlas@yorku.ca; jiyeli2007@gmail.com
OI , Henry/0000-0002-7010-6455
CR Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15
[Anonymous], 2016, ACM TRANS MANAG INF, DOI DOI 10.1145/2843948
[Anonymous], 2008, P 2 INT C UB INF MAN, DOI 10.1145/1352793.1352837
Fowler J., 2014, THE NEXT WEB
Guy I., 2015, Social Recommender Systems, P511
Hennig-Thurau T, 2012, J MARKETING, V76, P89, DOI 10.1509/jm.10.0537
Kantardzic M., 2011, Data mining: concepts, models, methods, and algorithms
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
NR 9
TC 12
Z9 15
U1 1
U2 33
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2017
VL 60
IS 5
BP 613
EP 620
DI 10.1016/j.bushor.2017.05.005
PG 8
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA FH6QW
UT WOS:000411302800006
DA 2024-03-27
ER
PT J
AU Eryarsoy, E
Piramuthu, S
AF Eryarsoy, Enes
Piramuthu, Selwyn
TI Experimental evaluation of sequential bias in online customer reviews
SO INFORMATION & MANAGEMENT
LA English
DT Article
DE Recommender systems; Online product reviews; Sequential bias
ID INFORMATION SEARCH
AB Explicit information used in the development of recommender systems includes online customer feedback. Such explicit input from customers suffers from several types of bias, which directly affect the quality of the resulting recommender system. Recent research has identified sequential bias to be present in online customer reviews. However, to our knowledge, no study to date has confirmed its existence in this context. Given the nature of sequential bias, confirming its existence necessitates a controlled experimental study, which is lacking in extant published literature. We attempt to address this gap. Results from our study show evidence for the existence of sequential bias. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Eryarsoy, Enes] Istanbul Sehir Univ, Istanbul, Turkey.
[Piramuthu, Selwyn] Univ Florida, Gainesville, FL 32611 USA.
[Piramuthu, Selwyn] RFID European Lab, Paris, France.
C3 Istanbul Sehir University; State University System of Florida;
University of Florida
RP Piramuthu, S (autor correspondiente), Univ Florida, Gainesville, FL 32611 USA.
EM eryarsoy@gmail.com; selwyn@ufl.edu
RI Eryarsoy, Enes/AAY-3816-2020
OI Eryarsoy, Enes/0000-0002-5918-0558
CR [Anonymous], 1957, MODELS MAN SOCIAL RA
Deffuant G., 2007, M2M C MARS MARCH
DEFFUANT G, 2006, 1 WORLD C SOC SIM KY
DITTO PH, 1992, J PERS SOC PSYCHOL, V63, P568, DOI 10.1037/0022-3514.63.4.568
Edwards K, 1996, J PERS SOC PSYCHOL, V71, P5, DOI 10.1037/0022-3514.71.1.5
FESTINGER L., 1964, CONFLICT DECIS DISSO
FREY D, 1981, J EXP SOC PSYCHOL, V17, P51, DOI 10.1016/0022-1031(81)90006-8
HOLTON B, 1989, PERS SOC PSYCHOL B, V15, P42, DOI 10.1177/0146167289151004
Hu N, 2012, DECIS SUPPORT SYST, V52, P674, DOI 10.1016/j.dss.2011.11.002
Hu N, 2011, DECIS SUPPORT SYST, V50, P627, DOI 10.1016/j.dss.2010.08.013
Jonas E, 2001, J PERS SOC PSYCHOL, V80, P557, DOI 10.1037/0022-3514.80.4.557
Kapoor G, 2009, J ORG COMP ELECT COM, V19, P85, DOI 10.1080/10919390902820764
Piramuthu S., 2010, P WORKSH DAT MIN MAR, P5
Piramuthu S, 2007, ADV SOFT COMP, V43, P298, DOI 10.1007/978-3-540-72575-6_48
Piramuthu S, 2012, DECIS SUPPORT SYST, V53, P418, DOI 10.1016/j.dss.2012.02.006
Rabin M, 1999, Q J ECON, V114, P37, DOI 10.1162/003355399555945
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Sikora RT, 2012, KNOWL-BASED SYST, V27, P314, DOI 10.1016/j.knosys.2011.10.011
Solloway T., 2010, The Emotional cognitive processing model
TVERSKY A, 1973, COGNITIVE PSYCHOL, V5, P207, DOI 10.1016/0010-0285(73)90033-9
You L., 2014, INF SYST E BUS MANAG
NR 21
TC 17
Z9 19
U1 0
U2 49
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0378-7206
EI 1872-7530
J9 INFORM MANAGE-AMSTER
JI Inf. Manage.
PD DEC
PY 2014
VL 51
IS 8
BP 964
EP 971
DI 10.1016/j.im.2014.09.001
PG 8
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA AX8EE
UT WOS:000347142300003
DA 2024-03-27
ER
PT J
AU Köcher, S
Jugovac, M
Jannach, D
Holzmüller, HH
AF Koecher, Soeren
Jugovac, Michael
Jannach, Dietmar
Holzmueller, Hartmut H.
TI New Hidden Persuaders: An Investigation of Attribute-Level Anchoring
Effects of Product Recommendations
SO JOURNAL OF RETAILING
LA English
DT Article
DE Recommender systems; Anchoring effects; Numerical attributes; Decision
making; Attention; Online shopping
ID WILLINGNESS-TO-PAY; PREFERENCE CONSTRUCTION; DECISION-MAKING;
E-COMMERCE; SYSTEMS; CHOICE; PRICE; CONTEXT; IMPACT; CONSUMERS
AB Recommender systems on marketers' websites are typically designed to facilitate purchase decisions by helping customers easily identify products that match their tastes and needs. However, such product recommendations can not only support but also influence decision-making and outcomes. Expanding on previous research on the persuasiveness of product recommendations, this paper demonstrates that recommender systems can affect online decision-making through an anchoring effect such that consumers' decision-making processes and, ultimately, choices are biased toward numerical attributes of (even random) product recommendations; we refer to this phenomenon as attribute-level anchoring effect. The findings of the current research reveal that this effect occurs because consumers tend to pay more attention to alternatives that are similar to a recommended option. From a practical perspective, these results indicate the potential to shape consumer interests and choices through recommender systems and highlight possible customer protection issues in online shopping environments. (C) 2018 New York University. Published by Elsevier Inc. All rights reserved.
C1 [Koecher, Soeren; Holzmueller, Hartmut H.] TU Dortmund Univ, Fac Business & Econ, Dept Mkt, Otto Hahn Str 6, D-44227 Dortmund, Germany.
[Jugovac, Michael] TU Dortmund Univ, Fac Comp Sci, Otto Hahn Str 12, D-44227 Dortmund, Germany.
[Jannach, Dietmar] Alpen Adria Univ Klagenfurt, Fac Tech Sci, Dept Appl Informat, Univ Str 65-67, A-9020 Klagenfurt, Austria.
C3 Dortmund University of Technology; Dortmund University of Technology;
University of Klagenfurt
RP Köcher, S (autor correspondiente), TU Dortmund Univ, Fac Business & Econ, Dept Mkt, Otto Hahn Str 6, D-44227 Dortmund, Germany.
EM soeren.koecher@tu-dortmund.de; michaeljugovac@tu-dortmund.de;
dietmar.jannach@aau.at; hartmut.holzmueller@tu-dortmund.de
OI Kocher, Soren/0000-0002-7277-238X
CR Adaval R, 2011, J MARKETING RES, V48, P355, DOI 10.1509/jmkr.48.2.355
Adomavicius G, 2013, INFORM SYST RES, V24, P956, DOI 10.1287/isre.2013.0497
[Anonymous], INT J RES MARKETING, DOI [DOI 10.1016/0167-8116(95)00027-5, DOI 10.1016/0167-8116%2895%2900027-5]
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Ariely D, 2003, Q J ECON, V118, P73, DOI 10.1162/00335530360535153
ARIELY D, 1995, ORGAN BEHAV HUM DEC, V63, P223, DOI 10.1006/obhd.1995.1075
Armel KC, 2008, JUDGM DECIS MAK, V3, P396
Atalay AS, 2012, J CONSUM RES, V39, P848, DOI 10.1086/665984
Bar-Hillel Maya, 2011, DISCUSSION PAPER SER, Vdp580
Bettman JR, 1998, J CONSUM RES, V25, P187, DOI 10.1086/209535
Biswas D, 2010, J MARKETING RES, V47, P508, DOI 10.1509/jmkr.47.3.508
Blair E, 2006, J ACAD MARKET SCI, V34, P4, DOI 10.1177/0092070305283778
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Brewer NT, 2002, J BEHAV DECIS MAKING, V15, P65, DOI 10.1002/bdm.403
Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
CALDER BJ, 1981, J CONSUM RES, V8, P197, DOI 10.1086/208856
Chandon P, 2009, J MARKETING, V73, P1, DOI 10.1509/jmkg.73.6.1
Chapman GB, 1999, ORGAN BEHAV HUM DEC, V79, P115, DOI 10.1006/obhd.1999.2841
Cosley D., 2003, PROC SIGCHI C HUMAN, P585
Edwards SM, 2002, J ADVERTISING, V31, P83, DOI 10.1080/00913367.2002.10673678
Epley N, 2001, PSYCHOL SCI, V12, P391, DOI 10.1111/1467-9280.00372
Felfernig A, 2007, LECT NOTES COMPUT SC, V4744, P283, DOI 10.1007/978-3-540-77006-0_34
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Flynn LR, 1999, J BUS RES, V46, P57, DOI 10.1016/S0148-2963(98)00057-5
Furnham A., 2011, J SOCIO-ECON, V40, P35, DOI [10.1016/j.socec.2010.10.008, DOI 10.1016/J.SOCEC.2010.10.008]
Gedikli F, 2014, INT J HUM-COMPUT ST, V72, P367, DOI 10.1016/j.ijhcs.2013.12.007
GORDON ME, 1986, ACAD MANAGE REV, V11, P191, DOI 10.2307/258340
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Häubl G, 2003, J CONSUM PSYCHOL, V13, P75
Hayes AF., 2018, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, DOI DOI 10.1111/JEDM.12050
Herlocker J.L., 2000, P 2000 ACM C COMP SU, P241, DOI DOI 10.1145/358916.358995
HERR PM, 1989, J CONSUM RES, V16, P67, DOI 10.1086/209194
Higgins E.T., 1996, SOCIAL PSYCHOL HDB B, DOI DOI 10.1109/EMBC44109.2020.9176396
Hostler RE, 2011, INFORM MANAGE-AMSTER, V48, P336, DOI 10.1016/j.im.2011.08.002
Hostler RE, 2005, DECIS SUPPORT SYST, V41, P313, DOI 10.1016/j.dss.2004.07.002
Howard DJ, 2006, J MARKETING, V70, P185, DOI 10.1509/jmkg.70.4.185
HUBER J, 1982, J CONSUM RES, V9, P90, DOI 10.1086/208899
Iyengar SS, 2000, J PERS SOC PSYCHOL, V79, P995, DOI 10.1037//0022-3514.79.6.995
Jannach D., 2009, P 3 ACM C REC SYST, P205, DOI 10.1145/1639714.1639749
Jiang YC, 2010, DECIS SUPPORT SYST, V48, P470, DOI 10.1016/j.dss.2009.06.006
Jonas E, 2002, EUR J SOC PSYCHOL, V32, P147, DOI 10.1002/ejsp.112
Jung MH, 2016, J MARKETING RES, V53, P354, DOI 10.1509/jmr.14.0238
Kirshenbaum Evan, 2012, Machine Learning and Knowledge Discovery in Databases. Proceedings of the European Conference (ECML PKDD 2012), P51, DOI 10.1007/978-3-642-33486-3_4
Konstan JA, 2012, USER MODEL USER-ADAP, V22, P101, DOI 10.1007/s11257-011-9112-x
Krajbich I, 2010, NAT NEUROSCI, V13, P1292, DOI 10.1038/nn.2635
Kramer T, 2007, J MARKETING RES, V44, P224, DOI 10.1509/jmkr.44.2.224
Kridel Donald J., 2013, J SERVICE SCI MANAGE, V6, P32, DOI DOI 10.4236/JSSM.2013.65A004
LeBoeuf RA, 2006, J BEHAV DECIS MAKING, V19, P393, DOI 10.1002/bdm.535
Levav J, 2010, J POLIT ECON, V118, P274, DOI 10.1086/652463
Liang TP, 2006, J MANAGE INFORM SYST, V23, P45, DOI 10.2753/MIS0742-1222230303
LYNCH JG, 1991, J CONSUM RES, V18, P284, DOI 10.1086/209260
LYNCH JG, 1982, J CONSUM RES, V9, P225, DOI 10.1086/208919
Manning KC, 2007, J RETAILING, V83, P411, DOI 10.1016/j.jretai.2007.03.011
Mazumdar T, 2005, J MARKETING, V69, P84, DOI 10.1509/jmkg.2005.69.4.84
Mussweiler T, 1999, J EXP SOC PSYCHOL, V35, P136, DOI 10.1006/jesp.1998.1364
Mussweiler T, 2000, PERS SOC PSYCHOL B, V26, P1142, DOI 10.1177/01461672002611010
Muthukrishnan AV, 2001, J CONSUM RES, V28, P89, DOI 10.1086/321949
Nanou T, 2010, MULTIMEDIA SYST, V16, P219, DOI 10.1007/s00530-010-0190-0
Nilashi M, 2016, ELECTRON COMMER R A, V19, P70, DOI 10.1016/j.elerap.2016.09.003
NORTHCRAFT GB, 1987, ORGAN BEHAV HUM DEC, V39, P84, DOI 10.1016/0749-5978(87)90046-X
Nunes JC, 2004, J MARKETING RES, V41, P457, DOI 10.1509/jmkr.41.4.457.47014
Nyström M, 2013, BEHAV RES METHODS, V45, P272, DOI 10.3758/s13428-012-0247-4
Oppenheimer DA, 2008, COGNITION, V106, P13, DOI 10.1016/j.cognition.2006.12.008
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Pu P, 2011, P 5 ACM C REC SYST, P157, DOI [10.1145/2043932.2043962, DOI 10.1145/2043932.2043962]
Raghubir P, 1999, J MARKETING RES, V36, P313, DOI 10.2307/3152079
Raghubir P, 2002, J CONSUM RES, V29, P335, DOI 10.1086/344430
Raghubir P, 1996, J CONSUM RES, V23, P26, DOI 10.1086/209464
REDELMEIER DA, 1995, JAMA-J AM MED ASSOC, V273, P302, DOI 10.1001/jama.1995.03520280048038
Rooderkerk RP, 2011, J MARKETING RES, V48, P767, DOI 10.1509/jmkr.48.4.767
Schafer J. Ben, 1999, Proceedings of the 1st ACM conference on Electronic commerce, P158, DOI [DOI 10.1145/336992.337035, 10.1145/336992.337035]
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Shiv B, 2005, J MARKETING RES, V42, P383, DOI 10.1509/jmkr.2005.42.4.383
SIMONSON I, 1992, J MARKETING RES, V29, P281, DOI 10.2307/3172740
SIMONSON I, 1989, J CONSUM RES, V16, P158, DOI 10.1086/209205
Simonson I, 2004, J CONSUM RES, V31, P681, DOI 10.1086/425103
SLOVIC P, 1995, AM PSYCHOL, V50, P364, DOI 10.1037/0003-066X.50.5.364
Sridhar S, 2012, J MARKETING, V76, P70, DOI 10.1509/jm.10.0377
Srinivasan SS, 2002, J RETAILING, V78, P41, DOI 10.1016/S0022-4359(01)00065-3
Strack F, 1997, J PERS SOC PSYCHOL, V73, P437, DOI 10.1037/0022-3514.73.3.437
Swaminathan V, 2003, J CONSUM PSYCHOL, V13, P93, DOI 10.1207/S15327663JCP13-1&2_08
Teppan Erich, 2011, P RECSYS 2011 WORKSH
TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
TVERSKY A, 1993, MANAGE SCI, V39, P1179, DOI 10.1287/mnsc.39.10.1179
Vijayasarathy L. R., 2001, Electronic Markets, V11, P75, DOI 10.1080/10196780151105483
Wang WQ, 2007, J MANAGE INFORM SYST, V23, P217, DOI 10.2753/MIS0742-1222230410
Wansink B, 1998, J MARKETING RES, V35, P71, DOI 10.2307/3151931
Wong KFE, 2000, ORGAN BEHAV HUM DEC, V82, P314, DOI 10.1006/obhd.2000.2900
Wu LL, 2013, P ANN HICSS, P2753, DOI 10.1109/HICSS.2013.461
Xiao B, 2007, MIS QUART, V31, P137
Ying YP, 2006, J MARKETING RES, V43, P355, DOI 10.1509/jmkr.43.3.355
Yoo K.-H., 2013, SpringerBriefs in electrical and computer engineering
Zanker M, 2006, LECT NOTES COMPUT SC, V4082, P51
2013, DECIS SUPPORT SYST, V56, P180, DOI DOI 10.1016/J.DSS.2013.05.023
2018, DECIS SUPPORT SYST, V110, P46, DOI DOI 10.1016/J.DSS.2018.03.005
2014, MIS Q, V38, P379
2006, PSYCHOL SCI, V17, P311
NR 97
TC 21
Z9 21
U1 8
U2 61
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0022-4359
EI 1873-3271
J9 J RETAILING
JI J. Retail.
PD MAR
PY 2019
VL 95
IS 1
BP 24
EP 41
DI 10.1016/j.jretai.2018.10.004
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HV8SB
UT WOS:000466252600004
DA 2024-03-27
ER
PT J
AU Hsieh, MT
Lee, SJ
Wu, CH
Hou, CL
Ouyang, CS
Lin, ZP
AF Hsieh, Mi-Tsuen
Lee, Shie-Jue
Wu, Chih-Hung
Hou, Chun-Liang
Ouyang, Chen-Sen
Lin, Zhan-Pei
TI Leveraging attribute latent features for addressing new item cold-start
issue
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE E-commerce; Recommender system; Item cold-start problem; Matrix
factorization; Neural networks
AB A recommender system employs an information filtering technology aiming to recommend items that are likely to be of interest to users, based on user behavior, rating feedback of items, or item characteristics. The cold-start problem occurs when the recommender system has difficulties in drawing any recommendations due to lack of information. In this paper, we focus on the item cold-start problem. When new items are added to the catalogue, they can easily be overlooked because no feedback information is accessible for recommending them to users, impeding the promotion of new products online. We propose a hybrid recommender system, called ALFNCF (Attribute Latent Features with Neural Collaborative Filtering), which combines the advantages of collaborative filtering, content-based filtering, and neural network technologies, to address the item cold-start issue. Like collaborative filtering, ALFNCF considers the interaction between users with similar behavior and items with similar feedback, while it also takes into account the influence of user's and item's attributes on individual behaviors. As in content-based filtering, attribute information is used in ALFNCF to establish links between new and old items. ALFNCF not only can retain the linear interactions between users and items, but also can learn the non-linear interactions by neural network units. Through the training on users' past rating feedback information for old items, ALFNCF can predict the user's ratings for new items. Experimental results show that our proposed method is effective and superior to other methods in the promotion of new items.
C1 [Hsieh, Mi-Tsuen; Lee, Shie-Jue] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung, Taiwan.
[Lee, Shie-Jue] Natl Sun Yat Sen Univ, Intelligent Elect Commerce Res Ctr, Kaohsiung, Taiwan.
[Wu, Chih-Hung] Natl Univ Kaohsiung, Dept Elect Engn, Kaohsiung, Taiwan.
[Hou, Chun-Liang] Chunghwa Telecom Co Ltd, Informat Technol Dept, Southern Taiwan Business Grp, Kaohsiung, Taiwan.
[Ouyang, Chen-Sen] I Shou Univ, Dept Informat Engn, Kaohsiung, Taiwan.
[Lin, Zhan-Pei] ROC Mil Acad, Dept Elect Engn, Kaohsiung, Taiwan.
C3 National Sun Yat Sen University; National Sun Yat Sen University;
National University Kaohsiung; Chunghwa Telecom; I Shou University
RP Lee, SJ (autor correspondiente), Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung, Taiwan.
EM mcsie@itlm.ee.nsysu.edu.tw; leesj@mail.ee.nsysu.edu.tw;
johnw@nuk.edu.tw; hjl@cht.com.tw; ouyangcs@isu.edu.tw;
ccdavidlin@gmail.com
FU Ministry of Science and Technology [MOST-108-2221-E-110-046-MY2,
MOST-109-2622-E-110-009]; NSYSU-KMU Joint Research Project [NSYSUKMU
110-I001]; Intelligent Electronic Commerce Research Center" from the
Featured Areas Research Center Program
FX The authors would like to express their sincere appreciations to the
Guest Editors and Reviewers for their comments which were very helpful
in improving the quality and presentation of the paper. This work was
supported by the grants MOST-108-2221-E-110-046-MY2 and
MOST-109-2622-E-110-009, Ministry of Science and Technology, the
NSYSU-KMU Joint Research Project (#NSYSUKMU 110-I001), and the
"Intelligent Electronic Commerce Research Center" from the Featured
Areas Research Center Program within the framework of the Higher
Education Sprout Project by the Ministry of Education in Taiwan.
CR Ajaegbu C., 2021, J AMBIENT INTELL HUM, V12, P10629, DOI 10.1007/s12652-020-02876-1
Aleksandrova M, 2017, J INTELL INF SYST, V48, P365, DOI 10.1007/s10844-016-0418-3
Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
Cheng H.-T., 2016, P 1 WORKSH DEEP LEAR
Covington P, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P191, DOI 10.1145/2959100.2959190
Das Barman S, 2019, 2019 8TH INTERNATIONAL CONFERENCE ON SOFTWARE AND COMPUTER APPLICATIONS (ICSCA 2019), P258, DOI 10.1145/3316615.3316732
Desrosiers C, 2011, RECOMMENDER SYSTEMS HANDBOOK, P107, DOI 10.1007/978-0-387-85820-3_4
Ebesu T, 2018, ACM/SIGIR PROCEEDINGS 2018, P515, DOI 10.1145/3209978.3209991
Enrich M, 2013, LECT NOTES BUS INF P, V152, P101
Ghazanfar MA, 2012, INFORM SCIENCES, V208, P81, DOI 10.1016/j.ins.2012.04.012
Glorot X., 2011, P 14 INT C ARTIFICIA, P315
Guo GB, 2015, AAAI CONF ARTIF INTE, P123
Guo HF, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1725
Guo X, 2019, IEEE ACCESS, V7, P11349, DOI 10.1109/ACCESS.2019.2891544
He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
Hu L, 2019, AAAI CONF ARTIF INTE, P3830
Ji K, 2015, KNOWL-BASED SYST, V83, P42, DOI 10.1016/j.knosys.2015.03.008
Kim D, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P233, DOI 10.1145/2959100.2959165
Kingma D. P., 2014, ARXIV
Koren Y, 2008, 14 ACM SIGKDD INT C
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Li JJ, 2019, AAAI CONF ARTIF INTE, P4189
Li ZY, 2019, COMPLEXITY, DOI 10.1155/2019/6325654
Ma D., 2011, P 4 ACM INT C WEB SE, P287
Pan FY, 2019, PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), P695, DOI 10.1145/3331184.3331268
Park S.T., 2009, P 3 ACM C REC SYST, P21, DOI DOI 10.1145/1639714.1639720
Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P325
Qian TY, 2022, IEEE T KNOWL DATA EN, V34, P3597, DOI 10.1109/TKDE.2020.3038234
Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
Santos C.N., 2014, P COLING 2014 25 INT, P69
Tang JX, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P565, DOI 10.1145/3159652.3159656
Volkovs M, 2017, ADV NEUR IN, V30
Wang DH, 2018, KNOWL-BASED SYST, V157, P1, DOI 10.1016/j.knosys.2018.05.001
Xiong F, 2020, IEEE T CYBERNETICS, V50, P4186, DOI 10.1109/TCYB.2019.2939390
Xiong F, 2020, IEEE T SYST MAN CY-S, V50, P3804, DOI 10.1109/TSMC.2018.2854000
Xu JW, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P3981
Zheng L, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P425, DOI 10.1145/3018661.3018665
Zhu Y, 2020, IEEE T KNOWL DATA EN, V32, P631, DOI 10.1109/TKDE.2019.2891530
NR 38
TC 2
Z9 2
U1 21
U2 71
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD JUL-AUG
PY 2022
VL 54
AR 101177
DI 10.1016/j.elerap.2022.101177
EA JUL 2022
PG 12
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 5A7LR
UT WOS:000863065400003
DA 2024-03-27
ER
PT J
AU Yang, ZK
Lin, ZJ
AF Yang, Zekun
Lin, Zhijie
TI Interpretable video tag recommendation with multimedia deep learning
framework
SO INTERNET RESEARCH
LA English
DT Article
DE Interpretable AI; Machine learning; Recommender system; User-generated
content; Multimedia; Convolutional neural network
AB Purpose Tags help promote customer engagement on video-sharing platforms. Video tag recommender systems are artificial intelligence-enabled frameworks that strive for recommending precise tags for videos. Extant video tag recommender systems are uninterpretable, which leads to distrust of the recommendation outcome, hesitation in tag adoption and difficulty in the system debugging process. This study aims at constructing an interpretable and novel video tag recommender system to assist video-sharing platform users in tagging their newly uploaded videos. Design/methodology/approach The proposed interpretable video tag recommender system is a multimedia deep learning framework composed of convolutional neural networks (CNNs), which receives texts and images as inputs. The interpretability of the proposed system is realized through layer-wise relevance propagation. Findings The case study and user study demonstrate that the proposed interpretable multimedia CNN model could effectively explain its recommended tag to users by highlighting keywords and key patches that contribute the most to the recommended tag. Moreover, the proposed model achieves an improved recommendation performance by outperforming state-of-the-art models. Practical implications The interpretability of the proposed recommender system makes its decision process more transparent, builds users' trust in the recommender systems and prompts users to adopt the recommended tags. Through labeling videos with human-understandable and accurate tags, the exposure of videos to their target audiences would increase, which enhances information technology (IT) adoption, customer engagement, value co-creation and precision marketing on the video-sharing platform. Originality/value The proposed model is not only the first explainable video tag recommender system but also the first explainable multimedia tag recommender system to the best of our knowledge.
C1 [Yang, Zekun] Renmin Univ China, Sch Informat Resource Management, Beijing, Peoples R China.
[Lin, Zhijie] Tsinghua Univ, Sch Econ & Management, Dept Management Sci & Engn, Beijing, Peoples R China.
C3 Renmin University of China; Tsinghua University
RP Lin, ZJ (autor correspondiente), Tsinghua Univ, Sch Econ & Management, Dept Management Sci & Engn, Beijing, Peoples R China.
EM dr.zhijie.lin@gmail.com
RI YANG, Zekun/JCO-3822-2023
OI YANG, Zekun/0000-0003-4040-8476
FU National Natural Science Foundation of China [71801217, 72022007,
71872080]; Tsinghua University Initiative Scientific Research Program
[2019THZWJC12]
FX The authors appreciate the editors and the anonymous reviewers for their
detailed and constructive comments. This work was supported in part by
the National Natural Science Foundation of China [Grants 71801217,
72022007 and 71872080] and Tsinghua University Initiative Scientific
Research Program [Grant 2019THZWJC12].
CR Arras L, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0181142
Bach S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0130140
Belém FM, 2017, J ASSOC INF SCI TECH, V68, P830, DOI 10.1002/asi.23736
Bellini V, 2018, PROCEEDINGS OF THE 3RD WORKSHOP ON DEEP LEARNING FOR RECOMMENDER SYSTEMS (DLRS), P24, DOI 10.1145/3270323.3270327
Brodie RJ, 2011, J SERV RES-US, V14, P252, DOI 10.1177/1094670511411703
China Internet Network Information Center, 2019, 44 STAT REPORT INTER
Dominguez V, 2019, PROCEEDINGS OF IUI 2019, P408, DOI 10.1145/3301275.3302274
Grave E, 2018, PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018), P3483
He X., 2015, P 24 ACM INT C INF K, P1661, DOI [10.1145/2806416.2806504, DOI 10.1145/2806416.2806504]
Kenny EM, 2021, ARTIF INTELL, V294, DOI 10.1016/j.artint.2021.103459
Kingma D. P., 2015, arXiv preprint arXiv:1412.6980
Komiak SYX, 2006, MIS QUART, V30, P941
Lau RYK, 2018, PROD OPER MANAG, V27, P1775, DOI 10.1111/poms.12737
Li MM, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P509, DOI 10.1145/3357384.3357912
Lopezosa C, 2020, JOURNAL PRACT, V14, P465, DOI 10.1080/17512786.2019.1628657
Lu J, 2015, DECIS SUPPORT SYST, V74, P12, DOI 10.1016/j.dss.2015.03.008
Lv GY, 2016, AAAI CONF ARTIF INTE, P3000
Matt C, 2019, INTERNET RES, V29, P1526, DOI 10.1108/INTR-06-2018-0274
Montaner M., 2002, Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, P304
Montavon G., 2019, Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, V11700 LNCS, P193, DOI [10.1007/978-3-030-28954-6_10/COVER, DOI 10.1007/978-3-030-28954-6_10, DOI 10.1007/978-3-030-28954-610]
Montavon G, 2017, PATTERN RECOGN, V65, P211, DOI 10.1016/j.patcog.2016.11.008
Nunes I, 2017, USER MODEL USER-ADAP, V27, P393, DOI 10.1007/s11257-017-9195-0
Oh S, 2017, INTERNET RES, V27, P691, DOI 10.1108/IntR-01-2016-0005
Pansari A, 2017, J ACAD MARKET SCI, V45, P294, DOI 10.1007/s11747-016-0485-6
Parra E, 2018, INT C PROGRAM COMPRE, P222, DOI 10.1145/3196321.3196351
Rantanen A, 2020, INTERNET RES, V30, P45, DOI 10.1108/INTR-07-2018-0318
Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
Richardson A, 2018, P 2 WORKSH EXPL ART, V2018, P137
Sakaji H, 2016, INT C NETWB INFO, P550, DOI 10.1109/NBiS.2016.46
Schaefer C., 2021, Hawaii International Conference on System Sciences, P2347, DOI DOI 10.24251/HICSS.2021.286
Sheh R., 2017, AAAI Workshop-Technical Report, P628
Sheh R.K., 2017, 2017 AAAI FALL S SER
Sigurbjornsson B., 2008, P 17 INT C WORLD WID, P327, DOI DOI 10.1145/1367497.1367542
Tafesse W, 2020, INTERNET RES, V30, P1689, DOI 10.1108/INTR-10-2019-0406
Toderici G, 2010, PROC CVPR IEEE, P3447, DOI 10.1109/CVPR.2010.5539985
van der Waa J, 2020, INT J HUM-COMPUT ST, V144, DOI 10.1016/j.ijhcs.2020.102493
Vance A, 2008, J MANAGE INFORM SYST, V24, P73, DOI 10.2753/MIS0742-1222240403
Wang N, 2018, ACM/SIGIR PROCEEDINGS 2018, P165, DOI 10.1145/3209978.3210010
Wang YL, 2017, AAAI CONF ARTIF INTE, P210
Wei YW, 2019, PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19), P1446, DOI 10.1145/3343031.3350858
Yang C, 2020, IEEE ACCESS, V8, P78252, DOI 10.1109/ACCESS.2020.2989473
Yang WM, 2019, ACM T KNOWL DISCOV D, V13, DOI 10.1145/3332932
Yang ZK, 2020, AAAI CONF ARTIF INTE, V34, P9426
You Z, 2015, EXPERT SYST APPL, V42, P3357, DOI 10.1016/j.eswa.2014.12.022
Zhang YD, 2020, FRONT COMPUT SCI-CHI, V14, DOI 10.1007/s11704-018-8106-9
NR 45
TC 7
Z9 7
U1 11
U2 67
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1066-2243
J9 INTERNET RES
JI Internet Res.
PD MAR 15
PY 2022
VL 32
IS 2
SI SI
BP 518
EP 535
DI 10.1108/INTR-08-2020-0471
EA JUL 2021
PG 18
WC Business; Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science; Telecommunications
GA ZY6TT
UT WOS:000677661200001
DA 2024-03-27
ER
PT J
AU Militaru, D
Zaharia, C
AF Militaru, Dorin
Zaharia, Costin
TI ONLINE COLLABORATIVE FILTERING-BASED SYSTEMS: SEMANTICS AND EFFICIENCY
SO ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
LA English
DT Article
DE Electronic Commerce; Collaborative filtering; Recommender Systems;
Marketing; Experimentation
AB Internet services operate on a vastly larger scale and allow virtual interactions. Both Web and Internet have created vast new opportunities, providing an infrastructure that enables buyers and sellers to find each other online. Companies can now offer many products, services and information easily and with lower costs. It becomes more and more difficult for customers to find quickly what they are looking for. Nevertheless, recommendation systems are playing a major role. Collaborative filtering (CF), or recommender system based-CF, has appeared as one methodology designed to perform such a recommendation task. These systems allow people to use expressed preferences of thousands of other people in order to find the product they desire based on the level of similarity between tastes. The concept has appeared from convergent research on search browsers, intelligent agents and data mining, and it allows to avoid the difficult question of "why" consumers prefer this or that product or brand.
Early studies of electronic markets tools and recommender systems took a simplistic view of consumers as economic agents whose behavior was guided by the search for the lowest cost transactions. Moreover, most studies take into account only technical aspects of these systems like algorithms' development and computational problems. No study had been interested in recommendation's efficiency of collaborative filtering-based systems.
This article explores the current state of research in recommender systems-based collaborative filtering and proposes an experiment to find if such electronic recommendations are better than human recommendations.
C1 [Militaru, Dorin] Grp Sup Co Amiens Picardie, ESC Amiens, Dept Mkt, F-80038 Amiens, France.
[Zaharia, Costin] MCF Univ Mans IUT GEA, F-75007 Paris, France.
[Zaharia, Costin] IAE Paris 1, ENSAM Paris, ESTP Cachan, Paris, France.
C3 Arts et Metiers Institute of Technology
RP Militaru, D (autor correspondiente), Grp Sup Co Amiens Picardie, ESC Amiens, Dept Mkt, 18 Pl St Michel, F-80038 Amiens, France.
EM dorin.militaru@supco-amiens.fr; Costin.Zaharia@univ-lemans.fr
CR [Anonymous], 1998, MSRTR9812
[Anonymous], 1994, 1994 ACM C COMPUTER, DOI DOI 10.1145/192844.192905
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Das A. S., 2007, WWW
GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867
GOLDBERG K, 2000, M0041 UCB EL RES LAB
HERLOCKER JL, 2000, CSCW 00 PHIL PA DEC
MCKIM R, 2000, J DATABASE MARKETING, V7, P385
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Rich E., 1979, Cognitive Science, V3, P335
Rosen E., 2000, The Anatomy of Buzz
SARWAR B, 2000, EC 00 MINN MINN ACM
Schafer J.B., 1999, Recommender Systems in E-Commerce
Shardanand U., 1995, SOCIAL INFORM FILTER
Vapnik V.N., 1998, STAT LEARNING THEORY, P732
NR 15
TC 0
Z9 0
U1 0
U2 5
PU ACAD ECONOMIC STUDIES
PI BUCHAREST
PA 15-17 CALEA DOROBANTI, SECTOR 1, BUCHAREST, 00000, ROMANIA
SN 0424-267X
J9 ECON COMPUT ECON CYB
JI Econ. Comput. Econ. Cybern. Stud.
PY 2011
VL 45
IS 1
BP 203
EP 214
PG 12
WC Economics; Mathematics, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Mathematics
GA 743JR
UT WOS:000289015100013
DA 2024-03-27
ER
PT J
AU Lee, D
Hosanagar, K
AF Lee, Dokyun
Hosanagar, Kartik
TI How Do Recommender Systems Affect Sales Diversity? A Cross-Category
Investigation via Randomized Field Experiment
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE e-commerce; personalization; recommender systems; sales diversity;
consumer purchase behavior; collaborative filtering; Gini coefficient
ID EMPIRICAL-ANALYSIS; IMPACT; ASSORTMENT; INTERNET; COMMERCE; VARIETY;
SEARCH; TAIL
AB We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon's "Customers who bought this item also bought") commonly used in e-commerce on sales diversity. We use data from a randomized field experiment run on the website of a top retailer in North America across 82,290 products and 1,138,238 users. We report four main findings. First, we demonstrate and quantify across a wide range of product categories that the use of traditional collaborative filters (CFs) is associated with a decrease in sales diversity relative to a world without product recommendations. Furthermore, the design of the CF matters. CFs based on purchase data are associated with a greater effect size than those based on product views. Second, the decrease in aggregate sales diversity may not always be accompanied by a corresponding decrease in individual-level consumption diversity. In fact, it is even possible for individual consumption diversity to increase while aggregate sales diversity decreases. Third, copurchase network analyses show that while recommenders can help individuals explore new products, similar users still end up exploring the same kinds of products, resulting in concentration bias at the aggregate level. Fourth and finally, there is a difference between absolute and relative impact on niche items. Specifically, absolute sales and views for niche items in fact increase, but their gains are smaller compared with the gains in views and sales for popular items. Thus, whereas niche items gain in absolute terms, they lose out in terms of market share. We discuss economic impacts and managerial implications.
C1 [Lee, Dokyun] Carnegie Mellon Univ, Tepper Sch Business, Pittsburgh, PA 15293 USA.
[Hosanagar, Kartik] Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA.
C3 Carnegie Mellon University; University of Pennsylvania
RP Lee, D (autor correspondiente), Carnegie Mellon Univ, Tepper Sch Business, Pittsburgh, PA 15293 USA.
EM dokyun@cmu.edu; kartikh@wharton.upenn.edu
OI Lee, Dokyun/0000-0002-3186-3349
FU Jay H. Baker Retailing Center; Wharton Risk Management and Decision
Processes Center; Mack Institute for Innovation Management;
Fishman-Davidson Center for Service and Operations Management
FX The authors gratefully acknowledge financial support from the Jay H.
Baker Retailing Center, the Wharton Risk Management and Decision
Processes Center, the Mack Institute for Innovation Management, and the
Fishman-Davidson Center for Service and Operations Management.
CR Adamopoulos P, 2015, ACM T INTEL SYST TEC, V5, DOI 10.1145/2559952
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Adomavicius G, 2014, INFORMS J COMPUT, V26, P351, DOI 10.1287/ijoc.2013.0570
Anderson Chris., 2004, The Long Tail: Why the Future of Business is Selling Less of More
[Anonymous], 2010, Proceedings of the for the 10th ACM SIGCOMM Conference on Internet Measurement, DOI DOI 10.1145/1879141.1879193
Borle S, 2005, MARKET SCI, V24, P616, DOI 10.1287/mksc.1050.0121
Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
Brynjolfsson E, 2000, MANAGE SCI, V46, P563, DOI 10.1287/mnsc.46.4.563.12061
Brynjolfsson E, 2006, MIT SLOAN MANAGE REV, V47, P67
Brynjolfsson E, 2011, MANAGE SCI, V57, P1373, DOI 10.1287/mnsc.1110.1371
Celma O., 2008, P 2 KDD WORKSH LARG, P1
Cooke ADJ, 2002, J MARKETING RES, V39, P488, DOI 10.1509/jmkr.39.4.488.19121
De P, 2010, MANAGE SCI, V56, P1930, DOI 10.1287/mnsc.1100.1233
Dias MB, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P291
Elberse A, 2008, HARVARD BUS REV, V86, P88
Fisher M.L., 2003, Operations management: critical perspectives on business and management, V4, P73
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Gallino S, 2017, MANAGE SCI, V63, P2813, DOI 10.1287/mnsc.2016.2479
Good P., 2005, Permutation, parametric and bootstrap tests of hypotheses, V3rd ed
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Hinz O, 2010, BUS INFORM SYST ENG+, V2, P67, DOI 10.1007/s12599-010-0092-x
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
Jannach Dietmar, 2013, User Modeling, Adaptation, and Personalization. 21th International Conference, UMAP 2013. Proceedings., P25, DOI 10.1007/978-3-642-38844-6_3
Jannach D., 2009, P 3 ACM C REC SYST, P205, DOI 10.1145/1639714.1639749
Jannach D, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P7, DOI 10.1145/2959100.2959186
Jannach D, 2015, USER MODEL USER-ADAP, V25, P427, DOI 10.1007/s11257-015-9165-3
Jiang BJ, 2011, MARKET SCI, V30, P757, DOI 10.1287/mksc.1110.0656
Jiang H, 2014, OPER RES, V62, P973, DOI 10.1287/opre.2014.1292
Kök AG, 2009, INT SER OPER RES MAN, V122, P99, DOI 10.1007/978-0-387-78902-6_6
Lamere P., 2008, SUN JAVAONE C
Lee S, 2011, IEEE INT C BIO BIO W, P725, DOI 10.1109/BIBMW.2011.6112460
Lewis GJ, 2005, SUPPLY CHAIN MANAG, V10, P349, DOI 10.1108/13598540510624179
Lin ZJ, 2017, MIS QUART, V41, P397, DOI 10.25300/MISQ/2017/41.2.04
Mooney R. J., 2000, ACM 2000. Digital Libraries. Proceedings of the Fifth ACM Conference on Digital Libraries, P195, DOI 10.1145/336597.336662
Oestreicher-Singer G, 2012, MIS QUART, V36, P65, DOI 10.2307/41410406
Pariser Eli, 2011, FILTER BUBBLE WHAT I
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Sarwar B., 2001, P 10 INT C WORLD WID, P285
SCHAFER J.B., 1999, P 1 ACM C EL COMM, P158, DOI DOI 10.1145/336992.337035
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Tan TF, 2017, INFORM SYST RES, V28, P643, DOI 10.1287/isre.2017.0712
Thompson Clive., 2008, The New York Times
van Herpen E, 2002, MARKET SCI, V21, P331, DOI 10.1287/mksc.21.3.331.144
Walter FE, 2012, INF SYST E-BUS MANAG, V10, P367, DOI 10.1007/s10257-011-0170-8
Wu L.-L., 2011, System Sciences (HICSS), 2011 44th Hawaii International Conference on, P1, DOI DOI 10.1109/HICSS.2011.357
Zhu K., 2001, Electronic Markets, V11, P273, DOI 10.1080/101967801753405562
NR 48
TC 62
Z9 71
U1 20
U2 155
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD MAR
PY 2019
VL 30
IS 1
BP 239
EP 259
DI 10.1287/isre.2018.0800
PG 21
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA HU1PR
UT WOS:000465044800015
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Lau, RYK
AF Lau, Raymond Y. K.
TI Towards a web services and intelligent agents-based negotiation system
for B2B eCommerce
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article; Proceedings Paper
CT 7th International Conference on Electronic Commerce (ICEC 2005)
CY AUG 15-17, 2005
CL Xian, PEOPLES R CHINA
SP Xian Jiaotong Univ, Sun Yat-Sen Univ
DE automated negotiations; intelligent agents; web services; eCommerce
ID ARGUMENTATION; LOGIC
AB With the explosive growth of the number of transactions conducted via electronic channels, there is a pressing need for the development of intelligent support tools to improve the degree and sophistication of automation for eCommerce. With reference to the BBT business model, negotiation is one of key steps for B2B eCommerce. Nevertheless, classical negotiation models are ineffective for supporting multi- agent multi- issue negotiations often encountered in eBusiness environment. The first contribution of this paper is the exploitation of Web services and intelligent agent techniques for the design and development of a distributed service discovery and negotiation system to streamline B2B eCommerce. In addition, an effective and efficient integrative negotiation mechanism is developed to conduct multi- party multi- issue negotiations for B2B eCommerce. Finally, an empirical study is conducted to evaluate our intelligent agents- based negotiation mechanism and to compare the negotiation performance of our software agents with that of their human counterparts. Our research work opens the door to the development of the next generation of intelligent system solutions to support B2B eCommerce. (C) 2006 Elsevier B. V. All rights reserved.
C1 City Univ Hong Kong, Dept Informat Syst, Kowloon, Hong Kong, Peoples R China.
C3 City University of Hong Kong
RP Lau, RYK (autor correspondiente), City Univ Hong Kong, Dept Informat Syst, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China.
EM raylau@cityu.edu.hk
RI Novoa, Kevin/J-2867-2014
OI Lau, Raymond/0000-0002-5751-4550
CR [Anonymous], ELECT COMMERCE RES
BAZERMAN MH, 1985, ORGAN BEHAV HUM DEC, V35, P294, DOI 10.1016/0749-5978(85)90026-3
BOX D, 2000, ACCESS PROTOCOL SOAP
Chen Minder., 2003, Journal of Electronic Commerce Research, V4, P128
Curbera F, 2002, IEEE INTERNET COMPUT, V6, P86, DOI 10.1109/4236.991449
Davies NJ, 2004, BT TECHNOL J, V22, P118, DOI 10.1023/B:BTTJ.0000015502.09747.39
DELANEY M, 1997, DECIS SUPPORT SYST, V20, P53
DUMAS M, 2002, ELECTRON COMMER R A, V1, P29
Ehtamo H, 2001, EUR J OPER RES, V130, P54, DOI 10.1016/S0377-2217(00)00019-9
Faratin P, 2002, ARTIF INTELL, V142, P205, DOI 10.1016/S0004-3702(02)00290-4
Fatima SS, 2004, ARTIF INTELL, V152, P1, DOI 10.1016/S0004-3702(03)00115-2
Governatori G, 2000, FR ART INT, V54, P469
GUTTMAN R, 1999, LECT NOTES COMPUTER, V1571, P70
Guttman RH, 1998, KNOWL ENG REV, V13, P147, DOI 10.1017/S0269888998002082
HARSANYI J, 1973, MANAGE SCI, V18, P80
He MH, 2003, IEEE T KNOWL DATA EN, V15, P985, DOI 10.1109/TKDE.2003.1209014
Heiskanen P, 1999, EUR J OPER RES, V117, P578, DOI 10.1016/S0377-2217(98)00276-8
Jaiswal A, 2004, ELECTRON COMMER R A, V3, P355, DOI DOI 10.1016/J.ELERAP.2004.06.005
JENNINGS N, 2002, INT J GROUP DECISION, V10, P199
Keeney R. L., 1993, DECISIONS MULTIPLE O, DOI DOI 10.1017/CBO9781139174084
KOWALCZYK R, 2001, LECT NOTES ARTIF INT, P31
Kraus S, 1998, ARTIF INTELL, V104, P1, DOI 10.1016/S0004-3702(98)00078-2
KRAUS S, 1998, ARTIF INTELL, V94, P79
KRAUSE P, 1995, COMPUT INTELL, V11, P113, DOI 10.1111/j.1467-8640.1995.tb00025.x
Krovi R, 1999, IEEE T SYST MAN CY C, V29, P15, DOI 10.1109/5326.740666
Lau RYK, 2005, SEVENTH INTERNATIONAL CONFERENCE ON ELECTRONIC COMMERCE, VOLS 1 AND 2, SELECTED PROCEEDINGS, P271
Lau RYK, 2004, LECT NOTES ARTIF INT, V3157, P605
Lau RYK, 2003, IEEE/WIC INTERNATIONAL CONFERENCE ON INTELLIGENT AGENT TECHNOLOGY, PROCEEDINGS, P196, DOI 10.1109/IAT.2003.1241068
LAU RYK, 1984, P 4 IEEE WIC INT C I, P295
Leymann F, 2002, IBM SYST J, V41, P198, DOI 10.1147/sj.412.0198
Lomuscio AR, 2003, GROUP DECIS NEGOT, V12, P31, DOI 10.1023/A:1022232410606
Ma M, 1999, COMMUN ACM, V42, P78, DOI 10.1145/295685.295708
Maes P, 1999, COMMUN ACM, V42, P81, DOI 10.1145/295685.295716
MATWIN S, 1991, IEEE T SYST MAN CYB, V21, P102, DOI 10.1109/21.101141
Nash JF, 1950, ECONOMETRICA, V18, P155, DOI 10.2307/1907266
PRUITT S, 1981, NEGOTIATION BEHAV
Raiffa Howard., 1982, ART SCI NEGOTIATION
ROSENSCHEIN J, 1994, RULES ENCOUNTER DESI, P29
Rubenstein-Montano B, 2002, IEEE T EVOLUT COMPUT, V6, P366, DOI 10.1109/TEVC.2002.802874
RUBINSTEIN A, 1982, ECONOMETRICA, V50, P97, DOI 10.2307/1912531
SAMUEL PM, 2001, COMPUTER NETWORKS, V37, P195
SHAW MJ, 2000, HDB ELECT COMMERCE, P3
SIERRA C, 1999, P EUSFLAT ESTYLF JOI, P155
Sycara K, 1997, P 14 NAT C ART INT 9, P36
Sycara K., 1989, DISTRIBUTED ARTIFICI, V2, P119
Tremblett P, 2002, DR DOBBS J, V27, P34
von Neumann J., 1994, THEORY GAMES EC BEHA
*W3C, 2004, WEB SERV ACT
WILKENFELD J, 1995, DECIS SUPPORT SYST, V14, P369, DOI 10.1016/0167-9236(94)00027-P
Williams JD, 2003, PHYS PLASMAS, V10, P103, DOI 10.1063/1.1526695
WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115, DOI 10.1017/S0269888900008122
Wurman P. R., 1998, Proceedings of the Second International Conference on Autonomous Agents, P301, DOI 10.1145/280765.280847
YING H, 2003, ELECTRON COMMER R A, V2, P15
Zeng DJ, 1998, INT J HUM-COMPUT ST, V48, P125, DOI 10.1006/ijhc.1997.0164
[No title captured]
NR 55
TC 44
Z9 51
U1 4
U2 35
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD FAL
PY 2007
VL 6
IS 3
BP 260
EP 273
DI 10.1016/j.elerap.2006.06.007
PG 14
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH); Conference Proceedings Citation Index - Science (CPCI-S)
SC Business & Economics; Computer Science
GA 225HY
UT WOS:000250508900004
DA 2024-03-27
ER
PT J
AU Sun, LH
Guo, JP
Zhu, YL
AF Sun, Lihua
Guo, Junpeng
Zhu, Yanlin
TI A multi-aspect user-interest model based on sentiment analysis and
uncertainty theory for recommender systems
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Recommender system; Sentiment analysis; Uncertainty theory; Product
reviews; User interest
ID PRODUCT; STATE; WEB
AB This work presents a new multi-aspect user-interest model for recommender systems to improve recommendation and prediction accuracy. We introduce the overall user satisfaction for a product to build a user-interest profile by computing the user-interest levels from multi-aspect reviews. A domain emotional dictionary is built to overcome the gap in quantity between negative and positive words for sentiment polarity analysis. A sentiment analysis model is designed to characterize the users' sentiment polarity and strength based on uncertainty theory and the domain emotional dictionary. Accordingly, a new multi-aspect user-interest model is proposed by considering the sentiment analysis model with the user-interest profile. Then, our proposed model is applied to recommender systems and experimentally tested on five products of different categories from three e-commerce websites. Our model not only outperforms the traditional and state-of-the-art models on rating prediction tasks but also improves the recommendation accuracy in multiple domains.
C1 [Sun, Lihua; Guo, Junpeng; Zhu, Yanlin] Tianjin Univ, Coll Management Econ, Tianjin 300072, Peoples R China.
C3 Tianjin University
RP Guo, JP (autor correspondiente), Tianjin Univ, Coll Management Econ, Tianjin 300072, Peoples R China.
EM guojp@tju.edu.cn
RI Zhang, Yihao/JGM-3514-2023
FU National Natural Science Foundation of China [71671121]
FX This work is supported by the National Natural Science Foundation of
China (Grant No. 71671121).
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Aljukhadar M, 2012, INT J ELECTRON COMM, V17, P41, DOI 10.2753/JEC1086-4415170202
[Anonymous], 2013, CLIN TRANSL ONCOL
Appel O, 2016, KNOWL-BASED SYST, V108, P110, DOI 10.1016/j.knosys.2016.05.040
Bao Y, 2014, AAAI CONF ARTIF INTE, P2
Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
Chen L, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P239, DOI 10.1145/2567948.2577276
Chen L, 2015, USER MODEL USER-ADAP, V25, P99, DOI 10.1007/s11257-015-9155-5
Chen XW, 2012, EUR J OPER RES, V222, P312, DOI 10.1016/j.ejor.2012.05.010
Chen YC, 2009, ELECTRON COMMER R A, V8, P48, DOI 10.1016/j.elerap.2008.09.001
DILLON M, 1983, INFORM PROCESS MANAG, V19, P402, DOI 10.1016/0306-4573(83)90062-6
Ding SB, 2013, APPL MATH COMPUT, V223, P139, DOI 10.1016/j.amc.2013.07.083
Ding SB, 2014, J INTELL FUZZY SYST, V26, P483, DOI 10.3233/IFS-130919
Dong Z., 2006, Hownet and the computation of meaning
Esparza SG, 2012, KNOWL-BASED SYST, V29, P3, DOI 10.1016/j.knosys.2011.07.007
Ganu G, 2012, INFORM SYST, V38, P1, DOI 10.1016/j.is.2012.03.001
Gao Y, 2016, IEEE T FUZZY SYST, V24, P981, DOI 10.1109/TFUZZ.2015.2500267
Gao Y, 2015, INFORM SCIENCES, V296, P61, DOI 10.1016/j.ins.2014.10.048
Gao Y, 2012, APPL MATH MODEL, V36, P2592, DOI 10.1016/j.apm.2011.09.042
Gao Y, 2011, COMPUT MATH APPL, V62, P2591, DOI 10.1016/j.camwa.2011.07.058
Hu N, 2014, DECIS SUPPORT SYST, V57, P42, DOI 10.1016/j.dss.2013.07.009
Joachims T., 1998, P 10 EUR C MACH LEAR, P137, DOI DOI 10.1007/BFB0026683
Koohi H, 2016, MEASUREMENT, V91, P134, DOI 10.1016/j.measurement.2016.05.058
Koren Y, 2011, RECOMMENDER SYSTEMS HANDBOOK, P145, DOI 10.1007/978-0-387-85820-3_5
Lakiotaki K, 2011, IEEE INTELL SYST, V26, P64, DOI 10.1109/MIS.2011.33
Ling G, 2014, PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14), P105, DOI 10.1145/2645710.2645728
Liu B., 2010, Journal of Uncertain Systems, V4, P83
Liu B.D., 2007, Uncertainty Theory, V2nd, DOI DOI 10.1007/978-3-642-13959-8
Liu BD, 2012, FUZZY OPTIM DECIS MA, V11, P387, DOI 10.1007/s10700-012-9128-7
Liu B, 2011, DATA CENTRIC SYST AP, P459, DOI 10.1007/978-3-642-19460-3_11
Liu W, 2010, INFORMATION-TOKYO, V13, P1693
Loia V, 2014, KNOWL-BASED SYST, V58, P75, DOI 10.1016/j.knosys.2013.09.024
Lu J, 2015, DECIS SUPPORT SYST, V74, P12, DOI 10.1016/j.dss.2015.03.008
McAuley J., 2013, P 7 ACM C REC SYST, P165
Musat C.-C., 2013, IJCAI 13, P2684
Musto C, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P321, DOI 10.1145/3109859.3109905
Qiu L, 2016, KNOWL-BASED SYST, V110, P233, DOI 10.1016/j.knosys.2016.07.033
Ravi K, 2015, KNOWL-BASED SYST, V89, P14, DOI 10.1016/j.knosys.2015.06.015
Turney PD, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P417
Vapnik, 2013, NATURE STAT LEARNING
Varshney S, 2017, 2017 2ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATION AND NETWORKS (TEL-NET), P270
WANG H, 2014, P 2014 C EMP METH NA, V2014, P1192
Wang JY, 2018, LECT NOTES COMPUT SC, V10745, P433, DOI 10.1007/978-3-319-74521-3_46
Wang XS, 2012, INFORMATION-TOKYO, V15, P449
Yang Y., 1997, ICML, V97, P412, DOI DOI 10.5555/645526.657137
Yao K, 2015, IEEE T FUZZY SYST, V23, P1333, DOI 10.1109/TFUZZ.2014.2360551
Yao K, 2012, IEEE T FUZZY SYST, V20, P1154, DOI 10.1109/TFUZZ.2012.2194152
Zhai CY, 2016, 2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI), P1119, DOI [10.1109/CSCI.2016.212, 10.1109/CSCI.2016.0213]
Zhang YF, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P83, DOI 10.1145/2600428.2609579
NR 49
TC 10
Z9 13
U1 5
U2 61
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD DEC
PY 2020
VL 20
IS 4
BP 857
EP 882
DI 10.1007/s10660-018-9319-6
PG 26
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA OH9EB
UT WOS:000582891600008
DA 2024-03-27
ER
PT J
AU Pathak, B
Garfinkel, R
Gopal, RD
Venkatesan, R
Yin, F
AF Pathak, Bhavik
Garfinkel, Robert
Gopal, Ram D.
Venkatesan, Rajkumar
Yin, Fang
TI Empirical Analysis of the Impact of Recommender Systems on Sales
SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
LA English
DT Article
DE collaborative filtering; electronic commerce; e-tail; experience goods;
recommender systems
ID CONSUMER DECISION-MAKING; WORD-OF-MOUTH; ONLINE; INFORMATION; REVIEWS;
PERSUASION; COMMERCE; CRITICS; AGENTS; COSTS
AB Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is how recommender systems affect sales. We take into consideration the interaction among recommendations, sales, and price. We then develop a robust empirical model that incorporates the indirect effect of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive effect on sales. Moreover, this effect is moderated by the recency effect, where more recently released recommended items positively affect the cross-selling efforts of sellers. We also show that recommender systems help to reinforce the long-tail phenomenon of electronic commerce, and obscure recommendations positively affect cross-selling. We also found a positive effect of recommendations on prices. These results suggest that recommendations not only improve sales but they also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher effect on sales than does consumer feedback. Our empirical results show that providing value-added services, such as digital word of mouth and recommendations, allows retailers to charge higher prices while at the same time increasing demand by providing more information regarding the quality and match of products.
C1 [Pathak, Bhavik] Indiana Univ S Bend, Sch Business & Econ, South Bend, IN USA.
[Garfinkel, Robert; Gopal, Ram D.] Univ Connecticut, Sch Business, Operat & Informat Management Dept, Storrs, CT 06269 USA.
[Venkatesan, Rajkumar] Univ Virginia, Darden Business Sch, Charlottesville, VA 22903 USA.
[Yin, Fang] Univ Oregon, Lundquist Coll Business, Dept Decis Sci, Eugene, OR 97403 USA.
C3 Indiana University System; Indiana University South Bend; University of
Connecticut; University of Virginia; University of Oregon
RP Pathak, B (autor correspondiente), Indiana Univ S Bend, Sch Business & Econ, South Bend, IN USA.
RI Gopal, Ram/M-9077-2019
OI Gopal, Ram/0000-0003-4241-9355
CR ADAMS WJ, 1976, Q J ECON, V90, P475, DOI 10.2307/1886045
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Alba J, 1997, J MARKETING, V61, P38, DOI 10.2307/1251788
[Anonymous], 2006, Hyperion
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Ben Schafer J, 2001, DATA MIN KNOWL DISC, V5, P115, DOI 10.1023/A:1009804230409
Benbasat I, 2003, MIS QUART, V27, P183
BERGEMANN D, 2006, 1563 YAL U
Brynjolfsson E, 2003, MANAGE SCI, V49, P1580, DOI 10.1287/mnsc.49.11.1580.20580
Caves RE, 1996, INT J IND ORGAN, V14, P29, DOI 10.1016/0167-7187(95)00478-5
Chen P., 2004, P 25 INT C INF SYST, P710
Chevalier J, 2003, QME-QUANT MARK ECON, V1, P203, DOI 10.1023/A:1024634613982
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Dellarocas C, 2003, MANAGE SCI, V49, P1407, DOI 10.1287/mnsc.49.10.1407.17308
Eliashberg J, 1997, J MARKETING, V61, P68, DOI 10.2307/1251831
FLYNN LJ, 2006, NY TIMES 0123
Ghose A, 2006, INFORM SYST RES, V17, P3, DOI 10.1287/isre.1050.0072
Grenci RT, 2002, COMMUN ACM, V45, P64, DOI 10.1145/504729.504730
Gretzel U, 2006, INT J ELECTRON COMM, V11, P81, DOI 10.2753/JEC1086-4415110204
GUERNSEY L, 2003, NY TIMES 0501
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hostler RE, 2005, DECIS SUPPORT SYST, V41, P313, DOI 10.1016/j.dss.2004.07.002
Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126
Kumar N, 2006, INFORM SYST RES, V17, P425, DOI 10.1287/isre.1060.0107
Latcovich S, 2001, OXFORD REV ECON POL, V17, P217, DOI 10.1093/oxrep/17.2.217
Liang TP, 2006, J MANAGE INFORM SYST, V23, P45, DOI 10.2753/MIS0742-1222230303
Liang TP, 2006, J MANAGE INFORM SYST, V23, P9, DOI 10.2753/MIS0742-1222230301
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Mild A., 2003, Journal of Retailing and Consumer Services, V10, P123, DOI [DOI 10.1016/S0969-6989(03)00003-1, 10.1016/S0969-6989(03)00003-1]
MOBASHER B, 2001, 3 ACM WORKSH WEB INF
MUI YQ, 2006, WASHINGTON POST 0107
NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630
NELSON P, 1974, J POLIT ECON, V82, P729, DOI 10.1086/260231
Nikolaeva R, 2006, INT J ELECTRON COMM, V11, P101, DOI 10.2753/JEC1086-4415110205
NULL C, 2003, NETFLIX FIXING HOLLY
OESTREICHERSING.G, 2006, P 27 INT C INF SYST, P382
Reinstein DA, 2005, J IND ECON, V53, P27, DOI 10.1111/j.0022-1821.2005.00244.x
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Schafer J. Ben, 1999, Proceedings of the 1st ACM conference on Electronic commerce, P158, DOI [DOI 10.1145/336992.337035, 10.1145/336992.337035]
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Shardanand U., 1995, Human Factors in Computing Systems. CHI'95 Conference Proceedings, P210, DOI 10.1145/223904.223931
Smith MD, 2001, J IND ECON, V49, P541, DOI 10.1111/1467-6451.00162
Stiglitz J., 1989, HDB IND ORG, P769, DOI DOI 10.1016/S1573-448X(89)01016-2
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Vakratsas D, 1999, J MARKETING, V63, P26, DOI 10.2307/1251999
Wang WQ, 2007, J MANAGE INFORM SYST, V23, P217, DOI 10.2753/MIS0742-1222230410
WESTBROOK RA, 1987, J MARKETING RES, V24, P258, DOI 10.2307/3151636
Xiao B, 2007, MIS QUART, V31, P137
Yang Y., 2005, Journal of Electronic Commerce Research, V6, P112
NR 50
TC 170
Z9 191
U1 8
U2 156
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND
SN 0742-1222
EI 1557-928X
J9 J MANAGE INFORM SYST
JI J. Manage. Inform. Syst.
PD FAL
PY 2010
VL 27
IS 2
BP 159
EP 188
DI 10.2753/MIS0742-1222270205
PG 30
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA 679DB
UT WOS:000284138500006
DA 2024-03-27
ER
PT J
AU Karn, AL
Karna, RK
Kondamudi, BR
Bagale, G
Pustokhin, DA
Pustokhina, IV
Sengan, S
AF Karn, Arodh Lal
Karna, Rakshha Kumari
Kondamudi, Bhavana Raj
Bagale, Girish
Pustokhin, Denis A.
Pustokhina, Irina, V
Sengan, Sudhakar
TI Customer centric hybrid recommendation system for E-Commerce
applications by integrating hybrid sentiment analysis
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Hybrid recommender system; E-Commerce; Sentiment analysis; Neural
network; Accuracy; Filtering; Deep learning; Bert
AB Internet applications such as Online Social Networking and Electronic commerce are becoming incredibly common, resulting in more content being available. Recommender systems (RS) assist users in identifying appropriate information out of a large pool of options. In today's internet applications, RS are extremely important. To increase user satisfaction, this type of system supports personalized recommendations based on a massive volume of data. These suggestions assist clients in selecting products, while concerns can increase product utilization. We discovered that much research work is going on in the field of recommendation and that there are some effective systems out there. In the context of social information, sentimental analysis (SA) can aid in increasing the knowledge of a user's behaviour, views, and reactions, which is helpful for incorporating into RS to improve recommendation accuracy. RS has been found to resolve information overload challenges in information retrieval, but they still have issues with cold-start and data sparsity. SA, on the other hand, is well-known for interpreting text and conveying user choices. It's frequently used to assist E-Commerce in tracking customer feedback on their products and attempting to comprehend customer needs and preferences. To improve the accuracy and correctness of any RS, this paper proposes a recommendation model based on a Hybrid Recommendation Model (HRM) and hybrid SA. In the proposed method, we first generate a preliminary recommendation list using a HRM. To generate the final recommendation list, the HRM with SA is used. In terms of various evaluation criteria, the HRM with SA outperforms traditional models.
C1 [Karn, Arodh Lal] Xian Jiaotong Liverpool Univ, Sch Math & Phys, Dept Financial & Actuarial Math, Suzhou 215123, Jiangsu, Peoples R China.
[Karna, Rakshha Kumari] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
[Kondamudi, Bhavana Raj] Inst Publ Enterprise, Dept Management Studies, Hyderabad 500101, India.
[Bagale, Girish] SVKMs NMIMS Univ, SBMs Pravin Dalal Sch Entrepreneurship & Family B, Mumbai 400056, Maharashtra, India.
[Pustokhin, Denis A.] State Univ Management, Dept Logist, Moscow 109542, Russia.
[Pustokhina, Irina, V] Plekhanov Russian Univ Econ, Dept Entrepreneurship & Logist, Moscow 117997, Russia.
[Sengan, Sudhakar] PSN Coll Engn & Technol, Dept Comp Sci & Engn, Tirunelveli 627152, Tamil Nadu, India.
C3 Xi'an Jiaotong-Liverpool University; Harbin Institute of Technology;
SVKM's NMIMS (Deemed to be University); State University of Management;
Plekhanov Russian University of Economics
RP Sengan, S (autor correspondiente), PSN Coll Engn & Technol, Dept Comp Sci & Engn, Tirunelveli 627152, Tamil Nadu, India.
EM alkmaithili@foxmail.com; rakshha.karna@gmail.com;
bhavana_raj_83@yahoo.com; girishbagale08@gmail.com;
dpustokhin@yandex.ru; ivpustokhina@yandex.ru; sudhasengan@gmail.com
RI Pustokhina, Irina/D-3508-2019; Pustokhin, Denis/AEU-9889-2022
OI Pustokhina, Irina/0000-0001-5480-8871; KARN, ARODH
LAL/0000-0003-4557-1889
CR Aaron O., 2019, SHOPIFY PLUS 0131
Agarwal B, 2015, COMPUT INTEL NEUROSC, V2015, DOI 10.1155/2015/715730
Alhamid MF, 2015, MULTIMED TOOLS APPL, V74, P11399, DOI 10.1007/s11042-014-2236-3
[Anonymous], 2018, ADV POSITIONING FLOW
Cheng T., 2016, WWW, P2787
Dang CN, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21165666
Dang NC, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9030483
Devipriya K., 2020, INT J SCI TECHNOL RE, V9, P3812
Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
Fayyaz Z, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10217748
Fu MS, 2019, IEEE T CYBERNETICS, V49, P1084, DOI 10.1109/TCYB.2018.2795041
García-Cumbreras MA, 2013, EXPERT SYST APPL, V40, P6758, DOI 10.1016/j.eswa.2013.06.049
Ghabayen A, 2014, J COMPUTER SCI, V10, P1166, DOI [10.3844/jcssp.2014.1166.1173, DOI 10.3844/JCSSP.2014.1166.1173]
Ghabayen A.S., 2017, International Journal on Advanced Science, Engineering and Information Technology, V7, P2063, DOI [DOI 10.18517/IJASEIT.7.6.1826, 10.18517/ijaseit.7.6.1826]
Guimaraes R, 2016, I SYMP CONSUM ELECTR, P71, DOI 10.1109/ISCE.2016.7797377
Hanafizadeh P, 2021, ELECTRON MARK, V31, P269, DOI 10.1007/s12525-020-00449-w
He P, 2022, ELECTRON COMMER RES, DOI 10.1007/s10660-022-09609-w
He X., 2015, P 24 ACM INT C INF K, P1661, DOI [10.1145/2806416.2806504, DOI 10.1145/2806416.2806504]
Hu FY, 2023, ELECTRON COMMER RES, V23, P2647, DOI 10.1007/s10660-022-09557-5
Hu Y., 2011, P ISMIR, P103
Jaiswal Sapna, 2020, ITM Web of Conferences, V32, DOI 10.1051/itmconf/20203203034
Ka-Hou Chan, 2020, Neural Information Processing. 27th International Conference, ICONIP 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12532), P693, DOI 10.1007/978-3-030-63830-6_58
Kamatchi SB, 2019, PROCEDIA COMPUT SCI, V165, P724, DOI 10.1016/j.procs.2020.01.023
Kang LS, 2021, ELECTRON MARK, V31, P253, DOI 10.1007/s12525-020-00416-5
Kashyap R, 2023, ELECTRON COMMER RES, V23, P2183, DOI 10.1007/s10660-022-09531-1
Kumar S, 2020, IEEE T COMPUT SOC SY, V7, P915, DOI 10.1109/TCSS.2020.2993585
Kundu S, 2022, ELECTRON COMMER RES, V22, P925, DOI 10.1007/s10660-020-09429-w
Lei XJ, 2016, IEEE T MULTIMEDIA, V18, P1910, DOI 10.1109/TMM.2016.2575738
Li GL, 2019, IEEE ACCESS, V7, P126539, DOI 10.1109/ACCESS.2019.2937375
Lin David, 2015, 2015 IEEE International Test Conference (ITC), P1, DOI 10.1109/TEST.2015.7342397
Melville P., 2010, ENCY MACHINE LEARNIN, DOI DOI 10.1007/978-0-387-30164-8_705
Nimirthi P, 2019, Social Network Forensics, Cyber Security, and Machine Learning, P59
Osman NA, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0248695
Pan R., 2012, TAG BASED SYSTEMS SP, P355
Peleja F, 2013, MULTIMEDIA SYST, V19, P543, DOI 10.1007/s00530-013-0310-8
Preethi G, 2017, INT CONF COMP INFO, P93, DOI 10.1109/CITS.2017.8035341
Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
Salehi Mojtaba, 2013, International Journal of Business Information Systems, V14, P480, DOI 10.1504/IJBIS.2013.057501
Sánchez-Moreno D, 2020, 2020 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2020), P502, DOI 10.1109/WIIAT50758.2020.00075
Selmene S, 2020, 4TH INTERNATIONAL CONFERENCE ON E-COMMERCE, E-BUSINESS AND E-GOVERNMENT, ICEEG 2020, P96, DOI 10.1145/3409929.3414744
Shafqat W, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12104107
SHAREEF S. M., 2020, Engineering and Technology Journal, V38, P6
Son J, 2017, EXPERT SYST APPL, V89, P404, DOI 10.1016/j.eswa.2017.08.008
Strub Florian, 2016, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, P11
Su X., 2009, Advances in artificial intelligence, V2009, P4, DOI [10.1155/2009/421425, DOI 10.1155/2009/421425]
Wang B, 2018, FUTURE INTERNET, V10, DOI 10.3390/fi10120117
Wang Y., 2018, INT C EL BUS 2018 P, P211
Wang YB, 2018, WIREL COMMUN MOB COM, DOI 10.1155/2018/2734219
Wu XQ, 2021, ELECTRON COMMER RES, V21, P689, DOI 10.1007/s10660-019-09339-6
Zhang HW, 2021, IEEE ACCESS, V9, P48039, DOI 10.1109/ACCESS.2021.3061279
Zhou LC, 2020, ELECTRON COMMER RES, V20, P321, DOI 10.1007/s10660-020-09411-6
Zhuang YY, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13148039
Ziani A., 2017, P 2 INT C AUT CONTR
NR 53
TC 15
Z9 16
U1 51
U2 121
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD MAR
PY 2023
VL 23
IS 1
SI SI
BP 279
EP 314
DI 10.1007/s10660-022-09630-z
EA OCT 2022
PG 36
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA F6MZ6
UT WOS:000875791900001
DA 2024-03-27
ER
PT J
AU Khan, ZA
Chaudhary, NI
Zubair, S
AF Khan, Zeshan Aslam
Chaudhary, Naveed Ishtiaq
Zubair, Syed
TI Fractional stochastic gradient descent for recommender systems
SO ELECTRONIC MARKETS
LA English
DT Article
DE Recommender systems; E-Commerce; Fractional calculus; Stochastic
gradient descent
ID MATRIX FACTORIZATION; PARAMETER-ESTIMATION; ADAPTIVE STRATEGY; ORDER
CIRCUITS; IDENTIFICATION; ALGORITHM; STABILITY; DESIGN; LMS
AB Recently, recommender systems are getting popular in the e-commerce industry for retrieving and recommending most relevant information about items for users from large amounts of data. Different stochastic gradient descent (SGD) based adaptive strategies have been proposed to make recommendations more precise and efficient. In this paper, we propose a fractional variant of the standard SGD, named as fractional stochastic gradient descent (FSGD), for recommender systems. We compare its convergence and estimated accuracy with standard SGD against a number of features with different learning rates and fractional orders. The performance of our proposed method is evaluated using the root mean square error (RMSE) as a quantitative evaluation measure. We examine that the proposed strategy is more accurate in terms of RMSE than the standard SGD for all values of fractional orders and different numbers of features. The contribution of fractional calculus has not been explored yet to solve the recommender systems problem; therefore, we exploit FSGD for solving this problem. The results show that our proposed method performs significantly well in terms of estimated accuracy and convergence as compared to the standard SGD.
C1 [Khan, Zeshan Aslam; Chaudhary, Naveed Ishtiaq; Zubair, Syed] Int Islamic Univ, Dept Elect Engn, Islamabad 44000, Pakistan.
C3 International Islamic University, Pakistan
RP Khan, ZA (autor correspondiente), Int Islamic Univ, Dept Elect Engn, Islamabad 44000, Pakistan.
EM zeeshan.aslam@iiu.edu.pk; naveed.ishtiaq@iiu.edu.pk; szubair@iiu.edu.pk
RI Chaudhary, Naveed/I-7754-2019
OI Chaudhary, Naveed Ishtiaq/0000-0002-9568-3216
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Aggarwal C. C., 2001, KDD-2001. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P227, DOI 10.1145/502512.502543
Aggarwal C. C., 2016, RECOMMENDER SYSTEMS, V1
[Anonymous], STAT ANAL MISSING DA
[Anonymous], MODARES J ELECT ENG
[Anonymous], NEURAL COMPUTING APP
[Anonymous], 2007, P KDD CUP WORKSH
[Anonymous], INT C ALG APPL MAN
[Anonymous], 2011, FRACTIONAL CALCULUS, DOI DOI 10.1007/978-94-007-0747-4
[Anonymous], 4 IFAC WORKSH FRACT
Aslam MS, 2017, NONLINEAR DYNAM, V87, P519, DOI 10.1007/s11071-016-3058-9
Aslam MS, 2015, SIGNAL PROCESS, V107, P433, DOI 10.1016/j.sigpro.2014.04.012
Badri V, 2016, IEEE T CONTR SYST T, V24, P1059, DOI 10.1109/TCST.2015.2462739
Baskonus HM, 2015, OPEN MATH, V13, P547, DOI 10.1515/math-2015-0052
Baskonus HM, 2015, ENTROPY-SWITZ, V17, P5771, DOI 10.3390/e17085771
Bell RM, 2007, IEEE DATA MINING, P43, DOI 10.1109/ICDM.2007.90
Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
Bouzeriba A, 2016, NEURAL COMPUT APPL, V27, P1349, DOI 10.1007/s00521-015-1938-4
Bulut Hasan, 2013, ABSTR APPL AN, V2013
Chaudhary NI, 2018, APPL MATH MODEL, V55, P698, DOI 10.1016/j.apm.2017.11.023
Chaudhary NI, 2017, IET SIGNAL PROCESS, V11, P975, DOI 10.1049/iet-spr.2016.0578
Chaudhary NI, 2017, ISA T, V68, P189, DOI 10.1016/j.isatra.2017.03.011
Chaudhary NI, 2015, NONLINEAR DYNAM, V82, P1811, DOI 10.1007/s11071-015-2279-7
Chen DL, 2015, APPL MATH COMPUT, V257, P537, DOI 10.1016/j.amc.2015.01.012
Chen DL, 2013, CENT EUR J PHYS, V11, P1414, DOI 10.2478/s11534-013-0241-1
Chen P.-L., 2011, KDD-Cup, P21
Chin WS, 2015, ACM T INTEL SYST TEC, V6, DOI 10.1145/2668133
Ortigueira MD, 2006, SIGNAL PROCESS, V86, P2503, DOI 10.1016/j.sigpro.2006.02.001
Elwakil AS, 2010, IEEE CIRC SYST MAG, V10, P40, DOI 10.1109/MCAS.2010.938637
Funk S., 2006, Netflix update: Try this at home
Gangola S, 2018, ADV ENV ENG GREEN TE, P1, DOI 10.4018/978-1-5225-3126-5.ch001
Gemulla R., 2011, P 17 ACM SIGKDD INT
Geravanchizadeh M, 2014, Iranian Journal of Electrical and Electronic Engineering, V10, P256
Harper FM, 2016, ACM T INTERACT INTEL, V5, DOI 10.1145/2827872
He C, 2016, EXPERT SYST APPL, V56, P9, DOI 10.1016/j.eswa.2016.02.013
Heimbach I, 2015, ELECTRON MARK, V25, P125, DOI 10.1007/s12525-015-0187-9
Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774
Kawai M, 2016, INT INF I TOKYO INF, V19, P2177
Köhler S, 2016, ELECTRON MARK, V26, P369, DOI 10.1007/s12525-016-0232-3
Koren Y, 2008, 14 ACM SIGKDD INT C
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Lu J, 2015, DECIS SUPPORT SYST, V74, P12, DOI 10.1016/j.dss.2015.03.008
Luo X, 2013, KNOWL-BASED SYST, V37, P154, DOI 10.1016/j.knosys.2012.07.016
Luo X, 2012, KNOWL-BASED SYST, V27, P271, DOI 10.1016/j.knosys.2011.09.006
Machado JT, 2011, COMMUN NONLINEAR SCI, V16, P1140, DOI 10.1016/j.cnsns.2010.05.027
Malek H, 2016, IET POWER ELECTRON, V9, P1608, DOI 10.1049/iet-pel.2015.0636
Mnih Andriy, 2007, ADV NEURAL INFORM PR, P1257, DOI DOI 10.1145/1390156.1390267
Ortigueira MD, 2015, SIGNAL PROCESS, V107, P197, DOI 10.1016/j.sigpro.2014.10.002
Pahnehkolaei SMA, 2017, INFORM SCIENCES, V418, P703, DOI 10.1016/j.ins.2017.08.046
Pahnehkolaei SMA, 2017, CHAOS SOLITON FRACT, V103, P488, DOI 10.1016/j.chaos.2017.06.003
Pahnehkolaei SMA, 2017, COMMUN NONLINEAR SCI, V47, P328, DOI 10.1016/j.cnsns.2016.11.013
Paterek A., 2007, P KDD CUP WORKSH, V2007, P5
Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P325
Psychalinos C, 2016, CIRC SYST SIGNAL PR, V35, P1807, DOI 10.1007/s00034-016-0285-8
Pu YF, 2017, IEEE T NEUR NET LEAR, V28, P2319, DOI 10.1109/TNNLS.2016.2582512
Raja MAZ, 2015, SIGNAL PROCESS, V107, P327, DOI 10.1016/j.sigpro.2014.06.015
Raja MAZ, 2014, IET SIGNAL PROCESS, V8, P968, DOI 10.1049/iet-spr.2013.0438
Rennie Jasson DM, 2005, P 22 INT C MACH LEAR, P713, DOI DOI 10.1145/1102351.1102441
Rostami M, 2015, SIGNAL PROCESS, V107, P361, DOI 10.1016/j.sigpro.2014.03.042
Sabatier J, 2007, ADV FRACTIONAL CALCU
Safarinejadian B, 2016, INT J CONTROL, V89, P2277, DOI 10.1080/00207179.2016.1155237
Salter J, 2006, IEEE INTELL SYST, V21, P35, DOI 10.1109/MIS.2006.4
Sarwar B., 2000, Application of dimensionality reduction in recommender system-a case study
Schafer J. B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P291
Srebro N., 2004, ADV NEURAL INFORM PR, P1329
Takács G, 2009, J MACH LEARN RES, V10, P623
Machado JAT, 2015, APPL MATH MODEL, V39, P4095, DOI 10.1016/j.apm.2014.12.037
Wang ZJ, 2016, COMM COM INF SC, V643, P199, DOI 10.1007/978-981-10-2663-8_21
Wu GC, 2016, J VIB CONTROL, V22, P2092, DOI 10.1177/1077546315574649
Yu HF, 2014, KNOWL INF SYST, V41, P793, DOI 10.1007/s10115-013-0682-2
Yu HF, 2012, IEEE DATA MINING, P765, DOI 10.1109/ICDM.2012.168
Zubair S, 2018, SIGNAL PROCESS, V142, P441, DOI 10.1016/j.sigpro.2017.08.009
NR 72
TC 34
Z9 35
U1 1
U2 17
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD JUN
PY 2019
VL 29
IS 2
BP 275
EP 285
DI 10.1007/s12525-018-0297-2
PG 11
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA IE7EA
UT WOS:000472536000012
DA 2024-03-27
ER
PT J
AU Panniello, U
Hill, S
Gorgoglione, M
AF Panniello, Umberto
Hill, Shawndra
Gorgoglione, Michele
TI The impact of profit incentives on the relevance of online
recommendations
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Consumer trust; e-Commerce; Incentive-centered design; Recommender
systems
ID E-COMMERCE; PRODUCT RECOMMENDATIONS; INTEGRATIVE MODEL; CONSUMER
REVIEWS; SYSTEMS; TRUST; CONTEXT; PERSONALIZATION; CUSTOMERS; VARIETY
AB Recommender systems are commonly used by firms to improve consumers' online shopping experiences, with the secondary benefit of increased sales and profits. Prior research has demonstrated that a trade-off between relevance and profit exists, and that recommendations' manipulations and biases may hurt the credibility of recommender systems, and thus reduces customer trust. While many of the proposed designs suggest simple heuristics to bias recommendations toward higher-margin items, very little is known about consumers' reactions (in terms of purchasing behavior and trust) to recommender algorithms that balance recommendations' relevance and profitability or the drivers of this behavior. We aim to fill this gap. Data from an online randomized field experiment showed that balancing recommendations' accuracy and profit has a positive effect on consumers' purchasing behavior and does not affect their trust. We also found that the profit made during our experiment was due to a balance of several variables. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Panniello, Umberto; Gorgoglione, Michele] Politecn Bari, Bari, Italy.
[Hill, Shawndra] Univ Penn, Wharton Business Sch, Philadelphia, PA 19104 USA.
C3 Politecnico di Bari; University of Pennsylvania
RP Panniello, U (autor correspondiente), Politecn Bari, Bari, Italy.
EM umberto.panniello@poliba.it
OI Panniello, Umberto/0000-0002-7902-5993
CR Aamir M., 2015, INT J COMPUT APPL, V120, P25
Adomavicius G, 2005, ACM T INFORM SYST, V23, P103, DOI 10.1145/1055709.1055714
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15
Adomavicius G, 2013, INFORM SYST RES, V24, P956, DOI 10.1287/isre.2013.0497
Akoglu L., 2010, 2010 IEEE INT C DAT
AKSOY L, 2001, SOC CONS RES WINT C
[Anonymous], 1993, ADAPTIVE DECISION MA, DOI DOI 10.1017/CBO9781139173933
[Anonymous], 2001, P 2 DELOS NETW EXC W
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Ansari A, 2003, J MARKETING RES, V40, P131, DOI 10.1509/jmkr.40.2.131.19224
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Azaria A., 2013, P 7 ACM C REC SYST H
Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124
Baum D, 2014, INT J ELECTRON COMM, V19, P129, DOI 10.2753/JEC1086-4415190104
BAUMOL WJ, 1956, MANAGE SCI, V3, P93, DOI 10.1287/mnsc.3.1.93
Beldad A, 2010, COMPUT HUM BEHAV, V26, P857, DOI 10.1016/j.chb.2010.03.013
Ben Schafer J, 2001, DATA MIN KNOWL DISC, V5, P115, DOI 10.1023/A:1009804230409
Benbasat I, 2005, Journal of the Association for Information Systems, V6, P4, DOI [DOI 10.17705/1JAIS.00065, 10.17705/1jais.00065]
Benlian A, 2012, J MANAGE INFORM SYST, V29, P237, DOI 10.2753/MIS0742-1222290107
Bharati P, 2004, DECIS SUPPORT SYST, V37, P187, DOI 10.1016/S0167-9236(03)00006-X
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Brand M., 2005, SIAM INT C DAT MIN N
Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
Broniarczyk SM, 1998, J MARKETING RES, V35, P166, DOI 10.2307/3151845
Brynjolfsson E, 2003, MANAGE SCI, V49, P1580, DOI 10.1287/mnsc.49.11.1580.20580
Chen LS, 2008, INFORM SCIENCES, V178, P1032, DOI 10.1016/j.ins.2007.09.027
Cho Y.C., 2015, IJMIS, V19, P16
Choi J, 2011, INT J ELECTRON COMM, V16, P129, DOI 10.2753/JEC1086-4415160105
Cooke ADJ, 2002, J MARKETING RES, V39, P488, DOI 10.1509/jmkr.39.4.488.19121
Das A., 2009, MAXIMIZING PROFIT US
Doney PM, 1997, J MARKETING, V61, P35, DOI 10.2307/1251829
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Ganesan S., 1994, J MARKETING, V58, P1, DOI DOI 10.2307/1252265
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Gorgoglione M., 2011, Proc. of the fifth ACM conference on RS, P85, DOI DOI 10.1145/2043932.2043951
Gretzel U, 2006, INT J ELECTRON COMM, V11, P81, DOI 10.2753/JEC1086-4415110204
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
HERPEN EV, 2002, MARKET SCI, V21, P331
Hess TJ, 2005, J MANAGE INFORM SYST, V22, P15, DOI 10.2753/MIS0742-1222220302
Hoch SJ, 1999, MARKET SCI, V18, P527, DOI 10.1287/mksc.18.4.527
Hosanagar K., 2008, P INT C INF SYST PAR
Hu R., 2011, DIVERS RECSYS
Iwata T, 2008, IEEE T KNOWL DATA EN, V20, P1254, DOI 10.1109/TKDE.2008.55
Jarvenpaa S. L., 1998, Journal of Management Information Systems, V14, P29
KAHN BE, 1991, J RETAILING, V67, P274
Komiak SYX, 2006, MIS QUART, V30, P941
Liang TP, 2006, J MANAGE INFORM SYST, V23, P45, DOI 10.2753/MIS0742-1222230303
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
McGinty L, 2006, INT J ELECTRON COMM, V11, P35, DOI 10.2753/JEC1086-4415110202
McKnight DH, 2002, INFORM SYST RES, V13, P334, DOI 10.1287/isre.13.3.334.81
Mooney R.J., 2000, P 5 ACM C DIG LIB SA
Palmisano C, 2008, IEEE T KNOWL DATA EN, V20, P1535, DOI 10.1109/TKDE.2008.110
Panniello U, 2016, INFORM SYST RES, V27, P182, DOI 10.1287/isre.2015.0610
Panniello U, 2014, USER MODEL USER-ADAP, V24, P35, DOI 10.1007/s11257-012-9135-y
Panniello U, 2012, ELECTRON COMMER RES, V12, P1, DOI 10.1007/s10660-012-9087-7
Panniello U, 2009, LECT NOTES COMPUT SC, V5692, P348, DOI 10.1007/978-3-642-03964-5_32
Park SH, 2013, INT J ELECTRON COMM, V18, P51, DOI 10.2753/JEC1086-4415180202
Pathak A, 2015, COMPANION PROCEEDINGS OF THE SECOND ACM IKDD CONFERENCE ON DATA SCIENCES (CODS), DOI 10.1145/2778865.2778873
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Pavlou PA, 2003, INT J ELECTRON COMM, V7, P101, DOI 10.1080/10864415.2003.11044275
Pavlou PA, 2007, MIS QUART, V31, P105
Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P325
Pham A., 2005, INFORM SYST RES, V23, P231
Schoorman FD, 2007, ACAD MANAGE REV, V32, P344, DOI 10.5465/AMR.2007.24348410
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
SHANNON CE, 1948, BELL SYST TECH J, V27, P379, DOI 10.1002/j.1538-7305.1948.tb01338.x
Shardanand U., 1995, SIGCHI C HUM FACT CO
Simonson I, 2005, J MARKETING, V69, P32, DOI 10.1509/jmkg.69.1.32.55512
Soboroff I., 1999, IJCAI WORKSH MACH LE
Sun Y., 2015, P INT MULTICONFERENC
Swearingen K., 2001, ACM SIGIR WORKSH REC, P393
Umberto P., 2015, Int. J. Electron. Commer. Stud., V6, P1, DOI DOI 10.7903/IJECS.1348
Xiao B, 2007, MIS QUART, V31, P137
Zhang M., 2008, ACM C REC SYST LAUS
NR 76
TC 19
Z9 21
U1 2
U2 51
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD NOV-DEC
PY 2016
VL 20
BP 87
EP 104
DI 10.1016/j.elerap.2016.10.003
PG 18
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA EI9JS
UT WOS:000392824700007
DA 2024-03-27
ER
PT J
AU Molaie, MM
Lee, W
AF Molaie, Mir Majid
Lee, Wonjae
TI Economic corollaries of personalized recommendations
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Artificial intelligence; Consumer purchase diversity; E-commerce; Field
experiment; Machine learning; Neural networks; Personalization;
Recommender systems; Sale diversity
ID LONG TAIL; SYSTEMS; CONTEXT; COSTS
AB The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales.
C1 [Molaie, Mir Majid; Lee, Wonjae] Korea Adv Inst Sci & Technol, Grad Sch Culture Technol, Daejeon, South Korea.
C3 Korea Advanced Institute of Science & Technology (KAIST)
RP Lee, W (autor correspondiente), Korea Adv Inst Sci & Technol, Grad Sch Culture Technol, Daejeon, South Korea.
EM majid@kaist.ac.kr; wnjlee@kaist.ac.kr
OI Molaie, Mir Majid/0000-0001-8063-7383
CR Adomavicius G, 2018, INFORM SYST RES, V29, P84, DOI 10.1287/isre.2017.0703
Anderson A, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P2155, DOI 10.1145/3366423.3380281
[Anonymous], 2006, Hyperion
Baier D, 2010, J RETAIL CONSUM SERV, V17, P173, DOI 10.1016/j.jretconser.2010.03.005
Brynjolfsson E, 2011, MANAGE SCI, V57, P1373, DOI 10.1287/mnsc.1110.1371
Chaney AJB, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P224, DOI 10.1145/3240323.3240370
Chen SS, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102573
Chinchanachokchai S, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102528
Claussen J., 2019, DATA EXTERNALITIES O
Donnelly R., 2021, LONG TAIL EFFECT PER
Elberse A, 2013, BLOCKBUSTERS HIT MAK
Gerlach M, 2016, PHYS REV X, V6, DOI 10.1103/PhysRevX.6.021009
Good P., 2013, PERMUTATION TESTS PR
Hauser JR, 2014, J BUS RES, V67, P1688, DOI 10.1016/j.jbusres.2014.02.015
He XN, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P173, DOI 10.1145/3038912.3052569
Holtz David, 2020, EC '20: Proceedings of the 21st ACM Conference on Economics and Computation, P75, DOI 10.1145/3391403.3399532
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
Hoskins JD, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102234
Jannach D, 2019, ACM TRANS MANAG INF, V10, DOI 10.1145/3370082
Kim J, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102381
Konstan JA, 2012, USER MODEL USER-ADAP, V22, P101, DOI 10.1007/s11257-011-9112-x
Kumar A, 2014, MIS QUART, V38, P1057, DOI 10.25300/MISQ/2014/38.4.06
Lee D, 2019, INFORM SYST RES, V30, P239, DOI 10.1287/isre.2018.0800
Lee S., 2011, PROC RECSYS 11, P93, DOI DOI 10.1145/2043932.2043952
Lee Y, 2019, J RETAIL CONSUM SERV, V51, P437, DOI 10.1016/j.jretconser.2017.10.016
Li X., 2021, Information Systems Research
Liaukonyte J., 2021, PERSONALIZED SOCIAL
Lombardi S, 2013, EXPERT SYST APPL, V40, P5219, DOI 10.1016/j.eswa.2013.03.009
Milano S, 2020, AI SOC, V35, P957, DOI 10.1007/s00146-020-00950-y
Möller J, 2018, INFORM COMMUN SOC, V21, P959, DOI 10.1080/1369118X.2018.1444076
Morales PR, 2021, THEOR COMPUT SCI, V859, P80, DOI 10.1016/j.tcs.2021.01.013
Morik M, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P429, DOI 10.1145/3397271.3401100
Panniello U, 2016, INFORM SYST RES, V27, P182, DOI 10.1287/isre.2015.0610
Pariser Eli, 2011, FILTER BUBBLE WHAT I
Rendle S., 2009, P 25 C UNCERTAINTY A, P452, DOI DOI 10.5555/1795114.1795167
Resnick P., 1994, Transcending Boundaries, CSCW '94. Proceedings of the Conference on Computer Supported Cooperative Work, P175, DOI 10.1145/192844.192905
Schreiner T, 2019, J RETAIL CONSUM SERV, V48, P87, DOI 10.1016/j.jretconser.2019.02.010
Srivastava A, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.02.018
Suglia A, 2017, PROCEEDINGS OF THE 25TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'17), P202, DOI 10.1145/3079628.3079684
Verkijika SF, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102743
Virosztek D, 2021, ADV MATH, V380, DOI 10.1016/j.aim.2021.107595
Yucesoy B, 2018, EPJ DATA SCI, V7, DOI 10.1140/epjds/s13688-018-0135-y
Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
NR 43
TC 2
Z9 2
U1 11
U2 38
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD SEP
PY 2022
VL 68
AR 103003
DI 10.1016/j.jretconser.2022.103003
EA MAY 2022
PG 9
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1Q3ON
UT WOS:000802601300006
DA 2024-03-27
ER
PT J
AU Wang, J
Kamran, A
Shahzad, F
Syed, NA
AF Wang, Jian
Kamran, Asif
Shahzad, Fakhar
Syed, Nadeem Ahmad
TI Enhancing group recommender systems: A fusion of social tagging and
collaborative filtering for cohesive recommendations
SO SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE
LA English
DT Article; Early Access
DE group recommender system; collaborative filtering; group
decision-making; tagging information; user experience
ID TRUST; STATE
AB This study examines the challenges and opportunities of using group recommendation systems in an information overload scenario. Social network recommendation systems are increasingly important because they deliver users customized choices. Most existing solutions are geared for single users, making it difficult to propose for a group with different interests. This paper analyses group recommendation systems and exposes their flaws. This study tested whether the suggested approach outperforms the one without tagging information in recall, precision, and user satisfaction. Empirical evidence indicates that the algorithm exhibits appropriate levels of reliability and accuracy compared to conventional methods. The proposed approach has the potential to substantially enhance the existing state of social network group recommendation systems, thereby facilitating users in their quest to identify and participate in groups that align with their preferences.
C1 [Wang, Jian] Zhengzhou Univ Light Ind, Coll Econ & Management, Zhengzhou, Peoples R China.
[Kamran, Asif] Nazeer Hussain Univ, Fac Business Management & Study, Karachi, Pakistan.
[Shahzad, Fakhar] Shenzhen Univ, Res Inst Business Analyt & Supply Chain Management, Coll Management, Shenzhen, Peoples R China.
[Syed, Nadeem Ahmad] Khadim Ali Shah Bukhari Inst Technol, Dept Business Adm, Karachi, Pakistan.
C3 Zhengzhou University of Light Industry; Shenzhen University
RP Shahzad, F (autor correspondiente), Shenzhen Univ, Res Inst Business Analyt & Supply Chain Management, Coll Management, Shenzhen, Peoples R China.
EM fshahzad51@szu.edu.cn
RI Shahzad, Fakhar/F-7336-2012; Kamran, Asif/F-1939-2016
OI Shahzad, Fakhar/0000-0002-6408-2848; Kamran, Asif/0000-0001-7134-4394
FU Philosophy and social Science planning project of Henan Province, China
FX No Statement Availabler No Statement Available
CR Aljunid MF, 2021, CAAI T INTELL TECHNO, V6, P480, DOI 10.1049/cit2.12048
Allam H, 2019, INT J INFORM MANAGE, V45, P211, DOI 10.1016/j.ijinfomgt.2018.11.005
Amer-Yahia S, 2009, PROC VLDB ENDOW, V2
Bok K, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11167681
Bok K, 2019, ELECTRONICS-SWITZ, V8, DOI 10.3390/electronics8101165
Breese H Heckerman D., 1998, P 14 C UNC ART INT, p1998 43
Cao D, 2021, IEEE T KNOWL DATA EN, V33, P1195, DOI 10.1109/TKDE.2019.2936475
Castro J, 2018, INT J INTELL SYST, V33, P2058, DOI 10.1002/int.21922
Castro J, 2015, INT J INTELL SYST, V30, P887, DOI 10.1002/int.21730
Cechinel C, 2013, INFORM PROCESS MANAG, V49, P34, DOI 10.1016/j.ipm.2012.07.004
Chen YL, 2008, EXPERT SYST APPL, V34, P2082, DOI 10.1016/j.eswa.2007.02.008
Choi B, 2017, TELEMAT INFORM, V34, P550, DOI 10.1016/j.tele.2016.11.005
Dara S, 2020, J INTELL INF SYST, V54, P271, DOI 10.1007/s10844-018-0542-3
DESANCTIS G, 1987, MANAGE SCI, V33, P589, DOI 10.1287/mnsc.33.5.589
Duchateau F., 2011, Proceedings 2011 27th IEEE International Conference on Data Engineering Workshops (ICDEW 2011), P218, DOI 10.1109/ICDEW.2011.5767663
Fang G., 2018, Wireless Communications and Mobile Computing, V2018, P1, DOI [10.1155/2018/6709607, DOI 10.1155/2018/6709607]
Fogel J, 2009, COMPUT HUM BEHAV, V25, P153, DOI 10.1016/j.chb.2008.08.006
Gabriel HH, 2014, EXPERT SYST APPL, V41, P457, DOI 10.1016/j.eswa.2013.07.071
Garcia I, 2011, LECT NOTES COMPUT SC, V6786, P547, DOI 10.1007/978-3-642-21934-4_45
Garcia I, 2009, LECT NOTES COMPUT SC, V5692, P26, DOI 10.1007/978-3-642-03964-5_4
Hao BW, 2022, INT J INTELL SYST, V37, P10880, DOI 10.1002/int.23011
Hsu SW, 2018, ELECTRON COMMER R A, V28, P30, DOI 10.1016/j.elerap.2018.01.006
Jameson A., 2004, 2 METHODS ENHANCING, P447, DOI DOI 10.1145/989863.989948
Jia JJ, 2021, SCI PROGRAMMING-NETH, V2021, DOI 10.1155/2021/1969118
Jin QB, 2020, IEEE ACCESS, V8, P17594, DOI 10.1109/ACCESS.2020.2965595
Karimpour D, 2021, 2021 26TH INTERNATIONAL COMPUTER CONFERENCE, COMPUTER SOCIETY OF IRAN (CSICC), DOI 10.1109/CSICC52343.2021.9420562
Kim JK, 2010, INT J INFORM MANAGE, V30, P212, DOI 10.1016/j.ijinfomgt.2009.09.006
King DL, 2013, CLIN PSYCHOL REV, V33, P331, DOI 10.1016/j.cpr.2013.01.002
Kumar S., 2015, Asian Social Science, V11, P202, DOI [10.5539/ass.v11n2p202, DOI 10.5539/ASS.V11N2P202]
Laughlin PR, 2011, GROUP PROCESS INTERG, V14, P63, DOI 10.1177/1368430210372524
Lee J, 2011, INTERNET RES, V21, P187, DOI 10.1108/10662241111123766
Liao SH, 2021, TECHNOL SOC, V66, DOI 10.1016/j.techsoc.2021.101655
Masthoff J, 2006, USER MODEL USER-ADAP, V16, P281, DOI 10.1007/s11257-006-9008-3
McCarthy J, 2014, INTERNET RES, V24, P181, DOI 10.1108/IntR-08-2012-0154
McCarthy K., 2006, 2006 International Conference on Intelligent User Interfaces, P267, DOI 10.1145/1111449.1111506
McCarthy K, 2006, LECT NOTES ARTIF INT, V4106, P196
Milicevic AK, 2010, ARTIF INTELL REV, V33, P187, DOI 10.1007/s10462-009-9153-2
Morawski J, 2017, INT J INTELL SYST, V32, P1062, DOI 10.1002/int.21884
Mou N., 2018, Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, V24, P1261, DOI [10.13196/j.cims.2018.05.020, DOI 10.13196/J.CIMS.2018.05.020]
Nozari RB, 2020, KNOWL-BASED SYST, V205, DOI 10.1016/j.knosys.2020.106296
Papadakis H, 2022, KNOWL INF SYST, V64, P35, DOI 10.1007/s10115-021-01628-7
Pavlou PA, 2006, INFORM SYST RES, V17, P198, DOI 10.1287/isre.1060.0094
Pérez-Almaguer Y, 2021, EXPERT SYST APPL, V184, DOI 10.1016/j.eswa.2021.115444
Qi JY, 2014, SYST RES BEHAV SCI, V31, P424, DOI 10.1002/sres.2279
Rekha VS, 2018, INFORM SOFTWARE TECH, V101, P51, DOI 10.1016/j.infsof.2018.04.009
Ridings C.M., 2004, J COMPUT-MEDIAT COMM, V10, DOI [10.1111/j.1083-6101.2004.tb00229, DOI 10.1111/J.1083-6101.2004.TB00229.X, 10.1111/j.1083-6101.2004.tb00229.x]
Rocca B., 2019, Introduction to recommender systems: Overview of some major recommendation algorithms
Salehi-Abari A., 2015, Preference-oriented social networks: Group recommendation and inference, P35, DOI DOI 10.1145/2792838.2800190
Samer R, 2020, UMAP'20: PROCEEDINGS OF THE 28TH ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, P221, DOI 10.1145/3340631.3394851
Seo YD, 2021, EXPERT SYST APPL, V183, DOI 10.1016/j.eswa.2021.115396
Seo YD, 2018, EXPERT SYST APPL, V93, P299, DOI 10.1016/j.eswa.2017.10.027
Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259
Song TT, 2017, INFORM MANAGE-AMSTER, V54, P1072, DOI 10.1016/j.im.2017.03.003
Souabi S., 2021, Advances in Science, Technology and Engineering Systems, V6, P719, DOI [10.25046/aj060178, DOI 10.25046/AJ060178]
Srifi M, 2020, INFORMATION, V11, DOI 10.3390/info11060317
Sun LF, 2017, IEEE T MULTIMEDIA, V19, P609, DOI 10.1109/TMM.2016.2635589
Wang W, 2019, IEEE T SYST MAN CY-S, V49, P1152, DOI 10.1109/TSMC.2017.2699690
Wu Y, 2023, IEEE ACCESS, V11, P1854, DOI 10.1109/ACCESS.2022.3224598
Xie XQ, 2016, LECT NOTES COMPUT SC, V9931, P292, DOI 10.1007/978-3-319-45814-4_24
Xu B, 2022, COGN COMPUT, V14, P1055, DOI 10.1007/s12559-022-10001-x
Yin HZ, 2022, IEEE T KNOWL DATA EN, V34, P3447, DOI 10.1109/TKDE.2020.3023787
Yin HZ, 2019, PROC INT CONF DATA, P566, DOI 10.1109/ICDE.2019.00057
Yin XC, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101337
Yuan Q, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P163, DOI 10.1145/2623330.2623616
Yun YD, 2018, J INF SCI, V44, P331, DOI 10.1177/0165551517692955
Zapata A, 2015, INT J HUM-COMPUT ST, V76, P22, DOI 10.1016/j.ijhcs.2014.12.002
Zervas Panagiotis, 2016, Smart Learning Environments, V3, DOI 10.1186/s40561-016-0037-z
Zhang Y, 2017, MOBILE NETW APPL, V22, P1182, DOI 10.1007/s11036-017-0864-3
Zhang Y, 2016, IEEE T SERV COMPUT, V9, P786, DOI 10.1109/TSC.2016.2592520
Zhen Y., 2009, TAGICOFI TAG INFORM, P69, DOI DOI 10.1145/1639714.1639727
Zheng XH, 2019, APPL INTELL, V49, P2623, DOI 10.1007/s10489-018-01406-z
NR 71
TC 0
Z9 0
U1 3
U2 3
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1092-7026
EI 1099-1743
J9 SYST RES BEHAV SCI
JI Syst. Res. Behav. Sci.
PD 2024 FEB 14
PY 2024
DI 10.1002/sres.3000
EA FEB 2024
PG 16
WC Management; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Business & Economics; Social Sciences - Other Topics
GA HU6S4
UT WOS:001162065100001
DA 2024-03-27
ER
PT J
AU Adomavicius, G
Bockstedt, JC
Curley, SP
Zhang, JJ
AF Adomavicius, Gediminas
Bockstedt, Jesse C.
Curley, Shawn P.
Zhang, Jingjing
TI Effects of Online Recommendations on Consumers' Willingness to Pay
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE behavioral economics; electronic commerce; laboratory experiments;
preferences; recommender systems; willingness to pay
ID FINANCIAL INCENTIVES; SYSTEMS; JUDGMENT
AB Recommender systems are an integral part of the online retail environment. Prior research has focused largely on computational approaches to improving recommendation accuracy, and only recently researchers have started to study their behavioral implications and potential side effects. We used three controlled experiments, in the context of purchasing digital songs, to explore the willingness-to-pay judgments of individual consumers after being shown personalized recommendations. In Study 1, we found strong evidence that randomly assigned song recommendations affected participants' willingness to pay, even when controlling for participants' preferences and demographics. In Study 2, participants viewed actual system-generated recommendations that were intentionally perturbed ( introducing recommendation error), and we observed similar effects. In Study 3, we showed that the influence of personalized recommendations on willingness-to-pay judgments was obtained even when preference uncertainty was reduced through immediate and mandatory song sampling prior to pricing. The results demonstrate the existence of important economic side effects of personalized recommender systems and inform our understanding of how system recommendations can influence our everyday preference judgments. The findings have significant implications for the design and application of recommender systems as well as for online retail practices.
C1 [Adomavicius, Gediminas; Curley, Shawn P.] Univ Minnesota, Carlson Sch Management, Informat & Decis Sci, Minneapolis, MN 55455 USA.
[Bockstedt, Jesse C.] Emory Univ, Goizueta Business Sch, Informat Syst & Operat Management, Atlanta, GA 30322 USA.
[Zhang, Jingjing] Indiana Univ, Kelley Sch Business, Operat & Decis Technol, Bloomington, IN 47405 USA.
C3 University of Minnesota System; University of Minnesota Twin Cities;
Emory University; Indiana University System; Indiana University
Bloomington; IU Kelley School of Business
RP Adomavicius, G (autor correspondiente), Univ Minnesota, Carlson Sch Management, Informat & Decis Sci, Minneapolis, MN 55455 USA.
EM gedas@umn.edu; bockstedt@emory.edu; curley@umn.edu; jjzhang@indiana.edu
OI Bockstedt, Jesse/0000-0002-4274-9744; Zhang,
Jingjing/0000-0002-6805-8685
FU Carlson School of Management
FX This work was supported in part by a research grant provided by the
Carlson School of Management.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Adomavicius G, 2013, INFORM SYST RES, V24, P956, DOI 10.1287/isre.2013.0497
Amatriain Xavier, 2012, NETFLIX TECH BL 0405
Ariely D, 2006, J ECON BEHAV ORGAN, V60, P1, DOI 10.1016/j.jebo.2004.10.003
Ariely D, 2003, Q J ECON, V118, P73, DOI 10.1162/00335530360535153
BECKER GM, 1964, BEHAV SCI, V9, P226, DOI 10.1002/bs.3830090304
BOX GEP, 1954, ANN MATH STAT, V25, P484, DOI 10.1214/aoms/1177728717
BOX GEP, 1954, ANN MATH STAT, V25, P290, DOI 10.1214/aoms/1177728786
Camerer CF, 1999, J RISK UNCERTAINTY, V19, P7, DOI 10.1023/A:1007850605129
Chapman GB, 1996, APPL COGNITIVE PSYCH, V10, P519
Chapman GB, 2002, HEURISTICS BIASES PS, P120, DOI DOI 10.1017/CBO9780511808098.008
Chen YB, 2005, MARKET SCI, V24, P218, DOI 10.1287/mksc.1040.0089
Cosley D., 2003, PROC SIGCHI C HUMAN, P585
de Matos MG, 2016, MANAGE SCI, V62, P2563, DOI 10.1287/mnsc.2015.2258
Donaldson C, 1998, APPL ECON, V30, P667, DOI 10.1080/000368498325651
Epley N, 2010, J CONSUM PSYCHOL, V20, P20, DOI 10.1016/j.jcps.2009.12.005
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
JACOWITZ KE, 1995, PERS SOC PSYCHOL B, V21, P1161, DOI 10.1177/01461672952111004
Kahneman D., 2011, Thinking, Fast and Slow
Lanning S., 2007, P KDD CUP WORKSH, P3
Leonard TC, 2008, CONST POLITICAL ECON, V19, P356, DOI 10.1007/s10602-008-9056-2
Lichtenstein S, 2006, CONSTRUCTION OF PREFERENCE, P1, DOI 10.2277/ 0521542200
Marshall M., 2006, Aggregate knowledge raises $5m from Kleiner, on a roll
Mauchly JW, 1940, ANN MATH STAT, V11, P204, DOI 10.1214/aoms/1177731915
Müller H, 2012, MARKET LETT, V23, P73, DOI 10.1007/s11002-011-9137-2
Mussweiler T, 1999, J EXP SOC PSYCHOL, V35, P136, DOI 10.1006/jesp.1998.1364
NORTHCRAFT GB, 1987, ORGAN BEHAV HUM DEC, V39, P84, DOI 10.1016/0749-5978(87)90046-X
Ricci F, 2015, RECOMMENDER SYSTEMS
Salganik MJ, 2008, SOC PSYCHOL QUART, V71, P338, DOI 10.1177/019027250807100404
Salganik MJ, 2006, SCIENCE, V311, P854, DOI 10.1126/science.1121066
Sarwar Badrul, 2001, P 10 INT C WORLD WID, P285, DOI 10.1145/371920.372071
SCHKADE DA, 1989, ORGAN BEHAV HUM DEC, V44, P203, DOI 10.1016/0749-5978(89)90025-3
Shapiro C., 1998, Information Rules: A Strategic Guide to the Network Economy
Surowiecki J., 2004, WISDOM CROWDS WHY MA
Tucker CE, 2011, MANAGE SCI, V57, P828, DOI 10.1287/mnsc.1110.1312
TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
TVERSKY A, 1988, PSYCHOL REV, V95, P371, DOI 10.1037/0033-295X.95.3.371
Wilson TD, 1996, J EXP PSYCHOL GEN, V125, P387, DOI 10.1037/0096-3445.125.4.387
WRIGHT WF, 1989, ORGAN BEHAV HUM DEC, V44, P68, DOI 10.1016/0749-5978(89)90035-6
Zhang JJ, 2012, MANAGE SCI, V58, P892, DOI 10.1287/mnsc.1110.1459
NR 41
TC 66
Z9 75
U1 19
U2 219
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD MAR
PY 2018
VL 29
IS 1
BP 84
EP 102
DI 10.1287/isre.2017.0703
PG 19
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA FZ6OM
UT WOS:000427718900006
DA 2024-03-27
ER
PT J
AU Nguyen, VD
Sriboonchitta, S
Huynh, VN
AF Van-Doan Nguyen
Sriboonchitta, Songsak
Van-Nam Huynh
TI Using community preference for overcoming sparsity and cold-start
problems in collaborative filtering system offering soft ratings
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Recommender systems; E-commerce; Soft ratings; Community preferences;
Dempster-Shafer theory
ID BELIEF-THEORETIC APPROACH; RECOMMENDER SYSTEMS; TRUST; CLASSIFICATION;
ALLEVIATE; ALGORITHM
AB This paper introduces a new collaborative filtering recommender system that is capable of offering soft ratings as well as integrating with a social network containing all users. Offering soft ratings is known as a new methodology for modeling subjective, qualitative, and imperfect information about user preferences, as well as a more realistic and flexible means for users to express their preferences on products and services. Additionally, in the system, community preferences that are extracted from the social network are employed for overcoming sparsity and cold-start problems. In the experiment, the new system is tested using a data set culled from Flixster, a social network focused on movies. The experiment's results show that this system is more effective than the selected baseline in terms of recommendation accuracy. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Van-Doan Nguyen; Van-Nam Huynh] Japan Adv Inst Sci & Technol, Nomi, Japan.
[Sriboonchitta, Songsak] Chiang Mai Univ, Chiang Mai, Thailand.
C3 Japan Advanced Institute of Science & Technology (JAIST); Chiang Mai
University
RP Nguyen, VD (autor correspondiente), Japan Adv Inst Sci & Technol, Nomi, Japan.
EM nvdoan@jaist.ac.jp
RI Huynh, Van/GSM-7997-2022
OI yamaka, woraphon/0000-0002-0787-1437; Nguyen, Doan/0000-0001-5834-3709
FU JSPS KAKENHI [25240049]; Grants-in-Aid for Scientific Research
[25240049] Funding Source: KAKEN
FX This research work was supported by JSPS KAKENHI Grant No. 25240049.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
[Anonymous], SYNTHESIS LECT DATA
[Anonymous], P 13 PAC RIM INT C
Barjasteh Iman., 2015, RecSys, P91, DOI DOI 10.1145/2792838.2800196
Bauer J, 2014, DECIS SUPPORT SYST, V68, P77, DOI 10.1016/j.dss.2014.09.005
Bloch I, 1996, PATTERN RECOGN LETT, V17, P905, DOI 10.1016/0167-8655(96)00039-6
DEMPSTER AP, 1967, ANN MATH STAT, V38, P325, DOI 10.1214/aoms/1177698950
Ghazanfar MA, 2014, EXPERT SYST APPL, V41, P3261, DOI 10.1016/j.eswa.2013.11.010
Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799
Grcar M., 2005, Advances in Web Mining and Web Usage Analysis. 7th International Workshop on Knowledge Discovery on the Web, WebKDD 2005. Revised Papers (Lecture Notes in Artificial Intelligence Vol. 4198), P58
Gregory S, 2008, LECT NOTES ARTIF INT, V5211, P408, DOI 10.1007/978-3-540-87479-9_45
Gregory S, 2007, LECT NOTES ARTIF INT, V4702, P91
Guo GB, 2015, AAAI CONF ARTIF INTE, P123
Guo GB, 2014, KNOWL-BASED SYST, V57, P57, DOI 10.1016/j.knosys.2013.12.007
Herlocker JL, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P230, DOI 10.1145/312624.312682
Huang Z, 2004, ACM T INFORM SYST, V22, P116, DOI 10.1145/963770.963775
Hwang SY, 2010, ELECTRON COMMER R A, V9, P323, DOI 10.1016/j.elerap.2010.01.001
Jannach D, 2012, LECT NOTES BUS INF P, V123, P76
Jiang CQ, 2015, DECIS SUPPORT SYST, V79, P195, DOI 10.1016/j.dss.2015.09.002
Jierui Xie, 2012, Advances in Knowledge Discovery and Data Mining. Proceedings 16th Pacific-Asia Conference (PAKDD 2012), P25, DOI 10.1007/978-3-642-30220-6_3
Kim HN, 2010, ELECTRON COMMER R A, V9, P73, DOI 10.1016/j.elerap.2009.08.004
Kim HK, 2011, ELECTRON COMMER R A, V10, P501, DOI 10.1016/j.elerap.2011.03.002
Kim J.K., 2002, Electronic Commerce Research and Applications, V1, P301, DOI DOI 10.1016/S1567-4223(02)00022-4
Konstas I, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P195, DOI 10.1145/1571941.1571977
Li YM, 2013, DECIS SUPPORT SYST, V55, P740, DOI 10.1016/j.dss.2013.02.009
Lika B, 2014, EXPERT SYST APPL, V41, P2065, DOI 10.1016/j.eswa.2013.09.005
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103
Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133
Nguyen VD, 2016, LECT NOTES COMPUT SC, V9795, P170, DOI 10.1007/978-3-319-42345-6_15
Palla G, 2005, NATURE, V435, P814, DOI 10.1038/nature03607
Papagelis M, 2005, LECT NOTES COMPUT SC, V3477, P224
Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
Rohitha KK, 2007, IEEE T SYST MAN CY B, V37, P1446, DOI 10.1109/TSMCB.2007.903536
Shafer G., 1976, A mathematical theory of evidence, V76
Shambour Q, 2011, INT J INTELL SYST, V26, P814, DOI 10.1002/int.20495
SMETS P, 1994, ARTIF INTELL, V66, P191, DOI 10.1016/0004-3702(94)90026-4
Smets P, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P612
Sun ZB, 2015, J SYST SOFTWARE, V99, P109, DOI 10.1016/j.jss.2014.09.019
Nguyen VD, 2015, LECT NOTES ARTIF INT, V9161, P429, DOI 10.1007/978-3-319-20807-7_39
Wickramarathne TL, 2011, IEEE T KNOWL DATA EN, V23, P175, DOI 10.1109/TKDE.2010.88
Wu H, 2016, KNOWL-BASED SYST, V97, P111, DOI 10.1016/j.knosys.2016.01.011
NR 42
TC 21
Z9 22
U1 3
U2 29
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD NOV-DEC
PY 2017
VL 26
BP 101
EP 108
DI 10.1016/j.elerap.2017.10.002
PG 8
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA FN8MF
UT WOS:000416278500010
DA 2024-03-27
ER
PT J
AU Vézina, R
Militaru, D
AF Vézina, R
Militaru, D
TI Collaborative filtering:: theoretical positions and a research agenda in
marketing
SO INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT
LA English
DT Article
DE electronic commerce; collaborative filtering; recommender systems;
marketing; experimentation
ID INFORMATION
AB Internet offers many choices of products, services and content. But the Multitude of choices has made it more difficult for customers to find quickly what they are looking for. Collaborative Filtering (CF), or recommender system based-CF, is a methodology designed to perform such a recommendation task. These systems allow users to use expressed preferences of thousands of other people to find the product they desire, based on the level of similarity between tastes.
The concept has emerged from convergent research on search browsers, intelligent agents and data mining, and it permits to escape the difficult question of 'why' consumers prefer a particular product or brand. Furthermore, CF is open for the end-user and allows customers to discover things within an information environment that they probably never would have discovered otherwise. On a more practical perspective, CF through internet allows us to focus exclusively on the similarity of preferences without 'social contamination': the consumer obtains recommendations to purchase a given product or brand on the basis of his or her own past preferences and on the basis of the preferences of a large group of anonymous consumers.
In this paper, we will review the Current state of research in consumer behaviour that provides the theoretical foundations underlying collaborative filtering. Then we will propose a research agenda in marketing keeping in mind the perspective of users i.e. consumers or marketers.
C1 Univ Laval, Fac Sci Adm, Dept Mkt, Quebec City, PQ G1K 7P4, Canada.
GRID, F-94235 Cachan, France.
C3 Laval University
RP Univ Laval, Fac Sci Adm, Dept Mkt, Quebec City, PQ G1K 7P4, Canada.
EM Richard.Vezina@fsa.ulaval.ca; militaru@grid.ens-cachan.fr
CR [Anonymous], 1998, MSRTR9812
[Anonymous], 1971, CONSUMER DEMAND NEW
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
ARNDT J, 1967, RISK TAING INFORMATI
Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124
BELK RW, 1971, P 2ND ANN C ASS CONS, P470
Bhargava HK, 1999, COMPUTER, V32, P31, DOI 10.1109/2.751326
BILLSUS D, 1999, P 7 INT C US MOD JUN
BIONDO J, 1971, J PERS, V39, P407, DOI 10.1111/j.1467-6494.1971.tb00051.x
BONE PF, 1992, ADV CONSUM RES, V19, P579
BREHM JW, 1989, ADV CONSUM RES, V16, P72
Bristor J., 1990, RES CONSUMER BEHAV, V4, P51
BURKE R, 1999, WORKSH AI EL COMM AA
BURKE R, 2000, ENCY LIB INFORMATION, V69
BURNSTEIN E, 1977, J EXP SOC PSYCHOL, V13, P315, DOI 10.1016/0022-1031(77)90002-6
BURNSTEIN E, 1975, J EXP SOC PSYCHOL, V11, P412, DOI 10.1016/0022-1031(75)90045-1
BURNSTEIN E, 1973, J EXP SOC PSYCHOL, V9, P236, DOI 10.1016/0022-1031(73)90012-7
CARPENTER GS, 1994, J MARKETING RES, V31, P339, DOI 10.2307/3152221
CECI SJ, 1982, PUBLIC OPIN QUART, V46, P228, DOI 10.1086/268715
Dubois B., 1999, EUROPEAN ADV CONSUME, VIV, P201
Eliashberg J, 1997, J MARKETING, V61, P68, DOI 10.2307/1251831
EZINA R, 1997, MARKETNG PROGR PROSP, P1300
FEICK LF, 1986, ADV CONSUM RES, V13, P301
GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867
GOLDBERG K, 2000, M0041 UCB EL RES LAB
Good N, 1999, SIXTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-99)/ELEVENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE (IAAI-99), P439
HERLOCKER JL, 2000, CSCW 00
HOFFMAN T, 1999, P 16 INT JOINT C ART, P688
LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131
MCKIM R, 2000, J DATABASE MARKETING, V7, P385
MYERS JH, 1972, J MARKETING RES, V9, P41, DOI 10.2307/3149604
NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630
RESICK P, 1994, P ACM 1994 C COMP SU, P175
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Rich E., 1979, Cognitive Science, V3, P335
Richins MarshaL., 1988, ADV CONSUM RES, V15, P32
Rosen E., 2000, The Anatomy of Buzz
SARWAR B, 2000, EC 00
SHARDANAND U, 1995, SOCIAL INFORMATION F
Tepper K, 1997, RES CON BEH, V8, P209
Vapnik V.N., 1998, STAT LEARNING THEORY, P732
VOLLE M, 2000, E CONOMIE
Wilkie W.L., 1991, PERSPECTIVES CONSUME, P1
NR 43
TC 9
Z9 10
U1 1
U2 12
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 0267-5730
EI 1741-5276
J9 INT J TECHNOL MANAGE
JI Int. J. Technol. Manage.
PY 2004
VL 28
IS 1
BP 31
EP 45
DI 10.1504/IJTM.2004.005051
PG 15
WC Engineering, Multidisciplinary; Management; Operations Research &
Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
Science
GA 853DQ
UT WOS:000223807100003
DA 2024-03-27
ER
PT J
AU Mushtaque, U
Pazour, JA
AF Mushtaque, Uzma
Pazour, Jennifer A.
TI Assortment optimization under cardinality effects and novelty for
unequal profit margin items
SO JOURNAL OF REVENUE AND PRICING MANAGEMENT
LA English
DT Article
DE Recommender Systems; Novelty; Diversity; Assortment Optimization;
Consideration Sets; Online Retail
AB This work focuses on assortment optimization problems concerned with the three ways a recommender system increases conversion rates: (1) improve accuracy, (2) provide consideration sets and (3) introduce novelty and diversity. To do so, we introduce a new random utility-based model, which in addition to item and user attributes, captures context effects, as well as the need to introduce novel items. We use this new model and an existing random utility model to study assortment optimization problems in which the value that a customer derives from an assortment reaches a maximum and then begins to decline with increase in size. We focus on an online retail environment, in which a trade-off exists between recommending items with high profit margins, but low consumer choice probability. We present polynomial-time algorithms to solve both optimization models. We computationally analyze the performance of the algorithms using real-world online transactional data.
C1 [Mushtaque, Uzma; Pazour, Jennifer A.] Rensselaer Polytech Inst, 110 8th St, Troy, NY 12180 USA.
C3 Rensselaer Polytechnic Institute
RP Mushtaque, U (autor correspondiente), Rensselaer Polytech Inst, 110 8th St, Troy, NY 12180 USA.
EM mushtu@rpi.edu; Pazouj@rpi.edu
FU NSF (National Science Foundation) [1550532]; Directorate For
Engineering; Div Of Civil, Mechanical, & Manufact Inn [1550532] Funding
Source: National Science Foundation
FX This work is supported by NSF (National Science Foundation) Award
Number: 1550532.
CR Aljukhadar M, 2012, INT J ELECTRON COMM, V17, P41, DOI 10.2753/JEC1086-4415170202
[Anonymous], 2011, Proceedings of International Workshop on Diversity in Document Retrieval (DDR)
Ben-Akiva, 1984, TRANSPORT POLICY DEC, V4
Berry D, 2018, P 2018 IISE ANN C
Besbes O, 2016, PROD OPER MANAG, V25, P114, DOI 10.1111/poms.12402
Bollen D., 2010, P 4 ACM C REC SYST, P63, DOI [10.1145/1864708.1864724, DOI 10.1145/1864708.1864724]
Cachon G. P., 2005, Manufacturing & Service Operations Management, V7, P330, DOI 10.1287/msom.1050.0088
Castells P, 2015, RECOMMENDER SYSTEMS, DOI DOI 10.1007/978-1-4899-7637-6_26
Chaptini BassamH., 2005, USE DISCRETE CHOICE
Chen D., 2012, J DATABASE MARKETING, V19, P197, DOI DOI 10.1057/dbm.2012.17
Chen YC, 2009, ELECTRON COMMER R A, V8, P48, DOI 10.1016/j.elerap.2008.09.001
Echenique F, 2018, MATH SOC SCI, V93, P67, DOI 10.1016/j.mathsocsci.2018.02.004
Edmunds A, 2000, INT J INFORM MANAGE, V20, P17, DOI 10.1016/S0268-4012(99)00051-1
Eppler MJ, 2004, INFORM SOC, V20, P325, DOI 10.1080/01972240490507974
Flores A, 2019, EUR J OPER RES, V273, P1052, DOI 10.1016/j.ejor.2018.08.047
Gallego G, 2015, OPER RES, V63, P212, DOI 10.1287/opre.2014.1328
Garaus M, 2016, J BUS RES, V69, P3459, DOI 10.1016/j.jbusres.2016.01.040
Goyal V, 2016, OPER RES, V64, P219, DOI 10.1287/opre.2015.1450
Gross R., 2014, International Journal of Business Management Economic Research, V5, P64
Hahsler M., 2011, RECOMMENDERLAB FRAME, P1
Hauser JR, 2014, J BUS RES, V67, P1688, DOI 10.1016/j.jbusres.2014.02.015
Tuu HH, 2013, ASIA PAC J MARKET LO, V25, P590, DOI 10.1108/APJML-09-2012-0102
Holton AE, 2012, CYBERPSYCH BEH SOC N, V15, P619, DOI 10.1089/cyber.2011.0610
Huang SL, 2011, ELECTRON COMMER R A, V10, P398, DOI 10.1016/j.elerap.2010.11.003
Hübner A, 2016, EUR J OPER RES, V250, P505, DOI 10.1016/j.ejor.2015.11.007
Hunter, 2005, J PERSONAL SELLING S, V24, P91
Hunter G.L., 2004, J PERS SELL SALES M, V24, P91, DOI DOI 10.2753/PSS0885-3134260201
JACOBY J, 1977, J MARKETING RES, V14, P569, DOI 10.2307/3151201
JACOBY J, 1974, J MARKETING RES, V11, P63, DOI 10.2307/3150994
KAMAKURA WA, 1984, J MARKETING RES, V21, P420, DOI 10.2307/3151468
Kivetz R, 2004, J MARKETING RES, V41, P262, DOI 10.1509/jmkr.41.3.262.35993
Kivetz R, 2004, J MARKETING RES, V41, P237, DOI 10.1509/jmkr.41.3.237.35990
Kök AG, 2015, INT SER OPER RES MAN, V223, P175, DOI 10.1007/978-1-4899-7562-1_8
Konstan JA, 2012, USER MODEL USER-ADAP, V22, P101, DOI 10.1007/s11257-011-9112-x
Laker LF, 2018, PROD OPER MANAG, V27, P2213, DOI 10.1111/poms.12777
LANCASTER K, 1990, MARKET SCI, V9, P189, DOI 10.1287/mksc.9.3.189
LANCASTER KJ, 1966, J POLIT ECON, V74, P132, DOI 10.1086/259131
Li CY, 2016, TELEMAT INFORM, V33, P834, DOI 10.1016/j.tele.2015.12.007
Li MX, 2016, INFORM MANAGE-AMSTER, V53, P467, DOI 10.1016/j.im.2015.11.002
Louviere JJ, 2008, J CHOICE MODEL, V1, P128
Manrai AK, 1998, EUR J OPER RES, V111, P193, DOI 10.1016/S0377-2217(98)00145-3
Mehta N, 2003, MARKET SCI, V22, P58, DOI 10.1287/mksc.22.1.58.12849
NEMHAUSER GL, 1978, MATH PROGRAM, V14, P265, DOI 10.1007/BF01588971
Noone BM, 2016, J REVENUE PRICING MA, V15, P264, DOI 10.1057/rpm.2016.8
Park JY, 2013, TOURISM MANAGE, V35, P1, DOI 10.1016/j.tourman.2012.05.004
Pazour, 2020, IISE 2020 C P
Pazour J., 2020, J REVENUE PRICING MA
Ramdas K, 2003, PROD OPER MANAG, V12, P79, DOI 10.1111/j.1937-5956.2003.tb00199.x
Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
Rieskamp J, 2006, J ECON LIT, V44, P631, DOI 10.1257/jel.44.3.631
Rooderkerk RP, 2011, J MARKETING RES, V48, P767, DOI 10.1509/jmkr.48.4.767
Sarwar B., 2000, EC'00. Proceedings of the 2nd ACM Conference on Electronic Commerce, P158, DOI 10.1145/352871.352887
Sicilia M, 2010, ELECTRON COMMER R A, V9, P183, DOI 10.1016/j.elerap.2009.03.004
Speier-Pero, 2019, J DECIS SYST, P1
Talluri K, 2004, MANAGE SCI, V50, P15, DOI 10.1287/mnsc.1030.0147
Todd PM, 2003, J ECON PSYCHOL, V24, P143, DOI 10.1016/S0167-4870(02)00200-3
Topaloglu, 2019, ASSORTMENT OPTIMIZAT
Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1, DOI 10.1017/CBO9780511805271
Tseng VS, 2013, IEEE T KNOWL DATA EN, V25, P1772, DOI 10.1109/TKDE.2012.59
van Ryzin G, 1999, MANAGE SCI, V45, P1496, DOI 10.1287/mnsc.45.11.1496
Vargas S., 2011, P 5 ACM C REC SYST, P109, DOI DOI 10.1145/2043932.2043955
Vargas S, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1281, DOI 10.1145/2600428.2610382
Wang RX, 2013, J REVENUE PRICING MA, V12, P254, DOI 10.1057/rpm.2012.40
Weber EU, 2009, ANNU REV PSYCHOL, V60, P53, DOI 10.1146/annurev.psych.60.110707.163633
Working H., 1929, J Am Stat Assoc, V24, P73, DOI [10.1080/01621459.1929.10506274, DOI 10.1080/01621459.1929.10506274]
Yücel E, 2009, EUR J OPER RES, V199, P759, DOI 10.1016/j.ejor.2008.08.004
Zhou T, 2010, P NATL ACAD SCI USA, V107, P4511, DOI 10.1073/pnas.1000488107
NR 67
TC 2
Z9 2
U1 1
U2 11
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1476-6930
EI 1477-657X
J9 J REVENUE PRICING MA
JI J. Revenue Pricing Manag.
PD FEB
PY 2022
VL 21
IS 1
BP 106
EP 126
DI 10.1057/s41272-020-00279-7
EA FEB 2021
PG 21
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA ZA0VQ
UT WOS:000617401900001
DA 2024-03-27
ER
PT J
AU Pollmann, K
Loh, W
Fronemann, N
Ziegler, D
AF Pollmann, Kathrin
Loh, Wulf
Fronemann, Nora
Ziegler, Daniel
TI Entertainment vs. manipulation: Personalized human-robot interaction
between user experience and ethical design
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Human -robot interaction; User experience; Ethical design;
personalization; Design patterns
ID ANTHROPOMORPHISM; PATERNALISM; CARE
AB A personalized human-robot interaction (HRI) can increase the acceptance of robots through positive effects on the user experience (UX), as well as the user's attitude towards and perception of the robot. From an ethical perspective, however, personalized HRI poses certain risks with regard to autonomy and manipulation of the users. Taking the scenario of a personalized quizmaster robot as an example, this paper combines the usercentered design of a personalized robot behavior with ethical design perspectives. Based on motivation strategies of the robot quiz master, the paper assesses and generalizes which interaction behaviors may be ethically permissible and at the same time enjoyable, engaging and motivating. Balancing the two perspectives of UX and ethical design, we propose transferable recommendations for the design of personalized HRI based on the approach of cascading models of design.
C1 [Pollmann, Kathrin; Fronemann, Nora; Ziegler, Daniel] Inst Ind Engn, Fraunhofer IAO, Stuttgart, Germany.
[Loh, Wulf] Univ Tubingen, Ctr Ethics Sci & Humanities IZEW, Tubingen, Germany.
[Pollmann, Kathrin] Nobelstr 12, D-70563 Stuttgart, Germany.
C3 Eberhard Karls University of Tubingen
RP Pollmann, K (autor correspondiente), Nobelstr 12, D-70563 Stuttgart, Germany.
EM kathrin.pollmann@iao.fraunhofer.de
RI Loh, Wulf/AAG-9989-2021
OI Loh, Wulf/0000-0001-7643-1614
FU German Federal Ministry for Edu- cation and Research (BMBF) [16SV7941,
16SV7944]
FX Funding This research was funded by the German Federal Ministry for Edu-
cation and Research (BMBF; 16SV7941, 16SV7944) .
CR Aaltonen I, 2017, ACMIEEE INT CONF HUM, P53, DOI 10.1145/3029798.3038362
Alonso-Martín F, 2015, SENSORS-BASEL, V15, P15799, DOI 10.3390/s150715799
[Anonymous], 2017, EUPRESOLUTION2015210
[Anonymous], 2008, P 7 INT JOINT C AUTO
[Anonymous], 2018, 9241112018 DIN EN IS
[Anonymous], 2000, What We Owe to Each Other, DOI 10.2307/j.ctv134vmrn
Bartneck C, 2004, RO-MAN 2004: 13TH IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, PROCEEDINGS, P591, DOI 10.1109/ROMAN.2004.1374827
Beauchamp TL., 2019, PRINCIPLES BIOMEDICA
Bosse C, 2013, ROBOETHICS, P30
Breazeal C, 2003, INT J HUM-COMPUT ST, V59, P119, DOI 10.1016/S1071-5819(03)00018-1
Breazeal C, 2002, MU S ART SOC SIM ORG, V3, P149
Breazeal C., 2004, Designing Sociable Robots
Brooks AndrewG., 2004, COMPUT ENTERTAIN, V2, P10, DOI DOI 10.1145/1027154.1027171
Bryson JJ, 2018, ETHICS INF TECHNOL, V20, P15, DOI 10.1007/s10676-018-9448-6
Calo MR, 2012, INTELL ROBOT AUTON, P187
Churamani N, 2017, PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON HUMAN AGENT INTERACTION (HAI'17), P171, DOI 10.1145/3125739.3125756
Coeckelbergh M, 2016, AI SOC, V31, P455, DOI 10.1007/s00146-015-0626-3
Coons Christian, 2014, Manipulation: Theory and practice
Darling Kate, 2016, ROBOT LAW
DARWALL SL, 1977, ETHICS, V88, P36, DOI 10.1086/292054
Dautenhahn K, 2004, RO-MAN 2004: 13TH IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, PROCEEDINGS, P17, DOI 10.1109/ROMAN.2004.1374720
Dautenhahn K, 2002, IEEE ROMAN 2002, PROCEEDINGS, P192, DOI 10.1109/ROMAN.2002.1045621
Davidson D. L., 1980, Electron Microscopy and Analysis, 1979, P21
Denning T, 2009, UBICOMP'09: PROCEEDINGS OF THE 11TH ACM INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING, P105
Drury J.L., 2004, CHI 04 EXTENDED ABST
Duffy BR, 2003, ROBOT AUTON SYST, V42, P177, DOI 10.1016/S0921-8890(02)00374-3
DWORKIN G, 1972, MONIST, V56, P64, DOI 10.5840/monist197256119
Dworkin Gerald., 1988, THEORY PRACTICE AUTO
Edwards A, 2019, COMPUT HUM BEHAV, V90, P308, DOI 10.1016/j.chb.2018.08.042
Ermi L., 2005, Fundamental components of the gameplay experience: Analysing immersion
Eyal Nir, 2014, Hooked: How to Build Habit-Forming Products, V1st
Fan HY, 2006, J ORG COMP ELECT COM, V16, P179, DOI 10.1207/s15327744joce1603&4_2
Fischer A., 2017, MANIPULATION
Forlizzi J., 2007, 2007 2nd Annual Conference on Human-Robot Interaction (HRI), P129
FRANKENA WK, 1986, PHILOS TOPICS, V14, P149, DOI 10.5840/philtopics19861428
Frankfurt Harry., 1987, RESPONSIBILITY CHARA
Fronemann N, 2022, AI SOC, V37, P517, DOI 10.1007/s00146-021-01210-3
Fronemann N, 2014, PROCEEDINGS OF THE NORDICHI'14: THE 8TH NORDIC CONFERENCE ON HUMAN-COMPUTER INTERACTION: FUN, FAST, FOUNDATIONAL, P727, DOI 10.1145/2639189.2641203
Gorin Moti, 2014, Manipulation: Theory and Practice, V2014, P73
Gransche B., 2014, WANDEL AUTONOMIE KON
Guggemos J, 2020, BRIT J EDUC TECHNOL, V51, P1864, DOI 10.1111/bjet.13006
Gunkel DJ, 2018, ROBOT RIGHTS
Hassenzahl M., 2007, COMMUNICATION PR SEN, P10
Hellman Deborah, 2008, When Is Discrimination Wrong?
Honneth Axel., 1996, The struggle for recognition: moral grammar of social conflicts, V1st
HUSAK DN, 1981, PHILOS PUBLIC AFF, V10, P27
Johnson DO, 2016, INT J SOC ROBOT, V8, P247, DOI 10.1007/s12369-015-0331-x
Karami AB, 2013, PROC INT C TOOLS ART, P626, DOI 10.1109/ICTAI.2013.98
Kidd CD, 2008, 2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS, P3230, DOI 10.1109/IROS.2008.4651113
Kirby R, 2010, ROBOT AUTON SYST, V58, P322, DOI 10.1016/j.robot.2009.09.015
Kobsa A, 2001, USER MODEL USER-ADAP, V11, P49, DOI 10.1023/A:1011187500863
Korsgaard Christine., 2009, SELF CONSTITUTION AG
Lamers M.H., 2011, LECT NOTES I COMPUTE, V59, DOI [10.1007/978-3-642-19385-9, DOI 10.1007/978-3-642-19385-9]
Leite Iolanda, 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, P669, DOI 10.1109/ROMAN.2009.5326256
Loh J., 2022, SOCIAL ROBOTICS GOOD
Looije R., 2008, J PHYS AGENTS, V2, P13, DOI [10.14198/JoPha.2008.2.2.03, DOI 10.14198/JOPHA.2008.2.2.03]
McGonical Jane., 2011, REALITY IS BROKEN WH
Millar J, 2015, IEEE TECHNOL SOC MAG, V34, P47, DOI 10.1109/MTS.2015.2425612
Nielsen J., 1994, USABILITY ENG
Nishimuta I, 2017, PROCEEDINGS 2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), P1787, DOI 10.1109/CSCI.2017.313
Noggle R., 2018, ETHICS MANIPULATION
Norman D. A., 2013, DESIGN EVERYDAY THIN, DOI DOI 10.15358/9783800648108
Ötting SK, 2022, HUM FACTORS, V64, P1027, DOI 10.1177/0018720820966433
Pollmann Kathrin, 2020, MuC'20: Proceedings of the Conference on Mensch und Computer, P485, DOI 10.1145/3404983.3410414
Pollmann K, 2021, MULTIMODAL TECHNOLOG, V5, DOI 10.3390/mti5090049
Pontes HM, 2021, INT J MENT HEALTH AD, V19, P508, DOI 10.1007/s11469-019-00088-z
Poole S., 2000, TRIGGER HAPPY VIDEOG
Rawls J, 1971, THEORY JUSTICE
Reiss S, 2004, REV GEN PSYCHOL, V8, P179, DOI 10.1037/1089-2680.8.3.179
Reyes RSJ, 2020, 2020 THE 5TH INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS ENGINEERING (ICCRE 2020), P25, DOI [10.1109/iccre49379.2020.9096267, 10.1109/ICCRE49379.2020.9096267]
Ritschel H, 2017, IEEE ROMAN, P378, DOI 10.1109/ROMAN.2017.8172330
RUDINOW J, 1978, ETHICS, V88, P338, DOI 10.1086/292086
Ryan Marie-Laure., 2001, NARRATIVE VIRTUAL RE
Scheutz M, 2012, INTELL ROBOT AUTON, P205
Seibt J., 2016, P ROBOPHILOSOPHY 201, V290
Sheldon KM, 2001, J PERS SOC PSYCHOL, V80, P325, DOI 10.1037/0022-3514.80.2.325
Sorell T, 2014, ETHICS INF TECHNOL, V16, P183, DOI 10.1007/s10676-014-9344-7
Starcevic V., 2017, Current Addiction Reports, V4, P317, DOI DOI 10.1007/S40429-017-0158-7
Sudhkar RS, 2015, 1ST INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION ICCUBEA 2015, P661, DOI 10.1109/ICCUBEA.2015.135
Susser D, 2019, INTERNET POLICY REV, V8, DOI 10.14763/2019.2.1410
Syrdal Dag Sverre, 2007, 16th IEEE International Conference on Robot and Human Interactive Communication, P1143
Tanaka F, 2011, ACMIEEE INT CONF HUM, P265, DOI 10.1145/1957656.1957763
Thompson C, 2017, 2017 IEEE 5TH INTERNATIONAL SYMPOSIUM ON ROBOTICS AND INTELLIGENT SENSORS (IRIS), P173, DOI 10.1109/IRIS.2017.8250117
Thunberg S, 2020, ACMIEEE INT CONF HUM, P482, DOI 10.1145/3371382.3378294
Turkle S., 2010, Close engagements with artificial companions: Key social, psychological, ethical and design issues, P3, DOI DOI 10.1075/NLP.8.03TUR
Vorderer P., 2006, PLAYING VIDEO GAMES
Wada K, 2007, IEEE T ROBOT, V23, P972, DOI 10.1109/TRO.2007.906261
Weber, 2014, MANIPULATION THEORY, P17, DOI DOI 10.1093/ACPROF:OSO/9780199338207.003.0002
Williams B., 1985, Ethics and the Limits of Philosophy
Williams Bernard., 1981, Moral Luck, P101
Yogeeswaran K, 2016, J HUM-ROBOT INTERACT, V5, P29, DOI 10.5898/JHRI.5.2.Yogeeswaran
Ziegler D., 2019, MENSCH COMPUTER 2019
NR 92
TC 5
Z9 5
U1 22
U2 40
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD APR
PY 2023
VL 189
AR 122376
DI 10.1016/j.techfore.2023.122376
EA FEB 2023
PG 13
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA 9R7JE
UT WOS:000945823800001
OA hybrid
DA 2024-03-27
ER
PT J
AU Chandrashekhar, H
Bhasker, B
AF Chandrashekhar, Hemalatha
Bhasker, Bharat
TI PERSONALIZED RECOMMENDER SYSTEM USING ENTROPY BASED COLLABORATIVE
FILTERING TECHNIQUE
SO JOURNAL OF ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Recommender System; Collaborative Filtering; Personalization; Entropy;
Ecommerce
ID OF-THE-ART; E-COMMERCE
AB This paper introduces a novel collaborative filtering recommender system for ecommerce which copes reasonably well with the ratings sparsity issue through the use of the notion of selective predictability and the use of the information theoretic measure known as entropy to estimate the same. It exploits the predictable portion(s) of apparently complex relationships between users when picking out mentors for an active user. The potential of the proposed approach in providing novel as well as good quality recommendations have been demonstrated through comparative experiments on popular datasets such as MovieLens and Jester. The approach. s additional capability to come up with explanations for its recommendations will enhance the user. s comfort level in accepting the personalized recommendations.
C1 [Chandrashekhar, Hemalatha] Indian Inst Management, Ranchi, Bihar, India.
[Bhasker, Bharat] Indian Inst Management, Lucknow, Uttar Pradesh, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
Management Ranchi; Indian Institute of Management (IIM System); Indian
Institute of Management Lucknow
RP Chandrashekhar, H (autor correspondiente), Indian Inst Management, Ranchi, Bihar, India.
EM hemalatha@iimranchi.ac.in; bhasker@iiml.ac.in
RI Bhasker, Bharat/Q-5025-2019; Chandrashekhar, Hemalatha/AAH-5434-2020
OI Bhasker, Bharat/0000-0002-7128-1423
CR Adolphs C, 2010, J ELECTRON COMMER RE, V11, P326
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Aggarwal C. C., 1999, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P201, DOI [DOI 10.1145/312129.312230, 10.1145/312129.312230]
Bell R. M., 2007, P 13 ACM SIGKDD INT
Bell Robert M., 2007, P KDD CUP WORKSH 13, P7, DOI DOI 10.1007/S007790170019
Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
Cho YH, 2002, EXPERT SYST APPL, V23, P329, DOI 10.1016/S0957-4174(02)00052-0
Delgado J., 1999, P ACM SIGIR 99 WORKS
Desmarais-Frantz A, 2005, INT CONF E BUS ENG, P229, DOI 10.1109/ICEBE.2005.39
GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Herlocker JL, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P230, DOI 10.1145/312624.312682
Hill W., 1995, Human Factors in Computing Systems. CHI'95 Conference Proceedings, P194
Huang Z, 2007, IEEE INTELL SYST, V22, P68, DOI 10.1109/MIS.2007.4338497
Kim J.K., 2002, Electronic Commerce Research and Applications, V1, P301, DOI DOI 10.1016/S1567-4223(02)00022-4
Koren Y, 2008, 14 ACM SIGKDD INT C
Lee WP, 2002, EXPERT SYST APPL, V22, P275, DOI 10.1016/S0957-4174(02)00015-5
Palanivel K, 2010, J ELECTRON COMMER RE, V11, P140
Resnick P., 1994, Transcending Boundaries, CSCW '94. Proceedings of the Conference on Computer Supported Cooperative Work, P175, DOI 10.1145/192844.192905
Sarwar B., 2001, P 10 INT C WORLD WID, P285
Schickel-Zuber V., 2006, P 21 NAT C ART INT, V2, P1413
Schickel-Zuber V, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P599
SHANNON CE, 1948, BELL SYST TECH J, V27, P379, DOI 10.1002/j.1538-7305.1948.tb01338.x
Shardanand U., 1995, Human Factors in Computing Systems. CHI'95 Conference Proceedings, P210, DOI 10.1145/223904.223931
Srikumar K., 2005, INT J ELECT BUSINESS, V3, P4
Yang Y., 2005, Journal of Electronic Commerce Research, V6, P112
NR 26
TC 22
Z9 23
U1 1
U2 16
PU CALIFORNIA STATE UNIV
PI LONG BEACH
PA COLL BUSINESS, LONG BEACH, CA 90840 USA
SN 1526-6133
EI 1938-9027
J9 J ELECTRON COMMER RE
JI J. Electron. Commer. Res.
PY 2011
VL 12
IS 3
BP 214
EP 237
PG 24
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 893SP
UT WOS:000300376000005
DA 2024-03-27
ER
PT J
AU Wang, Q
Yu, JJ
Deng, WW
AF Wang, Qian
Yu, Jijun
Deng, Weiwei
TI An adjustable re-ranking approach for improving the individual and
aggregate diversities of product recommendations
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Electronic commerce; Recommender system; Product recommendation;
Individual diversity; Aggregate diversity; Re-ranking approach
ID PERSONALIZED RECOMMENDATION; NETWORK ANALYSIS; LONG TAIL; SYSTEMS;
ACCURACY; IMPACT
AB The effectiveness of product recommendations is previously assessed based on recommendation accuracy. Recently, individual diversity and aggregate diversity of product recommendations have been recognized as important dimensions in evaluating the recommendation effectiveness. However, the gain of either diversity is usually at the cost of accuracy and the increase of one diversity does not guarantee a significant improvement in the other. A few attempts have been made to achieve reasonable trade-offs either between recommendation accuracy and individual diversity or between recommendation accuracy and aggregate diversity. Little attention has been paid to obtain a balance among the three important aspects of product recommendations. To address this problem, we propose an adjustable re-ranking approach that incorporates two new ranking criteria for improving both diversities. Three ranking lists are generated to guarantee recommendation accuracy, individual diversity, and aggregate diversity, respectively. The three ranking lists are finally merged with tunable parameters to generate a recommendation list. To evaluate the proposed method, experiments are conducted on a data set obtained from Alibaba. The results show that the proposed method achieves much higher improvements in both diversities than the baseline methods when sacrificing the same amount of recommendation accuracy.
C1 [Wang, Qian; Yu, Jijun] Sun Yat Sen Univ, Dept Informat Syst & Engn, Sch Business, Guangzhou, Guangdong, Peoples R China.
[Deng, Weiwei] City Univ Hong Kong, Coll Business, Dept Informat Syst, Kowloon Tong, 83 Tat Chee Ave, Hong Kong, Peoples R China.
C3 Sun Yat Sen University; City University of Hong Kong
RP Deng, WW (autor correspondiente), City Univ Hong Kong, Coll Business, Dept Informat Syst, Kowloon Tong, 83 Tat Chee Ave, Hong Kong, Peoples R China.
EM wwdeng3-c@my.cityu.edu.hk
FU National Natural Science Foundation of China [71772187, 70971141]
FX This work was supported by the National Natural Science Foundation of
China (Grant Nos. 71772187, 70971141).
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Adomavicius G., 2009, Workshop on Information Technologies and Systems, P79
Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15
Adomavicius G, 2014, INFORMS J COMPUT, V26, P351, DOI 10.1287/ijoc.2013.0570
Borgatti SP, 2006, SOC NETWORKS, V28, P466, DOI 10.1016/j.socnet.2005.11.005
Breese H Heckerman D., 1998, P 14 C UNC ART INT, p1998 43
Brynjolfsson E, 2011, MANAGE SCI, V57, P1373, DOI 10.1287/mnsc.1110.1371
Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13
Burke R, 2007, The adaptive web: methods and strategies of web personalization, P377, DOI DOI 10.1007/978-3-540-72079-9_12
Chen L., 2017, ARXIV170905135CS
Chien BC, 2009, INFORM SYST FRONT, V11, P551, DOI 10.1007/s10796-008-9126-z
Deb K., 2014, SEARCH METHODOLOGIES, P403, DOI DOI 10.1007/978-1-4614-6940-7_15
Di Noia T, 2014, PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14), P285, DOI 10.1145/2645710.2645774
Dias MB, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P291
Ding S, 2017, DECIS SUPPORT SYST, V101, P106, DOI 10.1016/j.dss.2017.06.005
Dwork C., 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Gan MX, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0114662
Gan MX, 2013, DECIS SUPPORT SYST, V55, P811, DOI 10.1016/j.dss.2013.03.006
Ghoshal A, 2015, INFORM SYST RES, V26, P532, DOI 10.1287/isre.2015.0583
Girardi D, 2016, J BIOMED INFORM, V63, P66, DOI 10.1016/j.jbi.2016.07.021
Gogna A, 2017, KNOWL-BASED SYST, V125, P83, DOI 10.1016/j.knosys.2017.03.023
Hafshejani ZY, 2018, ELECTRON COMMER RES, V18, P1
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hsu FM, 2012, EXPERT SYST APPL, V39, P6221, DOI 10.1016/j.eswa.2011.12.005
Jing N, 2018, ELECTRON COMMER RES, V18, P159, DOI 10.1007/s10660-017-9275-6
Karakaya Mo, 2017, KNOWL INF SYST, V56, P1
Kim HK, 2012, EXPERT SYST APPL, V39, P7403, DOI 10.1016/j.eswa.2012.01.066
Konak A, 2006, RELIAB ENG SYST SAFE, V91, P992, DOI 10.1016/j.ress.2005.11.018
Kunaver M, 2017, KNOWL-BASED SYST, V123, P154, DOI 10.1016/j.knosys.2017.02.009
Lika B, 2014, EXPERT SYST APPL, V41, P2065, DOI 10.1016/j.eswa.2013.09.005
Ma Y, 2017, ELECTRON COMMER RES, V17, P3, DOI 10.1007/s10660-016-9240-9
Muter I, 2017, INFORMS J COMPUT, V29, P405, DOI 10.1287/ijoc.2016.0741
Niemann K, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P955
Oestreicher-Singer G, 2012, MIS QUART, V36, P65, DOI 10.2307/41410406
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Shani G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P257, DOI 10.1007/978-0-387-85820-3_8
Tucker C., 2007, 465507 MIT
Wang JK, 2016, P ANN HICSS, P688, DOI 10.1109/HICSS.2016.91
Wang Q, 2017, DECIS SUPPORT SYST, V103, P46, DOI 10.1016/j.dss.2017.09.001
Weng SS, 2008, EXPERT SYST APPL, V34, P1857, DOI 10.1016/j.eswa.2007.02.023
Zhang M, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P123
Zhang Q, 2017, DECIS SUPPORT SYST, V104, P49, DOI 10.1016/j.dss.2017.10.002
Ziegler C-N, 2005, P 14 INT C WORLD WID, P22, DOI DOI 10.1145/1060745.1060754
NR 44
TC 9
Z9 10
U1 9
U2 42
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD MAR
PY 2019
VL 19
IS 1
BP 59
EP 79
DI 10.1007/s10660-018-09325-4
PG 21
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HL1ZY
UT WOS:000458504200003
OA hybrid
DA 2024-03-27
ER
PT J
AU Adomavicius, G
Bockstedt, JC
Curley, SP
Zhang, JJ
AF Adomavicius, Gediminas
Bockstedt, Jesse C.
Curley, Shawn P.
Zhang, Jingjing
TI Do Recommender Systems Manipulate Consumer Preferences? A Study of
Anchoring Effects
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE anchoring effects; behavioral decision making; behavioral economics;
electronic commerce; experimental research; preferences; recommender
systems
ID UNCERTAINTY; INTERFACES; JUDGMENTS; TRUST
AB Recommender systems are becoming a salient part of many e-commerce websites. Much research has focused on advancing recommendation technologies to improve accuracy of predictions, although behavioral aspects of using recommender systems are often overlooked. In our studies, we explore how consumer preferences at the time of consumption are impacted by predictions generated by recommender systems. We conducted three controlled laboratory experiments to explore the effects of system recommendations on preferences. Studies 1 and 2 investigated user preferences for television programs across a variety of conditions, which were surveyed immediately following program viewing. Study 3 investigated the granularity of the observed effects within individual participants. Results provide strong evidence that the rating presented by a recommender system serves as an anchor for the consumer's constructed preference. Viewers' preference ratings are malleable and can be significantly influenced by the recommendation received. The effect is sensitive to the perceived reliability of a recommender system and, thus, not a purely numerical or priming-based effect. Finally, the effect of anchoring is continuous and linear, operating over a range of perturbations of the system. These general findings have a number of important implications (e.g., on recommender systems performance metrics and design, preference bias, potential strategic behavior, and trust), which are discussed.
C1 [Adomavicius, Gediminas; Curley, Shawn P.] Univ Minnesota, Carlson Sch Management, Minneapolis, MN 55455 USA.
[Bockstedt, Jesse C.] Univ Arizona, Eller Coll Management, Tucson, AZ 85721 USA.
[Zhang, Jingjing] Indiana Univ, Kelley Sch Business, Bloomington, IN 47405 USA.
C3 University of Minnesota System; University of Minnesota Twin Cities;
University of Arizona; Indiana University System; IU Kelley School of
Business; Indiana University Bloomington
RP Adomavicius, G (autor correspondiente), Univ Minnesota, Carlson Sch Management, Minneapolis, MN 55455 USA.
EM gedas@umn.edu; bockstedt@email.arizona.edu; curley@umn.edu;
jjzhang@indiana.edu
OI Zhang, Jingjing/0000-0002-6805-8685
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Ariely D, 2003, Q J ECON, V118, P73, DOI 10.1162/00335530360535153
Bell Robert M., 2007, P KDD CUP WORKSH 13, P7, DOI DOI 10.1007/S007790170019
Breese H Heckerman D., 1998, P 14 C UNC ART INT, p1998 43
Chapman GB, 1996, APPL COGNITIVE PSYCH, V10, P519
Chapman GB, 2002, HEURISTICS BIASES PS, P120, DOI DOI 10.1017/CBO9780511808098.008
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Cosley D., 2003, PROC SIGCHI C HUMAN, P585
Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776
Epley N, 2010, J CONSUM PSYCHOL, V20, P20, DOI 10.1016/j.jcps.2009.12.005
Flynn LJ, 2006, NY TIMES
Funk S., 2006, Netflix update: Try this at home
Goldberg K, 2001, INFORM RETRIEVAL, V4, P133, DOI 10.1023/A:1011419012209
JACOWITZ KE, 1995, PERS SOC PSYCHOL B, V21, P1161, DOI 10.1177/01461672952111004
Johnson JEV, 2009, J BEHAV DECIS MAKING, V22, P410, DOI 10.1002/bdm.640
Komiak SYX, 2006, MIS QUART, V30, P941
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Koren Yehuda, 2009, Netflix prize documentation, V81, P1
Ku G, 2006, J PERS SOC PSYCHOL, V90, P975, DOI 10.1037/0022-3514.90.6.975
Lam S, 2004, 13 INT C WORLD WID W, P353
Lanning S., 2007, P KDD CUP WORKSH, P3
Lichtenstein S, 2006, CONSTRUCTION OF PREFERENCE, P1, DOI 10.2277/ 0521542200
McNee SM, 2003, LECT NOTES ARTIF INT, V2702, P178
Mobasher B, 2007, ACM T INTERNET TECHN, V7, DOI 10.1145/1278366.1278372
Mussweiler T, 2000, J EXP SOC PSYCHOL, V36, P495, DOI 10.1006/jesp.1999.1414
NORTHCRAFT GB, 1987, ORGAN BEHAV HUM DEC, V39, P84, DOI 10.1016/0749-5978(87)90046-X
Piotte Matrin., 2009, The Pragmatic Theory solution to the Netflix Grand Prize
Pu P, 2007, KNOWL-BASED SYST, V20, P542, DOI 10.1016/j.knosys.2007.04.004
Russo JE, 2010, J CONSUM PSYCHOL, V20, P25, DOI 10.1016/j.jcps.2009.12.006
Sarwar Badrul, 2001, P 10 INT C WORLD WID, P285, DOI 10.1145/371920.372071
SCHKADE DA, 1989, ORGAN BEHAV HUM DEC, V44, P203, DOI 10.1016/0749-5978(89)90025-3
Schonfeld E, 2007, CNNMONEY JUL
Strack F, 1997, J PERS SOC PSYCHOL, V73, P437, DOI 10.1037/0022-3514.73.3.437
Swearingen K., 2001, ACM SIGIR 2001 WORKS
Thorsteinson TJ, 2008, ORGAN BEHAV HUM DEC, V107, P29, DOI 10.1016/j.obhdp.2008.01.003
Tintarev N, 2011, RECOMMENDER SYSTEMS HANDBOOK, P479, DOI 10.1007/978-0-387-85820-3_15
Toscher Andreas, 2009, NETFLIX PRIZE DOCUME, P1
TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
Wang WQ, 2007, J MANAGE INFORM SYST, V23, P217, DOI 10.2753/MIS0742-1222230410
Wilson R., 2010, Journal of Direct, Data and Digital Marketing Practice, V12, P69, DOI [10.1057/dddmp.2010.19, DOI 10.1057/DDDMP.2010.19]
Yoo KH, 2011, RECOMMENDER SYSTEMS HANDBOOK, P455, DOI 10.1007/978-0-387-85820-3_14
NR 41
TC 110
Z9 129
U1 22
U2 190
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD DEC
PY 2013
VL 24
IS 4
BP 956
EP 975
DI 10.1287/isre.2013.0497
PG 20
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA 273MW
UT WOS:000328540100006
DA 2024-03-27
ER
PT J
AU Huang, CL
Huang, WL
AF Huang, Cheng-Lung
Huang, Wei-Liang
TI Handling sequential pattern decay: Developing a two-stage collaborative
recommender system
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Recommender systems; Clustering; Sequential pattern; Collaborative
filtering; Electronic commerce
AB This study proposes a sequential pattern based collaborative recommender system that predicts the customer's time-variant purchase behavior in an e-commerce environment where the customer's purchase patterns may change gradually. A new two-stage recommendation process is developed to predict customer purchase behavior for the product categories, as well as for product items. The time window weight is introduced to produce sequential patterns closer to the current time period that possess a larger impact on the prediction than patterns relatively far from the current time period. This study is the first to propose time-decaying sequential patterns within a collaborative recommender system. The experimental results show that the proposed system outperforms the traditional collaborative system using a public food mart dataset and a synthetic dataset. (C) 2008 Elsevier B.V. All rights reserved.
C1 [Huang, Cheng-Lung; Huang, Wei-Liang] Natl Kaohsiung First Univ Sci & Technol, Dept Informat Management, Kaohsiung 811, Taiwan.
C3 National Kaohsiung University of Science & Technology
RP Huang, CL (autor correspondiente), Natl Kaohsiung First Univ Sci & Technol, Dept Informat Management, 2 Juoyue Rd, Kaohsiung 811, Taiwan.
EM clhuang@ccms.nkfust.edu.tw
FU National Science Council of the Republic of China, Taiwan [NSC
96-2221-E-327-008]
FX The authors would like to thank the anonymous referees for their
valuable comments, which helped in improving the quality of this paper.
The authors would also like to thank the National Science Council of the
Republic of China, Taiwan for financially supporting this research under
Contract No. NSC 96-2221-E-327-008.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
AGRAWAL R, 1995, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.1995.380415
Agrawal R., 1994, 9910 RJ IBM ALM RES
[Anonymous], 2011, Pei. data mining concepts and techniques
Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124
Chen YL, 2006, DECIS SUPPORT SYST, V42, P1203, DOI 10.1016/j.dss.2005.10.006
Chen YL, 2005, IEEE T SYST MAN CY B, V35, P959, DOI 10.1109/TSMCB.2005.847741
Cho YB, 2005, EXPERT SYST APPL, V28, P359, DOI 10.1016/j.eswa.2004.10.015
CONOVER W. J., 1980, PRACTICAL NONPARAMET
Cowgill MC, 1999, COMPUT MATH APPL, V37, P99, DOI 10.1016/S0898-1221(99)00090-5
Davidovitch N, 2006, SCI CONTEXT, V19, P309, DOI 10.1017/S0269889706000949
DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224, DOI 10.1109/TPAMI.1979.4766909
Garfinkel R, 2006, DECIS SUPPORT SYST, V42, P1974, DOI 10.1016/j.dss.2006.05.005
GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867
Goldberg D., 1989, GENETIC ALGORITHMS S
HUANG JP, 2003, P 4 C INF MAN NEW TE, P53
Kaymak U, 2001, JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5, P1038, DOI 10.1109/NAFIPS.2001.944748
Kazienko P, 2007, INFORM SCIENCES, V177, P2269, DOI 10.1016/j.ins.2007.01.002
KOHONEN T, 1997, SELF ORGANIZED MAPS
KOWALSKI G., 1997, INFORM RETRIEVAL SYS
Krulwich B., 1996, P AAAI SPRING S MACH, V25, P110
Kyoung-jae Kim, 2005, Artificial Intelligence and Simulation. 13th International Conference on AI, Simulation, and Planning in High Autonomy Systems, AIS 2004. Revised Selected Papers (Lecture Notes in Artificial Intelligence Vol.3397), P409
Lang K., 1995, P 12 INT C MACHINE L, P331
Lee HJ, 2007, EXPERT SYST APPL, V32, P143, DOI 10.1016/j.eswa.2005.11.010
Lekakos G, 2006, INTERACT COMPUT, V18, P410, DOI 10.1016/j.intcom.2005.11.004
Maulik U, 2000, PATTERN RECOGN, V33, P1455, DOI 10.1016/S0031-3203(99)00137-5
Mild A., 2003, Journal of Retailing and Consumer Services, V10, P123, DOI [DOI 10.1016/S0969-6989(03)00003-1, 10.1016/S0969-6989(03)00003-1]
Min SH, 2005, EXPERT SYST APPL, V28, P189, DOI 10.1016/j.eswa.2004.10.001
Pei J, 2004, IEEE T KNOWL DATA EN, V16, P1424, DOI 10.1109/TKDE.2004.77
Pei J., 2001, P 2001 INT C DAT ENG
Perugini S, 2004, J INTELL INF SYST, V23, P107, DOI 10.1023/B:JIIS.0000039532.05533.99
Rashid A.M., 2002, P 7 INT C INT US INT, P127, DOI [10.1145, DOI 10.1145/502716.502737]
Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI DOI 10.1145/192844.192905
Rijsbergen V., 1979, INFORM RETRIEVAL, VSecond Edi
Rucker J, 1997, COMMUN ACM, V40, P73, DOI 10.1145/245108.245125
Salzberg SL, 1997, DATA MIN KNOWL DISC, V1, P317, DOI 10.1023/A:1009752403260
Sarwar S., 2000, PROCEEDING 2 ACM C E, P158, DOI DOI 10.1145/352871.352887
Schafer J., 2001, DATA MIN KNOWL DISC, V5, P115, DOI DOI 10.1023/A:1009804230409
Schafer J. Ben, 1999, Proceedings of the 1st ACM conference on Electronic commerce, P158, DOI [DOI 10.1145/336992.337035, 10.1145/336992.337035]
Shardanand U., 1995, Human Factors in Computing Systems. CHI'95 Conference Proceedings, P210, DOI 10.1145/223904.223931
Srikant R., 1996, Advances in Database Technology - EDBT '96. 5th International Conference on Extending Database Technology. Proceedings, P3
Tseng LY, 2001, PATTERN RECOGN, V34, P415, DOI 10.1016/S0031-3203(00)00005-4
Ujjin S, 2003, PROCEEDINGS OF THE 2003 IEEE SWARM INTELLIGENCE SYMPOSIUM (SIS 03), P124, DOI 10.1109/SIS.2003.1202257
Wang YF, 2004, EXPERT SYST APPL, V26, P427, DOI 10.1016/j.eswa.2003.10.001
WEI K, 2007, INT C SERV SYST SERV, P1
Yang WJ, 2004, IEEE SYS MAN CYBERN, P4135
Yang YM, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42, DOI 10.1145/312624.312647
NR 47
TC 38
Z9 45
U1 0
U2 12
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD MAY-JUN
PY 2009
VL 8
IS 3
BP 117
EP 129
DI 10.1016/j.elerap.2008.10.001
PG 13
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 448OP
UT WOS:000266272200002
DA 2024-03-27
ER
PT J
AU Li, P
Zhu, XR
Su, XJ
AF Li, Peng
Zhu, Xinru
Su, Xinjie
TI Neural_BPR: Multi-processing popularity bias mitigating method in
recommendation systems
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE E-commerce platform economy; Recommender system; Popularity bias;
Artificial intelligence; Small and medium enterprises
AB E-commerce recommendations address the problem of information overload, but recent research has identified the phenomenon of popularity bias in recommendation mechanisms. This phenomenon tends to homogenise recommendation results, i.e. popular products are widely recommended and non-popular products are under-exposed. The persistent presence of popularity bias in recommendation systems can lead to several negative consequences: (1) Consumers are unable to attain satisfactory personalized shopping experiences. (2) Small and medium enterprises are deprived of fair competitive opportunities, making survival challenging. (3) The user base of e-commerce platforms dwindles, leading to reduced transaction volumes. In this paper, we propose a multi-process fusion debiasing method. Convolutional neural networks are used to extract feature information to be embedded in the recommendation algorithm for pre-processing, in-processing and post-processing in turn to mitigate popularity bias and simultaneously enhance recommendation utility. The effectiveness of the proposed debiasing method is validated using the Bayesian Personalized Ranking (BPR) recommendation model as an example. Experimental results on two public datasets demonstrate that our model outperforms two baseline models and a state-of-the-art model. Specifically, compared to the traditional BPR model, the alleviation of popularity bias is around 70% to 80%, and the recommendation utility increases by approximately 30% to 50%.
C1 [Li, Peng; Zhu, Xinru; Su, Xinjie] Harbin Univ Commerce, Sch Management, Harbin, Peoples R China.
C3 Harbin University of Commerce
RP Li, P (autor correspondiente), Harbin Univ Commerce, Sch Management, Harbin, Peoples R China.
EM lipeng@hrbcu.edu.cn
FU Federation of Social Science of Heilongjiang Province of China [22310]
FX This work was supported by the Federation of Social Science of
Heilongjiang Province of China, grant number 22310.
CR Abdollahpouri H, 2019, Arxiv, DOI arXiv:1901.07555
Abdollahpouri H, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P42, DOI 10.1145/3109859.3109912
Acs ZJ, 2022, TEC EMPRESARIAL, V16, P1, DOI 10.18845/te.v16i1.5926
Basu S, 2021, ELECTRON COMMER R A, V48, DOI 10.1016/j.elerap.2021.101074
Beutel A, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2212, DOI 10.1145/3292500.3330745
Bose AJ, 2019, PR MACH LEARN RES, V97
Cutolo D, 2021, ACAD MANAGE PERSPECT, V35, P584, DOI 10.5465/amp.2019.0103
Ghanem N, 2022, ELECTRON COMMER R A, V55, DOI 10.1016/j.elerap.2022.101195
Harper FM, 2016, ACM T INTERACT INTEL, V5, DOI 10.1145/2827872
Lee H., 2009, ICML
Li K, 2022, J SMALL BUS MANAGE, V60, P93, DOI 10.1080/00472778.2019.1695495
Liu DC, 2017, WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P583, DOI 10.1145/3041021.3054202
Liu YD, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2765, DOI 10.1145/3292500.3330707
Liu Z., 2023, ACM Trans. Inf. Syst., V41, P1
Mansoury M, 2022, ACM T INFORM SYST, V40, DOI 10.1145/3470948
Manyika J., 2016, Digital globalization: The new era of global flows
Ranzato M, 2007, PROC CVPR IEEE, P1429
Rastegarpanah B, 2019, PROCEEDINGS OF THE TWELFTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'19), P231, DOI 10.1145/3289600.3291002
Rendle S, 2012, Arxiv, DOI [arXiv:1205.2618, DOI 10.48550/ARXIV.1205.2618]
Saito Y, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P309, DOI 10.1145/3397271.3401114
Sha X, 2021, ELECTRON COMMER R A, V48, DOI 10.1016/j.elerap.2021.101071
Steck H, 2019, Arxiv, DOI arXiv:1904.13033
Steck Harald, 2011, P 5 ACM C REC SYST, P125
Xiao T, 2022, WSDM'22: PROCEEDINGS OF THE FIFTEENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P1158, DOI 10.1145/3488560.3498521
Xue F, 2019, ACM T INFORM SYST, V37, DOI 10.1145/3314578
Yih WT, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2, P643
Yoo B, 2012, ELECTRON COMMER R A, V11, P180, DOI 10.1016/j.elerap.2011.12.007
Zhu ZW, 2022, WSDM'22: PROCEEDINGS OF THE FIFTEENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P1497, DOI 10.1145/3488560.3498427
Zhu ZW, 2021, WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P85, DOI 10.1145/3437963.3441820
Zhu ZW, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P449, DOI 10.1145/3397271.3401177
NR 30
TC 0
Z9 0
U1 12
U2 12
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD NOV-DEC
PY 2023
VL 62
AR 101323
DI 10.1016/j.elerap.2023.101323
EA OCT 2023
PG 11
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA W9JI7
UT WOS:001094714600001
DA 2024-03-27
ER
PT J
AU Lee, HC
Rim, HC
Lee, DG
AF Lee, Ho-Chang
Rim, Hae-Chang
Lee, Do-Gil
TI Learning to rank products based on online product reviews using a
hierarchical deep neural network
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Product ranking; Online product reviews; Hierarchical deep neural
network
ID WORD-OF-MOUTH; FORECASTING SALES; CONSUMER REVIEWS; IMPACT
AB Product ranking based on online product reviews is a task of inferring relative user preferences between different products as a variant of entity-level sentiment analysis. Despite the complex relationship between the overall user's preference and individual diverse opinions, existing approaches generally employ empirical assumptions about sentiment features of the products of interest. In this paper, we propose a novel unified approach for learning to rank products based on online product reviews. Unlike existing approaches, it uses deep-learning techniques to extract the high-level latent review representation that contains the most semantic information in the learning process. For this approach, we extend the recently proposed hierarchical attention network to operate in the ranking domain. This network hierarchically learns optimal feature representations of the products and their reviews through the use of two-level attention-based encoders. To construct a more advanced ranking model, several features were added to give sufficient information about the relative user preferences, and two representative ranking loss functions, RankNet and ListNet, were applied. Furthermore, we demonstrate that this network outperforms the existing methods in sales rank prediction based on online product reviews.
C1 [Lee, Ho-Chang; Rim, Hae-Chang] Korea Univ, Dept Comp Sci, Seoul, South Korea.
[Lee, Do-Gil] Korea Univ, Res Inst Korean Studies, Seoul, South Korea.
C3 Korea University; Korea University
RP Lee, DG (autor correspondiente), Korea Univ, Res Inst Korean Studies, Seoul, South Korea.
EM pery@korea.ac.kr; rim@korea.ac.kr; motdg@korea.ac.kr
OI Lee, Ho-Chang/0000-0003-2864-2972
FU Next-Generation Information Computing Development Program through the
National Research Foundation of Korea (NRF) - Ministry of Science, ICT
Future Plannig [2012M3C4A7033344]
FX This research was supported by Next-Generation Information Computing
Development Program through the National Research Foundation of Korea
(NRF) funded by the Ministry of Science, ICT & Future Plannig
(2012M3C4A7033344).
CR [Anonymous], P INT C WEB INT MIN
[Anonymous], ARXIV160309025
Appel O, 2016, KNOWL-BASED SYST, V108, P110, DOI 10.1016/j.knosys.2016.05.040
Bahdanau D., 2015, ARXIV14090473
Bashir S, 2016, APPL SOFT COMPUT, V38, P151, DOI 10.1016/j.asoc.2015.10.001
Bickart B., 2001, J INTERACT MARK, V15, P31, DOI [10.1002/dir.1014, DOI 10.1002/DIR.1014]
Burges CJ, 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363
Cao T., 2007, P 24 INT C MACH LEAR, P129, DOI [10.1145/1273496.1273513, DOI 10.1145/1273496.1273513]
Chen T, 2017, EXPERT SYST APPL, V72, P221, DOI 10.1016/j.eswa.2016.10.065
Decker R, 2010, INT J RES MARK, V27, P293, DOI 10.1016/j.ijresmar.2010.09.001
Dellarocas C, 2007, J INTERACT MARK, V21, P23, DOI 10.1002/dir.20087
Du JF, 2014, EXPERT SYST APPL, V41, P1680, DOI 10.1016/j.eswa.2013.08.065
Fan ZP, 2017, J BUS RES, V74, P90, DOI 10.1016/j.jbusres.2017.01.010
Ganesan K, 2012, INFORM RETRIEVAL, V15, P116, DOI 10.1007/s10791-011-9174-8
Gerani Shima, 2014, P 2014 C EMP METH NA, P1602, DOI [10.1007/978-3-642-14834-7_15, DOI 10.1007/978-3-642-14834-7_15]
He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
Huang Z., 2015, ARXIV
Jannach D, 2016, COMMUN ACM, V59, P94, DOI 10.1145/2891406
Jindal N., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P244, DOI 10.1145/1148170.1148215
Kessler Wiltrud, 2013, P 2013 C EMP METH NA, P1892
Kim Y, 2014, P 2014 C EMP METH NA, V2014, P1746, DOI [DOI 10.3115/V1/D14-1181, 10.3115/v1/D14-1181]
Kingma D. P., 2014, ARXIV
Liu Y, 2017, INFORM FUSION, V36, P149, DOI 10.1016/j.inffus.2016.11.012
Liu Y, 2008, IEEE DATA MINING, P443, DOI 10.1109/ICDM.2008.94
Liu Yang, 2007, P 30 ANN INT ACM SIG, P607, DOI [10.1145/1277741.1277845, DOI 10.1145/1277741.1277845]
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
McAuley J, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/2766462.2767755
Najmi E, 2015, COMPUTING, V97, P843, DOI 10.1007/s00607-015-0439-8
Quanchao Liu, 2013, Advanced Data Mining and Applications. 9th International Conference, ADMA 2013. Proceedings: LNCS 8347, P300, DOI 10.1007/978-3-642-53917-6_27
Raffel C., 2015, INT C LEARNING REPRE
Romeo Salvatore., 2016, Proceedings of COLING 2016, P1734
Saumya S, 2018, ELECTRON COMMER R A, V29, P78, DOI 10.1016/j.elerap.2018.03.008
Schneider MJ, 2016, INT J FORECASTING, V32, P243, DOI 10.1016/j.ijforecast.2015.08.005
Song YQ, 2014, PROCEEDINGS OF THE THIRD NORTHEAST ASIA INTERNATIONAL SYMPOSIUM ON LANGUAGE, LITERATURE AND TRANSLATION, VOLS 1 AND 2, P83
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Sun Jianshu, 2009, Polibits, V0, P33
Varathan KD, 2017, J ASSOC INF SCI TECH, V68, P811, DOI 10.1002/asi.23716
Wang B., 2017, ARXIV170206106
Wang Lu, 2016, ARXIV160602785
Xie SH, 2015, PROCEEDINGS OF THE 2015 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (IEEE DSAA 2015), P464
Xu K, 2015, PR MACH LEARN RES, V37, P2048
Yagci IA, 2018, ELECTRON COMMER R A, V30, P102, DOI 10.1016/j.elerap.2018.05.010
Yang ZJ, 2016, ADV SOC SCI EDUC HUM, V64, P1480
Yu J., 2011, IEEE T GEOSCI REMOTE, V49, P1
Zhang A, 2010, J OPER OCEANOGR, V3, P11, DOI 10.1080/1755876X.2010.11020114
Zhang K., 2011, OCEANS 2011 IEEE - Spain, P1, DOI [DOI 10.1145/2378104.2378116, 10.1109/Oceans-Spain.2011.6003503, DOI 10.1109/OCEANS-SPAIN.2011.6003503]
Zhang Z, 2013, ACM TRANS MANAG INF, V3, DOI 10.1145/2407740.2407744
Zheng L, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P425, DOI 10.1145/3018661.3018665
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
2011, CLUTT HDB RES INT ED, P1, DOI DOI 10.1109/CEWIT.2011.6135866
NR 51
TC 21
Z9 21
U1 9
U2 83
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD JUL-AUG
PY 2019
VL 36
AR 100874
DI 10.1016/j.elerap.2019.100874
PG 10
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA IM0HV
UT WOS:000477669200013
DA 2024-03-27
ER
PT J
AU Panniello, U
Gorgoglione, M
Tuzhilin, A
AF Panniello, Umberto
Gorgoglione, Michele
Tuzhilin, Alexander
TI In CARSs We Trust: How Context-Aware Recommendations Affect Customers'
Trust and Other Business Performance Measures of Recommender Systems
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE business value of IT; case studies; economics of IS; electronic
commerce; field experiments; recommender systems; context aware
ID INTEGRATIVE MODEL; E-COMMERCE; INFORMATION; DETERMINANTS; SATISFACTION;
VARIETY; IMPACT
AB Most of the work on context-aware recommender systems has focused on demonstrating that the contextual information leads to more accurate recommendations. Little work has been done, however, on studying how much the contextual information affects the business performance. In this paper, we study how including context in recommendations affects customers' trust, sales, and other crucial business-related performance measures. To do this, we delivered content-based and context-aware recommendations through a live controlled experiment with real customers of a commercial European online publisher. We measured the recommendations' accuracy and diversification, how much customers spent purchasing products during the experiment, the quantity and price of their purchases, and the customers' level of trust. We show that collecting and using contextual information in recommendations affects business-related performance measures, such as company sales, by improving the accuracy and diversification of recommendations, which in turn improves trust and, ultimately, business performance results.
C1 [Panniello, Umberto; Gorgoglione, Michele] Politecn Bari, I-70126 Bari, Italy.
[Tuzhilin, Alexander] NYU, Stern Sch Business, 550 1St Ave, New York, NY 10012 USA.
C3 Politecnico di Bari; New York University
RP Panniello, U; Gorgoglione, M (autor correspondiente), Politecn Bari, I-70126 Bari, Italy.; Tuzhilin, A (autor correspondiente), NYU, Stern Sch Business, 550 1St Ave, New York, NY 10012 USA.
EM umberto.panniello@poliba.it; michele.gorgoglione@poliba.it;
atuzhili@stern.nyu.edu
CR Adomavicius G, 2005, ACM T INFORM SYST, V23, P103, DOI 10.1145/1055709.1055714
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15
Adomavicius G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P217, DOI 10.1007/978-0-387-85820-3_7
Allen J, 2020, ARCHAEOL OCEAN, V55, P1, DOI 10.1002/arco.5207
[Anonymous], STAT ANAL ENG SCI
[Anonymous], 2012, P RECSYS
[Anonymous], 2012, P 6 ACM C REC SYST
[Anonymous], 2012, NETFLIX RECOMMENDA 1
[Anonymous], 2012, PROC ACM RECSYS
BAUMOL WJ, 1956, MANAGE SCI, V3, P93, DOI 10.1287/mnsc.3.1.93
Beldad A, 2010, COMPUT HUM BEHAV, V26, P857, DOI 10.1016/j.chb.2010.03.013
Ben Schafer J, 2001, DATA MIN KNOWL DISC, V5, P115, DOI 10.1023/A:1009804230409
Benbasat I, 2005, Journal of the Association for Information Systems, V6, P4, DOI [DOI 10.17705/1JAIS.00065, 10.17705/1jais.00065]
Bharati P, 2004, DECIS SUPPORT SYST, V37, P187, DOI 10.1016/S0167-9236(03)00006-X
Bollen D., 2010, P 4 ACM C REC SYST, P63, DOI [10.1145/1864708.1864724, DOI 10.1145/1864708.1864724]
Brynjolfsson E, 2003, MANAGE SCI, V49, P1580, DOI 10.1287/mnsc.49.11.1580.20580
Colson E, 2014, P 8 ACM C REC SYST
Cooke ADJ, 2002, J MARKETING RES, V39, P488, DOI 10.1509/jmkr.39.4.488.19121
Doney PM, 1997, J MARKETING, V61, P35, DOI 10.2307/1251829
Dourish P, 2004, PERS UBIQUIT COMPUT, V8, P19, DOI 10.1007/s00779-003-0253-8
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Flynn LJ, 2006, NY TIMES BK REV, P9
Ganesan S., 1994, J MARKETING, V58, P1, DOI DOI 10.2307/1252265
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Gorgoglione M., 2011, Proc. of the fifth ACM conference on RS, P85, DOI DOI 10.1145/2043932.2043951
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hu R., 2011, Proc. of the 16th Int'l Conf. on Intelligent User Interfaces (IUI '11), P347, DOI [10.1145/1943403.1943462, DOI 10.1145/1943403.1943462]
Hunt N, 2014, 8 ACM C REC SYST FOS
Jarvenpaa S. L., 1998, Journal of Management Information Systems, V14, P29
KAHN BE, 1991, J RETAILING, V67, P274
Knijnenburg BP, 2012, USER MODEL USER-ADAP, V22, P441, DOI 10.1007/s11257-011-9118-4
Knijnenburg BP, 2012, 6 ACM C REC SYST
Koenigstein N., 2012, Proceedings of the sixth ACM Conference on Recommender Systems, P281
Komiak SYX, 2006, MIS QUART, V30, P941
Kwon O, 2009, EXPERT SYST APPL, V36, P1893, DOI 10.1016/j.eswa.2007.12.064
Lenzini G, 2009, ADBIS WORKSH, P121
Liang TP, 2006, J MANAGE INFORM SYST, V23, P45, DOI 10.2753/MIS0742-1222230303
Liu Q., 2012, P 6 ACM C RECOMMENDE, P285
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
McDonald DG, 2003, COMMUN RES, V30, P60, DOI 10.1177/0093650202239026
McGinty L, 2003, LECT NOTES ARTIF INT, V2689, P276
McKnight DH, 2002, INFORM SYST RES, V13, P334, DOI 10.1287/isre.13.3.334.81
Mui YQ, 2006, WASHINGTON POST
Panniello U, 2015, WORKING PAPER
Panniello U, 2014, USER MODEL USER-ADAP, V24, P35, DOI 10.1007/s11257-012-9135-y
Panniello Umberto, 2009, P 3 ACM C REC SYST, P265, DOI [10.1145/1639714.1639764, DOI 10.1145/1639714.1639764]
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Pavlou PA, 2007, MIS QUART, V31, P105
Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P325
Pu P, 2011, 5 ACM C REC SYST, P23
Schein AI, 2012, 25 INT ACM SIGIR C R, P253
Schoorman FD, 2007, ACAD MANAGE REV, V32, P344, DOI 10.5465/AMR.2007.24348410
Shmueli G, 2011, MIS QUART, V35, P553
Simonson I, 2005, J MARKETING, V69, P32, DOI 10.1509/jmkg.69.1.32.55512
Smyth B, 2001, LECT NOTES ARTIF INT, V2080, P347
Smyth B, 2012, P 6 ACM C REC SYST, P289
Swearingen K., 2001, ACM SIGIR WORKSH REC, P393
Swearingen K, 2002, C DES INT SYST, P25
Vargas S., 2011, P 5 ACM C REC SYST, P109, DOI DOI 10.1145/2043932.2043955
Xiao B, 2007, MIS QUART, V31, P137
Zhang C, 2011, MIS Q, V35, P859
Zhang M, 2009, 2009 ACM C REC SYST, P123
Ziegler C-N, 2005, P 14 INT C WORLD WID, P22, DOI DOI 10.1145/1060745.1060754
NR 64
TC 47
Z9 50
U1 6
U2 134
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD MAR
PY 2016
VL 27
IS 1
BP 182
EP 196
DI 10.1287/isre.2015.0610
PG 15
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA DL4JI
UT WOS:000375600200011
DA 2024-03-27
ER
PT J
AU Iwanaga, J
Nishimura, N
Sukegawa, N
Takano, Y
AF Iwanaga, Jiro
Nishimura, Naoki
Sukegawa, Noriyoshi
Takano, Yuichi
TI Improving collaborative filtering recommendations by estimating user
preferences from clickstream data
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Collaborative filtering; User preference; Rating matrix; Clickstream
data; E-commerce; Recommender system
ID MATRIX-FACTORIZATION; SYSTEMS; RATINGS; ONTOLOGY; COMMERCE; CONTEXT;
CHOICE; NOISY; MODEL; TRUST
AB For practical applications of collaborative filtering, we need a user-item rating matrix that encodes user preferences for items. However, estimation of user preferences is inevitably affected by some degree of noise, which can markedly degrade the recommender performance. The primary aim of this research is to obtain a high-quality rating matrix by the effective use of clickstream data, which are a record of a user's page view (PV) history on an e-commerce site. To this end, we use the shape-restricted optimization model for estimating item-choice probabilities from the recency and frequency of each user's previous PVs. Experimental results based on real-world clickstream data demonstrate that higher recommender performance is achieved with our method than with baseline methods for constructing a rating matrix. Moreover, high recommender performance is maintained by our shape-restricted estimation even when only a limited number of training samples are available.
C1 [Iwanaga, Jiro] Retty Inc, Minato Ku, Sumitomo Fudosan Azabu Juban Bldg,1-4-1 Mita, Tokyo 1080073, Japan.
[Nishimura, Naoki] Recruit Lifestyle Co Ltd, Chiyoda Ku, GranTokyo South Tower,1-9-2 Marunouchi, Tokyo 1006640, Japan.
[Sukegawa, Noriyoshi] Tokyo Univ Sci, Katsushika Ku, 6-3-1 Niijuku, Tokyo 1258585, Japan.
[Takano, Yuichi] Univ Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058577, Japan.
C3 Tokyo University of Science; University of Tsukuba
RP Sukegawa, N (autor correspondiente), Tokyo Univ Sci, Katsushika Ku, 6-3-1 Niijuku, Tokyo 1258585, Japan.
EM sukegawa@rs.tus.ac.jp
RI Sukegawa, Noriyoshi/AAW-8223-2021
OI Sukegawa, Noriyoshi/0000-0002-3560-0036
FU JSPS KAKENHI [JP17K12983]
FX The authors would like to thank Recruit Lifestyle Co., Ltd.2 for
providing the clickstream data used in the experiments. This work was
supported by JSPS KAKENHI Grant No. JP17K12983.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Akcayol MA, 2018, ELECTRON COMMER R A, V28, P86, DOI 10.1016/j.elerap.2018.01.013
Amatriain X, 2009, LECT NOTES COMPUT SC, V5535, P247, DOI 10.1007/978-3-642-02247-0_24
Amatriain Xavier, 2009, P 3 ACM C REC SYST R, P247, DOI [10.1145/1639714.1639744, DOI 10.1145/1639714.1639744]
[Anonymous], 2016, RECOMMENDER SYSTEMS, DOI DOI 10.1007/978-3-319-29659-3
Ansari S, 2001, 2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P27, DOI 10.1109/ICDM.2001.989497
Ar Y, 2016, EXPERT SYST APPL, V61, P122, DOI 10.1016/j.eswa.2016.05.021
Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
Bobadilla J, 2012, INFORM SCIENCES, V185, P1, DOI 10.1016/j.ins.2011.09.014
Bucklin RE, 2009, J INTERACT MARK, V23, P35, DOI 10.1016/j.intmar.2008.10.004
Campos PG, 2014, USER MODEL USER-ADAP, V24, P67, DOI 10.1007/s11257-012-9136-x
Campos PG, 2010, PROCEEDINGS OF THE RECSYS'2010 ACM CHALLENGE ON CONTEXT-AWARE MOVIE RECOMMENDATION (CAMRA2010), P20, DOI 10.1145/1869652.1869655
Chen G, 2009, INFORM PROCESS MANAG, V45, P368, DOI 10.1016/j.ipm.2008.12.004
Cho YH, 2004, EXPERT SYST APPL, V26, P233, DOI 10.1016/S0957-4174(03)00138-6
Ding Y, 2006, P 17 AUSTR DAT C ADC, V49, P99
Ding Y., 2005, PROC 14 ACM INT C IN, P485, DOI DOI 10.1145/1099554.1099689
Ekstrand Michael D., 2010, Foundations and Trends in Human-Computer Interaction, V4, P81, DOI 10.1561/1100000009
Gadanho S, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P97
Gordea S, 2007, LECT NOTES COMPUT SC, V4831, P171
Guo GB, 2014, ELECTRON COMMER R A, V13, P440, DOI 10.1016/j.elerap.2014.10.003
Hafshejani ZY, 2018, ELECTRON COMMER RES, V18, P813, DOI 10.1007/s10660-018-9287-x
Han H, 2018, ELECTRON COMMER R A, V30, P38, DOI 10.1016/j.elerap.2018.05.005
Pham HX, 2013, MULTIMED TOOLS APPL, V65, P119, DOI 10.1007/s11042-012-1119-8
Hernando A, 2016, KNOWL-BASED SYST, V97, P188, DOI 10.1016/j.knosys.2015.12.018
Hong WX, 2012, EXPERT SYST APPL, V39, P12398, DOI 10.1016/j.eswa.2012.04.082
Hwang WY, 2018, ELECTRON COMMER R A, V29, P12, DOI 10.1016/j.elerap.2018.03.002
Hwangbo H, 2018, ELECTRON COMMER R A, V28, P94, DOI 10.1016/j.elerap.2018.01.012
Iwanaga J, 2016, KNOWL-BASED SYST, V99, P157, DOI 10.1016/j.knosys.2016.02.006
Jahrer Michael, 2010, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD'10, P693, DOI [DOI 10.1145/1835804.1835893, 10.1145/1835804.1835893]
Jawaheer G, 2014, ACM T INTERACT INTEL, V4, DOI 10.1145/2512208
Ji K, 2016, NEUROCOMPUTING, V173, P912, DOI 10.1016/j.neucom.2015.08.046
Kai Y, 2002, SIAM PROC S, P59
Karatzoglou Alexandros., 2011, Proceedings of the fifth ACM conference on Recommender systems, P313
Kermany NR, 2017, ELECTRON COMMER R A, V21, P50, DOI 10.1016/j.elerap.2016.12.005
Kim YS, 2011, EXPERT SYST APPL, V38, P13320, DOI 10.1016/j.eswa.2011.04.154
Koohi H, 2017, EXPERT SYST APPL, V83, P30, DOI 10.1016/j.eswa.2017.04.027
Koren Y, 2010, ACM T KNOWL DISCOV D, V4, DOI 10.1145/1644873.1644874
Koren Y, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P447
Kwon Y, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P307
Larrain Santiago, 2015, P 9 ACM C REC SYST, P269
Latha R, 2015, COMM COM INF SC, V566, P46, DOI 10.1007/978-3-319-26404-2_4
Leal F, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.11.004
Lee DD, 1999, NATURE, V401, P788, DOI 10.1038/44565
Lee DD, 2001, ADV NEUR IN, V13, P556
Lee TQ, 2008, EXPERT SYST APPL, V34, P3055, DOI 10.1016/j.eswa.2007.06.031
Li B, 2013, WORLD WIDE WEB, V16, P677, DOI 10.1007/s11280-012-0161-9
Li SSY, 2015, J ASSOC INF SYST, V16, P72, DOI 10.17705/1jais.00389
Li X, 2015, EXPERT SYST APPL, V42, P4274, DOI 10.1016/j.eswa.2015.01.054
Liao CL, 2016, ELECTRON COMMER R A, V18, P1, DOI 10.1016/j.elerap.2016.05.001
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Liu GM, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P155, DOI 10.1145/2939672.2939674
Liu MS, 2017, KNOWL-BASED SYST, V119, P178, DOI 10.1016/j.knosys.2016.12.010
Liu Nathan N, 2010, P 19 ACM INT C INF K, P1445, DOI DOI 10.1145/1871437.1871643
Liu XJ, 2017, CLUSTER COMPUT, V20, P1281, DOI 10.1007/s10586-017-0807-6
Luo X, 2014, IEEE T IND INFORM, V10, P1273, DOI 10.1109/TII.2014.2308433
Ma X, 2017, ELECTRON COMMER R A, V25, P29, DOI 10.1016/j.elerap.2017.06.005
Mobasher B., 2001, PROC IJCAI 2001 WORK, P53
Najafabadi MK, 2017, COMPUT HUM BEHAV, V67, P113, DOI 10.1016/j.chb.2016.11.010
Nilashi M, 2018, EXPERT SYST APPL, V92, P507, DOI 10.1016/j.eswa.2017.09.058
Nilashi M, 2016, ELECTRON COMMER R A, V19, P70, DOI 10.1016/j.elerap.2016.09.003
Nilashi M, 2015, INFORM SCIENCES, V293, P235, DOI 10.1016/j.ins.2014.09.012
Nishimura N, 2018, INFORM SCIENCES, V429, P406, DOI 10.1016/j.ins.2017.11.014
O'Mahony M. P., 2006, 2006 International Conference on Intelligent User Interfaces, P109, DOI 10.1145/1111449.1111477
Parra D., 2011, P CARS 2011
Polatidis N, 2016, EXPERT SYST APPL, V48, P100, DOI 10.1016/j.eswa.2015.11.023
Resnick P., 1994, Transcending Boundaries, CSCW '94. Proceedings of the Conference on Computer Supported Cooperative Work, P175, DOI 10.1145/192844.192905
Ricci F, 2015, RECOMMENDER SYSTEMS
Salah A, 2016, NEUROCOMPUTING, V175, P206, DOI 10.1016/j.neucom.2015.10.050
Sarwar B., 2001, P 10 INT C WORLD WID, P285
Su Q, 2015, ELECTRON COMMER R A, V14, P1, DOI 10.1016/j.elerap.2014.10.002
Tian G, 2017, INFORM SYST FRONT, V19, P75, DOI 10.1007/s10796-015-9590-1
Umyarov A, 2011, ACM T WEB, V5, DOI 10.1145/1921591.1921594
Nguyen VD, 2017, ELECTRON COMMER R A, V26, P101, DOI 10.1016/j.elerap.2017.10.002
Villegas NM, 2018, KNOWL-BASED SYST, V140, P173, DOI 10.1016/j.knosys.2017.11.003
Xu Y, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P313, DOI [10.1145/2939672.2939703, 10.1007/978-3-319-32025-0_20]
Yang JF, 2010, PROCEEDINGS OF THE ELEVENTH INTERNATIONAL SYMPOSIUM ON STRUCTURAL ENGINEERING, VOL I AND II, P19
Yera R, 2016, APPL SOFT COMPUT, V40, P187, DOI 10.1016/j.asoc.2015.10.060
Toledo RY, 2015, KNOWL-BASED SYST, V76, P96, DOI 10.1016/j.knosys.2014.12.011
Yu X, 2018, KNOWL-BASED SYST, V141, P80, DOI 10.1016/j.knosys.2017.11.010
Zhang J, 2016, APPL INTELL, V45, P230, DOI 10.1007/s10489-015-0756-9
Zhang S, 2006, SIAM PROC S, P549
Zimdars A., 2001, P 17 C UNC ART INT U, P580
NR 82
TC 17
Z9 17
U1 0
U2 36
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD SEP-OCT
PY 2019
VL 37
AR 100877
DI 10.1016/j.elerap.2019.100877
PG 12
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA JC1QV
UT WOS:000489053400002
DA 2024-03-27
ER
PT J
AU Deng, WW
AF Deng, Weiwei
TI Leveraging consumer behaviors for product recommendation: an approach
based on heterogeneous network
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Product recommendation; Recommender system; Consumer behavior;
Heterogeneous network; Electronic commerce
ID PERSONALIZED RECOMMENDATION; RELEVANCE MEASURE; INFORMATION; SYSTEM
AB Consumer behaviors (e.g., clicking products, adding products to favorites, adding products to carts, and purchasing products) play important roles in inferring consumers' interests for product recommendation. Although studies have been conducted to incorporate the consumer behaviors for product recommendation, the heterogeneity of the behaviors and their composites were seldom explored for product recommendation. There is a need to capture the heterogeneity of the consumer behaviors and reveal their importance in the product recommendation because the behaviors indicate different consumer preferences for products. To bridge the gap, this research proposes a heterogeneous network-based approach to leverage the consumer behaviors for product recommendation. The proposed approach represents consumers and products as different types of nodes and behaviors as different types of edges. Meta paths that describe behavioral relationships between the consumers and products are used to calculate their similarities, which are further used to generate recommendations. To select informative meta paths for product recommendation, a heuristic selection mechanism is proposed. Besides, the research uses a non-negative matrix factorization method to learn the weights of the selected meta paths and then makes personalized recommendations for consumers. Experimental results based on real-world data demonstrate that the proposed approach not only helps to understand the roles of different consumer behaviors in product recommendation, but also achieves better recommendation performance than several baseline methods.
C1 [Deng, Weiwei] South China Normal Univ, Sch Econ & Management, Waihuan Xi Rd 378, Guangzhou, Guangdong, Peoples R China.
C3 South China Normal University
RP Deng, WW (autor correspondiente), South China Normal Univ, Sch Econ & Management, Waihuan Xi Rd 378, Guangzhou, Guangdong, Peoples R China.
EM weiweideng@m.scnu.edu.cn
CR Castro J, 2018, INT J INTELL SYST, V33, P2058, DOI 10.1002/int.21922
Castro J, 2015, INT J INTELL SYST, V30, P887, DOI 10.1002/int.21730
Cooper C, 2014, WWW'14 COMPANION: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P811, DOI 10.1145/2567948.2579244
DENG W., 2017, ICIS 2017 P
He XJ, 2019, KNOWL-BASED SYST, V184, DOI 10.1016/j.knosys.2019.104899
Hidasi B., 2016, ICLR, P1, DOI DOI 10.48550/ARXIV.1511.06939
Hu L, 2017, IEEE ACCESS, V5, P19773, DOI 10.1109/ACCESS.2017.2751682
Jing N, 2018, ELECTRON COMMER RES, V18, P159, DOI 10.1007/s10660-017-9275-6
Kang D, 2019, MULTIMED TOOLS APPL, V78, P3267, DOI 10.1007/s11042-018-6733-7
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Lao N., 2010, P 16 ACM SIGKDD INT, P881
Lin CJ, 2007, NEURAL COMPUT, V19, P2756, DOI 10.1162/neco.2007.19.10.2756
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Liu HZ, 2019, KNOWL-BASED SYST, V181, DOI 10.1016/j.knosys.2019.05.027
Liu Y, 2019, EXPERT SYST, V36, DOI 10.1111/exsy.12365
Meng XF, 2014, LECT NOTES COMPUT SC, V8709, P636, DOI 10.1007/978-3-319-11116-2_61
Mishra R, 2015, DECIS SUPPORT SYST, V75, P1, DOI 10.1016/j.dss.2015.04.004
Oramas S, 2017, ACM T INTEL SYST TEC, V8, DOI 10.1145/2926718
Pourgholamali F, 2017, ELECTRON COMMER R A, V25, P70, DOI 10.1016/j.elerap.2017.08.001
Ru Jia, 2017, 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), P1, DOI 10.1109/CITS.2017.8035294
Shani G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P257, DOI 10.1007/978-0-387-85820-3_8
Shi C, 2019, IEEE T KNOWL DATA EN, V31, P357, DOI 10.1109/TKDE.2018.2833443
Shi C, 2017, IEEE T KNOWL DATA EN, V29, P17, DOI 10.1109/TKDE.2016.2598561
Shi C, 2014, IEEE T KNOWL DATA EN, V26, P2479, DOI 10.1109/TKDE.2013.2297920
Sun JS, 2020, ELECTRON COMMER RES, V20, P147, DOI 10.1007/s10660-019-09390-3
Sun LH, 2020, ELECTRON COMMER RES, V20, P857, DOI 10.1007/s10660-018-9319-6
Sunt YZ, 2011, PROC VLDB ENDOW, V4, P992
Wang JZ, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P839, DOI 10.1145/3219819.3219869
Wang Q, 2019, INT J ELECTRON COMM, V23, P147, DOI 10.1080/10864415.2018.1564549
Wang Q, 2017, DECIS SUPPORT SYST, V103, P46, DOI 10.1016/j.dss.2017.09.001
Wang Q, 2019, ELECTRON COMMER RES, V19, P59, DOI 10.1007/s10660-018-09325-4
Wei X, 2019, LECT NOTES ARTIF INT, V11775, P72, DOI 10.1007/978-3-030-29551-6_7
Wu S, 2019, AAAI CONF ARTIF INTE, P346
Xu W, 2016, J NETW COMPUT APPL, V59, P362, DOI 10.1016/j.jnca.2015.01.003
Xu YH, 2019, DECIS SUPPORT SYST, V119, P1, DOI 10.1016/j.dss.2019.01.004
Yu JL, 2018, CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P357, DOI 10.1145/3269206.3271725
Zhang JH, 2018, INFORM SYST FRONT, V20, P617, DOI 10.1007/s10796-016-9697-z
Zhang MY, 2019, ACM T KNOWL DISCOV D, V13, DOI 10.1145/3320277
Zhou LC, 2020, ELECTRON COMMER RES, V20, P321, DOI 10.1007/s10660-020-09411-6
NR 39
TC 2
Z9 2
U1 12
U2 46
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD DEC
PY 2022
VL 22
IS 4
BP 1079
EP 1105
DI 10.1007/s10660-020-09441-0
EA NOV 2020
PG 27
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5W8QD
UT WOS:000589577500001
DA 2024-03-27
ER
PT J
AU Dong, XS
Tu, HQ
Zhu, HZ
Liu, TL
Zhao, X
Xie, K
AF Dong, Xiaosong
Tu, Hanqi
Zhu, Hanzhe
Liu, Tianlang
Zhao, Xing
Xie, Kai
TI Does diversity facilitate consumer decisions: a comparative perspective
based on single-category versus multi-category products
SO ASIA PACIFIC JOURNAL OF MARKETING AND LOGISTICS
LA English
DT Article; Early Access
DE Information diversity; Online visitor heterogeneity; Consumer decision
making; Recommender systems; Online marketing
ID SEGMENTATION; COMPLEXITY
AB PurposeThis study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors - direct, hesitant and hedonic - in the relationship between product information diversity and consumer decision making.Design/methodology/approachThe research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.FindingsThe study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.Originality/valueFirst, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.
C1 [Dong, Xiaosong; Tu, Hanqi; Liu, Tianlang; Xie, Kai] Nanchang Univ, Res Ctr Econ & Social Dev Cent China, Sch Econ & Management, Nanchang, Peoples R China.
[Dong, Xiaosong] Shanghai Univ Engn Sci, Sch Management, Shanghai, Peoples R China.
[Zhu, Hanzhe] Univ Alberta, Edmonton, AB, Canada.
[Zhao, Xing] Shanghai Univ Engn Sci, Sch Management, Shanghai, Peoples R China.
C3 Nanchang University; Shanghai University of Engineering Science;
University of Alberta; Shanghai University of Engineering Science
RP Zhao, X (autor correspondiente), Shanghai Univ Engn Sci, Sch Management, Shanghai, Peoples R China.
EM 1246191958@qq.com
FU This work was supported by the National Natural Science Foundation of
China and "14th Five-Year Plan" (2021) Fund Project of Social Sciences
in Jiangxi Province (Grant number 72272072, 21GL01). [2021]; National
Natural Science Foundation of China [72272072, 21GL01]; Fund Project of
Social Sciences in Jiangxi Province
FX This work was supported by the National Natural Science Foundation of
China and "14th Five-Year Plan" (2021) Fund Project of Social Sciences
in Jiangxi Province (Grant number 72272072, 21GL01).
CR Battistello L, 2021, CIRP J MANUF SCI TEC, V35, P517, DOI 10.1016/j.cirpj.2021.08.008
Brzeczek T, 2020, REV MANAG SCI, V14, P1009, DOI 10.1007/s11846-018-0315-y
Burson KA, 2007, J CONSUM RES, V34, P104, DOI 10.1086/513051
[车敬上 Che Jingshang], 2019, [心理科学进展, Advances in Psychological Science], V27, P1758
CHEWNING EG, 1990, ACCOUNT ORG SOC, V15, P527, DOI 10.1016/0361-3682(90)90033-Q
Dellaert BGC, 2012, J MARKETING RES, V49, P424, DOI 10.1509/jmr.09.0315
Feng YY, 2000, OPER RES, V48, P332, DOI 10.1287/opre.48.2.332.13373
Hu HF, 2019, J BUS RES, V100, P27, DOI 10.1016/j.jbusres.2019.03.011
Huseynov F, 2019, SAGE OPEN, V9, DOI 10.1177/2158244019854639
Ireland NJ, 2007, J IND ECON, V55, P235, DOI 10.1111/j.1467-6451.2007.00310.x
Li YR, 2021, BEHAV INFORM TECHNOL, V40, P1448, DOI 10.1080/0144929X.2020.1759691
Liao SH, 2020, ASIA PAC J MARKET LO, V32, P589, DOI 10.1108/APJML-03-2019-0211
Lin Y., 2021, Journal of Information Science, V49, P302
Liu HP, 2021, J INTELL FUZZY SYST, V40, P6529, DOI 10.3233/JIFS-189491
Liu W, 2022, DISCRETE DYN NAT SOC, V2022, DOI 10.1155/2022/1326579
Liu Y, 2015, ANN OPER RES, V233, P263, DOI 10.1007/s10479-013-1443-z
Lo Papa G, 2011, GEOMORPHOLOGY, V135, P332, DOI 10.1016/j.geomorph.2011.02.015
Lurie NH, 2004, J CONSUM RES, V30, P473, DOI 10.1086/380283
Ma BJ, 2012, ELECTRON COMMER R A, V11, P560, DOI 10.1016/j.elerap.2012.09.001
Moe WW, 2003, J CONSUM PSYCHOL, V13, P29, DOI 10.1207/153276603768344762
Muvengwi J, 2019, URBAN FOR URBAN GREE, V43, DOI 10.1016/j.ufug.2019.126370
Nambiar A., 2022, Plos One, V17
Otondo RF, 2008, INFORM MANAGE-AMSTER, V45, P21, DOI 10.1016/j.im.2007.09.003
Park SH, 2013, INT J ELECTRON COMM, V18, P51, DOI 10.2753/JEC1086-4415180202
Pipitwanichakarn T, 2021, ASIA PAC J MARKET LO, V33, P1415, DOI 10.1108/APJML-10-2018-0448
Punhani A, 2022, IEEE ACCESS, V10, P115025, DOI 10.1109/ACCESS.2022.3215568
Qin R, 2017, ELECTRON COMMER R A, V24, P68, DOI 10.1016/j.elerap.2017.07.001
Sasaki T, 2011, J ECON PSYCHOL, V32, P391, DOI 10.1016/j.joep.2011.02.010
Sohrabi M, 2024, MOL BIOTECHNOL, V66, P424, DOI 10.1007/s12033-022-00630-w
Sondhi N, 2023, ASIA PAC J MARKET LO, V35, P380, DOI 10.1108/APJML-07-2021-0492
Song Keyong-Seog, 2020, [Journal of Digital Convergence, 디지털융복합연구], V18, P137, DOI 10.14400/JDC.2020.18.4.137
Tan AH, 2022, IEEE T CYBERNETICS, V52, P192, DOI 10.1109/TCYB.2020.2973379
Tjahjaningsih E, 2020, J ASIAN FINANC ECON, V7, P481, DOI 10.13106/jafeb.2020.vol7.no12.481
Torres A., 2007, IADIS INT C E COMM
Tsekouras D, 2020, J ACAD MARKET SCI, V48, P186, DOI 10.1007/s11747-019-00682-6
Wan X, 2017, INT J PROD ECON, V186, P123, DOI 10.1016/j.ijpe.2017.02.002
Wu IC, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102323
Wu JH, 2014, DECIS SUPPORT SYST, V58, P43, DOI 10.1016/j.dss.2013.01.015
Yang Z., 2023, Iscience, V26
Zavali M, 2023, IEEE T ENG MANAGE, V70, P1353, DOI 10.1109/TEM.2021.3070069
NR 40
TC 0
Z9 0
U1 30
U2 30
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 1355-5855
EI 1758-4248
J9 ASIA PAC J MARKET LO
JI Asia Pac. J. Market. Logist.
PD 2023 NOV 7
PY 2023
DI 10.1108/APJML-05-2023-0395
EA NOV 2023
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA W9ML8
UT WOS:001094797300001
DA 2024-03-27
ER
PT J
AU McGinty, L
Smyth, B
AF McGinty, Lorraine
Smyth, Barry
TI Adaptive selection: An analysis of critiquing and preference-based
feedback in conversational recommender systems
SO INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
LA English
DT Article
DE adaptive e-commerce applications; conversational recommender systems;
critiquing; feedback elicitation; preference-based feedback; similarity
and diversity
AB E-commerce recommender systems help consumers to locate products within a complex product-space. Conversational recommender systems engage the user in a multi-cycle session, suggesting one or more products during each cycle, and using the feedback to inform the suggestions for the next cycle. By combining user feedback over several cycles, the system obtains a clear picture of the product the user wishes to purchase. As demonstrated under several experimental conditions, the performance of recommender systems is dramatically improved by the technique of adaptive selection, which employs critiquing and preference-based feedback, and emphasizes product diversity rather than similarity as a selection constraint.
C1 Univ Coll Dublin, Sch Comp Sci & Informat, Dublin, Ireland.
C3 University College Dublin
RP McGinty, L (autor correspondiente), Univ Coll Dublin, Sch Comp Sci & Informat, Dublin, Ireland.
EM lorraine.mcginty@ucd.ie; barry.smyth@ucd.ie
RI Smyth, Barry/AAY-9953-2020
OI Smyth, Barry/0000-0003-0962-3362
CR [Anonymous], P 17 INT JOINT C ART
Bridge D, 2002, LECT NOTES ARTIF INT, V2416, P43
BRIDGE D, 2001, WORKSH CBR EL COMM, P1
Bridge DerekG., 2002, ECCBR Workshops, P9
Burke R., 2000, ENCY LIB INFORM SYST, V69, P32
Burke RD, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P462
Chen LC, 2004, LECT NOTES COMPUT SC, V3073, P1
COSTELLO E, 2006, P 11 INT C INT US IN, P282
Coyle L, 2004, LECT NOTES COMPUT SC, V3155, P560
DOYLE M, 2000, P 5 EUR WORKSH CAS B, P49
Faltings Boi, 2004, P 9 INT C INTELLIGEN, P22
GOKER M, 2000, P 5 EUR WORKSH CAS B, P99
Huan Pearl, 2004, P 5 ACM C EL COMM, P208
LEAKE DB, 1996, CASE BASED REASONING
McCarthy K, 2004, LECT NOTES COMPUT SC, V3137, P176
MCCARTHY K, 2004, P INT C INT US INT, P175
McCarthy K., 2005, P INT FLAIRS C 2005, P123
McGinty L, 2002, LECT NOTES ARTIF INT, V2416, P575
McGinty L, 2003, LECT NOTES ARTIF INT, V2689, P276
McGinty L., 2003, P WORKSHOP PERSONALI, P20
MCGINTY L, 2002, P WORKSH PERS ECOMM, P80
McSherry D, 2002, LECT NOTES ARTIF INT, V2416, P219
NADER M, 2005, P IEEE INT C ETECHN, P772
PRICE B, 2005, P 20 NAT C ART INT, P541
Pu P, 2004, IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2004), PROCEEDINGS, P724, DOI 10.1109/WI.2004.10058
Pu P, 2004, CONSTRAINTS, V9, P289, DOI 10.1023/B:CONS.0000049205.05581.24
Reilly J, 2004, LECT NOTES COMPUT SC, V3155, P763
Ricci F., 2005, Information and Communication Technologies in Tourism 2005, P172
Schafer J., 2001, DATA MIN KNOWL DISC, V5, P115, DOI DOI 10.1023/A:1009804230409
Shardanand U., 1995, Human Factors in Computing Systems. CHI'95 Conference Proceedings, P210, DOI 10.1145/223904.223931
Shearin S., 2001, The 6th International Conference on Intelligent User Interfaces (IUI '01), P145
Shimazu H., 2001, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), P1443
Smyth B, 2001, LECT NOTES ARTIF INT, V2080, P347
SMYTH B, 2003, P 18 INT JOINT C ART, P127
NR 34
TC 35
Z9 39
U1 1
U2 11
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1086-4415
EI 1557-9301
J9 INT J ELECTRON COMM
JI Int. J. Electron. Commer.
PD WIN
PY 2006
VL 11
IS 2
BP 35
EP 57
DI 10.2753/JEC1086-4415110202
PG 23
WC Business; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 119YF
UT WOS:000243049700003
DA 2024-03-27
ER
PT J
AU Farias, VF
Li, AA
AF Farias, Vivek F.
Li, Andrew A.
TI Learning Preferences with Side Information
SO MANAGEMENT SCIENCE
LA English
DT Article
DE personalization; e-commerce; online retail; recommender systems;
collaborative filtering; matrix recovery; tensor recovery; side
information; multi-interaction data
ID RECOMMENDER SYSTEMS; MATRIX; NUMBER; DECOMPOSITIONS; OPTIMIZATION
AB Product and content personalization is now ubiquitous in e-commerce. There are typically not enough available transactional data for this task. As such, companies today seek to use a variety of information on the interactions between a product and a customer to drive personalization decisions. We formalize this problem as one of recovering a large-scale matrix with side information in the form of additional matrices of conforming dimension. Viewing the matrix we seek to recover and the side information we have as slices of a tensor, we consider the problem of slice recovery, which is to recover specific slices of "simple" tensors from noisy observations of the entire tensor. We propose a definition of simplicity that on the one hand elegantly generalizes a standard generative model for our motivating problem and on the other hand subsumes low-rank tensors for a variety of existing definitions of tensor rank. We provide an efficient algorithm for slice recovery that is practical for massive data sets and provides a significant performance improvement over state-of-the-art incumbent approaches to tensor recovery. Furthermore, we establish near-optimal recovery guarantees that, in an important regime, represent an order improvement over the best available results for this problem. Experiments on data from a music streaming service demonstrate the performance and scalability of our algorithm.
C1 [Farias, Vivek F.] MIT, Sloan Sch Management, Cambridge, MA 02142 USA.
[Li, Andrew A.] MIT, Operat Res Ctr, Cambridge, MA 02142 USA.
C3 Massachusetts Institute of Technology (MIT); Massachusetts Institute of
Technology (MIT)
RP Farias, VF (autor correspondiente), MIT, Sloan Sch Management, Cambridge, MA 02142 USA.
EM vivekf@mit.edu; aali@mit.edu
OI Farias, Vivek/0000-0002-5856-9246; /0000-0002-9552-6421
CR Acar E, 2005, LECT NOTES COMPUT SC, V3495, P256
Achlioptas D, 2007, J ACM, V54, DOI 10.1145/1219092.1219097
Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
[Anonymous], DAIMI REP
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Ansari A, 2003, J MARKETING RES, V40, P131, DOI 10.1509/jmkr.40.2.131.19224
Bahadori Mohammad Taha, 2014, ADV NEURAL INFORM PR, P3491
Bernhardsson E, 2014, MACH LEARN C APR 14
Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127
Besbes O., 2015, MANUFACTURING SERVIC, V18, P15
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Candès EJ, 2011, IEEE T INFORM THEORY, V57, P2342, DOI 10.1109/TIT.2011.2111771
Candès EJ, 2010, IEEE T INFORM THEORY, V56, P2053, DOI 10.1109/TIT.2010.2044061
CARROLL JD, 1970, PSYCHOMETRIKA, V35, P283, DOI 10.1007/BF02310791
CATTELL RB, 1966, MULTIVAR BEHAV RES, V1, P245, DOI 10.1207/s15327906mbr0102_10
Chatterjee S, 2015, ANN STAT, V43, P177, DOI 10.1214/14-AOS1272
Chiang K.-Y., 2015, Advances in Neural Information Processing Systems, P3429
Chung TS, 2009, MARKET SCI, V28, P52, DOI 10.1287/mksc.1080.0371
Demirezen EM, 2016, PROD OPER MANAG, V25, P593, DOI 10.1111/poms.12540
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Gandy S, 2011, INVERSE PROBL, V27, DOI 10.1088/0266-5611/27/2/025010
Garfinkel R, 2008, DECIS SUPPORT SYST, V46, P61, DOI 10.1016/j.dss.2008.05.006
Gavish M, 2014, IEEE T INFORM THEORY, V60, P5040, DOI 10.1109/TIT.2014.2323359
Ghose A, 2012, MARKET SCI, V31, P493, DOI 10.1287/mksc.1110.0700
GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867
Grey P, 2015, EXPORT X 1211
Gross D, 2011, IEEE T INFORM THEORY, V57, P1548, DOI 10.1109/TIT.2011.2104999
GROVER R, 1987, J MARKETING RES, V24, P139, DOI 10.2307/3151504
Harris A., 2015, FAST CO
HENRION R, 1994, CHEMOMETR INTELL LAB, V25, P1, DOI 10.1016/0169-7439(93)E0086-J
Hoff PD, 2007, J AM STAT ASSOC, V102, P674, DOI 10.1198/016214506000001310
Hosanagar K., 2008, P 2008 INT C INF SYS
Huang B, 2015, PAC J OPTIM, V11, P339
Jacobs BJD, 2016, MARKET SCI, V35, P389, DOI 10.1287/mksc.2016.0985
Jain P., 2014, Advances in Neural Information Processing Systems, P1431
Jain P. K., 2013, WORKING PAPER
Keshavan RH, 2010, IEEE T INFORM THEORY, V56, P2980, DOI 10.1109/TIT.2010.2046205
Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140
Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X
Koltchinskii V, 2011, ANN STAT, V39, P2302, DOI 10.1214/11-AOS894
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Liu J, 2013, IEEE T PATTERN ANAL, V35, P208, DOI 10.1109/TPAMI.2012.39
Liu Y., 2014, P ADV NEUR INF PROC, V2, P1763
Morup M, 2011, WIRES DATA MIN KNOWL, V1, P24, DOI 10.1002/widm.1
Mu C, 2013, WORKING PAPER
Naik P, 2008, MARKET LETT, V19, P201, DOI 10.1007/s11002-008-9036-3
Oliver NM, 2000, IEEE T PATTERN ANAL, V22, P831, DOI 10.1109/34.868684
Owen AB, 2009, ANN APPL STAT, V3, P564, DOI 10.1214/08-AOAS227
Papadimitriou C. H., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, P159, DOI 10.1145/275487.275505
Pastur L.A., 1967, MATH USSR SB, V1, P457, DOI [10.1070/SM1967v001n04ABEH001994, 10.1070/sm1967v001n04abeh001994, DOI 10.1070/SM1967V001N04ABEH001994]
Recht B, 2011, J MACH LEARN RES, V12, P3413
Romera-Paredes B., 2013, Advances in Neural Information Processing Systems, P2967
Romera-Paredes Bernardino, 2013, ICML, P1444
Shi Y, 2014, ACM COMPUT SURV, V47, DOI 10.1145/2556270
Signoretto M, 2014, MACH LEARN, V94, P303, DOI 10.1007/s10994-013-5366-3
Singh A, 2008, INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, P650
SIROVICH L, 1987, J OPT SOC AM A, V4, P519, DOI 10.1364/JOSAA.4.000519
So AMC, 2007, MATH PROGRAM, V109, P367, DOI 10.1007/s10107-006-0040-1
Soni A, 2016, IEEE T INFORM THEORY, V62, P3636, DOI 10.1109/TIT.2016.2549040
Statista, 2016, ANN NUMB WORLDW ACT
Sun Jian-Tao., 2005, PROC WWW 05, P382, DOI DOI 10.1145/1060745.1060803
Suzuki T, 2015, PR MACH LEARN RES, V37, P1273
Tomioka R., 2011, ADV NEURAL INFORM PR, V24, P972
Tomioka Ryota, 2013, ADV NEURAL INFORM PR, P1331
Troyanskaya O, 2001, BIOINFORMATICS, V17, P520, DOI 10.1093/bioinformatics/17.6.520
Vasilescu MAO, 2002, LECT NOTES COMPUT SC, V2350, P447
WOLD S, 1978, TECHNOMETRICS, V20, P397, DOI 10.2307/1267639
Xu M., 2013, P C ADV NEUR INF PRO, P2301, DOI DOI 10.5555/2999792.2999869
YIN YQ, 1988, PROBAB THEORY REL, V78, P509, DOI 10.1007/BF00353874
Yuan M, 2016, FOUND COMPUT MATH, V16, P1031, DOI 10.1007/s10208-015-9269-5
Zheng Q., 2015, ADV NEURAL INFORM PR, V28, P3106
Zhou H, 2013, J AM STAT ASSOC, V108, P540, DOI 10.1080/01621459.2013.776499
NR 74
TC 28
Z9 32
U1 5
U2 52
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD JUL
PY 2019
VL 65
IS 7
BP 3131
EP 3149
DI 10.1287/mnsc.2018.3092
PG 19
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA IJ2CB
UT WOS:000475704700010
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Liu, X
Datta, A
Rzadca, K
AF Liu, Xin
Datta, Anwitaman
Rzadca, Krzysztof
TI Trust beyond reputation: A computational trust model based on
stereotypes
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Security and trust; Computational trust; E-commerce; Multiagent systems;
Information retrieval; Distributed systems; Recommender systems
ID PEER; CONTEXT; SYSTEM
AB Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"-essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information. (c) 2012 Elsevier B.V. All rights reserved.
C1 [Liu, Xin] Ecole Polytech Fed Lausanne, Sch Comp & Commun Sci, CH-1015 Lausanne, Switzerland.
[Datta, Anwitaman] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore.
[Rzadca, Krzysztof] Univ Warsaw, Inst Informat, PL-00325 Warsaw, Poland.
C3 Swiss Federal Institutes of Technology Domain; Ecole Polytechnique
Federale de Lausanne; Nanyang Technological University; University of
Warsaw
RP Liu, X (autor correspondiente), Ecole Polytech Fed Lausanne, Sch Comp & Commun Sci, CH-1015 Lausanne, Switzerland.
EM x.liu@epfl.ch; anwitaman@ntu.edu.sg; krzadca@mimuw.edu.pl
RI Datta, .Anwitaman/A-3713-2011
OI Datta, Anwitaman/0000-0002-4203-1572
FU A* Star TSRP [1021580038]
FX This work has been supported in part by A* Star TSRP grant number
1021580038.
CR Aringhieri R, 2006, J AM SOC INF SCI TEC, V57, P528, DOI 10.1002/asi.20307
Bartoszynski R, 2008, PROBABILITY AND STATISTICAL INFERENCE, 2ND EDITION, P1
Blewitt ME, 2008, NAT GENET, V40, P663, DOI 10.1038/ng.142
Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
Buchegger S., 2004, P P2PECON
Burnett C., 2010, Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, V1, P241, DOI DOI 10.1016/J.DSS.2005.05.019
Burnett C., 2011, P 14 INT WORKSH TRUS, P25
Cachin Christian, 2009, SIGACT News, V40, P81, DOI 10.1145/1556154.1556173
Conte R., 2002, REPUTATION ARTIFICIA
Coons E. E., 2010, BIOFEEDBACK, V38, P101
Corbitt B. J., 2003, ELECTRON COMMER R A, V2, P203, DOI [DOI 10.1016/S1567-4223(03)00024-3, 10.1016/s1567-4223(03)00024-3]
Gambetta D., 2000, Can We Trust Trust?, V13, P213, DOI [DOI 10.2307/591021, 10.2307/2234217]
Josang A., 2003, P WORKSH FORM ASP SE
Josang Audun., 2006, Proceedings of the 29th Australasian Computer Science Conference, ACSC, P85
Josang Audun., 2002, Proceedings of the 15th bled electronic commerce conference, V5, P2502
Kamvar S. D., 2003, P 12 INT C WORLD WID, P640, DOI [10.1145/775152.775242, DOI 10.1145/775152.775242]
Kass Gordon V., 1980, Applied Statistics, V29, P119, DOI DOI 10.2307/2986296
Kaufman LM, 2009, IEEE SECUR PRIV, V7, P61, DOI 10.1109/MSP.2009.87
Kim HW, 2012, ELECTRON COMMER R A, V11, P241, DOI 10.1016/j.elerap.2011.06.003
Liu X., 2011, P 22 INT JOINT C ART
Liu X., 2009, P 18 ACM C INF KNOWL
Liu X., 2010, P 2010 IEEE 2 INT C
Liu X., 2012, AAAI, P1938
Liu X, 2012, J NETW SYST MANAG, V20, P200, DOI 10.1007/s10922-011-9198-9
MacKay DJ, 2003, Information theory, inference, and learning algorithms
Macskassy S. A., 2010, P 2010 INT C ADV SOC
Mui L., 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences, P2431, DOI 10.1109/HICSS.2002.994181
Oxenham M. G., 2003, SPIE C SERIES
Palmisano C, 2008, IEEE T KNOWL DATA EN, V20, P1535, DOI 10.1109/TKDE.2008.110
Quinlan J.R., 1992, Proceedings of the 5th Australian Joint Conference on Artificial Intelligence (AI'92), P343, DOI DOI 10.1142/9789814536271
Rahman AA., 1997, PROC ACM NEW SECURIT, P48, DOI 10.1145/283699.283739
Ravichandran A., 2006, SACMAT 2006. Proceedings of Eleventh ACM Symposium on Access Control Models and Technologies, P71
Regan K., 2006, P 21 NAT C ART INT, V2
Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/S15516709COG0304_3
Sabater J., 2001, Proceedings of the Fifth International Conference on Autonomous Agents, P194, DOI 10.1145/375735.376110
Su X., 2009, Advances in artificial intelligence, V2009, P4, DOI [10.1155/2009/421425, DOI 10.1155/2009/421425]
Sun L, 2005, LECT NOTES COMPUT SC, V3828, P651
Swamynathan G, 2005, LECT NOTES COMPUT SC, V3759, P82
Tadelis S, 2003, ECON THEOR, V21, P635, DOI 10.1007/s00199-002-0257-z
Teacy WTL, 2006, AUTON AGENT MULTI-AG, V12, P183, DOI 10.1007/s10458-006-5952-x
Tirole J, 1996, REV ECON STUD, V63, P1, DOI 10.2307/2298112
Wu F, 2011, ELECTRON COMMER R A, V10, P428, DOI 10.1016/j.elerap.2010.09.004
Xin L., 2011, 11030086 ARXIV
Xin L., 2011, P 10 INT C AUT AG MU
Xiong L, 2004, IEEE T KNOWL DATA EN, V16, P843, DOI 10.1109/TKDE.2004.1318566
Zhou RF, 2007, IEEE T PARALL DISTR, V18, P460, DOI 10.1109/TPDS.2007.1015
NR 46
TC 37
Z9 45
U1 0
U2 48
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD JAN-FEB
PY 2013
VL 12
IS 1
BP 24
EP 39
DI 10.1016/j.elerap.2012.07.001
PG 16
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 091AZ
UT WOS:000315022400003
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Gupta, S
Dixit, VS
AF Gupta, Shalini
Dixit, Veer Sain
TI A Meta-Heuristic Algorithm Approximating Optimized Recommendations for
E-Commerce Business Promotions
SO INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY PROJECT MANAGEMENT
LA English
DT Article
DE Clickstream; E-Commerce; Implicit Preference; Information Retrieval;
Recommender System; Sequential Path; Sessions; Web Log Mining
ID COLLABORATIVE RECOMMENDER; USER PROFILES; SYSTEM; BEHAVIOR; USAGE
AB To provide personalized services such as online-product recommendations, it is usually necessary to model clickstream behavior of users if implicit preferences are taken into account. To accomplish this, web log mining is a promising approach that mines clickstream sessions and depicts frequent sequential paths that a customer follows while browsing e-commerce websites. Strong attributes are identified from the navigation behavior of users. These attributes reflect absolute preference (AP) of the customer towards a product viewed. The preferences are obtained only for the products clicked. These preferences are further refined by calculating the sequential preference (SP) of the user for the products. This paper proposes an intelligent recommender system known as SAPRS (sequential absolute preference-based recommender system) that embed these two approaches that are integrated to improve the quality of recommendation. The performance is evaluated using information retrieval methods. Extensive experiments were carried out to evaluate the proposed approach against state-of-the-art methods.
C1 [Gupta, Shalini; Dixit, Veer Sain] Univ Delhi, Atma Ram Sanatan Dharma Coll, Dept Comp Sci, Delhi, India.
C3 University of Delhi; Atma Ram Sanatan Dharma College
RP Gupta, S (autor correspondiente), Univ Delhi, Atma Ram Sanatan Dharma Coll, Dept Comp Sci, Delhi, India.
RI Dixit, Veer Sain/AAT-4833-2021; dixit, veer sain/C-8030-2013
OI Gupta, Shalini/0000-0002-4270-1222
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Adomavicius G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P217, DOI 10.1007/978-0-387-85820-3_7
Aloysius G, 2013, J KING SAUD UNIV-COM, V25, P77, DOI 10.1016/j.jksuci.2012.07.001
[Anonymous], 2007, P 2007 INT C SERV SY, DOI DOI 10.1109/ICSSSM.2007.4280214
Barragáns-Martínez AB, 2010, INFORM SCIENCES, V180, P4290, DOI 10.1016/j.ins.2010.07.024
Ben-Shimon David, 2015, P 9 ACM C RECOMMENDE, P357, DOI DOI 10.1145/2792838.2798723
Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
Chen W, 2014, WORLD WIDE WEB, V17, P271, DOI 10.1007/s11280-012-0187-z
Chen YL, 2009, ELECTRON COMMER R A, V8, P241, DOI 10.1016/j.elerap.2009.03.002
Chiang RD, 2013, ELECTRON COMMER R A, V12, P362, DOI 10.1016/j.elerap.2013.06.002
Christakou C, 2007, INT J ARTIF INTELL T, V16, P771, DOI 10.1142/S0218213007003540
Cleger-Tamayo S, 2012, KNOWL-BASED SYST, V27, P180, DOI 10.1016/j.knosys.2011.11.017
Degemmis M, 2007, USER MODEL USER-ADAP, V17, P217, DOI 10.1007/s11257-006-9023-4
Dwivedi P, 2015, EXPERT SYST, V32, P264, DOI 10.1111/exsy.12061
Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6
Ghauth KI, 2010, AUSTRALAS J EDUC TEC, V26, P764
Ghazanfar MA, 2015, INTELL DATA ANAL, V19, P845, DOI 10.3233/IDA-150748
Greening D. R., 1998, RECOMMENDER SYSTEMS, P53
Hamidi H, 2018, J GLOB INF MANAG, V26, P41, DOI 10.4018/JGIM.2018010103
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hong Yu., 2012, J CHINESE COMPUTER S, V1, P24
Hu YF, 2008, IEEE DATA MINING, P263, DOI 10.1109/ICDM.2008.22
Iaquinta Leo, 2007, 7th International Conference on Hybrid Intelligent Systems, HIS 2007, P47
Jeong B, 2009, EXPERT SYST APPL, V36, P6181, DOI 10.1016/j.eswa.2008.07.085
Jung KY, 2004, LECT NOTES COMPUT SC, V3036, P295
Kim HN, 2010, ELECTRON COMMER R A, V9, P73, DOI 10.1016/j.elerap.2009.08.004
Kim SC, 2016, MULTIMED TOOLS APPL, V75, P4957, DOI 10.1007/s11042-013-1814-0
Kim YS, 2013, J INF SCI ENG, V29, P63
Kim YS, 2011, EXPERT SYST APPL, V38, P13320, DOI 10.1016/j.eswa.2011.04.154
Kim YS, 2005, EXPERT SYST APPL, V28, P381, DOI 10.1016/j.eswa.2004.10.017
Koren Y, 2008, 14 ACM SIGKDD INT C
Lee TQ, 2008, EXPERT SYST APPL, V34, P3055, DOI 10.1016/j.eswa.2007.06.031
Li J, 2016, J GLOB INF MANAG, V24, P45, DOI 10.4018/JGIM.2016100103
Li Y, 2010, TEXTILE BIOENGINEERING AND INFORMATICS SYMPOSIUM PROCEEDINGS, VOLS 1-3, P955, DOI 10.3993/tbis2010168
Liu H., 2012, J COMPUTATIONAL INFO, V8, P1383
Mabroukeh NR, 2010, ACM COMPUT SURV, V43, DOI 10.1145/1824795.1824798
Martínez L, 2009, 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 3, P187
Park YJ, 2009, EXPERT SYST APPL, V36, P1932, DOI 10.1016/j.eswa.2007.12.034
Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943
Puntheeranurak S, 2007, 2007 CIT: 7TH IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY, PROCEEDINGS, P223, DOI 10.1109/CIT.2007.54
Rathipriya R., 2011, ARXIV11096726
Resnick P., 1994, Transcending Boundaries, CSCW '94. Proceedings of the Conference on Computer Supported Cooperative Work, P175, DOI 10.1145/192844.192905
Ricci F., 2011, INTRO RECOMMENDER SY
Rojsattarat E, 2003, LECT NOTES COMPUT SC, V2690, P337
Sarwar B., 2001, P 10 INT C WORLD WID, P285
Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259
Su Q, 2015, ELECTRON COMMER R A, V14, P1, DOI 10.1016/j.elerap.2014.10.002
Tan, 2011, J TIANJIN U SCI TECH, V3, P18
Nguyen TTS, 2014, IEEE T KNOWL DATA EN, V26, P2574, DOI 10.1109/TKDE.2013.78
Tran T, 2006, 2006 ICEC: Eighth International Conference on Electronic Commerce, Proceedings, P512
Yong Zheng, 2015, Web Information Systems Engineering - WISE 2015. 16th International Conference. Proceedings: LNCS 9418, P431, DOI 10.1007/978-3-319-26190-4_29
Yue H. E., 2012, SYSTEMS ENG THEORY P, V6, P23
Zaki MJ, 2001, MACH LEARN, V42, P31, DOI 10.1023/A:1007652502315
Zanker M, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P139
Zhang Z, 2013, INFORM SCIENCES, V235, P117, DOI 10.1016/j.ins.2013.01.025
Zhao XY, 2013, KNOWL-BASED SYST, V48, P46, DOI 10.1016/j.knosys.2013.04.009
Zheng L., 2010, P INT C ADV COMP THE, V5, pV5, DOI [10.1109/ICACTE.2010.5579511, DOI 10.1109/ICACTE.2010.5579511]
NR 58
TC 0
Z9 0
U1 0
U2 4
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1938-0232
EI 1938-0240
J9 INT J INF TECHNOL PR
JI Int. J. Inf. Technol. Proj. Manag.
PD APR-JUN
PY 2020
VL 11
IS 2
BP 23
EP 49
DI 10.4018/IJITPM.2020040103
PG 27
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA LK8DY
UT WOS:000531091700003
DA 2024-03-27
ER
PT J
AU Yang, ZK
Feng, J
AF Yang, Zekun
Feng, Juan
TI Explainable multi-task convolutional neural network framework for
electronic petition tag recommendation
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Recommender systems; E-government; E-petition; Explainable machine
learning; Multi-task learning; User-generated content
ID E-GOVERNMENT SERVICES; POLITICAL-PARTICIPATION; INTERNET; ADOPTION;
RESPONSIVENESS; REGRESSION; GROWTH
AB Electronic petition (e-petition) is an electronic government (e-government) service that allows citizens to file petitions to governments via the internet. The complexity of the e-petition filing process and the unexplainable e-government tools would reduce the perceived ease of use and trust of users, which causes citizens to make errors when tagging their e-petition. These errors may lead to failure and delay in resolving the e-petition, which further restrains citizen engagement and electronic participation (e-participation) adoption on e-petition platforms (EPP). The purpose of this study is to develop an explainable tag recommender system to assist citizens in tagging their e-petitions. Specifically, we design an explainable multi-task learning framework for e-petition tag recommendation based on convolutional neural networks and layer-wise relevance propagation. We also conduct both quantitative and qualitative experiments to demonstrate the recommendation effec-tiveness as well as interpretability of our model. This is among the first attempt to design an e-petition tag recommender system and an explainable e-government recommender system. The practical implications of our research are two-fold. For citizens, our proposed model recommends more accurate tags with human -understandable explanations, which could assist citizens' tagging decisions and increase the possibility for an e-petition to be resolved. For governments, the e-petition service quality of governments would be enhanced, which further promotes e-participation adoption, citizen engagement, and e-government success on EPPs.
C1 [Yang, Zekun] Renmin Univ China, Res Ctr Digital Humanities, Sch Informat Resource Management, Beijing 100872, Peoples R China.
[Feng, Juan] Tsinghua Univ, Sch Econ & Management, Dept Management Sci & Engn, Beijing, Peoples R China.
[Feng, Juan] Tsinghua Univ, Shenzhen Int Grad Sch, Beijing, Peoples R China.
C3 Renmin University of China; Tsinghua University; Tsinghua University
RP Feng, J (autor correspondiente), Tsinghua Univ, Sch Econ & Management, Dept Management Sci & Engn, Beijing, Peoples R China.; Feng, J (autor correspondiente), Tsinghua Univ, Shenzhen Int Grad Sch, Beijing, Peoples R China.
EM fengjuan@sem.tsinghua.edu.cn
RI YANG, Zekun/JCO-3822-2023
OI YANG, Zekun/0000-0003-4040-8476
FU Fundamental Research Funds for the Central Universities; Research Funds
of Renmin University of China [23XNF040]; National Natural Science
Foundation of China [72204255]
FX The authors appreciate the editors and the anonymous reviewers for their
detailed and constructive comments. This research was supported by the
Fundamental Research Funds for the Central Universities, and the
Research Funds of Renmin University of China [Grant 23XNF040] as well as
the National Natural Science Foundation of China [Grant 72204255] .
CR Abu-Shanab E, 2019, ELECTRON COMMER R A, V38, DOI 10.1016/j.elerap.2019.100892
Alathur S, 2012, TRANSFORM GOV-PEOPLE, V6, P392, DOI 10.1108/17506161211267536
Aljazzaf ZM, 2020, INT J ADV COMPUT SC, V11, P192
Alshibly H, 2015, ELECTRON COMMER R A, V14, P393, DOI 10.1016/j.elerap.2015.05.003
Anduiza E, 2010, J INF TECHNOL POLITI, V7, P356, DOI 10.1080/19331681003791891
[Anonymous], 2010, ACM Sigkdd Explorations Newsletter, DOI DOI 10.1145/1882471.1882480
Ayachi R, 2016, UNIVERSAL ACCESS INF, V15, P681, DOI 10.1007/s10209-015-0442-z
Bach S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0130140
Baldi P., 2013, ADV NEURAL INFORM PR, P26
Batista G. E. A. P. A., 2004, ACM SIGKDD EXPL NEWS, V6, P20, DOI [10.1145/1007730.1007735, DOI 10.1145/1007730.1007735]
Belém FM, 2017, J ASSOC INF SCI TECH, V68, P830, DOI 10.1002/asi.23736
Bellini V, 2018, PROCEEDINGS OF THE 3RD WORKSHOP ON DEEP LEARNING FOR RECOMMENDER SYSTEMS (DLRS), P24, DOI 10.1145/3270323.3270327
Bershadskaya L, 2014, P 8 ICEGOV2014 INT C, P395, DOI [10.1145/2691195.2691198, DOI 10.1145/2691195.2691198]
Bershadskaya L., 2013, ACM International Conference Proceeding Series, P73, DOI DOI 10.1145/2591888.2591900
Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012
Chen W, 2019, J COMPUT SCI TECH-CH, V34, P957, DOI 10.1007/s11390-019-1954-4
Colesca SE, 2008, J APPL RES TECHNOL, V6, P204
Cruickshank P., 2010, Electronic Government and Electronic Participation, P275
Dominguez V, 2019, PROCEEDINGS OF IUI 2019, P408, DOI 10.1145/3301275.3302274
Dumas CL, 2015, BIG DATA SOC, V2, DOI 10.1177/2053951715598170
Dyczkowski K., 2012, CCIS, P441, DOI DOI 10.1007/978-3-642-31715-6_47
Estevez E, 2013, GOV INFORM Q, V30, pS94, DOI 10.1016/j.giq.2012.11.001
Rodriguez-Hevía LF, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12176807
Gao X., 2020, STATE CAPACITY BUILD, P29
Gao YZ, 2016, IEEE T MED IMAGING, V35, P1532, DOI 10.1109/TMI.2016.2519264
Gibreel O, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9122181
Grave E, 2018, PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018), P3483
Guy I, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P194
Hagen L., 2018, POLICY ANAL MODELLIN, P205, DOI [10.1007/978-3-319-61762-6_9, DOI 10.1007/978-3-319-61762-6_9]
Hagen L, 2018, INFORM PROCESS MANAG, V54, P1292, DOI 10.1016/j.ipm.2018.05.006
Hagen L, 2016, GOV INFORM Q, V33, P783, DOI 10.1016/j.giq.2016.07.006
Hajiramezanali E, 2018, ADV NEUR IN, V31
Hale S. A., 2013, P 5 ANN ACM WEB SCI, P132, DOI [10.1145/2464464.2464518, DOI 10.1145/2464464.2464518]
Harbin Institute of Technology, 2022, RES CTR SOC COMP INF
Hassan HAM, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P465, DOI 10.1145/3240323.3240409
Heitin L., 2016, Education week, V36, P5
Hernon P., 2002, United States government information: Policies and sources
Hessel M, 2018, AAAI CONF ARTIF INTE, P3215
Hinton G. E., 2012, arXiv, DOI [10.48550/arXiv.1207.0580, DOI 10.48550/ARXIV.1207.0580]
Hoerl AE, 2000, TECHNOMETRICS, V42, P80, DOI 10.2307/1271436
Hossain MD, 2011, ELECTRON COMMER R A, V10, P576, DOI 10.1016/j.elerap.2010.12.003
Jaeger PT, 2003, GOV INFORM Q, V20, P323, DOI 10.1016/j.giq.2003.08.003
Jiang JY, 2019, GOVERNANCE, V32, P531, DOI 10.1111/gove.12391
Jiang M, 2009, SOC SCI COMPUT REV, V27, P174, DOI 10.1177/0894439308327313
Jieba, 2022, JIEB
Jochumsen H, 2012, NEW LIB WORLD, V113, P586, DOI 10.1108/03074801211282948
Jungherr A, 2010, POLICY INTERNET, V2, P131, DOI 10.2202/1944-2866.1084
Kenny EM, 2021, ARTIF INTELL, V294, DOI 10.1016/j.artint.2021.103459
Kim TY, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12125191
Kraus M, 2019, DECIS SUPPORT SYST, V125, DOI 10.1016/j.dss.2019.113100
Krestel R., 2009, P 3 ACM C REC SYST, P61, DOI DOI 10.1145/1639714.1639726
Lee HI, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12030969
Li F., 2019, EXPLORE-NY, V6, P91
Li F, 2019, TELEMAT INFORM, V42, DOI 10.1016/j.tele.2019.06.001
Li H., 2019, E GOVT, V3, P72
Li XH, 2008, IEEE T SIGNAL PROCES, V56, P675, DOI 10.1109/TSP.2007.907820
Lindner R., 2009, INT PARLIAMENTARY E, V1, P1, DOI DOI 10.29379/JEDEM.V1I1.3
Lindner R, 2011, POLICY INTERNET, V3, DOI 10.2202/1944-2866.1083
Liu J., 2009, P 25 C UNC ART INT, P339
Liu WB, 2017, NEUROCOMPUTING, V234, P11, DOI 10.1016/j.neucom.2016.12.038
Liu YZ, 2016, J HOUS ECON, V33, P82, DOI 10.1016/j.jhe.2016.05.002
Ma L, 2020, INT REV ADM SCI, V86, P80, DOI 10.1177/0020852317749025
McDonald GC, 2009, WIRES COMPUT STAT, V1, DOI 10.1002/wics.14
Meng TG, 2020, CHINA REV, V20, P13
Message Board for Leaders, 2022, HOM MESS BOARD LEAD
Messina P, 2019, USER MODEL USER-ADAP, V29, P251, DOI 10.1007/s11257-018-9206-9
Mohammed R, 2020, INT CONF INFORM COMM, P243, DOI 10.1109/ICICS49469.2020.239556
Montavon G., 2019, Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, V11700 LNCS, P193, DOI [10.1007/978-3-030-28954-6_10/COVER, DOI 10.1007/978-3-030-28954-6_10, DOI 10.1007/978-3-030-28954-610]
Montavon G, 2017, PATTERN RECOGN, V65, P211, DOI 10.1016/j.patcog.2016.11.008
Nguyen HTH, 2017, LECT NOTES ARTIF INT, V10234, P186, DOI 10.1007/978-3-319-57454-7_15
Noble WS, 2006, NAT BIOTECHNOL, V24, P1565, DOI 10.1038/nbt1206-1565
Panagiotopoulos P, 2011, TRANSFORM GOV-PEOPLE, V5, P20, DOI 10.1108/17506161111114626
Preisach C, 2010, LECT NOTES ARTIF INT, V6118, P348
Reusens M, 2017, DECIS SUPPORT SYST, V98, P26, DOI 10.1016/j.dss.2017.04.002
Sabucedo L. A., 2012, 2012 Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP), P409, DOI 10.1109/DICTAP.2012.6215392
Sæbo O, 2008, GOV INFORM Q, V25, P400, DOI 10.1016/j.giq.2007.04.007
Santoyo-González A, 2018, 2018 IEEE CONFERENCE ON STANDARDS FOR COMMUNICATIONS AND NETWORKING (IEEE CSCN)
Scherer S., 2014, P 8 INT C THEORY PRA, P61, DOI [10.1145/2691195.2691237, DOI 10.1145/2691195.2691237]
Shao Z., 2020, COMP EC SOC SYST, V1, P114
Shi WS, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), P229, DOI 10.1109/ICWS.2017.37
Song Y, 2011, ACM T WEB, V5, DOI 10.1145/1921591.1921595
Steinbach M, 2020, J INF TECHNOL POLITI, V17, P12, DOI 10.1080/19331681.2019.1676361
Su Z, 2016, SOC SCI RES, V59, P52, DOI 10.1016/j.ssresearch.2016.04.017
Sun C, 2011, INT J CHEM ENG, V2011, DOI 10.1155/2011/545234
Tai KT, 2020, PUBLIC MANAG REV, V22, P278, DOI 10.1080/14719037.2019.1584233
Tajbakhsh MS, 2016, 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW), P252, DOI 10.1109/W-FiCloud.2016.59
Terán L, 2019, GOV INFORM Q, V36, P520, DOI 10.1016/j.giq.2019.03.003
Tibshirani R, 1996, J ROY STAT SOC B, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x
Tin Kam Ho, 1995, Proceedings of the Third International Conference on Document Analysis and Recognition, P278, DOI 10.1109/ICDAR.1995.598994
UK Government and Parliament, 2022, PET
van der Waa J, 2020, INT J HUM-COMPUT ST, V144, DOI 10.1016/j.ijhcs.2020.102493
Verkijika SF, 2018, ELECTRON COMMER R A, V30, P83, DOI 10.1016/j.elerap.2018.05.012
Wang YL, 2017, AAAI CONF ARTIF INTE, P210
Wang Z, 2020, J URBAN MANAG, V9, P228, DOI 10.1016/j.jum.2019.11.006
We the People, 2020, PET WHIT HOUS ISS MA
Xavier Glorot, 2011, 14 INT C ARTIF INTEL, P315, DOI DOI 10.1002/ECS2.1832
Xu BC, 2019, IEEE INT CON AUTO SC, P812, DOI [10.1109/COASE.2019.8843151, 10.1109/coase.2019.8843151]
Yang GB, 2007, CHINA QUART, P122, DOI 10.1017/S030574100600083X
Yang ZK, 2020, AAAI CONF ARTIF INTE, V34, P9426
Yao Y, 2015, J ECON GROWTH, V20, P405, DOI 10.1007/s10887-015-9116-1
Zhang YF, 2020, FOUND TRENDS INF RET, V14, P1, DOI 10.1561/1500000066
Zhang Y, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1011, DOI 10.1145/3357384.3358005
Zhang ZJ, 2018, 2018 IEEE/ACM 26TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS), DOI 10.1109/IWQoS.2018.8624183
Zhong SR, 2017, 2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), P671, DOI 10.1109/ICBDA.2017.8078720
Zhou PY, 2019, INFORM SOFTWARE TECH, V109, P1, DOI 10.1016/j.infsof.2019.01.002
Zhou PY, 2017, 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), P272, DOI 10.1109/SANER.2017.7884628
Zolotov MN, 2018, COMPUT HUM BEHAV, V81, P350, DOI 10.1016/j.chb.2017.12.031
NR 107
TC 1
Z9 1
U1 12
U2 14
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD MAY-JUN
PY 2023
VL 59
AR 101263
DI 10.1016/j.elerap.2023.101263
EA MAY 2023
PG 15
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA I8QH5
UT WOS:001005370500001
DA 2024-03-27
ER
PT J
AU Rahman, MS
Bag, S
Hossain, MA
Fattah, FAMA
Gani, MO
Rana, NP
AF Rahman, Muhammad Sabbir
Bag, Surajit
Hossain, Md Afnan
Fattah, Fadi Abdel Muniem Abdel
Gani, Mohammad Osman
Rana, Nripendra P.
TI The new wave of AI-powered luxury brands online shopping experience: The
role of digital multisensory cues and customers' engagement
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE AI-powered digital assistance; Digital multisensory cue; Customer
engagement; Online platform; Online shopping experience
ID QUALITATIVE COMPARATIVE-ANALYSIS; ARTIFICIAL-INTELLIGENCE;
INFORMATION-SYSTEMS; COMPLEXITY THEORY; FUZZY-SETS; PERFORMANCE;
ACCEPTANCE; MARKET; SEM; TECHNOLOGY
AB Academic literature retains a dearth of empirical evidence of the cutting-edge aspect of artificial intelligence (AI)-powered digital assistance and digital multisensory cues, despite the prospect of these factors on real-life customers' luxury brand online shopping experience. Thus, the aim of this study is to examine the significant pathway and effects of AI-powered digital assistance toward customers' luxury brand online shopping experi-ence. Drawing on S-O-R (Stimulus, organism, and response) and TRAM (Technology Readiness and Acceptance Model) paradigm, a multi-method research design was deployed to investigate constructs. Firstly, semi-structured interviews were utilized to explore customers' online behavior under the luxury brands and infor-mation technology aspect. Secondly, survey data were collected and analyzed by using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The PLS-based analysis of quantitative data confirmed the exploratory insights of qualitative findings, establishing the con-nections of AI-powered digital assistance, customer engagement, and customers' luxury brand online shopping experience. Research findings also suggest that customer engagement plays a mediation role in the relationship between AI-powered digital assistance and customers' luxury brand online shopping experience. Besides, digital multisensory cues moderate the relationship between AI-powered digital assistance and customer engagement. Further, fsQCA complements the findings of PLS-SEM that reveal the significant combination of factors that lead to the perceptions of customers' luxury brand online shopping experience.
C1 [Rahman, Muhammad Sabbir; Hossain, Md Afnan] North South Univ, Sch Business & Econ, Dept Mkt & Int Business, Dhaka 1229, Bangladesh.
[Bag, Surajit] Univ Johannesburg, Dept Transport & Supply Chain Management, Johannesburg, South Africa.
[Hossain, Md Afnan] Univ Wollongong, Fac Business & Law, Sch Business, Wollongong, NSW 2522, Australia.
[Fattah, Fadi Abdel Muniem Abdel] Asharqiyah Univ, Coll Business Adm, Ibra, Oman.
[Gani, Mohammad Osman] Bangladesh Univ Profess BUP, Fac Business Studies FBS, Dept Mkt, Dhaka, Bangladesh.
[Rana, Nripendra P.] Qatar Univ, Coll Business & Econ, POB 2713, Doha, Qatar.
[Gani, Mohammad Osman] Hiroshima Univ, Grad Sch Humanities & Social Sci, Higashihiroshima, Japan.
C3 North South University (NSU); University of Johannesburg; University of
Wollongong; Qatar University; Hiroshima University
RP Rahman, MS (autor correspondiente), North South Univ, Sch Business & Econ, Dept Mkt & Int Business, Dhaka 1229, Bangladesh.
EM rahman.sabbir@northsouth.edu; surajit.bag@gmail.com;
mah619@uowmail.edu.au; fadi.fattah@asu.edu.om; osman.gani100@gmail.com;
nrana@qu.edu.qa
RI Rau, Lea/IXW-9119-2023; Rahman, Muhammad Sabbir/G-3968-2018; Hossain, Dr
Md Afnan/M-6626-2017; AbdelFattah, Fadi/L-7441-2014
OI Hossain, Dr Md Afnan/0000-0003-2954-1823; AbdelFattah,
Fadi/0000-0002-4665-4777; GANI, MOHAMMAD OSMAN/0000-0002-9724-4006
CR Agrebi S, 2015, J RETAIL CONSUM SERV, V22, P16, DOI 10.1016/j.jretconser.2014.09.003
Al-Shidhani S.S.S., 2021, SAGE OPEN, V13
Amatulli C, 2012, INT REV RETAIL DISTR, V22, P189, DOI 10.1080/09593969.2011.652647
An MA, 2020, J BUS RES, V120, P389, DOI 10.1016/j.jbusres.2020.02.044
Anderson CL, 2010, MIS QUART, V34, P613
[Anonymous], 2021, WATER AIR SOIL POLL
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Athwal N, 2019, INFORM TECHNOL PEOPL, V32, P603, DOI 10.1108/ITP-01-2018-0017
Atwal G, 2017, J BRAND MANAG ADV CO, P43, DOI 10.1007/978-3-319-51127-6_3
Balakrishnan J, 2024, ANN OPER RES, V333, P653, DOI 10.1007/s10479-021-04049-5
Barsalou LW, 1999, BEHAV BRAIN SCI, V22, P637, DOI 10.1017/S0140525X99532147
Bassano C., 2020, P 53 HAWAII INT C SY, P1618
Batat W., 2022, J STRATEG MARK, P1
Beauloye FE, 2022, How personalisation and artificial intelligence are transforming luxury retail online: AI and the future of luxury e-commerce
Benbasat I, 1999, MIS QUART, V23, P3, DOI 10.2307/249403
BLEE KM, 2002, METHODS SOCIAL MOVEM, V0016
Brodie RJ, 2011, J SERV RES-US, V14, P252, DOI 10.1177/1094670511411703
Butt AH, 2021, PSYCHOL MARKET, V38, P1014, DOI 10.1002/mar.21487
Moreno FC, 2016, J BUS RES, V69, P1396, DOI 10.1016/j.jbusres.2015.10.114
Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
Castillo D, 2021, SERV IND J, V41, P900, DOI 10.1080/02642069.2020.1787993
Chan L., 2022, APPL ARTIF INTELL, P213, DOI [10.1007/978-3-031-05740-3_14, DOI 10.1007/978-3-031-05740-3_14]
Chandon JL, 2016, J BUS RES, V69, P299, DOI 10.1016/j.jbusres.2015.08.001
Chang CW, 2021, SERV IND J, V41, P330, DOI 10.1080/02642069.2019.1611784
Cheng Y, 2022, J PROD BRAND MANAG, V31, P252, DOI 10.1108/JPBM-05-2020-2907
Chin W. W., 2010, HDB PARTIAL LEAST SQ, P655, DOI DOI 10.1007/978-3-540-32827-8_29
Chin WW, 2008, J MARKET THEORY PRAC, V16, P287, DOI 10.2753/MTP1069-6679160402
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Chung N, 2015, COMPUT HUM BEHAV, V50, P588, DOI 10.1016/j.chb.2015.02.068
Cowan K, 2021, J BUS RES, V123, P86, DOI 10.1016/j.jbusres.2020.09.058
Creswell W., 2009, Research design: Qualitative, quantitative, and mixed methods approaches, V3rd, DOI DOI 10.1080/15424065.2022.2046231
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
DAY GS, 1994, J MARKETING, V58, P37, DOI 10.2307/1251915
De Bruyn A, 2020, J INTERACT MARK, V51, P91, DOI 10.1016/j.intmar.2020.04.007
Disko, 2018, LUX PHYG CULT SHOCK
DONOVAN RJ, 1994, J RETAILING, V70, P283, DOI 10.1016/0022-4359(94)90037-X
Dubois B, 2005, MARKET LETT, V16, P115, DOI 10.1007/s11002-005-2172-0
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
EASTERBY-SMITH M., 2008, MANAGEMENT RES, V3rd
Eroglu SA, 2001, J BUS RES, V54, P177, DOI 10.1016/S0148-2963(99)00087-9
Fang JM, 2016, INT J INFORM MANAGE, V36, P1205, DOI 10.1016/j.ijinfomgt.2016.08.006
Fionda AM, 2009, J BRAND MANAG, V16, P347, DOI 10.1057/bm.2008.45
Fiss PC, 2011, ACAD MANAGE J, V54, P393, DOI 10.5465/AMJ.2011.60263120
Fournier S, 1998, J CONSUM RES, V24, P343, DOI 10.1086/209515
Frankel S., 2022, INT TEXTILE APPAREL, V78, P1
Garg P, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2020.102069
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Godey B, 2012, J BUS RES, V65, P1461, DOI 10.1016/j.jbusres.2011.10.012
Grant RM, 1996, STRATEGIC MANAGE J, V17, P109, DOI 10.1002/smj.4250171110
Grewal D, 2017, J RETAILING, V93, P55, DOI 10.1016/j.jretai.2016.12.001
Gulfraz MB, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103000
Guo KX, 2023, J HOSP TOUR RES, V47, P1017, DOI 10.1177/10963480211030319
Gupta S, 2020, J INTERACT MARK, V51, P26, DOI 10.1016/j.intmar.2020.04.003
Rodríguez PG, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102201
Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
Hagtvedt H, 2009, J CONSUM PSYCHOL, V19, P608, DOI 10.1016/j.jcps.2009.05.007
Han MS, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103061
Hausman A, 2000, J CONSUM MARK, V17, P403, DOI 10.1108/07363760010341045
Hayes AF, 2011, COMMUN SER, P434
Hernández B, 2010, J BUS RES, V63, P964, DOI 10.1016/j.jbusres.2009.01.019
Hernandez-Ortega B, 2021, PSYCHOL MARKET, V38, P1122, DOI 10.1002/mar.21497
HOLBROOK MB, 1982, J CONSUM RES, V9, P132, DOI 10.1086/208906
Hollebeek L, 2011, J STRATEG MARK, V19, P555, DOI 10.1080/0965254X.2011.599493
Hollebeek LD, 2014, J INTERACT MARK, V28, P149, DOI 10.1016/j.intmar.2013.12.002
Holmqvist J, 2020, J BUS RES, V116, P503, DOI 10.1016/j.jbusres.2019.10.015
Holt DB, 2002, J CONSUM RES, V29, P70, DOI 10.1086/339922
Hossain MA, 2021, J BUS RES, V131, P287, DOI 10.1016/j.jbusres.2021.03.063
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
Hung J.Y., 2012, GLOB J BUS RES, V6, P45
Hyun H, 2022, J STRATEG MARK, DOI 10.1080/0965254X.2022.2052937
Izogo EE, 2018, J CONSUM BEHAV, V17, P379, DOI 10.1002/cb.1715
Jackson T, 2020, Virtual reality shopping, AI store assistants, branded online avatars and apps aplenty-what the 5G revolution means for luxury fashion
Jacoby J, 2002, J CONSUM PSYCHOL, V12, P51, DOI 10.1207/S15327663JCP1201_05
Jang SC, 2009, J BUS RES, V62, P451, DOI 10.1016/j.jbusres.2008.01.038
Jin SV, 2023, INT J HUM-COMPUT INT, V39, P1874, DOI 10.1080/10447318.2022.2129277
Jin T, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102466
Kamboj S, 2018, INT J INFORM MANAGE, V39, P169, DOI 10.1016/j.ijinfomgt.2017.12.001
Kauppinen-Räisänen H, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102251
Kaya B, 2020, J BUS RES, V114, P278, DOI 10.1016/j.jbusres.2020.04.016
KIM Won-Gu, 2020, [The Journal of Industrial Distribution & Business, 산경연구논집], V11, P41
Kim YJ, 2014, COMPUT HUM BEHAV, V33, P256, DOI 10.1016/j.chb.2014.01.015
Klaus P, 2022, J PROD BRAND MANAG, V31, P368, DOI 10.1108/JPBM-10-2020-3164
Klaus P, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102248
Klaus P, 2020, J SERV MANAGE, V31, P1175, DOI 10.1108/JOSM-05-2020-0159
Knowles J., 2020, MIT Sloan Management Review, V61, P2
Ko E, 2016, J BUS RES, V69, P5749, DOI 10.1016/j.jbusres.2016.04.170
Kreuzer M, 2020, J BUS RES, V116, P482, DOI 10.1016/j.jbusres.2019.11.033
Kumar P., 2022, PSYCHIAT RES
Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
Lambert D.M., 1990, J BUS LOGIST, V11, DOI DOI 10.1007/978-3-319-10966-4
Larson JS, 2014, J CONSUM PSYCHOL, V24, P188, DOI 10.1016/j.jcps.2013.09.001
Lee CT, 2022, BEHAV INFORM TECHNOL, V41, P1270, DOI 10.1080/0144929X.2021.1872704
Lee LDW, 2020, EUR J MARKETING, V54, P615, DOI 10.1108/EJM-02-2019-0219
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Li DM, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102355
Lim SH, 2020, INT J HUM-COMPUT INT, V36, P1304, DOI 10.1080/10447318.2020.1739882
Lin CH, 2007, PSYCHOL MARKET, V24, P641, DOI 10.1002/mar.20177
Loureiro S., 2011, AUSTR NZ MARKETING A
Loureiro S.M. C., 2018, INT J FASH DES TECHN, V11, P310, DOI [10.1080/17543266.2018.1431810, DOI 10.1080/17543266.2018.1431810]
Radziwill NM, 2017, Arxiv, DOI [arXiv:1704.04579, DOI 10.48550/ARXIV.1704.04579]
Malhotra N., 2018, Marketing Research, V3rd
Manthiou A, 2017, J TRAVEL TOUR MARK, V34, P531, DOI 10.1080/10548408.2016.1208786
Mayer R., 2020, WATER AIR SOIL POLL
Mazaheri E, 2014, J BUS RES, V67, P253, DOI 10.1016/j.jbusres.2013.05.011
McLean G, 2021, J BUS RES, V124, P312, DOI 10.1016/j.jbusres.2020.11.045
Mehrabian A., 1974, APPROACH ENV PSYCHOL
Miao M, 2022, J GLOB FASH MARK, V13, P30, DOI 10.1080/20932685.2021.1960581
Miao Y., 2020, INT C MACHINE LEARNI, P170
Mohseni S, 2018, CURR ISSUES TOUR, V21, P616, DOI 10.1080/13683500.2016.1200539
Nagy S, 2021, AMFITEATRU ECON, V23, P155, DOI 10.24818/EA/2021/56/155
Nath P, 2010, IND MARKET MANAG, V39, P317, DOI 10.1016/j.indmarman.2008.09.001
Nilsson Nils, 2010, The Quest for Artificial Intelligence
Nyadzayo MW, 2020, J FASH MARK MANAG, V24, P589, DOI 10.1108/JFMM-05-2018-0070
Oh H. M., 2007, Journal of Travel Research, V46, P119, DOI 10.1177/0047287507304039
Olya HGT, 2016, J BUS RES, V69, P2791, DOI 10.1016/j.jbusres.2015.11.015
Ou YC, 2017, J ACAD MARKET SCI, V45, P336, DOI 10.1007/s11747-016-0477-6
Panetta K, 2019, CIOS CAN SEPARATE AI
Pansari A, 2017, J ACAD MARKET SCI, V45, P294, DOI 10.1007/s11747-016-0485-6
Pantano E, 2021, J BUS RES, V123, P117, DOI 10.1016/j.jbusres.2020.09.049
Pantano E, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102096
Pantano E, 2020, J BUS RES, V116, P209, DOI 10.1016/j.jbusres.2020.05.036
Pantano E, 2018, J RETAIL CONSUM SERV, V43, P94, DOI 10.1016/j.jretconser.2018.03.012
Pappas IO, 2017, TELEMAT INFORM, V34, P730, DOI 10.1016/j.tele.2016.08.021
Parasuraman W, 2000, Journal of Service Research, V2, P307, DOI [10.1177/109467050024001, DOI 10.1177/109467050024001]
Parguel B, 2016, J BUS RES, V69, P341, DOI 10.1016/j.jbusres.2015.08.006
Park J, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102272
Peck J., 2008, HDB CONSUMER PSYCHOL, P193
Pekovic S, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102171
Peng C, 2014, J INTERNET COMMER, V13, P159, DOI 10.1080/15332861.2014.944437
Peng N, 2021, INT J HOSP MANAG, V94, DOI 10.1016/j.ijhm.2021.102891
Perez-Vega R, 2021, J BUS RES, V129, P902, DOI 10.1016/j.jbusres.2020.11.002
Petit O, 2019, J INTERACT MARK, V45, P42, DOI 10.1016/j.intmar.2018.07.004
Phau I., 2000, J BRAND MANAG, V8, P122
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
Pine BJ., 1999, The experience economy: work is theatre every business a stage
Prashar S., 2017, Vikalpa: The Journal For Decision Makers, V42, P1, DOI [https://doi.org/10.1177/0256090916686681, DOI 10.1177/0256090916686681, DOI 10.1016/j.dss.2011.10.022]
Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879
Prentic C, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102186
Prentice C, 2020, J HOSP MARKET MANAG, V29, P739, DOI 10.1080/19368623.2020.1722304
Prentice C, 2018, J RETAIL CONSUM SERV, V43, P325, DOI 10.1016/j.jretconser.2018.05.003
Ragin CC, 2005, SOCIOL METHOD RES, V33, P423, DOI 10.1177/0049124105274499
Ragin CC, 2008, SOCIOL METHOD RES, V36, P431, DOI 10.1177/0049124107313903
Ragin CharlesC., 2009, CONFIGURATIONAL COMP, P87, DOI DOI 10.4135/9781452226569.N5
Rahman MS, 2022, INFORM TECHNOL PEOPL, V35, P316, DOI 10.1108/ITP-12-2019-0621
Raju B, 2021, J NEUROSURG, V135, P373, DOI 10.3171/2020.5.JNS201288
Ransbotham S, 2017, MIT Sloan Manag Rev, V59
Richter NF, 2016, INT MARKET REV, V33, P376, DOI 10.1108/IMR-04-2014-0148
Rigdon EE, 2012, LONG RANGE PLANN, V45, P341, DOI 10.1016/j.lrp.2012.09.010
Rodrigues LF, 2016, COMPUT HUM BEHAV, V63, P392, DOI 10.1016/j.chb.2016.05.063
Roozen I., 2019, Journal of Relationship Marketing, V18, P247
Rosemann M, 2008, MIS QUART, V32, P1
Roy SK, 2022, J STRATEG MARK, V30, P45, DOI 10.1080/0965254X.2019.1642937
Rubin H. J., 2012, QUALITATIVE INTERVIE
Russell S., 2010, Artificial intelligence a modern approach, V3rd
Russo I, 2019, AUSTRALAS MARK J, V27, P129, DOI 10.1016/j.ausmj.2018.11.001
Salem SF, 2022, J GLOB FASH MARK, V13, P75, DOI 10.1080/20932685.2021.1978308
Sambamurthy V, 2003, MIS QUART, V27, P237
Saunders M., 2012, RES METHODS BUSINESS, DOI DOI 10.1007/S13398-014-0173-7.2
Schmitt B, 2015, J CONSUM PSYCHOL, V25, P166, DOI 10.1016/j.jcps.2014.09.001
Schweiger E., 2020, RES HDB LUXURY BRAND
Sekaran U., 2011, Research methods for business: A skill building approach, V5th ed., DOI DOI 10.1108/17506200710779521
Serazio M, 2015, TELEV NEW MEDIA, V16, P599, DOI 10.1177/1527476413491015
Shah D, 2021, J BUS RES, V125, P772, DOI 10.1016/j.jbusres.2020.06.062
Sharma A, 2020, J BUS RES, V111, P25, DOI 10.1016/j.jbusres.2020.02.009
Sharma M, 2022, INFORM SYST FRONT, DOI 10.1007/s10796-022-10339-5
Sheth JN, 2020, J INT MARKETING, V28, P3, DOI 10.1177/1069031X19897044
Shmueli G, 2016, J BUS RES, V69, P4552, DOI 10.1016/j.jbusres.2016.03.049
Shukla P, 2011, J WORLD BUS, V46, P242, DOI 10.1016/j.jwb.2010.11.002
Silva ES, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2020.102430
Singh J, 2019, J PERS SELL SALES M, V39, P2, DOI 10.1080/08853134.2018.1557525
SLATER SF, 1995, J MARKETING, V59, P63, DOI 10.2307/1252120
Spence C, 2011, PSYCHOL MARKET, V28, P267, DOI 10.1002/mar.20392
Strickland E, 2019, IEEE SPECTRUM, V56, P24, DOI 10.1109/MSPEC.2019.8678513
Sung E, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102382
Thang D.C.L., 2003, J RETAIL CONSUM SERV, V10, P193, DOI DOI 10.1016/S0969-6989(02)00006-1
Thapa S, 2022, J PROD BRAND MANAG, V31, P984, DOI 10.1108/JPBM-05-2021-3500
Truong Y, 2009, J BRAND MANAG, V16, P375, DOI 10.1057/bm.2009.1
Tynan C, 2010, J BUS RES, V63, P1156, DOI 10.1016/j.jbusres.2009.10.012
Ul Islam J, 2017, TELEMAT INFORM, V34, P96, DOI 10.1016/j.tele.2017.01.004
van Doorn J, 2010, J SERV RES-US, V13, P253, DOI 10.1177/1094670510375599
Van Esch P, 2021, J ASSOC CONSUM RES, V6, P394, DOI 10.1086/714503
Varadarajan R, 2020, IND MARKET MANAG, V89, P89, DOI 10.1016/j.indmarman.2020.03.003
Venkatesh V, 2016, J ASSOC INF SYST, V17, P328, DOI 10.17705/1jais.00428
Verhoef PC, 2009, J RETAILING, V85, P31, DOI 10.1016/j.jretai.2008.11.001
Vickers J.S., 2003, Marketing Review, V3, P459, DOI [https://doi.org/10.1362/146934703771910071, DOI 10.1362/146934703771910071]
Vlacic B, 2021, J BUS RES, V128, P187, DOI 10.1016/j.jbusres.2021.01.055
Wang YJ, 2011, J RETAILING, V87, P46, DOI 10.1016/j.jretai.2010.09.002
Widanengsih E, 2021, J IND ENG MANAG RES, V2, P73, DOI 10.7777/jiemar.v2i1.115
Wiedmann K., 2013, Marketing Review St. Gallen, V30, P60, DOI DOI 10.1365/S11621-013-0300-4
Wong CS, 2002, LEADERSHIP QUART, V13, P243, DOI 10.1016/S1048-9843(02)00099-1
Woodside AG, 2015, PSYCHOL MARKET, V32, P65, DOI 10.1002/mar.20763
NR 192
TC 16
Z9 17
U1 61
U2 133
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD MAY
PY 2023
VL 72
AR 103273
DI 10.1016/j.jretconser.2023.103273
EA FEB 2023
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 8X4LS
UT WOS:000931986400001
DA 2024-03-27
ER
PT J
AU Oestreicher-Singer, G
Sundararajan, A
AF Oestreicher-Singer, Gal
Sundararajan, Arun
TI RECOMMENDATION NETWORKS AND THE LONG TAIL OF ELECTRONIC COMMERCE
SO MIS QUARTERLY
LA English
DT Article
DE Networks; social networks; electronic commerce; recommender systems;
Gini coefficient; long tail; influence; social media; Web 2.0
ID CONSUMER; DIFFUSION
AB It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or "blockbusters" to less popular or "niche" products, and that electronic markets will therefore be characterized by a "long tail" of demand and revenue. We test this conjecture using the revenue distributions of books in over 200 distinct categories on Amazon. corn and detailed daily snapshots of co-purchase recommendation networks in which the products of these categories are situated. We measure how much a product is influenced by its position in this hyperlinked network of recommendations using a variant of Google's PageRank measure of centrality. We then associate the average influence of the network on each category with the inequality in the distribution of its demand and revenue, quantifying this inequality using the Gini coefficient derived from the category's Lorenz curve. We establish that categories whose products are influenced more by the recommendation network have significantly flatter demand and revenue distributions, even after controlling for variation in average category demand, category size, and price differentials. Our empirical findings indicate that doubling the average network influence on a category is associated with an average increase of about 50 percent in the relative revenue for the least popular 20 percent of products, and with an average reduction of about 15 percent in the relative revenue for the most popular 20 percent of products. We also show that this effect is enhanced by higher assortative mixing and lower clustering in the network, and is greater in categories whose products are more evenly influenced by recommendations. The direction of these results persists over time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work illustrates how the microscopic economic data revealed by online networks can be used to define and answer new kinds of research questions, offers a fresh perspective on the influence of networked IT artifacts on business outcomes, and provides novel empirical evidence about the impact of visible recommendations on the long tail of electronic commerce.
C1 [Oestreicher-Singer, Gal] Tel Aviv Univ, Recanati Grad Sch Business, IL-69978 Tel Aviv, Israel.
NYU, Stern Sch Business, New York, NY 10012 USA.
C3 Tel Aviv University; New York University
RP Oestreicher-Singer, G (autor correspondiente), Tel Aviv Univ, Recanati Grad Sch Business, IL-69978 Tel Aviv, Israel.
EM galos@post.tau.ac.il; asundara@stern.nyu.edu
RI Oestreicher - Singer, Gal/JYQ-4365-2024
FU NET Institute
FX We thank Vijay Gurbaxani, Ravi Bapna, and the members of the review team
for their careful and detailed suggestions during the review process. We
also thank Vasant Dhar, Nicholas Economides, William Greene, Panos
Ipeirotis, Roy Radner, and seminar participants at the International
Workshop and Conference on Network Science, the Wharton School, New York
University, Tel-Aviv University, the Zentrum fur Europaische
Wirtschaftsforschung, the Statistical Challenges in Electronic Commerce
Research Symposium, the Telecommunications Policy Research Conference,
the INFORMS Conference on Information Systems and Technology, and the
International Conference on Information Systems for their feedback.
Financial support from the NET Institute (http://www.netinst.org/) is
gratefully acknowledged.
CR Anderson C, 2006, LONG TAIL WHY FUTURE
Aral S., 2007, CTR DIGITAL BUSINESS, V9
Aral S, 2009, P NATL ACAD SCI USA, V106, P21544, DOI 10.1073/pnas.0908800106
Bailey J., 2008, 20 WORKSH INF SYST E
Barrot C., 2008, ROLE SPATIAL PROXIMI
Bradlow ET, 2005, MARKET LETT, V16, P267, DOI 10.1007/s11002-005-5891-3
Bramoullé Y, 2007, J ECON THEORY, V135, P478, DOI 10.1016/j.jet.2006.06.006
Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
Brin S., 1999, 19990120 STANF U COM
Brynjolfsson E, 2003, MANAGE SCI, V49, P1580, DOI 10.1287/mnsc.49.11.1580.20580
Brynjolfsson E, 2006, MIT SLOAN MANAGE REV, V47, P67
Choi J, 2011, J MARKETING RES, V48, P670, DOI 10.1509/jmkr.48.4.670
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
Corrado Gini., 1921, ECON J, V31, P124, DOI [DOI 10.2307/2223319, 10.2307/2223319]
Dellarocas Chris., 2010, MEDIA AGGREGATORS LI
Desai PS, 2000, MARKET SCI, V19, P381, DOI 10.1287/mksc.19.4.381.11793
Dhar V., 2009, CEDER0906 NEW YORK U
Elberse A., HARVARD BUSINESS REV, V86, P88
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Galeotti A, 2010, REV ECON STUD, V77, P218, DOI 10.1111/j.1467-937X.2009.00570.x
Goh K. H., 2008, 20 WORKSH INF SYST E
Goldenberg J, 2001, MARKET LETT, V12, P211, DOI 10.1023/A:1011122126881
Hervas-Drane A., 2007, 0741 NEW YORK U STER
Hill S, 2006, STAT SCI, V21, P256, DOI 10.1214/088342306000000222
Katona Z, 2008, MARKET SCI, V27, P764, DOI 10.1287/mksc.1070.0349
Keller E., 2003, INFLUENCES
Langville A., 2004, INTERNET MATH, V1, P335, DOI DOI 10.1080/15427951.2004.10129091
Lariviere MA, 1997, MARKET SCI, V16, P112, DOI 10.1287/mksc.16.2.112
Li SB, 2005, J MARKETING RES, V42, P233, DOI 10.1509/jmkr.42.2.233.62288
Lorenz MO, 1905, J AM STAT ASSOC, V9, P209
Mayzlin D., 2008, Link to Success: How Blogs Build an Audience by Promoting Rivals
Muller Eitan., 2009, Innovation Diffusion and New Product Growth
Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480
Niraj R, 2008, MARKET SCI, V27, P225, DOI 10.1287/mksc.1070.0299
Oestreicher-Singer G., 2012, MANAGEMENT IN PRESS
Oh J., 2008, Examining the diffusion of user-generated content in online social networks
Sriram S, 2010, MARKET SCI, V29, P291, DOI 10.1287/mksc.1090.0506
Sundararajan A, 2007, BE J THEOR ECON, V7
Tucker C., 2008, LONG TAIL STEEP TAIL
Valente TW, 1996, SOC NETWORKS, V18, P69, DOI 10.1016/0378-8733(95)00256-1
van Den Bulte Christophe., 2007, Social Networks and Marketing
Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918
Watts DJ, 2007, J CONSUM RES, V34, P441, DOI 10.1086/518527
Wedel M, 2004, J MARKETING RES, V41, P448, DOI 10.1509/jmkr.41.4.448.47017
Zhao X., 2008, INFORMS C INF SYST T
NR 45
TC 139
Z9 170
U1 7
U2 194
PU SOC INFORM MANAGE-MIS RES CENT
PI MINNEAPOLIS
PA UNIV MINNESOTA-SCH MANAGEMENT 271 19TH AVE SOUTH, MINNEAPOLIS, MN 55455
USA
SN 0276-7783
J9 MIS QUART
JI MIS Q.
PD MAR
PY 2012
VL 36
IS 1
BP 65
EP 83
DI 10.2307/41410406
PG 19
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA 895EY
UT WOS:000300480200005
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Yan, SR
Pirooznia, S
Heidari, A
Navimipour, NJ
Unal, M
AF Yan, Shu-Rong
Pirooznia, Sina
Heidari, Arash
Navimipour, Nima Jafari
Unal, Mehmet
TI Implementation of a Product-Recommender System in an IoT-Based Smart
Shopping Using Fuzzy Logic and Apriori Algorithm
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Apriori algorithm; filtering; fuzzy logic; Internet of things;
recommender systems; shopping cart; smartening
AB The Internet of Things (IoT) has recently become important in accelerating various functions, from manufacturing and business to healthcare and retail. A recommender system can handle the problem of information and data buildup in IoT-based smart commerce systems. These technologies are designed to determine users' preferences and filter out irrelevant information. Identifying items and services that customers might be interested in and then convincing them to buy is one of the essential parts of effective IoT-based smart shopping systems. Due to the relevance of product-recommender systems from both the consumer and shop perspectives, this article presents a new IoT-based smart product-recommender system based on an apriori algorithm and fuzzy logic. The suggested technique employs association rules to display the interdependencies and linkages among many data objects. The most common use of association rule discovery is "shopping cart analysis." Customers' buying habits and behavior are studied based on the numerous goods they place in their shopping carts. As a result, the association rules are generated using a fuzzy system. The apriori algorithm then selects the product based on the provided fuzzy association rules. The results revealed that the suggested technique had achieved acceptable results in terms of mean absolute error, root-mean-square error, precision, recall, diversity, novelty, and catalog coverage when compared to cutting-edge methods. Finally, themethod helps increase recommender systems' diversity in IoT-based smart shopping.
C1 [Yan, Shu-Rong] Hunan Univ, Sch Business Adm, Changsha 410082, Peoples R China.
[Pirooznia, Sina; Heidari, Arash] Islamic Azad Univ, Tabriz Branch, Dept Comp Engn, Tabriz, Iran.
[Navimipour, Nima Jafari] Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Turkey.
[Unal, Mehmet] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkey.
C3 Hunan University; Islamic Azad University; Kadir Has University;
Nisantasi University
RP Yan, SR (autor correspondiente), Hunan Univ, Sch Business Adm, Changsha 410082, Peoples R China.; Navimipour, NJ (autor correspondiente), Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Turkey.
EM sryan@ygu.edu.cn; sina.pirooznia@gmail.com; arash_heidari@ieee.org;
Nima.navimipour@khas.edu.tr; mehmet.unal@nisantasi.edu.tr
RI Heidari, Arash/AAK-9761-2021; Jafari Navimipour, Nima/AAF-5662-2021;
Ünal, Mehmet/AAO-6590-2021
OI Heidari, Arash/0000-0003-4279-8551; Jafari Navimipour,
Nima/0000-0002-5514-5536; Ünal, Mehmet/0000-0003-4927-649X; pirooznia,
sina/0009-0007-0388-7398
FU key program of the National Social Science Foundation of China
[18AJY013]
FX This work was supported by the key program of the National Social
Science Foundation of China under Grant 18AJY013. Reviewof this
manuscript was arranged by Department Editor D. Cetindamar.
CR Ahmad M. B., 2021, J EMERG TECHNOL INNO
Ahmadian S, 2020, KNOWL-BASED SYST, V192, DOI 10.1016/j.knosys.2019.105371
Al-Hadi I. A. A.-Q., 2017, P 2 INT C INT THINGS, P1
Bian WJ, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.884673
Bouazza H, 2022, J KING SAUD UNIV-COM, V34, P5633, DOI 10.1016/j.jksuci.2022.02.003
Cepeda-Pacheco JC, 2022, NEURAL COMPUT APPL, V34, P7691, DOI 10.1007/s00521-021-06872-0
Chen LS, 2008, INFORM SCIENCES, V178, P1032, DOI 10.1016/j.ins.2007.09.027
Chen X., 2019, P 36 INT C MACHINE L, P1052
Dong Jiahua, 2021, IEEE Trans Pattern Anal Mach Intell, VPP, DOI 10.1109/TPAMI.2021.3128560
Du H, 2022, IEEE T WIREL COMMUN, V21, P8322, DOI [10.1109/TWC.2022.3165588, 10.1109/TNNLS.2022.3204090]
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Himeur Y, 2022, COMPUT SCI REV, V43, DOI 10.1016/j.cosrev.2021.100439
Imani M, 2022, IEEE INTELL SYST, V37, P44, DOI 10.1109/MIS.2022.3163227
Imani M, 2022, IEEE T NEUR NET LEAR, V33, P5913, DOI 10.1109/TNNLS.2021.3071958
Jabeen F, 2019, PEER PEER NETW APPL, V12, P1263, DOI 10.1007/s12083-019-00733-3
JAFARZADEH H, 2015, INDIAN J SCI TECHNOL, V8, P707
Jalili M, 2018, IEEE ACCESS, V6, P74003, DOI 10.1109/ACCESS.2018.2883742
Jiang YQ, 2011, LECT NOTES COMPUT SC, V6988, P196, DOI 10.1007/978-3-642-23982-3_25
Ju CH, 2019, MULTIMED TOOLS APPL, V78, P30097, DOI 10.1007/s11042-018-6980-7
Katarya R, 2020, MULTIMED TOOLS APPL, V79, P35927, DOI 10.1007/s11042-020-09199-5
Li, 2019, COMPUTER DIGITAL ENG, V47, P1293
Li DY, 2021, IEEE T CONTROL NETW, V8, P89, DOI 10.1109/TCNS.2020.3034523
LI M, IN PRESS
Liu GH, 2021, IEEE COMMUN MAG, V59, P132, DOI 10.1109/MCOM.001.2000921
Ma ZY, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.353
Mekouar L, 2022, COMPUT COMMUN, V187, P1, DOI 10.1016/j.comcom.2022.01.020
Meng SM, 2022, IEEE T IND INFORM, V18, P5637, DOI 10.1109/TII.2022.3143103
Ojagh S, 2020, FUTURE GENER COMP SY, V108, P97, DOI 10.1016/j.future.2020.02.041
Raza S, 2020, IEEE INT CONF BIG DA, P551, DOI 10.1109/BigData50022.2020.9378340
Rezaee Abbas Ali, 2020, Proceedings of the 10th International Conference on Computer and Knowledge Engineering (ICCKE 2020), P665, DOI 10.1109/ICCKE50421.2020.9303619
Ruiz-García G, 2019, IEEE T FUZZY SYST, V27, P2381, DOI 10.1109/TFUZZ.2019.2898582
Saleem Y, 2022, IEEE T EMERG TOP COM, V10, P280, DOI 10.1109/TETC.2020.3014722
Sánchez P, 2022, ACM COMPUT SURV, V54, DOI 10.1145/3510409
Shannon C. E., 1948, BELL SYST TECH J, V27, P623, DOI [DOI 10.1002/J.1538-7305.1948.TB00917.X, DOI 10.1002/J.1538-7305.1948.TB01338.X]
Walti C., 2022, U.S. Patent, Patent No. [11,321,761, 11321761]
Wang J., 2015, PROC INT AAAI C WEB
Wang J, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12147219
Woo Lee Hyung, 2019, [Journal of Internet Computing and Services, 인터넷정보학회논문지], V20, P57, DOI 10.7472/jksii.2019.20.1.57
Wu HH, 2022, APPL SOFT COMPUT, V120, DOI 10.1016/j.asoc.2022.108626
Wu XX, 2021, IEEE T SOFTWARE ENG, V48, P2541, DOI 10.1109/TSE.2021.3063727
Zhang GR, 2023, INT J CONSUM STUD, V47, P74, DOI 10.1111/ijcs.12806
ZHANG Y, IN PRESS
Zheng WF, 2021, PATTERN RECOGN, V120, DOI 10.1016/j.patcog.2021.108153
Zheng WF, 2021, PEERJ COMPUT SCI, V7, DOI 10.7717/peerj-cs.613
ZHONG L, IN PRESS
Zhou LY, 2022, J BUS RES, V143, P72, DOI 10.1016/j.jbusres.2022.01.060
NR 46
TC 11
Z9 11
U1 11
U2 15
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 4940
EP 4954
DI 10.1109/TEM.2022.3207326
EA OCT 2022
PG 15
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA IR4M3
UT WOS:001007844500001
DA 2024-03-27
ER
PT J
AU Rausch, TM
Derra, ND
Wolf, L
AF Rausch, Theresa Maria
Derra, Nicholas Daniel
Wolf, Lukas
TI Predicting online shopping cart abandonment with machine learning
approaches
SO INTERNATIONAL JOURNAL OF MARKET RESEARCH
LA English
DT Article
DE classification; e-commerce; machine learning; prediction; shopping cart
abandonment; supervised learning
ID ARTIFICIAL NEURAL-NETWORKS; WEB SITE; BIG DATA; CLICKSTREAM; BEHAVIOR;
MODEL; CLASSIFICATION; DATAFICATION; REGRESSION
AB Excessive online shopping cart abandonment rates constitute a major challenge for e-commerce companies and can inhibit their success within their competitive environment. Simultaneously, the emergence of the Internet's commercial usage results in steadily growing volumes of data about consumers' online behavior. Thus, data-driven methods are needed to extract valuable knowledge from such big data to automatically identify online shopping cart abandoners. Hence, this contribution analyzes clickstream data of a leading German online retailer comprising 821,048 observations to predict such abandoners by proposing different machine learning approaches. Thereby, we provide methodological insights to gather a comprehensive understanding of the practicability of classification methods in the context of online shopping cart abandonment prediction: our findings indicate that gradient boosting with regularization outperforms the remaining models yielding an F-1-Score of 0.8569 and an AUC value of 0.8182. Nevertheless, as gradient boosting tends to be computationally infeasible, a decision tree or boosted logistic regression may be suitable alternatives, balancing the trade-off between model complexity and prediction accuracy.
C1 [Rausch, Theresa Maria; Derra, Nicholas Daniel; Wolf, Lukas] Univ Bayreuth, Bayreuth, Germany.
C3 University of Bayreuth
RP Derra, ND (autor correspondiente), Univ Bayreuth, Res Inst Small & Medium Sized Enterprises BF M, Mainstr 5, D-95444 Bayreuth, Germany.
EM nicholas.derra@bfm-bayreuth.de
OI Wolf, Lukas/0000-0003-0832-6155; Derra, Nicholas
Daniel/0000-0003-4448-0221
CR Abu-Nimeh S., 2007, P ANTIPHISHING WORKI, P60
Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
Blewitt ME, 2008, NAT GENET, V40, P663, DOI 10.1038/ng.142
Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
Bradley AP, 1997, PATTERN RECOGN, V30, P1145, DOI 10.1016/S0031-3203(96)00142-2
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Bucklin RE, 2003, J MARKETING RES, V40, P249, DOI 10.1509/jmkr.40.3.249.19241
Caruana R, 2006, P 23 INT C MACH LEAR, P161, DOI [DOI 10.1145/1143844.1143865, 10.1145/1143844.1143865.]
Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906
Chen HC, 2012, MIS QUART, V36, P1165
Cheung KW, 2003, DECIS SUPPORT SYST, V35, P231, DOI 10.1016/S0167-9236(02)00108-2
Chipman HA, 1998, J AM STAT ASSOC, V93, P935, DOI 10.2307/2669832
Cho CH, 2006, CYBERPSYCHOL BEHAV, V9, P261, DOI 10.1089/cpb.2006.9.261
Chollet F, 2018, Deep learning in R
Climent F, 2019, J BUS RES, V101, P885, DOI 10.1016/j.jbusres.2018.11.015
Close AG, 2010, J BUS RES, V63, P986, DOI 10.1016/j.jbusres.2009.01.022
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964
Cui G, 2006, MANAGE SCI, V52, P597, DOI 10.1287/mnsc.1060.0514
Davis J, 2006, P 23 INT C MACH LEAR, P233, DOI [10.1145/1143844.1143874, DOI 10.1145/1143844.1143874]
Deng L., 2011, P AS SUMM C APSIPA A, P1
Dietterich T. G., 2002, The Handbook of Brain Theory and Neural Networks, VVol. 2, P110
Doshi-Velez F., 2017, ARXIV170208608
Duda R. O., 2000, PATTERN CLASSIFICATI
Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010
Fayyad U, 1996, AI MAG, V17, P37
Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504
Friedman J, 2000, ANN STAT, V28, P337, DOI 10.1214/aos/1016218223
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Friedman JH, 2002, COMPUT STAT DATA AN, V38, P367, DOI 10.1016/S0167-9473(01)00065-2
GEISSER S, 1975, J AM STAT ASSOC, V70, P320, DOI 10.2307/2285815
Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
Hand DJ, 2009, MACH LEARN, V77, P103, DOI 10.1007/s10994-009-5119-5
Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
Huang GH, 2018, J BUS RES, V85, P165, DOI 10.1016/j.jbusres.2017.12.008
Jain AK, 1996, COMPUTER, V29, P31, DOI 10.1109/2.485891
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
Kelly S, 2017, J ASSOC INF SYST, V18, P872, DOI 10.17705/1jais.00477
Khadivi S., 2014, 2014 6 C INF KNOWL T
Kohavi R, 2001, DATA MIN KNOWL DISC, V5, P5, DOI 10.1023/A:1009840925866
Kohavi R., 1995, INT JOINT C ART INT, DOI DOI 10.1067/MOD.2000.109031
Kukar-Kinney M, 2010, J ACAD MARKET SCI, V38, P240, DOI 10.1007/s11747-009-0141-5
L'Heureux A, 2017, IEEE ACCESS, V5, P7776, DOI 10.1109/ACCESS.2017.2696365
Liu B., 2018, 2018 IEEE INT C CONS
Lycett M, 2013, EUR J INFORM SYST, V22, P381, DOI 10.1057/ejis.2013.10
Moe WW, 2004, J INTERACT MARK, V18, P5, DOI 10.1002/dir.10074
Moe WW, 2004, MANAGE SCI, V50, P326, DOI 10.1287/mnsc.1040.0153
Moe WW, 2003, J CONSUM PSYCHOL, V13, P29, DOI 10.1207/153276603768344762
Mokryn O, 2019, ELECTRON COMMER R A, V34, DOI 10.1016/j.elerap.2019.100836
Montgomery AL, 2004, MARKET SCI, V23, P579, DOI 10.1287/mksc.1040.0073
Montgomery AL, 2001, INTERFACES, V31, P90, DOI 10.1287/inte.31.2.90.10630
Moro S, 2014, DECIS SUPPORT SYST, V62, P22, DOI 10.1016/j.dss.2014.03.001
Muhammad Iqbal, 2015, ICTACT Journal on Soft Computing, V5, P946
Opitz D., 1999, J ARTIF INTELL RES, V11, P169, DOI [10.1613/jair.614, DOI 10.1613/JAIR.614]
Provost F, 2001, MACH LEARN, V42, P203, DOI 10.1023/A:1007601015854
Provost F., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, P43
Rajamma RK, 2009, J PROD BRAND MANAG, V18, P188, DOI 10.1108/10610420910957816
Razi MA, 2005, EXPERT SYST APPL, V29, P65, DOI 10.1016/j.eswa.2005.01.006
Rokach L, 2010, ARTIF INTELL REV, V33, P1, DOI 10.1007/s10462-009-9124-7
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Sakar CO, 2019, NEURAL COMPUT APPL, V31, P6893, DOI 10.1007/s00521-018-3523-0
SAMUEL AL, 1959, IBM J RES DEV, V3, P211, DOI 10.1147/rd.441.0206
Schapire RE, 1998, ANN STAT, V26, P1651
Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
Sismeiro C, 2004, J MARKETING RES, V41, P306, DOI 10.1509/jmkr.41.3.306.35985
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x
Suykens JAK, 1999, NEURAL PROCESS LETT, V9, P293, DOI 10.1023/A:1018628609742
Tambe P, 2019, CALIF MANAGE REV, V61, P15, DOI 10.1177/0008125619867910
Tibshirani RJ, 2009, ANN APPL STAT, V3, P822, DOI 10.1214/08-AOAS224
Vafeiadis T, 2015, SIMUL MODEL PRACT TH, V55, P1, DOI 10.1016/j.simpat.2015.03.003
Van den Poel D, 2005, EUR J OPER RES, V166, P557, DOI 10.1016/j.ejor.2004.04.022
Vapnik V, 2006, ESTIMATION DEPENDENC
Williams N, 2006, ACM SIGCOMM COMP COM, V36, P7, DOI 10.1145/1163593.1163596
Zhang GQ, 1998, INT J FORECASTING, V14, P35, DOI 10.1016/S0169-2070(97)00044-7
ZHANG P, 1993, ANN STAT, V21, P299, DOI 10.1214/aos/1176349027
Zhang QC, 2018, INFORM FUSION, V42, P146, DOI 10.1016/j.inffus.2017.10.006
NR 79
TC 9
Z9 9
U1 3
U2 41
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1470-7853
EI 2515-2173
J9 INT J MARKET RES
JI Int. J. Market Res.
PD JAN
PY 2022
VL 64
IS 1
BP 89
EP 112
AR 1470785320972526
DI 10.1177/1470785320972526
EA NOV 2020
PG 24
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA XY4WS
UT WOS:000631216300001
DA 2024-03-27
ER
PT J
AU Lee, D
Hosanagar, K
AF Lee, Dokyun
Hosanagar, Kartik
TI How Do Product Attributes and Reviews Moderate the Impact of Recommender
Systems Through Purchase Stages?
SO MANAGEMENT SCIENCE
LA English
DT Article
DE e-commerce; personalization; recommender systems; product attributes;
consumer reviews; awareness; salience; purchase journey
ID WORD-OF-MOUTH; CONSUMER-BEHAVIOR; E-COMMERCE; SEARCH; SALES; EXPERIENCE;
UTILITARIAN; UNCERTAINTY; INFORMATION; COMMUNICATION
AB We investigate the moderating effect of product attributes and review ratings on views, conversion|views (conversion conditional on views), and final conversion of a purchase-based collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top retailer with 184,375 users split into a recommender-treated group and a control group. We tag theory-driven attributes of 37,125 unique products via Amazon Mechanical Turk to augment the usual product data (e.g., review ratings, descriptions). By examining the recommender's impact through different stages-awareness (views), salience (conversion|views), and final conversion-and across product types, we provide nuanced insights. The study confirms that the recommender increases views, conversion|views, and final conversion rates by 15.3%, 21.6%, and 7.5%, respectively, but this lift is moderated by product attributes and review ratings. We find that the lift on product views is greater for utilitarian products compared with hedonic products as well as for experience products compared with search products. In contrast, the lift on conversion| views rate is greater for hedonic products compared with utilitarian products. Furthermore, the lift on views rate is greater for products with higher average review ratings, which suggests that a recommender acts as a complement to review ratings, whereas the opposite is true for conversion|views, where recommender and review ratings are substitutes. Additionally, a recommender's awareness lift is greater than its saliency impact. We discuss the potential mechanisms behind our results as well as their managerial implications.
C1 [Lee, Dokyun] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA.
[Hosanagar, Kartik] Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA.
C3 Carnegie Mellon University; University of Pennsylvania
RP Lee, D (autor correspondiente), Carnegie Mellon Univ, Pittsburgh, PA 15213 USA.
EM dokyun@cmu.edu; kartikh@wharton.upenn.edu
OI Lee, Dokyun/0000-0002-3186-3349; hosanagar, kartik/0000-0002-6442-9434
FU Berkman Faculty Fund; Jay H. Baker Retailing Center; Mack Institute for
Innovation Management; FishmanDavidson Center for Service and Operations
Management
FX The authors gratefully acknowledge the financial support from the
Berkman Faculty Fund, the Jay H. Baker Retailing Center, the Mack
Institute for Innovation Management, and the FishmanDavidson Center for
Service and Operations Management.
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Adomavicius G., 2011, RecSys 2011 workshop on human decision making in recommender systems, P35
Adomavicius G, 2013, INFORM SYST RES, V24, P956, DOI 10.1287/isre.2013.0497
Aggarwal P., 2005, International Journal of Internet Marketing and Advertising, V2, P38, DOI 10.1504/IJIMA.2005.007503
Ai CR, 2003, ECON LETT, V80, P123, DOI 10.1016/S0165-1765(03)00032-6
Anderson Chris., 2004, The Long Tail: Why the Future of Business is Selling Less of More
Angrist JD, 2009, MOSTLY HARMLESS ECONOMETRICS: AN EMPIRICISTS COMPANION, P1
Animesh A, 2010, INFORM SYST RES, V21, P190, DOI 10.1287/isre.1080.0222
[Anonymous], 1986, Limited dependent and qualitative variables in econometrics
[Anonymous], 1975, CLUSTERING ALGORITHM
Arnold MJ, 2003, J RETAILING, V79, P77, DOI 10.1016/S0022-4359(03)00007-1
BABIN BJ, 1994, J CONSUM RES, V20, P644, DOI 10.1086/209376
Bagwell K, 2007, Handbook of Industrial Organization, V3, P1701, DOI DOI 10.1016/S1573-448X(06)03028-7
Bart Y, 2014, J MARKETING RES, V51, P270, DOI 10.1509/jmr.13.0503
Benbasat I, 1996, DECIS SUPPORT SYST, V17, P241, DOI 10.1016/0167-9236(96)00003-6
Benbasat I, 2005, Journal of the Association for Information Systems, V6, P4, DOI [DOI 10.17705/1JAIS.00065, 10.17705/1jais.00065]
Berger J, 2014, J CONSUM PSYCHOL, V24, P586, DOI 10.1016/j.jcps.2014.05.002
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
BUTTERS GR, 1977, REV ECON STUD, V44, P465, DOI 10.2307/2296902
CAUDILL SB, 1988, OXFORD B ECON STAT, V50, P425
Chang C.C., 2010, World Academy of Science, Engineering and Technology, V66, P111
Chen P., 2004, INT C INF SYST, V12, P711
Chen P.-Y., 2009, System Sciences, P1
Chen YB, 2008, MANAGE SCI, V54, P477, DOI 10.1287/mnsc.1070.0810
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chiu CM, 2014, INFORM SYST J, V24, P85, DOI 10.1111/j.1365-2575.2012.00407.x
Choi J, 2011, INT J ELECTRON COMM, V16, P129, DOI 10.2753/JEC1086-4415160105
Clemons EK, 2008, J MANAGE INFORM SYST, V25, P13, DOI 10.2753/MIS0742-1222250202
Dai H., 2014, ADV CONSUMER RES
Das A. S., 2007, P 16 INT C WORLD WID, P271
De P, 2013, INFORM SYST RES, V24, P998, DOI 10.1287/isre.2013.0487
De P, 2010, MANAGE SCI, V56, P1930, DOI 10.1287/mnsc.1100.1233
Dhar R, 2000, J MARKETING RES, V37, P60, DOI 10.1509/jmkr.37.1.60.18718
Dias MB, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P291
Dimoka A, 2012, MIS QUART, V36, P395
Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001
Econsultancy and Monetate, 2013, REALITIES ONLINE PER
FEICK L, 1992, J ADVERTISING, V21, P9, DOI 10.1080/00913367.1992.10673364
Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974
Gigerenzer G, 2001, DAHL WS ENV, P37
Girard T, 2003, J BUS PSYCHOL, V18, P101, DOI 10.1023/A:1025087021768
Girard T, 2010, J BUS RES, V63, P1079, DOI 10.1016/j.jbusres.2008.12.011
Goldfarb A, 2011, MARKET SCI, V30, P389, DOI 10.1287/mksc.1100.0583
Greevy R, 2004, J AM STAT ASSOC, V99, P7, DOI 10.1198/016214504000000025
Gu B, 2012, INFORM SYST RES, V23, P182, DOI 10.1287/isre.1100.0343
Hann IH, 2003, MANAGE SCI, V49, P1563, DOI 10.1287/mnsc.49.11.1563.20586
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Häubl G, 2006, MIT SLOAN MANAGE REV, V47, P8
Häubl G, 2003, J CONSUM PSYCHOL, V13, P75
Hinz O, 2010, BUS INFORM SYST ENG+, V2, P67, DOI 10.1007/s12599-010-0092-x
HIRSCHMAN EC, 1982, J MARKETING, V46, P92, DOI 10.2307/1251707
Hong Y, 2014, INFORM SYST RES, V25, P328, DOI 10.1287/isre.2014.0520
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
Hsieh YC, 2005, J RETAILING, V81, P75, DOI 10.1016/j.jretai.2005.01.006
Huang P, 2009, J MARKETING, V73, P55, DOI 10.1509/jmkg.73.2.55
JACOBY J, 1971, J APPL PSYCHOL, V55, P570, DOI 10.1037/h0032045
Jannach D., 2009, P 3 ACM C REC SYST, P205, DOI 10.1145/1639714.1639749
Khan Uzma., 2005, INSIDE CONSUMPTION C, P144
Kim C, 2012, ELECTRON COMMER R A, V11, P374, DOI 10.1016/j.elerap.2012.04.002
Klein LR, 2003, J INTERACT MARK, V17, P29, DOI 10.1002/dir.10058
Klein LR, 1998, J BUS RES, V41, P195, DOI 10.1016/S0148-2963(97)00062-3
Komiak S. X., 2004, Information Technology & Management, V5, P181, DOI 10.1023/B:ITEM.0000008081.55563.d4
Komiak SYX, 2006, MIS QUART, V30, P941
Krishnan B.C., 2001, J SERV MARK, V15, P328
Kumar A, 2019, INFORM SYST RES, V30, P819, DOI 10.1287/isre.2018.0833
Kumar A, 2015, MANAGE SCI, V61, P1921, DOI 10.1287/mnsc.2014.2086
Lee D.-h., 2014, Proc. of IEEE Workshop on Signal Processing Systems (SiPS), P1, DOI DOI 10.1109/REDW.2014.7004595
Lee D, 2019, INFORM SYST RES, V30, P239, DOI 10.1287/isre.2018.0800
Lee D, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P85, DOI 10.1145/2872427.2882976
Li XT, 2013, P ANN HICSS, P2908, DOI 10.1109/HICSS.2013.397
Lin ZJ, 2017, MIS QUART, V41, P397, DOI 10.25300/MISQ/2017/41.2.04
Lunt M, 2014, AM J EPIDEMIOL, V179, P226, DOI 10.1093/aje/kwt212
Monetate, 2013, MAXIMIZE ONLINE SALE
MOORE R, 2001, ADV CONSUM RES, V28, P128
NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630
NELSON P, 1974, J POLIT ECON, V82, P729, DOI 10.1086/260231
Oestreicher-Singer G, 2012, MANAGE SCI, V58, P1963, DOI 10.1287/mnsc.1120.1536
Oestreicher-Singer G, 2012, MIS QUART, V36, P65, DOI 10.2307/41410406
Paraschiv Corina., 2002, ELECTRON COMMER RES, V2, P255
Pavlou PA, 2003, INT J ELECTRON COMM, V7, P101, DOI 10.1080/10864415.2003.11044275
Sarkar A., 2011, INT MANAGEMENT REV, V7, P58
Sarwar Badrul, 2001, P 10 INT C WORLD WID, P285, DOI 10.1145/371920.372071
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Stafford MR, 2002, J ADVERTISING, V31, P17, DOI 10.1080/00913367.2002.10673664
Strahilevitz M, 1998, J CONSUM RES, V24, P434, DOI 10.1086/209519
Sun M, 2012, MANAGE SCI, V58, P696, DOI 10.1287/mnsc.1110.1458
Swaminathan V, 2003, J CONSUM PSYCHOL, V13, P93, DOI 10.1207/S15327663JCP13-1&2_08
Thompson Clive., 2008, The New York Times
To PL, 2007, TECHNOVATION, V27, P774, DOI 10.1016/j.technovation.2007.01.001
Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1, DOI 10.1017/CBO9780511805271
Tucker CE, 2011, MANAGE SCI, V57, P828, DOI 10.1287/mnsc.1110.1312
Weathers D, 2007, J RETAILING, V83, P393, DOI 10.1016/j.jretai.2007.03.009
WEITZMAN ML, 1979, ECONOMETRICA, V47, P641, DOI 10.2307/1910412
WOLINSKY A, 1983, REV ECON STUD, V50, P647, DOI 10.2307/2297767
Xiao B, 2007, MIS QUART, V31, P137
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 96
TC 32
Z9 34
U1 20
U2 132
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD JAN
PY 2021
VL 67
IS 1
BP 524
EP 546
DI 10.1287/mnsc.2019.3546
PG 23
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA RN8XS
UT WOS:000640637000001
DA 2024-03-27
ER
PT J
AU Pfeiffer, J
Pfeiffer, T
Meissner, M
Weiss, E
AF Pfeiffer, Jella
Pfeiffer, Thies
Meissner, Martin
Weiss, Elisa
TI Eye-Tracking-Based Classification of Information Search Behavior Using
Machine Learning: Evidence from Experiments in Physical Shops and
Virtual Reality Shopping Environments
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE virtual reality; recommender system; mobile eye tracking; goal-directed
search; exploratory search; electronic commerce field experiments;
laboratory experiments; decision support systems
ID DECISION-MAKING; DESIGN SCIENCE; ONLINE; TECHNOLOGY; ATTENTION; CHOICE;
IMPACT; MODEL; TIME; INDIVIDUALS
AB Classifying information search behavior helps tailor recommender systems to individual customers' shopping motives. But how can we identify these motives without requiring users to exert too much effort? Our research goal is to demonstrate that eye tracking can be used at the point of sale to do so. We focus on two frequently investigated shopping motives: goal-directed and exploratory search. To train and test a prediction model, we conducted two eye-tracking experiments in front of supermarket shelves. The first experiment was carried out in immersive virtual reality; the second, in physical reality-in other words, as a field study in a real supermarket. We conducted a virtual reality study, because recently launched virtual shopping environments suggest that there is great interest in using this technology as a retail channel. Our empirical results show that support vector machines allow the correct classification of search motives with 80% accuracy in virtual reality and 85% accuracy in physical reality. Our findings also imply that eye movements allow shopping motives to be identified relatively early in the search process: our models achieve 70% prediction accuracy after only 15 seconds in virtual reality and 75% in physical reality. Applying an ensemble method increases the prediction accuracy substantially, to about 90%. Consequently, the approach that we propose could be used for the satisfiable classification of consumers in practice. Furthermore, both environments' best predictor variables overlap substantially. This finding provides evidence that in virtual reality, information search behavior might be similar to the one used in physical reality. Finally, we also discuss managerial implications for retailers and companies that are planning to use our technology to personalize a consumer assistance system.
C1 [Pfeiffer, Jella] Justus Liebig Univ Giessen, D-35394 Giessen, Germany.
[Pfeiffer, Thies] Univ Appl Sci Emden Leer, D-26723 Emden, Germany.
[Meissner, Martin] Zeppelin Univ, D-88045 Friedrichshafen, Germany.
[Weiss, Elisa] Karlsruhe Inst Technol, D-76133 Karlsruhe, Germany.
C3 Justus Liebig University Giessen; Zeppelin University; Helmholtz
Association; Karlsruhe Institute of Technology
RP Pfeiffer, J (autor correspondiente), Justus Liebig Univ Giessen, D-35394 Giessen, Germany.
EM jella.pfeiffer@wirtschaft.uni-giessen.de;
thies.pfeiffer@hs-emden-leer.de; martin.meissner@zu.de;
elisa@fam-weiss.de
RI Meißner, Martin/O-3209-2019
OI Meißner, Martin/0000-0002-3574-4283; Pfeiffer, Thies/0000-0001-6619-749X
CR Aggarwal CC, 2015, DATA MINING, DOI 10.1007/978-3-319-14142-8
Ajanki A, 2009, USER MODEL USER-ADAP, V19, P307, DOI 10.1007/s11257-009-9066-4
Bednarik Roman., 2012, Proceedings of the symposium on eye tracking research and applications, P83, DOI [DOI 10.1145/2168556.2168569, 10.1145/2168556.2168569, 10.1145/2168556.2168569.]
Berg LP, 2017, VIRTUAL REAL-LONDON, V21, P1, DOI 10.1007/s10055-016-0293-9
Bigné E, 2016, J BUS RES, V69, P1423, DOI 10.1016/j.jbusres.2015.10.119
Blattgerste J, 2018, COMMUNICATION BY GAZE INTERACTION (COGAIN 2018), DOI 10.1145/3206343.3206349
Bonetti F, 2018, PROGR IS, P119, DOI 10.1007/978-3-319-64027-3_9
Bowman DA, 2007, COMPUTER, V40, P36, DOI 10.1109/MC.2007.257
Boyd DE, 2019, J BUS RES, V100, P590, DOI 10.1016/j.jbusres.2018.06.007
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Burke R.R., 2018, Innovative Research Methodologies in Management: Volume II: Futures, Biometrics and Neuroscience Research, P63, DOI DOI 10.1007/978-3-319-64400-4_3
Cacioppo JT, 1996, PSYCHOL BULL, V119, P197, DOI 10.1037/0033-2909.119.2.197
Chandon P, 2009, J MARKETING, V73, P1, DOI 10.1509/jmkg.73.6.1
Chiou JS, 2011, COMPUT HUM BEHAV, V27, P203, DOI 10.1016/j.chb.2010.07.037
Cowan K, 2019, J BUS RES, V100, P483, DOI 10.1016/j.jbusres.2018.10.063
David ME, 2017, J CONSUM BEHAV, V16, pE26, DOI 10.1002/cb.1651
De Bruyn A, 2008, MARKET SCI, V27, P443, DOI 10.1287/mksc.1070.0306
de Melo EV, 2015, PROC INT C TOOLS ART, P644, DOI 10.1109/ICTAI.2015.98
DeFanti TA, 1993, Proceedings of the 20st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, P135, DOI [DOI 10.1145/166117.166134, 10.1145/166117.166134]
Diaz G, 2013, J VISION, V13, DOI 10.1167/13.1.20
Dickinger A., 2011, Information Technology and Tourism, V13, P239, DOI 10.3727/109830512X13283928066995
Duchowski A, 2002, BEHAV RES METH INS C, V34, P573, DOI 10.3758/BF03195486
Duchowski A. T., 2002, Proceedings ETRA 2002. Eye Tracking Research and Applications Symposium, P103, DOI 10.1145/507072.507094
Duchowski A T., 2004, Proceedings o f ETRA: Eye Tracking Research Applications Symposium, P35, DOI [10.1145/968363.968369, DOI 10.1145/968363.968369]
EHRENBERG ASC, 1993, J ROY STAT SOC A STA, V156, P167, DOI 10.2307/2982727
Foulsham T, 2011, VISION RES, V51, P1920, DOI 10.1016/j.visres.2011.07.002
Glöckner A, 2011, J BEHAV DECIS MAKING, V24, P71, DOI 10.1002/bdm.684
Godin G, 2004, P 10 EUR S VIRT ENV, P7
Guenter B, 2012, ACM T GRAPHIC, V31, DOI 10.1145/2366145.2366183
Gutirrez Alonso M. A., 2008, STEPPING VIRTUAL REA
Guyon I., 2003, J MACH LEARN RES, V3, P1157, DOI DOI 10.1162/153244303322753616
Hastie T, 2013, ELEMENTS STAT LEARNI
Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
HIRSCHMAN EC, 1982, J MARKETING, V46, P92, DOI 10.2307/1251707
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
Holmqvist K., 2011, EYE TRACKING COMPREH
Hui SK, 2009, MARKET SCI, V28, P320, DOI 10.1287/mksc.1080.0400
Hyönä J, 2003, MIND'S EYE: COGNITIVE AND APPLIED ASPECTS OF EYE MOVEMENT RESEARCH, P313, DOI 10.1016/B978-044451020-4/50018-9
Iacovou CL, 1995, MIS QUART, V19, P465, DOI 10.2307/249629
Inman JJ, 2017, J RETAILING, V93, P7, DOI 10.1016/j.jretai.2016.12.006
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
Janiszewski C, 1998, J CONSUM RES, V25, P290, DOI 10.1086/209540
Kahn BE, 2017, J RETAILING, V93, P29, DOI 10.1016/j.jretai.2016.11.004
Kaltcheva VD, 2006, J MARKETING, V70, P107, DOI 10.1509/jmkg.2006.70.1.107
Kramida G, 2016, IEEE T VIS COMPUT GR, V22, P1912, DOI 10.1109/TVCG.2015.2473855
Krol M, 2017, JUDGM DECIS MAK, V12, P596
Kuhn M, 2018, caret: Classification and Regression Training
Kuncheva L.I, 2014, Combining pattern classifiers: methods and algorithms
Lai ML, 2013, EDUC RES REV-NETH, V10, P90, DOI 10.1016/j.edurev.2013.10.001
Lau KW, 2019, VIRTUAL REAL-LONDON, V23, P255, DOI 10.1007/s10055-018-0362-3
Lee Leonard., 2018, Journal of the Association for Consumer Research, V3, P277, DOI 10.1086/698414
Léger PM, 2014, J ASSOC INF SYST, V15, P651
Loomis JM, 1999, BEHAV RES METH INS C, V31, P557, DOI 10.3758/BF03200735
MacLean K.E., 2008, REV HUMAN FACTORS ER, V4, P149, DOI [DOI 10.1518/155723408X342826, 10.1518/155723408X342826]
Mariscal G, 2010, KNOWL ENG REV, V25, P137, DOI 10.1017/S0269888910000032
Meiβner M., 2017, J BUS RES, DOI [DOI 10.1016/J.JBUSRES.2017.09.028, 10.1016/j.jbusres.2017.09.028]
Meiner M, 2016, J MARKETING RES, V53, P1, DOI 10.1509/jmr.13.0467
Meyer DD., 2017, Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071)
Moe WW, 2003, J CONSUM PSYCHOL, V13, P29, DOI 10.1207/153276603768344762
Moeslund TB, 2001, COMPUT VIS IMAGE UND, V81, P231, DOI 10.1006/cviu.2000.0897
Opitz D., 1999, J ARTIF INTELL RES, V11, P169, DOI [10.1613/jair.614, DOI 10.1613/JAIR.614]
Orquin JL, 2013, ACTA PSYCHOL, V144, P190, DOI 10.1016/j.actpsy.2013.06.003
Peffers K, 2007, J MANAGE INFORM SYST, V24, P45, DOI 10.2753/MIS0742-1222240302
Peukert C, 2019, J MANAGE INFORM SYST, V36, P755, DOI 10.1080/07421222.2019.1628889
Pfeiffer T., 2016, EYEWEAR COMPUTING SK, V23, P199, DOI [10. 4230/DagRep. 6. 1. 160, DOI 10.4230/DAGREP.6.1.160]
Pieters R, 1999, INT J RES MARK, V16, P1, DOI 10.1016/S0167-8116(98)00022-6
Rayner K, 1998, PSYCHOL BULL, V124, P372, DOI 10.1037/0033-2909.124.3.372
RUSSO JE, 1994, J CONSUM RES, V21, P274, DOI 10.1086/209397
Scholz M, 2017, EUR J OPER RES, V260, P388, DOI 10.1016/j.ejor.2016.12.022
Shi SW, 2013, MANAGE SCI, V59, P1009, DOI 10.1287/mnsc.1120.1625
Shimojo S, 2003, NAT NEUROSCI, V6, P1317, DOI 10.1038/nn1150
Shmueli G, 2011, MIS QUART, V35, P553
Shmueli G, 2010, STAT SCI, V25, P289, DOI 10.1214/10-STS330
Slater M., 2003, Presence Connect, V3, P1
Slater M., 1995, ACM Transactions on Computer-Human Interaction, V2, P201, DOI [10.1145/210079.210084, DOI 10.1145/210079.210084, DOI 10.1145/210079]
Speier C, 1999, DECISION SCI, V30, P337, DOI 10.1111/j.1540-5915.1999.tb01613.x
STEUER J, 1992, J COMMUN, V42, P73, DOI 10.1111/j.1460-2466.1992.tb00812.x
Sutherland I., 1968, P AFIPS FALL JOINT C, V33, P757, DOI [10.1145/1476589.1476686, DOI 10.1145/1476589.1476686]
Tanriverdi V., 2000, CHI 2000 Conference Proceedings. Conference on Human Factors in Computing Systems. CHI 2000. The Future is Here, P265, DOI 10.1145/332040.332443
Tien T, 2014, J SURG RES, V191, P169, DOI 10.1016/j.jss.2014.04.032
van der Lans R, 2008, MARKET SCI, V27, P922, DOI 10.1287/mksc.1070.0327
van der Lans R, 2011, BEHAV RES METHODS, V43, P239, DOI 10.3758/s13428-010-0031-2
van Nierop E, 2010, J MARKETING RES, V47, P63, DOI 10.1509/jmkr.47.1.63
Vidal M, 2013, UBICOMP'13: PROCEEDINGS OF THE 2013 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, P439, DOI 10.1145/2493432.2493477
WEBSTER DM, 1994, J PERS SOC PSYCHOL, V67, P1049, DOI 10.1037/0022-3514.67.6.1049
Wirth R., 2000, Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, P29
Witten IH, 2011, MOR KAUF D, P1
Wolfinbarger M, 2001, CALIF MANAGE REV, V43, P34, DOI 10.2307/41166074
Xiao B, 2007, MIS QUART, V31, P137
Xu SH, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P83
Zhu R, 2004, IEEE T NEUR SYS REH, V12, P295, DOI 10.1109/TNSRE.2004.827825
NR 91
TC 44
Z9 49
U1 44
U2 219
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD SEP
PY 2020
VL 31
IS 3
BP 675
EP 691
DI 10.1287/isre.2019.0907
PG 17
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA NV5UN
UT WOS:000574386400018
OA Green Published, Bronze
DA 2024-03-27
ER
PT J
AU Li, G
Li, N
AF Li, Guo
Li, Na
TI Customs classification for cross-border e-commerce based on text-image
adaptive convolutional neural network
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Customs classification; Cross-border e-commerce; Convolutional neural
network; Text and image classification
ID PATTERN-RECOGNITION; EFFICIENCY; SYSTEM; ENERGY; MODEL
AB Customs classification is an essential international procedure to import cross-border goods traded by various companies and individuals. Proper classification of such goods with high efficiency in light of the rapidly increasing amount of international trade is still challenging. The current abundant e-commence data and advanced machine learning techniques provide an opportunity for cross-border e-commerce sellers to classify goods efficiently. Thus, in this paper, we propose a text-image adaptive convolutional neural network to effectively utilize website information and facilitate the customs classification process. The proposed model includes two independent submodels: one for text and the other for image. The submodels are fused by a novel method, which can adjust the value of parameters according to the model training result. Finally, we conduct a case study and comparison experiments based on a group of customs tariff codes and a data set from an e-commerce website. Experiment results indicate the effectiveness of text and image combination in performance improvement, the outperformance of the adaptive fusion method, as well as the potential of this approach when applied to customs classification.
C1 [Li, Guo; Li, Na] Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.
[Li, Guo; Li, Na] Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing 100081, Peoples R China.
[Li, Guo] Sustainable Dev Res Inst Econ & Soc Beijing, Beijing 100081, Peoples R China.
C3 Beijing Institute of Technology; Beijing Institute of Technology
RP Li, G (autor correspondiente), Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China.; Li, G (autor correspondiente), Beijing Inst Technol, Ctr Energy & Environm Policy Res, Beijing 100081, Peoples R China.; Li, G (autor correspondiente), Sustainable Dev Res Inst Econ & Soc Beijing, Beijing 100081, Peoples R China.
EM liguo@bit.edu.cn
RI Li, Guo/ACY-6481-2022; Li, Na/AAO-7841-2021; N'Dri, Amoin
Bernadine/IWD-7811-2023
OI Li, Guo/0000-0002-7127-1102; Li, Na/0000-0003-3768-500X
CR Aanen SS, 2015, EXPERT SYST APPL, V42, P1298, DOI 10.1016/j.eswa.2014.09.032
Abbasi A, 2010, MIS QUART, V34, P435
Abdolshah M, 2017, EXPERT SYST APPL, V77, P57, DOI 10.1016/j.eswa.2017.01.030
Appels T., 1998, INT J LOGIST MANAG, V15, P111
Bai X, 2017, PATTERN RECOGN, V66, P437, DOI 10.1016/j.patcog.2016.12.005
Bi WJ, 2016, IEEE T IND INFORM, V12, P1270, DOI 10.1109/TII.2016.2547584
Cheng F, 2018, PATTERN RECOGN, V83, P340, DOI 10.1016/j.patcog.2018.06.005
Dakhlia S, 2013, J PUBLIC ECON THEORY, V15, P803, DOI 10.1111/jpet.12055
Ding LY, 2015, PROCEDIA COMPUT SCI, V60, P1462, DOI 10.1016/j.procs.2015.08.224
Farid DM, 2014, EXPERT SYST APPL, V41, P1937, DOI 10.1016/j.eswa.2013.08.089
Frecknall Hughes J., 2001, EUROPEAN MANAGEMENT, V19, P651, DOI [10.1016/S0263-2373(01)00090-1, DOI 10.1016/S0263-2373(01)00090-1]
FUKUSHIMA K, 1980, BIOL CYBERN, V36, P193, DOI 10.1007/BF00344251
Grüschow RM, 2016, ELECTRON COMMER R A, V18, P27, DOI 10.1016/j.elerap.2016.06.001
Han DM, 2018, EXPERT SYST APPL, V95, P43, DOI 10.1016/j.eswa.2017.11.028
Han W, 2020, ELECTRON COMMER RES, V20, P651, DOI 10.1007/s10660-018-9318-7
He LF, 2018, ELECTRON COMMER RES, V18, P277, DOI 10.1007/s10660-018-9292-0
Hsu CI, 2009, COMPUT IND ENG, V57, P506, DOI 10.1016/j.cie.2008.02.003
Hsu VN, 2011, M&SOM-MANUF SERV OP, V13, P163, DOI 10.1287/msom.1100.0312
Huang AZ, 2018, ELECTRON COMMER RES, V18, P143, DOI 10.1007/s10660-017-9262-y
Jing N, 2018, ELECTRON COMMER RES, V18, P159, DOI 10.1007/s10660-017-9275-6
Kalva PR, 2007, PROC INT CONF DOC, P561
Kazemian HB, 2015, EXPERT SYST APPL, V42, P1166, DOI 10.1016/j.eswa.2014.08.046
Kim S, 2018, COMPUT NETW, V137, P119, DOI 10.1016/j.comnet.2018.03.006
LeCun Y, 1989, NEURAL COMPUT, V1, P541, DOI 10.1162/neco.1989.1.4.541
Lee H, 2018, ELECTRON COMMER RES, V18, P433, DOI 10.1007/s10660-017-9268-5
Lee J., 2004, Electronic Commerce Research, V4, P335, DOI 10.1023/B:ELEC.0000037081.43372.6a
Li G, 2020, DECISION SCI, V51, P691, DOI 10.1111/deci.12340
Li G, 2018, J CLEAN PROD, V197, P124, DOI 10.1016/j.jclepro.2018.06.177
Ma Y, 2017, ELECTRON COMMER RES, V17, P3, DOI 10.1007/s10660-016-9240-9
Martin AK, 2013, TELECOMMUN POLICY, V37, P715, DOI 10.1016/j.telpol.2013.05.005
Pourakbar M, 2018, EUR J OPER RES, V271, P331, DOI 10.1016/j.ejor.2018.05.012
Ramesh G, 2017, J INF SECUR APPL, V35, P75, DOI 10.1016/j.jisa.2017.06.001
Raus M, 2009, GOV INFORM Q, V26, P246, DOI 10.1016/j.giq.2008.11.008
Robinson R, 2012, ELECTRON COMMER RES, V12, P301, DOI 10.1007/s10660-012-9095-7
Rovetta D, 2009, LEG ISS ECON INTEGR, V36, P7
Schu G, 2018, EXPERT SYST APPL, V98, P57, DOI 10.1016/j.eswa.2017.12.045
Song L, 2017, ELECTRON COMMER RES, V17, P51, DOI 10.1007/s10660-016-9244-5
Sun JS, 2018, STRUCT CHANGE ECON D, V47, P57, DOI 10.1016/j.strueco.2018.07.007
Sun JS, 2018, J CLEAN PROD, V175, P561, DOI 10.1016/j.jclepro.2017.12.042
Swapna G., 2018, Procedia Computer Science, V132, P1253, DOI 10.1016/j.procs.2018.05.041
Tanha J, 2017, INT J MACH LEARN CYB, V8, P355, DOI 10.1007/s13042-015-0328-7
Urciuoli L, 2013, GOV INFORM Q, V30, P473, DOI 10.1016/j.giq.2013.06.001
Ward BT, 2012, INFORM SYST MANAGE, V29, P331, DOI 10.1080/10580530.2012.716995
Wei YC, 2016, IEEE T PATTERN ANAL, V38, P1901, DOI 10.1109/TPAMI.2015.2491929
Wu HF, 2018, J ELECTROMYOGR KINES, V42, P136, DOI 10.1016/j.jelekin.2018.07.005
Wu YF, 2011, TELECOMMUN POLICY, V35, P603, DOI 10.1016/j.telpol.2011.05.002
Xiao B, 2007, MIS QUART, V31, P137
Xiao B, 2011, MIS QUART, V35, P169
Yapar BK, 2015, WORLD CONFERENCE ON TECHNOLOGY, INNOVATION AND ENTREPRENEURSHIP, P642, DOI 10.1016/j.sbspro.2015.06.145
Zhang XM, 2018, MULTIMED TOOLS APPL, V77, P7469, DOI 10.1007/s11042-017-4657-2
Zhu FY, 2019, NEUROCOMPUTING, V328, P182, DOI 10.1016/j.neucom.2018.02.099
NR 51
TC 26
Z9 26
U1 5
U2 50
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD DEC
PY 2019
VL 19
IS 4
SI SI
BP 779
EP 800
DI 10.1007/s10660-019-09334-x
PG 22
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JM2DA
UT WOS:000496029800003
DA 2024-03-27
ER
PT J
AU Fu, JD
Mouakket, S
Sun, Y
AF Fu, Jindi
Mouakket, Samar
Sun, Yuan
TI The role of chatbots' human-like characteristics in online shopping
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Chatbots; Technology Readiness Index; Human -like characteristics; Trust
ID TECHNOLOGY READINESS; SOCIAL PRESENCE; CONSUMERS ACCEPTANCE;
ANTHROPOMORPHISM; AGENT; TRUST; EMPLOYEES; RESPONSIVENESS; PERCEPTIONS;
VARIABLES
AB Despite their importance for online shopping, little is known about what motivates customers to trust and use chatbots. This study fills this gap by investigating factors leading customers to trust chatbots, eventually leading to a willingness to use them. We examined the antecedents of trust from two perspectives: first, the customer perspective, represented by innovativeness and optimism, and second, the chatbots' human-like characteristics, represented by anthropomorphism, empathy, and social presence. The model was examined using a represen-tative sample of 395 online shopping customers. Our findings show that customer's readiness characteristics (optimism and innovativeness), and the human-like characteristics of chatbots (empathy and social presence) have a positive influence on customers' trust in chatbots, while perceived anthropomorphism has a negative influence on trust. In addition, our results indicate that customers' trust significantly affects their willingness to use this new technology. The results of the study provide key insights for chatbot developers and marketing managers on how to build trust in chatbots, which will, in turn, lead to an increase in customers' willingness to use this technology in the online shopping context.
C1 [Fu, Jindi] Hangzhou Dianzi Univ, Sch Management, Hangzhou, Peoples R China.
[Fu, Jindi] Zhejiang Univ, Sch Management, Hangzhou, Peoples R China.
[Mouakket, Samar] Univ Sharjah, Coll Comp & Informat, Sharjah, U Arab Emirates.
[Sun, Yuan] Zhejiang Gongshang Univ, Key Res Inst Humanities & Social Sci Univ, Modern Business Res Ctr, Minist Educ China, Hangzhou, Peoples R China.
[Sun, Yuan] Zhejiang Gongshang Univ, Sch Business Adm, Hangzhou City, Peoples R China.
[Sun, Yuan] Zhejiang Gongshang Univ, Zheshang Res Inst, Hangzhou, Peoples R China.
C3 Hangzhou Dianzi University; Zhejiang University; University of Sharjah;
Zhejiang Gongshang University; Zhejiang Gongshang University; Zhejiang
Gongshang University
RP Sun, Y (autor correspondiente), Zhejiang Gongshang Univ, Key Res Inst Humanities & Social Sci Univ, Modern Business Res Ctr, Minist Educ China, Hangzhou, Peoples R China.; Sun, Y (autor correspondiente), Zhejiang Gongshang Univ, Sch Business Adm, Hangzhou City, Peoples R China.; Sun, Y (autor correspondiente), Zhejiang Gongshang Univ, Zheshang Res Inst, Hangzhou, Peoples R China.
EM 3030630052@163.com; samar@sharjah.ac.ae; sunyuan@mail.zjgsu.edu.cn
RI Mouakket, Samar/HJY-0400-2023
OI SUN, Yuan/0000-0002-8659-1870
FU National Natural Science Foundation of China [72102058]; China
Postdoctoral Science Foundation [2021M702794, 2023T160579]; Zhejiang
Postdoctoral Science Foundation [ZJ2021057]; Major Project of National
Social Sci- ence Fund of China [21 ZD119]; Zhejiang Provincial Natural
Sci- ence Foundation [LR23G020001]
FX This work was supported by grants awarded by the National Natural
Science Foundation of China (72102058) , China Postdoctoral Science
Foundation (2021M702794, 2023T160579) , Zhejiang Postdoctoral Science
Foundation (ZJ2021057) , Major Project of National Social Sci- ence Fund
of China (21 & ZD119) , and Zhejiang Provincial Natural Sci- ence
Foundation (LR23G020001) .
CR Aggarwal P, 2012, J CONSUM RES, V39, P307, DOI 10.1086/662614
Ameen N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106548
Aoki N, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2020.101490
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Barclay D., 1995, PARTIAL LEAST SQUARE
Bartneck C, 2009, INT J SOC ROBOT, V1, P71, DOI 10.1007/s12369-008-0001-3
Baylor A.L., 2005, International Journal of Artificial Intelligence in Education, V15, P95, DOI DOI 10.1007/BF02504991
Ben Mimoun M.S., 2013, Improving Consumer Performance and Perceived Service Quality in a New Type of Commercial Environment in 3D, Thanks to Embodied Virtual Sales Agents, P233, DOI [10.4018/978-1-4666-3954-6.ch014, DOI 10.4018/978-1-4666-3954-6.CH014]
Birnbaum GE, 2016, COMPUT HUM BEHAV, V63, P416, DOI 10.1016/j.chb.2016.05.064
BRISLIN RW, 1970, J CROSS CULT PSYCHOL, V1, P185, DOI 10.1177/135910457000100301
Cameron D, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106561
Carmines EG., 1979, RELIABILITY VALIDITY, V17, P37, DOI [DOI 10.4135/9781412985642, 10.4135/9781412985642.n4]
Carrion G.C., 2017, PARTIAL LEAST SQUARE, DOI DOI 10.1007/978-3-319-64069-3_8
Cassell J., 2000, EMBODIED CONVERSATIO
Castellano S, 2018, MANAGE DECIS, V56, P1232, DOI 10.1108/MD-04-2017-0444
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Chang YW, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102325
Chen JV, 2021, INTERNET RES, V31, P1376, DOI 10.1108/INTR-11-2019-0447
Chen MF, 2018, INTERNET RES, V28, P351, DOI 10.1108/IntR-03-2017-0099
Chen Q, 2022, J BUS RES, V145, P552, DOI 10.1016/j.jbusres.2022.02.088
Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
Chin WW, 1998, QUANT METH SER, P295
Chin WW, 2013, SPRINGER P MATH STAT, V56, P221, DOI 10.1007/978-1-4614-8283-3_15
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Ciechanowski L, 2019, FUTURE GENER COMP SY, V92, P539, DOI 10.1016/j.future.2018.01.055
Cruz-Cárdenas J, 2021, J BUS RES, V122, P217, DOI 10.1016/j.jbusres.2020.08.054
de Kervenoael R, 2020, TOURISM MANAGE, V78, DOI 10.1016/j.tourman.2019.104042
Dhagarra D, 2020, INT J MED INFORM, V141, DOI 10.1016/j.ijmedinf.2020.104164
Dikmen M, 2017, IEEE SYS MAN CYBERN, P1093, DOI 10.1109/SMC.2017.8122757
Dirsehan T, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101361
Edwards C, 2016, COMPUT HUM BEHAV, V65, P666, DOI 10.1016/j.chb.2016.07.003
Etemad-Sajadi R, 2014, INT J QUAL RELIAB MA, V31, P419, DOI 10.1108/IJQRM-05-2013-0077
Fernandes T, 2021, J BUS RES, V122, P180, DOI 10.1016/j.jbusres.2020.08.058
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gao LL, 2017, INFORM SYST FRONT, V19, P525, DOI 10.1007/s10796-015-9611-0
Gefen D, 2004, OMEGA-INT J MANAGE S, V32, P407, DOI 10.1016/j.omega.2004.01.006
Gefen D., 2003, e-Service Journal, V2, P7, DOI DOI 10.2979/ESJ.2003.2.2.7
Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Gong L, 2007, HUM COMMUN RES, V33, P163, DOI 10.1111/j.1468-2958.2007.00295.x
Goudey A, 2016, RECH APPL MARKET-ENG, V31, P2, DOI 10.1177/2051570716643961
Griol D, 2013, APPL ARTIF INTELL, V27, P759, DOI 10.1080/08839514.2013.835230
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Haddad A, 2020, ADV INTELL SYST COMP, V1016, P249, DOI 10.1007/978-981-13-9364-8_19
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Han MC, 2021, J INTERNET COMMER, V20, P46, DOI 10.1080/15332861.2020.1863022
Harman H.H., 1976, MODERN FACTOR ANAL, DOI DOI 10.2307/2282293
Heerink M, 2010, INT J SOC ROBOT, V2, P361, DOI 10.1007/s12369-010-0068-5
Henseler J, 2014, ORGAN RES METHODS, V17, P182, DOI 10.1177/1094428114526928
Huang JM, 2015, J HOSP TOUR TECHNOL, V6, P26, DOI 10.1108/JHTT-02-2015-0013
Jiang H, 2022, COMPUT HUM BEHAV, V134, DOI 10.1016/j.chb.2022.107329
Jiang QL, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.103074
Jiang Y, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107485
Kasilingam DL, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101280
Kaushik AK, 2015, TOUR MANAG PERSPECT, V16, P278, DOI 10.1016/j.tmp.2015.09.002
Kim Y, 2012, COMPUT HUM BEHAV, V28, P241, DOI 10.1016/j.chb.2011.09.006
Konya-Baumbach E, 2023, COMPUT HUM BEHAV, V139, DOI 10.1016/j.chb.2022.107513
Larivière B, 2017, J BUS RES, V79, P238, DOI 10.1016/j.jbusres.2017.03.008
Lin JSC, 2011, MANAG SERV QUAL, V21, P424, DOI 10.1108/09604521111146289
Lin XL, 2022, IND MARKET MANAG, V101, P45, DOI 10.1016/j.indmarman.2021.11.016
Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
Lu J, 2012, J ELECTRON COMMER RE, V13, P50
Lu L, 2019, INT J HOSP MANAG, V80, P36, DOI 10.1016/j.ijhm.2019.01.005
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
McLean G, 2019, COMPUT HUM BEHAV, V99, P28, DOI 10.1016/j.chb.2019.05.009
Meng J, 2009, J INT CONSUM MARK, V22, P19, DOI 10.1080/08961530902844915
Mostafa RB, 2022, EUR J MARKETING, V56, P1748, DOI 10.1108/EJM-02-2020-0084
Murray J, 2019, INT J RETAIL DISTRIB, V47, P1364, DOI 10.1108/IJRDM-01-2019-0016
Nugroho MA, 2017, PROCEDIA COMPUT SCI, V124, P319, DOI 10.1016/j.procs.2017.12.161
Parasuraman A, 2015, J SERV RES-US, V18, P59, DOI 10.1177/1094670514539730
Parasuraman A., 2001, Techno-Ready Marketing: How and Why Your Customers Adopt Technology
Parasuraman W, 2000, Journal of Service Research, V2, P307, DOI [10.1177/109467050024001, DOI 10.1177/109467050024001]
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
PODSAKOFF PM, 1986, J MANAGE, V12, P531, DOI 10.1177/014920638601200408
Powell PA, 2017, COMPUT HUM BEHAV, V68, P137, DOI 10.1016/j.chb.2016.11.024
Rosenbaum MS, 2015, J BUS RES, V68, P1862, DOI 10.1016/j.jbusres.2015.01.014
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Siau K., 2018, Cutter business technology journal, V31, P47
Sun S, 2020, INT J HOSP MANAG, V90, DOI 10.1016/j.ijhm.2020.102633
Sundar S. S., 2008, Digital media, youth, and credibility, P73, DOI DOI 10.1162/DMAL.9780262562324.073
Tsai WHS, 2021, J RES INTERACT MARK, V15, P460, DOI 10.1108/JRIM-12-2019-0200
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Verhagen T, 2014, J COMPUT-MEDIAT COMM, V19, P529, DOI 10.1111/jcc4.12066
Wagner K, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P1386
Walczuch R, 2007, INFORM MANAGE-AMSTER, V44, P206, DOI 10.1016/j.im.2006.12.005
Wang CC, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103325
Waytz A, 2014, J EXP SOC PSYCHOL, V52, P113, DOI 10.1016/j.jesp.2014.01.005
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Yoo Y, 2001, MIS QUART, V25, P371, DOI 10.2307/3250922
Zhao XS, 2010, J CONSUM RES, V37, P197, DOI 10.1086/651257
NR 92
TC 0
Z9 0
U1 49
U2 49
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD SEP-OCT
PY 2023
VL 61
AR 101304
DI 10.1016/j.elerap.2023.101304
EA AUG 2023
PG 11
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA T7RS0
UT WOS:001079924200001
DA 2024-03-27
ER
PT J
AU Sivathanu, B
Pillai, R
Metri, B
AF Sivathanu, Brijesh
Pillai, Rajasshrie
Metri, Bhimaraya
TI Customers' online shopping intention by watching AI-based deepfake
advertisements
SO INTERNATIONAL JOURNAL OF RETAIL & DISTRIBUTION MANAGEMENT
LA English
DT Article
DE Online shopping intention; Deepfake video advertising; PLS-SEM; Media
richness; Information manipulation; Personalization; Perceived trust
ID MANIPULATION THEORY 2; MEDIA-RICHNESS; ARTIFICIAL-INTELLIGENCE;
INTEGRATED MODEL; SOCIAL PRESENCE; COGNITIVE LOAD; BIG DATA; PLS-SEM;
TRUST; IMPACT
AB Purpose The purpose of this study was to investigate the online shopping intention of customers by watching artificial intelligence (AI)-based deepfake video advertisements using media richness (MR) theory and Information Manipulation Theory 2 (IMT2). Design/methodology/approach A conceptual model was developed to understand customers' online shopping intention by watching deepfake videos. A quantitative survey was conducted among the 1,180 customers using a structured questionnaire to test the conceptual model, and data were analyzed with partial least squares structural equation modeling. Findings The outcome of this research provides the antecedents of the online shopping intention of customers after watching AI-based deepfake videos. These antecedents are MR, information manipulation tactics, personalization and perceived trust. Perceived deception negatively influences customers' online shopping intention, and cognitive load has no effect. It also elucidates the manipulation tactics used by the managers to develop AI-based deepfake videos. Practical implications The distinctive model that emerged is insightful for senior executives and managers in the e-commerce and retailing industry to understand the influence of AI-based deepfake videos. This provides the antecedents of online shopping intention due to deepfakes, which are helpful for designers, marketing managers and developers. Originality/value The authors amalgamate the MR and IMT2 theory to understand the online shopping intention of the customers after watching AI-based deepfake videos. This work is a pioneer in examining the effect of AI-based deepfakes on the online shopping intention of customers by providing a framework that is empirically validated.
C1 [Sivathanu, Brijesh] Coll Engn Pune, Dept Management, Pune, Maharashtra, India.
[Pillai, Rajasshrie] Pune Inst Business Management, Dept Management, Pune, Maharashtra, India.
[Metri, Bhimaraya] Indian Inst Management, Nagpur, Maharashtra, India.
C3 College of Engineering Pune; Indian Institute of Management (IIM
System); Indian Institute of Management Nagpur
RP Pillai, R (autor correspondiente), Pune Inst Business Management, Dept Management, Pune, Maharashtra, India.
EM rajasshriel@gmail.com
RI S, BRIJESH/AAQ-4753-2021; Pillai, Rajasshrie/GRO-0859-2022
OI Sivathanu, Dr. Brijesh/0000-0003-2505-9140
CR Adee S., 2020, IEEE SPECTRUM
Aiolfi S, 2021, INT J RETAIL DISTRIB, V49, P1089, DOI 10.1108/IJRDM-10-2020-0410
Al-Debei MM, 2015, INTERNET RES, V25, P707, DOI 10.1108/IntR-05-2014-0146
Al-Maghrabi T, 2011, INT J RETAIL DISTRIB, V39, P899, DOI 10.1108/09590551111183308
Alamäki A, 2019, INFORM PROCESS MANAG, V56, P756, DOI 10.1016/j.ipm.2019.01.003
All Pakistan Textile Mills Association, 2020, AI GENERATED TEXT IS
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Arora S., 2021, J INTERNET COMMER, V21, P1
Banerjee S, 2022, INT J INFORM MANAGE, V62, DOI 10.1016/j.ijinfomgt.2021.102416
Bankole FO, 2017, TELEMAT INFORM, V34, P490, DOI 10.1016/j.tele.2016.08.004
Baron K., 2019, FORBES
Bergin R., 2013, MEDIA RICHNESS THEOR
Bianchi C, 2018, INT J RETAIL DISTRIB, V46, P364, DOI 10.1108/IJRDM-02-2017-0035
Blake, 2020, 50 STATS SHOWING POW
Bleier A, 2015, J RETAILING, V91, P390, DOI 10.1016/j.jretai.2015.04.001
Brunelle E, 2009, J INTERNET COMMER, V8, P222, DOI 10.1080/15332860903467649
Campbell C, 2022, J ADVERTISING, V51, P22, DOI 10.1080/00913367.2021.1909515
Cao LL, 2021, INT J RETAIL DISTRIB, V49, P958, DOI 10.1108/IJRDM-09-2020-0350
Chen CC, 2018, TELEMAT INFORM, V35, P1512, DOI 10.1016/j.tele.2018.03.019
Chen MF, 2014, INT J HOSP MANAG, V36, P221, DOI 10.1016/j.ijhm.2013.09.006
Chen MY, 2013, ELECTRON COMMER RES, V13, P1, DOI 10.1007/s10660-013-9104-5
Chen YM, 2018, J RETAIL CONSUM SERV, V41, P281, DOI 10.1016/j.jretconser.2017.04.004
Chetioui Y, 2021, EUROMED J BUS, V16, P544, DOI 10.1108/EMJB-05-2020-0046
Chiluwa I.E., 2019, HDB RES DECEPTION FA
Chitrakorn K., 2021, BRANDS CAN TAP FUTUR
Chopra K, 2019, INT J RETAIL DISTRIB, V47, P331, DOI 10.1108/IJRDM-11-2018-0251
Cui G., 2014, J MARKETING, V1
DAFT RL, 1987, MIS QUART, V11, P355, DOI 10.2307/248682
Dan A., 2020, FORBES
Dialani P., 2021, NOTHE POSITIVE POTEN
Dubey R, 2018, INT J LOGIST MANAG, V29, P485, DOI 10.1108/IJLM-02-2017-0039
Etemad-Sajadi R, 2016, COMPUT HUM BEHAV, V61, P227, DOI 10.1016/j.chb.2016.03.045
Fan XJ, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101986
Giertz JN, 2022, J SERV MANAGE, V33, P485, DOI 10.1108/JOSM-06-2021-0211
Grewal D, 2018, J MARKET EDUC, V40, P85, DOI 10.1177/0273475318755838
Grewal D, 2017, J RETAILING, V93, P1, DOI 10.1016/j.jretai.2016.12.008
GUADAGNOLI E, 1988, PSYCHOL BULL, V103, P265, DOI 10.1037/0033-2909.103.2.265
Guide VDR, 2015, J OPER MANAG, V37, pV, DOI 10.1016/S0272-6963(15)00056-X
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
Hamouda M, 2018, J ENTERP INF MANAG, V31, P426, DOI 10.1108/JEIM-07-2017-0101
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Huang TL, 2015, ELECTRON COMMER RES, V15, P269, DOI 10.1007/s10660-014-9163-2
Ishii K, 2019, HUM BEHAV EMERG TECH, V1, P124, DOI 10.1002/hbe2.138
Kalinic Z, 2016, INF SYST E-BUS MANAG, V14, P367, DOI 10.1007/s10257-015-0287-2
Kalpokas Ignas, 2021, SN Soc Sci, V1, P1, DOI 10.1007/s43545-020-00010-8
Kang M, 2016, INFORM TECHNOL PEOPL, V29, P580, DOI 10.1108/ITP-01-2015-0023
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Kietzmann J, 2021, INT J ADVERT, V40, P473, DOI 10.1080/02650487.2020.1834211
Kietzmann J, 2020, BUS HORIZONS, V63, P135, DOI 10.1016/j.bushor.2019.11.006
Kim JH, 2010, J FASH MARK MANAG, V14, P247, DOI 10.1108/13612021011046093
Kim YJ, 2014, COMPUT HUM BEHAV, V33, P256, DOI 10.1016/j.chb.2014.01.015
Koo DM, 2010, COMPUT HUM BEHAV, V26, P377, DOI 10.1016/j.chb.2009.11.009
Kwok AOJ, 2021, CURR ISSUES TOUR, V24, P1798, DOI 10.1080/13683500.2020.1738357
Lai YY, 2018, INT J LOGIST MANAG, V29, P676, DOI 10.1108/IJLM-06-2017-0153
Lee J., 2022, SSRN ELECT J, DOI 10.2139/ssrn.4059116
Lee J, 2011, INTERNET RES, V21, P187, DOI 10.1108/10662241111123766
Li CH, 2016, INT J CONTEMP HOSP M, V28, P924, DOI 10.1108/IJCHM-05-2014-0227
Lim JS., 2020, Journal of Marketing Communications, V26, P145, DOI DOI 10.1080/13527266.2018.1469543
Lipowski M, 2018, BALT J MANAG, V13, P169, DOI 10.1108/BJM-04-2017-0127
Luthera N., 2020, FORBES
Mahmud B., 2020, ARXIV PREPRINT, V5, P13
Maity M, 2018, J BUS RES, V87, P36, DOI 10.1016/j.jbusres.2018.02.003
Maras MH, 2019, INT J EVID PROOF, V23, P255, DOI 10.1177/1365712718807226
MCCORNACK SA, 1992, COMMUN MONOGR, V59, P1, DOI 10.1080/03637759209376245
McCornack SA, 2014, J LANG SOC PSYCHOL, V33, P348, DOI 10.1177/0261927X14534656
Mitra G., 2020, EXPRESS COMPUTER
Morosan C, 2014, INT J CONTEMP HOSP M, V26, P246, DOI 10.1108/IJCHM-11-2012-0221
Mustafi M.A. A., 2020, Journal of Contemporary Marketing Science, V3, P385, DOI [DOI 10.1108/JCMARS-05-2020-0024, 10.1108/jcmars-05-2020-0024]
Nguyen T.T., 2019, Deep Learning for Deepfakes Creation and Detection
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Ogara SO, 2014, COMPUT HUM BEHAV, V36, P453, DOI 10.1016/j.chb.2014.03.064
Ogonowski A, 2014, J RETAIL CONSUM SERV, V21, P482, DOI 10.1016/j.jretconser.2014.03.004
PAAS FGWC, 1994, PERCEPT MOTOR SKILL, V79, P419, DOI 10.2466/pms.1994.79.1.419
Patel L., 2020, FORBES
Paul H, 2021, DATA MIN KNOWL DISC, V35, P1830, DOI 10.1007/s10618-021-00772-6
Peng K, 2020, INTERNET RES, V30, P1143, DOI 10.1108/INTR-03-2019-0095
Peng L., 2009, ACCOUNT FINANC, V24, P75
Peng L, 2016, J CONSUM MARK, V33, P269, DOI 10.1108/JCM-01-2015-1281
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Qin X., 2019, J ADVERTISING, V48, P1
Ringle C.M., 2005, Smart PLS 2.0
Riquelme IP, 2014, J BUS ETHICS, V119, P405, DOI 10.1007/s10551-013-1628-z
Román S, 2007, J BUS ETHICS, V72, P131, DOI 10.1007/s10551-006-9161-y
Roy SK, 2017, TECHNOL FORECAST SOC, V124, P257, DOI 10.1016/j.techfore.2016.09.022
Samuel LHS, 2015, J INTERNET COMMER, V14, P233, DOI 10.1080/15332861.2015.1028250
Schmeck A, 2015, INSTR SCI, V43, P93, DOI 10.1007/s11251-014-9328-3
Schrader C, 2012, COMPUT HUM BEHAV, V28, P648, DOI 10.1016/j.chb.2011.11.011
Sengar N., 2022, EC TIMES, P1
Sohn K, 2021, INT J RETAIL DISTRIB, V49, P61, DOI 10.1108/IJRDM-03-2020-0091
Sokolova K, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.01.011
Somers M., 2020, SLOAN
Souiden N, 2019, J RETAIL CONSUM SERV, V50, P286, DOI 10.1016/j.jretconser.2018.07.023
Srivastava S., 2020, ANAL INSIGHT
Stover D, 2018, B ATOM SCI, V74, P283, DOI 10.1080/00963402.2018.1486618
Suh KS, 1999, INFORM MANAGE, V35, P295, DOI 10.1016/S0378-7206(98)00097-4
Tolosana Ruben, 2020, Information Fusion, V64, P131, DOI 10.1016/j.inffus.2020.06.014
Sebastian AT, 2021, COGENT BUS MANAG, V8, DOI 10.1080/23311975.2021.1876545
Tseng CH, 2020, INT J INFORM MANAGE, V50, P353, DOI 10.1016/j.ijinfomgt.2019.08.010
Tseng FC, 2017, INTERNET RES, V27, P520, DOI 10.1108/IntR-06-2016-0181
Tsou HT, 2015, INT J INFORM MANAGE, V35, P1, DOI 10.1016/j.ijinfomgt.2014.09.001
Tukachinsky R, 2019, COMMUN THEOR, V29, P297, DOI 10.1093/ct/qty032
Tuomi A, 2021, ANN TOUR RES EMPIR I, V2, DOI 10.1016/j.annale.2021.100027
Voropanova E, 2015, INT REV RETAIL DISTR, V25, P529, DOI 10.1080/09593969.2015.1089304
Walczyk JJ, 2014, J LANG SOC PSYCHOL, V33, P424, DOI 10.1177/0261927X14535395
Wang Y, 2019, ASIA PAC J MARKET LO, V31, P55, DOI 10.1108/APJML-06-2017-0112
Wang YY, 2018, INT J INFORM MANAGE, V38, P7, DOI 10.1016/j.ijinfomgt.2017.07.003
Westerlund M, 2019, TECHNOL INNOV MANAG, V9, P39, DOI 10.22215/timreview/1282
Whittaker L, 2021, AUSTRALAS MARK J, V29, P204, DOI 10.1177/1839334921999479
Williams D.E., 2020, INT REV RETAIL DISTR, V31, P1
Wu WY, 2017, INF SYST E-BUS MANAG, V15, P689, DOI 10.1007/s10257-016-0323-x
Yang, 2020, ONLINE BEHAV ADVERTI
Yeh YS, 2009, ONLINE INFORM REV, V33, P1066, DOI 10.1108/14684520911011016
Yoo K., 2014, INFORM COMMUNICATION, P49
Yoo K. H., 2010, Information Technology and Tourism, V12, P139, DOI 10.3727/109830510X12887971002701
Zhang M, 2020, SERV IND J, V40, P656, DOI 10.1080/02642069.2019.1576642
Zhu YQ, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2020.103314
Zhu YQ, 2016, COMPUT HUM BEHAV, V65, P442, DOI 10.1016/j.chb.2016.08.048
Zhuang, 2010, BUYER BEWARE CONSUME
Zhuang MZ, 2018, J BUS RES, V87, P24, DOI 10.1016/j.jbusres.2018.02.016
NR 121
TC 3
Z9 4
U1 79
U2 153
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0959-0552
EI 1758-6690
J9 INT J RETAIL DISTRIB
JI Int. J. Retail Distrib. Manag.
PD JAN 2
PY 2023
VL 51
IS 1
BP 124
EP 145
DI 10.1108/IJRDM-12-2021-0583
EA SEP 2022
PG 22
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 7I7HO
UT WOS:000853274200001
DA 2024-03-27
ER
PT J
AU Louta, M
Roussaki, I
Pechlivanos, L
AF Louta, Malamati
Roussaki, Ioanna
Pechlivanos, Lambros
TI An intelligent agent negotiation strategy in the electronic marketplace
environment
SO EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
LA English
DT Article; Proceedings Paper
CT 20th European Conference on Operational Research (EURO XX)
CY JUL 04-07, 2004
CL Rhodes Isl, GREECE
DE intelligent agents; negotiation protocol and model; strategy; ranking
mechanism
ID COMPUTING PARETO SOLUTIONS; ISSUE NEGOTIATIONS; INFORMATION; MODEL
AB E-commerce will strongly penetrate the market if coupled with appropriate technologies and mechanisms. Mobile agents may enhance the intelligence and improve the efficiency of systems in the e-marketplace. We propose a dynamic multi-lateral negotiation model and construct an efficient negotiation strategy based on a ranking mechanism that does not require a complicated rationale on behalf of the buyer agents. This strategy can be used to extend the functionality of autonomous intelligent agents, so that they quickly reach to an agreement aiming to maximise their owner's utility. The framework proposed considers both contract and decision issues, is based on real market conditions, and has been empirically evaluated. Moreover, it is shown that in a linear framework like the one we employ, more elaborate ranking mechanisms do not necessarily improve efficiency. (C) 2006 Elsevier B.V. All rights reserved.
C1 [Louta, Malamati] Technol Educ Inst Western Macedonia, Dept Business Adm, Koila 50100, Kozani, Greece.
[Roussaki, Ioanna] Natl Tech Univ Athens, GR-15773 Athens, Greece.
[Pechlivanos, Lambros] Athens Univ Econ & Business, Athens 10434, Greece.
C3 National Technical University of Athens; Athens University of Economics
& Business
RP Louta, M (autor correspondiente), Technol Educ Inst Western Macedonia, Dept Business Adm, Koila 50100, Kozani, Greece.
EM louta@telecom.ntua.gr; nanario@telecom.ntua.gr; lpech@aueb.gr
RI Roussaki, Ioanna G/AIF-3176-2022
OI Louta, Malamati/0009-0005-2283-7383; Pechlivanos,
Lambros/0000-0001-8667-3561
CR Aarts E., 1989, Simulated Annealing and Boltzmann Machines
AXELROD R, 1981, SCIENCE, V211, P1390, DOI 10.1126/science.7466396
Axelrod R., 1997, COMPLEXITY COOPERATI
Bac M, 1996, GAME ECON BEHAV, V13, P125, DOI 10.1006/game.1996.0028
Brugha C, 2004, J OPER RES SOC, V55, P1156, DOI 10.1057/palgrave.jors.2601777
Busch LA, 2002, GAME ECON BEHAV, V41, P169, DOI 10.1016/S0899-8256(02)00505-5
CHEN MK, 2002, AGENDAS MULTI ISSUE
CONTIZER V, 2003, P 5 WORKSH AG MED EL
Crane M., 1991, CONJOINT ANAL GUIDE
Ehtamo H, 2001, EUR J OPER RES, V130, P54, DOI 10.1016/S0377-2217(00)00019-9
Faratin P, 1998, ROBOT AUTON SYST, V24, P159, DOI 10.1016/S0921-8890(98)00029-3
Fatima S. S., 2002, Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, P143
Fatima SS, 2004, ARTIF INTELL, V152, P1, DOI 10.1016/S0004-3702(03)00115-2
Fershtman C, 2000, GAME ECON BEHAV, V30, P216, DOI 10.1006/game.1999.0727
FIELD S, 2000, ELECT COMMERCE SPECI, V32
Figueira J, 2005, MULTIPLE CRITERIA DE, V78
Gerding EnricoH., 2000, Scientific approaches and techniques for negotiation. a game theoretic and artificial intelligence perspective
Ghosh S, 1998, HARVARD BUS REV, V76, P126
GLITHO R, 1998, FEATURE TOPIC IEEE C, V36
Gosling J., 2005, Java Language Specification, Vthird
He MH, 2003, IEEE T KNOWL DATA EN, V15, P985, DOI 10.1109/TKDE.2003.1209014
Heiskanen P, 1999, EUR J OPER RES, V117, P578, DOI 10.1016/S0377-2217(98)00276-8
Heiskanen P, 2001, EUR J OPER RES, V133, P44, DOI 10.1016/S0377-2217(00)00179-X
In Y, 2004, J ECON BEHAV ORGAN, V53, P385, DOI 10.1016/S0167-2681(03)00087-8
Inderst R, 2000, GAME ECON BEHAV, V30, P64, DOI 10.1006/game.1998.0710
Jennings NR, 2001, GROUP DECIS NEGOT, V10, P199, DOI 10.1023/A:1008746126376
Kaelbling LP, 1996, J ARTIF INTELL RES, V4, P237, DOI 10.1613/jair.301
KALAI E, 1977, ECONOMETRICA, V45, P1623, DOI 10.2307/1913954
KAPETANAKIS S, 2002, P NAT C INN APPL ART
Karlins M., 1970, PERSUASION OPINIONS, V2nd
KARP A, 2003, REPRESENTING UTILITY
Keeney RL., 1993, Decisions with Multiple Objectives: Preferences and Value Tradeoffs
KLEIN M, 2002, P ACM INT JOINT C AU
KRAOUS S, 1998, ARTIF INTELL, V104, P1
KRAUS S, 1995, COMPUT INTELL, V11, P132, DOI 10.1111/j.1467-8640.1995.tb00026.x
LI C, 2003, P ACM C EL COMM SAND
Louta A, 2002, COMPUT COMMUN, V25, P662, DOI 10.1016/S0140-3664(01)00393-0
LOUTA M, 2005, P IEEE WIC ACM INT C
LOUTA M, 2004, P IEEE INT C INT SYS
Luo XD, 2003, ARTIF INTELL, V148, P53, DOI 10.1016/S0004-3702(03)00041-9
MYERSON RB, 1981, ECONOMETRICA, V49, P883, DOI 10.2307/1912508
Papadimitriou C.H., 1982, Combinational Optimization: Algorithms and Complexity
Pardalos P.M., 1995, Advances in Multicriteria Analysis
PETERS H, 1986, ECONOMETRICA, V54, P153, DOI 10.2307/1914163
Ponsati C, 1997, INT J GAME THEORY, V26, P501, DOI 10.1007/BF01813888
Ponsati C, 1998, ECON THEOR, V11, P667, DOI 10.1007/s001990050208
Pruitt D.G., 1981, NEGOTIATION BEHAV
RAIFFA H, 1982, AR SCI NEGOTIATION
Raith MG, 2000, MATH SOC SCI, V39, P303, DOI 10.1016/S0165-4896(99)00032-3
Rosenschein J. S., 1994, RULES ENCOUNTER DESI
ROUSSAKI I, 2003, THESIS NATL TECHNICA
ROUSSAKI I, 2004, P 4 INT C INT DES AP
RUBINSTEIN A, 1982, ECONOMETRICA, V50, P97, DOI 10.2307/1912531
RUBINSTEIN A, 1985, ECONOMETRICA, V53, P1151, DOI 10.2307/1911016
Sandholm T.W., 1999, DISTRIBUTED RATIONAL
Sycara K., 1989, DISTRIBUTED ARTIFICI, V2, P119
SYCARA KP, 1990, EUR J OPER RES, V46, P216, DOI 10.1016/0377-2217(90)90133-V
TAG M, 1996, P INT 1996 C NAR JAP
Vulkan N, 1999, ECON J, V109, pF67, DOI 10.1111/1468-0297.00403
ZUIDWEG H, 1999, P 6 INT C INT SERV N
NR 60
TC 31
Z9 33
U1 3
U2 20
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0377-2217
EI 1872-6860
J9 EUR J OPER RES
JI Eur. J. Oper. Res.
PD JUN 16
PY 2008
VL 187
IS 3
BP 1327
EP 1345
DI 10.1016/j.ejor.2006.09.016
PG 19
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Science (CPCI-S)
SC Business & Economics; Operations Research & Management Science
GA 253ZS
UT WOS:000252556700042
DA 2024-03-27
ER
PT J
AU Liébana-Cabanillas, F
Marinkovic, V
de Luna, IR
Kalinic, Z
AF Liebana-Cabanillas, Francisco
Marinkovic, Veljko
de Luna, Iviane Ramos
Kalinic, Zoran
TI Predicting the determinants of mobile payment acceptance: A hybrid
SEM-neural network approach
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Mobile payment; NFC; SEM; Neural network; Behavioral intention
ID INTERNET BANKING ADOPTION; M-COMMERCE ADOPTION; PERSONAL INNOVATIVENESS;
PERCEIVED EASE; INITIAL TRUST; INFORMATION-TECHNOLOGY; CUSTOMER
SATISFACTION; BEHAVIORAL INTENTION; CONSUMER ADOPTION; SOCIAL INFLUENCES
AB As a modern alternative to cash, check or credit cards, the interest in mobile payments is growing in our society, from consumers to merchants. The present study develops a new research model used for the prediction of the most significant factors influencing the decision to use m-payment. To this end, the authors have carried out a study through an online survey of a national panel of Spanish users of smartphones. Two techniques were used: first, structural equation modeling (SEM) was used to determine which variables had significant influence on mobile payment adoption; in a second phase, the neural network model was used to rank the relative influence of significant predictors obtained by SEM. This research found that the most significant variables impacting the intention to use were perceived usefulness and perceived security variables. On the other side, the results of neural network analysis confirmed many SEM findings, but also gave slightly different order of influence of significant predictors. The conclusions and implications for management provide companies with alternatives to consolidate this new business opportunity under the new technological developments.
C1 [Liebana-Cabanillas, Francisco; de Luna, Iviane Ramos] Univ Granada, Dept Mkt & Market Res, Granada, Spain.
[Kalinic, Zoran] Univ Kragujevac, Fac Econ, Djure Pucara Starog 3, Kragujevac 34000, Serbia.
[Marinkovic, Veljko] Univ Kragujevac, Fac Econ, Mkt Res & Consumer Behav, Kragujevac, Serbia.
C3 University of Granada; University of Kragujevac; University of
Kragujevac
RP Kalinic, Z (autor correspondiente), Univ Kragujevac, Fac Econ, Djure Pucara Starog 3, Kragujevac 34000, Serbia.
EM franlieb@ugr.es; vmarinkovic@kg.ac.rs; iviane@correo.ugr.es;
zkalinic@kg.ac.rs
RI Kalinic, Zoran/AAH-1448-2020; Liebana-Cabanillas, F./I-1063-2015; Ramos
de Luna, Iviane/F-2450-2018
OI Kalinic, Zoran/0000-0001-8137-9005; Ramos de Luna,
Iviane/0000-0002-0056-9438; LIEBANA-CABANILLAS,
FRANCISCO/0000-0002-3255-0651
FU Andalusia Regional Government [P10-SEJ-6768]; Capes Foundation-Ministry
of Education of Brazil [BEX 0739/13-8]; Ministry of Education, Science
and Technological Development of the Republic of Serbia [III-44010]
FX This study has being conducted thanks to the financial support received
through the Excellence Research Project P10-SEJ-6768 from the Andalusia
Regional Government, Research Grant BEX 0739/13-8 of the Capes
Foundation-Ministry of Education of Brazil and also through the Research
Project III-44010 of the Ministry of Education, Science and
Technological Development of the Republic of Serbia.
CR Abdullah F, 2016, COMPUT HUM BEHAV, V56, P238, DOI 10.1016/j.chb.2015.11.036
AJZEN I, 1977, PSYCHOL BULL, V84, P888, DOI 10.1037/0033-2909.84.5.888
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
[Anonymous], 2016, On the pulse of the networked society
[Anonymous], 2015, REV INTEGRATIVE BUSI
[Anonymous], 2015, GARTN SAYS 2018 50 P
Bagozzi RP, 1988, Journal of the Academy of Marketing Science, V16, P74, DOI DOI 10.1007/BF02723327
Baptista G, 2016, COMPUT HUM BEHAV, V63, P480, DOI 10.1016/j.chb.2016.05.074
Bhattacherjee A, 2001, DECIS SUPPORT SYST, V32, P201, DOI 10.1016/S0167-9236(01)00111-7
Bhatti T., 2007, Journal of Internet Banking and Commerce, Volume, V12
BLUM A, 1992, NEURAL NETWORKS C PL
Borges Monica S., 2015, International Journal of Electronic Business, V12, P70, DOI 10.1504/IJEB.2015.068316
Business Insider, 2015, BI INT
Byrne B.M., 1998, STRUCTURAL EQUATION
Cavus N., 2015, GLOBAL J INF TECHNOL, V5, P62, DOI DOI 10.18844/GJIT.V5I2.196
Chan FTS, 2012, DECIS SUPPORT SYST, V54, P621, DOI 10.1016/j.dss.2012.08.009
Chong AYL, 2015, INT J PROD ECON, V159, P66, DOI 10.1016/j.ijpe.2014.09.034
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Chong AYL, 2013, EXPERT SYST APPL, V40, P523, DOI 10.1016/j.eswa.2012.07.068
Cobanoglu C., 2015, HOSPITALITY REV, V31, P6
Dahlberg T, 2015, ELECTRON COMMER R A, V14, P265, DOI 10.1016/j.elerap.2015.07.006
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dennehy D., 2015, J Innov Manag, V3, P49, DOI [10.24840/2183-0606_003.001_0006, DOI 10.24840/2183-0606_003.001_0006]
Devaraj S, 2002, INFORM SYST RES, V13, P316, DOI 10.1287/isre.13.3.316.77
Di Pietro L, 2015, TRANSPORT RES C-EMER, V56, P463, DOI 10.1016/j.trc.2015.05.001
eMarketer, 2015, MOB PAYM WILL TRIPL
eMarketer, 2017, PERS MOB PAYM RIS EU
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Forrester, 2016, FORR FOR MOB PAYM RE
Francisco LC, 2015, INT J INF TECH DECIS, V14, P421, DOI 10.1142/S0219622015500078
Fung Global Retail & Technology, 2016, MOB PAYM SUPP EUR MO
Gao LL, 2017, INFORM SYST FRONT, V19, P525, DOI 10.1007/s10796-015-9611-0
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Ghezzi Antonio, 2010, Info, V12, P3, DOI 10.1108/14636691011071130
Hair J.F., 2009, Multivariate data analysis
Hanafizadeh P, 2014, TELEMAT INFORM, V31, P62, DOI 10.1016/j.tele.2012.11.001
Harris MA, 2016, INT J INFORM MANAGE, V36, P441, DOI 10.1016/j.ijinfomgt.2016.02.004
Hartono E, 2014, DECIS SUPPORT SYST, V62, P11, DOI 10.1016/j.dss.2014.02.006
Haykin S., 2001, Neural Networks: A Comprehensive Foundation
Hernández-García A, 2011, J UNIVERS COMPUT SCI, V17, P1314
Hsiao CH, 2015, SCIENTOMETRICS, V105, P1091, DOI 10.1007/s11192-015-1749-5
Huang JH, 2004, INT MARKET REV, V21, P598, DOI 10.1108/02651330410568033
IBM Corporation, 2012, IBM SPSS NEUR NETW 2
Jiang CH, 2016, COMPUT HUM BEHAV, V64, P754, DOI 10.1016/j.chb.2016.07.046
Kalinic Z, 2016, INF SYST E-BUS MANAG, V14, P367, DOI 10.1007/s10257-015-0287-2
Kesharwani A, 2012, INT J BANK MARK, V30, P303, DOI 10.1108/02652321211236923
Kim C, 2010, COMPUT HUM BEHAV, V26, P310, DOI 10.1016/j.chb.2009.10.013
Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Li H., 2014, PACIS, P231
Liébana-Cabanillas F, 2018, SERV BUS, V12, P25, DOI 10.1007/s11628-017-0336-7
Liébana-Cabanillas F, 2017, ECON RES-EKON ISTRAZ, V30, P892, DOI 10.1080/1331677X.2017.1305784
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Liébana-Cabanillas F, 2015, TECHNOL ANAL STRATEG, V27, P1031, DOI 10.1080/09537325.2015.1047757
Liébana-Cabanillas F, 2014, COMPUT HUM BEHAV, V35, P464, DOI 10.1016/j.chb.2014.03.022
Lu J, 2005, J STRATEGIC INF SYST, V14, P245, DOI 10.1016/j.jsis.2005.07.003
Lu JN, 2014, INTERNET RES, V24, P134, DOI 10.1108/IntR-05-2012-0100
Lu YB, 2011, INFORM MANAGE-AMSTER, V48, P393, DOI 10.1016/j.im.2011.09.006
Luarn P, 2005, COMPUT HUM BEHAV, V21, P873, DOI 10.1016/j.chb.2004.03.003
Luna I.R, 2012, THESIS, P2012
Mahajan Vijay., 1985, Models for Innovation Adoption
Malaquias RF, 2016, COMPUT HUM BEHAV, V54, P453, DOI 10.1016/j.chb.2015.08.039
Marinkovic V, 2017, ONLINE INFORM REV, V41, P138, DOI 10.1108/OIR-11-2015-0364
Masamila B, 2010, COMM COM INF SC, V87, P175
Mastercard, 2016, PAG MOV CON TECN NFC
Meixin X, 2014, STUDY INTENTION USE
Merhi MI, 2016, COMPUT HUM BEHAV, V60, P253, DOI 10.1016/j.chb.2016.02.072
Montazemi AR, 2015, INFORM MANAGE-AMSTER, V52, P210, DOI 10.1016/j.im.2014.11.002
Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192
Moosmayer DC, 2013, EXPERT SYST APPL, V40, P3028, DOI 10.1016/j.eswa.2012.12.018
Morosan, 2016, INFORM COMMUNICATION, P427
Morosan C, 2016, INT J HOSP MANAG, V53, P17, DOI 10.1016/j.ijhm.2015.11.003
Negnevitsky M., 2011, ARTIFICIAL INTELLIGE, DOI DOI 10.1007/S00894-010-0935-X
Oliveira T, 2016, COMPUT HUM BEHAV, V61, P404, DOI 10.1016/j.chb.2016.03.030
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
Ozturk AB, 2016, INT J INFORM MANAGE, V36, P1350, DOI 10.1016/j.ijinfomgt.2016.04.005
Parasuraman A, 2005, J SERV RES-US, V7, P213, DOI 10.1177/1094670504271156
Patel N, 2016, STRATEGY ANAL, P1
Pham TTT, 2015, TECHNOL SOC, V43, P159, DOI 10.1016/j.techsoc.2015.05.004
Plouffe CR, 2001, INFORM SYST RES, V12, P208, DOI 10.1287/isre.12.2.208.9697
Ramos-de-Luna I, 2016, INF SYST E-BUS MANAG, V14, P293, DOI 10.1007/s10257-015-0284-5
Riquelme HE, 2010, INT J BANK MARK, V28, P328, DOI 10.1108/02652321011064872
Rogers, 2003, DIFFUSION INNOVATION
Sam KM, 2014, IN C IND ENG ENG MAN, P1121, DOI 10.1109/IEEM.2014.7058813
Martín HS, 2012, TOURISM MANAGE, V33, P341, DOI 10.1016/j.tourman.2011.04.003
Schierz PG, 2010, ELECTRON COMMER R A, V9, P209, DOI 10.1016/j.elerap.2009.07.005
Shaikh AA, 2015, COMPUT HUM BEHAV, V49, P541, DOI 10.1016/j.chb.2015.03.059
Shaikh AA, 2015, TELEMAT INFORM, V32, P129, DOI 10.1016/j.tele.2014.05.003
Shanko Genet, 2016, International Journal of Networking and Virtual Organisations, V16, P143
Sharma S.K, 2017, INFORM SYST FRONT, DOI [10.1007/s10976-017-9775-x, DOI 10.1007/S10976-017-9775-X]
Sharma SK, 2015, MANAG RES REV, V38, P750, DOI 10.1108/MRR-06-2014-0139
Sheela KG, 2013, MATH PROBL ENG, V2013, DOI 10.1155/2013/425740
Shibchurn J, 2015, COMPUT HUM BEHAV, V44, P103, DOI 10.1016/j.chb.2014.10.059
Shin DH, 2009, COMPUT HUM BEHAV, V25, P1343, DOI 10.1016/j.chb.2009.06.001
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Slade E, 2015, J STRATEG MARK, V23, P209, DOI 10.1080/0965254X.2014.914075
Slade EL, 2015, PSYCHOL MARKET, V32, P860, DOI 10.1002/mar.20823
Su P, 2017, TECH ANAL STRAT MANA, P1
Tan GWH, 2014, COMPUT HUM BEHAV, V36, P198, DOI 10.1016/j.chb.2014.03.052
Tan GWH, 2014, TELEMAT INFORM, V31, P292, DOI 10.1016/j.tele.2013.06.002
Thakur R, 2014, INTERNET RES, V24, P369, DOI 10.1108/IntR-12-2012-0244
Upadhyay P, 2016, INTERNET RES, V26, P38, DOI 10.1108/IntR-05-2014-0143
van der Heijden H, 2003, INFORM MANAGE-AMSTER, V40, P541, DOI 10.1016/S0378-7206(02)00079-4
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Wei TT, 2009, IND MANAGE DATA SYST, V109, P370, DOI 10.1108/02635570910939399
Wu JH, 2005, INFORM MANAGE-AMSTER, V42, P719, DOI 10.1016/j.im.2004.07.001
Yadav R, 2016, J ENTERP INF MANAG, V29, P222, DOI 10.1108/JEIM-04-2015-0034
Yang SQ, 2012, COMPUT HUM BEHAV, V28, P129, DOI 10.1016/j.chb.2011.08.019
Zarmpou T, 2012, ELECTRON COMMER RES, V12, P225, DOI 10.1007/s10660-012-9092-x
Zhang LY, 2012, COMPUT HUM BEHAV, V28, P1902, DOI 10.1016/j.chb.2012.05.008
Zhou T, 2011, INFORM DEV, V27, P290, DOI 10.1177/0266666911424075
Zhou T, 2011, INTERNET RES, V21, P527, DOI 10.1108/10662241111176353
Zhou T, 2010, COMPUT HUM BEHAV, V26, P760, DOI 10.1016/j.chb.2010.01.013
Zolkepli IA, 2015, COMPUT HUM BEHAV, V43, P189, DOI 10.1016/j.chb.2014.10.050
Zupanovic D, 2015, PROCEDIA ENGINEER, V100, P1396, DOI 10.1016/j.proeng.2015.01.509
NR 117
TC 222
Z9 231
U1 15
U2 220
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD APR
PY 2018
VL 129
BP 117
EP 130
DI 10.1016/j.techfore.2017.12.015
PG 14
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA GD8LH
UT WOS:000430763400010
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Mukhopadhyay, S
Samaddar, S
Nargundkar, S
AF Mukhopadhyay, Somnath
Samaddar, Subhashish
Nargundkar, Satish
TI PREDICTING ELECTRONIC COMMERCE GROWTH: AN INTEGRATION OF DIFFUSION AND
NEURAL NETWORK MODELS
SO JOURNAL OF ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE E-commerce; Dotcom; Forecasting; Neural Network; Diffusion models; and
E-market Planning
ID PRODUCT GROWTH; FORECASTING METHODS; ADOPTION
AB There is a growing recognition that e-market planners and various planning agencies in Information Technology sectors have a significant interest in measuring and forecasting the growth of e-commerce. The difficulties lie in finding a forecasting model that can incorporate both internal and external influences on diffusion, as well as an acceptable measure for e-commerce growth. This study uses models based on the knowledge of traditional diffusion theories as well as artificial neural networks. Additionally, it integrates the two into a hybrid model in order to study e-commerce growth. A count of dot-com hosts is used as a reliable measure of e-commerce growth in all the models. Our study demonstrates that a simple Neural Network model, if properly calibrated, can create a very flexible response function to forecast e-commerce diffusion growth. The neural network model successfully modeled both the internal and external influences in the data, while the traditional formulations could only model the internal influences. The predictive validation of the results was enhanced by replicating the comparisons on simulated data with various degrees of external influence. The study suggests that when external influences are present, the neural network model will be superior to the best traditional diffusion model.
C1 [Mukhopadhyay, Somnath] Univ Texas El Paso, El Paso, TX 79968 USA.
[Samaddar, Subhashish; Nargundkar, Satish] Georgia State Univ, Dept Managerial Sci, Atlanta, GA 30303 USA.
C3 University of Texas System; University of Texas El Paso; University
System of Georgia; Georgia State University
RP Mukhopadhyay, S (autor correspondiente), Univ Texas El Paso, El Paso, TX 79968 USA.
EM smukhopadhyay@utep.edu; s-samaddar@gsu.edu; snargundkar@gsu.edu
CR Adya M, 1998, J FORECASTING, V17, P481, DOI 10.1002/(SICI)1099-131X(1998090)17:5/6<481::AID-FOR709>3.0.CO;2-Q
Armstrong JS, 2001, INT SER OPER RES MAN, V30, P15
ARMSTRONG JS, 1992, INT J FORECASTING, V8, P69, DOI 10.1016/0169-2070(92)90008-W
BARNDEN JA, 1995, SIMULATION EVALUATIO, P247
BASS FM, 1969, MANAGE SCI, V15, P215, DOI 10.1287/mnsc.15.5.215
Bishop C.M., 1995, NEURAL NETWORKS PATT
CARBONE R, 1982, J FORECASTING, V1, P215, DOI 10.1002/for.3980010207
CHANGSU K, 2004, J ELECTRON COMMER RE, V5, P199
*CISC SYST, 1999, MEAS INT EC
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Coleman JS, 1966, MED INNOVATION DIFFU
Dutta A, 2004, INT J ELECTRON COMM, V9, P143, DOI 10.1080/10864415.2005.11044329
Elman J. L., 1987, 8701 UC SAN DIEG I C
FOURT L.A., 1960, J MARKETING, V25, P31
Golder PN, 1997, MARKET SCI, V16, P256, DOI 10.1287/mksc.16.3.256
GURBAXANI V, 1990, COMMUN ACM, V33, P65, DOI 10.1145/96267.96283
Hecht-Nielsen R., 1990, NEUROCOMPUTING
HEELER RM, 1980, MANAGE SCI, V26, P1007, DOI 10.1287/mnsc.26.10.1007
KAHN RE, 1994, COMMUN ACM, V37, P15, DOI 10.1145/179606.179729
Lippmann R. P., 1987, IEEE ASSP Magazine, V4, P4, DOI 10.1145/44571.44572
MAHAJAN V, 1988, MANAGE SCI, V34, P1188, DOI 10.1287/mnsc.34.10.1188
MAHAJAN V, 1979, J MARKETING, V43, P55, DOI 10.2307/1250271
MANSFIELD E, 1961, ECONOMETRICA, V29, P741, DOI 10.2307/1911817
Press L, 1997, COMMUN ACM, V40, P11, DOI 10.1145/265684.265686
Rai A, 1998, COMMUN ACM, V41, P97, DOI 10.1145/286238.286253
Rogers E., 1983, DIFFUSIONS INNOVATIO
Roy A, 1997, IEEE T NEURAL NETWOR, V8, P402, DOI 10.1109/72.557694
Rumelhart DE., 1985, LEARNING INTERNAL RE, DOI [DOI 10.7551/MITPRESS/5236.001.0001, 10.1016/b978-1-4832-1446-7.50035-2]
RUSSELL TD, 1987, MANAGEMENT SCI OCT, P1267
Sarle W. S., 1995, Computing Science and Statistics. Vol.27. Proceedings of the 27th Symposium on the Interface. Statistics and Manufacturing with Subthemes in Environmental Statistics, Graphics and Imaging, P352
Sejnowski T. J., 1987, Complex Systems, V1, P145
SHARDA R, 1992, J INTELL MANUF, V3, P317, DOI 10.1007/BF01577272
Somers MJ, 2001, J OCCUP ORGAN PSYCH, V74, P47, DOI 10.1348/096317901167226
TAM KY, 1992, MANAGE SCI, V38, P926, DOI 10.1287/mnsc.38.7.926
Valente TW, 1996, SOC NETWORKS, V18, P69, DOI 10.1016/0378-8733(95)00256-1
Van den Bulte C, 1997, MARKET SCI, V16, P338, DOI 10.1287/mksc.16.4.338
VENKATRAMAN N, 1994, MANAGE SCI, V40, P496, DOI 10.1287/mnsc.40.4.496
Wang YCW, 2004, J ELECTRON COMMER RE, V5, P85
Weiland A., 1988, GEOMETRIC ANAL NEUR, P385
YAO JT, 2004, J ELECTRON COMMER RE, V5, P54
NEURAL NETWORK FAQ 3
NETCRAFT 2004
NR 42
TC 4
Z9 8
U1 1
U2 9
PU CALIFORNIA STATE UNIV
PI LONG BEACH
PA COLL BUSINESS, LONG BEACH, CA 90840 USA
SN 1526-6133
EI 1938-9027
J9 J ELECTRON COMMER RE
JI J. Electron. Commer. Res.
PY 2008
VL 9
IS 4
BP 280
EP 295
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA V17EZ
UT WOS:000207921800003
DA 2024-03-27
ER
PT J
AU Ma, C
Wu, JY
Sun, HY
Zhou, X
Sun, XY
AF Ma, Chao
Wu, Jingyi
Sun, Heyuan
Zhou, Xin
Sun, Xiyan
TI Enhancing user experience in digital payments: A hybrid approach using
SEM and neural networks
SO FINANCE RESEARCH LETTERS
LA English
DT Article
DE Digital payments; Digital currency; Structural equation modeling (SEM);
Artificial neural network (ANN); User experience; Technology Acceptance;
Central bank digital currencies (CBDCs); Technology acceptance model
(TAM); UTAUT
ID MOBILE PAYMENT; ADOPTION; ACCEPTANCE; INTENTION; DETERMINANTS;
FRAMEWORK; SECURITY; ATTITUDE; UTAUT
AB This article proposes a hybrid approach utilizing Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) techniques to predict and enhance user experience (UX) in digital payments. The study introduces a novel Technology Acceptance Model to predict the critical factors influencing users' adoption of digital currencies and subsequently improve their experience. The research findings reveal that perceived ease of use and perceived usefulness significantly influence attitudes towards adopting digital currencies. Furthermore, the accuracy of the SEM results is confirmed through the implementation of the Artificial Neural Network. These conclusions hold significant implications for the future promotion of digital currencies.
C1 [Ma, Chao] Ningbo Univ, Pan Tianshou Coll Architecture & Art Design, Ningbo, Zhejiang, Peoples R China.
[Wu, Jingyi] Kookmin Univ, Fash design, 77 Jeongneung Ro, Seoul, South Korea.
[Sun, Heyuan] Kookmin Univ, Sustainable Design & Mat Innovat Dept, 80-7 Yeonhui Dong, Seoul, South Korea.
[Zhou, Xin] Yango Univ, Digital Media Art Design Coll, Bldg 9,Area A,18 Huaxi South Rd, Fuzhou, Fujian, Peoples R China.
[Sun, Xiyan] Yunnan Arts Univ, Art & Design, 1577 Yuhua Rd, Kunming, Yunnan, Peoples R China.
C3 Ningbo University; Kookmin University; Kookmin University; Yunnan Arts
University
RP Sun, HY (autor correspondiente), Kookmin Univ, Sustainable Design & Mat Innovat Dept, 80-7 Yeonhui Dong, Seoul, South Korea.; Zhou, X (autor correspondiente), Yango Univ, Digital Media Art Design Coll, Bldg 9,Area A,18 Huaxi South Rd, Fuzhou, Fujian, Peoples R China.; Sun, XY (autor correspondiente), Yunnan Arts Univ, Art & Design, 1577 Yuhua Rd, Kunming, Yunnan, Peoples R China.
EM sunayuanwind@163.com; 124789269g@gmail.com; sunxiyan_02@163.com
OI MA, CHAO/0000-0003-2264-5275
FU Achievements of social science planning in Zhejiang Province
[22NDQN222YB]; Fundamental Research Funds for the Provincial
Universities of Zhejiang [SJWY2022003]
FX Achievements of social science planning in Zhejiang Province.
(22NDQN222YB) . The Fundamental Research Funds for the Provincial
Universities of Zhejiang. (SJWY2022003) . Research Achievements of High
Quality Development Action in Mountain (Island) Counties Empowered by
Social Sciences.
CR AJZEN I, 1977, PSYCHOL BULL, V84, P888, DOI 10.1037/0033-2909.84.5.888
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Apanasevic T, 2016, INT J BANK MARK, V34, P37, DOI 10.1108/IJBM-06-2014-0064
Balapour A, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102063
Ben Arfi W, 2021, TECHNOL FORECAST SOC, V167, DOI 10.1016/j.techfore.2021.120688
Bojjagani S, 2021, J AMB INTEL HUM COMP, DOI 10.1007/s12652-021-03316-4
Chawla D, 2019, INT J BANK MARK, V37, P1590, DOI 10.1108/IJBM-09-2018-0256
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Gao LL, 2015, COMPUT HUM BEHAV, V53, P249, DOI 10.1016/j.chb.2015.07.014
Hanif Y, 2021, TECHNOL SOC, V67, DOI 10.1016/j.techsoc.2021.101693
KLEMPERER P, 1987, Q J ECON, V102, P375, DOI 10.2307/1885068
Koenig-Lewis N, 2015, SERV IND J, V35, P537, DOI 10.1080/02642069.2015.1043278
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Liébana-Cabanillas F, 2014, COMPUT HUM BEHAV, V35, P464, DOI 10.1016/j.chb.2014.03.022
Liu CH, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.830842
Ma C, 2023, J INTERNET TECHNOL, V24, P759, DOI 10.53106/160792642023052403019
Ma C, 2022, MOB INF SYST, V2022, DOI 10.1155/2022/4846372
Malarvizhi CA, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.870793
Munoz-Leiva F., 2017, SPAN J MARKET-ESIC, V21, P25, DOI [10.1016/j.sjme.2016.12.001, DOI 10.1016/J.SJME.2016.12.001]
Oliveira T, 2016, COMPUT HUM BEHAV, V61, P404, DOI 10.1016/j.chb.2016.03.030
Phonthanukitithaworn C, 2016, SAGE OPEN, V6, DOI 10.1177/2158244016675397
Posey C, 2014, INFORM MANAGE-AMSTER, V51, P551, DOI 10.1016/j.im.2014.03.009
Qu B, 2022, FINANC INNOV, V8, DOI 10.1186/s40854-021-00312-7
Roussou I, 2019, INF SYST E-BUS MANAG, V17, P223, DOI 10.1007/s10257-019-00426-7
Schierz PG, 2010, ELECTRON COMMER R A, V9, P209, DOI 10.1016/j.elerap.2009.07.005
SHAPIRO SP, 1987, AM J SOCIOL, V93, P623, DOI 10.1086/228791
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Slade EL, 2015, PSYCHOL MARKET, V32, P860, DOI 10.1002/mar.20823
Tam C, 2019, INFORM MANAGE-AMSTER, V56, P356, DOI 10.1016/j.im.2018.07.009
Tsiakis T, 2005, COMPUT SECUR, V24, P10, DOI 10.1016/j.cose.2004.11.001
Upadhyay N, 2022, INT J BANK MARK, V40, P960, DOI 10.1108/IJBM-06-2021-0262
Wei MF, 2021, J THEOR APPL EL COMM, V16, P618, DOI 10.3390/jtaer16040037
NR 32
TC 1
Z9 1
U1 28
U2 28
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1544-6123
EI 1544-6131
J9 FINANC RES LETT
JI Financ. Res. Lett.
PD DEC
PY 2023
VL 58
AR 104376
DI 10.1016/j.frl.2023.104376
EA SEP 2023
PN B
PG 13
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA T3NR9
UT WOS:001077092800001
DA 2024-03-27
ER
PT J
AU Khan, AN
Cao, XF
Pitafi, AH
AF Khan, Ali Nawaz
Cao, Xiongfei
Pitafi, Abdul Hameed
TI Personality Traits as Predictor of M-Payment Systems: A SEM-Neural
Networks Approach
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Agreeableness; Artificial Neural Networks; Conscientiousness;
Extraversion; M-Commerce; Mobile Payment; Neuroticism; Openness to
Experience; Personality Traits; SEM
ID MOBILE CREDIT CARD; BIG 5; TECHNOLOGY ACCEPTANCE; SMALL BUSINESSES;
5-FACTOR MODEL; UNIFIED THEORY; ADOPTION; DETERMINANTS; INTENTION;
GENDER
AB Mobile phones have led to a great revolution of modern society, helpful for many businesses to reorient their sales methods towards effective commercial formats. The m-payment, for instance, as an emergent technology to these novel commercial setups, is now undertaking the adoption process. Individual users are known to vary in their tendency to accept new technologies. Not surprisingly, some conceptual models describe how and why individuals use m-payments. Until recently, however, the role of personality in overall, and the big five model of personality, in particular, had remained mostly unexplored. This article aims to ascertain the impact of personality traits on m-payment adoption. Data were collected from 323 m-payment customers and analyzed using a two-step research methodology. SEM was applied to test the hypothesis, and significant antecedents of m-payment were identified. Next significant personality factors were input to a neural network model for ranking. The results showed that conscientious and agreeableness is the two main predictors of m-payment adoption.
C1 [Khan, Ali Nawaz] Tongji Univ Shanghai, Sch Econ & Management, Shanghai, Peoples R China.
[Cao, Xiongfei; Pitafi, Abdul Hameed] Hefei Univ Technol, Sch Management, Hefei, Peoples R China.
C3 Tongji University; Hefei University of Technology
RP Khan, AN (autor correspondiente), Tongji Univ Shanghai, Sch Econ & Management, Shanghai, Peoples R China.
RI KHAN, ALI/AAJ-9493-2021
CR AbuShanab E., 2010, COMMUNICATIONS ASS I, V26
Ahani A, 2017, COMPUT HUM BEHAV, V75, P560, DOI 10.1016/j.chb.2017.05.032
Ali A, 2019, ASIAN BUS MANAG, V18, P98, DOI 10.1057/s41291-018-00050-2
Ali A, 2019, COMPUT HUM BEHAV, V91, P115, DOI 10.1016/j.chb.2018.09.033
ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
Bagozzi RP, 1988, Journal of the Academy of Marketing Science, V16, P74, DOI DOI 10.1007/BF02723327
Bano S, 2019, CHILD YOUTH SERV REV, V103, P200, DOI 10.1016/j.childyouth.2019.06.002
Barnett T, 2015, EUR J INFORM SYST, V24, P374, DOI 10.1057/ejis.2014.10
BARRICK MR, 1991, PERS PSYCHOL, V44, P1, DOI 10.1111/j.1744-6570.1991.tb00688.x
Butt S, 2008, COMPUT HUM BEHAV, V24, P346, DOI 10.1016/j.chb.2007.01.019
Cao XF, 2019, J EDUC COMPUT RES, V57, P1083, DOI 10.1177/0735633118781907
Carmines E.G., 1981, SOCIAL MEASUREMENT C, P65, DOI DOI 10.1207/S15327965PLI0404_1
Chen XG, 2017, J COMPUT INFORM SYST, V57, P287, DOI 10.1080/08874417.2016.1180649
Dahlberg T, 2015, ELECTRON COMMER R A, V14, P265, DOI 10.1016/j.elerap.2015.07.006
de Reuver M, 2015, ELECTRON COMMER R A, V14, P331, DOI 10.1016/j.elerap.2014.08.004
Devaraj S, 2008, INFORM SYST RES, V19, P93, DOI 10.1287/isre.1070.0153
Donner J, 2008, ASIAN J COMMUN, V18, P318, DOI 10.1080/01292980802344190
Eichhorn B.R., 2014, MIDWEST SAS USERS GR
Forgays DK, 2014, COMPUT HUM BEHAV, V31, P314, DOI 10.1016/j.chb.2013.10.053
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Garrett JL, 2014, FAM CONSUM SCI RES J, V42, P358, DOI 10.1111/fcsr.12069
Gupta S., 2014, J ORG END USER COMPU
Hair J. F., 1998, Multivariate data analysis with readings, V5th ed.
Hardie E., 2007, Australian Journal of Emerging Technologies and Society, V5, P34
Harman H. H., 1976, MODERN FACTOR ANAL
Hartmann T, 2017, J ORGAN END USER COM, V29, P1, DOI 10.4018/JOEUC.2017040101
Haykin S., 2001, Neural Networks: A Comprehensive Foundation
Huang JH, 2010, SOC BEHAV PERSONAL, V38, P673, DOI 10.2224/sbp.2010.38.5.673
Huang LK, 2017, J ORGAN END USER COM, V29, P1, DOI 10.4018/JOEUC.2017010101
Jacques P.H., 2009, J BUS MANAG, V15, P143
Jani D, 2014, INT J HOSP MANAG, V37, P11, DOI 10.1016/j.ijhm.2013.10.007
Judge TA, 2000, J APPL PSYCHOL, V85, P237, DOI 10.1037//0021-9010.85.2.237
Katrimpouza A., 2017, TWITTER ADOPTION STU
Khan AN, 2018, WIRELESS PERS COMMUN, V103, P2529, DOI 10.1007/s11277-018-5945-5
Kim C, 2010, COMPUT HUM BEHAV, V26, P310, DOI 10.1016/j.chb.2009.10.013
Korukonda AR, 2005, INFORM SCIENCES, V170, P309, DOI 10.1016/j.ins.2004.03.007
Kuan KKY, 2001, INFORM MANAGE-AMSTER, V38, P507, DOI 10.1016/S0378-7206(01)00073-8
Kumar A., 2008, J SERVICE MARKETING, V22, P568, DOI 10.1108/08876040810909695
Lai J.-Y., 2016, J ORG END USER COMPU
Lakhal S, 2017, J COMPUT EDUC, V4, P251, DOI 10.1007/s40692-017-0086-5
Lakhal S, 2012, INT J MANAG EDUC-OXF, V10, P88, DOI 10.1016/j.ijme.2012.03.003
Leong LY, 2017, INTERNET RES, V27, P786, DOI 10.1108/IntR-04-2016-0107
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Liébana-Cabanillas F, 2017, TECHNOL FORECAST SOC, V120, P32, DOI 10.1016/j.techfore.2017.04.002
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Liébana-Cabanillas F, 2014, INT J INFORM MANAGE, V34, P151, DOI 10.1016/j.ijinfomgt.2013.12.006
Mallat N., 2008, E SERVICE J, V6, P24, DOI DOI 10.2979/ESJ.2008.6.2.24
Matzler K, 2011, J MARKET MANAG-UK, V27, P874, DOI 10.1080/0267257X.2010.543634
MCCRAE RR, 1987, J PERS SOC PSYCHOL, V52, P81, DOI 10.1037/0022-3514.52.1.81
Merritt C., 2011, J PAYMENTS STRATEGY, V5
Mooradian TA, 2008, SOC PERSONAL PSYCHOL, V2, P1703, DOI 10.1111/j.1751-9004.2008.00128.x
Mouakket S., 2018, The Journal of High Technology Management Research, V29, P124, DOI [DOI 10.1016/J.HITECH.2016.10.003, 10.1016/j.hitech.2016.10.003]
Nov O., 2008, Proceedings of the Annual Hawaii International Conference on System Sciences, P1
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Oliveira T, 2016, COMPUT HUM BEHAV, V61, P404, DOI 10.1016/j.chb.2016.03.030
Özbek V, 2014, PROCD SOC BEHV, V150, P541, DOI 10.1016/j.sbspro.2014.09.073
Pavlou PA, 2006, INFORM SYST RES, V17, P198, DOI 10.1287/isre.1060.0094
Pham TTT, 2015, TECHNOL SOC, V43, P159, DOI 10.1016/j.techsoc.2015.05.004
Pitafi A. H., 2018, TECHNOL SOC, V55, DOI [10.1016/j.techsoc.2018.08.002, DOI 10.1016/J.TECHS0C.2018.08.002]
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Reza M., 2014, Journal of Information Systems Research and Innovation, P1
Rogers E., 2003, Diffusion of innovation
Ross C, 2009, COMPUT HUM BEHAV, V25, P578, DOI 10.1016/j.chb.2008.12.024
Salahshour Rad M., 2017, INFORM TECHNOLOGY AD
Schaupp LC, 2016, J ORGAN END USER COM, V28, P49, DOI 10.4018/JOEUC.2016070104
Schmitt DP, 2008, J PERS SOC PSYCHOL, V94, P168, DOI 10.1037/0022-3514.94.1.168
Sharma SK, 2019, INFORM SYST FRONT, V21, P815, DOI 10.1007/s10796-017-9775-x
Sharma SK, 2017, BEHAV INFORM TECHNOL, V36, P1053, DOI 10.1080/0144929X.2017.1340973
Soltani S., 2013, J CONSUM AFF, DOI DOI 10.1111/J.1745-6606.1986.TB00382.X
Stachl C, 2017, EUR J PERSONALITY, V31, P701, DOI 10.1002/per.2113
Statista Corporation, 2015, MOB PAYM REV WORLDW
Tan GWH, 2014, TELEMAT INFORM, V31, P292, DOI 10.1016/j.tele.2013.06.002
Terzis V, 2012, COMPUT HUM BEHAV, V28, P1985, DOI 10.1016/j.chb.2012.05.019
Ting HR, 2016, PROCD SOC BEHV, V224, P368, DOI 10.1016/j.sbspro.2016.05.390
Ul Islam J, 2017, MARK INTELL PLAN, V35, P510, DOI 10.1108/MIP-10-2016-0193
Venkatesh V, 2000, ORGAN BEHAV HUM DEC, V83, P33, DOI 10.1006/obhd.2000.2896
Venkatesh V, 2012, MIS QUART, V36, P157
Walczak S, 1999, INFORM SOFTWARE TECH, V41, P107, DOI 10.1016/S0950-5849(98)00116-5
Walczak S, 1999, INFORM SCIENCES, V119, P1, DOI 10.1016/S0020-0255(99)00057-2
Walczak S., 2001, J MANAGEMENT INFORMA, V17
Walczak S, 2016, J THEOR APPL EL COMM, V11, P67, DOI 10.4067/S0718-18762016000100005
Wang YS, 2012, BRIT J EDUC TECHNOL, V43, P139, DOI 10.1111/j.1467-8535.2010.01151.x
Weidmann R., 2017, J RES PERSONALITY, V69
Xiong Y., 2016, J THEORETICAL APPL I, V87
Yang SQ, 2012, COMPUT HUM BEHAV, V28, P129, DOI 10.1016/j.chb.2011.08.019
Yoon HS, 2013, COMPUT HUM BEHAV, V29, P1133, DOI 10.1016/j.chb.2012.10.005
Zhang L, 2014, J HOSP TOUR TECHNOL, V5, P299, DOI 10.1108/JHTT-03-2014-0008
ZMUD RW, 1979, MANAGE SCI, V25, P966, DOI 10.1287/mnsc.25.10.966
NR 88
TC 47
Z9 49
U1 2
U2 31
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PD OCT-DEC
PY 2019
VL 31
IS 4
BP 89
EP 110
DI 10.4018/JOEUC.2019100105
PG 22
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA KH2NC
UT WOS:000510483300006
DA 2024-03-27
ER
PT J
AU Mkedder, N
Özata, FZ
AF Mkedder, Nadjim
Ozata, Fatma Zeynep
TI I will buy virtual goods if I like them: a hybrid PLS-SEM-artificial
neural network (ANN) analytical approach
SO JOURNAL OF MARKETING ANALYTICS
LA English
DT Article
DE Consumption value theory; F2P games; PLS-SEM; Artificial neural network
(ANN); Virtual goods; Perceived value; Purchase intention
ID PERCEIVED VALUE; ONLINE GAME; DISCRIMINANT VALIDITY; PURCHASE INTENTION;
CUSTOMIZATION; CONSUMPTION; DETERMINANTS; SATISFACTION; COMMUNITIES;
MOTIVATIONS
AB Despite the popularity of Free-to-Play (F2P) games in recent years, the motivations behind players' intention to purchase virtual goods in F2P games still require further investigations. This study aims to address this dilemma by investigating the antecedents of functional, emotional, and social values in shaping the purchase intention of virtual goods in F2P games. Using purposive sampling, data were collected through a survey from 352 F2P game participants in the United States. A hybrid PLS-SEM-Artificial Neural Network (ANN) modeling approach was employed to examine the impact of these factors on the intention to purchase virtual goods. The results reveal that perceived value positively influences the purchase intention of virtual goods. The findings also show that functional, emotional, and social values significantly impact the perceived value and purchase intention of virtual goods. Further, perceived value mediates the relationship between quality, achievement, enjoyment, aesthetics, customization, self-presentation, and the intention to purchase virtual goods. The ANN results reveal that quality and social presence are the most critical factors since they achieve the greatest normalized importance ratio compared to the others. The model illustrated considerable explanatory evidence for purchase intention in the context of F2P games. Additionally, this research significantly strengthens the marketing literature by developing an understanding of the intention to buy virtual goods in F2P games. The proposed model can provide insights for F2P game providers to design their games and marketing strategies.
C1 [Mkedder, Nadjim] Univ Abou Bekr Belkaid Tlemcen, Grad Sch Business & Management, Dept Mkt, 22 St Abi Ayad Abdelkarim Fg Pasteur,Bp 119, Tilimsen, Algeria.
[Ozata, Fatma Zeynep] Anadolu Univ, Grad Sch Social Sci, Dept Mkt, Yunus Emre Campus,POB 26470, Eskisehir, Turkiye.
C3 Universite Abou Bekr Belkaid; Anadolu University
RP Mkedder, N (autor correspondiente), Univ Abou Bekr Belkaid Tlemcen, Grad Sch Business & Management, Dept Mkt, 22 St Abi Ayad Abdelkarim Fg Pasteur,Bp 119, Tilimsen, Algeria.
EM mkeddernadjim@yahoo.com; fzozata@anadolu.edu.tr
RI Mkedder, Nadjim/GPS-8499-2022; Ozata, Fatma Zeynep/R-1499-2019
OI Mkedder, Nadjim/0000-0002-4523-2038; Ozata, Fatma
Zeynep/0000-0002-3338-0308
CR Al-Sharafi MA, 2022, INT J BANK MARK, V40, P1071, DOI 10.1108/IJBM-07-2021-0291
Aryadoust V, 2016, EDUC ASSESS, V21, P135, DOI 10.1080/10627197.2016.1166343
Bleize D N., 2019, Journal of Marketing Communications, V25, P403, DOI DOI 10.1080/13527266.2016.1278028
Cai XW, 2022, PLOS ONE, V17, DOI 10.1371/journal.pone.0262998
Chauhan S, 2021, SYMMETRY-BASEL, V13, DOI 10.3390/sym13081545
Chen CC, 2018, LIBR HI TECH, V36, P583, DOI 10.1108/LHT-01-2018-0007
Chen H, 2022, BEHAV INFORM TECHNOL, V41, P1171, DOI 10.1080/0144929X.2020.1864017
Chen HJ, 2014, ASIA PAC J MARKET LO, V26, P232, DOI 10.1108/APJML-07-2013-0084
Cialdini R. B., 2001, INFLUENCE SCI PRACTI
DODDS WB, 1991, J MARKETING RES, V28, P307, DOI 10.2307/3172866
ESA, 2021, Essential facts about the video game industry
Esteves J, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103452
Franke G, 2019, INTERNET RES, V29, P430, DOI 10.1108/IntR-12-2017-0515
Ghazali EM, 2023, KYBERNETES, V52, P1937, DOI 10.1108/K-08-2021-0678
Guo Y, 2012, J COMPUT INFORM SYST, V52, P18
Guo Y, 2011, INFORM MANAGE-AMSTER, V48, P303, DOI 10.1016/j.im.2011.07.004
Gupta S, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102511
Gupta S, 2019, INT REV RETAIL DISTR, V29, P178, DOI 10.1080/09593969.2018.1562955
Hair J.F., 2009, Multivariate data analysis
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hair Jr J.F., 2016, PRIMER PARTIAL LEAST
Hamari J, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102040
Hamari J, 2019, INT J HUM-COMPUT INT, V35, P804, DOI 10.1080/10447318.2018.1497115
Hamari J, 2017, COMPUT HUM BEHAV, V71, P59, DOI 10.1016/j.chb.2017.01.042
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Högberg J, 2019, USER MODEL USER-ADAP, V29, P619, DOI 10.1007/s11257-019-09223-w
Hollebeek LD, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102892
Hsiao KL, 2020, LIBR HI TECH, V38, P638, DOI 10.1108/LHT-09-2018-0123
Hsiao KL, 2016, ELECTRON COMMER R A, V16, P18, DOI 10.1016/j.elerap.2016.01.001
Hsieh JK, 2018, J ELECTRON COMMER RE, V19, P164
Huang E, 2012, INTERNET RES, V22, P252, DOI 10.1108/10662241211235644
Huang LY, 2011, INTERNET RES, V21, P581, DOI 10.1108/10662241111176380
Huang TZ, 2017, PROGRAM-ELECTRON LIB, V51, P259, DOI 10.1108/PROG-12-2016-0078
Hussain A, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103128
Hussain A, 2022, J SERV MARK, V36, P398, DOI 10.1108/JSM-05-2020-0164
InsiderIntelligence, 2022, US VID GAM IND 2022
Jang M, 2021, INF SYST E-BUS MANAG, V19, P439, DOI 10.1007/s10257-019-00420-z
Katz E., 1974, The uses of mass communications: Current perspectives on gratifications research, DOI DOI 10.1561/106.00000008
Kaur P, 2020, TELEMAT INFORM, V53, DOI 10.1016/j.tele.2020.101376
Kaur P, 2018, ONLINE INFORM REV, V42, P205, DOI 10.1108/OIR-12-2015-0383
Kim HW, 2012, ELECTRON COMMER R A, V11, P241, DOI 10.1016/j.elerap.2011.06.003
Kim HW, 2011, INFORM MANAGE-AMSTER, V48, P228, DOI 10.1016/j.im.2011.05.004
Kock N., 2017, In partial least squares path modeling, P245, DOI [10.1007/978-3-319-64069-3_11, DOI 10.1007/978-3-319-64069-3]
Konuk FA, 2019, J RETAIL CONSUM SERV, V50, P103, DOI 10.1016/j.jretconser.2019.05.005
Kumar A, 2023, J RES INTERACT MARK, V17, P176, DOI 10.1108/JRIM-02-2020-0035
Lau O, 2021, FASH TEXT, V8, DOI 10.1186/s40691-021-00270-9
Lee HT, 2022, INTERNET RES, V32, P1288, DOI 10.1108/INTR-01-2021-0050
Lee J, 2012, CYBERPSYCH BEH SOC N, V15, P643, DOI 10.1089/cyber.2012.0093
Lehdonvirta V, 2009, ELECTRON COMMER RES, V9, P97, DOI 10.1007/s10660-009-9028-2
Leong LY, 2020, EXPERT SYST APPL, V162, DOI 10.1016/j.eswa.2020.113849
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Li HX, 2015, COMPUT HUM BEHAV, V48, P261, DOI 10.1016/j.chb.2015.01.053
Li K, 2018, INTERNET RES, V28, P1103, DOI 10.1108/IntR-06-2017-0246
Liao GY, 2022, ELECTRON COMMER R A, V53, DOI 10.1016/j.elerap.2022.101147
Liao GY, 2019, INTERNET RES, V29, P349, DOI 10.1108/IntR-11-2017-0463
Liébana-Cabanillas F, 2018, TECHNOL FORECAST SOC, V129, P117, DOI 10.1016/j.techfore.2017.12.015
Liengaard BD, 2021, DECISION SCI, V52, P362, DOI 10.1111/deci.12445
Liew LWL, 2018, BEHAV INFORM TECHNOL, V37, P558, DOI 10.1080/0144929X.2018.1464599
Lima XJ, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2020.102422
Lin CY, 2015, INTERNET RES, V25, P829, DOI 10.1108/IntR-12-2013-0268
LYNN M, 1992, BASIC APPL SOC PSYCH, V13, P67, DOI 10.1207/s15324834basp1301_6
Mäntymäki M, 2015, INT J INFORM MANAGE, V35, P124, DOI 10.1016/j.ijinfomgt.2014.10.004
Merhi MI, 2016, COMPUT HUM BEHAV, V60, P253, DOI 10.1016/j.chb.2016.02.072
Mishra AK, 2023, INT J CONSUM STUD, V47, P563, DOI 10.1111/ijcs.12850
Mkedder N., 2023, Journal of Global Marketing, P1, DOI [10.1080/08911762.2023.2236583, DOI 10.1080/08911762.2023.2236583]
Mkedder N, 2021, CENT EUR MANAG J, V29, P124, DOI 10.7206/cemj.2658-0845.62
Mooi E, 2011, CONCISE GUIDE TO MARKET RESEARCH: THE PROCESS, DATA, AND METHODS USING IBM SPSS STATISTICS, P11, DOI 10.1007/978-3-642-12541-6_2
Nitzl C, 2016, IND MANAGE DATA SYST, V116, P1849, DOI 10.1108/IMDS-07-2015-0302
Nowak KL, 2003, PRESENCE-TELEOP VIRT, V12, P481, DOI 10.1162/105474603322761289
Ozkara BY, 2017, J RETAIL CONSUM SERV, V37, P119, DOI 10.1016/j.jretconser.2017.04.001
Park K, 2017, WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, P445, DOI 10.1145/3041021.3054176
Park SB, 2011, COMPUT HUM BEHAV, V27, P2372, DOI 10.1016/j.chb.2011.07.016
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Ratan R, 2015, COMPUT HUM BEHAV, V50, P367, DOI 10.1016/j.chb.2015.04.010
Reer F, 2022, COMPUT HUM BEHAV, V132, DOI 10.1016/j.chb.2022.107242
Rezaei S, 2014, COMPUT HUM BEHAV, V35, P252, DOI 10.1016/j.chb.2014.03.002
Ringle C M., 2022, SmartPLS 4
Ringle CM, 2023, DATA BRIEF, V48, DOI 10.1016/j.dib.2023.109074
Ringle CM, 2016, IND MANAGE DATA SYST, V116, P1865, DOI 10.1108/IMDS-10-2015-0449
Sarstedt M, 2020, INT J MARKET RES, V62, P288, DOI 10.1177/1470785320915686
Schuberth F, 2018, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.02541
Sharma A, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106919
Sharma M, 2022, INFORM SYST FRONT, DOI 10.1007/s10796-022-10339-5
Sharma PN, 2023, EUR J MARKETING, V57, P1662, DOI 10.1108/EJM-08-2020-0636
Sharma TG, 2022, BEHAV INFORM TECHNOL, V41, P348, DOI 10.1080/0144929X.2020.1811770
SHETH JN, 1991, J BUS RES, V22, P159, DOI 10.1016/0148-2963(91)90050-8
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Shmueli G, 2016, J BUS RES, V69, P4552, DOI 10.1016/j.jbusres.2016.03.049
Sohaib O, 2020, IEEE ACCESS, V8, P13138, DOI 10.1109/ACCESS.2019.2960083
Statista, 2020, CONS SPEND GAM US 2
Sweeney JC, 2001, J RETAILING, V77, P203, DOI 10.1016/S0022-4359(01)00041-0
Syahrivar J, 2022, INT J RETAIL DISTRIB, V50, P117, DOI 10.1108/IJRDM-09-2020-0358
Talukder Shamim, 2019, Journal of Systems and Information Technology, V21, P419, DOI 10.1108/JSIT-10-2017-0096
Teng CI, 2017, INT J INFORM MANAGE, V37, P601, DOI 10.1016/j.ijinfomgt.2017.06.006
Teng CI, 2010, COMPUT HUM BEHAV, V26, P1547, DOI 10.1016/j.chb.2010.05.029
Wang L, 2022, J BUS RES, V147, P308, DOI 10.1016/j.jbusres.2022.04.011
Wang L, 2022, INFORM SYST J, V32, P1097, DOI 10.1111/isj.12384
Wang L, 2021, INFORM TECHNOL PEOPL, V34, P297, DOI 10.1108/ITP-10-2019-0527
Wang L, 2023, EXPERT SYST APPL, V212, DOI 10.1016/j.eswa.2022.118799
Wedel M, 2020, INT J RES MARK, V37, P443, DOI 10.1016/j.ijresmar.2020.04.004
Weilbull L., 1985, MEDIA GRATIFICATIONS, P123
Wu JH, 2010, COMPUT HUM BEHAV, V26, P1862, DOI 10.1016/j.chb.2010.07.033
Wu SL, 2018, J BUS RES, V92, P242, DOI 10.1016/j.jbusres.2018.07.035
Yang H, 2016, TELEMAT INFORM, V33, P256, DOI 10.1016/j.tele.2015.08.007
Yee N, 2006, CYBERPSYCHOL BEHAV, V9, P772, DOI 10.1089/cpb.2006.9.772
ZEITHAML VA, 1988, J MARKETING, V52, P2, DOI 10.2307/1251446
Zhao YS, 2021, INT SYM MIX AUGMENT, P52, DOI 10.1109/ISMAR-Adjunct54149.2021.00020
NR 108
TC 3
Z9 3
U1 9
U2 9
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 2050-3318
EI 2050-3326
J9 J MARK ANAL
JI J. Market. Anal.
PD MAR
PY 2024
VL 12
IS 1
SI SI
BP 42
EP 70
DI 10.1057/s41270-023-00252-4
EA SEP 2023
PG 29
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA IX8O1
UT WOS:001063358700001
DA 2024-03-27
ER
PT J
AU Thongsri, N
Tripak, O
AF Thongsri, Nattaporn
Tripak, Orawan
TI Does social banking matter in times of crisis? Evidence from the
COVID-19 pandemic: a combined SEM-neural network approach
SO INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS
LA English
DT Article
DE Social banking; Structural equation modeling (SEM); Neural network
model; COVID-19; Thailand
ID WORD-OF-MOUTH; MOBILE BANKING; PERCEIVED RISK; INFORMATION-TECHNOLOGY;
CONSUMER ACCEPTANCE; USER ACCEPTANCE; METHOD BIAS; ADOPTION;
ANTECEDENTS; INTENTIONS
AB Purpose - The purpose of this study was to investigate the factors that would influence the intention to use social banking during the coronavirus disease 2019 (COVID-19) pandemic. This study integrated two theories, namely the integrated technology acceptance model (TAM), which focused on the acceptance of technology by consumers, and electronic word of mouth (eWOM), which focused on consumer behavior. This study also applied the significant variables in the context of Thailand, which were trust and perceived risk.Design/methodology/approach- A quantitative research method was applied by collecting data from 411 consumers during the COVID-19 pandemic in Thailand. A combined multi-analytic approach of a structural equation model (SEM)-neural network was used to analyze the data. In the first step, the SEM was used to determine the important factors that affected the adoption of social banking. In the second step, a neural network model was used to prioritize the important factors to confirm the results of the SEM method in step 1.Findings - The empirical results of the data analysis using the SEM method showed that the perceived ease of use, perceived usefulness and trust were the most significant determinants of adopting social banking. This was consistent with the neural network method of the important factors. Practical implications - The results of this research could initiate issues that should be developed for the continued use of online banking among consumers in the context of developing countries, such as Thailand.Originality/value-This research model provided guidelines for the effective development of mobile banking applications for use on mobile devices. The results of this research made strong theoretical contributions to the existing literature on online banking and offered procedures and information to the relevant sectors.Peer review - The peer review history for this article is available at: https://publons.com/publon/10.1108/ IJSE-10-2022-0709
C1 [Thongsri, Nattaporn] Prince Songkla Univ, Fac Sci & Ind Technol, Surat Thani Campus, Surat Thani, Thailand.
[Tripak, Orawan] Prince Songkla Univ, Fac Sci, Hat Yai, Thailand.
C3 Prince of Songkla University; Prince of Songkla University
RP Thongsri, N (autor correspondiente), Prince Songkla Univ, Fac Sci & Ind Technol, Surat Thani Campus, Surat Thani, Thailand.
EM nattaporn.th@psu.ac.th; orawan.t@psu.ac.th
OI Thongsri, Nattaporn/0000-0002-4077-8517
CR Agatonovic-Kustrin S, 2000, J PHARMACEUT BIOMED, V22, P717, DOI 10.1016/S0731-7085(99)00272-1
Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
Alalwan AA, 2016, J ENTERP INF MANAG, V29, P118, DOI 10.1108/JEIM-04-2015-0035
Anyasi F., 2009, RES J INF TECHNOL, V1, P1
Arif I, 2020, TECHNOL SOC, V61, DOI 10.1016/j.techsoc.2020.101231
Awa HO, 2015, J SCI TECHNOL POLICY, V6, P76, DOI 10.1108/JSTPM-04-2014-0012
Bank of Thailand, 2020, CBDC PROGR THAIL
Bank of Thailand, 2020, BOT STRAT PLAN ADDR
Bronner F, 2016, ANN TOURISM RES, V57, P94, DOI 10.1016/j.annals.2015.12.012
Chen CS, 2013, MANAG SERV QUAL, V23, P410, DOI 10.1108/MSQ-10-2012-0137
Cheung CMK, 2012, DECIS SUPPORT SYST, V54, P461, DOI 10.1016/j.dss.2012.06.008
Cheung CMK, 2009, LECT NOTES ARTIF INT, V5736, P501, DOI 10.1007/978-3-642-04754-1_51
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Deb M, 2014, MARK INTELL PLAN, V32, P475, DOI 10.1108/MIP-07-2013-0119
Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001
Eagly A. H., 1993, PSYCHOL ATTITUDES
Falk R.F., 1992, A primer for soft modeling
Fayad R, 2015, PROC ECON FINANC, V26, P1000, DOI 10.1016/S2212-5671(15)00922-3
Garner SarahJane., 1986, MID ATLAN J BUS, V24, P49
GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
Gopinath S, 2014, MARKET SCI, V33, P241, DOI 10.1287/mksc.2013.0820
Govender I., 2014, Mediterranean Journal of Social Sciences, V5, P451, DOI DOI 10.5901/MJSS.2014.V5N7P451
Gramlich J., 2019, 10 FACTS AMERICANS F
Hair J.F., 2006, Proceedings of the 7th International Conference on, P1
Hanafizadeh P, 2014, TELEMAT INFORM, V31, P62, DOI 10.1016/j.tele.2012.11.001
Hassan HE, 2020, TELEMAT INFORM, V46, DOI 10.1016/j.tele.2019.101312
Hau YS, 2016, INT J INFORM MANAGE, V36, P520, DOI 10.1016/j.ijinfomgt.2016.02.008
Hoque R, 2017, INT J MED INFORM, V101, P75, DOI 10.1016/j.ijmedinf.2017.02.002
Im I, 2008, INFORM MANAGE-AMSTER, V45, P1, DOI 10.1016/j.im.2007.03.005
Islam AKMN, 2020, TECHNOL FORECAST SOC, V159, DOI 10.1016/j.techfore.2020.120201
Kasikorn Bank, 2019, AN ADM DEP
Koksal MH, 2016, INT J BANK MARK, V34, P327, DOI 10.1108/IJBM-03-2015-0025
Kostic S.C., 2020, NEW NORMAL STRATEGIC
Liao ZQ, 2017, J RETAIL CONSUM SERV, V39, P258, DOI 10.1016/j.jretconser.2017.06.003
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Lin HF, 2011, INT J INFORM MANAGE, V31, P252, DOI 10.1016/j.ijinfomgt.2010.07.006
Mahittivanicha Narongyod, 2020, GLOBAL SOCIAL MEDIA
Malaquias RF, 2019, INT J INFORM MANAGE, V44, P132, DOI 10.1016/j.ijinfomgt.2018.10.004
Malaquias RF, 2016, COMPUT HUM BEHAV, V54, P453, DOI 10.1016/j.chb.2015.08.039
Malaquias RF, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101260
Marketingoops.Com, 2020, UPD THAI BEH HAS CHA
Meade A.W., 2007, 22 ANN M SOC IND ORG, DOI [10.1037/e518532013-132, DOI 10.1037/E518532013-132]
Mehrad D, 2017, TELEMAT INFORM, V34, P1351, DOI 10.1016/j.tele.2016.08.009
Merhi M, 2019, TECHNOL SOC, V59, DOI 10.1016/j.techsoc.2019.101151
Mortazavi M, 2014, J RES INTERACT MARK, V8, P102, DOI 10.1108/JRIM-08-2013-0057
Mullan J, 2017, INT J BANK MARK, V35, P1152, DOI 10.1108/IJBM-09-2015-0145
Ngo V.M., 2022, OPER MANAGE RES, P1
Nguyen HH, 2021, RES INT BUS FINANC, V56, DOI 10.1016/j.ribaf.2021.101380
Nilashi M, 2018, J COMPUT SCI-NETH, V28, P168, DOI 10.1016/j.jocs.2018.09.006
Phanachai Areepermphon, 2020, COVID 19 CHANGED CON
Podsakoff PM, 2012, ANNU REV PSYCHOL, V63, P539, DOI 10.1146/annurev-psych-120710-100452
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Roy G, 2017, GLOB BUS REV, V18, P198, DOI 10.1177/0972150916666966
Ruangkanjanases A., 2018, J TELECOMMUNICATION, V10, P147
Shaikh AA, 2015, TELEMAT INFORM, V32, P129, DOI 10.1016/j.tele.2014.05.003
Shankar A., 2016, Journal of Internet Banking and Commerce, V21, P1
Shankar A, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101920
Sharma SK, 2018, GOV INFORM Q, V35, P557, DOI 10.1016/j.giq.2018.07.002
Siemsen E, 2010, ORGAN RES METHODS, V13, P456, DOI 10.1177/1094428109351241
Singh AK, 2023, EVALUATION REV, V47, P350, DOI 10.1177/0193841X221121244
Stockdale LA, 2020, J ADOLESCENCE, V79, P173, DOI 10.1016/j.adolescence.2020.01.010
Talukder MS, 2020, TECHNOL FORECAST SOC, V150, DOI 10.1016/j.techfore.2019.119793
Taylor J.W., 1974, Journal of marketing, V38, P54, DOI DOI 10.1177/002224297403800211
Teng SS, 2014, ONLINE INFORM REV, V38, P746, DOI 10.1108/OIR-04-2014-0089
Thongsri Nattaporn, 2018, Journal of Systems and Information Technology, V20, P278, DOI 10.1108/JSIT-11-2017-0107
Thongsri N., 12 INT C UB MED COMP, P314
Van Nguyen P., 2022, EVALUATION REV
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Ngo VM, 2023, INT J EMERG MARK, V18, P1355, DOI 10.1108/IJOEM-10-2021-1561
Wang R. Y., 1996, Journal of Management Information Systems, V12, P5
Wessels L, 2010, INT J BANK MARK, V28, P547, DOI 10.1108/02652321011085194
Xie KL, 2016, J TRAVEL TOUR MARK, V33, P211, DOI 10.1080/10548408.2015.1050538
Zhang Y., 1996, EUR J MARKETING, V30, P50, DOI DOI 10.1108/03090569610153309
NR 74
TC 1
Z9 1
U1 4
U2 8
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 0306-8293
EI 1758-6712
J9 INT J SOC ECON
JI Int. J. Soc. Econ.
PD FEB 7
PY 2024
VL 51
IS 2
SI SI
BP 227
EP 247
DI 10.1108/IJSE-10-2022-0709
EA MAR 2023
PG 21
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA HF8X2
UT WOS:000961047200001
DA 2024-03-27
ER
PT J
AU Peng, LF
Lai, LL
AF Peng, Lifang
Lai, Lingling
TI A service innovation evaluation framework for tourism e-commerce in
China based on BP neural network
SO ELECTRONIC MARKETS
LA English
DT Article
DE Tourism e-commerce; Service innovation evaluation; BP neural network
AB With the rapid development of tourism e-commerce in China, how to evaluate the effectiveness of tourism e-commerce service innovation in the e-commerce field has become an important and critical issue. Drawing on pertaining literature, this paper chooses the back propagation (BP) neural network model to evaluate the effectiveness of tourism e-commerce service innovation. The study first establishes the evaluation index system that is consistent with the characteristics of the tourism e-commerce service industry and selects ten tourism e-commerce service providers to conduct an empirical analysis. Then Matlab7.0 is employed to simulate this evaluation model and to draw the corresponding conclusions. Finally, this paper summarizes the limitations of the study and proposes future research avenues. The insightful results of this study can mirror the development situation of tourism e-commerce and provide an effective evaluation framework for the tourism e-commerce service innovation performance in China.
C1 [Peng, Lifang] Xiamen Univ, Sch Management, Xiamen 361005, Fujian Province, Peoples R China.
[Lai, Lingling] Xiamen City Univ, Dept Business, Xiamen 361008, Fujian Province, Peoples R China.
C3 Xiamen University
RP Peng, LF (autor correspondiente), Xiamen Univ, Sch Management, 422 South Siming Rd, Xiamen 361005, Fujian Province, Peoples R China.
EM lfpeng@xmu.edu.cn; lingling@xmcu.cn
RI Novoa, Kevin/J-2867-2014
CR Akoumianakis D, 2010, ELECTRON COMMER RES, V10, P43, DOI 10.1007/s10660-010-9045-1
[Anonymous], 2009, INTERNET WEEKLY JUN, P73
[Anonymous], 2003, EC POLICY REV
[Anonymous], 2011, IRESEARCH REPORT
BARRAS R, 1986, RES POLICY, V15, P161, DOI 10.1016/0048-7333(86)90012-0
Basheer IA, 2000, J MICROBIOL METH, V43, P3, DOI 10.1016/S0167-7012(00)00201-3
Berger Helmut, 2007, Virtual Reality, V11, P75, DOI 10.1007/s10055-006-0057-z
Bilderbeed P., 1998, 3 SI4S
Büyüközkan G, 2008, MATH COMPUT SIMULAT, V77, P464, DOI 10.1016/j.matcom.2007.11.015
Chen J., 2008, ECONOMICS, P66
Counter R., 2001, APPL OPTICS, V6, P4979
Djella F., 2001, Science and Public Policy, V28, P57
Drejer I, 2004, RES POLICY, V33, P551, DOI 10.1016/j.respol.2003.07.004
Gallouj F., 1998, EUROPEAN J INNOVATIO, V13, P123
Guan JC, 2006, EUR J OPER RES, V170, P971, DOI 10.1016/j.ejor.2004.07.054
Hipp C, 2005, RES POLICY, V34, P517, DOI 10.1016/j.respol.2005.03.002
Hipp C., 2000, INT J INNOV MANAG, V4, P417
Ho CI, 2007, TOURISM MANAGE, V28, P1434, DOI 10.1016/j.tourman.2006.12.002
HU L Y, 2008, INNOVATION RES, V22, P116
Hugo H., 2006, CAN WE MEASURE COMP
Jie Lu, 2001, Electronic Markets, V11, P107, DOI 10.1080/101967801300197025
Jin Liu, 2004, IEEE Circuits and Systems Magazine, V4, P4, DOI 10.1109/MCAS.2004.1330746
Kak S, 2002, INFORM SCIENCES, V148, P97, DOI 10.1016/S0020-0255(02)00281-5
Kline Stephen., 1986, POSITIVE SUM STRATEG, P289, DOI DOI 10.1108/14601069810368485
Milbergs E., 2004, Innovation Metrics: Measurement to Insight
Miles I, 2008, IBM SYST J, V47, P115, DOI 10.1147/sj.471.0115
Normann R., 1991, SERVICE MANAGEMENT S
Novak J, 2009, ELECTRON MARK, V19, P15, DOI 10.1007/s12525-009-0003-5
Patier D, 2010, PROCD SOC BEHV, V2, P6229, DOI 10.1016/j.sbspro.2010.04.033
PAVITT K, 1984, RES POLICY, V13, P343, DOI 10.1016/0048-7333(84)90018-0
Poon Auliana., 1993, TOURISM TECHNOLOGY C
Straub D., 2004, VALIDATION GUIDELINE, P13
Sun CC, 2010, EXPERT SYST APPL, V37, P7745, DOI 10.1016/j.eswa.2010.04.066
Watts RJ, 1997, TECHNOL FORECAST SOC, V56, P25, DOI 10.1016/S0040-1625(97)00050-4
Wen KL, 2008, ADV ENG SOFTW, V39, P137, DOI 10.1016/j.advengsoft.2006.12.002
Werthner H, 2004, COMMUN ACM, V47, P101, DOI 10.1145/1035134.1035141
Wu G. S., 2003, SERVICES INNOVATION
Zhang GJ, 2009, 2009 INTERNATIONAL FORUM ON INFORMATION TECHNOLOGY AND APPLICATIONS, VOL 3, PROCEEDINGS, P664, DOI 10.1109/IFITA.2009.273
NR 38
TC 21
Z9 24
U1 2
U2 103
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD MAR
PY 2014
VL 24
IS 1
BP 37
EP 46
DI 10.1007/s12525-013-0148-0
PG 10
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA AC6TC
UT WOS:000332656900004
DA 2024-03-27
ER
PT J
AU Pan, H
Zhou, HX
AF Pan, Hong
Zhou, Hanxun
TI Study on convolutional neural network and its application in data mining
and sales forecasting for E-commerce
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Convolutional neural network; E-commerce; Data mining; Sales forecasting
ID MODEL; PREDICTION; SENTIMENT
AB In recent years, the rapid development of e-commerce has brought great convenience to people. Compared with traditional business environment, e-commerce is more dynamic and complex, which brings many challenges. Data mining technology can help people better deal with these challenges. Traditional data mining technology cannot effectively use the massive data in the electricity supplier, it relies on the time-consuming and labour-consuming characteristic engineering, and the obtained model is not scalable. Convolutional neural network can effectively use a large amount of data, and can automatically extract effective features from the original data, with higher availability. In this paper, convolutional neural network is used to mine e-commerce data to achieve the prediction of commodity sales. First, this article combines the inherent nature of the relevant merchandise information with the original cargo log data that can be converted into a specific "data frame" format. Raw log data includes items sold over a long period of time, price, quantity view, browse, search, search, times collected, number of items added to cart, and many other metrics. Then, convolutional neural network is applied to extract effective features on the data frame. Finally, the final layer of the convolutional neural network uses these features to predict sales of goods. This method can automatically extract effective features from the original structured time series data by convolutional neural network, and further use these features to achieve sales forecast. The validity of the proposed algorithm is verified on the real e-commerce data set.
C1 [Pan, Hong] Liaoning Univ, Sch Econ, Shenyang 110036, Liaoning, Peoples R China.
[Zhou, Hanxun] Liaoning Univ, Sch Informat, Shenyang 110036, Liaoning, Peoples R China.
C3 Liaoning University; Liaoning University
RP Zhou, HX (autor correspondiente), Liaoning Univ, Sch Informat, Shenyang 110036, Liaoning, Peoples R China.
EM lidachao223@sohu.com
CR Bell D, 2018, SIMUL-T SOC MOD SIM, V94, P195, DOI 10.1177/0037549717743106
Borkar TS, 2019, IEEE T IMAGE PROCESS, V28, P6022, DOI 10.1109/TIP.2019.2924172
Clarke RT, 2007, HYDROL EARTH SYST SC, V11, P408, DOI 10.5194/hess-11-408-2007
Pham DH, 2018, DATA KNOWL ENG, V114, P26, DOI 10.1016/j.datak.2017.06.001
Guo ZQ, 2019, IET INTELL TRANSP SY, V13, P1374, DOI 10.1049/iet-its.2018.5530
Gysel P, 2018, IEEE T NEUR NET LEAR, V29, P5784, DOI 10.1109/TNNLS.2018.2808319
Hanson J, 2018, BIOINFORMATICS, V34, P4039, DOI 10.1093/bioinformatics/bty481
Jiang XH, 2018, NEUROCOMPUTING, V275, P1132, DOI 10.1016/j.neucom.2017.09.056
Kannadath BS, 2018, AM J GASTROENTEROL, V113, pS41
Khaled A, 2019, COMPUT HUM BEHAV, V101, P435, DOI 10.1016/j.chb.2018.08.051
Kuzovkin D, 2019, ACM T APPL PERCEPT, V16, DOI 10.1145/3333612
Liberis E, 2018, BIOINFORMATICS, V34, P2944, DOI 10.1093/bioinformatics/bty305
Lin JB, 2019, INFORM MANAGE-AMSTER, V56, P213, DOI 10.1016/j.im.2018.05.009
Mahdavinejad MS, 2018, DIGIT COMMUN NETW, V4, P161, DOI 10.1016/j.dcan.2017.10.002
Manogaran Gunasekaran, 2018, International Journal of Advanced Intelligence Paradigms, V10, P118
Mau S, 2018, INT J BANK MARK, V36, P1125, DOI 10.1108/IJBM-11-2016-0180
Ortega JA, 2018, BIOSYST ENG, V174, P231, DOI 10.1016/j.biosystemseng.2018.07.012
Qiu X., 2019, Journal of Banking and Financial Technology, V3, P33, DOI DOI 10.1007/S42786-018-00006-2
Ramos André Lubene, 2019, REM, Int. Eng. J., V72, P9
Rao H, 2019, APPL SOFT COMPUT, V74, P634, DOI 10.1016/j.asoc.2018.10.036
Rout JK, 2018, ELECTRON COMMER RES, V18, P181, DOI 10.1007/s10660-017-9257-8
Sarthak Yadav, 2018, INT J DIGITAL INFORM, V8, P174
Seinen C, 2018, PHYSICA D, V376, P78, DOI 10.1016/j.physd.2017.09.005
Sivasankar E, 2019, INT J BUSINESS INFOR, V31, P1, DOI DOI 10.1504/IJBIS.2019.099524
Stripling E, 2018, SWARM EVOL COMPUT, V40, P116, DOI 10.1016/j.swevo.2017.10.010
Vijaya J, 2019, CLUSTER COMPUT, V22, P10757, DOI 10.1007/s10586-017-1172-1
Wan SH, 2018, IEEE ACCESS, V6, P36825, DOI 10.1109/ACCESS.2018.2851382
Wang D, 2018, WIRELESS PERS COMMUN, V103, P941, DOI 10.1007/s11277-018-5488-9
Wang J, 2017, FRONT INFORM TECH EL, V18, P1817, DOI 10.1631/FITEE.1601468
Wu PJ, 2018, TELEMAT INFORM, V35, P237, DOI 10.1016/j.tele.2017.11.004
Wu T, 2018, COMPUT IND ENG, V116, P97, DOI 10.1016/j.cie.2017.11.032
Xu SX, 2017, PROD OPER MANAG, V26, P292, DOI 10.1111/poms.12638
Yao YQ, 2019, STAT PAP, V60, P235, DOI 10.1007/s00362-018-01068-6
Zhu LH, 2018, IEEE WIREL COMMUN, V25, P84, DOI 10.1109/MWC.2018.1700157
NR 34
TC 25
Z9 28
U1 5
U2 60
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD JUN
PY 2020
VL 20
IS 2
SI SI
BP 297
EP 320
DI 10.1007/s10660-020-09409-0
EA APR 2020
PG 24
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LK9BZ
UT WOS:000529581000001
DA 2024-03-27
ER
PT J
AU Tan, B
Yi, C
Chan, HC
AF Tan, Barney
Yi, Cheng
Chan, Hock C.
TI Deliberation Without Attention: The Latent Benefits of Distracting
Website Features for Online Purchase Decisions
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE Web design; human-computer interaction; unconscious thought theory;
deliberation without attention; online shopping
ID TECHNOLOGY ACCEPTANCE MODEL; UNCONSCIOUS THOUGHT THEORY; CONSCIOUS
THOUGHT; WEB PERSONALIZATION; USER EXPERIENCE; TASK; INFORMATION;
CHOICE; INTERRUPTION; SATISFACTION
AB Early studies on Web design typically caution against the use of distracting website features in electronic commerce, such as animated banners, pop-ups, and floating advertisements, because they may cause annoyance for online consumers and disrupt information processing, leading to poorer purchase decisions. Yet, the recently uncovered deliberation-without-attention (D-W-A) effect suggests that distracting consumers from the decision-making process may improve their decision quality when there are a large number of decision parameters to consider. To ascertain whether the D-W-A effect can be triggered through the use of distracting website features in the context of online shopping, two experiments are conducted. The first experiment reveals that the presence of distracting website features, in the form of pop-ups, gives rise to annoyance in general, but also leads to better purchase decisions when the decision to be made is complex. The second experiment supports the findings of the first and sheds further light on the underlying mode of thought triggered by these features. In particular, by eliminating a number of potential alternative mechanisms, including online judgments, the mere disruption of decision-related thought, and cognitively constrained conscious deliberation, the second experiment demonstrates that unconscious deliberation is likely to be the underlying cause of superior decision making. With these findings, this research supports a more balanced view in the recent human-computer interaction literature, which suggests that the usual advice to minimize the use of distracting website features should be examined more carefully. The research also uncovers evidence that contributes to the ongoing debate surrounding the D-W-A effect and unconscious thought theory.
C1 [Tan, Barney] Univ Sydney, Sch Business, Discipline Business Informat Syst, Sydney, NSW 2006, Australia.
[Yi, Cheng] Tsinghua Univ, Sch Econ & Management, Dept Management Sci & Engn, Beijing 100084, Peoples R China.
[Chan, Hock C.] Natl Univ Singapore, Dept Informat Syst, Singapore 117417, Singapore.
C3 University of Sydney; Tsinghua University; National University of
Singapore
RP Tan, B (autor correspondiente), Univ Sydney, Sch Business, Discipline Business Informat Syst, Sydney, NSW 2006, Australia.
EM barney.tan@sydney.edu.au; yich@sem.tsinghua.edu.cn;
chanhc@comp.nus.edu.sg
OI Tan, Barney/0000-0002-0687-9699
CR Acker F, 2008, JUDGM DECIS MAK, V3, P292
[Anonymous], 1993, ADAPTIVE DECISION MA, DOI DOI 10.1017/CBO9781139173933
Bailey BP, 2006, COMPUT HUM BEHAV, V22, P685, DOI 10.1016/j.chb.2005.12.009
Bargh JA, 2011, SOC COGNITION, V29, P629, DOI 10.1521/soco.2011.29.6.629
Baron R.S., 1986, ADV EXP SOC PSYCHOL, V19, P1, DOI [10.1016/S0065-2601(08)60211-7, 10.1016/S0065-2601, DOI 10.1016/S0065-2601, DOI 10.1016/S0065-2601(08)60211-7]
Beck J., 2001, The attention economy: Understanding the new currency of business, DOI 10.1145/375348.376626
Blackshaw P, 2003, ADVERTISING AGE, V74, P1
Blackwell RogerD., 2001, CONSUMER BEHAV, V9th
Bos MW, 2008, J EXP SOC PSYCHOL, V44, P1114, DOI 10.1016/j.jesp.2008.01.001
Bos MW, 2011, J CONSUM PSYCHOL, V21, P4, DOI 10.1016/j.jcps.2010.09.002
Brajnik G, 2010, INT J HUM-COMPUT INT, V26, P971, DOI 10.1080/10447318.2010.502100
Burns KS, 2006, J ADVERTISING, V35, P53, DOI 10.2753/JOA0091-3367350104
Carter M, 2014, EUR J INFORM SYST, V23, P185, DOI 10.1057/ejis.2012.55
Chen QM, 1999, J ADVERTISING RES, V39, P27
Chircu AM, 2006, DECIS SUPPORT SYST, V42, P898, DOI 10.1016/j.dss.2005.07.011
Cleeremans A, 2009, THINK NOT THIN UNPUB
Czerwinski M, 2000, P OZHCI SYDN
Devaraj S, 2002, INFORM SYST RES, V13, P316, DOI 10.1287/isre.13.3.316.77
Dijksterhuis A, 2006, SCIENCE, V311, P1005, DOI 10.1126/science.1121629
Dijksterhuis A, 2004, J PERS SOC PSYCHOL, V87, P586, DOI 10.1037/0022-3514.87.5.586
Dijksterhuis A, 2006, PERSPECT PSYCHOL SCI, V1, P95, DOI 10.1111/j.1745-6916.2006.00007.x
Dijksterhuis A, 2009, PSYCHOL SCI, V20, P1381, DOI 10.1111/j.1467-9280.2009.02451.x
Edwards SM, 2002, J ADVERTISING, V31, P83, DOI 10.1080/00913367.2002.10673678
Faber RJ, 2004, AM BEHAV SCI, V48, P447, DOI 10.1177/0002764204270281
Flavián C, 2006, INFORM MANAGE-AMSTER, V43, P1, DOI 10.1016/j.im.2005.01.002
Gao J, 2010, PACIS 2010 P TAIP TA
Gao Y., 2005, ADV TOPICS ELECT COM, P28
Gluck J, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P41
Ham J, 2009, SOC COGNITION, V27, P509, DOI 10.1521/soco.2009.27.4.509
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Hausman AV, 2009, J BUS RES, V62, P5, DOI 10.1016/j.jbusres.2008.01.018
Hong W, 2004, INFORM SYST RES, V15, P60, DOI 10.1287/isre.1040.0017
Hong W, 2007, J AM SOC INF SCI TEC, V58, P1467, DOI 10.1002/asi.20624
Jonides J, 1997, J COGNITIVE NEUROSCI, V9, P462, DOI 10.1162/jocn.1997.9.4.462
Kahneman D, 2003, AM PSYCHOL, V58, P697, DOI 10.1037/0003-066X.58.9.697
Kahneman D., 1973, ATTENTION EFFORT
Koufaris M, 2002, INFORM SYST RES, V13, P205, DOI 10.1287/isre.13.2.205.83
Lassiter GD, 2009, PSYCHOL SCI, V20, P671, DOI 10.1111/j.1467-9280.2009.02347.x
Lerouge D, 2009, J CONSUM RES, V36, P367, DOI 10.1086/599047
Li HR, 2002, J ADVERTISING, V31, P37, DOI 10.1080/00913367.2002.10673665
Ling M., 2006, WEB CONTENT PERSONAL
Liu W, 2008, J CONSUM RES, V35, P640, DOI 10.1086/592126
McCoy S, 2007, COMMUN ACM, V50, P84, DOI 10.1145/1226736.1226740
MILLER GA, 1956, SCI AM, V195, P42, DOI 10.1038/scientificamerican0856-42
Nadkarni S, 2007, MIS QUART, V31, P501
Newell BR, 2009, Q J EXP PSYCHOL, V62, P707, DOI 10.1080/17470210802215202
OLSHAVSKY RW, 1979, ORGAN BEHAV HUM PERF, V24, P300, DOI 10.1016/0030-5073(79)90032-1
PASADEOS Y, 1990, JOURNALISM QUART, V67, P35, DOI 10.1177/107769909006700107
Payne JW, 2008, PSYCHOL SCI, V19, P1118, DOI 10.1111/j.1467-9280.2008.02212.x
PAYNE JW, 1988, J EXP PSYCHOL LEARN, V14, P534, DOI 10.1037/0278-7393.14.3.534
Rey A, 2009, PSYCHOL RES-PSYCH FO, V73, P372, DOI 10.1007/s00426-008-0156-4
Schooler J.W., 1995, CREATIVE COGNITION A, P97
Schumann JH, 2014, J MARKETING, V78, P59, DOI 10.1509/jm.11.0316
Simon HA, 1955, Q J ECON, V69, P99, DOI 10.2307/1884852
Sloman SA, 1996, PSYCHOL BULL, V119, P3, DOI 10.1037/0033-2909.119.1.3
Smilek D, 2006, VIS COGN, V14, P543, DOI 10.1080/13506280500193487
Smith S. P., 2006, ECIS 2006 P
Speier C, 1999, DECISION SCI, V30, P337, DOI 10.1111/j.1540-5915.1999.tb01613.x
Speier C., 1997, PROC ICIS 97, P21
Strick M, 2011, SOC COGNITION, V29, P738, DOI 10.1521/soco.2011.29.6.738
Strick M, 2010, PSYCHOL SCI, V21, P484, DOI 10.1177/0956797610363555
Tam KY, 2006, MIS QUART, V30, P865
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Tan CH, 2010, INFORM SYST RES, V21, P305, DOI 10.1287/isre.1080.0232
Thorsteinson TJ, 2009, JUDGM DECIS MAK, V4, P235
Trafton JG, 2003, INT J HUM-COMPUT ST, V58, P583, DOI 10.1016/S1071-5819(03)00023-5
van de Heijden AHC, 1992, SELECTIVE ATTENTION
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Waroquier L, 2010, SOC PSYCHOL PERS SCI, V1, P111, DOI 10.1177/1948550609356597
WILSON TD, 1991, J PERS SOC PSYCHOL, V60, P181, DOI 10.1037/0022-3514.60.2.181
WILSON TD, 1993, PERS SOC PSYCHOL B, V19, P331, DOI 10.1177/0146167293193010
Zhang P., 2000, J ASSOC INF SYST, V1, P1
Zhang P, 2006, ADV MANAG INFORM SYS, V6, P70
Zhong CB, 2008, PSYCHOL SCI, V19, P912, DOI 10.1111/j.1467-9280.2008.02176.x
NR 74
TC 4
Z9 4
U1 12
U2 138
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD JUN
PY 2015
VL 26
IS 2
BP 437
EP 455
DI 10.1287/isre.2015.0566
PG 19
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA CL0CG
UT WOS:000356605700011
DA 2024-03-27
ER
PT J
AU Talukder, MS
Sorwar, G
Bao, YK
Ahmed, JU
Palash, MAS
AF Talukder, Md. Shamim
Sorwar, Golam
Bao, Yukun
Ahmed, Jashim Uddin
Palash, Md. Abu Saeed
TI Predicting antecedents of wearable healthcare technology acceptance by
elderly: A combined SEM-Neural Network approach
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Healthcare; Wearable technology; Adoption decision; SEM-Neural Network;
Elderly; Developing Country; China
ID INFORMATION-SYSTEMS CONTINUANCE; GOVERNMENT SERVICES; USERS
PERSPECTIVES; UNIFIED THEORY; ADOPTION; DETERMINANTS; MODEL; PERCEPTION;
VALIDITY; INTERNET
AB Wearable healthcare technology (WHT) has the potential to improve access to healthcare information especially to the older population and empower them to play an active role in self-management of their health. Despite their potential benefits, the acceptance and usage of WHT among the elderly are considerably low. However, little research has been conducted to describe any systematic study of the elderly's intention to adopt WHT. The objective of this study was to develop a theoretical model on the basis of extended Unified Theory of Acceptance and Use of Technology (UTAUT2) with additional constructs- resistance to change, technology anxiety, and self-actualization, to investigate the key predictors of WHT adoption by elderly. The model used in the current study was analyzed in two steps. In the first step, a Structural Equation Modeling (SEM) was used to determine significant determinants that affect the adoption of WHT. In the second step, a neural network model was applied to validate the findings in step 1 and establish the relative importance of each determinant to the adoption of WHT. The findings revealed that social influence, performance expectancy, functional congruence, self-actualization, and hedonic motivation had a positive relationship with the adoption of WHT. In addition, technology anxiety and resistance to change posed important but negative influences on WHT acceptance. Surprisingly, the study did not find any significant relationship between effort expectancy and facilitating conditions with behavioral intention to use WHT by the elderly. The results of this research have strong theoretical contributions to the existing literature of WHT. It also provides valuable information for WHT developers and social planners in the design and execution of WHT for the elderly.
C1 [Talukder, Md. Shamim; Bao, Yukun; Palash, Md. Abu Saeed] Huazhong Univ Sci & Technol, Sch Management, Ctr Modern Informat Management, Wuhan 430074, Hubei, Peoples R China.
[Talukder, Md. Shamim; Ahmed, Jashim Uddin] North South Univ, Dept Management, Dhaka, Bangladesh.
[Sorwar, Golam] Southern Cross Univ, Sch Business & Tourism, Lismore, NSW, Australia.
C3 Huazhong University of Science & Technology; North South University
(NSU); Southern Cross University
RP Bao, YK (autor correspondiente), Huazhong Univ Sci & Technol, Sch Management, Ctr Modern Informat Management, Wuhan 430074, Hubei, Peoples R China.
EM Shamim.talukder@northsouth.edu; golam.sorwar@scu.edu.au;
yukunbao@hust.edu.cn; jashim.ahmed@northsouth.edu
RI Ahmed, Jashim Uddin/P-5988-2019
OI Ahmed, Jashim Uddin/0000-0001-8145-6912; Talukder, Dr.
Shamim/0000-0003-2095-4699
FU National Natural Science Foundation of China [71810107003]
FX This study was supported by the National Natural Science Foundation of
China under Project No. 71810107003.
CR Ahmad SZ, 2017, INT J INFORM MANAGE, V37, P367, DOI 10.1016/j.ijinfomgt.2017.03.008
Aldholay AH, 2018, TELEMAT INFORM, V35, P1421, DOI 10.1016/j.tele.2018.03.012
[Anonymous], 2013, EXPLORING PREDICTORS
[Anonymous], 2017, World population prospects 2017: Data booklet
[Anonymous], RADIOL INFECT DIS
[Anonymous], ED INF TECHNOL
[Anonymous], 2004, J ASSOC INF SYST, DOI DOI 10.17705/1JAIS.00059
[Anonymous], DETERMINANTS IT RELA
[Anonymous], GOV INF Q
[Anonymous], COMP 2 FORMULATIONS
ATCHLEY RC, 1989, GERONTOLOGIST, V29, P183, DOI 10.1093/geront/29.2.183
BAGOZZI RP, 1991, ADMIN SCI QUART, V36, P421, DOI 10.2307/2393203
Bao YK, 2017, INT J MED INFORM, V102, P12, DOI 10.1016/j.ijmedinf.2017.01.003
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Bhattacherjee A, 2008, J COMPUT INFORM SYST, V48, P69
Byrne B, 2010, INTERNATIONAL HANDBOOK OF PSYCHOLOGY IN EDUCATION, P3
Canhoto AI, 2017, J MARKET MANAG-UK, V33, P32, DOI 10.1080/0267257X.2016.1234505
Chan FTS, 2012, DECIS SUPPORT SYST, V54, P621, DOI 10.1016/j.dss.2012.08.009
Chen M, 2017, IEEE COMMUN MAG, V55, P54, DOI 10.1109/MCOM.2017.1600410CM
Chin WW, 1998, QUANT METH SER, P295
Chong AYL, 2015, INT J PROD ECON, V159, P66, DOI 10.1016/j.ijpe.2014.09.034
Chong AYL, 2010, INT J MOB COMMUN, V8, P129, DOI 10.1504/IJMC.2010.031444
Choudrie J., 2017, HAW INT C SYST SCI 5
Cimperman M, 2016, INT J MED INFORM, V90, P22, DOI 10.1016/j.ijmedinf.2016.03.002
Conci M, 2009, LECT NOTES COMPUT SC, V5726, P63, DOI 10.1007/978-3-642-03655-2_7
Czaja S.J., 2013, TECHNOLOGIES ACTIVE, P27, DOI DOI 10.1007/978-1-4419-8348-0_3
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Deng ZH, 2014, INT J MED INFORM, V83, P210, DOI 10.1016/j.ijmedinf.2013.12.002
Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
Dwivedi YK, 2015, INFORM SYST FRONT, V17, P143, DOI 10.1007/s10796-014-9500-y
Falk R.F., 1992, A primer for soft modeling
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gao S, 2016, LECT NOTES COMPUT SC, V9844, P280, DOI 10.1007/978-3-319-45234-0_26
Gao YW, 2015, IND MANAGE DATA SYST, V115, P1704, DOI 10.1108/IMDS-03-2015-0087
Godinho C, 2016, J NEUROENG REHABIL, V13, DOI 10.1186/s12984-016-0136-7
Goldberg E, 2016, PROCEEDINGS OF THE 2016 16TH CONFERENCE ON FORMAL METHODS IN COMPUTER-AIDED DESIGN (FMCAD 2016), P49, DOI 10.1109/FMCAD.2016.7886660
Guo XT, 2013, ELECTRON MARK, V23, P49, DOI 10.1007/s12525-012-0112-4
Hair J F., 2010, Technometrics, V15
Hair J.F., 2009, Multivariate data analysis
Hair J.F. Jr., 2016, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
Harman H. H., 1976, Modern Factor Analysis
Heinz M, 2013, J GERONTOL NURS, V39, P42, DOI 10.3928/00989134-20121204-04
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Hew JJ, 2017, TELEMAT INFORM, V34, P34, DOI 10.1016/j.tele.2016.12.009
HEYLIGHEN F, 1992, BEHAV SCI, V37, P39, DOI 10.1002/bs.3830370105
HIRSCHHEIM R, 1988, COMPUT J, V31, P398, DOI 10.1093/comjnl/31.5.398
Holden RJ, 2010, J BIOMED INFORM, V43, P159, DOI 10.1016/j.jbi.2009.07.002
Hoque MR, 2017, INFORM HEALTH SOC CA, V42, P1, DOI 10.3109/17538157.2015.1075541
Hoque R, 2017, INT J MED INFORM, V101, P75, DOI 10.1016/j.ijmedinf.2017.02.002
Hu ZY, 2017, J SYST SCI COMPLEX, V30, P1425, DOI 10.1007/s11424-017-5293-7
Huber F, 2010, J BUS RES, V63, P1113, DOI 10.1016/j.jbusres.2009.10.006
Irani Z, 2007, EUR J INFORM SYST, V16, P327, DOI 10.1057/palgrave.ejis.3000698
Jeong SC, 2017, TELEMAT INFORM, V34, P399, DOI 10.1016/j.tele.2016.09.001
Kaufman G, 2002, J AGING STUD, V16, P169, DOI 10.1016/S0890-4065(02)00042-7
Kekade S, 2018, COMPUT METH PROG BIO, V153, P137, DOI 10.1016/j.cmpb.2017.10.008
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Kijsanayotin B, 2009, INT J MED INFORM, V78, P404, DOI 10.1016/j.ijmedinf.2008.12.005
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Lallmahomed MZI, 2017, TELEMAT INFORM, V34, P57, DOI 10.1016/j.tele.2017.01.003
Lee B, 2011, COMPUT HUM BEHAV, V27, P1231, DOI 10.1016/j.chb.2011.01.003
Lee C, 2015, J PROD INNOVAT MANAG, V32, P747, DOI 10.1111/jpim.12176
Lee SY, 2018, TECHNOL FORECAST SOC, V129, P154, DOI 10.1016/j.techfore.2018.01.002
Li H, 2016, INT J MED INFORM, V88, P8, DOI 10.1016/j.ijmedinf.2015.12.010
Li HX, 2018, COMPUT HUM BEHAV, V85, P175, DOI 10.1016/j.chb.2018.03.048
Liu Y, 2017, INFORM SYST J, V27, P59, DOI 10.1111/isj.12094
Lowry PB, 2014, IEEE T PROF COMMUN, V57, P123, DOI 10.1109/TPC.2014.2312452
Luck D.J. Rubin., 1987, Marketing Research, V7th
Lunney A, 2016, COMPUT HUM BEHAV, V65, P114, DOI 10.1016/j.chb.2016.08.007
Marquié JC, 2002, BEHAV INFORM TECHNOL, V21, P273, DOI 10.1080/0144929021000020998
Maslow A.H., 2012, MOTIVATION PERSONALI, V3rd
Mathur A, 2005, PSYCHOL MARKET, V22, P969, DOI 10.1002/mar.20094
Meuter ML, 2003, J BUS RES, V56, P899, DOI 10.1016/S0148-2963(01)00276-4
Moschis G.P., 1992, Marketing to older consumers: A handbook of information for strategy development
Negahban A, 2014, COMPUT HUM BEHAV, V35, P75, DOI 10.1016/j.chb.2014.02.020
O'Brien RM, 2007, QUAL QUANT, V41, P673, DOI 10.1007/s11135-006-9018-6
Oliveira T, 2016, COMPUT HUM BEHAV, V61, P404, DOI 10.1016/j.chb.2016.03.030
Ooi KB, 2018, EXPERT SYST APPL, V93, P376, DOI 10.1016/j.eswa.2017.10.009
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
Phang CW, 2006, IEEE T ENG MANAGE, V53, P555, DOI 10.1109/TEM.2006.883710
Phichitchaisopa N, 2013, EXCLI J, V12, P413
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Premkumar G, 1995, DECISION SCI, V26, P303, DOI 10.1111/j.1540-5915.1995.tb01431.x
Reichheld FF, 2000, HARVARD BUS REV, V78, P105
Rho MJ, 2015, CLUSTER COMPUT, V18, P321, DOI 10.1007/s10586-014-0356-1
Ruhl K., 2004, Historical Soc. Res., V29, P171, DOI [10.12759/hsr.29.2004.4.171-177, DOI 10.12759/HSR.29.2004.4.171-177]
Saxena S, 2017, FORESIGHT, V19, P421, DOI 10.1108/FS-02-2017-0003
Scott JE, 2009, INFORM MANAGE-AMSTER, V46, P221, DOI 10.1016/j.im.2008.10.003
Sekaran U., 2011, Research methods for business: A skill building approach, V5th ed., DOI DOI 10.1108/17506200710779521
Sharma SK, 2018, GOV INFORM Q, V35, P557, DOI 10.1016/j.giq.2018.07.002
Sharma SK, 2016, COMPUT HUM BEHAV, V62, P61, DOI 10.1016/j.chb.2016.03.073
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
SIMONSON MR, 1987, J EDUC COMPUT RES, V3, P231, DOI 10.2190/7CHY-5CM0-4D00-6JCG
SIRGY MJ, 1985, J BUS RES, V13, P195, DOI 10.1016/0148-2963(85)90026-8
Srizongkhram S, 2018, PORTL INT CONF MANAG
Steele R, 2009, INT J MED INFORM, V78, P788, DOI 10.1016/j.ijmedinf.2009.08.001
Talukder MS, 2019, TECHNOL SOC, V56, P147, DOI 10.1016/j.techsoc.2018.09.013
Talukder MS, 2019, IND MANAGE DATA SYST, V119, P170, DOI 10.1108/IMDS-01-2018-0009
Tedesco S, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17061277
Teo TSH, 2003, OMEGA-INT J MANAGE S, V31, P483, DOI 10.1016/j.omega.2003.08.005
Turner P, 2007, BEHAV INFORM TECHNOL, V26, P287, DOI 10.1080/01449290601173499
Veeramootoo N, 2018, GOV INFORM Q, V35, P161, DOI 10.1016/j.giq.2018.03.004
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Venkatesh V, 2011, INFORM SYST J, V21, P527, DOI [10.1111/j.1365-2575.2011.00373.x, 10.1111/J.1365-2575.2011.00373.x]
Vroman KG, 2015, COMPUT HUM BEHAV, V43, P156, DOI 10.1016/j.chb.2014.10.018
Wang T, 2014, BEHAV INFORM TECHNOL, V33, P1048, DOI 10.1080/0144929X.2013.781221
Wei J, 2014, IEEE CONSUM ELECTR M, V3, P53, DOI 10.1109/MCE.2014.2317895
Wei TT, 2009, IND MANAGE DATA SYST, V109, P370, DOI 10.1108/02635570910939399
Williams MD, 2015, J ENTERP INF MANAG, V28, P443, DOI 10.1108/JEIM-09-2014-0088
Woodside AG, 2014, J BUS RES, V67, P2495, DOI 10.1016/j.jbusres.2014.07.006
Woodside AG, 2013, J BUS RES, V66, P463, DOI 10.1016/j.jbusres.2012.12.021
Xue LS, 2012, INT J MED INFORM, V81, P637, DOI 10.1016/j.ijmedinf.2012.04.008
NR 114
TC 135
Z9 138
U1 27
U2 174
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD JAN
PY 2020
VL 150
AR 119793
DI 10.1016/j.techfore.2019.119793
PG 13
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA JW8VM
UT WOS:000503324600007
OA hybrid
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Sabbir, MM
Islam, M
Das, S
AF Sabbir, Md. Mahiuddin
Islam, Mazharul
Das, Samir
TI Understanding the determinants of online pharmacy adoption: a two-staged
SEM-neural network analysis approach
SO JOURNAL OF SCIENCE AND TECHNOLOGY POLICY MANAGEMENT
LA English
DT Article
DE UTAUT; Health literacy; Personal innovativeness; Neural network
analysis; Online pharmacy
ID TECHNOLOGY ACCEPTANCE MODEL; INFORMATION-TECHNOLOGY; BEHAVIORAL
INTENTION; COMMERCE ADOPTION; HEALTH LITERACY; PERSONAL INNOVATIVENESS;
USER ACCEPTANCE; MOBILE; SERVICES; SYSTEM
AB Purpose
This study aims to understand the determinants of online pharmacy or epharmacy adoption among young consumers in Bangladesh using an extended unified theory of acceptance and use of technology (UTAUT) model.
Design/methodology/approach
A structured Google Docs questionnaire was sent out to 420 respondents using messenger service; 285 useable responses were finally extracted. Data were empirically validated using the two-staged structural equation model (SEM)-neural network analysis approach.
Findings
The robustness of the classical UTAUT model remains intact in the context of online pharmacy adoption. Among the integrated variables, while perceived trust and health literacy were found significant, perceived risk and personal innovativeness were found insignificant in determining consumers' intention to adopt online pharmacy. The neural network analysis provided further verification of these findings derived from the SEM.
Practical implications
The findings of this study would facilitate in devising better strategies for entering or expanding online pharmacy business in developing countries such as Bangladesh.
Originality/value
The originality of the current study relates to the two-fold contributions of this study. First, while this study extended the classical UTAUT model by incorporating perceived risk, perceived trust, personal innovativeness and health literacy, the inclusion of the following two variables is fresh within the extant online pharmacy literature. Second, by using a two-staged SEM-neural network analysis approach, this study advances the past studies on e-commerce adoption in pharmaceutical settings and provides a general understanding of the customers of developing countries.
C1 [Sabbir, Md. Mahiuddin; Islam, Mazharul; Das, Samir] Univ Barishal, Fac Business Studies, Dept Mkt, Barishal, Bangladesh.
C3 University of Barishal
RP Sabbir, MM (autor correspondiente), Univ Barishal, Fac Business Studies, Dept Mkt, Barishal, Bangladesh.
EM mmsabbir@bu.ac.bd; mazharul97@yahoo.com; samir.bumkt@gmail.com
RI Sabbir, Mahiuddin/AAZ-9248-2020
OI Sabbir, Mahiuddin/0000-0001-5804-3343; Islam,
Mazharul/0000-0003-0643-7003
CR Abanmy N, 2017, SAUDI PHARM J, V25, P891, DOI 10.1016/j.jsps.2017.02.001
Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204
Ahmed H.U., 2019, FINANCIAL EXPRESS
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
Alam MZ, 2020, INT J INFORM MANAGE, V50, P128, DOI 10.1016/j.ijinfomgt.2019.04.016
Alsadoun AA, 2020, International Journal of Advanced Science and Technology, V29, P262, DOI [10.31838/jcr.07.16.43, DOI 10.31838/JCR.07.16.43]
Aref MM, 2020, REV ECON POLIT SCI-R, V5, P21, DOI 10.1108/REPS-10-2018-0013
Awa HO, 2015, J SCI TECHNOL POLICY, V6, P76, DOI 10.1108/JSTPM-04-2014-0012
Bessell TL, 2002, EUR J CLIN PHARMACOL, V58, P567, DOI 10.1007/s00228-002-0519-5
Boontarig W., 2012, 2012 IEEE Symposium on Humanities, Science and Engineering Research (SHUSER), P479, DOI 10.1109/SHUSER.2012.6268881
Brusch I, 2020, J RETAIL CONSUM SERV, V54, DOI 10.1016/j.jretconser.2019.101936
Brusselaers J, 2020, FOREST POLICY ECON, V111, DOI 10.1016/j.forpol.2019.102026
Chan FTS, 2012, DECIS SUPPORT SYST, V54, P621, DOI 10.1016/j.dss.2012.08.009
Childers TL, 2001, J RETAILING, V77, P511, DOI 10.1016/S0022-4359(01)00056-2
Chong AYL, 2013, EXPERT SYST APPL, V40, P523, DOI 10.1016/j.eswa.2012.07.068
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Desai C, 2016, INDIAN J PHARMACOL, V48, P615, DOI 10.4103/0253-7613.194865
DOLL WJ, 1994, MIS QUART, V18, P453, DOI 10.2307/249524
Dowell D, 2015, IND MARKET MANAG, V44, P119, DOI 10.1016/j.indmarman.2014.10.016
Featherman MS, 2003, INT J HUM-COMPUT ST, V59, P451, DOI 10.1016/S1071-5819(03)00111-3
Ferdous M., 2013, J BUSINESS STUDIES, V34
Fittler A, 2018, J MED INTERNET RES, V20, DOI 10.2196/11115
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Grewal D., 2004, J RELATIONSHIP MARKE, V2, P31, DOI DOI 10.1300/J366V02N03_03
Hair J.F., 2009, Multivariate data analysis
Hew TS, 2016, J COMPUT INFORM SYST, V56, P352, DOI 10.1080/08874417.2016.1164497
Hinkin TR, 1998, ORGAN RES METHODS, V1, P104, DOI 10.1177/109442819800100106
Holden RJ, 2010, J BIOMED INFORM, V43, P159, DOI 10.1016/j.jbi.2009.07.002
Hoque R, 2017, INT J MED INFORM, V101, P75, DOI 10.1016/j.ijmedinf.2017.02.002
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Hussain A, 2019, INT J INFORM MANAGE, V44, P76, DOI 10.1016/j.ijinfomgt.2018.09.016
Ignjatovi D., 2019, FINIZ 2019 DIGITIZAT, P78, DOI [10.15308/finiz-2019-78-84, DOI 10.15308/FINIZ-2019-78-84]
Isaac O, 2019, ASIA PAC MANAG REV, V24, P335, DOI 10.1016/j.apmrv.2018.12.003
Karakaya F, 2014, J EDUC BUS, V89, P13, DOI 10.1080/08832323.2012.740520
Khan AN, 2018, WIRELESS PERS COMMUN, V103, P2529, DOI 10.1007/s11277-018-5945-5
Khan J. J., 2020, DAILY STAR
Kline R. B., 2015, PRINCIPLES PRACTICE
Koufaris M, 2004, INFORM MANAGE-AMSTER, V41, P379, DOI 10.1016/j.im.2003.08.004
Lee SY, 2014, TELEMAT INFORM, V31, P308, DOI 10.1016/j.tele.2013.06.001
Liébana-Cabanillas F, 2018, TECHNOL FORECAST SOC, V129, P117, DOI 10.1016/j.techfore.2017.12.015
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Lu J, 2005, J STRATEGIC INF SYST, V14, P245, DOI 10.1016/j.jsis.2005.07.003
Mancuso JM, 2008, NURS HEALTH SCI, V10, P248, DOI 10.1111/j.1442-2018.2008.00394.x
Natarajan T, 2017, J RETAIL CONSUM SERV, V37, P8, DOI 10.1016/j.jretconser.2017.02.010
Nicola M, 2020, INT J SURG, V78, P185, DOI 10.1016/j.ijsu.2020.04.018
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Nuq PA, 2013, INT J HEALTHCARE MAN, V6, P217, DOI 10.1179/2047971913Y.0000000033
Nutbeam D, 2000, HEALTH PROMOT INT, V15, P259, DOI 10.1093/heapro/15.3.259
Ofori D, 2019, COGENT BUS MANAG, V6, DOI 10.1080/23311975.2019.1644715
Orizio G, 2011, J MED INTERNET RES, V13, DOI 10.2196/jmir.1795
Orsolini L, 2015, HUM PSYCHOPHARM CLIN, V30, P302, DOI 10.1002/hup.2466
Osborne RH, 2013, BMC PUBLIC HEALTH, V13, DOI 10.1186/1471-2458-13-658
Otto L., 2019, PACIS 2019 P
Paasche-Orlow MK, 2007, AM J HEALTH BEHAV, V31, pS19
Pipitwanichakarn T, 2019, J SCI TECHNOL POLICY, V10, P193, DOI 10.1108/JSTPM-12-2017-0074
Porter Eileen J, 2002, Care Manag J, V3, P192, DOI 10.1891/cmaj.3.4.192.57452
Rahman SU, 2018, S ASIAN J BUS STUD, V7, P111, DOI 10.1108/SAJBS-11-2016-0088
Rana NP, 2016, COMPUT HUM BEHAV, V59, P265, DOI 10.1016/j.chb.2016.02.019
Ratten V, 2015, J SCI TECHNOL POLICY, V6, P25, DOI 10.1108/JSTPM-06-2014-0029
Rogers, 2003, DIFFUSION INNOVATION
Rogers E. M., 1995, DIFFUSION INNOVATION
Sabbir M.M., 2018, COMILLA U J BUSINESS, V5, P123
Sarker S.K., 2019, J SHAHEED SUHRAWARDY, V11, P43, DOI 10.3329/jssmc.v11i1.43178
Scott JE, 2009, INFORM MANAGE-AMSTER, V46, P221, DOI 10.1016/j.im.2008.10.003
Shafique-ur Rehman, 2019, J GLOB ENTREP RES, V9, DOI 10.1186/s40497-019-0155-5
Shareef MA, 2017, INT J INFORM MANAGE, V37, P257, DOI 10.1016/j.ijinfomgt.2017.02.003
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Singh H, 2020, Journal of Drug Delivery and Therapeutics, P335
Sivathanu B, 2019, J SCI TECHNOL POLICY, V10, P143, DOI 10.1108/JSTPM-07-2017-0033
Soh PY, 2020, J AMB INTEL HUM COMP, DOI 10.1007/s12652-020-01718-4
Sorensen K, 2012, BMC PUBLIC HEALTH, V12, DOI 10.1186/1471-2458-12-80
Speros C, 2005, J ADV NURS, V50, P633, DOI 10.1111/j.1365-2648.2005.03448.x
Steele R, 2009, INT J MED INFORM, V78, P788, DOI 10.1016/j.ijmedinf.2009.08.001
Su L., 2011, J MED MARKETING DEVI, V11, P197, DOI [10.1177/1745790411405712, DOI 10.1177/1745790411405712]
Svorc J., 2012, Journal of Systems Integration, V3, P3
Tabachnick B. G., 2007, Using Multivariate Statistics, V5, P481, DOI DOI 10.1037/022267
Tan GWH, 2014, COMPUT HUM BEHAV, V36, P198, DOI 10.1016/j.chb.2014.03.052
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2008, MIS QUART, V32, P483
Venkatesh V, 2012, MIS QUART, V36, P157
Vermeir I, 2006, J AGR ENVIRON ETHIC, V19, P169, DOI 10.1007/s10806-005-5485-3
Wei YC, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-017-02648-0
Wiedmann K. P., 2010, J. Cust. Behav, V9, P175, DOI [DOI 10.1362/147539210X511362, 10.1362/147539210X511362]
Yang SQ, 2016, INTERNET RES, V26, P982, DOI 10.1108/IntR-01-2015-0010
Yi MY, 2006, INFORM MANAGE-AMSTER, V43, P350, DOI 10.1016/j.im.2005.08.006
Yin M., 2016, ACM INT C P SER 17 1, P1, DOI [DOI 10.1145/2971603.2971616, 10.1145/2971603.2971616]
Yiu CS, 2007, INT J INFORM MANAGE, V27, P336, DOI 10.1016/j.ijinfomgt.2007.03.002
Yu YY, 2014, J SCI TECHNOL POLICY, V5, P41, DOI 10.1108/JSTPM-02-2013-0004
NR 90
TC 14
Z9 15
U1 1
U2 10
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2053-4620
EI 1758-5538
J9 J SCI TECHNOL POLICY
JI J. Sci. Technol. Policy Manag.
PD SEP 21
PY 2021
VL 12
IS 4
BP 666
EP 687
DI 10.1108/JSTPM-07-2020-0108
EA NOV 2020
PG 22
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA US5IA
UT WOS:000592700700001
DA 2024-03-27
ER
PT J
AU Guan, SU
Chan, TK
Zhu, FM
AF Guan, Sheng-Uei
Chan, Tai Kheng
Zhu, Fangming
TI Evolutionary intelligent agents for e-commerce: Generic preference
detection with feature analysis
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article; Proceedings Paper
CT 6th International Conference on Electronic Commerce
CY OCT, 2004
CL Delft, NETHERLANDS
DE generic preference; e-commerce; generic attributes; feature analysis;
genetic algorithm
ID ACQUISITION; RECOMMENDATION
AB Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer's generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers' generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising. (c) 2005 Elsevier B.V. All rights reserved.
C1 Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 119260, Singapore.
C3 National University of Singapore
RP Guan, SU (autor correspondiente), Natl Univ Singapore, Dept Elect & Comp Engn, 10 Kent Ridge Crescent, Singapore 119260, Singapore.
EM eleguans@nus.edu.sg
RI Novoa, Kevin/J-2867-2014; Zhu, Fang/L-3411-2016; Zhu, Fang/IRY-9603-2023
CR Alba E, 2002, IEEE T EVOLUT COMPUT, V6, P443, DOI 10.1109/TEVC.2002.800880
[Anonymous], 1996, Genetic Algorithms + Data Structures = Evolution Programs
CHU SC, 1999, P 3 INT C KNOWL BAS, P492
Davis L., 1987, Research Notes in Artificial Intelligence
Guan S, 2004, J RES PRACT INF TECH, V36, P35
GUAN S, 2002, J INF TECHNOL, V1, P473
Guan Steven., 2002, Electronic Commerce Research and Applications, V1, P314, DOI DOI 10.1016/S1567-4223(02)00023-6
Guan SU, 2003, INT J INTELL SYST, V18, P1173, DOI 10.1002/int.10145
GUAN SU, 2004, ELECTRON COMMER R A, V3, P39
GUAN SU, 2004, ELECTRON COMMER R A, V3, P232
GUO Y, 2003, P 2003 INT C AUT AG, P1004
Han E. H., 2001, P 5 PAC AS C KNOWL D, P53
INGBER L, 1992, MATH COMPUT MODEL, V16, P87, DOI 10.1016/0895-7177(92)90108-W
Jung SY, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P235, DOI 10.1109/ICDM.2002.1183908
Kwak M, 2001, ISIE 2001: IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS PROCEEDINGS, VOLS I-III, P625, DOI 10.1109/ISIE.2001.931866
Kwon O.-W., 2000, Proceedings of the 5th International Workshop on Information Retrieval with Asian Languages, P9
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Luke S., 1997, Proceedings of the First International Conference on Autonomous Agents, P59, DOI 10.1145/267658.267668
NEUMANN F, 2002, P INT C DAT ENG ICDE
NWANA H, 1996, BT TECHNOLOGY J, V14
Shahabi C, 2003, DISTRIB PARALLEL DAT, V14, P173, DOI 10.1023/A:1024888710505
Sheth B., 1993, Proceedings. The Ninth Conference on Artificial Intelligence for Applications (Cat. No.93CH3254-0), P345, DOI 10.1109/CAIA.1993.366590
Shibata H, 2002, IWADS: 2ND INTERNATIONAL WORKSHOP ON AUTONOMOUS DECENTRALIZED SYSTEM, PROCEEDINGS, P176
SUNG HH, 2002, IEEE INTELL SYST APP, V17, P34
Tamma V, 2002, LECT NOTES ARTIF INT, V2531, P219
NR 25
TC 14
Z9 17
U1 0
U2 8
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD WIN
PY 2005
VL 4
IS 4
BP 377
EP 394
DI 10.1016/j.elerap.2005.07.002
PG 18
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH); Conference Proceedings Citation Index - Science (CPCI-S)
SC Business & Economics; Computer Science
GA 113VT
UT WOS:000242626800008
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Varzaru, AA
Bocean, CG
AF Varzaru, Anca Antoaneta
Bocean, Claudiu George
TI A Two-Stage SEM-Artificial Neural Network Analysis of Mobile Commerce
and Its Drivers
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE m-commerce; e-commerce; total sales; GDP; mobile device; internet users;
U; S
ID CUSTOMER SATISFACTION; HYBRID SEM; IMPACT; CONSUMERS; BUSINESS; ADOPTION
AB The COVID-19 health and economic crisis has affected all areas of social life globally, including the economy. The world economy has declined due to purchasing power for individuals who have been forced to stay at home and cannot perform work. These restrictions to prevent the spread of SARS-Cov-2 have led to an increase in electronic commerce and mobile commerce as tools for procuring goods and services. In this paper, we conducted a longitudinal analysis of mobile commerce as an essential electronic commerce component, establishing the main drivers of mobile commerce and the intensity of their influences. The research focuses on mobile commerce in the United States (U.S.). It covers the period 2010-2020, the last year of this period capturing the context of the COVID-19 pandemic and its impact on electronic commerce (e-commerce) and mobile commerce (m-commerce). In the macroeconomic analysis of competitiveness, we established the main drivers of m-commerce, using artificial neural networks and the mediation effects found between the variables that describe m-commerce, e-commerce, and total sales, using structural equation modeling. The research results indicate an increase in the share of e-commerce in total sales and a predominance of the m-commerce share in e-commerce on the background of traffic restrictions and social distance rules imposed due to the COVID-19 pandemic. Stakeholders in the m-commerce area should consider the following enhancing drivers: increasing internet speed, expanding 5G and Wi-Fi networks, and increasing accessibility and trust in mobile devices and applications.
C1 [Varzaru, Anca Antoaneta] Univ Craiova, Dept Econ Accounting & Int Business, Craiova 200585, Romania.
[Bocean, Claudiu George] Univ Craiova, Dept Management Mkt & Business Adm, Craiova 200585, Romania.
C3 University of Craiova; University of Craiova
RP Varzaru, AA (autor correspondiente), Univ Craiova, Dept Econ Accounting & Int Business, Craiova 200585, Romania.
EM anca.varzaru@edu.ucv.ro; bocean.claudiu@ucv.ro
RI Bocean, Claudiu George/AAG-9932-2020
OI Bocean, Claudiu George/0000-0001-9714-1333; Varzaru, Anca
Antoaneta/0000-0001-6045-204X
CR Abubakar AM, 2019, PERS REV, V49, P67, DOI 10.1108/PR-06-2017-0180
Alaimo LS, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12229594
Taha VA, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13041710
Alsharif MH, 2018, T EMERG TELECOMMUN T, V29, DOI 10.1002/ett.3254
andWShazia Dr Khan, 2019, P 10 INT C DIGITAL S
Bollen KA, 2011, P NATL ACAD SCI USA, V108, P15639, DOI 10.1073/pnas.1010661108
Bureau of Economic Analysis (BEA), NAT INC PROD ACC TAB
Chang BY, 2015, S AFR J BUS MANAG, V46, P53, DOI 10.4102/sajbm.v46i4.109
Chung K.C., 2019, P USIPCO, P1, DOI [10.1145/3332324.3332335, DOI 10.1145/3302509.3311041]
De Best R., MOBILE PAYMENTS WORL
Du S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11061580
Duch-Brown N, 2017, INT J IND ORGAN, V52, P30, DOI 10.1016/j.ijindorg.2017.01.009
Dumanska I., 2021, T ENV DEV, V17, P386, DOI [10.37394/232015.2021.17.38, DOI 10.37394/232015.2021.17.38]
Dzaka D., 2015, SINGAPOREAN J BUSINE, V3, P1, DOI [10.12816/0010936, DOI 10.12816/0010936]
eMarketer, WORLDW EC WILL APPR
Falk M, 2015, INT J PROD ECON, V170, P357, DOI 10.1016/j.ijpe.2015.10.003
Sanz LF, 2020, TEH VJESN, V27, P2062, DOI 10.17559/TV-20190524075657
Fouskas Konstantinos, 2020, Strategic Innovative Marketing and Tourism, V2020, P687
Fuchs M, 2010, J TRAVEL RES, V49, P165, DOI 10.1177/0047287509336469
Fulgoni G., MOBILE PATH PURCHASE
Gupta S., 2020, P INT C INN COMP COM, DOI [10.2139/ssrn.359530410.2139/ssrn.3595304, DOI 10.2139/SSRN.359530410.2139/SSRN.3595304]
Hew JJ, 2017, TELEMAT INFORM, V34, P43, DOI 10.1016/j.tele.2016.04.003
Hew TS, 2016, J COMPUT INFORM SYST, V56, P352, DOI 10.1080/08874417.2016.1164497
Holmes A, 2013, INT J RETAIL DISTRIB, V42, P25, DOI 10.1108/IJRDM-10-2012-0096
Jahanshahi A.A., 2012, Afr. J. Bus. Manage, V6, P6486
Jakimoski K., 2014, International Journal of U- and e-Service, Science and Technology, V7, P13, DOI [DOI 10.14257/IJUNESST.2014.7.6, DOI 10.14257/IJUNESST.2014.7.6.02]
Jehangir M, 2011, COMM COM INF SC, V189, P440
Kaabi S., 2019, P INT C DIG EC, P183, DOI DOI 10.1007/978-3-030-30874-2_14
Kalinic Z, 2021, EXPERT SYST APPL, V175, DOI 10.1016/j.eswa.2021.114803
Kalinic Z, 2020, J ENTERP INF MANAG, V33, P71, DOI 10.1108/JEIM-05-2019-0136
Kareem T.S., 2014, International Journal of Academic Research in Business and Social Sciences, V4, P215
Katsikeas C, 2020, INT MARKET REV, V37, P405, DOI 10.1108/IMR-02-2019-0080
Kim EA, 2020, SAF HEALTH WORK-KR, V11, P275, DOI 10.1016/j.shaw.2020.07.006
Kumar, 2021, APPL ARTIFICIAL NEUR, P154
Kuo T., 2016, P 17 AS PAC IND ENG, P7
Liébana-Cabanillas F, 2018, TECHNOL FORECAST SOC, V129, P117, DOI 10.1016/j.techfore.2017.12.015
Marinao-Artigas E, 2020, ELECTRON COMMER R A, V39, DOI 10.1016/j.elerap.2019.100919
Marinkovic V, 2017, ONLINE INFORM REV, V41, P138, DOI 10.1108/OIR-11-2015-0364
Ngubelanga A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13115973
Nilashi M, 2015, J RETAIL CONSUM SERV, V26, P57, DOI 10.1016/j.jretconser.2015.05.002
Oday A, 2021, J HOSP TOUR TECHNOL, V12, P471, DOI 10.1108/JHTT-01-2020-0007
Oh LB, 2012, J OPER MANAG, V30, P368, DOI 10.1016/j.jom.2012.03.001
Ozturk Ozgurol, 2013, Design, User Experience, and Usability. Web, Mobile, and Product Design. Second International Conference, DUXU 2013 Held as Part of HCI International 2013. Proceedings. LNCS 8015, P623, DOI 10.1007/978-3-642-39253-5_69
Pantelimon F. V., 2020, INFORM EC, V24, P27, DOI [https://doi.org/10.24818/issn14531305/24.2.2020.03, DOI 10.24818/ISSN14531305/24.2.2020.03, 10.24818/issn14531305/24.2.2020.03]
Pew Research Center (PRC), MOB FACT SHEET
Pousttchi K, 2015, INT J ELECTRON COMM, V19, P1, DOI 10.1080/10864415.2015.1029351
Rossi, 2017, DEV MARKETING SCI, DOI [10.1007/978-3-319-66023-3_230, DOI 10.1007/978-3-319-66023-3_230]
Salwani Mohamed Intan, 2009, Information Management & Computer Security, V17, P166, DOI 10.1108/09685220910964027
Sardjono W, 2021, J PHYS CONF SER, V1832, DOI 10.1088/1742-6596/1832/1/012024
Saritas O, 2021, SCIENTOMETRICS, V126, P1553, DOI 10.1007/s11192-020-03807-9
Sobihah A.H. M., 2014, INT J BUSINESS MANAG, V9, P56, DOI [10.5539/ijbm.v9n1p56, DOI 10.5539/IJBM.V9N1P56]
Sobihah A. H. M., 2015, MEDITERR J SOC SCI, V6, P61, DOI DOI 10.5901/MJSS.2015.V6N2P61
Statista, US MOB RET COMM REV
Statista, SHAR AD US WHO US IN
Tang AKY, 2019, ELECTRON COMMER R A, V37, DOI 10.1016/j.elerap.2019.100885
Thakur R, 2019, EUR J MARKETING, V53, P1278, DOI 10.1108/EJM-11-2017-0895
United States Census Bureau (USCB), EST ANN US RET TRAD
United States Census Bureau (USCB), EST Q US RET SAL TOT
Wu JH, 2006, ELECTRON COMMER R A, V5, P192, DOI 10.1016/j.elerap.2005.09.004
Wu QQ, 2017, IEEE WIREL COMMUN, V24, P72, DOI 10.1109/MWC.2017.1600343
Wurmser Y., MOBILE SHOPPING GAIN
Xu, 2013, P 34 INT C INF SYST, P2591
Yakubu MN, 2020, EDUC INF TECHNOL, V25, P3515, DOI 10.1007/s10639-020-10110-w
Zarmpou T, 2012, ELECTRON COMMER RES, V12, P225, DOI 10.1007/s10660-012-9092-x
Zhang SQ, 2017, IEEE COMMUN SURV TUT, V19, P33, DOI 10.1109/COMST.2016.2594120
Zhu K, 2005, INFORM SYST RES, V16, P61, DOI 10.1287/isre.1050.0045
NR 66
TC 15
Z9 14
U1 3
U2 60
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD SEP
PY 2021
VL 16
IS 6
BP 2304
EP 2318
DI 10.3390/jtaer16060127
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA UV7DF
UT WOS:000699633400001
OA gold
DA 2024-03-27
ER
PT J
AU Sarkar, JG
Mukherjee, T
Lahiri, I
AF Sarkar, Juin Ghosh
Mukherjee, Tuhin
Lahiri, Isita
TI Deep Learning-Based Classification of Customers Towards Online Purchase
Behaviour: A Recent Empirical Study for Home Appliances
SO INTERNATIONAL JOURNAL OF ONLINE MARKETING
LA English
DT Article
DE Consumer Behaviour; E-Commerce; Home Appliances; Neural Network; Online
Shopping; Technology
AB Online shopping is the new trend and is quickly becoming an integral part of our lifestyle. Due to the internet revolution and massive e-commerce usage by traders, online shopping has seen mammoth growth in recent years. In today's intensely competitive and dynamic environment with technological innovation in every sphere, knowing the consumer mind is the most daunting task for the success of any business. In this backdrop, the researchers have developed a neural network model. They have also made an attempt to classify the customers into two disjoint classes that are interested and uninterested online customers regarding purchase of home appliances through internet in and around Kolkata based on five demographic attributes, namely age, gender, place of residence, occupation, and income. The paper also focuses to optimise the parameters of the proposed neural network and test the efficiency of the constructed model and compare the result by reviewing the existing literatures on the related topic.
C1 [Sarkar, Juin Ghosh] MCKV Inst Engn, Howrah, W Bengal, India.
[Mukherjee, Tuhin] Univ Kalyani, Dept Business Adm, Kalyani, W Bengal, India.
[Lahiri, Isita] Univ Kalyani, Kalyani, W Bengal, India.
C3 Kalyani University; Kalyani University
RP Sarkar, JG (autor correspondiente), MCKV Inst Engn, Howrah, W Bengal, India.
OI Ghosh Sarkar, Juin/0000-0001-6158-0975
CR Awan P D. A. G., 2015, European Journal of Business and Management, V7, P96
Isita L., 2018, INT J BUSINESS MANAG, V7, P1
Iyer R, 2006, J MARKET THEORY PRAC, V14, P57, DOI 10.2753/MTP1069-6679140104
JAMES L, 2003, IEEE T FUZZY SYST, V11, P223, DOI DOI 10.1109/TFUZZ.2003.809900
Kanimozhi M. K, 2016, FACTORS INFLUENCING
Kiplangat R. J., 2015, INT J SCI RES PUBLIC, V5, P1
Kotler P., 2011, FRAMEWORK MARKETING, V5th
Lee GG, 2005, INT J RETAIL DISTRIB, V33, P161, DOI 10.1108/09590550510581485
Lin BS, 1995, J COMPUT INFORM SYST, V36, P15
Manisha, 2016, INT J INNOVATION APP, V14, P218
Misha K., 2013, INT J SOFT COMPUTING, V3, P99
Mo W., 2002, BUILDING ONLINE PURC
Mokhtar NF., 2015, INT J BUSINESS SOCIA, V6, P59
Niharika, 2015, INT J SCI TECHNOLOGY, V4, P15
Nisha G., 2017, INT C REC DEV ENG SC, V3, P56
Nittala R, 2015, INT J ONLINE MARKET, V5, P38, DOI 10.4018/IJOM.2015010103
Punj G, 2012, J BUS RES, V65, P634, DOI 10.1016/j.jbusres.2011.03.003
Saha A., 2015, IOSR J BUSINESS MANA, V2, P74
Sen R. A., 2014, INT J MANAGEMENT COM, V2, P44
Sheth J.N., 1999, CUSTOMER BEHAV CONSU
Thakur P, 2019, INT J ONLINE MARKET, V9, P13, DOI 10.4018/IJOM.2019040102
Vasudeva S, 2019, INT J ONLINE MARKET, V9, P1, DOI 10.4018/IJOM.2019100101
Wu S. I., 2003, MARKETING INTELLIGEN
Zhou, 2004, DECIS SUPPORT SYST, V41, P514
NR 24
TC 3
Z9 3
U1 2
U2 8
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 2156-1753
EI 2156-1745
J9 INT J ONLINE MARKET
JI Int. J. Online Market
PD OCT-DEC
PY 2020
VL 10
IS 4
BP 74
EP 86
DI 10.4018/IJOM.2020100105
PG 13
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA PC1OT
UT WOS:000596779400005
DA 2024-03-27
ER
PT J
AU Payne, EM
Peltier, JW
Barger, VA
AF Payne, Elizabeth Manser
Peltier, James W.
Barger, Victor A.
TI Mobile banking and AI-enabled mobile banking The differential effects of
technological and non-technological factors on digital natives'
perceptions and behavior
SO JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
LA English
DT Article
DE Mobile marketing; Financial services; Services marketing; Human-computer
interaction; Online consumer behavior; Young consumers
ID SELF-SERVICE TECHNOLOGY; CUSTOMER SATISFACTION; EMPIRICAL-EXAMINATION;
CONSUMER ADOPTION; INITIAL TRUST; ONLINE; ACCEPTANCE; INTERNET;
INTENTION; QUALITY
AB Purpose The rapid growth of technology, including artificial intelligence (AI), in the banking industry has played a disrupting role in traditional banking channels. This study aims to investigate factors that influence the attitudes and perceptions of digital natives pertaining to mobile banking and comfort interacting with AI-enabled mobile banking activities.
Design/methodology/approach Data were collected from 218 digital natives. This paper uses multivariate regression and two separate multiple regression analyses to examine the differential effects of technology-based (i.e. attitudes toward AI, relative advantage, perceived trust and security in specific mobile banking activities) and non-technology based (i.e. need for service, quality of service) factors on mobile banking usage and AI-enabled mobile banking services.
Findings This study identifies determining factors for mobile banking and AI-enabled mobile banking services. Results indicate a divide in how digital natives perceive relative advantage between our two dependent variables. Consistent with previous studies, the relative advantage construct has the most impact on mobile banking usage. However, relative advantage was not significant for AI-enabled mobile banking, suggesting an extra layer of complexity that goes beyond convenient fast banking.
Research limitations/implications A limitation of this study is that it does not incorporate age groups outside of digital natives. Further research is needed to test for differential effects between age groups. In addition, the discovery of no significant impact of relative advantage on AI mobile banking warrants more research on the similarities and differences between mobile banking and AI-enabled mobile banking.
Practical implications To better appeal to digital natives, it is suggested that the banking industry emphasize mobile banking's anywhere/anytime access to financial accounts, as this is important to college-age customers who may not live near their local banking institution. Moreover, the paper suggests that improvement to mobile banking features for one-on-one interpersonal contact with bank employees is needed.
Originality/value This study addresses the gap in the understanding of how digital natives perceive mobile banking in comparison to AI-enabled mobile banking services.
C1 [Payne, Elizabeth Manser] Marian Univ, Sch Business, Fond Du Lac, WI USA.
[Payne, Elizabeth Manser; Peltier, James W.; Barger, Victor A.] Univ Wisconsin Whitewater, Coll Business & Econ, Dept Mkt, Whitewater, WI 53190 USA.
C3 University of Wisconsin System; University of Wisconsin Whitewater
RP Peltier, JW (autor correspondiente), Univ Wisconsin Whitewater, Coll Business & Econ, Dept Mkt, Whitewater, WI 53190 USA.
EM peltierj@uww.edu
RI Payne, Elizabeth Manser/AAF-6338-2021; Barger, Victor A/B-2102-2009
OI Manser Payne, Elizabeth/0000-0002-7734-2282
CR Al-Jabri IM, 2012, J ELECTRON COMMER RE, V13, P373
[Anonymous], INT J RES MARKETING, DOI [DOI 10.1016/0167-8116(95)00027-5, DOI 10.1016/0167-8116%2895%2900027-5]
[Anonymous], 2018, STRATEGIC FINANCE
[Anonymous], 2018, Mobile Fact Sheet
Ariff MSM, 2013, PROCD SOC BEHV, V81, P469, DOI 10.1016/j.sbspro.2013.06.462
Babin BJ, 1998, J BUS RES, V42, P271, DOI 10.1016/S0148-2963(97)00124-0
Bhatt A., 2016, Journal of Internet Banking and Commerce, V21, P1
Brown I, 2003, INT J INFORM MANAGE, V23, P381, DOI 10.1016/S0268-4012(03)00065-3
Burleigh TJ, 2013, COMPUT HUM BEHAV, V29, P759, DOI 10.1016/j.chb.2012.11.021
Chen LD, 2002, INFORM MANAGE-AMSTER, V39, P705, DOI 10.1016/S0378-7206(01)00127-6
Choudhury V, 2008, MIS QUART, V32, P179
Choudrie J, 2018, J BUS RES, V88, P449, DOI 10.1016/j.jbusres.2017.11.029
Colgate M, 2001, J CONSUM MARK, V18, P332, DOI 10.1108/07363760110393001
Cruz P, 2010, INT J BANK MARK, V28, P342, DOI 10.1108/02652321011064881
Cummins S, 2014, J RES INTERACT MARK, V8, P169, DOI 10.1108/JRIM-04-2013-0019
Cunningham LF, 2009, J SERV MARK, V23, P11, DOI 10.1108/08876040910933057
Curran J.M., 2003, Journal of Service Research, V5, P209, DOI [10.1177/1094670502238916, DOI 10.1177/1094670502238916]
Dabholkar P.A., 1999, HDB SERVICES MARKETI
Dabholkar PA, 2002, J ACAD MARKET SCI, V30, P184, DOI 10.1177/0092070302303001
Dahl A.J., 2018, J RES INTERACTIVE MA
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Davis FD, 1996, INT J HUM-COMPUT ST, V45, P19, DOI 10.1006/ijhc.1996.0040
Deb M, 2014, MARK INTELL PLAN, V32, P475, DOI 10.1108/MIP-07-2013-0119
Digital Banking Report, 2017, INN RET BANK
Federal Reserve Board, 2016, CONS MOB FIN SERV 20
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
Grabner-Kraeuter S, 2002, J BUS ETHICS, V39, P43, DOI 10.1023/A:1016323815802
Gu ZY, 2017, MIS QUART, V41, P583, DOI 10.25300/MISQ/2017/41.2.11
Guo YS, 2013, J FINANC SERV MARK, V18, P27, DOI 10.1057/fsm.2013.3
Haridasan AC, 2018, J RES INTERACT MARK, V12, P215, DOI 10.1108/JRIM-07-2017-0060
Harrison T, 2017, J FINANC SERV MARK, V22, P139, DOI 10.1057/s41264-017-0038-1
Hausman A., 2014, J RES INTERACTIVE MA, V8
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Iberahim H, 2015, PROC ECON FINANC, V37, P13, DOI 10.1016/S2212-5671(16)30086-7
Illia A., 2015, Journal of Management Information and Decision Sciences, V18, P111
Karthikeyan P., 2016, J INTERNET BANKING C, V21, P1
Kim G, 2009, INFORM SYST J, V19, P283, DOI 10.1111/j.1365-2575.2007.00269.x
Kleijnen M., 2004, J FINANC SERV MARK, V8, P206, DOI [DOI 10.1057/PALGRAVE.FSM.4770120, 10.1057/palgrave.fsm.4770120]
Klein Micheal., 2011, Policy Research Working Paper 5664 - The World Bank, Financial and Private Sector Development
Kolodinsky J.M., 2004, INT J BANK MARK, V22, P238, DOI DOI 10.1108/02652320410542536
Korzeniowski P., 2017, CRM MAGAZINE
Kumar A., 2008, J SERVICE MARKETING, V22, P568, DOI 10.1108/08876040810909695
Langeard E., 1981, Services Marketing: New Insights from Consumers and Managers
Laukkanen T, 2007, BUS PROCESS MANAG J, V13, P788, DOI 10.1108/14637150710834550
Laukkanen T, 2016, J BUS RES, V69, P2432, DOI 10.1016/j.jbusres.2016.01.013
Lee HJ, 2017, J RES INTERACT MARK, V11, P214, DOI 10.1108/JRIM-04-2016-0036
Lee HJ, 2016, COMPUT HUM BEHAV, V60, P322, DOI 10.1016/j.chb.2016.02.051
Lee KC, 2009, INTERACT COMPUT, V21, P385, DOI 10.1016/j.intcom.2009.06.004
Lee MC, 2009, ELECTRON COMMER R A, V8, P130, DOI 10.1016/j.elerap.2008.11.006
Lepkowska-White E., 2015, J MARKETING THEORY P, V12, P36
Lin HF, 2011, INT J INFORM MANAGE, V31, P252, DOI 10.1016/j.ijinfomgt.2010.07.006
Luarn P, 2005, COMPUT HUM BEHAV, V21, P873, DOI 10.1016/j.chb.2004.03.003
Luo X, 2010, DECIS SUPPORT SYST, V49, P222, DOI 10.1016/j.dss.2010.02.008
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
McKnight D. H., 2002, Information Systems Research, V13, P334, DOI 10.1287/isre.13.3.334.81
Meuter ML, 2000, J MARKETING, V64, P50, DOI 10.1509/jmkg.64.3.50.18024
Mistry D., 2018, Chatbots in banking can play the role of 'financial concierge'
Mittal B, 1996, J RETAILING, V72, P95, DOI 10.1016/S0022-4359(96)90007-X
Muggleton S, 2014, AI COMMUN, V27, P3, DOI 10.3233/AIC-130579
NEWELL A, 1958, PSYCHOL REV, V65, P151, DOI 10.1037/h0048495
Ngo VM, 2016, J COMPETITIVENESS, V8, P103, DOI 10.7441/joc.2016.02.08
Nijssen EJ, 2016, J SERV MANAGE, V27, P276, DOI 10.1108/JOSM-08-2015-0233
Oblinger D., 2005, ED NET GENERATION
Orenga-Roglá S, 2016, SPRINGERPLUS, V5, DOI 10.1186/s40064-016-3128-y
PARASURAMAN A, 1988, J RETAILING, V64, P12
Payne EM, 2017, J RES INTERACT MARK, V11, P185, DOI 10.1108/JRIM-08-2016-0091
Prensky M., 2001, HORIZON, V9, P1, DOI [10.1108/10748120110424816, DOI 10.1108/10748120110424816, 10.1108/10748120110424843]
Pueschel J, 2010, INT J BANK MARK, V28, P389, DOI 10.1108/02652321011064908
Reeves M, 2013, J ENTERP COMMUNITIES, V7, P155, DOI 10.1108/17506201311325805
Rogers E., 2005, Diffusion of Innovations, V4th ed., DOI DOI 10.1007/978-3-642-79868-9_2
Rogers E. M., 1995, DIFFUSION INNOVATION
Russell S. J., 1995, ARTIFICIAL INTELLIGE
Rust RT, 2014, MARKET SCI, V33, P206, DOI 10.1287/mksc.2013.0836
Shaikh AA, 2015, TELEMAT INFORM, V32, P129, DOI 10.1016/j.tele.2014.05.003
Shankar A., 2016, Journal of Internet Banking and Commerce, V21, P1
Shareef MA, 2018, J RETAIL CONSUM SERV, V43, P54, DOI 10.1016/j.jretconser.2018.03.003
Shen YC, 2010, BEHAV INFORM TECHNOL, V29, P497, DOI 10.1080/01449290903490658
Shin S., 2014, Journal of Applied Business Research, V30, P1365, DOI DOI 10.19030/JABR.V30I5.8793
Silva B., 2013, INT J MANAGEMENT SCI, V1, P27
Singer D.D., 2012, Journal of Internet Banking and Commerce, V17, P1
Singh S, 2014, ENERGY SUSTAIN DEV, V19, P1, DOI 10.1016/j.esd.2014.01.007
Smith T.J., 2015, The Journal of Business Diversity, V15, P39
So KKF, 2016, J SERV MANAGE, V27, P170, DOI 10.1108/JOSM-05-2015-0176
Statista, 2016, DISTR MOB BANK US US
Sureshchandar G., 2002, J SERV MARK, V16, P363, DOI [10.1108/08876040210433248, DOI 10.1108/08876040210433248]
Tan E, 2016, YOUNG CONSUM, V17, P18, DOI 10.1108/YC-07-2015-00537
TORNATZKY LG, 1982, IEEE T ENG MANAGE, V29, P28, DOI 10.1109/TEM.1982.6447463
Trudeau C., 2017, IUP J BUSINESS STRAT, V14, P20
Turing A.M.I., 1950, MIND, VLIX, P433, DOI [10.1093/mind/LIX.236.433, DOI 10.1093/MIND/LIX.236.433]
Wang SW, 2014, J MARK CHANNELS, V21, P43, DOI 10.1080/1046669X.2013.832466
Yalif G., 2018, How chime bank is using ai to drive growth and open more accounts
Yousafzai SY, 2010, J APPL SOC PSYCHOL, V40, P1172, DOI 10.1111/j.1559-1816.2010.00615.x
Zainuddin N, 2016, J SERV MARK, V30, P586, DOI 10.1108/JSM-02-2016-0075
Zhou T, 2012, INFORM TECHNOL MANAG, V13, P27, DOI 10.1007/s10799-011-0111-8
Zhou T, 2011, INTERNET RES, V21, P527, DOI 10.1108/10662241111176353
NR 95
TC 53
Z9 57
U1 22
U2 167
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-7122
EI 2040-7130
J9 J RES INTERACT MARK
JI J. Res. Interact. Mark.
PY 2018
VL 12
IS 3
BP 328
EP 346
DI 10.1108/JRIM-07-2018-0087
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA GX1SD
UT WOS:000447497600005
DA 2024-03-27
ER
PT J
AU Ruan, YY
Mezei, J
AF Ruan, Yanya
Mezei, Jozsef
TI When do AI chatbots lead to higher customer satisfaction than human
frontline employees in online shopping assistance? Considering product
attribute type
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Customer satisfaction; AI chatbots; Human FLEs; Product attribute type
ID COGNITIVE-AFFECTIVE MODEL; SELF-SERVICE TECHNOLOGY; MODERATING ROLE;
INFORMATION QUALITY; WAITING TIME; CONSUMER SATISFACTION; CONSUMPTION
EMOTIONS; USER SATISFACTION; EXPERIENCE; SMILE
AB The increasing adoption of AI chatbots in online shopping assistance, as a complement or substitute for human frontline employees (HFLEs), leads to the question whether HFLEs perform better than AI service robots and why. From the perspective of product attribute type (experiential/functional) and focusing on customer satisfaction, this study explores how the impact of service agent on customer satisfaction varies along with product attribute type. A scenario-based experiment was designed and completed by 567 participants. Although HFLEs lead to higher customer satisfaction when the product attribute is experiential, AI chatbots perform better than HFLEs when the product attribute is functional. We make use of perceived information quality, perceived waiting time, and positive emotions, three determinants of customer satisfaction, to explain the variation of the role of different service agent types. The findings offer useful implications for companies when selecting service agent types in online shopping assistance.
C1 [Ruan, Yanya] Guangzhou Univ, Higher Educ Mega Ctr, Sch Management, 230 Outer Ring West Rd, Guangzhou 510006, Peoples R China.
[Mezei, Jozsef] Abo Akad Univ, Fac Social Sci Business & Econ, Vanrikinkatu 3 B, FI-20500 Turku, Finland.
C3 Guangzhou University; Abo Akademi University
RP Mezei, J (autor correspondiente), Abo Akad Univ, Fac Social Sci Business & Econ, Vanrikinkatu 3 B, FI-20500 Turku, Finland.
EM ruanyy@gzhu.edu.cn; jmezei@abo.fi
OI Ruan, Yanya/0000-0003-3339-3640
FU Humanities and Social Sciences Research Project of Ministry of Education
in China [18YJCZH144]
FX Acknowledgements This work was supported by the Humanities and Social
Sciences Research Project of Ministry of Education in China (Grant
No.18YJCZH144) .
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Ahearne M, 2007, J ACAD MARKET SCI, V35, P603, DOI 10.1007/s11747-006-0013-1
Anderson KC, 2014, J RETAIL CONSUM SERV, V21, P773, DOI 10.1016/j.jretconser.2014.05.007
Antonides G, 2002, J CONSUM PSYCHOL, V12, P193, DOI 10.1207/S15327663JCP1203_02
Arnold MJ, 2012, J RETAILING, V88, P399, DOI 10.1016/j.jretai.2011.12.004
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Babin BarryJ., 1995, ACAD MARKETING SCI J, V23, P94
Bae S, 2011, ELECTRON MARK, V21, P255, DOI 10.1007/s12525-011-0072-0
Bagozzi RP, 1999, J ACAD MARKET SCI, V27, P184, DOI 10.1177/0092070399272005
Bagozzi RP, 1988, Journal of the Academy of Marketing Science, V16, P74, DOI DOI 10.1007/BF02723327
Baker J., 1996, J ACAD MARKET SCI, V24, P338, DOI 10.1177/0092070396244005
Baltas G, 2017, J CONSUM BEHAV, V16, pE1, DOI 10.1002/cb.1649
Barbopoulos I, 2016, J CONSUM MARK, V33, P75, DOI 10.1108/JCM-08-2014-1091
Barney C, 2020, J BUS RES, V108, P401, DOI 10.1016/j.jbusres.2019.11.061
Bigne J.E., 2008, J SERV MARK, V22, P303, DOI [10.1108/08876040810881704, DOI 10.1108/08876040810881704]
Bock DE, 2020, J SERV MARK, V34, P317, DOI 10.1108/JSM-01-2019-0047
Bolin JH, 2014, J EDUC MEAS, V51, P335, DOI 10.1111/jedm.12050
Boukis A, 2020, TOURISM MANAGE, V77, DOI 10.1016/j.tourman.2019.103997
Brakus JJ, 2014, J BUS RES, V67, P2291, DOI 10.1016/j.jbusres.2014.06.017
Brakus JJ, 2009, J MARKETING, V73, P52, DOI 10.1509/jmkg.73.3.52
Branco-Illodo I, 2020, EUR J MARKETING, V54, P857, DOI 10.1108/EJM-05-2018-0309
Chang EC, 2014, J BUS RES, V67, P1368, DOI 10.1016/j.jbusres.2013.08.016
Chattaraman V, 2010, J CONSUM BEHAV, V9, P18, DOI 10.1002/cb.290
Chatterjee S, 2021, J BUS RES, V131, P815, DOI 10.1016/j.jbusres.2020.10.043
Cheng FF, 2020, COMPUT HUM BEHAV, V107, DOI 10.1016/j.chb.2018.10.018
Chi T, 2018, J INTERNET COMMER, V17, P189, DOI 10.1080/15332861.2018.1451970
Choi S, 2019, INT J HOSP MANAG, V82, P32, DOI 10.1016/j.ijhm.2019.03.026
Christ-Brendemühl S, 2020, J BUS RES, V117, P378, DOI 10.1016/j.jbusres.2020.06.021
CLORE GL, 1987, J PERS SOC PSYCHOL, V53, P751, DOI 10.1037/0022-3514.53.4.751
Coursaris CK, 2016, INFORM MANAGE-AMSTER, V53, P252, DOI 10.1016/j.im.2015.10.003
Couwenberg LE, 2017, INT J RES MARK, V34, P355, DOI 10.1016/j.ijresmar.2016.10.005
Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
Das G, 2019, J BUS RES, V96, P147, DOI 10.1016/j.jbusres.2018.11.007
Das G, 2017, J RETAIL CONSUM SERV, V38, P177, DOI 10.1016/j.jretconser.2017.06.008
Deloitte, REP GLOB DEV
Demoulin N, 2019, J BUS RES, V104, P295, DOI 10.1016/j.jbusres.2019.07.004
Djelassi S, 2018, DECIS SUPPORT SYST, V111, P38, DOI 10.1016/j.dss.2018.04.004
Eren BA, 2021, INT J BANK MARK, V39, P294, DOI 10.1108/IJBM-02-2020-0056
Ertekin N, 2020, PROD OPER MANAG, V29, P1232, DOI 10.1111/poms.13077
Estes Z, 2018, J BUS RES, V85, P396, DOI 10.1016/j.jbusres.2017.10.017
Farrell A.M., 2001, J MARKET MANAG, V17, P577, DOI DOI 10.1362/026725701323366944
Fergurson JR, 2021, J CONSUM MARK, V38, P652, DOI 10.1108/JCM-09-2020-4126
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Geebren A, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106584
Ghasemaghaei M, 2015, INFORM MANAGE-AMSTER, V52, P965, DOI 10.1016/j.im.2015.07.001
Giroux M, 2022, J BUS ETHICS, V178, P1027, DOI 10.1007/s10551-022-05056-7
Guido G, 2007, INT J MARKET RES, V49, P365, DOI 10.1177/147078530704900307
Gunarathne P, 2018, MIS QUART, V42, P489, DOI 10.25300/MISQ/2018/14290
Hämmerli A, 2012, INT J COSMETIC SCI, V34, P161, DOI 10.1111/j.1468-2494.2011.00696.x
Hatfield C., 1994, STUDIES EMOTION SOCI
Henkel AP, 2020, J SERV MANAGE, V31, P247, DOI 10.1108/JOSM-05-2019-0160
Hochstein B, 2021, J RETAILING, V97, P336, DOI 10.1016/j.jretai.2020.10.003
Hochstein B, 2019, J ACAD MARKET SCI, V47, P118, DOI 10.1007/s11747-018-0609-2
Holmqvist J, 2019, J RETAILING, V95, P115, DOI 10.1016/j.jretai.2019.03.004
Hong SJ, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101917
Hsieh SH, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102482
Hughes DE, 2019, J RETAILING, V95, P130, DOI 10.1016/j.jretai.2019.03.003
Hunneman A, 2017, J BUS RES, V78, P133, DOI 10.1016/j.jbusres.2017.05.012
Kalra A, 2021, J SERV RES-US, V24, P269, DOI 10.1177/1094670520958070
Kastenholz E, 2018, J TRAVEL TOUR MARK, V35, P189, DOI 10.1080/10548408.2017.1350617
Keiningham T, 2017, J SERV MARK, V31, P148, DOI 10.1108/JSM-09-2016-0337
Keller P.A., 1994, J CONSUM PSYCHOL, V3, P29
Kim M, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102562
Kong DM, 2020, J CONSUM BEHAV, V19, P80, DOI 10.1002/cb.1796
Krampf R., 2003, MARK MANAG, V13, P32
Ladhari R, 2017, J RETAIL CONSUM SERV, V34, P10, DOI 10.1016/j.jretconser.2016.09.005
Le D, 2020, INT J HOSP MANAG, V88, DOI 10.1016/j.ijhm.2020.102542
Lechner AT, 2019, J BUS RES, V94, P195, DOI 10.1016/j.jbusres.2017.06.009
Lee D, 2021, MANAGE SCI, V67, P524, DOI 10.1287/mnsc.2019.3546
Lee EJ, 2016, COMPUT HUM BEHAV, V64, P152, DOI 10.1016/j.chb.2016.06.017
Lee S, 2011, J BUS RES, V64, P1195, DOI 10.1016/j.jbusres.2011.06.022
Lee Y, 2012, MIS QUART, V36, P365
Lee YW, 2002, INFORM MANAGE-AMSTER, V40, P133, DOI 10.1016/S0378-7206(02)00043-5
Lehdonvirta V, 2009, ELECTRON COMMER RES, V9, P97, DOI 10.1007/s10660-009-9028-2
LEHMANN DR, 1974, J MARKETING, V38, P36, DOI 10.2307/1250195
Li CZE, 2020, J PHYS CONF SER, V1575, DOI 10.1088/1742-6596/1575/1/012192
Li JJ, 2020, J MARKETING, V84, P127, DOI 10.1177/0022242920911628
Li ML, 2021, INT J HOSP MANAG, V95, DOI 10.1016/j.ijhm.2021.102930
Li XS, 2019, J CONSUM RES, V45, P973, DOI 10.1093/jcr/ucy016
Liljander V, 1997, INT J SERV IND MANAG, V8, P148, DOI 10.1108/09564239710166272
Lin CY, 2017, J SERV MANAGE, V28, P107, DOI 10.1108/JOSM-08-2015-0251
Lin IY, 2016, J SERV MARK, V30, P692, DOI 10.1108/JSM-08-2015-0258
Lin JSC, 2011, J SERV MANAGE, V22, P183, DOI 10.1108/09564231111124217
Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Ma RJ, 2021, J BUS RES, V134, P443, DOI 10.1016/j.jbusres.2021.05.057
Maguire L, 2015, J SERV MARK, V29, P211, DOI 10.1108/JSM-02-2014-0047
Malhotra N, 2013, J BUS RES, V66, P1338, DOI 10.1016/j.jbusres.2012.02.034
Marques C, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104293
Caro LM, 2007, J BUS RES, V60, P108, DOI 10.1016/j.jbusres.2006.10.008
Matthews L, 2020, J BUS RES, V114, P111, DOI 10.1016/j.jbusres.2020.04.002
McLean G, 2017, COMPUT HUM BEHAV, V76, P494, DOI 10.1016/j.chb.2017.08.005
McLeay F, 2021, J SERV RES-US, V24, P104, DOI 10.1177/1094670520933354
Moliner-Velázquez B, 2019, SERV BUS, V13, P199, DOI 10.1007/s11628-018-0378-5
Nunally J.C., 1978, Psychometric theory
Nunkoo R, 2020, INT J HOSP MANAG, V91, DOI 10.1016/j.ijhm.2019.102414
OLIVER RL, 1993, J CONSUM RES, V20, P418, DOI 10.1086/209358
Otterbring T, 2017, J SERV MANAGE, V28, P284, DOI 10.1108/JOSM-11-2015-0372
Palcu J, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00881
Pantano E, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102096
Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
Peng L, 2019, J ACAD MARKET SCI, V47, P960, DOI 10.1007/s11747-019-00662-w
Ponte EB, 2015, TOURISM MANAGE, V47, P286, DOI 10.1016/j.tourman.2014.10.009
Prentic C, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102186
Prentice C, 2020, INT J HOSP MANAG, V90, DOI 10.1016/j.ijhm.2020.102629
Pruyn A, 1998, INT J RES MARK, V15, P321, DOI 10.1016/S0167-8116(98)00008-1
Puzakova M, 2013, J MARKETING, V77, P81, DOI 10.1509/jm.11.0510
Rhim J, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.107034
Richins ML, 1997, J CONSUM RES, V24, P127, DOI 10.1086/209499
Robinson S, 2020, J BUS RES, V116, P366, DOI 10.1016/j.jbusres.2019.08.038
Rod M, 2016, J RETAIL CONSUM SERV, V30, P212, DOI 10.1016/j.jretconser.2016.02.005
Román S, 2010, J BUS ETHICS, V95, P373, DOI 10.1007/s10551-010-0365-9
Ryu K, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13179549
Salem MZ, 2018, MANAGE DECIS, V56, P1748, DOI 10.1108/MD-04-2017-0363
Sang Yong Kim, 2001, Electronic Markets, V11, P148, DOI 10.1080/101967801681007988
Schill M, 2022, J SERV MARK, V36, P769, DOI 10.1108/JSM-11-2020-0472
Sela A, 2012, J MARKETING RES, V49, P942, DOI 10.1509/jmr.11.0142
Setia P, 2013, MIS QUART, V37, P565, DOI 10.25300/MISQ/2013/37.2.11
Slevitch L, 2010, INT J HOSP MANAG, V29, P559, DOI 10.1016/j.ijhm.2009.09.004
Song J, 2017, INT J HOSP MANAG, V66, P66, DOI 10.1016/j.ijhm.2017.06.015
SPIELMAN LA, 1988, AM BEHAV SCI, V31, P296, DOI 10.1177/000276488031003003
Stein A, 2020, J SERV MANAGE, V31, P51, DOI 10.1108/JOSM-04-2019-0113
Steinhoff L, 2021, J BUS RES, V129, P70, DOI 10.1016/j.jbusres.2021.02.016
Sun XY, 2019, DECIS SUPPORT SYST, V124, DOI 10.1016/j.dss.2019.113099
Tueanrat Y, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102550
Turel O, 2013, INFORM MANAGE-AMSTER, V50, P98, DOI 10.1016/j.im.2013.02.004
Vigolo V, 2020, PSYCHOL MARKET, V37, P408, DOI 10.1002/mar.21307
Wang CY, 2015, INT J HOSP MANAG, V51, P96, DOI 10.1016/j.ijhm.2015.09.001
Wang QZ, 2017, ELECTRON COMMER RES, V17, P609, DOI 10.1007/s10660-016-9237-4
Wang R. Y., 1996, Journal of Management Information Systems, V12, P5
Wang SS, 2018, J MARKETING RES, V55, P524, DOI 10.1509/jmr.16.0453
Wang YR, 2018, ELECTRON COMMER R A, V29, P1, DOI 10.1016/j.elerap.2018.03.003
Weijters B, 2007, J SERV RES-US, V10, P3, DOI 10.1177/1094670507302990
Wien AH, 2021, J BUS RES, V137, P13, DOI 10.1016/j.jbusres.2021.08.016
Wilcox K, 2011, J CONSUM RES, V38, P763, DOI 10.1086/660702
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Wixom BH, 2005, INFORM SYST RES, V16, P85, DOI 10.1287/isre.1050.0042
Wong IA, 2013, J TRAVEL RES, V52, P29, DOI 10.1177/0047287512457263
Xiao L, 2021, J SERV RES-US, V24, P9, DOI 10.1177/1094670519878881
Xu XY, 2020, J ELECTRON COMMER RE, V21, P144
Yan RN, 2011, INT J RETAIL DISTRIB, V39, P346, DOI 10.1108/09590551111130775
Ying J., 2006, Journal of Services Marketing, V20 Iss, P211, DOI DOI 10.1108/08876040610674562
Zablah AR, 2017, J SERV RES-US, V20, P76, DOI 10.1177/1094670516675405
Zhang M, 2021, J SERV MARK, V35, P312, DOI 10.1108/JSM-01-2020-0009
Zhou RG, 2019, ELECTRON COMMER RES, V19, P477, DOI 10.1007/s10660-018-9301-3
NR 145
TC 31
Z9 31
U1 66
U2 225
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD SEP
PY 2022
VL 68
AR 103059
DI 10.1016/j.jretconser.2022.103059
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 2V1DD
UT WOS:000823591800007
OA Green Published
DA 2024-03-27
ER
PT J
AU Chaturvedi, AR
Choubey, AK
Roan, J
AF Chaturvedi, AR
Choubey, AK
Roan, J
TI Active replication and update of content in electronic commerce
SO INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
LA English
DT Article
DE client-server computing; distributed systems; electronic commerce;
intelligent agents
ID DISTRIBUTED DATABASE-SYSTEMS; FILE ALLOCATION
AB This paper describes a strategy for actively replicating and updating content for electronic commerce. Active replication and updating of content is achieved by intelligent agents (IA) using a time-invariant fragmentation approach to partitioning and replicating data in a distributed computing environment. Taking into account the time-sensitivity property of data, IAs derive time-invariant fragments for their respective nodes from the query history. A time-invariant fragment (TIF) is that portion of the database whose values do not change during a specified time interval. The algorithm that IAs use in creating TIFs for each node, for a given time interval, is presented. The active replication approach is compared with three other approaches, Full-replication, nonreplication, and materialized view, in terms of data transmission costs. Results indicate that the active approach can be most effective for electronic commerce because of the high percentage of modification queries, the large sire of the network, and the greet number of transactions.
C1 Purdue Univ, Krannert Grad Sch Management, SEAS Lab, W Lafayette, IN 47907 USA.
C3 Purdue University System; Purdue University
RP Purdue Univ, Krannert Grad Sch Management, SEAS Lab, W Lafayette, IN 47907 USA.
EM alok@mgmt.purdue.edu; achoubey@lucent.com; jsroan@mail.nsysu.edu.tw
OI Chaturvedi, Alok/0000-0003-4666-0197
CR ADIBA ME, 1980, P 6 INT C VER LARG D, P86
ALONSO R, 1990, ACM T DATABASE SYST, V15, P359, DOI 10.1145/88636.87848
ALONSO R, 1988, LECT NOTES COMPUT SC, V303, P443
APERS PMG, 1988, ACM T DATABASE SYST, V13, P263, DOI 10.1145/44498.45063
Blakeley J.A., 1986, Proceedings of Special Interest Group on Management of Data (SIGMOD'86), Washington, P61
BONABEAU FH, INTELLIGENT AGENTS T
CHATURVEDI AR, 1993, EXPERT SYST APPL, V6, P23, DOI 10.1016/0957-4174(93)90016-Y
CHATURVEDI AR, 1994, IEEE T ENG MANAGE, V41, P194, DOI 10.1109/17.293386
CHESS CH, 1997, LECT NOTES COMPUTER, V1222
CHU WW, 1969, IEEE T COMPUT, VC 18, P885, DOI 10.1109/T-C.1969.222542
Di Caro G, 1998, P ANN HICSS, P74, DOI 10.1109/HICSS.1998.649179
DOWDY LW, 1982, COMPUT SURV, V14, P287, DOI 10.1145/356876.356883
DUTTA A, 1985, INT J COMPUT INF SCI, V14, P29, DOI 10.1007/BF00990899
HANSON EN, 1987, P SIGMOD C SAN FRANC, P440
HSIAO HI, 1993, DISTRIB PARALLEL DAT, V1, P53, DOI 10.1007/BF01277520
Kahler B., 1987, Proceedings of the Thirteenth International Conference on Very Large Data Bases: 1987 13th VLDB, P389
Lange DB, 1999, COMMUN ACM, V42, P88, DOI 10.1145/295685.298136
MINAR N, 1999, SOFTWARE AGENTS FUTU, pCH12
RAKES TR, 1984, COMPUT OPER RES, V11, P387, DOI 10.1016/0305-0548(84)90039-X
RAM S, 1989, J SYST SOFTWARE, V10, P77, DOI 10.1016/0164-1212(89)90021-6
RAM S, 1989, INFORM MANAGE, V17, P169, DOI 10.1016/0378-7206(89)90018-9
ROUSSOPOULOS N, 1986, COMPUTER, V19, P19, DOI 10.1109/MC.1986.1663124
SACCA D, 1983, P INT C VLDB, P242
Saharia AN, 1990, INFORM SYST RES, V1, P277, DOI 10.1287/isre.1.3.277
Samaras G, 1996, INT CON DISTR COMP S, P683, DOI 10.1109/ICDCS.1996.508020
Segev A., 1989, IEEE Transactions on Knowledge and Data Engineering, V1, P173, DOI 10.1109/69.87958
YU PS, 1994, P IEEE, V82, P140, DOI 10.1109/5.259432
NR 27
TC 0
Z9 0
U1 0
U2 2
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1086-4415
EI 1557-9301
J9 INT J ELECTRON COMM
JI Int. J. Electron. Commer.
PD SPR
PY 2000
VL 4
IS 3
BP 45
EP 67
DI 10.1080/10864415.2000.11518371
PG 23
WC Business; Computer Science, Software Engineering
WE Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 323GJ
UT WOS:000087557500004
DA 2024-03-27
ER
PT J
AU Kim, D
Benbasat, I
AF Kim, Dongmin
Benbasat, Izak
TI The effects of trust-assuring arguments on consumer trust in Internet
stores: Application of Toulmin's model of argumentation
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE trust-assuring arguments; trust; electronic commerce; Toulmin; model of
argumentation; claim; data; backing; human-computer interaction
ID E-COMMERCE; IMPACT; INVOLVEMENT; ACCEPTANCE; SYSTEMS; SCALE
AB A trust-assuring argument refers to "a claim and its supporting statements used in an Internet store to address trust-related issues." Although trust-assuring arguments often appear in Internet stores, little research has been conducted to understand their effects on consumer trust in an Internet store. The goals of this study are (1) to investigate whether or not the provision of trust-assuring arguments on the website of an Internet store increase consumer trust in that Internet store and (2) to identify the most effective form of trust-assuring arguments to provide guidelines for their implementation.
Toulmin's (1958) model of argumentation is proposed as a basis to identify the elements of an argument and to strengthen the effects of trust-assuring arguments on consumer trust in an Internet store. Based on Toulmin's (1958) model of argumentation, three elements of arguments that commonly appear in daily communication; namely, claim, data, and backing, are identified. Data refers to the grounds for a claim, while backing is used for providing reasons for why the data should be accepted. By combining these three elements, three forms of trust-assuring arguments (claim only, claim plus data, and claim plus data and backing) are developed. The effects of these three forms of trust-assuring arguments on consumer trust in an Internet store are tested by comparing them to a no trust-assuring argument condition in a laboratory experiment with 112 participants.
The results indicate (1) providing trust-assuring arguments that consist of claim plus data or claim plus data and backing increases consumers' trusting belief but displaying arguments that contain claim only does not and (2) trust-assuring arguments that include claim plus data and backing lead to the highest level of trusting belief among the three forms of arguments examined in this study. Based on the results, we argue that Toulmin's (1958) model of argumentation is an effective basis for website designers to develop convincing trust-assuring arguments and to improve existing trust-assuring arguments in Internet stores.
C1 Univ New Brunswick, Fac Business, St John, NB E2L 4L5, Canada.
Univ British Columbia, Sauder Sch Business, Vancouver, BC V6T 1Z2, Canada.
C3 University of New Brunswick; University of British Columbia
RP Kim, D (autor correspondiente), Univ New Brunswick, Fac Business, POB 5050, St John, NB E2L 4L5, Canada.
EM dongmin@unbsj.ca; izak.benbasat@ubc.ca
CR [Anonymous], 1958, THE USE OF ARGUMENT
[Anonymous], J GLOBAL INFORM MANA, DOI DOI 10.4018/JGIM.2004010102
[Anonymous], 2002, US TODAY
[Anonymous], 1964, ADAPTION LEVEL THEOR
[Anonymous], 1999, eCommerce trust study
[Anonymous], 1999, P INT C INF SYST
[Anonymous], 2000, P 21 INT C INF SYST
Ba SL, 2002, MIS QUART, V26, P243, DOI 10.2307/4132332
Bakker AB, 2006, J SOC PSYCHOL, V146, P31, DOI 10.3200/SOCP.146.1.31-50
Bhattacherjee A, 2002, J MANAGE INFORM SYST, V19, P211
Cook D.P., 2003, Eservice Journal, V2, P71
Cooper D., 1995, BUSINESS RES METHODS, V5th
DEIGHTON J, 1985, ADV CONSUM RES, V12, P432
Doney PM, 1997, J MARKETING, V61, P35, DOI 10.2307/1251829
Fogg B. J., 1999, C HUM FACT COMP SYST, P15
FOGG BJ, 2001, CHI, V3, P61
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Gefen D., 1997, Building users' trust in freeware providers and the effects of this trust on users' perceptions of usefulness, ease of use and intended use
Gefen D., 2000, COMMUN ASSOC INF SYS, V4, P407, DOI DOI 10.17705/1CAIS.00407
Gefen D., 2003, e-Service Journal, V2, P7, DOI DOI 10.2979/ESJ.2003.2.2.7
Gregor S, 1999, MIS QUART, V23, P497, DOI 10.2307/249487
HEAD M, 2002, Q J ELECT COMMERCE, V2, P307
Hoffman DL, 1999, COMMUN ACM, V42, P80, DOI 10.1145/299157.299175
Jarvenpaa S. L., 2000, Information Technology & Management, V1, P45, DOI 10.1023/A:1019104520776
Jiang ZH, 2004, J MANAGE INFORM SYST, V21, P111, DOI 10.1080/07421222.2004.11045817
Kaplan S. E., 2003, International Journal of Accounting Information Systems, V4, P95, DOI 10.1016/S1467-0895(03)00005-8
Kim D., 2003, J ELECTRON COMMER RE, V4, P49
Kim H., 2004, J ASSOC INF SYST, V5, P392, DOI [10.17705/1jais.00056, DOI 10.17705/1JAIS.00056]
Kimery K., 2002, J INFORM TECHNOLOGY, V4, P63
Kirk R.E., 1995, EXPT DESIGN, V3rd
Kovar S.E., 2000, Review of Accounting Information Systems, V4, P39
Lee MKO, 2001, INT J ELECTRON COMM, V6, P75, DOI 10.1080/10864415.2001.11044227
Lim KH, 2000, MIS QUART, V24, P449, DOI 10.2307/3250969
LIM KH, 2006, IN PRESS J MANAGEMEN
MACKENZIE SB, 1992, J CONSUM RES, V18, P519, DOI 10.1086/209278
MAHESWARAN D, 1990, J MARKETING RES, V27, P361, DOI 10.2307/3172593
Malhotra NK, 2004, INFORM SYST RES, V15, P336, DOI 10.1287/isre.1040.0032
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
McKinney V, 2002, INFORM SYST RES, V13, P296, DOI 10.1287/isre.13.3.296.76
Mcknight DH, 1998, ACAD MANAGE REV, V23, P473, DOI 10.5465/AMR.1998.926622
McKnight DH, 2001, INT J ELECTRON COMM, V6, P35, DOI 10.1080/10864415.2001.11044235
McKnight DH, 2002, INFORM SYST RES, V13, P334, DOI 10.1287/isre.13.3.334.81
MUNCH JM, 1993, J CONSUM RES, V20, P294, DOI 10.1086/209350
NOTEBERG A, 1999, P 20 INT C INF SYST, P472
O'Keefe D.J., 2002, Persuasion: Theory and research, V2
Pavlou PA, 2004, INFORM SYST RES, V15, P37, DOI 10.1287/isre.1040.0015
Pavlou PA, 2003, INT J ELECTRON COMM, V7, P101, DOI 10.1080/10864415.2003.11044275
Pennington R, 2003, J MANAGE INFORM SYST, V20, P197, DOI 10.1080/07421222.2003.11045777
Petty R.E., 1981, ADV CONSUM RES, V8, P20
Petty R.E., 1986, The Elaboration Likelihood Model of Persuasion, DOI DOI 10.1007/978-1-4612-4964-1_1
PETTY RE, 1984, J PERS SOC PSYCHOL, V46, P69, DOI 10.1037/0022-3514.46.1.69
Rousseau DM, 1998, ACAD MANAGE REV, V23, P393, DOI 10.5465/AMR.1998.926617
SMITH GF, 1991, ORGAN BEHAV HUM DEC, V48, P291, DOI 10.1016/0749-5978(91)90016-M
Stewart KJ, 2003, ORGAN SCI, V14, P5, DOI 10.1287/orsc.14.1.5.12810
Suh B, 2003, INT J ELECTRON COMM, V7, P135
Verlinden J.G., 1998, Verifiers in everyday argument: An expansion of the Toulmin model
Wang SJ, 2004, J INTERACT MARK, V18, P53, DOI 10.1002/dir.10071
WANG W, 2004, IMPACT EXPLANATIONS
WETSCH L, 2000, P MARK PUBL POL C AM
YE LR, 1995, MIS QUART, V19, P157, DOI 10.2307/249686
ZAICHKOWSKY JL, 1994, J ADVERTISING, V23, P59, DOI 10.1080/00913367.1943.10673459
NR 61
TC 139
Z9 155
U1 4
U2 75
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD SEP
PY 2006
VL 17
IS 3
BP 286
EP 300
DI 10.1287/isre.1060.0093
PG 15
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA 085LY
UT WOS:000240606800006
DA 2024-03-27
ER
PT J
AU Liang, TP
Doong, HS
AF Liang, TP
Doong, HS
TI Effect of bargaining in electronic commerce
SO INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
LA English
DT Article
DE electronic commerce; intelligent agents; price bargaining
ID COMPUTER SELF-EFFICACY; COGNITIVE-STYLE; INDIVIDUAL-DIFFERENCES;
DECISION-MAKING; INFORMATION; BEHAVIOR; NEGOTIATION; PERSUASION;
PERCENTAGE; JUDGMENT
AB Internet business has grown at an unprecedented rate in the post several years. Recent research has found that the functions provided by a store have a significant impact on customer purchase decisions. Price bargaining is a common practice in traditional businesses, and this study investigates its effect in electronic commerce, Focusing on three different bargaining strategies. An intelligent agent that allows customers to bargain for a better price was implemented and integrated into experimental stores. The results show that consumers prefer shopping at bargaining stores even when there is no financial gain. Different bargaining strategies and customer personalities may also affect the outcome and customer satisfaction.
RI liang, ting/JFB-4960-2023
CR [Anonymous], STRUCTURE EQUATIONS
[Anonymous], J BUSINESS, DOI DOI 10.1086/296365
[Anonymous], 1982, MODELS BOUNDED RATIO
ARNDT J, 1979, J MARKETING, V43, P69, DOI 10.2307/1250272
Barry B, 1996, ORGAN BEHAV HUM DEC, V67, P127, DOI 10.1006/obhd.1996.0069
BARTOS OJ, 1967, J CONFLICT RESOLUT, V11, P481, DOI 10.1177/002200276701100406
BENBASAT I, 1985, MANAGE SCI, V31, P1348, DOI 10.1287/mnsc.31.11.1348
BRUGGEN GH, 1998, MANAGE SCI, V44, P545
CACIOPPO JT, 1982, J PERS SOC PSYCHOL, V42, P116, DOI 10.1037/0022-3514.42.1.116
CHAIKEN S, 1989, P212
CHAIKEN S, 1980, J PERS SOC PSYCHOL, V39, P752, DOI 10.1037/0022-3514.39.5.752
Chaiken S., 1987, Social influence: The Ontario symposium, V5
COLE CA, 1990, J MARKETING RES, V27, P175, DOI 10.2307/3172844
COMPEAU DR, 1995, MIS QUART, V19, P189, DOI 10.2307/249688
DARKE PR, 1993, J APPL PSYCHOL, V78, P960, DOI 10.1037/0021-9010.78.6.960
DARKE PR, 1995, J APPL SOC PSYCHOL, V25, P1597, DOI 10.1111/j.1559-1816.1995.tb02635.x
DARKE PR, 1995, J APPL PSYCHOL, V80, P580
DOLL WJ, 1988, MIS QUART, V12, P259, DOI 10.2307/248851
DORRIS JW, 1972, J PERS SOC PSYCHOL, V22, P387, DOI 10.1037/h0032871
Epstein S, 1996, J PERS SOC PSYCHOL, V71, P390, DOI 10.1037/0022-3514.71.2.390
Fouraker L., 1963, BARGAINING BEHAV
GALBRAITH S, 1993, J BUS ETHICS, V8, P234
HAMMER WC, 1969, EC BARGAINING
HAMMER WC, 1974, J PERS SOC PSYCHOL, V30, P458
Harnett D.L., 1980, Bargaining behavior: An international study
HENDERSON JC, 1980, MANAGE SCI, V26, P371, DOI 10.1287/mnsc.26.4.371
HUBER GP, 1983, MANAGE SCI, V29, P567, DOI 10.1287/mnsc.29.5.567
HUYSMANS JHBM, 1970, MANAGE SCI, V17, P92, DOI 10.1287/mnsc.17.1.92
IVES B, 1983, COMMUN ACM, V26, P785, DOI 10.1145/358413.358430
KAHNEMAN D, 1984, AM PSYCHOL, V39, P341, DOI 10.1037/0003-066X.39.4.341
KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185
Kalakota R., 1996, FRONTIERS ELECT COMM
Kerlinger F.N., 1978, Foundations of Behavioral Research
KOMORITA SS, 1968, J PERS SOC PSYCHOL, V9, P15, DOI 10.1037/h0025700
LUSK EJ, 1979, MANAGE SCI, V25, P787, DOI 10.1287/mnsc.25.8.787
Maes P, 1999, COMMUN ACM, V42, P81, DOI 10.1145/295685.295716
MAHESWARAN D, 1991, J PERS SOC PSYCHOL, V61, P13, DOI 10.1037/0022-3514.61.1.13
Marakas GM, 1998, INFORM SYST RES, V9, P126, DOI 10.1287/isre.9.2.126
Oliver J. R., 1996, Journal of Management Information Systems, V13, P83
Osgood CharlesE., 1962, ALTERNATIVE WAR SURR
Powell M, 1997, J ECON PSYCHOL, V18, P605, DOI 10.1016/S0167-4870(97)00026-3
QUALLS WJ, 1987, J CONSUM RES, V14, P264, DOI 10.1086/209111
Raiffa Howard., 1982, ART SCI NEGOTIATION
Rangaswamy A, 1997, MANAGE SCI, V43, P1147, DOI 10.1287/mnsc.43.8.1147
RATNESHWAR S, 1991, J CONSUM RES, V18, P52, DOI 10.1086/209240
ROTH AE, 1988, AM ECON REV, V78, P806
Rubin Jeffrey, 1975, The Social Psychology of Bargaining and Negotiation
RUSSELL T, 1985, AM ECON REV, V75, P1071
Sandholm T, 1999, COMMUN ACM, V42, P84, DOI 10.1145/295685.295866
Schelling Thomas C., 1960, The Strategy of Conflict
Siegel S., 1960, BARGAINING GROUP DEC
SIMON HA, 1986, J BUS, V59, P209
Simon HA, 1955, Q J ECON, V69, P99, DOI 10.2307/1884852
STIGLER GJ, 1961, J POLIT ECON, V69, P213, DOI 10.1086/258464
THALER R, 1980, J ECON BEHAV ORGAN, V1, P39, DOI 10.1016/0167-2681(80)90051-7
Thaler RH, 2008, MARKET SCI, V27, P15, DOI 10.1287/mksc.1070.0330
TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
TVERSKY A, 1981, SCIENCE, V211, P251
VISWANATHAN M, 1993, J APPL PSYCHOL, V78, P741, DOI 10.1037/0021-9010.78.5.741
WEBSTER J, 1992, MIS QUART, V16, P201, DOI 10.2307/249576
Winer R.S., 1988, Issues in Pricing. Theory and Research
Witkin H.A., 1971, A manual for the embedded figures tests
NR 62
TC 25
Z9 27
U1 2
U2 17
PU M E SHARPE INC
PI ARMONK
PA 80 BUSINESS PARK DR, ARMONK, NY 10504 USA
SN 1086-4415
J9 INT J ELECTRON COMM
JI Int. J. Electron. Commer.
PD SPR
PY 2000
VL 4
IS 3
BP 23
EP 43
DI 10.1080/10864415.2000.11518370
PG 21
WC Business; Computer Science, Software Engineering
WE Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 323GJ
UT WOS:000087557500003
DA 2024-03-27
ER
PT J
AU McLean, G
Osei-Frimpong, K
AF McLean, Graeme
Osei-Frimpong, Kofi
TI Chat now... Examining the variables influencing the use of online live
chat
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Live chat; Human computer interaction; Online customer support; Online
service encounter
ID WEB SITE DESIGN; CUSTOMER EXPERIENCE; INFORMATION-TECHNOLOGY;
MEASUREMENT INVARIANCE; SOCIAL SUPPORT; E-COMMERCE; SERVICE;
SATISFACTION; ACCEPTANCE; CREDIBILITY
AB This paper advances our theoretical understanding of online service delivery with regard to live chat technology. Online customer support via live chat offers customers a way in which they can interact with service personnel in the online environment. Through the use of an online questionnaire and conducing structural equation modelling, the aim of this research is to understand the variables directly influencing the use of a live chat function with a customer service representative during use of a website. The findings outline eight variables that motivate the use of live chat, accounting for 71% explained variance. The research illustrates the variables influencing such use is dependent on the context for initiating the chat discussion, namely for search/navigation support or decision support. This paper offers key managerial implications, highlighting the importance of offering customers a live chat function and why website users are motivated to use live chat. The paper illustrates the role of online live chat as a service recovery tool and a service feedback tool.
C1 [McLean, Graeme] Univ Strathclyde, Business Sch, Stenhouse Wing, Glasgow G4 0QU, Lanark, Scotland.
[Osei-Frimpong, Kofi] GIMPA Business Sch, Room 1,D Block, Achimota, Accra, Ghana.
C3 University of Strathclyde
RP McLean, G (autor correspondiente), Univ Strathclyde, Business Sch, Stenhouse Wing, Glasgow G4 0QU, Lanark, Scotland.
EM graeme.mclean@strath.ac.uk; Kosei-frimpong@gimpa.edu.gh
RI Osei-Frimpong, Kofi/JPX-4096-2023; Osei-Frimpong, Kofi/JCR-4418-2023
OI Osei-Frimpong, Kofi/0000-0002-3956-2495; McLean,
Graeme/0000-0003-3758-5279
CR Abbattista F., 2002, 1 INT JOINT C AAMAS
Agarwal R, 2000, MIS QUART, V24, P665, DOI 10.2307/3250951
Alexa, 2016, COMPL WEB AN TOOLK
[Anonymous], 2018, J BUS RES
[Anonymous], INT J MAN MACHINE ST
[Anonymous], HUMAN FACTORS WEB DE
[Anonymous], 2004, SEEKING MEANING PROC
Ansari A, 2003, J MARKETING RES, V40, P131, DOI 10.1509/jmkr.40.2.131.19224
Azab C, 2017, J BUS RES, V72, P57, DOI 10.1016/j.jbusres.2016.11.012
Bitner M J., 2000, Handbook of Services Marketing and Management, P37
Brehm J.W., 1996, THEORY PSYCHOL REACT
Byrne A, 2013, PALGRAVE STUD CULT, P3
Carter J, 2015, WHY LIVE CHAT COULD
Cassell J., 2000, EMBODIED CONVERSATIO
Chattaraman V, 2019, COMPUT HUM BEHAV, V90, P315, DOI 10.1016/j.chb.2018.08.048
Chattaraman V, 2012, COMPUT HUM BEHAV, V28, P2055, DOI 10.1016/j.chb.2012.06.009
Cheung CMK, 2006, J AM SOC INF SCI TEC, V57, P479, DOI 10.1002/asi.20312
Ciechanowski L, 2019, FUTURE GENER COMP SY, V92, P539, DOI 10.1016/j.future.2018.01.055
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Coughlan J., 2008, STRUCTURAL EQUATION, V9, P233
Cox J., 2001, Managing Service Quality: An International Journal, V11, P121, DOI [10.1108/09604520110387257, DOI 10.1108/09604520110387257]
DAFT RL, 1986, MANAGE SCI, V32, P554, DOI 10.1287/mnsc.32.5.554
Dailey L, 2004, J BUS RES, V57, P795, DOI 10.1016/S0148-2963(02)00364-8
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
Dennis AR, 2006, DECIS SUPPORT SYST, V42, P810, DOI 10.1016/j.dss.2005.05.032
Dixon M, 2013, J OPER MANAG, V31, P138, DOI 10.1016/j.jom.2012.12.002
Edvardsson B., 2005, MANAG SERV QUAL, V15, P127, DOI DOI 10.1108/09604520510585316
Etemad-Sajadi R, 2014, INT J QUAL RELIAB MA, V31, P419, DOI 10.1108/IJQRM-05-2013-0077
Faiola A, 2013, COMPUT HUM BEHAV, V29, P1113, DOI 10.1016/j.chb.2012.10.003
Fan WS, 2010, TOTAL QUAL MANAG BUS, V21, P1141, DOI 10.1080/14783363.2010.529335
Flanagin AJ, 2000, JOURNALISM MASS COMM, V77, P515, DOI 10.1177/107769900007700304
Flanagin AJ, 2007, NEW MEDIA SOC, V9, P319, DOI 10.1177/1461444807075015
Flavián-Blanco C, 2011, COMPUT HUM BEHAV, V27, P540, DOI 10.1016/j.chb.2010.10.002
Fogg B. J., 1999, P SIGCHI C HUM FACT, P80, DOI [10.1145/302979.303001, DOI 10.1145/302979.303001, 10.1145/302979.303001.3]
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gefen D, 2003, IEEE T ENG MANAGE, V50, P307, DOI 10.1109/TEM.2003.817277
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Gummerus J., 2004, J SERV MARK, V18, P175, DOI [DOI 10.1108/08876040410536486, 10.1108/08876040410536486]
Guo X., 2012, ASIAN SOCIAL SCI, V8, P40, DOI [10.5539/ass.v8n13p40, DOI 10.5539/ASS.V8N13P40]
Ha S, 2009, J BUS RES, V62, P565, DOI 10.1016/j.jbusres.2008.06.016
Hair J.F., 2009, Multivariate data analysis
Hajli N, 2015, TECHNOL FORECAST SOC, V96, P232, DOI 10.1016/j.techfore.2015.03.014
Hoffman DL, 2009, J INTERACT MARK, V23, P23, DOI 10.1016/j.intmar.2008.10.003
Hong WY, 2013, MIS QUART, V37, P383, DOI 10.25300/MISQ/2013/37.2.04
Hopkins CD, 2009, J INTERNET COMMER, V8, P23, DOI 10.1080/15332860903182487
HORN JL, 1992, EXP AGING RES, V18, P117, DOI 10.1080/03610739208253916
Hsu CL, 2004, INFORM MANAGE-AMSTER, V41, P853, DOI 10.1016/j.im.2003.08.014
Hulland J, 1999, STRATEGIC MANAGE J, V20, P195, DOI 10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.3.CO;2-Z
Katerattanakul P, 2002, INFOR, V40, P57
Kim H.Y, 2017, INT J FASHION DESIGN, V10
Kim M., 2006, Managing Service Quality, V16, P51
Klaus P, 2013, J SERV MARK, V27, P443, DOI 10.1108/JSM-02-2012-0030
Kuhlthau C.C., 1994, Advances in Librarianship, V18, P57, DOI 10.1108/S0065-2830(1994)0000018004
Kuhlthau CC, 2008, INFORM RES, V13
Lee A. S., 2010, J HOSP TOUR MANAG, V3, P47
Lee CH, 2011, TOURISM MANAGE, V32, P987, DOI 10.1016/j.tourman.2010.08.011
Lee G, 2005, MANAGE DECIS, V43, P171
Lee Y., 2010, MIS Q, V36, P365
Legris P, 2003, INFORM MANAGE-AMSTER, V40, P191, DOI 10.1016/S0378-7206(01)00143-4
Lew Z, 2018, J COMPUT-MEDIAT COMM, V23, P201, DOI 10.1093/jcmc/zmy009
Li C.Y, 2017, TECHNOL FORECAST SOC
Li S, 2019, TECHNOL FORECAST SOC, V139, P303, DOI 10.1016/j.techfore.2018.11.018
Liljander V, 1997, INT J SERV IND MANAG, V8, P148, DOI 10.1108/09564239710166272
Liu C, 2000, INFORM MANAGE, V38, P23, DOI 10.1016/S0378-7206(00)00049-5
Liu X, 2008, INT J RETAIL DISTRIB, V36, P919, DOI 10.1108/09590550810911683
Loiacono E.T., 2002, MARKETING EDUCATORS, V13, P432
Loiacono ET, 2007, INT J ELECTRON COMM, V11, P51, DOI 10.2753/JEC1086-4415110302
Lucassen T, 2013, J AM SOC INF SCI TEC, V64, P254, DOI 10.1002/asi.22743
Lucassen T, 2011, J AM SOC INF SCI TEC, V62, P1232, DOI 10.1002/asi.21545
Lv ZP, 2018, ELECTRON COMMER R A, V28, P102, DOI 10.1016/j.elerap.2018.01.003
Martin J, 2015, J RETAIL CONSUM SERV, V25, P81, DOI 10.1016/j.jretconser.2015.03.008
Mathwick C, 2001, J RETAILING, V77, P39, DOI 10.1016/S0022-4359(00)00045-2
McKinney L.N., 2004, INT J CONSUMER STUDI, V28, P268
McLean G, 2017, MARK INTELL PLAN, V35
McLean G, 2018, J BUS RES, V85, P325, DOI 10.1016/j.jbusres.2018.01.018
McLean G, 2017, COMPUT HUM BEHAV, V76, P494, DOI 10.1016/j.chb.2017.08.005
McLean G, 2016, COMPUT HUM BEHAV, V60, P602, DOI 10.1016/j.chb.2016.02.084
McMillan SJ, 2002, J ADVERTISING, V31, P29, DOI 10.1080/00913367.2002.10673674
Mero J, 2018, ELECTRON MARK, V28, P205, DOI 10.1007/s12525-017-0281-2
Metzger MJ, 2007, J AM SOC INF SCI TEC, V58, P2078, DOI 10.1002/asi.20672
Metzger MJ, 2013, J PRAGMATICS, V59, P210, DOI 10.1016/j.pragma.2013.07.012
Meuter ML, 2000, J MARKETING, V64, P50, DOI 10.1509/jmkg.64.3.50.18024
Micu AE, 2019, INT ENTREP MANAG J, V15, P651, DOI 10.1007/s11365-019-00564-x
Oliver RL, 1999, J MARKETING, V63, P33, DOI 10.2307/1252099
Osei-Frimpong K, 2018, TECHNOL FORECAST SOC, V126, P14, DOI 10.1016/j.techfore.2016.04.009
Pallant J., 2016, SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS
Parasuraman A, 2005, J SERV RES-US, V7, P213, DOI 10.1177/1094670504271156
Petzer DJ, 2017, INT J BANK MARK, V35, P241, DOI 10.1108/IJBM-04-2016-0047
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Ranaweera C, 2014, J BUS RES, V67, P2645, DOI 10.1016/j.jbusres.2014.04.002
Reinecke K., 2013, P SIGCHI C HUM FACT, P2049, DOI [10, https://doi.org/10.1145/2470654.2481281, DOI 10.1145/2470654.2481281, 10.1145/2470654.2481281]
Rieh S.Y., 2009, ENCY LIB INFORM SCI, P1337, DOI [10.1081/e-elis3-120044103, DOI 10.1081/E-ELIS3-120044103]
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
SHUMAKER SA, 1984, J SOC ISSUES, V40, P11, DOI 10.1111/j.1540-4560.1984.tb01105.x
SOLOMON MR, 1985, J MARKETING, V49, P99, DOI 10.2307/1251180
Song JH, 2008, J MARKETING, V72, P99, DOI 10.1509/jmkg.72.2.99
Srinivasan SS, 2002, J RETAILING, V78, P41, DOI 10.1016/S0022-4359(01)00065-3
Sundar S. S., 2008, Digital media, youth, and credibility, P73, DOI DOI 10.1162/DMAL.9780262562324.073
Pham TSH, 2017, TECHNOL FORECAST SOC, V124, P332, DOI 10.1016/j.techfore.2017.04.003
THOITS PA, 1986, J CONSULT CLIN PSYCH, V54, P416, DOI 10.1037/0022-006X.54.4.416
Tombs A., 2003, Marketing Theory, V3, P447, DOI DOI 10.1177/1470593103040785
Torkzadeh G, 2002, INFORM SYST RES, V13, P187, DOI 10.1287/isre.13.2.187.87
Turel O, 2013, INT J INFORM MANAGE, V33, P674, DOI 10.1016/j.ijinfomgt.2013.03.005
Turel O, 2013, INFORM MANAGE-AMSTER, V50, P98, DOI 10.1016/j.im.2013.02.004
Vandenberg RJ, 2000, ORGAN RES METHODS, V3, P4, DOI 10.1177/109442810031002
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2008, DECIS SCI, V39
Venkatesh V, 2012, MIS QUART, V36, P157
Verhagen T, 2015, COMPUT HUM BEHAV, V48, P340, DOI 10.1016/j.chb.2015.01.061
Verhagen T, 2014, J COMPUT-MEDIAT COMM, V19, P529, DOI 10.1111/jcc4.12066
Verhoef PC, 2009, J RETAILING, V85, P31, DOI 10.1016/j.jretai.2008.11.001
Wang XH, 2011, J SERV RES-US, V14, P149, DOI 10.1177/1094670511400722
WeAreSocial, 2019, DIG 2019 SPORTL E CO
Westerwick A, 2013, J COMPUT-MEDIAT COMM, V18, P80, DOI 10.1111/jcc4.12006
Whiting A, 2006, J SERV RES-US, V8, P234, DOI 10.1177/1094670505281703
Wünderlich NV, 2013, J SERV RES-US, V16, P3, DOI 10.1177/1094670512448413
Yang Z, 2001, P EMAC C MAY 2001 BE
Yang Zhilin., 2003, Journal of Services Marketing, V17, P685, DOI DOI 10.1108/08876040310501241
[杨祖元 Yang Zuyuan], 2002, [重庆大学学报, Journal of Chongqing University], V25, P19
Yoo B., 2001, Q J ELECT COMMERCE, V2, P31, DOI DOI 10.1007/978-3-319-11885-7_129
Yoon C, 2007, ELECTRON COMMER R A, V6, P102, DOI 10.1016/j.elerap.2006.06.009
Yoon C, 2010, COMPUT HUM BEHAV, V26, P1296, DOI 10.1016/j.chb.2010.04.001
Yu YT, 2001, INT J SERV IND MANAG, V12, P234, DOI 10.1108/09564230110393239
Zeithaml V.A., 2000, Implications for Future Research and Managerial Practice, P1
NR 126
TC 40
Z9 41
U1 6
U2 44
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD SEP
PY 2019
VL 146
BP 55
EP 67
DI 10.1016/j.techfore.2019.05.017
PG 13
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA JR9FS
UT WOS:000499922800006
DA 2024-03-27
ER
PT J
AU Ekelik, H
Emir, S
AF Ekelik, Haydar
Emir, Senol
TI A Comparison of Machine Learning Classifiers for Evaluation of
Remarketing Audiences in E-Commerce
SO ESKISEHIR OSMANGAZI UNIVERSITESI IIBF DERGISI-ESKISEHIR OSMANGAZI
UNIVERSITY JOURNAL OF ECONOMICS AND ADMINISTRATIVE SCIENCES
LA English
DT Article
DE Remarketing; Google Analytics; Artificial Neural Networks; CART; Random
Forest
ID FRAMEWORK
AB In this study, user data of an e-commerce site operating in Turkey is examined. Users are those who have visited the site before, that is, they are in the remarketing audience pool. The main goal is to make accurate predictions for remarketing and thus offer customized ad packages for new visitors. Visitors are labeled as "Shoppers" and "Non-shoppers" based on their previous visits. The data set is divided into two portions that do not intersect with each other as training and test sets. Three classification models based on artificial neural networks, classification and regression trees (CART), and random forest are built to make predictions and then classification performances of these models are compared.
C1 [Ekelik, Haydar; Emir, Senol] Istanbul Univ, Iktisat Fak, Ekonometri Bolumu, Istanbul, Turkey.
C3 Istanbul University
RP Ekelik, H (autor correspondiente), Istanbul Univ, Iktisat Fak, Ekonometri Bolumu, Istanbul, Turkey.
EM haydar.ekelik@istanbul.edu.tr; senol.emir@istanbul.edu.tr
RI Ekelik, Haydar/AAS-4275-2020; Emir, Şenol/GLU-3691-2022
OI Ekelik, Haydar/0000-0002-0661-4164; Emir, Senol/0000-0002-6762-9351
CR Breiman, 1984, CHAPMAN HALLCRC TEXT
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Charlesworth A., 2018, Digital Marketing: A Practical Approach
Ciaburro GiuseppeBalaji Venkateswaran., 2017, Neural Networks with R
Derevitskii I, 2019, 2019 SIXTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS), P321, DOI [10.1109/snams.2019.8931852, 10.1109/SNAMS.2019.8931852]
Devi, 2020, SOLID STATE TECHNOL, V63, P6251
Dogan, 2020, INTELLIGENT FUZZY TE, P39
Gupta R, 2014, PROCEDIA COMPUT SCI, V36, P599, DOI 10.1016/j.procs.2014.09.060
Haykin S., 2008, NEURAL NETWORKS LEAR, V3
Ho TK, 1998, IEEE T PATTERN ANAL, V20, P832, DOI 10.1109/34.709601
Jäger G, 2019, BIG DATA COGN COMPUT, V3, DOI 10.3390/bdcc3040051
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
Lee SS, 2000, COMPUT STAT DATA AN, V34, P165, DOI 10.1016/S0167-9473(99)00095-X
Levin N., 2001, J INTERACT MARK, V15, P2, DOI DOI 10.1002/DIR.1007
Mitchell Tom M, 1980, NEED BIASES LEARNING
Niu X, 2017, AMCIS 2017 PROCEEDINGS
Oztemel E., 2006, Yapay Sinir Aglari
Patrutiu-Baltes L., 2016, B TRANSILVANIA U BRA, V9, P61
Rokach L., 2010, Pattern Classification Using Ensemble Methods
Rokach Lior., 2015, Data Mining with Decision Trees: Theory and Applications, V2nd
Safa NS, 2014, MALAYS J COMPUT SCI, V27, P171
Silva I.N. da., 2017, Artificial Neural Networks
Ballestar MT, 2018, J BUS RES, V88, P407, DOI 10.1016/j.jbusres.2017.11.047
Therneau B., 2019, RPART RECURSIVE PART
Venables W.N., 2002, Modern Applied Statistics with S Fourth, DOI DOI 10.2307/2685660
Wolpert DavidH., 1995, No free lunch theorems for search, V10
Yasmin A., 2015, INT J MANAG SCI BUS, V1, P69
Zhu G., 2019, Science Journal of Business and Management, V7, P33, DOI DOI 10.11648/J.SJBM.20190701.15
NR 29
TC 1
Z9 1
U1 0
U2 7
PU ESKISEHIR OSMANGAZI UNIV, FAC EDUCATION
PI ESKISEHIR
PA ESKISEHIR OSMANGAZI UNIV, FAC EDUCATION, ESKISEHIR, 26480, TURKEY
SN 1306-6730
J9 ESKISEH OSMAN UNIV I
JI Eskiseh. Osman. Univ. IIBF Derg.
PD AUG
PY 2021
VL 16
IS 2
BP 341
EP 359
DI 10.17153/oguiibf.879105
PG 19
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA SU7JP
UT WOS:000663309100004
OA gold, Green Submitted
DA 2024-03-27
ER
PT J
AU Ferreira, KJ
Lee, BHA
Simchi-Levi, D
AF Ferreira, Kris Johnson
Lee, Bin Hong Alex
Simchi-Levi, David
TI Analytics for an Online Retailer: Demand Forecasting and Price
Optimization
SO M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
LA English
DT Article
DE online retailing; flash sales; initial pricing; revenue management;
price optimization; machine learning; regression trees; demand
forecasting; demand interdependency; model implementation
ID DECISION-SUPPORT-SYSTEM; SUBSTITUTABLE PRODUCTS; SALES; LINE
AB We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer's main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product's demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multiproduct price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La's daily use. We conduct a field experiment and find that sales does not decrease because of implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of [2.3%, 17.8%].
C1 [Ferreira, Kris Johnson] Harvard Univ, Sch Business, Technol & Operat Management Unit, Boston, MA 02163 USA.
[Lee, Bin Hong Alex] MIT, Engn Syst Div, Cambridge, MA 02193 USA.
[Simchi-Levi, David] MIT, Engn Syst Div, Dept Civil & Environm Engn, Inst Data Syst & Soc, Cambridge, MA 02193 USA.
[Simchi-Levi, David] MIT, Ctr Operat Res, Cambridge, MA 02193 USA.
C3 Harvard University; Massachusetts Institute of Technology (MIT);
Massachusetts Institute of Technology (MIT); Massachusetts Institute of
Technology (MIT)
RP Ferreira, KJ (autor correspondiente), Harvard Univ, Sch Business, Technol & Operat Management Unit, Boston, MA 02163 USA.; Lee, BHA (autor correspondiente), MIT, Engn Syst Div, Cambridge, MA 02193 USA.; Simchi-Levi, D (autor correspondiente), MIT, Engn Syst Div, Dept Civil & Environm Engn, Inst Data Syst & Soc, Cambridge, MA 02193 USA.; Simchi-Levi, D (autor correspondiente), MIT, Ctr Operat Res, Cambridge, MA 02193 USA.
EM kferreira@hbs.edu; binhong@mit.edu; dslevi@mit.edu
FU Accenture through the MIT Alliance in Business Analytics
FX The authors thank Murali Narayanaswamy, the vice president of pricing
and operations strategy at Rue La La; Jonathan Waggoner, the former
chief operating officer at Rue La La; and Philip Roizin, the former
chief financial officer at Rue La La, for their continuing support and
for sharing valuable business expertise through numerous discussions and
providing a considerable amount of time and resources to ensure a
successful project. The integration of the authors' pricing decision
support tool with their ERP system could not have been done without the
help of Hemant Pariawala and Debadatta Mohanty. The authors also thank
the numerous other Rue La La executives and employees for their
assistance and support throughout this project. This research also
benefitted from discussions with Roy Welsch (MIT), Ozalp Ozer
(University of Texas at Dallas), Matt O'Kane (Accenture), Andy Fano
(Accenture), Paul Mahler (Accenture), Marjan Baghaie (Accenture), and
students in D. Simchi-Levi's research group at MIT. Finally, the authors
thank the referees and area editor, whose comments significantly helped
the presentation and analysis in this paper. This work was supported by
Accenture through the MIT Alliance in Business Analytics.
CR Anupindi R, 1998, MARKET SCI, V17, P406, DOI 10.1287/mksc.17.4.406
BERRY S, 1995, ECONOMETRICA, V63, P841, DOI 10.2307/2171802
Birge JR, 1998, INT J FLEX MANUF SYS, V10, P407, DOI 10.1023/A:1008061605260
Bitran G., 2003, Manufacturing & Service Operations Management, V5, P203, DOI 10.1287/msom.5.3.203.16031
Caro F, 2012, OPER RES, V60, P1404, DOI 10.1287/opre.1120.1102
Choi TM, 2007, INT J PROD ECON, V106, P146, DOI 10.1016/j.ijpe.2006.05.009
Corder GW., 2014, NONPARAMETRIC STAT S
Du D.-Z., 1998, Handbook of Combinatorial Optimization, V1
Elmaghraby W, 2003, MANAGE SCI, V49, P1287, DOI 10.1287/mnsc.49.10.1287.17315
Emery F., 1970, Pricing Strategy, P98
EVERITT B. S., 2011, CLUSTER ANAL
Ferreira K. J., 2015, WORKING PAPER
Garro A, 2011, THESIS MIT CAMBRIDGE
Gaur V, 2005, PROD OPER MANAG, V14, P377, DOI 10.1111/j.1937-5956.2005.tb00227.x
Hastie T., 2009, ELEMENTS STAT LEARNI, V2, P1, DOI DOI 10.1007/978-0-387-84858-7
LAU L. C., 2011, Iterative Methods in Combinatorial Optimization, V46
Levy M, 2004, J RETAILING, V80, pXIII, DOI 10.1016/j.jretai.2004.08.003
LITTLE JDC, 1980, J BUS, V53, pS199, DOI 10.1086/296112
Local Offer Network, 2011, DAIL DEAL PHEN YEAR, P11
Maddah B, 2007, NAV RES LOG, V54, P315, DOI 10.1002/nav.20209
Mazumdar T, 2005, J MARKETING, V69, P84, DOI 10.1509/jmkg.2005.69.4.84
McKitterick W, 2015, ONLINE FASHION SAMPL
Musalem A, 2010, MANAGE SCI, V56, P1180, DOI 10.1287/mnsc.1100.1170
Natter M, 2007, MARKET SCI, V26, P576, DOI 10.1287/mksc.1060.0217
Ostapenko N., 2013, INT J BUSINESS SOCIA, V3, P60
Ozer O ., 2012, The Oxford handbook of pricing management
REIBSTEIN DJ, 1984, J MARKETING RES, V21, P259, DOI 10.2307/3151602
Rice J.A., 2006, Mathematical Statistics and Data Analysis
Sen A, 2008, INT J PROD ECON, V114, P571, DOI 10.1016/j.ijpe.2007.05.022
Smith SA, 1998, MANAGE SCI, V44, P285, DOI 10.1287/mnsc.44.3.285
Strobl C, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-307
Subrahmanyan Saroja., 2000, Journal of Product and Brand Management, V9, P304, DOI DOI 10.1108/10610420010347100
Talluri K, 2005, THEORY PRACTICE REVE
Vulcano G, 2012, OPER RES, V60, P313, DOI 10.1287/opre.1110.1012
Winer Russell., 1985, MARKET SCI, V4, P74, DOI [10.1287/mksc.4.1.74, DOI 10.1287/MKSC.4.1.74]
Wolverson R, 2012, TIME MAGAZINE, V180, P9
Wu JH, 2014, DECIS SUPPORT SYST, V58, P43, DOI 10.1016/j.dss.2013.01.015
NR 37
TC 192
Z9 240
U1 26
U2 454
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1523-4614
EI 1526-5498
J9 M&SOM-MANUF SERV OP
JI M&SOM-Manuf. Serv. Oper. Manag.
PD WIN
PY 2016
VL 18
IS 1
SI SI
BP 69
EP 88
DI 10.1287/msom.2015.0561
PG 20
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA DL4JT
UT WOS:000375601500006
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Park, J
Ahn, H
Kim, D
Park, E
AF Park, Jinhee
Ahn, Hyeongjin
Kim, Dongjae
Park, Eunil
TI GNN-IR: Examining graph neural networks for influencer recommendations
in social media marketing
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Influencer marketing; Recommendation; Link prediction; Bipartite graph;
YouTube commercial
AB With the notable growth of the Internet, a number of platforms have emerged and attracted an enormous number of users. Based on the impact of these platforms, some 'influencers' are highlighted. These influencers wield significant power, shaping consumer behavior. This influence spawned the concept of influencer marketing, where companies leverage these personalities to advertise their products. YouTube stands out as a prominent platform in this trend. However, considering the limited number of influencers and their concepts, the majority of companies, which hope to conduct their marketing campaigns with influencers face challenges in identifying suitable influencers for their campaigns. With this trend, we introduce GNN-IR, a graph neural network for influencer recommendation, based on the connections between companies and influencers of YouTube, one of the largest content platforms. In developing GNN-IR, we adopted a data -driven methodology utilizing a meticulously curated dataset collected in-house. Our dataset comprises a total of 25,174 relationship entries between advertisers and influencers, involving 1,886 distinct advertisers and 3,812 unique YouTube influencers. It encompasses diverse data modalities, including images, text, and assorted metadata. The data was sourced from two primary platforms: YouTube and ugwanggi. Ugwanggi provided valuable insights into the relationships between advertisers and influencers via their information. Meanwhile, YouTube offered more comprehensive and detailed influencer -centric information. We employed PyTorch Geometric to construct a bipartite graph representing interconnected data. Our recommendation system operates via link prediction, suggesting the Topk influencers to advertisers based on the calculated connection probability between nodes. To assess GNN-IR's performance, we employed a range of evaluation metrics. For link prediction, we measured Accuracy, Precision, Recall, and F1 -score. Additionally, in the recommendation phase, we evaluated Precision@k, Recall@k, and F1-score@k. Using GNN-IR and incorporating profile images from YouTube, keyword features, metadata, and sentiment gleaned from YouTube comments, we achieved Precision levels of 96.51% at k=1 and 93.68% at k=10. Based on the experimental results, several implications and limitations are presented.
C1 [Park, Jinhee; Ahn, Hyeongjin; Kim, Dongjae; Park, Eunil] Sungkyunkwan Univ, Dept Appl Artificial Intelligence, Seoul 03063, South Korea.
[Park, Jinhee; Kim, Dongjae] Univ Toronto, Toronto, ON M5S 1A8, Canada.
[Park, Eunil] Teach Co, Seoul 03063, South Korea.
C3 Sungkyunkwan University (SKKU); University of Toronto
RP Park, E (autor correspondiente), 310 Int Hall,25-2 Sungkyunkwan Ro, Seoul 03063, South Korea.
EM eunilpark@skku.edu
OI Ahn, Hyeongjin/0000-0001-8545-7867
FU Institute of Information & communi-cations Technology Planning &
Evaluation (IITP) - Korea government (MSIT) [RS-2023-00254129]; MSIT,
Korea, under the ICAN (ICT Challenge and Advanced Network of HRD)
[IITP-2023-RS-2023-00259497]
FX This work was supported by Institute of Information & communi-cations
Technology Planning & Evaluation (IITP) grant funded by the Korea
government (MSIT) (No. RS-2023-00254129, Graduate School of Metaverse
Convergence (Sungkyunkwan University) ) . This research was also
supported by the MSIT, Korea, under the ICAN (ICT Challenge and Advanced
Network of HRD) support program (IITP-2023-RS-2023-00259497) supervised
by the IITP.
CR Acikgoz F, 2021, INT J INTERNET MARK, V15, P201, DOI 10.1504/IJIMA.2021.114331
Adamic LA, 2003, SOC NETWORKS, V25, P211, DOI 10.1016/S0378-8733(03)00009-1
Alamäki A, 2019, INFORM PROCESS MANAG, V56, P756, DOI 10.1016/j.ipm.2019.01.003
Andonopoulos V, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103521
Aum J, 2023, BEHAV INFORM TECHNOL, V42, P775, DOI 10.1080/0144929X.2022.2042737
Bakshy E., 2011, P 4 ACM INT C WEB SE, P65, DOI DOI 10.1145/1935826.1935845
Barabási AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509
Barman Sanket, 2023, 2023 8th International Conference on Communication and Electronics Systems (ICCES), P626, DOI 10.1109/ICCES57224.2023.10192645
Barta S, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103149
Behera RK, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.03.026
Belanche D, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102585
Bhattacharya A, 2023, NMIMS MANAG REV, V31, P203, DOI 10.1177/09711023231205509
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Carter A.E., 2018, Ph.D. thesis
Cha J, 2022, HUM SOC SCI COMMUN, V9, DOI 10.1057/s41599-022-01313-2
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Clark K., 2020, INT C LEARN REPR
Das Debashis, 2017, International Journal of Computer Applications, V160, P7, DOI [10.5120/ijca2017913081, DOI 10.5120/IJCA2017913081]
DataReporter, 2022, Dital 2022: YouTube's AD reach passes 2.5 billion
DataReporter, 2023, Global social media statistics
Daud NN, 2020, J NETW COMPUT APPL, V166, DOI 10.1016/j.jnca.2020.102716
De Veirman M, 2017, INT J ADVERT, V36, P798, DOI 10.1080/02650487.2017.1348035
Demandsage, 2023, YouTube statistics for 2023 (demographics & usage)
Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
Dimitrieska Savica., 2021, Economics and Management, V18, P109
Farivar S, 2022, J RETAIL CONSUM SERV, V67, DOI 10.1016/j.jretconser.2022.103026
Grover A, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P855, DOI 10.1145/2939672.2939754
Hamilton WL, 2017, ADV NEUR IN, V30
Hearst MA, 1998, IEEE INTELL SYST APP, V13, P18, DOI 10.1109/5254.708428
Hu XJ, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103142
Huang X, 2017, P 2017 SIAM INT C DA, P633, DOI DOI 10.1137/1.9781611974973.71
Hudders L, 2023, INT J ADVERT, V42, P151, DOI 10.1080/02650487.2022.2137318
Hwang S, 2023, INT J MACH LEARN CYB, V14, P3205, DOI 10.1007/s13042-023-01828-3
Isinkaye FO, 2015, EGYPT INFORM J, V16, P261, DOI 10.1016/j.eij.2015.06.005
Jeong D, 2022, EXPERT SYST APPL, V210, DOI 10.1016/j.eswa.2022.118375
Jiang Hongyun, 2022, 2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC), P660, DOI 10.1109/ICNISC57059.2022.00135
Kaya B, 2020, J INF SCI, V46, P53, DOI 10.1177/0165551518824577
Kim D, 2024, COMPUT VIS IMAGE UND, V238, DOI 10.1016/j.cviu.2023.103879
Kim S, 2021, WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P319, DOI 10.1145/3437963.3441803
Kipf T. N., 2017, INT C LEARN REPRESEN, DOI DOI 10.48550/ARXIV.1609.02907
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Kurtin K.S., 2018, J SOCIAL MEDIA SOC, V7, P233
Lee J, 2023, TELEMAT INFORM, V85, DOI 10.1016/j.tele.2023.102066
Lee MY, 2023, IEEE T AUTOM SCI ENG, DOI 10.1109/TASE.2023.3251383
Lee S, 2022, KNOWL-BASED SYST, V242, DOI 10.1016/j.knosys.2022.108437
Lee S, 2023, APPL MATH COMPUT, V458, DOI 10.1016/j.amc.2023.128219
Lee S, 2023, TELEMAT INFORM, V78, DOI 10.1016/j.tele.2023.101948
Li Y, 2021, MARK INTELL PLAN, V39, P960, DOI 10.1108/MIP-04-2021-0104
Liang ZH, 2016, IEEE INT C BIOINFORM, P493, DOI 10.1109/BIBM.2016.7822567
Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591
Lin ZH, 2022, PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22), P2320, DOI 10.1145/3485447.3512104
Liu GG, 2022, ALEX ENG J, V61, P905, DOI 10.1016/j.aej.2021.04.081
Lou C., 2019, Journal of Interactive Advertising, V19, P58, DOI DOI 10.1080/15252019.2018.1533501
Martensen A, 2018, J FASH MARK MANAG, V22, P335, DOI 10.1108/JFMM-09-2017-0095
Masuda H, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121246
Molazadeh Farsangi A., 2021, Ph.D. thesis BCE Kommunikacio es Szociologia Intezet
Nagarnaik P, 2015, 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEMS (ICECS), P1603, DOI 10.1109/ECS.2015.7124857
Newman MEJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.025102
Niwattanakul Suphakit, 2013, IMECS 2013 Proceedings of International Multiconference of Engineers and Computer Scientists, P380
Pan Shirui, 2016, NETWORK, P1895
Park C., 2023, Int. J. Adv. Smart Converg., V12, P1
Park E, 2023, J BIG DATA-GER, V10, DOI 10.1186/s40537-022-00674-4
Perozzi B, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P701, DOI 10.1145/2623330.2623732
Peterson L. E., 2009, SCHOLARPEDIA, V4, P1883, DOI DOI 10.4249/SCHOLARPEDIA.1883
Polatidis N, 2017, COMPUT STAND INTER, V51, P14, DOI 10.1016/j.csi.2016.10.014
Le Q, 2014, PR MACH LEARN RES, V32, P1188
Rieder B, 2023, SOC MEDIA SOC, V9, DOI 10.1177/20563051231180628
Rish I, 2001, IJCAI 2001 workshop on empirical methods in artificial intelligence, V3, P41, DOI DOI 10.1039/B104835J
Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605
Shang KK, 2019, CHAOS, V29, DOI 10.1063/1.5107440
Sharma S, 2022, EDUC INF TECHNOL, V27, P1523, DOI 10.1007/s10639-021-10643-8
Shi Y., 2021, P 30 INT JOINT C ART, P1548, DOI 10.24963/ijcai.2021/214
Singh R. H., 2020, International Journal of Engineering and Advanced Technology, V9, P556, DOI 10.35940/ijeat.E9666.069520
Sodeinde O.A., 2022, SAU J. Manag. Soc. Sci., V5, P15
Sokolova K, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.01.011
Srivastava A, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.02.018
Statistia, 2023, Number of YouTubers creators and influencers in the United States as of June 2023, by follower count
Statistia, 2023, Influencer marketing platform market size worldwide from 2022 to 2025
Statistia, 2022, Hours of video uploaded to YouTube every minute as of February 2022
Statistia, 2023, Number of influencers brands worked with worldwide as of February 2023
Sun X., 2021, Acad. J. Bus. Manag., V3, P1
Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
Tran GA, 2019, PSYCHOL MARKET, V36, P923, DOI 10.1002/mar.21245
Velickovic P., 2017, ARXIV, DOI DOI 10.48550/ARXIV.1710.10903
Venciute D, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103506
Victoria priscilla C., 2022, Computer Networks, Big Data and IoT: Proceedings of ICCBI 2021. Lecture Notes on Data Engineering and Communications Technologies (117), P383, DOI 10.1007/978-981-19-0898-9_30
Wang SH, 2016, CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1753, DOI 10.1145/2983323.2983756
Wu SW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3535101
Xiao M., 2023, J. Interact. Advert., V23, P166
Yang C, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P2111
Zhang LL, 2020, MULTIMED TOOLS APPL, V79, P13197, DOI 10.1007/s11042-019-08451-x
Zhang MH, 2018, ADV NEUR IN, V31
Zhou T, 2009, EUR PHYS J B, V71, P623, DOI 10.1140/epjb/e2009-00335-8
Zhou Z., 2023, INT C MACHINE LEARNI, P42644
Zhu ZW, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3483, DOI 10.1145/3308558.3313678
NR 95
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD MAY
PY 2024
VL 78
AR 103705
DI 10.1016/j.jretconser.2024.103705
EA JAN 2024
PG 14
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JA5N5
UT WOS:001170446300001
DA 2024-03-27
ER
PT J
AU Tinyakova, VI
Lavrinenko, YB
Evlaev, AN
Sulyan, GS
Tusova, AA
AF Tinyakova, Viktoriya, I
Lavrinenko, Yaroslav B.
Evlaev, Andrey N.
Sulyan, Gagik S.
Tusova, Alexandra A.
TI Specific Features and Trends in the Field of Application of Artificial
Neural Networks in Marketing
SO TURISMO-ESTUDOS E PRATICAS
LA English
DT Article
DE marketing; artificial neural network; marketing strategy; example
artificial neural network; retargeting in marketing
AB Purpose: The purpose of the article is to demonstrate the applied capabilities of neural networks in relation to the up-to-date task of marketing - predicting the effectiveness of buying a banner space for retargeting. Design/methodology/approach: The authors evaluate the development of neural networks, which are more and more involved in people's lives. They are becoming an essential tool in modern marketing, as well. The article considers the history of the development of neural networks and identifies the prerequisites for their wide use. The effectiveness of the use of neural networks in solving the tasks of a marketer in the following subject areas was evaluated: marketing analysis, the formation of ideas and strategies, content creation, automation of technical work and work with clients. The influence of neural networks and digitalization of marketing leads to the fact that for the marketer it is no longer important whether it is "a consumer or a person" as such. Findings: Modern marketing has been shown to increase its effectiveness with the help of neural networks. However, neural network marketing, considering all its advantages, has several disadvantages. The authors identified the following: the impossibility of creating new approaches by neural networks, the content of unpredictable cognitive distortions by neural networks. As a result of the use of neural networks, marketers will become less in demand, and their work will become significantly accelerated and simplified. Therefore, for marketers to remain competitive, it is necessary to study related areas. Originality / value: An example of the effective use of neural networks in marketing is the retargeting of site visitors through advertising sites. The neural network selects and estimates the price for displaying an advertising banner based on basic functions and preliminary deep training. The neural network presented in the article offers optimal solutions, evaluating a significant number of factors.
C1 [Tinyakova, Viktoriya, I] State Univ Management, Moscow, Russia.
[Lavrinenko, Yaroslav B.] Voronezh State Tech Univ, Voronezh, Russia.
[Evlaev, Andrey N.] Russian Univ Transport, Moscow, Russia.
[Sulyan, Gagik S.] Gubkin Russian State Univ Oil & Gas, Natl Res Univ, Moscow, Russia.
[Tusova, Alexandra A.] Russian State Social Univ, Moscow, Russia.
C3 State University of Management; Voronezh State Technical University;
Russian University of Transport; Gubkin Russian State University of Oil
& Gas; Russian State Social University (RSSU)
RP Tinyakova, VI (autor correspondiente), State Univ Management, Moscow, Russia.
EM tviktoria@yandex.ru; yaroslav_lav1@bk.ru; acadra@yandex.ru;
zirmanya@mail.ru; dance-fresh@rambler.ru
RI Evlaev, Andrey/AAQ-9115-2021; Tusova, Aleksandra/AAL-3773-2021;
Lavrinenko, Yaroslav Borisovich/Q-9593-2018
OI Evlaev, Andrey/0000-0002-4114-7645
CR Chang CC, 2015, COGENT ENG, V2, DOI 10.1080/23311916.2014.995785
De Tienne K. B., 2017, POLISH J MANAGEMENT, V25, P289
DeTienne KB, 2017, J STRATEG MARK, V25, P289, DOI 10.1080/0965254X.2015.1076881
Hew JJ, 2019, TECHNOL FORECAST SOC, V144, P311, DOI 10.1016/j.techfore.2017.10.007
Lavrinenko Y., 2018, MODERN EC PROBLEMS S, V3, P77
Lavrinenko Y., 2014, MARKETING RUSSIA ABR, V3, P125
Leong L. Y., 2018, J ELECT COMMERCE RES, V19
Luiz M., 2015, QUANTITATIVE MODELLI
Metcalf L, 2019, CALIF MANAGE REV, V61, P84, DOI 10.1177/0008125619862256
Paschen J., 2019, Journal of Business Industrial Marketing
Sitokina N., 2018, REGION SYSTEMS EC MA, V3, P31
Tinyakova V, 2018, EC SOC DEVELOP, P907
NR 12
TC 0
Z9 0
U1 0
U2 1
PU UNIV ESTADO RIO GRANDE NORTE
PI MOSSORO
PA RUA ANTONIO VICTOR 116, BAIRRO RINCAO, MOSSORO, RN CEP59626-310, BRAZIL
SN 2316-1493
J9 TURISMO
JI Turismo
PD SEP
PY 2020
SU 4
PG 9
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA NE6AP
UT WOS:000562682200012
DA 2024-03-27
ER
PT J
AU Bui, MT
Tran, TTH
AF Bui, My-Trinh
Tran, Thi-Thanh-Huyen
TI The internal and external effect of environmental complexity on business
responses: a PLS-SEM and artificial neural network approach
SO JOURNAL OF HOSPITALITY AND TOURISM INSIGHTS
LA English
DT Article; Early Access
DE Creativity; Traditionality; Mimetic pressure; Status certainty; Digital
technology speed; Business response; Performance
ID EMPLOYEE CREATIVITY; KNOWLEDGE TRANSFER; INNOVATION; FIRM; REPLICATION;
UNCERTAINTY; MODEL; ORGANIZATIONS; ENTRAINMENT; PERFORMANCE
AB PurposeIn the wake of severe socio-economic damage, many firms have made creative and technological progress in their responses to the COVID-19 crisis. This paper examines internal and external environmental complexity elements as antecedents of business responses and builds a framework for tourism firms to respond to the pandemic crisis.Design/methodology/approachThis study obtained survey data from 395 respondents in the Vietnamese tourism and hospitality industry. A partial least squares structural equation modeling-artificial neural network approach was used to examine various combinations of internal and external environmental complexity elements that have different impacts on business responses and firms' performance.FindingsThe knowledge and practice created by the firm's employees (individual creativity), obtained from traditional contexts (traditionality) were identified as internal environmental complexity factors while practice learned from other firms (mimetic pressure), information processing (status certainty) and digital transformation (digital technology speed) were treated as external environmental complexity factors. Internal and external environmental complexity factors influence business responses and firms' performance positively but differently.Practical implicationsThis study demonstrates that firms should integrate their internal environment of creativity and traditionality with external environmental factors of mimetic pressure, status certainty and digital technology speed to create better business responses, and thus firm performance in the COVID-19 era.Originality/valueThis investigation contributes to environmental research and narrows the existing research gap relating to the association between types of environmental complexity and firms' responsive action, which then influence firms' performance in terms of sustainable competitiveness.
C1 [Bui, My-Trinh] Vietnam Natl Univ Hanoi, Int Sch, Hanoi, Vietnam.
[Tran, Thi-Thanh-Huyen] Banking Acad Vietnam, Hanoi, Vietnam.
C3 Vietnam National University Hanoi; Banking Academy of Vietnam
RP Bui, MT (autor correspondiente), Vietnam Natl Univ Hanoi, Int Sch, Hanoi, Vietnam.
EM buimytrinh@gmail.com
RI Bui, My-Trinh/ABC-8687-2021
OI Bui, My-Trinh/0000-0001-9125-5655; Tran,
Thi-Thanh-Huyen/0009-0001-4909-4832
FU Vietnam National Foundation for Science and Technology Development
(NAFOSTED) [502.02-2019.310]
FX "This research is funded by Vietnam National Foundation for Science and
Technology Development (NAFOSTED) under grant number 502.02-2019.310."
CR Albert D, 2015, ACAD MANAGE REV, V40, P210, DOI 10.5465/amr.2012.0177
Amabile TM, 2016, RES ORGAN BEHAV, V36, P157, DOI 10.1016/j.riob.2016.10.001
Ancona D, 1996, RES ORGAN BEHAV, V18, P251
Ansong A, 2023, J HOSP TOUR INSIGHTS, V6, P1890, DOI 10.1108/JHTI-06-2022-0218
Argote L, 2000, ORGAN BEHAV HUM DEC, V82, P150, DOI 10.1006/obhd.2000.2893
Battram A., 1999, Navigating complexity
Becker JM, 2023, INT J CONTEMP HOSP M, V35, P321, DOI 10.1108/IJCHM-04-2022-0474
Capaldo A, 2017, J MANAGE, V43, P503, DOI 10.1177/0149206314535442
Carson SJ, 2006, ACAD MANAGE J, V49, P1058, DOI 10.2307/20159816
Chen GZ, 2020, J TRAVEL RES, V59, P864, DOI 10.1177/0047287519861170
Chen MJ, 2015, STRATEGIC MANAGE J, V36, P758, DOI 10.1002/smj.2245
Cheng TM, 2023, J HOSP TOUR RES, V47, P590, DOI 10.1177/10963480211012459
Choi M, 2023, INT J CONTEMP HOSP M, V35, P469, DOI 10.1108/IJCHM-02-2022-0157
DAFT RL, 1984, ACAD MANAGE REV, V9, P284, DOI 10.2307/258441
De Massis A, 2016, ACAD MANAGE PERSPECT, V30, P93, DOI 10.5465/amp.2015.0017
Dedeoglu BB, 2023, J HOSP TOUR INSIGHTS, V6, P191, DOI 10.1108/JHTI-02-2021-0048
Dibrell C, 2015, J BUS RES, V68, P19, DOI 10.1016/j.jbusres.2014.05.012
Su DN, 2021, CURR ISSUES TOUR, V24, P3189, DOI 10.1080/13683500.2020.1863930
DIMAGGIO PJ, 1983, AM SOCIOL REV, V48, P147, DOI 10.2307/2095101
Du J, 2021, INT J HOSP MANAG, V98, DOI 10.1016/j.ijhm.2021.103034
Elgarhy SD, 2023, INT J CONTEMP HOSP M, V35, P1986, DOI 10.1108/IJCHM-04-2022-0508
Fadaee A., 2014, International Journal of Economy, Management and Social Sciences, V3, P1, DOI DOI 10.1177/1938965510393723
Ferreira J, 2020, TECHNOVATION, V92-93, DOI 10.1016/j.technovation.2018.11.004
Franco S, 2022, J KNOWL MANAG, V26, P486, DOI 10.1108/JKM-05-2021-0356
Galunic DC, 1998, STRATEGIC MANAGE J, V19, P1193, DOI 10.1002/(SICI)1097-0266(1998120)19:12<1193::AID-SMJ5>3.3.CO;2-6
González-González T, 2021, INT J HOSP MANAG, V94, DOI 10.1016/j.ijhm.2021.102877
Gulati R, 1998, ADMIN SCI QUART, V43, P781, DOI 10.2307/2393616
Guo H, 2020, FRONT BUS RES CHINA, V14, DOI 10.1186/s11782-020-00087-1
Han MS, 2021, TECHNOL FORECAST SOC, V173, DOI 10.1016/j.techfore.2021.121146
Ho GKS, 2023, J HOSP TOUR INSIGHTS, V6, P835, DOI 10.1108/JHTI-09-2021-0242
Hon AHY, 2011, CORNELL HOSP Q, V52, P125, DOI 10.1177/1938965511403921
Hossain MS, 2022, J HOSP TOUR INSIGHTS, V5, P79, DOI 10.1108/JHTI-08-2020-0152
Houchin K, 2005, BRIT J MANAGE, V16, P149, DOI 10.1111/j.1467-8551.2005.00427.x
Jeong M, 2022, INT J CONTEMP HOSP M, V34, P836, DOI 10.1108/IJCHM-07-2021-0831
Johannessen JA, 1999, INT J INFORM MANAGE, V19, P121, DOI 10.1016/S0268-4012(99)00004-3
Kahveci E, 2023, J HOSP TOUR INSIGHTS, V6, P1569, DOI 10.1108/JHTI-08-2021-0232
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Kodama M, 2001, INT J HUM RESOUR MAN, V12, P1062
KOGUT B, 1992, ORGAN SCI, V3, P383, DOI 10.1287/orsc.3.3.383
Kruesi MA, 2023, J HOSP TOUR INSIGHTS, V6, P549, DOI 10.1108/JHTI-10-2021-0270
Lee C, 2016, TOURISM MANAGE, V53, P215, DOI 10.1016/j.tourman.2015.09.017
Lee RP, 2004, J MARKETING, V68, P157, DOI 10.1509/jmkg.68.4.157.42730
Li YX, 2018, ACAD MANAGE J, V61, P2000, DOI 10.5465/amj.2015.1101
Li Z, 2018, TOURISM MANAGE, V69, P23, DOI 10.1016/j.tourman.2018.05.004
Liang HG, 2007, MIS QUART, V31, P59
Lin Y, 2019, J MANAGE, V45, P3114, DOI 10.1177/0149206318777589
Liu D, 2017, ACAD MANAGE J, V60, P1164, DOI 10.5465/amj.2015.0230
Liu Y, 2023, TECHNOVATION, V119, DOI 10.1016/j.technovation.2022.102458
Luo CY, 2022, J HOSP TOUR MANAG, V51, P491, DOI 10.1016/j.jhtm.2022.05.006
Ma JN, 2023, TOURISM MANAGE, V95, DOI 10.1016/j.tourman.2022.104664
Mai NK, 2023, J HOSP TOUR INSIGHTS, V6, P1253, DOI 10.1108/JHTI-02-2022-0083
Mair S., 2020, The ConversationMarch 30
Marinova SV, 2019, J MANAGE, V45, P2045, DOI 10.1177/0149206318755301
Matsuno K, 2019, LONG RANGE PLANN, V52, P236, DOI 10.1016/j.lrp.2018.02.006
Meeus MTH, 2000, RES POLICY, V29, P41, DOI 10.1016/S0048-7333(99)00032-3
Mehran J, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101954
MILLIKEN FJ, 1987, ACAD MANAGE REV, V12, P133, DOI 10.2307/257999
Najar AH, 2023, J HOSP TOUR INSIGHTS, V6, P1357, DOI 10.1108/JHTI-03-2022-0100
Parente R, 2019, J INT BUS STUD, V50, P275, DOI 10.1057/s41267-018-0179-z
Park S, 2023, INT J HOSP MANAG, V114, DOI 10.1016/j.ijhm.2023.103568
Petruzzelli AM, 2014, LONG RANGE PLANN, V47, P224, DOI 10.1016/j.lrp.2012.09.001
Preko A, 2023, J HOSP TOUR INSIGHTS, V6, P779, DOI 10.1108/JHTI-10-2021-0294
Qi H, 2023, MANAGE DECIS, V61, P144, DOI 10.1108/MD-09-2021-1158
Ramus T, 2017, ACAD MANAGE J, V60, P1253, DOI 10.5465/amj.2015.0394
Ranucci R., 2022, Long. Range Plan, DOI [10.1016/j.lrp.2022.102268, DOI 10.1016/J.LRP.2022.102268]
SCOTT SG, 1994, ACAD MANAGE J, V37, P580, DOI 10.5465/256701
Shahzad M, 2020, J CLEAN PROD, V275, DOI 10.1016/j.jclepro.2020.124163
Sharma P, 2020, J BUS RES, V116, P188, DOI 10.1016/j.jbusres.2020.05.026
Sigala M, 2020, J BUS RES, V117, P312, DOI 10.1016/j.jbusres.2020.06.015
Silver M. S., 1988, Journal of Management Information Systems, V5, P51
Singh SH, 2021, TECHNOL FORECAST SOC, V164, DOI 10.1016/j.techfore.2019.02.002
Su F, 2023, LONG RANGE PLANN, V56, DOI 10.1016/j.lrp.2022.102292
Sun KA, 2021, J HOSP TOUR MANAG, V46, P468, DOI 10.1016/j.jhtm.2021.02.002
Tewari A, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2022.102938
Thompson JD., 2017, Organizations in Action, DOI [DOI 10.4324/9781315125930, https://doi.org/10.4324/9781315125930]
Tuomi A, 2023, J HOSP TOUR INSIGHTS, V6, P151, DOI 10.1108/JHTI-07-2021-0175
van Knippenberg D, 2017, ANNU REV ORGAN PSYCH, V4, P211, DOI 10.1146/annurev-orgpsych-032516-113240
Vatankhah S, 2023, J BUS RES, V155, DOI 10.1016/j.jbusres.2022.113350
Wang JY, 2024, LONG RANGE PLANN, V57, DOI 10.1016/j.lrp.2021.102140
Wang Z, 2023, INT J CONTEMP HOSP M, DOI 10.1108/IJCHM-11-2022-1498
Williams AM, 2022, ANN TOURISM RES, V92, DOI 10.1016/j.annals.2021.103346
Williams AM, 2021, J TRAVEL RES, V60, P293, DOI 10.1177/0047287519896012
Williams C, 2007, STRATEG MANAGE J, V28, P867, DOI 10.1002/smj.614
Williams TA, 2017, ACAD MANAG ANN, V11, P733, DOI 10.5465/annals.2015.0134
Winter SG, 2001, ORGAN SCI, V12, P730, DOI 10.1287/orsc.12.6.730.10084
WOODMAN RW, 1993, ACAD MANAGE REV, V18, P293, DOI 10.2307/258761
Wu B, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121214
Xie XM, 2022, TECHNOL FORECAST SOC, V177, DOI 10.1016/j.techfore.2022.121548
Yang EJ, 2023, TOURISM MANAGE, V95, DOI 10.1016/j.tourman.2022.104661
Yang M, 2021, INT J CONTEMP HOSP M, V33, P1230, DOI 10.1108/IJCHM-08-2020-0920
Zaki K, 2022, INT J CONTEMP HOSP M, V34, P1768, DOI 10.1108/IJCHM-08-2021-1043
Zhang JM, 2019, J MANAGE, V45, P2635, DOI 10.1177/0149206318761575
Zhang WZ, 2022, TOURISM MANAGE, V91, DOI 10.1016/j.tourman.2022.104525
Zhang XY, 2022, INT J CONTEMP HOSP M, V34, P2004, DOI 10.1108/IJCHM-10-2021-1262
NR 94
TC 0
Z9 0
U1 3
U2 3
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 2514-9792
EI 2514-9806
J9 J HOSP TOUR INSIGHTS
JI J. Hosp. Tour. Insights
PD 2023 DEC 19
PY 2023
DI 10.1108/JHTI-03-2023-0147
EA DEC 2023
PG 21
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA DA7A5
UT WOS:001129365800001
DA 2024-03-27
ER
PT J
AU Rettie, R
AF Rettie, R
TI An exploration of flow during Internet use
SO INTERNET RESEARCH-ELECTRONIC NETWORKING APPLICATIONS AND POLICY
LA English
DT Article
DE consumer behaviour; internet; electronic commerce; human-computer
interaction; Web design
ID COMPUTER; EXPERIENCE
AB Several authors have suggested that the concept of flow is useful for understanding consumer behaviour in computer-mediated environments. Previous Internet flow research has used self-completion questionnaires. This research uses focus groups to facilitate the identification and discussion of respondents' Internet experience. Explores respondents' awareness and experience of flow. Finds that half of the respondents recognised Internet flow experience and that Internet flow seems to prolong Internet and Web site usage. Identifies several factors that promote or inhibit Internet flow. These factors may help practitioners design Web sites that stimulate Row and encourage users to stay on the site.
C1 Kingston Univ, Surrey, England.
C3 Kingston University
RP Rettie, R (autor correspondiente), Kingston Univ, Surrey, England.
CR [Anonymous], 1988, OPTIMAL EXPERIENCE
CHEN H, 1998, 9 INF RES MAN ASS IN
Chen HS, 1999, ACTA PHYS-CHIM SIN, V15, P454, DOI 10.3866/PKU.WHXB19990513
CSIKSZENTMIHALY.M, 1990, FLOW PSYCHOL OPTIMAL
CSIKSZENTMIHALY.M, 1993, EVOLVING SELF PSYCHO
CSIKSZENTMIHALYI M, 1989, J PERS SOC PSYCHOL, V56, P815, DOI 10.1037/0022-3514.56.5.815
Csikszentmihalyi M., 1975, Beyond Boredom And Anxiety
Csikszentmihalyi M., 1982, REV PERSONALITY SOCI, V3, P13
Delle FaveA., 1988, OPTIMAL EXPERIENCE P, P193, DOI DOI 10.1017/CBO9780511621956.012
Dreze X., 1999, INTERNET ADVERTISING
GHANI JA, 1994, J PSYCHOL, V128, P381, DOI 10.1080/00223980.1994.9712742
Gordon W., 1988, Qualitative market research: A practitioner and buyers guide
Hoffman D. L., 1997, Information Society, V13, P43, DOI 10.1080/019722497129278
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
JENNER B, 2000, THESIS KINGSTON U
Larson R, 1983, NATURALISTIC APPROAC, V15
Mariampolski Hy., 1984, PUBLIC RELAT J, V40, P21
Morgan D.L., 1998, The Focus group kit
NEL V, 1999, INTERNET RES, V9
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
QUELCH JA, 1996, SLOAN MANAGEMENT SPR, P60
Schlosser AE, 1999, J INTERACT MARK, V13, P34, DOI 10.1002/(SICI)1520-6653(199922)13:3<34::AID-DIR3>3.0.CO;2-R
TREVINO LK, 1992, COMMUN RES, V19, P539, DOI 10.1177/009365092019005001
WEBSTER J, 1993, COMPUT HUM BEHAV, V9, P411, DOI 10.1016/0747-5632(93)90032-N
NR 24
TC 120
Z9 128
U1 2
U2 31
PU MCB U P LIMITED
PI BRADFORD
PA 60/62 TOLLER LANE, BRADFORD BD8 9BY, W YORKSHIRE, ENGLAND
SN 1066-2243
J9 INTERNET RES
JI Internet Res.-Electron. Netw. Appl. Policy
PY 2001
VL 11
IS 2
BP 103
EP 113
DI 10.1108/10662240110695070
PG 11
WC Business; Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science; Telecommunications
GA 437RG
UT WOS:000169006900002
OA Green Submitted, Green Accepted
DA 2024-03-27
ER
PT J
AU Biswas, B
Sanyal, MK
Mukherjee, T
AF Biswas, Biswajit
Sanyal, Manas Kumar
Mukherjee, Tuhin
TI AI-Based Sales Forecasting Model for Digital Marketing
SO INTERNATIONAL JOURNAL OF E-BUSINESS RESEARCH
LA English
DT Article
DE Artificial Neural Network; Digital Marketing; Sales Prediction
AB Sales prediction with minute accuracy plays a crucial role for an organization to sustain amidst the global competitive business environment. The use of artificial intelligence (AI) on top of the existing information technology environment has become one of the most exciting and promising area for any organizations in the current era of digital marketing. E-marketing provides customers to share their views with other customers. In this paper, the authors proposed a model which will be helpful to the digital marketers to find out the potential customers to extract value from customer feedback. The proposed model is based on artificial neural network and will make it possible to identify the customer demand depending on previous feedback and to predict the future sales volume of the product. The authors tried to utilize AI, mainly neural networks (NNs), to construct an intelligent sales prediction and also to apply ANNs for prediction regarding sales of mobile phone (Redmi, Note 6 Pro) one month ahead depending on customer feedback on two e-commerce platform, namely Amazon.in and Snapdeal.in.
C1 [Biswas, Biswajit; Sanyal, Manas Kumar; Mukherjee, Tuhin] Univ Kalyani, Dept Business Adm, Kalyani, India.
C3 Kalyani University
RP Biswas, B (autor correspondiente), Univ Kalyani, Dept Business Adm, Kalyani, India.
RI BISWAS, BISWAJIT/JFJ-0418-2023
OI BISWAS, BISWAJIT/0000-0002-5302-7675
CR Al-Imam A, 2019, A Gateway Towards Machine Learning: Predictive Analytics and Neural Networks in IBM-SPSS (SPSS v. 24)
Altman DG, 2000, STAT MED, V19, P453, DOI 10.1002/(SICI)1097-0258(20000229)19:4<453::AID-SIM350>3.0.CO;2-5
Aydin G, 2022, INT J E-BUS RES, V18, DOI 10.4018/IJEBR.309397
Baba N., 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, P565, DOI 10.1109/IJCNN.2000.859455
Bazerman M.H., 2012, Judgment in Managerial Decision Making, Veighth
Biswas B, 2021, INT J ONLINE MARKET, V11, P78, DOI 10.4018/IJOM.2021010105
Biswas B, 2019, 2019 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2019), P432, DOI [10.1109/confluence.2019.8776921, 10.1109/CONFLUENCE.2019.8776921]
Cheriyan S, 2018, 2018 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRONICS & COMMUNICATIONS ENGINEERING (ICCECE), P53, DOI 10.1109/iCCECOME.2018.8659115
Chong AYL, 2014, INT J PROD ECON, V154, P48, DOI 10.1016/j.ijpe.2014.04.005
Choudhary R., 2018, 2018 4 INT C COMP CO, DOI [10.1109/CCAA.2018.8777620, DOI 10.1109/CCAA.2018.8777620]
Collins G., 1988, IEEE INT C NEUR NETW, DOI [10.1109/ICNN.1988.23960, DOI 10.1109/ICNN.1988.23960]
Dreiseitl S, 2002, J BIOMED INFORM, V35, P352, DOI 10.1016/S1532-0464(03)00034-0
Floyd K, 2014, J RETAILING, V90, P217, DOI 10.1016/j.jretai.2014.04.004
Frank C, 2003, INT J CLOTH SCI TECH, V15, P107, DOI DOI 10.1108/09556220310470097
Gustriansyah R, 2022, EXPERT SYST APPL, V207, DOI 10.1016/j.eswa.2022.118043
Jagielska I., 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, P284, DOI 10.1109/ANNES.1993.323024
Kantasa-ard A, 2021, INT J PROD RES, V59, P7491, DOI 10.1080/00207543.2020.1844332
Kreutzer T., 2009, GENERATION MOBILE ON
Kumari JJA, 2022, INT J E-BUS RES, V18, DOI 10.4018/IJEBR.309388
Liu N, 2013, MATH PROBL ENG, V2013, DOI 10.1155/2013/738675
Mediavilla Mario Angos, 2022, Procedia CIRP, P1126, DOI 10.1016/j.procir.2022.05.119
Mitra A, 2022, IETE J RES, V3, DOI [10.1007/s43069-022-00166-4, DOI 10.1007/S43069-022-00166-4]
Naveen P, 2021, P 4 NAT C CURR EM PR, V2387, P14, DOI [10.1063/5.0068655, DOI 10.1063/5.0068655]
Penpece D., 2014, International Journal of Trade, Economics and Finance, V5, P435, DOI DOI 10.7763/IJTEF.2014.V5.411
Seyedan M, 2020, J BIG DATA-GER, V7, DOI 10.1186/s40537-020-00329-2
Sharma SK, 2019, INF SYST E-BUS MANAG, V17, P261, DOI 10.1007/s10257-019-00438-3
Sun ZL, 2008, DECIS SUPPORT SYST, V46, P411, DOI 10.1016/j.dss.2008.07.009
Vapnik VN., 2000, NATURE STAT LEARNING, P1, DOI DOI 10.1007/978-1-4757-3264-1
Yuan FC, 2020, SOFT COMPUT, V24, P2033, DOI 10.1007/s00500-019-04036-w
NR 29
TC 0
Z9 0
U1 3
U2 3
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1548-1131
EI 1548-114X
J9 INT J E-BUS RES
JI Int. J. E-Bus. Res.
PD JAN-JUN
PY 2023
VL 19
IS 1
AR 88
DI 10.4018/IJEBR.317888
PG 14
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA FF0C7
UT WOS:001144221100005
OA gold
DA 2024-03-27
ER
PT J
AU Mahnke, R
Benlian, A
Hess, T
AF Mahnke, Rolf
Benlian, Alexander
Hess, Thomas
TI A Grounded Theory of Online Shopping Flow
SO INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
LA English
DT Article
DE Flow theory; digital service innovation; user experience; human-computer
interaction; e-tail; grounded theory
ID WEB SITE; EXPERIENCE; MODEL; BEHAVIOR; COMMUNICATION; ENVIRONMENTS;
ACCEPTANCE; USAGE
AB With the increasing number of websites that have found their way into our daily lives, substantial resources are invested in enhancing user experience beyond mere functionality. Optimizing flow-the psychological state of deep focus while conducting a fluent activity-seems a promising approach, resulting in a win-win situation for both users and website operators. Flow has been found to result in "optimal" user experience leading to intrinsically motivated behavior, engagement, and loyalty. However, to date, there is little concrete knowledge of or advice on how to design a website for flow. This study develops a grounded theory of flow experiences in the context of online shopping, and sheds light on the theoretical relationships between concrete realizable website design options, corresponding latent constructs, and flow experience. Based on our findings we derive theoretical as well as practical implications for understanding and designing flow experience on the web.
C1 [Mahnke, Rolf] Univ Munich, Informat Syst, D-81377 Munich, Germany.
[Benlian, Alexander] Tech Univ Darmstadt, Informat Syst, Especially Elect Serv, Darmstadt, Germany.
[Hess, Thomas] Univ Munich, Informat Syst & Management, D-81377 Munich, Germany.
[Hess, Thomas] Univ Munich, Informat Syst & Management, Inst Informat Syst & New Media, D-81377 Munich, Germany.
[Hess, Thomas] Univ Munich, Informat Syst & Management, Ctr Internet Res & Media Integrat, D-81377 Munich, Germany.
C3 University of Munich; Technical University of Darmstadt; University of
Munich; University of Munich; University of Munich
RP Mahnke, R (autor correspondiente), Univ Munich, Informat Syst, Marchioninistr 15, D-81377 Munich, Germany.
EM mahnke@bwl.lmu.de; benlian@ise.tu-darmstadt.de; thess@bwl.lmu.de
RI Hess, Thomas K/A-1774-2016
OI Hess, Thomas/0000-0003-3969-7477
CR Adelaar T, 2003, J INF TECHNOL-UK, V18, P247, DOI 10.1080/0268396032000150799
Agarwal R, 2000, MIS QUART, V24, P665, DOI 10.2307/3250951
Bardhan IR, 2010, J MANAGE INFORM SYST, V26, P13, DOI 10.2753/MIS0742-1222260402
Birks DF, 2013, EUR J INFORM SYST, V22, P1, DOI 10.1057/ejis.2012.48
Bridges E, 2008, J BUS RES, V61, P309, DOI 10.1016/j.jbusres.2007.06.017
Brown T, 2008, HARVARD BUS REV, V86, P84
Cai S, 2011, INT J ELECTRON COMM, V15, P159, DOI 10.2753/JEC1086-4415150405
Carroll J. M., 1988, SIGCHI Bulletin, V19, P21, DOI 10.1145/49108.1045604
Chang HH, 2008, COMPUT HUM BEHAV, V24, P2336, DOI 10.1016/j.chb.2008.01.001
Charmaz K., 2014, Constructing grounded theory
Chen H, 1999, COMPUT HUM BEHAV, V15, P585, DOI 10.1016/S0747-5632(99)00038-2
Childers TL, 2001, J RETAILING, V77, P511, DOI 10.1016/S0022-4359(01)00056-2
Choi DH, 2007, INT J HUM-COMPUT ST, V65, P223, DOI 10.1016/j.ijhcs.2006.10.002
Csikszentmihalyi M, 2008, Flow the psychology of optimal experience
Csikszentmihalyi M., 1975, Beyond Boredom And Anxiety
Csikszentmihalyi M., 1994, The evolving self: A psychology for the third millennium
Dailey L, 2004, J BUS RES, V57, P795, DOI 10.1016/S0148-2963(02)00364-8
DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
Deng LQ, 2010, EUR J INFORM SYST, V19, P60, DOI 10.1057/ejis.2009.50
Engeser S, 2008, MOTIV EMOTION, V32, P158, DOI 10.1007/s11031-008-9102-4
Fishbein M., 1975, Beliefs, Attitude, Intention and Behavior: An Introduction to Theory and Research
Ghani J. A., 1991, Proceedings of the Twelfth International Conference on Information Systems, P229
GHANI JA, 1995, HUMAN COMPU, P291
GHANI JA, 1994, J PSYCHOL, V128, P381, DOI 10.1080/00223980.1994.9712742
Glaser B. G., 1967, BRIT J SOCIOLOGY, V20
Glaser B.G., 1992, EMERGENCE VS FORCING
Glaser B. G., 1967, The Discovery of Grounded Theory: Strategies for Qualitative Research
Grots A., 2009, MARKETING REV ST GAL, V26, P18, DOI [10.1007/s11621-009-0027-4, DOI 10.1007/S11621-009-0027-4]
Guru A, 2001, MANAGING INTERNET AND INTRANET TECHNOLOGIES IN ORGANIZATIONS: CHALLENGES AND OPPORTUNITIES, P50
Hampton-Sosa W, 2005, INT J ELECTRON COMM, V10, P55, DOI 10.1080/10864415.2005.11043965
Hoffman D. L., 1997, Information Society, V13, P43, DOI 10.1080/019722497129278
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
Hsiang Chen, 2000, Information Technology & People, V13, P263, DOI 10.1108/09593840010359473
Hsu CL, 2004, INFORM MANAGE-AMSTER, V41, P853, DOI 10.1016/j.im.2003.08.014
Huang MH, 2003, COMPUT HUM BEHAV, V19, P425, DOI 10.1016/S0747-5632(02)00080-8
Hutchison A.J., 2011, Qualitative Research in Psychology, V8, P247, DOI DOI 10.1080/14780880903304527
Jiang ZH, 2004, J MANAGE INFORM SYST, V21, P111, DOI 10.1080/07421222.2004.11045817
Kelle U., 1999, EINZELFALL TYPUS FAL
Klein LR, 2003, J INTERACT MARK, V17, P41, DOI 10.1002/dir.10046
Koh J, 2003, INT J ELECTRON COMM, V8, P75, DOI 10.1080/10864415.2003.11044295
Korzaan ML, 2003, J COMPUT INFORM SYST, V43, P25
Koufaris M, 2001, INT J ELECTRON COMM, V6, P115, DOI 10.1080/10864415.2001.11044233
Koufaris M, 2002, INFORM SYST RES, V13, P205, DOI 10.1287/isre.13.2.205.83
Kurz Andrea., 2007, Qualitative Marktforschung: Konzepte - Methoden - Analysen, V1, P463, DOI DOI 10.1007/978-3-8349-9258-1_29
Lamnek Siegfried., 2005, Qualitative Sozialforschung: Lehrbuch
Laugwitz B, 2008, LECT NOTES COMPUT SC, V5298, P63, DOI 10.1007/978-3-540-89350-9_6
Leonard Dorothy., 1995, WELLSPRINGS KNOWLEDG
Lin CS, 2005, INFORM MANAGE-AMSTER, V42, P683, DOI 10.1016/j.im.2004.04.003
Lindgaard G., 1999, P OZCHI 99 WAGG WAGG, P149
Liu SH, 2009, COMPUT EDUC, V52, P599, DOI 10.1016/j.compedu.2008.11.002
Lombard M., 1997, J COMPUT-MEDIAT COMM, DOI [DOI 10.1111/J.10836101.1997.TB00072.X, 10.1111/j.1083, 10.1111/j.1083-6101.1997.tb00072.x, DOI 10.1111/J.1083-6101.1997.TB00072.X]
Luna D, 2003, ADV CONSUM RES, V30, P280
Luna D, 2002, J ACAD MARKET SCI, V30, P397, DOI 10.1177/009207002236913
Mahnke R., 2012, 20 EUR C INF SYST EC, P1
Massimini F., 1991, AUSSERGEWOHNLICHE ER, P291
Mey G., 1999, ADOLESCENZ IDENTITAT
Minichiello V., 2008, IN DEPTH INTERVIEWIN
Nel D, 1999, INTERNET RES, V9, P109, DOI 10.1108/10662249910264873
Novak T.P., 1997, INFORMS MARK SCI INT, P1
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
Novak TP, 2003, J CONSUM PSYCHOL, V13, P3, DOI 10.1207/S15327663JCP13-1&2_01
Ostrom AL, 2010, J SERV RES-US, V13, P4, DOI 10.1177/1094670509357611
Pace S, 2004, INT J HUM-COMPUT ST, V60, P327, DOI 10.1016/j.ijhcs.2003.08.005
Pace S., 2004, Issues in Informing Science Information Technology, V1, P341
Park S.R., 2008, Journal of Virtual Worlds Research, V1, P1
Pöyry E, 2013, ELECTRON COMMER R A, V12, P224, DOI 10.1016/j.elerap.2013.01.003
Reinhardt C, 2006, Z SPORTPSYCHOL, V13, P140, DOI 10.1026/1612-5010.13.4.140
Rettie R, 2001, INTERNET RES, V11, P103, DOI 10.1108/10662240110695070
Rheinberg F, 2003, Z PSYCHOL, V211, P161, DOI 10.1026//0044-3409.211.4.161
Rheinberg F., 2003, TESTS TRENDS ED, V16, P261
Rheinberg F., 2008, MOTIVATION, V7
Rheinberg F., 2010, Motivation und Handeln, P365, DOI [10.1007/978-3-642-12693-2_14, DOI 10.1007/978-3-642-12693-2_14]
Rheinberg F., 2008, MOTIVATION ACTION, P329
Richard MO, 2005, J BUS RES, V58, P1019, DOI 10.1016/j.jbusres.2004.04.001
Sánchez-Franco MJ, 2006, BEHAV INFORM TECHNOL, V25, P19, DOI 10.1080/01449290500124536
Siekpe J.S., 2005, J ELECTRON COMMER RE, V6, P31
Skadberg YX, 2004, COMPUT HUM BEHAV, V20, P403, DOI 10.1016/S0747-5632(03)00050-5
Smith DN, 2004, J BUS RES, V57, P1199, DOI 10.1016/S0148-2963(02)00330-2
Stoll O., 2005, J SPORT PSYCHOL, V12, P75
TREVINO LK, 1992, COMMUN RES, V19, P539, DOI 10.1177/009365092019005001
van der Heijden H, 2004, MIS QUART, V28, P695, DOI 10.2307/25148660
WEBSTER J, 1993, COMPUT HUM BEHAV, V9, P411, DOI 10.1016/0747-5632(93)90032-N
Witzel A., 1996, TRUE STORIES THEORY, P49
WITZEL A, 1989, QUALITATIVE FORSCHUN, P227
Witzel A., 2000, Forum: Qualitative Social Research (Online Journal)
NR 85
TC 39
Z9 44
U1 7
U2 141
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1086-4415
EI 1557-9301
J9 INT J ELECTRON COMM
JI Int. J. Electron. Commer.
PD SPR
PY 2015
VL 19
IS 3
BP 54
EP 89
DI 10.1080/10864415.2015.1000222
PG 36
WC Business; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA CE1RR
UT WOS:000351590100005
DA 2024-03-27
ER
PT J
AU Zhang, M
Chen, YG
Zhang, S
Zhang, WY
Li, YX
Yang, SQ
AF Zhang, Miao
Chen, Yuangao
Zhang, Shuai
Zhang, Wenyu
Li, Yixiao
Yang, Shuiqing
TI Understanding mobile learning continuance from an online-cum-offline
learning perspective: a SEM-neural network method
SO INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS
LA English
DT Article
DE mobile learning; perceived integration; gratifications; neural network;
multi-analytic method
ID HIGHER-EDUCATION; GRATIFICATIONS; STUDENTS; ADOPTION; MODEL;
DETERMINANTS; TECHNOLOGY; ACCEPTANCE; UNIVERSITY; INTERNET
AB Based on uses, gratifications theory and literature related to perceived integration, this study investigated the factors that influence college students' mobile learning continuance from an online-cum-offline learning perspective. A research model was developed and tested against data collected from 261 college students who are the mobile learning users of an online flipped learning platform in China. A multi-analytic method was employed whereby the proposed model was first tested using structural equation modelling (SEM), and the results of the SEM were used as inputs for a neural network approach to explain mobile learning continuance. The results show that perceived integration affects mobile learning continuance directly and indirectly via students' extrinsic gratification (social need) and intrinsic gratifications (affective need and entertainment need). According to the normalised importance, affective need is the most significant factor affecting mobile learning continuance, following by social need and entertainment need.
C1 [Zhang, Miao] Zhejiang Univ Technol, Sch Educ Sci & Technol, Hangzhou 310014, Peoples R China.
[Chen, Yuangao; Zhang, Shuai; Zhang, Wenyu; Li, Yixiao; Yang, Shuiqing] Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, 18 XueYuan St, Hangzhou 310018, Peoples R China.
C3 Zhejiang University of Technology; Zhejiang University of Finance &
Economics
RP Yang, SQ (autor correspondiente), Zhejiang Univ Finance & Econ, Sch Informat Management & Artificial Intelligence, 18 XueYuan St, Hangzhou 310018, Peoples R China.
EM miranda.zhangm@foxmail.com; chenyg@zufe.edu.cn; zs760914@sina.com;
wyzhang@e.ntu.edu.sg; yxli@zufe.edu.cn; yangshuiqing@zufe.edu.cn
RI Chen, Yuangao/AAU-9505-2020; yang, sq/AAA-5465-2022
OI Chen, Yuangao/0000-0002-5960-9932; yang, sq/0000-0003-1864-814X
FU Key Project of Zhejiang University of Finance and Economics Ideological
and Political Excellence Project [2019SZ008]; National Natural Science
Foundation of China [61502414, 71472163]; Zhejiang Provincial Natural
Science Foundation of China [LY18G020013, LY18G020014]
FX This work was funded by Key Project of Zhejiang University of Finance
and Economics Ideological and Political Excellence Project, Grant No.
2019SZ008. This research was also funded by the National Natural Science
Foundation of China, Grant Nos. 61502414 and 71472163, and by Zhejiang
Provincial Natural Science Foundation of China, Grant Nos. LY18G020013
and LY18G020014.
CR Cao LL, 2015, J RETAILING, V91, P198, DOI 10.1016/j.jretai.2014.12.005
Chee KN, 2017, EDUC TECHNOL SOC, V20, P113
Chen YC, 2014, COMPUT HUM BEHAV, V31, P159, DOI 10.1016/j.chb.2013.10.040
Cheon J, 2012, COMPUT EDUC, V59, P1054, DOI 10.1016/j.compedu.2012.04.015
Cheung CMK, 2011, COMPUT HUM BEHAV, V27, P1337, DOI 10.1016/j.chb.2010.07.028
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Chung CJ, 2019, COMPUT EDUC, V129, P1, DOI 10.1016/j.compedu.2018.10.010
Chunmei Gan, 2015, Journal of Systems and Information Technology, V17, P351, DOI 10.1108/JSIT-06-2015-0052
DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
Delgado-Ballester E, 2008, INT J ELECTRON COMM, V12, P81, DOI 10.2753/JEC1086-4415120305
Gallego MD, 2016, COMPUT EDUC, V100, P81, DOI 10.1016/j.compedu.2016.05.001
Gefen D., 2000, COMMUN ASSOC INF SYS, V4, P407, DOI DOI 10.17705/1CAIS.00407
Guo ZX, 2010, COMMUN ASSOC INF SYS, V27, P339
Gwo-Jen Hwang, 2014, International Journal of Mobile Learning and Organisation, V8, P83, DOI 10.1504/IJMLO.2014.062346
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hamidi H, 2018, TELEMAT INFORM, V35, P1053, DOI 10.1016/j.tele.2017.09.016
Hashim KF, 2015, BRIT J EDUC TECHNOL, V46, P381, DOI 10.1111/bjet.12148
Haykin S., 1994, NEURAL NETWORKS COMP
Heflin H, 2017, COMPUT EDUC, V107, P91, DOI 10.1016/j.compedu.2017.01.006
Hew TS, 2016, J COMPUT INFORM SYST, V56, P352, DOI 10.1080/08874417.2016.1164497
Holbrook MB, 2006, J BUS RES, V59, P714, DOI 10.1016/j.jbusres.2006.01.008
Hsiao CH, 2016, TELEMAT INFORM, V33, P342, DOI 10.1016/j.tele.2015.08.014
Huang YM, 2015, BRIT J EDUC TECHNOL, V46, P437, DOI 10.1111/bjet.12147
Joo YJ, 2016, ETR&D-EDUC TECH RES, V64, P611, DOI 10.1007/s11423-016-9436-7
Karimi S, 2016, COMPUT HUM BEHAV, V63, P769, DOI 10.1016/j.chb.2016.06.014
Katz E., 1973, PUBLIC OPIN QUART, V37, DOI [10.1086/268109, DOI 10.1086/268109]
Kim DJ, 2012, INFORM SYST FRONT, V14, P409, DOI 10.1007/s10796-010-9267-8
Kim HW, 2007, DECIS SUPPORT SYST, V43, P111, DOI 10.1016/j.dss.2005.05.009
Kim HJ, 2017, COMPUT EDUC, V113, P108, DOI 10.1016/j.compedu.2017.05.015
Lee HH, 2010, J MARK CHANNELS, V17, P281, DOI 10.1080/1046669X.2010.512859
Liang HG, 2007, MIS QUART, V31, P59
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Mondi M, 2008, EDUC TECHNOL SOC, V11, P241
Motiwalla LF, 2007, COMPUT EDUC, V49, P581, DOI 10.1016/j.compedu.2005.10.011
Nie J, 2020, COMPUT EDUC, V156, DOI 10.1016/j.compedu.2020.103942
Nunnally J.C., 1978, Psychometric Theory, P86
Park SY, 2012, BRIT J EDUC TECHNOL, V43, P592, DOI 10.1111/j.1467-8535.2011.01229.x
Phua J, 2017, COMPUT HUM BEHAV, V72, P115, DOI 10.1016/j.chb.2017.02.041
Pimmer C, 2016, COMPUT HUM BEHAV, V63, P490, DOI 10.1016/j.chb.2016.05.057
PODSAKOFF PM, 1986, J MANAGE, V12, P531, DOI 10.1177/014920638601200408
Punyanunt-Carter NM, 2017, COMPUT HUM BEHAV, V75, P870, DOI 10.1016/j.chb.2017.06.034
Sarrab M, 2016, COMPUT HUM BEHAV, V55, P100, DOI 10.1016/j.chb.2015.09.003
Sharma SK, 2017, BEHAV INFORM TECHNOL, V36, P1053, DOI 10.1080/0144929X.2017.1340973
Shi N, 2010, P ANN HICSS, P3049
Shi S, 2016, COMPUT HUM BEHAV, V62, P578, DOI 10.1016/j.chb.2016.04.017
Shu H, 2018, INTERNET HIGH EDUC, V39, P13, DOI 10.1016/j.iheduc.2018.05.003
Sorebo O, 2009, COMPUT EDUC, V53, P1177, DOI 10.1016/j.compedu.2009.06.001
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Wang YS, 2009, BRIT J EDUC TECHNOL, V40, P92, DOI 10.1111/j.1467-8535.2007.00809.x
Wang YM, 2007, EXPERT SYST APPL, V32, P336, DOI 10.1016/j.eswa.2005.11.029
Wu JH, 2010, COMPUT HUM BEHAV, V26, P1862, DOI 10.1016/j.chb.2010.07.033
Wu WH, 2012, COMPUT EDUC, V59, P817, DOI 10.1016/j.compedu.2012.03.016
Yang SQ, 2019, BRIT J EDUC TECHNOL, V50, P626, DOI 10.1111/bjet.12634
Yang SQ, 2018, COMPUT HUM BEHAV, V89, P16, DOI 10.1016/j.chb.2018.07.032
Yang SQ, 2016, COMPUT HUM BEHAV, V64, P486, DOI 10.1016/j.chb.2016.07.010
Yang SQ, 2016, INTERNET RES, V26, P982, DOI 10.1108/IntR-01-2015-0010
Yang SQ, 2015, BEHAV INFORM TECHNOL, V34, P1135, DOI 10.1080/0144929X.2014.988176
Zhang LZ, 2013, COMPUT HUM BEHAV, V29, P2712, DOI 10.1016/j.chb.2013.07.009
Zydney JM, 2016, COMPUT EDUC, V94, P1, DOI 10.1016/j.compedu.2015.11.001
NR 59
TC 8
Z9 8
U1 5
U2 30
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 1470-949X
EI 1741-5217
J9 INT J MOB COMMUN
JI Int. J. Mob. Commun.
PY 2022
VL 20
IS 1
BP 105
EP 127
DI 10.1504/IJMC.2022.119995
PG 23
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA YC2KP
UT WOS:000739525400005
DA 2024-03-27
ER
PT J
AU Naysary, B
AF Naysary, Babak
TI A game theory-based analysis of merchants' mobile payment adoption using
hybrid SEM-neural network approach
SO COMPETITIVENESS REVIEW
LA English
DT Article; Early Access
DE Mobile payment; Merchants' perspective; Trust; Opportunism; Malaysia;
G23; G41
ID INTERFIRM RELATIONSHIPS; CONSUMER ADOPTION; PERCEIVED RISKS; FIT
INDEXES; TRUST; DETERMINANTS; OPPORTUNISM; SERVICES; SYSTEMS;
PERSPECTIVES
AB PurposeDriven by the evidence from the literature on the significance of mobile (m-)payment in economic growth and productivity and at the same time the relative dismal adoption of this service, the purpose of present paper is to elucidate the merchants' m-payment adoption from the perspective of trust, drawing upon the game theory framework, in the Malaysian context. Design/methodology/approachAn online survey consisting of 302 respondents was carried out to investigate the impact of trust and opportunism on merchants' perceived trustworthiness using a two-staged structural equation modeling-neural network approach to determine the significance and relative importance of variables. This study also applies a game-theoretic approach to analyze the impact of trust on the relationship between merchants and m-payment service providers. FindingsThe results indicate a positive and statistically significant relationship between merchant trust, merchant opportunism and perceived trustworthiness, and a statistically significant negative relationship was found between m-payment provider opportunism and perceived trustworthiness. The findings from the prisoner's dilemma two-player model indicate that the scenarios of mutual trust and mutual opportunism as paradigmatic of cooperation and defection produce the best and worse outcomes, respectively. An intriguing result was the positive impact of merchant opportunism on perceived trustworthiness, which indicates a very calculative orientation of merchants in m-payment contracting. Originality/valueTo the best of the authors' knowledge, this is among the first attempts to propose a game theory approach to the interaction between merchants and m-payment providers under the framework of trust and opportunism. A game theory study in the context of m-payment adoption can contribute to the theoretical literature by providing insights into the decision-making processes of merchants. By incorporating trust and opportunism into the game theory model, we can gain a better understanding of how they affect the decision-making process and overall adoption rates. The conclusions and implications provide useful insights for managers of both m-payment platforms and merchants in this relational exchange. The results of the present research can provide insights into the factors that influence merchant decisions and guide them toward suitable partnerships for successful adoption and can guide authorities for policy interventions and supporting adoption efforts.
C1 [Naysary, Babak] Birmingham City Univ, Business Sch, Birmingham, England.
C3 Birmingham City University
RP Naysary, B (autor correspondiente), Birmingham City Univ, Business Sch, Birmingham, England.
EM bnaysary@hotmail.com
RI Naysary, Babak/D-8911-2014
OI Naysary, Babak/0000-0003-0464-7402
CR Ahani A, 2017, COMPUT HUM BEHAV, V75, P560, DOI 10.1016/j.chb.2017.05.032
Al Momani K.M.D.K., 2021, BIG DATA DRIVEN DIGI, P59
Al-Bakri A., 2014, SMALL, V10, P49
Al-Sartawi A.M.A.M., 2022, INTERNET THINGS INTE
Apanasevic T, 2016, INT J BANK MARK, V34, P37, DOI 10.1108/IJBM-06-2014-0064
Arsenyan J, 2015, EXPERT SYST APPL, V42, P2073, DOI 10.1016/j.eswa.2014.10.010
Aryadoust V., 2014, CaMLA Work. Pap, V2, P1
Asa'd Israa Aref Abu, 2023, From the Internet of Things to the Internet of Ideas: The Role of Artificial Intelligence: Proceedings of EAMMIS 2022. Lecture Notes in Networks and Systems (557), P529, DOI 10.1007/978-3-031-17746-0_42
Bagozzi RP, 1988, Journal of the Academy of Marketing Science, V16, P74, DOI DOI 10.1007/BF02723327
Baidoo S.T., 2019, International Social Science Journal, V69, P63, DOI [DOI 10.1111/ISSJ.12200, 10.1111/issj.12200]
Barkhordari M, 2017, INF SYST E-BUS MANAG, V15, P89, DOI 10.1007/s10257-016-0311-1
BENTLER PM, 1980, PSYCHOL BULL, V88, P588, DOI 10.1037/0033-2909.107.2.238
BENTLER PM, 1990, PSYCHOL BULL, V107, P238, DOI 10.1037/0033-2909.107.2.238
Cao Q., 2019, INTEGRATING CONTEXT
Capgemini, 2016, TOP 10 TRENDS PAYM 2
Carbo-Valverde S., 2013, 26 AUSTRALASIAN FINA, DOI DOI 10.2139/SSRN.2310273
Chandra S, 2010, COMMUN ASSOC INF SYS, V27, P561
Chiles TH, 1996, ACAD MANAGE REV, V21, P73, DOI 10.2307/258630
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Cooper R, 1996, GAME ECON BEHAV, V12, P187, DOI 10.1006/game.1996.0013
Dahlberg T, 2015, ELECTRON COMMER R A, V14, P265, DOI 10.1016/j.elerap.2015.07.006
Dahlstrom R, 2014, INT J BANK MARK, V32, P268, DOI 10.1108/IJBM-03-2014-0042
Dasgupta P., 1988, Trust: Making and Breaking Cooperative Relations, P49
de Kerviler G, 2016, J RETAIL CONSUM SERV, V31, P334, DOI 10.1016/j.jretconser.2016.04.011
Elsharnouby, 2013, Journal of Relationship Marketing, vol, V12, P141, DOI DOI 10.1080/15332667.2013.794103
Esmaeili M, 2009, EUR J OPER RES, V195, P442, DOI 10.1016/j.ejor.2008.02.026
Guiso L., 2000, ROLE SOCIAL CAPITAL, P1
Guo J, 2016, TELECOMMUN POLICY, V40, P147, DOI 10.1016/j.telpol.2015.09.008
Gupta S, 2023, INT J HUM-COMPUT INT, V39, P3745, DOI 10.1080/10447318.2022.2104408
Hannoon A., 2021, RELATIONSHIP FINANCI, V974, P337
Hansen T, 2012, J SERV RES-US, V15, P280, DOI 10.1177/1094670512439105
HARRINGTON JE, 1995, GAME ECON BEHAV, V8, P364, DOI 10.1016/S0899-8256(05)80006-5
Hedayati A., 2019, J MONEY EC, V12, P419
HEIDE JB, 1992, J MARKETING, V56, P32, DOI 10.2307/1252040
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Jabarin M., 2019, Investment Management and Financial Innovations, V16, P156, DOI [10.21511/imfi.16(4).2019.14, DOI 10.21511/IMFI.16(4).2019.14]
Jap SD, 2001, INT J RES MARK, V18, P19, DOI 10.1016/S0167-8116(01)00028-3
Johnson VL, 2018, COMPUT HUM BEHAV, V79, P111, DOI 10.1016/j.chb.2017.10.035
Joshi AW, 1999, CAN J ADM SCI, V16, P334
Jurisic M., 2012, 2012 35th International Convention on Information and Communication Technology, Electronics and Microelectronics, P1093
Kapoor KK, 2015, INFORM SYST FRONT, V17, P1039, DOI 10.1007/s10796-014-9484-7
Kaur P, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102059
Kim C, 2010, COMPUT HUM BEHAV, V26, P310, DOI 10.1016/j.chb.2009.10.013
Koshel N.V., 2018, EUROPEAN RES STUDIES, V21, P144
Kreps D., 1990, PERSPECTIVES POSITIV, P90, DOI [DOI 10.1017/CBO9780511571657.006, 10.1017/CBO9780511571657.006, 10.1017/cbo9780511571657.006]
Lado AA, 2008, STRATEGIC MANAGE J, V29, P401, DOI 10.1002/smj.667
Leong LY, 2022, INTERNET RES, V32, P1757, DOI 10.1108/INTR-06-2021-0348
Liébana-Cabanillas F, 2017, TECHNOL FORECAST SOC, V120, P32, DOI 10.1016/j.techfore.2017.04.002
Liébana-Cabanillas F, 2018, TECHNOL FORECAST SOC, V129, P117, DOI 10.1016/j.techfore.2017.12.015
Mallat N, 2005, ICMB 2005: International Conference on Mobile Business, P347, DOI 10.1109/ICMB.2005.58
Mallat N., 2008, EXPLORING MERCHANT A, P347
March J.G., 1989, REDISCOVERING I ORG, P227
Meyers L.S., 2005, APPL MULTIVARIATE RE, P722
Mishra V, 2022, EUR J INFORM SYST, V31, P74, DOI 10.1080/0960085X.2021.1978338
Moghavvemi S, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102364
Najjar M, 2023, J SUSTAIN FINANC INV, V13, P744, DOI 10.1080/20430795.2022.2030663
Nguyen TDA, 2020, 2020 ACM/SIGDA INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE GATE ARRAYS (FPGA '20), P29, DOI 10.1145/3373087.3375305
Nour A., 2020, AN NAJAH U J RES B H, V36, P2181, DOI 10.35552/0247-036-010-006
Nour A., 2022, AN NAJAH U J RES, V36, P2180
Peters Hans J.M., 2015, Game theory: A Multileveled approach, V2nd
Pham TTT, 2015, TECHNOL SOC, V43, P159, DOI 10.1016/j.techsoc.2015.05.004
Pidugu K., 2015, GORDON I BUSINESS SC, V14449316, P1
Pu XD, 2020, INT J MOB COMMUN, V18, P343, DOI 10.1504/IJMC.2020.107145
Rouibah K, 2016, ELECTRON COMMER R A, V19, P33, DOI 10.1016/j.elerap.2016.07.001
Sachova R., 2016, SECURITY MOBILE PAYM, P487
Shao Z, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.100823
Sharma SK, 2019, INFORM SYST FRONT, V21, P815, DOI 10.1007/s10796-017-9775-x
Singh N, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101894
Singh S, 2020, MANAGE DECIS, V58, P1675, DOI 10.1108/MD-09-2019-1318
Sirdeshmukh D, 2002, J MARKETING, V66, P15, DOI 10.1509/jmkg.66.1.15.18449
van Witteloostuijn A., 2003, INT STUD MANAG ORG, V33, P53
Wathne KH, 2000, J MARKETING, V64, P36, DOI 10.1509/jmkg.64.4.36.18070
WILLIAMSON OE, 1993, J LAW ECON, V36, P453, DOI 10.1086/467284
Xin H, 2015, J COMPUT INFORM SYST, V55, P1, DOI 10.1080/08874417.2015.11645781
Xu XY, 2020, FINANC RES LETT, V35, DOI 10.1016/j.frl.2019.101310
Yang YQ, 2015, IND MANAGE DATA SYST, V115, P253, DOI 10.1108/IMDS-08-2014-0243
Yeboah E., 2020, Conference on e-business, e-services and e-society, P204
NR 77
TC 0
Z9 0
U1 2
U2 2
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1059-5422
EI 2051-3143
J9 COMPET REV
JI Compet. Rev.
PD 2023 AUG 15
PY 2023
DI 10.1108/CR-04-2023-0074
EA AUG 2023
PG 21
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA O6WY1
UT WOS:001045198300001
DA 2024-03-27
ER
PT J
AU Camilleri, MA
Troise, C
AF Camilleri, Mark Anthony
Troise, Ciro
TI Live support by chatbots with artificial intelligence: A future research
agenda
SO SERVICE BUSINESS
LA English
DT Article
DE Conversational agents; Online customer services; Customer experience;
Anthropomorphism; Artificial intelligence
ID ENGAGEMENT; COMMUNICATION; FRAMEWORK; RESPONSES; MACHINES; TRUST; USAGE
AB This research uses a Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol to investigate the utility of artificially intelligent (AI) conversational chatbots in service business settings. The findings shed light on key theoretical underpinnings focussed on human-computer interactions and clarify the benefits and costs of using responsive chatbot technologies. This contribution implies that, for the time being, works are still in progress for interactive, anthropomorphic chatbots to mimic human customer services agents' verbal, vocal and visual cues, when they respond to online queries. In conclusion it puts forward plausible research avenues in this promising area of study.
C1 [Camilleri, Mark Anthony] Univ Malta, Fac Media & Knowledge Sci, Dept Corp Commun, Msida 2080, Malta.
[Camilleri, Mark Anthony] Univ Edinburgh, Business Sch, Bucchleuch Pl, Edinburgh EH8 9JS, Midlothian, Scotland.
[Troise, Ciro] Univ Turin, Dept Management, Turin, Italy.
C3 University of Malta; University of Edinburgh; University of Turin
RP Camilleri, MA (autor correspondiente), Univ Malta, Fac Media & Knowledge Sci, Dept Corp Commun, Msida 2080, Malta.; Camilleri, MA (autor correspondiente), Univ Edinburgh, Business Sch, Bucchleuch Pl, Edinburgh EH8 9JS, Midlothian, Scotland.
EM mark.a.camilleri@um.edu.mt
RI Camilleri, Mark Anthony/R-4574-2016; Troise, Ciro/GRO-0940-2022; Paleja,
Heer/IQT-1538-2023
OI Camilleri, Mark Anthony/0000-0003-1288-4256; Troise,
Ciro/0000-0002-8899-8949;
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Adamopoulou E., 2020, MACHINE LEARNING APP, V2, DOI DOI 10.1016/J.MLWA.2020.100006
Balakrishnan J, 2021, PSYCHOL MARKET, V38, P643, DOI 10.1002/mar.21462
Becker M, 2023, SERV BUS, V17, P137, DOI 10.1007/s11628-022-00499-4
Belanche D, 2021, PSYCHOL MARKET, V38, P2357, DOI 10.1002/mar.21532
Belanche D, 2020, SERV IND J, V40, P203, DOI 10.1080/02642069.2019.1672666
Belanche D, 2020, J SERV MANAGE, V31, P267, DOI 10.1108/JOSM-05-2019-0156
Belanche D, 2021, ELECTRON MARK, V31, P477, DOI 10.1007/s12525-020-00432-5
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Borau S, 2021, PSYCHOL MARKET, V38, P1052, DOI 10.1002/mar.21480
Brachten F, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102375
Buhalis D., 2020, Information and Communication Technologies in Tourism 2020, P231, DOI [DOI 10.1007/978-3-030-36737-4_19, 10.1007/978-3-030-36737-4_19]
Caldarini G, 2022, INFORMATION, V13, DOI 10.3390/info13010041
Camilleri M.A., 2020, INT J BIG DATA MANAG, V1, P50, DOI DOI 10.1504/IJBDM.2020.106876
Camilleri MA, 2022, TECHNOL SOC, V71, DOI 10.1016/j.techsoc.2022.102098
Camilleri MA, 2022, J STRATEGY MANAG, V15, P377, DOI 10.1108/JSMA-02-2021-0045
Capriotti P, 2021, STRATEGIC CORPORATE COMMUNICATION IN THE DIGITAL AGE, P33, DOI 10.1108/978-1-80071-264-520211003
Castillo D, 2021, SERV IND J, V41, P900, DOI 10.1080/02642069.2020.1787993
Chen JS, 2021, INT J RETAIL DISTRIB, V49, P1512, DOI 10.1108/IJRDM-08-2020-0312
Cheng Y, 2020, J CONTING CRISIS MAN, V28, P339, DOI 10.1111/1468-5973.12319
Chong T, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102735
Chopra K, 2019, INT J RETAIL DISTRIB, V47, P331, DOI 10.1108/IJRDM-11-2018-0251
Ciechanowski L, 2019, FUTURE GENER COMP SY, V92, P539, DOI 10.1016/j.future.2018.01.055
Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
De Cicco R, 2020, INT J RETAIL DISTRIB, V48, P1213, DOI 10.1108/IJRDM-12-2019-0406
Dippold D, 2020, DISCOURSE CONTEXT ME, V37, DOI 10.1016/j.dcm.2020.100432
Eren BA, 2021, INT J BANK MARK, V39, P294, DOI 10.1108/IJBM-02-2020-0056
Flavian C, 2021, SERV IND J, V41, P853, DOI 10.1080/02642069.2021.1989177
Flavián C, 2022, J SERV MANAGE, V33, P293, DOI 10.1108/JOSM-10-2020-0378
Forgas-Coll S, 2022, SERV BUS, V16, P359, DOI 10.1007/s11628-022-00492-x
Grimes GM, 2021, DECIS SUPPORT SYST, V144, DOI 10.1016/j.dss.2021.113515
Hajarian M, 2021, STRATEGIC CORPORATE COMMUNICATION IN THE DIGITAL AGE, P235, DOI 10.1108/978-1-80071-264-520211014
Hildebrand C, 2021, J ACAD MARKET SCI, V49, P659, DOI 10.1007/s11747-020-00753-z
Hoyer WD, 2020, J INTERACT MARK, V51, P57, DOI 10.1016/j.intmar.2020.04.001
Huang JW, 2011, COMPUT HUM BEHAV, V27, P1963, DOI 10.1016/j.chb.2011.05.003
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huang YS, 2021, SERV IND J, V41, P964, DOI 10.1080/02642069.2021.1957845
Hung SC, 2019, SUN YAT-SEN J HUMANI, P1, DOI 10.1007/s11628-018-0364-y
Iovine A, 2020, DECIS SUPPORT SYST, V131, DOI 10.1016/j.dss.2020.113250
Jiménez-Barreto J, 2021, INT J CONTEMP HOSP M, V33, P3860, DOI 10.1108/IJCHM-10-2020-1244
Kasilingam DL, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101280
Kull AJ, 2021, J BUS RES, V135, P840, DOI 10.1016/j.jbusres.2021.03.005
Kushwaha AK, 2021, IND MARKET MANAG, V98, P207, DOI 10.1016/j.indmarman.2021.08.011
Lalicic L, 2021, J BUS RES, V129, P891, DOI 10.1016/j.jbusres.2020.11.005
Lee SM, 2020, SERV BUS, V14, P1, DOI 10.1007/s11628-019-00408-2
Leung XY, 2020, J HOSP TOUR MANAG, V45, P377, DOI 10.1016/j.jhtm.2020.09.004
Lewis SC, 2019, DIGIT JOURNAL, V7, P409, DOI 10.1080/21670811.2019.1577147
Li L, 2021, ELECTRON MARK, V31, P575, DOI 10.1007/s12525-020-00454-z
Lou C, 2022, INT J ADVERT, V41, P655, DOI 10.1080/02650487.2021.1951510
Lucia-Palacios L, 2021, SERV BUS, V15, P253, DOI 10.1007/s11628-021-00442-z
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Matiatou, 2019, STRAT PERSP DEST MKT, P174
McLean G, 2020, INT J CONTEMP HOSP M, V32, P1795, DOI 10.1108/IJCHM-07-2019-0605
Melián-González S, 2021, CURR ISSUES TOUR, V24, P192, DOI 10.1080/13683500.2019.1706457
Mero J, 2018, ELECTRON MARK, V28, P205, DOI 10.1007/s12525-017-0281-2
Moher D, 2010, INT J SURG, V8, P658, DOI [10.1016/j.ijsu.2010.07.299, 10.1136/bmj.b2700, 10.1371/journal.pmed.1000097, 10.1136/bmj.b2535, 10.1186/2046-4053-4-1, 10.1016/j.ijsu.2010.02.007, 10.1136/bmj.i4086]
Moon Y, 1996, COMMUN RES, V23, P651, DOI 10.1177/009365096023006002
Moriuchi E, 2021, J STRATEG MARK, V29, P375, DOI 10.1080/0965254X.2020.1740766
Mostafa RB, 2022, EUR J MARKETING, V56, P1748, DOI 10.1108/EJM-02-2020-0084
Mozafari N, 2022, J SERV MANAGE, V33, P221, DOI 10.1108/JOSM-10-2020-0380
Murtarelli G, 2021, J BUS RES, V129, P927, DOI 10.1016/j.jbusres.2020.09.018
Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
NASS C, 1994, HUMAN FACTORS IN COMPUTING SYSTEMS, CHI '94 CONFERENCE PROCEEDINGS - CELEBRATING INTERDEPENDENCE, P72, DOI 10.1145/191666.191703
Neuburger L., 2018, TOURISM PLANNING DES, P183, DOI DOI 10.1108/978-1-78756-291-220181009
Ngai EWT, 2021, ELECTRON COMMER R A, V50, DOI 10.1016/j.elerap.2021.101098
Nuruzzaman M, 2020, KNOWL-BASED SYST, V196, DOI 10.1016/j.knosys.2020.105810
Orden-Mejía M, 2022, CURR ISSUES TOUR, V25, P2854, DOI 10.1080/13683500.2021.1997942
Pantano E, 2022, J SERV RES-US, V25, P583, DOI 10.1177/10946705221103538
Pantano E, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102096
Paschou T, 2020, IND MARKET MANAG, V89, P278, DOI 10.1016/j.indmarman.2020.02.012
Pfeuffer N, 2019, BUS INFORM SYST ENG+, V61, P523, DOI 10.1007/s12599-019-00599-y
Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Ranoliya BR, 2017, 2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), P1525, DOI 10.1109/ICACCI.2017.8126057
Rese A., 2020, Journal of Retailing and Consumer Services, V56, P1, DOI DOI 10.1016/J.JRETCONSER.2020.102176
Rha JS, 2022, SERV BUS, V16, P77, DOI 10.1007/s11628-022-00481-0
Riikkinen M, 2018, INT J BANK MARK, V36, P1145, DOI 10.1108/IJBM-01-2017-0015
Roy R, 2021, J BUS RES, V126, P23, DOI 10.1016/j.jbusres.2020.12.051
Rutschi C, 2020, DATA BASE ADV INF SY, V51, P104, DOI 10.1145/3380799.3380808
Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
Schanke S, 2021, INFORM SYST RES, V32, P736, DOI 10.1287/isre.2021.1015
Schepers J, 2022, J SERV RES-US, V25, P565, DOI 10.1177/10946705221107704
Schuetzler RM, 2020, J MANAGE INFORM SYST, V37, P875, DOI 10.1080/07421222.2020.1790204
Seering Joseph, 2018, Proceedings of the ACM on Human-Computer Interaction, V2, DOI 10.1145/3274426
Selamat MA, 2021, TECHNOL SOC, V66, DOI 10.1016/j.techsoc.2021.101685
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shum HY, 2018, FRONT INFORM TECH EL, V19, P10, DOI 10.1631/FITEE.1700826
Sidaoui K, 2020, J SERV MANAGE, V31, P745, DOI 10.1108/JOSM-11-2019-0341
Stoeckli E, 2020, ELECTRON MARK, V30, P369, DOI 10.1007/s12525-019-00359-6
Thomaz F, 2020, J ACAD MARKET SCI, V48, P43, DOI 10.1007/s11747-019-00704-3
Tsai WHS, 2021, J RES INTERACT MARK, V15, P460, DOI 10.1108/JRIM-12-2019-0200
Van Pinxteren MME, 2020, J SERV MANAGE, V31, P203, DOI 10.1108/JOSM-06-2019-0175
Wang HS, 2019, J ACAD MARKET SCI, V47, P919, DOI 10.1007/s11747-019-00664-8
Wei W, 2021, ACM T INFORM SYST, V39, DOI 10.1145/3456414
Wilkinson D, 2021, ACM T INFORM SYST, V39, DOI 10.1145/3441715
Zhang JJY, 2023, J INTERNET COMMER, V22, P122, DOI 10.1080/15332861.2021.1966723
NR 96
TC 11
Z9 11
U1 21
U2 78
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1862-8516
EI 1862-8508
J9 SERV BUS
JI Serv. Bus.
PD MAR
PY 2023
VL 17
IS 1
SI SI
BP 61
EP 80
DI 10.1007/s11628-022-00513-9
EA NOV 2022
PG 20
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 9V4LT
UT WOS:000881556200001
DA 2024-03-27
ER
PT J
AU Abubakar, AM
Namin, BH
Harazneh, I
Arasli, H
Tunç, T
AF Abubakar, A. Mohammed
Namin, Boshra Hejraty
Harazneh, Ibrahim
Arasli, Huseyin
Tunc, Tugba
TI Does gender moderates the relationship between favoritism/nepotism,
supervisor incivility, cynicism and workplace withdrawal: A neural
network and SEM approach
SO TOURISM MANAGEMENT PERSPECTIVES
LA English
DT Article
DE Favoritism/nepotism; Employee cynicism; Supervisor incivility; Gender;
Work withdrawal; Northern Cyprus
ID HUMAN-RESOURCE MANAGEMENT; JOB DEMANDS; ORGANIZATIONAL JUSTICE;
EMOTIONAL EXHAUSTION; WORKGROUP INCIVILITY; EMPLOYEE CYNICISM; HOTEL
EMPLOYEES; TOP MANAGEMENT; MEDIATING ROLE; WORK
AB Organizational politics and workplace victimization are social stressors with significant implications on the wellbeing of employees. Applying Job Demand Resources framework, this study examines the impact of favoritism/nepotism, supervisor incivility on employee cynicism, and work withdrawal, and the moderating role of gender. Utilizing a cross-sectional design, data were gathered from frontline employees working in 3-star hotels in Northern Cyprus. Results from structural equation modeling and artificial neural network revealed that: (1) favoritism/nepotism has a positive impact on employee cynicism and work withdrawal; (2) employee cynicism has a positive impact on work withdrawal; (3) employee cynicism mediates the relationship between favoritism/nepotism, and work withdrawal; (4) the impact of employee cynicism on work withdrawal was about 6.7 times stronger for women; (5) the impact of favoritism/nepotismon work withdrawal was about 2.1 times stronger for men. Strategies to reduce this unwanted practices and how to keep employees productive are discussed. (C) 2017 Elsevier Ltd. All rights reserved.
C1 [Abubakar, A. Mohammed; Tunc, Tugba] Aksaray Univ, Dept Management Informat Syst, Aksaray, Turkey.
[Harazneh, Ibrahim] Girne Amer Univ, Hospitality & Tourism Dept, Via Mersin 10, Kyrenia, North Cyprus, Turkey.
[Namin, Boshra Hejraty; Arasli, Huseyin] Eastern Mediterranean Univ, Fac Tourism, POB 95,Via Mersin 10, Famagusta, North Cyprus, Turkey.
C3 Aksaray University; Girne American University; Eastern Mediterranean
University
RP Abubakar, AM (autor correspondiente), Aksaray Univ, Dept Management Informat Syst, Aksaray, Turkey.
EM Me@mohammedabubakar.com; huseyin.arasli@emu.edu.tr
RI arasli, huseyin/D-5503-2019; harazneh, ibrahim/IVU-7675-2023; harazneh,
ibrahim/I-2684-2018; ABUBAKAR, A Mohammed/L-7214-2018; harazneh,
ibrahim/AAA-3784-2020
OI harazneh, ibrahim/0000-0001-6572-1333; ABUBAKAR, A
Mohammed/0000-0002-1163-0185;
CR Abdalla HF, 1998, INT J MANPOWER, V19, P554, DOI 10.1108/01437729810242235
Abubakar AM, 2016, J MANAG DEV, V35, P1266, DOI 10.1108/JMD-11-2015-0164
Aladwan K, 2014, INT J ISLAMIC MIDDLE, V7, P126, DOI 10.1108/IMEFM-09-2013-0104
Altinay L., 2002, International Journal of Contemporary Hospitality Management, V14, P176, DOI 10.1108/09596110210427021
Andersson LM, 1997, J ORGAN BEHAV, V18, P449, DOI 10.1002/(SICI)1099-1379(199709)18:5<449::AID-JOB808>3.0.CO;2-O
[Anonymous], 2010, J INT BUSINESS ETHIC
[Anonymous], MIS Q
[Anonymous], 2015, STAT YB
Anwar M. N., 2011, INT ED STUDIES, V4
Arasli H, 2006, INT J SOCIOL SOC POL, V26, P295, DOI 10.1108/01443330610680399
Arasli H, 2008, SOC BEHAV PERSONAL, V36, P1237, DOI 10.2224/sbp.2008.36.9.1237
Aslan S., 2014, CLUT I INT AC C
Babakus E, 2008, INT J HOSP TOUR ADM, V9, P384, DOI 10.1080/15256480802427339
Bakker A.B., 2003, European Journal of Work and Organizational Psychology, V12, P393417, DOI 10.1080/13594320344000165
Bakker A. B., 2007, J Manag Psychol, V22, P309, DOI DOI 10.1108/02683940710733115
Bakker AB, 2004, HUM RESOUR MANAGE-US, V43, P83, DOI 10.1002/hrm.20004
Bedeian AG, 2007, ACAD MANAG LEARN EDU, V6, P9, DOI 10.5465/AMLE.2007.24401700
Belenky M., 1986, WOMENS WAY KNOWING
BENTLER PM, 1980, PSYCHOL BULL, V88, P588, DOI 10.1037/0033-2909.107.2.238
Berry C. M., 2010, J ORGAN BEHAV, V33, P678
Bodla M. A., 2008, INT J KNOWLEDGE CULT, V8, P9
Bommer WH, 2005, J ORGAN BEHAV, V26, P733, DOI 10.1002/job.342
Britton AR, 2012, PERS INDIV DIFFER, V52, P712, DOI 10.1016/j.paid.2011.12.038
Browne M.W., 1993, SOCIOL METHOD RES, P445
Burke R.J., 2009, Equal Opportunities International, V28, P452, DOI DOI 10.1108/02610150910980765
Chen CY, 2014, INT J HOSP MANAG, V37, P21, DOI 10.1016/j.ijhm.2013.10.006
Chiaburu DS, 2013, J VOCAT BEHAV, V83, P181, DOI 10.1016/j.jvb.2013.03.007
Cole MS, 2010, J MANAGE STUD, V47, P367, DOI 10.1111/j.1467-6486.2009.00864.x
Colquitt JA, 2001, J APPL PSYCHOL, V86, P425, DOI 10.1037//0021-9010.86.3.425
Cortina LM, 2002, LAW SOCIAL INQUIRY, V27, P235, DOI 10.1111/j.1747-4469.2002.tb00804.x
Cropanzano R, 2003, J APPL PSYCHOL, V88, P160, DOI 10.1037/0021-9010.88.1.160
Daskin M., 2013, J MANAGEMENT RES, V5, P1941
Daskin M, 2013, ANATOLIA, V24, P452, DOI 10.1080/13032917.2013.807396
Daskin M, 2012, TOURISM, V60, P273
Davidson M., 2006, LABOR TURNOVER COSTS
Dean JW, 1998, ACAD MANAGE REV, V23, P341, DOI 10.2307/259378
Deloitte, 2010, HOSP 2015 GAM CHANG
Demerouti E, 2001, J APPL PSYCHOL, V86, P499, DOI 10.1037//0021-9010.86.3.499
DRORY A, 1991, J ORGAN BEHAV, V12, P249, DOI 10.1002/job.4030120307
Eagly A. H., 1986, SEX DIFFERENCES SOCI, P128
Eagly A.H., 1987, JOHN M MACEACHRAN ME, DOI [10.1037/0022-3514.65.5.1010, DOI 10.4324/]
Erdogan B., 2002, HUM RESOUR MANAGE R, V12, P555, DOI [10.1016/S1053-4822(02)00070-0, DOI 10.1016/S1053-4822(02)00070-0]
Escartín J, 2011, J PERS PSYCHOL, V10, P157, DOI 10.1027/1866-5888/a000048
Ghosh R, 2011, ADV DEV HUM RESOUR, V13, P22, DOI 10.1177/1523422311410639
Glendinning PM, 2001, PUBLIC PERS MANAGE, V30, P269, DOI 10.1177/009102600103000301
Greenglass ER, 2001, J COMMUNITY APPL SOC, V11, P211, DOI 10.1002/casp.614
Griepentrog BK, 2012, PERS PSYCHOL, V65, P723, DOI 10.1111/peps.12000
Hair J. F., 1998, Multivariate data analysis with readings, V5th ed.
HANISCH KA, 1990, J VOCAT BEHAV, V37, P60, DOI 10.1016/0001-8791(90)90007-O
HANISCH KA, 1991, J VOCAT BEHAV, V39, P110, DOI 10.1016/0001-8791(91)90006-8
Hitlan R.T., 2006, North American Journal of Psychology, V8, P217
Holm K., 2015, MODELS WORKPLACE INC
Jaja S. A., 2004, ENTREPRENEURSHIP PAR
Jung J, 2012, INT J HUM RESOUR MAN, V23, P3636, DOI 10.1080/09585192.2012.654806
Kao FH, 2014, J OCCUP ORGAN PSYCH, V87, P755, DOI 10.1111/joop.12073
Kara D, 2012, INT J CONTEMP HOSP M, V24, P1047, DOI 10.1108/09596111211258919
Karadal H, 2009, SOC BEHAV PERSONAL, V37, P175, DOI 10.2224/sbp.2009.37.2.175
Karatepe OM, 2015, INT J CONTEMP HOSP M, V27, P4, DOI 10.1108/IJCHM-09-2013-0417
Karatepe OM, 2014, ECON RES-EKON ISTRAZ, V27, P414, DOI 10.1080/1331677X.2014.967533
Karatepe OM, 2011, INT J CONTEMP HOSP M, V23, P44, DOI 10.1108/09596111111101661
Karatepe OM, 2008, INT J TOUR RES, V10, P111, DOI 10.1002/jtr.645
Karimi L, 2008, COMMIMITY WORK FAM, V11, P283, DOI 10.1080/13668800802133958
Kim P, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0094333
Kim TY, 2009, HUM RELAT, V62, P1435, DOI 10.1177/0018726709340822
Kocoglu M., 2014, Global Business and Management Research: An International Journal, V6, P24
Kurt I., 2016, BUSINESS CHALLENGES, V2
Kwon I., 2006, BE J THEOR ECON, V6, P1
Leiter MP, 2015, RES OCCUP STRESS WEL, V13, P107, DOI 10.1108/S1479-355520150000013004
Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
Leung K, 2010, APPL PSYCHOL-INT REV, V59, P318, DOI 10.1111/j.1464-0597.2009.00392.x
Li J, 2016, INT J HOSP MANAG, V55, P1, DOI 10.1016/j.ijhm.2016.02.010
Li L., 2001, International Journal of Contemporary Hospitality Management, V13, P189, DOI 10.1108/09596110110389511
Lim S, 2005, J APPL PSYCHOL, V90, P483, DOI 10.1037/0021-9010.90.3.483
Lim S, 2008, J APPL PSYCHOL, V93, P95, DOI 10.1037/0021-9010.93.1.95
Lim VKG, 2009, INFORM MANAGE-AMSTER, V46, P419, DOI 10.1016/j.im.2009.06.006
Lisaniler G. F., 2012, 8 INT C CYPR STUD FA
Loi NM, 2015, J MANAG DEV, V34, P169, DOI 10.1108/JMD-12-2012-0152
Loo R., 2003, Women in Management Review, V18, P169, DOI DOI 10.1108/09649420310479372
Magley VJ, 2010, PSYCHOL BEHAV ASPECT, P239
MARSH HW, 1985, PSYCHOL BULL, V97, P562, DOI 10.1037/0033-2909.97.3.562
McCuddy MichaelK., 2010, Proceedings of ASBBS Annual Conference, Las Vegas, V17, P479
MCDONALD RP, 1990, PSYCHOL BULL, V107, P247, DOI 10.1037/0033-2909.107.2.247
Miner-Rubino K, 2010, J APPL SOC PSYCHOL, V40, P3148, DOI 10.1111/j.1559-1816.2010.00695.x
Murphy K. A., 1986, PSYCHOL BULL, V99, P128, DOI DOI 10.1037/0033-2909.99.1.133
Namazie P, 2007, INT J HUM RESOUR MAN, V18, P159, DOI 10.1080/09585190601068573
Olabimitan B., 2014, NIGERIAN J APPL BEHA, V2, P137
Paliaga M, 2011, ECON RES-EKON ISTRAZ, V24, P107, DOI 10.1080/1331677X.2011.11517448
Panwar S., 2012, VSRD INT J BUSINESS, V2, P81
Pearson CM, 2000, ORGAN DYN, V29, P123, DOI 10.1016/S0090-2616(00)00019-X
Pearson CM, 2001, HUM RELAT, V54, P1387, DOI 10.1177/00187267015411001
Pinar M, 2011, INT J HOSP MANAG, V30, P73, DOI 10.1016/j.ijhm.2010.06.007
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Porath CL, 2010, ORGAN DYN, V39, P64, DOI 10.1016/j.orgdyn.2009.10.006
Preacher KJ, 2004, BEHAV RES METH INS C, V36, P717, DOI 10.3758/BF03206553
Reio TG, 2011, ADV DEV HUM RESOUR, V13, P54, DOI 10.1177/1523422311410648
Reio TG, 2009, HUM RESOUR DEV Q, V20, P237, DOI 10.1002/hrdq.20020
Rogelberg SG, 2007, ORGAN RES METHODS, V10, P195, DOI 10.1177/1094428106294693
Rotundo M, 2001, J APPL PSYCHOL, V86, P914, DOI 10.1037//0021-9010.86.5.914
Schaap JI, 2008, INT J HOSP MANAG, V27, P87, DOI 10.1016/j.ijhm.2007.07.004
Schaufeli WB, 2004, J ORGAN BEHAV, V25, P293, DOI 10.1002/job.248
Shrout PE, 2002, PSYCHOL METHODS, V7, P422, DOI 10.1037//1082-989X.7.4.422
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Skalpe O, 2007, TOURISM MANAGE, V28, P845, DOI 10.1016/j.tourman.2006.06.005
Sliter M, 2012, J ORGAN BEHAV, V33, P121, DOI 10.1002/job.767
Smidt O, 2016, SA J IND PSYCHOL, V42
Spence HK, 2009, J NURS MANAGE, V17, P302, DOI 10.1111/j.1365-2834.2009.00999.x
Sulea C., 2012, Cognition, Brain, Behavior: An Interdisciplinary Journal, V16, P553
Sweeney PD, 1997, J ORGAN BEHAV, V18, P83, DOI 10.1002/(SICI)1099-1379(199701)18:1<83::AID-JOB779>3.0.CO;2-3
TANAKA JS, 1985, BRIT J MATH STAT PSY, V38, P197, DOI 10.1111/j.2044-8317.1985.tb00834.x
Töyry S, 2004, STRESS HEALTH, V20, P213, DOI 10.1002/smi.1009
Tracey JB, 2008, CORNELL HOSP Q, V49, P12, DOI 10.1177/0010880407310191
van Dolen WM, 2012, PSYCHOL MARKET, V29, P306, DOI 10.1002/mar.20523
Wan YKP, 2010, INT J HOSP MANAG, V29, P62, DOI 10.1016/j.ijhm.2009.06.002
Wang SH, 2012, INT J CROSS CULT MAN, V12, P211, DOI 10.1177/1470595812439871
West C, 1987, GENDER SOC, V1, P125, DOI 10.1177/0891243287001002002
Williams KD, 2007, ANNU REV PSYCHOL, V58, P425, DOI 10.1146/annurev.psych.58.110405.085641
Yavas U., 2004, Journal of Hospitality & Leisure Marketing, V11, P31, DOI 10.1300/J150v11n02_04
Yirik S., 2015, International Review of Management and Marketing, V5, P52
NR 118
TC 81
Z9 93
U1 9
U2 54
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2211-9736
EI 2211-9744
J9 TOUR MANAG PERSPECT
JI Tour. Manag. Perspect.
PD JUL
PY 2017
VL 23
BP 129
EP 139
DI 10.1016/j.tmp.2017.06.001
PG 11
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA FK7JT
UT WOS:000413682800015
DA 2024-03-27
ER
PT J
AU Gupta, P
Prashar, S
Parsad, C
Vijay, TS
AF Gupta, Priyanka
Prashar, Sanjeev
Parsad, Chandan
Vijay, T. Sai
TI Role of Shopping App Attributes in Creating Urges for Impulse Buying: An
Empirical Investigation Using SEM and Neural Network Technique
SO JOURNAL OF ELECTRONIC COMMERCE IN ORGANIZATIONS
LA English
DT Article
DE App Layout; Effort Expectancy; Impulse Buying; Impulse Buying Intention;
Mobile Applications (Apps); User Experience; User Satisfaction
ID CONSUMER SATISFACTION; MOBILE APPLICATION; PRICE DISCOUNTS; CUSTOMER
VALUE; MEDIATING ROLE; STORE LAYOUT; WEB SITE; IN-STORE; PURCHASE;
BEHAVIOR
AB With high speed internet, the retailers are continually engaged in upgrading mobile apps that facilitate shoppers in shopping anywhere-anytime and arousing their sudden urges to buy impulsively. The present study endeavors to decipher the antecedents of mobile app-based impulsive buying behavior and determining their relative significance in triggering impulsive urges. Using structural equation modeling, causal analysis was undertaken to identify the role of effort expectancy, price and discounts, atmosphere and layout of app, and user experience and satisfaction in creating impulsive buying intentions. It was observed that price and discounts and user experience didn't have any influence in stirring the consumer for impulsive buying. To determine the relative significance of remaining four, artificial neural network modeling was undertaken. Effort expectancy was noted to have highest influence in creating impulsive urges, followed by atmosphere and layout of an app. User satisfaction had minimum impact. The paper concludes with practical implications for m-commerce players.
C1 [Gupta, Priyanka] Indian Inst Management, Raipur, Madhya Pradesh, India.
[Prashar, Sanjeev] Indian Inst Management, Area Mkt, Raipur, Madhya Pradesh, India.
[Parsad, Chandan] Indian Inst Management IIM, Bodh Gaya, India.
[Vijay, T. Sai] Indian Inst Management, Ranchi, Bihar, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
Management Raipur; Indian Institute of Management (IIM System); Indian
Institute of Management Raipur; Indian Institute of Management (IIM
System); Indian Institute of Management Bodh Gaya; Indian Institute of
Management (IIM System); Indian Institute of Management Ranchi
RP Gupta, P (autor correspondiente), Indian Inst Management, Raipur, Madhya Pradesh, India.
RI Sai Vijay, Tata/IUQ-5585-2023
OI Parsad, Chandan/0000-0002-2616-6362
CR Agrawal D, 1996, J RETAILING, V72, P383, DOI 10.1016/S0022-4359(96)90020-2
Alnawas I, 2016, J RETAIL CONSUM SERV, V31, P313, DOI 10.1016/j.jretconser.2016.04.004
Arnold MJ, 2003, J RETAILING, V79, P77, DOI 10.1016/S0022-4359(03)00007-1
BABIN BJ, 1994, J CONSUM RES, V20, P644, DOI 10.1086/209376
Baker J., 1994, J ACAD MARKET SCI, V22, P328, DOI [DOI 10.1177/0092070394224002, 10.1177/0092070394224002]
Beatty SE, 1998, J RETAILING, V74, P169, DOI 10.1016/S0022-4359(99)80092-X
Bellman S, 2011, J INTERACT MARK, V25, P191, DOI 10.1016/j.intmar.2011.06.001
Brasel SA, 2014, J CONSUM PSYCHOL, V24, P226, DOI 10.1016/j.jcps.2013.10.003
Chan FTS, 2012, DECIS SUPPORT SYST, V54, P621, DOI 10.1016/j.dss.2012.08.009
Chan TKH, 2017, INFORM MANAGE-AMSTER, V54, P204, DOI 10.1016/j.im.2016.06.001
Chang TY, 2012, J SYST SOFTWARE, V85, P1157, DOI 10.1016/j.jss.2011.12.044
Chiang WYK, 2006, DECIS SUPPORT SYST, V41, P514, DOI 10.1016/j.dss.2004.08.016
Chiou WB, 2008, TOURISM MANAGE, V29, P146, DOI 10.1016/j.tourman.2007.03.016
Chong AYL, 2014, EXPERT SYST APPL, V41, P221, DOI 10.1016/j.eswa.2013.07.023
Chong AYL, 2013, EXPERT SYST APPL, V40, P523, DOI 10.1016/j.eswa.2012.07.068
Dholakia UM, 2000, PSYCHOL MARKET, V17, P955, DOI 10.1002/1520-6793(200011)17:11<955::AID-MAR3>3.0.CO;2-J
Drossos DA, 2014, ELECTRON COMMER R A, V13, P423, DOI 10.1016/j.elerap.2014.08.003
E-marketer, 2014, SMARTPH US WORLDW W
E-marketer, 2015, URB IND SMARTPH POP
Engelbrecht AP, 1996, IEEE IJCNN, P1274, DOI 10.1109/ICNN.1996.549081
EROGLU SA, 1993, ADV CONSUM RES, V20, P34
Éthier J, 2006, INFORM MANAGE-AMSTER, V43, P627, DOI 10.1016/j.im.2006.03.004
Farag S, 2007, TRANSPORT RES A-POL, V41, P125, DOI 10.1016/j.tra.2006.02.003
Flanagin AJ, 2001, HUM COMMUN RES, V27, P153, DOI 10.1093/hcr/27.1.153
Floh A, 2013, ELECTRON COMMER R A, V12, P425, DOI 10.1016/j.elerap.2013.06.001
Furner C.P., 2014, The Marketing Review, V14, P163, DOI [DOI 10.1362/146934714X14024778816913, 10.1362/146934714X14024778816913]
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hardesty DM, 2003, J RETAILING, V79, P17, DOI 10.1016/S0022-4359(03)00004-6
Harris MA, 2016, INT J INFORM MANAGE, V36, P441, DOI 10.1016/j.ijinfomgt.2016.02.004
Hassenzahl M., 2008, P IHM 08 20 INT C AS, DOI [10.1145/1512714.1512717, DOI 10.1145/1512714.1512717]
Heilman CM, 2002, J MARKETING RES, V39, P242, DOI 10.1509/jmkr.39.2.242.19081
Herabadi AG, 2009, ASIAN J SOC PSYCHOL, V12, P20, DOI 10.1111/j.1467-839X.2008.01266.x
Hew JJ, 2015, IND MANAGE DATA SYST, V115, P1269, DOI 10.1108/IMDS-01-2015-0028
Huang MH, 2003, INFORM MANAGE-AMSTER, V41, P39, DOI 10.1016/S0378-7206(03)00024-7
Hwang J, 2018, ASIA PAC J TOUR RES, V23, P359, DOI 10.1080/10941665.2018.1444648
Islam R., 2010, International Journal of Engineering Technology, V10, P104
KAHN BE, 1993, J CONSUM RES, V20, P257, DOI 10.1086/209347
Kang JW, 2018, INT J CONTEMP HOSP M, V30, P797, DOI 10.1108/IJCHM-09-2016-0537
Keng CJ, 2011, INTERNET RES, V21, P408, DOI 10.1108/10662241111158308
Khalifa M, 2007, EUR J INFORM SYST, V16, P780, DOI 10.1057/palgrave.ejis.3000711
Kim E., 2013, Journal of Interactive Advertising, V13, P53, DOI [10.1080/15252019.2013.782780, DOI 10.1080/15252019.2013.782780]
Kim SC., 2016, J MARK COMMUN, V22, P653, DOI DOI 10.1080/13527266.2014.951065
Kim SJ, 2015, J INTERACT MARK, V31, P28, DOI 10.1016/j.intmar.2015.05.004
KOTLER P, 1974, J RETAILING, V49, P48
Krasonikolakis I, 2018, EUR J MARKETING, V52, P1223, DOI 10.1108/EJM-03-2015-0183
Lane P. L., 2019, J INFORM SYSTEMS ED, V26, P3
Lee JA, 2008, J BUS RES, V61, P265, DOI 10.1016/j.jbusres.2007.06.006
Lee JE, 2018, FASH TEXT, V5, DOI 10.1186/s40691-018-0128-2
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Lester D.H., 2005, SERVICES MARKETING Q, V27, P123, DOI [10.1300/J396v27n02_08, DOI 10.1300/J396V27N02_08]
Li HR, 2002, J ADVERTISING, V31, P43, DOI 10.1080/00913367.2002.10673675
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Liu Y, 2013, DECIS SUPPORT SYST, V55, P829, DOI 10.1016/j.dss.2013.04.001
Malhotra N., 2007, Marketing research: An applied approach
Mazaheri E, 2010, J GLOB SCHOLARS MARK, V20, P123
Nikou SA, 2017, COMPUT HUM BEHAV, V68, P83, DOI 10.1016/j.chb.2016.11.020
O'Cass A., 2003, J RETAIL CONSUM SERV, V10, P81, DOI DOI 10.1016/S0969-6989(02)00004-8
Olaru D, 2008, J BUS IND MARK, V23, P554, DOI 10.1108/08858620810913362
Pandya P.R., 2018, IUP J MANAGEMENT RES, V17, P45
Parsad C., 2017, Journal of Business and Management, V23, P1, DOI [DOI 10.6347/JBM.201709_23(1_2).0001, 10.1108/eemcs-06-2016-0106]
Powers David Martin, 2011, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Prashar Sanjeev, 2017, International Journal of Manufacturing Technology and Management, V31, P494
Prashar S., 2016, International Journal of Business Information, V11, P175, DOI DOI 10.1016/J.TOURMAN.2015.01.028
Prashar S., 2016, ASIAN ACAD MANAGEMEN, V21
Prashar S, 2015, INT J INDIAN CULT BU, V11, P219, DOI 10.1504/IJICBM.2015.071308
Prashar S, 2015, DECISION-INDIA, V42, P403, DOI 10.1007/s40622-015-0109-x
Puri Radhika., 1996, Journal of Consumer Psychology, V5, P87, DOI DOI 10.1207/S15327663JCP0502_01
Pynoo B, 2011, COMPUT HUM BEHAV, V27, P568, DOI 10.1016/j.chb.2010.10.005
Rook DW, 1995, J CONSUM RES, V22, P305, DOI 10.1086/209452
Rose, 2017, BUSINESS COM TAP SUC
Ruiter RAC, 2001, HEALTH EDUC RES, V16, P307, DOI 10.1093/her/16.3.307
Saifi R, 2017, 2017 MOBILE APP MARK
Shaikh AA, 2018, INT J E-BUS RES, V14, P39, DOI 10.4018/IJEBR.2018040103
Sheehan D, 2019, J RETAIL CONSUM SERV, V51, P129, DOI 10.1016/j.jretconser.2019.06.001
Sherman E, 1997, PSYCHOL MARKET, V14, P361, DOI 10.1002/(SICI)1520-6793(199707)14:4<361::AID-MAR4>3.0.CO;2-7
Statista, 2017, NUMB SMARTPH US IND
Statista, 2016, COUNTR EXC GLOB AV P
statista, 2019, Global smartphone penetration rate as share of population from 2016 to 2022
STERN H, 1962, J MARKETING, V26, P59, DOI 10.2307/1248439
Sundström M, 2019, J RETAIL CONSUM SERV, V47, P150, DOI 10.1016/j.jretconser.2018.11.006
Tandon U., 2018, International Journal of Electronic Business, V14, P212, DOI DOI 10.1504/IJEB.2018.095959
Techcrunch, 2014, MOB APP US INCR 2014
TREVINO LK, 1992, COMMUN RES, V19, P539, DOI 10.1177/009365092019005001
Venkatesh V, 2012, MIS QUART, V36, P157
Vohs KD, 2007, J CONSUM RES, V33, P537, DOI 10.1086/510228
Vrechopoulos AP, 2004, J RETAILING, V80, P13, DOI 10.1016/j.jretai.2004.01.006
Web, 2015, IMPULSE ECOMMERCE 3
West GB, 1997, SCIENCE, V276, P122, DOI 10.1126/science.276.5309.122
Westland JC, 2010, ELECTRON COMMER R A, V9, P476, DOI 10.1016/j.elerap.2010.07.003
Wolin LD, 2003, INTERNET RES, V13, P375, DOI 10.1108/10662240310501658
Wood M, 1998, J ECON PSYCHOL, V19, P295, DOI 10.1016/S0167-4870(98)00009-9
Wu WY, 2013, INT J RETAIL DISTRIB, V42, P4, DOI 10.1108/IJRDM-01-2013-0035
Xu CY, 2015, DECIS SUPPORT SYST, V79, P171, DOI 10.1016/j.dss.2015.08.008
Xu RH, 2016, COMPUT HUM BEHAV, V62, P244, DOI 10.1016/j.chb.2016.04.011
Xu Y, 2014, SOC BEHAV PERSONAL, V42, P1293, DOI 10.2224/sbp.2014.42.8.1293
Yan Bo., 2011, P 9 INT C MOBILE SYS, P113
Yang HL, 2014, INFORM TECHNOL PEOPL, V27, P21, DOI 10.1108/ITP-11-2012-0130
Yang S. B., 1970, J INTERNET BANKING C, V20, P1
Yi S, 2020, J HOSP MARKET MANAG, V29, P662, DOI 10.1080/19368623.2020.1692267
Zhang GQ, 1998, INT J FORECASTING, V14, P35, DOI 10.1016/S0169-2070(97)00044-7
Zhang KZK, 2018, INTERNET RES, V28, P522, DOI 10.1108/IntR-12-2016-0377
NR 101
TC 0
Z9 1
U1 2
U2 29
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1539-2937
EI 1539-2929
J9 J ELECTRON COMMER OR
JI J. Electron. Commer. Organ.
PD JAN-MAR
PY 2021
VL 19
IS 1
BP 43
EP 64
DI 10.4018/JECO.2021010103
PG 22
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA PS5JI
UT WOS:000607956900003
DA 2024-03-27
ER
PT J
AU Cao, MK
Hu, Q
Kiang, MY
Hong, H
AF Cao, Mukun
Hu, Qing
Kiang, Melody Y.
Hong, Hong
TI A Portfolio Strategy Design for Human-Computer Negotiations in e-Retail
SO INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
LA English
DT Article
DE Electronic commerce; automated negotiation; negotiation strategy;
human-computer interaction; design science; human-computer negotiation;
software agents; online negotiation
ID SCIENCE RESEARCH; TASK COMPLEXITY; SOFTWARE AGENT; DECEPTION; COMMERCE;
SYSTEMS; MODEL; AUCTIONS; BEHAVIOR; TACTICS
AB Human-computer negotiation has the potential to play an important role in today's highly dynamic online environment, especially in business-to-consumer e-commerce transactions. However, the lack of research on effective automated negotiation algorithms to respond to human buyers' strategic and/or tactic offers has limited the development of automated human-computer negotiation systems for real-world applications. Intelligent software agents that are capable of dynamically adjusting their negotiation strategy in response to human buyers' offers can greatly improve the negotiation experience of human buyers. In this study, guided by design science principles, we design a portfolio strategy model, which implements four negotiation strategies (i.e., time-dependent, behavior-dependent, dynamic time-dependent, and impasse resolution) as the core of our software agent for negotiating with human buyers. To evaluate this novel model, we implement a prototype of the system and compare it with three benchmark single-strategy models (i.e., competitive, collaborative, and selection) in human-computer negotiation experiments. The results show that our model not only enables the software agent to outperform its human counterpart but also significantly increases the settlement ratio and the joint outcome of both parties.
C1 [Cao, Mukun] Xiamen Univ, Sch Management, Xiamen, Peoples R China.
[Hu, Qing] CUNY, Baruch Coll, Zicklin Sch Business, New York, NY 10021 USA.
[Kiang, Melody Y.] Calif State Univ Long Beach, Coll Business, Informat Syst Dept, 1250 Bellflower Blvd, Long Beach, CA 90840 USA.
[Hong, Hong] Harbin Inst Technol, Sch Management, Harbin, Heilongjiang, Peoples R China.
C3 Xiamen University; City University of New York (CUNY) System; Baruch
College (CUNY); California State University System; California State
University Long Beach; Harbin Institute of Technology
RP Kiang, MY (autor correspondiente), Calif State Univ Long Beach, Coll Business, Informat Syst Dept, 1250 Bellflower Blvd, Long Beach, CA 90840 USA.
EM Melody.kiang@csulb.edu
FU China Scholarship Council [201706315032]; Natural Science Foundation of
China [71671154]; Fundamental Research Funds for the Central
Universities of China [20720161052]
FX This work was supported by the China Scholarship Council
(Grant#201706315032), and the Natural Science Foundation of China
(Grant#71671154), and the Fundamental Research Funds for the Central
Universities of China (Grant#20720161052).
CR Adam MTP, 2017, INT J ELECTRON COMM, V21, P363, DOI 10.1080/10864415.2016.1319222
Adomavicius G, 2009, INFORM SYST RES, V20, P507, DOI 10.1287/isre.1080.0172
Axelrod R., 1984, The Evolution of Cooperation
Aydogan R., 2017, MODERN APPROACHES AG, P153, DOI [10.1007/978-3-319-51563-2, 10.1007/978-3-319-51563-2_10, DOI 10.1007/978-3-319-51563-2_10]
Baarslag T, 2013, ARTIF INTELL, V198, P73, DOI 10.1016/j.artint.2012.09.004
Bichler M, 2010, INFORM SYST RES, V21, P688, DOI 10.1287/isre.1100.0316
Bosse T, 2005, RRS 2005: RATIONAL, ROBUST, AND SECURE NEGOTIATION MECHANISMS IN MULTI-AGENT SYSTEMS, PROCEEDINGS, P11, DOI 10.1109/RRS.2005.8
Cao M, 2016, OPEN J SOC SCI, V4, P64
Cao MK, 2016, P ANN HICSS, P369, DOI 10.1109/HICSS.2016.52
Cao MK, 2015, FOURTEENTH WUHAN INTERNATIONAL CONFERENCE ON E-BUSINESS, P111
Cao MK, 2015, DECIS SUPPORT SYST, V73, P1, DOI 10.1016/j.dss.2015.02.012
Carbonneau RA, 2014, EXPERT SYST APPL, V41, P4035, DOI 10.1016/j.eswa.2013.12.029
Chohra A, 2011, LECT NOTES ARTIF INT, V6923, P352, DOI 10.1007/978-3-642-23938-0_36
FARATIN P, 1998, INT J ROB AUTON SYST, V24, P3
Fatima SS, 2004, ARTIF INTELL, V152, P1, DOI 10.1016/S0004-3702(03)00115-2
Gaspar JP, 2016, NEGOTIATION J, V32, P213, DOI 10.1111/nejo.12157
Giordano G.A., 2007, J COMPUT-MEDIAT COMM, V12, P23
GODE DK, 1993, J POLIT ECON, V101, P119, DOI 10.1086/261868
Gregor S, 2013, MIS QUART, V37, P337, DOI 10.25300/MISQ/2013/37.2.01
Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
Hinz O, 2011, MIS QUART, V35, P81
Huang SL, 2006, INT J ELECTRON COMM, V11, P85, DOI 10.2753/JEC1086-4415110104
Ilany L, 2016, AUTON AGENT MULTI-AG, V30, P697, DOI 10.1007/s10458-015-9302-8
Jain BA, 2000, INFORM MANAGE, V37, P161, DOI 10.1016/S0378-7206(99)00049-X
Jonker CM, 2007, AUTON AGENT MULTI-AG, V15, P221, DOI 10.1007/s10458-006-9009-y
Kersten GE, 2007, GROUP DECIS NEGOT, V16, P553, DOI 10.1007/s10726-007-9095-5
Ketter W, 2012, INFORM SYST RES, V23, P1263, DOI 10.1287/isre.1110.0415
KRAUS S, 1995, COMPUT INTELL, V11, P132, DOI 10.1111/j.1467-8640.1995.tb00026.x
Kraus S, 2008, ARTIF INTELL, V172, P1, DOI 10.1016/j.artint.2007.05.007
Lee CF, 2008, GROUP DECIS NEGOT, V17, P515, DOI 10.1007/s10726-008-9109-y
Lewicki R.J., 2003, NEGOTIATION, V4th
Lin R, 2008, ARTIF INTELL, V172, P823, DOI 10.1016/j.artint.2007.09.007
Lin R, 2014, DECIS SUPPORT SYST, V60, P1, DOI 10.1016/j.dss.2013.05.015
Lin R, 2014, COMPUT INTELL-US, V30, P48, DOI 10.1111/j.1467-8640.2012.00463.x
Lin R, 2010, COMMUN ACM, V53, P78, DOI 10.1145/1629175.1629199
Lopes F, 2008, ARTIF INTELL REV, V29, P1, DOI 10.1007/s10462-009-9107-8
Lumineau F, 2012, J OPER MANAG, V30, P382, DOI 10.1016/j.jom.2012.03.005
Luo XD, 2003, ARTIF INTELL, V148, P53, DOI 10.1016/S0004-3702(03)00041-9
Mell J, 2016, AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS, P1510
MICHELMAN JH, 1983, J BUS ETHICS, V2, P255, DOI 10.1007/BF00383182
Moukas A, 2000, INT J ELECTRON COMM, V4, P5, DOI 10.1080/10864415.2000.11518369
Nadler J, 2003, MANAGE SCI, V49, P4
Pak J, 2014, DECIS SUPPORT SYST, V63, P95, DOI 10.1016/j.dss.2013.08.010
Pan L, 2013, COMPUT INTELL-US, V29, P577, DOI 10.1111/j.1467-8640.2012.00434.x
Peffers K, 2007, J MANAGE INFORM SYST, V24, P45, DOI 10.2753/MIS0742-1222240302
Pruitt D., 2004, SOCIAL CONFLICT ESCA
Pruitt D.G., 1981, NEGOTIATION BEHAV
Putnam L.L., 1990, Research on negotiation in organizations, V2, P3
Raiffa Howard., 1982, ART SCI NEGOTIATION
Ren FH, 2014, DECIS SUPPORT SYST, V60, P29, DOI 10.1016/j.dss.2013.05.018
Resinas M, 2010, INT J ELECTRON COMM, V14, P93, DOI 10.2753/JEC1086-4415140305
Ribbink D, 2014, J OPER MANAG, V32, P114, DOI 10.1016/j.jom.2014.01.004
Ros R, 2006, AUTON AGENT MULTI-AG, V12, P163, DOI 10.1007/s10458-006-5837-z
SMITH VL, 1976, AM ECON REV, V66, P274
Thompson L., 2004, MIND HEART NEGOTIATO, V3rd
Vahidov R, 2017, ELECTRON COMMER R A, V26, P50, DOI 10.1016/j.elerap.2017.09.007
Vahidov R, 2014, DECIS SUPPORT SYST, V66, P135, DOI 10.1016/j.dss.2014.06.009
VANDEVLIERT E, 1997, COMPLEX INTERPERSONA
Walls JG, 1992, INFORM SYST RES, V3, P36, DOI 10.1287/isre.3.1.36
Wang KJ, 2003, ROBOT AUTON SYST, V43, P1, DOI 10.1016/S0921-8890(02)00359-7
Yang YP, 2014, IEEE INTELL SYST, V29, P36, DOI 10.1109/MIS.2013.106
Yang YP, 2012, J MANAGE INFORM SYST, V29, P223, DOI 10.2753/MIS0742-1222290307
NR 62
TC 7
Z9 7
U1 11
U2 69
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1086-4415
EI 1557-9301
J9 INT J ELECTRON COMM
JI Int. J. Electron. Commer.
PD JUL 2
PY 2020
VL 24
IS 3
BP 305
EP 337
DI 10.1080/10864415.2020.1767428
PG 33
WC Business; Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA MT6SE
UT WOS:000555101800003
DA 2024-03-27
ER
PT J
AU Shen, PY
Wan, DM
Li, JX
AF Shen, Pengyi
Wan, Demin
Li, Jinxiong
TI How human-computer interaction perception affects consumer well-being in
the context of online retail: from the perspective of autonomy
SO NANKAI BUSINESS REVIEW INTERNATIONAL
LA English
DT Article
DE Human-computer interaction; Consumer well-being; Autonomy; Psychological
resistance; Experiential purchase
ID PSYCHOLOGICAL REACTANCE; HAPPINESS; MOTIVATION; RECOMMENDATIONS;
EXPERIENCES; VALIDATION; PREFERENCE; PURCHASES; PRODUCT; ENHANCE
AB Purpose In recent years, the application of artificial intelligence and digital technology has increasingly become a priority for online retailers. It is crucial to choose a way to make use of human-computer interaction (HCI) design to exert the positive influence of intelligent technology on consumer welfare. Despite the increasing use of HCI design in online retail context, there remain limitations in their effect of consumer well-being improvement. Although there is extensive literature in the field of consumer well-being improvement, few studies have empirically examined how HCI design drives the improvement of consumer well-being in the online retail context. Therefor, this study aims to deeply and systematically analyze the psychological mechanism between HCI and consumer well-being in the online retail environment. Design/methodology/approach The empirical analysis is based on data collection of 476 samples of online shoppers through the online survey method. From the perspective of autonomy, this study deeply analyzes the influence mechanism of different dimensions of HCI perception on consumer well-being. Findings The results indicated that autonomy plays a positive intermediary role in the impact of perceived connectivity, perceived personalization, perceived control and perceived responsiveness on the eudaimonia and hedonic enjoyment. Also, it revealed that psychological resistance negatively regulates the impact of perceived connectivity, perceived personalization and perceived control on autonomy, while experience purchase positively regulates the impact of autonomy on hedonic enjoyment. Originality/value This paper expands the research situation of consumer well-being by making integration of the dual structure of subjective well-being and psychological well-being to define the psychological mechanism and boundary conditions of the impact of HCI perception on consumer well-being. The main contribution of this study is to provide enlightenment for online retail enterprises to improve HCI design and help consumers enhance long-term well-being.
C1 [Shen, Pengyi; Wan, Demin; Li, Jinxiong] Jiangxi Univ Finance & Econ, Sch Business Adm, Nanchang, Jiangxi, Peoples R China.
C3 Jiangxi University of Finance & Economics
RP Shen, PY (autor correspondiente), Jiangxi Univ Finance & Econ, Sch Business Adm, Nanchang, Jiangxi, Peoples R China.
EM pengyis2008@163.com
FU National Natural Science Foundation of China [71762011, 71972082];
Jiangxi Social Science Foundation Project [20GL13, 21GL56D]
FX The research is supported by National Natural Science Foundation of
China (Project No. 71762011; Project No.71972082), and Jiangxi Social
Science Foundation Project (Project No. 20GL13; Project No. 21GL56D).
CR Amoura C, 2014, MOTIV EMOTION, V38, P323, DOI 10.1007/s11031-013-9379-9
Andre Q., 2018, Customer Needs and Solutions, V5, P28, DOI 10.1007/s40547-017-0085-8
Brehm S.S., 1981, PSYCHOL REACTANCE
Calvo R.A., 2016, CHI 16 HUM FACT COMP
Caprariello PA, 2013, J PERS SOC PSYCHOL, V104, P199, DOI 10.1037/a0030953
Chan C, 2017, J CONSUM RES, V43, P913, DOI 10.1093/jcr/ucw067
Cho CH, 2004, J ADVERTISING, V33, P89, DOI 10.1080/00913367.2004.10639175
Christman Z, 2015, GISCI REMOTE SENS, V52, P543, DOI 10.1080/15481603.2015.1067859
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Deci EL, 2008, CAN PSYCHOL, V49, P14, DOI 10.1037/0708-5591.49.1.14
Deci EL, 2000, PSYCHOL INQ, V11, P227, DOI 10.1207/S15327965PLI1104_01
Dholakia R.R., 2000, INTERACTIVITY REVISI
Diener E, 2000, AM PSYCHOL, V55, P34, DOI 10.1037/0003-066X.55.1.34
Diener E, 2017, CAN PSYCHOL, V58, P87, DOI 10.1037/cap0000063
Donnell AJ, 2001, J SOC PSYCHOL, V141, P679, DOI 10.1080/00224540109600581
Fitzsimons GJ, 2004, MARKET SCI, V23, P82, DOI 10.1287/mksc.1030.0033
Gaggioli A, 2017, EMOTIONS AND AFFECT IN HUMAN FACTORS AND HUMAN-COMPUTER INTERACTION, P477, DOI 10.1016/B978-0-12-801851-4.00018-5
Gao Q, 2010, BEHAV INFORM TECHNOL, V29, P35, DOI 10.1080/01449290802666770
Gilovich Thomas., 2020, CONSUMER PSYCHOL REV, V3, P20, DOI [DOI 10.1002/ARCP.1053, 10.1002/arcp.1053]
He Y., 2016, RES STATUS PROSPECT, V38, P49
Hilken T, 2017, J ACAD MARKET SCI, V45, P884, DOI 10.1007/s11747-017-0541-x
Howell RT, 2012, J POSIT PSYCHOL, V7, P57, DOI 10.1080/17439760.2011.626791
Huang MH, 2017, J ACAD MARKET SCI, V45, P906, DOI 10.1007/s11747-017-0545-6
Hwang K, 2018, J BUS ETHICS, V151, P579, DOI 10.1007/s10551-016-3236-1
Kari T., 2016, 29 BLED ECONFERENCE
Kim J, 2016, SELF IDENTITY, V15, P727, DOI 10.1080/15298868.2016.1208623
Kumar A, 2016, J CONSUM PSYCHOL, V26, P169, DOI 10.1016/j.jcps.2015.06.013
Lee G, 2009, INFORM MANAGE-AMSTER, V46, P448, DOI 10.1016/j.im.2009.07.005
Lee JC, 2018, PSYCHOL SCI, V29, P1031, DOI 10.1177/0956797617736386
Lee T., 2005, J ELECTRON COMMER RE, V6, P165, DOI DOI 10.1145/1964921.1964953
Liu YP, 2002, J ADVERTISING, V31, P53, DOI 10.1080/00913367.2002.10673685
Newhagen JE, 2004, INFORM SOC, V20, P395, DOI 10.1080/01972240490508108
Ong CS, 2015, J BUS RES, V68, P591, DOI 10.1016/j.jbusres.2014.09.002
Pchelin P, 2014, J POSIT PSYCHOL, V9, P322, DOI 10.1080/17439760.2014.898316
Peterson C., 2005, Journal of Happiness Studies, V6, P25, DOI [10.1007/s10902-004-1278-z, DOI 10.1007/S10902-004-1278-Z]
Proshansky H.M., 1970, Environmental Psychology: Man and his physical setting
Rigoni D, 2012, CONSCIOUS COGN, V21, P1482, DOI 10.1016/j.concog.2012.04.004
RYAN RM, 1989, J PERS SOC PSYCHOL, V57, P749, DOI 10.1037/0022-3514.57.5.749
Ryan RM, 2001, ANNU REV PSYCHOL, V52, P141, DOI 10.1146/annurev.psych.52.1.141
Schneider S, 2018, LEARN INSTR, V58, P161, DOI 10.1016/j.learninstruc.2018.06.006
Sirgy MJ, 2001, SOC INDIC RES, V56, P125, DOI 10.1023/A:1012254826324
Sohn D, 2011, NEW MEDIA SOC, V13, P1320, DOI 10.1177/1461444811405806
Telfer E., 1980, Happiness
Thomas R, 2013, J PSYCHOL, V147, P345, DOI 10.1080/00223980.2012.694378
Van Boven L, 2003, J PERS SOC PSYCHOL, V85, P1193, DOI 10.1037/0022-3514.85.6.1193
Verduyn P, 2017, SOC ISS POLICY REV, V11, P274, DOI 10.1111/sipr.12033
Vogel J, 2015, J RETAIL CONSUM SERV, V27, P126, DOI 10.1016/j.jretconser.2015.07.007
Walz S.P., 2015, GAMEFUL WORLD APPROA
WATERMAN AS, 1993, J PERS SOC PSYCHOL, V64, P678, DOI 10.1037/0022-3514.64.4.678
Wheatley D, 2019, BRIT J SOCIOL, V70, P1971, DOI 10.1111/1468-4446.12644
Wu G., 2006, Journal of Current Issues and Research in Adverting, V28, P87
Xiao B, 2018, DECIS SUPPORT SYST, V110, P46, DOI 10.1016/j.dss.2018.03.005
Yoo WS, 2010, J RETAIL CONSUM SERV, V17, P89, DOI 10.1016/j.jretconser.2009.10.003
NR 53
TC 3
Z9 3
U1 11
U2 48
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-8749
EI 2040-8757
J9 NANKAI BUS REV NT
JI Nankai Bus. Rev. Int.
PD MAR 16
PY 2023
VL 14
IS 1
SI SI
BP 102
EP 127
DI 10.1108/NBRI-03-2022-0034
EA JUN 2022
PG 26
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA A0OV6
UT WOS:000804057200001
DA 2024-03-27
ER
PT J
AU Mishra, AK
Bansal, R
Maurya, PK
Kar, SK
Bakshi, PK
AF Mishra, Anand Kumar
Bansal, Rohit
Maurya, Prince Kumar
Kar, Sanjay Kumar
Bakshi, Palvinder Kaur
TI Predicting the antecedents of consumers' intention toward purchase of
mutual funds: A hybrid PLS-SEM-neural network approach
SO INTERNATIONAL JOURNAL OF CONSUMER STUDIES
LA English
DT Article
DE behavioural finance; consumer behaviour; COVID-19; financial awareness;
mutual funds; theory of planned behaviour
ID FINANCIAL LITERACY; SOCIAL MEDIA; INVESTMENT KNOWLEDGE; BEHAVIORAL
INTENTION; EMPIRICAL-ANALYSIS; SERVICE QUALITY; SELF-EFFICACY; IMPACT;
RISK; SATISFACTION
AB The current study intends to identify the behavioural antecedents of investors' attitude and investment intention toward mutual funds using a robust SEM-ANN approach. It focuses on novel factors in the purview of the COVID-19 pandemic, increasing digitalization and social media usage. The research outcome indicates that attitude (ATB), awareness (AW) and investment decision involvement (IDI) have a significant positive relation with investment intention (BI). In contrast, perceived barrier (PBR) negatively relates to investment intention. Herd behaviour (HB) and social media influence (SMI) do not influence investment intention toward mutual funds. Moreover, all the tested predictors share direct relation with the attitude toward mutual fund investment, barring perceived risk (PR), which has an inverse relationship. As per the outcome of ANN sensitivity analysis, attitude is the most crucial determinant of investment intention. It is followed by awareness (AW), perceived barriers (PBR) and investment decision involvement (IDI). Among the significant determinants of attitude, self-efficacy (SE) is the most important determinant, followed by perceived usefulness (PU), perceived emergency (PEMER), subjective norms (SN) and perceived risk (PR).
C1 [Mishra, Anand Kumar; Bansal, Rohit; Maurya, Prince Kumar; Kar, Sanjay Kumar] Rajiv Gandhi Inst Petr Technol, Dept Management Studies, Jais, Uttar Pradesh, India.
[Bakshi, Palvinder Kaur] Univ Delhi, New Delhi, India.
C3 University of Delhi
RP Bansal, R (autor correspondiente), Rajiv Gandhi Inst Petr Technol, Dept Management Studies, Amethi 229304, Uttar Pradesh, India.
EM rbansal@rgipt.ac.in
RI Maurya, Prince/HTN-7002-2023; Kar, Sanjay K/A-2971-2017; Maurya,
Prince/IXW-4855-2023; Mishra, Anand Kumar/HTS-7433-2023
OI Maurya, Prince/0000-0002-9657-8987; Mishra, Anand
Kumar/0000-0003-0256-4857; Kaur, Palvinder/0000-0002-4436-2842; bansal,
rohit/0000-0002-9914-9109
CR Abid AA, 2011, PERCEIVED BENEFITS B, V2011, P1
Agnew J.R., 2005, J BEHAV FINANC, V6, P57, DOI DOI 10.1207/S15427579JPFM0602_2
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
AJZEN I, 1974, HUM RELAT, V27, P1, DOI 10.1177/001872677402700101
Ajzen I., 2000, EUR REV SOC PSYCHOL, P1, DOI [https://doi.org/10.1080/14792779943000116, DOI 10.1080/14792779943000116, 10.1080/14792779943000116]
Albuquerque R.A., 2021, 7822021 ECGI FIN, DOI [10.2139/ssrn.3908464, DOI 10.2139/SSRN.3908464]
Alexander GJ., 2001, The Quarterly Review of Economics and Finance, V41, P387, DOI [10.1016/S1062-9769(00)00080-6, DOI 10.1016/S1062-9769(00)00080-6]
Alqadhib H, 2022, COGENT ECON FINANC, V10, DOI 10.1080/23322039.2022.2056361
Nguyen AN, 2018, J ECON STUD, V45, P1288, DOI 10.1108/JES-07-2017-0175
Anong ST, 2010, FAM CONSUM SCI RES J, V38, P405, DOI 10.1111/j.1552-3934.2010.00035.x
BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
Barber B. M., 2013, Handb. Econ. Finance, V2, P1533, DOI [10.1016/B978-0-44-459406-8.00022-6, DOI 10.1016/B978-0-44-459406-8.00022-6]
Bashir I, 2015, J INDIAN BUS RES, V7, P67, DOI 10.1108/JIBR-02-2014-0013
Beik I.S., 2019, J CONSUM SCI, V4, P37, DOI DOI 10.29244/JCS.4.1.37-47
Bialowolski P, 2022, INT J CONSUM STUD, V46, P88, DOI 10.1111/ijcs.12646
Bora D, 2021, J PUBLIC AFF, V21, DOI 10.1002/pa.2623
Bouri E, 2019, FINANC RES LETT, V29, P216, DOI 10.1016/j.frl.2018.07.008
Brahmana R, 2018, S E ASIAN J MANAGE, V12, P43, DOI 10.21002/seam.v12i1.7465
Buehler P, 2018, INT J BANK MARK, V36, P1073, DOI 10.1108/IJBM-12-2016-0182
Cade NL, 2018, ACCOUNT ORG SOC, V68-69, P63, DOI 10.1016/j.aos.2018.03.004
Calcagno R, 2015, J BANK FINANC, V50, P363, DOI 10.1016/j.jbankfin.2014.03.013
Cao C, 2008, J BANK FINANC, V32, P2111, DOI 10.1016/j.jbankfin.2007.12.035
Caparrelli F., 2004, J BEHAV FINANC, V5, P222, DOI [10.1207/s15427579jpfm0504_5, DOI 10.1207/S15427579JPFM0504_5]
Carhart MM, 1997, J FINANC, V52, P57, DOI 10.2307/2329556
Cashman G., 2014, J ECON FINANC-US, V38, P541
Chakraborty D, 2023, TECHNOVATION, V120, DOI 10.1016/j.technovation.2022.102481
Chan WS, 2003, J FINANC ECON, V70, P223, DOI 10.1016/S0304-405X(03)00146-6
Chang J, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2020.102452
Chase S., 2011, COMING CASH PINCH EM, DOI DOI 10.1162/003355397555109
Chawla D, 2014, GLOB BUS REV, V15, P493, DOI 10.1177/0972150914535136
Chiang TF, 2017, INT J CONSUM STUD, V41, P484, DOI 10.1111/ijcs.12356
Cho SH, 2014, INT J CONSUM STUD, V38, P701, DOI 10.1111/ijcs.12143
Chopdar PK, 2022, J BUS RES, V140, P657, DOI 10.1016/j.jbusres.2021.11.031
Chopdar PK, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121249
COHEN J, 1992, PSYCHOL BULL, V112, P155, DOI 10.1037/0033-2909.112.1.155
COX R, 1976, J MARKETING RES, V13, P320, DOI 10.2307/3150760
Cude BJ, 2019, INT J CONSUM STUD, V43, P503, DOI 10.1111/ijcs.12535
D'Arcangelis AM, 2021, J BUS RES, V129, P679, DOI 10.1016/j.jbusres.2019.11.016
Del Giudice A, 2017, RES INT BUS FINANC, V41, P600, DOI 10.1016/j.ribaf.2017.05.004
Dholakia UtpalM., 2001, EUR J MARKETING, V35, P1340, DOI DOI 10.1108/EUM0000000006479
Diacon S., 2004, International Journal of Bank Marketing, V22, P180, DOI [10.1108/02652320410530304, DOI 10.1108/02652320410530304]
Engelberg E., 2007, International Journal of Consumer Studies, V31, P95, DOI DOI 10.1111/J.1470-6431.2005.00494.X
Fama EF, 2010, J FINANC, V65, P1915, DOI 10.1111/j.1540-6261.2010.01598.x
Fernandez-Perez A, 2021, J BEHAV EXP FINANC, V29, DOI 10.1016/j.jbef.2020.100454
Ferriani F, 2021, J INT FINANC MARK I, V74, DOI 10.1016/j.intfin.2021.101391
Forbes J, 2010, J ECON PSYCHOL, V31, P435, DOI 10.1016/j.joep.2010.01.012
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Foxall G.R., 1998, INT J BANK MARK, V16, P180
Franck A., 2012, KREDIT KAPITAL, V45, P343, DOI DOI 10.3790/KUK.45.3.343
Frey S, 2014, J INT FINANC MARK I, V32, P219, DOI 10.1016/j.intfin.2014.05.006
FURNHAM A, 1985, J APPL SOC PSYCHOL, V15, P354, DOI 10.1111/j.1559-1816.1985.tb00912.x
Furnham A, 1999, J ECON PSYCHOL, V20, P677, DOI 10.1016/S0167-4870(99)00030-6
GEISSER S, 1974, BIOMETRIKA, V61, P101, DOI 10.2307/2334290
GINTER JL, 1979, J MARKETING RES, V16, P600, DOI 10.2307/3150840
Goodell JW, 2020, FINANC RES LETT, V35, DOI 10.1016/j.frl.2020.101512
Gordon-Wilson S, 2022, INT J CONSUM STUD, V46, P575, DOI 10.1111/ijcs.12701
Goyal K, 2021, INT J CONSUM STUD, V45, P80, DOI 10.1111/ijcs.12605
Guan C., 2021, Annals of Data Science, DOI DOI 10.1007/S40745-021-00353-W
Guan R, 2021, J SYST SCI SYST ENG, V30, P748, DOI 10.1007/s11518-021-5514-4
Gutsche G, 2020, SCHMALENBACH BUS REV, V72, P111, DOI 10.1007/s41464-020-00085-z
Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hira TK, 2013, INT J CONSUM STUD, V37, P29, DOI 10.1111/ijcs.12003
Hira TK, 2012, INT J CONSUM STUD, V36, P502, DOI 10.1111/j.1470-6431.2012.01115.x
Ho CS, 2017, DECIS SUPPORT SYST, V101, P69, DOI 10.1016/j.dss.2017.06.001
Hoffmann A, 2018, INT J CONSUM STUD, V42, P241, DOI 10.1111/ijcs.12412
Hogarth J.M., 2003, Financial Counseling and Planning, V14, P1, DOI [10.4324/9781315081816, DOI 10.4324/9781315081816]
Hsieh MF, 2011, QUANT FINANC, V11, P423, DOI 10.1080/14697688.2010.506882
Hwang YJ, 2007, DECIS SUPPORT SYST, V43, P746, DOI 10.1016/j.dss.2006.12.008
Jain M, 2019, INT J RES ANALYTICAL, V6, P1
Jank S, 2012, J BANK FINANC, V36, P3060, DOI 10.1016/j.jbankfin.2012.07.004
Jia DK, 2021, EMERG MARK FINANC TR, V57, P2153, DOI 10.1080/1540496X.2019.1643319
Jiang H, 2022, J FINANC ECON, V143, P277, DOI 10.1016/j.jfineco.2021.05.022
Jonsson S, 2017, MANAG FINANC, V43, P282, DOI 10.1108/MF-10-2015-0269
Jonsson S, 2009, STRATEGIC MANAGE J, V30, P517, DOI 10.1002/smj.739
Kale JR, 2012, IIMB MANAG REV, V24, P245, DOI 10.1016/j.iimb.2012.05.004
Kan M.P.H., 2017, ENCY PERSONALITY IND, P1, DOI DOI 10.1007/978-3-319-28099-8_1191-1
Kaniel R, 2017, J FINANC ECON, V123, P337, DOI 10.1016/j.jfineco.2016.11.003
Kaur I, 2018, QUAL RES FINANC MARK, V10, P395, DOI 10.1108/QRFM-09-2017-0084
Kaur I, 2016, J INDIAN BUS RES, V8, P19, DOI 10.1108/JIBR-04-2015-0051
Kautish P, 2019, J CLEAN PROD, V228, P1425, DOI 10.1016/j.jclepro.2019.04.389
Kengatharan L., 2014, ASIAN J FINANCE ACCO, DOI [DOI 10.5296/AJFA.V6I1.4893, 10.5296/ajfa.v6i1.4893]
Kennickell, 2005, FRANKFURT GERMANY GO
Kim J, 2008, INT J CONSUM STUD, V32, P619, DOI 10.1111/j.1470-6431.2008.00689.x
Kim MJ, 2022, INT J CONSUM STUD, V46, P449, DOI 10.1111/ijcs.12692
Klapper Leora., 2004, EMERG MARK REV, V5, P1, DOI DOI 10.1016/J.EMEMAR.2003.12.001
Kling L, 2023, J BANK FINANC, V154, DOI 10.1016/j.jbankfin.2022.106489
Kock N., 2020, Data Analysis Perspectives Journal, V2, P1
Kock N., 2017, In partial least squares path modeling, P245, DOI [10.1007/978-3-319-64069-3_11, DOI 10.1007/978-3-319-64069-3]
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015040101
Kozup J, 2008, J CONSUM AFF, V42, P37, DOI 10.1111/j.1745-6606.2007.00093.x
Laroche M., 2004, J SERV RES-US, V6, P373, DOI DOI 10.1177/1094670503262955
Lee JM, 2019, INT J FINANC STUD, V7, DOI 10.3390/ijfs7030053
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Leong LY, 2018, J ELECTRON COMMER RE, V19, P75
Li DF, 2019, J ASSOC INF SCI TECH, V70, P1000, DOI 10.1002/asi.24173
Lim TS, 2018, J BEHAV EXP FINANC, V20, P74, DOI 10.1016/j.jbef.2018.08.001
Lim X.J., 2017, ASIAN J BUSINESS RES, V7, P19, DOI [DOI 10.14707/AJBR.170035, 10.14707/ajbr.170035]
Liu F, 2019, INT J CONSUM STUD, V43, P23, DOI 10.1111/ijcs.12480
LIVINGSTONE S, 1993, HUM RELAT, V46, P963, DOI 10.1177/001872679304600804
Lobao J, 2017, CUAD ECON-SPAIN, V40, P268, DOI 10.1016/j.cesjef.2016.10.005
Lopes EL, 2022, INT J CONSUM STUD, V46, P803, DOI 10.1111/ijcs.12729
Lowry PB, 2014, IEEE T PROF COMMUN, V57, P123, DOI 10.1109/TPC.2014.2312452
Lusardi A., 2020, Mind Society, P20181, DOI DOI 10.1007/S11299-020-00246-0
Ma TJ, 2017, J INF TECHNOL-UK, V32, P234, DOI 10.1057/s41265-017-0037-7
Maheen MS, 2021, FUTUR BUS J, V7, DOI 10.1186/s43093-021-00081-w
Meesala A, 2018, J RETAIL CONSUM SERV, V40, P261, DOI 10.1016/j.jretconser.2016.10.011
Michaelidou Nina., 2008, Marketing Review, V8, P83, DOI DOI 10.1362/146934708X290403
Milakovic IK, 2021, INT J CONSUM STUD, V45, P1425, DOI 10.1111/ijcs.12672
Mirza N, 2020, FINANC RES LETT, V36, DOI 10.1016/j.frl.2020.101657
Mishra, 2012, IUP J BEHAV FINANCE, VIX, P27
Misra R, 2022, QUAL RES FINANC MARK, V14, P602, DOI 10.1108/QRFM-12-2020-0232
Montford W, 2016, INT J CONSUM STUD, V40, P101, DOI 10.1111/ijcs.12219
Mou J, 2017, BEHAV INFORM TECHNOL, V36, P125, DOI 10.1080/0144929X.2016.1203024
Murendo C, 2017, INT J CONSUM STUD, V41, P95, DOI 10.1111/ijcs.12318
Nanda AP, 2021, INT J CONSUM STUD, V45, P750, DOI 10.1111/ijcs.12668
Nayal P, 2022, J CONSUM AFF, V56, P359, DOI 10.1111/joca.12399
Nocetti D, 2010, BE J ECON ANAL POLI, V10
Nur AiniN S., 2019, J EC BUSINESS ACCOUN, V21, P401, DOI DOI 10.14414/JEBAV.V21I3.1663
O'Neill B, 2007, J FAM ECON ISS, V28, P321, DOI 10.1007/s10834-007-9063-x
Ortmann R, 2020, FINANC RES LETT, V37, DOI 10.1016/j.frl.2020.101717
Padmavathy C, 2019, J RETAIL CONSUM SERV, V51, P19, DOI 10.1016/j.jretconser.2019.05.014
Paliwal, 2018, PURUSHARTHA, V11, P39, DOI DOI 10.21844/PAJMES.V11I1.14608
Panigrahi S., 2018, INDIAN J MARKETING, V48, P28, DOI DOI 10.17010/IJOM/2018/V48/I1/120734
Pástor L, 2020, REV ASSET PRICING ST, V10, P791, DOI 10.1093/rapstu/raaa015
Paul J, 2021, INT J CONSUM STUD, V45, P937, DOI 10.1111/ijcs.12727
Paul J, 2020, J STRATEG MARK, V28, P681, DOI 10.1080/0965254X.2019.1569111
Paul J, 2016, INT J CONSUM STUD, V40, P635, DOI 10.1111/ijcs.12279
Paul J, 2016, INT J BANK MARK, V34, P606, DOI 10.1108/IJBM-03-2015-0030
Paul J, 2016, J RETAIL CONSUM SERV, V29, P123, DOI 10.1016/j.jretconser.2015.11.006
Paul J, 2012, J CONSUM MARK, V29, P412, DOI 10.1108/07363761211259223
Pellinen A, 2015, J FINANC SERV MARK, V20, P62, DOI 10.1057/fsm.2014.32
Pellinen A, 2011, INT J BANK MARK, V29, P107, DOI 10.1108/02652321111107611
PRATHER Laurie., 2004, REV FINANC ECON, V13, P305, DOI [10.1016/j.rfe.2003.11.002, DOI 10.1016/J.RFE.2003.11.002]
Purohit S, 2022, J CONSUM BEHAV, V21, P523, DOI 10.1002/cb.2017
Segarra-Moliner JR, 2016, J BUS RES, V69, P4694, DOI 10.1016/j.jbusres.2016.04.017
Rana J, 2020, INT J CONSUM STUD, V44, P162, DOI 10.1111/ijcs.12556
Rayburn SW, 2022, INT J CONSUM STUD, V46, P524, DOI 10.1111/ijcs.12698
Raza SA, 2020, J ISLAMIC MARK, V11, P1497, DOI 10.1108/JIMA-03-2019-0047
Reyers M, 2019, INT J CONSUM STUD, V43, P335, DOI 10.1111/ijcs.12520
Ribbinket D., 2004, Managing Service Quality, V14, P446, DOI [DOI 10.1108/09604520410569784, 10.1108/09604520410569784]
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Rizvi SKA, 2020, J ASSET MANAG, V21, P281, DOI 10.1057/s41260-020-00172-3
Robb C.A., 2009, Journal of Financial Counseling and Planning, V20, P25
Roszkiewicz M, 2014, INT J CONSUM STUD, V38, P602, DOI 10.1111/ijcs.12129
Samadi M., 2009, BUSINESS INTELLIGENC, V2, P261
Samira, 2020, MATER TODAY-PROC, DOI [10.1016/j.matpr.2020.11.134, DOI 10.1016/J.MATPR.2020.11.134]
Schmitt N, 2010, RES PRACT APPL LINGU, P1, DOI 10.1057/9780230293977
Shah, 2021, SOCIAL MEDIA ANAL MU, DOI [10.1007/978-981-15-5077-5_26, DOI 10.1007/978-981-15-5077-5_26]
Sharma A, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106919
Sharma GD, 2021, TOUR MANAG PERSPECT, V37, DOI 10.1016/j.tmp.2020.100786
Sherif M, 2020, J BEHAV EXP FINANC, V28, DOI 10.1016/j.jbef.2020.100403
Shim GY, 2008, J BUS RES, V61, P47, DOI 10.1016/j.jbusres.2006.05.008
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Shmueli G, 2016, J BUS RES, V69, P4552, DOI 10.1016/j.jbusres.2016.03.049
Sias R.W., 1997, REV QUANT FINANC ACC, V8, P245, DOI DOI 10.1023/A:1008262905223
Sivaramakrishnan S, 2017, INT J BANK MARK, V35, P818, DOI 10.1108/IJBM-01-2016-0012
Soon HT, 2017, INTERNET RES, V27, P942, DOI 10.1108/IntR-08-2016-0256
STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x
Tajurahim NNS, 2020, INT J CONSUM STUD, V44, P511, DOI 10.1111/ijcs.12585
Talwar M, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102341
Tan GWH, 2014, COMPUT HUM BEHAV, V36, P198, DOI 10.1016/j.chb.2014.03.052
Taneja A, 2019, INT J INFORM MANAGE, V45, P132, DOI 10.1016/j.ijinfomgt.2018.10.010
TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144
Teo AC, 2015, IND MANAGE DATA SYST, V115, P311, DOI 10.1108/IMDS-08-2014-0231
Teo Timothy, 2011, Campus-Wide Information Systems, V28, P124, DOI 10.1108/10650741111117824
Tong SC, 2018, J FINANC SERV MARK, V23, P179, DOI 10.1057/s41264-018-0054-9
Vaidyanathan R., 2000, J PRODUCT BRAND MANA, V9, P179, DOI [10.1108/10610420010332449, DOI 10.1108/10610420010332449]
Venkataramani R, 2023, MACROECON FINANC EME, V16, P157, DOI 10.1080/17520843.2021.1969086
Vörös Z, 2021, INT J CONSUM STUD, V45, P1292, DOI 10.1111/ijcs.12734
Vrontis D, 2021, INT J CONSUM STUD, V45, P617, DOI 10.1111/ijcs.12647
Waweru NelsonMaina., 2008, International Journal of Business and Emerging Markets, V1, P24, DOI [10.1504/IJBEM.2008.019243, DOI 10.1504/IJBEM.2008.019243, 10.1504/ijbem.2008.019243]
WOOD R, 1989, ACAD MANAGE REV, V14, P361, DOI 10.2307/258173
Xiao JJ, 2014, INT J CONSUM STUD, V38, P394, DOI 10.1111/ijcs.12106
Yao J, 2014, INT REV ECON FINANC, V29, P12, DOI 10.1016/j.iref.2013.03.002
Yap SAF, 2021, INT J CONSUM STUD, V45, P1239, DOI 10.1111/ijcs.12724
Yuh Y, 2010, J CONSUM AFF, V44, P70, DOI 10.1111/j.1745-6606.2010.01158.x
Zhu T, 2022, INT J CONSUM STUD, V46, P56, DOI 10.1111/ijcs.12643
NR 179
TC 11
Z9 11
U1 8
U2 38
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-6423
EI 1470-6431
J9 INT J CONSUM STUD
JI Int. J. Consum. Stud.
PD MAR
PY 2023
VL 47
IS 2
BP 563
EP 587
DI 10.1111/ijcs.12850
EA AUG 2022
PG 25
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 8U9YE
UT WOS:000837331800001
DA 2024-03-27
ER
PT J
AU Payne, EHM
Peltier, J
Barger, VA
AF Payne, Elizabeth H. Manser
Peltier, James
Barger, Victor A.
TI Enhancing the value co-creation process: artificial intelligence and
mobile banking service platforms
SO JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
LA English
DT Article
DE Mobile marketing; Web 2.0; Customer value; Services marketing; Service
quality; Information technology; Structural equation modeling; Eservice
quality; Human-computer interaction; Computer-mediated environments;
Digitalizations
ID SELF-SERVICE; CUSTOMER EXPERIENCE; AUGMENTED REALITY; CONSUMER ADOPTION;
ONLINE; TECHNOLOGY; INNOVATION; INTERNET; QUALITY; IMPACT
AB Purpose - The purpose of this study is to investigate the relationships that influence the value co-creation process and lead to consumer comfort with artificial intelligence (AI) and mobile banking (AIMB) service platforms.
Design/methodology/approach - A conceptual model was developed to investigate the value-in-use perceptions of AI-based mobile banking applications via five antecedents: baseline perceptions of current bank service delivery; service delivery configuration benefits; general data security; safety perceptions of specific mobile banking services; and perceptions of AI service delivery. Data were collected from 218 respondents and analyzed using structural equation modeling.
Findings - This study highlights the role and importance of the sequential relationships that impact the assessment of AIMB. The findings suggest that service delivery and the customer's role in value co-creation change as AI is introduced into a digital self-service technology channel. Furthermore, AIMB offers transaction-oriented (utilitarian) value propositions more so than relationship-oriented (hedonic) value propositions.
Research limitations/implications - The sample consisted on digital natives. Additional age cohorts are needed.
Practical implications - As financial institutions redirect their business models toward digital self-service technology channels, the need for customers to feel comfortable while interacting with an AI agent will be critical for enhancing the customer experience and firm performance.
Originality/value - The authors extend the service-dominant logic (SDL) literature by showing that value co-creation is a function of both firms' technologies and consumers' value-in-use, a finding that appears to be unique in the literature. The authors advance the digital transformation literature by evaluating AIMB as an interactive process that requires an understanding of key technology constructs, including perceptions of baseline service relationships, desired service configurations, security and safety issues and whether AI is useful for value co-creation. To the best of the authors' knowledge, this is the first SDL framework that investigates interactive and structural relationships to explain value-in-use perceptions of AIMB.
C1 [Payne, Elizabeth H. Manser] Univ South Dakota, Dept Mkt, Vermillion, SD USA.
[Payne, Elizabeth H. Manser; Barger, Victor A.] Univ Wisconsin, Coll Business & Econ, Dept Mkt, Whitewater, WI 53190 USA.
[Peltier, James] Univ Wisconsin, Dept Mkt, Whitewater, WI 53190 USA.
C3 University of South Dakota; University of Wisconsin System; University
of Wisconsin System
RP Peltier, J (autor correspondiente), Univ Wisconsin, Dept Mkt, Whitewater, WI 53190 USA.
EM peltierj@uww.edu
RI Barger, Victor A/B-2102-2009
OI Manser Payne, Elizabeth/0000-0002-7734-2282
CR Adapa S, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101901
Åkesson M, 2014, J SERV MANAGE, V25, P677, DOI 10.1108/JOSM-01-2013-0016
Akhter SH, 2015, J INT CONSUM MARK, V27, P207, DOI 10.1080/08961530.2014.994086
Albashrawi M, 2020, INFORM SYST MANAGE, V37, P16, DOI 10.1080/10580530.2020.1696530
Alzaydi ZM, 2018, BUS PROCESS MANAG J, V24, P295, DOI 10.1108/BPMJ-09-2016-0185
[Anonymous], 2019, EXPLORING OMNICHANNE
Arcand M, 2017, INT J BANK MARK, V35, P1066, DOI 10.1108/IJBM-10-2015-0150
Baabdullah AM, 2019, INT J INFORM MANAGE, V44, P38, DOI 10.1016/j.ijinfomgt.2018.09.002
Balapour A, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102063
BITNER MJ, 1992, J MARKETING, V56, P57, DOI 10.2307/1252042
Boateng SL, 2019, INT J BANK MARK, V37, P226, DOI 10.1108/IJBM-01-2018-0009
Boonlertvanich K, 2019, INT J BANK MARK, V37, P278, DOI 10.1108/IJBM-02-2018-0021
Brown I, 2003, INT J INFORM MANAGE, V23, P381, DOI 10.1016/S0268-4012(03)00065-3
Cambra-Fierro J, 2017, J RETAIL CONSUM SERV, V34, P219, DOI 10.1016/j.jretconser.2016.10.007
Castelli M, 2016, COMPUT INTEL NEUROSC, V2016, DOI 10.1155/2016/9139380
Chang HH, 2016, INFORM TECHNOL PEOPL, V29, P496, DOI 10.1108/ITP-08-2014-0171
Chaouali W, 2017, J RETAIL CONSUM SERV, V35, P57, DOI 10.1016/j.jretconser.2016.11.009
Chepurna M, 2018, J RES INTERACT MARK, V12, P452, DOI 10.1108/JRIM-01-2018-0018
Choudrie J, 2018, J BUS RES, V88, P449, DOI 10.1016/j.jbusres.2017.11.029
Veríssimo JMC, 2018, J BUS RES, V89, P442, DOI 10.1016/j.jbusres.2017.12.026
Curran JamesM., 2005, J SERV MARK, V19, P103, DOI [10.1108/08876040510591411, DOI 10.1108/08876040510591411]
Dabholkar PA, 2002, J ACAD MARKET SCI, V30, P184, DOI 10.1177/0092070302303001
Dahl A.J., 2020, J BUSINESS RES
Dahl AJ, 2019, J RES INTERACT MARK, V13, P47, DOI 10.1108/JRIM-11-2018-0153
Dahl AJ, 2018, J CONSUM AFF, V52, P562, DOI 10.1111/joca.12176
Dahl AJ, 2018, J RES INTERACT MARK, V12, P258, DOI 10.1108/JRIM-04-2018-0051
Dwivedi Y.K., 2020, J RETAIL CONSUM SERV, V53, P1
Elsharnouby TH, 2015, J RES INTERACT MARK, V9, P313, DOI 10.1108/JRIM-06-2014-0038
Fernandes T, 2019, J RETAIL CONSUM SERV, V50, P30, DOI 10.1016/j.jretconser.2019.01.018
Finsterwalder J, 2018, J RETAIL CONSUM SERV, V40, P276, DOI 10.1016/j.jretconser.2016.08.005
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Galindo-Martín MA, 2019, J BUS RES, V101, P522, DOI 10.1016/j.jbusres.2018.12.014
Ghosh D., 2017, IUP Journal of Management Research, V16, P34
Gronroos C., 2006, Marketing Theory, V6, P317
Grönroos C, 2011, J SERV MANAGE, V22, P5, DOI 10.1108/09564231111106893
Grönroos C, 2008, EUR BUS REV, V20, P298, DOI 10.1108/09555340810886585
Gummerus J, 2019, J SERV MARK, V33, P44, DOI 10.1108/JSM-10-2018-0292
Gupta S, 2017, INFORM TECHNOL DEV, V23, P127, DOI 10.1080/02681102.2016.1233855
Gustafsson A, 2012, J SERV MANAGE, V23, P311, DOI 10.1108/09564231211248426
Hair J., 2006, MULTIVARIATE DATA AN
Hallikainen H, 2019, J RETAIL CONSUM SERV, V50, P386, DOI 10.1016/j.jretconser.2018.07.014
Herhausen D, 2015, J RETAILING, V91, P309, DOI 10.1016/j.jretai.2014.12.009
Hilken T, 2018, J RES INTERACT MARK, V12, P509, DOI 10.1108/JRIM-01-2018-0023
Hossain TMT, 2019, J RETAIL CONSUM SERV, V49, P154, DOI 10.1016/j.jretconser.2019.03.019
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Izogo EE, 2018, J RES INTERACT MARK, V12, P193, DOI 10.1108/JRIM-02-2017-0015
Jacobson J, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.03.001
Jaksic M, 2019, RISK MANAG-UK, V21, P1, DOI 10.1057/s41283-018-0039-y
Johnson VL, 2018, COMPUT HUM BEHAV, V79, P111, DOI 10.1016/j.chb.2017.10.035
Khrais LT., 2020, International Journal of Applied Engineering Research, V15, P12, DOI DOI 10.1016/J.BIOPHA.2018.11.108
Korzeniowski P., 2017, CUSTOMER RELATIONSHI
Kristensson P, 2019, J SERV MARK, V33, P502, DOI 10.1108/JSM-01-2019-0031
Kucharska W, 2019, J RES INTERACT MARK, V13, P437, DOI 10.1108/JRIM-05-2018-0063
Laukkanen T, 2007, BUS PROCESS MANAG J, V13, P788, DOI 10.1108/14637150710834550
Laukkanen T, 2016, J BUS RES, V69, P2432, DOI 10.1016/j.jbusres.2016.01.013
Lee HJ, 2017, J RES INTERACT MARK, V11, P214, DOI 10.1108/JRIM-04-2016-0036
Lee J, 2019, J RETAIL CONSUM SERV, V48, P7, DOI 10.1016/j.jretconser.2019.01.007
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Lin HF, 2011, INT J INFORM MANAGE, V31, P252, DOI 10.1016/j.ijinfomgt.2010.07.006
Lin WR, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0227852
Lusch RF, 2008, IBM SYST J, V47, P5, DOI 10.1147/sj.471.0005
Manser Payne E., 2021, J RES INTERACTIVE MA
Markos E, 2017, J PUBLIC POLICY MARK, V36, P79, DOI 10.1509/jppm.15.159
Marous J., 2017, INNOVATION RETAIL BA
Mbama CI, 2018, J RES INTERACT MARK, V12, P432, DOI 10.1108/JRIM-01-2018-0026
Mistry D., 2018, Chatbots in banking can play the role of 'financial concierge'
Mondres T., 2019, ABA BANK J
Nijssen EJ, 2016, J SERV MANAGE, V27, P276, DOI 10.1108/JOSM-08-2015-0233
Parasuraman W, 2000, Journal of Service Research, V2, P307, DOI [10.1177/109467050024001, DOI 10.1177/109467050024001]
Park J, 2016, J RETAIL CONSUM SERV, V28, P310, DOI 10.1016/j.jretconser.2015.01.003
Patrício L, 2018, J SERV RES-US, V21, P3, DOI 10.1177/1094670517746780
Payne EM, 2018, J RES INTERACT MARK, V12, P328, DOI 10.1108/JRIM-07-2018-0087
Peltier J, 2020, J RES INTERACT MARK, V14, P239, DOI 10.1108/JRIM-01-2020-0010
Peltier JW, 2020, J BUS RES, V121, P724, DOI 10.1016/j.jbusres.2020.03.020
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Pueschel J, 2010, INT J BANK MARK, V28, P389, DOI 10.1108/02652321011064908
ROGERS E. M., 2003, DIFFUSION INNOVATION, V5th
Sandstrom S., 2008, Managing Service Quality, V18, P112, DOI [10.1108/09604520810859184, DOI 10.1108/09604520810859184]
Saunila M, 2019, INFORM TECHNOL PEOPL, V32, P627, DOI 10.1108/ITP-10-2016-0224
Sengupta R., 2019, EC REV FEDERAL RESER, V104, P43
Shankar A., 2016, JOURNAL, V21, P1
Shen YC, 2010, BEHAV INFORM TECHNOL, V29, P497, DOI 10.1080/01449290903490658
Shin S., 2014, Journal of Applied Business Research, V30, P1365, DOI DOI 10.19030/JABR.V30I5.8793
Sihi D, 2018, J RES INTERACT MARK, V12, P398, DOI 10.1108/JRIM-01-2018-0019
Silva B., 2013, International Journal of Management Science and Technology Information, V1, P1
Sklyar A, 2019, J BUS RES, V104, P450, DOI 10.1016/j.jbusres.2019.02.012
Souiden N, 2019, J RETAIL CONSUM SERV, V50, P286, DOI 10.1016/j.jretconser.2018.07.023
Sripalawat J, 2011, J COMPUT INFORM SYST, V51, P67
Srivastava S.K., 2018, JISTEM J INFORM SYST, V15
Straker K, 2015, J RES INTERACT MARK, V9, P110, DOI 10.1108/JRIM-06-2014-0039
Swan EL, 2019, J RES INTERACT MARK, V13, P602, DOI 10.1108/JRIM-03-2019-0039
Tarn M., 2019, J INF SYST TECHNOL M, V16, P1
Turetken O, 2019, BUS INFORM SYST ENG+, V61, P9, DOI 10.1007/s12599-018-0565-x
Vargo SL, 2004, J MARKETING, V68, P1, DOI 10.1509/jmkg.68.1.1.24036
Verhoef PC, 2009, J RETAILING, V85, P31, DOI 10.1016/j.jretai.2008.11.001
Yousafzai SY, 2010, J APPL SOC PSYCHOL, V40, P1172, DOI 10.1111/j.1559-1816.2010.00615.x
Zainuddin N, 2016, J SERV MARK, V30, P586, DOI 10.1108/JSM-02-2016-0075
NR 98
TC 88
Z9 90
U1 72
U2 342
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-7122
EI 2040-7130
J9 J RES INTERACT MARK
JI J. Res. Interact. Mark.
PD MAY 19
PY 2021
VL 15
IS 1
BP 68
EP 85
DI 10.1108/JRIM-10-2020-0214
EA FEB 2021
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA SF3GR
UT WOS:000614172200001
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Jin, E
Eastin, MS
AF Jin, Eunjoo
Eastin, Matthew S.
TI Birds of a feather flock together: matched personality effects of
product recommendation chatbots and users
SO JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
LA English
DT Article
DE E-commerce; Computer mediated communication; Human-computer interaction
ID ATTITUDE SIMILARITY; INTERPERSONAL-ATTRACTION; MANIFEST PERSONALITY;
SOCIAL RESPONSES; RECOGNITION; TRAITS; PEER; CUES; COMMUNICATION;
CONSISTENCY
AB Purpose - AI-driven product recommendation chatbots have markedly reduced operating costs and increased sales for marketers. However, previous literature has paid little attention to the effects of the personality of e-commerce chatbots. This study aimed to examine the ways that the interplay between the chatbot's and the user's personality can increase favorable product attitudes and future intentions to use the chatbot. Based on prior literature, we specifically focused on the degree of extroversion of both chatbot and user.
Design/methodology/approach - A total of 291 individuals participated in this study. Two different versions of chatbot were created for this study (i.e. extroversion: high vs. low). Participants self-reported their degree of extroversion. The PROCESS macro Model 1 and Model 7 with the Johnson-Neyman technique were employed to test the hypotheses.
Findings - The results showed that the high extroversion chatbot elicited greater user satisfactions and perceptions of chatbot friendliness among users with a high level of extroversion. On the contrary, the low extroversion chatbot resulted in greater user satisfactions and perceived chatbot friendliness among users with a low level of extroversion. This study further found that user satisfactions and perceived chatbot friendliness mediated the effects of the chatbot on greater intentions to use the chatbot and more favorable product attitudes.
Originality/value - By showing the effects of matching the personality of the chatbot and user, this study revealed that similarity-attraction effects also apply to human-chatbot interaction in e-commerce. Future studies would benefit by investigating the similarity-attraction effects in different characteristics, such as appearance, opinion and preference. This study also provides useful information for e-commerce marketers and chatbot UX/UI designers.
C1 [Jin, Eunjoo; Eastin, Matthew S.] Univ Texas Austin, Stan Richards Sch Advertising & Publ Relat, Austin, TX 78712 USA.
C3 University of Texas System; University of Texas Austin
RP Jin, E (autor correspondiente), Univ Texas Austin, Stan Richards Sch Advertising & Publ Relat, Austin, TX 78712 USA.
EM Eunjoo1125@utexas.edu
OI Jin, Eunjoo/0000-0003-4058-922X
CR [Anonymous], MARKET ANAL REPORT 2
Aquino A, 2020, COGN AFFECT BEHAV NE, V20, P536, DOI 10.3758/s13415-020-00784-7
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Barelds DPH, 2007, J SOC PERS RELAT, V24, P479, DOI 10.1177/0265407507079235
Baris A., 2020, GSI Journals Serie B: Advancements in Business and Economics, V3, P31
Batra R., 1990, MARKET LETT, V2, P159, DOI [DOI 10.1007/BF00436035, 10.1007/BF00436035]
Berger C.R., 1974, Hum. Commun. Res., V1, P99, DOI DOI 10.1111/J.1468-2958.1975.TB00258.X
Bernier Emily P., 2010, 2010 IEEE 9th International Conference on Development and Learning (ICDL 2010), P286, DOI 10.1109/DEVLRN.2010.5578828
BERNTSON GG, 1995, PSYCHOPHYSIOLOGY, V32, P162, DOI 10.1111/j.1469-8986.1995.tb03308.x
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Bolin JH, 2014, J EDUC MEAS, V51, P335, DOI 10.1111/jedm.12050
BUSCH P, 1976, J MARKETING RES, V13, P3, DOI 10.2307/3150896
Business Insider, 2021, Drone market outlook in 2021: industry growth trends, market stats and forecast
BYRNE D, 1969, J EXP RES PERS, V3, P179
Byrne D., 1970, PERSONALITY INT J, V1, P103
Byrne D. E., 1971, ATTRACTION PARADIGM
Cemalcilar Z, 2018, J RES PERS, V76, P139, DOI 10.1016/j.jrp.2018.07.009
CHAIKEN S, 1980, J PERS SOC PSYCHOL, V39, P752, DOI 10.1037/0022-3514.39.5.752
Chaves AP, 2021, INT J HUM-COMPUT INT, V37, P729, DOI 10.1080/10447318.2020.1841438
Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
CONDON JW, 1988, J PERS SOC PSYCHOL, V54, P789, DOI 10.1037/0022-3514.54.5.789
Craenen B, 2018, IEEE ROMAN, P626, DOI 10.1109/ROMAN.2018.8525672
CRITES SL, 1994, PERS SOC PSYCHOL B, V20, P619, DOI 10.1177/0146167294206001
Cuperman R, 2009, J PERS SOC PSYCHOL, V97, P667, DOI 10.1037/a0015741
Doroszuk M., 2019, ENCY PERSONALITY IND, DOI DOI 10.1007/978-3-319-28099-8_712-1
Edwards C, 2019, COMPUT HUM BEHAV, V90, P357, DOI 10.1016/j.chb.2018.08.027
Evans FB., 1963, American Behavioral Scientist, V6, P76, DOI DOI 10.1177/000276426300600922
Feine Jasper, 2020, Chatbot Research and Design. Third International Workshop, CONVERSATIONS 2019. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 11970), P79, DOI 10.1007/978-3-030-39540-7_6
Fogg B. J., 2002, UBIQUITY, V2002, P5, DOI [DOI 10.1145/764008.763957, 10.1145/763955.763957]
Foster JK, 2022, J RES INTERACT MARK, V16, P237, DOI 10.1108/JRIM-01-2020-0007
Galitsky Boris, 2021, HumanComputer Interaction Series, P93, DOI [10.1007/978-3-030-61641-0_3, DOI 10.1007/978-3-030-61641-0_3]
Gaur SS, 2012, INT REV RETAIL DISTR, V22, P315, DOI 10.1080/09593969.2012.682597
GEER JH, 1968, J PERS SOC PSYCHOL, V10, P148, DOI 10.1037/h0026293
Goldberg CB, 2005, GROUP ORGAN MANAGE, V30, P597, DOI 10.1177/1059601104267661
Goldberg L. R., 1992, Psychological Assessment, P26, DOI [10.1037/1040-3590.4.1.26, DOI 10.1037/1040-3590.4.1.26]
Hampton AJ, 2019, J SOC PERS RELAT, V36, P2221, DOI 10.1177/0265407518790411
HOGAN R, 1972, J COUNS PSYCHOL, V19, P238, DOI 10.1037/h0032707
Hsieh SH, 2021, J RES INTERACT MARK, V15, P267, DOI 10.1108/JRIM-11-2019-0179
Isbister K, 2000, INT J HUM-COMPUT ST, V53, P251, DOI 10.1006/ijhc.2000.0368
Ischen C, 2020, J BROADCAST ELECTRON, V64, P615, DOI 10.1080/08838151.2020.1834297
IZARD CE, 1960, J ABNORM SOC PSYCH, V61, P47, DOI 10.1037/h0040056
JAMIESON DW, 1987, J PERS SOC PSYCHOL, V53, P1052, DOI 10.1037/0022-3514.53.6.1052
Jin SV, 2018, COMPUT HUM BEHAV, V81, P31, DOI 10.1016/j.chb.2017.11.042
Lee KM, 2006, J COMMUN, V56, P754, DOI 10.1111/j.1460-2466.2006.00318.x
Lee SY, 2017, INT J HUM-COMPUT ST, V103, P95, DOI 10.1016/j.ijhcs.2017.02.005
Lim WM, 2023, J RES INTERACT MARK, V17, P232, DOI 10.1108/JRIM-11-2021-0276
Liu BJ, 2018, CYBERPSYCH BEH SOC N, V21, P625, DOI 10.1089/cyber.2018.0110
Liu D, 2017, J RES PERS, V70, P229, DOI 10.1016/j.jrp.2017.08.004
Lou C., 2021, INT J ADVERT, P1
Mairesse F, 2007, J ARTIF INTELL RES, V30, P457, DOI 10.1613/jair.2349
McDonnell M, 2019, INTERACT COMPUT, V31, P116, DOI 10.1093/iwc/iwz007
Mou Y, 2020, INT J HUM-COMPUT INT, V36, P591, DOI 10.1080/10447318.2019.1663008
Nass C, 1996, INT J HUM-COMPUT ST, V45, P669, DOI 10.1006/ijhc.1996.0073
Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
Nass C, 2001, J EXP PSYCHOL-APPL, V7, P171, DOI 10.1037//1076-898X.7.3.171
Ohadi J, 2018, COMPUT HUM BEHAV, V78, P126, DOI 10.1016/j.chb.2017.08.048
Pennebaker JW, 1999, J PERS SOC PSYCHOL, V77, P1296, DOI 10.1037/0022-3514.77.6.1296
Pratt JA, 2007, INTERACT COMPUT, V19, P512, DOI 10.1016/j.intcom.2007.02.003
Rapp A, 2021, INT J HUM-COMPUT ST, V151, DOI 10.1016/j.ijhcs.2021.102630
Reeves B, 2000, COMMUN ACM, V43, P65, DOI 10.1145/330534.330542
Reeves B., 1996, MEDIA EQUATION PEOPL
Robert LP, 2018, AMCIS 2018 PROCEEDINGS
Roy R, 2021, J BUS RES, V126, P23, DOI 10.1016/j.jbusres.2020.12.051
Ruane E., 2020, INT WORKSH CHATB RES, P32
Ruijten PAM, 2021, BEHAV INFORM TECHNOL, V40, P734, DOI 10.1080/0144929X.2020.1723701
Santamaria Tricia, 2017, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V61, P853, DOI 10.1177/1541931213601686
Schmitt DP, 2002, BRIT J SOC PSYCHOL, V41, P589, DOI 10.1348/014466602321149894
Seddon PB, 1997, INFORM SYST RES, V8, P240, DOI 10.1287/isre.8.3.240
Selfhout M, 2009, J PERS SOC PSYCHOL, V96, P1152, DOI 10.1037/a0014468
SETA JJ, 1979, J PERS SOC PSYCHOL, V37, P462, DOI 10.1037/0022-3514.37.3.462
Singh R, 2017, PERS RELATIONSHIP, V24, P203, DOI 10.1111/pere.12178
Strauss JP, 2001, J OCCUP ORGAN PSYCH, V74, P637, DOI 10.1348/096317901167569
STRODTBECK FL, 1959, AM SOCIOL REV, V24, P437, DOI 10.2307/2089421
Sundar SS, 2015, HBK COMMUN MEDIA, P47
Teeny JD, 2021, J CONSUM PSYCHOL, V31, P382, DOI 10.1002/jcpy.1198
Teo HH, 2003, INT J HUM-COMPUT ST, V58, P281, DOI 10.1016/S1071-5819(03)00008-9
TOUHEY JC, 1972, J PERS SOC PSYCHOL, V23, P8, DOI 10.1037/h0032886
Tsai WHS, 2021, J RES INTERACT MARK, V15, P460, DOI 10.1108/JRIM-12-2019-0200
Van den Broeck E, 2019, COMPUT HUM BEHAV, V98, P150, DOI 10.1016/j.chb.2019.04.009
van Noort G, 2020, J ADVERTISING, V49, P411, DOI 10.1080/00913367.2020.1795954
Wang CL, 2021, J RES INTERACT MARK, V15, P1, DOI 10.1108/JRIM-03-2021-270
Xu K, 2017, COMPUT HUM BEHAV, V74, P152, DOI 10.1016/j.chb.2017.04.043
Yorita A, 2019, IEEE SYS MAN CYBERN, P4094, DOI 10.1109/SMC.2019.8914583
Youyou Wu, 2017, Psychol Sci, V28, P276, DOI 10.1177/0956797616678187
ZAICHKOWSKY JL, 1985, J CONSUM RES, V12, P341, DOI 10.1086/208520
NR 86
TC 6
Z9 6
U1 62
U2 166
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-7122
EI 2040-7130
J9 J RES INTERACT MARK
JI J. Res. Interact. Mark.
PD MAY 3
PY 2023
VL 17
IS 3
BP 416
EP 433
DI 10.1108/JRIM-03-2022-0089
EA JUN 2022
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA F2RD9
UT WOS:000810497900001
DA 2024-03-27
ER
PT J
AU Srivastava, G
Bag, S
Rahman, MS
Pretorius, JHC
Gani, MO
AF Srivastava, Gautam
Bag, Surajit
Rahman, Muhammad Sabbir
Pretorius, Jan Harm Christiaan
Gani, Mohammad Osman
TI Examining the dark side of using gamification elements in online
community engagement: an application of PLS-SEM and ANN modeling
SO BENCHMARKING-AN INTERNATIONAL JOURNAL
LA English
DT Article
DE Adverse effects; Gamification; Marketing; Cyberbullying; Fatigue;
PLS-SEM; Artificial neural network
ID SOCIAL NETWORKING; PRIVACY; IMPACT; BIAS
AB Purpose The negative influence of gamification on online communities has received little attention in the available literature. The study examines the adverse effects of gamification during engaging in online communities. Design/methodology/approach Gap-spotting methods were used to develop the research questions, followed by model development using the social exchange and social-network theories. Data were collected from 429 samples. The study applied partial least squares structural equation modeling to test the research hypotheses followed by ANN application. Findings The study identified five factors related to gamification that have a significant adverse effect on the mental and emotional well-being of the users. Furthermore, the results of PLS-SEM were then compared through an artificial neural network (ANN) analytic process, revealing consistency for the model. This research presents a theoretical contribution by providing critical insights into online gamers' mental and emotional health. It implies that gamification can even bring mental and emotional disturbance. The resulting situation might lead to undesirable social consequences. Practical implications The result highlights the managerial and social relevance from the perspective of a developing country. As respondents are becoming more engrossed in online gaming, managers and decision-makers need to take preventive measures to overcome the dark side of online gaming. Originality/value The present study shows that the dark side of gamification has some adverse effects on human mental and emotional health. The study's findings can be used to improve gamification strategies while engaging online communities.
C1 [Srivastava, Gautam] GL Bajaj Inst Management & Res, Dept Management Studies, Greater Noida, India.
[Bag, Surajit] Inst Management Technol Ghaziabad, Ctr Data Sci, Ghaziabad, India.
[Rahman, Muhammad Sabbir] North South Univ, Sch Business & Econ, Dept Mkt & Int Business, Dhaka, Bangladesh.
[Pretorius, Jan Harm Christiaan] Univ Johannesburg, Fac Engn & Built Environm, Auckland Pk,Kingsway Campus, Johannesburg, South Africa.
[Gani, Mohammad Osman] Bangladesh Univ Profess, Dhaka, Bangladesh.
C3 Institute of Management Technology, Ghaziabad; North South University
(NSU); University of Johannesburg
RP Bag, S (autor correspondiente), Inst Management Technol Ghaziabad, Ctr Data Sci, Ghaziabad, India.
EM gautamshrivastav@gmail.com; surajit.bag@gmail.com;
rahman.sabbir@northsouth.edu; jhcpretorius@uj.ac.za; osman@bup.edu.bd
RI Srivastava, Gautam/N-5668-2019; Rahman, Muhammad/G-3968-2018
OI Srivastava, Gautam/0000-0001-9851-4103; Rahman,
Muhammad/0000-0003-1613-7944; BAG, SURAJIT/0000-0002-2344-9551; GANI,
MOHAMMAD OSMAN/0000-0002-9724-4006; Pretorius, Jan Harm
Christiaan/0000-0002-2023-749X
CR Abaido GM, 2020, INT J ADOLESC YOUTH, V25, P407, DOI 10.1080/02673843.2019.1669059
Andrade FRH, 2016, LECT NOTES COMPUT SC, V9684, P176, DOI 10.1007/978-3-319-39583-8_17
[Anonymous], 2011, The Straits Times
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Bag S, 2022, BENCHMARKING, V29, P2074, DOI 10.1108/BIJ-07-2021-0415
Bandara R, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101947
Behl A, 2021, AUSTRALAS J INF SYST, V25, DOI 10.3127/ajis.v25i0.2979
Behl A, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102140
Bleier A, 2020, INT J RES MARK, V37, P466, DOI 10.1016/j.ijresmar.2020.03.006
Bodhi R, 2022, BENCHMARKING, V29, P2450, DOI 10.1108/BIJ-03-2021-0152
Bonanno RA, 2013, J YOUTH ADOLESCENCE, V42, P685, DOI 10.1007/s10964-013-9937-1
Bradshaw J.L., 2020, COMPUTERS COMPOSITIO, V56, P1
Burt R. S., 1992, Structural holes
Cardador MT, 2017, HUM RESOUR MANAGE R, V27, P353, DOI 10.1016/j.hrmr.2016.09.014
Chun J, 2020, COMPUT HUM BEHAV, V113, DOI 10.1016/j.chb.2020.106485
Cook E.S., 2003, HDB SOCIAL PSYCHOL, P53, DOI [10.1007/978-94-007-6772-0_3, DOI 10.1007/978-94-007-6772-0_3]
Cortez RM, 2020, IND MARKET MANAG, V88, P125, DOI 10.1016/j.indmarman.2020.05.004
Craig W, 2020, J ADOLESCENT HEALTH, V66, pS100, DOI 10.1016/j.jadohealth.2020.03.006
Cruz AGB, 2018, INFORM SOC, V34, P15, DOI 10.1080/01972243.2017.1391909
D'Alessio FA, 2019, THINK SKILLS CREAT, V31, P275, DOI 10.1016/j.tsc.2019.02.002
de-Marcos L, 2014, COMPUT EDUC, V75, P82, DOI 10.1016/j.compedu.2014.01.012
Durnell E, 2020, INT J HUM-COMPUT INT, V36, P1834, DOI 10.1080/10447318.2020.1794626
Fabris MA, 2020, ADDICT BEHAV, V106, DOI 10.1016/j.addbeh.2020.106364
Faizan Ali Faizan Ali, 2014, Asia-Pacific Journal of Innovation in Hospitality and Tourism, V3, P175
Fazeli Sara, 2020, Addict Behav Rep, V12, P100307, DOI [10.1016/j.abrep.2020.100322, 10.1016/j.abrep.2020.100307]
Ferm LEC, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102584
Foo PY, 2018, EXPERT SYST APPL, V107, P1, DOI 10.1016/j.eswa.2018.04.013
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Franco JA, 2020, HUM BEHAV EMERG TECH, V2, P227, DOI 10.1002/hbe2.205
Graciyal DG, 2021, ASIA PAC MEDIA EDUC, V31, P99, DOI 10.1177/1326365X211003737
GRANOVETTER MS, 1973, AM J SOCIOL, V78, P1360, DOI 10.1086/225469
Groening C, 2021, INT J HUM-COMPUT INT, V37, P1130, DOI 10.1080/10447318.2020.1870828
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hammedi W, 2021, J BUS RES, V122, P256, DOI 10.1016/j.jbusres.2020.08.032
Harman H.H., 1967, Modern Factor Analysis
Harwood T, 2015, J SERV MARK, V29, P533, DOI 10.1108/JSM-01-2015-0045
Hilvert-Bruce Z, 2020, COMPUT HUM BEHAV, V102, P303, DOI 10.1016/j.chb.2019.09.003
Hofacker CF, 2016, J INTERACT MARK, V34, P25, DOI 10.1016/j.intmar.2016.03.001
HOUSE JS, 1988, SCIENCE, V241, P540, DOI 10.1037/0003-066X.59.8.676
Huotari K, 2017, ELECTRON MARK, V27, P21, DOI 10.1007/s12525-015-0212-z
Jia HY, 2022, COMPUT SECUR, V115, DOI 10.1016/j.cose.2022.102614
Juzwin M., 2014, Proceedings of the 2014 Multimedia, Interaction, Design and Innovation International Conference on Multimedia, Interaction, Design and Innovation 2014, P1, DOI [DOI 10.1145/2643572.2643587C, 10.1145/2643572, DOI 10.1145/2643572]
Kaur P, 2021, TECHNOL FORECAST SOC, V172, DOI 10.1016/j.techfore.2021.121039
Kim H, 2020, INT J HOSP MANAG, V89, DOI 10.1016/j.ijhm.2020.102577
Kim HJ, 2015, TECHNOL FORECAST SOC, V100, P255, DOI 10.1016/j.techfore.2015.07.009
Kim TW, 2016, ETHICS INF TECHNOL, V18, P157, DOI 10.1007/s10676-016-9401-5
Klosowski, 2020, NEW YORK TIMES
Koivisto J, 2019, INT J INFORM MANAGE, V45, P191, DOI 10.1016/j.ijinfomgt.2018.10.013
Lamb Y, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102198
Landers RN, 2019, J MANAGE INQUIRY, V28, P137, DOI 10.1177/1056492618790913
Lapierre KR, 2020, COMPUT HUM BEHAV, V113, DOI 10.1016/j.chb.2020.106497
Leclercq T, 2020, J MARKET MANAG-UK, V36, P550, DOI 10.1080/0267257X.2020.1736604
Leclercq T, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.07.007
Leclercq T, 2018, J INTERACT MARK, V44, P82, DOI 10.1016/j.intmar.2018.04.004
Lee VH, 2020, EXPERT SYST APPL, V157, DOI 10.1016/j.eswa.2020.113477
Li K, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102128
Lim A, 2022, SUPPLY CHAIN MANAG, V27, P611, DOI 10.1108/SCM-03-2021-0129
Lin KY, 2011, COMPUT HUM BEHAV, V27, P1152, DOI 10.1016/j.chb.2010.12.009
Lo PS, 2022, J BUS RES, V147, P325, DOI 10.1016/j.jbusres.2022.04.013
MacKenzie SB, 2012, J RETAILING, V88, P542, DOI 10.1016/j.jretai.2012.08.001
Maghsoudi R, 2020, COMPUT HUM BEHAV, V113, DOI 10.1016/j.chb.2020.106488
Marengo D, 2020, ADDICT BEHAV, V102, DOI 10.1016/j.addbeh.2019.106150
Marín-López I, 2020, CHILD YOUTH SERV REV, V116, DOI 10.1016/j.childyouth.2020.105144
Maturo AntonioFrancesco., 2018, Digital Health and the Gamification of Life: How Apps Can Promote a Positive Medicalization
Mheidly N, 2020, FRONT PUBLIC HEALTH, V8, DOI 10.3389/fpubh.2020.574969
Mitchell M.S., 2012, HDB SOCIAL RESOURCE, P99, DOI [DOI 10.1007/978-1-4614-4175-5_6, 10.1007/978-1-4614-4175-5_6]
Mitchell R, 2020, J BUS RES, V106, P323, DOI 10.1016/j.jbusres.2018.11.022
Moge CE, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e05340
Moqbel M, 2018, INFORM MANAGE-AMSTER, V55, P109, DOI 10.1016/j.im.2017.05.001
Mulcahy RF, 2021, EUR J MARKETING, V55, P2593, DOI 10.1108/EJM-05-2020-0341
Muñoz-Fernández N, 2020, COMPUT HUM BEHAV, V104, DOI 10.1016/j.chb.2019.106191
Neeli B.K., 2015, Gamification in education and business, P489, DOI DOI 10.1007/978-3-319-10208-5_25
Ng FZX, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102851
Ngien A, 2022, HEALTH COMMUN, V37, P1337, DOI 10.1080/10410236.2021.1888438
Nystrom T., 2021, Intelligent Computing, V285, P491, DOI DOI 10.1007/978-3-030-80129-8_35
Paciello M, 2020, COMPUT HUM BEHAV, V103, P190, DOI 10.1016/j.chb.2019.09.024
PARENT WA, 1983, LAW PHILOS, V2, P305, DOI 10.1007/BF00144949
Pillai R, 2020, BENCHMARKING, V27, P2599, DOI 10.1108/BIJ-04-2020-0186
Pizzi G, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102160
Rahi S, 2019, INT J INF LEARN TECH, V36, P2, DOI 10.1108/IJILT-05-2018-0045
Rahman MS, 2022, INFORM TECHNOL PEOPL, V35, P316, DOI 10.1108/ITP-12-2019-0621
Rahman MS, 2021, TOUR REV, V76, P358, DOI 10.1108/TR-12-2019-0505
Rasheed MI, 2020, COMPUT EDUC, V159, DOI 10.1016/j.compedu.2020.104002
Richter NF, 2016, EUR MANAG J, V34, P589, DOI 10.1016/j.emj.2016.08.001
Rigdon E., 2017, Marketing: ZFP-Journal of Research and Management, V39, P4, DOI [DOI 10.15358/0344-1369-2017-3-4, 10.15358/0344-1369-2017-3-4]
Sarstedt M, 2014, LONG RANGE PLANN, V47, P132, DOI 10.1016/j.lrp.2014.02.008
Schoffstall CL, 2011, SOC DEV, V20, P587, DOI 10.1111/j.1467-9507.2011.00609.x
Seiffert-Brockmann J, 2018, J COMMUN MANAG, V22, P67, DOI 10.1108/JCOM-12-2016-0096
Shmueli G, 2016, J BUS RES, V69, P4552, DOI 10.1016/j.jbusres.2016.03.049
Smith C, 2020, ORGANIZATION, V27, P840, DOI 10.1177/1350508419867201
Sohaib O, 2021, J GLOB INF MANAG, V29, P23, DOI 10.4018/JGIM.2021030102
Srivastava G., 2021, INT J TECHNOLOGY MAR, V15, P354
Srivastava G., 2021, INT J TECHNOLOGY MAR, V15, P143
Tajvidi M, 2020, J BUS RES, V121, P476, DOI 10.1016/j.jbusres.2018.06.008
Thorpe AS, 2019, J BUS ETHICS, V155, P597, DOI 10.1007/s10551-017-3501-y
Toda AM, 2018, COMM COM INF SC, V832, P143, DOI 10.1007/978-3-319-97934-2_9
Urbonavicius S, 2021, J BUS RES, V136, P76, DOI 10.1016/j.jbusres.2021.07.031
van der Lubbe LM, 2021, ENTERTAIN COMPUT, V38, DOI 10.1016/j.entcom.2020.100402
Wang C, 2022, IND MANAGE DATA SYST, V122, P1499, DOI 10.1108/IMDS-09-2021-0600
Wang GQ, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121345
Whelan E, 2020, INTERNET RES, V30, P869, DOI 10.1108/INTR-03-2019-0112
Wong LPW, 2023, INT J EDUC VOCAT GUI, V23, P719, DOI 10.1007/s10775-022-09525-3
Wright MF, 2020, J INTERPERS VIOLENCE, V35, P1719, DOI 10.1177/0886260517696858
Xi NN, 2019, INT J INFORM MANAGE, V46, P210, DOI 10.1016/j.ijinfomgt.2018.12.002
Yang HL, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102649
Yang HL, 2021, TECHNOL FORECAST SOC, V169, DOI 10.1016/j.techfore.2021.120839
Zabukovsek SS, 2019, CENT EUR J OPER RES, V27, P703, DOI 10.1007/s10100-018-0592-1
NR 108
TC 4
Z9 4
U1 10
U2 37
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 1463-5771
EI 1758-4094
J9 BENCHMARKING
JI Benchmarking
PD DEC 1
PY 2023
VL 30
IS 9
BP 2921
EP 2947
DI 10.1108/BIJ-03-2022-0160
EA JUL 2022
PG 27
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA HA7V4
UT WOS:000825366600001
DA 2024-03-27
ER
PT J
AU Pallud, J
Straub, DW
AF Pallud, Jessie
Straub, Detmar W.
TI Effective website design for experience-influenced environments: The
case of high culture museums
SO INFORMATION & MANAGEMENT
LA English
DT Article
DE User experience; Esthetics; Human computer interaction; Microsoft
usability guidelines; Museums; Website design
ID USER EXPERIENCE; WEB ACCEPTANCE; USABILITY; QUALITY; PARTICIPATION;
DIMENSIONS; AESTHETICS; FRAMEWORK; BEHAVIOR; ADOPTION
AB While most research on website has focused on functional tasks, the Internet offers many opportunities for leisure as well as experiential activities. Because of the evolution of developed society toward an experience economy, analyzing the role of technologies in the presence of prior user experiences makes sense. This research identifies variables that play a role and influence online behaviors in a specific experiential environment, namely the high culture museum website. Relying on the literature on experience, we propose a research model tested with two different websites. The results of the free simulation experiment indicates that (1) esthetics is the most important design criteria for experiential interfaces and (2) that website design influences intentions to visit a physical place. (C) 2014 Elsevier B.V. All rights reserved.
C1 [Pallud, Jessie] EM Strasbourg Business Sch, F-67085 Strasbourg, France.
[Straub, Detmar W.] J Mack Robinson Coll Business, Dept Comp Informat Syst, Atlanta, GA 30302 USA.
[Straub, Detmar W.] Korea Univ, Sch Business, Seoul 136701, South Korea.
C3 Korea University
RP Pallud, J (autor correspondiente), EM Strasbourg Business Sch, 61 Av Foret Noire, F-67085 Strasbourg, France.
EM jessie.pallud@em-strasbourg.eu; dstraub@gsu.edu
CR Addis M, 2005, EUR J MARKETING, V39, P729, DOI 10.1108/03090560510601734
Agarwal R, 2002, INFORM SYST RES, V13, P168, DOI 10.1287/isre.13.2.168.84
Agarwal R, 2000, MIS QUART, V24, P665, DOI 10.2307/3250951
AJZEN I, 1992, J LEISURE RES, V24, P207, DOI 10.1080/00222216.1992.11969889
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Ajzen I., 2006, CONSTRUCTING TPB QUE
Allen J, 2020, ARCHAEOL OCEAN, V55, P1, DOI 10.1002/arco.5207
Anderson Gail., 2004, Reinventing the Museum: Historical and Contemporary Perspectives on the Paradigm Shift
[Anonymous], 2002, DIG REP TECHN LANDSC
[Anonymous], FUTURE INTERACTION D
[Anonymous], 2008, INTERCONNECTIONS IML
Bélanger F, 2006, COMMUN ACM, V49, P114, DOI 10.1145/1183236.1183256
Blas N. D., 2002, P MUS WEB 2002
Blythe M.A., 2003, Funology: From usability to enjoyment
Bourdieu P., 1990, LOVE ART EUROPEAN AR
Bourgeon D, 2006, INT J ARTS MANAG, V9, P35
Bryan J, 2012, URBAN STUD, V49, P133, DOI 10.1177/0042098010396242
Burton C., 2003, International Journal of Arts Management, V5, P56
Castañeda JA, 2007, INFORM MANAGE-AMSTER, V44, P384, DOI 10.1016/j.im.2007.02.003
Cenfetelli RT, 2009, MIS QUART, V33, P689
Chin WW, 2003, INFORM SYST RES, V14, P189, DOI 10.1287/isre.14.2.189.16018
Chin WW, 1998, MIS QUART, V22, pVII
Chin WW, 1998, QUANT METH SER, P295
Chiou WC, 2010, INFORM MANAGE-AMSTER, V47, P282, DOI 10.1016/j.im.2010.06.002
Chonchúir MN, 2008, PERS UBIQUIT COMPUT, V12, P401, DOI 10.1007/s00779-007-0157-0
Cunliffe D., 2001, CURATOR, V19, P229
Cyr D, 2006, INFORM MANAGE-AMSTER, V43, P950, DOI 10.1016/j.im.2006.08.009
Debenedetti S., 2003, International Journal of Arts Management, V5, P52
Deng LQ, 2010, EUR J INFORM SYST, V19, P60, DOI 10.1057/ejis.2009.50
DESVALLEES Andre., 1992, Vagues: une anthologie de la nouvelle museologie
Dewey J., 1938, ART EXPERIENCE
DiMaggio P, 2004, POETICS, V32, P169, DOI 10.1016/j.poetic.2004.02.005
Falk J.H., 1992, MUSEUM EXPERIENCE
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
Flavián C, 2006, J RETAIL CONSUM SERV, V13, P363, DOI 10.1016/j.jretconser.2005.11.003
Forbes J.D., 1941, J AESTHET ART CRITIC, V1, P3
Fromkin H.L., 1976, Handbook of industrial and organizational psychology
Gefen D., 2000, J ASSOC INF SYST, V1, DOI [10.17705/1jais.00008, DOI 10.17705/1JAIS.00008]
Gefen D., 2000, COMMUN ASSOC INF SYS, V4, P407, DOI DOI 10.17705/1CAIS.00407
Gombault A., 2007, GRATUITE MUSEES MONU
Goodhue D.L., 2006, SYSTEM SCI, V8, p202b, DOI DOI 10.1109/HICSS.2006.381
Hassenzahl M, 2006, BEHAV INFORM TECHNOL, V25, P91, DOI 10.1080/01449290500330331
Hassenzahl M, 2004, HUM-COMPUT INTERACT, V19, P319, DOI 10.1207/s15327051hci1904_2
Hassenzahl M, 2001, INT J HUM-COMPUT INT, V13, P481, DOI 10.1207/S15327590IJHC1304_07
Hassenzahl M., 2007, Interfaces, V72, P10
Henning B, 2001, J COMMUN, V51, P100, DOI 10.1093/joc/51.1.100
Hoffman D. L., 1995, J COMPUT MEDIAT COMM, V3
Holbrook M. B., 2000, J CONSUM RES, V2, P178
HOLBROOK MB, 1982, J CONSUM RES, V9, P132, DOI 10.1086/208906
Hsu CL, 2004, INFORM MANAGE-AMSTER, V41, P853, DOI 10.1016/j.im.2003.08.014
ICOM, 2002, ICOM STAT
IMLS, 2008, IMLS NAT STUD US LIB
Institute of Museum and Library Services, 2006, STAT TECHN DIG NAT M
Jacques R., 1995, Canadian Journal of Educational Communication, V24, P49
Jain R, 2003, COMMUN ACM, V46, P48, DOI 10.1145/792704.792729
Jain R, 2001, COMMUN ACM, V44, P38, DOI 10.1145/365181.365186
Jin B, 2010, BEHAV INFORM TECHNOL, V29, P85, DOI 10.1080/01449290701497202
KEA European Affairs, 2009, IMP CULT CRAT STUDY
Kotler N., 2001, MUS MANAGE CURATOR, V19, P417
Kravchyna V., 2002, 1 MONDAY
Kuan H.H., 2003, 7th Pacific Asia Conference on Information Systems, Adelaide, South Australia, 2003, P369
Lagrosen S., 2003, Information Technology & People, V16, P132, DOI 10.1108/09593840310478667
LAHIRE Bernard., 2004, CULTURE INDIVIDUS DI
Lavie T, 2004, INT J HUM-COMPUT ST, V60, P269, DOI 10.1016/j.ijhcs.2003.09.002
Law ELC, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P719
Lee AS, 2003, INFORM SYST RES, V14, P221, DOI 10.1287/isre.14.3.221.16560
Limayem M., 2003, J ASSOC INF SYST, V4, P65, DOI 10.17705/1jais.00030
Lin A.C. H., 2006, International Review of Research in Open and Distance Learning, V7, P1, DOI DOI 10.19173/IRR0DL.V7I3.364
Lochon P., 2012, MUSEES MONDIAUX RESE
LOHMOLLER JB, 1988, MULTIVAR BEHAV RES, V23, P125, DOI 10.1207/s15327906mbr2301_7
Lu J, 2005, J COMPUT INFORM SYST, V46, P17
Mandryk RL, 2006, BEHAV INFORM TECHNOL, V25, P141, DOI 10.1080/01449290500331156
Martin A., 2002, CULT TRENDS, V12, P3
Marty P., 2004, CHANGING ROLE MUSEUM
Marty P.F., 2004, 1 MONDAY, V9
McCarthy J, 2004, INTERACTIONS, V11, P42
McCarthy J, 2006, PERS UBIQUIT COMPUT, V10, P369, DOI 10.1007/s00779-005-0055-2
McLellan H, 2000, CYBERPSYCHOL BEHAV, V3, P59, DOI 10.1089/109493100316238
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
O'Brien HL, 2008, J AM SOC INF SCI TEC, V59, P938, DOI 10.1002/asi.20801
Orlikowski WJ, 2001, INFORM SYST RES, V12, P121, DOI 10.1287/isre.12.2.121.9700
Park JY, 2007, DIGIT CREAT, V18, P175, DOI 10.1080/14626260701532066
Pavlou PA, 2006, MIS QUART, V30, P115
Pavlou PA, 2004, INFORM SYST RES, V15, P37, DOI 10.1287/isre.1040.0015
Petter S, 2008, EUR J INFORM SYST, V17, P236, DOI 10.1057/ejis.2008.15
Petter S, 2007, MIS QUART, V31, P623
Pine BJ., 1999, The experience economy: work is theatre every business a stage
Poulot Dominique, 2005, MUSEE MUSEOLOGIE
Ringle C.M., 2005, SMART PLS
Rosen S., 2001, Communication World, V18, P36
Ross Max., 2004, Museum and Society, V2, P84, DOI DOI 10.29311/MAS.V2I2.43
Sánchez-Franco MJ, 2005, INTERNET RES, V15, P21, DOI 10.1108/10662240510577059
Sauvage L., 2005, LOUVRE OFFRE NOUVELL
Schaupp C., 2006, P 39 INT C SYST SCI
Schuster JM, 2007, CULT TRENDS, V16, P99, DOI 10.1080/09548960701299815
Shin DH, 2009, INT J HUM-COMPUT INT, V25, P530, DOI 10.1080/10447310902963951
Soren BJ, 2005, MUS MANAGE CURATOR, V20, P131, DOI 10.1016/j.musmancur.2005.03.001
Straub D., 2004, ISWORLD QUANTIATIVE
Tractinsky N, 2000, INTERACT COMPUT, V13, P127, DOI 10.1016/S0953-5438(00)00031-X
Treiblmaier H, 2007, COMMUN ASSOC INF SYS, V19, P806
Tussyadiah IP, 2009, ANN TOURISM RES, V36, P24, DOI 10.1016/j.annals.2008.10.001
Valacich JS, 2007, COMMUN ACM, V50, P84, DOI 10.1145/1284621.1284624
van der Heijden H, 2004, MIS QUART, V28, P695, DOI 10.2307/25148660
Venkatesan R, 2007, J MARKETING, V71, P114, DOI 10.1509/jmkg.71.2.114
Venkatesh V, 2006, MIS QUART, V30, P181
Venkatesh V, 2006, MANAGE SCI, V52, P367, DOI 10.1287/mnsc.1050.0442
Vergo Peter, 1989, The New Museology
Wilson B., 2007, Australian and New Zealand Marketing Academy Conference, Otago, Australia, P791
Zhang P., 2005, J ASS INFORM SYSTEMS, V6, P227, DOI DOI 10.17705/1JAIS.00070
Zhang YX, 2011, INFORM MANAGE-AMSTER, V48, P192, DOI 10.1016/j.im.2011.05.003
NR 110
TC 91
Z9 99
U1 6
U2 123
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-7206
EI 1872-7530
J9 INFORM MANAGE-AMSTER
JI Inf. Manage.
PD APR
PY 2014
VL 51
IS 3
BP 359
EP 373
DI 10.1016/j.im.2014.02.010
PG 15
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA AH1HO
UT WOS:000335871400006
DA 2024-03-27
ER
PT J
AU Hew, JJ
Lee, VH
Leong, LY
AF Hew, Jun-Jie
Lee, Voon-Hsien
Leong, Lai-Ying
TI Why do mobile consumers resist mobile commerce applications? A hybrid
fsQCA-ANN analysis
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Mobile commerce; Mobile consumers; Active innovation resistance;
Resistance behaviours; fsQCA; Artificial neural network
ID ACTIVE INNOVATION RESISTANCE; TECHNOLOGY ADOPTION;
INFORMATION-TECHNOLOGY; BANKING ADOPTION; USER ACCEPTANCE; SOCIAL
COMMERCE; INTERNET; BARRIERS; MECHANISMS; POSTPONERS
AB Since its inception, mobile commerce (m-commerce) has introduced many disruptive changes to the business world via various types of m-commerce applications, which refer to the novel applications of m-commerce in conducting tasks that require mobility, for instance, mobile payment and mobile shopping. In view that not all mobile consumers around the world are keen to adopt mobile commerce applications, this study seeks to clarify the roles of active innovation resistance barriers (comprising functional and psychological barriers) on three distinct forms of resistance behaviour exhibited by mobile consumers (i.e., rejection, postponement, or opposition) towards m-commerce applications through the theoretical lens of Innovation Resistance Theory. For this purpose, an asymmetric and non-linear model was built and analysed through a configurational approach complemented by machine learning. The results indicate that all active innovation resistance barriers matter but are not equally important in triggering the resistance behaviours. Theoretically, this study has advanced the Innovation Resistance Theory and enriched the existing literature on m-commerce applications resistance. Practically, to lower the resistance behaviours of mobile consumers towards m-commerce applications, practitioners are advised to prioritise strategies that could overcome the psychological barriers.
C1 [Hew, Jun-Jie; Lee, Voon-Hsien; Leong, Lai-Ying] Univ Tunku Abdul Rahman, Fac Business & Finance, Kampar, Malaysia.
C3 Universiti Tunku Abdul Rahman (UTAR)
RP Lee, VH (autor correspondiente), Univ Tunku Abdul Rahman, Fac Business & Finance, Kampar, Malaysia.
EM hew.jun.jie@gmail.com; leevoonhsien@gmail.com; lyennlly@gmail.com
RI Lai-Ying, Leong/S-5659-2017; Lee, Voon-Hsien/S-6123-2017
OI Lai-Ying, Leong/0000-0001-7283-0300; Lee,
Voon-Hsien/0000-0002-8723-8219; Hew, Jun-Jie/0000-0003-4957-1050
CR Abu-Shanab Emad, 2012, International Journal of Technology Diffusion, V3, P36, DOI 10.4018/jtd.2012040104
Al-Janabi Samaher, 2020, Big Data and Networks Technologies. Lecture Notes in Networks and Systems (LNNS 81), P231, DOI 10.1007/978-3-030-23672-4_18
Alexa, 2020, TOP SIT MAL
Almeida Lucas G., 2023, ELECTRON COMMER R A
[Anonymous], 2020, LOW NET RUL REG
App Annie, 2020, APP ANN APP STOR STA
Aw ECX, 2023, SERV IND J, V43, P185, DOI 10.1080/02642069.2022.2161528
Benitez J, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.05.003
Benou P, 2012, INFORM TECHNOL MANAG, V13, P91, DOI 10.1007/s10799-012-0120-2
Benou P, 2010, ELECTRON COMMER RES, V10, P139, DOI 10.1007/s10660-010-9050-4
Buhalis D, 2023, TOURISM MANAGE, V97, DOI 10.1016/j.tourman.2023.104724
Chaouali W, 2019, J RETAIL CONSUM SERV, V50, P342, DOI 10.1016/j.jretconser.2018.07.009
Chen CC, 2022, J RETAIL CONSUM SERV, V67, DOI 10.1016/j.jretconser.2022.102974
Chen Q, 2019, INT J INFORM MANAGE, V47, P274, DOI 10.1016/j.ijinfomgt.2018.07.012
Chhonker MS, 2018, BOTTOM LINE, V31, P208, DOI 10.1108/BL-04-2018-0020
Chhonker MS, 2017, PROCEDIA COMPUT SCI, V122, P888, DOI 10.1016/j.procs.2017.11.451
Cornescu V, 2013, PROC ECON FINANC, V6, P457, DOI 10.1016/S2212-5671(13)00163-9
Dastane O, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102074
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Degirmenci K, 2020, INT J INFORM MANAGE, V50, P261, DOI 10.1016/j.ijinfomgt.2019.05.010
Desmal Abdulla Jaafar, 2019, Emerging Technologies in Computing. Second International Conference, iCETiC 2019. Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST 285), P203, DOI 10.1007/978-3-030-23943-5_15
Diwanji VS, 2023, INT J CONSUM STUD, V47, P2767, DOI 10.1111/ijcs.12889
Du S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11061580
Eze UC, 2013, J ELECTRON COMMER OR, V11, P46, DOI 10.4018/jeco.2013070103
Fiss PC, 2011, ACAD MANAGE J, V54, P393, DOI 10.5465/AMJ.2011.60263120
Ghasemy M, 2020, HIGH EDUC, V80, P1121, DOI 10.1007/s10734-020-00534-1
Ghazali EM, 2018, ASIA PAC J MARKET LO, V30, P1064, DOI 10.1108/APJML-05-2017-0093
Gligor D, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101929
Gligor DM, 2022, J BUS IND MARK, V37, P1542, DOI 10.1108/JBIM-09-2020-0436
Gong X, 2020, INT J ELECTRON COMM, V24, P279, DOI 10.1080/10864415.2020.1767427
Hadiana A, 2016, 2016 4TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT)
Hair J. F., 2022, A primer on partial least squares structural equation modeling (PLS-SEM), V3rd
Hair J.F., 2016, ESSENTIALS BUSINESS, DOI DOI 10.4324/9781315716862
Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hanafizadeh P, 2010, EXPERT SYST APPL, V37, P8879, DOI 10.1016/j.eswa.2010.06.008
Haque Mehruba, 2022, International Journal of Electronic Marketing and Retailing, V13, P259, DOI 10.1504/IJEMR.2022.123938
Heidenreich S, 2016, J BUS RES, V69, P2440, DOI 10.1016/j.jbusres.2016.01.014
Heidenreich S, 2013, INT J INNOV MANAG, V17, DOI 10.1142/S1363919613500217
Hew JJ, 2019, TECHNOL FORECAST SOC, V144, P311, DOI 10.1016/j.techfore.2017.10.007
Hew JJ, 2018, TOURISM MANAGE, V66, P121, DOI 10.1016/j.tourman.2017.10.005
Hew JJ, 2017, TELEMAT INFORM, V34, P34, DOI 10.1016/j.tele.2016.12.009
Hew JJ, 2017, TELEMAT INFORM, V34, P43, DOI 10.1016/j.tele.2016.04.003
Hew JJ, 2015, IND MANAGE DATA SYST, V115, P1269, DOI 10.1108/IMDS-01-2015-0028
Hossain M.A., 2022, ANN OPER RES
Hossain MA, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102537
Hult GTM, 2018, J INT MARKETING, V26, P1, DOI 10.1509/jim.17.0151
Insider Intelligence, 2022, MCOMM REP STAT MARK
Joachim V, 2018, IND MARKET MANAG, V71, P95, DOI 10.1016/j.indmarman.2017.12.011
Katsumata S, 2022, COMPUT HUM BEHAV REP, V5, DOI 10.1016/j.chbr.2022.100168
Kaur P, 2021, J ENTERP INF MANAG, V34, P1746, DOI 10.1108/JEIM-03-2020-0091
Kaur P, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102059
Khanra S, 2021, J HOSP TOUR MANAG, V46, P26, DOI 10.1016/j.jhtm.2020.11.004
Khaw KW, 2022, INT J HUM-COMPUT INT, V38, P1529, DOI 10.1080/10447318.2021.2004700
Kleijnen M, 2009, J ECON PSYCHOL, V30, P344, DOI 10.1016/j.joep.2009.02.004
Kock F, 2021, TOURISM MANAGE, V86, DOI 10.1016/j.tourman.2021.104330
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Laukkanen P, 2008, INT J BANK MARK, V26, P440, DOI 10.1108/02652320810902451
Laukkanen T, 2016, J BUS RES, V69, P2432, DOI 10.1016/j.jbusres.2016.01.013
Laukkanen T, 2007, J CONSUM MARK, V24, P419, DOI 10.1108/07363760710834834
Laukkanen T, 2009, INT J INFORM MANAGE, V29, P111, DOI 10.1016/j.ijinfomgt.2008.05.008
Lee VH, 2020, EXPERT SYST APPL, V157, DOI 10.1016/j.eswa.2020.113477
Lee ZWY, 2021, INFORM SYST J, V31, P33, DOI 10.1111/isj.12292
Leite FP, 2022, J MARKET THEORY PRAC, V30, P295, DOI 10.1080/10696679.2021.1935275
Leong LY, 2023, IND MANAGE DATA SYST, V123, P967, DOI 10.1108/IMDS-11-2021-0671
Leong LY, 2021, IND MANAGE DATA SYST, V121, P1784, DOI 10.1108/IMDS-12-2020-0741
Leong LY, 2020, J BUS RES, V110, P24, DOI 10.1016/j.jbusres.2019.11.056
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Leong LY, 2019, EXPERT SYST APPL, V133, P296, DOI 10.1016/j.eswa.2019.05.024
Li F, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103039
Lian JW, 2013, COMPUT HUM BEHAV, V29, P665, DOI 10.1016/j.chb.2012.10.009
Lou ZK, 2022, J BUS RES, V139, P81, DOI 10.1016/j.jbusres.2021.09.045
Luceri B, 2022, J RETAILING, V98, P178, DOI 10.1016/j.jretai.2022.02.002
Ma L, 2020, ONLINE INFORM REV, V44, P671, DOI 10.1108/OIR-06-2019-0203
Ma L, 2019, J EDUC COMPUT RES, V57, P571, DOI 10.1177/0735633118757732
MacKenzie SB, 2012, J RETAILING, V88, P542, DOI 10.1016/j.jretai.2012.08.001
Malaysia Communication and Multimedia Commission, 2020, Communications and Multimedia: Facts andfigures, 1Q 2020
Malaysian Communication and Multimedia Commission, 2021, Hand Phone User Survey 2021
Malaysian Communications and Multimedia Commission, 2022, COMMUNICATIONS MULTI, P2021
Matsuo M, 2018, J RETAIL CONSUM SERV, V45, P42, DOI 10.1016/j.jretconser.2018.08.005
Mattke J, 2022, COMMUN ASSOC INF SYS, V50, P208, DOI 10.17705/1CAIS.05008
Mzoughi N, 2013, INT J BANK MARK, V31, P388, DOI 10.1108/IJBM-10-2012-0105
Naicker V, 2018, INFORM TECHNOL PEOPL, V31, P507, DOI 10.1108/ITP-09-2016-0212
Nel J, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102368
Nikou S, 2022, J SMALL BUS MANAGE, DOI 10.1080/00472778.2022.2147190
Oliveira T, 2014, INT J INFORM MANAGE, V34, P689, DOI 10.1016/j.ijinfomgt.2014.06.004
Olya HGT, 2016, J BUS RES, V69, P2791, DOI 10.1016/j.jbusres.2015.11.015
Omar S, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102468
Ooi KB, 2021, IND MANAGE DATA SYST, V121, P312, DOI 10.1108/IMDS-04-2020-0239
Ooi KB, 2018, BEHAV INFORM TECHNOL, V37, P575, DOI 10.1080/0144929X.2018.1465997
Ooi KB, 2018, EXPERT SYST APPL, V93, P376, DOI 10.1016/j.eswa.2017.10.009
Pappas IO, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102310
Pappas IO, 2019, INTERNET RES, V29, P293, DOI 10.1108/IntR-12-2017-0479
Pappas IO, 2016, J BUS RES, V69, P794, DOI 10.1016/j.jbusres.2015.07.010
Park K, 2017, COMPUT HUM BEHAV, V69, P142, DOI 10.1016/j.chb.2016.12.017
Park Y, 2020, MIS QUART, V44, P1493, DOI 10.25300/MISQ/2020/13879
RAM S, 1987, ADV CONSUM RES, V14, P208
Ram S., 1989, J CONSUM MARK, V6, P5, DOI [10.1108/eum0000000002542, DOI 10.1108/EUM0000000002542, 10.1108/EUM0000000002542]
Rogers, 2003, DIFFUSION INNOVATION
Rogers E. M., 1962, Diffusion of Innovations
Salman A, 2023, ENVIRON DEV SUSTAIN, V25, P10261, DOI 10.1007/s10668-022-02488-z
Sham R, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103478
Sun Y, 2022, ASIA PAC J MARKET LO, V34, P1849, DOI 10.1108/APJML-12-2020-0865
Szmigin I, 1998, TECHNOVATION, V18, P459, DOI 10.1016/S0166-4972(98)00030-3
Talebian A, 2018, TRANSPORT RES C-EMER, V95, P363, DOI 10.1016/j.trc.2018.06.005
Talke K, 2014, J PROD INNOVAT MANAG, V31, P894, DOI 10.1111/jpim.12130
Talwar S, 2020, AUSTRALAS MARK J, V28, P286, DOI 10.1016/j.ausmj.2020.06.014
Turban E., 2018, ELECT COMMERCE 2018, P205
Turban E., 2015, ELECT COMMERCE MANAG, V8th, DOI DOI 10.1007/978-3-319-10091-3
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2021, IND MANAGE DATA SYST, V121, P30, DOI 10.1108/IMDS-08-2020-0506
Venkatesh V, 2012, MIS QUART, V36, P157
Xiao M, 2018, J MEDIA BUS STUD, V15, P188, DOI 10.1080/16522354.2018.1501146
Yang XP, 2023, J BUS RES, V155, DOI 10.1016/j.jbusres.2022.113406
Yu C.-S., 2016, International Journal of Electronic Commerce Studies, V7, P21, DOI [10.7903/ijecs.1375, DOI 10.7903/IJECS.1375]
Zhu B, 2022, INT J RETAIL DISTRIB, V50, P479, DOI 10.1108/IJRDM-05-2020-0174
NR 116
TC 5
Z9 5
U1 44
U2 46
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD NOV
PY 2023
VL 75
AR 103526
DI 10.1016/j.jretconser.2023.103526
EA AUG 2023
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA Q5KP9
UT WOS:001057912600001
DA 2024-03-27
ER
PT J
AU Chong, AYL
Li, BY
Ngai, EWT
Ch'ng, E
Lee, F
AF Chong, Alain Yee Loong
Li, Boying
Ngai, Eric W. T.
Ch'ng, Eugene
Lee, Filbert
TI Predicting online product sales via online reviews, sentiments, and
promotion strategies A big data architecture and neural network approach
SO INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT
LA English
DT Article
DE Big data; Neural network; Online reviews; Product demands; Valence;
Promotional marketing; Online marketplace
ID WORD-OF-MOUTH; USER-GENERATED CONTENT; GENETIC ALGORITHM; CONSUMER
REVIEWS; EMOTIONAL CONTAGION; CHAIN MANAGEMENT; IMPACT; DYNAMICS;
COMMUNICATION; DETERMINANTS
AB Purpose - The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.
Design/methodology/approach - The authors designed a big data architecture and deployed Node. js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.
Findings - This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.
Originality/value - This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.
C1 [Chong, Alain Yee Loong; Li, Boying] Univ Nottingham Ningbo China, Univ Nottingham, Business Sch China, Ningbo, Zhejiang, Peoples R China.
[Ngai, Eric W. T.] Hong Kong Polytech Univ, Management & Mkt Dept, Hong Kong, Hong Kong, Peoples R China.
[Ch'ng, Eugene] Univ Nottingham Ningbo China, Big Data & Visual Analyt Lab, Ningbo, Zhejiang, Peoples R China.
[Lee, Filbert] Univ Nottingham Ningbo China, Int Studies Dept, Ningbo, Zhejiang, Peoples R China.
C3 University of Nottingham Ningbo China; Hong Kong Polytechnic University;
University of Nottingham Ningbo China; University of Nottingham Ningbo
China
RP Chong, AYL (autor correspondiente), Univ Nottingham Ningbo China, Univ Nottingham, Business Sch China, Ningbo, Zhejiang, Peoples R China.
EM alain.chong@gmail.com
RI Ngai, Eric/ABC-2167-2020; Chong, Alain/ABD-6916-2021; Ch´ng,
Eugene/Q-8277-2019; Ngai, Eric W.T./L-8152-2015
OI Ch´ng, Eugene/0000-0003-3992-8335; Ngai, Eric W.T./0000-0002-7278-7434;
NGAI, WT Eric/0000-0001-6891-6750; Chong, Alain/0000-0002-0881-1612
FU National Natural Science Foundation of China (NSFC); International
Doctoral Innovation Centre; Ningbo Education Bureau; Ningbo Science and
Technology Bureau; China's MoST; University of Nottingham; NSFC
[71402076]; NBSTB [2012B10055]
FX "The authors acknowledge the financial support from the National Natural
Science Foundation of China (NSFC), International Doctoral Innovation
Centre, Ningbo Education Bureau, Ningbo Science and Technology Bureau,
China's MoST and The University of Nottingham. The project is partially
supported by NSFC No. 71402076 and NBSTB Project 2012B10055."
CR Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Ayat S, 2014, J MATH COMPUT SCI-JM, V8, P215, DOI 10.22436/jmcs.08.03.04
Baker BM, 2003, COMPUT OPER RES, V30, P787, DOI 10.1016/S0305-0548(02)00051-5
Bakos Y, 2011, MANAGE SCI, V57, P1944, DOI 10.1287/mnsc.1110.1390
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
Bolduc MC, 2010, EUR J OPER RES, V202, P122, DOI 10.1016/j.ejor.2009.05.008
Ch'ng E., 2014, 3 ASE BIG DAT SCI 20, P1
Ch'ng E, 2015, IND MANAGE DATA SYST, V115, P612, DOI 10.1108/IMDS-11-2014-0332
Chakravarty A, 2010, J INTERACT MARK, V24, P185, DOI 10.1016/j.intmar.2010.04.001
Chen SFS, 1998, J RETAILING, V74, P353, DOI 10.1016/S0022-4359(99)80100-6
Cheung CMK, 2012, DECIS SUPPORT SYST, V54, P461, DOI 10.1016/j.dss.2012.06.008
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chiang WYK, 2006, DECIS SUPPORT SYST, V41, P514, DOI 10.1016/j.dss.2004.08.016
Chong A.Y.-L., 2013, PACIS 2013 P PAC AS
Chong AYL, 2008, IND MANAGE DATA SYST, V108, P529, DOI [10.1108/026355708110868371, 10.1108/02635570810868371]
Chong AYL, 2014, INT J PROD ECON, V154, P48, DOI 10.1016/j.ijpe.2014.04.005
Chong AYL, 2013, INT J PROD RES, V51, P2006, DOI 10.1080/00207543.2012.701776
Chong AYL, 2013, EXPERT SYST APPL, V40, P523, DOI 10.1016/j.eswa.2012.07.068
Chong AYL, 2009, INT J PROD ECON, V122, P150, DOI 10.1016/j.ijpe.2009.05.012
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
Croom SR, 2005, INT J OPER PROD MAN, V25, P55, DOI 10.1108/01443570510572240
Cui G, 2012, INT J ELECTRON COMM, V17, P39, DOI 10.2753/JEC1086-4415170102
Davis Alanah, 2008, Electronic Markets, V18, P130, DOI 10.1080/10196780802044776
Dellarocas C., 2004, Working Paper, DOI [10.2139/ssrn.620821, DOI 10.2139/SSRN.620821]
Doern RR, 2006, J WORLD BUS, V41, P315, DOI 10.1016/j.jwb.2006.08.001
Drozdenko R, 2005, J PROD BRAND MANAG, V14, P264, DOI 10.1108/10610420510609285
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
East R, 2008, INT J RES MARK, V25, P215, DOI 10.1016/j.ijresmar.2008.04.001
EHRENBERG ASC, 1994, J ADVERTISING RES, V34, P11
Erenturk S, 2007, J FOOD ENG, V78, P905, DOI 10.1016/j.jfoodeng.2005.11.031
Floyd K, 2014, J RETAILING, V90, P217, DOI 10.1016/j.jretai.2014.04.004
Gandomi A, 2015, INT J INFORM MANAGE, V35, P137, DOI 10.1016/j.ijinfomgt.2014.10.007
Garcia D., 2011, Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and IEEE Third International Conference on Social Computing (PASSAT/SocialCom 2011), P483, DOI 10.1109/PASSAT/SocialCom.2011.219
Gardner MW, 1998, ATMOS ENVIRON, V32, P2627, DOI 10.1016/S1352-2310(97)00447-0
Garson G.D., 1998, NEURAL NETWORKS INTR
Gen MS, 2005, COMPUT IND ENG, V48, P799, DOI 10.1016/j.cie.2004.12.012
Gendall P, 2006, J PROD BRAND MANAG, V15, P458, DOI 10.1108/10610420610712847
George G, 2014, ACAD MANAGE J, V57, P321, DOI 10.5465/amj.2014.4002
Gevrey M, 2003, ECOL MODEL, V160, P249, DOI 10.1016/S0304-3800(02)00257-0
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
GUPTA S, 1992, J CONSUM RES, V19, P401, DOI 10.1086/209310
Hancock JT, 2008, CSCW: 2008 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, CONFERENCE PROCEEDINGS, P295
Hatfield E., 1994, EMOTIONAL CONTAGION
Haykin S., 1994, NEURAL NETWORKS COMP
Hu N, 2014, DECIS SUPPORT SYST, V57, P42, DOI 10.1016/j.dss.2013.07.009
Hu Nan., 2006, Proceedings of the 7th ACM Conference on Electronic Commerce. EC '06, P324, DOI DOI 10.1145/1134707.1134743
Hu Nan, 2016, ANN OPERATIONS RES
Huang YY, 2015, INT J OPER PROD MAN, V35, P2, DOI 10.1108/IJOPM-07-2013-0341
Ito TA, 1998, J PERS SOC PSYCHOL, V75, P887, DOI 10.1037/0022-3514.75.4.887
Jensen T, 2003, J INTERACT MARK, V17, P20, DOI 10.1002/dir.10052
Kramer ADI, 2014, P NATL ACAD SCI USA, V111, P8788, DOI 10.1073/pnas.1320040111
Lee J, 2008, ELECTRON COMMER R A, V7, P341, DOI 10.1016/j.elerap.2007.05.004
Li Y, 1998, INT J PROD ECON, V54, P65, DOI 10.1016/S0925-5273(97)00124-2
Liau BY, 2014, IND MANAGE DATA SYST, V114, P1344, DOI 10.1108/IMDS-07-2014-0225
LICHTENSTEIN DR, 1990, J MARKETING, V54, P54, DOI 10.2307/1251816
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Lopez L, 1998, EUR J OPER RES, V106, P317, DOI 10.1016/S0377-2217(97)00277-4
Lu XH, 2013, INFORM SYST RES, V24, P596, DOI 10.1287/isre.1120.0454
Luca M., 2011, WORKING PAPER
Marshall R., 2002, Journal of Product Brand Management, V11, P147, DOI DOI 10.1108/10610420210430042
McNeill L, 2013, ASIA PAC J MARKET LO, V25, P48, DOI 10.1108/13555851311290939
Mudambi SM, 2010, MIS QUART, V34, P185
Ng CSP, 2013, INFORM MANAGE-AMSTER, V50, P609, DOI 10.1016/j.im.2013.08.002
Ou CX, 2014, MIS QUART, V38, P209, DOI 10.25300/MISQ/2014/38.1.10
Pugh SD, 2001, ACAD MANAGE J, V44, P1018, DOI 10.5465/3069445
Ramanathan U, 2012, INT J OPER PROD MAN, V32, P676, DOI 10.1108/01443571211230925
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Smith AD, 2003, ONLINE INFORM REV, V27, P418, DOI 10.1108/14684520310510055
Sonnier GP, 2011, MARKET SCI, V30, P702, DOI 10.1287/mksc.1110.0642
Sparks BA, 2011, TOURISM MANAGE, V32, P1310, DOI 10.1016/j.tourman.2010.12.011
Suri R., 2000, Journal of Product Brand Management, V9, P193, DOI DOI 10.1108/10610420010332458
Tang TY, 2014, J MARKETING, V78, P41, DOI 10.1509/jm.13.0301
Tirunillai S, 2012, MARKET SCI, V31, P198, DOI 10.1287/mksc.1110.0682
Tsai WC, 2001, J MANAGE, V27, P497, DOI 10.1177/014920630102700406
Tyagi RK, 2010, J PROD INNOVAT MANAG, V27, P83, DOI 10.1111/j.1540-5885.2009.00701.x
Vermeulen IE, 2009, TOURISM MANAGE, V30, P123, DOI 10.1016/j.tourman.2008.04.008
Wang HW, 2014, IND MANAGE DATA SYST, V114, P1301, DOI 10.1108/IMDS-05-2014-0159
Wen C, 2014, INT J OPER PROD MAN, V34, P1506, DOI 10.1108/IJOPM-07-2013-0352
Yang J, 2012, EUR J MARKETING, V46, P1523, DOI 10.1108/03090561211259961
Ye QA, 2011, COMPUT HUM BEHAV, V27, P634, DOI 10.1016/j.chb.2010.04.014
Yin DZ, 2014, MIS QUART, V38, P539, DOI 10.25300/MISQ/2014/38.2.10
Yip L., 2002, Asia Pacific Journal of Tourism Research, V7, P36, DOI 10.1080/10941660208722108
Yu XH, 2012, IEEE T KNOWL DATA EN, V24, P720, DOI 10.1109/TKDE.2010.269
Zhang JQ, 2010, J BUS RES, V63, P1336, DOI 10.1016/j.jbusres.2009.12.011
Zhao CW, 2000, INT J PROD RES, V38, P385, DOI 10.1080/002075400189473
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 87
TC 97
Z9 112
U1 11
U2 171
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0144-3577
EI 1758-6593
J9 INT J OPER PROD MAN
JI Int. J. Oper. Prod. Manage.
PY 2016
VL 36
IS 4
BP 358
EP +
DI 10.1108/IJOPM-03-2015-0151
PG 27
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA DR1PN
UT WOS:000379677500001
OA Green Accepted
DA 2024-03-27
ER
PT J
AU Talwar, M
Corazza, L
Bodhi, R
Malibari, A
AF Talwar, Manish
Corazza, Laura
Bodhi, Rahul
Malibari, Areej
TI Why do consumers resist digital innovations? An innovation resistance
theory perspective
SO INTERNATIONAL JOURNAL OF EMERGING MARKETS
LA English
DT Article; Early Access
DE Artificial neural network; Consumer resistance; Digital marketing;
Financial products; Functional barriers; Psychological barriers
ID INFORMATION-TECHNOLOGY; INTERNET BANKING; ADOPTION; MODEL; ACCEPTANCE;
EXPERIENCE; BARRIERS; MEDIA; TRUST
AB PurposeDespite the efforts of governments and firms, consumer resistance toward digital innovations in the retail finance space continues to manifest rather visibly. Yet, the causes of consumer resistance toward innovations such as online procurement of financial products continue to remain under-explored. The present study attempts to address this gap by examining barriers that may constitute Indian consumers' resistance to buying financial products marketed digitally, using insurance as an exemplar. Precisely, the study measures five classic innovation resistance theory (IRT) barriers constituting consumers' resistance toward procuring digitally marketed insurance and examines the influence of consumers' demographic characteristics, measured through age and gender.Design/methodology/approachThe conceptual model, resting on the theoretical proposition of IRT, was tested using data collected from 420 smartphone users. Given that, the data did not satisfy the multivariate assumptions of normality, homoscedasticity and linearity, artificial neural network approach was used for analysis. The analysis served as the basis for determining the relative importance of the five barriers in influencing consumer resistance.FindingsThe results indicated that the image barrier was the most influential barrier impacting consumer resistance, followed by usage, tradition, risk and value barriers. Moreover, as revealed by the values of correlations, the direction of influence was positive. Notably, the relationship of all barriers except tradition with consumer resistance was found to be nonlinear.Originality/valueThe study makes a novel contribution in two ways - one by extending IRT to a new area, i.e., resistance to buying financial products online, thereby further enhancing its applicability, and the other by exploring consumer resistance to e-procurement of life and nonlife insurance, which to the best of the authors' knowledge, has not been examined so far despite the established exigency.
C1 [Talwar, Manish] Univ Mumbai, Alkesh Dinesh Mody Inst Financial & Management Stu, Mumbai, India.
[Corazza, Laura] Univ Turin, Dept Management, Turin, Italy.
[Bodhi, Rahul] Sch Business, UPES, Dehra Dun, India.
[Malibari, Areej] Princess Nourah Bint Abdulrahman Univ, Dept Coll Engn, Riyadh, Saudi Arabia.
[Malibari, Areej] King Abdulaziz Univ, Fac Comp NF & IT, Dept Comp Sci, Jeddah, Saudi Arabia.
C3 University of Mumbai; University of Turin; University of Petroleum &
Energy Studies (UPES); Princess Nourah bint Abdulrahman University; King
Abdulaziz University
RP Talwar, M (autor correspondiente), Univ Mumbai, Alkesh Dinesh Mody Inst Financial & Management Stu, Mumbai, India.
EM talwars25@gmail.com; laura.corazza@unito.it; rahulbodhi@outlook.com;
aamalibari@pnu.edu.sa
RI Bodhi, Rahul/B-9954-2019
OI Bodhi, Rahul/0000-0002-3015-9704; Talwar, Manish/0000-0002-7266-3300
CR Abbas M, 2017, COGENT BUS MANAG, V4, DOI 10.1080/23311975.2017.1312058
Accenture, 2022, DIG INS TRENDS OPP K
Antioco M, 2010, EUR J MARKETING, V44, P1700, DOI 10.1108/03090561011079846
Artur Lugmayr., 2012, PROCEEDING 16 INT AC, P231
Bhattacherjee A, 2007, EUR J INFORM SYST, V16, P725, DOI 10.1057/palgrave.ejis.3000717
Chakraborty A., 2022, J ASIAN FINANC ECON, V1, P1, DOI [10.1504/ijmef.2022.10045060, DOI 10.1504/IJMEF.2022.10045060]
Chemingui H, 2013, INT J BANK MARK, V31, P574, DOI 10.1108/IJBM-12-2012-0124
Cheng S, 2014, INT J SECUR APPL, V8, P167, DOI 10.14257/ijsia.2014.8.2.17
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dhir A, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102398
Do Cho S, 2008, IND MARKET MANAG, V37, P841, DOI 10.1016/j.indmarman.2008.04.004
Dutta M.M., 2020, VILAKSHAN XIMB J MAN, V17, P97, DOI [10.1108/xjm-07-2020-0021, DOI 10.1108/XJM-07-2020-0021]
Elbadrawy R., 2011, International Journal of Managing Information Technology, V3, P9, DOI DOI 10.5121/IJMIT.2011.3402
Gurtner S., 2014, 22 EUR C INF SYST, P1
Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
Heidenreich S, 2016, J PROD INNOVAT MANAG, V33, P277, DOI 10.1111/jpim.12273
Heidenreich S, 2013, INT J INNOV MANAG, V17, DOI 10.1142/S1363919613500217
Hennig-Thurau T, 2010, J SERV RES-US, V13, P311, DOI 10.1177/1094670510375460
Hongxia P., 2011, 2011 International Conference on E-Business and E-Government (ICEE), P1, DOI DOI 10.1109/ICEBEG.2011.5887081
Hosseini M.H., 2016, INT J ASIAN SOCIAL S, V6, P497, DOI DOI 10.18488/JOURNAL.1/2016.6.9/1.9.497.509
IBEF, 2022, INS IND REP
Ilyas AM, 2022, INT J EMERG MARK, V17, P1446, DOI 10.1108/IJOEM-10-2019-0869
Ilyas AM, 2020, INT J PRODUCT PERFOR, V69, P633, DOI 10.1108/IJPPM-04-2019-0147
Kaesler S., 2020, REVOLUTION MULTIACCE
Kapoor S., 2022, EXPLAINED CHALLENGE
Kaur P, 2021, J ENTERP INF MANAG, V34, P1746, DOI 10.1108/JEIM-03-2020-0091
Kaur P, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102059
Khalil A, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3202485
Khanra S, 2021, J HOSP TOUR MANAG, V46, P26, DOI 10.1016/j.jhtm.2020.11.004
Kim J, 2021, J BUS RES, V130, P552, DOI 10.1016/j.jbusres.2019.09.043
Kleijnen M, 2009, J ECON PSYCHOL, V30, P344, DOI 10.1016/j.joep.2009.02.004
Kuisma T, 2007, INT J INFORM MANAGE, V27, P75, DOI 10.1016/j.ijinfomgt.2006.08.006
Kumar S, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102444
Lapointe L, 2005, MIS QUART, V29, P461
Laukkanen P, 2008, INT J BANK MARK, V26, P440, DOI 10.1108/02652320810902451
Laukkanen T., 2007, INT C MANAGEMENT MOB, P42, DOI [10.1109/ICMB.2007.57, DOI 10.1109/ICMB.2007.57]
Laukkanen T, 2016, J BUS RES, V69, P2432, DOI 10.1016/j.jbusres.2016.01.013
Laukkanen T, 2007, J CONSUM MARK, V24, P419, DOI 10.1108/07363760710834834
Leeflang PSH, 2014, EUR MANAG J, V32, P1, DOI 10.1016/j.emj.2013.12.001
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Lian J., 2012, Computer Techology and Application, V3, P188
Lian JW, 2013, COMPUT HUM BEHAV, V29, P665, DOI 10.1016/j.chb.2012.10.009
Lombardi P., 2011, D11 IST STREP
Mahatanankoon P, 2007, J INTERNET COMMER, V6, P113, DOI 10.1080/15332860802086367
Mani Z, 2018, J PROD INNOVAT MANAG, V35, P780, DOI 10.1111/jpim.12463
Mani Z, 2017, J MARKET MANAG-UK, V33, P76, DOI 10.1080/0267257X.2016.1245212
Tiago MTPMB, 2014, BUS HORIZONS, V57, P703, DOI 10.1016/j.bushor.2014.07.002
Mullen M.R., 2010, 35 MACR C WYOM 2010
Nel J, 2019, J RETAIL CONSUM SERV, V47, P282, DOI 10.1016/j.jretconser.2018.12.003
Poddar P., 2019, DIGITAL INSURANCE CH
Ram S., 1989, J CONSUM MARK, V6, P5, DOI [10.1108/eum0000000002542, DOI 10.1108/EUM0000000002542, 10.1108/EUM0000000002542]
Rogers, 2003, DIFFUSION INNOVATION
Rogers E. M., 1995, DIFFUSION INNOVATION
Seth H, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102071
Simply Digital Marketing, 2012, WHAT IS DIGITAL MARK
Spreer P, 2016, J PERS SELL SALES M, V36, P240, DOI 10.1080/08853134.2016.1208100
Statista Research Department, 2022, INS IND IND STAT FAC
Sundjaja A. M., 2019, INT J SCI TECHNOLOGY, V8, P511
Talke K, 2014, J PROD INNOVAT MANAG, V31, P894, DOI 10.1111/jpim.12130
Talwar M, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2020.102396
Talwar M, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102341
Talwar S, 2021, AUSTRALAS J INF SYST, V25, DOI 10.3127/ajis.v25i0.3201
Talwar S, 2021, PSYCHOL MARKET, V38, P2142, DOI 10.1002/mar.21550
Talwar S, 2020, AUSTRALAS MARK J, V28, P286, DOI 10.1016/j.ausmj.2020.06.014
Talwar S, 2020, INT J HOSP MANAG, V89, DOI 10.1016/j.ijhm.2020.102593
Tandon A, 2021, FOOD QUAL PREFER, V88, DOI 10.1016/j.foodqual.2020.104077
Tansuhaj P., 1991, International Marketing Review, V8
van Offenbeek M, 2013, EUR J INFORM SYST, V22, P434, DOI 10.1057/ejis.2012.29
Yadav Chette Srinivas., 2018, IUP Journal of Management Research, V17, P35
Zheng YM, 2019, J PUBLIC POLICY MARK, V38, P246, DOI 10.1177/0743915618812453
Zhou T, 2011, INTERNET RES, V21, P527, DOI 10.1108/10662241111176353
NR 72
TC 1
Z9 1
U1 10
U2 25
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1746-8809
EI 1746-8817
J9 INT J EMERG MARK
JI Int. J. Emerg. Mark.
PD 2023 FEB 28
PY 2023
DI 10.1108/IJOEM-03-2022-0529
EA FEB 2023
PG 16
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA C1NA4
UT WOS:000959657500001
DA 2024-03-27
ER
PT J
AU Subramaniyaswamy, V
Logesh, R
Abejith, M
Umasankar, S
Umamakeswari, A
AF Subramaniyaswamy, V.
Logesh, R.
Abejith, M.
Umasankar, Sunil
Umamakeswari, A.
TI Sentiment Analysis of Tweets for Estimating Criticality and Security of
Events
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Artificial Intelligence; Human-Computer Interaction; Lexicon Based
Sentimental Analysis; Security; Social Aspects; Social Media Analytics
AB Social Media has become one of the major industries in the world. It has been noted that almost three fourth of the world's population use social media. This has instigated many researches towards social media. One such useful application is the sentimental analysis of real time social media data for security purposes. The insights that are generated can be used by law enforcement agencies and for intelligence purposes. There are many types of analyses that have been done for security purposes. Here, the authors propose a comprehensive software application which will meticulously scrape data from Twitter and analyse them using the lexicon based analysis to look for possible threats. They propose a methodology to obtain a quantitative result called criticality to assess the level of threat for a public event. The results can be used to understand people's opinions and comments with regard to specific events. The proposed system combines this lexicon based sentimental analysis along with deep data collection and segregates the emotions into different levels to analyse the threat for an event.
C1 [Subramaniyaswamy, V.; Logesh, R.; Abejith, M.; Umasankar, Sunil] SASTRA Univ, Sch Comp, Thanjavur, India.
[Umamakeswari, A.] SASTRA Univ, Sch Comp, Dept Comp Sci & Engn, Thanjavur, India.
C3 Shanmugha Arts, Science, Technology & Research Academy (SASTRA);
Shanmugha Arts, Science, Technology & Research Academy (SASTRA)
RP Subramaniyaswamy, V (autor correspondiente), SASTRA Univ, Sch Comp, Thanjavur, India.
OI A, Umamakeswari/0000-0001-9724-1115; R, Logesh/0000-0002-0034-4714; ,
Subramaniyaswamy/0000-0001-5328-7672
FU Science and Engineering Research Board (SERB), Department of Science &
Technology, New Delhi [YSS/2014/000718/ES]
FX The authors are grateful to Science and Engineering Research Board
(SERB), Department of Science & Technology, New Delhi, for the financial
support (No. YSS/2014/000718/ES). Authors also thank SASTRA University,
Thanjavur, for providing the infrastructural facilities to carry out
this research work.
CR Abdulla N., 2013, 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), P1, DOI [DOI 10.1109/AEECT.2013.6716448, 10.1109/AEECT.2013.6716448]
Avanço LV, 2014, 2014 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), P277, DOI 10.1109/BRACIS.2014.57
Basave A. E. C., 2013, P 6 INT JOINT C NAT, P109
Colbaugh Richard, 2013, 2013 European Intelligence and Security Informatics Conference (EISIC), P45, DOI 10.1109/EISIC.2013.14
Colbaugh R., 2011, 2011 European Intelligence and Security Informatics Conference, P327, DOI 10.1109/EISIC.2011.65
Esuli Andrea., 2006, SENTIWORDNET PUBLICL
Glass K., 2012, CORRABS12126810
Go A., 2009, TWITTER SENTIMENT CL
Jurek A, 2014, 2014 IEEE JOINT INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (JISIC), P48, DOI 10.1109/JISIC.2014.17
Jurek Anna., 2015, Security Informatics, V4, P9, DOI DOI 10.1186/S13388-015-0024-X
Ngoc PT, 2014, 2014 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2014), P444, DOI 10.1109/ICOIN.2014.6799721
Spitters M, 2014, 2014 IEEE JOINT INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (JISIC), P216, DOI 10.1109/JISIC.2014.39
Taboada M, 2011, COMPUT LINGUIST, V37, P267, DOI 10.1162/COLI_a_00049
NR 13
TC 17
Z9 18
U1 3
U2 71
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PD OCT-DEC
PY 2017
VL 29
IS 4
BP 51
EP 71
DI 10.4018/JOEUC.2017100103
PG 21
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA FE1ZH
UT WOS:000408015600004
DA 2024-03-27
ER
PT J
AU Xia, ML
Zhang, Y
AF Xia, Menglong
Zhang, Yang
TI Linear and nonlinear relationships: a hybrid SEM-neural network approach
to verify the links of online experience with luxury hotel branding
SO JOURNAL OF HOSPITALITY AND TOURISM INSIGHTS
LA English
DT Article
DE SOR framework; Online experience; Mobile hotel branding; Brand image;
SEM-ANN analysis
ID CUSTOMER SATISFACTION; BEHAVIORAL INTENTION; PERCEIVED USEFULNESS;
TRAVELER ACCEPTANCE; WEBSITE QUALITY; MODEL; IMPACT; IMAGE;
DETERMINANTS; INFORMATION
AB Purpose - Mobile technologies have recently come to serve as the primary reservation option for the hospitality industry. This study examines the role of online experience in determining potential consumers' perceived hotel brand image, through a three-stage model based on the stimulus-organism-response (SOR) framework.
Design/methodology/approach - A dual-stage analytical procedure, including structural equation modeling (SEM) and an artificial neural network (ANN) approach, was adopted to test the hypotheses.
Findings - Online experience of mobile applications (apps) can be influenced by perceived usefulness. As the indivisible component of consumers' cognitive beliefs, perceived ease of use exerts a positive impact on online experience. The online experience of mobile apps positively influenced brand awareness and satisfaction, further contributing to potential consumers' brand image formation.
Research limitations/implications - This study empirically verified the relationships among potential hotel consumers' perceptions of official hotel mobile app quality, online experience and brand image.
Practical implications - This study reiterates the importance of official hotel apps in implementing online marketing strategies, suggesting that hoteliers should pay attention to enhancing the quality of their official apps.
Originality/value - This study is one of the first to combine machine learning techniques with the traditional SEM approach to assess linear and nonlinear relationships in consumers' perceptual models. Additionally, the findings provide theoretical insights into the online experience of mobile apps and reveal the perceived brand image formation process of potential consumers.
C1 [Xia, Menglong; Zhang, Yang] Macau Univ Sci & Technol, Fac Hospitality & Tourism Management, Taipa, Macao, Peoples R China.
C3 Macau University of Science & Technology
RP Zhang, Y (autor correspondiente), Macau Univ Sci & Technol, Fac Hospitality & Tourism Management, Taipa, Macao, Peoples R China.
EM yangzhang@must.edu.mo
CR Alrawadieh Z, 2019, J HOSP TOUR TECHNOL, V10, P107, DOI [10.1108/jhtt-09-2017-0093, 10.1108/JHTT-09-2017-0093]
Andreassen T., 1998, J SERV RES-US, V1, P82, DOI [10.1177/109467059800100107, DOI 10.1177/109467059800100107]
Back K.J., 2005, J HOSPITALITY TOURIS, V29, P448, DOI 10.1177/1096348005276497
Bagozzi RP, 1988, Journal of the Academy of Marketing Science, V16, P74, DOI DOI 10.1007/BF02723327
Bai B, 2008, INT J HOSP MANAG, V27, P391, DOI 10.1016/j.ijhm.2007.10.008
Barreda AA, 2016, TOURISM MANAGE, V57, P180, DOI 10.1016/j.tourman.2016.06.007
Barreda AA, 2015, COMPUT HUM BEHAV, V50, P600, DOI 10.1016/j.chb.2015.03.023
Bazi S, 2020, J BUS RES, V112, P223, DOI 10.1016/j.jbusres.2020.02.032
Benslimane Y, 2007, MATH COMPUT MODEL, V46, P1191, DOI 10.1016/j.mcm.2006.12.008
Bilgihan A, 2014, INF TECHNOL TOUR, V14, P49, DOI 10.1007/s40558-013-0003-3
Boyer KK, 2006, J OPER MANAG, V24, P124, DOI 10.1016/j.jom.2005.04.002
Cenfetelli RT, 2008, INFORM SYST RES, V19, P161, DOI 10.1287/isre.1070.0163
Choi EK, 2016, J HOSP MARKET MANAG, V25, P771, DOI 10.1080/19368623.2016.1100102
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Eroglu SA, 2001, J BUS RES, V54, P177, DOI 10.1016/S0148-2963(99)00087-9
Esch FR, 2006, J PROD BRAND MANAG, V15, P98, DOI 10.1108/10610420610658938
Gu HM, 2012, TOUR MANAG PERSPECT, V4, P56, DOI 10.1016/j.tmp.2012.02.001
Ha H., 2005, Journal of Consumer Behavior, V4, P438, DOI DOI 10.1002/CB.29
Hair J.F., 2009, Multivariate data analysis
Herrero A, 2012, INT J HOSP MANAG, V31, P1178, DOI 10.1016/j.ijhm.2012.02.005
Homburg C, 2010, INT J RES MARK, V27, P201, DOI 10.1016/j.ijresmar.2010.03.004
Jani D, 2014, INT J HOSP MANAG, V37, P11, DOI 10.1016/j.ijhm.2013.10.007
Jeong M., 2003, International Journal of Hospitality Management, V22, P161, DOI 10.1016/S0278-4319(03)00016-1
JEONG M, 2020, J HOSPITALITY TOURIS
Joshi, 2018, BUSINESS PERSPECTIVE, V6, P13
Kandampully J., 2007, International Journal of Contemporary Hospitality Management, V19, P435, DOI 10.1108/09596110710775101
Kanje P, 2020, J HOSP TOUR INSIGHTS, V3, P273, DOI 10.1108/JHTI-04-2019-0074
KELLER KL, 1993, J MARKETING, V57, P1, DOI 10.2307/1252054
Kim DY, 2008, INT J TOUR RES, V10, P393, DOI 10.1002/jtr.669
Kim J, 2013, J RES INTERACT MARK, V7, P33, DOI 10.1108/17505931311316734
Kim J, 2016, INT J CONTEMP HOSP M, V28, P1535, DOI 10.1108/IJCHM-06-2015-0289
Kim M, 2014, INT J CONTEMP HOSP M, V26, P225, DOI 10.1108/IJCHM-09-2012-0165
Kim MJ, 2020, J TRAVEL RES, V59, P69, DOI 10.1177/0047287518818915
Lai IKW, 2015, J HOSP TOUR RES, V39, P401, DOI 10.1177/1096348013491596
Law R., 2008, Asia Pacific Journal of Tourism Research, V13, P435, DOI 10.1080/10941660802420994
Law R, 2019, INT J HOSP MANAG, V76, P2, DOI 10.1016/j.ijhm.2018.06.005
Lee H, 2021, J VACAT MARK, V27, P237, DOI 10.1177/1356766720987872
Lee JS, 2008, TOURISM MANAGE, V29, P331, DOI 10.1016/j.tourman.2007.03.002
Lee S, 2014, INT J HOSP MANAG, V40, P49, DOI 10.1016/j.ijhm.2014.03.008
Lee VH, 2020, EXPERT SYST APPL, V157, DOI 10.1016/j.eswa.2020.113477
Leong ChoonChiang Leong ChoonChiang, 2003, Asia Pacific Journal of Tourism Research, V8, P38, DOI 10.1080/10941660308725467
Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
Loureiro S.M., 2013, INT J ELECT COMMERCE, V4, P139, DOI DOI 10.7903/IJECS.1000
MacCallum RC, 1996, PSYCHOL METHODS, V1, P130, DOI 10.1037/1082-989X.1.2.130
Maditinos DI, 2010, INFORM TECHNOL PEOPL, V23, P312, DOI 10.1108/09593841011087789
Matthews D.R., 2014, J BUSINESS RETAIL MA, V9, P26
Mehrabian A., 1974, APPROACH ENV PSYCHOL
Morgan-Thomas A, 2013, J BUS RES, V66, P21, DOI 10.1016/j.jbusres.2011.07.019
Morosan C, 2008, INT J HOSP MANAG, V27, P284, DOI 10.1016/j.ijhm.2007.07.023
Morosan C, 2015, INT J HOSP MANAG, V47, P120, DOI 10.1016/j.ijhm.2015.03.008
Nam J, 2011, ANN TOURISM RES, V38, P1009, DOI 10.1016/j.annals.2011.01.015
Nambisan P, 2011, J BUS RES, V64, P889, DOI 10.1016/j.jbusres.2010.09.006
No E, 2014, INT J TOUR RES, V16, P534, DOI 10.1002/jtr.1945
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
O'Loughlin D., 2004, INT J BANK MARK, V22, P522
Ozturk AB, 2016, INT J INFORM MANAGE, V36, P1350, DOI 10.1016/j.ijinfomgt.2016.04.005
Rodrigues LF, 2016, COMPUT HUM BEHAV, V61, P114, DOI 10.1016/j.chb.2016.03.015
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
ROTH MS, 1995, J MARKETING RES, V32, P163, DOI 10.2307/3152045
Seric M, 2020, J HOSP TOUR INSIGHTS, V3, P451, DOI 10.1108/JHTI-11-2019-0119
Sharma SK, 2017, BEHAV INFORM TECHNOL, V36, P1053, DOI 10.1080/0144929X.2017.1340973
SHIH C.F., 1998, EUR J MARKETING, V32, P655
Su CT, 2002, AM J OCCUP THER, V56, P581, DOI 10.5014/ajot.56.5.581
Ul Islam J, 2017, TELEMAT INFORM, V34, P96, DOI 10.1016/j.tele.2017.01.004
Voorveld HAM, 2013, J BRAND MANAG, V20, P608, DOI 10.1057/bm.2013.3
Wang D, 2016, J TRAVEL RES, V55, P52, DOI 10.1177/0047287514535847
Wang D, 2014, ANN TOURISM RES, V48, P11, DOI 10.1016/j.annals.2014.04.008
Wang L, 2015, INT J HOSP MANAG, V47, P108, DOI 10.1016/j.ijhm.2015.03.012
Wu, 2018, INT J HOSP MANAG, V83, P247
Xia ML, 2018, J DESTIN MARK MANAGE, V8, P259, DOI 10.1016/j.jdmm.2017.05.002
Xiang Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/j.ijhm.2014.10.013
Zhang JJ, 2012, J HOSP MARKET MANAG, V21, P113, DOI 10.1080/19368623.2011.615017
NR 73
TC 1
Z9 1
U1 7
U2 39
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9792
EI 2514-9806
J9 J HOSP TOUR INSIGHTS
JI J. Hosp. Tour. Insights
PD DEC 7
PY 2022
VL 5
IS 5
BP 1062
EP 1079
DI 10.1108/JHTI-02-2021-0039
EA JUL 2021
PG 18
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA 6S9HB
UT WOS:000673195600001
DA 2024-03-27
ER
PT J
AU Zhou, LC
Zhang, Q
AF Zhou, Lichun
Zhang, Qian
TI RETRACTADO: Recognition of false comments in E-commerce based on deep
learning confidence network algorithm (Retracted Article)
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article; Retracted Publication
DE E-commerce; False comment recognition; Deep confidence network;
Multi-dimensional features; Convolutional neural network
AB Out of commercial interests, merchants will hire professional writers to write good reviews for their products or write bad reviews for competitors, which has a serious adverse impact on the ecological development of e-commerce platforms. This article uses the feature set of product reviews as an entry point, and uses a deep-confidence network algorithm based on deep learning to analyze and identify the credibility of product reviews for e-commerce transactions. According to the model recognition, the characteristics of normal consumers' comments are analyzed. A DBN (Deep Belief Network) model that uses deep learning methods to identify false reviews is constructed. The model uses LSTM (Long Short Term Memory) and bidirectional GRU (Gated Recurrent Unit) to mine deep semantic features, and uses CNN (Convolutional Neural Network) to process the traditional discrete features extracted in this paper, which is multi-dimensional. We connect semantic features with traditional features to establish a DBN model. By verifying the accuracy of the model and comparing with other shallow machine learning algorithms, it is found that the recognition accuracy of the deep confidence network for comment data is significantly higher than that of other shallow machine learning algorithms. This verifies the effectiveness of the model proposed in this paper.
C1 [Zhou, Lichun; Zhang, Qian] Shangqiu Normal Univ, Sch Media & Commun, Shangqiu 476000, Henan, Peoples R China.
C3 Shangqiu Normal University
RP Zhou, LC (autor correspondiente), Shangqiu Normal Univ, Sch Media & Commun, Shangqiu 476000, Henan, Peoples R China.
EM zhoulc666@163.com; 31427693@qq.com
FU Humanities and Social Science project of Henan Provincial Department of
Education [2021-ZZJH-287]
FX This work is supported by the Humanities and Social Science project of
Henan Provincial Department of Education (2021-ZZJH-286); Humanities and
Social Science project of Henan Provincial Department of Education
(2021-ZZJH-287).
CR Asghar MZ, 2020, SOFT COMPUT, V24, P3475, DOI 10.1007/s00500-019-04107-y
Bhargava R, 2019, J INTELL SYST, V28, P409, DOI 10.1515/jisys-2017-0501
Cheung M, 2019, ACM T MULTIM COMPUT, V15, DOI 10.1145/3311785
Gainsbury SM, 2019, NEW MEDIA SOC, V21, P1232, DOI 10.1177/1461444818815442
Hussain N, 2019, ADCAIJ-ADV DISTRIB C, V8, P61, DOI 10.14201/ADCAIJ2019826171
Jiang LL, 2019, J AMB INTEL HUM COMP, V10, P3023, DOI 10.1007/s12652-018-0928-7
Lee J, 2019, INT J ELECTRON COMM, V23, P595, DOI 10.1080/10864415.2019.1655207
Mahbub S, 2019, INT J WEB GRID SERV, V15, P139, DOI 10.1504/IJWGS.2019.099561
Mou J, 2019, ELECTRON COMMER RES, V19, P749, DOI 10.1007/s10660-019-09338-7
Nanduri J, 2020, INFORMS J APPL ANAL, V50, P64, DOI 10.1287/inte.2019.1017
Olukemi, 2019, J COMPUT SCI-NETH, V7, P49, DOI DOI 10.15640/JCSIT.V7N2A67,2
Shinde, 2019, INT J ENG MANAG RES, V9, P107, DOI [10.31033/ijemr.9.2.12, DOI 10.31033/IJEMR.9.2.12]
Tran V., 2020, INT J DATA NETW SCI, V4, P115, DOI [10.5267/j.ijdns.2020.2.005, DOI 10.5267/J.IJDNS.2020.2.005]
Ullrich, 2019, MAASTRICHT J EUR COM, V26, P558, DOI DOI 10.1177/1023263X19855073
Wang, 2019, CHINA EC, V14, P56, DOI [10.1080/10971475.2018.1523859, DOI 10.1080/10971475.2018.1523859]
Wang WC, 2019, INFORM MANAGE-AMSTER, V56, P418, DOI 10.1016/j.im.2018.08.002
Yaghoubi, 2019, INT J WEB RES, V2, P51
Yu CM, 2019, INFORM TECHNOL MANAG, V20, P123, DOI 10.1007/s10799-018-0288-1
Zhang W, 2019, J SYST SCI SYST ENG, V28, P731, DOI 10.1007/s11518-019-5438-4
NR 19
TC 15
Z9 16
U1 4
U2 49
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD AUG
PY 2023
VL 21
IS SUPPL 1
SU 1
BP 7
EP 7
DI 10.1007/s10257-021-00503-w
EA JAN 2021
PG 1
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA T4NO4
UT WOS:000608090600001
DA 2024-03-27
ER
PT J
AU Roy, S
Moorthi, YLR
AF Roy, Subhadip
Moorthi, Y. L. R.
TI Technology readiness, perceived ubiquity and M-commerce adoption The
moderating role of privacy
SO JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
LA English
DT Article
DE Consumer behavior; M-commerce; Structural equation modelling;
E-commerce; Technology readiness; Human-computer interaction; perceived
ubiquity
ID MOBILE COMMERCE; USAGE INTENTION; BEHAVIORAL INTENTIONS; ACCEPTANCE
MODEL; USER ACCEPTANCE; SERVICE QUALITY; SPECIAL-ISSUE; CONSUMER;
INTERNET; ONLINE
AB Purpose - The purpose of this study is to draw concepts from marketing and information systems research and integrate them in the context of M-commerce. The authors develop a conceptual model of technology readiness (TR) affecting perceived ubiquity (PQ) (of smartphones) and PQ affecting M-commerce adoption (MA) incorporating the moderating effect of privacy concerns (PC) on the relation between PQ and MA along with the constructs perceived usefulness (PU) and perceived ease of use (PEU).
Methodology - The conceptual model was formulated using a set of qualitative research procedures (four focus group discussions) and tested using two questionnaire-based surveys (with 372 and 431 respondents each) in India. Exploratory and confirmatory factor analyses were conducted followed by structural equation modeling for the quantitative data.
Findings - Results from the quantitative study indicate a significant effect of TR on PQ, PU and PEU. All three latter constructs had a significant effect on MA. A significant moderating effect of PC on the relation between PQ and MA was also observed.
Research implications - The study findings enhance the literature on the antecedents of successful adoption of M-commerce and establish the role of PQ as a significant influencer of MA.
Practical implications - The study findings would enable service providers with a new and relevant model of M-commerce adoption.
Originality - The major contribution of the study is the development and validation of a model that has attitudinal variables related to technology usage and their relations to M-commerce adoption.
C1 [Roy, Subhadip] Indian Inst Management Udaipur, Dept Mkt, Udaipur, India.
[Moorthi, Y. L. R.] Indian Inst Management Bangalore, Bangalore, Karnataka, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
Management Udaipur (IIMU); Indian Institute of Management (IIM System);
Indian Institute of Management Bangalore
RP Roy, S (autor correspondiente), Indian Inst Management Udaipur, Dept Mkt, Udaipur, India.
EM subhadip.roy@iimu.ac.in
CR Akesson M., 2007, 6 ANN GLOB MOB ROUND, P19
[Anonymous], 2014, MULTIPLE LINEAR REGR
Assocham. com, 2016, E COMM IND GEN 2 5 L
Balasubramanian S, 2002, J ACAD MARKET SCI, V30, P348, DOI 10.1177/009207002236910
Barnes S. J., 2002, Electronic Markets, V12, P14, DOI 10.1080/101967802753433227
Barnes SJ, 2002, INT J INFORM MANAGE, V22, P91, DOI 10.1016/S0268-4012(01)00047-0
Bigne E., 2005, J ELECTRON COMMER RE, V6, P193, DOI DOI 10.1509/JMKG.64.4.65.18071
Bitner MJ, 2000, J ACAD MARKET SCI, V28, P138, DOI 10.1177/0092070300281013
Boadi R.A., 2007, INFORM DEV, V23, P253, DOI [10.1177/0266666907084761, DOI 10.1177/0266666907084761]
Bolton RN, 2013, J SERV MANAGE, V24, P245, DOI 10.1108/09564231311326987
Boritz JE, 2011, J INF SYST, V25, P11, DOI 10.2308/isys-10090
Buckingham D., 2008, YOUTH IDENTITY DIGIT, V1, P24
Burke RR, 2002, J ACAD MARKET SCI, V30, P411, DOI 10.1177/009207002236914
Carlsson C., 2002, 15 BLED INT C E COMM, P463
Chen Jengchung V., 2008, Info, V10, P30, DOI 10.1108/14636690810887526
Chen SC, 2013, CYBERPSYCH BEH SOC N, V16, P604, DOI 10.1089/cyber.2012.0606
Chen SC, 2009, IND MANAGE DATA SYST, V109, P1248, DOI 10.1108/02635570911002306
Chin WW, 1998, QUANT METH SER, P295
Chong AYL, 2013, EXPERT SYST APPL, V40, P523, DOI 10.1016/j.eswa.2012.07.068
Chong AYL, 2012, DECIS SUPPORT SYST, V53, P34, DOI 10.1016/j.dss.2011.12.001
Chopra A., 2017, Number of Internet users in India could cross 450 million by June: report - Livemint
CHURCHILL GA, 1979, J MARKETING RES, V16, P64, DOI 10.2307/3150876
Clarke I.I., 2001, J BUSINESS STRATEGIE, V18, P133, DOI [10.54155/jbs.18.2.133-148, DOI 10.54155/JBS.18.2.133-148]
Colby C. L., 2003, ROCKBRIDGE NEWS, P2
Constantiou ID, 2010, J ELECTRON COMMER RE, V11, P127
Constantiou ID, 2009, DATA BASE ADV INF SY, V40, P132
Culnan MJ, 2000, J PUBLIC POLICY MARK, V19, P20, DOI 10.1509/jppm.19.1.20.16944
Dai H, 2009, DATA BASE ADV INF SY, V40, P43
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dazeinfo. com, 2016, MOB INT US IND 2016
Dhapola S., 2016, INDIAN EXPRESS
Diehl S., 2013, Media and convergence management
Eastin MS, 2016, COMPUT HUM BEHAV, V58, P214, DOI 10.1016/j.chb.2015.12.050
Elliot K.M., 2005, Marketing Management Journal, V15, P98
Elliott K., 2012, Services Marketing Quarterly, V33, P311, DOI DOI 10.1080/15332969.2012.715049
Fishbein M., 1975, Beliefs, Attitude, Intention and Behavior: An Introduction to Theory and Research
FORNELL C, 1981, J MARKETING RES, V18, P382, DOI 10.2307/3150980
Fouskas K. G., 2005, International Journal of Mobile Communications, V3, P350, DOI 10.1504/IJMC.2005.007023
FRIED C, 1968, YALE LAW J, V77, P475, DOI 10.2307/794941
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
GIST ME, 1992, ACAD MANAGE REV, V17, P183, DOI 10.2307/258770
Gupta UG, 1996, TECHNOL FORECAST SOC, V53, P185, DOI 10.1016/S0040-1625(96)00094-7
Hair J.F., 1995, MULTIVARIATE DATA AN
Hanafizadeh P, 2014, TELEMAT INFORM, V31, P62, DOI 10.1016/j.tele.2012.11.001
Hayes AF, 2015, MULTIVAR BEHAV RES, V50, P1, DOI 10.1080/00273171.2014.962683
Hsu CI, 2009, EXPERT SYST APPL, V36, P11760, DOI 10.1016/j.eswa.2009.04.010
Jaradat M.-I., 2013, INT J BUSINESS MANAG, V8, P102
Junglas I., 2006, COMMUNICATIONS ASS I, V17, P369
Junglas I. A., 2003, ICIS 2003 P
Kang HM, 2006, PSYCHOL MARKET, V23, P841, DOI 10.1002/mar.20136
Kao DT, 2009, CYBERPSYCHOL BEHAV, V12, P225, DOI 10.1089/cpb.2008.0212
Kim S, 2009, INFORM SYST FRONT, V11, P323, DOI 10.1007/s10796-008-9073-8
Kini R., 2006, ENCY MOBILE COMPUTIN
Kline R. B., 2015, PRINCIPLES PRACTICE
Koenig-Lewis N, 2010, INT J BANK MARK, V28, P410, DOI 10.1108/02652321011064917
Kourouthanassis PE, 2012, INT J ELECTRON COMM, V16, P5, DOI 10.2753/JEC1086-4415160401
Kumar S., 2003, International Journal of Mobile Communications, V1, P180, DOI 10.1504/IJMC.2003.002465
Kurup R., 2015, HINDU BUSINESSLINE
Langendoerfer P., 2002, M COMMERCE WHY IT DO
Larivière B, 2013, J SERV MANAGE, V24, P268, DOI 10.1108/09564231311326996
LAUFER RS, 1977, J SOC ISSUES, V33, P22, DOI 10.1111/j.1540-4560.1977.tb01880.x
Lee FH, 2011, EXPERT SYST APPL, V38, P7766, DOI 10.1016/j.eswa.2010.12.131
Lee YK, 2012, J BUS RES, V65, P1590, DOI 10.1016/j.jbusres.2011.02.044
Lewis BR, 2005, EUR J INFORM SYST, V14, P388, DOI 10.1057/palgrave.ejis.3000552
Liljander V, 2006, J RETAIL CONSUM SERV, V13, P177, DOI 10.1016/j.jretconser.2005.08.004
Limayem M, 2000, IEEE T SYST MAN CY A, V30, P421, DOI 10.1109/3468.852436
Lin CH, 2007, PSYCHOL MARKET, V24, P641, DOI 10.1002/mar.20177
Lin JSC, 2007, COMPUT HUM BEHAV, V23, P1597, DOI 10.1016/j.chb.2005.07.006
Lu HP, 2009, INTERNET RES, V19, P442, DOI 10.1108/10662240910981399
Lu J, 2005, J STRATEGIC INF SYST, V14, P245, DOI 10.1016/j.jsis.2005.07.003
Lwin M, 2007, J ACAD MARKET SCI, V35, P572, DOI 10.1007/s11747-006-0003-3
Maity M., 2010, INT J MOBILE MARKETI, V5
Malhotra NK, 2005, INT MARKET REV, V22, P256, DOI 10.1108/02651330510602204
Malhotra NK, 2004, INFORM SYST RES, V15, P336, DOI 10.1287/isre.1040.0032
Massey AP, 2007, DECISION SCI, V38, P277, DOI 10.1111/j.1540-5915.2007.00159.x
Meeker M., 2012, INTERNET TRENDS PRES
Meng J, 2009, J INT CONSUM MARK, V22, P19, DOI 10.1080/08961530902844915
Meuter ML, 2003, J BUS RES, V56, P899, DOI 10.1016/S0148-2963(01)00276-4
Meuter ML, 2000, J MARKETING, V64, P50, DOI 10.1509/jmkg.64.3.50.18024
Mishra S., 2014, J INTERNET BANKING C, V19, P1
MMA Global. com, 2009, MMA UPD DEF MOB MARK
Monroe KentB., 2003, PRICING MAKING PROFI
Nepomuceno MV, 2014, J RETAIL CONSUM SERV, V21, P619, DOI 10.1016/j.jretconser.2013.11.006
Netemeyer R.G., 2003, SCALE DEV SOCIAL SCI
Ngai EWT, 2007, DECIS SUPPORT SYST, V43, P3, DOI 10.1016/j.dss.2005.05.003
Nielsen, 2014, REPORT UNSTOPPABLE S
O'Donnell J., 2007, J THEOR APPL EL COMM, V2, P1
Okazaki S, 2013, J INTERACT MARK, V27, P98, DOI 10.1016/j.intmar.2012.10.001
Okazaki S, 2012, ELECTRON MARK, V22, P169, DOI 10.1007/s12525-012-0087-1
Okazaki S, 2009, J ADVERTISING, V38, P63, DOI 10.2753/JOA0091-3367380405
Okazaki S, 2009, INT J ADVERT, V28, P77, DOI 10.2501/S026504870909043X
PARASURAMAN A, 1988, J RETAILING, V64, P12
Parasuraman A., 2004, QUIS 9
Parasuraman W, 2000, Journal of Service Research, V2, P307, DOI [10.1177/109467050024001, DOI 10.1177/109467050024001]
Park J., 2007, Journal of Electronic Commerce Research, V8, P196
Pousttchi K, 2015, INT J ELECTRON COMM, V19, P1, DOI 10.1080/10864415.2015.1029351
Pymnts. com, 2015, GLOB M COMM SET LEAV
Roger E.M., 1995, DIFFUSION INNOVATION, V4th
Sadeh N., 2003, M COMMERCE TECHNOLOG
Martín SS, 2012, IND MANAGE DATA SYST, V112, P946, DOI 10.1108/02635571211238536
Sarker S, 2003, COMMUN ACM, V46, P35, DOI 10.1145/953460.953484
Sen S., 2015, The Financial Express
Shankar V, 2010, J INTERACT MARK, V24, P111, DOI 10.1016/j.intmar.2010.02.006
Shankar V, 2009, J INTERACT MARK, V23, P118, DOI 10.1016/j.intmar.2009.02.002
Sharma SK, 2014, J INDIAN BUS RES, V6, P155, DOI 10.1108/JIBR-02-2013-0013
Smith HJ, 1996, MIS QUART, V20, P167, DOI 10.2307/249477
Sraeel H., 2006, BANK TECHNOLOGY NEWS
Sultan F, 2009, J INTERACT MARK, V23, P308, DOI 10.1016/j.intmar.2009.07.003
Taylor R.E., 1994, Tourism Marketing and Management Handbook, V2nd, P535
Thakur R, 2013, J INDIAN BUS RES, V5, P52, DOI 10.1108/17554191311303385
Tscherning H., 2010, J INFORM TECHNOLOGY, V11, P24
Varnali K, 2010, INT J INFORM MANAGE, V30, P144, DOI 10.1016/j.ijinfomgt.2009.08.009
Varshney G., 2010, CURIE J, V3, P60
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 1996, DECISION SCI, V27, P451, DOI 10.1111/j.1540-5915.1996.tb01822.x
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
Venkatesh V, 2007, J ASSOC INF SYST, V8, P267, DOI 10.17705/1jais.00120
Vogt H, 2003, MOBILE NETW APPL, V8, P127, DOI 10.1023/A:1022276913209
Vrechopoulos A. P., 2002, P 15 BLED EL COMM C, P17
Watson RT, 2002, J ACAD MARKET SCI, V30, P333, DOI 10.1177/009207002236909
Wei TT, 2009, IND MANAGE DATA SYST, V109, P370, DOI 10.1108/02635570910939399
Wessels B, 2012, NEW MEDIA SOC, V14, P1251, DOI 10.1177/1461444812450679
Westin A.F., 1967, PRIVACY FREEDOM
Wright D., 2005, INFO, V7, P35
Wu JH, 2005, INFORM MANAGE-AMSTER, V42, P719, DOI 10.1016/j.im.2004.07.001
Xu H., 2012, Proceedings of 33rd International Conference on Information Systems, Orlando, Florida, P1
Yadav R, 2016, J ENTERP INF MANAG, V29, P222, DOI 10.1108/JEIM-04-2015-0034
Yang HE, 2009, PROD PLAN CONTROL, V20, P370, DOI 10.1080/09537280902843755
Yang K. C. C., 2005, Telematics and Informatics, V22, P257, DOI 10.1016/j.tele.2004.11.003
Yen HR, 2005, SERV IND J, V25, P641, DOI 10.1080/02642060500100833
Zeithaml V.A., 2006, SERVICES MARKETING I
Zhang RD, 2013, J COMPUT INFORM SYST, V53, P31, DOI 10.1080/08874417.2013.11645648
NR 134
TC 25
Z9 29
U1 1
U2 42
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-7122
EI 2040-7130
J9 J RES INTERACT MARK
JI J. Res. Interact. Mark.
PY 2017
VL 11
IS 3
BP 268
EP 295
DI 10.1108/JRIM-01-2016-0005
PG 28
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA FE0TN
UT WOS:000407932700004
DA 2024-03-27
ER
PT J
AU Salminen, J
Yoganathan, V
Corporan, J
Jansen, BJ
Jung, SG
AF Salminen, Joni
Yoganathan, Vignesh
Corporan, Juan
Jansen, Bernard J.
Jung, Soon-Gyo
TI Machine learning approach to auto-tagging online content for content
marketing efficiency: A comparative analysis between methods and content
type
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Machine learning; Auto-tagging; Web content; Content marketing; Neural
network; Digital marketing
ID RESPONSE MODELS; CLASSIFICATION; NETWORKS; SUPPORT; INFORMATION;
PERFORMANCE; DISCOVERY; SYSTEMS; TOPICS; IMPACT
AB As complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification - Random Forest, K-Nearest Neighbor, and Neural Network - to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.
C1 [Salminen, Joni; Jansen, Bernard J.; Jung, Soon-Gyo] Hamad Bin Khalifa Univ, Qatar Comp Res Inst, Doha, Qatar.
[Salminen, Joni] Univ Turku, Turku Sch Econ, Turku, Finland.
[Yoganathan, Vignesh] Univ Bradford, Sch Management, Emm Lane, Bradford BD9 4JL, W Yorkshire, England.
[Corporan, Juan] Banco Santa Cruz RD, Santo Domingo, Dominican Rep.
C3 Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar; Qatar
Computing Research Institute; University of Turku; University of
Bradford
RP Yoganathan, V (autor correspondiente), Univ Bradford, Sch Management, Emm Lane, Bradford BD9 4JL, W Yorkshire, England.
EM vj.yoganathan@gmail.com
CR Abu-Salih B, 2018, J KNOWL MANAG, V22, P949, DOI 10.1108/JKM-11-2016-0489
[Anonymous], International conference on machine learning
Antons D, 2018, J SERV RES-US, V21, P17, DOI 10.1177/1094670517738373
Balducci B, 2018, J ACAD MARKET SCI, V46, P557, DOI 10.1007/s11747-018-0581-x
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Cates S, 2017, J INVEST MANAG, V15, P39
Chun SH, 2018, ADV SCI LETT, V24, P5474, DOI 10.1166/asl.2018.11760
Cui DP, 2005, MARKET SCI, V24, P595, DOI 10.1287/mksc.1050.0123
Cui G, 2006, MANAGE SCI, V52, P597, DOI 10.1287/mnsc.1060.0514
Cui G, 2004, INT J MARKET RES, V46, P235, DOI 10.1177/147078530404600204
Davis DF, 2013, J BUS RES, V66, P1245, DOI 10.1016/j.jbusres.2012.02.020
de Oliveira R., 2006, US INT C PERV SYST C, P37
Deng N, 2018, TOURISM MANAGE, V65, P267, DOI 10.1016/j.tourman.2017.09.010
Dhaoui C, 2017, J CONSUM MARK, V34, P480, DOI 10.1108/JCM-03-2017-2141
Evans MP, 2007, INTERNET RES, V17, P21, DOI 10.1108/10662240710730470
Flake G. W., 2004, ACM T INTERNET TECHN, V4, P341
Ghose A, 2012, MARKET SCI, V31, P493, DOI 10.1287/mksc.1110.0700
Ha K, 2005, J INTERACT MARK, V19, P17, DOI 10.1002/dir.20028
Hofacker CF, 2012, J INTERACT MARK, V26, P1, DOI 10.1016/j.intmar.2011.10.001
Ilhan BE, 2018, J INTERACT MARK, V43, P33, DOI 10.1016/j.intmar.2018.01.003
Janardan SM, 2017, PROCEDIA COMPUT SCI, V122, P804, DOI 10.1016/j.procs.2017.11.440
Jansen BJ, 2017, J RES INTERACT MARK, V11, P142, DOI 10.1108/JRIM-07-2016-0073
Kilgour M, 2015, CORP COMMUN, V20, P326, DOI 10.1108/CCIJ-07-2014-0046
Klapdor S, 2014, J INTERACT MARK, V28, P285, DOI 10.1016/j.intmar.2014.07.001
Kumar V, 2018, J MARKETING, V82, P1, DOI 10.1509/jm.82.41
Kutlu M, 2018, INFORM PROCESS MANAG, V54, P37, DOI 10.1016/j.ipm.2017.09.002
Lee HJ, 2010, J INTERACT MARK, V24, P42, DOI 10.1016/j.intmar.2009.10.004
Li XM, 2018, INFORM PROCESS MANAG, V54, P1345, DOI 10.1016/j.ipm.2018.05.009
Machedon R., 2013, INT C SOC COMP 8 14
Martínez-López FJ, 2009, IND MARKET MANAG, V38, P714, DOI 10.1016/j.indmarman.2008.02.003
Mencia E. L., 2008, EFFICIENT PAIRWISE M
Montgomery AL, 2009, J INTERACT MARK, V23, P130, DOI 10.1016/j.intmar.2009.02.001
Moon S, 2014, EUR J MARKETING, V48, P2176, DOI 10.1108/EJM-06-2013-0291
Na JC, 2009, J INF SCI, V35, P709, DOI 10.1177/0165551509104233
Newman D, 2011, ADV NEURAL INFORM PR, V24, P496
Orriols-Puig A, 2013, J BUS RES, V66, P1332, DOI 10.1016/j.jbusres.2012.02.033
Pan B, 2011, J TRAVEL RES, V50, P365, DOI 10.1177/0047287510369558
Pandey L, 2018, INTERSPEECH, P112
Papagiannopoulou E, 2018, INFORM PROCESS MANAG, V54, P888, DOI 10.1016/j.ipm.2018.06.004
Pereira RB, 2018, INFORM PROCESS MANAG, V54, P359, DOI 10.1016/j.ipm.2018.01.002
Ramos Juan., 2003, USING TF IDF DETERMI, V242, P29, DOI DOI 10.15804/TNER.2015.42.4.03
Read J, 2011, MACH LEARN, V85, P333, DOI 10.1007/s10994-011-5256-5
Rowley J, 2008, J MARKET MANAG-UK, V24, P517, DOI 10.1362/026725708X325977
Salminen J., 2018, P INT AAAI C WEB SOC
SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
Singh JP, 2017, J BUS RES, V70, P346, DOI 10.1016/j.jbusres.2016.08.008
Sterne J., 2018, APPL MARKETING ANAL, V3, P298
Svetnik V, 2003, J CHEM INF COMP SCI, V43, P1947, DOI 10.1021/ci034160g
Syam N, 2018, IND MARKET MANAG, V69, P135, DOI 10.1016/j.indmarman.2017.12.019
Trohidis K, 2011, EURASIP J AUDIO SPEE, DOI 10.1186/1687-4722-2011-426793
Tsai CF, 2013, KYBERNETES, V42, P357, DOI 10.1108/03684921311323626
Uchinaka S, 2019, TOURISM MANAGE, V71, P137, DOI 10.1016/j.tourman.2018.10.008
Vázquez S, 2014, NEURAL NETWORKS, V58, P68, DOI 10.1016/j.neunet.2014.05.026
Vogrincic S, 2011, COMPUT SCI INF SYST, V8, P101, DOI 10.2298/CSIS100420034V
Wallach H. M., 2009, P 26 ANN INT C MACH
Xu J., 2018, Topic Modeling with LSA, PLSA, LDA lda2Vec
Zarrinkalam F, 2018, INFORM PROCESS MANAG, V54, P339, DOI 10.1016/j.ipm.2017.12.003
Zhang H, 2005, INFORM PROCESS MANAG, V41, P691, DOI 10.1016/j.ipm.2003.12.002
Zhang MM, 2011, ELECTRON MARK, V21, P161, DOI 10.1007/s12525-011-0065-z
Zhao WY, 2018, INFORM PROCESS MANAG, V54, P203, DOI 10.1016/j.ipm.2017.11.005
NR 60
TC 50
Z9 52
U1 8
U2 92
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD AUG
PY 2019
VL 101
BP 203
EP 217
DI 10.1016/j.jbusres.2019.04.018
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA IF8YK
UT WOS:000473379000019
OA Green Submitted, Green Accepted
DA 2024-03-27
ER
PT J
AU Ballestar, MT
Grau-Carles, P
Sainz, J
AF Teresa Ballestar, Maria
Grau-Carles, Pilar
Sainz, Jorge
TI Predicting customer quality in e-commerce social networks: a machine
learning approach
SO REVIEW OF MANAGERIAL SCIENCE
LA English
DT Article; Proceedings Paper
CT 8th Global-Innovation-and-Knowledge-Academy (GIKA) Conference on Digital
Innovation and Venturing
CY JUN 25-27, 2018
CL Cathol Univ ValenciaSan Vicente Martir, Valencia, SPAIN
SP Global Innovat & Knowledge Acad
HO Cathol Univ ValenciaSan Vicente Martir
DE Cashback; Social network; E-commerce; Machine learning; Artificial
neural network; Predictive model
ID ARTIFICIAL NEURAL-NETWORKS; EVOLUTION; FUTURE; MODELS
AB The digital transformation of companies is having a major impact on all business areas, especially marketing, where audiences are most volatile and loyalty is at its scarcest. Many large retail brands try to keep their client base interested by becoming partners in cashback websites. These websites are based on a specific type of affiliate marketing whereby customers access a wide range of merchants and obtain financial rewards based on their activities. Besides using this mix of traditional marketing strategies, cashback websites attract new target customers and increase existing customers' loyalty through recommendations, using a word-of-mouth marketing strategy built on economic incentives for users who refer others to these sites. The literature shows that this strategy is one of the major areas of success of this business model because customers who join following recommendation are more active and are therefore more profitable and loyal to the brand. Nevertheless, the new users who are referred to these sites vary considerably in terms of the number of transactions they make on the site. This study advances research on the design of recommendation-based digital marketing strategies by providing companies with a predictive model. This model uses data science, including machine learning methods and big data, to personalize financial incentives for users based on the quality of the new customers they refer to the cashback website. Companies can thus optimize and maximize the return on their marketing investment.
C1 [Teresa Ballestar, Maria] ESIC Business & Mkt Sch, Madrid, Spain.
[Grau-Carles, Pilar; Sainz, Jorge] Rey Juan Carlos Univ, Madrid, Spain.
[Sainz, Jorge] Univ Bath, Inst Policy Res, Bath, Avon, England.
C3 ESIC; ESIC Business & Marketing School; Universidad Rey Juan Carlos;
University of Bath
RP Ballestar, MT (autor correspondiente), ESIC Business & Mkt Sch, Madrid, Spain.
EM mariateresa.ballestar@esic.edu; pilar.grau@urjc.es; jorge.sainz@urjc.es
RI SAINZ, JORGE/AAG-5379-2021; Sainz, jorge/AGW-3813-2022; sainz,
jorge/T-9791-2019; Grau, Pilar/F-1288-2016
OI SAINZ, JORGE/0000-0001-8491-3154; Grau, Pilar/0000-0003-3917-1096;
Ballestar de las Heras, Maria Teresa/0000-0001-8526-7561
CR Altinkemer K, 2009, INF SYST E-BUS MANAG, V7, P39, DOI 10.1007/s10257-007-0062-0
[Anonymous], 2017, SSRN ELECT J, DOI DOI 10.4283/JKMS.2017.27.1.001
Brynjolfsson E., 2017, The Economics of Artificial Intelligence: An Agenda
Chen YL, 2008, LECT NOTES COMPUT SC, V5385, P556, DOI 10.1007/978-3-540-92185-1_62
Chung P, 2016, P 10 INT S COMP SCI
Duffy DL, 2005, J CONSUM MARK, V22, P161, DOI 10.1108/07363760510595986
Dzelihodzic A., 2016, Modeling of Artificial Intelligence, V3, P140, DOI DOI 10.13187/MAI.2016.11.140
Eggers F, 2017, J SMALL BUS MANAGE, V55, P270, DOI 10.1111/jsbm.12313
Flexer A., 1996, Cybernetics and Systems '96. Proceedings of the Thirteenth European Meeting on Cybernetics and Systems Research, P1005
Fu T, 2013, PHYSICA A, V392, P2807, DOI 10.1016/j.physa.2013.02.018
Ganganwar V., 2012, International Journal of Emerging Technology and Advanced Engineering, V2, P42, DOI DOI 10.46338/IJETAE0412_13
García-Fernández J, 2018, ECON RES-EKON ISTRAZ, V31, P1042, DOI 10.1080/1331677X.2018.1436455
Guo H., 2004, ACM SIGKDD Explorations Newsl, V6, P30, DOI DOI 10.1145/1007730.1007736
Harmeling CM, 2017, J ACAD MARKET SCI, V45, P312, DOI 10.1007/s11747-016-0509-2
Ho Y.C.C., 2013, P 34 INT C INF SYST
Ho YC, 2017, INFORM SYST RES, V28, P250, DOI 10.1287/isre.2017.0693
Hoge P, 2011, SAN FRANCISCO BUSINE
Homburg C, 2008, J MARKETING, V72, P110, DOI 10.1509/jmkg.72.5.110
Hosmer DW, 2013, APPL LOGISTIC REGRES
Hu XF, 2009, REMOTE SENS ENVIRON, V113, P2089, DOI 10.1016/j.rse.2009.05.014
Jahng J, 2007, EUR J INFORM SYST, V16, P254, DOI 10.1057/palgrave.ejis.3000665
Jensen HL., 1992, Manag. Financ, V18, P15, DOI [10.1108/eb013696, DOI 10.1108/EB013696]
Kavzoglu T, 2003, INT J REMOTE SENS, V24, P4907, DOI 10.1080/0143116031000114851
Kumar V, 2016, J ADVERTISING, V45, P302, DOI 10.1080/00913367.2016.1199335
Lamberton C, 2016, J MARKETING, V80, P146, DOI 10.1509/jm.15.0415
Li Z, 2006, P SPIE INT SOC OPTIC
Libai B., 2016, J SERV RES-US
Mahmood A, 2017, J INTERACT MARK, V37, P117, DOI 10.1016/j.intmar.2016.10.003
Monaghan S, 2018, IND INNOV, V25, P242, DOI 10.1080/13662716.2017.1315562
Moustafa A.A., 2017, COMPUTATIONAL MODELS
Paliwal M, 2009, EXPERT SYST APPL, V36, P2, DOI 10.1016/j.eswa.2007.10.005
Roig-Tierno N, 2018, REV MANAG SCI, V12, P379, DOI 10.1007/s11846-017-0266-8
Rust RT, 2006, MARKET SCI, V25, P560, DOI 10.1287/mksc.1050.0139
Shaffer G, 1995, MARKET SCI, V14, P395, DOI 10.1287/mksc.14.4.395
Spitzer J, 2018, INT J ENTREP BEHAV R
Suchánek P, 2018, ECON RES-EKON ISTRAZ, V31, P1330, DOI 10.1080/1331677X.2018.1484786
Sun YM, 2009, INT J PATTERN RECOGN, V23, P687, DOI 10.1142/S0218001409007326
Ballestar MT, 2018, J BUS RES, V88, P407, DOI 10.1016/j.jbusres.2017.11.047
Ballestar MT, 2016, PSYCHOL MARKET, V33, P1039, DOI 10.1002/mar.20937
Ballestar MT, 2016, J BUS RES, V69, P2101, DOI 10.1016/j.jbusres.2015.12.015
Tu JV, 1996, J CLIN EPIDEMIOL, V49, P1225, DOI 10.1016/S0895-4356(96)00002-9
Vana P, 2018, J MARKET RES
Wang Jun., 2000, Computational intelligence in manufacturing handbook
Yin LZ, 2013, NEUROCOMPUTING, V105, P3, DOI 10.1016/j.neucom.2012.04.039
NR 44
TC 40
Z9 41
U1 8
U2 116
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1863-6683
EI 1863-6691
J9 REV MANAG SCI
JI Rev. Manag. Sci.
PD JUN
PY 2019
VL 13
IS 3
SI SI
BP 589
EP 603
DI 10.1007/s11846-018-0316-x
PG 15
WC Management
WE Social Science Citation Index (SSCI); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH)
SC Business & Economics
GA HY6BS
UT WOS:000468214100006
DA 2024-03-27
ER
PT J
AU Binder, M
Heinrich, B
Hopf, M
Schiller, A
AF Binder, Markus
Heinrich, Bernd
Hopf, Marcus
Schiller, Alexander
TI Global reconstruction of language models with linguistic rules -
Explainable AI for online consumer reviews
SO ELECTRONIC MARKETS
LA English
DT Article
DE Explainable AI; Text analytics; Language models; BERT; Linguistic rules;
Online consumer reviews
AB Analyzing textual data by means of AI models has been recognized as highly relevant in information systems research and practice, since a vast amount of data on eCommerce platforms, review portals or social media is given in textual form. Here, language models such as BERT, which are deep learning AI models, constitute a breakthrough and achieve leading-edge results in many applications of text analytics such as sentiment analysis in online consumer reviews. However, these language models are "black boxes ": It is unclear how they arrive at their predictions. Yet, applications of language models, for instance, in eCommerce require checks and justifications by means of global reconstruction of their predictions, since the decisions based thereon can have large impacts or are even mandatory due to regulations such as the GDPR. To this end, we propose a novel XAI approach for global reconstructions of language model predictions for token-level classifications (e.g., aspect term detection) by means of linguistic rules based on NLP building blocks (e.g., part-of-speech). The approach is analyzed on different datasets of online consumer reviews and NLP tasks. Since our approach allows for different setups, we further are the first to analyze the trade-off between comprehensibility and fidelity of global reconstructions of language model predictions. With respect to this trade-off, we find that our approach indeed allows for balanced setups for global reconstructions of BERT's predictions. Thus, our approach paves the way for a thorough understanding of language model predictions in text analytics. In practice, our approach can assist businesses in their decision-making and supports compliance with regulatory requirements.
C1 [Binder, Markus; Heinrich, Bernd; Hopf, Marcus; Schiller, Alexander] Univ Regensburg, Fac Informat & Data Sci, Regensburg, Germany.
C3 University of Regensburg
RP Heinrich, B (autor correspondiente), Univ Regensburg, Fac Informat & Data Sci, Regensburg, Germany.
EM Markus1.Binder@ur.de; bernd.heinrich@ur.de; Marcus.Hopf@ur.de;
Alexander.Schiller@ur.de
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR Adadi A, 2018, IEEE ACCESS, V6, P52138, DOI 10.1109/ACCESS.2018.2870052
Ali A., 2022, PREPRINT, DOI DOI 10.48550/ARXIV.2202.07304
Askira-Gelman I, 1998, P ANN HICSS, P247, DOI 10.1109/HICSS.1998.648319
Augasta M. G., 2012, Proceedings of the 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), P404, DOI 10.1109/ICPRIME.2012.6208380
Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
Binder M., 2019, P EUR C INF SYST, P1
Bowman Samuel R., 2019, INT C LEARNING REPRE, P1
Brasoveanu A. M. P., 2022, INTEGRATING ARTIFICI, P213
Casey B, 2019, Berkeley Technol Law J., P143
Chatterjee S, 2021, J BUS RES, V131, P815, DOI 10.1016/j.jbusres.2020.10.043
Chatterjee S, 2019, DECIS SUPPORT SYST, V119, P14, DOI 10.1016/j.dss.2019.02.008
Coenen A, 2019, ADV NEUR IN, V32
Coheur L, 2020, INFORM PROCESSING MA, V1237, P29
Dai HL, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5268
Danilevsky M., 2020, PREPRINT, DOI DOI 10.48550/ARXIV.2010.00711
Dastin J., 2018, Reuters9 October
Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
Doran D., 2017, ARXIV, P2071, DOI DOI 10.48550/ARXIV.1710.00794
Fan ZF, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P2509
Fellbaum C., 2012, The encyclopedia of applied linguistics, DOI 10.1002/9781405198431.wbeal1285/abstract
Forster M., 2021, P 54 HAW INT C SYST, P1274
Forster M., 2020, P 28 EUROPEAN C INFO
Forster M., 2020, ICIS 2020 P
Fortune Business Insights, 2021, NATURAL LANGUAGE PRO
Gardner M., 2018, ARXIV, DOI DOI 10.48550/ARXIV.1803.07640
Geng ZQ, 2021, NEUROCOMPUTING, V429, P132, DOI 10.1016/j.neucom.2020.12.037
Gilpin LH, 2018, PR INT CONF DATA SC, P80, DOI 10.1109/DSAA.2018.00018
Goeken T., 2020, ECIS 2020 P
Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
Heidari M, 2020, 2020 SEVENTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORK ANALYSIS, MANAGEMENT AND SECURITY (SNAMS), P142, DOI 10.1109/SNAMS52053.2020.9336575
Heinrich B., 2022, ECIS 2022 P
Heinrich B, 2021, ELECTRON MARK, V31, P389, DOI 10.1007/s12525-019-00366-7
Heinrich B, 2022, INFORM SYST FRONT, V24, P267, DOI 10.1007/s10796-020-10071-y
Hewitt J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4129
Jumelet Jaap, 2018, P 2018 EMNLP WORKSH, P222, DOI [10.18653/v1/W18-5424, DOI 10.18653/V1/W18-5424]
Kamps J., 2004, P 4 INT C LANG RES E, V4, P1115
Kim N., 2019, 8 JOINT C LEX COMP S
Kokalj E., 2021, EACL HACKASHOP NEWS, P16
Kovaleva O, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P4365
Lan Z., 2019, ARXIV, DOI DOI 10.48550/ARXIV.1909.11942
Liu Q, 2015, PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), P1291
Liu SZ, 2021, IEEE INT CONF BIG DA, P1193, DOI 10.1109/BigData52589.2021.9671639
Lundberg SM, 2017, ADV NEUR IN, V30
Luo B, 2022, WIRES DATA MIN KNOWL, V12, DOI 10.1002/widm.1434
Malkiel I, 2022, PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22), P3259, DOI 10.1145/3485447.3512045
Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
Ni JM, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P188
O'Donovan J, 2019, REV FINANC STUD, V32, P4117, DOI 10.1093/rfs/hhz017
Peters M, 2018, STUD LATEINAMERIKA, V32, P1, DOI 10.5771/9783845286846
Peters M. E., P 2018 C EMPIRICAL M
Potnis, 2018, ILLUMINATING INSIGHT
Qi P, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020): SYSTEM DEMONSTRATIONS, P101
Ramon Y., 2020, PREPRINT, DOI DOI 10.48550/ARXIV.2003.04792
Repke T., 2021, SPRINGER EBOOK COLLE, P241, DOI [10.1007/978-3-030-66891-4_11, DOI 10.1007/978-3-030-66891-4_11]
Ribeiro MT, 2018, AAAI CONF ARTIF INTE, P1527
Schiller A., 2019, P 14 INT C BUSINESS
Shrestha YR, 2021, J BUS RES, V123, P588, DOI 10.1016/j.jbusres.2020.09.068
Steur AJ, 2022, ELECTRON MARK, V32, P1187, DOI 10.1007/s12525-022-00525-3
Sun C, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P380
Sushil M., 2018, P 2018 EMNLP WORKSH, P82
Szczepanski M, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-03100-6
Tenney I, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4593
van Aken B, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1823, DOI 10.1145/3357384.3358028
Vilone G, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.717899
Wang Alex, 2019, INT C LEARN REPR ICL
Xu H, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P2324
Xu SZ, 2020, ADV INTELL SYST COMP, V1130, P270, DOI 10.1007/978-3-030-39442-4_21
Yan H., 2022, PREPRINT, DOI DOI 10.48550/ARXIV.2202.09792
Yang Y, 2020, PREPRINT, DOI DOI 10.48550/ARXIV.2006.08097
Yin DZ, 2014, MIS QUART, V38, P539, DOI 10.25300/MISQ/2014/38.2.10
Young T, 2018, IEEE COMPUT INTELL M, V13, P55, DOI 10.1109/MCI.2018.2840738
Zafar M. B., 2021, PREPRINT, DOI DOI 10.48550/ARXIV.2112.12444
Zhang R., 2020, PREPRINT, DOI DOI 10.48550/ARXIV.2002.01861
NR 73
TC 2
Z9 2
U1 15
U2 45
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD DEC
PY 2022
VL 32
IS 4
BP 2123
EP 2138
DI 10.1007/s12525-022-00612-5
EA DEC 2022
PG 16
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 8P0YF
UT WOS:000898635000001
OA hybrid, Green Published
DA 2024-03-27
ER
PT J
AU Miralles-Pechuán, L
Ponce, H
Martínez-Villaseñor, L
AF Miralles-Pechuan, Luis
Ponce, Hiram
Martinez-Villasenor, Lourdes
TI A novel methodology for optimizing display advertising campaigns using
genetic algorithms
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Display advertising campaigns; Direct response; Optimization; Genetic
algorithms; Micro-targeting; Machine learning
ID OPTIMIZATION; ECONOMICS; INTERNET; MEDIA
AB Online advertising campaigns have attracted the attention of many advertisers willing to promote their business on the Internet. One of the main problems faced by advertisers, especially by those who have little experience in Internet advertising, is configuring their campaigns in an efficient way. To configure a campaign properly it is required to select the appropriate target, so it is guaranteed a high acceptance of users to adverts. It is also required that the number of visits that satisfy the configuration requirements is high enough to cover the advertisers' campaigns. Thus, this paper presents a novel methodology for optimizing the micro-targeting technique in direct response display advertising campaigns by using genetic algorithms as the basis optimization model and a machine-learning based click-through rate (CTR) model. We implement our methodology to optimize display advertising campaigns on mobile devices using a real dataset. Results show that our methodology is feasible to optimize the campaigns by selecting the set of the best features required. Also, customization of the advertising campaign selecting some features by an advertiser, e.g. applying micro-targeting, can be optimized efficiently. (C) 2017 Elsevier B.V. All rights reserved.
C1 [Miralles-Pechuan, Luis] Univ Coll Dublin, Ctr Appl Data Analyt Res CeADAR, Dublin 4, Ireland.
[Ponce, Hiram; Martinez-Villasenor, Lourdes] Univ Panamericana, Fac Ingn, Augusto Rodin 498, Ciudad De Mexico 03920, Mexico.
C3 University College Dublin
RP Ponce, H (autor correspondiente), Univ Panamericana, Fac Ingn, Augusto Rodin 498, Ciudad De Mexico 03920, Mexico.
EM luis.miralles@ucd.ie; hponce@up.edu.mx; lmartine@up.edu.mx
RI Ponce, Hiram/K-7593-2019; Miralles, Luis/O-2472-2016;
Martinez-Villasenor, Lourdes/N-7607-2018
OI Ponce, Hiram/0000-0002-6559-7501; Martinez-Villasenor,
Lourdes/0000-0002-9038-7821
CR Aksakalli V, 2012, ELECTRON COMMER R A, V11, P229, DOI 10.1016/j.elerap.2011.11.002
Aronowich M., 2014, US Patent App, Patent No. [14/314,151, 14314151]
Balseiro SR, 2014, MANAGE SCI, V60, P2886, DOI 10.1287/mnsc.2014.2017
Chapelle Olivier, 2014, ACM Transactions on Intelligent Systems and Technology, V5, DOI 10.1145/2532128
Chen G, 2016, IEEE COMMUN SURV TUT, V18, P2124, DOI 10.1109/COMST.2016.2519912
Chin WS, 2015, LECT NOTES ARTIF INT, V9077, P442, DOI 10.1007/978-3-319-18038-0_35
Danaher PJ, 2010, MARKET SCI, V29, P336, DOI 10.1287/mksc.1090.0507
Edelman B, 2007, DECIS SUPPORT SYST, V43, P192, DOI 10.1016/j.dss.2006.08.008
Evans DS, 2009, J ECON PERSPECT, V23, P37, DOI 10.1257/jep.23.3.37
Evans DS, 2008, REV NETW ECON, V7, P359
Goldfarb A, 2014, REV IND ORGAN, V44, P115, DOI 10.1007/s11151-013-9399-3
Goldfarb A, 2011, MARKET SCI, V30, P389, DOI 10.1287/mksc.1100.0583
IAB, 2016, INT ADV BUR
Lee K, 2013, P 7 INT WORKSH DAT M, P1
Lee K.-C., 2012, P 18 ACM SIGKDD INT
Li P., 2011, ADV NEURAL INFORM PR, P2672
Liu Y., 2012, Proc. 5th ACM Int. Conf. Web Search and Data Mining, P473, DOI [10.1145/2124295.2124353, DOI 10.1145/2124295.2124353]
Lucier B., 2012, P 21 INT C WORLD WID, P361, DOI [10.1145/2187836.2187886, DOI 10.1145/2187836.2187886]
McMahan HB, 2013, 19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), P1222
Miralles-Pechuán L, 2017, SOFT COMPUT, V21, P651, DOI 10.1007/s00500-016-2468-4
Muthukrishnan S, 2007, LECT NOTES COMPUT SC, V4858, P131
Pandey S, 2017, INTERFACES, V47, P195, DOI 10.1287/inte.2017.0886
Perlich C., 2012, P 18 ACM SIGKDD INT, P804
Provost F, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P707
Richardson M., 2007, P 16 INT C WORLD WID, P521, DOI DOI 10.1145/1242572.1242643
Scrucca L, 2013, J STAT SOFTW, V53, P1
Shan LL, 2016, ELECTRON COMMER R A, V16, P30, DOI 10.1016/j.elerap.2016.01.004
Sivadas E, 1998, J BUS RES, V41, P179, DOI 10.1016/S0148-2963(97)00060-X
Wilbur KC, 2009, MARKET SCI, V28, P293, DOI 10.1287/mksc.1080.0397
Yuan S., 2013, P 7 INT WORKSH DAT M, P3
NR 30
TC 15
Z9 17
U1 0
U2 20
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD JAN-FEB
PY 2018
VL 27
BP 39
EP 51
DI 10.1016/j.elerap.2017.11.004
PG 13
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA FX8NU
UT WOS:000426352300004
DA 2024-03-27
ER
PT J
AU Krishnan, K
Wan, Y
AF Krishnan, Kavita
Wan, Yun
TI The Detection of Fake Reviews in Bestselling Books: Exploration and
Findings
SO JOURNAL OF ELECTRONIC COMMERCE IN ORGANIZATIONS
LA English
DT Article
DE Clustering; E-Commerce; Manipulation; Neural Network; Online Reviews
ID WORD-OF-MOUTH; ONLINE CONSUMER REVIEWS; PRODUCT; SALES; IMPACT;
DYNAMICS; TRUST
AB This study detected the possible manipulation of reviews for bestseller books. The authors first used clustering analysis to identify the cluster of bestselling books and patterns of manipulated reviews and ratings. They then used an artificial neural network to predict the possibility of review manipulation in bestselling books based on the patterns identified. The prediction outcome has an accuracy rate of 89%. They found that fake or manipulated reviews for bestselling books could be identified by analyzing abnormal rating fluctuations. The findings could help e-commerce platforms identify review manipulations and thereby help customers make prudent purchase decisions.
C1 [Krishnan, Kavita] Univ Houston Victoria, Victoria, TX 77901 USA.
[Wan, Yun] Univ Houston Victoria, Comp Informat Syst, Victoria, TX USA.
C3 University of Houston System; University of Houston Victoria; University
of Houston; University of Houston System; University of Houston;
University of Houston Victoria
RP Krishnan, K (autor correspondiente), Univ Houston Victoria, Victoria, TX 77901 USA.
RI Wan, Yun/A-2531-2008; Wan, Chuyun/JXM-1785-2024
OI Wan, Yun/0000-0002-9038-5607;
CR Anderson E.W., 1998, J SERVICE RES, V1, P5, DOI DOI 10.1177/109467059800100102
[Anonymous], 2012, International Journal of Emerging Technology and Advanced Engineering
[Anonymous], 2013, 2013 IEEE JORD C APP, DOI DOI 10.1109/AEECT.2013.6716442
ARNDT J, 1967, J MARKETING RES, V4, P291, DOI 10.1177/002224376700400308
Balan U. M., 2015, 2015 IEEE WORKSH COM, P1, DOI DOI 10.1109/WCI.2015.7495517
Barbado R, 2019, INFORM PROCESS MANAG, V56, P1234, DOI 10.1016/j.ipm.2019.03.002
Bazaarvoice, 2014, BAZAARVOICE
Bernard H. Russell, 2017, RES METHODS ANTHR QU
Bughin Jacques., 2010, McKinsey Quarterly, P113
Chatterjee P, 2001, ADV CONSUM RES, V28, P129
Chen YH, 2007, IND MANAGE DATA SYST, V107, P21, DOI 10.1108/02635570710719034
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
Cones JohnW., 1992, FILM FINANCE DISTRIB
Dawson J, 2016, WHAT ARE BENEFITS E
Dellarocas C., 2006, WISE
Dellarocas C, 2007, J INTERACT MARK, V21, P23, DOI 10.1002/dir.20087
Dematis I, 2018, LECT NOTES COMPUT SC, V10706, P581, DOI 10.1007/978-3-319-73117-9_41
Dhar V, 2009, J INTERACT MARK, V23, P300, DOI 10.1016/j.intmar.2009.07.004
Doh SJ, 2009, CYBERPSYCHOL BEHAV, V12, P193, DOI 10.1089/cpb.2008.0109
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Fayyad U, 1996, COMMUN ACM, V39, P27, DOI 10.1145/240455.240464
Fei Geli., 2013, ICWSM
Feng S., 2012, ICWSM, V12, P98
Fontanarava J, 2017, PR INT CONF DATA SC, P658, DOI 10.1109/DSAA.2017.51
Forman C, 2008, INFORM SYST RES, V19, P291, DOI 10.1287/isre.1080.0193
Goddard W.Melville., 2004, RES METHODOLOGY INTR, V2nd
Gurun UG, 2012, J FINANC, V67, P561, DOI 10.1111/j.1540-6261.2012.01725.x
Hajek P, 2020, NEURAL COMPUT APPL, V32, P17259, DOI 10.1007/s00521-020-04757-2
He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
Hu N, 2012, DECIS SUPPORT SYST, V52, P674, DOI 10.1016/j.dss.2011.11.002
Jeff Bezon, 2017, DEFINITION AMAZON BE
Jindal Nitin., 2010, Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10, P1549
King RA, 2014, J INTERACT MARK, V28, P167, DOI 10.1016/j.intmar.2014.02.001
Kohavi R., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, P1137
Kornish L.J., 2009, Are user reviews systematically manipulated?
Kotthoff L, 2019, SPRING SER CHALLENGE, P81, DOI 10.1007/978-3-030-05318-5_4
Lackermair Georg, 2013, Adv. Econ. Bus., V1, P1, DOI [10.13189/aeb.2013.010101, DOI 10.13189/AEB.2013.010101]
Laliberte M., 2017, READERS DIGEST
Lee J, 2008, ELECTRON COMMER R A, V7, P341, DOI 10.1016/j.elerap.2007.05.004
Li HY, 2014, IEEE DATA MINING, P899, DOI 10.1109/ICDM.2014.47
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Luca M, 2016, MANAGE SCI, V62, P3412, DOI 10.1287/mnsc.2015.2304
Mayzlin D, 2006, MARKET SCI, V25, P155, DOI 10.1287/mksc.1050.0137
Merriam-Webster, 2017, DEF MAN
Morris, 2016, CALCULATE MANY BOOKS
Mudambi SM, 2010, MIS QUART, V34, P185
Nielson, 2013, NIELS BOOK RES MEAS
Otis Chandler, 2017, DEFINITION BESTSELLE
Ott M., 2011, P 49 ANN M ASS COMPU, V1, P309, DOI DOI 10.1145/2567948.2577293
Park DH, 2008, ELECTRON COMMER R A, V7, P386, DOI 10.1016/j.elerap.2007.11.004
Patel N.A., 2018, 2018 4 INT C COMPUTI, P1, DOI DOI 10.1109/CCAA.2018.8777594
SHETH JN, 1971, J ADVERTISING RES, V11, P15
White M. C., 2015, 6 CLUES ONLINE REV M
Williams Roger, 2010, TQM Journal, V22, P117, DOI 10.1108/17542731011024246
Wu X, 2017, IEEE INT CONF BIG DA, P494, DOI 10.1109/BigData.2017.8257963
Wu YY, 2020, DECIS SUPPORT SYST, V132, DOI 10.1016/j.dss.2020.113280
Xie S., 2012, P 18 ACM SIGKDD INT, P823, DOI DOI 10.1145/2339530.2339662
Xu Q, 2014, COMPUT HUM BEHAV, V33, P136, DOI 10.1016/j.chb.2014.01.027
Yubo Chen., 2003, Business Week, V7150, P1
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 61
TC 2
Z9 2
U1 0
U2 7
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1539-2937
EI 1539-2929
J9 J ELECTRON COMMER OR
JI J. Electron. Commer. Organ.
PD OCT-DEC
PY 2021
VL 19
IS 4
BP 64
EP 79
DI 10.4018/JECO.2021100104
PG 16
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA UM9LL
UT WOS:000693645600004
DA 2024-03-27
ER
PT J
AU Tung, VWS
Au, NM
AF Tung, Vincent Wing Sun
Au, Norman
TI Exploring customer experiences with robotics in hospitality
SO INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
LA English
DT Article
DE Embodiment; Human-robot interaction; Emotions; Experience co-creation;
User experience; Consumer reviews
ID TOURISM; ANTHROPOMORPHISM; WORD; MANAGEMENT; ANIMACY; SERVICE; TRUST
AB Purpose - The purpose of this study is to explore consumer reviews with robotics based on the five dimensions for evaluating user experiences (i.e. embodiment, emotion, human-oriented perception, feeling of security and co-experience), as derived from research in human-robot interactions (HRI).
Design/methodology/approach - The study first reviews the five dimensions for evaluating user experiences in HRI and then analyzes user experiences with robotics at four hotels (i.e. Yotel New York, Aloft Cupertino, Henn-na Hotel Japan and Marriott Residence Inn LAX) based on reviews on TripAdvisor, Agoda, Yelp and Booking.com.
Findings - The findings highlight the influence of robotic embodiment and human-oriented perceptions on consumer experiences. The findings also suggest that users and robots can co-create novel experiences, with some guests even proactively seeking new opportunities to interact and communicate with robots to develop a certain level of relationship with them.
Research limitations/implications - An understanding of user experiences from HRIs can inform future hospitality and tourism research and management.
Practical implications - This study contributes to hospitality and tourism management by highlighting current practices with robotics to suggest areas of improvements for enhancing future consumer experiences.
Social implications - Consumer experiences will change rapidly as hospitality and tourism management deploys robotics in the future.
Originality/value - This is one of the early studies in the field to explore consumer experiences with robotics based on the five dimensions for evaluating user experiences from research in HRI. In doing so, this study provides a number of theoretical and managerial implications relevant for hospitality and tourism research and practice.
C1 [Tung, Vincent Wing Sun; Au, Norman] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China.
C3 Hong Kong Polytechnic University
RP Tung, VWS (autor correspondiente), Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China.
EM vincent.tung@polyu.edu.hk
RI Tung, Vincent/K-9179-2019; Tung, Vincent/E-3876-2016; Paleja,
Heer/IQT-1538-2023
OI Tung, Vincent/0000-0001-9560-8761;
CR Adams B, 2000, IEEE INTELL SYST APP, V15, P25, DOI 10.1109/5254.867909
Alben L., 1996, INTERACTIONS, V3, P11, DOI DOI 10.1145/235008.235010
[Anonymous], ROBOT2013
[Anonymous], 2016, CHECK VELOCIRAPTOR W
[Anonymous], INT J SOCIAL ROBOTIC
[Anonymous], 2017, J THERM STRESS
[Anonymous], INT J CONT HOSPITALI
Bainbridge WA, 2011, INT J SOC ROBOT, V3, P41, DOI 10.1007/s12369-010-0082-7
Bartneck C, 2009, INT J SOC ROBOT, V1, P71, DOI 10.1007/s12369-008-0001-3
Battarbee K., 2005, CoDesign, V1, P5, DOI 10.1080/15710880412331289917
Battarbee K., 2003, DPPI 03, P109
Brakus JJ, 2009, J MARKETING, V73, P52, DOI 10.1509/jmkg.73.3.52
Braun V., 2006, Qualitative Research in Psychology, V3, P77, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA]
Broekens J, 2007, LECT NOTES COMPUT SC, V4451, P113
Buchmann A, 2017, CURR ISSUES TOUR, V20, P261, DOI 10.1080/13683500.2014.887058
Buchner R, 2012, ACMIEEE INT CONF HUM, P115
Chathoth PK, 2016, INT J CONTEMP HOSP M, V28, P222, DOI 10.1108/IJCHM-10-2014-0526
Cox C, 2009, J HOSP MARKET MANAG, V18, P743, DOI 10.1080/19368620903235753
Dautenhahn K., 2006, 1st Annual Conference on Human-Robot Interaction, P172
Dautenhahn K, 1998, APPL ARTIF INTELL, V12, P573, DOI 10.1080/088395198117550
de Graaf MMA, 2013, ROBOT AUTON SYST, V61, P1476, DOI 10.1016/j.robot.2013.07.007
Dickinger A, 2017, INT J CONTEMP HOSP M, V29, P803, DOI [10.1108/ijchm-10-2015-0597, 10.1108/IJCHM-10-2015-0597]
Dieck MCT, 2017, INT J CONTEMP HOSP M, V29, P530, DOI 10.1108/IJCHM-10-2015-0552
Duffy BR, 2003, ROBOT AUTON SYST, V42, P177, DOI 10.1016/S0921-8890(02)00374-3
Eyssel F, 2011, ACMIEEE INT CONF HUM, P61, DOI 10.1145/1957656.1957673
Eyssel F, 2010, 2010 IEEE RO-MAN, P646, DOI 10.1109/ROMAN.2010.5598687
Filieri R, 2015, TOURISM MANAGE, V51, P174, DOI 10.1016/j.tourman.2015.05.007
Fong T, 2003, ROBOT AUTON SYST, V42, P143, DOI 10.1016/S0921-8890(02)00372-X
Gibson E.Pick., 2000, ECOLOGICAL APPROACH
Golden F., 2014, ROYAL CARIBBEANS QUA
Hassenzahl M, 2006, BEHAV INFORM TECHNOL, V25, P91, DOI 10.1080/01449290500330331
Iglesias O, 2011, J BRAND MANAG, V18, P570, DOI 10.1057/bm.2010.58
Inversini A., 2009, Information Technology and Tourism, V11, P221, DOI 10.3727/109830509X12596187863991
Jindai M., 2007, 2007 IEEEASME INT C, P1
Klug M, 2013, IEEE 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL CYBERNETICS (ICCC 2013), P365, DOI 10.1109/ICCCyb.2013.6617620
Kuo CM, 2017, INT J CONTEMP HOSP M, V29, P1305, DOI [10.1108/ijchm-08-2015-0414, 10.1108/IJCHM-08-2015-0414]
Lin YZ, 2015, SMART SENSOR MEAS IN, V15, P321, DOI 10.1007/978-3-319-18191-2_14
Lisetti C.L., 2000, Pragmatics cognition, V8, P185
Luczak H, 2003, ERGONOMICS, V46, P1361, DOI 10.1080/00140130310001610883
McNamara N., 2006, Interactions, V13, P26, DOI 10.1145/1167948.1167972
Mitsui T, 2001, IROS 2001: PROCEEDINGS OF THE 2001 IEEE/RJS INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, P1189, DOI 10.1109/IROS.2001.976330
Murphy J, 2017, EUR J TOUR RES, V15, P104
Nardi B. A., 2000, CSCW 2000. ACM 2000 Conference on Computer Supported Cooperative Work, P79, DOI 10.1145/358916.358975
Niculescu Andreea I., 2014, Mobile Web Information Systems. 11th International Conference (MobiWIS 2014). Proceedings: LNCS 8640, P153, DOI 10.1007/978-3-319-10359-4_13
Nieto Diego, 2014, Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. 8th International Conference, UCAmI 2014. Proceedings: LNCS 8867, P21, DOI 10.1007/978-3-319-13102-3_5
Nowak KL, 2003, PRESENCE-TELEOP VIRT, V12, P481, DOI 10.1162/105474603322761289
Okita SY, 2006, INT J HUM ROBOT, V3, P393, DOI 10.1142/S0219843606000795
Pfeifer R, 2007, SCIENCE, V318, P1088, DOI 10.1126/science.1145803
Ritchie JRB, 2011, INT J CONTEMP HOSP M, V23, P419, DOI 10.1108/09596111111129968
Rossi A, 2017, ACMIEEE INT CONF HUM, P375, DOI 10.1145/3029798.3034822
Salem M, 2015, ACMIEEE INT CONF HUM, P141, DOI 10.1145/2696454.2696497
Silvera-Tawil D, 2015, ROBOT AUTON SYST, V63, P230, DOI 10.1016/j.robot.2014.09.008
Steinfeld A., 2006, 1st Annual Conference on Human-Robot Interaction, P33
Stock R.M., 2016, Emotion Transfer from Frontline Social Robots to Human Customers
Torres EN, 2016, INT J CONTEMP HOSP M, V28, P2132, DOI 10.1108/IJCHM-02-2015-0065
Tung VWS, 2017, INT J CONTEMP HOSP M, V29, P2498, DOI 10.1108/IJCHM-09-2016-0520
Tung VWS, 2017, J TRAVEL TOUR MARK, V34, P853, DOI 10.1080/10548408.2016.1260521
Tung VWS, 2011, ANN TOURISM RES, V38, P1367, DOI 10.1016/j.annals.2011.03.009
Viglia G, 2016, INT J CONTEMP HOSP M, V28, P2035, DOI 10.1108/IJCHM-05-2015-0238
Wada K, 2004, P IEEE, V92, P1780, DOI 10.1109/JPROC.2004.835378
Weiss Astrid., 2009, AISB2009 P S NEW FRO, V4, P11
Wu MY, 2017, INT J CONTEMP HOSP M, V29, P1096, DOI [10.1108/ijchm-01-2015-0014, 10.1108/IJCHM-01-2015-0014]
Yang SB, 2017, INT J CONTEMP HOSP M, V29, P817, DOI [10.1108/ijchm-11-2015-0643, 10.1108/IJCHM-11-2015-0643]
Yeoman I., 2012, 2050 Tomorrow's Tourism
Young JE, 2011, INT J SOC ROBOT, V3, P53, DOI 10.1007/s12369-010-0081-8
Yu YX, 2017, INT J CONTEMP HOSP M, V29, P1340, DOI [10.1108/IJCHM-07-2015-0371, 10.1108/ijchm-07-2015-0371]
Zanbaka C., 2006, Conference on Human Factors in Computing Systems. CHI2006, P1153
Ziemke T, 2014, FRONT ARTIF INTEL AP, V273, P49, DOI 10.3233/978-1-61499-480-0-49
NR 68
TC 215
Z9 230
U1 54
U2 317
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0959-6119
EI 1757-1049
J9 INT J CONTEMP HOSP M
JI Int. J. Contemp. Hosp. Manag.
PY 2018
VL 30
IS 7
BP 2680
EP 2697
DI 10.1108/IJCHM-06-2017-0322
PG 18
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA GT1NS
UT WOS:000444238200006
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Arora, M
Prakash, A
Mittal, A
Singh, S
AF Arora, Meenal
Prakash, Anshika
Mittal, Amit
Singh, Swati
TI Examining the slow acceptance of HR analytics in the Indian engineering
and construction industry: a SEM-ANN-based approach
SO ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
LA English
DT Article; Early Access
DE Technology adoption; HR analytics; Structural equation modeling (SEM);
Data availability; Quantitative self-efficacy; Artificial neural network
(ANN)
ID INFORMATION-TECHNOLOGY; ADOPTION; ISSUES
AB PurposeDespite the extensive benefits of human resource (HR) analytics, the intention to adopt such technology is still a matter of concern in the engineering and construction sectors. This study aims to examine the slow adoption of HR analytics among HR professionals in the engineering and construction sector.Design/methodology/approachA cross-sectional online survey including 376 HR executives working in Indian-based engineering and construction firms was conducted. Hierarchal regression, structural equation modeling and artificial neural networks (ANN) were applied to evaluate the relative importance of HR analytics predictors.FindingsThe results reveal that hedonic motivation (HM), data availability (DA) and performance expectancy (PE) influence the behavioral intention (BI) to use HR analytics, whereas effort expectancy (EE), quantitative self-efficacy (QSE), habit (HA) and social influence (SI) act as barriers to its adoption. Moreover, PE was the most influential predictor of BI.Practical implicationsBased on the findings of this study, engineering and construction industry managers can formulate strategies for the implementation and promotion of HR analytics to enhance organizational performance.Originality/valueThis study draws attention to evidence-based decision-making, emphasizing barriers to the adoption of HR analytics. This study also emphasizes the concept of DA and QSE to enhance adoption among HR professionals, specifically in the engineering and construction industry.
C1 [Arora, Meenal] New Delhi Inst Management, New Delhi, India.
[Prakash, Anshika] KR Mangalam Univ, Sch Management & Commerce, Gurugram, India.
[Mittal, Amit] Chitkara Univ, Chitkara Business Sch, Rajpura, India.
[Singh, Swati] Bhavans Usha & Lakshmi Mittal Inst Management BULM, New Delhi, India.
C3 Chitkara University, Punjab
RP Mittal, A (autor correspondiente), Chitkara Univ, Chitkara Business Sch, Rajpura, India.
EM meenal.bajaj20@gmail.com; anshika.prakash@krmangalam.edu.in;
amit.mittal@chitkara.edu.in; swatisingh@bulmim.ac.in
RI MITTAL, AMIT/AAD-2112-2019; Singh, Swati/JWQ-0018-2024; Arora,
Meenal/ADH-7267-2022
OI MITTAL, AMIT/0000-0002-1191-4620; Singh, Swati/0000-0002-3131-5203;
Arora, Meenal/0000-0001-7670-6948; Prakash, Dr.
Anshika/0000-0001-9052-7188
CR Ahmed V, 2018, ENG CONSTR ARCHIT MA, V25, P1436, DOI 10.1108/ECAM-01-2018-0035
Ajzen I., 2000, EUR REV SOC PSYCHOL, P1, DOI [https://doi.org/10.1080/14792779943000116, DOI 10.1080/14792779943000116, 10.1080/14792779943000116]
Al-Ajlouni M.I., 2020, J INF KNOWL MANAG, V18, P1
Anouze ALM, 2020, INT J BANK MARK, V38, P86, DOI 10.1108/IJBM-10-2018-0271
Aoun C., 2010, P 21 AUSTR C INF SYS
Arora M, 2021, 2021 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATION (DASA), DOI 10.1109/DASA53625.2021.9682325
BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
Bassi L., 2011, PeopleStrategy, V34/2, P14
Beath C, 2012, MIT SLOAN MANAGE REV, V53, P18
Brown K., 2015, 5 BAD HABITS HR NEED
Chen XC, 2022, ENG CONSTR ARCHIT MA, V29, P3181, DOI 10.1108/ECAM-02-2021-0172
Chen YL, 2019, ENG CONSTR ARCHIT MA, V26, P1878, DOI 10.1108/ECAM-11-2017-0246
Chin WW, 1998, MIS QUART, V22, pVII
Deloitte, 2021, 2022 ENG CONSTR IND
Deloitte, 2019, 2019 DEL GLOB HUM CA
Dubey R, 2019, TECHNOL FORECAST SOC, V144, P534, DOI 10.1016/j.techfore.2017.06.020
El Idrissi T, 2019, INT J INFORM MANAGE, V46, P263, DOI 10.1016/j.ijinfomgt.2018.09.011
Faul F, 2009, BEHAV RES METHODS, V41, P1149, DOI 10.3758/BRM.41.4.1149
Fernandez V, 2021, COMPET REV, V31, P162, DOI 10.1108/CR-12-2019-0163
Fishbein M., 1975, Beliefs, Attitude, Intention and Behavior: An Introduction to Theory and Research
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gleeds India I&A, 2022, IND CONSTR COSTS BIA
GUADAGNOLI E, 1988, PSYCHOL BULL, V103, P265, DOI 10.1037/0033-2909.103.2.265
Hair J.F., 2009, Multivariate data analysis
Haykin S., 2009, Neural Networks and Learning Machines, V3rd
Hernández B, 2008, TECHNOVATION, V28, P112, DOI 10.1016/j.technovation.2007.11.002
Hew TS, 2016, IND MANAGE DATA SYST, V116, P1557, DOI 10.1108/IMDS-11-2015-0475
Hooper D., 2008, Electron J Bus Res Methods, V6, P53, DOI [DOI 10.21427/D7CF7R, 10.21427/D7CF7R]
IBEF, 2022, INFR IND REP
James G., 2013, SPRINGER TEXTS STAT
Johnston A.C., 2006, THESIS
Johnston AC, 2010, MIS QUART, V34, P549
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
KPMG, 2019, The Future of HR 2019: In the Know or in the No
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
Lu YS, 2022, ENG CONSTR ARCHIT MA, V29, P3574, DOI 10.1108/ECAM-01-2021-0005
Ma GF, 2022, ENG CONSTR ARCHIT MA, V29, P3948, DOI 10.1108/ECAM-05-2021-0456
MacKenzie SB, 2011, MIS QUART, V35, P293
Manzoor B, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18116135
Marler JH, 2017, INT J HUM RESOUR MAN, V28, P3, DOI 10.1080/09585192.2016.1244699
McCartney S, 2022, MANAGE DECIS, V60, P25, DOI 10.1108/MD-12-2020-1581
Okoro CS, 2023, ENG CONSTR ARCHIT MA, V30, P2645, DOI 10.1108/ECAM-06-2021-0476
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
OrgVue, 2019, MAK PEOPL COUNT 2019
Ozgen K., 2008, Kastamonu Education Journal, V16, P517
Ozgen K., 2013, Journal of International Education Research, V9, P305
Qamar Y, 2022, PERS REV, V51, P251, DOI 10.1108/PR-04-2020-0247
Raman A., 2013, International Education Studies, V6, P157, DOI DOI 10.5539/IES.V6N7P157
Sang LH, 2021, ENG CONSTR ARCHIT MA, V28, P706, DOI 10.1108/ECAM-02-2020-0135
Sangwan S., 2022, INT J ELECT MARKETIN, V13, P391
Saxena Mansi, 2021, Int J Inf Technol, V13, P1649, DOI 10.1007/s41870-021-00677-z
Sharma SK, 2019, INT J INFORM MANAGE, V44, P65, DOI 10.1016/j.ijinfomgt.2018.09.013
Spector PE, 2019, J MANAGE, V45, P855, DOI 10.1177/0149206316687295
Spector PE, 2010, ORGAN RES METHODS, V13, P403, DOI 10.1177/1094428110366303
Tabachnick B. G., 2007, Using Multivariate Statistics, V5, P481, DOI DOI 10.1037/022267
Tan GWH, 2014, TELEMAT INFORM, V31, P292, DOI 10.1016/j.tele.2013.06.002
Tandon U, 2022, ENG CONSTR ARCHIT MA, V29, P4287, DOI 10.1108/ECAM-05-2021-0376
Tarhini A, 2017, J INT EDUC BUS, V10, P164, DOI 10.1108/JIEB-09-2016-0032
Teo AC, 2015, IND MANAGE DATA SYST, V115, P311, DOI 10.1108/IMDS-08-2014-0231
Uddin MA, 2020, EUR J INNOV MANAG, V23, P877, DOI 10.1108/EJIM-06-2019-0168
Uluskan M, 2023, TQM J, V35, P1048, DOI 10.1108/TQM-01-2022-0001
Vargas R., 2015, THESIS NOVA SO U
Vargas R, 2018, INT J HUM RESOUR MAN, V29, P3046, DOI 10.1080/09585192.2018.1446181
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Watson Willis Towers, 2018, STATE TALENT ANAL IN
Williams LJ, 2009, ACAD MANAG ANN, V3, P543, DOI 10.1080/19416520903065683
NR 68
TC 2
Z9 2
U1 12
U2 77
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0969-9988
EI 1365-232X
J9 ENG CONSTR ARCHIT MA
JI Eng. Constr. Archit. Manag.
PD 2022 DEC 22
PY 2022
DI 10.1108/ECAM-09-2021-0795
EA DEC 2022
PG 21
WC Engineering, Industrial; Engineering, Civil; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA 7E1VU
UT WOS:000900965600001
DA 2024-03-27
ER
PT J
AU Lovlie, AS
AF Lovlie, Anders Sundnes
TI CONSTRUCTIVE COMMENTS? Designing an online debate system for the Danish
Broadcasting Corporation
SO JOURNALISM PRACTICE
LA English
DT Article
DE Constructive journalism; design; human-computer interaction; interaction
design; media design; online comments; participatory journalism; user
experience
ID NEWSPAPER WEBSITES; NEWS; DELIBERATION; INCIVILITY; PATTERNS
AB This article brings together two strands of research that have the potential to inform the development of constructive forms of journalism: online comments and media design. Through a threeyear-long case study of the development of new formats for online comments on the website of the Danish Broadcasting Corporation, I explore the challenges encountered from two perspectives: the design features of the commenting system and the design process. While the broadcaster has emphasized developing features for strengthening editorial control, user engagement has faltered. A lack of attention to users in the design process seems to have contributed to the problems. These findings have implications for constructive journalism's ambitions to facilitate audience engagement, in particular when tied to online platforms.
C1 [Lovlie, Anders Sundnes] IT Univ Copenhagen, Digital Design Dept, Copenhagen, Denmark.
C3 IT University Copenhagen
RP Lovlie, AS (autor correspondiente), IT Univ Copenhagen, Digital Design Dept, Copenhagen, Denmark.
EM asun@itu.dk
CR Ahva Laura, 2017, C NORDM 2017 MED REA
[Anonymous], 2014, CONSTRUCTIVE NEWS
[Anonymous], 2013, NORSK MEDIETIDSSKRIF
Bardzell J., 2015, SYNTHESIS LECT HUMAN, V8, P1, DOI [DOI 10.2200/S00664ED1V01Y201508HCI031, 10.2200/S00664ED1V01Y201508HCI031]
Beyer Hugh, 2010, USER CTR AGILE METHO
Birchall C., 2015, HDB DIGITAL POLITICS, P264, DOI DOI 10.4337/9781782548768.00025
Bodker S, 2006, P 4 NORDIC C HUMAN C, P1, DOI [DOI 10.1145/1182475.1182476, 10.1145/1182475.1182476]
Bolter Jay David, 2013, Interactions, V20, P36, DOI 10.1145/2405716.2405726
Bolter JD, 2003, ELOQUENT IMAGES: WORD AND IMAGE IN THE AGE OF NEW MEDIA, P19
Boyd D, 2012, COMMUN ACM, V55, P29, DOI 10.1145/2240236.2240247
Canter L, 2013, JOURNAL PRACT, V7, P604, DOI 10.1080/17512786.2012.740172
Carlson C.R., 2006, INNOVATION 5 DISCIPL
Chung DS, 2008, J COMPUT-MEDIAT COMM, V13, P658, DOI 10.1111/j.1083-6101.2008.00414.x
Coe K, 2014, J COMMUN, V64, P658, DOI 10.1111/jcom.12104
Coleman R., 2007, Journalism, V8, P25, DOI DOI 10.1177/1464884907072418
Dahlgren P, 2005, POLIT COMMUN, V22, P147, DOI 10.1080/10584600590933160
Davies Todd., 2009, ONLINE DELIBERATION
Domingo D., 2011, Participatory journalism: Guarding open gates at online newspapers, P76
Domingo David., 2011, Participatory Journalism, P76
Elgesem Dag, 2016, JOURNALISM REEXAMINE, P141
Fagerjord A., 2015, J MEDIA INNOVATIONS, V2, P107
Figenschou T., 2016, JOURNAL PRACT, P1, DOI DOI 10.1080/17512786.2016.1220258
Harrison S., 2007, ALT CHI SESS SIGCHI, P1, DOI DOI 10.1234/12345678
Hartson R., 2012, The UX Book: Process and guidelines for ensuring a quality user experience
Hedemann Ole, 2010, IDEUTVIKLING MEDIEHU
Karasti Helena., 2004, P 8 C PARTICIPATORY, V1, P20, DOI DOI 10.1145/1011870.1011874
Karlsen J, 2017, DIGIT CREAT, V28, P224, DOI 10.1080/14626268.2017.1351992
Kies R, 2010, PROMISES AND LIMITS OF WEB-DELIBERATION, P1, DOI 10.1057/9780230106376
Koskinen I., 2006, NORDICOM REV, V27, P35
Krippendorf K., 2016, The international encyclopedia of communication theory and philosophy, P1, DOI [10.1002/9781118766804.wbiect264, DOI 10.1002/9781118766804.WBIECT264]
Krippendorff K., 2006, The semantic turn: A new foundation for design
Larsson AO, 2011, NEW MEDIA SOC, V13, P1180, DOI 10.1177/1461444811401254
Le Dantec CA, 2013, SOC STUD SCI, V43, P241, DOI 10.1177/0306312712471581
Lee EJ, 2017, HUM COMMUN RES, V43, P436, DOI 10.1111/hcre.12123
Lovlie AS, 2018, JOURNAL PRACT, V12, P362, DOI 10.1080/17512786.2017.1293490
Lovlie AS, 2011, INT J ARTS TECHNOL, V4, P234, DOI 10.1504/IJART.2011.041479
Lovlie Anders Sundnes, 2011, THESIS
Lovlie Anders Sundnes, 2016, J MEDIA INNOVATIONS, V3, DOI [10.5617/jmi.v3i2.2486, DOI 10.5617/JMI.V3I2.2486]
Lowgren J., 2013, Challenges, V4, P86, DOI [10.3390/challe4010086, DOI 10.3390/CHALLE4010086]
Lowgren J., 2013, Collaborative Media: Production, Consumption, and Design Interventions
Lunenfeld P, 2004, NEW MEDIA SOC, V6, P65, DOI 10.1177/1461444804039909
McCarthy J., 2004, TECHNOLOGY EXPERIENC, DOI [10.1145/1015530.1015549, DOI 10.1145/1015530.1015549]
Mclntyre Karen Elizabeth, 2015, THESIS
Morrison Andrew., 2011, INSIDE MULTIMODAL CO
Muddiman A, 2017, J COMMUN, V67, P586, DOI 10.1111/jcom.12312
Nyre L, 2012, CONVERGENCE-US, V18, P297, DOI 10.1177/1354856512441151
OIE KV, 2015, J MEDIA PRACTICE, V16, P245, DOI DOI 10.1080/14682753.2015.1116763
Reich Zvi, 2011, PARTICIPATORY JOURNA, P2424
Rogers Y., 2011, INTERACTION DESIGN H
Rowe I, 2015, J BROADCAST ELECTRON, V59, P539, DOI 10.1080/08838151.2015.1093482
Ruiz C, 2011, INT J PRESS/POLIT, V16, P463, DOI 10.1177/1940161211415849
Santana AD, 2014, JOURNAL PRACT, V8, P18, DOI 10.1080/17512786.2013.813194
Schwaber K., 2002, AGILE SOFTWARE DEV S
Simon H. A., 2019, The Sciences of the Artificial
Skjulstad Synne, 2008, THESIS
Sobieraj S, 2011, POLIT COMMUN, V28, P19, DOI 10.1080/10584609.2010.542360
Springer N, 2015, INFORM COMMUN SOC, V18, P798, DOI 10.1080/1369118X.2014.997268
Stroud NJ, 2015, J COMPUT-MEDIAT COMM, V20, P188, DOI 10.1111/jcc4.12104
Sunstein Cass., 2017, #Republic: Divided democracy in the age of social media
The Coral Project, RESEARCH
Toepfl F, 2018, NEW MEDIA SOC, V20, P2844, DOI 10.1177/1461444817733710
Wright S, 2007, NEW MEDIA SOC, V9, P849, DOI 10.1177/1461444807081230
Ziegele M, 2018, INFORM COMMUN SOC, V21, P1419, DOI 10.1080/1369118X.2017.1324505
NR 63
TC 6
Z9 6
U1 0
U2 19
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1751-2786
EI 1751-2794
J9 JOURNAL PRACT
JI Journal. Pract.
PY 2018
VL 12
IS 6
SI SI
BP 781
EP 798
DI 10.1080/17512786.2018.1473042
PG 18
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA GM6OQ
UT WOS:000438290800009
DA 2024-03-27
ER
PT J
AU Lin, CH
Lin, IC
Roan, JS
AF Lin, Chi-Hung
Lin, I. Chun
Roan, Jinsheng
TI To evaluate interface usability of an e-course platform: User
perspective
SO AFRICAN JOURNAL OF BUSINESS MANAGEMENT
LA English
DT Article
DE System usability; usability goals; user experience goals; human-computer
interaction; user perspective
ID E-LEARNING SYSTEMS; PSYCHOMETRIC EVALUATION; PERCEIVED USEFULNESS;
TECHNOLOGY; INFORMATION; ACCEPTANCE; EDUCATION; CONTEXT; EASE; TASK
AB As a core term in Human-Computer Interaction (HCI), the analysis of system usability continues to be one of priority focuses of HCI researchers. Through users' evaluation, a system's inherent problems can be learned and its design be improved. This research was conducted to obtain the overall evaluation from the participants over the e-Course platform based on the data gathered from questionnaires, interviews and scenario simulations. This study recruited five participants, three of whom were professors and the rest were teacher's assistant. We used nine constructs to evaluate interface usability of the system. The research results showed that the average scores of three scenarios from high to low were S1a, S1b and S2. In terms of usability goals, efficiency, learn ability, utility, effectiveness, their average score is high than 3. In terms of user's experiences goals, the average score is less then 3, but near to 3. Overall, the interface of e-Course platform is ease to use and acceptable. Finally, we organized interview results and provided nine suggestions for a director of computer department and system designer. These suggestions can let them to know about the priority of system improvement, and provide a useful reference for practice.
C1 [Lin, I. Chun] Hung Kuang Univ, Dept Comp Sci & Informat Management, Shalu 43302, Taichung, Taiwan.
[Lin, Chi-Hung; Roan, Jinsheng] Natl Chung Cheng Univ, Dept Informat Management, Taipei, Taiwan.
C3 National Chung Cheng University
RP Lin, IC (autor correspondiente), Hung Kuang Univ, Dept Comp Sci & Informat Management, Shalu 43302, Taichung, Taiwan.
EM caviar_lin@hk.edu.tw
CR Agarwal R, 2002, INFORM SYST RES, V13, P168, DOI 10.1287/isre.13.2.168.84
Alam GM, 2010, AFR J BUS MANAGE, V4, P534
Alam GM, 2009, SCI RES ESSAYS, V4, P1260
[Anonymous], P HUM FACT COMP SYST
ARDITO M, 2004, P WORK C ADV VIS INT, P25
Carroll JM, 2000, INTERACT COMPUT, V13, P41, DOI 10.1016/S0953-5438(00)00022-9
Chang HH, 2010, INT J HUM-COMPUT ST, V68, P69, DOI 10.1016/j.ijhcs.2009.09.010
Chen TY, 2010, INT J HUM-COMPUT ST, V68, P138, DOI 10.1016/j.ijhcs.2009.10.003
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Hashim F, 2010, INT J PHYS SCI, V5, P383
Henderson R, 2003, INT J HUM-COMPUT ST, V59, P383, DOI 10.1016/S1071-5819(03)00079-X
Hiltz SR, 2002, COMMUN ACM, V45, P56, DOI 10.1145/505248.505273
Hornbæk K, 2006, INT J HUM-COMPUT ST, V64, P79, DOI 10.1016/j.ijhcs.2005.06.002
Kihlstrom J.F., 2004, The Sage handbook of methods in social psychology, P195
Kim DJ, 2009, INT J HUM-COMPUT INT, V25, P134, DOI 10.1080/10447310802629561
Kopcha TJ, 2007, ETR&D-EDUC TECH RES, V55, P627, DOI 10.1007/s11423-006-9011-8
Lewis JR, 2002, INT J HUM-COMPUT INT, V14, P463, DOI 10.1080/10447318.2002.9669130
LEWIS JR, 1995, INT J HUM-COMPUT INT, V7, P57, DOI 10.1080/10447319509526110
MINE J, 2001, P HUM INT S, P413
Mullens J., 1999, VALIDATING ITEM RESP
Nielsen J., 1994, Heuristic evaluation
Norman D., 2004, EMOTIONAL DESIGN WHY
Norman D. A., 1993, THINGS MAKE US SMART
Norman D. A., 2013, DESIGN EVERYDAY THIN, DOI DOI 10.15358/9783800648108
Ruiz MDP, 2008, COMPUT STAND INTER, V30, P62, DOI 10.1016/j.csi.2007.07.006
Preece J., 2002, INTERACTION DESIGN
Rubin J., 1994, HDB USABILITY TESTIN
SALEEN JJ, 2007, J AM MED INFORM ASSN, V14, P684
Wang YS, 2007, COMPUT HUM BEHAV, V23, P1792, DOI 10.1016/j.chb.2005.10.006
Zhang DS, 2006, INFORM MANAGE-AMSTER, V43, P15, DOI 10.1016/j.im.2005.01.004
NR 30
TC 1
Z9 5
U1 2
U2 6
PU ACADEMIC JOURNALS
PI VICTORIA ISLAND
PA P O BOX 5170-00200 NAIROBI, VICTORIA ISLAND, LAGOS 73023, NIGERIA
SN 1993-8233
J9 AFR J BUS MANAGE
JI Afr. J. Bus. Manag.
PD JAN 4
PY 2011
VL 5
IS 1
BP 196
EP 202
PG 7
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 713GY
UT WOS:000286724700023
DA 2024-03-27
ER
PT J
AU Sharma, SK
Chakraborti, S
Jha, T
AF Sharma, Satyendra Kumar
Chakraborti, Swapnajit
Jha, Tanaya
TI Analysis of book sales prediction at Amazon marketplace in India: a
machine learning approach
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article
DE E-commerce; Sentiment analysis; Neural network; Decision tree;
Regression analysis; Predictive model
ID WORD-OF-MOUTH; ONLINE CONSUMER REVIEWS; PRODUCT REVIEWS; PROMOTION;
IMPACT; DETERMINANTS; STRATEGIES; DISCOUNTS
AB Prediction of customer demand is an important part of Supply Chain Management, as it helps to avoid over or under production and reduces delivery time. In the context of e-commerce, accurate prediction of customer demand, typically captured by sales volume, requires careful analysis of multiple factors, namely, type of product, country of purchase, price, discount rate, free delivery option, online review sentiment etc., and their interactions. For e-tailers such as, Amazon, this kind of prediction capability is also extremely important in order to manage the supply chain efficiently as well as ensure customer satisfaction. This study investigates the efficacy of various modeling techniques, namely, regression analysis, decision-tree analysis and artificial neural network, for predicting the sales of books at amazon.in, using various relevant factors and their interactions as predictor variables. Sentiment analysis is carried out to measure the polarity of online reviews, which are included as predictors in these models. The importance of each independent predictor variable, such as discount rate, review sentiment etc., is analyzed based on the outcome of each model to determine top significant predictors which can be controlled by the marketer to influence sales. In terms of accuracy of prediction, the artificial neural network model is found to perform better than the decision-tree based model. In addition, the regression analysis, with and without sentiment and interaction factors, generates comparable results. The comparative analysis of these models reveals several significant findings. Firstly, all three models confirm that review volume is the most important and significant predictor of sales of books at amazon.in. Secondly, discount rate, discount amount and average ratings have minimal or insignificant effect on sales prediction. Thirdly, both negative sentiment and positive sentiment of the reviews are individually significant predictors as per regression and decision-tree model, but they are not significant at all as per neural network model. This observation from the neural network model is contrary to the extant research which claims that both negative and positive sentiment are significant with the former having more influence in predicting sales. Finally, the interaction effects of review volume with negative and positive sentiment are also found to be significant predictors as per all three models. Hence, overall, out of various factors used for sales prediction of books, review volume, negative sentiment, positive sentiment and their interactions are found to be the most significant ones across all models. The results of this study can be utilized by online sellers to accurately predict the sales volume by adjusting these significant factors, thereby managing the supply chain effectively.
C1 [Sharma, Satyendra Kumar] Birla Inst Technol & Sci BITS Pilani, Dept Management, Pilani 333031, Rajasthan, India.
[Chakraborti, Swapnajit] SPJIMR, Informat Management, Mumbai, Maharashtra, India.
[Jha, Tanaya] Birla Inst Technol & Sci BITS Pilani, Dept Comp Sci, Pilani 333031, Rajasthan, India.
C3 Birla Institute of Technology & Science Pilani (BITS Pilani); S. P. Jain
Institute of Management & Research (SPJIMR); Birla Institute of
Technology & Science Pilani (BITS Pilani)
RP Chakraborti, S (autor correspondiente), SPJIMR, Informat Management, Mumbai, Maharashtra, India.
EM satyendrasharma@pilani.bits-pilan.ac.in;
swapnajit.chakraborti@spjimr.org; f2013304@pilani.bits-pilan.ac.in
RI Chakraborty, Swapnajit/AAU-3478-2021
OI Chakraborty, Swapnajit/0000-0002-5127-3733
CR [Anonymous], 2017, NATURAL LANGUAGE PRO
[Anonymous], 2012, SOCIAL MEDIA REPORT
[Anonymous], 2011, ONLINE INFLUENCE TRE
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Beautiful Soup, 2017, BEAUTIFUL SOUP DOCUM
Chen SFS, 1998, J RETAILING, V74, P353, DOI 10.1016/S0022-4359(99)80100-6
Cheung CMK, 2012, DECIS SUPPORT SYST, V54, P461, DOI 10.1016/j.dss.2012.06.008
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chong AYL, 2017, INT J PROD RES, V55, P5142, DOI 10.1080/00207543.2015.1066519
Chong AYL, 2016, INT J OPER PROD MAN, V36, P358, DOI 10.1108/IJOPM-03-2015-0151
Chong AYL, 2014, INT J PROD ECON, V154, P48, DOI 10.1016/j.ijpe.2014.04.005
Chong AYL, 2013, EXPERT SYST APPL, V40, P523, DOI 10.1016/j.eswa.2012.07.068
Chong AYL, 2009, INT J PROD ECON, V122, P150, DOI 10.1016/j.ijpe.2009.05.012
Cui G, 2012, INT J ELECTRON COMM, V17, P39, DOI 10.2753/JEC1086-4415170102
Davis Alanah, 2008, Electronic Markets, V18, P130, DOI 10.1080/10196780802044776
DELLAROCAS CN, 2004, SSRN ELECT J
Doern RR, 2006, J WORLD BUS, V41, P315, DOI 10.1016/j.jwb.2006.08.001
Drozdenko R, 2005, J PROD BRAND MANAG, V14, P264, DOI 10.1108/10610420510609285
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Faryabi M., 2012, JOUNAL BUSINESS STUD, V4, P197
Floyd K, 2014, J RETAILING, V90, P217, DOI 10.1016/j.jretai.2014.04.004
Gaikar D., 2015, International Journal of Computer Science and Information Technologies, V6, P2303
Gendall P, 2006, J PROD BRAND MANAG, V15, P458, DOI 10.1108/10610420610712847
Ghose A., 2006, P 16 ANN WORKSH INF
Gong J, 2015, J RETAILING, V91, P343, DOI 10.1016/j.jretai.2015.02.002
GUPTA S, 1992, J CONSUM RES, V19, P401, DOI 10.1086/209310
Hancock JT, 2008, CSCW: 2008 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, CONFERENCE PROCEEDINGS, P295
Hu N, 2012, DECIS SUPPORT SYST, V57, P42
Hu N, 2014, DECIS SUPPORT SYST, V57, P42, DOI 10.1016/j.dss.2013.07.009
Ito TA, 1998, J PERS SOC PSYCHOL, V75, P887, DOI 10.1037/0022-3514.75.4.887
Jain R, 2015, INT J ADV RES COMPUT, V3, P80
Kramer ADI, 2014, P NATL ACAD SCI USA, V111, P8788, DOI 10.1073/pnas.1320040111
Lee J, 2008, ELECTRON COMMER R A, V7, P341, DOI 10.1016/j.elerap.2007.05.004
Leino J, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P137
Li XX, 2010, MIS QUART, V34, P809
LICHTENSTEIN DR, 1990, J MARKETING, V54, P54, DOI 10.2307/1251816
Lu XH, 2013, INFORM SYST RES, V24, P596, DOI 10.1287/isre.1120.0454
Ludwig S, 2013, J MARKETING, V77, P87, DOI 10.1509/jm.11.0560
Marshall R., 2002, Journal of Product Brand Management, V11, P147, DOI DOI 10.1108/10610420210430042
McNeill L, 2013, ASIA PAC J MARKET LO, V25, P48, DOI 10.1108/13555851311290939
Mudambi SM, 2010, MIS QUART, V34, P185
NLTK, NAT LANG TOOLK DOC
Pang Bo, 2008, INFORM RETRIEVAL, V2, P1, DOI DOI 10.1561/1500000011
Python Software Foundation, 2017, REQ HTTP HUM
Salehan M, 2016, DECIS SUPPORT SYST, V81, P30, DOI 10.1016/j.dss.2015.10.006
Santibanez SF, 2015, CASE STUDY REAL ESTA
Schneider MJ, 2016, INT J FORECASTING, V32, P243, DOI 10.1016/j.ijforecast.2015.08.005
Tang TY, 2014, J MARKETING, V78, P41, DOI 10.1509/jm.13.0301
Tsai WC, 2001, J MANAGE, V27, P497, DOI 10.1177/014920630102700406
Xu N, 2017, ELECTRON COMMER R A, V21, P27, DOI 10.1016/j.elerap.2016.12.001
Yang J, 2012, EUR J MARKETING, V46, P1523, DOI 10.1108/03090561211259961
Yao R, 2013, 2013 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC), P396, DOI 10.1109/GrC.2013.6740443
Yu XH, 2012, IEEE T KNOWL DATA EN, V24, P720, DOI 10.1109/TKDE.2010.269
Zhou ZH, 2004, IEEE T KNOWL DATA EN, V16, P770, DOI 10.1109/TKDE.2004.11
Zhou ZH, 2002, ARTIF INTELL, V137, P239, DOI 10.1016/S0004-3702(02)00190-X
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 56
TC 10
Z9 11
U1 2
U2 61
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD DEC
PY 2019
VL 17
IS 2-4
BP 261
EP 284
DI 10.1007/s10257-019-00438-3
PG 24
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JR4UJ
UT WOS:000499621600003
DA 2024-03-27
ER
PT J
AU Yüksel, D
AF Yuksel, Dogus
TI Investigation of Web-Based Eye-Tracking System Performance under
Different Lighting Conditions for Neuromarketing
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE web-based eye tracking; neuromarketing; e-commerce; human computer
interaction; lighting conditions
ID MOVEMENTS; BEHAVIOR; ATTENTION; LAMPS
AB The increasing popularity of neuromarketing has led to the emergence of various measurement methods, such as webcam-based eye-tracking technology. Webcam-based eye-tracking technology is noteworthy not only for its use in laboratories but also for its ability to be applied to participants online in their natural environments through a link. However, the complexity of e-commerce interfaces necessitates high performance in eye-tracking methods. This complexity and the applicability of webcam-based eye-tracking technology in various environments have raised research questions about how its performance changes depending on the type and location of lighting. To answer these questions, experiments were conducted with 30 users in two different experimental environments illuminated by artificial and natural methods, with the lighting from the left, right, and front. Participants were asked to focus on targets located in specially prepared graphics for the experiment. In the heatmaps obtained in the eye-tracking tests, the distance and angular difference between the focal point and the target point were measured using the polar coordinate system. The findings indicate that measurements taken with lighting coming from the center were more efficient in both natural and artificial lighting types and measurements taken under natural lighting performed 24% better than artificial ones. Web camera-based eye-tracking technology is a promising method. However, detailed statistical analyses have demonstrated that for complex interfaces like e-commerce, the position and type of lighting are crucial parameters.
C1 [Yuksel, Dogus] OSTIM Tech Univ, Ind Engn Dept, TR-06374 Ankara, Turkiye.
C3 Ostim Technical University
RP Yüksel, D (autor correspondiente), OSTIM Tech Univ, Ind Engn Dept, TR-06374 Ankara, Turkiye.
EM dogus.yuksel@ostimteknik.edu.tr
FU Commission of Scientific Research Projects in Ostim Technical University
[202216]
FX This work was supported by the Commission of Scientific Research
Projects in Ostim Technical University. Project Number: 202216.
CR Abdrabou Y., 2023, P USABLE SECURITY MI
Adams A.L., 2023, Public Serv. Q, V19, P38, DOI [10.1080/15228959.2022.2155756, DOI 10.1080/15228959.2022.2155756]
Aldayel M, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10041525
Allopenna PD, 1998, J MEM LANG, V38, P419, DOI 10.1006/jmla.1997.2558
[Anonymous], 2023, Top websites ranking
Babcock J.S., 2004, ETRA 2004, P109, DOI DOI 10.1145/968363.968386
Baker J., 1994, J ACAD MARKET SCI, V22, P328, DOI [DOI 10.1177/0092070394224002, 10.1177/0092070394224002]
Beach P, 2019, INT J RES METHOD EDU, V42, P485, DOI 10.1080/1743727X.2018.1496415
Bojko A., 2013, Eye Tracking the User Experience: A Practical Guide to Research, P12
Bojko A., 2005, P UPA 2005 C MONTR Q
Brugarolas M, 2017, SENSORY AND AROMA MARKETING, P61, DOI 10.3920/978-90-8686-841-4_3
Bulling A, 2010, IEEE PERVAS COMPUT, V9, P8, DOI 10.1109/MPRV.2010.86
Carter BT, 2020, INT J PSYCHOPHYSIOL, V155, P49, DOI 10.1016/j.ijpsycho.2020.05.010
Chang WL, 2010, AFR J BUS MANAGE, V4, P3344
Chapman P., 2002, TRANSPORT RES F-TRAF, V5, P157, DOI DOI 10.1016/S1369-8478(02)00014-1
Cheung YM, 2015, IEEE T HUM-MACH SYST, V45, P419, DOI 10.1109/THMS.2015.2400442
Chua HF, 2005, P NATL ACAD SCI USA, V102, P12629, DOI 10.1073/pnas.0506162102
Colaço R, 2021, TRANSPORT RES REC, V2675, P379, DOI 10.1177/0361198121990672
Dalmaijer E.S., 2014, PeerJ Prepr, V4, P1, DOI [DOI 10.7287/PEERJ.PREPRINTS.141V2, 10.7287/peerj.preprints.585v1, 10. 7287/peerj. preprints. 585v1]
Dalmaijer ES, 2014, BEHAV RES METHODS, V46, P913, DOI 10.3758/s13428-013-0422-2
Demangeot C, 2007, INT J RETAIL DISTRIB, V35, P878, DOI 10.1108/09590550710828218
Duchowski AndrewT., 2017, Eye Tracking Methodology: Theory and Practice, P3, DOI DOI 10.1007/978-3-319-57883-5_1
Funke G., 2016, Proc Hum Factors Ergon Soc Annu Meet, P1240, DOI [DOI 10.1177/1541931213601289, 10.1177/1541931213601289]
Giraldo-Romero YI, 2021, J THEOR APPL EL COMM, V16, P1165, DOI 10.3390/jtaer16050066
Gómez-Carmona D, 2021, J THEOR APPL EL COMM, V16, P1648, DOI 10.3390/jtaer16050093
Hammou K.A., 2013, Journal of Management Research, V5, P20, DOI [10.5296/jmr.v5i4.4023, DOI 10.5296/JMR.V5I4.4023]
Hansen DW, 2005, COMPUT VIS IMAGE UND, V98, P155, DOI 10.1016/j.cviu.2004.07.013
Hoffman J.E., 2016, Attention, P119
Holmqvist K., 2017, Eye tracking: A Comprehensive Guide to Methods, Paradigms and Measures, V2nd ed., P5
Hsiao MH, 2009, TRANSPORT RES E-LOG, V45, P86, DOI 10.1016/j.tre.2008.06.002
Hulten B., 2009, What Is Sensory Marketing?, P1
Jacob RJK, 2003, MIND'S EYE: COGNITIVE AND APPLIED ASPECTS OF EYE MOVEMENT RESEARCH, P573, DOI 10.1016/B978-044451020-4/50031-1
JUST MA, 1980, PSYCHOL REV, V87, P329, DOI 10.1037/0033-295X.87.4.329
Kaufman A. E., 1993, Proceedings IEEE 1993 Symposium on Research Frontiers in Virtual Reality (Cat. No.93TH0585-0), P120, DOI 10.1109/VRAIS.1993.378254
Kauppinen-Räisänen H, 2014, PACKAG TECHNOL SCI, V27, P663, DOI 10.1002/pts.2061
Kiefer P, 2017, SPAT COGN COMPUT, V17, P1, DOI 10.1080/13875868.2016.1254634
King AJ, 2019, COMMUN METHODS MEAS, V13, P149, DOI 10.1080/19312458.2018.1558194
Krishna A, 2012, J CONSUM PSYCHOL, V22, P332, DOI 10.1016/j.jcps.2011.08.003
Kumar JA, 2023, EDUC INF TECHNOL, V28, P7877, DOI 10.1007/s10639-022-11504-8
ledbenchmark, FAQ-Comparision of LED and CFL Light Warm up Times
Li C, 2019, TELEMAT INFORM, V44, DOI 10.1016/j.tele.2019.101267
Lin YT, 2013, MULTIMED TOOLS APPL, V65, P543, DOI 10.1007/s11042-012-1202-1
Majaranta P, 2019, STUD NEUROSCI, P971, DOI 10.1007/978-3-030-20085-5_23
Manganari EE, 2009, EUR J MARKETING, V43, P1140, DOI 10.1108/03090560910976401
McDermott SL, 2008, OPT LASER TECHNOL, V40, P487, DOI 10.1016/j.optlastec.2007.07.013
Meissner M, 2019, ORGAN RES METHODS, V22, P590, DOI 10.1177/1094428117744882
Meng CN, 2017, IEEE ACCESS, V5, P19581, DOI 10.1109/ACCESS.2017.2754299
Mielczarski W, 1998, INT C HARMON QUAL PO, P1200, DOI 10.1109/ICHQP.1998.760210
Morin C, 2011, SOCIETY, V48, P131, DOI 10.1007/s12115-010-9408-1
Mormann M, 2020, MARKET LETT, V31, P381, DOI 10.1007/s11002-020-09520-0
Mounica MS, 2019, PROCEEDINGS OF THE 2019 3RD INTERNATIONAL CONFERENCE ON COMPUTING METHODOLOGIES AND COMMUNICATION (ICCMC 2019), P79, DOI [10.1109/iccmc.2019.8819645, 10.1109/ICCMC.2019.8819645]
Najjar LJ, 2011, LECT NOTES COMPUT SC, V6772, P292, DOI 10.1007/978-3-642-21669-5_35
Nakayasu H, 2011, J ADV COMPUT INTELL, V15, P1039, DOI 10.20965/jaciii.2011.p1039
Nichifor E, 2021, J THEOR APPL EL COMM, V16, P2319, DOI 10.3390/jtaer16060128
Othman Y, 2020, IEEE INT CONF INF VI, P729, DOI 10.1109/IV51561.2020.00128
Polonio L, 2015, GAME ECON BEHAV, V94, P80, DOI 10.1016/j.geb.2015.09.003
Rayner K, 2009, Q J EXP PSYCHOL, V62, P1457, DOI 10.1080/17470210902816461
ROBINSON DA, 1963, IEEE T BIO-MED ENG, VBM10, P137, DOI 10.1109/TBMEL.1963.4322822
Schifferstein HNJ, 2006, ACTA PSYCHOL, V121, P41, DOI 10.1016/j.actpsy.2005.06.004
Semmelmann K, 2018, BEHAV RES METHODS, V50, P451, DOI 10.3758/s13428-017-0913-7
Singh H., 2012, INT J SCI RES PUBLIC, V2, P1, DOI DOI 10.1371/J0URNAL.P0NE.0029319
Smilek D, 2010, PSYCHOL SCI, V21, P786, DOI 10.1177/0956797610368063
Solnais C, 2013, J ECON PSYCHOL, V36, P68, DOI 10.1016/j.joep.2013.02.011
Spence C, 2014, PSYCHOL MARKET, V31, P472, DOI 10.1002/mar.20709
Takemura K, 2014, IEEE T HUM-MACH SYST, V44, P531, DOI 10.1109/THMS.2014.2318324
tobii, Dark and Bright Pupil Tracking
TOPALIS FV, 1993, IEEE T POWER DELIVER, V8, P2038, DOI 10.1109/61.248319
Valenti R, 2012, INT J COMPUT VISION, V98, P324, DOI 10.1007/s11263-011-0511-6
Wade N., 2005, The Moving Tablet of the Eye: The Origins of Modern Eye Movement Research, P14
Wedel M., 2008, Review of marketing research, V4, P123, DOI [10.1108/S1548-6435(2008)0000004009, DOI 10.1108/S1548-6435(2008)0000004009]
Wedel M, 2006, FOUND TRENDS MARKET, V1, P231, DOI 10.1561/1700000011
Wiederhold BK, 2020, CYBERPSYCH BEH SOC N, V23, P437, DOI 10.1089/cyber.2020.29188.bkw
Xi GL, 2020, TRANSP LETT, V12, P157, DOI 10.1080/19427867.2018.1546797
Yuksel D., 2023, Bus. Econ. Res. J, V14, P433, DOI [10.20409/berj.2023.424, DOI 10.20409/BERJ.2023.424]
Yuksel D., 2023, Noro Logo Marka Yonetimi Bakis Acisiyla Logo Oranlarinin Goz Izleme Teknigiyle Incelenmesi, P65
Yuksel D., 2023, Isletme Arastirmalari Derg, V15, P2113
Zhaowei Li, 2021, Journal of Physics: Conference Series, V1802, DOI 10.1088/1742-6596/1802/4/042066
Zurawicki L., 2010, Neuromarketing: Exploring the Brain of the Consumer, P12
NR 78
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD DEC
PY 2023
VL 18
IS 4
BP 2092
EP 2106
DI 10.3390/jtaer18040105
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA DF7L3
UT WOS:001130678700001
OA gold
DA 2024-03-27
ER
PT J
AU van Esch, P
Black, JS
AF van Esch, Patrick
Black, J. Stewart
TI Artificial Intelligence (AI): Revolutionizing Digital Marketing
SO AUSTRALASIAN MARKETING JOURNAL
LA English
DT Article
DE artificial intelligence; digital marketing; AI in digital marketing;
marketing; AI in marketing
ID WORLD
AB Artificial intelligence (AI)-enabled digital marketing is revolutionizing the way organizations create content for campaigns, generate leads, reduce customer acquisition costs, manage customer experiences, market themselves to prospective employees, and convert their reachable consumer base via social media. Real-world examples of organizations who are using AI in digital marketing abound. For example, Red Balloon and Harley Davidson used AI to automate their digital advertising campaigns. However, we are early in the process of both the practical application of AI by firms broadly and by their marketing functions in particular. One could argue that we are even earlier in the research process of conceptualizing, theorizing, and researching the use and impact of AI. Importantly, as with most technologies of significant potential, the application of AI in marketing engenders not just practical considerations but ethical questions as well. The ability of AI to automate activities, that in the past people did, also raises the issue of whether marketing professionals will embrace AI as a means to free them from more mundane tasks to spend time on higher value activities, or will they view AI as a threat to their employment? Given the nascent nature of research on AI at this point, the full capabilities and limitations of AI in marketing are unknown. This special edition takes an important step in illuminating both what we know and what we yet need to research.
C1 [van Esch, Patrick] Kennesaw State Univ, Kennesaw, GA 30144 USA.
[Black, J. Stewart] INSEAD, San Francisco, CA USA.
C3 University System of Georgia; Kennesaw State University
RP van Esch, P (autor correspondiente), Kennesaw State Univ, Dept Mkt & Profess Sales, Michael J Coles Coll Business, 560 Parliament Garden Way NW, Kennesaw, GA 30144 USA.
EM pvanesch@kennesaw.edu
RI van Esch, Patrick/ABE-9472-2021
OI van Esch, Patrick/0000-0002-0541-9340
CR Black, 2020, HARVARD BUS REV
Black JS, 2021, BUS HORIZONS, V64, P513, DOI 10.1016/j.bushor.2021.02.015
Black JS, 2020, BUS HORIZONS, V63, P215, DOI 10.1016/j.bushor.2019.12.001
Camilleri M.A., 2018, TRAVEL MARKETING TOU, P69, DOI [10.1007/978-3-319-49849-2, 10.1007/978-3-319-49849-2_4, DOI 10.1007/978-3-319-49849-2_4]
Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
Cui YY, 2022, EUR J MARKETING, V56, P1650, DOI 10.1108/EJM-02-2020-0122
De Bruyn A, 2020, J INTERACT MARK, V51, P91, DOI 10.1016/j.intmar.2020.04.007
Feng CM, 2021, AUSTRALAS MARK J, V29, P252, DOI 10.1016/j.ausmj.2020.07.006
Floridi L., 2020, Philosophy & Technology, V33, P1, DOI DOI 10.1007/S13347-020-00396-6
Hanssens D.M., 2020, FUTURE MANAGEMENT AI
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Kietzmann J, 2020, BUS HORIZONS, V63, P135, DOI 10.1016/j.bushor.2019.11.006
Krafft M, 2020, J INTERACT MARK, V51, P1, DOI 10.1016/j.intmar.2020.06.001
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Ma LY, 2020, INT J RES MARK, V37, P481, DOI 10.1016/j.ijresmar.2020.04.005
Maeng K, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101327
Mogaji E, 2021, AUSTRALAS MARK J, V29, P235, DOI 10.1016/j.ausmj.2020.05.003
Narain K, 2019, J ADV MANAG RES, V16, P698, DOI 10.1108/JAMR-01-2019-0006
Neff G, 2016, INT J COMMUN-US
Oosthuizen K, 2021, AUSTRALAS MARK J, V29, P264, DOI 10.1016/j.ausmj.2020.07.007
Paschen J, 2021, AUSTRALAS MARK J, V29, P243, DOI 10.1016/j.ausmj.2020.06.004
Paschen J, 2020, BUS HORIZONS, V63, P403, DOI 10.1016/j.bushor.2020.01.003
Plotkina D, 2016, J RETAIL CONSUM SERV, V29, P1, DOI 10.1016/j.jretconser.2015.11.002
Reinartz W, 2019, INT J RES MARK, V36, P350, DOI 10.1016/j.ijresmar.2018.12.002
Robertson J, 2021, AUSTRALAS MARK J, V29, P215, DOI 10.1177/1839334921999487
Roggeveen AL, 2020, J RETAILING, V96, P299, DOI 10.1016/j.jretai.2020.08.0O1
Sharp M, 2018, J MANUF SYST, V48, P170, DOI 10.1016/j.jmsy.2018.02.004
Steward MD, 2018, J ACAD MARKET SCI, V46, P173, DOI 10.1007/s11747-017-0556-3
Stone M, 2020, BOTTOM LINE, V33, P183, DOI 10.1108/BL-03-2020-0022
Tong SL, 2020, J ACAD MARKET SCI, V48, P64, DOI 10.1007/s11747-019-00693-3
van Esch P, 2021, AUSTRALAS MARK J, V29, P225, DOI 10.1016/j.ausmj.2020.04.003
van Esch P, 2021, J ADVERTISING, V50, P63, DOI 10.1080/00913367.2020.1832939
van Esch P, 2019, BUS HORIZONS, V62, P729, DOI 10.1016/j.bushor.2019.07.004
van Esch P, 2019, COMPUT HUM BEHAV, V90, P215, DOI 10.1016/j.chb.2018.09.009
Verhoef PC, 2017, J INTERACT MARK, V40, P1, DOI 10.1016/j.intmar.2017.06.001
Whittaker L, 2021, AUSTRALAS MARK J, V29, P204, DOI 10.1177/1839334921999479
Whittaker L, 2020, IT PROF, V22, P90, DOI 10.1109/MITP.2020.2985492
Wilkins Jonathan, 2018, Network Security, V2018, P18, DOI 10.1016/S1353-4858(18)30046-1
Wirth N, 2018, INT J MARKET RES, V60, P435, DOI 10.1177/1470785318776841
Xu YZ, 2020, AUSTRALAS MARK J, V28, P189, DOI 10.1016/j.ausmj.2020.03.005
NR 40
TC 13
Z9 14
U1 196
U2 601
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1441-3582
EI 1839-3349
J9 AUSTRALAS MARK J
JI Australas. Mark. J.
PD AUG
PY 2021
VL 29
IS 3
BP 199
EP 203
AR 18393349211037684
DI 10.1177/18393349211037684
EA AUG 2021
PG 5
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA UJ5KG
UT WOS:000685076700001
DA 2024-03-27
ER
PT J
AU Chistyakov, A
Soto-Sanfiel, MT
Igarashi, T
Sakamoto, D
Carrabina, J
AF Chistyakov, Alexey
Soto-Sanfiel, Maria T.
Igarashi, Takeo
Sakamoto, Daisuke
Carrabina, Jordi
TI 3D DISPLAY INTERFACES IN E-COMERCE WEB APPLICATIONS: AN EXPLORATORY
STUDY
SO PROFESIONAL DE LA INFORMACION
LA English
DT Article
DE 3D displays; Usability; Human-computer interaction; Stereoscopic effect;
E-commerce; Web; Web applications
ID STEREO VISION; USABILITY; VISUALIZATION
AB The objective of the present research is to observe to what extent the stereoscopic effect presents a solution for enhancement of user interactions in the Web context. This paper describes an experiment conducted to detect differences in perception between 2D and 3D graphical user interfaces of an e-Commerce web application. The results of the conducted user study among 39 participants indicate significantly higher performance of the 2D interface in terms of efficiency, satisfaction, and, consequently, overall usability. Therefore, for the studied sample, the stereoscopic effect had mostly negative impact on user interactions.
C1 [Chistyakov, Alexey] Univ Autonoma Barcelona, Dept Comp Sci, Barcelona, Spain.
[Soto-Sanfiel, Maria T.] Univ Autonoma Barcelona, Audiovisual Commun & Advertising Dept, Barcelona, Spain.
[Igarashi, Takeo] Univ Tokyo, Dept Comp Sci, Tokyo, Japan.
[Sakamoto, Daisuke] Hokkaido Univ, Div Comp Sci & Informat Technol, Sapporo, Hokkaido, Japan.
[Carrabina, Jordi] Univ Autonoma Barcelona, Cephis, Escola Engn, Ctr Prototips & Soluc Hardware Software Cephis, Barcelona, Spain.
C3 Autonomous University of Barcelona; Autonomous University of Barcelona;
University of Tokyo; Hokkaido University; Autonomous University of
Barcelona
RP Chistyakov, A (autor correspondiente), Univ Autonoma Barcelona, Dept Comp Sci, Barcelona, Spain.
EM alexey.chistyakov@e-campus.uab.cat; mariateresa.soto@uab.es;
takeo@acm.org; sakamoto@ist.hokudai.ac.jp; jordi.carrabina@uab.cat
RI Soto-Sanfiel, María T./L-9833-2014; Igarashi, Takeo/ITT-5921-2023;
Sakamoto, Daisuke/AAA-5428-2022; Carrabina, Jordi/K-7916-2014
OI Soto-Sanfiel, María T./0000-0002-1364-8821; Sakamoto,
Daisuke/0000-0002-2219-4198; Carrabina, Jordi/0000-0002-9540-8759
FU Spanish government [MEF3\_IRX/SIRX-MMT (TEC2014-59679)]; Catalan
government [2014-SGR-01452]
FX We are especially grateful to the directorship and staff of the CMP
Group that donated their time and facilities for the experimentation. We
thank Dr. Jerome C. Foo for assistance with editing of the manuscript.
This work was partially done with the support of the Spanish government
project MEF3\_IRX/SIRX-MMT (TEC2014-59679) and the Catalan government
Grant 2014-SGR-01452.
CR [Anonymous], USABILITY ENG
[Anonymous], AUSTR C INF SYST HOB
[Anonymous], EXTENSIONS STEREOSCO
[Anonymous], HUMAN COMPUTER INTER
[Anonymous], HUMAN COMPUTER INTER
[Anonymous], P 29 ANN ACM S APPL
Chistyakov Alexey, 2013, Human-Computer Interaction. 6th Latin American Conference, CLIHC 2013. Proceedings: LNCS 8278, P22, DOI 10.1007/978-3-319-03068-5_5
Chistyakov A, 2016, LECT NOTES COMPUT SC, V10069, P503, DOI 10.1007/978-3-319-48746-5_52
Chistyakov A, 2016, J MED SYST, V40, DOI 10.1007/s10916-016-0616-0
Chittaro L., 2000, Adaptive Hypermedia and Adaptive Web-Based Systems. International Conference, AH 2000. Proceedings (Lecture Notes in Computer Science Vol.1892), P86
Chittaro L., 2002, P 7 INT C 3D WEB TEC, P145
Escobar MM, 2015, COMPUT BIOL MED, V61, P138, DOI 10.1016/j.compbiomed.2015.03.026
Fiorentino M, 2002, INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY, PROCEEDINGS, P86, DOI 10.1109/ISMAR.2002.1115077
Gediga G, 1999, BEHAV INFORM TECHNOL, V18, P151, DOI 10.1080/014492999119057
Gonzalez-Zuniga Diego, 2013, Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction. 7th International Conference, UCAmI 2013. Proceedings: LNCS 8276, P26, DOI 10.1007/978-3-319-03176-7_4
Hayashi H, 2000, IEEE SYS MAN CYBERN, P1289, DOI 10.1109/ICSMC.2000.886031
Heath MD, 2012, BRIT J NEUROSURG, V26, P662, DOI 10.3109/02688697.2012.672057
Holliman NS, 2011, IEEE T BROADCAST, V57, P362, DOI 10.1109/TBC.2011.2130930
Hornbæk K, 2006, INT J HUM-COMPUT ST, V64, P79, DOI 10.1016/j.ijhcs.2005.06.002
Ilgner J, 2006, PROC SPIE, V6055, DOI 10.1117/12.647591
Johnson A, 2006, IEEE COMPUT GRAPH, V26, P10, DOI 10.1109/MCG.2006.127
Johnston S, 2013, PROC SPIE, V8649, DOI 10.1117/12.2007594
KIRAKOWSKI J, 1993, BRIT J EDUC TECHNOL, V24, P210, DOI 10.1111/j.1467-8535.1993.tb00076.x
Kirakowski J, 1998, HUM FAC ERG SOC P, P424, DOI 10.1177/154193129804200405
Kline P., 2013, HDB PSYCHOL TESTING, DOI DOI 10.4324/9781315812274
LEWIS JR, 1995, INT J HUM-COMPUT INT, V7, P57, DOI 10.1080/10447319509526110
McIntire JP, 2014, DISPLAYS, V35, P18, DOI 10.1016/j.displa.2013.10.004
McIntire JP, 2012, PROC SPIE, V8383, DOI 10.1117/12.920017
Murray D, 2000, AUTON ROBOT, V8, P161, DOI 10.1023/A:1008987612352
Murray D, 1997, IEEE INT CONF ROBOT, P1694, DOI 10.1109/ROBOT.1997.614387
Oliveira C., 2000, Workshop on Application of Virtual Reality Technologies for Future Telecommunication Systems, IEEE Globecom, P288
Ortiz S, 2010, COMPUTER, V43, P14, DOI 10.1109/MC.2010.15
Padayachee I., 2010, ISO 9126 external systems quality characteristics, sub-characteristics and domain specific criteria for evaluating e-Learning systems
Perakakis E, 2015, IEEE T HUM-MACH SYST, V45, P534, DOI 10.1109/THMS.2015.2401975
Schild Jonas., 2013, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems-CHI '13, page, P169
Schild Jonas., 2012, Proceedings of the 2012 Association for Computing Machinery annual conference on Human Factors in Computing Systems, P89, DOI [10.1145/2207676.2207690, DOI 10.1145/2207676.2207690]
SCHNEIDEWIND NF, 1992, IEEE T SOFTWARE ENG, V18, P410, DOI 10.1109/32.135774
Seffah A, 2001, SECOND ASIA-PACIFIC CONFERENCE ON QUALITY SOFTWARE, PROCEEDINGS, P311, DOI 10.1109/APAQS.2001.990036
Seuntiens Pieter J. H., 2005, Proceedings of the SPIE - The International Society for Optical Engineering, V6016, P1, DOI 10.1117/12.627515
Shackel B., 1991, HUMAN FACTORS INFORM, P21, DOI DOI 10.1016/J.INTCOM.2009.04.007
van Beurden MHPH, 2012, 3D RES, V3, DOI 10.1007/3DRes.01(2012)3
Vonkeman C, 2017, INFORM MANAGE-AMSTER, V54, P1038, DOI 10.1016/j.im.2017.02.008
Wheatstone C., 1838, Philosophical Transactions of the Royal Society of London, V128, P371, DOI [10.1098/rstl.1838.0019, DOI 10.1098/RSTL.1838.0019]
Yim MYC, 2012, J ADVERTISING, V41, P113, DOI 10.2753/JOA0091-3367410208
NR 44
TC 0
Z9 0
U1 0
U2 22
PU EDICIONES PROFESIONALES INFORMACION SL-EPI
PI BARCELONA
PA MISTRAL, 36, BARCELONA, ALBOLOTE, SPAIN
SN 1386-6710
EI 1699-2407
J9 PROF INFORM
JI Prof. Inf.
PD SEP-OCT
PY 2018
VL 27
IS 5
BP 1116
EP 1127
DI 10.3145/epi.2018.sep.15
PG 12
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA GU7VV
UT WOS:000445537400015
OA Bronze, Green Published
DA 2024-03-27
ER
PT J
AU Xia, YF
Guo, XY
Li, YG
He, LY
Chen, XY
AF Xia, Yufei
Guo, Xinyi
Li, Yinguo
He, Lingyun
Chen, Xueyuan
TI Deep learning meets decision trees: An application of a heterogeneous
deep forest approach in credit scoring for online consumer lending
SO JOURNAL OF FORECASTING
LA English
DT Article
DE credit scoring; deep learning; financial regulation; gradient boosting
decision tree; macroeconomic variable; online consumer lending
ID GENETIC ALGORITHM; RISK-ASSESSMENT; MODEL; CLASSIFICATION; CLASSIFIERS;
INFERENCE; BUSINESS; COST
AB Online consumer lending has recently been growing rapidly, but it faces high credit risk. For this problem, developing powerful credit scoring models has become an effective solution and can be achieved from three aspects: modeling approach, data source, and evaluation measure. This paper proposes a novel model that departs from those in previous studies in threefold. First, a heterogeneous deep forest model that combines deep learning architecture and tree-based ensemble classifiers is proposed as the modeling approach. Second, a Bayesian-based macroeconomic variable optimization method is developed to determine the macroeconomic variables and the corresponding lag term, and the selected macroeconomic variables are used as supplementary data source for modeling. Lastly, a series of capital charge error measures is proposed to evaluate credit scoring models from a regulatory perspective. The proposal is evaluated on multiple large datasets under performance measures on predictive accuracy, profitability, and capital charge errors. Frequentist and Bayesian nonparametric significance tests are used to examine the statistical significance of heterogeneous deep forest and benchmarks. Three main conclusions can be reached from the comparison. First, heterogeneous deep forest significantly outperforms the industry benchmarks over all the evaluation measures. Second, the predictive performance is enhanced after incorporating the selected macroeconomic variables and the corresponding lag, and the result remains robust under cross-validation and forward-chaining validation. Third, the capital charge errors reflect the model performance from a regulatory perspective and thus lead to different rankings from those when evaluating predictive accuracy and profitability.
C1 [Xia, Yufei; Guo, Xinyi; Li, Yinguo; Chen, Xueyuan] Jiangsu Normal Univ, Business Sch, Xuzhou 221116, Jiangsu, Peoples R China.
[He, Lingyun] China Univ Min & Technol, Sch Econ & Management, Xuzhou, Jiangsu, Peoples R China.
C3 Jiangsu Normal University; China University of Mining & Technology
RP Li, YG (autor correspondiente), Jiangsu Normal Univ, Business Sch, Xuzhou 221116, Jiangsu, Peoples R China.
EM yinguo.li@jsnu.edu.cn
RI HE, Ling-Yun/K-2346-2012
OI Xia, Yufei/0000-0001-7805-8091
FU National Natural Science Foundation of China [72103082, 71874185];
National Social Science Foundation of China [15BTJ033]; Project of
Philosophy and Social Science Research in Colleges and Universities in
Jiangsu Province [2020SJA1018]; National Training Program of Innovation
and Entrepreneurship for Undergraduates [202110320012]
FX National Natural Science Foundation of China, Grant/Award Number:
72103082; 71874185; National Social Science Foundation of China,
Grant/Award Number: 15BTJ033; Project of Philosophy and Social Science
Research in Colleges and Universities in Jiangsu Province, Grant/Award
Number: 2020SJA1018; National Training Program of Innovation and
Entrepreneurship for Undergraduates, Grant/Award Number: 202110320012
CR Ala'raj M, 2021, J BIG DATA-GER, V8, DOI 10.1186/s40537-021-00461-7
Ala'raj M, 2016, KNOWL-BASED SYST, V104, P89, DOI 10.1016/j.knosys.2016.04.013
Bellotti T, 2009, J OPER RES SOC, V60, P1699, DOI 10.1057/jors.2008.130
Benavoli A, 2014, PR MACH LEARN RES, V32, P1026
Bensic M, 2005, INTELL SYST ACCOUNT, V13, P133, DOI 10.1002/isaf.261
Berg T, 2020, REV FINANC STUD, V33, P2845, DOI 10.1093/rfs/hhz099
Berger J, 2014, PHILOS PSYCHOL, V27, P829, DOI 10.1080/09515089.2013.771241
Bertsch C, 2020, J BANK FINANC, V116, DOI 10.1016/j.jbankfin.2020.105822
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Buchak G, 2018, J FINANC ECON, V130, P453, DOI 10.1016/j.jfineco.2018.03.011
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Dastile X, 2020, APPL SOFT COMPUT, V91, DOI 10.1016/j.asoc.2020.106263
de Andrade FWM, 2007, EUR J OPER RES, V183, P1569, DOI 10.1016/j.ejor.2006.07.049
Demsar J, 2006, J MACH LEARN RES, V7, P1
Di Maggio M., 2020, Fintech borrowers: lax-screening or cream-skimming?
Dirick L, 2019, J BUS ECON STAT, V37, P40, DOI 10.1080/07350015.2016.1260471
Djeundje VB, 2021, EXPERT SYST APPL, V163, DOI 10.1016/j.eswa.2020.113766
Djeundje VB, 2018, EUR J OPER RES, V271, P697, DOI 10.1016/j.ejor.2018.05.040
Dorogush A. V., 2018, ARXIV181011363, DOI DOI 10.48550/ARXIV.1810.11363
Einav L, 2013, RAND J ECON, V44, P249, DOI 10.1111/1756-2171.12019
EISENBEIS RA, 1977, J FINANC, V32, P875, DOI 10.2307/2326320
Feng XD, 2019, APPL INTELL, V49, P555, DOI 10.1007/s10489-018-1253-8
Feng XD, 2018, APPL SOFT COMPUT, V65, P139, DOI 10.1016/j.asoc.2018.01.021
Frazier P.I., 2018, TUTORIAL BAYESIAN OP
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
García S, 2010, INFORM SCIENCES, V180, P2044, DOI 10.1016/j.ins.2009.12.010
Geurts P, 2006, MACH LEARN, V63, P3, DOI 10.1007/s10994-006-6226-1
Gordy MB, 2003, J FINANC INTERMED, V12, P199, DOI 10.1016/S1042-9573(03)00040-8
Gunnarsson BR, 2021, EUR J OPER RES, V295, P292, DOI 10.1016/j.ejor.2021.03.006
Hand DJ, 2009, MACH LEARN, V77, P103, DOI 10.1007/s10994-009-5119-5
He HL, 2018, EXPERT SYST APPL, V98, P105, DOI 10.1016/j.eswa.2018.01.012
He N, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121371
Hurlin C, 2018, EUR J OPER RES, V268, P348, DOI 10.1016/j.ejor.2018.01.020
Jadhav S, 2018, APPL SOFT COMPUT, V69, P541, DOI 10.1016/j.asoc.2018.04.033
Ke G., 2017, Advances in Neural Information Processing Systems, V30, P3146, DOI DOI 10.5555/3294996.3295074
Kruschke JK, 2018, PSYCHON B REV, V25, P178, DOI 10.3758/s13423-016-1221-4
Lee E, 2012, ELECTRON COMMER R A, V11, P495, DOI 10.1016/j.elerap.2012.02.001
Lessmann S, 2015, EUR J OPER RES, V247, P124, DOI 10.1016/j.ejor.2015.05.030
Li K, 2021, APPL SOFT COMPUT, V107, DOI 10.1016/j.asoc.2021.107485
Liu WA, 2021, ENG APPL ARTIF INTEL, V97, DOI 10.1016/j.engappai.2020.104036
Lohmann C, 2019, J FORECASTING, V38, P375, DOI 10.1002/for.2545
Maldonado S, 2020, INFORM SCIENCES, V507, P700, DOI 10.1016/j.ins.2018.08.001
Malekipirbazari M, 2015, EXPERT SYST APPL, V42, P4621, DOI 10.1016/j.eswa.2015.02.001
Melo L, 2020, EXPERT SYST APPL, V152, DOI 10.1016/j.eswa.2020.113351
Messai A.S., 2013, International Journal of Economics and Financial Issues, V3, P852, DOI 10.14706/jecoss11312
Moosa IA, 2010, J BANK REGUL, V11, P95, DOI 10.1057/jbr.2010.2
Oskarsdóttir M, 2019, APPL SOFT COMPUT, V74, P26, DOI 10.1016/j.asoc.2018.10.004
Ozili PK, 2018, BORSA ISTANB REV, V18, P329, DOI 10.1016/j.bir.2017.12.003
Plawiak P, 2020, INFORM SCIENCES, V516, P401, DOI 10.1016/j.ins.2019.12.045
Plawiak P, 2019, APPL SOFT COMPUT, V84, DOI 10.1016/j.asoc.2019.105740
Roa L, 2021, EXPERT SYST APPL, V169, DOI 10.1016/j.eswa.2020.114486
Schotten PC, 2019, FINANC INNOV, V5, DOI 10.1186/s40854-019-0126-4
Serrano-Cinca C, 2016, DECIS SUPPORT SYST, V89, P113, DOI 10.1016/j.dss.2016.06.014
Shen F, 2021, APPL SOFT COMPUT, V98, DOI 10.1016/j.asoc.2020.106852
Shen F, 2020, TECHNOL ECON DEV ECO, V26, P405, DOI 10.3846/tede.2019.11337
Snoek H., 2012, ADV NEURAL INFORM PR, P2951, DOI DOI 10.48550/ARXIV.1206.2944
Tang LX, 2019, TECHNOL FORECAST SOC, V144, P563, DOI 10.1016/j.techfore.2018.03.007
Thomas LC, 2000, INT J FORECASTING, V16, P149, DOI 10.1016/S0169-2070(00)00034-0
Tian Y, 2018, APPL SOFT COMPUT, V73, P96, DOI 10.1016/j.asoc.2018.08.021
Wang CR, 2019, IEEE ACCESS, V7, P2161, DOI 10.1109/ACCESS.2018.2887138
Wang HM, 2021, J OPER RES SOC, V72, P923, DOI 10.1080/01605682.2019.1705193
Wolpert D. H., 1997, IEEE Transactions on Evolutionary Computation, V1, P67, DOI 10.1109/4235.585893
Xia YF, 2021, ELECTRON COMMER R A, V49, DOI 10.1016/j.elerap.2021.101095
Xia YF, 2021, INT J FORECASTING, V37, P1590, DOI 10.1016/j.ijforecast.2021.03.002
Xia YF, 2021, TECHNOL ECON DEV ECO, V27, P96, DOI 10.3846/tede.2020.13997
Xia YF, 2020, J FORECASTING, V39, P260, DOI 10.1002/for.2625
Xia YF, 2018, ELECTRON COMMER R A, V30, P111, DOI 10.1016/j.elerap.2018.05.011
Xia YF, 2018, EXPERT SYST APPL, V93, P182, DOI 10.1016/j.eswa.2017.10.022
Xia YF, 2017, ELECTRON COMMER R A, V24, P30, DOI 10.1016/j.elerap.2017.06.004
Xia YF, 2017, EXPERT SYST APPL, V78, P225, DOI 10.1016/j.eswa.2017.02.017
Xu DY, 2019, INT J FINANC ECON, V24, P903, DOI 10.1002/ijfe.1698
Yu L, 2008, EXPERT SYST APPL, V34, P1434, DOI 10.1016/j.eswa.2007.01.009
Yu LA, 2015, FINANC INNOV, V1, DOI 10.1186/s40854-015-0005-6
Zhang WY, 2021, EXPERT SYST APPL, V165, DOI 10.1016/j.eswa.2020.113872
Zhang WY, 2019, EXPERT SYST APPL, V121, P221, DOI 10.1016/j.eswa.2018.12.020
Zhou Z.-H., 2012, Ensemble Methods: Foundations and Algorithms
Zhou ZH, 2019, NATL SCI REV, V6, P74, DOI 10.1093/nsr/nwy108
NR 77
TC 7
Z9 7
U1 6
U2 20
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0277-6693
EI 1099-131X
J9 J FORECASTING
JI J. Forecast.
PD DEC
PY 2022
VL 41
IS 8
BP 1669
EP 1690
DI 10.1002/for.2891
EA JUL 2022
PG 22
WC Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5N7LT
UT WOS:000827794700001
DA 2024-03-27
ER
PT J
AU Cao, Q
Schniederjans, MJ
AF Cao, Qing
Schniederjans, Marc J.
TI Agent-mediated architecture for reputation-based electronic tourism
systems: A neural network approach
SO INFORMATION & MANAGEMENT
LA English
DT Article
DE E-commerce; multiple agent system; neural networks; management
information systems; tourism
ID BUSINESS; COMMERCE; SUPPORT; WEB
AB Agent technology can be used in searching and selecting a good e-tourism system. However, most such systems focus mainly on price and avoid important purchasing decision-making factors such as quality and reputation. Here we define a reputation-based architecture for an e-tourism system that we have called 'reputation-based electronic tourism' (RET). An artificial neural network model was created for a reputation agent to evaluate capabilities for selecting products and services in an e-tourism setting. The classification performance of the model was compared to another method. The results indicated that RET could outperform and be more accurate in classifying perspective products and services for consumers. (c) 2006 Elsevier B.V. All rights reserved.
C1 Univ Nebraska, Coll Business Adm, Lincoln, NE 68588 USA.
Univ Missouri, Henry W Bloch Sch Business & Publ Adm, Kansas City, MO 64110 USA.
C3 University of Nebraska System; University of Nebraska Lincoln;
University of Missouri System; University of Missouri Kansas City
RP Schniederjans, MJ (autor correspondiente), Univ Nebraska, Coll Business Adm, Lincoln, NE 68588 USA.
EM Mschniederjans1@unl.edu
CR BELLIFERNINE F, 2000, DEV MULT SYST JADE 7, P89
BO KW, 1995, INFORM MANAGE, V29, P141, DOI 10.1016/0378-7206(95)00023-P
Boyer KK, 2002, J OPER MANAG, V20, P357, DOI 10.1016/S0272-6963(02)00004-9
Brynjolfsson E, 2000, MANAGE SCI, V46, P563, DOI 10.1287/mnsc.46.4.563.12061
Bui T, 1999, DECIS SUPPORT SYST, V25, P225, DOI 10.1016/S0167-9236(99)00008-1
Camacho D, 2001, AUTON AGENT MULTI-AG, V4, P387, DOI 10.1023/A:1012767210241
CHEN KCW, 1992, AUDITING-J PRACT TH, V11, P30
Dellaert BGC, 2000, SPRING COMP SCI, P293
Dube L., 1999, Cornell Hotel and Restaurant Administration Quarterly, V40, P89, DOI 10.1016/S0010-8804(99)80237-7
Finin T., 1994, P 3 INT C INF KNOWL
Guttman RH, 1998, KNOWL ENG REV, V13, P147, DOI 10.1017/S0269888998002082
HOWDEN N, 2001, 5 INT C AUT AG MONTR
Hudson S., 2002, Journal of Vacation Marketing, V8, P155, DOI 10.1177/135676670200800205
Jain AK, 1999, COMMUN ACM, V42, P62, DOI 10.1145/295685.295702
JENNINGS NR, 1998, INT J AUTONOMOUS AGE, V1, P7
Joo J, 2002, INFORM SYST MANAGE, V19, P58, DOI 10.1201/1078/43201.19.3.20020601/37171.8
Klassen RD, 2001, J OPER MANAG, V19, P713, DOI 10.1016/S0272-6963(01)00071-7
Lau KinNam Lau KinNam, 2001, Cornell Hotel and Restaurant Administration Quarterly, V42, P55, DOI 10.1016/S0010-8804(01)81008-9
Lemire D, 2005, INFORM RETRIEVAL, V8, P129, DOI 10.1023/B:INRT.0000048492.50961.a6
LI EY, 1994, INFORM MANAGE, V27, P303, DOI 10.1016/0378-7206(94)90024-8
Maes P, 1999, COMMUN ACM, V42, P81, DOI 10.1145/295685.295716
MORRIS J, 2000, P 2 ACM C EL COMM NE
Nwana HS, 1999, APPL ARTIF INTELL, V13, P129, DOI 10.1080/088395199117513
Papazoglou MP, 2001, COMMUN ACM, V44, P71, DOI 10.1145/367211.367268
Partovi FY, 2002, COMPUT IND ENG, V41, P389, DOI 10.1016/S0360-8352(01)00064-X
SABATER J, 2000, REPORT ARTIFICIAL IN
SANTOS BLD, 1998, INFORMATION MANAGEME, V34, P175
Schniederjans M.J., 2002, E COMMERCE OPERATION
Sikora R, 1998, MANAGE SCI, V44, pS65, DOI 10.1287/mnsc.44.11.S65
TAM KY, 1992, MANAGE SCI, V38, P926, DOI 10.1287/mnsc.38.7.926
Wong BK, 1998, INFORM MANAGE-AMSTER, V34, P129, DOI 10.1016/S0378-7206(98)00050-0
NR 31
TC 14
Z9 16
U1 1
U2 34
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0378-7206
EI 1872-7530
J9 INFORM MANAGE-AMSTER
JI Inf. Manage.
PD JUL
PY 2006
VL 43
IS 5
BP 598
EP 606
DI 10.1016/j.im.2006.03.001
PG 9
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA 069VY
UT WOS:000239478200004
DA 2024-03-27
ER
PT J
AU Theerthaana, P
Manohar, HL
AF Theerthaana, P.
Manohar, Hansa Lysander
TI How a doer persuade a donor? Investigating the moderating effects of
behavioral biases in donor acceptance of donation crowdfunding
SO JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
LA English
DT Article
DE Internet marketing; E-commerce; Consumer behavior internet;
Human-computer interaction; Donation crowdfunding; Regret aversion bias;
Herding bias; Optimum bias; Overconfidence bias
ID CONSUMER TRUST; UNIFIED THEORY; ADOPTION; PERSPECTIVE; UTAUT;
INHIBITORS; STUDENTS; OPTIMISM; BANKING
AB Purpose
The concept of donation crowdfunding has been drawing enormous attention as it connects donors worldwide in a shorter time at a relatively lower cost. This paper aims to integrate two unified theories, namely, behavioral finance and unified theory of acceptance and use of technology model, to investigate on the motivators and deterrents that influence prospective donors to adopt and use donation crowdfunding. The study also substantiates the significance of donors' behavioral biases through the moderating effect in the crowdfunding adoption process.
Design/methodology/approach
The study used survey method for data collection and the data set was obtained from the sample of respondents belonging to India and Bangladesh. The proposed structural equation modeling is tested using SPSS 23.0 and AMOS 23.0.
Findings
The study reveals that performance expectancy, effort expectancy, facilitating conditions and trust significantly enhance the intention to adopt donation crowdfunding. Also, biases including overconfidence bias, herding bias and regret aversion bias are found to have significant moderating effects on the relationship between the behavioral intention to adopt donation crowdfunding and use behavior.
Practical implications
By investigating motivators and deterrents of the adoption of donation crowdfunding, the study renders lucrative insights for the donation crowdfunders in devising a donation fundraising campaign that motivates the prospective donors to provide financial contribution.
Originality/value
The study establishes its novelty in explaining the adoption behavior of donation crowdfunding with behavioral bias moderators as a theoretical paradigm. Furthermore, the unified theory of acceptance and use of technology model is extended by introducing, the variable "trust," while studying the adoption behavior of donation crowdfunding.
C1 [Theerthaana, P.] Loyola Inst Business Adm LIBA, MBA Dept, Chennai, Tamil Nadu, India.
[Manohar, Hansa Lysander] Anna Univ, Dept Management Studies, Chennai, Tamil Nadu, India.
C3 Loyola Institute of Business Administration (LIBA); Anna University;
Anna University Chennai
RP Theerthaana, P (autor correspondiente), Loyola Inst Business Adm LIBA, MBA Dept, Chennai, Tamil Nadu, India.
EM sahanatheerthee@gmail.com; auhansa@gmail.com
RI P, Theerthaana/AAU-7577-2020
OI P, Theerthaana/0000-0003-4475-7606
CR Akinkoye E., 2020, INT J BUSINESS MANAG, V4, P33
Al-Saedi K, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101293
Andreev P, 2012, INT J E-BUS RES, V8, P50, DOI 10.4018/jebr.2012070104
Annan-Diab F, 2017, INT J MANAG EDUC-OXF, V15, P73, DOI 10.1016/j.ijme.2017.03.006
[Anonymous], 2017, EC TIMES
Antony A, 2020, J PUBLIC AFF, V20, DOI 10.1002/pa.1996
Ariguzo G. C., 2006, International Journal of Business Information Systems, V1, P239, DOI 10.1504/IJBIS.2006.008598
Ba ZC, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102322
BAGOZZI RP, 1991, ADMIN SCI QUART, V36, P421, DOI 10.2307/2393203
Bagozzi RP, 1988, Journal of the Academy of Marketing Science, V16, P74, DOI DOI 10.1007/BF02723327
Bao ZS, 2021, J RES INTERACT MARK, V15, P10, DOI 10.1108/JRIM-02-2019-0027
Baptista G, 2015, COMPUT HUM BEHAV, V50, P418, DOI 10.1016/j.chb.2015.04.024
Barney-McNamara B, 2021, J RES INTERACT MARK, V15, P147, DOI 10.1108/JRIM-05-2020-0108
Behl A, 2023, TQM J, V35, P292, DOI 10.1108/TQM-06-2020-0139
Belleflamme P, 2014, J BUS VENTURING, V29, P585, DOI 10.1016/j.jbusvent.2013.07.003
Bhatia V, 2020, J RES INTERACT MARK, V14, P51, DOI 10.1108/JRIM-07-2018-0088
Biucky S.T., 2017, INT J ELECT COMMERCE, V8, P173, DOI DOI 10.7903/IJECS.1538
Bonney L, 2020, IND MARKET MANAG, V88, P339, DOI 10.1016/j.indmarman.2020.05.012
Brown R, 2019, IND MARKET MANAG, V80, P115, DOI 10.1016/j.indmarman.2018.02.003
Butticè V, 2020, INT SMALL BUS J, V38, P111, DOI 10.1177/0266242619883984
Camacho N, 2019, J MARKETING, V83, P138, DOI 10.1177/0022242918809673
Chandra S, 2010, COMMUN ASSOC INF SYS, V27, P561
Charoensukmongkol P., 2019, J GLOB MARK, V33, P141, DOI [DOI 10.1080/08911762.2019.1654586, 10.1080/08911762.2019]
CHURCHILL GA, 1979, J MARKETING RES, V16, P64, DOI 10.2307/3150876
Cimperman M, 2016, INT J MED INFORM, V90, P22, DOI 10.1016/j.ijmedinf.2016.03.002
Clement MB, 2005, J FINANC, V60, P307, DOI 10.1111/j.1540-6261.2005.00731.x
Compeau D. R., 1995, Management Information Systems Quarterly, V19, P189, DOI 10.2307/249688
Corrêa SCH, 2020, J RES INTERACT MARK, V14, P173, DOI 10.1108/JRIM-09-2019-0154
Costello A. B., 2005, PRACTICAL ASSESSMENT, V10, P7, DOI [10.7275/jyj1-4868, DOI 10.7275/JYJ1-4868]
Davvetas V, 2020, J INT MARKETING, V28, P40, DOI 10.1177/1069031X20910112
Dennis AR, 2020, J MANAGE INFORM SYST, V37, P39, DOI 10.1080/07421222.2019.1705505
Dickason-Koekemoer Z., 2020, INT J BUSINESMANAG, V10
Ding AW, 2019, J ACAD MARKET SCI, V47, P460, DOI 10.1007/s11747-018-0619-0
Engin A, 2020, ELECTRON COMMER R A, V40, DOI 10.1016/j.elerap.2020.100937
Fang YL, 2014, MIS QUART, V38, P407, DOI 10.25300/MISQ/2014/38.2.04
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gleason CA, 2003, ACCOUNT REV, V78, P193, DOI 10.2308/accr.2003.78.1.193
Goudarzi S., 2013, AUST J BASIC APPL SC, V7, P334
Graham JR, 1999, J FINANC, V54, P237, DOI 10.1111/0022-1082.00103
Guangming Y., 2011, E BUS E GOV ICEE 201, P1, DOI [10.1109/ICEBEG.2011.5881264, DOI 10.1109/ICEBEG.2011.5881264]
Handarkho YD, 2020, J RES INTERACT MARK, V14, P305, DOI 10.1108/JRIM-10-2018-0137
Hashim H., 2016, Creative Education, V7, P1147, DOI [DOI 10.4236/CE.2016.78119, 10.4236/ce.2016.78119]
Heerink M., 2009, Proceedings of the 2009 Bristish Computer Society Conference on Human-Computer Interaction, P430, DOI [10.14236/ewic/hci2009.54, DOI 10.14236/EWIC/HCI2009.54]
Helo P. T., 2003, International Journal of Mobile Communications, V1, P233, DOI 10.1504/IJMC.2003.003491
Hirschleifer D., 2003, EUR FINANC MANAG, V9, P25, DOI DOI 10.1111/1468-036X.00207
Hu Y., 2021, Customer Needs and Solutions, V8, P27, DOI [10.1007/s40547-020-00111-8, DOI 10.1007/S40547-020-00111-8]
Il Im, 2011, Information & Management, V48, P1, DOI 10.1016/j.im.2010.09.001
Izogo EE, 2020, J RES INTERACT MARK, V14, P431, DOI 10.1108/JRIM-09-2019-0145
Jayashree, 2014, INT J BUSINESS MANAG, V2, P24
Jeon HM, 2020, SERV BUS, V14, P533, DOI 10.1007/s11628-020-00425-6
Josefy M, 2017, ENTREP THEORY PRACT, V41, P161, DOI 10.1111/etap.12263
Kabra G, 2017, TELEMAT INFORM, V34, P1250, DOI 10.1016/j.tele.2017.05.010
Konietzny J., 2020, DEV MARKETING SCI P, P527, DOI [10.1007/978-3-030-42545-6_183, DOI 10.1007/978-3-030-42545-6_183]
Langley P, 2020, GEOFORUM, V115, P11, DOI 10.1016/j.geoforum.2020.06.014
Leech N.L., 2014, IBM SPSS INTERMEDIAT
Liang C.C., 2018, INT J ELECTRON COMM, V9, P71, DOI DOI 10.7903/IJECS.1556
Liao JY, 2020, J RES INTERACT MARK, V14, P413, DOI 10.1108/JRIM-07-2019-0115
Liu Y, 2011, COMPUT HUM BEHAV, V27, P890, DOI 10.1016/j.chb.2010.11.014
Maldonado UPT, 2009, ICCIT: 2009 FOURTH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCES AND CONVERGENCE INFORMATION TECHNOLOGY, VOLS 1 AND 2, P1431
Malik M., 2020, J Internet Bank Commer, V25, P1
Marcatto F, 2008, JUDGM DECIS MAK, V3, P87
Martins C, 2014, INT J INFORM MANAGE, V34, P1, DOI 10.1016/j.ijinfomgt.2013.06.002
Mochkabadi K, 2020, SMALL BUS ECON, V54, P75, DOI 10.1007/s11187-018-0081-x
Munim, 2020, ADV CROWDFUNDING, P297
Munyoka W, 2019, S AFR J INFORM MANAG, V21, DOI 10.4102/sajim.v21i1.983
Niemand T, 2018, INT J ENTREP BEHAV R, V24, P911, DOI 10.1108/IJEBR-07-2017-0256
Pando-Garcia J, 2016, J BUS RES, V69, P1731, DOI 10.1016/j.jbusres.2015.10.046
Pappas N, 2016, J RETAIL CONSUM SERV, V29, P92, DOI 10.1016/j.jretconser.2015.11.007
Patil P, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102144
Peltier J.W., 1995, Journal of Direct Marketing, V9, P72, DOI [10.1002/dir.4000090309, DOI 10.1002/DIR.4000090309]
Peltier J, 2020, J RES INTERACT MARK, V14, P239, DOI 10.1108/JRIM-01-2020-0010
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Pompian M.M., 2011, BEHAV FINANCE WEALTH, V667
Qin YS, 2020, J RES INTERACT MARK, V14, P337, DOI 10.1108/JRIM-08-2019-0138
Raut RK, 2020, GLOB BUS REV, V21, P818, DOI 10.1177/0972150918778915
Raza SA, 2021, J EDUC COMPUT RES, V59, P183, DOI 10.1177/0735633120960421
Raza SA, 2019, J ISLAMIC MARK, V10, P357, DOI 10.1108/JIMA-04-2017-0038
Raza SA, 2017, J EDUC COMPUT RES, V55, P295, DOI 10.1177/0735633116667357
Rey-Moreno M, 2017, J INNOV KNOWL, V2, P172, DOI 10.1016/j.jik.2017.01.001
Riaz T., 2015, Research Journal of Finance and Accounting, V6, P110
Rijanto A, 2018, J GEN MANAGE, V43, P79, DOI 10.1177/0306307017748125
Robiady ND, 2021, INT J RES MARK, V38, P492, DOI 10.1016/j.ijresmar.2020.03.001
Sair S., 2018, PAKISTAN J COMMERCE, V12, P501
SCHARFSTEIN DS, 1990, AM ECON REV, V80, P465
Schaupp LudwigChristian., 2010, P 43 HAWAII INT C SY, P1, DOI [10.1109/HICSS.2010.177, DOI 10.1109/HICSS.2010.177]
SCHEIER MF, 1994, J PERS SOC PSYCHOL, V67, P1063, DOI 10.1037/0022-3514.67.6.1063
Schibrowsky JohnA., 1995, Journal of Direct Marketing, V9, P8, DOI DOI 10.1002/DIR.4000090103
Slade EL, 2015, PSYCHOL MARKET, V32, P860, DOI 10.1002/mar.20823
Statista.,, 2020, DISTR UNS FUND PROJ
STRAUB DW, 1989, MIS QUART, V13, P147, DOI 10.2307/248922
Sultana J, 2020, EDUC INF TECHNOL, V25, P223, DOI 10.1007/s10639-019-09969-1
Sung H., 2015, International Journal of u-and e-Service, Science and Technology, V8, P197, DOI [10.14257/ijunesst.2015.8.9.21, DOI 10.14257/IJUNESST.2015.8.9.21]
Tan P.J. B., 2013, AM J COMPUTER TECHNO, V1, P14
Theerthaana P, 2020, J SIMUL, V14, P169, DOI 10.1080/17477778.2019.1664263
TORKZADEH G, 1992, BEHAV INFORM TECHNOL, V11, P99, DOI 10.1080/01449299208924324
Vazquez-Casielles R., 2018, INT J VOLUNTARY NONP, P1, DOI [10.1007/s11266-020-00213-w, DOI 10.1007/S11266-020-00213-W]
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Wang GY, 2020, FOODS, V9, DOI 10.3390/foods9111588
Wang KY, 2020, J ADV TRANSPORT, V2020, DOI 10.1155/2020/8849356
Wash R., 2013, 7 INT AAAI C WEBL SO
Welch I, 2000, J FINANC ECON, V58, P369, DOI 10.1016/S0304-405X(00)00076-3
Wojciechowski A, 2009, LECT NOTES COMPUT SC, V5872, P454, DOI 10.1007/978-3-642-05290-3_58
Xin H, 2015, J COMPUT INFORM SYST, V55, P1, DOI 10.1080/08874417.2015.11645781
Yegoryan, 2020, J ACAD MARKET SCI, P1
Zhang B., 2017, HDB GEOGRAPHIES MONE, DOI [10.4337/9781784719005, DOI 10.4337/9781784719005]
Zhang YL, 2020, COMPUT HUM BEHAV, V112, DOI 10.1016/j.chb.2020.106470
NR 107
TC 11
Z9 12
U1 7
U2 44
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 2040-7122
EI 2040-7130
J9 J RES INTERACT MARK
JI J. Res. Interact. Mark.
PD JUN 21
PY 2021
VL 15
IS 2
BP 243
EP 266
DI 10.1108/JRIM-06-2019-0097
EA MAY 2021
PG 24
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA SW1CO
UT WOS:000649442300001
DA 2024-03-27
ER
PT J
AU Chahal, J
Dagar, V
Dagher, L
Rao, AM
Udemba, EN
AF Chahal, Jyoti
Dagar, Vishal
Dagher, Leila
Rao, Amar
Udemba, Edmund Ntom
TI The crisis effect in TPB as a moderator for post-pandemic
entrepreneurial intentions among higher education students: PLS-SEM and
ANN approach
SO INTERNATIONAL JOURNAL OF MANAGEMENT EDUCATION
LA English
DT Article
DE Entrepreneurial intentions; Crisis-effect; Self-efficacy; Artificial
neural network (ANN); PLS-SEM; Post-pandemic
ID SELF-EFFICACY; PLANNED BEHAVIOR; MODELS; ATTITUDES; GENDER
AB This research examines college students' entrepreneurial inclinations using TPB, self-efficacy, and the crisis effect. It also examines the crisis effect's moderating influence post-pandemic. A unique analytical technique using Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) was used to evaluate the model's resilience. 310 Indian university students were surveyed online. Self-efficacy is a crucial predictor of entrepreneurial tendencies among higher education students. ANN analysis confirms SEM findings that self-efficacy and perceived behaviour control shape entrepreneurial desires. Despite its negative impact, the crisis effect doesn't appear to affect entrepreneurs' objectives. The crisis impact moderates all exogenous and endogenous factors except subjective norms and entrepreneurial goals, the research finds. The research also shows that students' education and geography affect their entrepreneurial inclinations. Gender, however, has little control. Policymakers and higher education administrators could boost entrepreneurial ambitions by fostering students' self-efficacy and perceived behaviour control. Understanding these elements allows higher education stakeholders to create targeted interventions and support systems to foster college student entrepreneurship.
C1 [Chahal, Jyoti] Govt Coll Women, Dept Geog, Sonipat 131301, Haryana, India.
[Dagar, Vishal] Great Lakes Inst Management Gurgaon, Dept Econ & Publ Policy, Gurugram 122413, Haryana, India.
[Dagher, Leila] Lebanese Amer Univ, Beirut 10000, Lebanon.
[Rao, Amar] BML Munjal Univ, Gurugram 122413, Haryana, India.
[Udemba, Edmund Ntom] Shanxi Technol & Business Coll, Business Sch, Taiyuan 030000, Peoples R China.
[Udemba, Edmund Ntom] Istanbul Nisantasi Univ, Istanbul, Turkiye.
[Udemba, Edmund Ntom] Ctr Econ Policy & Dev Res CEPDeR, Ota, Nigeria.
C3 Lebanese American University; BML Munjal University
RP Chahal, J (autor correspondiente), Govt Coll Women, Dept Geog, Sonipat 131301, Haryana, India.
EM jc4202@gmail.com; vishal.d@greatlakes.edu.in; leiladagher@gmail.com;
amarydvrao@gmail.com; eddy.ntom@gmail.com
RI Dagar, Vishal/U-8877-2018; dagher, leila/AAZ-4049-2021
OI Dagar, Vishal/0000-0001-5032-1783; dagher, leila/0000-0002-9355-2773; ,
Amar Rao/0000-0002-8787-7061; Chahal, Jyoti/0000-0001-9398-3584
CR Agarwal V, 2023, SOCIO-ECON PLAN SCI, V89, DOI 10.1016/j.seps.2023.101703
Agu AG, 2022, AFR J ECON MANAG STU, V13, P89, DOI 10.1108/AJEMS-05-2021-0227
Agu AG, 2021, INT J SUST HIGHER ED, V22, P659, DOI 10.1108/IJSHE-07-2020-0277
Ahmad R, 2019, 2019 13TH INTERNATIONAL CONFERENCE ON MATHEMATICS, ACTUARIAL SCIENCE, COMPUTER SCIENCE AND STATISTICS (MACS-13), DOI 10.1109/macs48846.2019.9024788
Ajzen I, 2002, J APPL SOC PSYCHOL, V32, P665, DOI 10.1111/j.1559-1816.2002.tb00236.x
AJZEN I, 1987, ADV EXP SOC PSYCHOL, V20, P1, DOI 10.1016/S0065-2601(08)60411-6
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Akhtar S, 2022, DISCRETE DYN NAT SOC, V2022, DOI 10.1155/2022/6767580
Akour I.A., 2022, Computers and Education: Artificial Intelligence, V3, P100052, DOI DOI 10.1016/J.CAEAI.2022.100052
Al-Ghani A., 2022, Theory of Planned Behaviour (TPB), V13, P10
Al-Jubari I, 2019, SAGE OPEN, V9, DOI 10.1177/2158244019853467
Al-Qadasi N, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1111934
Alam MMD, 2021, J BIOMED INFORM, V116, DOI 10.1016/j.jbi.2021.103722
Ali J, 2022, J PUBLIC AFF, V22, DOI 10.1002/pa.2399
Almohammad D, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13158602
Amofah K., 2022, Journal of Innovation and Entrepreneurship, V11, DOI DOI 10.1186/S13731-022-00197-5
Andreea Mortan Roxana, 2014, Rev. psicol. trab. organ., V30, P97
Anjum T, 2023, EDUC TRAIN, V65, P587, DOI 10.1108/ET-01-2021-0020
Ansell C, 2019, ADMIN SOC, V51, P1079, DOI 10.1177/0095399717747655
Armitage CJ, 2001, BRIT J SOC PSYCHOL, V40, P471, DOI 10.1348/014466601164939
Arrighetti A, 2016, INT J ENTREP BEHAV R, V22, P835, DOI 10.1108/IJEBR-12-2015-0326
BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
Bandura A., 1978, Advances in Behaviour Research and Therapy, V1, P139, DOI [10.1016/0146-6402(78)90002-4, DOI 10.1016/0146-6402(78)90002-4]
Batista-Canino RM, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e13046
Behroozi M, 2012, PROCD SOC BEHV, V46, P2414, DOI 10.1016/j.sbspro.2012.05.495
Bosnjak M, 2020, EUR J PSYCHOL, V16, P352, DOI 10.5964/ejop.v16i3.3107
Bouarir H, 2023, ADM SCI, V13, DOI 10.3390/admsci13020033
Chin W. W., 2010, HDB PARTIAL LEAST SQ, P655, DOI DOI 10.1007/978-3-540-32827-8_29
Chin WW, 1997, INFORM SYST RES, V8, P342, DOI 10.1287/isre.8.4.342
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Dagher L, 2023, ENERG POLICY, V183, DOI 10.1016/j.enpol.2023.113783
Davies J., 2002, J MARKET THEORY, V2, P29, DOI [10.1177/1470593102002001645, DOI 10.1177/1470593102002001645]
Dogan B, 2023, ENVIRON SCI POLLUT R, V30, P92162, DOI 10.1007/s11356-023-28770-9
Douglas E.J., 2002, Entrepreneurship Theory Practice, V26, P8190, DOI [10.1177/104225870202600305, DOI 10.1177/104225870202600305]
Easterby-Smith M., 2012, Management research
Elnadi M, 2021, INT J MANAG EDUC-OXF, V19, DOI 10.1016/j.ijme.2021.100458
Evermann J, 2016, J BUS RES, V69, P4565, DOI 10.1016/j.jbusres.2016.03.050
Fassott G, 2016, IND MANAGE DATA SYST, V116, P1887, DOI 10.1108/IMDS-06-2016-0248
Fayolle A, 2015, J SMALL BUS MANAGE, V53, P75, DOI 10.1111/jsbm.12065
Fishbein M., 1980, UNDERSTANDING ATTITU
Foo PY, 2018, EXPERT SYST APPL, V107, P1, DOI 10.1016/j.eswa.2018.04.013
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gupta RK, 2022, Promoting entrepreneurship to reduce graduate unemployment, P149
Guru S, 2023, MANAGE DECIS, V61, P1634, DOI 10.1108/MD-03-2022-0407
Hair J. F., 2022, A primer on partial least squares structural equation modeling (PLS-SEM), V3rd
Hair J.F., 2009, Multivariate data analysis
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hew JJ, 2019, TECHNOL FORECAST SOC, V144, P311, DOI 10.1016/j.techfore.2017.10.007
Jena RK, 2020, COMPUT HUM BEHAV, V107, DOI 10.1016/j.chb.2020.106275
Kakran S, 2023, COGENT ECON FINANC, V11, DOI 10.1080/23322039.2023.2254560
Krueger NF, 2000, J BUS VENTURING, V15, P411, DOI 10.1016/S0883-9026(98)00033-0
Kumar P, 2024, SUSTAIN DEV, V32, P795, DOI 10.1002/sd.2644
Kumar R, 2022, GLOB BUS REV, V23, P101, DOI 10.1177/0972150919844395
Lee VH, 2020, EXPERT SYST APPL, V157, DOI 10.1016/j.eswa.2020.113477
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Liguori E., 2020, ENTREPRENEURSHIP ED, P346, DOI [DOI 10.1177/2515127420916738, 10.1177/2515127420916738]
Linan F., 2008, INT ENTREP MANAG J, V4, P257, DOI [10.1007/s11365-008-0093-0, DOI 10.1007/S11365-008-0093-0]
Liñán F, 2009, ENTREP THEORY PRACT, V33, P593, DOI 10.1111/j.1540-6520.2009.00318.x
Lotfi H, 2023, DATA BRIEF, V49, DOI 10.1016/j.dib.2023.109410
Maheshwari G, 2022, INT J MANAG EDUC-OXF, V20, DOI 10.1016/j.ijme.2021.100553
Maritz A, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12114612
Mawardi M. K., 2020, P 2 ANN INT C BUS PU, DOI [10.2991/aebmr.k.201116.010, DOI 10.2991/AEBMR.K.201116.010]
Miriti D. G. M., 2020, An exploration of entrepreneurial intentions among university students in Kenya, V4, P9
Mohammed B. S, 2017, American Journal of Economics, V10
Mouselli S, 2017, BUS MANAGE ED, V15, P159, DOI 10.3846/bme.2017.386
Mwiya B., 2017, SSRN scholarly paper ID 3023294, DOI [10.2139/ssrn.3023294, DOI 10.2139/SSRN.3023294]
Na-Allah SR, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14094904
Nabi G, 2018, STUD HIGH EDUC, V43, P452, DOI 10.1080/03075079.2016.1177716
Nabil A.Q, 2020, Open Journal of Business and Management, V9, P114, DOI [10.4236/ojbm.2021.91007, DOI 10.4236/OJBM.2021.91007]
Nabil A.Q., 2020, African Journal of Business Management, V14, P196, DOI DOI 10.5897/AJBM2020.9017
Najafabadi MO, 2016, J EDUC BUS, V91, P338, DOI 10.1080/08832323.2016.1218318
Nawi NC, 2022, SAGE OPEN, V12, DOI 10.1177/21582440211069144
Nguyen PH, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9233075
Nithya D. N, 2022, 5. A psychology analysis of entrepreneurial intention among Indian students: A metadata analysis towards building a conceptual framework, V20, P25
Nunnally J. C, 1967, Psychometric theory, Vxiii
Paray ZA, 2020, J INT EDUC BUS, V13, P55, DOI 10.1108/JIEB-02-2019-0009
Parkinson J, 2017, J CONSUM BEHAV, V16, P413, DOI 10.1002/cb.1641
Piwowar-Sulej K, 2023, J BUS ETHICS, DOI 10.1007/s10551-023-05456-3
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Qasem YAM, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10144905
Rahmawati N., 2022, Journal Manajemen Universitas Bung Hatta, V17, P24, DOI [10.37301/jmubh.v17i1.19967, DOI 10.37301/JMUBH.V17I1.19967]
Rao AM, 2023, FINANC RES LETT, V56, DOI 10.1016/j.frl.2023.104093
Ratten V, 2021, INT J MANAG EDUC-OXF, V19, DOI 10.1016/j.ijme.2020.100432
Ratten V, 2021, INT J MANAG EDUC-OXF, V19, DOI 10.1016/j.ijme.2020.100431
Ratten V, 2020, J ENTERP COMMUNITIES, V14, P753, DOI 10.1108/JEC-06-2020-0121
Rehan M. F., 2021, Sustainable Business and Society in Emerging Economies, V3, P485, DOI [10.26710/sbsee.v3i4.2006, DOI 10.26710/SBSEE.V3I4.2006]
Roy R, 2022, INT J TECHNOL DES ED, V32, P555, DOI 10.1007/s10798-020-09596-9
Ruiz-Rosa I, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12176970
Santos FJ, 2016, J SMALL BUS MANAGE, V54, P49, DOI 10.1111/jsbm.12129
Sharma PN, 2021, DECISION SCI, V52, P567, DOI 10.1111/deci.12329
Shmueli G, 2016, J BUS RES, V69, P4552, DOI 10.1016/j.jbusres.2016.03.049
Solesvik MZ, 2013, EDUC TRAIN, V55, P253, DOI 10.1108/00400911311309314
Soper D.S., 2022, A-priori Sample Size Calculator for Structural Equation Models
Tang M., 2014, Journal of Entrepreneurship in Emerging Economies, V6, P163, DOI [DOI 10.1108/JEEE-02-2014-0002, 10.1108/JEEE-02-2014-0002]
Tavousi M., 2009, European Journal of Scientific Research, V30, P146
Teo AC, 2015, IND MANAGE DATA SYST, V115, P311, DOI 10.1108/IMDS-08-2014-0231
Teo TSH, 2008, J MANAGE INFORM SYST, V25, P99, DOI 10.2753/MIS0742-1222250303
Thompson R., 2006, Journal of Organizational and End User Computing, V18, P25, DOI 10.4018/joeuc.2006070102
Westland JC, 2010, ELECTRON COMMER R A, V9, P476, DOI 10.1016/j.elerap.2010.07.003
Wilson F, 2007, ENTREP THEORY PRACT, V31, P387, DOI 10.1111/j.1540-6520.2007.00179.x
Yeh CH, 2021, INT J MANAG EDUC-OXF, V19, DOI 10.1016/j.ijme.2021.100565
Zhang JP, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.643184
Zhao H, 2005, J APPL PSYCHOL, V90, P1265, DOI 10.1037/0021-9010.90.6.1265
NR 104
TC 2
Z9 2
U1 11
U2 11
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 1472-8117
EI 2352-3565
J9 INT J MANAG EDUC-OXF
JI Int. J. Manag. Educ.
PD NOV
PY 2023
VL 21
IS 3
AR 100878
DI 10.1016/j.ijme.2023.100878
EA SEP 2023
PG 18
WC Business; Education & Educational Research; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Education & Educational Research
GA W0JW8
UT WOS:001088588800001
DA 2024-03-27
ER
PT J
AU Triantoro, T
Gopal, R
Benbunan-Fich, R
Lang, GD
AF Triantoro, Tamilla
Gopal, Ram
Benbunan-Fich, Raquel
Lang, Guido
TI Personality and games: enhancing online surveys through gamification
SO INFORMATION TECHNOLOGY & MANAGEMENT
LA English
DT Article
DE Gamification; Big Five personality traits; Online surveys; User
experience design; Human computer interaction
ID 5-FACTOR MODEL; JOB-PERFORMANCE; CAREER SUCCESS; ENGAGEMENT; PREDICTORS;
DIMENSIONS; INTERESTS; MOBILITY; TRAITS; DESIGN
AB In this research, we evaluate the moderating role of personality on enjoyment and attention associated with a gamified data collection instrument, and the attractiveness of a surveying organization. In an online experiment, we compare a gamified survey with a traditional survey. The results suggest that gamified surveys are more enjoyable and users are more attentive when filling out gamified surveys. Specific personality traits moderate the effect of attention and enjoyment related to gamification, and the enjoyment associated with gamification increases the attractiveness of a surveying organization. These findings have theoretical and practical implications to improve the design of existing online surveys.
C1 [Triantoro, Tamilla; Lang, Guido] Quinnipiac Univ, Sch Business, Dept Comp Informat Syst, 275 Mt Carmel Ave, Hamden, CT 06518 USA.
[Gopal, Ram] Univ Warwick, Warwick Business Sch, Informat Syst & Management, Coventry CV4 7AL, W Midlands, England.
[Benbunan-Fich, Raquel] CUNY, Paul H Chook Dept Informat Syst & Stat, Zicklin Sch Business, Baruch College, One Bernard Baruch Way, New York, NY 10010 USA.
C3 Quinnipiac University; University of Warwick; City University of New
York (CUNY) System; Baruch College (CUNY)
RP Triantoro, T (autor correspondiente), Quinnipiac Univ, Sch Business, Dept Comp Informat Syst, 275 Mt Carmel Ave, Hamden, CT 06518 USA.
EM tamilla.triantoro@qu.edu; ram.gopal@wbs.ac.uk; rbfich@baruch.cuny.edu;
guido.lang@qu.edu
RI Triantoro, Tamilla/AAV-2323-2020
OI Triantoro, Tamilla/0000-0002-9964-6543
CR Ackerman PL, 1997, PSYCHOL BULL, V121, P219, DOI 10.1037/0033-2909.121.2.219
Adamopoulos P, 2018, INFORM SYST RES, V29, P612, DOI 10.1287/isre.2017.0768
Alcivar I, 2016, COMPUT HUM BEHAV, V58, P109, DOI 10.1016/j.chb.2015.12.018
Almlund M, 2011, HBK ECON, V4, P1
[Anonymous], 2014, GAME DESIGN WORKSHOP, DOI DOI 10.1201/B16671
Ashton MC, 2002, J PERS SOC PSYCHOL, V83, P245, DOI 10.1037//0022-3514.83.1.245
Bailey P, 2015, INT J MARKET RES, V57, P17, DOI 10.2501/IJMR-2015-003
BARRICK MR, 1991, PERS PSYCHOL, V44, P1, DOI 10.1111/j.1744-6570.1991.tb00688.x
Barrick MR, 2002, J APPL PSYCHOL, V87, P43, DOI 10.1037//0021-9010.87.1.43
Bendersky C, 2013, ACAD MANAGE J, V56, P387, DOI 10.5465/amj.2011.0316
Blohm I, 2013, BUS INFORM SYST ENG+, V5, P275, DOI 10.1007/s12599-013-0273-5
Boudreau JW, 2001, J VOCAT BEHAV, V58, P53, DOI 10.1006/jvbe.2000.1755
Brownell B., 2015, GAMIFICATION ED BUSI, P569, DOI DOI 10.1007/978-3-319-10208-5_29
Buettner R, 2017, ELECTRON MARK, V27, P247, DOI 10.1007/s12525-016-0228-z
Carter P, 2007, IQ APTITUDE TESTS
Cechanowicz J., 2013, Proceedings of the First International Conference on Gameful Design, Research, and Applications, Gamification'13, (New York, NY, USA), P58, DOI DOI 10.1145/2583008.2583016
Christy KR, 2014, COMPUT EDUC, V78, P66, DOI 10.1016/j.compedu.2014.05.005
de-Marcos L, 2014, COMPUT EDUC, V75, P82, DOI 10.1016/j.compedu.2014.01.012
Downes-Le Guin T, 2012, INT J MARKET RES, V54, P613, DOI 10.2501/IJMR-54-5-613-633
Fang RL, 2015, ORGAN SCI, V26, P1243, DOI 10.1287/orsc.2015.0972
Feldman DC, 2007, J MANAGE, V33, P350, DOI 10.1177/0149206307300815
GATTIKER UE, 1988, HUM RELAT, V41, P569, DOI 10.1177/001872678804100801
GOTTFREDSON GD, 1993, J COUNS PSYCHOL, V40, P518, DOI 10.1037/0022-0167.40.4.518
Hamari J, 2015, INT J INFORM MANAGE, V35, P419, DOI 10.1016/j.ijinfomgt.2015.04.006
Harms J, 2018, HUMAN COMPUTER INTER, P219
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
John O. P., 1999, HDB PERSONALITY THEO
Jones BA, 2014, PREV MED, V68, P76, DOI 10.1016/j.ypmed.2014.04.015
Judge TA, 2002, J APPL PSYCHOL, V87, P530, DOI 10.1037//0021-9010.87.3.530
Judge TA, 2002, J APPL PSYCHOL, V87, P765, DOI 10.1037/0021-9010.87.4.765
Kashdan TB, 2011, J PERS, V79, P1067, DOI 10.1111/j.1467-6494.2010.00697.x
Keusch F, 2017, SOC SCI COMPUT REV, V35, P147, DOI 10.1177/0894439315608451
Lee JJ, 2013, SIMULAT GAMING, V44, P349, DOI 10.1177/1046878112470539
Li MX, 2014, J MANAGE INFORM SYST, V30, P115, DOI 10.2753/MIS0742-1222300405
Lister C, 2014, JMIR SERIOUS GAMES, V2, P51, DOI 10.2196/games.3413
Liu D, 2017, MIS QUART, V41, P1011
Mavletova A, 2015, SOC SCI COMPUT REV, V33, P372, DOI 10.1177/0894439314545316
MCCRAE RR, 1992, J PERS, V60, P175, DOI 10.1111/j.1467-6494.1992.tb00970.x
Nettle D, 2006, AM PSYCHOL, V61, P622, DOI 10.1037/0003-066X.61.6.622
Ng TWH, 2005, PERS PSYCHOL, V58, P367, DOI 10.1111/j.1744-6570.2005.00515.x
Niess C, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0131115
Noftle EE, 2007, J PERS SOC PSYCHOL, V93, P116, DOI 10.1037/0022-3514.93.1.116
O'Brien HL, 2010, J AM SOC INF SCI TEC, V61, P50, DOI 10.1002/asi.21229
Podsakoff NP, 2013, J APPL PSYCHOL, V98, P863, DOI 10.1037/a0032588
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Saucier G., 2015, Handbook of Personality and Social Psychology, V4, P283
Seibert SE, 2001, J VOCAT BEHAV, V58, P1, DOI 10.1006/jvbe.2000.1757
Selfhout M, 2010, J PERS, V78, P509, DOI 10.1111/j.1467-6494.2010.00625.x
Suh A, 2017, J MANAGE INFORM SYST, V34, P268, DOI 10.1080/07421222.2017.1297642
Tang Jian, 2019, International Journal of Crowd Science, V3, DOI 10.1108/IJCS-09-2018-0025
Tolmie P, 2014, PERS UBIQUIT COMPUT, V18, P1763, DOI 10.1007/s00779-013-0755-y
Wayne JH, 2004, J VOCAT BEHAV, V64, P108, DOI 10.1016/S0001-8791(03)00035-6
Werbach K, 2012, WIN GAME THINKING CA
Wille B, 2010, J VOCAT BEHAV, V76, P547, DOI 10.1016/j.jvb.2010.01.007
Xi N, 2019, P 52 ANN HAW INT C S
NR 55
TC 10
Z9 10
U1 5
U2 57
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1385-951X
EI 1573-7667
J9 INFORM TECHNOL MANAG
JI Inf. Technol. Manag.
PD SEP
PY 2020
VL 21
IS 3
BP 169
EP 178
DI 10.1007/s10799-020-00314-4
EA APR 2020
PG 10
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA OD4GK
UT WOS:000558398900001
OA Green Accepted
DA 2024-03-27
ER
PT J
AU Chen, JS
Le, TTY
Florence, D
AF Chen, Ja-Shen
Le, Tran-Thien-Y
Florence, Devina
TI Usability and responsiveness of artificial intelligence chatbot on
online customer experience in e-retailing
SO INTERNATIONAL JOURNAL OF RETAIL & DISTRIBUTION MANAGEMENT
LA English
DT Article
DE Chatbot adoption; Online customer experience; Customer satisfaction;
Personality; e-retailing
ID AMAZONS MECHANICAL TURK; PERSONALITY-TRAITS; SERVICE; ENGAGEMENT;
SATISFACTION; CONVENIENCE; INTERNET; INTEGRATION; SUCCESS; WEBSITE
AB Purpose The rapid evolution in artificial intelligence (AI) has redefined the customer experience and created huge opportunities for companies to interact with customers using chatbots. This study explores the role of AI chatbots in influencing the online customer experience and customer satisfaction in e-retailing. Design/methodology/approach A research model based on the technology acceptance model and information system success model is proposed to describe the interrelationships among chatbot adoption, online customer experience and customer satisfaction. Personality is a moderator in the model. The authors used a quantitative approach to collect 425 useable online questionnaires and Statistical Product and Service Solutions (SPSS) and SmartPLS to analyze the measurement model and proposed hypotheses. Findings The usability of the chatbot had a positive influence on extrinsic values of customer experience, whereas the responsiveness of the chatbot had a positive impact on intrinsic values of customer experience. Furthermore, online customer experience had a positive relationship with customer satisfaction, and personality influenced the relationship between the usability of the chatbot and extrinsic values of customer experience. Originality/value This research extends understanding of the online customer experience with chatbots in e-retailing and provides empirical evidence by showing that extrinsic and intrinsic values of online customer experience are enhanced by chatbot adoption.
C1 [Chen, Ja-Shen; Le, Tran-Thien-Y; Florence, Devina] Yuan Ze Univ, Coll Management, Taoyuan, Taiwan.
[Le, Tran-Thien-Y] Can Tho Univ, Sch Econ, Can Tho, Vietnam.
C3 Yuan Ze University; Can Tho University
RP Chen, JS (autor correspondiente), Yuan Ze Univ, Coll Management, Taoyuan, Taiwan.
EM jchen@saturn.yzu.edu.tw
OI Florence, Devina/0000-0001-9272-4556; Chen, Ja-Shen/0000-0001-9099-1810
FU Ministry of Science and Technology, Taiwan [MoST 107-2410-H-155 -037
-MY3]
FX This work was partially supported by Ministry of Science and Technology,
Taiwan with grant number: MoST 107-2410-H-155 -037 -MY3.
CR Adamopoulou E., 2020, MACHINE LEARNING APP, V2, DOI DOI 10.1016/J.MLWA.2020.100006
Agichtein E, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P897, DOI 10.1145/3336191.3371882
Alt R, 2019, ELECTRON MARK, V29, P79, DOI 10.1007/s12525-019-00340-3
Amiel T, 2004, COMPUT HUM BEHAV, V20, P711, DOI 10.1016/j.chb.2004.09.002
An M., 2018, ARTIFICIAL INTELLIGE
Belanche D, 2019, IND MANAGE DATA SYST, V119, P1411, DOI 10.1108/IMDS-08-2018-0368
Buhrmester M, 2011, PERSPECT PSYCHOL SCI, V6, P3, DOI 10.1177/1745691610393980
Chopra K, 2019, INT J RETAIL DISTRIB, V47, P331, DOI 10.1108/IJRDM-11-2018-0251
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Correa T, 2010, COMPUT HUM BEHAV, V26, P247, DOI 10.1016/j.chb.2009.09.003
Da Cunha AD, 2007, COMMUN ACM, V50, P109, DOI 10.1145/1230819.1241672
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
De Cicco R, 2020, INT J RETAIL DISTRIB, V48, P1213, DOI 10.1108/IJRDM-12-2019-0406
DeLone WH, 2004, INT J ELECTRON COMM, V9, P31, DOI 10.1080/10864415.2004.11044317
Duarte P, 2018, J RETAIL CONSUM SERV, V44, P161, DOI 10.1016/j.jretconser.2018.06.007
Fazel-e-Hasan SM, 2019, INT J RETAIL DISTRIB, V47, P836, DOI 10.1108/IJRDM-10-2018-0235
Finstad K, 2010, INTERACT COMPUT, V22, P323, DOI 10.1016/j.intcom.2010.04.004
Flavián C, 2006, INFORM MANAGE-AMSTER, V43, P1, DOI 10.1016/j.im.2005.01.002
Follmer DJ, 2017, EDUC RESEARCHER, V46, P329, DOI 10.3102/0013189X17725519
Fryer LK, 2019, COMPUT HUM BEHAV, V93, P279, DOI 10.1016/j.chb.2018.12.023
Gallarza MG, 2017, J CONSUM BEHAV, V16, pE76, DOI 10.1002/cb.1661
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Hair JF, 2012, J ACAD MARKET SCI, V40, P414, DOI 10.1007/s11747-011-0261-6
Harrigan P, 2017, TOURISM MANAGE, V59, P597, DOI 10.1016/j.tourman.2016.09.015
Hollebeek LD, 2021, J SERV RES-US, V24, P3, DOI 10.1177/1094670520975110
Hu Tianran., 2018, P 2018 CHI C HUMAN F, P1, DOI DOI 10.1109/ICC.2018.8422598
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Irfan W, 2019, INT J RETAIL DISTRIB, V47, P350, DOI 10.1108/IJRDM-03-2018-0045
Jadin T, 2013, COMPUT HUM BEHAV, V29, P210, DOI 10.1016/j.chb.2012.08.007
John O. P., 1999, HDB PERSONALITY THEO
Joyce M, 2015, INT J HUM-COMPUT INT, V31, P506, DOI 10.1080/10447318.2015.1064657
Judge TA, 1999, PERS PSYCHOL, V52, P621, DOI 10.1111/j.1744-6570.1999.tb00174.x
Kang G.D., 2006, MANAG SERV QUAL, V16, P37, DOI [DOI 10.1108/09604520610639955, 10.1108/09604520610639955]
Kees J, 2017, J ADVERTISING, V46, P159, DOI 10.1080/00913367.2017.1281781
Kokkinou A, 2013, INT J HOSP MANAG, V33, P435, DOI 10.1016/j.ijhm.2012.11.003
Kraus D., 2019, VOICE CAN CHANGE CUS, V14, P23
Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
Li L, 2021, ELECTRON MARK, V31, P575, DOI 10.1007/s12525-020-00454-z
Lu L, 2019, INT J HOSP MANAG, V80, P36, DOI 10.1016/j.ijhm.2019.01.005
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
M?nster A., 2017, INT J RETAIL DISTRIB, V45, P122
Mason W, 2012, BEHAV RES METHODS, V44, P1, DOI 10.3758/s13428-011-0124-6
Mathwick C, 2001, J RETAILING, V77, P39, DOI 10.1016/S0022-4359(00)00045-2
McLean G, 2019, TECHNOL FORECAST SOC, V146, P55, DOI 10.1016/j.techfore.2019.05.017
Meuter ML, 2005, J MARKETING, V69, P61, DOI 10.1509/jmkg.69.2.61.60759
Molla A., 2011, Journal of Electronic Commerce Research, V2, P131, DOI DOI 10.1111/0033-3352.00196
Okuda T, 2018, FUJITSU SCI TECH J, V54, P4
Pappas IO, 2014, INT J RETAIL DISTRIB, V42, P187, DOI 10.1108/IJRDM-03-2012-0034
Peterson RA, 2014, J BUS RES, V67, P1035, DOI 10.1016/j.jbusres.2013.08.010
Petre M, 2006, BEHAV INFORM TECHNOL, V25, P189, DOI 10.1080/01449290500331198
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Pratt R., 2006, AC MAN M ATL GA
Prentice C, 2019, J RETAIL CONSUM SERV, V47, P339, DOI 10.1016/j.jretconser.2018.12.014
Pricilla C, 2018, 2018 5TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATICS: CONCEPTS, THEORY AND APPLICATIONS (ICAICTA 2018), P244, DOI 10.1109/ICAICTA.2018.8541320
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Quintino A.R.P., 2019, THESIS
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
Roy SK, 2018, J RETAIL CONSUM SERV, V44, P293, DOI 10.1016/j.jretconser.2018.07.018
Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
Sanny L., 2020, Management Science Letters, V10, P1225, DOI [https://doi.org/10.5267/j.msl.2019.11.036, DOI 10.5267/J.MSL.2019.11.036]
Sidaoui K, 2020, J SERV MANAGE, V31, P745, DOI 10.1108/JOSM-11-2019-0341
Sotolongo N., 2018, Applied Marketing Analytics, V4, P6
Sousa MJ, 2019, J BUS RES, V94, P257, DOI 10.1016/j.jbusres.2017.12.051
Stoeckli E, 2020, ELECTRON MARK, V30, P369, DOI 10.1007/s12525-019-00359-6
Tiwana A., 1998, Journal of Retailing and Consumer Services, P245, DOI DOI 10.1016/S0969-6989(97)00038-6
Tsao W., 2013, INT J MANAGEMENT, V30, P374
Ul Islam J, 2019, J RETAIL CONSUM SERV, V50, P277, DOI 10.1016/j.jretconser.2019.05.018
Ul Islam J, 2017, MARK INTELL PLAN, V35, P510, DOI 10.1108/MIP-10-2016-0193
Van den Broeck E, 2019, COMPUT HUM BEHAV, V98, P150, DOI 10.1016/j.chb.2019.04.009
van Griethuijsen RALF, 2015, RES SCI EDUC, V45, P581, DOI 10.1007/s11165-014-9438-6
Vassinen R., 2018, Journal of Brand Strategy, V7, P13
Wei W, 2016, INT J HOSP MANAG, V59, P105, DOI 10.1016/j.ijhm.2016.09.004
Wirth N, 2018, INT J MARKET RES, V60, P435, DOI 10.1177/1470785318776841
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Xi NN, 2019, INT J INFORM MANAGE, V46, P210, DOI 10.1016/j.ijinfomgt.2018.12.002
Youn S, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106721
Zarouali B, 2018, CYBERPSYCH BEH SOC N, V21, P491, DOI 10.1089/cyber.2017.0518
Zhang ML, 2017, INTERNET RES, V27, P839, DOI 10.1108/IntR-06-2016-0148
NR 79
TC 74
Z9 76
U1 71
U2 315
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0959-0552
EI 1758-6690
J9 INT J RETAIL DISTRIB
JI Int. J. Retail Distrib. Manag.
PD OCT 6
PY 2021
VL 49
IS 11
BP 1512
EP 1531
DI 10.1108/IJRDM-08-2020-0312
EA MAY 2021
PG 20
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA WC0HS
UT WOS:000647812800001
DA 2024-03-27
ER
PT J
AU Steinhoff, L
Arli, D
Weaven, S
Kozlenkova, IV
AF Steinhoff, Lena
Arli, Denni
Weaven, Scott
Kozlenkova, Irina V.
TI Online relationship marketing
SO JOURNAL OF THE ACADEMY OF MARKETING SCIENCE
LA English
DT Article
DE Relationship marketing; Relationship selling; Online relationships;
E-commerce; Online shopping; Online retailing; Social media; Mobile
shopping; Virtual assistants
ID WORD-OF-MOUTH; AUGMENTED-REALITY; PRIVACY CONCERNS; PARASOCIAL
INTERACTION; FLOW EXPERIENCE; MEDIA RICHNESS; CUSTOMERS; IMPACT; TRUST;
WEB
AB Online interactions have emerged as a dominant exchange mode for companies and customers. Cultivating online relationshipsdefined as relational exchanges that are mediated by Internet-based channelspresents firms with challenges and opportunities. In lockstep with exponential advancements in computing technology, a rich and ever-evolving toolbox is available to relationship marketers to manage customer relationships online, in settings including e-commerce, social media, online communities, mobile, big data, artificial intelligence, and augmented reality. To advance academic knowledge and guide managerial decision making, this study offers a comprehensive analysis of online relationship marketing in terms of its conceptual foundations, evolution in business practice, and empirical insights from academic research. The authors propose an evolving theory of online relationship marketing, characterizing online relationships as uniquely seamless, networked, omnichannel, personalized, and anthropomorphized. Based on these five essential features, six tenets and 11 corresponding propositions parsimoniously predict the performance effects of the manifold online relationship marketing strategies.
C1 [Steinhoff, Lena] Univ Rostock, Fac Business & Social Sci, Inst Mkt & Serv Res, Ulmenstr 69, D-18057 Rostock, Germany.
[Arli, Denni] Griffith Univ, Griffith Business Sch, Dept Mkt, 170 Kessels Rd, Nathan, Qld 4111, Australia.
[Weaven, Scott] Griffith Univ, Griffith Business Sch, Dept Mkt, Parklands Dr, Southport, Qld 4222, Australia.
[Kozlenkova, Irina V.] Univ Virginia, McIntire Sch Commerce, Mkt Dept, 125 Ruppel Dr, Charlottesville, VA 22903 USA.
C3 University of Rostock; Griffith University; Griffith University;
Griffith University - Gold Coast Campus; University of Virginia
RP Steinhoff, L (autor correspondiente), Univ Rostock, Fac Business & Social Sci, Inst Mkt & Serv Res, Ulmenstr 69, D-18057 Rostock, Germany.
EM lena.steinhoff@uni-rostock.de; d.arli@griffith.edu.au;
s.weaven@griffith.edu.au; irinak@virginia.edu
RI Arli, Denni/AAK-4715-2021; weaven, scott K/C-9937-2016
OI Weaven, Scott/0000-0001-9150-7111; Arli, Denni/0000-0002-6320-3994
CR Achen R.M., 2016, Journal of Relationship Marketing, V15, P247, DOI [DOI 10.1080/15332667.2016.1209054, https://doi.org/10.1080/15332667.2016.1209054]
Adjei MT, 2010, J ACAD MARKET SCI, V38, P634, DOI 10.1007/s11747-009-0178-5
Aguirre E, 2015, J RETAILING, V91, P34, DOI 10.1016/j.jretai.2014.09.005
Andrews M, 2016, MARKET SCI, V35, P218, DOI 10.1287/mksc.2015.0905
[Anonymous], 2011, SCVNGRS 1 NATL BRAND
[Anonymous], 2015, HARVARD BUSINESS REV
[Anonymous], 2015, UNDERSTANDING ALIBAB
[Anonymous], MY STARBUCKS IDEA ST
[Anonymous], BUILD STRONG CUSTOME
[Anonymous], 2017, MOBILE APP SUCCESS S
[Anonymous], LOYALT DEC HOW EM DR
[Anonymous], 2017, 8FIT HLTH FITNESS AP
[Anonymous], WHAT AM TEACH US CRM
[Anonymous], 2017, MCDONALDS MONOPOLY P
[Anonymous], J MARKETING
[Anonymous], WORST DATA BREACHES
[Anonymous], 2013, MCKINSEY Q
[Anonymous], 2017, GOOGLE STILL DOESNT
[Anonymous], ANNA IKEA IS INTELLE
[Anonymous], OREAL CASE STUDY AUG
[Anonymous], 2017, TOPOLOGY LETS YOU TR
[Anonymous], 2018, ROBOT HUMAN CONSUMER
[Anonymous], CASE STUDY GAME LAYE
[Anonymous], 2017, NIKE USE SOCIAL MEDI
[Anonymous], RESPOND TOMORROWS CU
[Anonymous], 2017, ALIBABA SMASHES ITS
[Anonymous], MARKETING SCI I WORK
[Anonymous], MOB MARK CAS STUD OR
[Anonymous], 2018, NUMB ACT CONS AL ONL
[Anonymous], GOOGLES NEW PRIVACY
[Anonymous], ABI RES
[Anonymous], 10 SUPERMARKETS 10 V
[Anonymous], VIRTUAL BECOMING VIA
[Anonymous], HARVARD BUSINESS REV
[Anonymous], WHAT IS UBERS COMPET
[Anonymous], P C COMP HUM FACT CO
[Anonymous], NUMB UN VIS WORLDW T
[Anonymous], MEDIUM
[Anonymous], CISC VIRT NETW IND F
[Anonymous], INTERNATIONAL JOURNA
[Anonymous], CUST HOM WORK APP HL
[Anonymous], ONL SHOPP FREQ INT U
[Anonymous], EMARKETER REPOR 0618
Ansari A, 2008, J MARKETING RES, V45, P60, DOI 10.1509/jmkr.45.1.60
Arli D, 2018, IND MARKET MANAG, V69, P169, DOI 10.1016/j.indmarman.2017.07.018
Armerding Taylor., 2018, 17 BIGGEST DATA BREA
Arora N, 2008, MARKET LETT, V19, P305, DOI 10.1007/s11002-008-9056-z
Ascarza E, 2017, J MARKETING RES, V54, P347, DOI 10.1509/jmr.15.0442
Ballantine PW, 2005, ADV CONSUM RES, V32, P197
Barnes NoraGanim., 2016, Use of Popular Tools Remains Constant as Use of Instagram Expands Quickly Among the 2016 Fortune 500
Bart Y, 2005, J MARKETING, V69, P133, DOI 10.1509/jmkg.2005.69.4.133
Ben Mimoun MS, 2012, J RETAIL CONSUM SERV, V19, P605, DOI 10.1016/j.jretconser.2012.07.006
BI Intelligence, 2016, AM CUST LOYALT EATS
Bleier A, 2015, J RETAILING, V91, P390, DOI 10.1016/j.jretai.2015.04.001
Brehm J. W., 1966, THEORY PSYCHOL REACT
Brown Morgan, 2014, AIRBNB GROWTH STORY
Burgoon JK, 2000, COMPUT HUM BEHAV, V16, P553, DOI 10.1016/S0747-5632(00)00029-7
Burke RR, 2002, J ACAD MARKET SCI, V30, P411, DOI 10.1177/009207002236914
Cassell J., 2000, EMBODIED CONVERSATIO
Castelo N., 2018, Consumer adoption of algorithms that blur the line between human and machine
Chan KW, 2010, J BUS RES, V63, P1033, DOI 10.1016/j.jbusres.2008.08.009
Chang YP, 2012, COMPUT HUM BEHAV, V28, P995, DOI 10.1016/j.chb.2012.01.001
Csikszentmihalyi M., 2008, FLOW PSYCHOL OPTIMAL
Csikzentimihalyi M., 1975, BOREDOM ANXIETY EXPE
DAFT RL, 1986, MANAGE SCI, V32, P554, DOI 10.1287/mnsc.32.5.554
Dans E., 2018, Forbes
Danziger Pamela N., 2018, AMAZONS CUSTOMER LOY
Darrow B., 2016, Fortune
de Haan E, 2018, J MARKETING, V82, P1, DOI 10.1509/jm.17.0113
DiSalvo C., 2003, Proceedings of the Conference on Designing Pleasurable Products and interfaces DPPI'03, P67, DOI DOI 10.1145/782896.782913
Duan JY, 2017, J CONSUM MARK, V34, P404, DOI 10.1108/JCM-07-2016-1871
Dubey A., 2007, MCKIN Q, V6, P1
DWYER FR, 1987, J MARKETING, V51, P11, DOI 10.2307/1251126
Eastlick MA, 2006, J BUS RES, V59, P877, DOI 10.1016/j.jbusres.2006.02.006
eMarketer, 2017, WORLDW RET EC SAL EM
Fischer E, 1996, ADV CONSUM RES, V23, P178
Fruend Melissa, 2017, 2017 COLLOQUY LOYALT
Gartner, 2011, GARTN CUST 360 SUMM
Gensler S, 2012, MARKET LETT, V23, P987, DOI 10.1007/s11002-012-9199-9
Giles DC, 2002, MEDIA PSYCHOL, V4, P279, DOI 10.1207/S1532785XMEP0403_04
GRANOVETTER MS, 1973, AM J SOCIOL, V78, P1360, DOI 10.1086/225469
Gray K, 2012, COGNITION, V125, P125, DOI 10.1016/j.cognition.2012.06.007
Grégoire Y, 2009, J MARKETING, V73, P18, DOI 10.1509/jmkg.73.6.18
Gwinner KP, 1998, J ACAD MARKET SCI, V26, P101, DOI 10.1177/0092070398262002
Henderson CM, 2011, J CONSUM PSYCHOL, V21, P256, DOI 10.1016/j.jcps.2011.02.007
Hilken T, 2017, J ACAD MARKET SCI, V45, P884, DOI 10.1007/s11747-017-0541-x
Hoerner J, 1999, ADVERT CONS, P135
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
HORTON D, 1956, PSYCHIATR, V19, P215, DOI 10.1080/00332747.1956.11023049
Huang TL, 2017, INTERNET RES, V27, P449, DOI 10.1108/IntR-11-2015-0321
Hume David., 1957, NATURAL HIST RELIG
Iacobucci D., 1996, Journal of Retailing and Consumer Services, V3, P195
Iyengar R, 2011, MARKET SCI, V30, P195, DOI 10.1287/mksc.1100.0566
Javornik A, 2016, J RETAIL CONSUM SERV, V30, P252, DOI 10.1016/j.jretconser.2016.02.004
Johnson JL, 2003, J ACAD MARKET SCI, V31, P74, DOI 10.1177/0092070302238603
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
Keeling K, 2013, J BUS RES, V66, P847, DOI 10.1016/j.jbusres.2011.06.010
Keeling K, 2010, J BUS RES, V63, P793, DOI 10.1016/j.jbusres.2008.12.015
Kim H, 2013, INFORM MANAGE-AMSTER, V50, P169, DOI 10.1016/j.im.2013.02.003
Kim SJ, 2015, J INTERACT MARK, V31, P28, DOI 10.1016/j.intmar.2015.05.004
Koch OF, 2015, J INTERACT MARK, V32, P37, DOI 10.1016/j.intmar.2015.09.005
Koda T, 1996, RO-MAN '96 - 5TH IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN COMMUNICATION, PROCEEDINGS, P189, DOI 10.1109/ROMAN.1996.568812
Koetsier J., 2018, FORBES
Kozinets RV, 2010, J MARKETING, V74, P71, DOI 10.1509/jmkg.74.2.71
Kozlenkova IV, 2017, J MARKETING, V81, P21, DOI 10.1509/jm.15.0430
KPMG, 2017, The truth about online consumers: 2017 Global Online Consumer Report
Krafft M, 2017, J INTERACT MARK, V39, P39, DOI 10.1016/j.intmar.2017.03.001
Kumar A, 2016, J MARKETING, V80, P7, DOI 10.1509/jm.14.0249
Kumar V, 2005, J INTERACT MARK, V19, P44, DOI 10.1002/dir.20034
Kushwaha T, 2013, J MARKETING, V77, P67, DOI 10.1509/jm.11.0297
Labrecque LI, 2014, J INTERACT MARK, V28, P134, DOI 10.1016/j.intmar.2013.12.003
Lee MC, 2010, INT J HUM-COMPUT INT, V26, P601, DOI 10.1080/10447311003781318
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Liu SX, 2015, INFORM SCIENCES, V306, P34, DOI 10.1016/j.ins.2015.01.034
Liu SH, 2009, COMPUT EDUC, V52, P599, DOI 10.1016/j.compedu.2008.11.002
Living Actor, 2016, YOK TOSH VIRT HELPD
Luna D, 2002, J ACAD MARKET SCI, V30, P397, DOI 10.1177/009207002236913
Lunden Ingrid, 2017, TECHCRUNCH
MacDorman K.F., 2005, COGSCI 2005 WORKSHOP, P106, DOI DOI 10.1016/J.CHB.2012.11.021
Marinova D, 2017, J SERV RES-US, V20, P29, DOI 10.1177/1094670516679273
Martin KD, 2017, J ACAD MARKET SCI, V45, P135, DOI 10.1007/s11747-016-0495-4
Martin KD, 2017, J MARKETING, V81, P36, DOI 10.1509/jm.15.0497
Mathwick C., 2002, Journal of Interactive Marketing, V16, P40, DOI DOI 10.1002/DIR.10003
McKenna KYA, 2002, J SOC ISSUES, V58, P9, DOI 10.1111/1540-4560.00246
MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
Mothersbaugh DL, 2012, J SERV RES-US, V15, P76, DOI 10.1177/1094670511424924
Mukherjee A, 2007, EUR J MARKETING, V41, P1173, DOI 10.1108/03090560710773390
Nakamura Jeanne., 2001, HDB POSITIVE PSYCHOL, P195
Naylor RW, 2012, J MARKETING, V76, P105, DOI 10.1509/jm.11.0105
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
Olson P., 2018, Forbes.
Palmatier RW, 2007, J MARKETING, V71, P172, DOI 10.1509/jmkg.71.4.172
Palmatier RW, 2007, J MARKETING RES, V44, P185, DOI 10.1509/jmkr.44.2.185
Palmatier RW, 2006, J MARKETING, V70, P136, DOI 10.1509/jmkg.70.4.136
Palmatier RW, 2009, J MARKETING, V73, P1, DOI 10.1509/jmkg.73.5.1
Palmatier RobertW., 2008, Relationship Marketing
Phan TQ, 2018, MARKET SCI, V37, P259, DOI 10.1287/mksc.2017.1077
Phelps J, 2000, J PUBLIC POLICY MARK, V19, P27, DOI 10.1509/jppm.19.1.27.16941
Rafaeli A, 2017, J SERV RES-US, V20, P91, DOI 10.1177/1094670516679275
Reeves B, 1996, MEDIA EQUATION PEOPL
Researchscape International, 2018, 2018 TRENDS PERS
Riquelme IP, 2016, J INTERACT MARK, V35, P16, DOI 10.1016/j.intmar.2016.01.002
Rochet JC, 2003, J EUR ECON ASSOC, V1, DOI 10.1162/154247603322493212
Rosario AB, 2016, J MARKETING RES, V53, P297, DOI 10.1509/jmr.14.0380
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
Saad S.B., 2016, J MARKET MANAG, V4, P63
Serenko A, 2007, COMPUT HUM BEHAV, V23, P478, DOI 10.1016/j.chb.2004.10.042
Shah DV, 2015, ANN AM ACAD POLIT SS, V659, P6, DOI 10.1177/0002716215572084
Shankar V, 2003, INT J RES MARK, V20, P153, DOI 10.1016/S0167-8116(03)00016-8
Sherman L., 2018, Forbes
Sheth J.N., 1995, INT BUS REV, V4, P397, DOI [DOI 10.1016/0969-5931, 10.1016/0969-5931(95)00018-6, DOI 10.1016/0969-5931(95)00018-6]
Son JY, 2008, MIS QUART, V32, P503
Statista, 2021, NUMBER APPS AVAILABL
Statista I., 2018, Number of social media users worldwide from 2010 to 2021
Steinhoff L, 2016, J ACAD MARKET SCI, V44, P88, DOI 10.1007/s11747-014-0405-6
Tam KY, 2006, MIS QUART, V30, P865
Taylor DG, 2009, ELECTRON COMMER RES, V9, P203, DOI 10.1007/s10660-009-9036-2
Thomas JS, 2005, J MARKETING, V69, P239, DOI 10.1509/jmkg.2005.69.4.239
Toker-Yildiz K, 2017, J MARKETING RES, V54, P364, DOI 10.1509/jmr.13.0482
Tolido Ron, 2016, TECHNOVISION 2016 NO
van Den Bulte Christophe., 2007, Social Networks and Marketing
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Venkatesan R, 2007, J RETAILING, V83, P309, DOI 10.1016/j.jretai.2006.04.002
Verhoef PC, 2015, J RETAILING, V91, P174, DOI 10.1016/j.jretai.2015.02.005
Verma V, 2016, J ACAD MARKET SCI, V44, P206, DOI 10.1007/s11747-015-0429-6
Viglia G, 2018, J BUS RES, V89, P404, DOI 10.1016/j.jbusres.2017.12.029
Wakefield Jane, 2016, WOULD YOU WANT TALK
Wallace DW, 2004, J RETAILING, V80, P249, DOI 10.1016/j.jretai.2004.10.002
Walsh G, 2010, J RETAIL CONSUM SERV, V17, P130, DOI 10.1016/j.jretconser.2009.11.003
Wang RJH, 2015, J RETAILING, V91, P217, DOI 10.1016/j.jretai.2015.01.002
Waytz A, 2010, PERSPECT PSYCHOL SCI, V5, P219, DOI 10.1177/1745691610369336
Yadav MS, 2014, J MARKETING, V78, P20, DOI 10.1509/jm.12.0020
Yazdani E, 2018, MARKET SCI, V37, P838, DOI 10.1287/mksc.2018.1101
Yoon D, 2008, PSYCHOL MARKET, V25, P602, DOI 10.1002/mar.20227
Zhang YC, 2017, J MARKETING, V81, P24, DOI 10.1509/jm.14.0344
Zhou T, 2011, COMPUT HUM BEHAV, V27, P883, DOI 10.1016/j.chb.2010.11.013
Zhu R, 2012, J MARKETING RES, V49, P394, DOI 10.1509/jmr.08.0499
NR 178
TC 171
Z9 198
U1 92
U2 766
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0092-0703
EI 1552-7824
J9 J ACAD MARKET SCI
JI J. Acad. Mark. Sci.
PD MAY
PY 2019
VL 47
IS 3
BP 369
EP 393
DI 10.1007/s11747-018-0621-6
PG 25
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HV5NS
UT WOS:000466031200001
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Mühlhoff, R
AF Muehlhoff, Rainer
TI Human-aided artificial intelligence: Or, how to run large computations
in human brains? Toward a media sociology of machine learning
SO NEW MEDIA & SOCIETY
LA English
DT Article
DE Artificial intelligence; audience labor; commercial content moderation;
cybernetics; deep learning; human computation; human-computer
interaction; social media; tracking; training data; user experience
design
ID COMPUTER
AB Today, artificial intelligence (AI), especially machine learning, is structurally dependent on human participation. Technologies such as deep learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of "media technologies of capture" in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term "cybernetic AI." This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control, and digital labor.
C1 [Muehlhoff, Rainer] Tech Univ Berlin, Cluster Sci Intelligence, Philosophy, Eth Design AI & Robot, Berlin, Germany.
C3 Technical University of Berlin
RP Mühlhoff, R (autor correspondiente), Tech Univ Berlin, Excellence Cluster Sci Intelligence, Str 17 Juni 135, D-10623 Berlin, Germany.
EM mail@rmuehlhoff.de
FU Collaborative Research Center SFB1171 Affective Societies, project B05,
at Freie Universitat Berlin - Deutsche Forschungsgemeinschaft (DFG)
FX The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: Research
of this article has in part been supportet by the Collaborative Research
Center SFB1171 Affective Societies, project B05, at Freie Universitat
Berlin, funded by the Deutsche Forschungsgemeinschaft (DFG), 2015-2019.
CR Ahn L-V, 2004, P SIGCHI C HUM FACT, P319, DOI DOI 10.1145/985692.985733
[Anonymous], COMPUTER ALS MEDIUM
Ashby W. R, 1957, INTRO CYBERNETICS
Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
Brooks R.A., 1991, INTELLIGENCE REASON
Copeland BJ, 2000, MIND MACH, V10, P519, DOI 10.1023/A:1011285919106
Deterding S., 2011, 15 INT AC MINDTREK C, V12, P9, DOI [DOI 10.1145/2181037.2181040, 10.1145/2181037.2181040]
Eubanks V., 2018, AUTOMATING INEQUALIT, DOI DOI 10.5555/3208509
Fisher E, 2015, DYN VIRTUAL WORK, P1, DOI 10.1057/9781137478573
Fisher E, 2015, DYN VIRTUAL WORK, P115
Fuchs C, 2010, INFORM SOC, V26, P179, DOI 10.1080/01972241003712215
Galloway A.R., 2007, The Exploit: A Theory of Networks
Geyer F, 1995, KYBERNETES, V24, P6, DOI 10.1108/03684929510089321
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Haugeland J, 1981, MIND DESIGN, P34
Haugeland J, 1989, Artificial Intelligence: The Very Idea, DOI DOI 10.7551/MITPRESS/1170.001.0001
Hutchins E., 2001, INT ENCY SOCIAL BEHA, P2068, DOI 10.1016/B0-08-043076-7/01636-3
Lanier Jaron., 2014, WHO OWNS FUTURE
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Leonard TC, 2008, CONST POLITICAL ECON, V19, P356, DOI 10.1007/s10602-008-9056-2
Levitt GeraldM., 2000, TURK CHESS AUTOMATON
Little Greg., 2010, Proceedings of the 23nd annual ACM symposium on User interface software and technology, P57, DOI DOI 10.1145/1866029.1866040
Muhlhoff R, 2019, ZFM Z MEDIENWISSENSC, V11, P56
Muhlhoff R, 2019, AFFEKT MACHT NETZ WE, P81
Muhlhoff R., 2018, Leviathan, V46, P551, DOI [10.5771/0340-0425-2018-4-551, DOI 10.5771/0340-0425-2018-4-551]
NEWELL A, 1976, COMMUN ACM, V19, P113, DOI 10.1145/360018.360022
Ng AY, 2017, COMMUNICATION 0125
Noble Safiya Umoja, 2018, Algorithms of Oppression: How Search Engines Reinforce Racism, DOI DOI 10.2307/J.CTT1PWT9W5
Norman D., 1988, PSYCHOL EVERYDAY THI
ONeil Cathy, 2016, WEAPONS MATH DESTRUC
OReilly T., 2005, WHAT IS WEB 2 0 DESI
Roberts S. T., 2016, MEDIA STUDIES PUBLIC, V12
Roberts ST., WI J MOBILE MEDIA, V10, P1
Rosenblueth A., 1943, Philos. Sci., V10, P18, DOI DOI 10.1086/286788
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Rumelhart DE., 1985, LEARNING INTERNAL RE, DOI [DOI 10.7551/MITPRESS/5236.001.0001, 10.1016/b978-1-4832-1446-7.50035-2]
Scholz Trebor., 2013, Digital Labor: The Internet As Playground and Factory
Sudmann A, 2018, MACHINE LEARNING MED, P9
Sun R, 2014, CAMBRIDGE HANDBOOK OF ARTIFICIAL INTELLIGENCE, P108
Terranova Tiziana., 2000, SOC TEXT, V18, P33, DOI DOI 10.1215/01642472-18-2_63-33
Turing A. M., 1996, Philos. Math., V4, P256, DOI [10.1093/philmat/4.3.256, DOI 10.1093/PHILMAT/4.3.256]
Turing AM, 1937, P LOND MATH SOC, V42, P230, DOI 10.1112/plms/s2-42.1.230
Turing AM., 1950, Mind, VLIX, P433, DOI 10.1093/mind/LIX.236.433
von Ahn L, 2003, LECT NOTES COMPUT SC, V2656, P294
von Ahn L, 2006, COMMUNICATION 0726
VON AHN L., 2006, SIGCHI C HUMAN FACTO, P55, DOI DOI 10.1145/1124772.1124782
Von Ahn L, 2005, THESIS
von Ahn L, 2008, SCIENCE, V321, P1465, DOI 10.1126/science.1160379
von Ahn L, 2006, COMPUTER, V39, P92, DOI 10.1109/MC.2006.196
WEISER M, 1991, SCI AM, V265, P94, DOI 10.1038/scientificamerican0991-94
WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/357980.357991
Wiener N., 1954, HUMAN USE HUMAN BEIN
Woodcock J, 2018, SOCIOL REV, V66, P542, DOI 10.1177/0038026117728620
NR 53
TC 28
Z9 31
U1 8
U2 74
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1461-4448
EI 1461-7315
J9 NEW MEDIA SOC
JI New Media Soc.
PD OCT
PY 2020
VL 22
IS 10
BP 1868
EP 1884
AR 1461444819885334
DI 10.1177/1461444819885334
EA NOV 2019
PG 17
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA NM8FH
UT WOS:000496074600001
OA hybrid
DA 2024-03-27
ER
PT J
AU Arpaci, I
AF Arpaci, Ibrahim
TI A Multianalytical SEM-ANN Approach to Investigate the Social
Sustainability of AI Chatbots Based on Cybersecurity and Protection
Motivation Theory
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE AI chatbots; artificial intelligence (AI); confidentiality; structural
equation modeling and artificial neural network (SEM-ANN) approach;
sustainability
ID INFORMATION SECURITY; FEAR APPEALS; BEHAVIORS; ADOPTION
AB With a primary focus on cybersecurity risks, this study endeavors to explore the sustainable deployment of artificial intelligence (AI) chatbots and, ultimately, to promote their social sustainability. The study introduces an enhanced model built upon the "Protection Motivation Theory" (PMT) to explore the factors that predict the social sustainability of AI chatbots. The proposed model is evaluated using both "structural equation modeling" and "artificial neural network" (ANN) analyses, leveraging data obtained from 1741 participants. The findings reveal that PMT factors significantly predict the sustainable use of AI chatbots. Moreover, cybersecurity concerns, including confidentiality and privacy, have emerged as significant predictors of sustainable use, impacting the social sustainability of AI chatbots. The indicated paths in the model explain 70% and 74% of the variance in sustainable use and social sustainability, respectively. The results from the ANN analysis also emphasize the critical role of confidentiality as the primary predictor. The significance of this study lies in the development of a unified model that integrates cybersecurity and PMT, offering a distinctive framework. In addition to its theoretical contributions, the study offers practical insights for service providers, application developers, and decision-makers in the field, thereby influencing the future of AI chatbots.
C1 [Arpaci, Ibrahim] Bandirma Onyedi Eylul Univ, Fac Engn & Nat Sci, Dept Software Engn, TR-10250 Balikesir, Turkiye.
C3 Bandirma Onyedi Eylul University
RP Arpaci, I (autor correspondiente), Bandirma Onyedi Eylul Univ, Fac Engn & Nat Sci, Dept Software Engn, TR-10250 Balikesir, Turkiye.
EM iarpaci@bandirma.edu.tr
RI ARPACI, Ibrahim/AAC-2389-2019
OI ARPACI, Ibrahim/0000-0001-6513-4569
CR Abbasi GA, 2023, J MARKET THEORY PRAC, DOI 10.1080/10696679.2023.2218098
Al-Emran M, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3237789
Al-Emran M, 2021, J ENTERP INF MANAG, V34, P1229, DOI 10.1108/JEIM-02-2020-0052
Al-Sharafi MA, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107708
Al-Sharafi MA, 2023, INT J HUM-COMPUT INT, V39, P2673, DOI 10.1080/10447318.2022.2084036
Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
[Anonymous], 2023, ChatGPT: Optimizing Language Models for Dialogue
Ariyaratne S, 2023, SKELETAL RADIOL, V52, P1755, DOI 10.1007/s00256-023-04340-5
Arpaci I, 2023, TECHNOL FORECAST SOC, V196, DOI 10.1016/j.techfore.2023.122858
Arpaci I, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107678
Arpaci I, 2022, TECHNOL SOC, V71, DOI 10.1016/j.techsoc.2022.102120
Arpaci I, 2022, INFORM DEV, V38, P218, DOI 10.1177/0266666921997512
Arpaci I, 2019, COMPUT HUM BEHAV, V90, P181, DOI 10.1016/j.chb.2018.09.005
Arpaci I, 2016, COMPUT HUM BEHAV, V58, P150, DOI 10.1016/j.chb.2015.12.067
Assefa G, 2007, TECHNOL SOC, V29, P63, DOI 10.1016/j.techsoc.2006.10.007
Belk R, 2021, SERV IND J, V41, P860, DOI 10.1080/02642069.2020.1727892
Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
Bijani M, 2022, GLOB ECOL CONSERV, V39, DOI 10.1016/j.gecco.2022.e02303
Boerman SC, 2021, COMMUN RES, V48, P953, DOI 10.1177/0093650218800915
Boucher EM, 2021, EXPERT REV MED DEVIC, V18, P37, DOI 10.1080/17434440.2021.2013200
Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
Breidbach CF, 2020, J SERV MANAGE, V31, P163, DOI 10.1108/JOSM-03-2019-0073
Chen TC, 2009, PROC EUR SOLID-STATE, P1
Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
Corallo A, 2023, IEEE T ENG MANAGE, V70, P3745, DOI 10.1109/TEM.2021.3084687
Daragmeh A., 2021, J. Open Innov. Technol. Market Complexity., V7
Doane AN, 2016, COMPUT HUM BEHAV, V60, P508, DOI 10.1016/j.chb.2016.02.010
Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
Faraz NA, 2021, CORP SOC RESP ENV MA, V28, P1171, DOI 10.1002/csr.2115
Floyd DL, 2000, J APPL SOC PSYCHOL, V30, P407, DOI 10.1111/j.1559-1816.2000.tb02323.x
Gao YW, 2015, IND MANAGE DATA SYST, V115, P1704, DOI 10.1108/IMDS-03-2015-0087
Graf A, 2023, NEUROSCIENCE, V515, P71, DOI 10.1016/j.neuroscience.2023.02.008
Gumasing MJJ, 2022, INT J DISAST RISK RE, V70, DOI 10.1016/j.ijdrr.2021.102759
Hair JF., 2022, MULTIVARIATE DATA AN
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hill J, 2015, COMPUT HUM BEHAV, V49, P245, DOI 10.1016/j.chb.2015.02.026
Homolak J, 2023, CROAT MED J, V64, P1, DOI 10.3325/cmj.2023.64.1
Hooda A, 2021, INT J ELECTRON GOV R, V17, P44, DOI 10.4018/IJEGR.2021070103
Hsieh HL, 2017, INT J ENV RES PUB HE, V14, DOI 10.3390/ijerph14010008
Hutson M, 2021, NATURE, V591, P22, DOI 10.1038/d41586-021-00530-0
Ifinedo P, 2012, COMPUT SECUR, V31, P83, DOI 10.1016/j.cose.2011.10.007
Jobin A, 2019, NAT MACH INTELL, V1, P389, DOI 10.1038/s42256-019-0088-2
Johnston AC, 2010, MIS QUART, V34, P549
Jorzik P, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3275643
Kalinic Z, 2021, EXPERT SYST APPL, V175, DOI 10.1016/j.eswa.2021.114803
Karatas K, 2023, EDUC URBAN SOC, V55, P674, DOI 10.1177/00131245221087999
Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
Keith MJ, 2015, INFORM SYST J, V25, P637, DOI 10.1111/isj.12082
King MR, 2023, CELL MOL BIOENG, V16, P1, DOI 10.1007/s12195-022-00754-8
Kretzschmar K, 2019, BIOMED INFORM INSIGH, V11, DOI 10.1177/1178222619829083
Lee Y, 2011, DECIS SUPPORT SYST, V50, P361, DOI 10.1016/j.dss.2010.07.009
Liu MQ, 2022, HUM VACC IMMUNOTHER, V18, DOI 10.1080/21645515.2022.2026136
Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
McGreevey JD, 2020, JAMA-J AM MED ASSOC, V324, P552, DOI 10.1001/jama.2020.2724
McTear M., 2021, Synthesis Lectures on Human Language Technologies.
Méndez-Suárez M, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3258686
Miorandi D, 2012, AD HOC NETW, V10, P1497, DOI 10.1016/j.adhoc.2012.02.016
Mou J, 2022, J ASSOC INF SYST, V23, P196, DOI 10.17705/1jais.00723
Alshabib HN, 2022, IEEE T ENG MANAGE, V69, P3664, DOI 10.1109/TEM.2021.3083330
Nguyen TH, 2022, INFORM SYST FRONT, V24, P797, DOI 10.1007/s10796-021-10212-x
Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
Picoto WN, 2023, INT J E-BUS RES, V19, DOI 10.4018/IJEBR.321647
PODSAKOFF PM, 1986, J MANAGE, V12, P531, DOI 10.1177/014920638601200408
Qin F, 2020, BRIT J EDUC TECHNOL, V51, P1693, DOI 10.1111/bjet.12994
Rainear AM, 2017, COMMUN RES REP, V34, P239, DOI 10.1080/08824096.2017.1286472
Roberts H, 2021, AI SOC, V36, P59, DOI 10.1007/s00146-020-00992-2
ROGERS RW, 1975, J PSYCHOL, V91, P93, DOI 10.1080/00223980.1975.9915803
Rogers RW., 1997, HDB HLTH BEHAV RES
Sedlakova J, 2023, AM J BIOETHICS, V23, P4, DOI 10.1080/15265161.2022.2048739
Sergueeva K, 2017, LECT NOTES COMPUT SC, V10293, P209, DOI 10.1007/978-3-319-58481-2_17
Shore A, 2022, COMPUT HUM BEHAV, V130, DOI 10.1016/j.chb.2022.107188
Siponen M, 2007, INT FED INFO PROC, V232, P133
Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
Tsai HYS, 2016, COMPUT SECUR, V59, P138, DOI 10.1016/j.cose.2016.02.009
Vance A, 2012, INFORM MANAGE-AMSTER, V49, P190, DOI 10.1016/j.im.2012.04.002
Verkijika SF, 2018, COMPUT SECUR, V77, P860, DOI 10.1016/j.cose.2018.03.008
von Solms R, 2013, COMPUT SECUR, V38, P97, DOI 10.1016/j.cose.2013.04.004
White GRT, 2022, IEEE T ENG MANAGE, V69, P3826, DOI 10.1109/TEM.2020.2994981
Wu DM, 2020, COMPUT HUM BEHAV, V105, DOI 10.1016/j.chb.2019.106229
Zhou J, 2024, FRONT INFORM TECH EL, V25, P6, DOI 10.1631/FITEE.2300089
NR 81
TC 0
Z9 0
U1 12
U2 12
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 1714
EP 1725
DI 10.1109/TEM.2023.3339578
PG 12
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA DT2F1
UT WOS:001134252100004
DA 2024-03-27
ER
PT J
AU Kim, YS
Hwangbo, H
Lee, HJ
Lee, WS
AF Kim, Yang Sok
Hwangbo, Hyunwoo
Lee, Hee Jun
Lee, Won Seok
TI Sequence aware recommenders for fashion E-commerce
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article; Early Access
DE Recommendation system; Sequence-aware recommendation; Deep learning;
Fashion E-commerce; Fashion product recommendation; Recurrent neural
network
ID ALGORITHM; SYSTEMS; SUPPORT; CLOTHES
AB In recent years, fashion e-commerce has become more and more popular. Since there are so many fashion products provided by e-commerce retailers, it is necessary to provide recommendation services to users to minimize information overload. When users look for a product on an e-commerce website, they usually click the product information sequentially. Previous recommenders, such as content-based recommenders and collaborative filtering recommenders, do not consider this important behavioral characteristic. To take advantage of this important characteristic, this study proposes sequence-aware recommenders for fashion product recommendation using a gated recurrent unit (GRU) algorithm. We conducted an experiment using a dataset collected from an e-commerce website of a Korean fashion company. Experimental results show that sequence aware recommenders outperform non-sequence aware recommender, and multiple sequence-based recommenders outperform a single sequence-based recommender because they consider the attributes of fashion products. Finally, we discuss the implications of our study on fashion recommendations and propose further research topics.
C1 [Kim, Yang Sok; Lee, Hee Jun; Lee, Won Seok] Keimyung Univ, Dept Management Informat Syst, Daegu 42061, South Korea.
[Hwangbo, Hyunwoo] Hana Financial Grp, Seoul 04523, South Korea.
C3 Keimyung University
RP Hwangbo, H (autor correspondiente), Hana Financial Grp, Seoul 04523, South Korea.
EM scotthwangbo@gmail.com
FU Keimyung University [20180731, 20190760, 20200589]
FX This research was supported by the Bisa Research Grant of Keimyung
University in 2018, 2019, and 2020 (No. 20180731, No. 20190760, and No.
20200589).
CR [Anonymous], 2010, RECSYS, DOI [DOI 10.1145/1864708.1864770, 10.1145/1864708.1864770.]
Aryabarzan N, 2018, EXPERT SYST APPL, V105, P129, DOI 10.1016/j.eswa.2018.03.041
Ay B., 2021, GENERATIVE ADVERSARI, P185
Bernhardsson E., 2014, RECURRENT NEURAL NET
Campos PG, 2014, USER MODEL USER-ADAP, V24, P67, DOI 10.1007/s11257-012-9136-x
Cheng WH, 2021, ACM COMPUT SURV, V54, DOI [10.1145/3552468.3554360, 10.1145/3447239]
Cho KYHY, 2014, Arxiv, DOI arXiv:1409.1259
Cunha T, 2018, INFORM SCIENCES, V423, P128, DOI 10.1016/j.ins.2017.09.050
Da'u A, 2020, ARTIF INTELL REV, V53, P2709, DOI 10.1007/s10462-019-09744-1
Devooght R., 2016, arXiv
Devooght R, 2017, PROCEEDINGS OF THE 25TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'17), P13, DOI 10.1145/3079628.3079670
Ding Y., 2005, PROC 14 ACM INT C IN, P485, DOI DOI 10.1145/1099554.1099689
Fang H, 2020, ACM T INFORM SYST, V39, DOI 10.1145/3426723
Fukuda M, 2011, LECT NOTES ENG COMP, P349
Glauber R, 2019, Arxiv, DOI arXiv:1912.08932
Gu SD, 2017, PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMMUNICATION AND INFORMATION PROCESSING (ICCIP 2017), P185, DOI 10.1145/3162957.3162982
Hidasi B, 2016, Arxiv, DOI arXiv:1511.06939
Hidasi B, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P241, DOI 10.1145/2959100.2959167
Hwangbo H, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11123336
Iwata T., 2011, IJCAI, V3, P2262
Jagadeesh V, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1925, DOI 10.1145/2623330.2623332
Jannach D, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P306, DOI 10.1145/3109859.3109872
Kolstad A., 2017, P RECSYSKTL WORKSHOP
Kuhn T, 2019, Arxiv, DOI arXiv:1908.09493
Lin YR, 2019, CACS INT AUTOMAT CON, DOI 10.1109/cacs47674.2019.9024361
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Liu K, 2017, INT CONF DAT MIN WOR, P773, DOI 10.1109/ICDMW.2017.107
Lu J, 2015, DECIS SUPPORT SYST, V74, P12, DOI 10.1016/j.dss.2015.03.008
Meena S., 2018, FORRESTER ANAL ONLIN
Moreira GDP, 2018, PROCEEDINGS OF THE 3RD WORKSHOP ON DEEP LEARNING FOR RECOMMENDER SYSTEMS (DLRS), P15, DOI 10.1145/3270323.3270328
Quadrana M., 2017, ALGORITHM SEQUENCE A
Quadrana M, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P539, DOI 10.1145/3240323.3241621
Quadrana M, 2018, ACM COMPUT SURV, V51, DOI 10.1145/3190616
Quanping H., 2015, INT J U AND E SERVIC, V8, P159, DOI 10.14257/ijunesst.2015.8.10.16
Rendle S., 2009, P 25 C UNCERTAINTY A, P452, DOI DOI 10.5555/1795114.1795167
Sato Ayaka, 2013, Human-Computer Interaction. Towards Intelligent and Implicit Interaction. 15th International Conference, HCI International 2013. Proceedings: LNCS 8008, P112, DOI 10.1007/978-3-642-39342-6_13
Shani G, 2005, J MACH LEARN RES, V6, P1265
Sherstinsky A, 2020, PHYSICA D, V404, DOI 10.1016/j.physd.2019.132306
Shewalkar A, 2019, J ARTIF INTELL SOFT, V9, P235, DOI 10.2478/jaiscr-2019-0006
Shi Y, 2014, ACM COMPUT SURV, V47, DOI 10.1145/2556270
Smirnova E., 2017, P 2 WORKSH DEEP LEAR, P2
Sottocornola G, 2018, COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), P1395, DOI 10.1145/3184558.3191582
Soysal ÖM, 2015, EXPERT SYST APPL, V42, P2582, DOI 10.1016/j.eswa.2014.10.049
Stan C, 2019, I C CONTR SYS COMP S, P210, DOI 10.1109/CSCS.2019.00042
Tan Y K, 2016, P 1 WORKSH DEEP LEAR, P17, DOI DOI 10.1145/2988450.2988452
Tavakol M, 2014, PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14), P33, DOI 10.1145/2645710.2645739
Tran T, 2016, INFORM SCIENCES, V369, P221, DOI 10.1016/j.ins.2016.06.027
Wang SY, 2021, APPL SOFT COMPUT, V110, DOI 10.1016/j.asoc.2021.107753
Wu C. Y., 2017, INT C LEARNING REPRE
Wu CY, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P495, DOI 10.1145/3018661.3018689
Xin Wang, 2019, Journal of Physics: Conference Series, V1325, DOI 10.1088/1742-6596/1325/1/012089
Yin RP, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P3434, DOI 10.1145/3308558.3313739
Yonezawa Y, 2009, LECT NOTES COMPUT SC, V5617, P323, DOI 10.1007/978-3-642-02556-3_37
Yu WH, 2018, WEB CONFERENCE 2018: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW2018), P649, DOI 10.1145/3178876.3186146
Yuan WH, 2020, INFORM SCIENCES, V510, P122, DOI 10.1016/j.ins.2019.09.007
Zhang YY, 2014, AAAI CONF ARTIF INTE, P1369
Zhou C, 2017, Arxiv, DOI arXiv:1711.06632
Zhu QN, 2019, AAAI CONF ARTIF INTE, P5973
Zhu T., 2021, IEEE T KNOWL DATA EN
NR 59
TC 1
Z9 2
U1 12
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD 2022 NOV 11
PY 2022
DI 10.1007/s10660-022-09627-8
EA NOV 2022
PG 21
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 6C2VR
UT WOS:000881879200001
OA hybrid
DA 2024-03-27
ER
PT J
AU Hettler, FM
Schumacher, JP
Anton, E
Eybey, B
Teuteberg, F
AF Hettler, Fabia Marie
Schumacher, Jan-Philip
Anton, Eduard
Eybey, Berna
Teuteberg, Frank
TI Understanding the user perception of digital nudging in platform
interface design
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article; Early Access
DE e-commerce; User perception; Digital nudging; Platform interface design;
Human-computer interaction; Choice architecture
ID EXPERIMENTAL VIGNETTE; E-COMMERCE; ELECTRONIC COMMERCE; PERCEIVED RISK;
STATUS-QUO; TRUST; TECHNOLOGY; ACCEPTANCE; MODEL; SYSTEMS
AB Given the nascent understanding of user perceptions toward digital nudges in e-commerce, our study examines key factors: perceived usefulness, ease of use, trust, and privacy risks. Via an online experiment of 273 participants, we examined the influence of digital nudging interventions - social norms, defaults, and scarcity warnings - against a control group. Employing descriptive and inferential statistics, notable trust variations were found between default and scarcity warning groups versus controls. To assess these findings, we interviewed 11 information systems and psychology experts. This research enriches our understanding of digital nudges in e-commerce and provides design insights. Theoretical implications span from providing propositions in order to enhance user involvement, conducting narrative accompanying research, analyzing diverse time points of nudging. Practical implications focus on emphasizing to users their choice autonomy and the highlighting that defaults and scarcity warnings are designed to mitigate inherent heuristics and biases for combining nudging with boosting elements.
C1 [Hettler, Fabia Marie; Anton, Eduard; Eybey, Berna; Teuteberg, Frank] Osnabruck Univ, Dept Accounting & Informat Syst, Katharinenstr 3, D-49074 Osnabruck, Germany.
[Schumacher, Jan-Philip] Osnabruck Univ, Work & Org Psychol, Lise Meitner Str 3, D-49076 Osnabruck, Germany.
C3 University Osnabruck; University Osnabruck
RP Hettler, FM (autor correspondiente), Osnabruck Univ, Dept Accounting & Informat Syst, Katharinenstr 3, D-49074 Osnabruck, Germany.
EM fabia.hettler@uos.de; jan.schumacher@uos.de; eduard.anton@uos.de;
beybey@uos.de; frank.teuteberg@uos.de
FU Universitt Osnabrck (3158)
FX No Statement Available
CR Aguinis H, 2014, ORGAN RES METHODS, V17, P351, DOI 10.1177/1094428114547952
Atzmüller C, 2010, METHODOLOGY-EUR, V6, P128, DOI 10.1027/1614-2241/a000014
Auf der Landwehr M., 2021, 29 EUR C INF SYST EC
Baldassarri D, 2017, ANNU REV SOCIOL, V43, P41, DOI 10.1146/annurev-soc-073014-112445
Barev T., 2021, Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS), DOI [10.24251/hicss.2021.500, DOI 10.24251/HICSS.2021.500]
Barev Torben Jan, 2021, The Next Wave of Sociotechnical Design: 16th International Conference on Design Science Research in Information Systems and Technology, DESRIST 2021, Proceedings. Lecture Notes in Computer Science, Information Systems and Applications, incl. Internet/Web, and HCI (12807), P299, DOI 10.1007/978-3-030-82405-1_30
Benamati J, 2010, IEEE T ENG MANAGE, V57, P380, DOI 10.1109/TEM.2009.2023111
Benbasat I, 2005, Journal of the Association for Information Systems, V6, P4, DOI [DOI 10.17705/1JAIS.00065, 10.17705/1jais.00065]
Berger M., 2020, 44ST INT C INF SYST, P1
Bhatia J, 2018, ACM T COMPUT-HUM INT, V25, DOI 10.1145/3267808
Bongard-Blanchy K, 2021, PROCEEDINGS OF THE 2021 ACM DESIGNING INTERACTIVE SYSTEMS CONFERENCE (DIS 2021), P763, DOI 10.1145/3461778.3462086
Brehm JW., 1966, A theory of psychological reactance
Brignull H., 2023, Deceptive design
Brusilovsky P., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P263
Calefato F, 2015, ELECTRON COMMER RES, V15, P453, DOI 10.1007/s10660-015-9194-3
Campos P, 2023, ELECTRON COMMER RES, DOI 10.1007/s10660-023-09725-1
Caraban A, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300733
Castagnos S., 2009, P 3 ACM C REC SYST, P361, DOI [10.1145/1639714.1639788, DOI 10.1145/1639714.1639788]
Chen S. C., 2003, Information Technology & Management, V4, P303, DOI 10.1023/A:1022962631249
Cialdini RB, 2004, ANNU REV PSYCHOL, V55, P591, DOI 10.1146/annurev.psych.55.090902.142015
Clavien C, 2018, J MORAL EDUC, V47, P366, DOI 10.1080/03057240.2017.1408577
Costello F. J., 2022, 55 HAW INT C SYST SC, DOI [10.24251/hicss.2022.531, DOI 10.24251/HICSS.2022.531]
Dalecke Sandor, 2020, WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics, P139, DOI 10.1145/3405962.3405975
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
Dennis AR, 2020, J MANAGE INFORM SYST, V37, P39, DOI 10.1080/07421222.2019.1705505
DiCosola BM, 2020, CHI'20: EXTENDED ABSTRACTS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3334480.3382877
Dinev T, 2006, INFORM SYST RES, V17, P61, DOI 10.1287/isre.1060.0080
Djurica D., 2017, 23 AM C INF SYST AMC, P1
Eigenbrod L., 2018, 26 EUR C INF SYST EC, P1
Esposito G, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0173333
European Commission, 2021, Directorate-General for Communications Networks, Content and Technology. Analytical paper on the structure of the online platform economy post COVID-19 outbreak: Analytical paper 6, DOI [10.2759/234588, DOI 10.2759/234588]
Featherman MS, 2003, INT J HUM-COMPUT ST, V59, P451, DOI 10.1016/S1071-5819(03)00111-3
Fechner Wim, 2021, UMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, P284, DOI 10.1145/3450614.3464620
Franzoi S., 2022, 13 EUR C INF SYST EC, P1
Friedrich T., 2018, 39 INT C INF SYST IC, P1
FRIESTAD M, 1994, J CONSUM RES, V21, P1, DOI 10.1086/209380
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Glaser J., 2010, Experteninterviews und qualitative Inhaltsanalyse, V4th, DOI DOI 10.1007/978-3-531-91538-8
Gottschewski P. O., 2022, WIRTSCH 2022 P, V18
Gray CM, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3174108
Greenberg S., 2014, C DES INT SYST, P523, DOI DOI 10.1145/2598510.2598541
Grose CR, 2014, ANNU REV POLIT SCI, V17, P355, DOI 10.1146/annurev-polisci-072012-174350
Hauser DJ, 2018, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.00998
Heinemann G., 2021, Der Neue Online-Handel: Geschaftsmodelle, Geschaftssysteme und Benchmarks Im E-Commerce
Hertwig R, 2017, PERSPECT PSYCHOL SCI, V12, P973, DOI 10.1177/1745691617702496
Ingendahl M, 2021, J CONSUM BEHAV, V20, P324, DOI 10.1002/cb.1861
Jachimowicz J. M., 2019, Behavioural Public Policy, V3, P159, DOI DOI 10.1017/BPP.2018.43
Jaekel M., 2017, Die Macht Der Digitalen Plattformen: Wegweiser Im Zeitalter Einer Expandierenden Digitalsphare und kunstlicher Intelligenz
Jesse M, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.729589
Jesse M, 2021, COMPUT HUM BEHAV REP, V3, DOI 10.1016/j.chbr.2020.100052
Johnson EJ, 2012, MARKET LETT, V23, P487, DOI 10.1007/s11002-012-9186-1
Josselson R., 2007, Handbook of narrative inquiry, P537, DOI [10.4135/9781452226552, DOI 10.4135/9781452226552, 10.4135/9781452226552.n21, DOI 10.4135/9781452226552.N21]
KAHNEMAN D, 1991, J ECON PERSPECT, V5, P193, DOI 10.1257/jep.5.1.193
Kahneman D., 2011, Thinking, Fast and Slow
Katner K, 2019, CHI EA '19 EXTENDED ABSTRACTS: EXTENDED ABSTRACTS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290607.3313065
Kim DJ, 2008, DECIS SUPPORT SYST, V44, P544, DOI 10.1016/j.dss.2007.07.001
Kim Y, 2017, J INTERACT MARK, V38, P44, DOI 10.1016/j.intmar.2017.01.001
Kissmer T., 2018, P 24 AM C INF SYST A
Klepsch M, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.01997
Kretzer M, 2018, J ASSOC INF SYST, V19, P1145, DOI 10.17705/1jais.00523
Kroll T, 2021, BEHAV INFORM TECHNOL, V40, P1, DOI 10.1080/0144929X.2019.1584644
Lankton NK, 2015, J ASSOC INF SYST, V16, P880, DOI 10.17705/1jais.00411
Lee MKO, 2001, INT J ELECTRON COMM, V6, P75, DOI 10.1080/10864415.2001.11044227
Lembcke T. B., 2019, P 27 EUR C INF SYST
Leonard TC, 2008, CONST POLITICAL ECON, V19, P356, DOI 10.1007/s10602-008-9056-2
Li Y., 2016, IBusiness, P48, DOI DOI 10.4236/IB.2016.83006
Lieberman A, 2020, CURR OPIN PSYCHOL, V31, P16, DOI 10.1016/j.copsyc.2019.06.022
Limayem M, 2000, IEEE T SYST MAN CY A, V30, P421, DOI 10.1109/3468.852436
Maier M., 2020, Human Technology, V16, P170, DOI [10.17011/ht/urn.202008245641, DOI 10.17011/HT/URN.202008245641]
Maier M, 2022, P NATL ACAD SCI USA, V119, DOI 10.1073/pnas.2200300119
McKnight D.H., 2011, ACM T MANAG INF SYST, V2, P1, DOI [10.1145/1985347.1985353, DOI 10.1145/1985347.1985353]
McKnight DH, 2002, INFORM SYST RES, V13, P334, DOI 10.1287/isre.13.3.334.81
Mertens S, 2022, P NATL ACAD SCI USA, V119, DOI 10.1073/pnas.2107346118
Meske C., 2020, WI2020 Zentrale Tracks, P1203, DOI DOI 10.30844/WI_2020_K5-MESKE
Meske C., 2017, 25 EUR C INF SYST EC, P2587
Meske C, 2020, COMMUN ASSOC INF SYS, V46, P402, DOI 10.17705/1CAIS.04617
Michels L., 2022, Proceedings of the 55th Hawaii International Conference on System Sciences, DOI [10.24251/hicss.2022.574, DOI 10.24251/HICSS.2022.574]
Michels L, 2023, EUR J INFORM SYST, DOI 10.1080/0960085X.2023.2229787
Mirbabaie M, 2023, PAC ASIA J ASSOC INF, V15, P65, DOI 10.17705/1pais.15303
Mirsch T., 2018, 39 INT C INF SYST IC, P1
Mirsch T., 2017, P 13 INT TAG WIRTSCH, P634
Münscher R, 2016, J BEHAV DECIS MAKING, V29, P511, DOI 10.1002/bdm.1897
Neslin SA, 2006, J SERV RES-US, V9, P95, DOI 10.1177/1094670506293559
Parker Geoffrey G., 2016, Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You
Pathak B, 2010, J MANAGE INFORM SYST, V27, P159, DOI 10.2753/MIS0742-1222270205
Pavlou PA, 2003, INT J ELECTRON COMM, V7, P101, DOI 10.1080/10864415.2003.11044275
Pavlou PA, 2007, MIS QUART, V31, P105
Ren SN, 2022, INFORM TECHNOL MANAG, DOI 10.1007/s10799-022-00371-x
Rohden SF, 2023, ELECTRON COMMER RES, V23, P2035, DOI 10.1007/s10660-022-09626-9
Román S, 2007, J BUS ETHICS, V72, P131, DOI 10.1007/s10551-006-9161-y
Saadé R, 2005, INFORM MANAGE-AMSTER, V42, P317, DOI 10.1016/j.im.2003.12.013
Sanchez-Loor DA, 2023, ELECTRON COMMER RES, V23, P1971, DOI 10.1007/s10660-021-09525-5
Schöning C, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P4395
Selinger E, 2011, SOCIOL COMPASS, V5, P923, DOI 10.1111/j.1751-9020.2011.00413.x
Shen Y, 2020, DECIS SUPPORT SYST, V137, DOI 10.1016/j.dss.2020.113365
Shipman M.D., 2014, LIMITATIONS SOCIAL R
Shopify, 2023, Shop ify
Shopify, Ultimate Sales Boost
Stanovich KE, 2000, BEHAV BRAIN SCI, V23, P645, DOI 10.1017/S0140525X00003435
Steffel M, 2016, J MARKETING RES, V53, P865, DOI 10.1509/jmr.14.0421
Steiner PM, 2006, KOLNER Z SOZIOL SOZ, V58, P117, DOI 10.1007/s11575-006-0006-9
Sunstein C.R., 2014, Why Nudge? The Politics of Libertarian Paternalism
Sunstein CR, 2016, DUKE LAW J, V66, P121
SurveyCircle, 2023, Research website SurveyCircle
Thaler R. H., 2013, Choice Architecture. The Behavioral Foundations of Public Policy, P428, DOI [10.2307/j.ctv550cbm.31, DOI 10.2307/J.CTV550CBM.31, 10.1515/9781400845347-029]
Thaler RH, 2003, AM ECON REV, V93, P175, DOI 10.1257/000282803321947001
TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
Valta M., 2022, P C COMP PEOPL RES, P1, DOI [10.1145/3510606.3550211, DOI 10.1145/3510606.3550211]
van der Heijden H, 2003, INFORM MANAGE-AMSTER, V40, P541, DOI 10.1016/S0378-7206(02)00079-4
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Wambsganss Thiemo, 2022, Proceedings of the ACM on Human-Computer Interaction, DOI 10.1145/3555633
Wambsganss T, 2022, COMPUT EDUC, V191, DOI 10.1016/j.compedu.2022.104644
Weiming Huang, 2019, Journal of Physics: Conference Series, V1325, DOI 10.1088/1742-6596/1325/1/012087
Weinmann M, 2016, BUS INFORM SYST ENG+, V58, P433, DOI 10.1007/s12599-016-0453-1
Wong MS, 2015, BMC PUBLIC HEALTH, V15, DOI 10.1186/s12889-015-2626-0
Xu H, 2005, Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol 5, P897
Xu JJ, 2013, MIS QUART, V37, P777, DOI 10.25300/MISQ/2013/37.3.05
Yadav M, 2017, TELEMAT INFORM, V34, P1294, DOI 10.1016/j.tele.2017.06.001
Zanker M., 2012, P 6 ACM C REC SYST, P269, DOI [10.1145/2365952.2366011, DOI 10.1145/2365952.2366011]
Zhang B, 2014, S US PRIV SEC SOUPS, P159
NR 120
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD 2024 MAR 18
PY 2024
DI 10.1007/s10660-024-09825-6
EA MAR 2024
PG 38
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LI2N7
UT WOS:001186096400001
OA hybrid
DA 2024-03-27
ER
PT J
AU Grange, C
Barki, H
AF Grange, Camille
Barki, Henri
TI The Nature and Role of User Beliefs Regarding a Website's Design Quality
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Design Quality Beliefs; Human-Computer Interaction; IT Acceptance;
Navigation Quality; Page Layout Quality; User Experience; Visual
Quality; Web Design
ID INFORMATION-SYSTEMS SUCCESS; WEB SITE DESIGN; EMPIRICAL-EXAMINATION;
MCLEAN MODEL; TECHNOLOGY; ACCEPTANCE; USABILITY; TRUST; SATISFACTION;
ANTECEDENTS
AB Researchers and practitioners have long been interested in identifying the criteria that users consider important in assessing whether a system is worth using. However, past research in this domain has not taken into account the characteristics of a system's design and their quality in a systematic and comprehensive manner, which is likely to have limited the development of actionable design guidelines. The article addresses this issue by suggesting a research model that links user beliefs-which have traditionally been used in IT acceptance and success research (i.e., information quality, system quality, usefulness, and ease of use)-to their beliefs regarding the quality of three categories of a system's design (i.e., visual quality, page layout quality, and navigation quality) and testing it in the context of organizational intranets. The analysis of data collected from 159 intranet website users in three organizations supported the model, suggesting that the three categories of design quality beliefs significantly influenced users' assessment of their system's information quality and system quality.
C1 [Grange, Camille; Barki, Henri] HEC Montreal, Informat Technol, Montreal, PQ, Canada.
C3 Universite de Montreal; HEC Montreal
RP Grange, C (autor correspondiente), HEC Montreal, Informat Technol, Montreal, PQ, Canada.
CR Agarwal R, 2002, INFORM SYST RES, V13, P168, DOI 10.1287/isre.13.2.168.84
Al-Natour S, 2009, J ASSOC INF SYST, V10, P661
Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961
Barnes S.J., 2002, Journal of Electronic commerce Quality, V3, P114, DOI DOI 10.1016/J.IM.2006.06.001
Barnes SJ, 2001, INT J ELECTRON COMM, V6, P11, DOI 10.1080/10864415.2001.11044225
Barnes SJ, 2012, INFORM MANAGE-AMSTER, V49, P164, DOI 10.1016/j.im.2012.02.002
Bearden WilliamO., 1999, Handbook of Marketing Scales: Multi- Item Measures for Marketing and Consumer Behavior Research
Benbasat I, 2007, J ASSOC INF SYST, V8, P211, DOI 10.17705/1jais.00126
Bergkvist L, 2007, J MARKETING RES, V44, P175, DOI 10.1509/jmkr.44.2.175
Burton-Jones A, 2013, INFORM SYST RES, V24, P632, DOI 10.1287/isre.1120.0444
Cenfetelli RT, 2008, INFORM SYST RES, V19, P161, DOI 10.1287/isre.1070.0163
Cyr D, 2008, J MANAGE INFORM SYST, V24, P47, DOI 10.2753/MIS0742-1222240402
Cyr D, 2014, J INF TECHNOL-UK, V29, P1, DOI 10.1057/jit.2013.25
DAVIS FD, 1993, INT J MAN MACH STUD, V38, P475, DOI 10.1006/imms.1993.1022
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60
Evermann J., 2009, P 30 INT C INF SYST
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Garrett J., 2011, The elements of user experience: user-centered design for the web and beyond
GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
Gregg DG, 2010, ELECTRON COMMER RES, V10, P1, DOI 10.1007/s10660-010-9044-2
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hartson HR, 2003, BEHAV INFORM TECHNOL, V22, P315, DOI 10.1080/01449290310001592587
Hassan S, 2005, J ELECTRON COMMER OR, V3, P46, DOI 10.4018/jeco.2005040104
Henseler J, 2014, ORGAN RES METHODS, V17, P182, DOI 10.1177/1094428114526928
Hoehle H, 2015, MIS QUART, V39, P435, DOI 10.25300/MISQ/2015/39.2.08
Hong W, 2001, J MANAGE INFORM SYST, V18, P97, DOI 10.1080/07421222.2002.11045692
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Kim G, 2010, MIS QUART, V34, P345
Lee Y, 2003, COM ASS INFORM SYST, V12, P752, DOI [DOI 10.17705/1CAIS.01250, 10.17705/1CAIS.01250]
Liang HG, 2007, MIS QUART, V31, P59
Loiacono ET, 2007, INT J ELECTRON COMM, V11, P51, DOI 10.2753/JEC1086-4415110302
McKinney V, 2002, INFORM SYST RES, V13, P296, DOI 10.1287/isre.13.3.296.76
Morville P., 2006, Information Architecture for the World Wide Web, V3rd
Nelson RR, 2005, J MANAGE INFORM SYST, V21, P199, DOI 10.1080/07421222.2005.11045823
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Palmer JW, 2002, INFORM SYST RES, V13, P151, DOI 10.1287/isre.13.2.151.88
Petter S, 2008, EUR J INFORM SYST, V17, P236, DOI 10.1057/ejis.2008.15
Petter S, 2013, J MANAGE INFORM SYST, V29, P7, DOI 10.2753/MIS0742-1222290401
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Rai A, 2002, INFORM SYST RES, V13, P50, DOI 10.1287/isre.13.1.50.96
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Ronkko M., 2011, INT C INF SYST
Seddon PB, 1997, INFORM SYST RES, V8, P240, DOI 10.1287/isre.8.3.240
Sonderegger A, 2010, APPL ERGON, V41, P403, DOI 10.1016/j.apergo.2009.09.002
Tan CW, 2013, MIS QUART, V37, P77
Tan CW, 2009, COMMUN ASSOC INF SYS, V24, P175
Tarafdar M, 2005, INF RESOUR MANAG J, V18, P62, DOI 10.4018/irmj.2005100104
TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144
Toms EG, 2002, J AM SOC INF SCI TEC, V53, P855, DOI 10.1002/asi.10094
Tractinsky N, 2000, INTERACT COMPUT, V13, P127, DOI 10.1016/S0953-5438(00)00031-X
Tractinsky N, 2018, HUM-COMPUT INTERACT, V33, P131, DOI 10.1080/07370024.2017.1298038
Vance A, 2008, J MANAGE INFORM SYST, V24, P73, DOI 10.2753/MIS0742-1222240403
Venkatesh V, 2006, MIS QUART, V30, P181
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
Venkatesh V, 2007, J ASSOC INF SYST, V8, P267, DOI 10.17705/1jais.00120
Walczak Steven, 2009, J. theor. appl. electron. commer. res., V4, P17, DOI 10.4067/S0718-18762009000300003
Wixom BH, 2001, MIS QUART, V25, P17, DOI 10.2307/3250957
Xu D. J., 2013, MIS Q, P777, DOI DOI 10.25300/MISQ/2013/37.3.05
Yamaguchi J., 1997, RASCH MEASUREMENT T, V11, P567
Yoo B., 2001, Q J ELECT COMMERCE, V2, P31, DOI DOI 10.1007/978-3-319-11885-7_129
NR 64
TC 20
Z9 20
U1 4
U2 28
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PD JAN-MAR
PY 2020
VL 32
IS 1
BP 75
EP 96
DI 10.4018/JOEUC.2020010105
PG 22
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA KH2NL
UT WOS:000510484400005
OA Bronze
DA 2024-03-27
ER
PT J
AU Maeng, Y
Lee, CC
Yun, H
AF Maeng, Yunho
Lee, Choong C.
Yun, Haejung
TI Understanding Antecedents That Affect Customer Evaluations of
Head-Mounted Display VR Devices through Text Mining and Deep Neural
Network
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE e-commerce; consumer behavior; big data; customer review; HMD VR; neural
network; text mining; sentiment analysis
ID SENTIMENT ANALYSIS; CONSUMER PERCEPTIONS; EMOTION; TWITTER; MODEL
AB Although the market for Head-Mounted Display Virtual Reality (HMD VR) devices has been growing along with the metaverse trend, the product has not been as widespread as initially expected. As each user has different purposes for use and prefers different features, various factors are expected to influence customer evaluations. Therefore, the present study aims to: (1) analyze customer reviews of hands-on HMD VR devices, provided with new user experience (UX), using text mining, and artificial neural network techniques; (2) comprehensively examine variables that affect user evaluations of VR devices; and (3) suggest major implications for the future development of VR devices. The research procedure consisted of four steps. First, customer reviews on HMD VR devices were collected from Amazon.com. Second, candidate variables were selected based on a literature review, and sentiment scores were extracted. Third, variables were determined through topic modeling, in-depth interviews, and a review of previous studies. Fourth, an artificial neural network analysis was performed by setting customer evaluation as a dependent variable, and the influence of each variable was checked through feature importance. The results indicate that feature importance can be derived from variables, and actionable implications can be identified, unlike in general sentiment analysis.
C1 [Maeng, Yunho; Lee, Choong C.] Yonsei Univ, Grad Sch Informat, Seoul 03722, South Korea.
[Yun, Haejung] Ewha Womans Univ, Coll Sci & Ind Convergence, Dept Int Off Adm, Seoul 03760, South Korea.
C3 Yonsei University; Ewha Womans University
RP Yun, H (autor correspondiente), Ewha Womans Univ, Coll Sci & Ind Convergence, Dept Int Off Adm, Seoul 03760, South Korea.
EM yunhj@ewha.ac.kr
RI Maeng, Yunho/KAM-2880-2024; Yun, Haejung/R-7767-2019
OI Yun, Haejung/0000-0002-8977-2044; Maeng, Yunho/0000-0003-1010-7840
CR Abarja R.A., 2020, Int. J, V8, P2156, DOI [10.30534/ijeter/2020/109852020, DOI 10.30534/IJETER/2020/109852020]
Ahmed AZ, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12031184
Ahmed N., 2005, P ACM S VIRT REAL SO, P257
Badenhausen K., 2015, ABOUT US
Balakrishnan V, 2022, J SUPERCOMPUT, V78, P7206, DOI 10.1007/s11227-021-04169-6
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Bloomberg, 2022, Virtual Reality Market Size Worth $87.0 Billion by 2030
Boas Yuri Antonio Goncalves Vilas, OVERVIEW VIRTUAL REA
Çalli L, 2023, INT J BANK MARK, V41, P428, DOI 10.1108/IJBM-08-2022-0351
Çalli L, 2023, TRANSPORT RES REC, V2677, P656, DOI 10.1177/03611981221112096
Chand S., 2003, GEOFLUIDS, V3, P275, DOI [10.1016/S0969-6989(02) 00062-0, DOI 10.1016/S0969-6989(02)00062-0]
Choi J., 2016, Master's Thesis
Chuang J, 2012, PROCEEDINGS OF THE INTERNATIONAL WORKING CONFERENCE ON ADVANCED VISUAL INTERFACES, P74, DOI 10.1145/2254556.2254572
Cohen MA, 2020, P NATL ACAD SCI USA, V117, P13821, DOI 10.1073/pnas.1922294117
Conneau A, 2016, arXiv, DOI DOI 10.48550/ARXIV.1606.01781
Ferreira FGDC, 2021, IEEE ACCESS, V9, P30898, DOI 10.1109/ACCESS.2021.3058133
Gogineni S., 2020, 2020 5th International Conference on Communication and Electronics Systems (ICCES), P1139, DOI 10.1109/ICCES48766.2020.9137994
Google Cloud, Best Practices for Creating Tabular Training Data|Vertex AI|
Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101
Horváth I, 2021, FRONT COMP SCI-SWITZ, V3, DOI 10.3389/fcomp.2021.673826
Imran AS, 2020, IEEE ACCESS, V8, P181074, DOI 10.1109/ACCESS.2020.3027350
Jo Y., 2011, P 4 ACM INT C WEB SE, P815, DOI [10.1145/1935826.1935932, DOI 10.1145/1935826.1935932]
John J., Virtual Reality Market Size Forecast to Reach
Khan ZY, 2021, ARTIF INTELL REV, V54, P95, DOI 10.1007/s10462-020-09892-9
Kim G., 2016, Master's Thesis
Kim HW, 2007, DECIS SUPPORT SYST, V43, P111, DOI 10.1016/j.dss.2005.05.009
Kim Y, 2014, P 2014 C EMP METH NA, DOI [DOI 10.3115/V1/D14-1181, 10.3115/v1/d14-1181, 10.3115/V1/D14-1181]
Korolov M., 2016, 98% of VR Headsets Sold This Year Are for Mobile Phones-Hypergrid Business
Kuyucuk T., 2022, Sak. Univ. J. Comput. Inf. Sci, V5, P371, DOI [10.35377/saucis ... 1121830 ..., DOI 10.35377/SAUCIS ... 1121830 ...]
La Viola J. J. Jr., 2000, SIGCHI Bulletin, V32, P47, DOI 10.1145/333329.333344
Lee Chungah, 2015, [The Journal of Society for e-Business Studies, 한국전자거래학회지], V20, P117, DOI 10.7838/jsebs.2015.20.1.117
Lee JH, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11082221
Lee S, 2019, PROCESSES, V7, DOI 10.3390/pr7120917
Legard R., 2003, Qualitative research practice, V1st, DOI DOI 10.1080/09687637.2016.1189876
Liu ZF, 2020, Arxiv, DOI arXiv:2012.06690
Lucini FR, 2020, J AIR TRANSP MANAG, V83, DOI 10.1016/j.jairtraman.2019.101760
Lundberg SM, 2017, ADV NEUR IN, V30
McCauley Michael E, 1992, Presence, V1, P311, DOI [DOI 10.1162/PRES.1992.1.3.311, 10.1162/pres.1992.1.3.311]
Mishev K, 2020, IEEE ACCESS, V8, P131662, DOI 10.1109/ACCESS.2020.3009626
Mudambi SM, 2010, MIS QUART, V34, P185
Nadeem W, 2020, TECHNOL FORECAST SOC, V151, DOI 10.1016/j.techfore.2019.119786
Ning XD, 2020, PATTERN RECOGN LETT, V132, P12, DOI 10.1016/j.patrec.2018.07.028
Park M.J., 2004, J. Commun. Res, V41, P29
Pezoa-Fuentes C, 2023, J THEOR APPL EL COMM, V18, P765, DOI 10.3390/jtaer18020039
Pouransari H., CS224N Proj, P1
Rohan, Virtual Reality Market Worth $15.89 Billion by 2020
Sagiroglu S, 2013, PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON COLLABORATION TECHNOLOGIES AND SYSTEMS (CTS), P42
Sharma AK, 2020, PROCEDIA COMPUT SCI, V167, P1139, DOI 10.1016/j.procs.2020.03.416
Shirani-Mehr H., 2014, Applications of deep learning to sentiment analysis of movie reviews
Sievert C., 2014, P WORKSH INT LANG LE, P63, DOI [DOI 10.3115/V1/W14-3110, 10.3115/v1/W14-3110]
Simons DJ, 1999, PERCEPTION, V28, P1059, DOI 10.1068/p2952
Suh KS, 2006, BEHAV INFORM TECHNOL, V25, P99, DOI 10.1080/01449290500330398
Suh KS, 2005, MIS QUART, V29, P673
Tan A.-H., 1999, Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases, P65
Ullah K, 2022, COMPLEXITY, V2022, DOI 10.1155/2022/5217491
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Yang C, 2021, J THEOR APPL EL COMM, V16, P1598, DOI 10.3390/jtaer16050090
Yang YX, 2022, J THEOR APPL EL COMM, V17, P1493, DOI 10.3390/jtaer17040075
ZEITHAML VA, 1988, J MARKETING, V52, P2, DOI 10.2307/1251446
Zhao JQ, 2017, IEEE ACCESS, V5, P2870, DOI 10.1109/ACCESS.2017.2672677
Zhao ZH, 2022, J THEOR APPL EL COMM, V17, P1, DOI 10.3390/jtaer17010001
NR 61
TC 1
Z9 1
U1 18
U2 18
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD SEP
PY 2023
VL 18
IS 3
BP 1238
EP 1256
DI 10.3390/jtaer18030063
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA S8NK9
UT WOS:001073679500001
OA gold
DA 2024-03-27
ER
PT J
AU Wang, GQ
Tan, GWH
Yuan, YP
Ooi, KB
Dwivedi, YK
AF Wang, Guoqiang
Tan, Garry Wei-Han
Yuan, Yunpeng
Ooi, Keng-Boon
Dwivedi, Yogesh K.
TI Revisiting TAM2 in behavioral targeting advertising: A deep
learning-based dual-stage SEM-ANN analysis
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Mobile advertising; Behavioral targeting advertising; Mobile commerce;
TAM; TAM2; Artificial neural network
ID TECHNOLOGY ACCEPTANCE MODEL; MOBILE CREDIT CARD; INFORMATION-TECHNOLOGY;
PERCEIVED USEFULNESS; SOCIAL MEDIA; INTENTION; ONLINE; TRUST; RISK;
DETERMINANTS
AB The study investigates the antecedents that affect consumers' acceptance of behavioral targeting advertising (BTA) services by extending technology acceptance Model 2 (TAM2) with perceived risk. A two-stage PLS-SEM-artificial-neural-network (ANN) predictive analytic approach was adopted to analyze the collected data, of which PLS-SEM was first applied to test the hypotheses, followed by the ANN technique to detect the nonlinear effect on the model. A total of 475 usable self-administered questionnaires were collected, and the results showed that only the relationship between the image and perceived usefulness (PU) was not supported. As per Model B, the ranking of subjective norms (SN) and PU between the PLS-SEM and ANN model does not match each other, implying that hidden attributes may exist in affecting the role of SN and PU under the practical context of which the relationship between variables may not fully be explained by a linear perspective. The finding is beneficial for advertising practitioners and software developers who wish to optimize BTA results. Theoretically, the study extends TAM2 in the context of advertising, which is a neglected research area. Methodologically, the study is the first to apply TAM2 using the hybrid PLS-SEM-ANN in the context of advertising.
C1 [Wang, Guoqiang; Tan, Garry Wei-Han; Yuan, Yunpeng; Ooi, Keng-Boon] UCSI Univ, UCSI Grad Business Sch, 1 Jalan Menara Gading, Cheras 56000, Wilayah Perseku, Malaysia.
[Tan, Garry Wei-Han] Nanchang Inst Technol, Sch Finance & Econ, Nanchang, Jiangxi, Peoples R China.
[Ooi, Keng-Boon] Chang Jung Christian Univ, Coll Management, Tainan, Guiren District, Taiwan.
[Dwivedi, Yogesh K.] Swansea Univ, Emerging Markets Res Ctr EMaRC, Sch Management, Room 323,Bay Campus, Swansea SA1 8EN, W Glam, Wales.
[Dwivedi, Yogesh K.] Symbiosis Inst Business Management, Pune, Maharashtra, India.
[Dwivedi, Yogesh K.] Symbiosis Int Deemed Univ, Pune, Maharashtra, India.
C3 UCSI University; Nanchang Institute Technology; Chang Jung Christian
University; Swansea University; Symbiosis International University;
Symbiosis Institute of Business Management (SIBM) Pune; Symbiosis
International University
RP Dwivedi, YK (autor correspondiente), Swansea Univ, Emerging Markets Res Ctr EMaRC, Sch Management, Room 323,Bay Campus, Swansea SA1 8EN, W Glam, Wales.; Dwivedi, YK (autor correspondiente), Symbiosis Inst Business Management, Pune, Maharashtra, India.; Dwivedi, YK (autor correspondiente), Symbiosis Int Deemed Univ, Pune, Maharashtra, India.
EM y.k.dwivedi@swansea.ac.uk
RI Dwivedi, Yogesh Kumar/A-5362-2008; Tan Wei Han, Garry/C-6565-2011; OOI,
Keng-Boon/I-4143-2019
OI Dwivedi, Yogesh Kumar/0000-0002-5547-9990; Tan Wei Han,
Garry/0000-0003-2974-2270; OOI, Keng-Boon/0000-0002-3384-1207
CR Agarwal R, 1997, DECISION SCI, V28, P557, DOI 10.1111/j.1540-5915.1997.tb01322.x
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Al-Omoush KS, 2021, J BUS RES, V132, P765, DOI 10.1016/j.jbusres.2020.10.056
Alwabel ASA, 2021, EXPERT SYST APPL, V185, DOI 10.1016/j.eswa.2021.115584
[Anonymous], 2009, P 18 INT C WORLD WID
[Anonymous], 2019, J RETAIL CONSUM SERV, DOI DOI 10.1016/j.jretconser.2017.11.001
Bagozzi RP, 2007, J ASSOC INF SYST, V8, P243
Barki H, 2001, MIS QUART, V25, P195, DOI 10.2307/3250929
Baudier P, 2020, TECHNOL FORECAST SOC, V153, DOI 10.1016/j.techfore.2018.06.043
Belanche D, 2012, J RETAIL CONSUM SERV, V19, P124, DOI 10.1016/j.jretconser.2011.11.001
Benbasat I, 2007, J ASSOC INF SYST, V8, P211, DOI 10.17705/1jais.00126
Bilgihan A, 2014, INT J CONTEMP HOSP M, V26, P349, DOI 10.1108/IJCHM-11-2012-0220
Bittner JV, 2014, J CONSUM MARK, V31, P391, DOI 10.1108/JCM-04-2014-0945
Chatterjee S, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120880
Cheung MFY, 2017, COMPUT HUM BEHAV, V76, P102, DOI 10.1016/j.chb.2017.07.011
China Internet Network Information Center (CINIC), 2021, 47 CHIN STAT REP INT
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Davis FD, 1996, INT J HUM-COMPUT ST, V45, P19, DOI 10.1006/ijhc.1996.0040
de Vries L, 2017, J MARKETING, V81, P1, DOI 10.1509/jm.15.0178
Dong L., 2021, INT J INF MANAGE, V61, P1
Dwivedi YK, 2020, INT J INFORM MANAGE, V55, DOI 10.1016/j.ijinfomgt.2020.102211
Dwivedi YK, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2020.102168
Elwalda A., 2016, J CUSTOMER BEHAV, V15, P123
Estrada-Jiménez J, 2017, COMPUT COMMUN, V100, P32, DOI 10.1016/j.comcom.2016.12.016
Fidriani A.P., 2019, ICORE, V5, P190
Fortune Business Insights.,, 2020, MOB ADV MARK SIZ SHA
Gan CM, 2018, COMPUT HUM BEHAV, V78, P306, DOI 10.1016/j.chb.2017.10.003
Ghosh T, 2021, J INTERACT MARK, V55, P52, DOI 10.1016/j.intmar.2021.01.002
GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
Halkola V, 2017, THESIS
HARTWICK J, 1994, MANAGE SCI, V40, P440, DOI 10.1287/mnsc.40.4.440
Haykin S, 1994, NEURAL NETWORKS COMP
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hew JJ, 2019, TECHNOL FORECAST SOC, V144, P311, DOI 10.1016/j.techfore.2017.10.007
Ismagilova E, 2021, EUR J MARKETING, V55, P1067, DOI 10.1108/EJM-07-2018-0472
Izuagbe R, 2017, LIBR REV, V66, P679, DOI 10.1108/LR-09-2016-0086
Jaradat Mohammed-Issa Riad Mousa, 2014, International Journal of Business Information Systems, V16, P271, DOI 10.1504/IJBIS.2014.063768
Jin B, 2008, INT MARKET REV, V25, P324, DOI 10.1108/02651330810877243
Karjaluoto H, 2019, INT J INFORM MANAGE, V47, P252, DOI 10.1016/j.ijinfomgt.2018.08.014
Kemp, 2020, DIGITAL 2020 GLOBAL
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Kiat YapChing., 2017, International Journal of Business and Social Science, V8, P92
Kim N.Y., 2017, J PROMOT MANAG, V24, P845
Knoesen Henri, 2019, SACJ, V31, P117, DOI 10.18489/sacj.v31i2.690
Kovco Ante, 2018, WSEAS Transactions on Business and Economics, V15, P73
Krishen AS, 2021, J BUS RES, V131, P183, DOI 10.1016/j.jbusres.2021.03.061
Kuo YF, 2009, COMPUT HUM BEHAV, V25, P103, DOI 10.1016/j.chb.2008.07.007
Lee YC, 2010, EXPERT SYST APPL, V37, P1745, DOI 10.1016/j.eswa.2009.07.034
Legris P, 2003, INFORM MANAGE-AMSTER, V40, P191, DOI 10.1016/S0378-7206(01)00143-4
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Lew SS, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101430
Li H, 2020, J CONSUM POLICY, V43, P723, DOI 10.1007/s10603-020-09469-7
Liang HG, 2007, MIS QUART, V31, P59
Lim A, 2022, SUPPLY CHAIN MANAG, V27, P611, DOI 10.1108/SCM-03-2021-0129
Lin CA, 2016, COMPUT HUM BEHAV, V64, P710, DOI 10.1016/j.chb.2016.07.027
Lin CW, 2017, INT J MOB COMMUN, V15, P104, DOI 10.1504/IJMC.2017.080580
Lin PJ, 2009, J HOSP MARKET MANAG, V18, P782, DOI 10.1080/19368620903235803
Loh XM, 2021, INTERNET RES, V31, P376, DOI 10.1108/INTR-04-2020-0175
Loh XM, 2022, J COMPUT INFORM SYST, V62, P39, DOI 10.1080/08874417.2019.1688733
Long Z, 2019, TRANSPORT RES A-POL, V129, P185, DOI 10.1016/j.tra.2019.08.006
Lu XH, 2016, ACM TRANS MANAG INF, V7, DOI 10.1145/2883816
Magni D, 2021, TECHNOL FORECAST SOC, V172, DOI 10.1016/j.techfore.2021.121022
Martins J, 2019, J BUS RES, V94, P378, DOI 10.1016/j.jbusres.2017.12.047
McLean G, 2020, J BUS RES, V106, P139, DOI 10.1016/j.jbusres.2019.08.032
Mei-ying Wu, 2011, WSEAS Transactions on Business and Economics, V8, P133
Mishra A., 2021, INT J INF MANAG
Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192
Muk A, 2015, J BUS RES, V68, P1, DOI 10.1016/j.jbusres.2014.06.001
Mun-Wai Siah, 2015, International Journal of Modelling in Operations Management, V5, P177
Narang U, 2019, REV MARKET RES, V16, P97, DOI 10.1108/S1548-643520190000016008
Nepomuceno MV, 2014, J RETAIL CONSUM SERV, V21, P619, DOI 10.1016/j.jretconser.2013.11.006
Nill A., 2014, J CURRENT ISSUES RES, V35, P126, DOI [10.1080/10641734.2014.899529, DOI 10.1080/10641734.2014.899529]
Nwagwu WE, 2016, ELECTRON LIBR, V34, P265, DOI 10.1108/EL-09-2014-0169
Ooi KB, 2021, IND MANAGE DATA SYST, V121, P312, DOI 10.1108/IMDS-04-2020-0239
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
Ozcelik AB, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.11.006
Paramaeswari Rr Putri Intan, 2020, 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), P505, DOI 10.1109/iSemantic50169.2020.9234271
Pfeffer J., 1982, ORG ORG THEORY
Martínez-Ruiz MP, 2017, FRONT PSYCHOL, V7, DOI 10.3389/fpsyg.2016.02018
Ringle CM, 2016, IND MANAGE DATA SYST, V116, P1865, DOI 10.1108/IMDS-10-2015-0449
Saba T.B., 2017, INT J ELECT COMMERCE, V8, P134
Saura J.R., BIG DATA RES
Saura JR, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102331
Schnall R, 2015, STUD HEALTH TECHNOL, V216, P467, DOI 10.3233/978-1-61499-564-7-467
Sharifzadeh MS, 2017, CROP PROT, V96, P88, DOI 10.1016/j.cropro.2017.01.014
Sharma A, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106919
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Sreejesh S, 2021, J BUS RES, V132, P88, DOI 10.1016/j.jbusres.2021.04.007
Statista, 2019, TOP MOB INT US WORLD
Tan GWH, 2018, TELEMAT INFORM, V35, P2270, DOI 10.1016/j.tele.2018.09.005
Tan GWH, 2018, TELEMAT INFORM, V35, P1617, DOI 10.1016/j.tele.2018.04.009
Tan GWH, 2014, TELEMAT INFORM, V31, P292, DOI 10.1016/j.tele.2013.06.002
Teo T, 2014, J COMPUT HIGH EDUC, V26, P124, DOI 10.1007/s12528-014-9080-3
Tew HT, 2022, J COMPUT INFORM SYST, V62, P527, DOI 10.1080/08874417.2020.1858731
TikTok, 2020, TIKT REC VID FOR
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2012, MIS QUART, V36, P157
Wang L, 2019, INFORM MANAGE-AMSTER, V56, DOI 10.1016/j.im.2019.02.005
Wang ZN, 2019, IND MANAGE DATA SYST, V119, P1625, DOI 10.1108/IMDS-01-2019-0011
Wei M, 2020, PROCEEDINGS OF THE 29TH USENIX SECURITY SYMPOSIUM, P145
Wilson RD, 2019, J BUS IND MARK, V35, P495, DOI 10.1108/JBIM-01-2019-0060
Wong CH, 2015, ONLINE INFORM REV, V39, P762, DOI 10.1108/OIR-05-2015-0152
Wong CH, 2015, TELEMAT INFORM, V32, P720, DOI 10.1016/j.tele.2015.03.003
Wong LW, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.08.005
Xia HS, 2016, FINANC INNOV, V2, DOI 10.1186/s40854-016-0041-x
Yan LY, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102300
Younas M., 2019, COMMUN COMPUT INF SC, V1054, P32
Yu CP, 2020, IND MARKET MANAG, V90, P481, DOI 10.1016/j.indmarman.2019.05.001
Zhang JQ, 2019, OMEGA-INT J MANAGE S, V89, P136, DOI 10.1016/j.omega.2018.10.007
Zhang XT, 2017, BMC MED INFORM DECIS, V17, DOI 10.1186/s12911-017-0470-0
NR 113
TC 39
Z9 40
U1 50
U2 66
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD FEB
PY 2022
VL 175
AR 121345
DI 10.1016/j.techfore.2021.121345
EA JAN 2022
PG 15
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA 0D4DC
UT WOS:000775946700037
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Hsieh, SH
Lee, CT
AF Hsieh, Sara H.
Lee, Crystal T.
TI Hey Alexa: examining the effect of perceived socialness in usage
intentions of AI assistant-enabled smart speaker
SO JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
LA English
DT Article
DE AI assistant; Smart speaker; Social response; Media richness; Parasocial
interaction; Trust; TAM; Mobile marketing; Online marketing; Online
consumer behavior; Quantitative research; Consumer behavior; Structural
equation modeling; Human-computer interaction
AB Purpose
Artificially intelligent (AI) assistant-enabled smart speaker not only can provide assistance by navigating the massive amount of product and brand information on the internet but also can facilitate two-way conversations with individuals, thus resembling a human interaction. Although smart speakers have substantial implications for practitioners, the knowledge of the underlying psychological factors that drive continuance usage remains limited. Drawing on social response theory and the technology acceptance model, this study aims to elucidate the adoption process of smart speakers.
Design/methodology/approach
A field survey of 391 smart speaker users were obtained. Partial least squares structural equation modeling was used to analyze the data.
Findings
Media richness (social cues) and parasocial interactions (social role) are key determinants affecting the establishment of trust, perceived usefulness and perceived ease of use, which, in turn, affect attitude, continuance usage intentions and online purchase intentions through AI assistants.
Originality/value
AI assistant-enabled smart speakers are revolutionizing how people interact with smart products. Studies of smart speakers have mainly focused on functional or technical perspectives. This study is the first to propose a comprehensive model from both functional and social perspectives of continuance usage intention of the smart speaker and online purchase intentions through AI assistants.
C1 [Hsieh, Sara H.] Tunghai Univ, Dept Business Adm, Taichung, Taiwan.
[Lee, Crystal T.] Shantou Univ, Dept Business Sch, Shantou, Peoples R China.
C3 Tunghai University; Shantou University
RP Lee, CT (autor correspondiente), Shantou Univ, Dept Business Sch, Shantou, Peoples R China.
EM crystal.ty.lee@gmail.com
RI Lee, Crystal T./KFA-4668-2024
OI Lee, Crystal T./0000-0002-2451-1353
FU Ministry of Science and Technology of Taiwan, R.O.C. [MOST
108-2410-H-029-054]
FX The research was supported by grant MOST 108-2410-H-029-054, from the
Ministry of Science and Technology of Taiwan, R.O.C.
CR Aljukhadar M, 2010, INT J ELECTRON COMM, V14, P103, DOI 10.2753/JEC1086-4415140404
[Anonymous], 2019, eMarketer
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Balaji MS, 2010, J INTERACT ONLINE LE, V9, P1
Bandura A, 2001, ANNU REV PSYCHOL, V52, P1, DOI 10.1146/annurev.psych.52.1.1
Barney-McNamara B, 2021, J RES INTERACT MARK, V15, P147, DOI 10.1108/JRIM-05-2020-0108
Ben Mimoun MS, 2017, INFORM MANAGE-AMSTER, V54, P545, DOI 10.1016/j.im.2016.11.008
Benbasat I., 2005, J ASSOC INF SYST, V6, P1
Brill TM, 2019, J MARKET MANAG-UK, V35, P1401, DOI 10.1080/0267257X.2019.1687571
Burgoon JK, 2002, J COMMUN, V52, P657, DOI 10.1093/joc/52.3.657
Castellano S, 2018, MANAGE DECIS, V56, P1232, DOI 10.1108/MD-04-2017-0444
Chandler J, 2014, BEHAV RES METHODS, V46, P112, DOI 10.3758/s13428-013-0365-7
Chattaraman V, 2014, J RES INTERACT MARK, V8, P144, DOI 10.1108/JRIM-08-2013-0054
Chen C.W., 2019, INT J BUSINESS EC, V18, P347
Chen CC, 2018, TELEMAT INFORM, V35, P1512, DOI 10.1016/j.tele.2018.03.019
Chérif E, 2019, RECH APPL MARKET-ENG, V34, P28, DOI 10.1177/2051570719829432
Chouk I, 2019, J SERV MARK, V33, P449, DOI 10.1108/JSM-01-2018-0046
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Coursaris CK, 2012, NEW MEDIA SOC, V14, P1128, DOI 10.1177/1461444812439552
DAFT RL, 1986, MANAGE SCI, V32, P554, DOI 10.1287/mnsc.32.5.554
DAFT RL, 1987, MIS QUART, V11, P355, DOI 10.2307/248682
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
Dawar N, 2018, HARVARD BUS REV, V96, P80
Edlund J, 2008, SPEECH COMMUN, V50, P630, DOI 10.1016/j.specom.2008.04.002
eMarketer, 2018, EMARKETER
Etemad-Sajadi R, 2016, COMPUT HUM BEHAV, V61, P227, DOI 10.1016/j.chb.2016.03.045
Fang YH, 2017, PSYCHOL MARKET, V34, P40, DOI 10.1002/mar.20972
Fogg BJ, 1997, INT J HUM-COMPUT ST, V46, P551, DOI 10.1006/ijhc.1996.0104
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gefen D, 2004, OMEGA-INT J MANAGE S, V32, P407, DOI 10.1016/j.omega.2004.01.006
Gefen D, 2000, OMEGA-INT J MANAGE S, V28, P725, DOI 10.1016/S0305-0483(00)00021-9
GIFFIN K, 1967, PSYCHOL BULL, V68, P104, DOI 10.1037/h0024833
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Goodman JK, 2013, J BEHAV DECIS MAKING, V26, P213, DOI 10.1002/bdm.1753
Goudey A, 2016, RECH APPL MARKET-ENG, V31, P2, DOI 10.1177/2051570716643961
Grewal D, 1998, J RETAILING, V74, P331, DOI 10.1016/S0022-4359(99)80099-2
Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
Han S, 2018, IND MANAGE DATA SYST, V118, P618, DOI 10.1108/IMDS-05-2017-0214
Handarkho YD, 2020, J RES INTERACT MARK, V14, P305, DOI 10.1108/JRIM-10-2018-0137
Hanus MD, 2015, INT J HUM-COMPUT ST, V84, P33, DOI 10.1016/j.ijhcs.2015.07.004
Hashim KF, 2015, INT J INFORM MANAGE, V35, P145, DOI 10.1016/j.ijinfomgt.2014.11.001
Hassanein K, 2005, INT J ELECTRON COMM, V10, P31, DOI 10.2753/JEC1086-4415100202
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hoffman DL, 2018, J CONSUM RES, V44, P1178, DOI 10.1093/jcr/ucx105
Hong A, 2020, TELECOMMUN POLICY, V44, DOI 10.1016/j.telpol.2019.101867
HORTON D, 1956, PSYCHIATR, V19, P215, DOI 10.1080/00332747.1956.11023049
Hsiao CC, 2012, INFORM MANAGE-AMSTER, V49, P292, DOI 10.1016/j.im.2012.09.002
Jin SAA, 2009, CYBERPSYCHOL BEHAV, V12, P723, DOI 10.1089/cpb.2008.0289
Kakalejcík L, 2020, J RES INTERACT MARK, V14, P17, DOI 10.1108/JRIM-01-2019-0012
Karahanna E, 2000, J ORG COMP ELECT COM, V10, P49, DOI 10.1207/S15327744JOCE100103
Keeling K, 2010, J BUS RES, V63, P793, DOI 10.1016/j.jbusres.2008.12.015
Kowalczuk P, 2018, J RES INTERACT MARK, V12, P418, DOI 10.1108/JRIM-01-2018-0022
Kwee-Meier ST, 2016, BEHAV INFORM TECHNOL, V35, P394, DOI 10.1080/0144929X.2016.1141986
Labrecque LI, 2014, J INTERACT MARK, V28, P134, DOI 10.1016/j.intmar.2013.12.003
Landwehr JR, 2011, J MARKETING, V75, P132, DOI 10.1509/jmkg.75.3.132
Lee H, 2020, TELEMAT INFORM, V49, DOI 10.1016/j.tele.2020.101377
Lee K, 2020, INFORM SYST FRONT, V22, P563, DOI 10.1007/s10796-019-09975-1
Lee MKO, 2007, J AM SOC INF SCI TEC, V58, P2066, DOI 10.1002/asi.20670
Lee MKO, 2005, INFORM MANAGE-AMSTER, V42, P1095, DOI 10.1016/j.im.2003.10.007
Lee SY, 2017, INT J HUM-COMPUT ST, V103, P95, DOI 10.1016/j.ijhcs.2017.02.005
LEVY MR, 1979, J BROADCASTING, V23, P69, DOI 10.1080/08838157909363919
Lim KH, 2000, MIS QUART, V24, P449, DOI 10.2307/3250969
Lin HX, 2020, J HOSP MARKET MANAG, V29, P530, DOI 10.1080/19368623.2020.1685053
Liu SH, 2009, COMPUT EDUC, V52, P599, DOI 10.1016/j.compedu.2008.11.002
Louie WYG, 2014, ASSIST TECHNOL, V26, P140, DOI 10.1080/10400435.2013.869703
Lowry PB, 2006, SMALL GR RES, V37, P631, DOI 10.1177/1046496406294322
Lu Y, 2014, COMPUT HUM BEHAV, V41, P40, DOI 10.1016/j.chb.2014.08.012
Lunney A, 2016, COMPUT HUM BEHAV, V65, P114, DOI 10.1016/j.chb.2016.08.007
Mamonov S, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2020.102091
Mani Z, 2019, J MARKET MANAG-UK, V35, P1460, DOI 10.1080/0267257X.2019.1667856
Moon Y, 2000, J CONSUM RES, V26, P323, DOI 10.1086/209566
Moriuchi E, 2019, PSYCHOL MARKET, V36, P489, DOI 10.1002/mar.21192
Mulcahy R, 2019, J MARKET MANAG-UK, V35, P1370, DOI 10.1080/0267257X.2019.1680568
NASS C, 1993, HUM COMMUN RES, V19, P504, DOI 10.1111/j.1468-2958.1993.tb00311.x
Nass C, 1996, INT J HUM-COMPUT ST, V45, P669, DOI 10.1006/ijhc.1996.0073
Nass C, 1999, J APPL SOC PSYCHOL, V29, P1093, DOI 10.1111/j.1559-1816.1999.tb00142.x
Nass C, 1997, J APPL SOC PSYCHOL, V27, P864, DOI 10.1111/j.1559-1816.1997.tb00275.x
Nguyen QN, 2019, INT J HUM-COMPUT INT, V35, P1362, DOI 10.1080/10447318.2018.1525023
Oppenheimer DM, 2009, J EXP SOC PSYCHOL, V45, P867, DOI 10.1016/j.jesp.2009.03.009
Park K, 2018, TELEMAT INFORM, V35, P2118, DOI 10.1016/j.tele.2018.07.013
Payne EHM, 2021, J RES INTERACT MARK, V15, P68, DOI 10.1108/JRIM-10-2020-0214
Peltier J, 2020, J RES INTERACT MARK, V14, P239, DOI 10.1108/JRIM-01-2020-0010
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Qin YS, 2020, J RES INTERACT MARK, V14, P337, DOI 10.1108/JRIM-08-2019-0138
Reeves B, 1996, MEDIA EQUATION PEOPL
Shin J, 2018, TECHNOL FORECAST SOC, V134, P246, DOI 10.1016/j.techfore.2018.06.029
Shum HY, 2018, FRONT INFORM TECH EL, V19, P10, DOI 10.1631/FITEE.1700826
Statista, 2018, GLOB INT ASS MARK SH
Stern SE, 2006, INT J HUM-COMPUT ST, V64, P43, DOI 10.1016/j.ijhcs.2005.07.002
Verhoef PC, 2017, J INTERACT MARK, V40, P1, DOI 10.1016/j.intmar.2017.06.001
Wagner K., 2019, P 52 HI INT C SYST S, P10
Wang EST, 2012, COMPUT HUM BEHAV, V28, P570, DOI 10.1016/j.chb.2011.11.002
Wang LC, 2007, J MARKETING, V71, P143, DOI 10.1509/jmkg.71.3.143
Wei CW, 2012, ETR&D-EDUC TECH RES, V60, P529, DOI 10.1007/s11423-012-9234-9
Wolkenfelt MRJ, 2020, J RES INTERACT MARK, V14, P89, DOI 10.1108/JRIM-11-2018-0141
Wu JT, 2017, J MARKET MANAG-UK, V33, P61, DOI 10.1080/0267257X.2016.1233132
Yang H, 2019, INF SYST E-BUS MANAG, V17, P65, DOI 10.1007/s10257-018-0375-1
Yang K. C. C., 2006, Telematics and Informatics, V23, P38, DOI 10.1016/j.tele.2005.03.001
Zhao SY, 2006, NEW MEDIA SOC, V8, P401, DOI 10.1177/1461444806061951
Zhao YM, 2020, COMPUT EDUC, V144, DOI 10.1016/j.compedu.2019.103721
NR 101
TC 45
Z9 46
U1 47
U2 239
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-7122
EI 2040-7130
J9 J RES INTERACT MARK
JI J. Res. Interact. Mark.
PD JUN 21
PY 2021
VL 15
IS 2
BP 267
EP 294
DI 10.1108/JRIM-11-2019-0179
EA MAY 2021
PG 28
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA SW1CO
UT WOS:000647757100001
DA 2024-03-27
ER
PT J
AU Hewapathirana, IU
AF Hewapathirana, Isuru Udayangani
TI Advancing tourism demand forecasting in Sri Lanka: evaluating the
performance of machine learning models and the impact of social media
data integration
SO JOURNAL OF TOURISM FUTURES
LA English
DT Article; Early Access
DE Tourism demand forecasting; Social media analytics; Machine learning;
Support vector regression; Random forest; Artificial neural network; Sri
Lanka
ID ARRIVALS; ARIMA
AB PurposeThis study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.Design/methodology/approachTwo sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.FindingsThe findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.Practical implicationsThe findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.Originality/valueThis study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.
C1 [Hewapathirana, Isuru Udayangani] Univ Kelaniya, Fac Sci, Kelaniya, Sri Lanka.
C3 University Kelaniya
RP Hewapathirana, IU (autor correspondiente), Univ Kelaniya, Fac Sci, Kelaniya, Sri Lanka.
EM ihewapathirana@kln.ac.lk
OI Hewapathirana, Isuru/0000-0003-4843-0993
FU University of Kelaniya [RP/03/02/09/01/2022]
FX The author extends her sincere gratitude to the reviewers for their
valuable feedback and acknowledges the significant contributions made in
improving the manuscript. The author also acknowledges the financial
support received for this study by the University Research Grant
RP/03/02/09/01/2022 from the University of Kelaniya. The support
provided by this grant has been instrumental in conducting and
completing the research.
CR Adil M, 2021, PROCESSES, V9, DOI 10.3390/pr9101759
Andariesta DT, 2022, J TOUR FUTURES, DOI 10.1108/JTF-10-2021-0239
Arlot S, 2010, STAT SURV, V4, P40, DOI 10.1214/09-SS054
Aslanargun A, 2007, J STAT COMPUT SIM, V77, P29, DOI 10.1080/10629360600564874
Balabaeva K, 2019, PROCEDIA COMPUT SCI, V156, P87, DOI 10.1016/j.procs.2019.08.183
Basnayake BRPM, 2022, STAT APPL, V20, P103
Biau G, 2016, TEST-SPAIN, V25, P197, DOI 10.1007/s11749-016-0481-7
BOX GEP, 1974, ROY STAT SOC C-APP, V23, P158, DOI 10.2307/2346997
Colladon AF, 2019, DECIS SUPPORT SYST, V123, DOI 10.1016/j.dss.2019.113075
Diunugala Hemantha Premakumara, 2020, Journal of Tourism, Heritage & Services Marketing, V6, P3, DOI 10.5281/zenodo.4055960
DIXON WJ, 1946, J AM STAT ASSOC, V41, P557, DOI 10.2307/2280577
Fiesler C., 2020, P INT AAAI C WEB SOC, V14, P187
Gunter U, 2016, ANN TOURISM RES, V61, P199, DOI 10.1016/j.annals.2016.10.007
Hadavandi E, 2011, TOURISM MANAGE, V32, P1196, DOI 10.1016/j.tourman.2010.09.015
Höpken W, 2021, J TRAVEL RES, V60, P998, DOI 10.1177/0047287520921244
Hu MM, 2021, TOURISM MANAGE, V84, DOI 10.1016/j.tourman.2020.104263
Hutto C. J., 2014, P INT AAAI C WEB SOC, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
Jassim R.S.Al., 2023, EAI Endorsed Transactions on Creative Technologies, V9, pe1, DOI [10.4108/eetct.v9i31.2986, DOI 10.4108/EETCT.V9I31.2986]
Jiang ZX, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1965
Kodippili A., 2017, An International Peer-Reviewed Journal, V29
Konarasinghe K.M.U.B., 2016, Review of Integrative Business and Economics ResearchOnlineCDROM, V5, P161
Kurukulasooriya N., 2014, Time Series Behaviour of Burgeoning, V1, P1
Li HY, 2020, ANN TOURISM RES, V83, DOI 10.1016/j.annals.2020.102912
Li X, 2021, J TRAVEL RES, V60, P1213, DOI 10.1177/0047287520934871
Li X, 2017, TOURISM MANAGE, V59, P57, DOI 10.1016/j.tourman.2016.07.005
Li Y., 2022, Journal of Digital Economy, V1, P32, DOI [10.1016/j.jdec.2022.08.006, DOI 10.1016/J.JDEC.2022.08.006]
Lutkepohl H., 2004, APPL TIME SERIES ECO
Marutho D., 2022, 2022 international Seminar on intelligent Technology and its applications (ISITIA), P98, DOI [10.1109/ISITIA56226.2022.9855341, DOI 10.1109/ISITIA56226.2022.9855341]
Mishra RK, 2021, INT J ADV COMPUT SC, V12, P55
Moreno MA, 2013, CYBERPSYCH BEH SOC N, V16, P708, DOI 10.1089/cyber.2012.0334
Park S, 2017, J TRAVEL TOUR MARK, V34, P357, DOI 10.1080/10548408.2016.1170651
Plevris V., 2022, INT J COAL PREP UTIL, P1, DOI [10.23967/eccomas.2022.155, DOI 10.23967/ECCOMAS.2022.155]
Priyangika J., 2016, S STAT COMP MOD APPL
Rajangam V., 2022, 2022 4 INT C BIOM EN, P7, DOI [10.1109/IBIOMED56408.2022.9988439, DOI 10.1109/IBIOMED56408.2022.9988439]
Reda O., 2023, Bulletin of Electrical Engineering and Informatics, V12, P1115, DOI [10.11591/eei.v12i2.4588, DOI 10.11591/EEI.V12I2.4588]
Samarathunga W., 2020, Available at SSRN 3581509, DOI [10.2139/ssrn.3581509, DOI 10.2139/SSRN.3581509]
Smola AJ, 2004, STAT COMPUT, V14, P199, DOI 10.1023/B:STCO.0000035301.49549.88
Song H., 2008, Tourism demand modelling and forecasting A review of recent research
Song HY, 2010, TOURISM ECON, V16, P63, DOI 10.5367/000000010790872213
Taecharungroj V, 2019, TOURISM MANAGE, V75, P550, DOI 10.1016/j.tourman.2019.06.020
Tealab Ahmed, 2018, Future Computing and Informatics Journal, V3, P334, DOI 10.1016/j.fcij.2018.10.003
Thushara SC, 2019, COGENT ECON FINANC, V7, DOI 10.1080/23322039.2019.1699884
Thushara S.C., 2016, Forecasting international tourist arrivals to Sri Lanka using sarima approach, V1
Tofallis C, 2015, J OPER RES SOC, V66, P1352, DOI 10.1057/jors.2014.103
Wickramasinghe K, 2021, CURR ISSUES TOUR, V24, P2740, DOI 10.1080/13683500.2020.1849049
Wolfram H., 2017, Information and Communication Technologies in Tourism 2017, P187, DOI DOI 10.1007/978-3-319-51168-9_14
Yu G. M., 2006, Journal of Travel Research, V45, P194, DOI 10.1177/0047287506291594
Zhang YS, 2021, J TRAVEL RES, V60, P981, DOI 10.1177/0047287520919522
NR 48
TC 0
Z9 0
U1 1
U2 1
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 2055-5911
EI 2055-592X
J9 J TOUR FUTURES
JI J. Tour. Futures
PD 2023 DEC 15
PY 2023
DI 10.1108/JTF-06-2023-0149
EA DEC 2023
PG 25
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA CR3L3
UT WOS:001126930000001
OA hybrid
DA 2024-03-27
ER
PT J
AU McAfee, RP
AF McAfee, R. Preston
TI The Design of Advertising Exchanges
SO REVIEW OF INDUSTRIAL ORGANIZATION
LA English
DT Article
DE Auctions; Winner's curse; Machine learning; Display advertising;
Internet advertising
ID AUCTION
AB Internet advertising exchanges possess three characteristics-fast delivery, low values, and automated systems-that influence market design. Automated learning systems induce the winner's curse when several pricing types compete. Bidders frequently compete with different data, which induces randomization in equilibrium. Machine learning causes the value of information to leak across participants. Discrimination may be used to induce efficient exploration, although publishers (websites) may balk at participating. The creation of "learning accounts," which divorce payments from receipts, may be used to internalize learning externalities. Under some learning mechanisms the learning account eventually shows a surplus. The solution is illustrated computationally.
C1 Yahoo Res, Burbank, CA 91504 USA.
C3 Yahoo! Inc; Yahoo! Inc United States
RP McAfee, RP (autor correspondiente), Yahoo Res, 3333 Empire Blvd, Burbank, CA 91504 USA.
EM preston@mcafee.cc
CR ABDULKADIROGLU A, 2005, AEA PAPERS P, P368
Abraham I., 2011, Peaches, lemons, and cookies: Designing auction markets with dispersed information
BAX E, 2011, COMPARING PREDICTED
Edelman B, 2007, AM ECON REV, V97, P242, DOI 10.1257/aer.97.1.242
HENDRICKS K, 1988, AM ECON REV, V78, P865
KENNEY RW, 1983, J LAW ECON, V26, P497, DOI 10.1086/467048
Li SM, 2010, LECT NOTES COMPUT SC, V6484, P294, DOI 10.1007/978-3-642-17572-5_24
Luenberger D. G., 1979, Introduction to Dynamic Systems: Theory, Models, and Applications
Manne H.G., 1966, Insider trading and the stock market
MCAFEE P, 2010, BETTER LIVING EC, P168
MCAFEE P, 2010, MAXIMALLY REPR UNPUB
MCMILLAN J, 1994, J ECON PERSPECT, V8, P145, DOI 10.1257/jep.8.3.145
Milgrom P, 2000, J POLIT ECON, V108, P245, DOI 10.1086/262118
ROTH A, 2010, BETTER LIVING EC, P206
Roth AE, 2005, AM ECON REV, V95, P376, DOI 10.1257/000282805774669989
Tietenberg T., 2010, Better Living Through Economics, P42
Varian HR, 2007, INT J IND ORGAN, V25, P1163, DOI 10.1016/j.ijindorg.2006.10.002
Wilson R, 2002, ECONOMETRICA, V70, P1299, DOI 10.1111/1468-0262.00334
WILSON RB, 1969, MANAGE SCI, V15, P446, DOI 10.1287/mnsc.15.7.446
NR 19
TC 19
Z9 23
U1 2
U2 12
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0889-938X
EI 1573-7160
J9 REV IND ORGAN
JI Rev. Ind. Organ.
PD NOV
PY 2011
VL 39
IS 3
BP 169
EP 185
DI 10.1007/s11151-011-9300-1
PG 17
WC Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 826DT
UT WOS:000295332300001
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Kowalczuk, P
AF Kowalczuk, Pascal
TI Consumer acceptance of smart speakers: a mixed methods approach
SO JOURNAL OF RESEARCH IN INTERACTIVE MARKETING
LA English
DT Article
DE Qualitative research; SEM; Consumer behaviour; Twitter; Data mining;
Human-computer interaction
ID USER ACCEPTANCE; INFORMATION-TECHNOLOGY; SOCIAL MEDIA; MODEL;
NETNOGRAPHY; ADOPTION; FUTURE; USAGE; TAM
AB Purpose Voice-activated smart speakers such as Amazon Echo and Google Home were recently developed and are gaining popularity. Understanding and theorizing the underlying mechanisms that encourage or impede consumers to use smart speakers is fundamental for enhancing acceptance and future development of these new devices. Therefore, building on technology acceptance research, this study aims to develop and test an acceptance model for investigating consumers' intention to use smart speakers.
Design/methodology/approach First, antecedents that may significantly affect the usage intention of smart speakers were identified through an explorative approach by a netnographic analysis of customer reviews (N = 2,186) and Twitter data (N = 899). Afterward, these results and contemporary literature were used to develop and validate an acceptance model for smart speakers. Structural equation modeling (SEM) was used to test the proposed hypotheses on data collected from 293 participants of an online survey.
Findings Besides perceived ease of use and perceived usefulness, the quality and diversity of a system, its enjoyment, consumer's technology optimism and risk (surveillance anxiety and security/privacy risk) strongly affect the acceptance of smart speakers. Among these variables, enjoyment had the strongest effect on behavioral intention to use smart speakers.
Originality/value This is the first study that incorporates netnography and SEM for investigating technology acceptance and applies it to the field of interactive smart devices.
C1 [Kowalczuk, Pascal] Univ Duisburg Essen, Chair Mkt, Duisburg, Germany.
C3 University of Duisburg Essen
RP Kowalczuk, P (autor correspondiente), Univ Duisburg Essen, Chair Mkt, Duisburg, Germany.
EM pascal.kowalczuk@uni-due.de
RI Kowalczuk, Pascal/AAV-7791-2021
CR Ahn T., 2004, Electronic Commerce Research and Applications, V3, P405, DOI DOI 10.1016/J.ELERAP.2004.05.001
Belz FM, 2010, CREAT INNOV MANAG, V19, P304, DOI 10.1111/j.1467-8691.2010.00571.x
Brem A, 2015, J ENG TECHNOL MANAGE, V37, P40, DOI 10.1016/j.jengtecman.2015.08.004
Bruner GC, 2005, J BUS RES, V58, P553, DOI 10.1016/j.jbusres.2003.08.002
Chalmers RP, 2012, J STAT SOFTW, V48, P1
Childers TL, 2001, J RETAILING, V77, P511, DOI 10.1016/S0022-4359(01)00056-2
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
Fishbein M., 1980, UNDERSTANDING ATTITU
Fishbein M., 1975, Beliefs, Attitude, Intention and Behavior: An Introduction to Theory and Research
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Ha S, 2009, J BUS RES, V62, P565, DOI 10.1016/j.jbusres.2008.06.016
Hair J., 2014, Multivariate data analysis, V7
Hennessy J, 1999, COMPUTER, V32, P27, DOI 10.1109/2.781631
Joo J, 2013, COMPUT HUM BEHAV, V29, P2512, DOI 10.1016/j.chb.2013.06.002
Kim KJ, 2015, INTERNET RES, V25, P527, DOI 10.1108/IntR-05-2014-0126
King WR, 2006, INFORM MANAGE-AMSTER, V43, P740, DOI 10.1016/j.im.2006.05.003
Kozinets RV, 2002, J MARKETING RES, V39, P61, DOI 10.1509/jmkr.39.1.61.18935
Kummer TF, 2017, INFORM MANAGE-AMSTER, V54, P73, DOI 10.1016/j.im.2016.04.002
Lee MC, 2009, ELECTRON COMMER R A, V8, P130, DOI 10.1016/j.elerap.2008.11.006
Maxwell J.A., 2009, SAGE HDB APPL SOCIAL, V2nd, DOI DOI 10.4135/9781483348858.N7
MILES MB, 1979, ADMIN SCI QUART, V24, P590, DOI 10.2307/2392365
Miorandi D, 2012, AD HOC NETW, V10, P1497, DOI 10.1016/j.adhoc.2012.02.016
Moon JW, 2001, INFORM MANAGE-AMSTER, V38, P217, DOI 10.1016/S0378-7206(00)00061-6
Muniz AM, 2001, J CONSUM RES, V27, P412, DOI 10.1086/319618
Nunnally J., 1978, PSYCHOMETRIC METHODS
Pagani M, 2004, J INTERACT MARK, V18, P46, DOI 10.1002/dir.20011
Pak A., 2010, COMPUTER, V10, P1320, DOI [DOI 10.1371/J0URNAL.P0NE.0026624, DOI 10.1371/JOURNAL.PONE.0026624]
Parasuraman A., 2001, TECHNO READY MARKETI
Parasuraman W, 2000, Journal of Service Research, V2, P307, DOI [10.1177/109467050024001, DOI 10.1177/109467050024001]
Park E, 2015, BEHAV INFORM TECHNOL, V34, P741, DOI 10.1080/0144929X.2014.963672
Park E, 2013, PROGRAM-ELECTRON LIB, V47, P188, DOI 10.1108/00330331311313762
Perry A, 2016, J RETAIL CONSUM SERV, V33, P171, DOI 10.1016/j.jretconser.2016.08.018
Rauniar R, 2014, J ENTERP INF MANAG, V27, P6, DOI 10.1108/JEIM-04-2012-0011
Satorra A., 1994, LATENT VARIABLES ANA, P399, DOI DOI 10.2307/2291370
Sinkovics RR, 2005, QUAL MARK RES, V8, P9, DOI 10.1108/13522750510575426
Tong X, 2010, INT J RETAIL DISTRIB, V38, P742, DOI 10.1108/09590551011076524
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
Venkatesh V, 2012, MIS QUART, V36, P157
Walczuch R, 2007, INFORM MANAGE-AMSTER, V44, P206, DOI 10.1016/j.im.2006.12.005
Yang H, 2017, IND MANAGE DATA SYST, V117, P68, DOI 10.1108/IMDS-01-2016-0017
Yang H, 2016, TELEMAT INFORM, V33, P256, DOI 10.1016/j.tele.2015.08.007
NR 45
TC 87
Z9 91
U1 30
U2 133
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2040-7122
EI 2040-7130
J9 J RES INTERACT MARK
JI J. Res. Interact. Mark.
PY 2018
VL 12
IS 4
SI SI
BP 418
EP 431
DI 10.1108/JRIM-01-2018-0022
PG 14
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HA4CW
UT WOS:000450206500002
DA 2024-03-27
ER
PT J
AU Sun, Y
Shao, X
Li, XT
Guo, Y
Nie, K
AF Sun, Yuan
Shao, Xiang
Li, Xiaotong
Guo, Yue
Nie, Kun
TI A 2020 perspective on "How live streaming influences purchase intentions
in social commerce: An IT affordance perspective"
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Human-computer interaction; Live-streaming shopping; User behavior; User
experience; Social commerce
AB Live-streaming shopping has become increasingly popular in social commerce. Reviewing recent research on live-streaming shopping, we find that most papers about this topic have focused on user behavior and user experience. While these previous studies, including Sun et al. (2019), have provided useful insights into different aspects of live-streaming in social commerce, most of them have used cross-sectional surveys as the primary research method. Therefore, limitations such as the self-reporting bias or the common research method bias may hinder our understanding of the real effect of live-streaming on consumer purchase behavior. The actual causal relationship between live-streaming and consumer purchases should be further examined using more robust econometric models, natural experiments, or longitudinal studies.
C1 [Sun, Yuan; Shao, Xiang; Nie, Kun] Zhejiang Gongshang Univ, Sch Business Adm, Hangzhou, Zhejiang, Peoples R China.
[Sun, Yuan] Zhejiang Gongshang Univ, Zheshang Res Inst, Hangzhou, Zhejiang, Peoples R China.
[Li, Xiaotong] Univ Alabama, Coll Business, Huntsville, AL 35899 USA.
[Guo, Yue] Southern Univ Sci & Technol, Shenzhen, Peoples R China.
C3 Zhejiang Gongshang University; Zhejiang Gongshang University; University
of Alabama System; University of Alabama Huntsville; Southern University
of Science & Technology
RP Li, XT (autor correspondiente), Univ Alabama, Coll Business, Huntsville, AL 35899 USA.
EM d05sunyuan@zju.edu.cn; lixi@uah.edu; niekun@mail.zjgsu.edu.cn
RI Li, xiaotong/GYV-4890-2022
OI SUN, Yuan/0000-0002-8659-1870
FU National Natural Science Foundation of China [71772162, 71872061];
Contemporary Business and Trade Research Center; Zhejiang Gongshang
University [2019SMYJ12ZC]; Zhejiang Provincial Philosophy and Social
Sciences Project [20NDJC102YB]
FX This work was supported by grants awarded by the National Natural
Science Foundation of China (71772162, 71872061), the Contemporary
Business and Trade Research Center, and the Zhejiang Gongshang
University (2019SMYJ12ZC), the Zhejiang Provincial Philosophy and Social
Sciences Project (20NDJC102YB).
CR Bengio Samy, 2019, ARXIV190710247
Cai J, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P2548
Diwanji V, 2020, COMPUT HUM BEHAV, V105, DOI 10.1016/j.chb.2019.106221
Park HJ, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101934
Sun Y, 2019, ELECTRON COMMER R A, V37, DOI 10.1016/j.elerap.2019.100886
Wang X, 2019, PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019), P266, DOI [10.1109/ITNEC.2019.8729444, 10.1109/itnec.2019.8729444]
Zhou JL, 2019, ELECTRON COMMER R A, V34, DOI 10.1016/j.elerap.2018.11.002
NR 7
TC 37
Z9 37
U1 34
U2 424
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD MAR-APR
PY 2020
VL 40
AR 100958
DI 10.1016/j.elerap.2020.100958
PG 2
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA LH7CR
UT WOS:000528943600031
DA 2024-03-27
ER
PT J
AU Caleb-Solly, P
Dogramadzi, S
Huijnen, CAGJ
van den Heuvel, H
AF Caleb-Solly, Praminda
Dogramadzi, Sanja
Huijnen, Claire A. G. J.
van den Heuvel, Herjan
TI Exploiting ability for human adaptation to facilitate improved
human-robot interaction and acceptance
SO INFORMATION SOCIETY
LA English
DT Article
DE Assistive robotics; Human-Robot Interaction; Usability and User
Experience Evaluation; Socially assistive robots
AB This article reports findings from a usability and user experience evaluations conducted in the last 2 years of a 4-year assistive robotics research project using the Kompai robot. It focuses on the evaluations that were conducted with older adults in an assisted living studio in the United Kingdom (which was arranged as an open plan studio apartment), a UK residential care home, and an older couple's own home in the Netherlands over 2 days. It examines emergent adaptive human behaviour in human-robot interaction (HRI) to consider whether we are approaching the embodiment and functionality of service robots correctly. It discusses possible improvements that could be made at the systems level that better exploit people's natural ability to adapt and find workarounds to technologies and their limitations.
C1 [Caleb-Solly, Praminda; Dogramadzi, Sanja] Univ West England, Bristol Robot Lab, Bristol, Avon, England.
[Huijnen, Claire A. G. J.; van den Heuvel, Herjan] Smart Homes, Eindhoven, Netherlands.
C3 University of West England; University of Bristol
RP Caleb-Solly, P (autor correspondiente), Univ West England, Bristol Robot Lab, Fac Environm & Technol, Bristol BS16 1QY, Avon, England.
EM praminda.caleb-solly@uwe.ac.uk
RI Dogramadzi, Sanja/AAE-4706-2020
OI Caleb-Solly, Praminda/0000-0001-8821-0464
FU European Commission [FP7-248434]; AHRC [AH/N004108/1, AH/N004108/2]
Funding Source: UKRI
FX This work was developed within the MOBISERV project partially funded by
the European Commission under the 7th Framework Programme (FP7-248434).
The authors wish to thank all project members, particularly Anne
Allardice and Daniel Davies for their work in the trials, and all the
study participants.
CR [Anonymous], LEARNING BEHAV
[Anonymous], 2013, DESIGN EVERYDAY THIN
Bajones M., 2014, COGN ARCH HUM ROB IN
Bartneck C, 2007, AI SOC, V21, P217, DOI 10.1007/s00146-006-0052-7
Caleb-Solly P, 2014, ACMIEEE INT CONF HUM, P374, DOI 10.1145/2559636.2559681
Chenxia Wu, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), P4362, DOI 10.1109/CVPR.2015.7299065
de Graaf MMA, 2015, COMPUT HUM BEHAV, V43, P1, DOI 10.1016/j.chb.2014.10.030
DICK MB, 1988, J GERONTOL, V43, pP127, DOI 10.1093/geronj/43.5.P127
European Commission, 2012, SPEC EUR 382 WAV EB7
Fiorini L., 2015, IET INT C TECHN ACT
Frennert S, 2014, INT J SOC ROBOT, V6, P299, DOI 10.1007/s12369-013-0225-8
Green A, 2004, RO-MAN 2004: 13TH IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, PROCEEDINGS, P575, DOI 10.1109/ROMAN.2004.1374824
HUBER A, 2016, ACMIEEE INT CONF HUM, P367
Huijnen C, 2011, LECT NOTES COMPUT SC, V7040, P324, DOI 10.1007/978-3-642-25167-2_44
Koppula HS, 2013, INT J ROBOT RES, V32, P951, DOI 10.1177/0278364913478446
Martins G. S., 2016, CONTEXT AWARE ADAPTA
Ferrús RM, 2016, ROBOT AUTON SYST, V79, P99, DOI 10.1016/j.robot.2016.01.010
Nani M., 2010, 4 COMP ROB WORKSH BR
Nygård L, 2007, AGING MENT HEALTH, V11, P144, DOI 10.1080/13607860600844168
Pino M, 2015, FRONT AGING NEUROSCI, V7, DOI 10.3389/fnagi.2015.00141
Rosenthal-von der Puetten AM, 2017, ACMIEEE INT CONF HUM, P483, DOI 10.1145/2909824.3020251
Salem M., 2015, EM POL ETH HUM ROB I
Sandoval EB, 2014, LECT NOTES ARTIF INT, V8755, P54, DOI 10.1007/978-3-319-11973-1_6
Saunders J., 2013, Assist Technol Res Series, V33, P49, DOI [10.3233/978-1-61499-304-9-49, DOI 10.3233/978-1-61499-304-9-49]
Sun R, 2001, COGNITIVE SCI, V25, P203, DOI 10.1016/S0364-0213(01)00035-0
Sung J, 2010, INT J SOC ROBOT, V2, P417, DOI 10.1007/s12369-010-0065-8
Syrdal DS, 2014, COGN COMPUT, V6, P741, DOI 10.1007/s12559-014-9284-x
Weiss Astrid., 2009, AISB2009 P S NEW FRO, V4, P11
Zimmerman J, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P395
NR 29
TC 20
Z9 21
U1 2
U2 32
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0197-2243
EI 1087-6537
J9 INFORM SOC
JI Inf. Soc.
PY 2018
VL 34
IS 3
SI SI
BP 153
EP 165
DI 10.1080/01972243.2018.1444255
PG 13
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA GE2CR
UT WOS:000431023700004
DA 2024-03-27
ER
PT J
AU Uluskan, M
AF Uluskan, Meryem
TI Structural equation modelling - artificial neural network based hybrid
approach for assessing quality of university cafeteria services
SO TQM JOURNAL
LA English
DT Article
DE Artificial intelligence; Service quality; Artificial neural networks;
SEM-ANN hybrid approach; Student satisfaction; University cafeterias
ID DETERMINANTS; MANAGEMENT; SATISFACTION; ANTECEDENTS; INTEGRATION;
ACCEPTANCE; STUDENTS
AB Purpose This study aims to show the effectiveness and applicability of artificial intelligence applications in the measurement and evaluation of university services. Universities can gain competitive advantage through providing their students with quality services in various aspects, such as bookstores, dormitories, recreation centers as well as cafeterias. Among these facilities, university cafeterias are places where students spend a significant amount of time. Therefore, this study aims to integrate artificial intelligence application in the evaluation of university cafeteria services based on students' perceptions with two-stage structural equation modeling (SEM) and artificial neural network (ANN) approach. Design/methodology/approach An artificial intelligence based SEM-ANN hybrid approach was used to determine the factors that have significant influence on student satisfaction, sufficiency-of-services and likelihood-of-recommendation. Data were collected from 373 students through a face-to-face questionnaire. Initially, four service quality dimensions were attained through factor analysis. Then, hypotheses, which were determined via literature review, were tested through SEM-ANN hybrid approach. Findings Incorporating the results of SEM analysis into the ANN technique resulted in superior models with good prediction performance. Based on four ANN models created and ANN sensitivity analyses conducted, significant predictors of satisfaction, sufficiency, reliability and recommendation are determined and ranked. Originality/value Prior studies have assessed service quality using traditional techniques, whereas, this study integrates artificial intelligence in the assessment of higher-educational institutions' services quality. Also, as a distinction from previous studies, this study ranked importance levels of predictor variables through ANN sensitivity analysis.
C1 [Uluskan, Meryem] Eskisehir Osmangazi Univ, Fac Engn & Architecture, Dept Ind Engn, Eskisehir, Turkey.
C3 Eskisehir Osmangazi University; Anadolu University
RP Uluskan, M (autor correspondiente), Eskisehir Osmangazi Univ, Fac Engn & Architecture, Dept Ind Engn, Eskisehir, Turkey.
EM meryemulus@yahoo.com
RI Uluskan, Meryem/AAC-3884-2022
OI Uluskan, Meryem/0000-0003-1287-8286
CR Abirami S, 2020, ADV COMPUT, V117, P339, DOI 10.1016/bs.adcom.2019.09.007
Andaleeb S. S., 2007, Journal of Foodservice Business Research, V10, P51, DOI 10.1300/J369v10n02_04
[Anonymous], 2015, INT FORUM
[Anonymous], 2020, MERRIAM WEBSTER OFFI
Awan MU, 2013, QUAL QUANT, V47, P1093, DOI 10.1007/s11135-011-9587-x
Bollen K.A., 1989, STRUCTURAL EQUATIONS, DOI DOI 10.1002/9781118619179
Bowen N., 2011, STRUCTURAL EQUATION
Cerquitelli T., 2017, TRANSPARENT DATA MIN
Cha SS, 2019, J ASIAN FINANC ECON, V6, P187, DOI 10.13106/jafeb.2019.vol6.no1.187
Chakraborty M, 2019, NEW GENERAT COMPUT, V37, P67, DOI 10.1007/s00354-018-0048-0
Chang D.M.L., 2018, INT J ENG TECHNOLOGY, V7, P61, DOI [DOI 10.14419/IJET.V7I3.21.17097, 10.14419/ijet.v7i3.21.17097]
Chang MLD., 2014, Asian Social Science, V10, P202
Chen SH, 2012, QUAL QUANT, V46, P1279, DOI 10.1007/s11135-011-9441-1
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Cruz-Jesus F, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e04081
Czarniecka-Skubina E, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16193699
Deng LF, 2018, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.00580
Dwaikat NY, 2021, TQM J, V33, P841, DOI 10.1108/TQM-06-2020-0133
Fern YS, 2012, CURRENT ISSUES IN HOSPITALITY AND TOURISM RESEARCH AND INNOVATIONS, P81
Galabo N. R., 2019, INT J SCI TECHNOLOGY, V8, P114
Garg A, 2017, EUR J TOUR HOSP RECR, V8, P96, DOI 10.1515/ejthr-2017-0009
George B.P., 2004, International journal of bank marketing, V22, P390, DOI [10.1108/02652320410559321, DOI 10.1108/02652320410559321]
GOLDBERGER AS, 1972, ECONOMETRICA, V40, P979, DOI 10.2307/1913851
Ha S.J., 2008, J KOREAN I INTERIOR, V17, P91
Han J, 2009, INT CHAIN NETW, V7, P1, DOI 10.3920/978-90-8686-684-7
Hanafi, 2018, INT J HUMANITIES SOC, V6, P112
Jain R., 2007, Journal of Services Research, V7, P37
Jang I, 2018, ENERG EXPLOR EXPLOIT, V36, P433, DOI 10.1177/0144598717729490
Johnson M., 2003, QUALITY MANAGEMENT J, V10, P8, DOI DOI 10.1080/10686967.2003.11919049
Jung J, 2009, NUTR RES PRACT, V3, P295, DOI 10.4162/nrp.2009.3.4.295
Kaufman BE, 2015, HUM RESOUR MANAG J, V25, P19, DOI 10.1111/1748-8583.12056
Kimani S W., 2011, International Journal of Business Administration, V2, P98, DOI https://doi.org/10.5430/ijba.v2n1p98
Kuleto V, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su131810424
Kumar A., 2011, MARK MANAG
Kwun D. J. W., 2013, Journal of Foodservice Business Research, V16, P276, DOI 10.1080/15378020.2013.810534
Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
Liang X., 2009, International Journal of Quality and Service Science, V1, P113, DOI DOI 10.1108/17566690910945903
Liébana-Cabanillas F, 2018, TECHNOL FORECAST SOC, V129, P117, DOI 10.1016/j.techfore.2017.12.015
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Masserini L, 2019, SOC INDIC RES, V146, P91, DOI 10.1007/s11205-018-1927-y
Mensah I., 2018, Journal of Tourism, Heritage & Services Marketing, V4, P27
Nadzirah S., 2013, International Food Research Journal, V20, P1459
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
Pakurár M, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11041113
Palma M, 2019, SOC INDIC RES, V146, P327, DOI 10.1007/s11205-018-1889-0
Pei K.J., 2015, Open Access Library Journal, V02, P1
Pickard Q., 2008, ARCHITECTS HDB
Piotrowski C.M., 2010, Designing commercial interiors
Saglik E., 2014, American International Journal of Contemporary Research, V4, P100
Sainlez M, 2011, COMPUT-AIDED CHEM EN, V29, P1784
Schumacker R. E., 2004, A beginner's guide to structural equation modeling
Shanka T., 2005, Asia Pacific Journal of Tourism Research, V10, P329, DOI 10.1080/10941660500309754
Sharma SK, 2019, INFORM SYST FRONT, V21, P815, DOI 10.1007/s10796-017-9775-x
Sharma SK, 2019, INFORM SYST MANAGE, V36, P243, DOI 10.1080/10580530.2019.1620504
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Singh Gurjeet., 2011, International Journal of Earth Sciences and Engineering, V4, P443
Smith RA., 2020, International Hospitality Review., V34, P13, DOI [DOI 10.1108/IHR-06-2019-0010, 10.1108/IHR-06-2019-0010]
Uluskan M, 2020, TOTAL QUAL MANAG BUS, V31, P1811, DOI 10.1080/14783363.2018.1520597
Uluskan M, 2017, TOTAL QUAL MANAG BUS, V28, P1526, DOI 10.1080/14783363.2016.1150173
Young Jung Hyun, 2013, [Journal of the Korean Society of Food Science and Nutrition, 한국식품영양과학회지], V42, P633
Zabukovsek SS, 2019, CENT EUR J OPER RES, V27, P703, DOI 10.1007/s10100-018-0592-1
Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
Zeide E, 2019, Educause Review, V54, P31
Zhenxing Ge, 2020, Journal of Physics: Conference Series, V1533, DOI 10.1088/1742-6596/1533/3/032089
NR 64
TC 2
Z9 2
U1 6
U2 21
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1754-2731
EI 1754-274X
J9 TQM J
JI TQM J.
PD MAY 4
PY 2023
VL 35
IS 4
BP 1048
EP 1071
DI 10.1108/TQM-01-2022-0001
EA MAY 2022
PG 24
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA F3IN1
UT WOS:000799618600001
DA 2024-03-27
ER
PT J
AU Saw, CC
Inthiran, A
AF Saw, Chian Chyi
Inthiran, Anushia
TI Designing for Trust on E-Commerce Websites Using Two of the Big Five
Personality Traits
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE keyword trust; personality traits; e-commerce websites; design features;
human computer interaction; psychology
ID HUMAN-COMPUTER INTERACTION; CONSUMER TRUST; INITIAL TRUST; WEB DESIGN;
SATISFACTION; DIMENSIONS; ATTRIBUTES; CULTURES; IMPACT; MODEL
AB Online consumers perceived performing an online transaction as risky. The inability to trust the website is one reason why online consumers are reluctant to perform an online transaction. In this research study, 46 design features are examined to identify features that are able to increase the value of trust. Eighty-nine individuals participated in this research study. Participants completed one questionnaire which was divided into four parts. The questionnaire collected information on demographics, personality traits, trust and website design features. Data were analysed using quantitative statistical methods. A pilot test was conducted prior to the main experiment. Results indicate there are sixteen design features that have the ability to increase the level of trust amongst participants with the neuroticism trait. Fourteen design features had the ability to increase the level of trust amongst participants with the conscientiousness personality trait. E-commerce website designers could use these design features to increase the online consumer's perception of trust on e-commerce websites.
C1 [Saw, Chian Chyi] Univ Canterbury, Business Informat Syst, Business Taught Masters Degree Programme, Christchurch 8041, New Zealand.
[Inthiran, Anushia] Univ Canterbury, Dept Accounting & Informat Syst, Christchurch 8041, New Zealand.
C3 University of Canterbury; University of Canterbury
RP Inthiran, A (autor correspondiente), Univ Canterbury, Dept Accounting & Informat Syst, Christchurch 8041, New Zealand.
EM chianchyi.saw@gmail.com; anushia.inthiran@canterbury.ac.nz
OI Inthiran, Anushia/0000-0002-7691-0890
CR Abed S, 2018, J ADV MANAG RES, V15, P146, DOI 10.1108/JAMR-05-2017-0057
[Anonymous], 2020, CHINA INTERNET WATCH
Asif MS, 2020, J CLEAN PROD, V276, DOI 10.1016/j.jclepro.2020.124191
Aslam W., 2019, Business Perspectives and Research, V8, P186, DOI DOI 10.1177/2278533719887451
Azam A., 2013, Journal of WEI Business and Economics, V2, P31
Beatty P, 2011, ACM COMPUT SURV, V43, DOI 10.1145/1922649.1922651
Benet-Martinez V, 1998, J PERS SOC PSYCHOL, V75, P729, DOI 10.1037/0022-3514.75.3.729
Bhatnagar A, 2004, J BUS RES, V57, P1352, DOI 10.1016/S0148-2963(03)00067-5
Bratu S., 2019, Review of Contemporary Philosophy, V18, P157, DOI [DOI 10.22381/RCP18201910, 10.22381/RCP18201910, https://doi.org/10.22381/RCP18201910]
Carroll JM, 1997, INT J HUM-COMPUT ST, V46, P501, DOI 10.1006/ijhc.1996.0101
CHANDLER GN, 1992, J BUS VENTURING, V7, P223, DOI 10.1016/0883-9026(92)90028-P
Clement J., 2019, E COMMERCE WORLDWIDE
Clemmensen T., 2006, Information Technology & People, V19, P121, DOI 10.1108/09593840610673793
Cunningham S.M., 1967, RISK TAKING INFORM H
Cyr D, 2008, J MANAGE INFORM SYST, V24, P47, DOI 10.2753/MIS0742-1222240402
Davis Evan., 2017, Post Truth: Why We Have Reached Peak Bullshit and What We Can Do About It
Doney PM, 1997, J MARKETING, V61, P35, DOI 10.2307/1251829
Egger F. N., 2001, P INT C AFF HUM FACT
Endeshaw A, 2001, INF COMMUN TECHNOL L, V10, P203
Ferraro A, 1998, 1 MONDAY, V3, DOI DOI 10.5210/FM.V3I6.601
Fimberg K., 2020, ADV CREATIVITY INNOV, P267
Ganguly Boudhayan, 2010, International Journal of Electronic Business, V8, P302, DOI 10.1504/IJEB.2010.035289
Garver Abe., 2012, Forbes
Gefen D, 2000, OMEGA-INT J MANAGE S, V28, P725, DOI 10.1016/S0305-0483(00)00021-9
GOLDBERG LR, 1993, AM PSYCHOL, V48, P26, DOI 10.1037/0003-066X.48.1.26
Hansen JM, 2018, COMPUT HUM BEHAV, V80, P197, DOI 10.1016/j.chb.2017.11.010
Jarvenpaa S., 1999, JCMC, V5, P1, DOI [10.1111/j.1083-6101.1999.tb00337.x, DOI 10.1111/J.1083-6101.1999.TB00337.X]
Jeon H, 2017, J QUAL ASSUR HOSP TO, V18, P125, DOI [10.1080/1528008X.2016.1169473, 10.1080/1528008x.2016.1169473]
Jourdan Z., 2011, TRUST E BUSINESS CRO
Karimov FP, 2011, J ELECTRON COMMER RE, V12, P272
Kim D., 2003, J ELECTRON COMMER RE, V4, P49
Ko H., 2013, Journal of Interactive Advertising, V4, P20, DOI [10.1080/15252019.2004.10722084, DOI 10.1080/15252019.2004.10722084]
Koufaris M, 2004, INFORM MANAGE-AMSTER, V41, P379, DOI 10.1016/j.im.2003.08.004
Lee JD, 2004, HUM FACTORS, V46, P50, DOI 10.1518/hfes.46.1.50.30392
Lee SM, 2005, J COMPUT INFORM SYST, V46, P85
Leen J. Y. A., 2010, INT J ECON MANAG ACC, V4, P2010
Littauer F., 2005, PERSONALITY PLUS UND
Lowry PB, 2014, INT J HUM-COMPUT INT, V30, P63, DOI 10.1080/10447318.2013.839899
Lumsden J, 2006, 2006 ICEC: EIGHTH INTERNATIONAL CONFERENCE ON ELECTRONIC COMMERCE, PROCEEDINGS, P471
Lynch PD, 2001, J ADVERTISING RES, V41, P15, DOI 10.2501/JAR-41-3-15-23
May AY C., 2021, Econ. Manag. Financ. Mark, V16, P32, DOI [10.22381/emfm16220212, DOI 10.22381/EMFM16220212]
MCCRAE RR, 1987, J PERS SOC PSYCHOL, V52, P81, DOI 10.1037/0022-3514.52.1.81
McDowell WC, 2016, J BUS RES, V69, P4837, DOI 10.1016/j.jbusres.2016.04.040
McKnight DH, 2002, J STRATEGIC INF SYST, V11, P297, DOI 10.1016/S0963-8687(02)00020-3
Menon S, 2002, J RETAILING, V78, P31, DOI 10.1016/S0022-4359(01)00064-1
Mirica C, 2009, EC MANAG FINANC MARK, V14, P15
Mohd NAB, 2019, PROCEDIA COMPUT SCI, V161, P1199, DOI 10.1016/j.procs.2019.11.233
Faisal CMN, 2017, IEEE T HUM-MACH SYST, V47, P847, DOI 10.1109/THMS.2016.2620901
Nah F., 2002, The Journal of Electronic Commerce Research, V3, P98
Oliveira T, 2017, COMPUT HUM BEHAV, V71, P153, DOI 10.1016/j.chb.2017.01.050
Olson GM, 2003, ANNU REV PSYCHOL, V54, P491, DOI 10.1146/annurev.psych.54.101601.145044
Pandey S, 2020, BUILD TRUST ECOMMERC
Pavlou P., 2002, P INT C INFORM SYSTE, V15, P1
Pickup O, 2017, TELEGRAPH
Riegelsberger J, 2005, INT J HUM-COMPUT ST, V62, P381, DOI 10.1016/j.ijhcs.2005.01.001
Rodríguez-Ardura Inma, 2008, J. theor. appl. electron. commer. res., V3, P18
Roy MC, 2001, INTERNET RES, V11, P388, DOI 10.1108/10662240110410165
Santos V., 2017, International Journal of Business Administration, V8, P130, DOI DOI 10.5430/IJBA.V8N7P130
Sebastianelli R, 2018, ONLINE INFORM REV, V42, P506, DOI 10.1108/OIR-02-2016-0039
Shah B.S., 2019, LIB PHILOS PRACTICE
Song J, 2005, MANAGE SCI, V51, P1219, DOI 10.1287/mnsc.1050.0427
Sulikowski P., 2019, P 16 INT C BUS ENG I, P208
Sulikowski P, 2018, PROCEDIA COMPUT SCI, V126, P1587, DOI 10.1016/j.procs.2018.08.132
Tam E.-C., 2002, P INT C INFORM SYSTE
Tan F. B., 2004, Journal of Electronic Commerce in Organizations, V2, P40, DOI 10.4018/jeco.2004070103
Thatcher JB, 2013, COMMUN ASSOC INF SYS, V32, P107
Tractinsky, 2003, P 36 HAW INT C SYST
Tseng KT, 2014, LECT NOTES COMPUT SC, V8512, P90, DOI 10.1007/978-3-319-07227-2_10
Vance A, 2008, J MANAGE INFORM SYST, V24, P73, DOI 10.2753/MIS0742-1222240403
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Volkel S.T., 2019, OPPORTUNITIES CHALLE
Walczuch R, 2004, INFORM MANAGE-AMSTER, V42, P159, DOI 10.1016/j.im.2003.12.009
Wang Y., 2009, IT IS NOT EASY ENACT
Wang YD, 2005, COMPUT HUM BEHAV, V21, P105, DOI 10.1016/j.chb.2003.11.008
Weijters B, 2013, PSYCHOL METHODS, V18, P320, DOI 10.1037/a0032121
Xu-Priour DL, 2017, INT J ELECTRON COMM, V21, P299, DOI 10.1080/10864415.2016.1319206
Yong-Hui L., 2009, Proceedings of World Academy of Science, V41, P919
Zhou X., 2005, THESIS LULEA U TECHN
NR 78
TC 4
Z9 4
U1 4
U2 25
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD JUN
PY 2022
VL 17
IS 2
BP 375
EP 393
DI 10.3390/jtaer17020020
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 2M4KW
UT WOS:000817671300001
OA gold
DA 2024-03-27
ER
PT J
AU Wells, JD
Fuerst, WL
Palmer, JW
AF Wells, JD
Fuerst, WL
Palmer, JW
TI Designing consumer interfaces for experiential tasks: an empirical
investigation
SO EUROPEAN JOURNAL OF INFORMATION SYSTEMS
LA English
DT Article
DE experiential tasks; interface design; metaphor; electronic commerce;
domain familiarity; mental models; consumer behavior; human-computer
interaction; information presentation
ID GRAPHICAL INFORMATION PRESENTATION; CUSTOMER INTERFACES; COMPUTING
SYSTEMS; DECISION-SUPPORT; METAPHOR; IMPACT; ONLINE; WEB; PERFORMANCE;
TECHNOLOGY
AB In the early adoption phase of business-to-consumer (B2C) ecommerce, the tasks that proved most conducive to online consumer interaction typically were goal-directed, being clear in sequence and structure. A key challenge in ecommerce is the ability to design interfaces that support experiential tasks in addition to goal-directed tasks. Most of the ecommerce research on interface design, however, has focused on goal-directed tasks and has not addressed experiential tasks. Based on the literature from interface metaphors and mental models, this paper explores the use of tangible attributes derived from the physical business domain as a technique for designing an interface that effectively supports experiential tasks. A laboratory experiment was designed and conducted to test the impact of two types of interfaces and business domain familiarity when completing an experiential task. Because consumers need to retain and recall information to evaluate products/services or to make brand associations, retention/recall of information was measured on both the day of the treatment and after a 2-day lag. Results revealed that the interface based upon the business domain metaphor stimulated higher levels of retention and recall of information and thus provided the desired support for experiential tasks. Further, users with weaker domain familiarity showed the greatest improvement in retention and recall, particularly after a 2-day lag, when using the interface with the business domain metaphor design.
C1 Washington State Univ, Coll Business & Econ, Dept Informat Syst, Pullman, WA 99164 USA.
Univ Kansas, Sch Business, Lawrence, KS 66045 USA.
Coll William & Mary, Sch Business, Williamsburg, VA 23187 USA.
C3 Washington State University; University of Kansas; William & Mary
RP Wells, JD (autor correspondiente), Washington State Univ, Coll Business & Econ, Dept Informat Syst, Pullman, WA 99164 USA.
EM wellsjd@wsu.edu
CR Agarwal R, 2002, INFORM SYST RES, V13, P168, DOI 10.1287/isre.13.2.168.84
Alba J, 1997, J MARKETING, V61, P38, DOI 10.2307/1251788
Ashcraft M.H., 1994, HUMAN MEMORY COGNITI, V2nd
AYKIN NM, 1991, COMPUT IND ENG, V20, P373, DOI 10.1016/0360-8352(91)90009-U
Baecker R., 1995, Readings in human-computer interaction: Towards the Year 2000
BENBASAT I, 1986, MIS QUART, V10, P59, DOI 10.2307/248881
BENBASAT I, 1986, COMMUN ACM, V29, P1094, DOI 10.1145/7538.7545
BETTMAN JR, 1977, J CONSUM RES, V3, P233, DOI 10.1086/208672
BETTMAN JR, 1979, J MARKETING, V43, P37, DOI 10.1177/002224297904300205
Biocca F., 1997, J COMPUTER MEDIATED, V3
Burke RR, 2002, J ACAD MARKET SCI, V30, P411, DOI 10.1177/009207002236914
Carroll J. M., 1988, Handbook of human-computer interaction, P67, DOI DOI 10.1016/B978-0-444-70536-5.50008-7
CARROLL JM, 1982, IEEE T SYST MAN CYB, V12, P107, DOI 10.1109/TSMC.1982.4308795
CARROLL JM, 1985, INT J MAN MACH STUD, V22, P39, DOI 10.1016/S0020-7373(85)80076-6
Chau PYK, 2000, J ORG COMP ELECT COM, V10, P1
DAVIES G, 1991, J MICROCOMPUT APPL, V14, P105, DOI 10.1016/0745-7138(91)90003-A
DICKSON GW, 1986, COMMUN ACM, V29, P40, DOI 10.1145/5465.5469
Dieberger A, 1998, J VISUAL LANG COMPUT, V9, P597, DOI 10.1006/jvlc.1998.0100
Ebbinghaus H., 1885, MEMORY CONTRIBUTION
Erickson T., 1990, ART HUMAN COMPUTER I, P65
Gentner D., 1983, MENTAL MODELS
GILLAN DJ, 1995, HUM FAC ERG SOC P, P243
Golovchinsky G, 1997, INFORM PROCESS MANAG, V33, P663, DOI 10.1016/S0306-4573(97)00024-1
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
JACCARD J, 1998, SERIES QUANTITATIVE
Jahng JJ, 2002, EUR J INFORM SYST, V11, P181, DOI 10.1057/palgrave.ejis.3000431
Jarvenpaa S.L., 1996, INT J ELECTRON COMM, V1, P59, DOI DOI 10.1080/10864415.1996.11518283
JARVENPAA SL, 1988, COMMUN ACM, V31, P764, DOI 10.1145/62959.62971
Jenkins JG, 1924, AM J PSYCHOL, V35, P605, DOI 10.2307/1414040
Jiang Z., 2003, P 24 INT C INF SYST, P960
Johnson A.L.A., 1984, Zitteliana, V11, P1
Kay Alan., 1990, The Art of Human-Computer Interface Design, P191
Kim J, 1999, BEHAV INFORM TECHNOL, V18, P213, DOI 10.1080/014492999119093
Koufaris M, 2002, INFORM SYST RES, V13, P205, DOI 10.1287/isre.13.2.205.83
Lackoff George., 1980, Metaphors We Live By
Li H., 2001, Journal of Interactive Marketing, V15, P13, DOI [https://doi.org/10.1002/dir.1013, DOI 10.1002/DIR.1013, 10.1002/dir.1013]
Li HR, 2002, J ADVERTISING, V31, P43, DOI 10.1080/00913367.2002.10673675
Lohse GL, 1998, COMMUN ACM, V41, P81, DOI 10.1145/278476.278491
LUCAS HC, 1980, MANAGE SCI, V26, P982, DOI 10.1287/mnsc.26.10.982
LUCAS HC, 1981, MANAGE SCI, V27, P757, DOI 10.1287/mnsc.27.7.757
MADSEN KH, 1994, COMMUN ACM, V37, P57, DOI 10.1145/198366.198381
Marcus A., 1998, Journal of Computer Documentation, V22, P43, DOI 10.1145/291391.291397
Mathwick C, 2002, J RETAILING, V78, P51, DOI 10.1016/S0022-4359(01)00066-5
MEYER DL, 1991, PSYCHOL BULL, V110, P571, DOI 10.1037/0033-2909.110.3.571
Murphy K., 2004, STAT POWER ANAL
Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470
Nielsen J., 2000, DESIGNING WEB USABIL
Norman DonaldA., 1990, DESIGN EVERYDAY THIN
O'Keefe RM, 1998, COMMUN ACM, V41, P71, DOI 10.1145/272287.272300
Palmer JW, 2002, INFORM SYST RES, V13, P151, DOI 10.1287/isre.13.2.151.88
Pitkow JE, 1996, COMMUN ACM, V39, P106, DOI 10.1145/228503.228525
ROSNOW RL, 1991, PSYCHOL BULL, V110, P574, DOI 10.1037/0033-2909.110.3.574
Satzinger J. W., 1998, Journal of Management Information Systems, V14, P167
Shayo C., 2000, Journal of End User Computing, V12, P3, DOI 10.4018/joeuc.2000010101
Smith-Jentsch KA, 2001, J ORGAN BEHAV, V22, P179, DOI 10.1002/job.88
UMANATH NS, 1988, COMMUN ACM, V31, P562, DOI 10.1145/42411.42419
UMANATH NS, 1990, DECISION SCI, V21, P216, DOI 10.1111/j.1540-5915.1990.tb00326.x
Vaughan MW, 1998, INT J HUM-COMPUT INT, V10, P265, DOI 10.1207/s15327590ijhc1003_4
VESSEY I, 1991, DECISION SCI, V22, P219, DOI 10.1111/j.1540-5915.1991.tb00344.x
Vessey I, 1991, INFORM SYST RES, V2, P63, DOI 10.1287/isre.2.1.63
Vora P., 1998, HUMAN FACTORS WEB DE, P153
Wolfinbarger M, 2001, CALIF MANAGE REV, V43, P34, DOI 10.2307/41166074
NR 62
TC 28
Z9 30
U1 5
U2 37
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 0960-085X
EI 1476-9344
J9 EUR J INFORM SYST
JI Eur. J. Inform. Syst.
PD SEP
PY 2005
VL 14
IS 3
BP 273
EP 287
DI 10.1057/palgrave.ejis.3000516
PG 15
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA 964IK
UT WOS:000231872800007
DA 2024-03-27
ER
PT J
AU Sundar, SS
AF Sundar, S. Shyam
TI Rise of Machine Agency: A Framework for Studying the Psychology of
Human-AI Interaction (HAII)
SO JOURNAL OF COMPUTER-MEDIATED COMMUNICATION
LA English
DT Article
DE Source Interactivity; Machine Heuristic; Artificial Intelligence (AI);
Algorithms; User Experience; Human-AI Interaction (HAII); Theory of
Interactive Media Effects (TIME)
ID ATTRIBUTION
AB Advances in personalization algorithms and other applications of machine learning have vastly enhanced the ease and convenience of our media and communication experiences, but they have also raised significant concerns about privacy, transparency of technologies and human control over their operations. Going forth, reconciling such tensions between machine agency and human agency will be important in the era of artificial intelligence (AI), as machines get more agentic and media experiences become increasingly determined by algorithms. Theory and research should be geared toward a deeper understanding of the human experience of algorithms in general and the psychology of Human-AI interaction (HAII) in particular. This article proposes some directions by applying the dual-process framework of the Theory of Interactive Media Effects (TIME) for studying the symbolic and enabling effects of the affordances of AI-driven media on user perceptions and experiences.
C1 [Sundar, S. Shyam] Penn State Univ, Media Effects Res Lab, Donald P Bellisario Coll Commun, University Pk, PA 16802 USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE);
Pennsylvania State University; Pennsylvania State University -
University Park
RP Sundar, SS (autor correspondiente), Penn State Univ, Media Effects Res Lab, Donald P Bellisario Coll Commun, University Pk, PA 16802 USA.
EM sss12@psu.edu
CR Alvarado O, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3173860
[Anonymous], 2016, INFORM COMMUNICATION
Bandura A, 2001, ANNU REV PSYCHOL, V52, P1, DOI 10.1146/annurev.psych.52.1.1
Bellavista P, 2008, IEEE PERVAS COMPUT, V7, P85, DOI 10.1109/MPRV.2008.34
Biocca F., 1997, J Comput Mediat Commun, V3, DOI [10.1111/j.1083-6101.1997.tb00070.x, DOI 10.1111/J.1083-6101.1997.TB00070.X, https://doi.org/10.1111/j.1083-6101.1997.tb00070.x]
Blin F, 2016, LANG STUD SCI ENGINE, V2, P41, DOI 10.1075/lsse.2.03bli
CAPPELLA JN, 1989, SAGE AR CR, V17, P24
Carr N., 2010, SHALLOWS INTERNET IS
Chen TW, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3174111
Clark M., 2013, STATELINE
DeVito MA, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P3163, DOI 10.1145/3025453.3025659
Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
Eslami M, 2016, 34TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2016, P2371, DOI 10.1145/2858036.2858494
Eslami M, 2015, CHI 2015: PROCEEDINGS OF THE 33RD ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, P153, DOI 10.1145/2702123.2702556
Evans SK, 2017, J COMPUT-MEDIAT COMM, V22, P35, DOI 10.1111/jcc4.12180
French M., 2017, 67 ANN C INT COMMUNI, DOI DOI 10.2139/SSRN.2910571
Hassan N, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1803, DOI 10.1145/3097983.3098131
Jakesch M, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300469
Jarrahi MH, 2018, BUS HORIZONS, V61, P577, DOI 10.1016/j.bushor.2018.03.007
Jhaver S, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3173995
Jia HY, 2012, UBICOMP'12: PROCEEDINGS OF THE 2012 ACM INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING, P1185
Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
KIESLER S, 1984, AM PSYCHOL, V39, P1123, DOI 10.1037/0003-066X.39.10.1123
Kim KJ, 2016, J COMPUT-MEDIAT COMM, V21, P420, DOI 10.1111/jcc4.12177
Lee MK, 2018, BIG DATA SOC, V5, DOI 10.1177/2053951718756684
Lombard M., 1997, J COMPUT-MEDIAT COMM, DOI [DOI 10.1111/J.10836101.1997.TB00072.X, 10.1111/j.1083, 10.1111/j.1083-6101.1997.tb00072.x, DOI 10.1111/J.1083-6101.1997.TB00072.X]
McLuhan M., 1964, UNDERST MEDIA
Mosier KL, 1996, PROCEEDINGS OF THE HUMAN FACTORS AND ERGONOMICS SOCIETY - 40TH ANNUAL MEETING, VOLS 1 AND 2, P204
Norman D., 1988, PSYCHOL EVERYDAY THI
O'Reilly Tim., 2017, WTF?: What's the Future and why It's Up to Us
Perez-Rosas V., 2017, AUTOMATIC DETECTION
Rader E, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3173677
Rammert W., 2008, PARADOXES INTERACTIV, P62, DOI [10.14361/9783839408421-004, DOI 10.14361/9783839408421-004]
Reeves B, 1996, MEDIA EQUATION PEOPL
Rheingold Howard., 1985, Tools for Thought: The History and Future of Mind-Expanding Technology
Roloff MichaelE., 1981, Interpersonal Communication
Rusbult C.E., 1997, HDB PERSONAL RELATIO, V2nd, P221
Schuppe Jon, 2018, NBC News
Shao CC, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-018-06930-7
Stavrositu C, 2012, J COMPUT-MEDIAT COMM, V17, P369, DOI 10.1111/j.1083-6101.2012.01587.x
Subramanian Samanth, 2017, Wired
Sundar S., 2007, OXFORD HDB INTERNET, DOI DOI 10.1093/OXFORDHB/9780199561803.013.0007
Sundar SS, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300768
Sundar SS, 2015, HBK COMMUN MEDIA, P47
Sundar SS, 2010, HUM COMMUN RES, V36, DOI 10.1111/j.1468-2958.2010.01377.x
Sundar SS, 2000, COMMUN RES, V27, P683, DOI 10.1177/009365000027006001
Takayama L, 2015, HBK COMMUN MEDIA, P160
Treem J. W., 2013, Communication Yearbook, V36, P143, DOI DOI 10.1080/23808985.2013.11679130
Twenge Jean M, 2017, Atlantic
Vincent J., 2018, VERGE 0403
Waddell TF, 2019, J MASS COMMUN Q, V96, P82, DOI 10.1177/1077699018815891
Walther J.B., 2002, HDB INTERPERSONAL CO, P529, DOI DOI 10.1177/0893318916687397
Xu B, 2016, ACM CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW 2016), P1662, DOI 10.1145/2818048.2819948
Zhang B, 2019, INT J HUM-COMPUT ST, V128, P86, DOI 10.1016/j.ijhcs.2019.03.002
Zhang B, 2014, 32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014), P111
NR 55
TC 171
Z9 181
U1 153
U2 479
PU OXFORD UNIV PRESS INC
PI CARY
PA JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA
SN 1083-6101
J9 J COMPUT-MEDIAT COMM
JI J. Comput.-Mediat. Commun.
PD JAN
PY 2020
VL 25
IS 1
SI SI
BP 74
EP 88
DI 10.1093/jcmc/zmz026
PG 15
WC Communication; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science
GA NN8QV
UT WOS:000569052500007
OA Bronze
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Sharma, A
Fadahunsi, A
Abbas, H
Pathak, VK
AF Sharma, Anshuman
Fadahunsi, Akinola
Abbas, Haidar
Pathak, Vivek Kumar
TI A multi-analytic approach to predict social media marketing influence on
consumer purchase intention
SO JOURNAL OF INDIAN BUSINESS RESEARCH
LA English
DT Article
DE Purchase intention; Inspiration; Artificial neural network (ANN);
Consumer-based brand equity (CBBE); Reflective-formative higher order
construct (R-F-HOC); Social media marketing (SMM)
ID PLS-SEM GUIDELINES; BRAND EQUITY; INSPIRE ME; IMPACT; COMMERCE; REVIEWS;
MODEL; DETERMINANTS; ANTECEDENTS; CONSTRUCTS
AB Purpose Based on the stimulus-organism-response (SOR) framework, this study aims to investigate the effect of social media marketing (SMM) activities on consumers' purchase intention (PI), as well as to test the mediation effect of consumer-based brand equity (CBBE) and consumer inspiration (INS) between the relationship of SMM and PI. Further, this study has also proposed and validated SMM as a reflective-formative higher-order construct (R-F-HOC) with its five first-order dimensions: customization, entertainment, interaction, trendiness and word of mouth (WoM). Design/methodology/approach Using a non-probability purposive sampling method, a structured questionnaire survey using Google forms was used to collect data from a sample of 236 UAE consumers. Subsequently, the data was analyzed with a hybrid method that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis. Findings The findings suggest that SMM has a direct effect on CBBE, INS and PI. Both proposed mediation effects are statistically significant, and there is a partial complementary mediation effect of CBBE and INS between SMM and PI. This study validated the operationalization of SMM as R-F-HOC. Further, the results of the ANN analysis validate the results of the PLS-SEM, suggesting that SMM is the strongest predictor of PI followed by CBBE and INS. Research limitations/implications In terms of theoretical significance, this study has advanced our understanding of the process by which the influence of SMM is transferred to PI via CBBE and INS. This study has also made a significant contribution by validating SMM as a R-F-HOC. In terms of practical implications, this study suggests that SMM should be best assessed as a R-F-HOC construct with five dimensions: customization, entertainment, interaction, trendiness and WoM. This study has also demonstrated the importance of CBBE and INS in transmitting the effect of SMM on PI to marketers. Originality/value This study contributes to the digital advertising literature by filling a knowledge gap about the mediation effect of CBBE and INS between SMM and PI via the SOR framework. SMM's multidimensionality as a R-F-HOC has also been established.
C1 [Sharma, Anshuman; Fadahunsi, Akinola] Ajman Univ, Coll Business Adm, Dept Mkt, Ajman, U Arab Emirates.
[Abbas, Haidar] Univ Technol & Appl Sci, Salalah Coll Appl Sci, Dept Business Adm, Salalah, Oman.
[Pathak, Vivek Kumar] Arunachal Univ Studies, Fac Commerce & Management, Namsai, India.
C3 Ajman University
RP Sharma, A (autor correspondiente), Ajman Univ, Coll Business Adm, Dept Mkt, Ajman, U Arab Emirates.
EM profasharma@gmail.com
RI Pathak, Vivek Kumar/P-9291-2018; Pathak, Vivek/GRX-8733-2022
OI Abbas, Haidar/0000-0001-8063-879X
CR Aguirre-Urreta MI, 2018, MIS QUART, V42, P1001, DOI 10.25300/MISQ/2018/13587
Ajzen I., 1985, SSSP SPRINGER SERIES, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2]
Alalwan AA, 2018, INT J INFORM MANAGE, V42, P65, DOI 10.1016/j.ijinfomgt.2018.06.001
Alves H, 2016, PSYCHOL MARKET, V33, P1029, DOI 10.1002/mar.20936
[Anonymous], 2021, GLOBAL MEDIA INTELLI
Ashley C, 2015, PSYCHOL MARKET, V32, P15, DOI 10.1002/mar.20761
Baalbaki S, 2016, J BRAND MANAG, V23, P229, DOI 10.1057/bm.2016.11
Batool F, 2022, LEADERSHIP ORG DEV J, V43, P71, DOI 10.1108/LODJ-06-2021-0264
Batra R, 2016, J MARKETING, V80, P122, DOI 10.1509/jm.15.0419
Becker JM, 2012, LONG RANGE PLANN, V45, P359, DOI 10.1016/j.lrp.2012.10.001
Beneke J, 2016, INT REV RETAIL DISTR, V26, P171, DOI 10.1080/09593969.2015.1068828
Böttger T, 2017, J MARKETING, V81, P116, DOI 10.1509/jm.15.0007
Bragg MA, 2020, PEDIATR OBES, V15, DOI 10.1111/ijpo.12606
Carlson J, 2018, J SERV MARK, V32, P83, DOI 10.1108/JSM-02-2017-0059
Chakraborty U, 2019, J RES INTERACT MARK, V13, P142, DOI 10.1108/JRIM-06-2018-0080
Chang HH, 2009, SERV IND J, V29, P1687, DOI 10.1080/02642060902793557
Charoensukmongkol P, 2020, J ASIA BUS STUD, V14, P441, DOI 10.1108/JABS-07-2018-0204
Cheah JH, 2018, INT J CONTEMP HOSP M, V30, P3192, DOI [10.1108/ijchm-10-2017-0649, 10.1108/IJCHM-10-2017-0649]
Chen SC, 2019, TECHNOL FORECAST SOC, V140, P22, DOI 10.1016/j.techfore.2018.11.025
Chetioui Y., 2021, Journal of Global Marketing, V34, P220, DOI DOI 10.1080/08911762.2021.1891359
Cheung ML, 2021, AUSTRALAS MARK J, V29, P118, DOI 10.1016/j.ausmj.2020.03.006
Chin WW, 1998, MIS QUART, V22, pVII
Cordero-Guti?rrez R., 2020, SPANISH J MARKETING, V24, P247, DOI DOI 10.1108/SJME-09-2019-0075
DataReportal, 2021, DIG 2021 APR GLOB ST
Dedeoglu BB, 2020, J VACAT MARK, V26, P33, DOI 10.1177/1356766719858644
Diamantopoulos A, 2006, BRIT J MANAGE, V17, P263, DOI 10.1111/j.1467-8551.2006.00500.x
Dolan R, 2019, EUR J MARKETING, V53, P2213, DOI 10.1108/EJM-03-2017-0182
Duffett RG, 2015, INTERNET RES, V25, P498, DOI 10.1108/IntR-01-2014-0020
Dwivedi A, 2018, EUR J MARKETING, V52, P1387, DOI 10.1108/EJM-11-2016-0665
Ebrahim R.S., 2020, J RELATIONSHIP MARKE, V19, P287, DOI [10.1080/15332667.2019.1705742, DOI 10.1080/15332667.2019.1705742]
Erdogmus IE, 2015, PROCD SOC BEHV, V207, P189, DOI 10.1016/j.sbspro.2015.10.087
Fang JM, 2017, INT J INFORM MANAGE, V37, P269, DOI 10.1016/j.ijinfomgt.2017.03.003
Fisher G, 2017, ORGAN RES METHODS, V20, P438, DOI 10.1177/1094428116689707
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Guha S, 2021, J RES MARK ENTREP, V23, P339, DOI 10.1108/JRME-07-2020-0097
Hafez M, 2021, INT J BANK MARK, V39, P1353, DOI 10.1108/IJBM-02-2021-0067
Hair J. F., 2022, A primer on partial least squares structural equation modeling (PLS-SEM), V3rd
Hall KR, 2022, J BUS IND MARK, V37, P1787, DOI 10.1108/JBIM-04-2021-0218
Hamilton M, 2016, J INTERACT MARK, V36, P121, DOI 10.1016/j.intmar.2016.07.001
Hasan M, 2021, J INT CONSUM MARK, V33, P350, DOI 10.1080/08961530.2020.1795043
Hayes AF, 2018, COMMUN MONOGR, V85, P4, DOI 10.1080/03637751.2017.1352100
Henseler J, 2020, Composite-based Structural Equation Modeling: Analyzing Latent and Emergent Variables
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hertzog MA, 2008, RES NURS HEALTH, V31, P180, DOI 10.1002/nur.20247
Hu X, 2016, INT J INFORM MANAGE, V36, P1218, DOI 10.1016/j.ijinfomgt.2016.08.005
Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
Hutter K, 2013, J PROD BRAND MANAG, V22, P342, DOI 10.1108/JPBM-05-2013-0299
Izogo EE, 2020, J RES INTERACT MARK, V14, P431, DOI 10.1108/JRIM-09-2019-0145
Jarvis CB, 2003, J CONSUM RES, V30, P199, DOI 10.1086/376806
Jebarajakirthy C, 2021, INT J CONSUM STUD, V45, P1258, DOI 10.1111/ijcs.12728
KELLER KL, 1993, J MARKETING, V57, P1, DOI 10.2307/1252054
Kim AJ, 2012, J BUS RES, V65, P1480, DOI 10.1016/j.jbusres.2011.10.014
Kim D, 2013, INT J HOSP MANAG, V35, P246, DOI 10.1016/j.ijhm.2013.05.012
Kim JS, 2021, INT J TOUR RES, V23, P13, DOI 10.1002/jtr.2388
Koay KY, 2021, ASIA PAC J MARKET LO, V33, P53, DOI 10.1108/APJML-07-2019-0453
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015010101
Lamberton C, 2016, J MARKETING, V80, P146, DOI 10.1509/jm.15.0415
Lee EB, 2017, IND MANAGE DATA SYST, V117, P1011, DOI 10.1108/IMDS-06-2016-0229
Lee J, 2016, INT J INFORM MANAGE, V36, P360, DOI 10.1016/j.ijinfomgt.2016.01.001
Liang JP, 2016, J CONSUM PSYCHOL, V26, P283, DOI 10.1016/j.jcps.2015.09.001
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Lim XJ, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120763
MacKenzie SB, 2012, J RETAILING, V88, P542, DOI 10.1016/j.jretai.2012.08.001
Majeed M, 2021, COGENT BUS MANAG, V8, DOI 10.1080/23311975.2021.1944008
MARDIA KV, 1970, BIOMETRIKA, V57, P519, DOI 10.2307/2334770
Martin L., 2021, International Business Development: A Concise Textbook Focusing on International B-to-B Contexts
Martins J, 2019, J BUS RES, V94, P378, DOI 10.1016/j.jbusres.2017.12.047
Mason AN, 2021, COGENT BUS MANAG, V8, DOI 10.1080/23311975.2020.1870797
Mathur M, 2018, INT REV RETAIL DISTR, V28, P554, DOI 10.1080/09593969.2018.1509799
McClure C, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101975
Mehrabian A., 1974, APPROACH ENV PSYCHOL
Memon MA., 2018, Journal of Applied Structural Equation Modeling, V2, pi, DOI [10.47263/jasem.2(1)01, DOI 10.47263/JASEM.2(1)01]
Mohajeri K, 2020, MIS QUART, V44, P525, DOI 10.25300/MISQ/2020/13932
Moreira AC, 2017, J BUS ECON MANAG, V18, P68, DOI 10.3846/16111699.2016.1252793
Nikabadi M.S., 2015, J PROMOTION MANAGEME, V21, P13, DOI DOI 10.1080/10496491.2014.946208
Oleynick VC, 2014, FRONT HUM NEUROSCI, V8, DOI 10.3389/fnhum.2014.00436
Park N, 2009, CYBERPSYCHOL BEHAV, V12, P729, DOI 10.1089/cpb.2009.0003
Pham Phuoc H. M., 2015, International Journal of Internet Marketing and Advertising, V9, P321
Raji R.A., 2018, J MARK COMMUN, V25, P511
Rasoolimanesh SM, 2021, J HOSP TOUR MANAG, V48, P395, DOI 10.1016/j.jhtm.2021.07.013
Ratriyana I. N., 2021, J DIGITAL SOCIAL MED, V8, P365
Rauschnabel PA, 2019, J RETAIL CONSUM SERV, V49, P43, DOI 10.1016/j.jretconser.2019.03.004
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Rungtusanatham M, 2014, J OPER MANAG, V32, P99, DOI 10.1016/j.jom.2014.01.002
Ryu S, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102094
Sarstedt M, 2019, AUSTRALAS MARK J, V27, P197, DOI 10.1016/j.ausmj.2019.05.003
Schivinski B., 2014, J MARK COMMUN, V22, P1, DOI DOI 10.1080/13527266.2013.871323
Seo EJ, 2018, J AIR TRANSP MANAG, V66, P36, DOI 10.1016/j.jairtraman.2017.09.014
Shareef MA, 2019, J RETAIL CONSUM SERV, V46, P58, DOI 10.1016/j.jretconser.2017.11.001
Sharma Anshuman, 2021, World Review of Science, Technology and Sustainable Development, V17, P348, DOI 10.1504/WRSTSD.2021.117842
Sharma A, 2022, J INDIAN BUS RES, V14, P108, DOI 10.1108/JIBR-02-2021-0057
Sharma A, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106919
Sharma SK, 2019, INFORM SYST FRONT, V21, P815, DOI 10.1007/s10796-017-9775-x
Sharma SK, 2019, INFORM SYST MANAGE, V36, P243, DOI 10.1080/10580530.2019.1620504
Sheng H, 2020, IND MANAGE DATA SYST, V120, P1041, DOI 10.1108/IMDS-10-2019-0548
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Sigurdsson V, 2021, J INNOV KNOWL, V6, P257, DOI 10.1016/j.jik.2021.07.001
Swani K, 2017, IND MARKET MANAG, V62, P77, DOI 10.1016/j.indmarman.2016.07.006
Tang YY, 2020, J BUS RES, V115, P101, DOI 10.1016/j.jbusres.2020.04.031
Thien LM, 2020, INT J RES METHOD EDU, V43, P243, DOI 10.1080/1743727X.2019.1662779
Thota S.C., 2018, Academy of Marketing Studies Journal, V22, P1
Thrash TM, 2003, J PERS SOC PSYCHOL, V84, P871, DOI 10.1037/0022-3514.84.4.871
Thrash TM, 2017, J PERS SOC PSYCHOL, V113, P466, DOI 10.1037/pspp0000094
Thrash TM, 2010, J PERS SOC PSYCHOL, V98, P469, DOI 10.1037/a0017907
Verma P., 2021, Journal of Promotion Management, V27, P103, DOI DOI 10.1080/10496491.2020.1809591
Walrave M., 2016, Journal of Marketing Communications, P1, DOI [10.1080/13527266.2016.1182938, DOI 10.1080/13527266.2016.1182938]
Wang W, 2019, COMPUT HUM BEHAV, V93, P176, DOI 10.1016/j.chb.2018.12.007
Wang Z., 2020, E3S WEB C, V214
Yadav M, 2017, TELEMAT INFORM, V34, P1294, DOI 10.1016/j.tele.2017.06.001
Yazdanparast A, 2016, YOUNG CONSUM, V17, P243, DOI 10.1108/YC-03-2016-00590
Yoo B, 2001, J BUS RES, V52, P1, DOI 10.1016/S0148-2963(99)00098-3
Zhai XS, 2020, INTERACT LEARN ENVIR, V28, P586, DOI 10.1080/10494820.2019.1696841
Zhao XS, 2010, J CONSUM RES, V37, P197, DOI 10.1086/651257
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
Zollo L, 2020, J BUS RES, V117, P256, DOI 10.1016/j.jbusres.2020.05.001
NR 116
TC 13
Z9 13
U1 6
U2 38
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1755-4195
EI 1755-4209
J9 J INDIAN BUS RES
JI J. Indian Bus. Res.
PD MAY 17
PY 2022
VL 14
IS 2
SI SI
BP 125
EP 149
DI 10.1108/JIBR-08-2021-0313
EA JAN 2022
PG 25
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 1I3FH
UT WOS:000749599400001
DA 2024-03-27
ER
PT J
AU Akter, S
Sultana, S
Mariani, M
Wamba, SF
Spanaki, K
Dwivedi, YK
AF Akter, Shahriar
Sultana, Saida
Mariani, Marcello
Wamba, Samuel Fosso
Spanaki, Konstantina
Dwivedi, Yogesh K.
TI Advancing algorithmic bias management capabilities in AI-driven
marketing analytics research
SO INDUSTRIAL MARKETING MANAGEMENT
LA English
DT Article
DE Algorithms; Algorithmic bias; AI-driven marketing analytics; Artificial
intelligence
ID CUSTOMER EQUITY DRIVERS; PLS-SEM GUIDELINES; ARTIFICIAL-INTELLIGENCE;
BIG DATA; DYNAMIC CAPABILITIES; SOCIAL MEDIA; LOYALTY; IMPACT; TRUST;
TRUSTWORTHINESS
AB Algorithms in the age of artificial intelligence (AI) constantly transform customer behaviour, marketing programs, and marketing strategies in industrial markets. However, algorithms often fail to perform as expected due to various data, model, and market biases. Motivated by this challenge, this study presents a framework of algorithmic bias management capabilities for industrial markets that contribute to customer equity in terms of value, brand and relationship equity. Drawing on the dynamic capability theory, this study fills this gap by conducting a literature review, thematic analysis, and two rounds of surveys (n=200 analytics professionals and n=200 business customers) in the financial service industry in Australia. The findings show that algorithmic bias management capability consists of three primary dimensions (data, model, and deployment capabilities) and nine subdimensions. These findings have important implications for scholars and managers interested in developing algorithmic bias management capabilities to influence customer equity in industrial markets.
C1 [Akter, Shahriar; Sultana, Saida] Univ Wollongong, Sch Business, Wollongong, NSW 2522, Australia.
[Mariani, Marcello] Univ Reading Greenlands, Henley Business Sch, Henley On Thames RG9 3AU, Oxon, England.
[Wamba, Samuel Fosso] TBS Business Sch, 1 Pl Alphonse Jourdain, F-31068 Toulouse, France.
[Spanaki, Konstantina] Audencia Business Sch, Nantes, France.
[Dwivedi, Yogesh K.] Swansea Univ, Sch Management, Digital Futures Sustainable Business & Soc Res Grp, Bay Campus, Swansea SA1 8EN, Wales.
[Dwivedi, Yogesh K.] Symbiosis Inst Business Management, Dept Management, Pune, Maharashtra, India.
[Dwivedi, Yogesh K.] Symbiosis Int Deemed Univ, Pune, Maharashtra, India.
[Mariani, Marcello] Univ Bologna, Bologna, Italy.
C3 University of Wollongong; University of Reading; Audencia; Swansea
University; Symbiosis International University; Symbiosis Institute of
Business Management (SIBM) Pune; Symbiosis International University;
University of Bologna
RP Dwivedi, YK (autor correspondiente), Swansea Univ, Sch Management, Digital Futures Sustainable Business & Soc Res Grp, Bay Campus, Swansea SA1 8EN, Wales.; Dwivedi, YK (autor correspondiente), Symbiosis Inst Business Management, Dept Management, Pune, Maharashtra, India.; Dwivedi, YK (autor correspondiente), Symbiosis Int Deemed Univ, Pune, Maharashtra, India.
EM sakter@uow.edu.au; ss089@uowmail.edu.au; m.mariani@henley.ac.uk;
s.fosso-wamba@tbs-education.fr; kspanaki@audencia.com;
y.k.dwivedi@swansea.ac.uk
RI Dwivedi, Yogesh Kumar/A-5362-2008; Mariani, Marcello/ABW-5250-2022;
Fosso Wamba, Samuel/AAB-4953-2019
OI Dwivedi, Yogesh Kumar/0000-0002-5547-9990; Mariani,
Marcello/0000-0002-7916-2576; Fosso Wamba, Samuel/0000-0002-1073-058X;
Spanaki, Konstantina/0000-0001-6332-1731
CR ABC, 2020, Federal Government ends Robodebt class action with settlement worth $1.2 billion
Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Adomavicius G, 2019, MIT SLOAN MANAGE REV, V60, P13
Akter S, 2022, J BUS RES, V144, P201, DOI 10.1016/j.jbusres.2022.01.083
Akter S, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa3
Akter S, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102387
Akter S, 2017, PROD PLAN CONTROL, V28, P1011, DOI 10.1080/09537287.2016.1267411
Akter S, 2011, J AM SOC INF SCI TEC, V62, P100, DOI 10.1002/asi.21442
Ali Muhammad, 2019, Proceedings of the ACM on Human-Computer Interaction, V3, DOI 10.1145/3359301
Almquist E, 2018, HARVARD BUS REV, V96, P18
AMIT R, 1993, STRATEGIC MANAGE J, V14, P33, DOI 10.1002/smj.4250140105
Anees-ur-Rehman M, 2019, J STRATEG MARK, V27, P730, DOI 10.1080/0965254X.2018.1482943
Angwin Julia, 2017, PROPUBLICA
[Anonymous], 2021, WALL STR J
[Anonymous], 2020, USA Today
Anshari Muhammad, 2019, Applied Computing and Informatics, V15, P94, DOI 10.1016/j.aci.2018.05.004
Baker RS, 2022, INT J ARTIF INTELL E, V32, P1052, DOI 10.1007/s40593-021-00285-9
Balayn A, 2021, VLDB J, V30, P739, DOI 10.1007/s00778-021-00671-8
Barocas S, 2017, COMMUN ACM, V60, P23, DOI 10.1145/3144172
BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
Batra M.M., 2017, Competition Forum, V15, P198
Beat V., 2021, Report: AI investments see largest year-over-year growth in 20 years
Becker JM, 2012, LONG RANGE PLANN, V45, P359, DOI 10.1016/j.lrp.2012.10.001
Bellamy RKE, 2019, IBM J RES DEV, V63, DOI 10.1147/JRD.2019.2942287
Bhutta N., 2020, FEDS Notes, P2020, DOI 10.17016/2380-7172.2797
Braun V., 2006, Qualitative Research in Psychology, V3, P77, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA]
Bughin J., 2018, Notes from the AI frontier modeling the impact of AI on the world economy, V4
Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
Cartwright S, 2021, IND MARKET MANAG, V97, P35, DOI 10.1016/j.indmarman.2021.06.005
Castaneda J, 2022, ALGORITHMS, V15, DOI 10.3390/a15090303
Christofi M, 2021, INT BUS REV, V30, DOI 10.1016/j.ibusrev.2019.101625
Chui M., 2018, Notes from the AI frontier: Insights from hundreds of use cases, V2
Colquitt JA, 2007, J APPL PSYCHOL, V92, P909, DOI 10.1037/0021-9010.92.4.909
Columbus L., 2020, 2020 is the year AI goes mainstream in marketing
Commonwealth Bank Australia, 2020, Helping our customers understand the options available tothem
Coombs C, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102311
Cowgill Bo., 2020, Columbia Business School Research Paper, DOI DOI 10.2139/SSRN.3361280
Dalenberg DJ, 2018, COMPUT LAW SECUR REV, V34, P615, DOI 10.1016/j.clsr.2017.11.009
Danks D, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4691
Dash R., 2019, J. Strateg. Innov. Sustain, V14, P43, DOI DOI 10.33423/JSIS.V14I3.2105
Dastin J., 2018, Reuters9 October
Davenport T., 2021, Harvard Data Science Review, V3, DOI 10.1162/99608f92.90814c32
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Davenport TH, 2018, HARVARD BUS REV, V96, P108
De Luca LM, 2021, J ACAD MARKET SCI, V49, P790, DOI 10.1007/s11747-020-00739-x
de Visser EJ, 2020, INT J SOC ROBOT, V12, P459, DOI 10.1007/s12369-019-00596-x
Deloitte and Salesforce, 2018, Consumer experience in the retail renaissance
Dilmegani C., 2022, Bias in AI: What it is, Types, Examples & 6 Ways to Fix it in 2022
Doney PM, 1997, J MARKETING, V61, P35, DOI 10.2307/1251829
Duan YQ, 2019, INT J INFORM MANAGE, V48, P63, DOI 10.1016/j.ijinfomgt.2019.01.021
Durach CF, 2017, J SUPPLY CHAIN MANAG, V53, P67, DOI 10.1111/jscm.12145
Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
Dwivedi YK, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2020.102168
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Eisenhardt KM, 2000, STRATEGIC MANAGE J, V21, P1105, DOI 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
Elia S, 2021, J BUS RES, V132, P158, DOI 10.1016/j.jbusres.2021.04.010
Eyers, 2020, CommBank using AI to help triage loan deferral customers
Fahse T, 2021, L N INF SYST ORGAN, V47, P94, DOI 10.1007/978-3-030-86797-3_7
Fairlie R, 2022, MANAGE SCI, V68, P2377, DOI 10.1287/mnsc.2021.3998
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Clavell GG, 2020, PROCEEDINGS OF THE 3RD AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY AIES 2020, P265, DOI 10.1145/3375627.3375852
Gartner, 2020, Healthcare technology innovations for identifying and managing COVID-19 patients
Gebru T., 2020, Datasheets for datasets, V2
Gianfrancesco MA, 2018, JAMA INTERN MED, V178, P1544, DOI 10.1001/jamainternmed.2018.3763
Gkikas DC., 2022, Advances in Artificial Intelligence-based Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis-Vol. 1, P147, DOI [10.1007/978-3-030-80571-5_10, DOI 10.1007/978-3-030-80571-5_10]
Grewal R., 2022, Handbook of Business-to-Business Marketing, P2
Grote T, 2022, CAMB Q HEALTHC ETHIC, V31, P83, DOI 10.1017/S0963180121000839
Gudivada V., 2017, INT J ADV SOFTW, V10, P1
Guha S, 2000, INFORM SYST, V25, P345, DOI 10.1016/S0306-4379(00)00022-3
Gurbaxani V, 2019, MIS Q EXEC, V18, P209, DOI 10.17705/2msqe.00017
Hagen L, 2020, MARKET LETT, V31, P361, DOI 10.1007/s11002-020-09535-7
Hair J.F., 2021, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Classroom Companion, DOI DOI 10.1007/978-3-030-80519-7_7
Hardesty L., 2018, MIT NewsFebruary 11
Hartmann J, 2021, J MARKETING RES, V58, P1159, DOI 10.1177/00222437211037258
Hartmann K, 2021, POLICY SCI, V54, P269, DOI 10.1007/s11077-020-09414-y
Hassani B.K., 2021, AI and Ethics, V1, P239, DOI DOI 10.1007/S43681-020-00026-Z
Hawkins L., 2020, IUP J MARKETING MANA, V19, P7, DOI DOI 10.2139/SSRN.3445173
Hayes AF, 2011, COMMUN SER, P434
Helfat C., 2007, DYNAMIC CAPABILITIES
Helfat CE, 2003, STRATEGIC MANAGE J, V24, P997, DOI 10.1002/smj.332
Helfat CE, 2002, IND CORP CHANGE, V11, P725, DOI 10.1093/icc/11.4.725
Helfat CE, 2015, J MANAGE, V41, P1281, DOI 10.1177/0149206314561301
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Ho Mia Hsiao-Wen., 2020, Journal of Business Research, V121, P13, DOI DOI 10.1016/J.JBUSRES.2020.07.046
Hooker S, 2021, PATTERNS, V2, DOI 10.1016/j.patter.2021.100241
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huang XL, 2018, MOBILE NETW APPL, V23, P68, DOI 10.1007/s11036-017-0962-2
Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
Hussain I, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12072939
Indriasari Elisa, 2019, 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI), P863, DOI 10.1109/IIAI-AAI.2019.00175
Israeli A., 2020, Reference no. 9-521-020)
Janssen M, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2020.101493
Jones-Jang SM, 2022, J COMPUT-MEDIAT COMM, V28, DOI 10.1093/jcmc/zmac029
Keller K.L., 2003, INTERACTIVE MARKETIN, V5, P7, DOI [DOI 10.1057/PALGRAVE.IM.4340213, 10.1057/palgrave.im.4340213]
Kenny D. A., 2020, Measuring Model Fit
Kim AJ, 2012, J BUS RES, V65, P1480, DOI 10.1016/j.jbusres.2011.10.014
Kim W, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101963
Kitchens B, 2018, J MANAGE INFORM SYST, V35, P540, DOI 10.1080/07421222.2018.1451957
Kordzadeh N, 2022, EUR J INFORM SYST, V31, P388, DOI 10.1080/0960085X.2021.1927212
Kumar A, 2020, IND MARKET MANAG, V90, P493, DOI 10.1016/j.indmarman.2019.05.003
Kumar A, 2020, Arxiv, DOI [arXiv:2006.04944, 10.48550/arXiv.2006.04944, DOI 10.48550/ARXIV.2006.04944]
Kumar P, 2023, INT J INFORM MANAGE, V69, DOI 10.1016/j.ijinfomgt.2022.102598
Kumar P, 2023, INFORM SYST FRONT, V25, P2197, DOI [10.1007/s10796-021-10136-6, 10.1109/TNSE.2021.3089435]
Kumar V, 2007, J ACAD MARKET SCI, V35, P157, DOI 10.1007/s11747-007-0028-2
Lambrecht A., 2016, SSRN, DOI DOI 10.2139/SSRN.2852260
Lambrecht A, 2019, MANAGE SCI, V65, P2966, DOI 10.1287/mnsc.2018.3093
Lee NT, 2018, J INF COMMUN ETHICS, V16, P252, DOI 10.1108/JICES-06-2018-0056
Lee SM, 2020, SERV BUS, V14, P1, DOI 10.1007/s11628-019-00408-2
Lemon KN, 2001, MARK MANAG, V10, P20
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Lind E.A., 2001, ADV ORG JUSTICE, P56
Ma LY, 2020, INT J RES MARK, V37, P481, DOI 10.1016/j.ijresmar.2020.04.005
Majhi SG, 2022, INFORM SYST FRONT, V24, P1435, DOI 10.1007/s10796-021-10145-5
Mariani M, 2023, INT J CONTEMP HOSP M, V35, P2929, DOI 10.1108/IJCHM-08-2022-1006
Mariani MM, 2023, TECHNOVATION, V122, DOI 10.1016/j.technovation.2022.102623
Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
Mariani MM, 2021, TECHNOL FORECAST SOC, V172, DOI 10.1016/j.techfore.2021.121009
Mariani MM, 2020, J BUS RES, V121, P338, DOI 10.1016/j.jbusres.2020.09.012
Markl Nina, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P521, DOI 10.1145/3531146.3533117
Marr B., 2019, Artificial intelligence in practice: How 50 successful companies used AI and machine learning to solve problems
Martin K, 2019, J BUS ETHICS, V160, P835, DOI 10.1007/s10551-018-3921-3
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
Meade AW, 2012, PSYCHOL METHODS, V17, P437, DOI 10.1037/a0028085
Mikalef P, 2021, IND MARKET MANAG, V98, P80, DOI 10.1016/j.indmarman.2021.08.003
Miller Barbara D., 2017, Cultural anthropology
Moon S, 2022, FOUND TRENDS MARKET, V15, P213, DOI 10.1561/1700000073
Moradi A., 2022, Entrepreneurship Knowledge, V2
Nelson RR, 2005, J MANAGE INFORM SYST, V21, P199, DOI 10.1080/07421222.2005.11045823
Newman DT, 2020, ORGAN BEHAV HUM DEC, V160, P149, DOI 10.1016/j.obhdp.2020.03.008
Ntoutsi E, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1356
ODonnellan R., 2020, Intuition
Okuda T, 2018, FUJITSU SCI TECH J, V54, P4
Ou YC, 2017, J ACAD MARKET SCI, V45, P336, DOI 10.1007/s11747-016-0477-6
Palmatier RW, 2018, J ACAD MARKET SCI, V46, P1, DOI 10.1007/s11747-017-0563-4
Panch T, 2019, J GLOB HEALTH, V9, DOI 10.7189/jogh.09.020318
Pandya J., 2019, Forbes
Parikh RB, 2019, JAMA-J AM MED ASSOC, V322, P2377, DOI 10.1001/jama.2019.18058
Paulus JK, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-0304-9
Peters Uwe, 2022, Philos Technol, V35, P25, DOI 10.1007/s13347-022-00512-8
Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879
Puspita H, 2021, J GLOB FASH MARK, V12, P133, DOI 10.1080/20932685.2020.1853584
Raghavan M, 2020, FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P469, DOI 10.1145/3351095.3372828
Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
Rajkomar A, 2018, ANN INTERN MED, V169, P866, DOI 10.7326/M18-1990
Ramaseshan B, 2013, J BUS IND MARK, V28, P335, DOI 10.1108/08858621311313929
Ransbotham S., 2017, MIT SLOAN MANAGE REV, V59, P1
Razzaq Z, 2017, ASIA PAC J MARKET LO, V29, P239, DOI 10.1108/APJML-03-2016-0053
Ringle C M., 2022, SmartPLS 4
Rozado D, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0231189
Rust R.T., 2000, DRIVING CUSTOMER EQU
Rust RT, 2020, INT J RES MARK, V37, P15, DOI 10.1016/j.ijresmar.2019.08.002
Rust RT, 2004, J MARKETING, V68, P109, DOI 10.1509/jmkg.68.1.109.24030
Ryan J., 2013, The Marketing Review, V13, P39, DOI DOI 10.1362/146934713X13590250137745
Salvato M, 2018, MON NOT R ASTRON SOC, V473, P4937, DOI 10.1093/mnras/stx2651
Sarstedt M, 2019, AUSTRALAS MARK J, V27, P197, DOI 10.1016/j.ausmj.2019.05.003
Sathi A., 2017, Engaging customers using big data: How Marketing analytics are transforming business
Sawhney M, 2002, J ACAD MARKET SCI, V30, P313, DOI 10.1177/009207002236908
Schweidel D. A., 2023, Artificial Intelligence in Marketing, P125, DOI [10.1108/S1548-643520230000020006, DOI 10.1108/S1548-643520230000020006]
Sengupta E, 2018, PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON SYSTEM MODELING & ADVANCEMENT IN RESEARCH TRENDS (SMART), P226, DOI 10.1109/SYSMART.2018.8746946
Seo H., 2023, Asia Marketing Journal, V24, P178, DOI [10.53728/2765-6500.1600, DOI 10.53728/2765-6500.1600]
Seyyed-Kalantari L, 2021, NAT MED, V27, P2176, DOI 10.1038/s41591-021-01595-0
Shamma H, 2013, BENCHMARKING, V20, P377, DOI 10.1108/14635771311318144
Shellenbarger Sue., 2019, Wall Street Journal
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Siala H, 2022, SOC SCI MED, V296, DOI 10.1016/j.socscimed.2022.114782
Simmering MJ, 2015, ORGAN RES METHODS, V18, P473, DOI 10.1177/1094428114560023
Simonite Tom, 2015, MIT Technology Review
Sipior JC, 2020, INT J INFORM MANAGE, V55, DOI 10.1016/j.ijinfomgt.2020.102170
SITKIN SB, 1993, ORGAN SCI, V4, P367, DOI 10.1287/orsc.4.3.367
Sivarajah U, 2017, J BUS RES, V70, P263, DOI 10.1016/j.jbusres.2016.08.001
Slaughter R. K., 2020, YALE J TECH, V23, P1
Smith G, 2020, Mitigating Bias in Artificial Intelligence: An Equity Fluent Leadership Playbook
Snyder H, 2019, J BUS RES, V104, P333, DOI 10.1016/j.jbusres.2019.07.039
Srinivasan A. V., 2020, Maandblad voor Accountancy en Bedrijfseconomie, V94, P147
Stahl BC, 2022, INT J INFORM MANAGE, V62, DOI 10.1016/j.ijinfomgt.2021.102441
Sun WL, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0235502
Suresh H, 2021, PROCEEDINGS OF 2021 ACM CONFERENCE ON EQUITY AND ACCESS IN ALGORITHMS, MECHANISMS, AND OPTIMIZATION, EAAMO 2021, DOI 10.1145/3465416.3483305
Takshi S., 2020, JL & Health, V34, P215
Teece D., 2003, HDB KNOWLEDGE MANAGE, P195, DOI DOI 10.1007/978-3-540-24748-7_10
Teece DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/smj.640
Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Thiem A, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0233625
Thompson J, 2021, LECT NOTES COMPUT SC, V13095, P574, DOI 10.1007/978-3-030-90963-5_43
Tranfield D, 2003, BRIT J MANAGE, V14, P207, DOI 10.1111/1467-8551.00375
Tsamados A, 2022, AI SOC, V37, P215, DOI 10.1007/s00146-021-01154-8
Valentine S., 2019, Fordham Urban Law Journal, V46, P364
van Giffen B, 2022, J BUS RES, V144, P93, DOI 10.1016/j.jbusres.2022.01.076
Varsha PS, 2021, J GLOB INF MANAG, V29, P221, DOI 10.4018/JGIM.20210701.oa10
Vermeer SAM, 2019, INT J RES MARK, V36, P492, DOI 10.1016/j.ijresmar.2019.01.010
Verster A, 2019, S AFR J BUS MANAG, V50, DOI 10.4102/sajbm.v50i1.1372
Vigdor Neil, 2019, NEW YORK TIMES
Vogel V, 2008, J MARKETING, V72, P98, DOI 10.1509/jmkg.72.6.98
Walsh CG, 2020, JAMIA OPEN, V3, P9, DOI 10.1093/jamiaopen/ooz054
Wamba SF, 2019, BUS PROCESS MANAG J, V25, P512, DOI 10.1108/BPMJ-08-2017-0218
Wan MT, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P618, DOI 10.1145/3336191.3371855
Wang CL, 2007, INT J MANAG REV, V9, P31, DOI 10.1111/j.1468-2370.2007.00201.x
Watson R, 2018, J PROD INNOVAT MANAG, V35, P254, DOI 10.1111/jpim.12394
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Wetzels M, 2009, MIS QUART, V33, P177, DOI 10.2307/20650284
Whitney H. M., 2017, U. Chi. Legal F., P537
Wirtz BW, 2020, INT J PUBLIC ADMIN, V43, P818, DOI 10.1080/01900692.2020.1749851
Wixom BH, 2005, INFORM SYST RES, V16, P85, DOI 10.1287/isre.1050.0042
Xu Z, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2021.102800
Yang S.J., 2021, COMPUTERS ED ARTIFIC, V2, P100008
Yu XL, 2019, ASIA PAC J MARKET LO, V31, P1233, DOI 10.1108/APJML-01-2018-0034
Zhang JL, 2020, IEEE T IND ELECTRON, V67, P7025, DOI 10.1109/TIE.2019.2938462
Zhou R., 2021, 2021 IEEE 5 C EN INT, P3318, DOI [10.1109/EI252483.2021.9713542, DOI 10.1109/EI252483.2021.9713542]
NR 208
TC 0
Z9 0
U1 52
U2 52
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0019-8501
EI 1873-2062
J9 IND MARKET MANAG
JI Ind. Mark. Manage.
PD OCT
PY 2023
VL 114
BP 243
EP 261
DI 10.1016/j.indmarman.2023.08.013
EA AUG 2023
PG 19
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA S8SC3
UT WOS:001073801500001
OA hybrid, Green Submitted
DA 2024-03-27
ER
PT J
AU Yi, MY
Fiedler, KD
Park, JS
AF Yi, Mun Y.
Fiedler, Kirk D.
Park, Jae S.
TI Understanding the role of individual innovativeness in the acceptance of
IT-based innovations: Comparative analyses of models and measures
SO DECISION SCIENCES
LA English
DT Article
DE adopter category; behavioral intention; hierarchical regression;
innovation diffusion theory; innovativeness; online shopping; path
analysis; personal digital assistant; personal innovativeness in
information technology
ID COMPUTER SELF-EFFICACY; TECHNOLOGY ACCEPTANCE; INFORMATION-SYSTEMS; USER
ACCEPTANCE; PERCEIVED EASE; E-MAIL; ADOPTION; BEHAVIOR; IMPLEMENTATION;
PERCEPTIONS
AB As new technological innovations are rapidly introduced and changed, identifying an individual characteristic that has a persistent effect on the acceptance decisions across multiple technologies is of substantial value for the successful implementation of information systems. Augmenting prior work on individual innovativeness within the context of information technology, we developed a new measure of adopter category innovativeness (ACI) and compared its effectiveness with the existing measure of personal innovativeness in IT (PIIT). Further, we examined two alternative models in which the role of individual innovativeness was theorized differently-either as a moderator of the effects the perceived innovation characteristics of usefulness, ease of use, and compatibility have on future use intention (moderator model) or as a direct determinant of the innovation characteristics (direct determinant model). To ensure the generalizability of the study findings, two field studies (N = 634) were conducted, each of which examined the two models (moderator and direct determinant) and measured individual innovativeness using the two measures (ACI and PIIT). Study 1 surveyed the online buying practices of 412 individuals, and Study 2 surveyed personal digital assistant adoption of 222 healthcare professionals. Across the markedly different adoption contexts, the study results consistently show that individual innovativeness is a direct determinant of the innovation characteristics, and the two measures share many commonalities. The new measure offers some additional utilities not found in the PIIT measure by allowing individuals to be directly classified and mapped into adopter categories. Implications are drawn for future research and practice.
C1 Univ S Carolina, Moore Sch Business, Columbia, SC 29208 USA.
Kosin Univ, Pusan, South Korea.
C3 University of South Carolina System; University of South Carolina
Columbia
RP Yi, MY (autor correspondiente), Univ S Carolina, Moore Sch Business, 1705 Coll St, Columbia, SC 29208 USA.
EM myi@moore.sc.edu; fiedler@moore.sc.edu; jpark@kosin.ac.kr
RI Yi, Mun Yong/C-2065-2011
CR Agarwal R, 1997, DECISION SCI, V28, P557, DOI 10.1111/j.1540-5915.1997.tb01322.x
Agarwal R, 2000, MIS QUART, V24, P665, DOI 10.2307/3250951
Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204
Agarwal R, 1999, DECISION SCI, V30, P361, DOI 10.1111/j.1540-5915.1999.tb01614.x
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Ajzen I., 1985, SSSP SPRINGER SERIES, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2]
BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
Bhatnagar A, 2000, COMMUN ACM, V43, P98, DOI 10.1145/353360.353371
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Boyer KK, 2005, DECISION SCI, V36, P569, DOI 10.1111/j.1540-5414.2005.00103.x
Brancheau JC, 1990, INFORM SYST RES, V1, P115, DOI 10.1287/isre.1.2.115
Carte TA, 2003, MIS QUART, V27, P479
CHURCHILL GA, 1979, J MARKETING RES, V16, P64, DOI 10.2307/3150876
Compeau D. R., 1995, Management Information Systems Quarterly, V19, P189, DOI 10.2307/249688
Cook T.D., 1979, Quasi‐experimentation: design analysis issues for field setting
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dishaw MT, 1999, INFORM MANAGE-AMSTER, V36, P9, DOI 10.1016/S0378-7206(98)00101-3
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
Fichman R.G., 1992, INFORM TECHNOLOGY DI
FLIEGEL FC, 1968, RURAL SOCIOL, V33, P437
Gefen D, 1997, MIS QUART, V21, P389, DOI 10.2307/249720
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
GILLOOLY C, 1998, INFORM WEEK, V669, P46
Goldsmith R.E., 1991, Journal of the Academy of Marketing Science, V19, P209, DOI [10.1007/BF02726482, DOI 10.1007/BF02726482, 10.1007/BF02726497, DOI 10.1007/BF02726497]
Goldsmith R.E., 2011, The Journal of Applied Business Research, V7, P89, DOI [DOI 10.19030/JABR.V7I2.6249, 10.19030/jabr.v7i2.6249]
Hurt H.T., 1977, HUM COMMUN RES, V4, P58, DOI DOI 10.1111/J.1468-2958.1977.TB00597.X
Jaccard J., 1990, INTERACTION EFFECTS
JAMES LR, 1984, J APPL PSYCHOL, V69, P307, DOI 10.1037/0021-9010.69.2.307
KING RT, 1994, WALL STREET J E EDIT, V27, pB5
Kirk R. E., 1995, Experimental design: Procedures for the behavioural sciences
KIRTON M, 1976, J APPL PSYCHOL, V61, P622, DOI 10.1037/0021-9010.61.5.622
Kwon T.H., 1987, CRITICAL ISSUES INFO, DOI DOI 10.5555/54905
Leavitt C., 1975, ADV CONSUM RES, V2, P545
Lee Y, 2003, COM ASS INFORM SYST, V12, P752, DOI [DOI 10.17705/1CAIS.01250, 10.17705/1CAIS.01250]
LEONARDBARTON D, 1988, MANAGE SCI, V34, P1252, DOI 10.1287/mnsc.34.10.1252
Lewis W, 2003, MIS QUART, V27, P657
Looney CA, 2006, DECISION SCI, V37, P205, DOI 10.1111/j.1540-5915.2006.00122.x
Lucas HC, 1999, DECISION SCI, V30, P291, DOI 10.1111/j.1540-5915.1999.tb01611.x
Marakas GM, 1998, INFORM SYST RES, V9, P126, DOI 10.1287/isre.9.2.126
McCarroll T., 1991, Time, V138, P44
MIDGLEY DF, 1978, J CONSUM RES, V4, P229, DOI 10.1086/208701
Moore G., 1991, CROSSING CHASM MARKE
Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192
MOORE TA, 1999, CROSSING CHASM
Neter J., 1996, APPL LINEAR STAT MOD
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Parthasarathy M, 1998, INFORM SYST RES, V9, P362, DOI 10.1287/isre.9.4.362
Rogers, 2003, DIFFUSION INNOVATION
Rogers E. M., 1962, Diffusion of Innovations
Rothman J., 1974, PLANNING ORG SOCIAL
STRAUB DW, 1994, INFORM SYST RES, V5, P23, DOI 10.1287/isre.5.1.23
Sultan F, 2000, IEEE T ENG MANAGE, V47, P106, DOI 10.1109/17.820730
TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144
TORNATZKY LG, 1982, IEEE T ENG MANAGE, V29, P28, DOI 10.1109/TEM.1982.6447463
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 1996, DECISION SCI, V27, P451, DOI 10.1111/j.1540-5915.1996.tb01822.x
Venkatesh V, 2000, MIS QUART, V24, P115, DOI 10.2307/3250981
WEBSTER J, 1992, MIS QUART, V16, P201, DOI 10.2307/249576
Yi MY, 2003, INT J HUM-COMPUT ST, V59, P431, DOI 10.1016/S1071-5819(03)00114-9
Yi MY, 2003, INFORM SYST RES, V14, P146, DOI 10.1287/isre.14.2.146.16016
NR 61
TC 252
Z9 278
U1 8
U2 115
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0011-7315
EI 1540-5915
J9 DECISION SCI
JI Decis. Sci.
PD AUG
PY 2006
VL 37
IS 3
BP 393
EP 426
DI 10.1111/j.1540-5414.2006.00132.x
PG 34
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 091AD
UT WOS:000240996900004
DA 2024-03-27
ER
PT J
AU Alantari, HJ
Currim, IS
Deng, YT
Singh, S
AF Alantari, Huwail J.
Currim, Imran S.
Deng, Yiting
Singh, Sameer
TI An empirical comparison of machine learning methods for text-based
sentiment analysis of online consumer reviews
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE Automated text analysis; Sentiment analysis; Online reviews; User
generated content; Machine learning; Natural language processing
ID USER-GENERATED CONTENT; WORD-OF-MOUTH; CONJOINT-ANALYSIS; SEARCH
ENGINES; CHOICE MODELS; CHATTER; DESIGN
AB The amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. Marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. We focus on the fundamental relationship between a consumer's overall empirical evaluation, and the text-based explanation of their evaluation. We study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. We incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. For generalizability, we analyze 25,241 products in nine product categories, and 260,489 reviews across five review platforms. We find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as Latent Dirichlet Allocation offer deeper diagnostics. However, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. Consequently, future selection of methods to process text reviews is likely to be based on analysts' goals of prediction versus diagnostics. Published by Elsevier B.V.
C1 [Alantari, Huwail J.; Currim, Imran S.] Univ Calif Irvine, Paul Merage Sch Business, Irvine, CA 92697 USA.
[Deng, Yiting] UCL, UCL Sch Management, London, England.
[Singh, Sameer] Univ Calif Irvine, Donald Bren Sch Informat & Comp Sci, Irvine, CA USA.
C3 University of California System; University of California Irvine;
University of London; University College London; University of
California System; University of California Irvine
RP Alantari, HJ (autor correspondiente), Univ Calif Irvine, Paul Merage Sch Business, Irvine, CA 92697 USA.
RI Deng, Yiting/CAH-2594-2022
OI Deng, Yiting/0000-0003-2330-1554; Singh, Sameer/0000-0003-0621-6323
CR Abramson C, 2000, J MARKETING RES, V37, P410, DOI 10.1509/jmkr.37.4.410.18791
Anderson ET, 2014, J MARKETING RES, V51, P249, DOI 10.1509/jmr.13.0209
Andrews RL, 2008, MANAGE SCI, V54, P83, DOI 10.1287/mnsc.1070.0749
Andrews RL, 2011, J BUS ECON STAT, V29, P319, DOI 10.1198/jbes.2010.07225
Andrews RL, 2002, J MARKETING RES, V39, P479, DOI 10.1509/jmkr.39.4.479.19124
Andrews RL, 2002, J MARKETING RES, V39, P87, DOI 10.1509/jmkr.39.1.87.18936
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Bai X, 2020, INFORM SYST RES, V31, P1132, DOI 10.1287/isre.2020.0935
Bell G, 2009, SCIENCE, V323, P1297, DOI 10.1126/science.1170411
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Borgman CL, 2015, BIG DATA, LITTLE DATA, NO DATA: SCHOLARSHIP IN THE NETWORKED WORLD, P1
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Chakraborty I, 2021, 2176R2 COWL FDN
Chen Z, 2017, J CONSUM RES, V44, P613, DOI 10.1093/jcr/ucx055
CHURCH KW, 1990, 27TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P76
GENSCH DH, 1979, J MARKETING RES, V16, P124, DOI 10.2307/3150883
Ghose A, 2019, MANAGE SCI, V65, P1363, DOI 10.1287/mnsc.2017.2991
Ghose A, 2012, MARKET SCI, V31, P493, DOI 10.1287/mksc.1110.0700
Goes PB, 2014, INFORM SYST RES, V25, P222, DOI 10.1287/isre.2013.0512
Grasser F, 2018, DH '18: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON DIGITAL HEALTH, P121, DOI 10.1145/3194658.3194677
GREEN PE, 1978, J CONSUM RES, V5, P103, DOI 10.1086/208721
Harbert T., 2021, Tapping the power of unstructured data
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
Heitmann M., 2020, MORE FEELING BENCHMA
HILL RJ, 1977, CONTEMP SOCIOL, V6, P244, DOI 10.2307/2065853
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Jurafsky D., 2009, P HUMAN LANGUAGE TEC, P638, DOI DOI 10.3115/1620754.1620847
Kirmani A, 2017, J MARKETING, V81, P103, DOI 10.1509/jm.15.0369
Kubler RV, 2020, J INTERACT MARK, V50, P136, DOI 10.1016/j.intmar.2019.08.001
Lee TY, 2011, J MARKETING RES, V48, P881, DOI 10.1509/jmkr.48.5.881
Liu X, 2019, J MARKETING RES, V56, P918, DOI 10.1177/0022243719866690
Ludwig S, 2013, J MARKETING, V77, P87, DOI 10.1509/jm.11.0560
McAuley J, 2019, ARXIV PREPRINT ARXIV
McAuley J, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/2766462.2767755
Ordenes FV, 2017, J CONSUM RES, V43, P875, DOI 10.1093/jcr/ucw070
Packard G, 2017, J MARKETING RES, V54, P572, DOI 10.1509/jmr.15.0248
Puranam D, 2017, MARKET SCI, V36, P726, DOI 10.1287/mksc.2017.1048
Rafieian O, 2021, MARKET SCI, V40, P193, DOI 10.1287/mksc.2020.1235
Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
Ransbotham S, 2019, MARKET SCI, V38, P773, DOI 10.1287/mksc.2018.1115
Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
Rubera G, 2015, MARKET SCI, V34, P98, DOI 10.1287/mksc.2014.0875
Sridhar S, 2012, J MARKETING, V76, P70, DOI 10.1509/jm.10.0377
STECKEL JH, 1993, MARKET SCI, V12, P415, DOI 10.1287/mksc.12.4.415
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Tirunillai S, 2017, MARKET SCI, V36, P862, DOI 10.1287/mksc.2017.1040
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Tirunillai S, 2012, MARKET SCI, V31, P198, DOI 10.1287/mksc.1110.0682
van Laer T, 2019, J CONSUM RES, V46, P267, DOI 10.1093/jcr/ucy067
Vermeer SAM, 2019, INT J RES MARK, V36, P492, DOI 10.1016/j.ijresmar.2019.01.010
Wan MT, 2018, 12TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS), P86, DOI 10.1145/3240323.3240369
Wang Y, 2018, J MARKETING RES, V55, P163, DOI 10.1509/jmr.15.0511
Wu CH, 2015, MARKET SCI, V34, P906, DOI 10.1287/mksc.2015.0944
Yoganarasimhan H, 2020, MANAGE SCI, V66, P1045, DOI 10.1287/mnsc.2018.3255
Zhang YC, 2018, MARKET SCI, V37, P425, DOI 10.1287/mksc.2017.1076
NR 56
TC 29
Z9 32
U1 15
U2 79
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD MAR
PY 2022
VL 39
IS 1
BP 1
EP 19
DI 10.1016/j.ijresmar.2021.10.011
EA FEB 2022
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA ZI2OZ
UT WOS:000761467400001
OA Green Published
DA 2024-03-27
ER
PT J
AU Al-Sharafi, MA
Al-Qaysi, N
Iahad, NA
Al-Emran, M
AF Al-Sharafi, Mohammed A.
Al-Qaysi, Noor
Iahad, Noorminshah A.
Al-Emran, Mostafa
TI Evaluating the sustainable use of mobile payment contactless
technologies within and beyond the COVID-19 pandemic using a hybrid
SEM-ANN approach
SO INTERNATIONAL JOURNAL OF BANK MARKETING
LA English
DT Article
DE Mobile payment; Contactless technologies; Protection motivation theory;
Expectation-confirmation model; Trust; PLS-SEM; Artificial neural
network; COVID-19
ID PROTECTION MOTIVATION THEORY; NEURAL-NETWORK; SELF-EFFICACY; FEAR
APPEALS; ACCEPTANCE; EXPECTATION; CONTINUANCE; INTENTION; ADOPTION;
ATTITUDE
AB Purpose While there is an abundant amount of literature studies on mobile payment adoption, there is a scarce of knowledge concerning the sustainable use of mobile payment contactless technologies. As those technologies are mainly concerned with security and users' trust, the question of how security factors and trust can influence the sustainable use of those technologies within and beyond the COVID-19 pandemic is still unanswered. This research thus develops a theoretical model based on integrating the protection motivation theory (PMT) and the expectation-confirmation model (ECM), extended with perceived trust (PT) to explore the sustainable use of mobile payment contactless technologies. Design/methodology/approach The developed model is evaluated based on data collected through a web-based survey from 523 users who used contactless payment technologies. Unlike the existing literature, the collected data were analyzed using a hybrid structural equation modeling-artificial neural network (SEM-ANN) technique. Findings The data analysis results reinforced all the proposed relationships in the developed model. The sensitivity analysis results showed that PT has the largest impact on the sustainable use of mobile payment contactless technologies with 97.2% normalized importance, followed by self-efficacy (SE) (77%), satisfaction (72.1%), perceived vulnerability (PV) (48.9%), perceived usefulness (PU) (48.2%), perceived severity (PS) (40.7%), response efficacy (RE) (28.7%) and response costs (RCs) (24.1%). Originality/value The originality of this research lies behind the development of an integrated model based on PMT and ECM to understand the sustainable use of mobile payment contactless technologies. The study provides several managerial implications for decision-makers, policy-makers and service providers to ensure the sustainability of those contactless technologies within and beyond the COVID-19 pandemic.
C1 [Al-Sharafi, Mohammed A.; Iahad, Noorminshah A.] Univ Teknol Malaysia, Azman Hashim Int Business Sch, Dept Informat Syst, Skudai, Malaysia.
[Al-Qaysi, Noor] Univ Pendidikan Sultan Idris, Fac Art Comp & Creat Ind, Tanjung Malim, Malaysia.
[Iahad, Noorminshah A.] Airlangga Univ, Fac Sci & Technol, Informat Syst, Surabaya, Indonesia.
[Al-Emran, Mostafa] British Univ Dubai, Fac Engn & IT, Dubai, Dubai, U Arab Emirates.
C3 Universiti Teknologi Malaysia; Universiti Pendidikan Sultan Idris;
Airlangga University
RP Iahad, NA (autor correspondiente), Univ Teknol Malaysia, Azman Hashim Int Business Sch, Dept Informat Syst, Skudai, Malaysia.; Iahad, NA (autor correspondiente), Airlangga Univ, Fac Sci & Technol, Informat Syst, Surabaya, Indonesia.
EM alsharafi@ieee.org; noor.j.alqaysi@gmail.com; minshah@utm.my;
mustafa.n.alemran@gmail.com
RI Al-Qaysi, Noor/HTN-4399-2023; Al-Sharafi, Mohammed A./E-1530-2017;
Al-Emran, Mostafa/W-4466-2018
OI Al-Sharafi, Mohammed A./0000-0003-0726-6031; Al-Emran,
Mostafa/0000-0002-5269-5380
CR Al-Emran M, 2021, J ENTERP INF MANAG, V34, P1229, DOI 10.1108/JEIM-02-2020-0052
Al-Emran M, 2022, INTERACT LEARN ENVIR, V30, P1897, DOI 10.1080/10494820.2020.1753216
Al-Emran M, 2019, ADV INTELL SYST COMP, V845, P644, DOI 10.1007/978-3-319-99010-1_59
Al-Saedi K., 2021, RECENT ADV TECHNOLOG, P79, DOI DOI 10.1007/978-3-030-64987-6_6
Al-Saedi K, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101293
Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
Ali Z, 2018, J COMPUT EDUC, V5, P297, DOI 10.1007/s40692-018-0114-0
Aljawder M., 2019, INT J COMPUTING DIGI, V8, P669, DOI 10.12785/ijcds/080614
Alkawsi GA, 2021, ALEX ENG J, V60, P227, DOI 10.1016/j.aej.2020.07.002
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Arpaci I, 2021, MULTIMED TOOLS APPL, V80, P11943, DOI 10.1007/s11042-020-10340-7
Arpaci I, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101468
BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
Bhattacherjee A, 2004, MIS QUART, V28, P229
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Bhattacherjee Anol, 2011, Asia Pacific Journal of Information Systems, V21, P1
Cao XF, 2018, INTERNET RES, V28, P456, DOI 10.1108/IntR-11-2016-0359
Chin W. W., 2010, HDB PARTIAL LEAST SQ, P655, DOI DOI 10.1007/978-3-540-32827-8_29
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Compeau D. R., 1995, Management Information Systems Quarterly, V19, P189, DOI 10.2307/249688
Daragmeh A., 2021, Journal of Open Innovation: Technology, Market, and Complexity, V7, P132, DOI [DOI 10.3390/JOITMC7020132, 10.3390/joitmc7020132]
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dawes J, 2008, INT J MARKET RES, V50, P61, DOI 10.1177/147078530805000106
Duy DP, 2015, COMPUT SECUR, V48, P281, DOI 10.1016/j.cose.2014.11.002
Foth M, 2012, J PUBLIC HEALTH-HEID, V20, P253, DOI 10.1007/s10389-011-0456-9
Gaube S., 2019, Perceived Safety: A Multidisciplinary Perspective, P101, DOI DOI 10.1007/978-3-030-11456-5_7
Hair J., 2017, International Journal of Multivariate Data Analysis, V1, P107, DOI [DOI 10.1504/IJMDA.2017.087624, https://doi.org/10.1504/IJMDA.2017.087624, 10.1504/ijmda.2017.10008574]
Hair JF, 2012, J ACAD MARKET SCI, V40, P414, DOI 10.1007/s11747-011-0261-6
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair Jr J.F., 2016, PRIMER PARTIAL LEAST
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Johnston AC, 2010, MIS QUART, V34, P549
Kalinic Z, 2021, EXPERT SYST APPL, V175, DOI 10.1016/j.eswa.2021.114803
Karjaluoto H, 2020, INT J BANK MARK, V38, P332, DOI 10.1108/IJBM-04-2019-0155
Khayer A, 2020, TECHNOL SOC, V60, DOI 10.1016/j.techsoc.2019.101225
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015040101
Lee VH, 2020, EXPERT SYST APPL, V157, DOI 10.1016/j.eswa.2020.113477
Leong LY, 2020, J BUS RES, V110, P24, DOI 10.1016/j.jbusres.2019.11.056
Loh XM, 2021, INTERNET RES, V31, P376, DOI 10.1108/INTR-04-2020-0175
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
MORRIS SA, 2004, IND MANAGE DATA SYST
Nguyen P., 2013, Contemporary Management Research, V9, P399
OLIVER RL, 1977, J APPL PSYCHOL, V62, P480, DOI 10.1037/0021-9010.62.4.480
Ooi KB, 2018, EXPERT SYST APPL, V93, P376, DOI 10.1016/j.eswa.2017.10.009
Park E, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101318
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Puriwat W., 2021, Emerging Science Journal, V5, P85, DOI DOI 10.28991/ESJ-2021-01260
Qasem, 2018, INDIAN J SCI TECHNOL, V11, P1, DOI DOI 10.17485/IJST/2018/V11I20/91928
Qasem YAM, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10144905
Rahi S, 2021, J ENTERP INF MANAG, V34, P1874, DOI 10.1108/JEIM-04-2020-0156
Ramayah T., 2018, Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated guide and practical guide to statistical analysis, V2nd, DOI DOI 10.1213/01.ANE.0000105862.78906.3D
Roger E., 2003, DIFFUSION INNOVATION, V4th, DOI DOI 10.1111/J.1467-9523.1970.TB00071.X
ROGERS RW, 1975, J PSYCHOL, V91, P93, DOI 10.1080/00223980.1975.9915803
Rogers RW., 1997, HDB HLTH BEHAV RES
Sekaran U.Bougie., 2010, RES METHODS BUSINESS, DOI DOI 10.1007/978-94-007-0753-5_102084
Sim JJ, 2014, TELEMAT INFORM, V31, P569, DOI 10.1016/j.tele.2013.11.005
Singh S, 2020, AUSTRALAS J INF SYST, V24, DOI 10.3127/ajis.v24i0.2695
Tam C, 2020, INFORM SYST FRONT, V22, P243, DOI 10.1007/s10796-018-9864-5
Tan GWH, 2014, TELEMAT INFORM, V31, P292, DOI 10.1016/j.tele.2013.06.002
Teo AC, 2015, INT J MOB COMMUN, V13, P478, DOI 10.1504/IJMC.2015.070961
Tew HT, 2022, J COMPUT INFORM SYST, V62, P527, DOI 10.1080/08874417.2020.1858731
Tongco M. D. C., 2007, Ethnobotany Research and Applications, V5, P147
Trutsch T., 2020, Swiss Journal of Economics and Statistics, V156, P1, DOI DOI 10.1186/S41937-020-00050-0
Tull K., 2017, MOBILE PAYMENTS TODA
Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
Wang YM, 2007, EXPERT SYST APPL, V32, P336, DOI 10.1016/j.eswa.2005.11.029
WITTE K, 1992, COMMUN MONOGR, V59, P329, DOI 10.1080/03637759209376276
Xiu-Ming Loh, 2022, Journal of Computer Information Systems, V62, P39, DOI 10.1080/08874417.2019.1688733
Zhang X, 2019, ONLINE INFORM REV, V44, P1, DOI 10.1108/OIR-11-2016-0339
NR 69
TC 44
Z9 43
U1 9
U2 41
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 0265-2323
EI 1758-5937
J9 INT J BANK MARK
JI Int. J. Bank Mark.
PD JUN 8
PY 2022
VL 40
IS 5
SI SI
BP 1071
EP 1095
DI 10.1108/IJBM-07-2021-0291
EA DEC 2021
PG 25
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1V9TF
UT WOS:000733286600001
DA 2024-03-27
ER
PT J
AU Flavién, C
Akdim, K
Casaló, LV
AF Flavian, Carlos
Akdim, Khaoula
Casalo, Luis V.
TI Effects of voice assistant recommendations on consumer behavior
SO PSYCHOLOGY & MARKETING
LA English
DT Article
DE artificial intelligence; consumer behavior; e-WOM; online consumer
review; recommendations; virtual assistant; voice assistant
ID WORD-OF-MOUTH; MEDIA-RICHNESS; ONLINE REVIEWS; PLS-SEM; ELECTRONIC MAIL;
CREDIBILITY; INFORMATION; IMPACT; SEARCH; EXPERIENCE
AB The present study compares the influence of text-based recommendations; traditionally known as online consumer reviews, and the influence of voice-based recommendations provided by voice-driven virtual assistants on consumer behaviors. Based on media richness theory, the research model investigates how voice versus text modality influences consumers' perceptions of credibility and usefulness, as well as their behavioral intentions and actual behaviors. In addition, the study analyses if these relationships vary based on the type of product and compares the influence of masculine and feminine voices. Two studies were conducted using between-subjects experimental designs, partial least squares-structural equation modeling, and logistic regression. The core finding is that voice-based recommendations are more effective than online consumer reviews in altering consumer behaviors. In addition, the first study showed that the influence of recommendations on behavioral intentions is mediated by consumers' perceptions of their credibility and usefulness. The second study confirmed, in a realistic setting, that voice-based recommendations affect consumer choices to a greater extent. Recommendations for search products and provided by males are also found to be more effective. These results contribute to the voice assistant and e-WOM literature by highlighting the effectiveness of voice-based recommendations in predicting consumer behaviors, confirming that credibility and usefulness are key factors that determine the influence of recommendations, and showing that recommendations are more effective when they focus on search products.
C1 [Flavian, Carlos; Akdim, Khaoula; Casalo, Luis V.] Univ Zaragoza, Dept Mkt Management & Market Res, Zaragoza, Spain.
[Flavian, Carlos; Akdim, Khaoula] Univ Zaragoza, Fac Econ & Business, Zaragoza, Spain.
[Casalo, Luis V.] Univ Zaragoza, Fac Business & Publ Management, Huesca, Spain.
C3 University of Zaragoza; University of Zaragoza; University of Zaragoza
RP Flavién, C (autor correspondiente), Univ Zaragoza, Dept Mkt Management & Market Res, Zaragoza, Spain.
EM cflavian@unizar.es
RI Flavian, Carlos/G-4365-2013
OI Flavian, Carlos/0000-0001-7118-9013
FU Spanish Ministry of Science and Innovation; European Social Fund;
Government of Aragon (Group "METODO"); [PID2019-105468RB-I00];
[S20_20R; LMP51_21]; [C135/2017]
FX This research was supported by the Spanish Ministry of Science and
Innovation (PID2019-105468RB-I00) and the European Social Fund and the
Government of Aragon (Group "METODO" S20_20R; LMP51_21 and pre-doctoral
grant 2017-2021 C135/2017).
CR Agarwal J, 2022, PSYCHOL MARKET, V39, P1946, DOI 10.1002/mar.21710
Ahn J, 2022, J BUS RES, V141, P50, DOI 10.1016/j.jbusres.2021.12.007
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Anandarajan M, 2010, IEEE T PROF COMMUN, V53, P132, DOI 10.1109/TPC.2010.2046082
[Anonymous], 2006, 649 SFB
Bagozzi RP, 2016, PSYCHOL MARKET, V33, P629, DOI 10.1002/mar.20905
Balakrishnan J, 2021, PSYCHOL MARKET, V38, P643, DOI 10.1002/mar.21462
Bampo M, 2008, INFORM SYST RES, V19, P273, DOI 10.1287/isre.1070.0152
Basil DZ, 2006, PSYCHOL MARKET, V23, P1035, DOI 10.1002/mar.20145
Baumgartner H, 2021, J ACAD MARKET SCI, V49, P221, DOI 10.1007/s11747-020-00766-8
Belanche D, 2021, PSYCHOL MARKET, V38, P2357, DOI 10.1002/mar.21532
Berger J, 2013, J CONSUM RES, V40, P567, DOI 10.1086/671345
BJORK RA, 1970, J VERB LEARN VERB BE, V9, P255, DOI 10.1016/S0022-5371(70)80059-7
Bloch P.H., 1981, ADV CONSUM RES, V8, P61
Braeuer P, 2022, COMPUT HUM BEHAV, V135, DOI 10.1016/j.chb.2022.107362
Brill TM, 2019, J MARKET MANAG-UK, V35, P1401, DOI 10.1080/0267257X.2019.1687571
Campbell John, 2006, Journal of Information, Information Technology & Organizations, V1, P87
Capgemini, 2019, SMART TALK ORG CONSU
Caplan S.E., 2014, COMMUNICATION TECHNO, P53
Casaló LV, 2011, COMPUT HUM BEHAV, V27, P622, DOI 10.1016/j.chb.2010.04.013
Casaló LV, 2010, TOURISM MANAGE, V31, P898, DOI 10.1016/j.tourman.2010.04.007
Chang Y, 2016, INFORM DEV, V32, P226, DOI 10.1177/0266666914529294
Chatterjee P, 2001, ADV CONSUM RES, V28, P129
Cheng YH, 2015, J BUS RES, V68, P883, DOI 10.1016/j.jbusres.2014.11.046
Chérif E, 2019, RECH APPL MARKET-ENG, V34, P28, DOI 10.1177/2051570719829432
Cheung CMK, 2008, INTERNET RES, V18, P229, DOI 10.1108/10662240810883290
Cheung CMK, 2012, DECIS SUPPORT SYST, V54, P461, DOI 10.1016/j.dss.2012.06.008
Cheung MY, 2009, INT J ELECTRON COMM, V13, P9, DOI 10.2753/JEC1086-4415130402
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chi OHX, 2022, J TRAVEL RES, V61, P170, DOI 10.1177/0047287520971054
Chin W. W., 2010, HDB PARTIAL LEAST SQ, P655, DOI DOI 10.1007/978-3-540-32827-8_29
Chin WW, 1998, MIS QUART, V22, pVII
Chiu CM, 2009, ONLINE INFORM REV, V33, P761, DOI 10.1108/14684520910985710
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
ComScore, 2021, SMART SPEAKERS EVOLV
Craciun G, 2019, COMPUT HUM BEHAV, V97, P104, DOI 10.1016/j.chb.2019.03.010
DAFT RL, 1986, MANAGE SCI, V32, P554, DOI 10.1287/mnsc.32.5.554
Dellaert BGC, 2020, MARKET LETT, V31, P335, DOI 10.1007/s11002-020-09537-5
DOWLING GR, 1994, J CONSUM RES, V21, P119, DOI 10.1086/209386
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Eyssel F, 2012, J APPL SOC PSYCHOL, V42, P2213, DOI 10.1111/j.1559-1816.2012.00937.x
Feine J, 2019, INT J HUM-COMPUT ST, V132, P138, DOI 10.1016/j.ijhcs.2019.07.009
Filieri R, 2021, INT J CONTEMP HOSP M, V33, P199, DOI 10.1108/IJCHM-05-2020-0402
Filieri R, 2018, INFORM MANAGE-AMSTER, V55, P956, DOI 10.1016/j.im.2018.04.010
Filieri R, 2018, COMPUT HUM BEHAV, V80, P122, DOI 10.1016/j.chb.2017.10.039
Filieri R, 2016, ANN TOURISM RES, V58, P46, DOI 10.1016/j.annals.2015.12.019
Filieri R, 2015, TOURISM MANAGE, V51, P174, DOI 10.1016/j.tourman.2015.05.007
Filieri R, 2015, J BUS RES, V68, P1261, DOI 10.1016/j.jbusres.2014.11.006
Filieri R, 2014, J TRAVEL RES, V53, P44, DOI 10.1177/0047287513481274
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Foster ME, 2007, LANG RESOUR EVAL, V41, P305, DOI 10.1007/s10579-007-9055-3
Grewal L, 2019, J MARKETING RES, V56, P197, DOI 10.1177/0022243718821960
Grover T, 2020, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2020, P390, DOI 10.1145/3377325.3377507
Hair J F., 2014, European Business Review, V26, P106, DOI [10.1108/ebr-10-2013-0128, DOI 10.1108/EBR-10-2013-0128]
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Hernandez-Ortega B, 2021, PSYCHOL MARKET, V38, P1122, DOI 10.1002/mar.21497
Hosany S, 2010, J TRAVEL RES, V49, P351, DOI 10.1177/0047287509346859
Hsieh YC, 2005, J RETAILING, V81, P75, DOI 10.1016/j.jretai.2005.01.006
Hu HS, 2021, TOURISM MANAGE, V86, DOI 10.1016/j.tourman.2021.104316
Huang P.H., 2020, INT J MULTINATIONAL, V3, P108
Huang P, 2009, J MARKETING, V73, P55, DOI 10.1509/jmkg.73.2.55
Iribarren M., 2021, GLOBAL DIGITAL TRAVE
Jiménez FR, 2013, J INTERACT MARK, V27, P226, DOI 10.1016/j.intmar.2013.04.004
KAMINS MA, 1989, J ADVERTISING, V18, P4, DOI 10.1080/00913367.1989.10673146
Keil M., 2002, J INFORM SYSTEMS ED, V13, P295
Keller E, 2007, J ADVERTISING RES, V47, P448, DOI 10.2501/S0021849907070468
Klaus P, 2020, J SERV MARK, V34, P389, DOI 10.1108/JSM-01-2019-0043
Klein LR, 1998, J BUS RES, V41, P195, DOI 10.1016/S0148-2963(97)00062-3
Kontogiorgos D, 2019, PROCEEDINGS OF THE 19TH ACM INTERNATIONAL CONFERENCE ON INTELLIGENT VIRTUAL AGENTS (IVA' 19), P133, DOI 10.1145/3308532.3329466
Kraus D., 2019, FACTORS INFLUENCING
Lau Josephine, 2018, Proceedings of the ACM on Human-Computer Interaction, V2, DOI 10.1145/3274371
Lieberman A, 2020, CURR OPIN PSYCHOL, V31, P16, DOI 10.1016/j.copsyc.2019.06.022
Lim WM, 2022, PSYCHOL MARKET, V39, P1129, DOI 10.1002/mar.21654
Lim YS, 2015, J COMPUT-MEDIAT COMM, V20, P67, DOI 10.1111/jcc4.12093
Ling EC, 2021, PSYCHOL MARKET, V38, P1031, DOI 10.1002/mar.21491
Lis B, 2013, JMM-INT J MEDIA MANA, V15, P229, DOI 10.1080/14241277.2013.863099
Liu BJ, 2018, CYBERPSYCH BEH SOC N, V21, P625, DOI 10.1089/cyber.2018.0110
Liu HH, 2020, PSYCHOL MARKET, V37, P1586, DOI 10.1002/mar.21404
Lo A.S., 2019, INT J CONTEMP HOSP M, V11, P228
Ma WWK, 2014, COMPUT HUM BEHAV, V39, P51, DOI 10.1016/j.chb.2014.06.015
Maity M, 2018, J BUS RES, V87, P36, DOI 10.1016/j.jbusres.2018.02.003
Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
MCCRAE RR, 1987, J PERS SOC PSYCHOL, V52, P81, DOI 10.1037/0022-3514.52.1.81
McKnight H., 2007, 9 INT C ELECT COMMER, P423
McLean G, 2019, COMPUT HUM BEHAV, V99, P28, DOI 10.1016/j.chb.2019.05.009
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
MEYER P, 1988, JOURNALISM QUART, V65, P567, DOI 10.1177/107769908806500301
Miller DI, 2013, FRONT NEUROL, V4, DOI 10.3389/fneur.2013.00107
Moon Y, 1996, COMMUN RES, V23, P651, DOI 10.1177/009365096023006002
Moriuchi E, 2019, PSYCHOL MARKET, V36, P489, DOI 10.1002/mar.21192
Mudambi SM, 2010, MIS QUART, V34, P185
Nass C, 1997, J APPL SOC PSYCHOL, V27, P864, DOI 10.1111/j.1559-1816.1997.tb00275.x
NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630
Neuninger R, 2017, J RETAIL CONSUM SERV, V35, P98, DOI 10.1016/j.jretconser.2016.12.003
Nunnally J., 1994, INTRO RES HLTH SCI
Ostrom AL., 2018, Handbook of service science, Vii, P77, DOI DOI 10.1007/978-3-319-98512-1_5
Park C, 2009, J BUS RES, V62, P61, DOI 10.1016/j.jbusres.2007.11.017
Patrakosol B, 2013, SERV BUS, V7, P329, DOI 10.1007/s11628-012-0162-x
Paul Grice H., 1975, Syntax and Semantics, P47, DOI [10.1057/9780230005853_5, DOI 10.1163/9789004368811_003]
PERLOFF RM, 1993, INT J PUBLIC OPIN R, V5, P167, DOI 10.1093/ijpor/5.2.167
Petrock Victoria., 2020, Voice assistant and smart speaker users 2020
Petty RE, 2002, J PERS SOC PSYCHOL, V82, P722, DOI 10.1037//0022-3514.82.5.722
Pitardi V, 2021, PSYCHOL MARKET, V38, P626, DOI 10.1002/mar.21457
Pizzi G, 2021, J BUS RES, V129, P878, DOI 10.1016/j.jbusres.2020.11.006
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Purnawirawan N, 2012, J INTERACT MARK, V26, P244, DOI 10.1016/j.intmar.2012.04.002
Qiu LY, 2009, J MANAGE INFORM SYST, V25, P145, DOI 10.2753/MIS0742-1222250405
Rhee CE, 2020, COMPUT HUM BEHAV, V109, DOI [10.1016/j.chb.2020.102659, 10.1016/j.chb.2020.106359]
Ringle CM, 2016, IND MANAGE DATA SYST, V116, P1865, DOI 10.1108/IMDS-10-2015-0449
Ringle CM, 2012, MIS QUART, V36, pIII
Rosenthal-von der Pütten AM, 2019, COMPUT HUM BEHAV, V90, P397, DOI 10.1016/j.chb.2018.08.047
Rosenthal-von der Pütten AM, 2016, LECT NOTES ARTIF INT, V10011, P256, DOI 10.1007/978-3-319-47665-0_23
Rudovic O, 2018, SCI ROBOT, V3, DOI 10.1126/scirobotics.aao6760
Saima, 2021, Journal of Promotion Management, V27, P503, DOI DOI 10.1080/10496491.2020.1851847
Schepers J, 2022, J SERV RES-US, V25, P565, DOI 10.1177/10946705221107704
Schindler RM, 2012, J CONSUM BEHAV, V11, P234, DOI 10.1002/cb.1372
SCHMITZ J, 1991, COMMUN RES, V18, P487, DOI 10.1177/009365091018004003
Schweitzer F, 2019, J MARKET MANAG-UK, V35, P693, DOI 10.1080/0267257X.2019.1596970
Sciuto A, 2018, DIS 2018: PROCEEDINGS OF THE 2018 DESIGNING INTERACTIVE SYSTEMS CONFERENCE, P857, DOI 10.1145/3196709.3196772
Cantallops AS, 2014, INT J HOSP MANAG, V36, P41, DOI 10.1016/j.ijhm.2013.08.007
Spence PR, 2019, COMPUT HUM BEHAV, V90, P285, DOI 10.1016/j.chb.2018.09.014
SPROULL L, 1986, MANAGE SCI, V32, P1492, DOI 10.1287/mnsc.32.11.1492
Statista, 2021, NUMB DIG VOIC ASS US
statista, 2022, GLOB SMART SPEAK MAR
Stoll B, 2016, COMMUN STUD, V67, P530, DOI 10.1080/10510974.2016.1215339
Suh KS, 1999, INFORM MANAGE, V35, P295, DOI 10.1016/S0378-7206(98)00097-4
Sussman SW, 2003, INFORM SYST RES, V14, P47, DOI 10.1287/isre.14.1.47.14767
Tabassum Madiha, 2019, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, V3, DOI 10.1145/3369807
Tassiello V, 2021, PSYCHOL MARKET, V38, P1069, DOI 10.1002/mar.21488
Taylor KM, 2021, IND MARKET MANAG, V92, P295, DOI 10.1016/j.indmarman.2020.05.007
Teng SS, 2014, ONLINE INFORM REV, V38, P746, DOI 10.1108/OIR-04-2014-0089
Tolmeijer S, 2021, EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21), DOI 10.1145/3411763.3451623
van Esch P, 2021, PSYCHOL MARKET, V38, P1081, DOI 10.1002/mar.21494
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Verma S, 2021, J INTERACT MARK, V53, P111, DOI 10.1016/j.intmar.2020.07.001
Viglia G, 2021, IND MARKET MANAG, V98, P193, DOI 10.1016/j.indmarman.2021.08.007
Viglia G, 2016, INT J CONTEMP HOSP M, V28, P2035, DOI 10.1108/IJCHM-05-2015-0238
Wathen CN, 2002, J AM SOC INF SCI TEC, V53, P134, DOI 10.1002/asi.10016
Weathers D, 2007, J RETAILING, V83, P393, DOI 10.1016/j.jretai.2007.03.009
Westlund JMK, 2017, FRONT HUM NEUROSCI, V11, DOI 10.3389/fnhum.2017.00295
Xiao H, 2023, CURR PSYCHOL, V42, P2488, DOI 10.1007/s12144-021-02253-x
ZAICHKOWSKY JL, 1986, J ADVERTISING, V15, P4, DOI 10.1080/00913367.1986.10672999
ZAICHKOWSKY JL, 1985, J CONSUM RES, V12, P341, DOI 10.1086/208520
Zhang HZ, 2014, ASIAN J COMMUN, V24, P158, DOI 10.1080/01292986.2013.856452
Zhao JY, 2020, COMPUT HUM BEHAV, V108, DOI 10.1016/j.chb.2020.106334
Zhao XS, 2010, J CONSUM RES, V37, P197, DOI 10.1086/651257
NR 147
TC 15
Z9 15
U1 34
U2 85
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0742-6046
EI 1520-6793
J9 PSYCHOL MARKET
JI Psychol. Mark.
PD FEB
PY 2023
VL 40
IS 2
BP 328
EP 346
DI 10.1002/mar.21765
EA NOV 2022
PG 19
WC Business; Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA 7L6AP
UT WOS:000892520700001
OA hybrid, Green Published
DA 2024-03-27
ER
PT J
AU Pantano, E
Pizzi, G
AF Pantano, Eleonora
Pizzi, Gabriele
TI Forecasting artificial intelligence on online customer assistance:
Evidence from chatbot patents analysis
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
ID RECOMMENDATION AGENTS; CONVERSATIONAL AGENTS; SERVICE; TECHNOLOGY;
INNOVATION; KNOWLEDGE; ROBOTS; ANTHROPOMORPHISM; PERSONALIZATION;
ARCHITECTURE
C1 [Pantano, Eleonora] Univ Bristol, Sch Management, Howard House,Queens Ave, Bristol BS8 1SD, Avon, England.
Univ Bologna, Dept Management, Via Capo di Lucca 34, I-3440126 Bologna, Italy.
C3 University of Bristol; University of Bologna
RP Pantano, E (autor correspondiente), Univ Bristol, Sch Management, Howard House,Queens Ave, Bristol BS8 1SD, Avon, England.
EM e.pantano@bristol.ac.uk; gabriele.pizzi@unibo.it
OI Pantano, Eleonora/0000-0001-9480-3346
CR Abraham BP, 2001, TECHNOVATION, V21, P245, DOI 10.1016/S0166-4972(00)00040-7
Admoni H, 2017, J HUM-ROBOT INTERACT, V6, P25, DOI 10.5898/JHRI.6.1.Admoni
Aldiri K, 2008, INT J E-BUS RES, V4, P58, DOI 10.4018/jebr.2008100105
Alfano M, 2011, RECENT PAT NANOTECH, V5, P37, DOI 10.2174/187221011794474976
Almansor E.H., 2019, ADV INTELLIGENT SYST, P534
Andre Q., 2018, Customer Needs and Solutions, V5, P28, DOI 10.1007/s40547-017-0085-8
[Anonymous], 1999, J SERV MARK, DOI DOI 10.1108/08876049910273763
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Ariely D, 2004, J CONSUM PSYCHOL, V14, P81, DOI 10.1207/s15327663jcp1401&2_10
Arsovski S, 2019, EXPERT SYST APPL, V137, P343, DOI 10.1016/j.eswa.2019.07.014
Arsovski S, 2018, INT J ADV COMPUT SC, V9, P402
Beck A, 2013, INT J SOC ROBOT, V5, P325, DOI 10.1007/s12369-013-0193-z
Belavina E, 2017, MANAGE SCI, V63, P1781, DOI 10.1287/mnsc.2016.2430
Beldad A, 2016, COMPUT HUM BEHAV, V60, P62, DOI 10.1016/j.chb.2016.02.046
Bell DR, 2018, MANAGE SCI, V64, P1629, DOI 10.1287/mnsc.2016.2684
Ben Mimoun MS, 2017, INFORM MANAGE-AMSTER, V54, P545, DOI 10.1016/j.im.2016.11.008
Ben Mimoun MS, 2012, J RETAIL CONSUM SERV, V19, P605, DOI 10.1016/j.jretconser.2012.07.006
Bitner MJ, 2000, J ACAD MARKET SCI, V28, P138, DOI 10.1177/0092070300281013
Block J, 2013, FAM BUS REV, V26, P180, DOI 10.1177/0894486513477454
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Bozer YA, 2018, INT J PROD RES, V56, P3838, DOI 10.1080/00207543.2018.1424364
Burke RR, 2002, J ACAD MARKET SCI, V30, P411, DOI 10.1177/009207002236914
Castro-González A, 2016, INT J HUM-COMPUT ST, V90, P27, DOI 10.1016/j.ijhcs.2016.02.004
Chakrabarti C, 2015, EXPERT SYST APPL, V42, P6878, DOI 10.1016/j.eswa.2015.04.067
Chakraborty R, 2016, DECIS SUPPORT SYST, V83, P47, DOI 10.1016/j.dss.2015.12.007
Chang HH, 2016, INFORM TECHNOL PEOPL, V29, P496, DOI 10.1108/ITP-08-2014-0171
Chang SB, 2012, TECHNOL FORECAST SOC, V79, P3, DOI 10.1016/j.techfore.2011.07.002
Chatterjee A, 2019, COMPUT HUM BEHAV, V93, P309, DOI 10.1016/j.chb.2018.12.029
Choi Y.K., 2001, J INTERACTIVE ADVERT, V2, P19, DOI [https://doi.org/10.1080/15252019.2001.10722055, DOI 10.1080/15252019.2001.10722055]
Culley KE, 2013, COMPUT HUM BEHAV, V29, P577, DOI 10.1016/j.chb.2012.11.023
Daim TU, 2006, TECHNOL FORECAST SOC, V73, P981, DOI 10.1016/j.techfore.2006.04.004
Davelembayeva D., MAPPING EC SOCIAL TE
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Davenport TH, 2018, J BUS ANAL, V1, P73, DOI 10.1080/2573234X.2018.1543535
Davenport TH, 2016, MIT SLOAN MANAGE REV, V57, P21
De Bruyn A, 2008, MARKET SCI, V27, P443, DOI 10.1287/mksc.1070.0306
De Keyser A, 2019, J SERV MANAGE, V30, P156, DOI 10.1108/JOSM-03-2018-0082
Demmers J, 2018, INT J ELECTRON COMM, V22, P8, DOI 10.1080/10864415.2018.1396110
Felfernig A., 2014, RECOMMENDATION SYSTE, P15, DOI [10.1007/978-3-642-45135-5_2, DOI 10.1007/978-3-642-45135-5_2]
Felfernig A, 2006, INT J ELECTRON COMM, V11, P11, DOI 10.2753/JEC1086-4415110201
Flanagin AJ, 2014, ELECTRON COMMER RES, V14, P1, DOI 10.1007/s10660-014-9139-2
Fryer LK, 2019, COMPUT HUM BEHAV, V93, P279, DOI 10.1016/j.chb.2018.12.023
Ghahramani Z, 2015, NATURE, V521, P452, DOI 10.1038/nature14541
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Grand View Research, 2017, CHATB MARK SIZ REACH
Gretzel U, 2006, INT J ELECTRON COMM, V11, P81, DOI 10.2753/JEC1086-4415110204
Grewal D, 2017, J RETAILING, V93, P1, DOI 10.1016/j.jretai.2016.12.008
Griol D, 2016, COGN COMPUT, V8, P336, DOI 10.1007/s12559-015-9352-x
Griol D, 2013, APPL ARTIF INTELL, V27, P759, DOI 10.1080/08839514.2013.835230
Hill J, 2015, COMPUT HUM BEHAV, V49, P245, DOI 10.1016/j.chb.2015.02.026
Holzwarth M, 2006, J MARKETING, V70, P19, DOI 10.1509/jmkg.70.4.19
Hostler RE, 2011, INFORM MANAGE-AMSTER, V48, P336, DOI 10.1016/j.im.2011.08.002
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huang MH, 2017, J ACAD MARKET SCI, V45, P906, DOI 10.1007/s11747-017-0545-6
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
JACOBY J, 1977, J MARKETING RES, V14, P569, DOI 10.2307/3151201
Keeling K, 2010, J BUS RES, V63, P793, DOI 10.1016/j.jbusres.2008.12.015
Kim G, 2017, TECHNOL FORECAST SOC, V117, P228, DOI 10.1016/j.techfore.2016.11.023
Läpple D, 2015, FOOD POLICY, V51, P1, DOI 10.1016/j.foodpol.2014.11.003
Lai HC, 2000, INT J ELECTRON COMM, V4, P69, DOI 10.1080/10864415.2000.11518372
Larivière B, 2017, J BUS RES, V79, P238, DOI 10.1016/j.jbusres.2017.03.008
Lee C, 2011, TECHNOL FORECAST SOC, V78, P690, DOI 10.1016/j.techfore.2010.11.010
Lee SY, 2017, INT J HUM-COMPUT ST, V103, P95, DOI 10.1016/j.ijhcs.2017.02.005
Lee S, 2009, TECHNOL FORECAST SOC, V76, P769, DOI 10.1016/j.techfore.2009.01.003
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Mathur MB, 2016, COGNITION, V146, P22, DOI 10.1016/j.cognition.2015.09.008
McGinty L, 2006, INT J ELECTRON COMM, V11, P35, DOI 10.2753/JEC1086-4415110202
Mou Y, 2017, COMPUT HUM BEHAV, V72, P432, DOI 10.1016/j.chb.2017.02.067
Müller VC, 2016, SYNTH LIBR, V376, P553, DOI 10.1007/978-3-319-26485-1_33
Murray KB, 2009, J INTERACT MARK, V23, P138, DOI 10.1016/j.intmar.2009.02.009
Nelson A, 2014, RES POLICY, V43, P927, DOI 10.1016/j.respol.2014.01.010
Nowak KL, 2008, COMPUT HUM BEHAV, V24, P1473, DOI 10.1016/j.chb.2007.05.005
Nuruzzaman M, 2018, INT CONF E BUS ENG, P54, DOI 10.1109/ICEBE.2018.00019
Pantano E, 2019, J RETAIL CONSUM SERV, V49, P297, DOI 10.1016/j.jretconser.2019.01.019
Pantano E, 2018, J RETAIL CONSUM SERV, V44, P150, DOI 10.1016/j.jretconser.2018.06.004
Pantano E, 2017, J RETAIL CONSUM SERV, V34, P88, DOI 10.1016/j.jretconser.2016.10.001
Papagiannidis S, 2017, INFORM TECHNOL PEOPL, V30, P163, DOI 10.1108/ITP-03-2015-0069
Park C.-H., 2003, International Journal of Retail and Distribution Management, V31, P16, DOI DOI 10.1108/09590550310457818
Park Y, 2005, TECHNOL SOC, V27, P471, DOI 10.1016/j.techsoc.2005.08.003
Potdar V, 2018, INFORM TECHNOL PEOPL, V31, P595, DOI 10.1108/ITP-02-2017-0035
Qiu LY, 2009, J MANAGE INFORM SYST, V25, P145, DOI 10.2753/MIS0742-1222250405
Rust RT, 2014, MARKET SCI, V33, P206, DOI 10.1287/mksc.2013.0836
Satzger B, 2006, LECT NOTES COMPUT SC, V4082, P31
Saunila M, 2019, INFORM TECHNOL PEOPL, V32, P627, DOI 10.1108/ITP-10-2016-0224
SEIFODDINI H, 1991, COMPUT IND ENG, V21, P263, DOI 10.1016/0360-8352(91)90099-R
Shah H, 2016, COMPUT HUM BEHAV, V58, P278, DOI 10.1016/j.chb.2016.01.004
Shankar V, 2018, J RETAILING, V94, pVI, DOI 10.1016/S0022-4359(18)30076-9
Silver C, 2015, J MIX METHOD RES, V9, P386, DOI 10.1177/1558689814538833
Sivaramakrishnan S, 2007, J INTERACT MARK, V21, P60, DOI 10.1002/dir.20075
Swaminathan V, 2003, J CONSUM PSYCHOL, V13, P93, DOI 10.1207/S15327663JCP13-1&2_08
Syam N, 2018, IND MARKET MANAG, V69, P135, DOI 10.1016/j.indmarman.2017.12.019
Tay BTC, 2016, COMPUT HUM BEHAV, V60, P19, DOI 10.1016/j.chb.2016.01.042
Van den Broeck E, 2019, COMPUT HUM BEHAV, V98, P150, DOI 10.1016/j.chb.2019.04.009
Verhagen T, 2014, J COMPUT-MEDIAT COMM, V19, P529, DOI 10.1111/jcc4.12066
Willems K, 2017, TECHNOL FORECAST SOC, V124, P228, DOI 10.1016/j.techfore.2016.10.066
Wilson JR, 2017, LECT NOTES ARTIF INT, V10652, P334, DOI 10.1007/978-3-319-70022-9_33
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Yoo WS, 2010, J RETAIL CONSUM SERV, V17, P89, DOI 10.1016/j.jretconser.2009.10.003
Yoon VY, 2013, DECIS SUPPORT SYST, V55, P883, DOI 10.1016/j.dss.2012.12.024
Zanker M., 2011, NEW DIRECTIONS INTEL, P1
NR 100
TC 94
Z9 95
U1 19
U2 153
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD JUL
PY 2020
VL 55
AR 102096
DI 10.1016/j.jretconser.2020.102096
PG 9
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LZ3ZH
UT WOS:000541166100015
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Sim, Y
Lee, SK
Sutherland, I
AF Sim, Youngseok
Lee, Seul Ki
Sutherland, Ian
TI The impact of latent topic valence of online reviews on purchase
intention for the accommodation industry
SO TOURISM MANAGEMENT PERSPECTIVES
LA English
DT Article
DE Latent Dirichlet allocation; Convolutional neural network; Text
analytics; Online customer reviews; Purchase intention
ID WORD-OF-MOUTH; CONSUMER REVIEWS; SOCIAL MEDIA; PRODUCT; RATINGS;
SATISFACTION; PERFORMANCE; WEBSITE; HOTELS; SALES
AB This study utilizes machine learning (ML) natural language processing (NLP) algorithms and statistical methods in order to measure the impact that qualitative textual reviews have on booking intentions for accommodations. Using over 400,000 online reviews from 1256 accommodations in South Korea, latent Dirichlet allocation (LDA) is used to determine the topic of the review content, convolutional neural networks (CNNs) are used to identify the valence of the reviews, and spatial probit models are used to determine the impact of the review content and valence on booking intention, while controlling for several other variables. It is found that positive reviews about an accommodation's ambiance, value, service, front office, accessibility, surrounding neighborhood and room capacity result in significantly higher booking intentions, while negative reviews in the service, front office and surrounding neighborhood result in lower probability of booking. A number of explanatory variables also have varying effects on booking intentions.
C1 [Sim, Youngseok] Small Enterprise Policy Res Ctr, Small Enterprise & Market Serv, 246 Bomun Ro, Daejeon, South Korea.
[Lee, Seul Ki] Sejong Univ, Coll Hotel & Tourism Management HTM, Tourism Ind Data Analyt Lab TIDAL, Off Int Affairs OIA, 98 Gunja Dong, Seoul 143747, South Korea.
[Sutherland, Ian] Sejong Univ, Coll Hotel & Tourism Management HTM, Tourism Ind Data Analyt Lab TIDAL, Room 824a,98 Gunja Dong, Seoul 143747, South Korea.
C3 Sejong University; Sejong University
RP Sutherland, I (autor correspondiente), Sejong Univ, Coll Hotel & Tourism Management HTM, Tourism Ind Data Analyt Lab TIDAL, Room 824a,98 Gunja Dong, Seoul 143747, South Korea.
EM iamssys@gmail.com; seulkilee@sejong.ac.kr; sutherland@sejong.ac.kr
RI Sutherland, Ian/AAU-7274-2020; Lee, Seul Ki/K-2440-2013
OI Sutherland, Ian/0000-0002-0481-5827;
CR Abdi H., 2007, ENCY MEASUREMENT STA, P651, DOI DOI 10.4135/9781412952644.N299
Anderson C, 2012, IMPACT SOCIAL MEDIA
Anderson E.W., 1998, J SERVICE RES, V1, P5, DOI DOI 10.1177/109467059800100102
[Anonymous], 2017, TOUL VER C EXC SERV
ANSELIN L, 1992, REG SCI URBAN ECON, V22, P509, DOI 10.1016/0166-0462(92)90042-Y
ANSELIN L, 1988, GEOGR ANAL, V20, P1, DOI 10.1111/j.1538-4632.1988.tb00159.x
Anselin Luc., 1999, GEOGRAPHIC INFORM SC, V5, P67, DOI [DOI 10.1080/10824009909480516, 10.1080/10824009909480516]
Appel G, 2020, J ACAD MARKET SCI, V48, P79, DOI 10.1007/s11747-019-00695-1
Arun R, 2010, LECT NOTES ARTIF INT, V6118, P391
Avant T., 2013, RESPONDING TRIPADVIS
Baek H, 2012, INT J ELECTRON COMM, V17, P99, DOI 10.2753/JEC1086-4415170204
Bansal H.S., 2000, Journal of Service Research, DOI [10.1177/109467050032005, DOI 10.1177/109467050032005]
Berezina K, 2016, J HOSP MARKET MANAG, V25, P1, DOI 10.1080/19368623.2015.983631
Bivand RS, 2008, USE R, P237
Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Bordogna G, 2016, IEEE INT CONGR BIG, P42, DOI 10.1109/BigDataCongress.2016.78
Buttle F. A., 1998, J STRATEGIC MARKETIN, V6, P241, DOI DOI 10.1080/096525498346658
Cao J, 2009, NEUROCOMPUTING, V72, P1775, DOI 10.1016/j.neucom.2008.06.011
Casaló LV, 2015, INT J HOSP MANAG, V49, P28, DOI 10.1016/j.ijhm.2015.05.005
Chan ICC, 2017, INT J HOSP MANAG, V66, P54, DOI 10.1016/j.ijhm.2017.06.007
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
DEVEAUD R, 2014, DOCUMENT NUMERIQUE, V0017
Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001
El-Said OA, 2020, TOUR MANAG PERSPECT, V33, DOI 10.1016/j.tmp.2019.100604
Floh A, 2013, J MARKET MANAG-UK, V29, P646, DOI 10.1080/0267257X.2013.776620
Gavilan D, 2018, TOURISM MANAGE, V66, P53, DOI 10.1016/j.tourman.2017.10.018
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101
Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
Helbich M, 2014, URBAN STUD, V51, P390, DOI 10.1177/0042098013492234
Hilbrink E. M, 2017, EFFECTS VALENCE LANG
Hunold M, 2018, INT J IND ORGAN, V61, P542, DOI 10.1016/j.ijindorg.2018.03.008
Kapoor KK, 2018, INFORM SYST FRONT, V20, P531, DOI 10.1007/s10796-017-9810-y
Khozaei F., 2016, International Journal of Research in Tourism and Hospitality, V2, P30, DOI DOI 10.20431/24550043.0202004
Kiatkawsin K, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12166673
Kim WG, 2015, INT J HOSP MANAG, V44, P165, DOI 10.1016/j.ijhm.2014.10.014
Ladhari R, 2015, INT J HOSP MANAG, V46, P36, DOI 10.1016/j.ijhm.2015.01.010
Lai IKW, 2016, TOURISM MANAGE, V55, P139, DOI 10.1016/j.tourman.2016.01.007
Lee J, 2008, ELECTRON COMMER R A, V7, P341, DOI 10.1016/j.elerap.2007.05.004
Lee SK, 2013, J TRAVEL RES, V52, P56, DOI 10.1177/0047287512457268
Lee SK, 2012, CORNELL HOSP Q, V53, P165, DOI 10.1177/1938965512441056
LeSage J, 2009, STAT TEXTB MONOGR, P1
Li G, 2015, TOURISM MANAGE, V46, P311, DOI 10.1016/j.tourman.2014.06.015
Li HY, 2013, ASIA PAC J TOUR RES, V18, P784, DOI 10.1080/10941665.2012.708351
Li XX, 2008, INFORM SYST RES, V19, P456, DOI 10.1287/isre.1070.0154
Liu ZW, 2015, TOURISM MANAGE, V47, P140, DOI 10.1016/j.tourman.2014.09.020
Lu WL, 2012, TOURISM MANAGE, V33, P702, DOI 10.1016/j.tourman.2011.08.003
Mauri AG, 2013, INT J HOSP MANAG, V34, P99, DOI 10.1016/j.ijhm.2013.02.012
Nieto J, 2014, TOURISM MANAGE, V45, P115, DOI 10.1016/j.tourman.2014.03.009
Pavlou PA, 2003, INT J ELECTRON COMM, V7, P101, DOI 10.1080/10864415.2003.11044275
Philander K, 2016, INT J HOSP MANAG, V55, P16, DOI 10.1016/j.ijhm.2016.02.001
Phillips P, 2017, J TRAVEL RES, V56, P235, DOI 10.1177/0047287516636481
Senecal S, 2004, J RETAILING, V80, P159, DOI 10.1016/j.jretai.2004.04.001
Singh N., 2007, International Journal of Hospitality Management, V26, P131, DOI 10.1016/j.ijhm.2005.10.002
Sparks BA, 2013, TOURISM MANAGE, V39, P1, DOI 10.1016/j.tourman.2013.03.007
Sparks BA, 2010, J HOSP MARKET MANAG, V19, P797, DOI 10.1080/19368623.2010.508010
Sparks BA, 2011, TOURISM MANAGE, V32, P1310, DOI 10.1016/j.tourman.2010.12.011
Stepchenkova S, 2009, J TRAVEL RES, V47, P454, DOI 10.1177/0047287508326509
Stringam BB, 2010, J QUAL ASSUR HOSP TO, V11, P73, DOI 10.1080/1528008X.2010.482000
Sun M, 2012, MANAGE SCI, V58, P696, DOI 10.1287/mnsc.1110.1458
Sutherland I, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083402
Sutherland I, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12051821
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Torres EN, 2015, INT J HOSP MANAG, V50, P77, DOI 10.1016/j.ijhm.2015.07.012
Tuominen P., 2011, 19 ANN FRONT SERV C, P1
Vermeulen IE, 2009, TOURISM MANAGE, V30, P123, DOI 10.1016/j.tourman.2008.04.008
Wang R, 2019, ASIA PAC J TOUR RES, V24, P1092, DOI 10.1080/10941665.2019.1665558
Wooldridge J., 2002, INTRO ECONOMETRICS M
Xiang Z, 2010, TOURISM MANAGE, V31, P179, DOI 10.1016/j.tourman.2009.02.016
Xie KL, 2014, INT J HOSP MANAG, V43, P1, DOI 10.1016/j.ijhm.2014.07.007
Xu X, 2016, INT J HOSP MANAG, V55, P57, DOI 10.1016/j.ijhm.2016.03.003
Yang B., 2013, EFFECT ONLINE CUSTOM
Ye QA, 2011, COMPUT HUM BEHAV, V27, P634, DOI 10.1016/j.chb.2010.04.014
Ye Q, 2009, INT J HOSP MANAG, V28, P180, DOI 10.1016/j.ijhm.2008.06.011
Zhao XY, 2015, INT J CONTEMP HOSP M, V27, P1343, DOI 10.1108/IJCHM-12-2013-0542
Zhong ZhuLing Zhong ZhuLing, 2014, Journal of Applied Sciences, V14, P341
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 79
TC 12
Z9 12
U1 5
U2 63
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2211-9736
EI 2211-9744
J9 TOUR MANAG PERSPECT
JI Tour. Manag. Perspect.
PD OCT
PY 2021
VL 40
AR 100903
DI 10.1016/j.tmp.2021.100903
EA NOV 2021
PG 12
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA XB6XX
UT WOS:000721471200006
DA 2024-03-27
ER
PT J
AU Hou, LF
AF Hou, Linfang
TI RETRACTADO: Decision support model of e-commerce enterprise investment
based on deep learning algorithm (Retracted Article)
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article; Retracted Publication
DE Deep learning; E-commerce; Investment decision; Neural network model
AB According to the current situation and future trend of e-commerce investment market research at home and abroad, combined with the established research objectives, this paper studies and analyzes the existing methods of investment quality evaluation. This paper puts forward the modeling idea of the evaluation index system of investment decision quality, and takes the deep neural network as the modeling tool and python as the simulation tool to complete the establishment of the investment decision support model of e-commerce listed companies and get the experimental results. In this paper, based on the deep learning portfolio replication algorithm, the features of the portfolio model are extracted by the automatic encoder to realize the portfolio replication. First, the definition of deep learning model and the framework of model training will be given. Then, a process of trestle automatic encoder model will be introduced, and then the detailed process of portfolio replication algorithm based on deep learning will be explained in detail. Finally, the trained model is used to evaluate the samples of the test set, and the results are good, which shows that the index evaluation system is feasible and effective.
C1 [Hou, Linfang] Zhoukou Normal Univ, Sch Econ & Management, Zhoukou 466000, Henan, Peoples R China.
C3 Zhoukou Normal University
RP Hou, LF (autor correspondiente), Zhoukou Normal Univ, Sch Econ & Management, Zhoukou 466000, Henan, Peoples R China.
EM lf18103872337@163.com
CR Cardenas CE, 2018, INT J RADIAT ONCOL, V101, P468, DOI 10.1016/j.ijrobp.2018.01.114
Dhote S, 2020, ELECTRON COMMER RES, V20, P259, DOI 10.1007/s10660-019-09383-2
Gao MH, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20051452
Guan Y, 2019, DECIS SUPPORT SYST, V118, P58, DOI 10.1016/j.dss.2019.01.003
Hadad Y, 2013, INT J PROJ MANAG, V31, P532, DOI 10.1016/j.ijproman.2012.10.004
Jasek P, 2019, PRAGUE ECON PAP, V28, P648, DOI 10.18267/j.pep.714
Ju RH, 2019, IEEE ACM T COMPUT BI, V16, P244, DOI 10.1109/TCBB.2017.2776910
Kratzwald B, 2018, DECIS SUPPORT SYST, V115, P24, DOI 10.1016/j.dss.2018.09.002
Kumar APP., 2018, J COMPUTER COMMUNICA, V06, P45, DOI [10.4236/jcc.2018.66004, DOI 10.4236/JCC.2018.66004]
Leng JW, 2016, KNOWL-BASED SYST, V100, P188, DOI 10.1016/j.knosys.2016.03.008
[彭海霞 Peng Haixia], 2014, [光电子·激光, Journal of Optoelectronics·Laser], V25, P1771
Rangra A, 2019, INT J INF SYST MODEL, V10, P59, DOI 10.4018/IJISMD.2019070104
Ren JL, 2014, J INFRARED MILLIM W, V33, P122, DOI 10.3724/SP.J.1010.2014.00122
Thung KH, 2017, LECT NOTES COMPUT SC, V10553, P160, DOI 10.1007/978-3-319-67558-9_19
Twa MD, 2018, OPTOMETRY VISION SCI, V95, P275, DOI 10.1097/OPX.0000000000001210
Vo NNY, 2019, DECIS SUPPORT SYST, V124, DOI 10.1016/j.dss.2019.113097
Wang YH, 2018, KNOWL-BASED SYST, V143, P248, DOI 10.1016/j.knosys.2017.11.005
Zhang ZH, 2015, INT J SIMUL MODEL, V14, P647, DOI 10.2507/IJSIMM14(4)7.318
NR 18
TC 2
Z9 2
U1 4
U2 31
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD AUG
PY 2023
VL 21
IS SUPPL 1
SU 1
BP 13
EP 13
DI 10.1007/s10257-021-00507-6
EA FEB 2021
PG 1
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA T4NO4
UT WOS:000613583600001
DA 2024-03-27
ER
PT J
AU Stenbom, A
Wiggberg, M
Norlund, T
AF Stenbom, Agnes
Wiggberg, Mattias
Norlund, Tobias
TI Exploring Communicative AI: Reflections from a Swedish Newsroom
SO DIGITAL JOURNALISM
LA English
DT Article
DE Computational journalism; machine learning; natural language generation;
search engine optimization; human-machine communication; communicative
AI
ID DIGITAL JOURNALISM; AUTOMATION; MACHINE
AB This article contributes to the emerging field of research on computational journalism with a practical illustration of an attempt to utilize Machine Learning to generate Search Engine Optimized headlines in a major Swedish newsroom. By using its technical results as a springboard for reflections among internal stakeholders, the experiment serves as a catalyzing innovation revealing deliberations on computational approaches in journalism in general and communicative Artificial Intelligence (AI) in specific. The study concludes with three ideas to support decision makers involved in evaluating potential use cases for communicative AI in journalism.
C1 [Stenbom, Agnes] Schibsted, Stockholm, Sweden.
[Stenbom, Agnes; Wiggberg, Mattias] KTH Royal Inst Technol, Dept Ind Econ & Management, Stockholm, Sweden.
[Norlund, Tobias] Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden.
C3 Royal Institute of Technology; Chalmers University of Technology
RP Stenbom, A (autor correspondiente), Schibsted, Stockholm, Sweden.; Stenbom, A (autor correspondiente), KTH Royal Inst Technol, Dept Ind Econ & Management, Stockholm, Sweden.
EM astenbom@kth.se
RI Wiggberg, Mattias/GPP-6721-2022
OI Wiggberg, Mattias/0000-0001-9636-1545
CR Anderson CW, 2013, NEW MEDIA SOC, V15, P1005, DOI 10.1177/1461444812465137
[Anonymous], 2021, SWEDEN OECD DATA
Bahdanau D, 2016, Arxiv, DOI [arXiv:1409.0473, DOI 10.48550/ARXIV.1409.0473]
Becker BE, 2006, J MANAGE, V32, P898, DOI 10.1177/0149206306293668
Beckett, 2019, POLIS LONDON SCH EC
Bilton, 2018, REUTERS NEW AUTOMATI
Broussard M, 2018, ARTIFICIAL UNINTELLIGENCE: HOW COMPUTERS MISUNDERSTAND THE WORLD
Brown T. B., 2020, Neural Information Processing Systems (NeurIPS)
Carlson M, 2015, DIGIT JOURNAL, V3, P416, DOI 10.1080/21670811.2014.976412
Coddington M, 2015, DIGIT JOURNAL, V3, P331, DOI 10.1080/21670811.2014.976400
Cohen S, 2011, COMMUN ACM, V54, P66, DOI 10.1145/2001269.2001288
Cornia A, 2020, JOURNALISM, V21, P172, DOI 10.1177/1464884918779919
Deuze M, 2018, JOURNALISM, V19, P165, DOI 10.1177/1464884916688550
Diakopoulos N., 2019, Automating the News: How Algorithms Are Rewriting the Media
Diakopoulos N, 2019, DIGIT JOURNAL, V7, P1180, DOI 10.1080/21670811.2019.1682938
Dick M, 2011, JOURNAL PRACT, V5, P462, DOI 10.1080/17512786.2010.551020
Dörr KN, 2016, DIGIT JOURNAL, V4, P700, DOI 10.1080/21670811.2015.1096748
Duffy A, 2019, DIGIT JOURNAL, V7, P378, DOI 10.1080/21670811.2019.1568899
Eldridge SA, 2019, DIGIT JOURNAL, V7, P386, DOI 10.1080/21670811.2019.1599724
Frey CB, 2017, TECHNOL FORECAST SOC, V114, P254, DOI 10.1016/j.techfore.2016.08.019
Giomelakis D, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11120261
Graefe A., 2016, Guide to Automated Journalism
Grubenmann S, 2016, DIGIT JOURNAL, V4, P160, DOI 10.1080/21670811.2015.1093274
Guzman, 2018, WHAT IS HUMAN MACHIN, P1
Guzman AL, 2020, NEW MEDIA SOC, V22, P70, DOI 10.1177/1461444819858691
Hermida, 2016, SAGE HDB DIGITAL JOU
Hollis Martin., 2002, PHILOS SOCIAL SCI IN, P1
Ilya Wu., 2019, OPENAI BLOG, V1
Jones B, 2019, DIGIT JOURNAL, V7, P1032, DOI 10.1080/21670811.2019.1609371
Jones R, 2019, DIGIT JOURNAL, V7, P1157, DOI 10.1080/21670811.2019.1609372
K├a┬Nrhus, 2020, Q1 2020 RESULTS
Karlsson M., 2018, RETHINKING RES METHO
Krys Kochut., 2017, ARXIV PREPRINT ARXIV
Kung L, 2015, RISJ CHALLENGES, P1
Kung L., 2013, Media Innovations: A Multidisciplinary Study of Change, P9, DOI 10.13140/2.1.1328.9284
Latzer M., 2016, Handbook on the Economics of the Internet
Lewis SC, 2019, DIGIT JOURNAL, V7, P409, DOI 10.1080/21670811.2019.1577147
Lewis SC, 2019, J MASS COMMUN Q, V96, P673, DOI 10.1177/1077699019859901
Lewis SC, 2016, CONVERGENCE-US, V22, P543, DOI 10.1177/1354856515623865
Lewis SC, 2015, DIGIT JOURNAL, V3, P19, DOI 10.1080/21670811.2014.927986
Mattias Wiggberg., 2020, DIGITAL SPETSKOMPETE
McNiff J., 2016, YOU YOUR ACTION RES
Nedelkoska L., 2018, AUTOMATION SKILLS US, DOI DOI 10.1787/2E2F4EEA-EN
Newman Nic., 2011, REUTERS I STUDY JOUR, V58
Posetti J., 2018, TIME STEP AWAY BRIGH
Raffel C, 2020, J MACH LEARN RES, V21
Royal C, 2020, J MASS COMMUN Q, V97, P597, DOI 10.1177/1077699020933872
See A, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P1073, DOI 10.18653/v1/P17-1099
Steensen S, 2015, DIGIT JOURNAL, V3, P1, DOI 10.1080/21670811.2014.927984
Syvertsen T., 2019, NORDIC J MEDIA STUDI, V1, P11, DOI DOI 10.2478/NJMS-2019-0002
Tandoc EC, 2020, DIGIT JOURNAL, V8, P548, DOI 10.1080/21670811.2020.1762102
Taylor, 2008, ACTION INQUIRY INTER, V2, P239
Thurman N, 2017, DIGIT JOURNAL, V5, P1240, DOI 10.1080/21670811.2017.1289819
Torbert W., 2004, Action inquiry: The secret of timely and transforming leadership
Vaswani A, 2017, ADV NEUR IN, V30
Vollstedt M, 2019, ICME-13 MONOGR, P81, DOI 10.1007/978-3-030-15636-7_4
Wagemans A, 2019, CONVERGENCE-US, V25, P209, DOI 10.1177/1354856519834880
Wu SY, 2019, DIGIT JOURNAL, V7, P428, DOI 10.1080/21670811.2019.1620112
NR 58
TC 4
Z9 4
U1 24
U2 71
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 2167-0811
EI 2167-082X
J9 DIGIT JOURNAL
JI Digit. Journal.
PD OCT 21
PY 2023
VL 11
IS 9
BP 1622
EP 1640
DI 10.1080/21670811.2021.2007781
EA NOV 2021
PG 19
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA Y4RU5
UT WOS:000725462300001
OA Green Published
DA 2024-03-27
ER
PT J
AU Bigné, E
Oltra, E
Andreu, L
AF Bigne, Enrique
Oltra, Enrique
Andreu, Luisa
TI Harnessing stakeholder input on Twitter: A case study of short breaks in
Spanish tourist cities
SO TOURISM MANAGEMENT
LA English
DT Article
DE Social media marketing; Destination marketing organisations; Twitter;
Text mining; Artificial neural network; Hotel occupancy; Short-break
holidays
ID WORD-OF-MOUTH; ARTIFICIAL NEURAL-NETWORKS; USER-GENERATED CONTENT;
SOCIAL MEDIA; MARKET-SEGMENTATION; ONLINE REVIEWS; DEMAND; IMPACT;
BRAND; INFORMATION
AB Knowledge of how destination marketing organisations (DMOs) use Twitter is still limited. This study aimed to assess how DMOs' Twitter activity affects hotel occupancy in short-break holidays. Key dimensions of Twitter that may affect hotel occupancy in tourist destinations were first identified. A longitudinal study using data for 10 Spanish DMOs was conducted to forecast hotel occupancy. Twitter application programming interfaces were used to gather data on tweets by DMOs and retweets and likes by users. Text mining was used to analyse the tweets by DMOs, differentiating between tweets related to events, attractions, socialisation, and marketing. Data were analysed using artificial neural networks. The best fit was achieved with a multilayer perceptron artificial neural network. Findings suggest that the number of retweets and replies by users and the number of event tweets, tourist attraction tweets, and retweets by DMOs can predict the hotel occupancy rate for a given destination.
C1 [Bigne, Enrique; Andreu, Luisa] Univ Valencia, Fac Econ, Dept Mkt, Av Naranjos S-N, Valencia 46022, Spain.
[Oltra, Enrique] Inst Serrallarga, C Joan Benejam 1, Blanes, Gerona, Spain.
C3 University of Valencia
RP Andreu, L (autor correspondiente), Univ Valencia, Fac Econ, Dept Mkt, Av Naranjos S-N, Valencia 46022, Spain.
EM Enrique.Bigne@uv.es; kike.oltra.llopis@gmail.com; Luisa.Andreu@uv.es
RI Andreu, Luisa/K-8428-2014; Bigné, Enrique/D-9287-2015
OI Andreu, Luisa/0000-0001-8852-6506; Bigné, Enrique/0000-0002-6529-7605
FU Ministry of Economy and Competitiveness (Spain) [ECO2014-53837R]
FX This work was supported by the Ministry of Economy and Competitiveness
(Spain) under Grant ECO2014-53837R. The authors are grateful to Juergen
Gnoth (University of Otago, New Zealand) for comments on an earlier
draft of this manuscript. We thank the anonymous reviewers for their
comments throughout the review process.
CR Ågerfalk PJ, 2014, EUR J INFORM SYST, V23, P593, DOI 10.1057/ejis.2014.35
Akin M, 2015, TOURISM MANAGE, V48, P64, DOI 10.1016/j.tourman.2014.11.004
Anderson C, 2012, CORNELL HOSPITALITY, V12, P6
Andre Paul., 2012, P ACM 2012 C COMPUTE, P471, DOI DOI 10.1145/2145204
[Anonymous], 1988, PARALLEL DISTRIBUTED
[Anonymous], 2015, Revista Europea de Direccion y Economia de la Empresa, DOI [DOI 10.1016/J.REDEE.2015.03.002, 10.1016/j.redee.2015.03.002]
[Anonymous], 2011, J COMPUT SCI-NETH, DOI DOI 10.1016/j.jocs.2010.12.007
[Anonymous], 1994, Econometric Reviews, DOI DOI 10.1080/07474939408800273
[Anonymous], 2010, P 2010 43 HAWAII INT, DOI [DOI 10.1353/JSM.2016.0009, DOI 10.1109/HICSS.2010.412]
Aral S, 2011, MANAGE SCI, V57, P1623, DOI 10.1287/mnsc.1110.1421
Attoh-Okine NO, 1999, ADV ENG SOFTW, V30, P291, DOI 10.1016/S0965-9978(98)00071-4
Bansal A., 1993, Journal of Management Information Systems, V10, P11
Barke M., 1996, Tourism in Spain: Critical Issues, P3
Bigné E, 2016, EUR J MANAG BUS ECON, V25, P89, DOI 10.1016/j.redeen.2016.09.001
Bigné E, 2010, NEURAL COMPUT APPL, V19, P1, DOI 10.1007/s00521-008-0226-y
Bloom JZ, 2004, TOURISM MANAGE, V25, P723, DOI 10.1016/j.tourman.2003.07.004
Bokunewicz JF, 2017, J HOSP TOUR TECHNOL, V8, P205, DOI 10.1108/JHTT-09-2016-0057
Boyd DM, 2007, J COMPUT-MEDIAT COMM, V13, P210, DOI 10.1111/j.1083-6101.2007.00393.x
Breiman L, 2001, STAT SCI, V16, P199, DOI 10.1214/ss/1009213726
Burger CJSC, 2001, TOURISM MANAGE, V22, P403, DOI 10.1016/S0261-5177(00)00068-6
Castellanos-Verdugo M, 2009, INT J CONTEMP HOSP M, V21, P251, DOI 10.1108/09596110910948288
Cha M., 2010, P 4 INT AAAI C WEBLO, V4, P10
Chan NL, 2011, J TRAVEL TOUR MARK, V28, P345, DOI 10.1080/10548408.2011.571571
Chu SC, 2016, INT J ADVERT, V35, P421, DOI 10.1080/02650487.2015.1037708
Chu SC, 2015, ELECTRON COMMER R A, V14, P251, DOI 10.1016/j.elerap.2015.04.002
Claveria O., 2018, SSRN ELECT J, V1, P1
Counts S., 2011, P INT AAAI C WEB SOC, P97, DOI DOI 10.1609/ICWSM.V5I1.14103
Crofton S. O., 2012, J TOURISM RES HOSPIT, V1, DOI [10.4172/2324-8807, DOI 10.4172/2324-8807]
DeTienne KB, 2003, ORGAN RES METHODS, V6, P236, DOI 10.1177/1094428103251907
DeTienne KB, 2017, J STRATEG MARK, V25, P289, DOI 10.1080/0965254X.2015.1076881
Dholakia UM, 2009, J SERV RES-US, V12, P208, DOI 10.1177/1094670509338618
Dijkmans C, 2015, TOURISM MANAGE, V47, P58, DOI 10.1016/j.tourman.2014.09.005
Evans L., 2010, SOCIAL MEDIA MARKETI
Feldman R., 2006, TEXT MINING HDB ADV, DOI 10.1017/cbo9780511546914
Fielding AH, 1997, ENVIRON CONSERV, V24, P38, DOI 10.1017/S0376892997000088
Ghose A, 2012, MARKET SCI, V31, P493, DOI 10.1287/mksc.1110.0700
Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
Hand DJ, 2000, SOC SCI COMPUT REV, V18, P442, DOI 10.1177/089443930001800407
Hays S, 2013, CURR ISSUES TOUR, V16, P211, DOI 10.1080/13683500.2012.662215
He W, 2013, INT J INFORM MANAGE, V33, P464, DOI 10.1016/j.ijinfomgt.2013.01.001
Hennig-Thurau T, 2015, J ACAD MARKET SCI, V43, P375, DOI 10.1007/s11747-014-0388-3
Hewett K, 2016, J MARKETING, V80, P1, DOI 10.1509/jm.15.0033
HINTON GE, 1992, SCI AM, V267, P145, DOI 10.1038/scientificamerican0992-144
Jabreel M, 2017, J DESTIN MARK MANAGE, V6, P170, DOI 10.1016/j.jdmm.2016.03.004
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
Karlaftis MG, 2011, TRANSPORT RES C-EMER, V19, P387, DOI 10.1016/j.trc.2010.10.004
Keiningham TL, 2018, J SERV MANAGE, V29, P2, DOI 10.1108/JOSM-03-2017-0077
Kirilenko AP, 2017, TOURISM MANAGE, V63, P54, DOI 10.1016/j.tourman.2017.06.007
Köhler CF, 2011, J MARKETING, V75, P93, DOI 10.1509/jm.75.2.93
Költringer C, 2015, J BUS RES, V68, P1836, DOI 10.1016/j.jbusres.2015.01.011
Kozinets RV, 2010, J MARKETING, V74, P71, DOI 10.1509/jmkg.74.2.71
Kumar A, 2016, J MARKETING, V80, P7, DOI 10.1509/jm.14.0249
Kumar V, 2017, J ACAD MARKET SCI, V45, P268, DOI 10.1007/s11747-016-0484-7
Kwok L, 2013, CORNELL HOSP Q, V54, P84, DOI 10.1177/1938965512458360
Sánchez VL, 2017, TOURISM ECON, V23, P1255, DOI 10.1177/1354816616675996
Law R., 1998, International Journal of Contemporary Hospitality Management, V10, P234, DOI 10.1108/09596119810232301
Law R, 1999, TOURISM MANAGE, V20, P89, DOI 10.1016/S0261-5177(98)00094-6
Law R, 2000, TOURISM MANAGE, V21, P331, DOI 10.1016/S0261-5177(99)00067-9
Leung D, 2013, J TRAVEL TOUR MARK, V30, P3, DOI 10.1080/10548408.2013.750919
Leung XY, 2013, J TRAVEL TOUR MARK, V30, P58, DOI 10.1080/10548408.2013.751211
Llodrà-Riera I, 2015, TOURISM MANAGE, V48, P319, DOI 10.1016/j.tourman.2014.11.012
MACINNIS DJ, 1991, J MARKETING, V55, P32, DOI 10.2307/1251955
Malaga Tourism Observatory, 2014, IMP EC TUR SEM SANT
Malthouse EC, 2013, J INTERACT MARK, V27, P270, DOI 10.1016/j.intmar.2013.09.008
Martínez-Navarro J, 2017, J ELECTRON COMMER RE, V18, P52
Meier F., 2014, Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, P346
Milano R, 2011, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2011, P471
Miller AR, 2013, INFORM SYST RES, V24, P52, DOI 10.1287/isre.1120.0466
Milwood P, 2013, J TRAVEL TOUR MARK, V30, P165, DOI 10.1080/10548408.2013.751287
Moro S, 2016, J BUS RES, V69, P3341, DOI 10.1016/j.jbusres.2016.02.010
Mostafa MM, 2013, EXPERT SYST APPL, V40, P4241, DOI 10.1016/j.eswa.2013.01.019
Moutinho L, 2015, TOUR ANAL, V20, P619, DOI 10.3727/108354215X14464845877913
Munar AM, 2014, TOURISM MANAGE, V43, P46, DOI 10.1016/j.tourman.2014.01.012
Nielsen, 2017, 2016 NIELS SOC MED R
Oggionni T, 2018, J DESTIN MARK MANAGE, V9, P85, DOI 10.1016/j.jdmm.2017.11.003
Olden JD, 2002, ECOL MODEL, V154, P135, DOI 10.1016/S0304-3800(02)00064-9
Oliveira N, 2017, EXPERT SYST APPL, V73, P125, DOI 10.1016/j.eswa.2016.12.036
Padhi SS, 2011, INT J HOSP MANAG, V30, P725, DOI 10.1016/j.ijhm.2010.12.007
Palmer A, 2006, TOURISM MANAGE, V27, P781, DOI 10.1016/j.tourman.2005.05.006
Park S, 2016, J TRAVEL TOUR MARK, V33, P885, DOI 10.1080/10548408.2015.1071688
Pattie DC, 1996, ANN TOURISM RES, V23, P151, DOI 10.1016/0160-7383(95)00052-6
Peters K, 2013, J INTERACT MARK, V27, P281, DOI 10.1016/j.intmar.2013.09.007
Phillips P, 2015, TOURISM MANAGE, V50, P130, DOI 10.1016/j.tourman.2015.01.028
Pike S., 2003, J TRAVEL RES, V41, P315
Pollach I, 2011, ORGAN RES METHODS, V14, P741, DOI 10.1177/1094428109356713
Rishika R, 2013, INFORM SYST RES, V24, P108, DOI 10.1287/isre.1120.0460
Rosario AB, 2016, J MARKETING RES, V53, P297, DOI 10.1509/jmr.14.0380
Rudat A, 2015, COMPUT HUM BEHAV, V46, P75, DOI 10.1016/j.chb.2015.01.005
Rutter R, 2016, J BUS RES, V69, P3096, DOI 10.1016/j.jbusres.2016.01.025
Sarle W. S., 1994, P SUGI19 P 19 ANN SA
Seiler S, 2017, MARKET SCI, V36, P838, DOI 10.1287/mksc.2017.1045
Sevin E, 2013, J PLACE MANAG DEV, V6, P227, DOI 10.1108/JPMD-10-2012-0037
Smith AN, 2012, J INTERACT MARK, V26, P102, DOI 10.1016/j.intmar.2012.01.002
Soboleva A, 2017, J MARKET MANAG-UK, V33, P1120, DOI 10.1080/0267257X.2017.1369142
Somers MJ, 2009, ORGAN RES METHODS, V12, P403, DOI 10.1177/1094428107309326
Sotiriadis MD, 2013, ELECTRON COMMER RES, V13, P103, DOI 10.1007/s10660-013-9108-1
Tang TY, 2014, J MARKETING, V78, P41, DOI 10.1509/jm.13.0301
Tsaur SH, 2002, TOURISM MANAGE, V23, P397, DOI 10.1016/S0261-5177(01)00097-8
Usakli A, 2017, J DESTIN MARK MANAGE, V6, P136, DOI 10.1016/j.jdmm.2017.02.001
Uysal M., 1999, Journal of Travel Research, V38, P111, DOI 10.1177/004728759903800203
Wang X, 2012, J INTERACT MARK, V26, P198, DOI 10.1016/j.intmar.2011.11.004
WARD S, 1974, J CONSUM RES, V1, P1, DOI 10.1086/208584
Weber P., 1990, BASIC CONTENT ANAL, V2nd
West PM, 1997, MARKET SCI, V16, P370, DOI 10.1287/mksc.16.4.370
Westerman D, 2012, COMPUT HUM BEHAV, V28, P199, DOI 10.1016/j.chb.2011.09.001
Wu DCG, 2017, INT J CONTEMP HOSP M, V29, P507, DOI [10.1108/ijchm-05-2015-0249, 10.1108/IJCHM-05-2015-0249]
Yang Y, 2014, J TRAVEL RES, V53, P433, DOI 10.1177/0047287513500391
Ye Q, 2009, INT J HOSP MANAG, V28, P180, DOI 10.1016/j.ijhm.2008.06.011
You Y, 2015, J MARKETING, V79, P19, DOI 10.1509/jm.14.0169
Youn H, 2010, INT J HOSP MANAG, V29, P120, DOI 10.1016/j.ijhm.2009.06.007
Zaman T. R., 2010, P WORKSHOP COMPUTATI, V104, P17599
Zeng BX, 2014, TOUR MANAG PERSPECT, V10, P27, DOI 10.1016/j.tmp.2014.01.001
Zhang J, 2009, AM J BUS, V24, P53, DOI 10.1108/19355181200900011
NR 113
TC 33
Z9 33
U1 3
U2 118
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0261-5177
EI 1879-3193
J9 TOURISM MANAGE
JI Tourism Manage.
PD APR
PY 2019
VL 71
BP 490
EP 503
DI 10.1016/j.tourman.2018.10.013
PG 14
WC Environmental Studies; Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Social Sciences - Other Topics;
Business & Economics
GA HD8NZ
UT WOS:000452815100043
DA 2024-03-27
ER
PT J
AU Moroz, M
AF Moroz, Miroslaw
TI TENDENCY TO USE THE VIRTUAL FITTING ROOM IN GENERATION Y - RESULTS OF
QUALITATIVE STUDY
SO FOUNDATIONS OF MANAGEMENT
LA English
DT Article
DE virtual fitting room; virtual dressing room; virtual changing room;
e-commerce; clothing industry; generation Y; augmented reality; 3D
mapping; human-computer interaction
ID CONSUMERS
AB E-commerce is growing rapidly on a global scale. Among many products purchased via the Internet, clothing is the first in terms of purchase frequency. However, there are growth barriers for this product category, which include, first of all, the client's fear of matching clothing to their own figure or complexion. This results in a high percentage of returns reaching up to 60% of transactions, which is more than that in other e-commerce sectors. One of the possible solutions to the abovementioned problem is the use of a virtual fitting room (VFR), which allows you to try on clothes in terms of size, fit, style, or color on a computer or smartphone screen. The main purpose of the article is to determine the propensity to use a VFR in the age group of generation Y. The second goal is to compare the propensity to use by type of VFR: 2D vs. 3D. The methodology is based on the qualitative exploratory approach. To conduct research, content analysis and sentiment analysis were used. The results of the study indicate that the participants of the research have an ambivalent attitude towards VFR. on the one hand, they perceive VFRs as an interesting solution for Internet users (not only generation Y). On the other hand, however, they themselves show a distance to use the VFR. The analysis also showed that a two-dimensional type of VFR based on augmented reality technology has greater market opportunities.
C1 [Moroz, Miroslaw] Wroclaw Univ Econ, Wroclaw, Poland.
C3 Wroclaw University of Economics & Business
RP Moroz, M (autor correspondiente), Wroclaw Univ Econ, Wroclaw, Poland.
EM miroslaw.moroz@ue.wroc.pl
CR Anagaha R., 2018, NCESC 2018 C P
[Anonymous], 2014, MARK RES
[Anonymous], 2016, INTERNET RETAILING F
Bakopoulos K., 2019, E COMMERCE SHARE TOT
Barclaycard, 2016, EM SER RET ONL SHOPP
Barclaycard, 2019, AG SER RET
Bazaki E., 2019, UNLOCKING POTENTIAL
Bolton RN, 2013, J SERV MANAGE, V24, P245, DOI 10.1108/09564231311326987
Bonetti F, 2018, PROGR IS, P119, DOI 10.1007/978-3-319-64027-3_9
Boonbrahm P, 2015, PROCEDIA COMPUT SCI, V75, P12, DOI 10.1016/j.procs.2015.12.189
Clancy O., 2016, BBC
Cullinane S., 2019, CONT OPERATIONS LOGI, P301, DOI DOI 10.1007/978-3-030-14493-7_16
Daaboul J., 2014, J REMANUFACTURING, V4, P7, DOI DOI 10.1186/S13243-014-0007-Y
de Leeuw S, 2016, INT J OPER PROD MAN, V36, P710, DOI 10.1108/IJOPM-01-2015-0010
DPD, 2018, E SHOPP BAR REP 2018
Dunn C., 2015, SIZING TECHNOLOGY CA
Duriau VJ, 2007, ORGAN RES METHODS, V10, P5, DOI 10.1177/1094428106289252
Duszczyk M., 2019, WIRTUALNA PRZYMIERZA
Ecommercenews, 2019, ECOMMERCENEWS
EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557
Erra U, 2018, MULTIMED TOOLS APPL, V77, P25077, DOI 10.1007/s11042-018-5758-2
Eurostat, 2018, E COMM STAT IND
Flick U., 2018, ANAL QUALITATIVE DAT, DOI DOI 10.4135/9781849208826.N10
Franco CE., 2016, INT J RES GRANTHAALA, V4, P7, DOI [10.29121/granthaalayah.v4.i3SE.2016.2771, DOI 10.29121/GRANTHAALAYAH.V4.I3SE.2016.2771]
Gemius, 2019, E COMM POLSC GEM DLA
Glamstorm, 2019, FITT ROOM
Hammersley M., 1995, Ethnography: Principles in practice
Huang TL, 2015, ELECTRON COMMER RES, V15, P269, DOI 10.1007/s10660-014-9163-2
Koch L., 2019, NIKE UNVEILS NIKE FI
Liu B., 2012, SENTIMENT ANAL OPINI
Narvar, 2017, MAK RET COMP ADV
Nielsen, 2018, TOP CATEGORIES PURCH
NVivo, 2019, MIX SENT COD
Patodiya P.K., 2017, REV J PHILOS SOCIAL, V42, P197
Smith S.M., 2005, Fundamentals of marketing research
Soares RR, 2017, J SERV MANAGE, V28, P520, DOI 10.1108/JOSM-08-2015-0256
Statista, 2019, SHAR INT US WHO HAV
Statista, 2020, RET E COMM SAL WORLD
Turban E., 2017, Electronic Commerce 2018: A Managerial and Social Networks Perspective
Urbanke P., 2015, 36 INT C INF SYST IC
Wearfits, 2019, DEM FITT ROOM
West P., 2017, VIRTUAL FITTING ROOM
Yen YY, 2017, PROC INT CONF COMP, P311
Yi Gao, 2014, HCI in Business. First International Conference, HCIB 2014. Held as Part of HCI International 2014. Proceedings: LNCS 8527, P307, DOI 10.1007/978-3-319-07293-7_30
NR 44
TC 8
Z9 9
U1 3
U2 18
PU SCIENDO
PI WARSAW
PA DE GRUYTER POLAND SP Z O O, BOGUMILA ZUGA 32A STR, 01-811 WARSAW, POLAND
SN 2080-7279
EI 2300-5661
J9 FOUND MANAGE
JI Found. Manag.
PD MAR
PY 2019
VL 11
IS 1
BP 239
EP 254
DI 10.2478/fman-2019-0020
PG 16
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA JZ1OC
UT WOS:000504873100009
OA gold, Green Submitted
DA 2024-03-27
ER
PT J
AU Zhao, HL
Yang, Q
Liu, ZH
AF Zhao, Huiliang
Yang, Qin
Liu, Zhenghong
TI Impact of online customer reviews and deep learning on product
innovation empirical study on mobile applications
SO BUSINESS PROCESS MANAGEMENT JOURNAL
LA English
DT Article
DE Product innovation (PI); Deep learning (DL); Online customer reviews
(OCR); Technology (T); Mobile apps (M apps)
AB Purpose The customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product reviews effect as market competition. This research study explains deep learning, online reviews and product innovation empirical evidence used by mobile apps. Design/methodology/approach Online reviews and product innovation are very important for every organization and firms to achieve a competitive advantage in a large business environment. When the authors see past traditional history, customers are not involved in product creating and innovating processes. Due to new technology changes, online systems and web 2.0 increase this ability. Findings For this research purpose, the authors use different analytical software to measure the impact among variables. This study is established on primary data; this study collected data from online customers and its users. For data collection, the authors use some questionnaires, and these questions are filled from 200 respondents. Research limitations/implications This research study used data from the Google app store - Google product selling application - and gathered customers' online reviews. Research found that customers' online reviews and deep learning positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability. Originality/value This research study used data from the Google app store Google product selling application and gathered customers' online reviews. Research founded that customers' online reviews and deep learning are positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.
C1 [Zhao, Huiliang] Guizhou Minzu Univ, Dept Prod Design, Guiyang, Peoples R China.
[Zhao, Huiliang; Yang, Qin; Liu, Zhenghong] Guizhou Univ, Sch Mech Engn, Guiyang, Peoples R China.
C3 Guizhou Minzu University; Guizhou University
RP Liu, ZH (autor correspondiente), Guizhou Univ, Sch Mech Engn, Guiyang, Peoples R China.
EM fightingzhl@163.com; qinyang_6@163.com; 415803168@qq.com
OI zhao, hui liang/0000-0003-3025-6560
FU Natural Science Foundation of the Guizhou Higher Education Institutions
of China [[2018]152, [2017]239]; Humanity and Social Science Foundation
of the Guizhou Higher Education Institutions of China [2018qn46]
FX Project supported by the Natural Science Foundation of the Guizhou
Higher Education Institutions of China (Grant No. [2018]152, No.
[2017]239). Project supported by the Humanity and Social Science
Foundation of the Guizhou Higher Education Institutions of China (Grant
No. 2018qn46).
CR Becker A., 2014, Journal of Promotion Management, V20, P436, DOI [10.1080/10496491.2014.930282, DOI 10.1080/10496491.2014.930282]
Bhuiyan N, 2004, MANAGE SCI, V50, P1690, DOI 10.1287/mnsc.1040.0309
Bickart B., 2001, J INTERACT MARK, V15, P31, DOI [10.1002/dir.1014, DOI 10.1002/DIR.1014]
Blythe M, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1467
Burton J, 2010, J MARKET MANAG-UK, V26, P230, DOI 10.1080/02672570903566268
Chua AYK, 2015, J ASSOC INF SCI TECH, V66, P354, DOI 10.1002/asi.23180
Constantinides E., 2008, Data and Digital Marketing Practice, V9, P231, DOI DOI 10.1057/PALGRAVE.DDDMP.4350098
Cummings JN, 2004, MANAGE SCI, V50, P352, DOI 10.1287/mnsc.1030.0134
De Dreu CKW, 2003, J APPL PSYCHOL, V88, P741, DOI 10.1037/0021-9010.88.4.741
Dellarocas C, 2010, MIT SLOAN MANAGE REV, V51, P33
Du JX, 2005, EDUC MEDIA INT, V42, P207, DOI 10.1080/09523980500161221
Edmondson AC, 2003, CALIF MANAGE REV, V45, P34, DOI 10.2307/41166164
Edmondson AC, 2002, ORGAN SCI, V13, P128, DOI 10.1287/orsc.13.2.128.530
Edmondson C, 2009, J PROD INNOVAT MANAG, V26, P123
Fan WG, 2014, COMMUN ACM, V57, P74, DOI 10.1145/2602574
Floh A, 2013, J MARKET MANAG-UK, V29, P646, DOI 10.1080/0267257X.2013.776620
Goldsmith R.E., 2006, Journal of Interactive Advertising, V6, P1, DOI DOI 10.1080/15252019.2006.10722114
Hennig-Thurau T, 2003, INT J ELECTRON COMM, V8, P51, DOI 10.1080/10864415.2003.11044293
Huang LS, 2015, BEHAV INFORM TECHNOL, V34, P33, DOI 10.1080/0144929X.2014.978378
Huang Z, 2013, ELECTRON COMMER R A, V12, P246, DOI 10.1016/j.elerap.2012.12.003
Iansiti M, 2004, HARVARD BUS REV, V82, P68
Jeppesen LB, 2003, TECHNOL ANAL STRATEG, V15, P363, DOI 10.1080/09537320310001601531
Klein LR, 2003, J INTERACT MARK, V17, P29, DOI 10.1002/dir.10058
Lee M, 2009, INT J ADVERT, V28, P473, DOI 10.2501/S0265048709200709
Lewis, 2013, E COMMERCE ORG DEV C, P85
Lu LC, 2014, COMPUT HUM BEHAV, V34, P258, DOI 10.1016/j.chb.2014.02.007
Prahalad CK, 2004, J INTERACT MARK, V18, P5, DOI 10.1002/dir.20015
Randall T, 2007, MARKET SCI, V26, P268, DOI 10.1287/mksc.1050.0116
Schmidt J.B., 1996, J ACAD MARKET SCI, V24, P246, DOI DOI 10.1177/0092070396243005
Wang HY, 2011, INT J HOSP MANAG, V30, P503, DOI 10.1016/j.ijhm.2010.07.009
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 31
TC 2
Z9 2
U1 10
U2 51
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1463-7154
EI 1758-4116
J9 BUS PROCESS MANAG J
JI Bus. Process. Manag. J.
PD OCT 12
PY 2021
VL 27
IS 6
BP 1912
EP 1925
DI 10.1108/BPMJ-12-2020-0542
EA APR 2021
PG 14
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA WF3XN
UT WOS:000636485100001
DA 2024-03-27
ER
PT J
AU Perez-Vega, R
Kaartemo, V
Lages, CR
Razavi, NB
Männistö, J
AF Perez-Vega, Rodrigo
Kaartemo, Valtteri
Lages, Cristiana R.
Razavi, Niloofar Borghei
Mannisto, Jaakko
TI Reshaping the contexts of online customer engagement behavior via
artificial intelligence: A conceptual framework
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Artificial intelligence; Online customer engagement behaviors;
Stimulus-organism-response; Information processing systems
ID WORD-OF-MOUTH; BIG DATA; BUSINESS INTELLIGENCE; BRAND ENGAGEMENT;
ANALYTICS; CONSUMERS; DECISION; PURCHASE; IMPACT; MODEL
AB As new applications of artificial intelligence continue to emerge, there is an increasing interest to explore how this type of technology can improve automated service interactions between the firm and its customers. This paper aims to develop a conceptual framework that details how firms and customers can enhance the outcomes of firm-solicited and firm-unsolicited online customer engagement behaviors through the use of information processing systems enabled by artificial intelligence. By building on the metaphor of artificial intelligence systems as organisms and taking a Stimulus-Organism-Response theory perspective, this paper identifies different types of firm-solicited and firm-unsolicited online customer engagement behaviors that act as stimuli for artificial intelligence organisms to process customer-related information resulting in both artificial intelligence and human responses which, in turn, shape the contexts of future online customer engagement behaviors.
C1 [Perez-Vega, Rodrigo; Lages, Cristiana R.; Razavi, Niloofar Borghei] Univ Reading, Henley Business Sch, Whiteknights Campus, Reading RG6 6UD, Berks, England.
[Kaartemo, Valtteri] Univ Turku, Turku Sch Econ, Rehtorinpellonkatu 3, Turku 20500, Finland.
[Mannisto, Jaakko] Feedbackly Oy, Itamerenkatu 1, Helsinki 00180, Finland.
C3 University of Reading; University of Turku
RP Perez-Vega, R (autor correspondiente), Univ Reading, Henley Business Sch, Whiteknights Campus, Reading RG6 6UD, Berks, England.
EM r.perezvega@henley.ac.uk
RI Perez-Vega, Rodrigo/AAR-2603-2021; Lages, Cristiana/ABD-3563-2020;
Kaartemo, Valtteri/JCD-8259-2023
OI Lages, Cristiana/0000-0002-7024-9536; Kaartemo,
Valtteri/0000-0003-2915-0240; Perez Vega, Rodrigo/0000-0003-1619-317X
FU Academy of Finland [315604]; Academy of Finland (AKA) [315604] Funding
Source: Academy of Finland (AKA)
FX This research is funded by the Academy of Finland (315604).
CR Akter S, 2016, ELECTRON MARK, V26, P173, DOI 10.1007/s12525-016-0219-0
Alexander MJ, 2018, J SERV MANAGE, V29, P333, DOI 10.1108/JOSM-08-2016-0237
Assaf AG, 2015, INT J HOSP MANAG, V44, P77, DOI 10.1016/j.ijhm.2014.09.009
Bauer J, 2014, DECIS SUPPORT SYST, V68, P77, DOI 10.1016/j.dss.2014.09.005
Beckers SFM, 2018, J ACAD MARKET SCI, V46, P366, DOI 10.1007/s11747-017-0539-4
Bhuiyan T., 2009, P 9 AS PAC COMPL SYS, P385
Bijmolt THA, 2010, J SERV RES-US, V13, P341, DOI 10.1177/1094670510375603
BITNER MJ, 1992, J MARKETING, V56, P57, DOI 10.2307/1252042
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
Bonnin G, 2006, J SERVICES RES, V6, P43
Brodie RJ, 2011, J SERV RES-US, V14, P252, DOI 10.1177/1094670511411703
Chau M, 2012, MIS QUART, V36, P1189
Chen HC, 2012, MIS QUART, V36, P1165
Chen YC, 2009, ELECTRON COMMER R A, V8, P48, DOI 10.1016/j.elerap.2008.09.001
Cheng H.-T., 2016, P 1 WORKSH DEEP LEAR, P7, DOI [10.1145/2988450.2988454, DOI 10.1145/2988450.2988454]
Chica M, 2017, J MARKETING RES, V54, P752, DOI 10.1509/jmr.15.0443
Chiu YB, 2005, INT J SERV IND MANAG, V16, P416, DOI 10.1108/09564230510625741
Choudhury MM, 2014, J STRATEG MARK, V22, P149, DOI 10.1080/0965254X.2013.876069
Culotta A, 2015, AAAI CONF ARTIF INTE, P72
Domingos P., 2015, The Master Algorithm. How the Quest for the Ultimate Learning Machine Will Remake Our World, DOI DOI 10.5555/3217330
Eroglu SA, 2001, J BUS RES, V54, P177, DOI 10.1016/S0148-2963(99)00087-9
Estelami H., 2014, ROUTLEDGE COMPANION, P376
Fagella D., 2018, ARTIFICIAL INTELLIGE
Fill C., 2016, MARKETING COMMUNICAT
Fortini-Campbell L, 1992, J CONSUMER MARKET, V9, P73
Fournier S, 2009, HARVARD BUS REV, V87, P105
Freitag D, 2000, MACH LEARN, V39, P169, DOI 10.1023/A:1007601113994
Fulgoni GM, 2014, J ADVERTISING RES, V54, P377, DOI 10.2501/JAR-54-4-377-380
Gamberini L, 2017, LECT NOTES COMPUT SC, V9961, P1, DOI 10.1007/978-3-319-57753-1_1
Greenberg P, 2010, J BUS IND MARK, V25, P410, DOI 10.1108/08858621011066008
Gummerus J, 2012, MANAG RES REV, V35, P857, DOI 10.1108/01409171211256578
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772
Hirschman AlbertO., 1972, EXIT VOICE LOYALTY R
Hollebeek LD, 2014, J PROD BRAND MANAG, V23, P62, DOI 10.1108/JPBM-06-2013-0332
Hollebeek L, 2011, J STRATEG MARK, V19, P555, DOI 10.1080/0965254X.2011.599493
Hollebeek LD, 2019, J ACAD MARKET SCI, V47, P161, DOI 10.1007/s11747-016-0494-5
Hoyer WD, 2010, J SERV RES-US, V13, P283, DOI 10.1177/1094670510375604
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
HUNT SD, 1995, J BUS RES, V33, P81, DOI 10.1016/0148-2963(94)00057-L
Isinkaye FO, 2015, EGYPT INFORM J, V16, P261, DOI 10.1016/j.eij.2015.06.005
Jaakkola E, 2014, J SERV RES-US, V17, P247, DOI 10.1177/1094670514529187
Jacucci G, 2014, LECT NOTES COMPUT SC, V8820, P3, DOI 10.1007/978-3-319-13500-7_1
Jahn B, 2012, J SERV MANAGE, V23, P344, DOI 10.1108/09564231211248444
Kaartemo V., 2018, Journal of Creating Value, V4, P211, DOI 10.1177/2394964318805625
Karimi J, 2001, J MANAGE INFORM SYST, V17, P125, DOI 10.1080/07421222.2001.11045661
Kitchens B, 2018, J MANAGE INFORM SYST, V35, P540, DOI 10.1080/07421222.2018.1451957
Koren Y, 2008, P 14 ACM SIGKDD INT
KOTLER P, 1974, J RETAILING, V49, P48
Kumar V, 2016, J ACAD MARKET SCI, V44, P24, DOI 10.1007/s11747-015-0426-9
Kumar V, 2013, J SERV MANAGE, V24, P330, DOI 10.1108/09564231311327021
Kunz W, 2017, J SERV MARK, V31, P161, DOI 10.1108/JSM-10-2016-0352
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Li ST, 2008, EXPERT SYST APPL, V35, P739, DOI 10.1016/j.eswa.2007.07.049
MacInnis DJ, 2011, J MARKETING, V75, P136, DOI 10.1509/jmkg.75.4.136
Marbach J, 2016, J MARKET MANAG-UK, V32, P502, DOI 10.1080/0267257X.2015.1128472
Marinova D, 2017, J SERV RES-US, V20, P29, DOI 10.1177/1094670516679273
Maslowska E, 2016, J MARKET MANAG-UK, V32, P469, DOI 10.1080/0267257X.2015.1134628
McCarthy J., 1955, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
Mehrabian A., 1974, APPROACH ENV PSYCHOL, V12
Oelke D., 2009, Visual Analytics Science and Technology, P187
Ordenes FV, 2014, J SERV RES-US, V17, P278, DOI 10.1177/1094670514524625
Pansari A, 2017, J ACAD MARKET SCI, V45, P294, DOI 10.1007/s11747-016-0485-6
Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1
Rust RT, 2014, MARKET SCI, V33, P206, DOI 10.1287/mksc.2013.0836
Sahoo N, 2012, MIS QUART, V36, P1329
Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283
Smith AN, 2012, J INTERACT MARK, V26, P102, DOI 10.1016/j.intmar.2012.01.002
Sterne J., 2017, Artificial intelligence for marketing: practical applications
Syam N, 2018, IND MARKET MANAG, V69, P135, DOI 10.1016/j.indmarman.2017.12.019
Trusov M, 2009, J MARKETING, V73, P90, DOI 10.1509/jmkg.73.5.90
Vafeiadis T, 2015, SIMUL MODEL PRACT TH, V55, P1, DOI 10.1016/j.simpat.2015.03.003
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
van Doorn J, 2010, J SERV RES-US, V13, P253, DOI 10.1177/1094670510375599
Vargo Stephen L., 2008, European Management Journal, V26, P145, DOI 10.1016/j.emj.2008.04.003
Verhoef PC, 2010, J SERV RES-US, V13, P247, DOI 10.1177/1094670510375461
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Weinberg BD, 2015, BUS HORIZONS, V58, P615, DOI 10.1016/j.bushor.2015.06.005
Wirtz J, 2004, INT J SERV IND MANAG, V15, P150, DOI 10.1108/09564230410532484
Wirtz J, 2013, J SERV MANAGE, V24, P223, DOI 10.1108/09564231311326978
Zhu DH, 2016, TELEMAT INFORM, V33, P8, DOI 10.1016/j.tele.2015.06.001
Ziegler Cai-Nicolas, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, P932, DOI 10.1109/WIIAT.2008.69
NR 82
TC 53
Z9 53
U1 52
U2 240
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD MAY
PY 2021
VL 129
BP 902
EP 910
DI 10.1016/j.jbusres.2020.11.002
EA APR 2021
PG 9
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA RL7AA
UT WOS:000639120000077
OA Green Accepted, hybrid
DA 2024-03-27
ER
PT J
AU Deng, ZH
Guo, MW
AF Deng, Zhihang
Guo, Meiwen
TI Research on the impact of the application of artificial intelligence
technology on the sustainable development of mobile e-commerce
SO BENCHMARKING-AN INTERNATIONAL JOURNAL
LA English
DT Article; Early Access
DE Mobile e-commerce; AI; Sustainable development; Grounded theory
ID PRIVACY
AB Purpose - This article aims to reveal the factors influencing the sustainable development of mobile e-commerce from both user and operational perspectives. It fills the gap in qualitative research on the sustainable development of artificial intelligence (AI) technology in mobile e-commerce based on the grounded theory. This study provides valuable insights and inspiration for sustainable development in this field and lays the theoretical foundation and research reference for future studies. Design/methodology/approach - Based on the grounded theory (GT), interview method was used to conduct the study. Findings - The impact of AI applications on mobile e-commerce is mainly reflected in three stages of the customer shopping process. They are pre-shopping, mid-shopping and after-shopping AI services and each of the three stages has its own separate dimensions that need attention. The study and its persistence aspects are discussed. Practical implications - The results of this study can provide forward-looking suggestions and paths for the construction and optimization of future e-commerce platforms, contribute to the sustainable development of e-commerce and contribute to the sustainable and healthy growth of the social economy. Originality/value - This study proposes sustainable development measures for the application of AI in mobile e-commerce, from operation to supervision, which is an important reference for promoting coordinated and rapid socio-economic development.
C1 [Deng, Zhihang; Guo, Meiwen] Guangzhou Xinhua Univ, Guangzhou, Peoples R China.
RP Guo, MW (autor correspondiente), Guangzhou Xinhua Univ, Guangzhou, Peoples R China.
EM gmw@xhsysu.edu.cn
FU collaborative education project of industry-university cooperation of
the Ministry of Education [202102209025]; "The 13th Five-Year" plan
research project of philosophy and social sciences of Guangdong province
[GD17YGL03]; Guangdong Youth Research Project [2019GJ043]
FX This paper was funded by the collaborative education project of
industry-university cooperation of the Ministry of Education (No:
202102209025), "The 13th Five-Year" plan research project of philosophy
and social sciences of Guangdong province (No: GD17YGL03) and Guangdong
Youth Research Project (No: 2019GJ043).
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Bawack RE, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102309
Bedué P, 2022, J ENTERP INF MANAG, V35, P530, DOI 10.1108/JEIM-06-2020-0233
Borland J, 2017, AUST ECON REV, V50, P377, DOI 10.1111/1467-8462.12245
Cai YJ, 2020, INT J PROD ECON, V229, DOI 10.1016/j.ijpe.2020.107729
Feng M., 2009, Guangdong Science and Technology (In Chinese), P65
Glaser Barney., 2007, The Grounded Theory Seminar Reader
Glaser Barney G., 1967, DISCOV GROUNDED THEO, DOI DOI 10.4324/9780203793206
Goode, 2021, BIOMETRIC TECHNOL TO, V2021, P7, DOI [10.1016/S0969-4765(21)00083-7, DOI 10.1016/S0969-4765(21)00083-7]
Guo MZ, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020926
Guo MW, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-01732-9
Guo MW, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph182010729
Huang A.Z., 2022, Technological Forecasting and Social Change, V188
Ju X., 2020, E-Commerce, P21
Kasilingam DL, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101280
Kurzweil R., 2001, Work and Think in the New Age of Intelligent Machines, V38, P351
Lin J., 2019, China Business Theory (In Chinese), P19
Liu YL, 2021, TELECOMMUN POLICY, V45, DOI 10.1016/j.telpol.2021.102155
Lu Y., 2017, National Circulation Economy, P14
Lu Z., 2020, Popular Investment Guide, P58
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Lv Jian, 2021, Journal of Physics: Conference Series, V1881, DOI 10.1088/1742-6596/1881/3/032062
Nilsson N.J., 1998, ARTIFICIAL INTELLIGE
Omar S, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102468
Pallathadka Harikumar, 2023, Materials Today: Proceedings, P2610, DOI 10.1016/j.matpr.2021.06.419
Pan W., 2021, Journal of Lvliang College of Education, V38, P35
Pratap M.chandra., 2021, International Journal of Business Strategy and Automation, V2, P20
Shao Y., 2018, Science and Technology Communication, V10, P106
Simkova N, 2021, J THEOR APPL EL COMM, V16, P1186, DOI 10.3390/jtaer16050067
Strauss A.L., 2003, QUALITATIVE ANAL SOC
Su X, 2019, IEEE T IND INFORM, V15, P4266, DOI 10.1109/TII.2019.2908056
Suddaby R, 2006, ACAD MANAGE J, V49, P633, DOI 10.5465/AMJ.2006.22083020
Sun K., 2019, Guizhou Social Science, P136
Vimalkumar M, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106763
Wang F.Q., 2020, Foreign Economy and Management (In Chinese), V42, P48
Wang Y, 2017, CABI SER TOUR MANAGE, P136, DOI 10.1079/9781786391896.0136
Wu Q.J., 2019, Shandong Social Science (In Chinese), V3, P73
Xia Song, 2019, Journal of Physics: Conference Series, V1302, DOI 10.1088/1742-6596/1302/3/032030
Yang GY, 2022, ANN OPER RES, V308, P703, DOI 10.1007/s10479-020-03602-y
Zhang D, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102304
Zhu Z., 2016, Computer Knowledge and Technology, V12, P263
Zhu Z.F., 2018, Logistics Engineering and Management (In Chinese), V40, P31
NR 42
TC 0
Z9 0
U1 6
U2 6
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 1463-5771
EI 1758-4094
J9 BENCHMARKING
JI Benchmarking
PD 2023 SEP 25
PY 2023
DI 10.1108/BIJ-11-2022-0697
EA SEP 2023
PG 24
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA S0LR4
UT WOS:001068176100001
DA 2024-03-27
ER
PT J
AU Ng, FZX
Yap, HY
Tan, GWH
Lo, PS
Ooi, KB
AF Ng, Felicity Zi-Xuan
Yap, Hui-Yee
Tan, Garry Wei-Han
Lo, Pei-San
Ooi, Keng-Boon
TI Fashion shopping on the go: A Dual-stage predictive-analytics SEM-ANN
analysis on usage behaviour, experience response and cross-category
usage
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Mobile commerce; Fashion; Usage intention; Usage behaviour; Experience
response; Cross category usage; Individual lifestyle; Artificial neural
network; PLS-SEM
ID EMPLOYEES EXTENDED USE; MOBILE; ACCEPTANCE; INTENTION; ADOPTION; IMPACT;
PRODUCTS; SERVICES; DRIVERS; TOOL
AB With the proliferation of mobile commerce, mobile shopping has become the buzzword in the electronic commerce industry. To examine the predictive factors that affect the usage behaviour, experience response, and cross-category usage in mobile fashion shopping, an integrated research framework, comprising of the Mobile Technology Acceptance Model and individual attributes in terms of lifestyle orientations was proposed. The quantitative data, derived from 500 qualified responses, collected through a survey questionnaire, was validated via a two-stage predictive-analytics SEM-ANN approach to identify the non-compensatory and non-linear relationship. All six of the ANN models showed consistent relationships and rankings with the SEM results. The findings imply that mobile commerce developers and designers should ensure that the functions provided can satisfy the evaluation criteria of users with different lifestyle orientations, whereby the advantages of the mobile commerce platforms should be highlighted in the marketing messages to drive first-time usage, as well as extended usage across different mobile commerce platforms (i.e., mobile sites and mobile applications) and product categories. From the theoretical perspective, the findings revealed the indirect influence of the individual attributes on the usage intention of innovative mobile technology. The research is also the first to adopt non-compensatory neural network analysis to compensate the linear SEM analysis in the study on mobile shopping of fashion products.
C1 [Ng, Felicity Zi-Xuan; Yap, Hui-Yee] UCSI Univ, Fac Business & Management, 1 Jalan Menara Gading, Kuala Lumpur 56000, Malaysia.
[Tan, Garry Wei-Han; Lo, Pei-San; Ooi, Keng-Boon] UCSI Univ, UCSI Grad Business Sch, 1 Jalan Menara Gading, Kuala Lumpur 56000, Malaysia.
[Tan, Garry Wei-Han] Nanchang Inst Technol, Sch Finance & Econ, Nanchang, Jiangxi, Peoples R China.
[Ooi, Keng-Boon] Chang Jung Christian Univ, Coll Management, Tainan, Taiwan.
C3 UCSI University; UCSI University; Nanchang Institute Technology; Chang
Jung Christian University
RP Tan, GWH (autor correspondiente), UCSI Univ, UCSI Grad Business Sch, 1 Jalan Menara Gading, Kuala Lumpur 56000, Malaysia.
EM xuan99.fn@gmail.com; huiyee097@gmail.com; garrytanweihan@gmail.com;
oeiansan@gmail.com; ooikengboon@gmail.com
RI Lo, Pei San/ADV-5389-2022; OOI, Keng-Boon/I-4143-2019; Lo, Pei
San/AAD-7683-2022; Tan Wei Han, Garry/C-6565-2011
OI OOI, Keng-Boon/0000-0002-3384-1207; Lo, Pei San/0000-0002-7061-2307; Tan
Wei Han, Garry/0000-0003-2974-2270
CR Abdallah N., 2020, J THEORETICAL APPL I, V98, P1479
Ahmad Norzieiriani, 2010, Business Strategy Series, V11, P227, DOI 10.1108/17515631011063767
Akkaya M, 2021, EUR RES MANAG BUS EC, V27, DOI 10.1016/j.iedeen.2021.100155
[Anonymous], 1997, J FASH MARK MANAG, DOI DOI 10.1108/EB022500
Bourdieu Pierre., 2012, Food and Culture: A Reader, P31, DOI [10.4324/9780203079751-11, DOI 10.4324/9780203079751-11]
Broeckelmann P, 2010, QUAL MARK RES, V13, P414, DOI 10.1108/13522751011078827
Caiati V, 2020, TRANSPORT RES A-POL, V131, P123, DOI 10.1016/j.tra.2019.09.029
Sánchez-Prieto JC, 2016, COMPUT HUM BEHAV, V55, P519, DOI 10.1016/j.chb.2015.07.002
Cho YC., 2015, INT J MANAGEMENT INF, V19, P21, DOI DOI 10.19030/IJMIS.V19I1.9086
Chong AYL, 2012, DECIS SUPPORT SYST, V53, P34, DOI 10.1016/j.dss.2011.12.001
Chopdar PK, 2019, BEHAV INFORM TECHNOL, V38, P42, DOI 10.1080/0144929X.2018.1513563
Dahana WD, 2019, J BUS RES, V99, P319, DOI 10.1016/j.jbusres.2019.02.049
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dijkstra TK, 2015, MIS QUART, V39, P297, DOI 10.25300/MISQ/2015/39.2.02
Donovan RJ, 1999, PSYCHOL MARKET, V16, P613, DOI 10.1002/(SICI)1520-6793(199910)16:7<613::AID-MAR4>3.0.CO;2-F
El Hedhli K, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102260
Ericsson KA, 2006, CAMBRIDGE HANDBOOK OF EXPERTISE AND EXPERT PERFORMANCE, P683
Escobar-Rodríguez T, 2017, INF SYST E-BUS MANAG, V15, P599, DOI 10.1007/s10257-016-0319-6
Faul F, 2009, BEHAV RES METHODS, V41, P1149, DOI 10.3758/BRM.41.4.1149
Gefen D, 2005, COMMUN ASSOC INF SYS, V16, P91, DOI 10.17705/1CAIS.01605
Gorgoglione M, 2018, INT J BANK MARK, V36, P1311, DOI 10.1108/IJBM-06-2017-0124
Gross M, 2015, INT J RETAIL DISTRIB, V43, P221, DOI 10.1108/IJRDM-06-2013-0119
Gunter B., 2016, PSYCHOL CONSUMER PRO
Hair J.F., 2016, A premier on partial least squares structural equation modeling (PLS-SEM), V2nd
Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
Hew JJ, 2019, TECHNOL FORECAST SOC, V144, P311, DOI 10.1016/j.techfore.2017.10.007
Hew JJ, 2017, TELEMAT INFORM, V34, P914, DOI 10.1016/j.tele.2017.04.001
Hongkun Peng, 2020, Advances in Intelligent Information Hiding and Multimedia Signal Processing. Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT. Smart Innovation, Systems and Technologies (SIST 156), P21, DOI 10.1007/978-981-13-9714-1_3
Hsieh JJPA, 2007, EUR J INFORM SYST, V16, P216, DOI 10.1057/palgrave.ejis.3000663
Hubert M, 2017, PSYCHOL MARKET, V34, P175, DOI 10.1002/mar.20982
Hur HJ, 2017, COMPUT HUM BEHAV, V73, P353, DOI 10.1016/j.chb.2017.03.051
Kaynak E., 2015, International Journal of Advertising, V20, P455, DOI DOI 10.1080/02650487.2001.11104906
Kim D., 2000, P 21 INT C INF SYST, V76, P688
Kim N, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102457
Leung L, 2017, TELEMAT INFORM, V34, P1638, DOI 10.1016/j.tele.2017.07.010
Lew SS, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101430
Li SCS, 2013, TELECOMMUN POLICY, V37, P639, DOI 10.1016/j.telpol.2012.12.009
Lian JWE, 2021, J RETAIL CONSUM SERV, V62, DOI 10.1016/j.jretconser.2021.102631
Liang HG, 2007, MIS QUART, V31, P59
Liébana-Cabanillas F, 2018, TECHNOL FORECAST SOC, V129, P117, DOI 10.1016/j.techfore.2017.12.015
Liu H, 2022, J RETAILING, V98, P241, DOI 10.1016/j.jretai.2021.02.002
Loh XK, 2022, INFORM SYST FRONT, V24, P1887, DOI 10.1007/s10796-021-10202-z
Loh XM, 2021, INTERNET RES, V31, P376, DOI 10.1108/INTR-04-2020-0175
Madan K, 2018, ASIA PAC J MARKET LO, V30, P139, DOI 10.1108/APJML-02-2017-0023
Mali N, 2020, BIG COMMERCE
Menon S, 2002, J RETAILING, V78, P31, DOI 10.1016/S0022-4359(01)00064-1
Mohamed N, 2014, IND MANAGE DATA SYST, V114, P1453, DOI 10.1108/IMDS-07-2014-0201
Mowen J.C., 1998, CONSUMER BEHAV
Mowen John C., 1999, The 3M Model of Motivation and Personality: Theory and Empirical Applications to Consumer Behavior
Murray KB, 2007, J CONSUM RES, V34, P77, DOI 10.1086/513048
Nawi NC, 2019, SOCIETIES, V9, DOI 10.3390/soc9010010
Nikhashemi SR, 2019, J RETAIL CONSUM SERV, V48, P122, DOI 10.1016/j.jretconser.2019.01.014
Omar S, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102468
Ooi KB, 2021, IND MANAGE DATA SYST, V121, P312, DOI 10.1108/IMDS-04-2020-0239
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Raman P, 2021, YOUNG CONSUM, V22, P387, DOI 10.1108/YC-05-2020-1148
Ruiz-Mafe C, 2014, ONLINE INFORM REV, V38, P362, DOI 10.1108/OIR-05-2013-0101
Russell G.J., 1997, MARKET LETT, V8, P297, DOI 10.1023/A:1007960529932
Shao Z, 2019, J ENTERP INF MANAG, V32, P626, DOI 10.1108/JEIM-07-2018-0160
Shin E, 2021, J BUS RES, V132, P416, DOI 10.1016/j.jbusres.2021.04.022
Siau K., 2004, International Journal of Electronic Business, V2, P283, DOI 10.1504/IJEB.2004.005143
Soni M, 2019, J GLOB FASH MARK, V10, P358
Statista, 2020, FASH ECOMMERCE REP 2
Statista Digital Market Outlook, 2021, ECOMMERCE REP 2021
Tan GWH, 2018, TELEMAT INFORM, V35, P2270, DOI 10.1016/j.tele.2018.09.005
Tan GWH, 2018, TELEMAT INFORM, V35, P1617, DOI 10.1016/j.tele.2018.04.009
Tan GWH, 2014, COMPUT HUM BEHAV, V36, P198, DOI 10.1016/j.chb.2014.03.052
Tanusondjaja A, 2016, INT J MARKET RES, V58, P401, DOI 10.2501/IJMR-2016-026
Taylor S, 1995, MIS QUART, V19, P561, DOI 10.2307/249633
Tew HT, 2022, J COMPUT INFORM SYST, V62, P527, DOI 10.1080/08874417.2020.1858731
Pham TSH, 2017, TECHNOL FORECAST SOC, V124, P332, DOI 10.1016/j.techfore.2017.04.003
Venkatesh V, 2012, MIS QUART, V36, P157
Vic-Qi Pan, 2015, International Journal of Modelling in Operations Management, V5, P83
Ward P, 2014, J LEISURE RES, V46, P84, DOI 10.1080/00222216.2014.11950314
We are Social & Hootsuite, 2021, DIG 2021 MAL
Wong C.H., 2012, INT J NETWORK MOBILE, V3, P24
Wong CH, 2015, ONLINE INFORM REV, V39, P762, DOI 10.1108/OIR-05-2015-0152
Wu JH, 2006, ELECTRON COMMER R A, V5, P192, DOI 10.1016/j.elerap.2005.09.004
Xiu-Ming Loh, 2022, Journal of Computer Information Systems, V62, P39, DOI 10.1080/08874417.2019.1688733
Yan LY, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102300
Yang CY, 2011, INT J HUM-COMPUT INT, V27, P583, DOI 10.1080/10447318.2011.555300
Yon Heong Tung, 2016, HERES WHY MILLENNIAL, pE27
Yuan YP, 2021, TELEMAT INFORM, V64, DOI 10.1016/j.tele.2021.101676
Zalora, 2020, SE AS TREND REP 2020
Zha XJ, 2020, ASLIB J INFORM MANAG, V72, P705, DOI 10.1108/AJIM-08-2019-0213
NR 86
TC 24
Z9 25
U1 14
U2 44
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD MAR
PY 2022
VL 65
AR 102851
DI 10.1016/j.jretconser.2021.102851
EA JAN 2022
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 0U8HG
UT WOS:000787887700011
DA 2024-03-27
ER
PT J
AU Das, PK
Kumar, T
AF Das, P. K.
Kumar, Talleen
TI E-commerce sellers' ratings: Is user feedback adequate?
SO INTERNATIONAL JOURNAL OF CONSUMER STUDIES
LA English
DT Article
DE artificial neural network; electronic commerce; principal component
analysis; reputation systems; seller rating; Wilcoxon signed-rank test
ID PRINCIPAL COMPONENT ANALYSIS; REPUTATION MANAGEMENT; ONLINE; REVIEWS;
SYSTEMS; MARKETPLACES; DISCLOSURE; CHALLENGES; EXPERIENCE; MODEL
AB The literature on the theory of public procurement points out two well-known informational problems arising out of information asymmetry: (i) adverse selection and (ii) moral hazard. To reduce these issues and foster credibility and trust in the procurement process while maintaining quality and efficiency in public procurement, e-procurement platforms have turned to reputation or rating systems. Therefore, the research and design of such rating systems are crucial. In this study, we discuss the theoretical underpinnings of procurement and employ the information-theoretic, regression analysis, artificial neural network and principal component analysis (PCA) approaches to estimate the weights of the variables entering the rating system. Using real data from Government e-Marketplace, a business-to-business public e-commerce portal, we empirically determine the weights of the rating variables derived from the transaction-level and user feedback data for sellers. The weights obtained from the PCA are the most applicable compared with the other three methods. We compare the old rating system with the newly proposed design using the Wilcoxon signed-rank test. This results in a statistically significant difference between the two ratings. The canonical correlation and Wilks' trial reveal that the ratings derived from transaction-level data and user feedback are uncorrelated to a great extent. Hence, considering only transaction-level data or user feedback is unlikely to divulge sellers' intrinsic worth. E-commerce platforms can use this approach to quickly implement methods to obtain rating scores on a real-time basis for sellers on online platforms.
C1 [Das, P. K.] Indian Inst Foreign Trade, 1583 Madurdaha, Chowbaga Rd, Kolkata 700107, West Bengal, India.
[Kumar, Talleen] Govt e Marketplace, Space Atom Energy & Earth Commiss, New Delhi, India.
RP Das, PK (autor correspondiente), Indian Inst Foreign Trade, 1583 Madurdaha, Chowbaga Rd, Kolkata 700107, West Bengal, India.
EM pkdas@iift.edu
CR Aggarwal C.C., 2018, NEURAL NETWORKS DEEP
AKERLOF GA, 1970, Q J ECON, V84, P488, DOI 10.2307/1879431
Albano G. L., 2013, CHARTING COURSE PUBL
Alston C. M., 2016, ADVERSE SELECTION
Anderson T.W., 2003, INTRO MULTIVARIATE S
ANDERSON TW, 1954, J AM STAT ASSOC, V49, P765, DOI 10.2307/2281537
Aulkemeier F, 2016, INF SYST E-BUS MANAG, V14, P469, DOI 10.1007/s10257-015-0291-6
Badenhorst-Weiss J., 2010, J CONTEMP ISS BUS GO, V16, P69
Banerjee S, 2017, DECIS SUPPORT SYST, V96, P17, DOI 10.1016/j.dss.2017.01.006
Banks DL, 2006, STAT SCI, V21, P234, DOI 10.1214/088342306000000204
Baranowski P, 2018, APPL ECON LETT, V25, P1226, DOI 10.1080/13504851.2017.1412070
Basheka B. C., 2021, Public procurement, corruption and the crisis of governance in Africa, P113
Basheka B. C., 2013, CHARTING COURSE PUBL, P289
BOX GEP, 1964, J ROY STAT SOC B, V26, P211, DOI 10.1111/j.2517-6161.1964.tb00553.x
Butler D, 2021, GAMES-BASEL, V12, DOI 10.3390/g12040073
Castillo A, 2018, DECIS SUPPORT SYST, V108, P69, DOI 10.1016/j.dss.2018.02.006
Chatterjee S, 2019, DECIS SUPPORT SYST, V119, P14, DOI 10.1016/j.dss.2019.02.008
Chen S. C., 2003, Information Technology & Management, V4, P303, DOI 10.1023/A:1022962631249
Chrisidu-Budnik A., 2017, WROCLAW REV LAW ADM, V7, P154, DOI DOI 10.1515/WRLAE-2015-0059
Chung C, 2018, J GLOB MARK, V31, P128
Chung-Tzer Liu, 2008, Journal of Organizational and End User Computing, V20, P74, DOI 10.4018/joeuc.2008100104
Clemons EK, 2007, J MANAGE INFORM SYST, V24, P43, DOI 10.2753/MIS0742-1222240203
Cox JC, 1996, GAME ECON BEHAV, V17, P147, DOI 10.1006/game.1996.0100
Dai CY, 2007, I C WIREL COMM NETW, P3617
Dellarocas C, 2003, MANAGE SCI, V49, P1407, DOI 10.1287/mnsc.49.10.1407.17308
Dewally M, 2006, J BUS, V79, P693, DOI 10.1086/499169
Duan H., 2012, IEEE 11 INT C TRUS
Fienberg SE, 2006, STAT SCI, V21, P143, DOI 10.1214/088342306000000240
Flanagin AJ, 2014, ELECTRON COMMER RES, V14, P1, DOI 10.1007/s10660-014-9139-2
Flynn A, 2014, J PUBLIC PROCUR, V14, P139, DOI 10.1108/JOPP-14-02-2014-B001
GeM, 2019, INTERNAL REPORT
GeM, 2023, HOME GEM STA
Ghose A, 2006, STAT SCI, V21, P131, DOI 10.1214/088342306000000187
Goes P, 2013, DECIS SUPPORT SYST, V56, P247, DOI 10.1016/j.dss.2013.06.007
Goldsmith E., 2008, J CONSUMER STUDIES H, V24, P124, DOI DOI 10.1046/J.1365-2737.2000.00150.X
Hackney R., 2003, INFORM TECHNOL MANAG, V4, P135
Hendrikx F, 2015, J PARALLEL DISTR COM, V75, P184, DOI 10.1016/j.jpdc.2014.08.004
Hoffman K, 2009, ACM COMPUT SURV, V42, DOI 10.1145/1592451.1592452
IBM Corp, 2020, IBM SPSS Statistics for Windows, DOI DOI 10.1186/S12910-020-00557-4
IEZZONI AF, 1991, HORTSCIENCE, V26, P334, DOI 10.21273/HORTSCI.26.4.334
Irmatov A., 2018, ARXIV
Irmatova E., 2017, RUSSIAN J RUSSIAN E, V18, P20
Ivanova O, 2017, DECIS SUPPORT SYST, V104, P64, DOI 10.1016/j.dss.2017.10.003
Jank W, 2006, STAT SCI, V21, P113, DOI 10.1214/088342306000000178
Jelinek R, 2021, BUS HORIZONS, V64, P61, DOI 10.1016/j.bushor.2020.10.002
Johnson R. A., 2015, APPL MULTIVARIATE ST
Johnson R.A., 2019, Statistics: principles and methods
Jung H, 2014, ADV MATER RES-SWITZ, V919-921, P1716, DOI 10.4028/www.scientific.net/AMR.919-921.1716
Kalia P, 2021, COMPUT HUM BEHAV, V115, DOI 10.1016/j.chb.2020.106608
Khoman S, 2017, PUBLIC ADM GOV GLOB, V18, P249, DOI 10.1007/978-3-319-49280-3_14
Kleisiari C, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084392
Koala K, 2018, J PUBLIC PROCUR, V18, P282, DOI 10.1108/JOPP-11-2018-017
Komakech R.A., 2016, PUBLIC POLICY ADM RE, V6, P20
Kostyk A, 2017, J CONSUM BEHAV, V16, P434, DOI 10.1002/cb.1643
Kotha S, 2004, J BUS, V77, pS109, DOI 10.1086/381639
Labib K, 2006, ANN TELECOMMUN, V61, P218, DOI 10.1007/BF03219975
Lakshmanaprabu SK, 2018, COMPLEXITY, DOI 10.1155/2018/3569351
Leonidou LC, 2018, PSYCHOL MARKET, V35, P341, DOI 10.1002/mar.21090
Li HF, 2019, MIS QUART, V43, P207, DOI 10.25300/MISQ/2019/14201
Lin HW, 2020, IEEE-CAA J AUTOMATIC, V7, P954, DOI 10.1109/JAS.2020.1003225
Liu HY, 2020, IEEE T COMPUT SOC SY, V7, P1358, DOI 10.1109/TCSS.2020.3033302
Lysons K., 2020, Procurement and Supply Chain Management
Melnik MI, 2002, J IND ECON, V50, P337
Minitab Inc., 2007, COMP SOFTW STAT COLL
Mishra Monika, 2020, [Journal of Internet Computing and Services, 인터넷정보학회논문지], V21, P33, DOI 10.7472/jksii.2020.21.6.33
Mudambi SM, 2010, MIS QUART, V34, P185
Ngai EWT, 2002, INFORM MANAGE-AMSTER, V39, P415, DOI 10.1016/S0378-7206(01)00107-0
Park DH, 2007, INT J ELECTRON COMM, V11, P125, DOI 10.2753/JEC1086-4415110405
Paul J, 2020, INT BUS REV, V29, DOI 10.1016/j.ibusrev.2020.101717
Qu Z, 2008, DECIS SUPPORT SYST, V46, P440, DOI 10.1016/j.dss.2008.08.004
Rabby F, 2016, MANAG DECIS ECON, V37, P95, DOI 10.1002/mde.2696
RDSO, 2020, INTERNAL REPORT
Sanderson J., 2015, Health Services and Delivery Research, V3, P1, DOI DOI 10.3310/HSDR03180
Shinde S., 2022, 58 E COMMERCE CONSUM
Smith G., 2015, Essential statistics, regression, and econometrics, DOI [10.1016/B978-0-12-382221-5.00010-6, DOI 10.1016/B978-0-12-382221-5.00010-6]
Snider KF, 2012, J PUBLIC AFF EDUC, V18, P327, DOI 10.1080/15236803.2012.12001687
Statista, 2021, WORLDW SHAR CONS SHO
Suchacka G, 2017, INF SYST E-BUS MANAG, V15, P751, DOI 10.1007/s10257-016-0329-4
Sun T, 2011, INT J CONSUM STUD, V35, P330, DOI 10.1111/j.1470-6431.2010.00938.x
THAI K.V., 2017, GLOBAL PUBLIC PROCUR
Thai K.V., 2005, Challenges in public procurement
Thakur S, 2019, ELECTRON COMMER RES, V19, P23, DOI 10.1007/s10660-017-9280-9
Tzeng DY, 2005, COLOR RES APPL, V30, P84, DOI 10.1002/col.20086
Vaithianathan S, 2010, ELECTRON COMMER RES, V10, P83, DOI 10.1007/s10660-010-9046-0
Varzaru AA, 2021, J THEOR APPL EL COMM, V16, P2304, DOI 10.3390/jtaer16060127
Vecchi V., 2022, PUBLIC WORKS MANAG P, V0, P1
von Helversen B, 2018, DECIS SUPPORT SYST, V113, P1, DOI 10.1016/j.dss.2018.05.006
Wang M., 2005, E SERVICE J, V4, P29
WTO, 2020, ELECT COMMERCE
Xu YZ, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11195521
Yang C, 2021, J THEOR APPL EL COMM, V16, P1598, DOI 10.3390/jtaer16050090
Yu B, 2002, COMPUT INTELL-US, V18, P535, DOI 10.1111/1467-8640.00202
Yu CM, 2019, INFORM TECHNOL MANAG, V20, P123, DOI 10.1007/s10799-018-0288-1
Zhang YJ, 2020, IEEE-CAA J AUTOMATIC, V7, P1038, DOI 10.1109/JAS.2020.1003243
NR 94
TC 0
Z9 0
U1 9
U2 19
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-6423
EI 1470-6431
J9 INT J CONSUM STUD
JI Int. J. Consum. Stud.
PD JUL
PY 2023
VL 47
IS 4
BP 1561
EP 1578
DI 10.1111/ijcs.12938
EA APR 2023
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA I1JE5
UT WOS:000967873200001
DA 2024-03-27
ER
PT J
AU Malthouse, E
Copulsky, J
AF Malthouse, Edward
Copulsky, Jonathan
TI Artificial intelligence ecosystems for marketing communications
SO INTERNATIONAL JOURNAL OF ADVERTISING
LA English
DT Article
DE machine learning algorithms; customer data; digital environments;
digital content assets; IT infrastructure
ID PITFALLS; NEED
AB The goal of this article is to help advertising scholars, students and practitioners understand and anticipate the effects of artificial intelligence (AI) and machine learning (ML) on advertising and, more generally, marketing communications (Marcom). While many discussions of AI centre on algorithms and models, we argue that to understand AI in Marcom, one must consider the broader ecosystem in which these algorithms operate. This article develops a framework that shows the Marcom-AI ecosystem and its outcomes, consisting of the following mutually reinforcing components: (1) algorithms and models, (2) customer data (3) digital environments (e.g. mobile devices, digital signage), (4) digital content assets (e.g. images, videos, copy) and (5) information technology infrastructure. We briefly sketch the uses of AI within Marcom. Most or all components of the ecosystem are usually necessary for AI to address Marcom opportunities and challenges. In conjunction with these components, the ecosystem comprises a broad set of stakeholders: consumers, influencers, brands/advertisers, media and messaging platforms, data platforms, publishers and content creators, MarTech/AdTech vendors, AI/ML service providers, device manufacturers and regulators. The combination of these components and stakeholders enables marketers to optimize touchpoints through targeting and choice architectures, create platforms for testing, derive insights from data, and support marketing processes and workflows. Building from the framework, we close by identifying future research directions for advertising scholars, including understanding consumer response to AI touchpoints, privacy, interactions between stakeholders, and how the ecosystem will evolve.
C1 [Malthouse, Edward; Copulsky, Jonathan] Northwestern Univ, Medill Spiegel Res Ctr, Integrated Mkt Commun, Evanston, IL 60208 USA.
C3 Northwestern University
RP Malthouse, E (autor correspondiente), Northwestern Univ, Medill Spiegel Res Ctr, Integrated Mkt Commun, Evanston, IL 60208 USA.
EM ecm@northwestern.edu
CR Ahn J, 2021, INT J ADVERT, V40, P1366, DOI 10.1080/02650487.2021.1982529
[Anonymous], 1988, PARALLEL DISTRIBUTED
[Anonymous], 2018, US MARKETING TECHNOL
Araujo T, 2020, J ADVERTISING, V49, P428, DOI 10.1080/00913367.2020.1795756
Araujo T, 2020, AI SOC, V35, P611, DOI 10.1007/s00146-019-00931-w
Ascarza E., 2021, HARVARD BUSINESS REV
Baek TH, 2022, INT J ADVERT, V41, P850, DOI 10.1080/02650487.2021.2011654
Ben Schafer J, 2001, DATA MIN KNOWL DISC, V5, P115, DOI 10.1023/A:1009804230409
Bird J., 2022, FORBES
Buhalis D, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102253
Creswell Julie., 2021, The New York Times
De Bruyn A, 2020, J INTERACT MARK, V51, P91, DOI 10.1016/j.intmar.2020.04.007
Edelman D. C., 2022, HARVARD BUS REV
Hastie T., 2009, ELEMENTS STAT LEARNI, V2, DOI [DOI 10.1007/978-0-387-21606-5, 10.1007/978-0-387-21606-5, 10.1007/978-0-387-84858-7]
Hayes A., 2021, BUSINESS ECOSYSTEM
Helberger N, 2020, J ADVERTISING, V49, P377, DOI 10.1080/00913367.2020.1811179
Holzer, 2021, MARKETING OPERATIONS
Huh J, 2020, J ADVERTISING, V49, P367, DOI 10.1080/00913367.2020.1795759
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
Khatibloo F., 2017, CONSUMER DATA 1 3 PA
Kietzmann J, 2021, INT J ADVERT, V40, P473, DOI 10.1080/02650487.2020.1834211
Kim J, 2022, INT J ADVERT, V41, P997, DOI 10.1080/02650487.2021.1963098
Konstan JA, 2021, AI MAG, V42, P31, DOI 10.1609/aaai.12015
Kuhl N., 2020, ARXIV
Lacerda A, 2015, INFORM RETRIEVAL J, V18, P95, DOI 10.1007/s10791-014-9249-4
Lee HK, 2020, INT J CULT POLICY, V26, P544, DOI 10.1080/10286632.2019.1577401
Lee Kai-Fu, 2018, AI Superpowers: China, Silicon Valley, and the New World Order
Lou C, 2022, INT J ADVERT, V41, P655, DOI 10.1080/02650487.2021.1951510
Luscombe NM, 2001, METHOD INFORM MED, V40, P346, DOI 10.1055/s-0038-1634431
Malthouse EC, 1999, J INTERACT MARK, V13, P10, DOI 10.1002/(SICI)1520-6653(199923)13:4<10::AID-DIR2>3.3.CO;2-V
Malthouse EC, 2018, INT J ADVERT, V37, P769, DOI 10.1080/02650487.2018.1461733
Malthouse EC, 2017, J ADVERTISING, V46, P227, DOI 10.1080/00913367.2017.1299653
Moore J.F., 1997, DEATH COMPETITION
MOORE JF, 1993, HARVARD BUS REV, V71, P75
Mumtaz F, 2018, BIOMED PHARMACOTHER, V105, P1205, DOI 10.1016/j.biopha.2018.05.086
PRAAGMAN J, 1985, EUR J OPER RES, V19, P144, DOI 10.1016/0377-2217(85)90321-2
Quinlan JR., 2014, C4 5 PROGRAMS MACHIN
Quinn T, 2018, TLS-TIMES LIT SUPPL, P31
Rice, 2012, SMU COMMENCEMENT SPE
Rusk N, 2016, NAT METHODS, V13, P35, DOI 10.1038/nmeth.3707
Schwartz EM, 2017, MARKET SCI, V36, P500, DOI 10.1287/mksc.2016.1023
Sotolongo N., 2018, APPL MARKETING ANAL, V4, P11
Strycharz J, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106750
Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
Taylor CR, 2022, INT J ADVERT, V41, P383, DOI 10.1080/02650487.2022.2058786
Taylor CR, 2020, INT J ADVERT, V39, P889, DOI 10.1080/02650487.2020.1822104
Taylor CR, 2019, INT J ADVERT, V38, P649, DOI 10.1080/02650487.2019.1618032
Wang RJH, 2015, J RETAILING, V91, P217, DOI 10.1016/j.jretai.2015.01.002
Willemsen MC, 2016, USER MODEL USER-ADAP, V26, P347, DOI 10.1007/s11257-016-9178-6
Williams L, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102215
Yuan Shuai, 2013, P 7 INT WORKSH DAT M, P1, DOI DOI 10.1145/2501040.2501980
Yun JT, 2020, J ADVERTISING, V49, P446, DOI 10.1080/00913367.2020.1795757
NR 52
TC 6
Z9 6
U1 47
U2 125
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0265-0487
EI 1759-3948
J9 INT J ADVERT
JI Int. J. Advert.
PD JAN 2
PY 2023
VL 42
IS 1
SI SI
BP 128
EP 140
DI 10.1080/02650487.2022.2122249
EA SEP 2022
PG 13
WC Business; Communication
WE Social Science Citation Index (SSCI)
SC Business & Economics; Communication
GA 8Z3EL
UT WOS:000854242400001
DA 2024-03-27
ER
PT J
AU Phillips, P
Zigan, K
Silva, MMS
Schegg, R
AF Phillips, Paul
Zigan, Krystin
Santos Silva, Maria Manuela
Schegg, Roland
TI The interactive effects of online reviews on the determinants of Swiss
hotel performance: A neural network analysis
SO TOURISM MANAGEMENT
LA English
DT Article
DE User generated content; Online reviews; Determinants of performance;
Artificial neural network; Hotels and tourism; Switzerland
ID WORD-OF-MOUTH; USER-GENERATED CONTENT; MARKET-SEGMENTATION; TOURISM
DEMAND; BEHAVIOR; HOSPITALITY; RELIABILITY; PREDICTION; SYSTEM; IMPACT
AB From a strategy perspective, the growth of social media accelerates the need for tourism organisations to constantly re-appraise their competitive strategies. This study contributes theoretically to the tourism performance literature by validating a new approach to examining the determinants of hotel performance. Drawing from and extending prior hotel determinants studies, this study uses artificial neural network model with ten input variables to investigate the relationships among user generated online reviews, hotel characteristics, and Revpar. The sample includes 235 Swiss hotels for the period 2008 -2010, with 59,688 positive reviews from 69 online sources.
The empirical findings reveal four hidden nodes that have a significant impact on RevPar. Three of these have negative impacts: room quality, positive regional review, hotel regional reputation, and regional room star rating has a positive impact. Further, the findings imply that there may be boundaries to reputational benefits for Swiss hotels. (C) 2015 Elsevier Ltd. All rights reserved.
C1 [Phillips, Paul] Univ Kent, Kent Business Sch, Canterbury CT2 7PE, Kent, England.
[Zigan, Krystin] Univ Kent, Kent Business Sch, Chatham Maritim ME4 4AG, Kent, England.
[Santos Silva, Maria Manuela] Univ Coimbra, Fac Econ, P-3004512 Coimbra, Portugal.
[Schegg, Roland] Univ Appl Sci & Arts Western Switzerland Valais, Inst Tourism, CH-3960 Sierre, Switzerland.
C3 University of Kent; University of Kent; Universidade de Coimbra
RP Phillips, P (autor correspondiente), Univ Kent, Kent Business Sch, Canterbury CT2 7PE, Kent, England.
EM P.A.Phillips@kent.ac.uk; K.Zigan@kent.ac.uk; nelinha@fe.uc.pt;
Roland.Schegg@hevs.ch
CR Anderson C. K., 2012, Cornell Hospitality Report, V12, P1
[Anonymous], 2007, J TRAVEL TOUR MARK, DOI DOI 10.1300/J073V21N04_03
[Anonymous], 2001, J MARKET MANAG
[Anonymous], 2008, Information and Communications Technology in Tourism
Ayeh JK, 2013, J TRAVEL RES, V52, P437, DOI 10.1177/0047287512475217
B e CURRY., 1993, European Journal of Marketing, V, V27, P5, DOI DOI 10.1108/03090569310040325
Bentz Y, 2000, J FORECASTING, V19, P177, DOI 10.1002/(SICI)1099-131X(200004)19:3<177::AID-FOR738>3.0.CO;2-6
Bishop C.M., 1995, NEURAL NETWORKS PATT
Blal I, 2014, CORNELL HOSP Q, V55, P365, DOI 10.1177/1938965514533419
Bloom JZ, 2005, ANN TOURISM RES, V32, P93, DOI 10.1016/j.annals.2004.05.001
Bloom JZ, 2004, TOURISM MANAGE, V25, P723, DOI 10.1016/j.tourman.2003.07.004
Boone DS, 2002, MARKET LETT, V13, P317, DOI 10.1023/A:1020321132568
Burger CJSC, 2001, TOURISM MANAGE, V22, P403, DOI 10.1016/S0261-5177(00)00068-6
Cardoso JB, 2008, ADV ENG SOFTW, V39, P505, DOI 10.1016/j.advengsoft.2007.03.015
Castro G.M., 2004, Journal of Intellectual Capital, V5, P575, DOI [10.1108/14691930410567022, DOI 10.1108/14691930410567022]
Chen HN, 2013, EUR J MARKETING, V47, P1758, DOI 10.1108/EJM-11-2011-0625
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Loureiro SMC, 2011, INT J HOSP MANAG, V30, P575, DOI 10.1016/j.ijhm.2010.10.007
Davies G, 2010, STRATEGIC MANAGE J, V31, P530, DOI 10.1002/smj.825
Deng WJ, 2009, EXPERT SYST APPL, V36, P3774, DOI 10.1016/j.eswa.2008.02.063
DeTienne KB, 2003, ORGAN RES METHODS, V6, P236, DOI 10.1177/1094428103251907
Dickinger A., 2008, Information and communication technologies in tourism 2008, P244, DOI [10.1007/978-3-211-77280-5_22, DOI 10.1007/978-3-211-77280-5_22]
Du JP, 2007, LECT NOTES COMPUT SC, V4493, P80
Duverger P, 2013, J TRAVEL RES, V52, P465, DOI 10.1177/0047287513478498
Gan C., 2005, Journal of Social Sciences, V1, P211
Ghose A., 2006, P WORKSHOP INFORM TE, P1
Gronholdt L, 2005, INT J MARKET RES, V47, P121
Haykin S., 2008, NUERAL NETWORKS LEAR
Jost A., 1993, CREDIT WORLD, V2, P26
Jun SH, 2010, J TRAVEL TOUR MARK, V27, P579, DOI 10.1080/10548408.2010.507151
Khan ZabirHaider., 2011, International Journal of Computer Applications, V22, P42
Kim J, 2003, TOURISM MANAGE, V24, P25, DOI 10.1016/S0261-5177(02)00050-X
Kim J, 2010, J HOSP MARKET MANAG, V19, P735, DOI 10.1080/19368623.2010.508005
Kuo RJ, 2001, EUR J OPER RES, V129, P496, DOI 10.1016/S0377-2217(99)00463-4
Leung D, 2013, J TRAVEL TOUR MARK, V30, P3, DOI 10.1080/10548408.2013.750919
Levy SE, 2013, CORNELL HOSP Q, V54, P49, DOI 10.1177/1938965512464513
Li SL, 2000, DECIS SUPPORT SYST, V27, P395, DOI 10.1016/S0167-9236(99)00061-5
Lopes N., 2003, Neural, Parallel & Scientific Computations, V11, P253
Lu WL, 2012, TOURISM MANAGE, V33, P702, DOI 10.1016/j.tourman.2011.08.003
Mazanec J. A., 1992, Journal of Travel & Tourism Marketing, V1, P39, DOI 10.1300/J073v01n01_04
McGuire K., 2013, PRICING SOCIAL WORLD
Mikulic J, 2012, EXPERT SYST APPL, V39, P5144, DOI 10.1016/j.eswa.2011.11.026
Mitchell VW, 1999, J BUS RES, V46, P167, DOI 10.1016/S0148-2963(98)00020-4
Moutinho L, 2008, SERV BUS, V2, P219, DOI 10.1007/s11628-008-0037-3
Moutinho Luiz., 1996, J RETAILING CONSUMER, V3, P135
Nieto J, 2014, TOURISM MANAGE, V45, P115, DOI 10.1016/j.tourman.2014.03.009
Palmer A, 2006, TOURISM MANAGE, V27, P781, DOI 10.1016/j.tourman.2005.05.006
Phillips P., 1999, Management Decision, V37, P279, DOI 10.1108/00251749910264514
Phillips P.A., 2002, Journal of Market-Focused Management, V5, P219
Porter ME, 1998, HARVARD BUS REV, V76, P77
Robson K, 2013, INT J MARKET RES, V55, P521, DOI 10.2501/IJMR-2013-046
Romero I, 2011, TOURISM MANAGE, V32, P297, DOI 10.1016/j.tourman.2010.02.006
Roos G., 2005, Managing Intellectual Capital in Practice
Sainaghi R, 2011, INT J CONTEMP HOSP M, V23, P297, DOI 10.1108/09596111111122497
Saldamli A., 2008, Istambul Ticaret Universitesi Sosyal Bilimler Dergisi, V7, P351
Scaglione M, 2009, TECHNOVATION, V29, P625, DOI 10.1016/j.technovation.2009.05.011
Schegg R., 2013, INFORM COMMUNICATION, P801
Cantallops AS, 2014, INT J HOSP MANAG, V36, P41, DOI 10.1016/j.ijhm.2013.08.007
Silva M, 2009, EUR J MARKETING, V43, P421, DOI 10.1108/03090560910935505
Sparks BA, 2011, TOURISM MANAGE, V32, P1310, DOI 10.1016/j.tourman.2010.12.011
Sund KJ, 2006, TOUR PLAN DEV, V3, P1, DOI 10.1080/14790530600638788
Swingler K., 1996, APPL NEURAL NETWORKS
Swiss Tourism Federation, 2010, SWISS TOUR FIG 2010
Swiss Tourism Federation, 2011, SWISS TOUR FIG 2011
Swiss Tourism Federation, 2013, SWISS TOUR FIG 2012
Thieme RJ, 2000, J MARKETING RES, V37, P499, DOI 10.1509/jmkr.37.4.499.18790
Toh RS, 2011, CORNELL HOSP Q, V52, P181, DOI 10.1177/1938965511400409
TripAdvisor, 2014, FACT SHEET
TripAdvisor, 2014, TRIPADVISOR NOW OFF
Tsaur S., 2002, TOURISM MANAGE, V25, P367
Tuominen P., 2011, 19 ANN FRONT SERV C, P1
Vellido A, 2000, INT J ELECTRON COMM, V4, P83, DOI 10.1080/10864415.2000.11518380
Venugopal V., 1994, MARKETING INTELLIGEN, V12, P30, DOI DOI 10.1108/02634509410065555
Vila M, 2010, TOURISM MANAGE, V31, P232, DOI 10.1016/j.tourman.2009.02.015
Wallis Kanton, 2013, ENTWICKLUNGSSTRATEGI
West PM, 1997, MARKET SCI, V16, P370, DOI 10.1287/mksc.16.4.370
Yacouel N, 2012, J TRAVEL RES, V51, P219, DOI 10.1177/0047287511400611
Ye QA, 2011, COMPUT HUM BEHAV, V27, P634, DOI 10.1016/j.chb.2010.04.014
Ye Q, 2009, INT J HOSP MANAG, V28, P180, DOI 10.1016/j.ijhm.2008.06.011
NR 79
TC 135
Z9 145
U1 7
U2 224
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0261-5177
EI 1879-3193
J9 TOURISM MANAGE
JI Tourism Manage.
PD OCT
PY 2015
VL 50
BP 130
EP 141
DI 10.1016/j.tourman.2015.01.028
PG 12
WC Environmental Studies; Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Social Sciences - Other Topics;
Business & Economics
GA CJ8QV
UT WOS:000355769500020
DA 2024-03-27
ER
PT J
AU Capatina, A
Kachour, M
Lichy, J
Micu, A
Micu, AE
Codignola, F
AF Capatina, Alexandru
Kachour, Maher
Lichy, Jessica
Micu, Adrian
Micu, Angela-Eliza
Codignola, Federica
TI Matching the future capabilities of an artificial intelligence-based
software for social media marketing with potential users' expectations
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Artificial intelligence; Machine learning; Social media marketing;
Audience analysis; Image analysis; Sentiment analysis
ID REGRESSION-MODELS; STRATEGIES; INNOVATION
AB The increasing use of Artificial Intelligence (AI) in Social Media Marketing (SMM) triggered the need for this research to identify and further analyze such expectations of potential users of an AI-based software for Social Media Marketing; a software that will be developed in the next two years, based on its future capabilities.
In this research, we seek to discover how the potential users of this AI-based software (owners and employees from digital agencies based in France, Italy and Romania, as well as freelancers from these countries, with expertise in SMM) perceive the capabilities that we offer, as a way to differentiate our technological solution from other available in the market.
We propose a causal model to find out which expected capabilities of the future AI-based software can explain potential users' intention to test and use this innovative technological solution for SMM, based on integer valued regression models. With this purpose, R software is used to analyze the data provided by the respondents. We identify different causal configurations of upcoming capabilities of the AI-based software, classified in three categories (audience, image and sentiment analysis), and will trigger potential users' intention to test and use the software, based on an fsQCA approach.
C1 [Capatina, Alexandru; Micu, Adrian] Dunarea de Jos Univ Galati, Business Adm Dept, Galati, Romania.
[Kachour, Maher] ESSCA Sch Management, Angers, France.
[Lichy, Jessica] IDRAC Business Sch, Lyon, France.
[Micu, Angela-Eliza] Ovidius Univ Constanta, Constanta, Romania.
[Codignola, Federica] Univ Milano Bicocca, Milan, Italy.
C3 Dunarea De Jos University Galati; ESSCA School of Management; Ovidius
University; University of Milano-Bicocca
RP Capatina, A (autor correspondiente), Dunarea de Jos Univ Galati, Business Adm Dept, Galati, Romania.
EM acapatana@ugal.ro; maher.kachour@essca.fr; jessica.lichy1@idraclyon.com;
amicu@ugal.ro; federica.codignola@unimib.it
RI Micu, Angela Eliza/B-7129-2018; Micu, Adrian/V-7868-2017; sami,
nusrat/AAK-9622-2020; Micu, Adrian/AAJ-9641-2020; Capatina,
Alexandru/A-7596-2018
OI Lichy, Jessica/0000-0002-7091-9448; Micu, Adrian/0000-0003-3161-5748;
Capatina, Alexandru/0000-0002-5439-838X; Micu, Angela
Eliza/0000-0001-5254-0015
FU project FutureWeb by Romanian Ministry of Research and Innovation, CCCDI
- UEFISCDI within PNCDI III
[PN-III-P1-1.2-PCCDI-2017-0800/86PCCDI/2018]; project "Excellence,
performance and competitiveness in the Research, Development and
Innovation activities at "Dunarea de Jos" University of Galati", acronym
"EXPERT" - Romanian Ministry of Research and Innovation
[14PFE/17.10.2018]
FX This research was conducted within the framework of the project
FutureWeb, launched by Romanian Ministry of Research and Innovation,
CCCDI - UEFISCDI, project number
PN-III-P1-1.2-PCCDI-2017-0800/86PCCDI/2018, within PNCDI III. This work
was supported by the project "Excellence, performance and
competitiveness in the Research, Development and Innovation activities
at "Dunarea de Jos" University of Galati", acronym "EXPERT", financed by
the Romanian Ministry of Research and Innovation in the framework of
Programme 1 - Development of the national research and development
system, Sub-programme 1.2 - Institutional Performance - Projects for
financing excellence in Research, Development and Innovation, Contract
no. 14PFE/17.10.2018. Romanian authors are grateful for the
collaboration with the colleagues from France and Italy, which led to a
cross-country approach of the results.
CR [Anonymous], 2010, 4 INT AAAI C WEBL SO
[Anonymous], 28 AAAI C ART INT
[Anonymous], 2017, P 50 HAW INT C SYST
Ashley C, 2015, PSYCHOL MARKET, V32, P15, DOI 10.1002/mar.20761
Barbour R, 2008, DOING FOCUS GROUPS
Bengio Y, 2009, FOUND TRENDS MACH LE, V2, P1, DOI 10.1561/2200000006
Bhimani H, 2019, TECHNOL FORECAST SOC, V144, P251, DOI 10.1016/j.techfore.2018.10.007
Borth D, 2013, P 21 ACM INT C MULT, P223, DOI [10.1145/2502081.2502282, DOI 10.1145/2502081.2502282]
Brannen J., 2005, Qualitative Research, V5, P523, DOI [10.1177/1468794105059508, DOI 10.1177/1468794105059508]
Cambria E, 2012, MULTIMED TOOLS APPL, V59, P557, DOI 10.1007/s11042-011-0815-0
Cao DL, 2016, MULTIMEDIA SYST, V22, P479, DOI 10.1007/s00530-014-0407-8
Chen SC, 2019, TECHNOL FORECAST SOC, V140, P22, DOI 10.1016/j.techfore.2018.11.025
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Ding Y, 2013, 2013 INTERNATIONAL CONFERENCE ON SENSOR NETWORK SECURITY TECHNOLOGY AND PRIVACY COMMUNICATION SYSTEM (SNS & PCS), P13
Dutang C., 2017, SOME EXPLANATIONS IW
Engel R.J. Schutt., 2012, PRACTICE RES SOCIAL
Farquhar J, 2006, BUS PROCESS MANAG J, V12, P162, DOI 10.1108/14637150610657512
Fiss PC, 2007, ACAD MANAGE REV, V32, P1180, DOI 10.5465/AMR.2007.26586092
Garimella VRK, 2016, 34TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2016, P5543, DOI 10.1145/2858036.2858234
Guo-Fitoussi L., 2019, INT J INTELLECTUAL P, V9, P136, DOI DOI 10.1504/IJIPM.2019.100213
Habernal I., 2013, P 4 WORKSH COMP APPR, P65
He W, 2013, INT J INFORM MANAGE, V33, P464, DOI 10.1016/j.ijinfomgt.2013.01.001
Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
Holsapple CW, 2018, DECIS SUPPORT SYST, V110, P32, DOI 10.1016/j.dss.2018.03.004
Iqbal R, 2020, TECHNOL FORECAST SOC, V153, DOI 10.1016/j.techfore.2018.03.024
Jia J., 2012, Proc. ACM Multimedia, P857
Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
Karpathy A, 2015, PROC CVPR IEEE, P3128, DOI 10.1109/CVPR.2015.7298932
Kawano Y, 2015, MULTIMED TOOLS APPL, V74, P5263, DOI 10.1007/s11042-014-2000-8
Khedhaouria A, 2017, TECHNOL FORECAST SOC, V120, P48, DOI 10.1016/j.techfore.2017.04.005
Kosinski M, 2013, P NATL ACAD SCI USA, V110, P5802, DOI 10.1073/pnas.1218772110
Kouloumpis E., 2011, P INT AAAI C WEB SOC
Kurnia Parama Fadli, 2018, Procedia Computer Science, V135, P5, DOI 10.1016/j.procs.2018.08.144
Lassen N.B., 2017, The SAGE Handbook of Social Media Research Methods, P328
Lee I, 2018, BUS HORIZONS, V61, P199, DOI 10.1016/j.bushor.2017.11.002
Liu Jingjing, 2007, P 2007 JOINT C EMPIR, P334
Lopez R, 2012, BUILDING AMERICAN PUBLIC HEALTH: URBAN PLANNING, ARCHITECTURE, AND THE QUEST FOR BETTER HEALTH IN THE UNITED STATES, P81
MCCULLAGH P, 1980, J ROY STAT SOC B MET, V42, P109
Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
Mostafa MM, 2013, EXPERT SYST APPL, V40, P4241, DOI 10.1016/j.eswa.2013.01.019
Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79, DOI 10.3115/1118693.1118704
Pappas IO, 2016, J BUS RES, V69, P794, DOI 10.1016/j.jbusres.2015.07.010
Paul J, 2018, EUR J INT MANAG, V12, P722, DOI 10.1504/EJIM.2018.094466
Phillips F, 2016, TECHNOL FORECAST SOC, V105, P158, DOI 10.1016/j.techfore.2016.01.007
Ragin C. C., 2009, Redesigning social inquiry: Fuzzy sets and beyond, DOI DOI 10.7208/CHICAGO/9780226702797.001.0001
Soriano DR, 2010, J BUS RES, V63, P463, DOI 10.1016/j.jbusres.2009.04.003
Sterne J., 2017, Artificial intelligence for marketing: practical applications
Suwajanakorn S, 2017, ACM T GRAPHIC, V36, DOI 10.1145/3072959.3073640
Trkman M, 2018, ECON RES-EKON ISTRAZ, V31, P1091, DOI 10.1080/1331677X.2018.1456355
Tsimonis G, 2014, MARK INTELL PLAN, V32, P328, DOI 10.1108/MIP-04-2013-0056
Vázquez S, 2014, NEURAL NETWORKS, V58, P68, DOI 10.1016/j.neunet.2014.05.026
Wang YL, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), P1584, DOI 10.1109/ICDMW.2015.142
Wilson T, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P761
WINSHIP C, 1984, AM SOCIOL REV, V49, P512, DOI 10.2307/2095465
Woodside AG, 2014, J BUS RES, V67, P2495, DOI 10.1016/j.jbusres.2014.07.006
Wu R., 2015, Deep image: Scaling up image recognition
You QZ, 2016, PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), P13, DOI 10.1145/2835776.2835779
You QZ, 2015, AAAI CONF ARTIF INTE, P381
Zikmund W. G., 2003, Business Research Methods, V7th
NR 60
TC 44
Z9 46
U1 20
U2 107
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD FEB
PY 2020
VL 151
AR 119794
DI 10.1016/j.techfore.2019.119794
PG 11
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA KG3CA
UT WOS:000509818900053
DA 2024-03-27
ER
PT J
AU Pandey, P
Rai, AK
AF Pandey, Palima
Rai, Alok Kumar
TI Consumer Adoption of AI-powered Virtual Assistants (AIVA): An Integrated
Model Based on the SEM-ANN Approach
SO FIIB BUSINESS REVIEW
LA English
DT Article; Early Access
DE Artificial Intelligence; Artificial Neural Network; Consumer Adoption;
Structural Equation Modelling; Uncanny Valley; Virtual Assistants
ID ARTIFICIAL-INTELLIGENCE; CUSTOMER LOYALTY; VOICE ASSISTANTS; USER
ACCEPTANCE; TECHNOLOGY; REGRESSION; BEHAVIOR; WARMTH; TRUST; USAGE
AB Artificial intelligence (AI) has lured consumers to orchestrate their routine activities relying on such technologies. Though AI-powered virtual assistants (AIVAs) have gained traction among service providers, these are still lagging on the demand front. This study intends to develop an 'AIVA adoption model' delineated under a holistic framework based on structural equation modelling and deep neural network incorporating multilayer perceptron algorithm. The sensitivity analysis designated 'effort expectancy' as the most dominant antecedent of AIVA adoption, followed by 'perceived innovativeness'. While 'perceived risk' held high relevance, the tech users were equally concerned about the performance of AIVA in conjunction with its anthropomorphic response; however, they gave the least consideration to subjective norms. The parallel mediation analysis revealed that the adopters preferred transactional relationships with AIVA more than the communal one, while the simultaneous application of both the perspectives better generates loyal customers. The moderation analysis unveiled that the uncanny valley paradigm could not always be supportive, especially in the context of AIVA. The developed model may serve the basis to generate as well as sustain adoption and loyalty of the specified technology.
C1 [Pandey, Palima] Banaras Hindu Univ, RGSC, Inst Management Studies, Dept MBA Agribusiness, Varanasi, Uttar Pradesh, India.
[Rai, Alok Kumar] Banaras Hindu Univ, Inst Management Studies, Dept Management, Varanasi, Uttar Pradesh, India.
[Rai, Alok Kumar] Univ Lucknow, Lucknow, Uttar Pradesh, India.
[Pandey, Palima] Banaras Hindu Univ, RGSC, Inst Management Studies, Varanasi 221005, Uttar Pradesh, India.
C3 Banaras Hindu University (BHU); Banaras Hindu University (BHU); Lucknow
University; Banaras Hindu University (BHU)
RP Pandey, P (autor correspondiente), Banaras Hindu Univ, RGSC, Inst Management Studies, Varanasi 221005, Uttar Pradesh, India.
EM palimapandey@gmail.com
RI Pandey, Palima/KEH-4932-2024
CR Acosta L. H., 2021, Pervasive and Mobile Computing, V80, P1
Agarwal A, 2023, FORESIGHT, V25, P67, DOI 10.1108/FS-10-2021-0199
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Al-Emran M, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3237789
Anantasiska V., 2022, The International Journal of Social Sciences World, V4, P101
[Anonymous], 2021, McKinsey Report
[Anonymous], 2023, McKinsey Report
Aruldoss A, 2023, J MARKET THEORY PRAC, DOI 10.1080/10696679.2023.2221442
Ashrafi DM, 2023, INT J INNOV TECHNOL, V20, DOI 10.1142/S0219877023500049
Bauer R. A., 1960, CONSUMER BEHAV RISK
Bawack R., 2022, In HICSS: Proceedings of the 55th Hawaii International Conference on System Sciences, P4306
Bedi K., 2022, Adoption and implementation of AI in customer relationship management, P26
Belanche D, 2021, PSYCHOL MARKET, V38, P2357, DOI 10.1002/mar.21532
BENTLER PM, 1990, PSYCHOL BULL, V107, P238, DOI 10.1037/0033-2909.107.2.238
Bhadauria VS, 2022, INF COMPUT SECUR, V30, P730, DOI 10.1108/ICS-07-2021-0096
Bhonsle Vishnu Sai, 2022, Communication and Control for Robotic Systems. Smart Innovation, Systems and Technologies (229), P177, DOI 10.1007/978-981-16-1777-5_11
Bilgihan A, 2016, COMPUT HUM BEHAV, V61, P103, DOI 10.1016/j.chb.2016.03.014
Bolton T, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21072312
Burbach L, 2019, IEEE INT PROF COMMUN, P101, DOI 10.1109/ProComm.2019.00025
Cao C., 2022, Frontiers in Psychology, V13, P1
Carmines E.G., 1988, RELIABILITY VALIDITY
Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
Chi OH, 2020, J HOSP MARKET MANAG, V29, P757, DOI 10.1080/19368623.2020.1721394
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Choudhury A, 2023, J MED INTERNET RES, V25, DOI 10.2196/47184
Chow CSK, 2023, J ELECTRON COMMER RE, V24, P84
Chu L., 2019, Master's thesis, University of Twente, P1
Chung D, 2022, INT J INNOV TECHNOL, V19, DOI 10.1142/S0219877022500250
Cochran W.G., 1963, Sampling Technique, V2nd
Collins C, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102383
Coskun-Setirek Abide, 2017, International Journal of E-Services and Mobile Applications, V9, P1, DOI 10.4018/IJESMA.2017070101
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Sebastián MGD, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.993935
Fang SJ, 2023, CORNELL HOSP Q, V64, P246, DOI 10.1177/19389655221102383
Fernandes S., 2015, INDIAN J SCI TECHNOL, V8, P95
Fernandes S., 2019, Journal of management research, V19, P131, DOI DOI 10.17485/IJST/2015/V8IS6/64720
Fernandes S. F., 2018, Journal of Commerce and Management Thought, V9, P273
Fernandes T, 2021, J BUS RES, V122, P180, DOI 10.1016/j.jbusres.2020.08.058
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
Fiske ST, 2002, J PERS SOC PSYCHOL, V82, P878, DOI 10.1037//0022-3514.82.6.878
Fiske ST, 2007, TRENDS COGN SCI, V11, P77, DOI 10.1016/j.tics.2006.11.005
Frank DA, 2023, PSYCHOL MARKET, V40, P554, DOI 10.1002/mar.21740
Gopinath K, 2023, INT J CONSUM STUD, V47, P2367, DOI 10.1111/ijcs.12933
Gunasinghe A, 2020, INT J TECHNOL ENHANC, V12, P458, DOI 10.1504/IJTEL.2020.110056
Hair J.F., 2009, Multivariate data analysis
Han S, 2018, IND MANAGE DATA SYST, V118, P618, DOI 10.1108/IMDS-05-2017-0214
Hasan R, 2021, J BUS RES, V131, P591, DOI 10.1016/j.jbusres.2020.12.012
Hu K, 2023, ARTIF INTELL REV, V56, P1833, DOI 10.1007/s10462-022-10210-8
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Hu Q, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102250
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huh J, 2023, J RES INTERACT MARK, V17, P794, DOI 10.1108/JRIM-10-2022-0328
Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
Hurley AE, 1997, J ORGAN BEHAV, V18, P667, DOI 10.1002/(SICI)1099-1379(199711)18:6<667::AID-JOB874>3.0.CO;2-T
Jang H., 2022, The impact of the humanness of AI influencers on the success of influencer marketing, DOI [10.26153/tsw/42156, DOI 10.26153/TSW/42156]
Jenneboer L, 2022, J THEOR APPL EL COMM, V17, P212, DOI 10.3390/jtaer17010011
Johnson N, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15129224
Kaaria A., 2017, Master Thesis
Kallel A, 2023, J STRATEG MARK, DOI 10.1080/0965254X.2023.2175020
Kessler S. K., 2017, Master Thesis, P1
Kim SY, 2019, MARKET LETT, V30, P1, DOI 10.1007/s11002-019-09485-9
Kontogiorgos D, 2019, PROCEEDINGS OF THE 19TH ACM INTERNATIONAL CONFERENCE ON INTELLIGENT VIRTUAL AGENTS (IVA' 19), P133, DOI 10.1145/3308532.3329466
Lavuri R, 2023, INT J EMERG MARK, V18, P958, DOI 10.1108/IJOEM-12-2020-1530
Lee KW, 2023, J RETAIL CONSUM SERV, V74, DOI 10.1016/j.jretconser.2023.103447
Liu X, 2022, ANN TOURISM RES, V92, DOI 10.1016/j.annals.2021.103324
Lowe B, 2019, EUR J MARKETING, V53, P1038, DOI 10.1108/EJM-06-2019-966
MacKenzie SB, 2001, J CONSUM RES, V28, P159, DOI 10.1086/321954
Malhan A., 2021, The Economic Times
Malodia S, 2023, J SERV MARK, V37, P25, DOI 10.1108/JSM-12-2021-0488
Mariani MM, 2023, J BUS RES, V161, DOI 10.1016/j.jbusres.2023.113838
Maroufkhani P, 2022, TECHNOL FORECAST SOC, V183, DOI 10.1016/j.techfore.2022.121870
Mokhtar S. A., 2017, Science International Lahore, V29, P829
Molinillo S, 2023, SERV BUS, V17, P167, DOI 10.1007/s11628-022-00509-5
Mori M., 1970, IEEE Spectrum
Moriuchi E, 2023, PSYCHOL MARKET, V40, P169, DOI 10.1002/mar.21753
Moussawi S, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P115
Moussawi S, 2021, ELECTRON MARK, V31, P343, DOI 10.1007/s12525-020-00411-w
Mukherjee S, 2022, FIIB BUS REV, V11, P438, DOI 10.1177/23197145211059243
Musil CM, 1998, RES NURS HEALTH, V21, P271
Muthukumaran A., 2020, Indian Journal of Science and Technology, V13, P4407, DOI DOI 10.17485/IJST/v13i43.1911
Nasirian F., 2017, Twenty-third Americas Conference on Information Systems, P1
Nunkoo R, 2012, CURR ISSUES TOUR, V15, P777, DOI 10.1080/13683500.2011.641947
Pal D, 2020, IEEE INTERNET THINGS, V7, P10852, DOI 10.1109/JIOT.2020.2991791
Pan Y., 2023, AMCIS 2023 Proceedings, V4, P1178
Pandey P., 2020, Purushartha, V13, P30
Pentina I, 2023, COMPUT HUM BEHAV, V140, DOI 10.1016/j.chb.2022.107600
Phaosathianphan N, 2019, INT J INNOV TECHNOL, V16, DOI 10.1142/S0219877019500603
Prasad B, 2022, FIIB BUS REV, V11, P405, DOI 10.1177/23197145211046126
PwC's Report, 2023, AI-sizing the price
Qadiri R. M., 2020, International Journal of Finance, Insurance Risk Management, V10, P44
RAM S, 1987, ADV CONSUM RES, V14, P208
Rana S., 2023, Journal of Promotion Management, P1
Rogers E. M., 1962, Diffusion of Innovations
Saxena Charu, 2023, 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET), P1, DOI 10.1109/GlobConET56651.2023.10150155
Shao Z., 2021, Working Paper
Singh S, 2021, J HOSP TOUR MANAG, V49, P528, DOI 10.1016/j.jhtm.2021.10.014
Sohn K, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101324
Song Y. W., 2019, Doctoral dissertation
Soofastaei A., 2021, Virtual assistants
Sun CC, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app112210618
Sung B, 2023, INT J CONSUM STUD, V47, P1772, DOI 10.1111/ijcs.12946
Sweeney JC, 2001, J RETAILING, V77, P203, DOI 10.1016/S0022-4359(01)00041-0
Tan S, 2019, STUD HEALTH TECHNOL, V264, P1787, DOI [10.3233/SHT1190648, 10.3233/SHTI190648]
Traiyatha P., 2023, Jurnal Nasional Pendidikan Teknik Informatika: Janapati, V12, P119, DOI [10.23887/janapati.v12i1.53524, DOI 10.23887/JANAPATI.V12I1.53524]
Tran HTT, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/12659
Tsai W. H. S., 2023, The Palgrave handbook of interactive marketing, P255
Tschopp M, 2023, CYBERPSYCHOLOGY, V17, DOI 10.5817/CP2023-3-9
Tuzovic S., 2018, SERVSIG Conference Proceedings 2018 Paris: Opportunities for services in a challenging world, P107
van Bussel MJP, 2022, BMC HEALTH SERV RES, V22, DOI 10.1186/s12913-022-08189-7
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Wagner K, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P1386
Weiz D, 2016, PROGR IS, P1, DOI 10.1007/978-3-319-30376-5_1
White KM, 2009, BRIT J SOC PSYCHOL, V48, P135, DOI 10.1348/014466608X295207
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Xue JL, 2023, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2227834
Yadav H., 2021, International Journal of Computer Science and Wireless, Security (IJCSWS), V8, P39
Yilmaz MB, 2022, J ENG DES TECHNOL, V20, P1738, DOI 10.1108/JEDT-02-2021-0084
Yu XY, 2023, INFORM TECHNOL PEOPL, V36, P454, DOI 10.1108/ITP-04-2021-0254
Zhang SL, 2023, IND MANAGE DATA SYST, V123, P578, DOI 10.1108/IMDS-11-2021-0662
Zhang SY, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.728495
Zhang YT, 2023, APPL ENERG, V333, DOI 10.1016/j.apenergy.2022.120600
Zhao Y. G., 2023, P 56 HAWAII INT C SY
NR 122
TC 2
Z9 2
U1 0
U2 0
PU SAGE PUBLICATIONS INDIA PVT LTD
PI NEW DELHI
PA B-1-I-1 MOHAN CO-OPERATIVE INDUSTRIAL AREA, MATHURA RD, POST BAG NO 7,
NEW DELHI 110 044, INDIA
SN 2319-7145
EI 2455-2658
J9 FIIB BUS REV
JI FIIB Bus. Rev.
PD 2023 OCT 11
PY 2023
DI 10.1177/23197145231196066
EA OCT 2023
PG 19
WC Business; Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA HP7E6
UT WOS:001160767200001
DA 2024-03-27
ER
PT J
AU Ellickson, PB
Kar, W
Reeder, JC
AF Ellickson, Paul B.
Kar, Wreetabrata
Reeder, James C.
TI Estimating Marketing Component Effects: Double Machine Learning from
Targeted Digital Promotions
SO MARKETING SCIENCE
LA English
DT Article
DE digital marketing; causal machine learning; targeted digital promotions;
robust inference; advertising
ID SALES PROMOTIONS; CAUSAL; PERCEPTIONS; INFERENCE
AB We estimate the causal effects of different targeted email promotions on the opening and purchase decisions of the consumers who receive them. To do so, we synthesize and extend recent advances in causal machine learning techniques to capture heterogeneity in the content of the email subject line itself as well as heterogeneous consumer responses to the promotional offers and semantic choices contained therein. We find that content and framing are important for driving performance. We identify precise causal estimates of the effects of individual deal components, personalized content, and various semantic choices on consumer outcomes all the way down the conversion funnel. The decompositional nature of our methodology allows us to show how different combinations of key words and promotional inducements produce significantly different outcomes, both within a given stage and across all stages of the funnel. Notably, discounts framed as clearance events sharply outperform those tied to particular products. We also find components that drive engagement at the top of the funnel don't always lead to conversion at the bottom: their efficacy, across the funnel, is significantly moderated by the engagement levels of the consumers who receive them. Finally, leveraging both aspects of heterogeneity, we use off-policy evaluation to demonstrate the potential for significant gains from improved targeting.
C1 [Ellickson, Paul B.] Univ Rochester, Simon Sch Business, Rochester, NY 14627 USA.
[Kar, Wreetabrata; Reeder, James C.] Purdue Univ, Krannert Sch Management, W Lafayette, IN 47907 USA.
C3 University of Rochester; Purdue University System; Purdue University
RP Ellickson, PB (autor correspondiente), Univ Rochester, Simon Sch Business, Rochester, NY 14627 USA.
EM paul.ellickson@simon.rochester.edu; wkar@purdue.edu; jreederi@purdue.edu
OI Ellickson, Paul/0000-0003-4020-5716
CR [Anonymous], 1935, Supplement to the Journal of the Royal Statistical Society, DOI DOI 10.2307/2983637
Ansari A, 2003, J MARKETING RES, V40, P131, DOI 10.1509/jmkr.40.2.131.19224
Ascarza E, 2018, J MARKETING RES, V55, P80, DOI 10.1509/jmr.16.0163
Athey S., 2019, Estimating Treatment Effects with Causal Forests: An Application, V5, P37
Athey S, 2019, ANNU REV ECON, V11, P685, DOI 10.1146/annurev-economics-080217-053433
Athey S, 2016, P NATL ACAD SCI USA, V113, P7353, DOI 10.1073/pnas.1510489113
Bembom O, 2007, STAT APPL GENET MOL, V6
Bonfrer A, 2009, MARKET SCI, V28, P251, DOI 10.1287/mksc.1080.0393
BOX GEP, 1951, J R STAT SOC B, V13, P1, DOI 10.1111/j.2517-6161.1951.tb00067.x
Chatterjee P, 2010, J APPL BUS RES, V26
Chernozhukov V, 2017, GEN MACHINE LEARNING
Chernozhukov V, 2018, ECONOMET J, V21, pC1, DOI 10.1111/ectj.12097
Clarke RT, 2007, HYDROL EARTH SYST SC, V11, P408, DOI 10.5194/hess-11-408-2007
Cook TD, 2002, EXPT QUASIEXPERIMENT
Cox D.R., 1958, PLANNING EXPT
Crump RK, 2009, BIOMETRIKA, V96, P187, DOI 10.1093/biomet/asn055
DIAMOND WD, 1990, ADV CONSUM RES, V17, P494
Dudik M., 2011, ARXIV11034601
Dudík M, 2014, STAT SCI, V29, P485, DOI 10.1214/14-STS500
Garrett Johnson, 2017, SSRN, DOI [10.2139/ssrn.2701578, DOI 10.2139/SSRN.2701578]
Gopalakrishnan A, 2021, MARKET SCI, V40, P48, DOI 10.1287/mksc.2020.1232
Gordon B. R., 2022, PREPRINT
Gordon BR, 2019, MARKET SCI, V38, P193, DOI 10.1287/mksc.2018.1135
Grimmer J, 2017, POLIT ANAL, V25, P413, DOI 10.1017/pan.2017.15
Heckman J., 2007, HDB ECONOMETRICS, P4779, DOI DOI 10.1016/S1573-4412(07)06070-9
Hernán MA, 2006, J EPIDEMIOL COMMUN H, V60, P578, DOI 10.1136/jech.2004.029496
Hernan MA., 2020, Causal inference: what if
Hitsch GJ, 2018, PREPRINT, DOI [10.2139/ssrn.3111957, DOI 10.2139/SSRN.3111957]
HOLLAND PW, 1986, J AM STAT ASSOC, V81, P945, DOI 10.2307/2289064
Imai K, 2013, ANN APPL STAT, V7, P443, DOI 10.1214/12-AOAS593
Imai K, 2011, POLIT ANAL, V19, P1, DOI 10.1093/pan/mpq035
Imbens GW, 2015, CAUSAL INFERENCE FOR STATISTICS, SOCIAL, AND BIOMEDICAL SCIENCES: AN INTRODUCTION, P1, DOI 10.1017/CBO9781139025751
Jacob D, 2019, PREPRINT
Johnson G, 2020, PREPRINT, DOI [10.2139/ssrn.3581396, DOI 10.2139/SSRN.3581396]
KALWANI MU, 1990, J MARKETING RES, V27, P251, DOI 10.2307/3172584
Kodinariya T.M., 2013, International Journal, V1, DOI DOI 10.18576/AMIS/100428
KRISHNA A, 1991, J MARKETING, V55, P4, DOI 10.2307/1252233
Kumar V, 2014, J MARKETING RES, V51, P403, DOI 10.1509/jmr.13.0169
Lee BK, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018174
Lewis RA, 2015, Q J ECON, V130, P1941, DOI 10.1093/qje/qjv023
Liaukonyte J, 2015, MARKET SCI, V34, P311, DOI 10.1287/mksc.2014.0899
McCaffrey DF, 2013, STAT MED, V32, P3388, DOI 10.1002/sim.5753
Park CH, 2018, INT J RES MARK, V35, P453, DOI 10.1016/j.ijresmar.2018.05.001
QUANDT RE, 1958, J AM STAT ASSOC, V53, P873, DOI 10.2307/2281957
ROBINS JM, 1995, J AM STAT ASSOC, V90, P122, DOI 10.2307/2291135
Rose S, 2011, SPRINGER SER STAT, P3, DOI 10.1007/978-1-4419-9782-1
RUBIN DB, 1978, ANN STAT, V6, P34, DOI 10.1214/aos/1176344064
RUBIN DB, 1980, J AM STAT ASSOC, V75, P591, DOI 10.2307/2287653
Sahni NS, 2018, MARKET SCI, V37, P236, DOI 10.1287/mksc.2017.1066
Sahni NS, 2017, MANAGE SCI, V63, P2688, DOI 10.1287/mnsc.2016.2450
Semenova V, 2021, ECONOMET J, V24, P264, DOI 10.1093/ectj/utaa027
Sinha I, 2000, PSYCHOL MARKET, V17, P257, DOI 10.1002/(SICI)1520-6793(200003)17:3<257::AID-MAR4>3.3.CO;2-G
WASSERTHEIL S, 1970, BIOMETRICS, V26, P588, DOI 10.2307/2529115
Yi Y, 2011, PSYCHOL MARKET, V28, P879, DOI 10.1002/mar.20416
Yoganarasimhan H, 2020, DESIGN EVALUATION PE
Zeelenberg M, 2005, PSYCHOL MARKET, V22, P611, DOI 10.1002/mar.20076
Zhang X, 2017, J MARKETING RES, V54, P851, DOI 10.1509/jmr.16.0210
NR 57
TC 2
Z9 2
U1 28
U2 63
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD JUL-AUG
PY 2023
VL 42
IS 4
DI 10.1287/mksc.2022.1401
EA SEP 2022
PG 26
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA CC3H1
UT WOS:000864731700001
DA 2024-03-27
ER
PT J
AU Xu, XY
Jia, QD
Tayyab, SMU
AF Xu, Xiao-Yu
Jia, Qing-Dan
Tayyab, Syed Muhammad Usman
TI Exploring the stimulating role of augmented reality features in
E-commerce: A three-staged hybrid approach
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Augmented eality (AR) retailing; Stimuli-organism-response (S-O-R);
Structural equation modeling (SEM); Artificial-neural-network (ANN);
Mixed-method
ID MIXED-METHODS RESEARCH; PURCHASE INTENTION; METHOD VARIANCE;
INFORMATION; ANTECEDENTS; GUIDELINES; VARIABLES; ADOPTION; MODEL; TRUST
AB The application of Augmented Reality (AR) in business applications has seen colossal growth in recent years with even healthier future growth expectations. To advance the understanding of the paramount role played by AR features in shaping consumers' perceptions and behaviors. Applying a mixed-method approach, this study endeavors to contextualize the Stimuli-Organism-Response (S-O-R) framework in a novel context of AR retailing. Specifically, this study aims to determine the key influential AR features in the context of e-commerce and explores their relevant effects on facilitating consumers' in-depth understanding of the products and producing a playful atmosphere for customers resulting in enhanced consumer experiences. A three-stage hybrid research design is adopted in this study. First, an in-depth qualitative interview is applied to produce a comprehensive list of context-dependent key AR features. The SEM analysis using survey data in stage two and the artificial neural network (ANN) analytical technique in stage three unveil how the AR features influence customers' different reactions and rank the significance of AR features. Some cardinal theoretical and practical implications are also provided in the end.
C1 [Xu, Xiao-Yu; Jia, Qing-Dan] Xi An Jiao Tong Univ, Sch Econ & Finance, Xian, Shaanxi, Peoples R China.
[Tayyab, Syed Muhammad Usman] McGill Univ, Desautels Fac Management, Montreal, PQ, Canada.
[Jia, Qing-Dan] Xi An Jiao Tong Univ, Sch Econ & Finance, 74 Yanta West Rd, Xian, Shaanxi, Peoples R China.
C3 Xi'an Jiaotong University; McGill University; Xi'an Jiaotong University
RP Jia, QD (autor correspondiente), Xi An Jiao Tong Univ, Sch Econ & Finance, 74 Yanta West Rd, Xian, Shaanxi, Peoples R China.
EM xuxiaoyu@xjtu.edu.cn; qingdan@stu.xjtu.edu.cn;
syed.tayyab@mail.mcgill.ca
FU Nature Science Foundation of Shaanxi [2023-JC-QN-0794]
FX This work was supported by the Nature Science Foundation of Shaanxi
(2023-JC-QN-0794) .
CR Alimamy S, 2022, INFORM TECHNOL PEOPL, V35, P577, DOI 10.1108/ITP-07-2020-0455
[Anonymous], 2020, Forbes 11.5
Arghashi V, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102756
Ayaburi EW, 2020, INT J INFORM MANAGE, V50, P171, DOI 10.1016/j.ijinfomgt.2019.05.014
Barhorst JB, 2021, J BUS RES, V122, P423, DOI 10.1016/j.jbusres.2020.08.041
BERLYNE DE, 1963, J EXP PSYCHOL, V66, P560, DOI 10.1037/h0045767
Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
Castillo A, 2021, DECIS SUPPORT SYST, V145, DOI 10.1016/j.dss.2021.113516
Chan TKH, 2017, INFORM MANAGE-AMSTER, V54, P204, DOI 10.1016/j.im.2016.06.001
Chandra S, 2018, J ELECTRON COMMER RE, V19, P237
Cheung C.M., 2011, 32nd International Conference on Information System 2011, ICIS 2011, P3105
Cheung CMY, 2012, J ASSOC INF SYST, V13, P618
Chin W. W., 1998, QUANT METH SER, P295, DOI DOI 10.1016/J.AAP.2008.12.010
Chiu CL, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102561
Daassi M, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103453
DAFT RL, 1986, MANAGE SCI, V32, P554, DOI 10.1287/mnsc.32.5.554
DONOVAN RJ, 1982, J RETAILING, V58, P34
Eroglu SA, 2003, PSYCHOL MARKET, V20, P139, DOI 10.1002/mar.10064
Faul F, 2009, BEHAV RES METHODS, V41, P1149, DOI 10.3758/BRM.41.4.1149
Fazal-e-Hasan SM, 2021, COMPUT HUM BEHAV, V117, DOI 10.1016/j.chb.2020.106622
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
French AM, 2017, INFORM MANAGE-AMSTER, V54, P802, DOI 10.1016/j.im.2016.10.006
Grand View Research, 2021, AUGM REAL MARK SIZ S
Haile TT, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12219185
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Hsu SHY, 2021, J RETAIL CONSUM SERV, V62, DOI 10.1016/j.jretconser.2021.102649
Huang TL, 2019, J SERV MARK, V31, P851, DOI 10.1108/JSM-12-2018-0366
Javornik A, 2016, J MARKET MANAG-UK, V32, P987, DOI 10.1080/0267257X.2016.1174726
Jiang Y, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102720
Jung TH, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102575
Kang HJ, 2020, J INTERACT MARK, V49, P70, DOI 10.1016/j.intmar.2019.07.002
KBV research, 2021, Augmented reality market size, growth & forecast 2020-2026
Kim JH, 2021, J RES INTERACT MARK, V15, P502, DOI 10.1108/JRIM-07-2020-0148
Kline R. B., 2015, PRINCIPLES PRACTICE
Kowalczuk P, 2021, J BUS RES, V124, P357, DOI 10.1016/j.jbusres.2020.10.050
Lee H, 2021, J FASH MARK MANAG, V25, P45, DOI 10.1108/JFMM-05-2019-0092
Leong LY, 2020, J BUS RES, V110, P24, DOI 10.1016/j.jbusres.2019.11.056
Leonidou LC, 2022, J BUS RES, V151, P339, DOI 10.1016/j.jbusres.2022.06.027
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Liu MT, 2007, J CONSUM MARK, V24, P358, DOI 10.1108/07363760710822945
Lombard M, 2002, HUM COMMUN RES, V28, P587, DOI 10.1111/j.1468-2958.2002.tb00826.x
MacKenzie SB, 2012, J RETAILING, V88, P542, DOI 10.1016/j.jretai.2012.08.001
McLean G, 2019, COMPUT HUM BEHAV, V101, P210, DOI 10.1016/j.chb.2019.07.002
MEHRABIA.A, 1974, PERCEPT MOTOR SKILL, V38, P283, DOI 10.2466/pms.1974.38.1.283
Molinillo S, 2020, COMPUT HUM BEHAV, V108, DOI 10.1016/j.chb.2019.04.004
Nikhashemi SR, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102464
Noy C, 2008, INT J SOC RES METHOD, V11, P327, DOI 10.1080/13645570701401305
Park M, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101912
Poushneh A, 2018, J RETAIL CONSUM SERV, V41, P169, DOI 10.1016/j.jretconser.2017.12.010
Qasem Z, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102254
Qin H, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102680
Qin H, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102337
Rademaker M., 2020, cSEM: Composite - Based Structural Equation Modeling (0.2.0)
Raut RD, 2018, TECHNOL FORECAST SOC, V134, P98, DOI 10.1016/j.techfore.2018.05.020
Sharma A, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106919
Sharma PN, 2019, J ASSOC INF SYST, V20, P346, DOI 10.17005/1.jais.00538
Shiau W.L., 2022, Inf. Technol. People.
Smink AR, 2020, J BUS RES, V118, P474, DOI 10.1016/j.jbusres.2020.07.018
Smink AR, 2019, ELECTRON COMMER R A, V35, DOI 10.1016/j.elerap.2019.100854
Song HK, 2020, INFORM TECHNOL PEOPL, V33, P1214, DOI 10.1108/ITP-02-2019-0092
Spector PE, 2006, ORGAN RES METHODS, V9, P221, DOI 10.1177/1094428105284955
Suh A, 2018, COMPUT HUM BEHAV, V86, P77, DOI 10.1016/j.chb.2018.04.019
Venkatesh V, 2016, J ASSOC INF SYST, V17, P435, DOI 10.17705/1jais.00433
Venkatesh V, 2013, MIS QUART, V37, P21
Wang GQ, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121345
Wang YN, 2022, ASIA PAC J MARKET LO, V34, P110, DOI 10.1108/APJML-11-2019-0684
Whang JB, 2021, J BUS RES, V133, P275, DOI 10.1016/j.jbusres.2021.04.057
Williams LJ, 2010, ORGAN RES METHODS, V13, P477, DOI 10.1177/1094428110366036
Xiao X, 2020, MIS QUART, V44, P1811, DOI 10.25300/MISQ/2020/13216
Xu XY, 2020, J ELECTRON COMMER RE, V21, P144
Xu X, 2020, DECIS SUPPORT SYST, V136, DOI 10.1016/j.dss.2020.113344
Yuan CL, 2021, INT J ADVERT, V40, P922, DOI 10.1080/02650487.2020.1869387
Zheng XB, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102043
NR 74
TC 0
Z9 0
U1 23
U2 23
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD MAR
PY 2024
VL 77
AR 103682
DI 10.1016/j.jretconser.2023.103682
EA JAN 2024
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA GR5T4
UT WOS:001154419800001
DA 2024-03-27
ER
PT J
AU Qi, BT
Shen, YB
Xu, TY
AF Qi, Bitian
Shen, Yanbo
Xu, Tieyu
TI An artificial-intelligence-enabled sustainable supply chain model for
B2C E-commerce business in the international trade
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE International trade; Business to consumer (B2C); Artificial
intelligence; Sustainable supply chain; Electronic commerce (E-commerce)
ID SYSTEMS
AB This work aims to improve the efficiency of logistics distribution in the international trade environment by optimizing the distribution path of the e-commerce supply chain model (SCM). First, this work analyzes the international trade and e-commerce backgrounds and unveils the existing problems in the sustainable supply chain model (SCM). Then, an experiment is designed to study sustainable SCM under the Business to Consumer (B2C) E-commerce business model and compare it to the Consumer to Consumer (C2C) business model. This research has important reference value for improving resource efficiency in the logistics field. The optimal experimental results of the model show that given an order quantity of 2000, the vehicle competition ratio (VCR) is only 1.1 when the vehicle capacity (VC) is 12. In contrast, when the VC is 72, the VCR is 1.45. Therefore, the greater that the load of a single vehicle in the logistics management system is, the higher the logistics distribution efficiency of the supply chain. The research content has practical application value for improving the efficiency of logistics distribution under the current e-commerce environment and promoting the digital and electronic development of international trade logistics.
C1 [Qi, Bitian] Yichun Univ, Sch Econ & Management, Yichun 336000, Jiangxi, Peoples R China.
[Shen, Yanbo; Xu, Tieyu] Suzhou Univ, Sch Business Coll, Anhui 215000, Peoples R China.
C3 Yichun University; Suzhou University
RP Xu, TY (autor correspondiente), Suzhou Univ, Sch Business Coll, Anhui 215000, Peoples R China.
EM ture1983@163.com
CR Al-Adwan AS, 2019, J THEOR APPL EL COMM, V14, P51, DOI 10.4067/S0718-18762019000100105
Alamdari PM, 2020, IEEE ACCESS, V8, P115694, DOI 10.1109/ACCESS.2020.3002803
Atkin D, 2020, ANNU REV ECON, V12, P213, DOI 10.1146/annurev-economics-081919-041554
Bach A, 2020, J DAIRY SCI, V103, P5709, DOI 10.3168/jds.2019-17304
Bag S., 2023, TECHNOL FORECAST SOC, V186
Beltran A.D., 2019, BMC VET RES, V15, P1
Belu MG., 2019, ROMANIAN EC J, V71, P2
Bueno S, 2021, J THEOR APPL EL COMM, V16, P1612, DOI 10.3390/jtaer16050091
Castillo VE, 2018, J BUS LOGIST, V39, P38, DOI 10.1111/jbl.12176
Chen LM, 2019, J INTELL FUZZY SYST, V37, P4697, DOI 10.3233/JIFS-179304
Chiu ML, 2022, J INNOV KNOWL, V7, DOI 10.1016/j.jik.2022.100264
Cui JB, 2022, TECHNOL FORECAST SOC, V176, DOI 10.1016/j.techfore.2022.121492
Dondi M, 2021, RESOUR CONSERV RECY, V168, DOI 10.1016/j.resconrec.2020.105271
Friel S, 2020, NAT FOOD, V1, P51, DOI 10.1038/s43016-019-0014-0
Grandón EE, 2018, J THEOR APPL EL COMM, V13, P1, DOI 10.4067/S0718-18762018000300102
Hayakawa K, 2021, ASIAN ECON PAP, V20, P123, DOI 10.1162/asep_a_00804
Hsu BX, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121302
Hulme PE, 2021, ONE EARTH, V4, P666, DOI 10.1016/j.oneear.2021.04.015
Hung BQ, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2022.100284
Ilyash O, 2021, B GEOGR-SOCIO-ECO, V51, P7, DOI 10.2478/bog-2021-0001
Ishfaq R, 2022, J BUS LOGIST, V43, P169, DOI 10.1111/jbl.12277
Jones R.W., 2018, WORLD SCI BOOK CHAPT, P233
Kibati P., 2022, EDITON CONSORTIUM J, V4, P146
Kumar S, 2021, ELECTRON COMMER RES, V21, P1, DOI 10.1007/s10660-021-09464-1
Lee TS, 2019, MATHEMATICS-BASEL, V7, DOI 10.3390/math7100918
Leonard LNK, 2021, J COMPUT INFORM SYST, V61, P240, DOI 10.1080/08874417.2019.1598829
Li HJ, 2020, IEEE T IND INFORM, V16, P5327, DOI 10.1109/TII.2019.2960835
Li QB, 2021, INT J SYST ASSUR ENG, V12, P813, DOI 10.1007/s13198-021-01108-9
Liu XL, 2021, IEEE ACCESS, V9, P36140, DOI 10.1109/ACCESS.2021.3059867
Manogaran G, 2021, IEEE T VEH TECHNOL, V70, P2404, DOI 10.1109/TVT.2021.3058689
Mastrocinque E, 2022, TECHNOL FORECAST SOC, V182, DOI 10.1016/j.techfore.2022.121813
Ortiz AMD, 2021, ONE EARTH, V4, P88, DOI 10.1016/j.oneear.2020.12.008
Parfenov A., 2021, J OPEN INNOV TECHNOL, V7, P1, DOI 10.3390/joitmc7010058
Qiao L, 2022, DIGIT COMMUN NETW, V8, P687, DOI 10.1016/j.dcan.2021.07.004
Qin JJ, 2021, INT J PROD ECON, V241, DOI 10.1016/j.ijpe.2021.108271
Qin XL, 2021, EUR J OPER RES, V289, P639, DOI 10.1016/j.ejor.2020.07.029
Rebs T, 2019, J CLEAN PROD, V208, P1265, DOI 10.1016/j.jclepro.2018.10.100
Ren R, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17010261
Rodríguez-Ardura I, 2020, J THEOR APPL EL COMM, V15, pI, DOI 10.4067/S0718-18762020000200101
Rosenbaum MS, 2021, BUS HORIZONS, V64, P93, DOI 10.1016/j.bushor.2020.10.001
Roy V, 2022, INT J MANAG REV, V24, P3, DOI 10.1111/ijmr.12258
Sadeeq M., 2020, TECHNOL REP KANSAI U, V62, P2365
Shi WQ, 2021, IEEE T WIREL COMMUN, V20, P453, DOI 10.1109/TWC.2020.3025446
Siragusa C, 2022, INT J SUSTAIN TRANSP, V16, P22, DOI 10.1080/15568318.2020.1847367
Villa E, 2018, J THEOR APPL EL COMM, V13, P39, DOI 10.4067/S0718-18762018000100104
Wiedmann T, 2018, NAT GEOSCI, V11, P314, DOI 10.1038/s41561-018-0113-9
Yang HH, 2019, IEEE T IND INFORM, V15, P4178, DOI 10.1109/TII.2019.2897128
Yang J, 2021, INT J PROD ECON, V231, DOI 10.1016/j.ijpe.2020.107878
Yuan QW, 2019, J INTELL FUZZY SYST, V37, P3393, DOI 10.3233/JIFS-179142
Zekhnini K, 2022, INT J PROD RES, V60, P6529, DOI 10.1080/00207543.2021.1994164
Zhao YY, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12083381
Zheng KP, 2022, J INNOV KNOWL, V7, DOI 10.1016/j.jik.2022.100256
NR 52
TC 4
Z9 4
U1 29
U2 62
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD JUN
PY 2023
VL 191
AR 122491
DI 10.1016/j.techfore.2023.122491
EA MAR 2023
PG 13
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA D8GO1
UT WOS:000971058300001
DA 2024-03-27
ER
PT J
AU Orea-Giner, A
Muñoz-Mazón, A
Villacé-Molinero, T
Fuentes-Moraleda, L
AF Orea-Giner, Alicia
Munoz-Mazon, Ana
Villace-Molinero, Teresa
Fuentes-Moraleda, Laura
TI Cultural tourist and user experience with artificial intelligence: a
holistic perspective from the Industry 5.0 approach
SO JOURNAL OF TOURISM FUTURES
LA English
DT Article; Early Access
DE Artificial intelligence; Industry 5; 0; User service experiences;
Cultural institutions; Cultural tourist; User experience; Managers
ID SERVICE; TECHNOLOGY; HOSPITALITY; ACCEPTANCE; INNOVATION; HERITAGE;
FUTURE; ROBOTS; AI
AB PurposeThe purpose of this paper is to analyse the future of the implementation of artificial intelligence (AI) technologies in services experience provided by cultural institutions (e.g. museums, exhibition halls and cultural centres) from experts', cultural tourists' and users' point of view under the Industry 5.0 approach.Design/methodology/approachThe research was conducted using a qualitative approach, which was based on the analysis of the contents obtained from two roundtable discussions with experts and cultural tourists and users. A thematic analysis using NVivo was done to the data obtained.FindingsFrom a futuristic Industry 5.0 approach, AI is considered to be more than a tool - it as an integral part of the entire experience. AI aids in connecting cultural institutions with users and is beneficial since it allows the institutions to get to know the users better and provide a more integrated and immersive experience. Furthermore, AI is critical in establishing a community and nurturing it daily.Originality/valueThe most important contribution of this research is the theoretical model focused on the user experience and AI application in services experiences of museums and cultural institutions from an Industry 5.0 approach. This model includes the visitors' and managers' points of view through the following dimensions: the pre-experience, experience and post-experience. This model is focused on human-AI coworking (HAIC) in museums and cultural institutions.
C1 [Orea-Giner, Alicia; Munoz-Mazon, Ana; Villace-Molinero, Teresa; Fuentes-Moraleda, Laura] Rey Juan Carlos Univ, Fac Econ & Business, Madrid, Spain.
[Orea-Giner, Alicia] Univ Paris 1 Pantheon Sorbonne, EIREST, Paris, France.
C3 Universidad Rey Juan Carlos
RP Orea-Giner, A (autor correspondiente), Rey Juan Carlos Univ, Fac Econ & Business, Madrid, Spain.; Orea-Giner, A (autor correspondiente), Univ Paris 1 Pantheon Sorbonne, EIREST, Paris, France.
EM alicia.orea@urjc.es
RI MAZON, ANA isabel MUÑOZ/Q-4954-2018; Villacé-Molinero,
Teresa/U-1313-2019; Muñoz, Ana/JCN-6031-2023; Orea-Giner,
Alicia/AAZ-5581-2020
OI Villacé-Molinero, Teresa/0000-0001-9322-3673; Orea-Giner,
Alicia/0000-0001-8198-8169; Munoz Mazon, Ana Isabel/0000-0001-7636-7982;
Fuentes-Moraleda, Laura/0000-0003-4612-8838
FU OpenInnova Research Group
FX The authors acknowledge the funding support by OpenInnova Research
Group.
CR Akundi A, 2022, APPL SYST INNOV, V5, DOI 10.3390/asi5010027
Anhong Guo, 2020, ACM SIGACCESS Accessibility and Computing, DOI 10.1145/3386296.3386298
[Anonymous], INFORM COMMUNICATION
[Anonymous], ANAL INTERPRETING QU
Arefieva V, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104318
Armbrecht J, 2014, TOURISM MANAGE, V42, P141, DOI 10.1016/j.tourman.2013.11.010
Bartneck C, 2007, AI SOC, V21, P217, DOI 10.1007/s00146-006-0052-7
Belanche D, 2019, IND MANAGE DATA SYST, V119, P1411, DOI 10.1108/IMDS-08-2018-0368
Bickmore TW, 2013, AUTON AGENT MULTI-AG, V27, P254, DOI 10.1007/s10458-012-9216-7
Bonacini E, 2022, CULT TRENDS, V31, P3, DOI 10.1080/09548963.2021.1910490
Braun V., 2006, Qualitative Research in Psychology, V3, P77, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA]
Cachia M, 2011, QUAL RES ORGAN MANAG, V6, P265, DOI 10.1108/17465641111188420
Calero-Sanz J, 2022, PROCEDIA COMPUT SCI, V200, P1078, DOI 10.1016/j.procs.2022.01.307
Camarero C, 2015, J CULT HERIT, V16, P228, DOI 10.1016/j.culher.2014.05.006
Capriotti P, 2010, CORP COMMUN, V15, P281, DOI 10.1108/13563281011068131
Carayannis EG, 2022, J KNOWL ECON, V13, P3445, DOI 10.1007/s13132-021-00854-2
Chuah SHW, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102551
Cristobal-Fransi E, 2021, ACM J COMPUT CULT HE, V14, DOI 10.1145/3464977
Damian IM, 2022, TOUR REV, V77, P515, DOI 10.1108/TR-07-2020-0292
Demir Kadir Alpaslan, 2019, Procedia Computer Science, V158, P688, DOI 10.1016/j.procs.2019.09.104
Di Pietro L, 2018, J HERIT TOUR, V13, P97, DOI 10.1080/1743873X.2017.1384478
Dieck MCT, 2018, CURR ISSUES TOUR, V21, P2014, DOI 10.1080/13683500.2016.1224818
Falk J.H., 2016, MUSEUM EXPERIENCE RE
Ferreiro-Rosende É, 2022, J BRAND MANAG, V29, P383, DOI 10.1057/s41262-021-00267-1
French A., 2019, HUMANIZING DIGITAL P, P101
Fritzsche A, 2022, GENEVA PAP R I-ISS P, V47, P817, DOI 10.1057/s41288-021-00244-4
Fuentes-Moraleda L, 2022, LIBR HI TECH, V40, P894, DOI 10.1108/LHT-07-2020-0180
Gaia G, 2019, SPR SER CULT COMPUT, P309, DOI 10.1007/978-3-319-97457-6_15
García-Muiña FE, 2019, INT J CONTEMP HOSP M, V31, P4357, DOI 10.1108/IJCHM-03-2018-0260
Gasteiger N, 2021, CLIN INTERV AGING, V16, P941, DOI 10.2147/CIA.S282709
Glisczinski D, 2018, J TRANSFORM EDUC, V16, P175, DOI 10.1177/1541344618777367
Gravari-Barbas M., 2020, STAR ARCHITECTURE, P203
Hanafiah MH, 2019, COMPET REV, V29, P592, DOI 10.1108/CR-07-2018-0045
Hausmann A, 2021, J HERIT TOUR, V16, P669, DOI 10.1080/1743873X.2020.1819300
Heerink M, 2010, INT J SOC ROBOT, V2, P361, DOI 10.1007/s12369-010-0068-5
Hennink MM, 2019, QUAL HEALTH RES, V29, P1483, DOI 10.1177/1049732318821692
Huang L, 2022, SCI PROGRAMMING-NETH, V2022, DOI 10.1155/2022/2490033
Hughes P, 2004, NONPROF VOLUNT SEC Q, V33, P203, DOI 10.1177/0899764004263320
Ivanov D, 2018, ANNU REV CONTROL, V46, P134, DOI 10.1016/j.arcontrol.2018.10.014
Ivanov S, 2019, J HOSP TOUR TECHNOL, V10, P489, DOI 10.1108/JHTT-08-2018-0087
Ivanov S, 2018, TECHNOL SOC, V55, P24, DOI 10.1016/j.techsoc.2018.06.004
Jobin A, 2019, NAT MACH INTELL, V1, P389, DOI 10.1038/s42256-019-0088-2
Kim M, 2015, J HOSP TOUR RES, V39, P170, DOI 10.1177/1096348012471376
Kirby R, 2010, ROBOT AUTON SYST, V58, P322, DOI 10.1016/j.robot.2009.09.015
Kirchner TA, 2007, INT J NONPROFIT VOLU, V12, DOI 10.1002/nvsm.285
Koukoulis K., 2016, Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection, P792, DOI 10.1007/978-3-319-48496-9_63
Kuo CM, 2017, INT J CONTEMP HOSP M, V29, P1305, DOI [10.1108/ijchm-08-2015-0414, 10.1108/IJCHM-08-2015-0414]
Li ML, 2021, INT J HOSP MANAG, V95, DOI 10.1016/j.ijhm.2021.102930
Lindsay G, 2018, J URBAN DES, V23, P193, DOI 10.1080/13574809.2017.1399793
Maddikunta PKR, 2022, J IND INF INTEGR, V26, DOI 10.1016/j.jii.2021.100257
Majd M, 2017, 2017 19TH CSI INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP), P195, DOI 10.1109/AISP.2017.8324080
Marasco A, 2018, INT J CONTEMP HOSP M, V30, P2364, DOI [10.1108/ijchm-01-2018-0043, 10.1108/IJCHM-01-2018-0043]
Markovic S, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120883
Mich L., 2022, APPL DATA SCI TOURIS, P3
Minkiewicz J, 2016, J SERV MARK, V30, P749, DOI 10.1108/JSM-04-2015-0156
Niemczyk A, 2014, ECON SOCIOL, V7, P204, DOI 10.14254/2071-789X.2014/7-1/18
Noy C, 2008, INT J SOC RES METHOD, V11, P327, DOI 10.1080/13645570701401305
Orea-Giner A, 2022, TOURISM MANAGE, V93, DOI 10.1016/j.tourman.2022.104586
Orea-Giner A, 2021, TOUR PLAN DEV, V18, P147, DOI 10.1080/21568316.2021.1873831
Orea-Giner A, 2021, MUS MANAGE CURATOR, V36, P48, DOI 10.1080/09647775.2019.1700468
Ozdemir-Guzel S., 2021, GENERATION MARKETING, P141
Park S, 2020, ANN TOURISM RES, V81, DOI 10.1016/j.annals.2020.102888
Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
Pinillos R, 2016, ROBOT AUTON SYST, V79, P40, DOI 10.1016/j.robot.2016.01.014
Polishuk A., 2011, 4 KNOWLEDGE CITIES W, P319
Pop F., 2018, CYBERSPACE SAFETY SE, P291
Primawati S., 2018, P ENTER2018 PHD WORK
Richards G, 2020, TOUR REV, V75, P232, DOI 10.1108/TR-04-2019-0139
Richards G, 2018, J HOSP TOUR MANAG, V36, P12, DOI 10.1016/j.jhtm.2018.03.005
Ruel Huub, 2021, Journal of Tourism Futures, V7, P53, DOI 10.1108/JTF-03-2020-0032
Russo AP., 2016, Reinventing the local in tourism planning: Producing, consuming and negotiating place
Samala N, 2022, J TOUR FUTURES, V8, P73, DOI 10.1108/JTF-07-2019-0065
Samara D, 2020, J HOSP TOUR TECHNOL, V11, P343, DOI 10.1108/JHTT-12-2018-0118
Sarmah B, 2018, ONLINE INFORM REV, V42, P1146, DOI 10.1108/OIR-03-2017-0079
Scavarelli A, 2021, VIRTUAL REAL-LONDON, V25, P257, DOI 10.1007/s10055-020-00444-8
Schepers J, 2007, INFORM MANAGE-AMSTER, V44, P90, DOI 10.1016/j.im.2006.10.007
Singh G., 2021, ResearchBerg Review of Science and Technology, V1, P16
Smith P., 2021, AI and Ethics, V1, P81, DOI DOI 10.1007/S43681-020-00004-5
Solima L, 2018, J HERIT TOUR, V13, P115, DOI 10.1080/1743873X.2017.1337776
Stock RM, 2018, PROCEEDINGS OF THE 51ST ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS), P1056
Tung VWS, 2018, INT J CONTEMP HOSP M, V30, P2680, DOI 10.1108/IJCHM-06-2017-0322
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Varitimiadis S, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11199160
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Vitezic V, 2021, SERV IND J, V41, P926, DOI 10.1080/02642069.2021.1974406
Webster C., 2022, Journal of Smart Tourism, V2, P33, DOI 10.1007/s10055-020-00444-8
Weiss Astrid., 2009, AISB2009 P S NEW FRO, V4, P11
WELCH JK, 1992, MOD LANG J, V76, P543, DOI 10.2307/330063
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Wu HC, 2018, J HOSP MARKET MANAG, V27, P151, DOI 10.1080/19368623.2017.1344952
Yap N., 2020, 2020 INT S NETWORKS, P1
Zheng DN, 2020, J SUSTAIN TOUR, V28, P1782, DOI 10.1080/09669582.2020.1760288
NR 92
TC 2
Z9 2
U1 13
U2 29
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 2055-5911
EI 2055-592X
J9 J TOUR FUTURES
JI J. Tour. Futures
PD 2022 DEC 6
PY 2022
DI 10.1108/JTF-04-2022-0115
EA DEC 2022
PG 18
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA 6S9KV
UT WOS:000893302900001
OA gold
DA 2024-03-27
ER
PT J
AU Khayer, A
Talukder, MS
Bao, YK
Hossain, MN
AF Khayer, Abul
Talukder, Md Shamim
Bao, Yukun
Hossain, Md Nahin
TI Application-based mobile payment systems: continuance intention and
intention to recommend
SO INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS
LA English
DT Article
DE application-based mobile payment; expectation-confirmation model; ECM;
continuance intention; intention to recommend; structural equation
modelling; SEM; artificial neural network; ANN
ID NEURAL NETWORK APPROACH; GOVERNMENT SERVICES; WEARABLE TECHNOLOGY; USER
ACCEPTANCE; ADOPTION; DETERMINANTS; MODEL; SEM; TRUST; COMMERCE
AB This research paper valuates the factors of continuance intention and intention to recommend application-based mobile payment systems. The research model has been developed based on several strands of theories in information systems. The model has been tested using 360 respondents' data collected from Wuhan, PR China. This study used a hybrid method by relating structural equation modelling (SEM) and artificial neural network (ANN) for analysing data. The SEM confirms that satisfaction, perceived usefulness, perceived enjoyment, habit, and context are the key predictors of the continuance intention. The continuance intention positively affects the intention to recommend the application-based mobile payment. This study also confirms the moderating effect of context on the relationship between satisfaction and continuance intention. The ANN concludes that the most significant predictor of continuance intention is satisfaction, while the least important factor is habit. This study's implications provide worthy insights to the researchers, practitioners, and managers, which assist them in devising effective strategies for implementing application-based mobile payment.
C1 [Khayer, Abul] Univ Dhaka, Dept Int Business, Dhaka, Bangladesh.
[Talukder, Md Shamim] North South Univ, Dept Management, Dhaka, Bangladesh.
[Bao, Yukun] Huazhong Univ Sci & Technol, Ctr Modern Informat Management, Sch Management, Wuhan 430074, Peoples R China.
[Hossain, Md Nahin] Army Inst Business Adm, Dept Management, Savar, Bangladesh.
C3 University of Dhaka; North South University (NSU); Huazhong University
of Science & Technology
RP Bao, YK (autor correspondiente), Huazhong Univ Sci & Technol, Ctr Modern Informat Management, Sch Management, Wuhan 430074, Peoples R China.
EM akhayer@du.ac.bd; shamim.talukder@northsouth.edu; yukunbao@hust.edu.cn;
nahin@aibasavar.edu.bd
RI Hossain, MD Nahin/HDM-0518-2022
FU National Natural Science Foundation of China [71810107003]
FX This study was supported by the National Natural Science Foundation of
China under Project No. 71810107003.
CR Abel F, 2010, IEEE T LEARN TECHNOL, V3, P165, DOI 10.1109/TLT.2009.40
Abul Khayer, 2019, BOTTOM LINE, V32, P211, DOI 10.1108/BL-07-2019-0097
Al-Emran M, 2020, EDUC INF TECHNOL, V25, P2899, DOI 10.1007/s10639-019-10094-2
Alalwan AA, 2020, INT J INFORM MANAGE, V50, P28, DOI 10.1016/j.ijinfomgt.2019.04.008
Amoroso D, 2017, INT J INFORM MANAGE, V37, P693, DOI 10.1016/j.ijinfomgt.2017.05.003
[Anonymous], 2016, J IND ENG ENG MANAG
BAGOZZI RP, 1991, ADMIN SCI QUART, V36, P421, DOI 10.2307/2393203
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Bölen MC, 2020, INT J MOB COMMUN, V18, P101, DOI 10.1504/IJMC.2020.104423
Cao Q, 2019, INT J IND ERGONOM, V69, P9, DOI 10.1016/j.ergon.2018.09.004
Cao XF, 2018, INTERNET RES, V28, P456, DOI 10.1108/IntR-11-2016-0359
Chan FTS, 2012, DECIS SUPPORT SYST, V54, P621, DOI 10.1016/j.dss.2012.08.009
Chen CC, 2015, MULTIMEDIA SYST, V21, P61, DOI [10.1007/978-3-642-36620-8_1, 10.1007/s00530-013-0348-7]
Chen XG, 2017, J COMPUT INFORM SYST, V57, P287, DOI 10.1080/08874417.2016.1180649
Cheng S, 2020, INT J MOB COMMUN, V18, P462, DOI 10.1504/IJMC.2020.108288
Chin WW, 2003, INFORM SYST RES, V14, P189, DOI 10.1287/isre.14.2.189.16018
Chin WW, 1998, QUANT METH SER, P295
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Chuah SHW, 2017, J SERV THEOR PRACT, V27, P616, DOI 10.1108/JSTP-05-2016-0107
Cimperman M, 2016, INT J MED INFORM, V90, P22, DOI 10.1016/j.ijmedinf.2016.03.002
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
Dayour F, 2020, AFR J MANAGE, V6, P85, DOI 10.1080/23322373.2020.1753495
DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gallego Daniel, 2013, 2013 IEEE Frontiers in Education Conference (FIE), P1393, DOI 10.1109/FIE.2013.6685060
Gallego D, 2012, PROC FRONT EDUC CONF
Gao YW, 2015, IND MANAGE DATA SYST, V115, P1704, DOI 10.1108/IMDS-03-2015-0087
Gogo KO, 2018, INT CONF SOFT COMP, P114, DOI 10.1109/ISCMI.2018.8703247
Ha I, 2007, INFORM MANAGE-AMSTER, V44, P276, DOI 10.1016/j.im.2007.01.001
Hair J.F., 2016, A premier on partial least squares structural equation modeling (PLS-SEM), V2nd
Hair J.F., 2009, Multivariate data analysis
Hampshire C, 2017, INT J BANK MARK, V35, P354, DOI 10.1108/IJBM-08-2016-0105
Harman H. H., 1976, MODERN FACTOR ANAL
Haykin S., 2009, Neural Networks and Learning Machines, V3rd
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hillman S, 2017, COMPUT HUM BEHAV, V70, P10, DOI 10.1016/j.chb.2016.12.061
Hoque MR, 2015, TELEMED E-HEALTH, V21, P845, DOI 10.1089/tmj.2014.0128
Houston L, 2018, ORGAN BEHAV HUM DEC, V144, P85, DOI 10.1016/j.obhdp.2017.10.001
Humbani M, 2019, INT J BANK MARK, V37, P646, DOI 10.1108/IJBM-03-2018-0072
Hussein T., 2009, WORKSH CONT AW REC S
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Khalifa M., 2004, Journal of Information Technology Theory and Application (JITTA), V5, P4
Khayer A, 2020, TECHNOL SOC, V60, DOI 10.1016/j.techsoc.2019.101225
Khayer A, 2020, IND MANAGE DATA SYST, V120, P963, DOI 10.1108/IMDS-06-2019-0327
Khayer A, 2021, VINE J INF KNOWL MAN, V51, P64, DOI 10.1108/VJIKMS-05-2019-0064
Kim B, 2012, TELECOMMUN POLICY, V36, P69, DOI 10.1016/j.telpol.2011.11.011
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Kumar A, 2018, INT J BANK MARK, V36, P1170, DOI 10.1108/IJBM-04-2017-0077
Lee T., 2005, J ELECTRON COMMER RE, V6, P165, DOI DOI 10.1145/1964921.1964953
Leong LY, 2020, J BUS RES, V110, P24, DOI 10.1016/j.jbusres.2019.11.056
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Leong LY, 2019, EXPERT SYST APPL, V133, P296, DOI 10.1016/j.eswa.2019.05.024
Liang TP, 2011, PERS UBIQUIT COMPUT, V15, P187, DOI 10.1007/s00779-010-0300-1
Liao CC, 2009, INT J INFORM MANAGE, V29, P309, DOI 10.1016/j.ijinfomgt.2009.03.004
Liébana-Cabanillas F, 2014, COMPUT HUM BEHAV, V35, P464, DOI 10.1016/j.chb.2014.03.022
Lim SH, 2019, INT J HUM-COMPUT INT, V35, P886, DOI 10.1080/10447318.2018.1507132
Limayem M, 2007, MIS QUART, V31, P705, DOI 10.2307/25148817
Lowry PB, 2014, IEEE T PROF COMMUN, V57, P123, DOI 10.1109/TPC.2014.2312452
Lu YB, 2009, COMPUT HUM BEHAV, V25, P29, DOI 10.1016/j.chb.2008.06.002
Nascimento B, 2018, J RETAIL CONSUM SERV, V43, P157, DOI 10.1016/j.jretconser.2018.03.017
O'Brien RM, 2007, QUAL QUANT, V41, P673, DOI 10.1007/s11135-006-9018-6
Oghuma AP, 2016, TELEMAT INFORM, V33, P34, DOI 10.1016/j.tele.2015.05.006
Oliveira T, 2016, COMPUT HUM BEHAV, V61, P404, DOI 10.1016/j.chb.2016.03.030
OLSON JC, 1979, J APPL PSYCHOL, V64, P179, DOI 10.1037/0021-9010.64.2.179
Ozturk AB, 2017, INT J CONTEMP HOSP M, V29, P2027, DOI [10.1108/IJCHM-04-2016-0192, 10.1108/ijchm-04-2016-0192]
Pal D, 2020, UNIVERSAL ACCESS INF, V19, P261, DOI 10.1007/s10209-018-0639-z
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Rahi S, 2019, INT J INF LEARN TECH, V36, P2, DOI 10.1108/IJILT-05-2018-0045
Ringle CM, 2016, IND MANAGE DATA SYST, V116, P1865, DOI 10.1108/IMDS-10-2015-0449
Schilit B. N., 1994, Proceedings of the Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, P85
Shao Z, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.100823
Sharma SK, 2018, GOV INFORM Q, V35, P557, DOI 10.1016/j.giq.2018.07.002
Talukder M.S., 2020, TECHNOLOGICAL FORECA, V150, P119
Talukder MS, 2020, J GLOB INF MANAG, V28, P74, DOI 10.4018/JGIM.2020100105
Talukder MS, 2019, TECHNOL SOC, V56, P147, DOI 10.1016/j.techsoc.2018.09.013
Talukder MS, 2019, IND MANAGE DATA SYST, V119, P170, DOI 10.1108/IMDS-01-2018-0009
Tam C, 2020, INFORM SYST FRONT, V22, P243, DOI 10.1007/s10796-018-9864-5
Tarus JK, 2018, ARTIF INTELL REV, V50, P21, DOI 10.1007/s10462-017-9539-5
Tawafak RM, 2020, EDUC INF TECHNOL, V25, P817, DOI 10.1007/s10639-019-09984-2
Tehseen S., 2017, J MANAG SCI, V4, P142, DOI DOI 10.20547/JMS.2014.1704202
Thiprak S, 2015, 2015 12TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY (ECTI-CON)
Thong JYL, 2011, IEEE T ENG MANAGE, V58, P613, DOI 10.1109/TEM.2010.2058851
Turel O, 2011, MIS QUART, V35, P1043
Veeramootoo N, 2018, GOV INFORM Q, V35, P161, DOI 10.1016/j.giq.2018.03.004
Verkasalo Hannu, 2010, Telematics and Informatics, V27, P242, DOI 10.1016/j.tele.2009.11.001
Yang CH, 2009, RES POLICY, V38, P77, DOI 10.1016/j.respol.2008.09.001
Yang KC, 2020, TELEMAT INFORM, V51, DOI 10.1016/j.tele.2020.101400
Yao CB, 2017, IEEE T LEARN TECHNOL, V10, P104, DOI 10.1109/TLT.2015.2487977
Yap C.S., 2020, INT J PUBLIC ADMIN
Ye DP, 2016, MULTIMED TOOLS APPL, V75, P13977, DOI 10.1007/s11042-015-2693-3
Zhang XX, 2020, INT J NURS SCI, V7, P580, DOI 10.1016/j.ijnss.2020.07.009
Zhang Y., 2020, J CLEAN PROD, V242, P118
Zhou T, 2014, IND MANAGE DATA SYST, V114, P936, DOI 10.1108/IMDS-02-2014-0068
NR 97
TC 1
Z9 1
U1 11
U2 36
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 1470-949X
EI 1741-5217
J9 INT J MOB COMMUN
JI Int. J. Mob. Commun.
PY 2023
VL 21
IS 1
BP 19
EP 53
DI 10.1504/IJMC.2023.127374
PG 36
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA 6T3ZK
UT WOS:000893617300002
DA 2024-03-27
ER
PT J
AU Asante, IO
Jiang, YS
Hossin, AM
Luo, X
AF Asante, Isaac Owusu
Jiang, Yushi
Hossin, Atlab Md
Luo, Xiao
TI OPTIMIZATION OF CONSUMER ENGAGEMENT WITH ARTIFICIAL INTELLIGENCE
ELEMENTS ON ELECTRONIC COMMERCE PLATFORMS
SO JOURNAL OF ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE AI capability elements; Chatbot; Image search; Recommendation system;
Automated after-sales service
ID STRUCTURAL EQUATION MODELS; CUSTOMER ENGAGEMENT; BEHAVIORAL-RESEARCH;
BUSINESS VALUE; PLS-SEM; PERFORMANCE; ENVIRONMENTS; ANTECEDENTS;
INFORMATION; INTENTION
AB Artificial intelligence (AI) is reshaping the online shopping experience. However, there is limited information on consumers' interaction with AI elements embedded in electronic commerce (e-commerce) platforms and the behavioral outcomes of such interactions. AI application studies have focused on consumers' reluctance to use AI-powered services due to failed machine-human conversations. On the contrary, this study exploits the bright side of AI applications in e-commerce. It applies the stimuli-organism-response (S-O-R) paradigm to examine the effects of AI elements on consumer engagement attitudes, beyond purchase intentions, towards e-commerce platforms. Specifically, it examined the impact of chatbot efficiency, image search functionality, recommendation system efficiency, and automated after-sales service on consumer engagement. Furthermore, the study examined the moderating role of consumers' attention to the social comparison of consumption choices on the relationships between the AI capability elements and consumer engagement. The partial least square-structural equation modeling (PLS-SEM) approach was employed in analyzing 464 responses collected via an online survey from consumers of different e-commerce platforms. The findings indicate that AI capability elements, directly and indirectly, attract consumers' observable engagement behaviors. Also, attention to social comparison dampens the positive effects of chatbot efficiency and automated after-sales service on behavioral engagement. In contrast, it positively moderates the impact of recommendation system efficiency. The study contributes to academia by introducing consumers' attention to social comparison to advance the understanding of consumer engagement with AI applications in e -commerce. Practitioners can gain insight into improving consumer experience on e-commerce platforms.
C1 [Asante, Isaac Owusu; Jiang, Yushi] Southwest Jiaotong Univ, Sch Econ & Management, North Second Ring Rd, Chengdu 610031, Peoples R China.
[Asante, Isaac Owusu; Jiang, Yushi; Luo, Xiao] Southwest Jiaotong Univ, Yibin Res Inst, Yibin 644000, Peoples R China.
[Hossin, Atlab Md] Chengdu Univ, Sch Innovat & Entrepreneurship, Chengluo Ave, Chengdu 610106, Peoples R China.
C3 Southwest Jiaotong University; Southwest Jiaotong University; Chengdu
University
RP Asante, IO (autor correspondiente), Southwest Jiaotong Univ, Sch Econ & Management, North Second Ring Rd, Chengdu 610031, Peoples R China.; Asante, IO (autor correspondiente), Southwest Jiaotong Univ, Yibin Res Inst, Yibin 644000, Peoples R China.
EM asanteowusuisaac@swjtu.edu.cn; jiangyushi@swjtu.edu.cn;
atlabbd@cdu.edu.cn; luoxiaonec@hotmail.com
RI Asante, Isaac Owusu/AGU-4597-2022
OI Asante, Isaac Owusu/0000-0003-1213-9678
FU National Natural Science Foundation of China [72172129]; National Social
Science Foundation [20BSH103]; Humanities and Social Sciences Research
Program Fund of the Ministry of Education [21YJA63003]
FX Acknowledgments This study was funded by the National Natural Science
Foundation of China (72172129) , the National Social Science Foundation
(20BSH103) , and the Humanities and Social Sciences Research Program
Fund of the Ministry of Education (21YJA63003) .
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Agency G. E, 2021, CHIN ECOMMERCE PLATF
Aguinis H, 2005, J APPL PSYCHOL, V90, P94, DOI 10.1037/0021-9010.90.1.94
Amirpur M., 2015, P 36 INT C INF SYST, P1
ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
Asante IO, 2020, J PSYCHOL AFR, V30, P403, DOI 10.1080/14330237.2020.1821305
Asante IO, 2019, 2019 IEEE 10TH ANNUAL INFORMATION TECHNOLOGY, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (IEMCON), P763, DOI 10.1109/IEMCON.2019.8936291
Bagozzi RP, 1988, Journal of the Academy of Marketing Science, V16, P74, DOI DOI 10.1007/BF02723327
BEARDEN WO, 1990, J CONSUM RES, V16, P461, DOI 10.1086/209231
Becker JM, 2013, MIS QUART, V37, P665, DOI 10.25300/MISQ/2013/37.3.01
Ben Schafer J, 2001, DATA MIN KNOWL DISC, V5, P115, DOI 10.1023/A:1009804230409
Benitez J, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.05.003
Bharadwaj AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983
Bowden JLH, 2009, J MARKET THEORY PRAC, V17, P63, DOI 10.2753/MTP1069-6679170105
Brennan M., 2017, WECHAT USER REPORT I
Brodie RJ, 2013, J BUS RES, V66, P105, DOI 10.1016/j.jbusres.2011.07.029
CALDER BJ, 1981, J CONSUM RES, V8, P197, DOI 10.1086/208856
Cepeda-Carrion G, 2019, J KNOWL MANAG, V23, P67, DOI 10.1108/JKM-05-2018-0322
Chen XY, 2017, INT J INFORM MANAGE, V37, P1563, DOI 10.1016/j.ijinfomgt.2016.07.005
Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
Cheng XS, 2022, EUR J INFORM SYST, V31, P339, DOI 10.1080/0960085X.2020.1869508
Cheung CMK, 2015, ELECTRON COMMER R A, V14, P241, DOI 10.1016/j.elerap.2015.03.001
Chin WW, 1998, QUANT METH SER, P295
Chinchanachokchai S, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102528
Chou C., 2019, ALIZILA 0404
Chui M., 2018, AI Adoption Advances, but Foundational Barriers Remain
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
CXPA, 2018, ARTIF INTELL
Dagan A, 2021, SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P102, DOI 10.1145/3404835.3462950
Daqau M.A.M.A., 2019, INT REV MANAG MARK, V9, P22
Davenport TH, 2018, HARVARD BUS REV, V96, P108
De Cicco R, 2020, INT J RETAIL DISTRIB, V48, P1213, DOI 10.1108/IJRDM-12-2019-0406
De Keyzer F, 2022, J ELECTRON COMMER RE, V23, P138
Evans JR, 2005, INTERNET RES, V15, P195, DOI 10.1108/10662240510590360
Fang JM, 2017, INT J INFORM MANAGE, V37, P269, DOI 10.1016/j.ijinfomgt.2017.03.003
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Fornell C., 1994, ADV METHODS MARKETIN, P52, DOI DOI 10.1002/0471667196.ESS1914.PUB2
Gawali S. N, 2020, INT J MANAG ECON, VI, P15
Gnewuch Ulrich, 2017, ICIS, P13
Gupta M, 2016, INFORM MANAGE-AMSTER, V53, P1049, DOI 10.1016/j.im.2016.07.004
Hair J., 2017, International Journal of Multivariate Data Analysis, V1, P107, DOI [DOI 10.1504/IJMDA.2017.087624, https://doi.org/10.1504/IJMDA.2017.087624, 10.1504/ijmda.2017.10008574]
Hair JF, 2012, J ACAD MARKET SCI, V40, P414, DOI 10.1007/s11747-011-0261-6
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair Jr J., 2016, SAGE
Henseler J., 2010, HDB PARTIAL LEAST SQ, P713, DOI DOI 10.1007/978-3-540-32827-8_31
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Henseler J, 2014, ORGAN RES METHODS, V17, P182, DOI 10.1177/1094428114526928
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Hornung O, 2022, ELECTRON MARK, V32, P123, DOI 10.1007/s12525-021-00493-0
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Jamil R, 2019, J ACAD ETHICS, V17, P95, DOI 10.1007/s10805-018-9312-7
Jenkins A, 2003, FUTURES, V35, P779, DOI 10.1016/S0016-3287(03)00029-6
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Khan S., 2020, 2020 8THINTERNATIONA, DOI [DOI 10.1109/ICRITO48877.2020.9198004, 10.1109/icrito48877.2020.9198004]
Khrais LT, 2020, FUTURE INTERNET, V12, DOI 10.3390/fi12120226
Kim E, 2014, COMPUT HUM BEHAV, V37, P18, DOI 10.1016/j.chb.2014.04.020
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Kohli R, 2008, J ASSOC INF SYST, V9, P23, DOI 10.17705/1jais.00147
LENNOX RD, 1984, J PERS SOC PSYCHOL, V46, P1349, DOI 10.1037/0022-3514.46.6.1349
Li J, 2018, MIDDLEWARE INDUSTRY'18: PROCEEDINGS OF THE 2018 ACM/IFIP/USENIX MIDDLEWARE CONFERENCE (INDUSTRIAL TRACK), P9, DOI 10.1145/3284028.3284030
Lin H, 2014, INFORM MANAGE-AMSTER, V51, P595, DOI 10.1016/j.im.2014.03.010
Liu JW, 2022, J ACAD MARKET SCI, V50, P410, DOI 10.1007/s11747-021-00809-8
Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3
Luger E, 2016, 34TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2016, P5286, DOI 10.1145/2858036.2858288
Luo X., 2019, SSRN Electronic Journal, DOI [DOI 10.2139/SSRN.3435635, 10.2139/SSRN.3435635]
MacKenzie SB, 2011, MIS QUART, V35, P293
Matthews L., 2017, Partial least squares path modeling, P219, DOI [DOI 10.1007/978-3-319-64069-3_10, 10.1007/978-3-319-64069-3_10/COVER]
Mehrabian A., 1974, APPROACH ENV PSYCHOL
Mikalef P, 2021, INFORM MANAGEMENT AR
Müller T, 2018, J HOSP TOUR TECHNOL, V9, P249, DOI 10.1108/JHTT-09-2017-0106
Nunthiphatprueksa A., 2018, ASEAN J MANAGEMENT I, V5, P15, DOI DOI 10.14456/AJMI.2018.2
Oh C, 2017, INFORM MANAGE-AMSTER, V54, P25, DOI 10.1016/j.im.2016.03.004
Peters T, 2016, INT J INFORM MANAGE, V36, P773, DOI 10.1016/j.ijinfomgt.2016.05.003
Pfeuffer N., 2019, ICIS, P343, DOI 10.2/JQUERY.MIN.JS
Phua J, 2017, TELEMAT INFORM, V34, P412, DOI 10.1016/j.tele.2016.06.004
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879
Quoquab F, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12187784
Quoquab F, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17145258
Quoquab F, 2018, ASIA PAC J MARKET LO, V30, P1087, DOI 10.1108/APJML-06-2017-0121
Ramey HL, 2019, J YOUTH DEV, V14, P88, DOI 10.5195/jyd.2019.769
Ransbotham S, 2018, MIT SLOAN BUSINESS R
Ray S, 2014, INFORM SYST RES, V25, P528, DOI 10.1287/isre.2014.0525
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Rowsell-Jones A., 2019, 2019 CIO SURVEY CIOS
Ruiz-Fernández A, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18062845
Sander T, 2014, NEW CHALLENGES OF ECONOMIC AND BUSINESS DEVELOPMENT - 2014, P346
Sashi CM, 2012, MANAGE DECIS, V50, P253, DOI 10.1108/00251741211203551
Schaufeli W.B., 2002, Journal of Happiness Studies, V3, P71, DOI [DOI 10.1023/A:1015630930326, 10.1023/A:1015630930326]
Schryen G, 2013, EUR J INFORM SYST, V22, P139, DOI 10.1057/ejis.2012.45
Singaraju SP, 2022, J ELECTRON COMMER RE, V23, P13
Sivapalan Sanjeevan, 2014, 2014 World Automation Congress (WAC), P179, DOI 10.1109/WAC.2014.6935763
Sudarsan Shambhavi, 2022, Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2021. Lecture Notes on Data Engineering and Communications Technologies (116), P823, DOI 10.1007/978-981-16-9605-3_57
Sutikno T., 2016, INT J ELECT COMPUTER, P909, DOI DOI 10.11591/IJECE.V6I3.10271
Tsai WHS, 2021, J RES INTERACT MARK, V15, P460, DOI 10.1108/JRIM-12-2019-0200
UNCTAD, 2021, GLOB E COMM JUMPS 26
van Doorn J, 2010, J SERV RES-US, V13, P253, DOI 10.1177/1094670510375599
Verhagen T, 2015, COMPUT HUM BEHAV, V48, P340, DOI 10.1016/j.chb.2015.01.061
Wei KN, 2007, I C SERV SYST SERV M, P734
Xia Song, 2019, Journal of Physics: Conference Series, V1302, DOI 10.1088/1742-6596/1302/3/032030
Xu Y, 2023, NANKAI BUS REV NT, V14, P249, DOI 10.1108/NBRI-06-2021-0041
Yang F, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2101, DOI 10.1145/3097983.3098162
Yoon HJ, 2016, INT J ADVERT, V35, P799, DOI 10.1080/02650487.2016.1145094
Zarifis A, 2021, J INTERNET COMMER, V20, P66, DOI 10.1080/15332861.2020.1832817
Zhang H, 2015, INFORM MANAGE-AMSTER, V52, P468, DOI 10.1016/j.im.2015.01.008
Zhang H, 2014, INFORM MANAGE-AMSTER, V51, P1017, DOI 10.1016/j.im.2014.07.005
Zhang YH, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P2489, DOI 10.1145/3357384.3357800
Zhang YH, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P993, DOI 10.1145/3219819.3219820
NR 109
TC 2
Z9 2
U1 16
U2 41
PU CALIFORNIA STATE UNIV
PI LONG BEACH
PA COLL BUSINESS, LONG BEACH, CA 90840 USA
SN 1526-6133
EI 1938-9027
J9 J ELECTRON COMMER RE
JI J. Electron. Commer. Res.
PD FEB
PY 2023
VL 24
IS 1
SI SI
BP 7
EP 28
PG 22
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA A8RC7
UT WOS:000957725600002
DA 2024-03-27
ER
PT J
AU Fonseka, K
Jaharadak, AA
Raman, M
AF Fonseka, Kapila
Jaharadak, Adam Amril
Raman, Murali
TI Impact of E-commerce adoption on business performance of SMEs in Sri
Lanka; moderating role of artificial intelligence
SO INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS
LA English
DT Article
DE E-commerce; E-business; Performance; Business strategy; Artificial
intelligence
ID CHATBOTS
AB Purpose - With the rapid development of technology in the 21st century, an ever-growing number of organisations are adopting digitalised technologies. The global economy connected with digitalisation is moving towards sustainable development. Individual firms adopt innovative technological strategies to consolidate their position in the competitive market. The study aimed to examine the management perception of the impact of E-commerce adoption (EC) on business performance (BP) - the moderating role of using artificial intelligence (AI).
Design/methodology/approach - A quantitative study using the deductive approach and the data collected from senior managers of the small and medium-sized enterprises (SMEs) in Sri Lanka, and 389 samples were collected using a simple random sampling method. EC, BP and AI were named as the independent, dependent and moderating variables in the model. Porters' generic strategies and resource-based views (RBVs) were applied as the foundation of the study.
Findings - The independent and moderating variables significantly influenced the BP. Managers' age, gender, education level and job position affect their perception.
Originality/value - The global economy is moving towards sustainable development using digitalisation. The firms should blend their strategies with digitalised platforms to survive in the competitive market.
C1 [Fonseka, Kapila; Jaharadak, Adam Amril] Management & Sci Univ, Shah Alam, Malaysia.
[Raman, Murali] Asia Pacific Univ Technol & Innovat, Kuala Lumpur, Malaysia.
C3 Management Science University; Asia Pacific University of Technology &
Innovation
RP Fonseka, K (autor correspondiente), Management & Sci Univ, Shah Alam, Malaysia.
EM kapilafonseka@gmail.com
RI FONSEKA, Kapila/ACX-3367-2022
OI FONSEKA, Kapila/0000-0002-5895-7249; Jaharadak, Adam
Amril/0000-0001-8441-1621
CR Abed SS, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102118
Abubakar AM, 2019, J INNOV KNOWL, V4, P104, DOI 10.1016/j.jik.2017.07.003
Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Agrawal A., 2019, The economics of artificial intelligence: An agenda, DOI DOI 10.7208/CHICAGO/9780226613475.001.0001
Ahmad SZ, 2019, INT J ENTREP BEHAV R, V25, P84, DOI 10.1108/IJEBR-08-2017-0299
Alderete María Verónica, 2019, Contad. Adm, V64, pe133, DOI 10.22201/fca.24488410e.2019.1922
Alzahrani J., 2019, Review of Economics and Political Science, V4, P73, DOI [10.1108/REPS-10-2018-013, DOI 10.1108/REPS-10-2018-013]
Ameen N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106548
[Anonymous], 2015, STRUCTURAL EQUATION
[Anonymous], 2021, STATISTA 2021 E COMM
Aravind A.S., 2021, INT J CREATIVE RES T, V9, P5023
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Chopra K, 2019, INT J RETAIL DISTRIB, V47, P331, DOI 10.1108/IJRDM-11-2018-0251
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Dahbi Salma, 2019, Procedia Computer Science, V158, P811, DOI 10.1016/j.procs.2019.09.118
Davis GF, 2021, J MANAGE, V47, P1684, DOI 10.1177/0149206320982650
Deloitte, 2018, ARTIF INTELL
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Ezzaouia I, 2020, TOUR MANAG PERSPECT, V34, DOI 10.1016/j.tmp.2020.100675
Fonseka K., 2021, J TELECOMMUNICATIONS, V9, P23, DOI [10.18080/jtde.v9n4.412, DOI 10.18080/JTDE.V9N4.412]
Fonseka K., 2020, Global Journal of Management and Business Research, P1, DOI [DOI 10.34257/GJMBRBVOL20IS6PG1, 10.34257/gjmbrbvol20is6pg1]
Govinnage D., 2019, Asian Journal of Advanced Research and Reports, V6, P1, DOI DOI 10.9734/AJARR/2019/V6I230147
Gunawardana Kennedy D., 2018, International Journal of Sociotechnology and Knowledge Development, V10, P54, DOI 10.4018/IJSKD.2018040104
Hidayati R., 2019, CENDEKIA NIAGA, V3, P31, DOI [10.52391/jcn.v3i1.460, DOI 10.52391/JCN.V3I1.460]
Howard MC, 2016, INT J HUM-COMPUT INT, V32, P51, DOI 10.1080/10447318.2015.1087664
Hu XH, 2019, ECON SOCIOL, V12, P80, DOI 10.14254/2071-789X.2019/12-1/4
Ibrahim W., 2018, International Journal of Business and Management, V14, P46, DOI DOI 10.5539/IJBM.V14N1P46
Jovanovic JS, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12176993
Kasilingam DL, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101280
KREJCIE RV, 1970, EDUC PSYCHOL MEAS, V30, P607, DOI 10.1177/001316447003000308
Kuruwitaarachchi N., 2020, International Journal of e-Education, e-Business, e-Management and e-Learning, V10, P95, DOI 10.17706/ijeeee.2020.10.1.95-103
Lim S., 2018, Management Science Letters, V8, DOI DOI 10.5267/J.MSL.2018.4
Mahliza F., 2019, International Journal of Business, Economics and Law, V8, P290
Masbuqin I., 2020, INT J PSYCHOSOCIAL R, V24, P779, DOI [10.37200/ijpr/v24i1/pr200182, DOI 10.37200/IJPR/V24I5/PR201747]
National Accounts of Sri Lanka, 2020, DEP C STAT MIN FIN E
NICHIFOR E, WWWAMFITEATRUECONOMI, V23, P87
Oktora K., 2020, IEOM SOC INT AFR INT, P7
Olise M C., 2020, Sumerianz Journal of Business Management and Marketing, V3, P212, DOI DOI 10.47752/SJBMM.312.212.218
Phiri, 2019, INT J ACAD RES BUSIN, V9, P32, DOI [10.6007/IJARBSS/v9-i4/5815, DOI 10.6007/IJARBSS/V9-I4/5815]
Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
Porter M. E., 1985, COMPETITIVE ADVANTAG
Prem E, 2019, TECHNOL INNOV MANAG, V9, P5, DOI 10.22215/timreview/1287
Putra POH, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e03568
Qamariah I., 2019, ECOFORUM, V8
Saravanan K., 2020, INT J MODERN TRENDS, V6, P14, DOI [10.46501/ijmtst0609s03, DOI 10.46501/IJMTST0609S03]
Schuetzler RM, 2020, J MANAGE INFORM SYST, V37, P875, DOI 10.1080/07421222.2020.1790204
Shumanov M, 2021, COMPUT HUM BEHAV, V117, DOI 10.1016/j.chb.2020.106627
Soloducho-Pelc L, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12218832
Song X., 2019, J PHYS C SERIES, V1302, DOI 10.1088/1742-6596/1302/3/032030
Tran AD, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102718
United Nations, 2019, DIG EC REP 2019 VAL
United Nations Conference on Trade and Development ( 2021 ), DIGITAL EC REPORT 20
Yadav R, 2019, J ELECTRON COMMER OR, V17, P44, DOI 10.4018/JECO.2019100104
Yoon C, 2020, COMPUT HUM BEHAV, V108, DOI 10.1016/j.chb.2020.106309
Zhang D, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102304
NR 55
TC 7
Z9 7
U1 11
U2 32
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0306-8293
EI 1758-6712
J9 INT J SOC ECON
JI Int. J. Soc. Econ.
PD AUG 25
PY 2022
VL 49
IS 10
BP 1518
EP 1531
DI 10.1108/IJSE-12-2021-0752
EA MAY 2022
PG 14
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 3X8HK
UT WOS:000794259300001
DA 2024-03-27
ER
PT J
AU Lacárcel, FJS
AF Lacarcel, Francisco Javier S.
TI Main Uses of Artificial Intelligence in Digital Marketing Strategies
Linked to Tourism
SO JOURNAL OF TOURISM SUSTAINABILITY AND WELL-BEING
LA English
DT Article
DE Digital Marketing; Artificial Intelligence; Tourism; Machine Learning;
Big Data Marketing
AB Migratory movements and tourism in general, together with the improvement of new technologies, have meant an increase in terms of accessibility and ease of information linked to tourism. At the same time, the use of artificial intelligence has opened new horizons in which digital marketing strategies linked to tourism can improve the industry, thus offering multiple possibilities in the short term. This new business ecosystem can analyze and extract large amounts of data for use in their marketing strategies. In this study, a systematic literature review (SLR) is conducted using the Web of Science (WOS) database. The main objective of this review is to identify the main uses of artificial intelligence in digital marketing strategies to understand the decision-making processes of future tourists, destination selection, automation of decision-making processes, and actions developed by tourists in the destination itself. In this context, through the applied methodology, 24 potential results have been identified, which have been classified gence algorithms, and (v)artificial intelligence strategies for the improvement of the user experience. Finally, theoretical and practical implications are identified to support companies that want to develop data-driven digital marketing actions and, from the applied perspective, to help future authors who want to make new academic contributions.
C1 [Lacarcel, Francisco Javier S.] Univ Alicante, Univ Inst Tourism Res, Alicante, Spain.
C3 Universitat d'Alacant
RP Lacárcel, FJS (autor correspondiente), Univ Alicante, Univ Inst Tourism Res, Alicante, Spain.
EM francisco@jlacarcel.net
RI S. Lacárcel, Francisco Javier/JOK-6507-2023
CR Agapito D, 2020, J SPAT ORGAN DYN, V8, P244
Agapito D, 2014, J SPAT ORGAN DYN, V2, P201
Amado A, 2018, EUR RES MANAG BUS EC, V24, P1, DOI 10.1016/j.iedeen.2017.06.002
Ameri F, 2012, J INTELL MANUF, V23, P1817, DOI 10.1007/s10845-010-0495-z
BARBOSA B., 2020, REV IBERICA SISTEMAS, V35, P152
Bassano C, 2019, CITIES, V87, P10, DOI 10.1016/j.cities.2018.12.025
Belo A, 2014, J SPAT ORGAN DYN, V2, P217
Bertello A, 2022, INT ENTREP MANAG J, V18, P89, DOI 10.1007/s11365-020-00727-1
Bhandari R.S., 2018, Jindal Journal of Business Research, V7, P23, DOI DOI 10.1177/2278682117754016
Burr C, 2020, SCI ENG ETHICS, V26, P2313, DOI 10.1007/s11948-020-00175-8
Caloghirou Y, 2022, INT ENTREP MANAG J, V18, P1319, DOI 10.1007/s11365-020-00674-x
Campbell C, 2020, BUS HORIZONS, V63, P469, DOI 10.1016/j.bushor.2020.03.003
Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199
Chu M. K., 2021, Open J. Soc. Sci., V9, P42
Csáji BC, 2006, ADV ENG INFORM, V20, P279, DOI 10.1016/j.aei.2006.01.001
De Carlo M, 2021, J BUS RES, V129, P936, DOI 10.1016/j.jbusres.2020.09.013
Duvnjak K, 2020, MANAG RES PRACT, V12, P18
Ekelik H, 2021, ESKISEH OSMAN UNIV I, V16, P341, DOI 10.17153/oguiibf.879105
Huete R, 2009, TURISTAS QUE LLEGAN, P281
Karagkiozidou M., 2019, STRATEGIC INNOVATIVE, P479, DOI DOI 10.1007/978-3-030-12453-3_54
Katsikeas C, 2020, INT MARKET REV, V37, P405, DOI 10.1108/IMR-02-2019-0080
Kerdpitak C., 2022, UNCERTAIN SUPPLY CHA, V10, P771, DOI [10.5267/j.uscm.2022.4.005, DOI 10.5267/J.USCM.2022.4.005]
Kikot T, 2014, J SPAT ORGAN DYN, V2, P229
Kim KS, 2008, EXPERT SYST APPL, V34, P128, DOI 10.1016/j.eswa.2006.08.009
Kirtil IG, 2021, ADV HOSP TOUR RES-AH, V9, P205, DOI 10.30519/ahtr.801690
Kitchen Philip J., 2015, Journal of Business Strategy, V36, P34, DOI 10.1108/JBS-06-2014-0070
Kopalle PK, 2021, J PUBLIC POLICY MARK, V40, P453, DOI 10.1177/0743915621999031
Kumar J, 2020, J SPAT ORGAN DYN, V8, P197
Lal B, 2020, ADV THE PRAC EMER MA, P3, DOI 10.1007/978-3-030-24374-6_1
Lee CK, 2020, J DESTIN MARK MANAGE, V18, DOI 10.1016/j.jdmm.2020.100500
Lee M, 2021, INT J CONTEMP HOSP M, V33, P2117, DOI 10.1108/IJCHM-06-2020-0587
Li D., 2022, WIREL COMMUN MOB COM, V2022, P5441170, DOI DOI 10.1155/2022/5441170
Li G, 2022, ADV METEOROL, V2022, DOI 10.1155/2022/5422210
Lv H, 2022, J HOSP MARKET MANAG, V31, P145, DOI 10.1080/19368623.2021.1937434
Mariani M, 2018, INT J CONTEMP HOSP M, V30, P3514, DOI 10.1108/IJCHM-07-2017-0461
Martin BAS, 2020, J HOSP TOUR MANAG, V44, P108, DOI 10.1016/j.jhtm.2020.06.004
Martin J.M.M., 2022, SUSTAINABLE TECHNOLO, V1, DOI [DOI 10.1016/J.STAE.2022.100005, 10.1016/j.stae.2022.100005]
Nadalipour Z, 2019, COMPET REV, V29, P314, DOI 10.1108/CR-12-2017-0086
Nedjah N., 2022, SUSTAINABLE TECHNOLO, V1, DOI [10.1016/j.stae.2022.100001, DOI 10.1016/J.STAE.2022.100001]
Nyagadza B., 2022, J DIGIT MEDIA POLICY
Ojo B. Y., 2019, Journal of Tourism Management Research, V6, P45, DOI 10.18488/journal.31.2019.61.45.59
Ostic D, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.678766
Prentice C, 2020, J HOSP MARKET MANAG, V29, P377, DOI 10.1080/19368623.2019.1647124
Saura JR, 2022, MANAGE DECIS, DOI 10.1108/MD-02-2022-0190
Saura JR, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9214603
Rather R.A., 2020, ROUTLEDGE HDB TOURIS, P271
Roscher R, 2020, IEEE ACCESS, V8, P42200, DOI 10.1109/ACCESS.2020.2976199
Samara D, 2020, J HOSP TOUR TECHNOL, V11, P343, DOI 10.1108/JHTT-12-2018-0118
Samuel A, 2021, COMPUT HUM BEHAV, V116, DOI 10.1016/j.chb.2020.106657
Sánchez EC, 2020, TOUR MANAG PERSPECT, V35, DOI 10.1016/j.tmp.2020.100718
Saura JR, 2022, J BUS RES, V142, P242, DOI 10.1016/j.jbusres.2021.12.052
Saura JR, 2021, IND MARKET MANAG, V98, P161, DOI 10.1016/j.indmarman.2021.08.006
Saura JR., 2021, J SMALL BUS MANAGE, DOI [10.1080/00472778.2021.1955127, DOI 10.1080/00472778.2021.1955127]
Schiessl D, 2022, J MARK ANAL, V10, P207, DOI 10.1057/s41270-021-00143-6
Sholichah N. L., 2022, PROCEDIA COMPUTER SC, V197, P445
Shuting Tao, 2017, Culinary Science & Hospitality Research, V23, P1, DOI 10.20878/cshr.2017.23.6.001
Sigala M., 2019, Big data and innovation in tourism, travel, and hospitality
Singh G., 2019, ADV ANAL APPL, P77, DOI 10.1007/978-981-13-1208-3
Tiago F, 2021, J INNOV KNOWL, V6, P27, DOI 10.1016/j.jik.2019.12.002
Topal I, 2019, IEEE ACCESS, V7, P162530, DOI 10.1109/ACCESS.2019.2947712
Veit DJ, 2002, APPL ARTIF INTELL, V16, P853, DOI 10.1080/08839510290030525
Wang RW, 2020, J DESTIN MARK MANAGE, V18, DOI 10.1016/j.jdmm.2020.100512
Xie D., 2022, HINDAWI MOBILE INFOR, DOI DOI 10.1155/2022/9154351
Yu N, 2007, P 14 INT C INT SYST, P1, DOI DOI 10.1109/ISAP.2007.4441590
NR 64
TC 7
Z9 7
U1 28
U2 46
PU CINTURS-RESEARCH CENTRE TOURISM SUSTAINABILITY & WELL-BEING
PI FARO
PA UNIV ALGARVE, GAMBELAS CAMPUS, FAC ECONOMICS, BLDG 9, OFF 2 76, FARO,
PORTUGAL
SN 2795-5044
J9 J TOUR SUSTAIN WELL
JI J. Tourism Sustainability Well-being
PY 2022
VL 10
IS 3
BP 215
EP 226
DI 10.34623/mppf-r253
PG 12
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA 5M4NO
UT WOS:000871074200005
DA 2024-03-27
ER
PT J
AU Thabet, Z
Albashtawi, S
Ansari, H
Al-Emran, M
Al-Sharafi, MA
AlQudah, AA
AF Thabet, Zeina
Albashtawi, Sara
Ansari, Hurmat
Al-Emran, Mostafa
Al-Sharafi, Mohammed A.
AlQudah, Adi Ahmad
TI Exploring the Factors Affecting Telemedicine Adoption by Integrating
UTAUT2 and IS Success Model: A Hybrid SEM-ANN Approach
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article; Early Access
DE Artificial neural network (ANN); Information System (IS) success model;
PLS-structural equation modeling (SEM); telemedicine adoption; Unified
Theory of Acceptance and Use of Technology (UTAUT2)
ID UNIFIED THEORY; INFORMATION-TECHNOLOGY; HEALTH; ACCEPTANCE; APPS;
PERCEPTIONS; TELEHEALTH; NETWORKS; SECURITY
AB Telemedicine adoption has steadily grown due to its ability to provide accessible and cost-effective healthcare services. However, individuals' adoption rate still faces challenges, necessitating a comprehensive understanding of the factors influencing their adoption. This article explores the factors affecting telemedicine adoption by integrating Unified Theory of Acceptance and Use of Technology, the Information System success model, and perceived security. The integrated model is evaluated using a hybrid structural equation modeling-artificial neural network (ANN) technique based on data collected from 152 individuals. The results showed that performance expectancy, hedonic motivation, perceived security, and user satisfaction significantly drive telemedicine adoption. Additionally, user satisfaction is affected substantially by information quality, system quality, and service quality. However, effort expectancy, social influence, and facilitating conditions do not significantly impact telemedicine adoption. The ANN findings revealed that user satisfaction is the most important driver for telemedicine adoption, with a normalized importance of 100%. This article contributes to telemedicine literature by providing a comprehensive framework that combines two well-established theories, offering insights into the multifaceted factors affecting telemedicine adoption. The findings also provide practical implications for decision-makers, policymakers, telemedicine service providers, software companies, and developers, emphasizing the importance of addressing the identified factors to promote widespread telemedicine adoption and ensure its long-term success.
C1 [Thabet, Zeina; Albashtawi, Sara; Ansari, Hurmat; Al-Emran, Mostafa; AlQudah, Adi Ahmad] British Univ Dubai, Fac Engn & IT, Dubai 345015, U Arab Emirates.
[Al-Emran, Mostafa] Dijlah Univ Coll, Dept Comp Tech Engn, Baghdad 00964, Iraq.
[Al-Sharafi, Mohammed A.] Univ Tenaga Nas, Inst Informat & Comp Energy, Kajang 43000, Malaysia.
C3 Dijlah University College; Universiti Tenaga Nasional
RP Al-Emran, M (autor correspondiente), British Univ Dubai, Fac Engn & IT, Dubai 345015, U Arab Emirates.; Al-Emran, M (autor correspondiente), Dijlah Univ Coll, Dept Comp Tech Engn, Baghdad 00964, Iraq.
EM zeina.thabet@outlook.com; sarabashtawi@hotmail.com;
hurmatsalmanansari@gmail.com; mustafa.n.alemran@gmail.com;
mohamed.a.alsharafi@gmail.com; adi.qudah@gmail.com
RI Al-Emran, Mostafa/W-4466-2018; Al-Sharafi, Mohammed A./E-1530-2017
OI Al-Emran, Mostafa/0000-0002-5269-5380; Al-Sharafi, Mohammed
A./0000-0003-0726-6031; AlQudah, Adi/0000-0003-3942-5869
CR Abbate S, 2023, IEEE T ENG MANAGE, V70, P2998, DOI 10.1109/TEM.2022.3212007
Abu-Shanab E, 2023, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2208990
Aggarwal A., 2018, INT J PUBLIC SECTOR, V4, P1, DOI [10.1504/ijpspm.2018.088691, DOI 10.1504/IJPSPM.2018.088691]
Akrong GB, 2022, INT J ACCOUNT INF SY, V47, DOI 10.1016/j.accinf.2022.100579
Al-Emran M., 2021, RECENT ADV TECHNOLOG, P159
Al-Emran M, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3237789
Al-Sharafi MA, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e16299
Al-Sharafi MA, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107708
Al-Sharafi MA, 2023, INTERACT LEARN ENVIR, V31, P7491, DOI 10.1080/10494820.2022.2075014
Al-Sharafi MA, 2022, INT J BANK MARK, V40, P1071, DOI 10.1108/IJBM-07-2021-0291
Al-Shihi H, 2018, EDUC INF TECHNOL, V23, P1805, DOI 10.1007/s10639-018-9691-9
Alaboudi A, 2016, J INFECT PUBLIC HEAL, V9, P725, DOI 10.1016/j.jiph.2016.09.001
Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
Alami R, 2021, INT WINT WORKSH BR, P253, DOI 10.1109/BCI51272.2021.9385323
Albanna H, 2022, INT J INFORM MANAGE, V63, DOI 10.1016/j.ijinfomgt.2021.102452
AlQudah AA, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app112210537
AlQudah AA, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3223520
Alshaafee AA, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132111916
Alzahrani AI, 2019, J LIBR INF SCI, V51, P291, DOI 10.1177/0961000617726123
Antrim A., 2020, PHARM TIMES
Boontarig W., 2012, 2012 IEEE Symposium on Humanities, Science and Engineering Research (SHUSER), P479, DOI 10.1109/SHUSER.2012.6268881
Camilleri MA, 2022, TECHNOL SOC, V68, DOI 10.1016/j.techsoc.2022.101881
Chakraborty I, 2023, INT J MED INFORM, V174, DOI 10.1016/j.ijmedinf.2023.105048
Chopdar PK, 2018, COMPUT HUM BEHAV, V86, P109, DOI 10.1016/j.chb.2018.04.017
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Crico C, 2018, ECANCERMEDICALSCIENC, V12, DOI 10.3332/ecancer.2018.853
Cushing M, 2022, VET CLIN N AM-SMALL, V52, P1069, DOI 10.1016/j.cvsm.2022.06.004
Dang T. Q., IN PRESS, DOI [10.1108/IJBM-05-2022-0202/FULL/PDF, DOI 10.1108/IJBM-05-2022-0202/FULL/PDF]
DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
Dwivedi YK, 2020, CURR OPIN PSYCHOL, V36, P13, DOI 10.1016/j.copsyc.2020.03.008
Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
Fadahunsi KP, 2021, J MED INTERNET RES, V23, DOI 10.2196/23479
Galloway A., 2005, Encyclopedia of Social Measurement, P859, DOI DOI 10.1016/B0-12-369398-5/00382-0
Giansanti D, 2017, TELEMED E-HEALTH, V23, P773, DOI 10.1089/tmj.2016.0255
Goetz D, 2023, INT J IND ORGAN, V89, DOI 10.1016/j.ijindorg.2023.102956
Gofeld M., 2023, INTERVENTIONAL PAIN, V2, DOI [10.1016/J.INPM.2023.100252, DOI 10.1016/J.INPM.2023.100252]
Hair Jr J.F., 2016, PRIMER PARTIAL LEAST
Hammood W.A., 2020, Telkomnika, V18, P2956, DOI DOI 10.12928/TELKOMNIKA.V18I6.14666
Harman H.H., 1976, MODERN FACTOR ANAL, DOI DOI 10.2307/2282293
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Isakovic M, 2016, J DIABETES RES, V2016, DOI 10.1155/2016/1604609
Kaium MA, 2020, INT J PHARM HEALTHC, V14, P251, DOI 10.1108/IJPHM-06-2019-0041
Kalinic Z, 2021, EXPERT SYST APPL, V175, DOI 10.1016/j.eswa.2021.114803
Kamal SA, 2020, TECHNOL SOC, V60, DOI 10.1016/j.techsoc.2019.101212
Kavandi H, 2020, HEALTH SOC CARE COMM, V28, P1827, DOI 10.1111/hsc.13011
Keikhosrokiani P, 2018, TELEMAT INFORM, V35, P753, DOI 10.1016/j.tele.2017.11.006
Khalilzadeh J, 2017, COMPUT HUM BEHAV, V70, P460, DOI 10.1016/j.chb.2017.01.001
Khodadad-Saryazdi A, 2021, TECHNOVATION, V107, DOI 10.1016/j.technovation.2021.102273
Kitsios F, 2019, COMPUT STAND INTER, V63, P16, DOI 10.1016/j.csi.2018.11.002
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Krasnova H, 2010, J INF TECHNOL-UK, V25, P109, DOI 10.1057/jit.2010.6
Kruse CS, 2018, J TELEMED TELECARE, V24, P4, DOI 10.1177/1357633X16674087
Mombeuil C, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102384
Mona Chang, 2022, Technology in Society, DOI 10.1016/j.techsoc.2022.102027
Nicolaou AI, 2013, DECIS SUPPORT SYST, V54, P986, DOI 10.1016/j.dss.2012.10.024
Nikolopoulou K, 2021, COMPUT EDUC OPEN, V2, DOI 10.1016/j.caeo.2021.100041
Ooi KB, 2018, EXPERT SYST APPL, V93, P376, DOI 10.1016/j.eswa.2017.10.009
Ostberg N, 2022, INT J MED INFORM, V161, DOI 10.1016/j.ijmedinf.2022.104726
Pratono AH, 2018, J AGING HEALTH, V30, P1556, DOI 10.1177/0898264318794732
Priyadarshinee P, 2017, COMPUT HUM BEHAV, V76, P341, DOI 10.1016/j.chb.2017.07.027
Qasem YAM, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10144905
Qazi A, 2023, IEEE T ENG MANAGE, V70, P371, DOI 10.1109/TEM.2020.3046749
Rahi S, 2021, ENTERP INF SYST-UK, V15, P769, DOI 10.1080/17517575.2020.1850872
Rahim NIM, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141912726
Rajeh MT, 2021, MED EDUC ONLINE, V26, DOI 10.1080/10872981.2021.1961348
Rana NP, 2017, INFORM SYST FRONT, V19, P549, DOI 10.1007/s10796-015-9613-y
Salehi S, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3235718
Salisbury WD, 2001, IND MANAGE DATA SYST, V101, P165, DOI 10.1108/02635570110390071
Serrano KM, 2021, INT J HEALTH CARE Q, V34, P35, DOI 10.1108/IJHCQA-06-2020-0098
Sharma A, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106919
Sharma S, 2024, IEEE T ENG MANAGE, V71, P1846, DOI 10.1109/TEM.2022.3157976
Shi JJ, 2021, FRONT PEDIATR, V9, DOI 10.3389/fped.2021.745687
Shiferaw KB, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0250220
Song T, 2021, J MED INTERNET RES, V23, DOI 10.2196/26670
Sulaman H, 2022, INT J MED INFORM, V158, DOI 10.1016/j.ijmedinf.2021.104653
Vaidya A., 2021, MHEALTHINTELLIGENCE
VanderSchaaf HP, 2023, IEEE T ENG MANAGE, V70, P631, DOI 10.1109/TEM.2021.3053966
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Voran David, 2015, Mo Med, V112, P129
Wang HL, 2021, JMIR MHEALTH UHEALTH, V9, DOI 10.2196/25960
Wei XY, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3140952
Wu D, 2021, HEALTH POLICY TECHN, V10, DOI 10.1016/j.hlpt.2021.100525
Yamin MAY, 2020, J INFECT PUBLIC HEAL, V13, P1845, DOI 10.1016/j.jiph.2020.10.017
Yuan SP, 2015, TELEMED E-HEALTH, V21, P735, DOI 10.1089/tmj.2014.0148
Yuan YP, 2024, IEEE T ENG MANAGE, V71, P2986, DOI 10.1109/TEM.2022.3204285
Zhang W, 2022, INT J MED INFORM, V166, DOI 10.1016/j.ijmedinf.2022.104854
Zhou M, 2019, INT J MED INFORM, V126, P118, DOI 10.1016/j.ijmedinf.2019.04.001
NR 89
TC 1
Z9 1
U1 11
U2 16
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD 2023 JUL 26
PY 2023
DI 10.1109/TEM.2023.3296132
EA JUL 2023
PG 13
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA O4IN3
UT WOS:001043468800001
DA 2024-03-27
ER
PT J
AU Barata, SFPG
Ferreira, FAF
Carayannis, EG
Ferreira, JJM
AF Barata, Sofia F. P. G.
Ferreira, Fernando A. F.
Carayannis, Elias G.
Ferreira, Joao J. M.
TI Determinants of E-Commerce, Artificial Intelligence, and Agile Methods
in Small- and Medium-Sized Enterprises
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article; Early Access
DE Agile methods; artificial intelligence (AI); cognitive mapping;
decision-making trial and evaluation laboratory (DEMATEL); e-commerce;
multiple-criteria decision analysis (MCDA); strategic options
development and analysis (SODA); technology
ID MULTICRITERIA DECISION-ANALYSIS; ADOPTION; SATISFACTION; MANAGEMENT
AB Small- and medium-sized enterprises (SMEs) are constrained by scarce resources, yet they are under strong pressure to maintain a competitive position in global markets, especially during crises such as the coronavirus disease-19 (COVID-19) pandemic. In this context, electronic commerce (hereafter e-commerce) platforms combining artificial intelligence (AI) and agile methods have been thriving. On the one hand, AI is a better way to process the data essential to platform customization and optimization. On the other hand, agile adoption ensures iterative software delivery as well as improved management practices that reduce uncertainty and allow SMEs to adapt more easily to new market requirements. A limited number of studies have covered all three topics, especially in relation to these companies. This research focused on creating an innovative model to identify determinants that favor or inhibit SME development of e-commerce, AI, and agile method projects. The model was developed using cognitive mapping-based on strategic options development and analysis-and decision-making trial and evaluation laboratory technique. Recommendations were generated to help SMEs use these tools to acquire a stronger competitive position in global markets.
C1 [Barata, Sofia F. P. G.] Univ Inst Lisbon, ISCTE Business Sch, P-1649026 Lisbon, Portugal.
[Ferreira, Fernando A. F.] Univ Inst Lisbon, ISCTE Business Sch, BRU IUL, P-1649026 Lisbon, Portugal.
[Ferreira, Fernando A. F.] Univ Memphis, Fogelman Coll Business & Econ, Memphis, TN 38152 USA.
[Carayannis, Elias G.] George Washington Univ, Sch Business, Dept Informat Syst & Technol Management, Washington, DC 20052 USA.
[Ferreira, Joao J. M.] Univ Beira Interior, NECE Res Unit, P-6200209 Covilha, Portugal.
C3 Instituto Universitario de Lisboa; Instituto Universitario de Lisboa;
University of Memphis; George Washington University; Universidade da
Beira Interior
RP Carayannis, EG (autor correspondiente), George Washington Univ, Sch Business, Dept Informat Syst & Technol Management, Washington, DC 20052 USA.
EM sofiabarata@outlook.pt; fernando.alberto.ferreira@iscte.pt;
caraye@gwu.edu; jjmf@ubi.pt
RI CARAYANNIS, ELIAS/H-3075-2014; Ferreira, João J.M./K-7669-2012
OI CARAYANNIS, ELIAS/0000-0003-2348-4311; Ferreira, João
J.M./0000-0002-5928-2474
FU Portuguese Foundation for Science and Technology [UIDB/00315/2020]
FX This work was supported in part by the Portuguese Foundation for Science
and Technology under Grant UIDB/00315/2020.
CR Abebe M, 2014, J SMALL BUS ENTERP D, V21, P100, DOI 10.1108/JSBED-10-2013-0145
Abuabara L, 2021, EUR J OPER RES, V292, P1051, DOI 10.1016/j.ejor.2020.11.032
[Anonymous], 2021, DESI DIG EC SOC IND
Barroso RMR, 2019, TECHNOL ECON DEV ECO, V25, P496, DOI 10.3846/tede.2019.9386
Bartoloni S, 2022, TECHNOVATION, V113, DOI 10.1016/j.technovation.2021.102413
Bhuyan HK, 2023, IEEE T ENG MANAGE, V70, P1655, DOI 10.1109/TEM.2021.3065699
Biesialska K., 2021, INFORM SOFTWARE TECH, V132, P106
Braga I., 2022, TECHNOL SOC, V66, P1
Carayannis EG, 2022, EUR J OPER RES, V300, P791, DOI 10.1016/j.ejor.2021.10.030
Carayannis EG, 2023, J KNOWL ECON, V14, P2420, DOI 10.1007/s13132-022-00991-2
Carayannis EG, 2022, J KNOWL ECON, V13, P926, DOI 10.1007/s13132-021-00763-4
Chan CML, 2019, INFORM SYST J, V29, P436, DOI 10.1111/isj.12215
Chaparro-Peláez J, 2016, J BUS RES, V69, P1277, DOI 10.1016/j.jbusres.2015.10.092
Chatterjee S, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120880
Chatterjee S, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120783
Chen CX, 2014, MANAGE ACCOUNT RES, V25, P187, DOI 10.1016/j.mar.2013.12.003
Chui Yin Wong, 2010, 2010 International Conference on Computer Applications and Industrial Electronics (ICCAIE), P288, DOI 10.1109/ICCAIE.2010.5735091
Cioffi R, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12020492
Darvishmotevali M, 2020, INT J HOSP MANAG, V87, DOI 10.1016/j.ijhm.2020.102499
DELBECQ AL, 1971, J APPL BEHAV SCI, V7, P466, DOI 10.1177/002188637100700404
Delbecq AL., 1975, Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Techniques
Dutot V, 2014, INT J INFORM MANAGE, V34, P672, DOI 10.1016/j.ijinfomgt.2014.06.006
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Eden C, 2001, GROUP DECIS NEGOT, V10, P119, DOI 10.1023/A:1008710816126
Ensslin L., 2000, International Transactions in Operational Research, V7, P79, DOI 10.1111/j.1475-3995.2000.tb00186.x
European Commission, 2003, OFFICIAL J EUROPEAN, V124, P36, DOI DOI 10.1093/NQ/S10-I.5.88-C
Ferreira F., 2011, Multiple Criteria Evaluation of Bank Branches: Decision Analysis Models and Applications
Freire CAR, 2023, IEEE T ENG MANAGE, V70, P1881, DOI 10.1109/TEM.2021.3098665
Gabus A, 1972, World problems, an invitation to further thought within the framework of DEMATEL
Goncalves R., 2014, P 9 IB C INF SYST TE, P1
IIBAInternational Institute of Business Analysis, 2011, AG EXT BABOK GUID CO
Ju XG, 2020, SOCIO-ECON PLAN SCI, V72, DOI 10.1016/j.seps.2019.100779
Khrais LT, 2020, FUTURE INTERNET, V12, DOI 10.3390/fi12120226
Kilic HS, 2021, J MANUF SYST, V59, P320, DOI 10.1016/j.jmsy.2021.03.003
Li G, 2019, INT J PROD ECON, V218, P135, DOI 10.1016/j.ijpe.2019.05.006
Lin CL, 2010, EXPERT SYST APPL, V37, P6723, DOI 10.1016/j.eswa.2010.01.014
Lip-Sam T, 2011, INT J BUS SOC, V12, P15
Maghrabie HF, 2019, TECHNOL FORECAST SOC, V146, P366, DOI 10.1016/j.techfore.2019.05.031
Mansor Z., 2013, SOCIAL BEHAV SCI, V107, P129
Martinidis G, 2022, J KNOWL ECON, V13, P849, DOI 10.1007/s13132-021-00722-z
Martins SM, 2020, J SERV MANAGE, V31, P291, DOI 10.1108/JOSM-03-2019-0084
Marttunen M, 2017, EUR J OPER RES, V263, P1, DOI 10.1016/j.ejor.2017.04.041
Milici A, 2023, IEEE T ENG MANAGE, V70, P495, DOI 10.1109/TEM.2020.3042458
Mullainathan S, 2017, J ECON PERSPECT, V31, P87, DOI 10.1257/jep.31.2.87
Murcia N., 2022, TECHNOLOGICAL FORECA, V174, P1
Niewöhner N, 2019, PROC CIRP, V84, P826, DOI [10.1016/j.procii.2019.04.295, 10.1016/j.procir.2019.04.295]
Nisar TM, 2017, J RETAIL CONSUM SERV, V39, P135, DOI 10.1016/j.jretconser.2017.07.010
Nunes S., 2021, J CLEANER PROD, V313, P1
Pérez-Gómeza P, 2018, BRQ-BUS RES Q, V21, P238, DOI 10.1016/j.brq.2018.08.003
Perkusich M, 2020, INFORM SOFTWARE TECH, V119, DOI 10.1016/j.infsof.2019.106241
Potdar PK, 2017, MATER TODAY-PROC, V4, P1744, DOI 10.1016/j.matpr.2017.02.016
Ribeiro MIF, 2017, TECHNOL ECON DEV ECO, V23, P140, DOI 10.3846/20294913.2016.1212742
Rodrigues A., 2022, Res. Int. Bus. Finance, V60, P1
Rodrigues RG, 2024, IEEE T ENG MANAGE, V71, P2524, DOI 10.1109/TEM.2022.3187650
Rosenhead J., 1989, OPERATIONAL RES SOCI
Sila I, 2013, ELECTRON COMMER RES, V13, P199, DOI 10.1007/s10660-013-9110-7
Sin KY, 2016, PROC ECON FINANC, V35, P434, DOI 10.1016/S2212-5671(16)00054-X
Song WY, 2017, COMPUT IND ENG, V110, P353, DOI 10.1016/j.cie.2017.06.020
Sumrit D, 2013, INT TRANS J ENG MANA, V4, P81
Tam C, 2020, INT J PROJ MANAG, V38, P165, DOI 10.1016/j.ijproman.2020.02.001
Tran LTT, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102287
TURBAN E., 2008, ELECT COMMERCE 2008
Walker JH, 2016, J INTERNET COMMER, V15, P189, DOI 10.1080/15332861.2016.1191049
Wolfslehner B, 2011, ECOL INDIC, V11, P274, DOI 10.1016/j.ecolind.2010.05.004
Yazdi M., 2020, Safety Science, V127, P1
Zhang CM, 2021, J IND INF INTEGR, V23, DOI 10.1016/j.jii.2021.100224
Zhuo ZL, 2021, AMFITEATRU ECON, V23, P174, DOI 10.24818/EA/2021/56/174
NR 67
TC 3
Z9 3
U1 35
U2 41
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD 2023 JUL 7
PY 2023
DI 10.1109/TEM.2023.3269601
EA JUL 2023
PG 15
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA M5NB7
UT WOS:001030672600001
DA 2024-03-27
ER
PT J
AU Gupta, AK
AF Gupta, Anoop Kumar
TI Is m-shopping a reasoned action? Evaluating the role of intention and
perceived risk in Indian m-shopping behaviour
SO INTERNATIONAL JOURNAL OF INDIAN CULTURE AND BUSINESS MANAGEMENT
LA English
DT Article
DE reasoned action; mobile shopping; perceived risk; neural network;
structural equation modelling; SEM; intention; attitude; perceived norm;
India
ID NEURAL-NETWORK ANALYSIS; RETAIL FORMAT CHOICE; MOBILE COMMERCE; PLANNED
BEHAVIOR; TECHNOLOGY ADOPTION; CONSUMER ATTITUDES; SOCIAL MEDIA; ONLINE;
ACCEPTANCE; MODEL
AB This study attempts to explain m-shopping behaviour of Indian consumer durable(1) customers through the theoretical lens of consumer perceived risk and reasoned action. For deeper understanding of the role of perceived risk in m-shopping behaviour, the study evaluates whether attitude and intention fully mediate the effect of risk on behaviour. A representative dataset of 485 respondents was analysed through multi-analytic techniques of structural equation modelling and neural network analysis for examining the contribution of the determinants of m-shopping behaviour. The perceived risk was found to have direct effect on m-purchasing action, besides having indirect effect through attitude and intention. This research offers new insights into the role played by perceived risk during check-out from mobile shopping cart and contributes in explaining the gap between intention and actual buying action.
C1 [Gupta, Anoop Kumar] Maharaja Agrasen Inst Technol, Dept Management, Sect 22, New Delhi 110086, India.
C3 Maharaja Agrasen Institute of Technology
RP Gupta, AK (autor correspondiente), Maharaja Agrasen Inst Technol, Dept Management, Sect 22, New Delhi 110086, India.
EM anoopkg@live.com
RI Gupta, Anoop Kumar/P-7652-2014
OI Gupta, Anoop Kumar/0000-0003-3314-3502
CR AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Ajzen I., 2008, The handbook of social psychology, P525, DOI DOI 10.4324/9780203809570.CH20
Ajzen I., 1988, ATTITUDES PERSONALIT
Ajzen I., 1985, SSSP SPRINGER SERIES, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2]
Akturan U, 2012, MARK INTELL PLAN, V30, P444, DOI 10.1108/02634501211231928
Alkailani M., 2009, Perceived risk, locus of control, product tactility and cultural impact: A study of online buying behavior in USA, India, and Jordan
Anand KS, 2009, INT REV RETAIL DISTR, V19, P505, DOI 10.1080/09593960903445376
Anwar A, 2021, J STRATEG MARK, V29, P722, DOI 10.1080/0965254X.2020.1786847
Armitage CJ, 2001, BRIT J SOC PSYCHOL, V40, P471, DOI 10.1348/014466601164939
Balapour A, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102063
Bang HK, 2000, PSYCHOL MARKET, V17, P449, DOI 10.1002/(SICI)1520-6793(200006)17:6<449::AID-MAR2>3.0.CO;2-8
Bashir S, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102285
Bauer R.A., 1967, RISK TAKING INFORM H, P23
Bezes C, 2016, INT J RETAIL DISTRIB, V44, P284, DOI 10.1108/IJRDM-02-2015-0019
Boudreau MC, 2001, MIS QUART, V25, P1, DOI 10.2307/3250956
Cases A.S., 2002, INT REV RETAIL DISTR, V12, P375, DOI [10.1080/09593960210151162, DOI 10.1080/09593960210151162]
Chan FTS, 2013, ONLINE INFORM REV, V37, P443, DOI 10.1108/OIR-01-2012-0012
Chen CS, 2013, MANAG SERV QUAL, V23, P410, DOI 10.1108/MSQ-10-2012-0137
Cheung GW, 2008, ORGAN RES METHODS, V11, P296, DOI 10.1177/1094428107300343
Chhonker MS, 2018, BOTTOM LINE, V31, P208, DOI 10.1108/BL-04-2018-0020
Cho JS, 2004, INFORM MANAGE-AMSTER, V41, P827, DOI 10.1016/j.im.2003.08.013
Choi JY, 2004, J ECON PSYCHOL, V25, P821, DOI 10.1016/j.joep.2003.08.006
Chong AYL, 2013, TECHNOL FORECAST SOC, V80, P1350, DOI 10.1016/j.techfore.2012.12.011
Chopdar PK, 2019, BEHAV INFORM TECHNOL, V38, P42, DOI 10.1080/0144929X.2018.1513563
Chopdar PK, 2018, COMPUT HUM BEHAV, V86, P109, DOI 10.1016/j.chb.2018.04.017
Chopdar PK, 2018, J INDIAN BUS RES, V10, P208, DOI 10.1108/JIBR-07-2017-0109
Chung KC, 2014, BEHAV INFORM TECHNOL, V33, P743, DOI 10.1080/0144929X.2013.805243
Conner M., 2020, HDB SPORT PSYCHOL, V3, DOI DOI 10.1002/9781119568124.CH1
Cox D., 1967, Risk taking and information handling in consumer behavior, P34
Cozzarin BP, 2016, ELECTRON COMMER RES, V16, P335, DOI 10.1007/s10660-015-9204-5
D2C, 2021, E COMM IND REP
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
de Mooij M, 2011, J INT CONSUM MARK, V23, P181, DOI 10.1080/08961530.2011.578057
Dowling G.R., 1986, PSYCHOL MARKETING, V3, P193, DOI [10.1002/mar.4220030307, DOI 10.1002/MAR.4220030307]
Eagly A. H., 1993, PSYCHOL ATTITUDES
Featherman MS, 2003, INT J HUM-COMPUT ST, V59, P451, DOI 10.1016/S1071-5819(03)00111-3
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
Fishbein M, 2011, PREDICTING AND CHANGING BEHAVIOR: THE REASONED ACTION APPROACH, P1
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Ghazali EM, 2018, ASIA PAC J MARKET LO, V30, P1064, DOI 10.1108/APJML-05-2017-0093
Giovanis A, 2019, INT J BANK MARK, V37, P1165, DOI 10.1108/IJBM-08-2018-0200
GREWAL D, 1994, J CONSUM RES, V21, P145, DOI 10.1086/209388
GroSS Michael, 2020, International Journal of Electronic Marketing and Retailing, V11, P50
Gross M, 2016, J RETAIL CONSUM SERV, V33, P109, DOI 10.1016/j.jretconser.2016.08.013
Gross M, 2015, INT REV RETAIL DISTR, V25, P215, DOI 10.1080/09593969.2014.988280
Gross M, 2015, INT J RETAIL DISTRIB, V43, P221, DOI 10.1108/IJRDM-06-2013-0119
Gupta A.K., 2018, THESIS
Gupta AK, 2019, J INT CONSUM MARK, V31, P469, DOI 10.1080/08961530.2019.1611518
Gupta AK, 2019, INT J INDIAN CULT BU, V18, P496, DOI 10.1504/IJICBM.2019.100309
Hair J.F., 2009, Multivariate data analysis
Hansen JM, 2018, COMPUT HUM BEHAV, V80, P197, DOI 10.1016/j.chb.2017.11.010
Hassan LM, 2016, J CONSUM BEHAV, V15, P72, DOI 10.1002/cb.1536
Hegner SM, 2017, J PROD BRAND MANAG, V26, P26, DOI 10.1108/JPBM-06-2016-1215
Crespo AH, 2009, J RISK RES, V12, P259, DOI 10.1080/13669870802497744
Hofstede G., 1980, CULTURES CONSEQUENCE, DOI DOI 10.1016/S0005-7967(02)00184-5
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Hubert M, 2017, PSYCHOL MARKET, V34, P175, DOI 10.1002/mar.20982
HUI CH, 1985, J CROSS CULT PSYCHOL, V16, P131, DOI 10.1177/0022002185016002001
Iacobucci D, 2010, J CONSUM PSYCHOL, V20, P90, DOI 10.1016/j.jcps.2009.09.003
Ismagilova E, 2020, INFORM SYST FRONT, V22, P1203, DOI 10.1007/s10796-019-09924-y
Jadil Y, 2021, J BUS RES, V132, P354, DOI 10.1016/j.jbusres.2021.04.052
Jain A., 2020, INDIA SURPASSED US B
Jain S, 2021, INT J INDIAN CULT BU, V22, P66
Jeyaraj A, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2021.102345
Joines JL, 2003, J CONSUM MARK, V20, P90, DOI 10.1108/07363760310464578
Joshi R, 2019, INT J INDIAN CULT BU, V19, P339
Kapoor AP, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102553
Khalifa M, 2012, J COMPUT INFORM SYST, V53, P14
Khare A, 2020, INT J INDIAN CULT BU, V20, P74
Khare A, 2019, INT J RETAIL DISTRIB, V47, P1093, DOI 10.1108/IJRDM-06-2017-0134
Kim E, 2013, INT J HOSP MANAG, V35, P203, DOI 10.1016/j.ijhm.2013.06.008
Kirk CP, 2015, J ADVERTISING RES, V55, P81, DOI 10.2501/JAR-55-1-081-094
Köster A, 2016, ELECTRON COMMER R A, V15, P26, DOI 10.1016/j.elerap.2015.11.002
Kongsompong K, 2009, AUSTRALAS MARK J, V17, DOI 10.1016/j.ausmj.2009.05.013
Lee C., 1991, Journal of International Consumer Marketing, V3, P27, DOI DOI 10.1300/J046V03N01_03
Leong LY, 2019, EXPERT SYST APPL, V133, P296, DOI 10.1016/j.eswa.2019.05.024
Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Li X, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102093
Li YM, 2010, COMPUT HUM BEHAV, V26, P673, DOI 10.1016/j.chb.2010.01.004
Li ZH, 2020, J BUS IND MARK, V35, P76, DOI 10.1108/JBIM-12-2018-0381
Liu ZZ, 2019, ELECTRON COMMER RES, V19, P575, DOI 10.1007/s10660-019-09349-4
Luna D, 2001, INT MARKET REV, V18, P45, DOI 10.1108/02651330110381998
Luo X, 2010, DECIS SUPPORT SYST, V49, P222, DOI 10.1016/j.dss.2010.02.008
Madan Khushbu, 2019, International Journal of Electronic Marketing and Retailing, V10, P130
Madan K, 2018, ASIA PAC J MARKET LO, V30, P139, DOI 10.1108/APJML-02-2017-0023
Mahapatra S, 2017, INT J RETAIL DISTRIB, V45, P930, DOI 10.1108/IJRDM-08-2016-0128
Malhotra NK, 2001, INT MARKET REV, V18, P235, DOI 10.1108/02651330110396505
MARKUS HR, 1991, PSYCHOL REV, V98, P224, DOI 10.1037/0033-295X.98.2.224
Marriott H. R., 2017, The Marketing Review, V17, P337, DOI [10.1362/146934717X14909733966254, DOI 10.1362/146934717X14909733966254]
Marriott HR, 2018, J RETAIL CONSUM SERV, V42, P133, DOI 10.1016/j.jretconser.2018.01.017
Marriott HR, 2017, INT J RETAIL DISTRIB, V45, P568, DOI 10.1108/IJRDM-09-2016-0164
Mathieson K, 1991, INFORM SYST RES, V2, P173, DOI 10.1287/isre.2.3.173
McCort D.J., 1993, J INT CONSUM MARK, V6, P91
McEachan RRC, 2011, HEALTH PSYCHOL REV, V5, P97, DOI 10.1080/17437199.2010.521684
McLean G, 2020, J BUS RES, V106, P139, DOI 10.1016/j.jbusres.2019.08.032
Mitchell V. W., 1998, British Food Journal, V100, P171, DOI 10.1108/00070709810207856
Mitchell V.W., 1999, EUR J MARKETING, V33, P163, DOI [10.1108/03090569910249229, DOI 10.1108/03090569910249229]
Mitchell V-W., 2015, P 1994 AC MARK SCI A, P350
Moore S., 2006, Journal of Website Promotion, V2, P71, DOI DOI 10.1080/15533610802104141
Morris SA, 2004, IND MANAGE DATA SYST, V104, P129, DOI 10.1108/02635570410522099
Morton NA, 2008, STRUCT EQU MODELING, V15, P491, DOI 10.1080/10705510802154331
Mukherjee W., 2018, EC TIMES
Mukherjee W., 2018, EC TIMES
Nel J, 2020, EUR J MARKETING, V54, P1433, DOI 10.1108/EJM-02-2018-0144
Khoi NH, 2018, ASIA PAC J MARKET LO, V30, P1112, DOI 10.1108/APJML-11-2017-0301
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Parry M.E., 2020, J INT CONSUMER MARKE, V33, P1
Patney M.B., 2010, THESIS
Paul J, 2016, INT J CONSUM STUD, V40, P635, DOI 10.1111/ijcs.12279
Paul J, 2016, J RETAIL CONSUM SERV, V29, P123, DOI 10.1016/j.jretconser.2015.11.006
Pelaez A, 2019, J COMPUT INFORM SYST, V59, P73, DOI 10.1080/08874417.2017.1300514
PETER JP, 1975, J CONSUM RES, V2, P29, DOI 10.1086/208613
Pookulangara S., 2010, International Journal of Electronic Commerce Studies, V1, P97
Preacher KJ, 2007, MULTIVAR BEHAV RES, V42, P185, DOI 10.1080/00273170701341316
Prodanova J, 2021, SERV IND J, V41, P648, DOI 10.1080/02642069.2018.1492561
Rajamma RK, 2009, J PROD BRAND MANAG, V18, P188, DOI 10.1108/10610420910957816
San-Martín S, 2013, J ORG COMP ELECT COM, V23, P350, DOI 10.1080/10919392.2013.837793
Sarkar S, 2020, INT J INFORM MANAGE, V50, P286, DOI 10.1016/j.ijinfomgt.2019.08.008
Schiffman L.G., 2007, CONSUMER BEHAV, V9th
Shankar A, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101920
SHEPPARD BH, 1988, J CONSUM RES, V15, P325, DOI 10.1086/209170
Shim S, 2001, J RETAILING, V77, P397, DOI 10.1016/S0022-4359(01)00051-3
Singh N, 2020, INT J INFORM MANAGE, V50, P191, DOI 10.1016/j.ijinfomgt.2019.05.022
Sohn S, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102129
Statista, 2020, NUMB SMARTPH US
Sutton S, 1998, J APPL SOC PSYCHOL, V28, P1317, DOI 10.1111/j.1559-1816.1998.tb01679.x
Thakur R, 2015, INT J RETAIL DISTRIB, V43, P148, DOI 10.1108/IJRDM-06-2013-0128
Thongpapanl N, 2018, J INT MARKETING, V26, P22, DOI 10.1509/jim.17.0129
Triandis H.C., 2018, INDIVIDUALISM COLLEC
Tyrväinen O, 2019, J INTERNET COMMER, V18, P221, DOI 10.1080/15332861.2019.1595364
Vahdat A, 2021, AUSTRALAS MARK J, V29, P187, DOI 10.1016/j.ausmj.2020.01.002
Van den Poel D., 1996, INT REV RETAIL DISTR, V6, P351, DOI DOI 10.1080/09593969600000039
Wang EST, 2017, BEHAV INFORM TECHNOL, V36, P2, DOI 10.1080/0144929X.2016.1143033
Wu IL, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2020.102099
Yang K, 2012, J RETAIL CONSUM SERV, V19, P484, DOI 10.1016/j.jretconser.2012.06.003
Zhang LY, 2012, COMPUT HUM BEHAV, V28, P1902, DOI 10.1016/j.chb.2012.05.008
NR 137
TC 0
Z9 0
U1 0
U2 1
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 1753-0806
EI 1753-0814
J9 INT J INDIAN CULT BU
JI Int. J. Indian Cult. Bus. Manag.
PY 2022
VL 27
IS 4
BP 436
EP 465
DI 10.1504/IJICBM.2022.127729
PG 31
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 7F1AZ
UT WOS:000901589700002
DA 2024-03-27
ER
PT J
AU Chen, SL
Li, XL
Liu, KC
Wang, XS
AF Chen, Shili
Li, Xiaolin
Liu, Kecheng
Wang, Xuesong
TI Chatbot or human? The impact of online customer service on consumers'
purchase intentions
SO PSYCHOLOGY & MARKETING
LA English
DT Article
DE artificial intelligence; chatbot; demand certainty; human employee;
online customer service; processing fluency; product type
ID ARTIFICIAL-INTELLIGENCE; PRODUCT TYPE; INFORMATION; EXPERIENCE; SEARCH;
FLUENCY; UNCERTAINTY; BENEFITS; REVIEWS; ROBOTS
AB Artificial intelligence (AI) chatbots and human employees have emerged as the dominant forms of online customer service. However, existing research rarely connects the service differences between them in terms of product type, ignoring the interactivity between the two. This study reveals the effect of matching customer service type (AI chatbot vs. human) to product type (search vs. experience) on consumers' purchase intentions through four experiments, revealing the psychological mechanism and boundary condition for the existence of this effect. It shows that (1) the match between customer service type and product type positively affects consumers' purchase intentions; (2) this matching effect is mediated by processing fluency and perceived service quality; and (3) the matching effect works only when consumers' demand certainty is low. These findings enrich the theoretical study of online customer service, and provide marketing insights for companies to improve the adoption of AI chatbots and human employees.
C1 [Chen, Shili; Li, Xiaolin; Wang, Xuesong] Sichuan Agr Univ, Business & Tourism Sch, Chengdu, Peoples R China.
[Liu, Kecheng] Univ Reading, Henley Business Sch, Whiteknights, England.
[Li, Xiaolin] Sichuan Agr Univ, Business & Tourism Sch, Chengdu 611830, Peoples R China.
C3 Sichuan Agricultural University; University of Reading; Sichuan
Agricultural University
RP Li, XL (autor correspondiente), Sichuan Agr Univ, Business & Tourism Sch, Chengdu 611830, Peoples R China.
EM shinely1203@163.com
RI WANG, JIAXUAN/JMP-8599-2023; Rau, Lea/IXW-9119-2023
OI chen, shili/0000-0003-3689-7019
FU National Philosophy and Social Science Foundation project: Research on
consumption decision and application problems of improving the
traceability of China's agricultural industry chain and supply chain
[21BGL161]
FX The National Philosophy and Social Science Foundation project: Research
on consumption decision and application problems of improving the
traceability of China's agricultural industry chain and supply chain,
Grant/Award Number: 21BGL161
CR Akdim K, 2023, SERV BUS, V17, P81, DOI 10.1007/s11628-023-00527-x
Alter AL, 2009, PERS SOC PSYCHOL REV, V13, P219, DOI 10.1177/1088868309341564
Ameen N, 2022, PSYCHOL MARKET, V39, P2110, DOI 10.1002/mar.21715
Aw ECX, 2022, TECHNOL FORECAST SOC, V180, DOI 10.1016/j.techfore.2022.121711
Balbo L, 2015, RECH APPL MARKET-ENG, V30, P28, DOI 10.1177/2051570714563686
Belanche D, 2020, SERV IND J, V40, P203, DOI 10.1080/02642069.2019.1672666
BITNER MJ, 1990, J MARKETING, V54, P69, DOI 10.2307/1251871
Bock DE, 2020, J SERV MARK, V34, P317, DOI 10.1108/JSM-01-2019-0047
Borau S, 2021, PSYCHOL MARKET, V38, P1052, DOI 10.1002/mar.21480
Calvo MG, 2001, COGNITION EMOTION, V15, P299, DOI 10.1080/0269993004200141
Casado-Aranda LA, 2022, J INTERACT MARK, V57, P296, DOI 10.1177/10949968221087259
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Chi OHX, 2022, J TRAVEL RES, V61, P170, DOI 10.1177/0047287520971054
CRONIN JJ, 1992, J MARKETING, V56, P55, DOI 10.2307/1252296
Filieri R, 2021, PSYCHOL MARKET, V38, P431, DOI 10.1002/mar.21440
Flavién C, 2023, PSYCHOL MARKET, V40, P328, DOI 10.1002/mar.21765
Flavian C, 2021, SERV IND J, V41, P853, DOI 10.1080/02642069.2021.1989177
Franke GR, 2004, J ACAD MARKET SCI, V32, P20, DOI 10.1177/0092070303257856
Gao X, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.102994
GOODSTEIN RC, 1993, J CONSUM RES, V20, P87, DOI 10.1086/209335
Graf LKM, 2018, J CONSUM PSYCHOL, V28, P393, DOI 10.1002/jcpy.1021
Gray HM, 2007, SCIENCE, V315, P619, DOI 10.1126/science.1134475
Haenlein M, 2021, J BUS RES, V124, P405, DOI 10.1016/j.jbusres.2020.10.042
Hafner M, 2010, J EXP SOC PSYCHOL, V46, P73, DOI 10.1016/j.jesp.2009.10.001
Haugeland IKF, 2022, INT J HUM-COMPUT ST, V161, DOI 10.1016/j.ijhcs.2022.102788
Hayes AF., 2018, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, DOI DOI 10.1111/JEDM.12050
Hilken T, 2022, PSYCHOL MARKET, V39, P1660, DOI 10.1002/mar.21678
Hill J, 2015, COMPUT HUM BEHAV, V49, P245, DOI 10.1016/j.chb.2015.02.026
Huang P, 2009, J MARKETING, V73, P55, DOI 10.1509/jmkg.73.2.55
Huang YH, 2018, INFORM MANAGE-AMSTER, V55, P430, DOI 10.1016/j.im.2017.10.003
Invernizzi AC, 2022, PSYCHOL MARKET, V39, P974, DOI 10.1002/mar.21635
Kallweit K, 2014, J RETAIL CONSUM SERV, V21, P268, DOI 10.1016/j.jretconser.2014.02.002
Kang G.D., 2006, MANAG SERV QUAL, V16, P37, DOI [DOI 10.1108/09604520610639955, 10.1108/09604520610639955]
Ketron S, 2020, J RETAIL CONSUM SERV, V54, DOI 10.1016/j.jretconser.2019.102021
Klein LR, 1998, J BUS RES, V41, P195, DOI 10.1016/S0148-2963(97)00062-3
Kotler P., 1967, Managerial marketing, planning, analysis, and control
Kramer M.W., 1999, Management Communication Quarterly, V13, P305, DOI [10.1177/0893318999132007, DOI 10.1177/0893318999132007]
Lee EJ, 2014, COMPUT HUM BEHAV, V31, P356, DOI 10.1016/j.chb.2013.10.050
Letheren K, 2020, J MARKET MANAG-UK, V36, P216, DOI 10.1080/0267257X.2019.1706306
Li CY, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2023.103264
Lim WM, 2022, PSYCHOL MARKET, V39, P1129, DOI 10.1002/mar.21654
Liu DW, 2023, TECHNOL SOC, V73, DOI 10.1016/j.techsoc.2023.102244
Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
Luan J, 2016, COMPUT HUM BEHAV, V65, P420, DOI 10.1016/j.chb.2016.08.037
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Mauri C, 2021, PSYCHOL MARKET, V38, P1323, DOI 10.1002/mar.21473
McLean G, 2019, COMPUT HUM BEHAV, V99, P28, DOI 10.1016/j.chb.2019.05.009
McLean G, 2017, COMPUT HUM BEHAV, V76, P494, DOI 10.1016/j.chb.2017.08.005
McLeay F, 2021, J SERV RES-US, V24, P104, DOI 10.1177/1094670520933354
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Mero J, 2018, ELECTRON MARK, V28, P205, DOI 10.1007/s12525-017-0281-2
Moriuchi E, 2021, J STRATEG MARK, V29, P375, DOI 10.1080/0965254X.2020.1740766
NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630
Northey G, 2020, J ADVERTISING RES, V60, P222, DOI 10.2501/JAR-2020-009
Pan B, 2006, ANN TOURISM RES, V33, P809, DOI 10.1016/j.annals.2006.03.006
PARASURAMAN A, 1988, J RETAILING, V64, P12
Pizzi G, 2023, PSYCHOL MARKET, V40, P1372, DOI 10.1002/mar.21813
Rabassa V, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121292
Rajaobelina L, 2021, PSYCHOL MARKET, V38, P2339, DOI 10.1002/mar.21548
Reynolds KE, 1999, J RETAILING, V75, P11, DOI 10.1016/S0022-4359(99)80002-5
Robinson S, 2020, J BUS RES, V116, P366, DOI 10.1016/j.jbusres.2019.08.038
Ruan YY, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103059
Rushton A.M., 1989, European Journal of Marketing, V23, P23, DOI DOI 10.1108/EUM0000000000582
Sands S, 2022, PSYCHOL MARKET, V39, P2039, DOI 10.1002/mar.21723
Septianto F, 2020, INT J RES MARK, V37, P621, DOI 10.1016/j.ijresmar.2019.11.003
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shin H, 2023, INT J CONSUM STUD, V47, P545, DOI 10.1111/ijcs.12849
Söderlund M, 2022, J RETAIL CONSUM SERV, V67, DOI 10.1016/j.jretconser.2022.102999
Song MM, 2022, ELECTRON COMMER R A, V55, DOI 10.1016/j.elerap.2022.101199
TAYLOR RS, 1968, COLL RES LIBR, V29, P178, DOI 10.5860/crl_29_03_178
Tsai WHS, 2021, PSYCHOL MARKET, V38, P2377, DOI 10.1002/mar.21556
URBANY JE, 1989, J CONSUM RES, V16, P208, DOI 10.1086/209209
Viglia G, 2021, IND MARKET MANAG, V98, P193, DOI 10.1016/j.indmarman.2021.08.007
Wang YW, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103002
Wansink B, 1996, J MARKETING, V60, P31, DOI 10.2307/1251886
Wien AH, 2021, J BUS RES, V137, P13, DOI 10.1016/j.jbusres.2021.08.016
Winkielman P., 2012, Cognitive consistency: A fundamental principle in social cognition, P89
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Wu RJ, 2022, J BUS RES, V141, P299, DOI 10.1016/j.jbusres.2021.12.037
Xiao L, 2021, J SERV RES-US, V24, P9, DOI 10.1177/1094670519878881
Xie ZH, 2022, PSYCHOL MARKET, V39, P1902, DOI 10.1002/mar.21706
Xu YZ, 2020, AUSTRALAS MARK J, V28, P189, DOI 10.1016/j.ausmj.2020.03.005
Yao Q, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103082
Zhang AD, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107415
Zhu YM, 2022, J BUS RES, V150, P642, DOI 10.1016/j.jbusres.2022.06.044
NR 85
TC 4
Z9 4
U1 120
U2 185
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0742-6046
EI 1520-6793
J9 PSYCHOL MARKET
JI Psychol. Mark.
PD NOV
PY 2023
VL 40
IS 11
BP 2186
EP 2200
DI 10.1002/mar.21862
EA JUN 2023
PG 15
WC Business; Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA T8XT9
UT WOS:001016823200001
DA 2024-03-27
ER
PT J
AU Arya, V
Paul, J
Sethi, D
AF Arya, Vikas
Paul, Justin
Sethi, Deepa
TI Like it or not! Brand communication on social networking sites triggers
consumer-based brand equity
SO INTERNATIONAL JOURNAL OF CONSUMER STUDIES
LA English
DT Article
DE artificial neural network; brand attachment; brand communication; brand
created content; consumer-based brand equity; social networking sites;
user generated content
ID MEDIA MARKETING ACTIVITIES; CUSTOMER SATISFACTION; ATTACHMENT;
EXPERIENCE; LOYALTY; ADVOCACY; SERVICES; IMPACT; MODEL; GRATIFICATIONS
AB This study examines how brand communication influences consumer-based brand equity (BEQ) through social networking sites in the presence of brand attachment (BAT) as a mediator. The outcomes related to consumer-BEQ, such as consumers' pay intention and loyalty to a brand and a brand's vocal ability, are also explored in this study. An empirical investigation for 498 responses was carried using Smart-PLS, Process-macro & artificial neural network modeling based hybrid approach. The analysis indicates that brand consumer-BEQ is high when a brand's communication on social media platforms is positive. A strong mediating role of BAT is confirmed. The study is unique in terms of explaining the role of brand communication on social networking sites and its impact on consumer-BEQ in the presence of BAT as a mediator. While focusing on Millennials' tech-savvy characteristics and considering SNSs as an advanced tool for brand communication, brands should refine their marketing strategy.
C1 [Arya, Vikas] Int Univ Rabat, Rabat Business Sch, Rabat, Morocco.
[Paul, Justin] Univ Puerto Rico, MBA Dept, San Juan, PR 00936 USA.
[Sethi, Deepa] Indian Inst Management, Kozhikode, India.
C3 Universite Internationale de Rabat; University of Puerto Rico; Indian
Institute of Management (IIM System); Indian Institute of Management
Kozhikode
RP Arya, V (autor correspondiente), Int Univ Rabat, Rabat Business Sch, Rabat, Morocco.
EM vikas.aryaa@yahoo.in
RI PAUL, JUSTIN/Y-5214-2019; Arya, Dr. Vikas/U-5500-2017
OI PAUL, JUSTIN/0000-0001-5062-8371; Arya, Dr. Vikas/0000-0001-8025-1528
CR Aaker DA, 1996, CALIF MANAGE REV, V38, P102, DOI 10.2307/41165845
Aaker DavidA., 1996, BUILDING STRONG BRAN
Al-Adwan AS, 2019, J THEOR APPL EL COMM, V14, P15, DOI 10.4067/S0718-18762019000200103
Algharabat R, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.01.016
ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
[Anonymous], 2018, #BrandsGetReal: What consumers want from brands in a divided society
Arya V, 2019, INT J INFORM MANAGE, V49, P142, DOI 10.1016/j.ijinfomgt.2019.03.013
Arya V, 2019, IIM KOZHIKODE SOC MA, V8, P87, DOI 10.1177/2277975219825508
Arya V, 2018, CORP COMMUN, V23, P648, DOI 10.1108/CCIJ-03-2018-0036
Banerjee S, 2016, IIMB MANAG REV, V28, P225, DOI 10.1016/j.iimb.2016.10.002
Belaid S, 2011, J PROD BRAND MANAG, V20, P37, DOI 10.1108/10610421111108003
Bhakar S., 2018, J PROMOTION MANAGEME, V24, P153
Biedenbach G, 2010, J BRAND MANAG, V17, P446, DOI 10.1057/bm.2009.37
BOWLBY J, 1979, BEHAV BRAIN SCI, V2, P637, DOI 10.1017/S0140525X00064955
Brakus JJ, 2009, J MARKETING, V73, P52, DOI 10.1509/jmkg.73.3.52
Bu LG, 2021, J CLEAN PROD, V280, DOI 10.1016/j.jclepro.2020.124413
Burmann C., 2008, USE GENERATED BRANDI
CFI Group, 2016, ONL SHOPP EXP RET OP
Cheah JH, 2020, AUSTRALAS MARK J, V28, P218, DOI 10.1016/j.ausmj.2020.04.005
Choudrie J, 2020, INFORM SYST FRONT, V22, P673, DOI [10.1007/s10796-018-9875-2, 10.1080/10298436.2018.1549325]
Chu SC, 2019, J GLOB FASH MARK, V10, P81
Clement J., 2019, GLOBAL NUMBER FACEBO
Correia LoureiroS M., 2012, Journal of Promotion Management, V18, P329, DOI [DOI 10.1080/10496491.2012.696460, https://doi.org/10.1080/10496491.2012.696460]
Damjana J., 2015, INNOVATIVE ISSUES AP, V8, P28, DOI DOI 10.12959/ISSN.1855-0541.IIASS-2015-NO1-ART02
Dash G, 2021, TECHNOL FORECAST SOC, V173, DOI 10.1016/j.techfore.2021.121092
Dash G, 2021, J BUS RES, V122, P608, DOI 10.1016/j.jbusres.2020.10.016
Datta H, 2017, J MARKETING, V81, P1, DOI 10.1509/jm.15.0340
de Almeida SO, 2018, J INTERACT MARK, V44, P29, DOI 10.1016/j.intmar.2018.05.006
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Devereux E, 2020, J CONSUM BEHAV, V19, P151, DOI 10.1002/cb.1800
Dholakia UM, 2009, J SERV RES-US, V12, P208, DOI 10.1177/1094670509338618
Faul F, 2007, BEHAV RES METHODS, V39, P175, DOI 10.3758/BF03193146
Foroudi P, 2018, J BUS RES, V89, P462, DOI 10.1016/j.jbusres.2018.01.031
Gao Q, 2016, COMPUT HUM BEHAV, V63, P868, DOI 10.1016/j.chb.2016.06.022
García-de los Salmones MD, 2021, J BUS ETHICS, V171, P583, DOI 10.1007/s10551-020-04466-9
Gilal RG, 2022, INT J CONSUM STUD, V46, P161, DOI 10.1111/ijcs.12653
Gohary A, 2020, CURR ISSUES TOUR, V23, P131, DOI 10.1080/13683500.2018.1560397
Gold AH, 2001, J MANAGE INFORM SYST, V18, P185, DOI 10.1080/07421222.2001.11045669
Gordon-Wilson S, 2022, INT J CONSUM STUD, V46, P575, DOI 10.1111/ijcs.12701
Grewal D, 1998, J RETAILING, V74, P331, DOI 10.1016/S0022-4359(99)80099-2
Hair J. F., 2017, ADV ISSUES PARTIAL L
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hair Jr J.F., 2016, PRIMER PARTIAL LEAST
Hamzah ZL, 2021, J RES INTERACT MARK, V15, P336, DOI 10.1108/JRIM-05-2020-0113
HANJEEHOON, 2020, [The Journal of the Korea Contents Association, 한국콘텐츠학회 논문지], V20, P31, DOI 10.5392/JKCA.2020.20.01.031
Hao AW, 2021, INT MARKET REV, V38, P46, DOI 10.1108/IMR-01-2019-0028
Hayes AF., 2018, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, DOI DOI 10.1111/JEDM.12050
HAYKIN S, 1997, NEURAL NETWORKS COMP
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Ho R.C., 2018, J RELATIONSHIP MARKE, V17, P204, DOI DOI 10.1080/15332667.2018.1492322
Hollebeek LD, 2014, J INTERACT MARK, V28, P149, DOI 10.1016/j.intmar.2013.12.002
Hosen M, 2021, COMPUT EDUC, V172, DOI 10.1016/j.compedu.2021.104262
Hsieh SH, 2016, J INTERACT MARK, V33, P13, DOI 10.1016/j.intmar.2015.10.001
Huaman-Ramirez R, 2019, EUR BUS REV, V31, P610, DOI 10.1108/EBR-02-2017-0039
Huang R, 2018, J RES INTERACT MARK, V12, P274, DOI 10.1108/JRIM-07-2017-0059
Huba, 2003, INC MAGAZINE
Hwang E, 2019, INT J HOSP MANAG, V76, P19, DOI 10.1016/j.ijhm.2018.03.009
Islam AKMN, 2019, INFORM TECHNOL PEOPL, V32, P269, DOI 10.1108/ITP-07-2017-0213
Islam T, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102357
Jahn S, 2012, PSYCHOL MARKET, V29, P445, DOI 10.1002/mar.20533
Jani D, 2014, INT J HOSP MANAG, V37, P11, DOI 10.1016/j.ijhm.2013.10.007
Japutra A, 2018, J STRATEG MARK, V26, P223, DOI 10.1080/0965254X.2016.1195862
Jawahar D, 2020, INT J TOUR CITIES, V6, P491, DOI 10.1108/IJTC-08-2019-0147
Jillapalli RK, 2010, J MARKET EDUC, V32, P328, DOI 10.1177/0273475310380880
Jones H., 2003, J FINANC SERV MARK, V8, P71, DOI [10.1057/palgrave.fsm.4770108, DOI 10.1057/PALGRAVE.FSM.4770108]
Joshi R, 2021, INT J CONSUM STUD, V45, P259, DOI 10.1111/ijcs.12618
Kampani N, 2021, BENCHMARKING, V28, P1247, DOI 10.1108/BIJ-09-2020-0492
Kapoor S, 2021, INT J CONSUM STUD, V45, P1047, DOI 10.1111/ijcs.12633
Katz E., 1973, PUBLIC OPIN QUART, V37, DOI [10.1086/268109, DOI 10.1086/268109]
Keller E, 2007, J ADVERTISING RES, V47, P448, DOI 10.2501/S0021849907070468
Keller K.L., 2019, Handbuch Markenfuhrung, P1409, DOI DOI 10.1007/978-3-658-13342-9_72
KELLER KL, 1993, J MARKETING, V57, P1, DOI 10.2307/1252054
Keller KL, 2003, MARK MANAG, V12, P26
Khatoon S, 2021, INT J CONSUM STUD, V45, P719, DOI 10.1111/ijcs.12665
Kim AJ, 2012, J BUS RES, V65, P1480, DOI 10.1016/j.jbusres.2011.10.014
Kim J, 2022, INT J CONSUM STUD, V46, P558, DOI 10.1111/ijcs.12700
Kizgin H, 2018, INFORM SYST FRONT, V20, P503, DOI 10.1007/s10796-017-9817-4
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015010101
Kujur F, 2020, J THEOR APPL EL COMM, V15, P30, DOI 10.4067/S0718-18762020000100104
Kujur F, 2017, ASIA PAC MANAG REV, V22, P16, DOI 10.1016/j.apmrv.2016.10.006
Kumar A, 2020, J BUS RES, V113, P384, DOI 10.1016/j.jbusres.2019.09.030
Kumar A, 2018, INT BUS REV, V27, P969, DOI 10.1016/j.ibusrev.2018.02.007
Lee J, 2018, DECIS SUPPORT SYST, V110, P1, DOI 10.1016/j.dss.2018.03.001
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Liu S, 2020, INT J HOSP MANAG, V90, DOI 10.1016/j.ijhm.2020.102611
Mägi AW, 2003, J RETAILING, V79, P97, DOI 10.1016/S0022-4359(03)00008-3
Mahapatra S, 2017, MARK INTELL PLAN, V35, P594, DOI 10.1108/MIP-01-2017-0007
Melancon JP, 2011, J ACAD MARKET SCI, V39, P341, DOI 10.1007/s11747-010-0206-5
Moreira AC, 2017, J BUS ECON MANAG, V18, P68, DOI 10.3846/16111699.2016.1252793
Naidoo C, 2018, J BRAND MANAG, V25, P3, DOI 10.1057/s41262-017-0072-2
Nisar TM, 2016, COMPUT HUM BEHAV, V62, P743, DOI 10.1016/j.chb.2016.04.042
Pappu R, 2005, J PROD BRAND MANAG, V14, P143, DOI 10.1108/10610420510601012
Park C.W., 2007, SEOUL NATL J, V12, P3, DOI DOI 10.4337/9781848446151.00007
Park C.W., 2014, HDB BRAND RELATIONSH, P349
Park CW, 2006, J CONSUM RES, V33, P16, DOI 10.1086/504122
Park CW, 2010, J MARKETING, V74, P1, DOI 10.1509/jmkg.74.6.1
Paul J, 2021, INT J CONSUM STUD, V45, P937, DOI 10.1111/ijcs.12727
Paul J, 2019, EUR MANAG J, V37, P299, DOI 10.1016/j.emj.2018.07.003
Paul J, 2018, EUR J INT MANAG, V12, P722, DOI 10.1504/EJIM.2018.094466
Paul J, 2015, MARK INTELL PLAN, V33, P691, DOI 10.1108/MIP-02-2014-0028
Piehler R, 2019, EUR J MARKETING, V53, P1833, DOI 10.1108/EJM-10-2017-0722
Podsakoff PM, 2012, ANNU REV PSYCHOL, V63, P539, DOI 10.1146/annurev-psych-120710-100452
Puligadda S, 2021, J RES INTERACT MARK, V15, P709, DOI 10.1108/JRIM-09-2019-0149
PUTREVU S, 1994, J ADVERTISING, V23, P77, DOI 10.1080/00913367.1994.10673443
Qin YS, 2020, J RES INTERACT MARK, V14, P337, DOI 10.1108/JRIM-08-2019-0138
Ringle CM, 2010, ANN INFORM SYST, V8, P19, DOI 10.1007/978-1-4419-1280-0_2
Romaniuk J., 2004, Marketing Theory, V4, P327, DOI DOI 10.1177/1470593104047643
Sarstedt M, 2019, AUSTRALAS MARK J, V27, P197, DOI 10.1016/j.ausmj.2019.05.003
Schivinski B, 2015, J RES INTERACT MARK, V9, P31, DOI 10.1108/JRIM-02-2014-0007
Schultz D, 2016, J PROD BRAND MANAG, V25, P507, DOI 10.1108/JPBM-07-2016-1260
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Seo EJ, 2018, J AIR TRANSP MANAG, V66, P36, DOI 10.1016/j.jairtraman.2017.09.014
Shah S., 2011, Asian Journal of Business Management, V4, P105
Sharma A, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106919
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Södergren J, 2021, INT J CONSUM STUD, V45, P645, DOI 10.1111/ijcs.12651
Sreejesh S, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102155
Taber KS, 2018, RES SCI EDUC, V48, P1273, DOI 10.1007/s11165-016-9602-2
Tang ZY, 2019, GOV INFORM Q, V36, DOI 10.1016/j.giq.2019.06.001
Tascioglu C., 2019, 16 EUR C PSYCH, P870
Thomson M, 2005, J CONSUM PSYCHOL, V15, P77, DOI 10.1207/s15327663jcp1501_10
Tsiros M, 2004, J CONSUM RES, V31, P476, DOI 10.1086/422124
Verduyn P, 2020, CURR OPIN PSYCHOL, V36, P32, DOI 10.1016/j.copsyc.2020.04.002
Wang WT, 2012, INTERNET RES, V22, P142, DOI 10.1108/10662241211214548
Wilk V, 2020, J PROD BRAND MANAG, V29, P415, DOI 10.1108/JPBM-10-2018-2090
Yannopoulou N, 2019, INT J CONSUM STUD, V43, P348, DOI 10.1111/ijcs.12514
Yap SAF, 2021, INT J CONSUM STUD, V45, P1239, DOI 10.1111/ijcs.12724
Yoo B, 2001, J BUS RES, V52, P1, DOI 10.1016/S0148-2963(99)00098-3
Zhang CB, 2019, J BUS IND MARK, V34, P1420, DOI 10.1108/JBIM-07-2018-0211
Zhang Z., 2018, Practical statistical power analysis using Webpower and R, DOI [10.35566/power, DOI 10.35566/POWER, 10.35566/power.]
Zhao XS, 2010, J CONSUM RES, V37, P197, DOI 10.1086/651257
NR 131
TC 38
Z9 38
U1 18
U2 81
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1470-6423
EI 1470-6431
J9 INT J CONSUM STUD
JI Int. J. Consum. Stud.
PD JUL
PY 2022
VL 46
IS 4
BP 1381
EP 1398
DI 10.1111/ijcs.12763
EA DEC 2021
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 2E6GY
UT WOS:000730663000001
DA 2024-03-27
ER
PT J
AU Kwark, Y
Lee, GM
Pavlou, PA
Qiu, LF
AF Kwark, Young
Lee, Gene Moo
Pavlou, Paul A.
Qiu, Liangfei
TI On the Spillover Effects of Online Product Reviews on Purchases:
Evidence from Clickstream Data
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE online product reviews; substitutive products; complementary products;
brand spillover; WOM spillover; topic modeling; machine learning
ID WORD-OF-MOUTH; RECOMMENDATION NETWORKS; MODERATING ROLE; SOCIAL MEDIA;
CONSUMER ATTITUDES; EMPIRICAL-ANALYSIS; CUSTOMER REVIEWS;
SELF-SELECTION; BRAND; SALES
AB We study the spillover effects of the online reviews of other covisited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by (a) whether the related (covisited) products are complementary or substitutive, (b) the choice of media channel (mobile or personal computer (PC)) used, (c) whether the related products are from the same or a different brand, (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of covisited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.
C1 [Kwark, Young; Qiu, Liangfei] Univ Florida, Warrington Coll Business, Dept Informat Syst & Operat Management, Gainesville, FL 32611 USA.
[Lee, Gene Moo] Univ British Columbia, Sauder Sch Business, Vancouver, BC V6T 1Z2, Canada.
[Pavlou, Paul A.] Univ Houston, CT Bauer Coll Business, Houston, TX 77204 USA.
C3 State University System of Florida; University of Florida; University of
British Columbia; University of Houston System; University of Houston
RP Kwark, Y (autor correspondiente), Univ Florida, Warrington Coll Business, Dept Informat Syst & Operat Management, Gainesville, FL 32611 USA.
EM young.kwark@warrington.ufl.edu; gene.lee@sauder.ubc.ca;
pavlou@bauer.uh.edu; liangfei.qiu@warrington.ufl.edu
RI Pavlou, Paul A/D-3561-2014
OI Pavlou, Paul A/0000-0002-8830-5727; Lee, Gene Moo/0000-0003-0657-6898;
Qiu, Liangfei/0000-0002-8771-9389
CR Aaker DavidA., 2009, MANAGING BRAND EQUIT
Aaker DavidA., 1996, BUILDING STRONG BRAN
Aggarwal R, 2012, INFORM SYST RES, V23, P976, DOI 10.1287/isre.1110.0402
Ahluwalia R, 2001, J MARKETING RES, V38, P458, DOI 10.1509/jmkr.38.4.458.18903
Anderson ET, 2013, J MARKETING RES, V50, P489, DOI 10.1509/jmr.11.0538
[Anonymous], 2014, P 35 INT C INF SYST
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Ariely D, 2016, TIME PRESSURE BEHAV
Balachander S, 2003, J MARKETING, V67, P4, DOI 10.1509/jmkg.67.1.4.18594
Bart Y, 2014, J MARKETING RES, V51, P270, DOI 10.1509/jmr.13.0503
Barwise P., 2002, J INTERACT MARK, V16, P14
Basuroy S, 2006, J MARKETING RES, V43, P287, DOI 10.1509/jmkr.43.2.287
Blackwell RogerD., 2001, CONSUMER BEHAV, V9th
Borah A, 2016, J MARKETING RES, V53, P143, DOI 10.1509/jmr.13.0009
Burtch G, 2018, MANAGE SCI, V64, P2065, DOI 10.1287/mnsc.2016.2715
Butcher D, 2010, MOBILE AD CAMPAIGNS
Cameron AC, 2008, REV ECON STAT, V90, P414, DOI 10.1162/rest.90.3.414
Carmi E., 2012, SSRN ELECT J, DOI [10.2139/ssrn.1694308, DOI 10.2139/SSRN.1694308]
Chae I, 2017, MARKET SCI, V36, P89, DOI 10.1287/mksc.2016.1001
Chen YB, 2005, MARKET SCI, V24, P218, DOI 10.1287/mksc.1040.0089
Chen YB, 2008, MANAGE SCI, V54, P477, DOI 10.1287/mnsc.1070.0810
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chintagunta PK, 1998, J MARKETING RES, V35, P43, DOI 10.2307/3151929
Chintagunta PK, 2010, MARKET SCI, V29, P944, DOI 10.1287/mksc.1100.0572
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
Connelly BL, 2011, J MANAGE, V37, P39, DOI 10.1177/0149206310388419
Dellarocas C, 2007, J INTERACT MARK, V21, P23, DOI 10.1002/dir.20087
Dellarocas C, 2006, MANAGE SCI, V52, P1577, DOI 10.1287/mnsc.1060.0567
Dellarocas C, 2010, J MANAGE INFORM SYST, V27, P127, DOI 10.2753/MIS0742-1222270204
Dimoka A, 2012, MIS QUART, V36, P395
DODSON JA, 1978, J MARKETING RES, V15, P72, DOI 10.2307/3150402
Drossos D., 2007, Journal of Interactive Advertising, V7, P16
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Duan W, 2009, MIS QUART, V33, P23
Entin EE, 1990, TR454 ALPH INC
EPPEN GD, 1991, SLOAN MANAGE REV, V32, P7
Erdem T, 2002, J MARKETING RES, V39, P408, DOI 10.1509/jmkr.39.4.408.19120
Erdem T, 1999, J ECONOMETRICS, V89, P159
Erdem T, 1998, J MARKETING RES, V35, P339, DOI 10.2307/3152032
Escalas JenniferE., 1998, J CONSUM PSYCHOL, V7, P131, DOI DOI 10.1207/S15327663JCP0702_02
Feng J, 2019, INFORM SYST RES, V30, P1107, DOI 10.1287/isre.2019.0852
Forman C, 2008, INFORM SYST RES, V19, P291, DOI 10.1287/isre.1080.0193
Gaeth GaryJ., 1991, MARKET LETT, V2, P47, DOI [DOI 10.1007/BF00435195, 10.1007/BF00435195]
Gao GD, 2015, MIS QUART, V39, P565, DOI 10.25300/MISQ/2015/39.3.03
Ghose A, 2013, INFORM SYST RES, V24, P613, DOI 10.1287/isre.1120.0453
Godes D, 2004, MARKET SCI, V23, P545, DOI 10.1287/mksc.1040.0071
Godes D, 2009, MARKET SCI, V28, P721, DOI 10.1287/mksc.1080.0444
Goes PB, 2014, INFORM SYST RES, V25, P222, DOI 10.1287/isre.2013.0512
GUILTINAN JP, 1987, J MARKETING, V51, P74, DOI 10.2307/1251130
Ho YC, 2017, INFORM SYST RES, V28, P626, DOI 10.1287/isre.2017.0694
Ho-Dac NN, 2013, J MARKETING, V77, P37, DOI 10.1509/jm.11.0011
Hong Y, 2014, INFORM SYST RES, V25, P328, DOI 10.1287/isre.2014.0520
Hu N, 2017, MIS QUART, V41, P449, DOI 10.25300/MISQ/2017/41.2.06
Jabr W, 2014, MIS QUART, V38, P635, DOI 10.25300/MISQ/2014/38.3.01
Kamakura WA, 2007, J RETAILING, V83, P159, DOI 10.1016/j.jretai.2006.02.006
KELLER KL, 1993, J MARKETING, V57, P1, DOI 10.2307/1252054
Khern-am-nuai W, 2018, INFORM SYST RES, V29, P871, DOI 10.1287/isre.2017.0750
KOTLER P., 2006, Marketing Management
Krishnan TV, 2012, INT J RES MARK, V29, P292, DOI 10.1016/j.ijresmar.2012.04.003
KUMAR V, 1988, J MARKETING RES, V25, P178, DOI 10.2307/3172649
Kwark Y, 2014, INFORM SYST RES, V25, P93, DOI 10.1287/isre.2013.0511
Lee D, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P85, DOI 10.1145/2872427.2882976
Lee GM, 2020, INFORM SYST RES, V31, P865, DOI 10.1287/isre.2020.0921
Lee YJ, 2015, MANAGE SCI, V61, P2241, DOI 10.1287/mnsc.2014.2082
Lewis R, 2015, QME-QUANT MARK ECON, V13, P93, DOI 10.1007/s11129-015-9155-0
Li XX, 2008, INFORM SYST RES, V19, P456, DOI 10.1287/isre.1070.0154
Li XT, 2018, INFORM SYST RES, V29, P739, DOI 10.1287/isre.2017.0728
Libai B, 2013, J MARKETING RES, V50, P161, DOI 10.1509/jmr.11.0305
Libai B, 2009, J MARKETING, V73, P19, DOI 10.1509/jmkg.73.3.19
Lin ZJ, 2017, MIS QUART, V41, P397, DOI 10.25300/MISQ/2017/41.2.04
Liu Y, 2017, INFORM SYST RES, V28, P723, DOI 10.1287/isre.2017.0715
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Lovett MJ, 2013, J MARKETING RES, V50, P427, DOI 10.1509/jmr.11.0458
Lu XH, 2013, INFORM SYST RES, V24, P596, DOI 10.1287/isre.1120.0454
Luca M, 2016, MANAGE SCI, V62, P3412, DOI 10.1287/mnsc.2015.2304
Luo XM, 2017, MIS QUART, V41, P371, DOI 10.25300/MISQ/2017/41.2.03
Luo XM, 2014, MANAGE SCI, V60, P1738, DOI 10.1287/mnsc.2013.1836
Manchanda P, 1999, MARKET SCI, V18, P95, DOI 10.1287/mksc.18.2.95
Maniar Nipan, 2008, Journal of Software, V3, P51, DOI 10.4304/jsw.3.4.51-61
Mas-Colell A., 1995, MICROECONOMIC THEORY
Maslowska E, 2017, DECIS SUPPORT SYST, V98, P1, DOI 10.1016/j.dss.2017.03.010
Mudambi SM, 2010, MIS QUART, V34, P185
MULHERN FJ, 1991, J MARKETING, V55, P63, DOI 10.2307/1251957
Nunamaker J. F. Jr., 1987, Journal of Management Information Systems, V3, P5
Oestreicher-Singer G, 2012, MANAGE SCI, V58, P1963, DOI 10.1287/mnsc.1120.1536
Oestreicher-Singer G, 2012, MIS QUART, V36, P65, DOI 10.2307/41410406
Parker P., 1994, International Journal ofResearch in Marketing, V11, P17, DOI DOI 10.1016/0167-8116(94)90032-9
Pavlou PA, 2006, INFORM SYST RES, V17, P392, DOI 10.1287/isre.1060.0106
Peres R, 2014, J MARKETING, V78, P83, DOI 10.1509/jm.12.0344
Rice SC, 2012, INFORM SYST RES, V23, P436, DOI 10.1287/isre.1110.0362
Roehm ML, 2006, J MARKETING RES, V43, P366, DOI 10.1509/jmkr.43.3.366
Russell GJ, 2000, J RETAILING, V76, P367, DOI 10.1016/S0022-4359(00)00030-0
RYNES SL, 1991, PERS PSYCHOL, V44, P487, DOI 10.1111/j.1744-6570.1991.tb02402.x
Seetharaman PB, 2005, MARKET LETT, V16, P239, DOI 10.1007/s11002-005-5888-y
Shin D, 2020, MIS QUART, V44, P1459, DOI 10.25300/MISQ/2020/14870
Shocker AD, 2004, J MARKETING, V68, P28, DOI 10.1509/jmkg.68.1.28.24032
Song R, 2019, J BUS ETHICS, V158, P743, DOI 10.1007/s10551-017-3720-2
Susarla A, 2012, INFORM SYST RES, V23, P23, DOI 10.1287/isre.1100.0339
Svenson O, 1985, KNOWLEDGE REPRESENTA, P134
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Tsang MM, 2004, INT J ELECTRON COMM, V8, P65, DOI 10.1080/10864415.2004.11044301
TVERSKY A, 1991, Q J ECON, V106, P1039, DOI 10.2307/2937956
TVERSKY A, 1993, MANAGE SCI, V39, P1179, DOI 10.1287/mnsc.39.10.1179
WALTERS RG, 1991, J MARKETING, V55, P17, DOI 10.2307/1252234
WERNERFELT B, 1988, RAND J ECON, V19, P458, DOI 10.2307/2555667
Yin DZ, 2014, MIS QUART, V38, P539, DOI 10.25300/MISQ/2014/38.2.10
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 107
TC 24
Z9 24
U1 36
U2 164
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD SEP
PY 2021
VL 32
IS 3
BP 895
EP 913
DI 10.1287/isre.2021.0998
PG 20
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA YI9CF
UT WOS:000744138200013
DA 2024-03-27
ER
PT J
AU Li, L
Lin, JB
Luo, WY
Luo, X
AF Li, Lei
Lin, Jiabao
Luo, Wenyi
Luo, Xin (Robert)
TI INVESTIGATING THE EFFECT OF ARTIFICIAL INTELLIGENCE ON CUSTOMER
RELATIONSHIP MANAGEMENT PERFORMANCE IN E-COMMERCE ENTERPRISES
SO JOURNAL OF ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE AI usage; CRM capabilities; CRM performance; E-commerce enterprise
ID BIG DATA ANALYTICS; SOCIAL MEDIA; FIRM PERFORMANCE; IMPACT;
CAPABILITIES; INNOVATION; USAGE; AI
AB Despite the importance of artificial intelligence (AI) technologies in improving customer relationships, AI usage in enabling customer relationship management (CRM) capabilities and, in turn, enhancing CRM performance has not yet been investigated. This study from the IT-enabled organizational capabilities perspective investigates the impact of AI usage on CRM performance and the mediating effect of CRM capabilities. We tested our core proposition and theory-driven research model using data collected from a sample of 193 e-commerce enterprises in China. The empirical results indicate that AI usage positively impacts CRM performance and that CRM capabilities positively mediate their relationship. Thus, this paper contributes to IS research with an eloquent theoretical explanation and strong empirical evidence on why e-commerce enterprises deploy AI initiatives to improve their CRM capabilities and performance.
C1 [Li, Lei] Northwest Agr & Forestry Univ, Coll Econ & Management, Yangling 712100, Peoples R China.
[Lin, Jiabao; Luo, Wenyi] South China Agr Univ, Coll Econ & Management, Guangzhou 510642, Peoples R China.
[Luo, Xin (Robert)] Univ New Mexico, Anderson Sch Management, Albuquerque, NM USA.
C3 Northwest A&F University - China; South China Agricultural University;
University of New Mexico
RP Lin, JB (autor correspondiente), South China Agr Univ, Coll Econ & Management, Guangzhou 510642, Peoples R China.
EM lilei0123@nwafu.edu.cn; linjb@scau.edu.cn; luowenyi1101@163.com;
luowenyi1101@163.com
RI wang, xiaoxuan/JMP-6531-2023; zhao, lin/JJF-0406-2023; zhang,
xiao/JCN-8822-2023; Yu, Xiaohan/KCK-5462-2024; Wang,
Xuechun/JRX-6509-2023; zhao, yan/JNT-6961-2023; li, yifan/JHU-9272-2023;
peng, yan/JCO-1763-2023; song, yu/KCZ-2003-2024; Zhang,
Yunyi/JHS-3626-2023; su, hang/KEH-2976-2024; Yang, Xiao/JDN-0082-2023;
yang, xiao/JLL-7721-2023
FU National Natural Science Foundation of China [71873047]; National Social
Science Foundation of China [18ZDA109]; Guangdong Basic and Applied
Basic Research Foundation [2023A1515011263]; National Natural Science
Foundation of Shaanxi Province [2023-JC-QN-0807, 2020JQ-282]; National
Social Science Foundation of Shaanxi Province [2020R042]; 111 Project
FX Acknowledgment This work was supported by grants from the National
Natural Science Foundation of China (71873047) , the National Social
Science Foundation of China (18ZDA109) , the Guangdong Basic and Applied
Basic Research Foundation (2023A1515011263) , the National Natural
Science Foundation of Shaanxi Province (2023-JC-QN-0807, 2020JQ-282) ,
National Social Science Foundation of Shaanxi Province (2020R042) and
the Support by the 111 Project.
CR Braojos J, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2020.103290
Brougham D, 2020, TECHNOL FORECAST SOC, V161, DOI 10.1016/j.techfore.2020.120276
Cao LM, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107669
Castillo A, 2021, J ASSOC INF SYST, V22, P1472, DOI 10.17705/1jais.00700
Chandra S, 2018, J ELECTRON COMMER RE, V19, P237
Chatterjee S, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120783
Collins C, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102383
Dalla Pozza I, 2018, J BUS RES, V89, P391, DOI 10.1016/j.jbusres.2018.02.004
Demlehner Q, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102317
Di Vaio A, 2020, J BUS RES, V121, P283, DOI 10.1016/j.jbusres.2020.08.019
Dondapati A, 2022, MATER TODAY-PROC, V56, P2232, DOI 10.1016/j.matpr.2021.11.557
Foltean FS, 2019, J BUS RES, V104, P563, DOI 10.1016/j.jbusres.2018.10.047
Ghasemaghaei M, 2021, INFORM SYST J, V31, P268, DOI 10.1111/isj.12310
Ghasemaghaei M, 2020, J BUS RES, V108, P147, DOI 10.1016/j.jbusres.2019.09.062
Ghasemaghaei M, 2017, DECIS SUPPORT SYST, V101, P95, DOI 10.1016/j.dss.2017.06.004
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Guerola Navarro V., 2021, TECHNOL FORECAST SOC, V169
Guerola-Navarro V, 2021, J BUS RES, V129, P83, DOI 10.1016/j.jbusres.2021.02.050
Gupta S, 2020, IND MARKET MANAG, V90, P581, DOI 10.1016/j.indmarman.2019.11.009
Hou TT, 2022, J BUS RES, V139, P496, DOI 10.1016/j.jbusres.2021.09.066
Hua Y, 2020, DECISION SCI, V51, P755, DOI 10.1111/deci.12372
Hung SY, 2010, DECIS SUPPORT SYST, V48, P592, DOI 10.1016/j.dss.2009.11.009
Jan Z, 2023, EXPERT SYST APPL, V216, DOI 10.1016/j.eswa.2022.119456
Johnson PC, 2022, TECHNOL FORECAST SOC, V179, DOI 10.1016/j.techfore.2022.121636
Komarek AM, 2020, AGR SYST, V178, DOI 10.1016/j.agsy.2019.102738
Li FY, 2022, SUSTAIN ENERGY TECHN, V52, DOI 10.1016/j.seta.2022.102103
Li L, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121355
Li LX, 2022, IND MARKET MANAG, V104, P28, DOI 10.1016/j.indmarman.2022.04.007
Li MN, 2015, INT J INFORM MANAGE, V35, P229, DOI 10.1016/j.ijinfomgt.2014.12.004
Li XC, 2020, J ELECTRON COMMER RE, V21, P75
Li YF, 2019, INFORM MANAGE-AMSTER, V56, P392, DOI 10.1016/j.im.2018.07.012
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Lin JB, 2021, DECIS SUPPORT SYST, V151, DOI 10.1016/j.dss.2021.113628
Lin SZ, 2022, INFORM MANAGE-AMSTER, V59, DOI 10.1016/j.im.2022.103680
Lindell MK, 2001, J APPL PSYCHOL, V86, P114, DOI 10.1037//0021-9010.86.1.114
Liu HF, 2013, DECIS SUPPORT SYST, V54, P1452, DOI 10.1016/j.dss.2012.12.016
Liu YY, 2018, TECHNOL FORECAST SOC, V130, P123, DOI 10.1016/j.techfore.2018.01.018
Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
Nazir S, 2023, TECHNOL SOC, V72, DOI 10.1016/j.techsoc.2022.102190
Olan F, 2022, J BUS RES, V145, P605, DOI 10.1016/j.jbusres.2022.03.008
Rahman MS, 2023, J BUS RES, V156, DOI 10.1016/j.jbusres.2022.113525
Saura JR, 2021, IND MARKET MANAG, V98, P161, DOI 10.1016/j.indmarman.2021.08.006
Shankar V, 2018, J RETAILING, V94, pVI, DOI 10.1016/S0022-4359(18)30076-9
Spanaki K, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2021.102350
Trainor KJ, 2014, J BUS RES, V67, P1201, DOI 10.1016/j.jbusres.2013.05.002
Turel O, 2019, INT J ACCOUNT INF SY, V33, P32, DOI 10.1016/j.accinf.2019.03.002
Verma S., 2021, INT J INFORM MANAGE, V1, DOI [10.1016/j.jjimei.2020.100002, DOI 10.1016/J.JJIMEI.2020.100002]
Wamba SF, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2022.102544
Wamba SF, 2017, J BUS RES, V70, P356, DOI 10.1016/j.jbusres.2016.08.009
Wamba-Taguimdje SL, 2020, BUS PROCESS MANAG J, V26, P1893, DOI 10.1108/BPMJ-10-2019-0411
Wang DY, 2022, DECIS SUPPORT SYST, V158, DOI 10.1016/j.dss.2022.113801
Wang S, 2016, INFORM MANAGE-AMSTER, V53, P197, DOI 10.1016/j.im.2015.09.011
Wang YG, 2012, MANAGE DECIS, V50, P115, DOI 10.1108/00251741211194903
Ye Y, 2022, J BUS RES, V139, P510, DOI 10.1016/j.jbusres.2021.10.008
Yu Y, 2018, INFORM MANAGE-AMSTER, V55, P245, DOI 10.1016/j.im.2017.07.001
Zhang CB, 2020, IND MARKET MANAG, V91, P483, DOI 10.1016/j.indmarman.2020.10.012
Zhao X, 2021, J ELECTRON COMMER RE, V22, P178
NR 58
TC 1
Z9 1
U1 19
U2 34
PU CALIFORNIA STATE UNIV
PI LONG BEACH
PA COLL BUSINESS, LONG BEACH, CA 90840 USA
SN 1526-6133
EI 1938-9027
J9 J ELECTRON COMMER RE
JI J. Electron. Commer. Res.
PD FEB
PY 2023
VL 24
IS 1
SI SI
BP 68
EP 83
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA A8RC7
UT WOS:000957725600005
DA 2024-03-27
ER
PT J
AU Zhang, HL
Song, M
AF Zhang, Haili
Song, Michael
TI How Big Data Analytics, AI, and Social Media Marketing Research Boost
Market Orientation Companies can use big data analytics, artificial
intelligence (AI), and social media marketing research to increase
market orientation.
SO RESEARCH-TECHNOLOGY MANAGEMENT
LA English
DT Article
DE Market orientation; Market-oriented firms; Big data analytics;
Artificial Intelligence; Social media marketing research
AB Overview: Previous research has shown that market-oriented firms outperform their peers. Why are some firms such outstanding performers in market orientation (MO)? This study identifies three categories of new technology tools that have emerged in the past few years-big data analytics, artificial intelligence (AI), and social media marketing research-and assesses how each category enhances MO. The empirical evidence from 442 firms indicates that all three categories have significant impacts on MO, with AI being the most effective, followed by big data analytics and social media marketing research, respectively. We outline how very successful firms adopt these new technology tools and offer managerial implications that can guide senior executives looking to increase MO.
C1 [Zhang, Haili] Xian Technol Univ, Innovat Management, Xian, Peoples R China.
[Song, Michael] Xian Technol Univ, Xian, Peoples R China.
C3 Xi'an Technological University; Xi'an Technological University
RP Zhang, HL (autor correspondiente), Xian Technol Univ, Innovat Management, Xian, Peoples R China.
EM zhanghaili@xatu.edu.cn; drmichaelsong@163.com
OI Song, Michael/0000-0002-8829-9961
CR DeVellis RF., 2017, SCALE DEV THEORY APP
Dillman DA, 2014, INTERNET PHONE MAIL, V4th
Frösén J, 2016, J MARKETING, V80, P60, DOI 10.1509/jm.15.0153
Gupta VK, 2019, REV MANAG SCI, V13, P649, DOI 10.1007/s11846-017-0262-z
Hu CJ, 2019, MARK INTELL PLAN, V37, P598, DOI 10.1108/MIP-08-2018-0325
JAWORSKI BJ, 1993, J MARKETING, V57, P53, DOI 10.2307/1251854
KOHLI AK, 1990, J MARKETING, V54, P1, DOI 10.2307/1251866
KOHLI AK, 1993, J MARKETING RES, V30, P467, DOI 10.2307/3172691
Mikalef P, 2018, INF SYST E-BUS MANAG, V16, P547, DOI 10.1007/s10257-017-0362-y
Moorman C, 2016, J MARKETING, V80, P6, DOI 10.1509/jm.15.0423
Song M, 2009, J ACAD MARKET SCI, V37, P144, DOI 10.1007/s11747-008-0114-0
Varadarajan Rajan., 2017, AMS Review, V7, P26, DOI DOI 10.1007/S13162-017-0094-7
Wang YJ, 2020, J BUS IND MARK, V35, P1001, DOI 10.1108/JBIM-04-2019-0152
Wang YF, 2020, RES TECHNOL MANAGE, V63, P35, DOI 10.1080/08956308.2020.1762447
Zhang HL, 2021, RES TECHNOL MANAGE, V64, P50, DOI 10.1080/08956308.2021.1942646
NR 15
TC 0
Z9 0
U1 34
U2 140
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0895-6308
EI 1930-0166
J9 RES TECHNOL MANAGE
JI Res.-Technol. Manage.
PD FEB 22
PY 2022
VL 65
IS 2
BP 64
EP 70
DI 10.1080/08956308.2022.2022907
PG 7
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA ZC6MR
UT WOS:000757632200008
DA 2024-03-27
ER
PT J
AU Yadav, R
Sharma, SK
Tarhini, A
AF Yadav, Rajan
Sharma, Sujeet Kumar
Tarhini, Ali
TI A multi-analytical approach to understand and predict the mobile
commerce adoption
SO JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT
LA English
DT Article
DE Neural network; India; Perceived trust; India; M-commerce; Variety of
services
ID TECHNOLOGY ACCEPTANCE MODEL; NEURAL NETWORK APPROACH;
INFORMATION-TECHNOLOGY; PERSONAL INNOVATIVENESS; INDIVIDUAL-DIFFERENCES;
EMPIRICAL-ANALYSIS; USER ACCEPTANCE; PERCEIVED RISK; SERVICES; INTERNET
AB Purpose - The advent of mobile telephony devices with strong internet capabilities has laid the foundation for mobile commerce (m-commerce) services. The purpose of this paper is to empirically examine predictors of m-commerce adoption using a modification of the widely used technology acceptance model and the unified theory of acceptance and use of technology model.
Design/methodology/approach - The data were collected from 213 respondents by means of an online survey. The data were analyzed through multi analytic approach by employing structural equation modeling (SEM) and neural network modeling.
Findings - The SEM results showed that variety of services, social influence, perceived usefulness, cost and perceived trust have significant influence on consumer's intention to adopt m-commerce. The only exception was perceived ease of use which observed statistically insignificant influence on adoption of m-commerce. Furthermore, the results obtained from SEM were employed as input to the neural network model and results showed that perceived usefulness, perceived trust and variety of services as most important predictors in adoption of m-commerce.
Practical implications - The findings of this study give an insight of key determinants that are important to develop suitable strategic framework to enhance the use of m-commerce adoption. In addition, it also provides an opportunity to academicians and researchers to use the framework of this study for further research.
Originality/value - The study is among a very few studies which analyzed m-commerce adoption by applying a linear and non-linear approach. The study offers a multi-analytical model to understand and predict m-commerce adoption in the developing nation like India.
C1 [Yadav, Rajan] Delhi Technol Univ, Dept Management, Delhi, India.
[Sharma, Sujeet Kumar] Sultan Qaboos Univ, Dept Operat Management & Business Stat, Muscat, Oman.
[Tarhini, Ali] Brunel Univ, Dept Informat Syst, London, England.
C3 Delhi Technological University; Sultan Qaboos University; Brunel
University
RP Sharma, SK (autor correspondiente), Sultan Qaboos Univ, Dept Operat Management & Business Stat, Muscat, Oman.
EM drsujeet@squ.edu.om
RI Tarhini, Ali/B-5045-2016; Sharma, Sujeet Kumar/HTP-3338-2023
OI Tarhini, Ali/0000-0002-8698-1764; Sharma, Sujeet
Kumar/0000-0001-6985-3798
CR Abbasi MS, 2015, J ENTERP INF MANAG, V28, P747, DOI 10.1108/JEIM-12-2014-0124
Aggarwal CC, 2012, ELECTRON COMMER R A, V11, P142, DOI 10.1016/j.elerap.2011.11.001
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Akturan U, 2012, MARK INTELL PLAN, V30, P444, DOI 10.1108/02634501211231928
[Anonymous], 2 BILLION SMART PHON
[Anonymous], 1975, BELIEFS ATTITUDES IN
[Anonymous], MIS Q
Chang YP, 2011, COMPUT HUM BEHAV, V27, P1840, DOI 10.1016/j.chb.2011.04.006
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Chong AYL, 2012, DECIS SUPPORT SYST, V53, P34, DOI 10.1016/j.dss.2011.12.001
Chong AYL, 2010, INT J MOB COMMUN, V8, P129, DOI 10.1504/IJMC.2010.031444
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Featherman MS, 2003, INT J HUM-COMPUT ST, V59, P451, DOI 10.1016/S1071-5819(03)00111-3
Gitau L., 2014, INT J BUSINESS HUMAN, V4, P84
Guriting P., 2006, MANAGE RES NEWS, V29, P6, DOI DOI 10.1108/01409170610645402
Hair J.F., 2010, MULTIVAR DATA ANAL, P168
Hanafizadeh P, 2014, TELEMAT INFORM, V31, P62, DOI 10.1016/j.tele.2012.11.001
Hsu CI, 2009, EXPERT SYST APPL, V36, P11760, DOI 10.1016/j.eswa.2009.04.010
Hsu CL, 2011, INT J MOB COMMUN, V9, P477, DOI 10.1504/IJMC.2011.042455
Karahanna E, 2000, J ORG COMP ELECT COM, V10, P49, DOI 10.1207/S15327744JOCE100103
Khalifa M, 2008, J ENTERP INF MANAG, V21, P110, DOI 10.1108/17410390810851372
Kim HW, 2007, DECIS SUPPORT SYST, V43, P111, DOI 10.1016/j.dss.2005.05.009
Kleijnen M., 2004, J FINANC SERV MARK, V8, P206, DOI [DOI 10.1057/PALGRAVE.FSM.4770120, 10.1057/palgrave.fsm.4770120]
Koenig-Lewis N, 2010, INT J BANK MARK, V28, P410, DOI 10.1108/02652321011064917
Lee CC, 2007, DECIS SUPPORT SYST, V43, P95, DOI 10.1016/j.dss.2005.05.008
Lee FH, 2011, EXPERT SYST APPL, V38, P7766, DOI 10.1016/j.eswa.2010.12.131
Lee YK, 2012, J BUS RES, V65, P1590, DOI 10.1016/j.jbusres.2011.02.044
Liao Z., 2006, INFORM MANAGE, V38, P299
Lu J, 2005, J STRATEGIC INF SYST, V14, P245, DOI 10.1016/j.jsis.2005.07.003
Lu U, 2003, INTERNET RES, V13, P206, DOI 10.1108/10662240310478222
Luam P., 2005, Computers in Human Behavior, V21, P873
Ngai EWT, 2007, DECIS SUPPORT SYST, V43, P3, DOI 10.1016/j.dss.2005.05.003
Qiang C.Z.W., 2009, PRIVATE SECTOR D NOV
Ramayah T., 2007, MANAGE RES NEWS, V30, P420
Riquelme HE, 2010, INT J BANK MARK, V28, P328, DOI 10.1108/02652321011064872
Roger E.M., 1995, DIFFUSION INNOVATION, V4th
Safeena Rahmath, 2013, International Journal of Computer Theory and Engineering, V5, P146, DOI 10.7763/IJCTE.2013.V5.665
Sharma S.K., 2014, ED BUSINESS SOC CONT, V7, P152, DOI DOI 10.1108/EBS-08-2013-0032
Sharma S.K., 2013, Education, Business and Society: Contemporary Middle Eastern Issues, V6, P87, DOI DOI 10.1108/EBS-12-2012-0055
Sharma SK, 2015, TRANSFORM GOV-PEOPLE, V9, P207, DOI 10.1108/TG-10-2014-0046
Sharma SK, 2015, MANAG RES REV, V38, P750, DOI 10.1108/MRR-06-2014-0139
Sharma SK, 2014, J INDIAN BUS RES, V6, P155, DOI 10.1108/JIBR-02-2013-0013
Szajna B, 1996, MANAGE SCI, V42, P85, DOI 10.1287/mnsc.42.1.85
Tarhini A, 2016, EDUC INF TECHNOL, V21, P1269, DOI 10.1007/s10639-015-9381-9
Tarhini A, 2015, BRIT J EDUC TECHNOL, V46, P739, DOI 10.1111/bjet.12169
Tarhini A, 2015, ELECTRON J E-LEARN, V13, P14
Tarhini A, 2014, COMPUT HUM BEHAV, V41, P153, DOI 10.1016/j.chb.2014.09.020
Thakur R, 2014, INTERNET RES, V24, P369, DOI 10.1108/IntR-12-2012-0244
Thakur R, 2013, J INDIAN BUS RES, V5, P52, DOI 10.1108/17554191311303385
Vance A, 2008, J MANAGE INFORM SYST, V24, P73, DOI 10.2753/MIS0742-1222240403
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Wang YM, 2007, EXPERT SYST APPL, V32, P336, DOI 10.1016/j.eswa.2005.11.029
Wei TT, 2009, IND MANAGE DATA SYST, V109, P370, DOI 10.1108/02635570910939399
Wu JH, 2005, INFORM MANAGE-AMSTER, V42, P719, DOI 10.1016/j.im.2004.07.001
Zhang LY, 2012, COMPUT HUM BEHAV, V28, P1902, DOI 10.1016/j.chb.2012.05.008
Zhou T, 2010, COMPUT HUM BEHAV, V26, P760, DOI 10.1016/j.chb.2010.01.013
NR 56
TC 107
Z9 124
U1 0
U2 48
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1741-0398
EI 1758-7409
J9 J ENTERP INF MANAG
JI J. Enterp. Inf. Manag.
PY 2016
VL 29
IS 2
BP 222
EP 237
DI 10.1108/JEIM-04-2015-0034
PG 16
WC Computer Science, Interdisciplinary Applications; Information Science &
Library Science; Management
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA DR1RC
UT WOS:000379681600005
DA 2024-03-27
ER
PT J
AU Salminen, J
Mustak, M
Corporan, J
Jung, SG
Jansen, BJ
AF Salminen, Joni
Mustak, Mekhail
Corporan, Juan
Jung, Soon-gyo
Jansen, Bernard J.
TI Detecting Pain Points from User-Generated Social Media Posts Using
Machine Learning
SO JOURNAL OF INTERACTIVE MARKETING
LA English
DT Article
DE marketing; artificial intelligence; AI; machine learning; customer
insight; user-generated content; UGC; pain points
ID MARKETING-RESEARCH; ARTIFICIAL-INTELLIGENCE; CUSTOMER; TOUCHPOINTS;
JOURNEY; SEARCH; DESIGN; AI
AB Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers' pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers' pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language-based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers' concerns.
C1 [Salminen, Joni] Univ Vaasa, Vaasa, Finland.
[Mustak, Mekhail] Turku Sch Econ & Business Adm, Turku, Finland.
[Corporan, Juan] Banco Santa Cruz RD, Santo Domingo, Dominican Rep.
[Jung, Soon-gyo] Hamad Bin Khalifa Univ, Qatar Comp Res Inst, Ar Rayyan, Qatar.
[Jansen, Bernard J.] Hamad Bin Khalifa Univ, Qatar Comp Res Inst, Social Comp Grp, Ar Rayyan, Qatar.
C3 University of Vaasa; University of Turku; Qatar Foundation (QF); Hamad
Bin Khalifa University-Qatar; Qatar Computing Research Institute; Qatar
Foundation (QF); Hamad Bin Khalifa University-Qatar; Qatar Computing
Research Institute
RP Salminen, J (autor correspondiente), Univ Vaasa, Vaasa, Finland.
EM salminen@uwasa.fi; mekhail.mustak@utu.fi; juan.nunez.corp@gmail.com;
sjung@hbku.edu.qa; bjansen@hbku.edu.qa
RI Mustak, Mekhail/P-9559-2019
OI Mustak, Mekhail/0000-0002-2111-2939
FU Mekhail Mustak expresses gratitude; Liikesivistysrahasto (The Foundation
for Economic Education, Finland)
FX The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: Mekhail
Mustak expresses gratitude to the Kone Foundation (Finland) and
Liikesivistysrahasto (The Foundation for Economic Education, Finland)
for their financial support of this research.
CR Abu-Salih B, 2018, J KNOWL MANAG, V22, P949, DOI 10.1108/JKM-11-2016-0489
Agrawal Tanay., 2020, Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
Alonso O, 2015, PROCEEDINGS OF THE 15TH ACM/IEEE-CS JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL'15), P101, DOI 10.1145/2756406.2757741
Alonso O, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P1089, DOI 10.1145/2766462.2776778
Amado A, 2018, EUR RES MANAG BUS EC, V24, P1, DOI 10.1016/j.iedeen.2017.06.002
Antons D, 2018, J SERV RES-US, V21, P17, DOI 10.1177/1094670517738373
Argyris YA, 2021, J INTERACT MARK, V55, P67, DOI 10.1016/j.intmar.2021.02.001
Arras L., 2017, PREPRINT
Asiri Sidath., 2018, Machine learning classifiers
Balducci B, 2018, J ACAD MARKET SCI, V46, P557, DOI 10.1007/s11747-018-0581-x
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Bergstra J, 2012, J MACH LEARN RES, V13, P281
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Brownlee J., 2016, MACHINE LEARNIN 0816
Chen RY, 2019, ELECTRON COMMER R A, V38, DOI 10.1016/j.elerap.2019.100889
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Cheng LC, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102613
Cui DP, 2005, MARKET SCI, V24, P595, DOI 10.1287/mksc.1050.0123
Cui G, 2006, MANAGE SCI, V52, P597, DOI 10.1287/mnsc.1060.0514
Davis DF, 2013, J BUS RES, V66, P1245, DOI 10.1016/j.jbusres.2012.02.020
Deshpande R., 1994, Marketing Letters, V5, P271
Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
Donges N., 2022, BUILT
Freelon D, 2014, J BROADCAST ELECTRON, V58, P59, DOI 10.1080/08838151.2013.875018
GRIFFIN A, 1993, MARKET SCI, V12, P1, DOI 10.1287/mksc.12.1.1
Gupta S, 2020, J INTERACT MARK, V51, P26, DOI 10.1016/j.intmar.2020.04.003
Handfield R.B., 2005, INT J SUPPLY CHAIN F, V6, P6
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
Hofacker CF, 2012, J INTERACT MARK, V26, P1, DOI 10.1016/j.intmar.2011.10.001
Homburg C, 2007, J ACAD MARKET SCI, V35, P523, DOI 10.1007/s11747-006-0009-x
Horev R., 2018, DATA SCI
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Hutto C. J., 2014, P INT AAAI C WEB SOC, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
Johnson DS, 2007, J INTERACT MARK, V21, P2, DOI 10.1002/dir.20091
Joulin A., 2016, PREPRINT
Kämpf M, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0141892
Kingma D. P., 2014, ARXIV
Klapdor S, 2014, J INTERACT MARK, V28, P285, DOI 10.1016/j.intmar.2014.07.001
Knocklein O., 2019, Em Towards Data Science, P1
KOHLI AK, 1990, J MARKETING, V54, P1, DOI 10.2307/1251866
Kotsiantis SB, 2007, INFORM-J COMPUT INFO, V31, P249
Kramer O, 2013, DIMENSIONALITY REDUC, P13, DOI DOI 10.1007/978-3-642-38652-7_2
Kranzbuhler AM, 2019, J ACAD MARKET SCI, V47, P308, DOI 10.1007/s11747-018-0594-5
Krogh A., 1995, Advances in Neural Information Processing Systems 7, P231
Kuehl N, 2020, ELECTRON MARK, V30, P351, DOI 10.1007/s12525-019-00351-0
Kuhl N., 2020, ARXIV
Kumar V, 2018, J MARKETING, V82, P1, DOI 10.1177/0022242918805485
Lee TY, 2011, J MARKETING RES, V48, P881, DOI 10.1509/jmkr.48.5.881
Li H, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2021.102784
Li YT, 2017, ADV NEUR IN, V30
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Liu L, 2020, MARKET SCI, V39, P669, DOI 10.1287/mksc.2020.1226
Liu X, 2017, J ADVERTISING, V46, P236, DOI 10.1080/00913367.2017.1297273
Liu X, 2016, MARKET SCI, V35, P363, DOI 10.1287/mksc.2015.0972
Liu Y, 2020, ABS190711692 CORR
Liu Y, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.11.002
Ma LY, 2020, INT J RES MARK, V37, P481, DOI 10.1016/j.ijresmar.2020.04.005
Macdonald E., 2012, Harvard Business Review
Malthouse EC, 2013, J INTERACT MARK, V27, P270, DOI 10.1016/j.intmar.2013.09.008
McHugh ML, 2012, BIOCHEM MEDICA, V22, P276, DOI 10.11613/bm.2012.031
MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
Mitchell T. M., 1997, MACH LEARN
Mumtaz F, 2018, BIOMED PHARMACOTHER, V105, P1205, DOI 10.1016/j.biopha.2018.05.086
Mustak M, 2021, J BUS RES, V124, P389, DOI 10.1016/j.jbusres.2020.10.044
Mustak M, 2013, MANAG SERV QUAL, V23, P341, DOI 10.1108/MSQ-03-2013-0046
Patel S., 2020, REVE CHAT
Patnaik D., 1999, Design management journal, V10, P37, DOI DOI 10.1111/J.1948-7169.1999.TB00250.X
Pawar U., 2020, ANAL VIDHYA
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Plangger K, 2020, J INTERACT MARK, V50, P32, DOI 10.1016/j.intmar.2019.10.004
Price R, 2016, J INT CONSUM MARK, V28, P92, DOI 10.1080/08961530.2015.1092405
Price RA, 2015, QUAL MARK RES, V18, P230, DOI 10.1108/QMR-03-2014-0024
Rambocas M, 2018, J RES INTERACT MARK, V12, P146, DOI 10.1108/JRIM-05-2017-0030
Rawson A, 2013, HARVARD BUS REV, V91, P90
Ray S., 2017, Analytics Vidhya
Reinstein I., 2017, KDNUGGETS
Reisenbichler M., 2021, SUPPORTING CONTENT M
Reisenbichler M., 2018, J BUSINESS EC, V89, P327, DOI [DOI 10.1007/S11573-018-0915-7, 10.1007/s11573-018-0915-7]
Rish I, 2001, IJCAI 2001 workshop on empirical methods in artificial intelligence, V3, P41, DOI DOI 10.1039/B104835J
Rojas R., 2013, Neural networks : A systematic introduction
Rooderkerk RP, 2016, J INTERACT MARK, V35, P1, DOI 10.1016/j.intmar.2015.12.003
Rydén P, 2015, J INTERACT MARK, V31, P1, DOI 10.1016/j.intmar.2015.03.001
Said E, 2015, J MARKET MANAG-UK, V31, P1158, DOI 10.1080/0267257X.2015.1037785
Salian I., 2018, SUPERVISED UNSUPERVI
Salminen J., 2018, P 5 INT C SOCIAL NET
Salminen J, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102771
Salminen J, 2020, HUM-CENTRIC COMPUT I, V10, DOI 10.1186/s13673-019-0205-6
Salminen J, 2019, J BUS RES, V101, P203, DOI 10.1016/j.jbusres.2019.04.018
Sanh V., 2019, ARXIV
Sawhney M, 2005, J INTERACT MARK, V19, P4, DOI 10.1002/dir.20046
Schaffhausen CR, 2015, J MECH DESIGN, V137, DOI 10.1115/1.4030161
Shalev-Shwartz S., 2014, Understanding Machine Learning: From Theory to Algorithms, DOI DOI 10.1017/CBO9781107298019
SHAP Latest Documentation, 2018, READ DOCS
Smith L.N, 2018, A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay
Srivastava N., 2013, THESIS U TORONTO, V182
Steinwart I., 2008, SUPPORT VECTOR MACHI
Straker K, 2021, J INT CONSUM MARK, V33, P239, DOI 10.1080/08961530.2020.1792387
Sugimura P., 2018, ARXIV
Sun BH, 2006, J INTERACT MARK, V20, P82, DOI 10.1002/dir.20069
Syam N, 2018, IND MARKET MANAG, V69, P135, DOI 10.1016/j.indmarman.2017.12.019
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
URBAN GL, 1988, MANAGE SCI, V34, P569, DOI 10.1287/mnsc.34.5.569
Verhoef PC, 2010, J INTERACT MARK, V24, P121, DOI 10.1016/j.intmar.2010.02.009
von Hippel E., 2009, J BETRIEBSWIRTSCHAFT, V1, P29, DOI [DOI 10.1007/S11301-004-0002-8, DOI 10.1260/175722209787951224]
VONHIPPEL E, 1986, MANAGE SCI, V32, P791, DOI 10.1287/mnsc.32.7.791
Wang BD, 2016, ENG APPL ARTIF INTEL, V47, P101, DOI 10.1016/j.engappai.2015.06.005
Wang W, 2022, CELL MOL NEUROBIOL, V42, P695, DOI 10.1007/s10571-020-00958-4
Wang Y, 2018, CIRP ANN-MANUF TECHN, V67, P145, DOI 10.1016/j.cirp.2018.04.018
Weinberg BD, 2013, J INTERACT MARK, V27, P299, DOI 10.1016/j.intmar.2013.09.006
Yang Y, 2022, J MARKETING, V86, P37, DOI 10.1177/00222429211033585
Yiu Tony., 2019, DATA SCI
Zhang JH, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102716
Zhang MM, 2011, ELECTRON MARK, V21, P161, DOI 10.1007/s12525-011-0065-z
Zhang Y, 2019, arXiv
Zhou F, 2020, J MECH DESIGN, V142, DOI 10.1115/1.4044435
NR 115
TC 11
Z9 11
U1 22
U2 67
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1094-9968
EI 1520-6653
J9 J INTERACT MARK
JI J. Interact. Mark.
PD AUG
PY 2022
VL 57
IS 3
BP 517
EP 539
DI 10.1177/10949968221095556
EA JUN 2022
PG 23
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 3A1OI
UT WOS:000823591200001
OA Green Accepted
DA 2024-03-27
ER
PT J
AU Ping, YN
Hill, C
Zhu, Y
Fresneda, J
AF Ping, Yanni
Hill, Chelsey
Zhu, Yun
Fresneda, Jorge
TI Antecedents and consequences of the key opinion leader status: an
econometric and machine learning approach
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Key opinion leader; Reviewer certification; Difference-in-difference;
Machine learning; Online consumer reviews
ID WORD-OF-MOUTH; ONLINE CONSUMER REVIEWS; PRODUCT REVIEWS; PROPENSITY
SCORE; PRICE PREMIUMS; HELPFULNESS; IMPACT; SALES; CREDIBILITY; USER
AB Key Opinion Leaders (KOLs) have an undeniable influence on businesses. Many online review communities, such as Yelp, give KOL users prominent status in their communities as cues of source trustworthiness. Using both econometric analysis and machine learning methods, we adopt an antecedents and consequences framework to investigate the drivers of KOL status and their economic impact on businesses. We find that a user's social activity is more important in determining KOL status than the reviews themselves. On the consequences side, the paper shows that the first KOL review significantly boosts sales, regardless of the actual rating assigned by the KOL. After confirming this sales boost, we use random forest regression to predict sales using KOL review characteristics, including text. It is found that the number of KOL reviews as the most influential feature in predicting sales. This research contributes to the existing literature by adding a more granular, holistic investigation into KOLs in online consumer review communities.
C1 [Ping, Yanni] St Johns Univ, Peter J Tobin Coll Business, Dept Business Analyt & Informat Syst, Queens, NY 11439 USA.
[Hill, Chelsey] Montclair State Univ, Feliciano Sch Business, Dept Informat Management & Business Analyt, Montclair, NJ 07043 USA.
[Zhu, Yun] St Johns Univ, Peter J Tobin Coll Business, Dept Econ & Finance, Queens, NY 11439 USA.
[Fresneda, Jorge] New Jersey Inst Technol, Martin Tuchman Sch Management, Dept Mkt, Newark, NJ 07103 USA.
C3 Saint John's University; Montclair State University; Saint John's
University; New Jersey Institute of Technology
RP Ping, YN (autor correspondiente), St Johns Univ, Peter J Tobin Coll Business, Dept Business Analyt & Informat Syst, Queens, NY 11439 USA.
EM pingy@stjohns.edu; hillc@montclair.edu; zhuy@stjohns.edu;
fresneda@njit.edu
RI Hill, Chelsey/JJF-2157-2023
OI Hill, Chelsey/0000-0003-3417-3121; Fresneda, Jorge
Eduardo/0000-0001-9985-8362
CR Agnihotri A, 2016, PSYCHOL MARKET, V33, P1006, DOI 10.1002/mar.20934
Aral S, 2011, MANAGE SCI, V57, P1623, DOI 10.1287/mnsc.1110.1421
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Austin PC, 2015, STAT MED, V34, P3661, DOI 10.1002/sim.6607
Ba SL, 2002, MIS QUART, V26, P243, DOI 10.2307/4132332
Baek H, 2012, INT J ELECTRON COMM, V17, P99, DOI 10.2753/JEC1086-4415170204
Bamakan SMH, 2019, EXPERT SYST APPL, V115, P200, DOI 10.1016/j.eswa.2018.07.069
Banerjee S, 2017, DECIS SUPPORT SYST, V96, P17, DOI 10.1016/j.dss.2017.01.006
Bapna R, 2018, MIS QUART, V42, P719, DOI 10.25300/MISQ/2018/13592
BasuMallick C., 2020, YELP VS GOOGLE WHICH
Bennett P., 1999, Risk Communication and Public Health, P3, DOI DOI 10.1093/ACPROF:OSO/9780199562848.001.0001
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Casalo LV, 2020, J BUS RES, V117, P510, DOI 10.1016/j.jbusres.2018.07.005
Chen P.-Y., 2008, ALL REV ARE NOT CREA
Chen Z, 2013, J MARKETING RES, V50, P463, DOI 10.1509/jmr.12.0063
Cheng LC, 2020, ELECTRON COMMER RES, V20, P807, DOI 10.1007/s10660-019-09332-z
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chong AYL, 2016, INT J OPER PROD MAN, V36, P358, DOI 10.1108/IJOPM-03-2015-0151
CHOWDHRY K, 1952, J ABNORM SOC PSYCH, V47, P51, DOI 10.1037/h0056411
Chua AYK, 2015, J ASSOC INF SCI TECH, V66, P354, DOI 10.1002/asi.23180
Cui G, 2012, INT J ELECTRON COMM, V17, P39, DOI 10.2753/JEC1086-4415170102
Dai WJ, 2018, QME-QUANT MARK ECON, V16, P289, DOI 10.1007/s11129-017-9194-9
De Langhe B, 2016, J CONSUM RES, V42, P817, DOI 10.1093/jcr/ucv047
Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001
Fang B, 2016, TOURISM MANAGE, V52, P498, DOI 10.1016/j.tourman.2015.07.018
Floyd K, 2014, J RETAILING, V90, P217, DOI 10.1016/j.jretai.2014.04.004
Folse JAG, 2016, PSYCHOL MARKET, V33, P747, DOI 10.1002/mar.20914
Forman C, 2008, INFORM SYST RES, V19, P291, DOI 10.1287/isre.1080.0193
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
Goh KY, 2013, INFORM SYST RES, V24, P88, DOI 10.1287/isre.1120.0469
Goldsmith RE, 2000, J ADVERTISING, V29, P43, DOI 10.1080/00913367.2000.10673616
Hass R.G., 1981, COGNITIVE RESPONSES, V1st
Hastie T., 2009, ELEMENTS STAT LEARNI, V2, P1, DOI DOI 10.1007/978-0-387-84858-7
He JX, 2020, DECIS SUPPORT SYST, V132, DOI 10.1016/j.dss.2020.113281
Heckman JJ, 1998, REV ECON STUD, V65, P261, DOI 10.1111/1467-937X.00044
Hong H, 2017, DECIS SUPPORT SYST, V102, P1, DOI 10.1016/j.dss.2017.06.007
Hu N, 2014, DECIS SUPPORT SYST, V57, P42, DOI 10.1016/j.dss.2013.07.009
Jiménez FR, 2013, J INTERACT MARK, V27, P226, DOI 10.1016/j.intmar.2013.04.004
Karimi S, 2017, DECIS SUPPORT SYST, V96, P39, DOI 10.1016/j.dss.2017.02.001
Kim JM, 2018, J INTERACT MARK, V43, P134, DOI 10.1016/j.intmar.2018.05.002
Kirmani A, 2000, J MARKETING, V64, P66, DOI 10.1509/jmkg.64.2.66.18000
Lee J, 2019, INT J ELECTRON COMM, V23, P595, DOI 10.1080/10864415.2019.1655207
Lee SG, 2018, J COMPUT INFORM SYST, V58, P344, DOI 10.1080/08874417.2016.1275954
Liu Y, 2008, IEEE DATA MINING, P443, DOI 10.1109/ICDM.2008.94
Liu ZW, 2015, TOURISM MANAGE, V47, P140, DOI 10.1016/j.tourman.2014.09.020
Lorla S., 2020, TEXTBLOB DOCUMENTATI
Lu SY, 2018, J INTERACT MARK, V44, P17, DOI 10.1016/j.intmar.2018.05.005
Lu XH, 2013, INFORM SYST RES, V24, P596, DOI 10.1287/isre.1120.0454
Luca M., 2012, SSRN ELECT J
Lyu W., 2022, ELECTRON COMMER RES, V1, P34
Mariani MM, 2020, INT J ELECTRON COMM, V24, P421, DOI 10.1080/10864415.2020.1806464
Ngo-Ye TL, 2014, DECIS SUPPORT SYST, V61, P47, DOI 10.1016/j.dss.2014.01.011
Ögüt H, 2012, SERV IND J, V32, P197, DOI 10.1080/02642069.2010.529436
Pavlou PA, 2006, INFORM SYST RES, V17, P392, DOI 10.1287/isre.1060.0106
Pearl J, 2009, STAT SURV, V3, P96, DOI 10.1214/09-SS057
Pranata I, 2016, ELECTRON COMMER R A, V20, P30, DOI 10.1016/j.elerap.2016.09.005
Rocks N., 2015, WHY WE CREATED YELP
Rosario AB, 2016, J MARKETING RES, V53, P297, DOI 10.1509/jmr.14.0380
ROSENBAUM PR, 1983, BIOMETRIKA, V70, P41, DOI 10.1093/biomet/70.1.41
Salesbacker, 2015, STEP BY STEP GUID GE
Shadish W.R., 2002, STAT CONCLUSION VALI
Shan Y, 2016, COMPUT HUM BEHAV, V55, P633, DOI 10.1016/j.chb.2015.10.013
Srivastava V, 2019, J INTERACT MARK, V48, P33, DOI 10.1016/j.intmar.2018.12.003
Stuart EA, 2013, J CLIN EPIDEMIOL, V66, pS84, DOI 10.1016/j.jclinepi.2013.01.013
Tobon S, 2021, J THEOR APPL EL COMM, V16, P748, DOI 10.3390/jtaer16040043
Wang X, 2019, INT J HOSP MANAG, V77, P438, DOI 10.1016/j.ijhm.2018.08.007
Weathers D, 2015, DECIS SUPPORT SYST, V79, P12, DOI 10.1016/j.dss.2015.07.009
Willemsen LM, 2012, J COMPUT-MEDIAT COMM, V18, P16, DOI 10.1111/j.1083-6101.2012.01598.x
Xu KQ, 2017, MANAGE SCI, V63, P1469, DOI 10.1287/mnsc.2015.2406
Xu Q, 2014, COMPUT HUM BEHAV, V33, P136, DOI 10.1016/j.chb.2014.01.027
Yang J, 2016, DECIS SUPPORT SYST, V89, P66, DOI 10.1016/j.dss.2016.06.009
Yang J, 2012, EUR J MARKETING, V46, P1523, DOI 10.1108/03090561211259961
Ye Q, 2009, INT J HOSP MANAG, V28, P180, DOI 10.1016/j.ijhm.2008.06.011
Yu C, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0252157
Zhang MY, 2020, DECIS SUPPORT SYST, V128, DOI 10.1016/j.dss.2019.113158
Zhang XF, 2016, ONLINE INFORM REV, V40, P119, DOI 10.1108/OIR-04-2015-0101
Zhu L, 2014, J ELECTRON COMMER RE, V15, P267
NR 78
TC 0
Z9 0
U1 7
U2 32
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD SEP
PY 2023
VL 23
IS 3
SI SI
BP 1459
EP 1484
DI 10.1007/s10660-022-09650-9
EA DEC 2022
PG 26
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA S4MI7
UT WOS:000896504100002
DA 2024-03-27
ER
PT J
AU Hossain, MA
Agnihotri, R
Rushan, MRI
Rahman, MS
Sumi, SF
AF Hossain, Md Afnan
Agnihotri, Raj
Rushan, Md Rifayat Islam
Rahman, Muhammad Sabbir
Sumi, Sumaiya Farhana
TI Marketing analytics capability, artificial intelligence adoption, and
firms? competitive advantage: Evidence from the manufacturing industry
SO INDUSTRIAL MARKETING MANAGEMENT
LA English
DT Article
DE Marketing analytics capability; AI adoption; Market sensing; Market
seizing; Market reconfiguration; Manufacturing industry; B2B export
market
ID BIG-DATA ANALYTICS; RESOURCE-BASED VIEW; BUSINESS MODEL INNOVATION;
DYNAMIC CAPABILITIES; SOCIAL MEDIA; SENSING CAPABILITY; DECISION-MAKING;
FINANCIAL PERFORMANCE; ORIENTATION; OPERATIONS
AB Data-driven analytics and artificial intelligence (AI) have become the most crucial aspects of today's industrial marketing management. Although many firms have embraced analytics and AI strategies, corresponding academic advances have been slow. This research investigates how industrial goods manufacturers sustain their competitive advantage in export markets, convincing buyers in a competitive data-rich business environment. The evidence has been taken from the RMG (readymade garment) industry, one of the largest manufacturing industries significantly attached to the export markets. Utilizing multi-phase research design, the study reveals that firms marketing analytics capability play a vital role in sensing, seizing, and reconfiguring the market, consequently leading to a sustained competitive advantage. The performance of sensing, seizing, and reconfiguring becomes higher for a firm when they adopt AI on the strength of the marketing analytics platform. These findings exhibit the latest avenue of exploration within marketing analytics and AI's academic research paradigm. Further, in practice, managers will be aware of the facts that create resilience in this specific industry context.
C1 [Hossain, Md Afnan] Univ Wollongong, Sch Business, Wollongong, NSW 2522, Australia.
[Agnihotri, Raj] Iowa State Univ, Ivy Coll Business, Dept Mkt, Ames, IA 50011 USA.
[Rushan, Md Rifayat Islam] Univ Manchester, Alliance Manchester Business Sch, Manchester M15 6PB, Lancs, England.
[Hossain, Md Afnan; Rahman, Muhammad Sabbir] North South Univ, Sch Business & Econ, Dept Mkt & Int Business, Dhaka 1229, Bangladesh.
[Sumi, Sumaiya Farhana] Univ Dhaka, Dhaka 1000, Bangladesh.
[Hossain, Md Afnan] Univ Melbourne, Fac Business & Econ, Dept Management & Mkt, Melbourne, Vic 3010, Australia.
C3 University of Wollongong; Iowa State University; University of
Manchester; Alliance Manchester Business School; North South University
(NSU); University of Dhaka; University of Melbourne
RP Agnihotri, R (autor correspondiente), Iowa State Univ, Ivy Coll Business, Dept Mkt, Ames, IA 50011 USA.
EM mah619@uowmail.edu.au; raj2@iastate.edu;
rifayat.islam@postgrad.manchester.ac.uk; rahman.sabbir@northsouth.edu;
ssumaiyafarhana@gmail.com
RI Rushan, Rifayat Islam/AAT-7796-2020; Rahman, Muhammad
Sabbir/G-3968-2018; Hossain, Dr Md Afnan/M-6626-2017; N'Dri, Amoin
Bernadine/IWD-7811-2023
OI Rushan, Rifayat Islam/0000-0001-5294-7253; Hossain, Dr Md
Afnan/0000-0003-2954-1823;
CR Addo PC, 2022, J MARKET THEORY PRAC, V30, P405, DOI 10.1080/10696679.2022.2056487
Adrodegari F, 2015, PROC CIRP, V30, P245, DOI 10.1016/j.procir.2015.02.105
Agnihotri R., 2021, A Research Agenda for Sales
Agnihotri R, 2020, IND MARKET MANAG, V90, P291, DOI 10.1016/j.indmarman.2020.07.017
Ahaskar A., 2019, MINT
Akter S, 2021, IND MARKET MANAG, V97, P258, DOI 10.1016/j.indmarman.2021.07.014
Akter S, 2021, INT MARKET REV, V38, P927, DOI 10.1108/IMR-11-2020-0256
Akter S, 2020, TECHNOL FORECAST SOC, V159, DOI 10.1016/j.techfore.2020.120180
Akter S, 2022, ANN OPER RES, V308, P7, DOI 10.1007/s10479-020-03620-w
Akter S, 2016, INT J PROD ECON, V182, P113, DOI 10.1016/j.ijpe.2016.08.018
Ali SM, 2021, EXPERT SYST APPL, V173, DOI 10.1016/j.eswa.2021.114690
Alinaghian L, 2020, IND MARKET MANAG, V85, P110, DOI 10.1016/j.indmarman.2019.10.003
Ambrosini V, 2009, BRIT J MANAGE, V20, pS9, DOI 10.1111/j.1467-8551.2008.00610.x
Amin A., 2022, NEW NATION
AMIT R, 1993, STRATEGIC MANAGE J, V14, P33, DOI 10.1002/smj.4250140105
[Anonymous], 2021, Forbes
[Anonymous], 2019, SAMAKAL NEWS
[Anonymous], 2019, BBC NEWS
[Anonymous], 2012, Mining Text Data, DOI [DOI 10.1007/978-1-4614-3223-4, DOI 10.1007/978-1-4614-3223-4_1, 10.1007/978-1-4614-3223-4_1]
[Anonymous], 2018, RMG TIMES
Apparel-Online, 2016, DEN SHOWS TOPP ONC A
Auh S, 2009, IND MARKET MANAG, V38, P757, DOI 10.1016/j.indmarman.2008.02.011
Aydiner AS, 2019, J BUS RES, V96, P228, DOI 10.1016/j.jbusres.2018.11.028
Baabdullah AM, 2021, IND MARKET MANAG, V98, P255, DOI 10.1016/j.indmarman.2021.09.003
Bag S, 2021, IND MARKET MANAG, V92, P178, DOI 10.1016/j.indmarman.2020.12.001
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Barrie Leonie., 2019, Just Style
Battistella C, 2017, J BUS RES, V73, P65, DOI 10.1016/j.jbusres.2016.12.007
Benitez J, 2018, INFORM MANAGE-AMSTER, V55, P131, DOI 10.1016/j.im.2017.09.004
Berg A, 2021, What's Next for Bangladesh's Garment Industry, after a Decade of Growth?
Bertello A, 2021, J MANAG GOV, V25, P1035, DOI 10.1007/s10997-020-09542-w
Bharadwaj N, 2014, J PROD INNOVAT MANAG, V31, P799, DOI 10.1111/jpim.12124
BHARADWAJ SG, 1993, J MARKETING, V57, P83, DOI 10.2307/1252221
Bordeleau FE, 2020, PROD PLAN CONTROL, V31, P173, DOI 10.1080/09537287.2019.1631458
Brynjolfsson E., 2017, Harvard Business Review, V95, P3
Business and Human Rights Resource Centre, 2020, 7 FASH BRANDS MAD BA
Cao GM, 2019, IND MARKET MANAG, V76, P72, DOI 10.1016/j.indmarman.2018.08.002
Capron L, 1999, J MARKETING, V63, P41, DOI 10.2307/1251944
Chaudhary S, 2020, INT J ENG BUS MANAG, V12, DOI 10.1177/1847979020975528
Chin W. W., 2010, HDB PARTIAL LEAST SQ, P655, DOI DOI 10.1007/978-3-540-32827-8_29
Colombi C, 2018, RES J TEXT APPAR, V22, P390, DOI 10.1108/RJTA-03-2018-0019
Conboy K, 2020, EUR J OPER RES, V281, P656, DOI 10.1016/j.ejor.2019.06.051
Cosic R, 2015, AUSTRALAS J INF SYST, V19, pS5
Coughlin T, 2018, 175 Zettabytes By 2025
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Davenport TH, 2018, HARVARD BUS REV, V96, P108
DAY GS, 1994, J MARKETING, V58, P37, DOI 10.2307/1251915
De Luca LM, 2021, J ACAD MARKET SCI, V49, P790, DOI 10.1007/s11747-020-00739-x
De Silva M, 2021, J BUS RES, V125, P733, DOI 10.1016/j.jbusres.2019.12.030
Delen D, 2018, J BUS ANAL, V1, P2, DOI 10.1080/2573234X.2018.1507324
Dong JQ, 2019, TECHNOL FORECAST SOC, V146, P923, DOI 10.1016/j.techfore.2018.07.050
Doshi Y., 2021, THAIL SKIN CAR PROD
Durmusoglu SS, 2021, IND MARKET MANAG, V93, P250, DOI 10.1016/j.indmarman.2021.01.014
Ebbes P, 2005, QME-QUANT MARK ECON, V3, P365, DOI 10.1007/s11129-005-1177-6
Eisenhardt KM, 2000, STRATEGIC MANAGE J, V21, P1105, DOI 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
Elia S, 2021, J BUS RES, V132, P158, DOI 10.1016/j.jbusres.2021.04.010
Endres H, 2020, IND MARKET MANAG, V90, P30, DOI 10.1016/j.indmarman.2020.06.004
Erevelles S, 2016, J BUS RES, V69, P897, DOI 10.1016/j.jbusres.2015.07.001
Farrokhi A, 2020, IND MARKET MANAG, V91, P257, DOI 10.1016/j.indmarman.2020.09.015
Fethi MD, 2010, EUR J OPER RES, V204, P189, DOI 10.1016/j.ejor.2009.08.003
Finney RZ, 2008, J BUS RES, V61, P925, DOI 10.1016/j.jbusres.2007.09.023
Fischer T, 2010, J SERV MANAGE, V21, P591, DOI 10.1108/09564231011079066
Fox R., 2018, Geek meets chic: Four actions to jump-start advanced analytics in apparel
Gaudenzi B, 2021, IND MARKET MANAG, V93, P124, DOI 10.1016/j.indmarman.2020.12.016
Germann F, 2013, INT J RES MARK, V30, P114, DOI 10.1016/j.ijresmar.2012.10.001
Giri C., 2019, FUNCTIONAL TEXTILES, P349
Greenley GE, 2005, J BUS RES, V58, P1483, DOI 10.1016/j.jbusres.2004.07.004
Gu Y., 2021, Gearing Up for the Future of Manufacturing in Bangladesh
Gupta S, 2020, IND MARKET MANAG, V90, P581, DOI 10.1016/j.indmarman.2019.11.009
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hanssens DM, 2016, J MARKETING, V80, P173, DOI 10.1509/jm.15.0417
Hayes AF, 2011, COMMUN SER, P434
Helfat C.E., 2009, Dynamic capabilities: undrestanding strategic change in organizations, P1
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Heusinkveld S, 2009, TECHNOVATION, V29, P509, DOI 10.1016/j.technovation.2009.02.003
Hossain M. A., 2020, TECHNOLOGICAL INNOVA, P117, DOI [10.4018/978-1-5225-9940-1.ch001, DOI 10.4018/978-1-5225-9940-1.CH001]
Hossain MA, 2021, J BUS RES, V131, P287, DOI 10.1016/j.jbusres.2021.03.063
Hossain MA, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102187
Hsu LC, 2012, BRIT J MANAGE, V23, P179, DOI 10.1111/j.1467-8551.2010.00718.x
Hughes P, 2019, IND MARKET MANAG, V83, P59, DOI 10.1016/j.indmarman.2018.11.005
Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
Iacobucci D, 2019, J MARK ANAL, V7, P152, DOI 10.1057/s41270-019-00059-2
Inigo EA, 2017, IND INNOV, V24, P515, DOI 10.1080/13662716.2017.1310034
JAWORSKI BJ, 1993, J MARKETING, V57, P53, DOI 10.2307/1251854
Jin BE, 2021, FASH TEXT, V8, DOI 10.1186/s40691-021-00259-4
Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
Keegan BJ, 2022, IND MARKET MANAG, V100, P36, DOI 10.1016/j.indmarman.2021.11.001
Khan O, 2021, J CLEAN PROD, V287, DOI 10.1016/j.jclepro.2020.125565
Kozjek D, 2020, INT J COMPUT INTEG M, V33, P169, DOI 10.1080/0951192X.2020.1718765
Kozlenkova IV, 2014, J ACAD MARKET SCI, V42, P1, DOI 10.1007/s11747-013-0336-7
Kumar V., 2017, APPL MARKETING ANAL, V3, P58
Kushwaha AK, 2021, IND MARKET MANAG, V98, P207, DOI 10.1016/j.indmarman.2021.08.011
Lay R., 2018, Digital transformation. The ultimate challenge for the fashion industry
Lee JM, 2021, J WORLD BUS, V56, DOI 10.1016/j.jwb.2021.101193
Lilien GL, 2011, J MARKETING, V75, P196, DOI 10.1509/jmkg.75.4.196
Lin JH, 2015, ASIA PAC MANAG REV, V20, P141, DOI 10.1016/j.apmrv.2014.12.013
Lütjen H, 2019, J BUS RES, V104, P506, DOI 10.1016/j.jbusres.2019.06.001
Martínez-López FJ, 2013, IND MARKET MANAG, V42, P489, DOI 10.1016/j.indmarman.2013.03.001
McKinsey Analytics, 2020, ACC AN NAV COVID 19
Michailova S, 2015, J WORLD BUS, V50, P576, DOI 10.1016/j.jwb.2014.10.001
Mikalef P., 2017, P BIG DAT AN CAP ANT
Mikalef P, 2021, IND MARKET MANAG, V98, P80, DOI 10.1016/j.indmarman.2021.08.003
Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2020.103412
Mikalef P, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.05.004
Moktadir MA, 2018, J CLEAN PROD, V174, P1366, DOI 10.1016/j.jclepro.2017.11.063
Moorman C, 2016, J ACAD MARKET SCI, V44, P562, DOI 10.1007/s11747-016-0483-8
Morash EA, 2002, J INT MARKETING, V10, P25, DOI 10.1509/jimk.10.1.25.19529
Mousavi S, 2017, J CLEAN PROD, V167, P1263, DOI 10.1016/j.jclepro.2017.07.146
Nath P, 2010, IND MARKET MANAG, V39, P317, DOI 10.1016/j.indmarman.2008.09.001
Ou YC, 2017, J ACAD MARKET SCI, V45, P336, DOI 10.1007/s11747-016-0477-6
Paiola M, 2020, IND MARKET MANAG, V89, P245, DOI 10.1016/j.indmarman.2020.03.009
Partida D., 2022, TECHNOLOGY
Pereira V, 2021, J BUS RES, V132, P557, DOI 10.1016/j.jbusres.2021.04.021
Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879
Rahman MS, 2022, J ENTERP INF MANAG, V35, P455, DOI 10.1108/JEIM-05-2020-0185
Rahman MS, 2022, INFORM TECHNOL PEOPL, V35, P316, DOI 10.1108/ITP-12-2019-0621
Randhawa K, 2021, J BUS RES, V130, P618, DOI 10.1016/j.jbusres.2020.05.046
Ravi R, 2021, FASHION IS REVAMPING
Safari A, 2020, J BUS IND MARK, V35, P635, DOI 10.1108/JBIM-11-2018-0324
Saha K, 2021, BUS STRATEG ENVIRON, V30, P1497, DOI 10.1002/bse.2670
Sarkar M, 2021, J INTERACT MARK, V53, P80, DOI 10.1016/j.intmar.2020.07.002
Schmelzer R., 2019, 5 BENEFITS BANKING I
Seagate, 2021, SEAGATE REPORT
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Silva E.S., 2019, Journal of Business Strategy, DOI [DOI 10.1108/JBS-04-2019-0062, 10.1108/JBS-04-2019-0062]
Slater SF, 2000, J ACAD MARKET SCI, V28, P120, DOI 10.1177/0092070300281011
Song M, 2007, J ACAD MARKET SCI, V35, P18, DOI 10.1007/s11747-006-0005-1
Song PJ, 2018, INFORM MANAGE-AMSTER, V55, P633, DOI 10.1016/j.im.2018.01.004
Stahl BC, 2021, J BUS RES, V124, P374, DOI 10.1016/j.jbusres.2020.11.030
Statista, 2021, TECH OV RMG
Swazan I.S., 2022, INT J GLOB BUS COMPE, P1
Tan FTC, 2016, INFORM MANAGE-AMSTER, V53, P878, DOI 10.1016/j.im.2016.07.002
Teece DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/smj.640
Teece DJ, 2018, LONG RANGE PLANN, V51, P40, DOI 10.1016/j.lrp.2017.06.007
Teece DJ, 2014, J INT BUS STUD, V45, P8, DOI 10.1057/jibs.2013.54
Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Thomassey S, 2018, SPR SER FASH BUS, P1, DOI 10.1007/978-981-13-0080-6_1
Trainor KJ, 2014, J BUS RES, V67, P1201, DOI 10.1016/j.jbusres.2013.05.002
Tupikovskaja-Omovie Z, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2020.102294
Uddin M., 2018, DAILY STAR
Urban G, 2020, MIT SLOAN MANAGE REV, V61, P71
Wade M, 2004, MIS QUART, V28, P107
Wamba SF, 2017, J BUS RES, V70, P356, DOI 10.1016/j.jbusres.2016.08.009
Wang CL, 2007, INT J MANAG REV, V9, P31, DOI 10.1111/j.1468-2370.2007.00201.x
Wang Z, 2017, J INTERACT MARK, V39, P15, DOI [10.13393/j.cnki.issn.1672-948X.2017.03.004, 10.1016/j.intmar.2017.02.004]
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Weinberg BD, 2015, BUS HORIZONS, V58, P615, DOI 10.1016/j.bushor.2015.06.005
WERNERFELT B, 1984, STRATEGIC MANAGE J, V5, P171, DOI 10.1002/smj.4250050207
Wiggers K., 2022, TECH CRUNCH
Wilden R, 2019, IND MARKET MANAG, V83, P21, DOI 10.1016/j.indmarman.2018.11.001
World Trade Organization, 2021, World Trade Statistics Review 2021
Wu LY, 2010, J BUS RES, V63, P27, DOI 10.1016/j.jbusres.2009.01.007
Xu ZN, 2016, J BUS RES, V69, P1562, DOI 10.1016/j.jbusres.2015.10.017
Cai YJ, 2020, EUR J OPER RES, V298, P169, DOI 10.1016/j.ejor.2021.05.021
Yalcin AS, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121193
Yasmin M, 2020, J BUS RES, V114, P1, DOI 10.1016/j.jbusres.2020.03.028
Yu WT, 2014, IND MARKET MANAG, V43, P25, DOI 10.1016/j.indmarman.2013.07.014
Zhang HS, 2020, IND MARKET MANAG, V86, DOI 10.1016/j.indmarman.2019.02.020
Zhou KZ, 2010, J BUS RES, V63, P224, DOI 10.1016/j.jbusres.2009.03.003
NR 159
TC 21
Z9 21
U1 61
U2 177
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0019-8501
EI 1873-2062
J9 IND MARKET MANAG
JI Ind. Mark. Manage.
PD OCT
PY 2022
VL 106
BP 240
EP 255
DI 10.1016/j.indmarman.2022.08.017
EA SEP 2022
PG 16
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 4N9ND
UT WOS:000854337800002
DA 2024-03-27
ER
PT J
AU Yuan, YP
Tan, GWH
Ooi, KB
AF Yuan, Yun-Peng
Tan, Garry Wei-Han
Ooi, Keng-Boon
TI Does COVID-19 Pandemic Motivate Privacy Self-Disclosure in Mobile
Fintech Transactions? A Privacy-Calculus-Based Dual-Stage SEM-ANN
Analysis
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Privacy; Pandemics; COVID-19; Decision making; Appraisal; Calculus;
Psychology; Artificial neural network (ANN); control agency theory;
coronavirus disease 2019 (COVID-19); mobile commerce; mobile financial
technology; partial least squares structural equation modeling; privacy
calculus
ID PROTECTION MOTIVATION; INFORMATION PRIVACY; FEAR APPEALS; E-COMMERCE;
RISK; DETERMINANTS; ANTECEDENTS; INTENTION; EPIDEMIC; EFFICACY
AB The emergence of mobile financial technology (mobile fintech) services raises numerous public concerns regarding privacy issues; consequently, researchers in mobile technology acceptance have focused on consumers' privacy self-disclosure behaviors under the usual scenario. However, there is still a lack of understanding on how external influences, such as a public health crisis, affect consumers' privacy decision-making process. Therefore, in this article, we examine the effects of privacy- and pandemic-related antecedents on mobile fintech users' information self-disclosure behavior during the coronavirus disease 2019 pandemic. The present research adopts a self-administered questionnaire with 712 effective responses for data collection and a two-stage partial least squares-structural equation modeling-artificial neural network (PLS-SEM-ANN) approach to test the theoretical lens proposed. The results indicate that the significant structural paths in the model are consistent with the proposed hypotheses and existing literature. Surprisingly, face-to-face avoidance (FFA) does not significantly influence consumers' self-disclosure willingness. Infection severity and infection susceptibility were insignificant with FFA. The present research is the first to investigate consumers' privacy-related behavior via integrating the privacy-calculus framework with control agency theory. This research focuses on consumers' decision-making during the pandemic, explicitly highlighting the macroenvironment's role in influencing an individual's behavior.
C1 [Yuan, Yun-Peng; Tan, Garry Wei-Han; Ooi, Keng-Boon] UCSI Univ, UCSI Grad Business Sch, Kuala Lumpur 56000, Malaysia.
[Tan, Garry Wei-Han; Ooi, Keng-Boon] Nanchang Inst Technol, Sch Finance & Econ, Nanchang 330029, Jiangxi, Peoples R China.
[Tan, Garry Wei-Han] Yunnan Normal Univ, Sch Econ & Management, Kunming 650092, Yunnan, Peoples R China.
[Ooi, Keng-Boon] Chang Jung Christian Univ, Coll Management, Tainan 711301, Taiwan.
C3 UCSI University; Nanchang Institute Technology; Yunnan Normal University
RP Tan, GWH (autor correspondiente), UCSI Univ, UCSI Grad Business Sch, Kuala Lumpur 56000, Malaysia.
EM yunpeng_yuan@163.com; fowler_1982@yahoo.com; ooikengboon@gmail.com
RI OOI, Keng-Boon/I-4143-2019; Tan Wei Han, Garry/C-6565-2011
OI OOI, Keng-Boon/0000-0002-3384-1207; Tan Wei Han,
Garry/0000-0003-2974-2270; Yuan, Yunpeng/0000-0002-6589-5568
CR Adam I, 2015, TOURISM MANAGE, V49, P99, DOI 10.1016/j.tourman.2015.02.016
Al-Saedi K., 2019, 2019 INT C 4 IND REV, P1, DOI DOI 10.1109/ICFIR.2019.8894794
Al-Sharafi MA, 2022, INT J BANK MARK, V40, P1071, DOI 10.1108/IJBM-07-2021-0291
Altman I., 1973, Social penetration: The development of interpersonal relationships
[Anonymous], 2021, 47 CHIN INT NETW INF
Bandura A, 2001, ANNU REV PSYCHOL, V52, P1, DOI 10.1146/annurev.psych.52.1.1
Bauer R.A., 1967, RISK TAKING INFORM H, P23
Boss SR, 2015, MIS QUART, V39, P837, DOI 10.25300/MISQ/2015/39.4.5
Bulgurcu B, 2010, MIS QUART, V34, P523
Cao Y. Q., 2021, CHINA DAILY AUG
Chen CY, 2020, INFORM SYST J, V30, P492, DOI 10.1111/isj.12264
Cho J, 2006, J RETAILING, V82, P25, DOI 10.1016/j.jretai.2005.11.002
Chong AYL, 2013, EXPERT SYST APPL, V40, P1240, DOI 10.1016/j.eswa.2012.08.067
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
COLLINS NL, 1994, PSYCHOL BULL, V116, P457, DOI 10.1037/0033-2909.116.3.457
Cui, 2018, SOHU JAN
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
De Meulenaer S, 2018, HEALTH COMMUN, V33, P291, DOI 10.1080/10410236.2016.1266573
Dinev T, 2006, INFORM SYST RES, V17, P61, DOI 10.1287/isre.1060.0080
Dinev T, 2013, EUR J INFORM SYST, V22, P295, DOI 10.1057/ejis.2012.23
Eichelberger L, 2007, SOC SCI MED, V65, P1284, DOI 10.1016/j.socscimed.2007.04.022
Fixson SK, 2017, IEEE T ENG MANAGE, V64, P16, DOI 10.1109/TEM.2016.2638847
Floyd DL, 2000, J APPL SOC PSYCHOL, V30, P407, DOI 10.1111/j.1559-1816.2000.tb02323.x
Foo PY, 2018, EXPERT SYST APPL, V107, P1, DOI 10.1016/j.eswa.2018.04.013
Fu Y., 2020, QIANZHAN OCT
Gao YW, 2015, IND MANAGE DATA SYST, V115, P1704, DOI 10.1108/IMDS-03-2015-0087
García-Milon A, 2021, TOURISM MANAGE, V87, DOI 10.1016/j.tourman.2021.104361
Gong X, 2020, INFORM TECHNOL PEOPL, V33, P1174, DOI 10.1108/ITP-03-2018-0132
Gong X, 2020, INT J INFORM MANAGE, V50, P310, DOI 10.1016/j.ijinfomgt.2019.08.006
Goode, 2021, BIOMETRIC TECHNOL TO, V2021, P7, DOI [10.1016/S0969-4765(21)00083-7, DOI 10.1016/S0969-4765(21)00083-7]
Hair J.F., 2009, Multivariate data analysis
Hajli N, 2017, IEEE T ENG MANAGE, V64, P594, DOI 10.1109/TEM.2017.2711042
Haykin S., 1994, Neural Network: A Comprehensive Foundation, V5, P363
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Hew JJ, 2019, TECHNOL FORECAST SOC, V144, P311, DOI 10.1016/j.techfore.2017.10.007
Hew TS, 2016, J COMPUT INFORM SYST, V56, P352, DOI 10.1080/08874417.2016.1164497
Jayanti RK, 1998, J ACAD MARKET SCI, V26, P6, DOI 10.1177/0092070398261002
Jiang Z, 2013, INFORM SYST RES, V24, P579, DOI 10.1287/isre.1120.0441
Jung Y, 2018, INT J INFORM MANAGE, V43, P15, DOI 10.1016/j.ijinfomgt.2018.05.007
Keller PA, 2006, J CONSUM RES, V33, P109, DOI 10.1086/504141
Lankton NK, 2020, IEEE T ENG MANAGE, V67, P697, DOI 10.1109/TEM.2019.2893541
LAUFER RS, 1977, J SOC ISSUES, V33, P22, DOI 10.1111/j.1540-4560.1977.tb01880.x
Lee VH, 2020, EXPERT SYST APPL, V157, DOI 10.1016/j.eswa.2020.113477
Lee YK, 2012, J BUS RES, V65, P1590, DOI 10.1016/j.jbusres.2011.02.044
Leong LY, 2020, J BUS RES, V110, P24, DOI 10.1016/j.jbusres.2019.11.056
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Li Y, 2020, INTERNET RES, V30, P511, DOI 10.1108/INTR-02-2018-0066
Liang HG, 2007, MIS QUART, V31, P59
Liu N, 2017, IEEE T ENG MANAGE, V64, P529, DOI 10.1109/TEM.2017.2703121
Liu ZL, 2016, INFORM MANAGE-AMSTER, V53, P53, DOI 10.1016/j.im.2015.08.006
Loh XM, 2021, INTERNET RES, V31, P376, DOI 10.1108/INTR-04-2020-0175
Lwin MO, 2010, HEALTH COMMUN, V25, P69, DOI 10.1080/10410230903473540
Ma XF, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106928
MADDUX JE, 1983, J EXP SOC PSYCHOL, V19, P469, DOI 10.1016/0022-1031(83)90023-9
Martin K, 2016, J ASSOC INF SCI TECH, V67, P1871, DOI 10.1002/asi.23500
Masser BM, 2011, TRANSFUSION, V51, P548, DOI 10.1111/j.1537-2995.2010.02887.x
McKnight D. H., 2002, Information Systems Research, V13, P334, DOI 10.1287/isre.13.3.334.81
Mcknight DH, 1998, ACAD MANAGE REV, V23, P473, DOI 10.5465/AMR.1998.926622
Menard P, 2017, J MANAGE INFORM SYST, V34, P1203, DOI 10.1080/07421222.2017.1394083
Milne S, 2000, J APPL SOC PSYCHOL, V30, P106, DOI 10.1111/j.1559-1816.2000.tb02308.x
Min J, 2016, J ASSOC INF SCI TECH, V67, P26, DOI 10.1002/asi.23376
Mutimukwe C, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2019.101413
Olya HGT, 2018, TOURISM MANAGE, V65, P279, DOI 10.1016/j.tourman.2017.10.015
Ooi KB, 2021, IND MANAGE DATA SYST, V121, P312, DOI 10.1108/IMDS-04-2020-0239
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
Person B, 2004, EMERG INFECT DIS, V10, P358, DOI 10.3201/eid1002.030750
Posey C, 2015, J MANAGE INFORM SYST, V32, P179, DOI 10.1080/07421222.2015.1138374
Rogers R.W., 1983, SOCIAL PSYCHOPHYSIOL, P153
ROGERS RW, 1975, J PSYCHOL, V91, P93, DOI 10.1080/00223980.1975.9915803
Shao Z, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.100823
Shih PH, 2018, KYBERNETES, V47, P539, DOI 10.1108/K-12-2016-0354
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Son JY, 2008, MIS QUART, V32, P503
STRONG P, 1990, SOCIOL HEALTH ILL, V12, P249, DOI 10.1111/1467-9566.ep11347150
Teo AC, 2015, INT J MOB COMMUN, V13, P478, DOI 10.1504/IJMC.2015.070961
Teo AC, 2015, IND MANAGE DATA SYST, V115, P311, DOI 10.1108/IMDS-08-2014-0231
Tew HT, 2022, J COMPUT INFORM SYST, V62, P527, DOI 10.1080/08874417.2020.1858731
Wang HY, 2017, CURR ISSUES TOUR, V20, P459, DOI 10.1080/13683500.2015.1062471
Wang JG, 2017, INFORM SYST RES, V28, P378, DOI 10.1287/isre.2016.0680
Wang L, 2019, INFORM MANAGE-AMSTER, V56, DOI 10.1016/j.im.2019.02.005
Wang ST, 2020, INT J BANK MARK, V38, P21, DOI 10.1108/IJBM-10-2018-0272
Wang ZN, 2019, IND MANAGE DATA SYST, V119, P1625, DOI 10.1108/IMDS-01-2019-0011
Wen H, 2022, J HOSP TOUR MANAG, V51, DOI 10.1016/j.jhtm.2022.03.009
Wheeless L.R., 1976, Human Communication Research, V3, P47
Wilson RD, 2019, J BUS IND MARK, V35, P495, DOI 10.1108/JBIM-01-2019-0060
Wong CH, 2015, ONLINE INFORM REV, V39, P762, DOI 10.1108/OIR-05-2015-0152
Xiang D, 2021, IEEE T ENG MANAGE, V68, P1590, DOI 10.1109/TEM.2020.2989136
Xiu-Ming Loh, 2022, Journal of Computer Information Systems, V62, P39, DOI 10.1080/08874417.2019.1688733
Xu H, 2012, INFORM SYST RES, V23, P1342, DOI 10.1287/isre.1120.0416
Xu H, 2011, J ASSOC INF SYST, V12, P798
Xu H, 2011, DECIS SUPPORT SYST, V51, P42, DOI 10.1016/j.dss.2010.11.017
Yan LY, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102300
Yang QW, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102065
Zheng DN, 2021, TOURISM MANAGE, V83, DOI 10.1016/j.tourman.2020.104261
Zlatolas LN, 2015, COMPUT HUM BEHAV, V45, P158, DOI 10.1016/j.chb.2014.12.012
NR 96
TC 10
Z9 10
U1 13
U2 58
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 2986
EP 3000
DI 10.1109/TEM.2022.3204285
EA SEP 2022
PG 15
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA EO4E7
UT WOS:000854577000001
DA 2024-03-27
ER
PT J
AU Bhatnagar, S
Kumra, R
AF Bhatnagar, Sushant
Kumra, Rajeev
TI Understanding consumer motivation to share IoT products data
SO JOURNAL OF INDIAN BUSINESS RESEARCH
LA English
DT Article
DE Electronic word of thing; Internet of things; Natural language
generation; eWOT; Online consumer behavior; Internet marketing
ID WORD-OF-MOUTH; INTERNET; INTENTION; BEHAVIOR
AB Purpose
Almost every study undertaken by academicians or practitioners on the Internet of Things (IoT) has mainly highlighted the privacy concerns and information security issues with the IoT products. On the contrary, this paper aims to explore the motivators that could encourage customers of an IoT product to share their IoT product's data with a third-party aggregator system to facilitate computer-generated product reviews which are defined as electronic Word of Thing (eWOT) in this paper.
Design/methodology/approach
An experiment was conducted with customized e-commerce prototypes of eWOT. Structural equation modeling analysis was conducted to test the measurement model by using confirmatory factor analysis and thereafter a structural model to test the relationships amongst the latent variables.
Findings
This paper found that five consumer motivators (personal innovativeness, enjoyment of helping, anticipated extrinsic rewards, moral obligations and venting negative feelings) contribute to eWOT intention.
Originality/value
This paper presents motivators for eWOT intention to share IoT product data. This is done through a novel concept of an experimental IoT-based prototype, namely, eWOT. These eWOT reviews can be generated from the IoT products data by applying analytics and using natural language generation. To the best of the authors' knowledge, no other study has been conducted on this subject.
C1 [Bhatnagar, Sushant] IIM Lucknow, Noida, India.
[Kumra, Rajeev] IIM Lucknow, Dept Mkt, Noida, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
Management Lucknow; Indian Institute of Management (IIM System); Indian
Institute of Management Lucknow
RP Bhatnagar, S (autor correspondiente), IIM Lucknow, Noida, India.
EM efpm01018@iiml.ac.in
RI Kumra, Rajeev/J-4069-2016
CR Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204
Anderson ET, 2014, J MARKETING RES, V51, P249, DOI 10.1509/jmr.13.0209
[Anonymous], 2011, Big data: The next frontier for innovation, competition, and productivity
[Anonymous], 2011, MARKETING RES APPL O
[Anonymous], 2007, MULTIVARIATE DATA
Ashton K., 2009, RFID J, V22, P97, DOI DOI 10.1016/J.AMJCARD.2013.11.014
Atzori L, 2014, IEEE COMMUN MAG, V52, P97, DOI 10.1109/MCOM.2014.6710070
Babbie E., 2003, The practice of social research
Bagozzi R.P., 1988, COGNITION EMOTION, V12, P1
Balasubramanian S, 2001, INT J ELECTRON COMM, V5, P103
Bartoli A, 2016, LECT NOTES COMPUT SC, V9817, P19, DOI 10.1007/978-3-319-45507-5_2
Bock GW, 2005, MIS QUART, V29, P87, DOI 10.2307/25148669
Bok B.G.J., 2016, 7 IBA BACH THES C EN
Bosnjak M, 2005, PSYCHOL MARKET, V22, P489, DOI 10.1002/mar.20070
Carmines E, 1981, SOCIAL MEASUREMENT C
Cecchinato M.E., 2015, Proceedings of the 33rd Annual ACM Conference extended abstracts on human factors in computing systems, P2133, DOI DOI 10.1145/2702613.2732837
Cheung CMK, 2012, DECIS SUPPORT SYST, V53, P218, DOI 10.1016/j.dss.2012.01.015
Cheyer A., 2014, US Patent, Patent No. 8677377
Chin WW, 1997, INFORM SYST RES, V8, P342, DOI 10.1287/isre.8.4.342
Choi H., 2018, P 11 INT NAT LANG GE, P165
Choi J, 2016, COMPUT HUM BEHAV, V63, P777, DOI 10.1016/j.chb.2016.06.007
de Arriba-Pérez F, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16091538
Dennison G., 2009, CISC VIS NETW IND GL
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Edson B., 2015, MICROSOFT REPORT
ETZIONI A, 1988, J ECON PSYCHOL, V9, P125, DOI 10.1016/0167-4870(88)90048-7
Evans H. I., 2015, INT J INFORM COMMUNI, V5
Farah M.F., 2017, J RETAILING CONSUMER, V39
Gartner, 2017, GARTN SAYS 84 BILL C
Haller S, 2009, LECT NOTES COMPUT SC, V5468, P14, DOI 10.1007/978-3-642-00985-3_2
Hennig-Thurau T., 2004, Journal Interactive Marketing, V18
Hongcharu B., 2011, International Journal of Management and Information Systems, V15, P113, DOI [10.19030/ijmis.v15i4.5808, DOI 10.19030/IJMIS.V15I4.5808]
Li W., 2014, PACIS 2014 P
Luo XM, 2009, MARKET SCI, V28, P148, DOI 10.1287/mksc.1080.0389
Manyika J., 2015, MCKINSEY 2025 INTERN
Mirkovic D., 2011, US Patent, Patent No. 8041570
Mukhopadhyay S.C., 2014, Internet of Things: Challenges and Opportunities
Neisse R, 2015, 2015 IEEE 2ND WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P789, DOI 10.1109/WF-IoT.2015.7389154
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
Perera C, 2015, IT PROF, V17, P32, DOI 10.1109/MITP.2015.34
Pittman D., 2013, BIG DATA RETAIL EXAM
Porter G, 2015, J CRIMINOL RES POLIC, V1, DOI 10.1108/JCRPP-10-2015-0049
Porter M.E., 2014, HARVARD BUSINESS REV, V11
Presi C, 2014, EUR J MARKETING, V48, P1600, DOI 10.1108/EJM-07-2012-0400
PwC, 2014, CONS INT SER
Sengul C, 2017, CONF INNOV CLOUD, P319, DOI 10.1109/ICIN.2017.7899432
Smits S.P., 2014, THESIS
Sundaram DS, 1998, ADV CONSUM RES, V25, P527
Verame J.M., 2016, P 2016 CHI C HUM FAC
Want R, 2015, COMPUTER, V48, P28, DOI 10.1109/MC.2015.12
Wasko MM, 2000, J STRATEGIC INF SYST, V9, P155, DOI 10.1016/S0963-8687(00)00045-7
Xia F, 2012, INT J COMMUN SYST, V25, P1101, DOI 10.1002/dac.2417
Yu J, 2005, INFORM MANAGE-AMSTER, V42, P965, DOI 10.1016/j.im.2004.11.001
NR 53
TC 5
Z9 6
U1 1
U2 10
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1755-4195
EI 1755-4209
J9 J INDIAN BUS RES
JI J. Indian Bus. Res.
PD JAN 25
PY 2020
VL 12
IS 1
SI SI
BP 5
EP 22
DI 10.1108/JIBR-09-2019-0268
EA JAN 2020
PG 18
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA LB2NW
UT WOS:000512308100001
DA 2024-03-27
ER
PT J
AU Cajias, M
Zeitler, JA
AF Cajias, Marcelo
Zeitler, Joseph-Alexander
TI Quantifying the drivers of residential housing demand - an interpretable
machine learning approach
SO JOURNAL OF EUROPEAN REAL ESTATE RESEARCH
LA English
DT Article
DE Machine learning; eXtreme gradient boosting; Online user-generated
search data; Residential real estate; German rental market; C33; C45;
D83; R211
ID INTERNET SEARCH BEHAVIOR; SELLING PRICE; MARKET; TIME; EFFICIENCY;
LIQUIDITY; DETERMINANTS; INFORMATION; HOT
AB PurposeThe paper employs a unique online user-generated housing search dataset and introduces a novel measure for housing demand, namely "contacts per listing" as explained by hedonic, geographic and socioeconomic variables. Design/methodology/approachThe authors explore housing demand by employing an extensive Internet search dataset from a German housing market platform. The authors apply state-of-the-art artificial intelligence, the eXtreme Gradient Boosting, to quantify factors that lead an apartment to be in demand.FindingsThe authors compare the results to alternative parametric models and find evidence of the superiority of the nonparametric model. The authors use eXplainable artificial intelligence (XAI) techniques to show economic meanings and inferences of the results. The results suggest that hedonic, socioeconomic and spatial aspects influence search intensity. The authors further find differences in temporal dynamics and geographical variations.Originality/valueTo the best of the authors' knowledge, it is the first study of its kind. The statistical model of housing search draws on insights from decision theory, AI and qualitative studies on housing search. The econometric approach employed is new as it considers standard regression models and an eXtreme Gradient Boosting (XGB or XGBoost) approach followed by a model-agnostic interpretation of the underlying effects.
C1 [Cajias, Marcelo] PATRIZIA SE, Investment Strategy & Res, Augsburg, Germany.
[Cajias, Marcelo] Univ Regensburg, Regensburg, Germany.
[Zeitler, Joseph-Alexander] IREBS, Regensburg, Germany.
C3 University of Regensburg
RP Cajias, M (autor correspondiente), PATRIZIA SE, Investment Strategy & Res, Augsburg, Germany.; Cajias, M (autor correspondiente), Univ Regensburg, Regensburg, Germany.
EM marcelocajias@hotmail.com; joseph-alexander.zeitler@hotmail.de
OI Cajias, Marcelo/0000-0002-0777-7459
CR Albrecht J, 2007, INT ECON REV, V48, P641, DOI 10.1111/j.1468-2354.2007.00440.x
Albrecht J, 2016, REV ECON DYNAM, V19, P218, DOI 10.1016/j.red.2015.05.002
Alexa, 2020, IMMOBILIENSCOUT24 CO
Allen MT, 2009, J REAL ESTATE FINANC, V38, P351, DOI 10.1007/s11146-007-9092-0
Anglin PM, 2003, J REAL ESTATE FINANC, V26, P95, DOI 10.1023/A:1021526332732
Askitas, 2009, APPL EC Q, V55, P107, DOI [10.2139/ssrn.1465341, DOI 10.3790/AEQ.55.2.107]
Benefield J., 2014, Journal of Real Estate Literature, V22, P149, DOI DOI 10.1080/10835547.2014.12090387
Benefield JD, 2011, J REAL ESTATE FINANC, V43, P401, DOI 10.1007/s11146-009-9219-6
Beracha E., 2015, Journal of Housing Research, V24, P147, DOI DOI 10.1080/10835547.2015.12092101
Boeing G, 2020, ENVIRON PLANN A, V52, P449, DOI 10.1177/0308518X19869678
Botterill Jacqueline., 2013, The Routledge Companion to Advertising and Promotional Culture, P326, DOI [10.4324/9780203071434-37, DOI 10.4324/9780203071434-37]
Bourassa SC, 1999, J HOUS ECON, V8, P160, DOI 10.1006/jhec.1999.0246
Breidenbach P, 2018, JAHRB NATL STAT, V238, P609, DOI 10.1515/jbnst-2017-0171
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Cajias M, 2018, J PROP INVEST FINANC, V36, P3, DOI 10.1108/JPIF-01-2017-0006
Cameron AC., 2013, REGRESSION ANAL COUN, V53, DOI DOI 10.1017/CBO9781139013567
Carrillo P.E., 2008, INFORM REAL ESTATE T
Carrillo PE, 2012, REG SCI URBAN ECON, V42, P189, DOI 10.1016/j.regsciurbeco.2011.08.010
CASE KE, 1989, AM ECON REV, V79, P125
CASE KE, 1990, AREUEA J, V18, P253
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Chernobai E, 2012, REAL ESTATE ECON, V40, P805, DOI 10.1111/j.1540-6229.2012.00329.x
Chuklin A, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P1217, DOI 10.1145/2505515.2507859
Cirman A, 2015, INZ EKON, V26, P4, DOI 10.5755/j01.ee.26.1.3905
Clark W.A. V., 1993, BEHAV ENV PSYCHOL GE, P298
Clayton J., 1998, J REAL ESTATE RES, V15, P41, DOI DOI 10.5555/REES.15.1.L4745676513K672U
Comscore, 2019, COMSC MED METR
D'urso V., 2002, 427102 MIT SLOAN SCH
Da Z, 2011, J FINANC, V66, P1461, DOI 10.1111/j.1540-6261.2011.01679.x
Das P, 2015, J REAL ESTATE FINANC, V51, P480, DOI 10.1007/s11146-015-9496-1
Díaz A, 2013, INT ECON REV, V54, P837, DOI 10.1111/iere.12019
Dunning R., 2012, INFORM SEARCH HOUSIN
European Parliament and Council, 2016, Official Journal of the European Union
Fisher A, 2019, J MACH LEARN RES, V20
Ford JS, 2005, J HOUS ECON, V14, P92, DOI 10.1016/j.jhe.2005.06.003
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Genesove D, 2012, J URBAN ECON, V72, P31, DOI 10.1016/j.jue.2012.01.002
Gesellschaft fur Konsumforschung, 2020, GFK WERB
Goodman AC, 2007, REAL ESTATE ECON, V35, P209, DOI 10.1111/j.1540-6229.2007.00188.x
Han Lu., 2015, HDB REGIONAL URBAN E, V5, P813, DOI [10.1016/B978-0-444-59531-7.00013-2, DOI 10.1016/B978-0-444-59531-7.00013-2]
Immobilienverband Deutschland, 2018, NUTZ IMM
Joint Center for Housing Studies, 2019, SUPPL STATE NAT HOUS
Knight JR, 2002, REAL ESTATE ECON, V30, P213, DOI 10.1111/1540-6229.00038
Krainer J, 2002, ECON THEOR, V19, P223, DOI 10.1007/PL00004214
Krainer J, 2001, J URBAN ECON, V49, P32, DOI 10.1006/juec.2000.2180
Krainer J., 1999, Economic Review, V3, P14
Kroft K, 2014, J LABOR ECON, V32, P259, DOI 10.1086/673374
Maclennan D., 1982, MODELLING HOUSING MA, P134, DOI DOI 10.4324/9781003182085-8
McLaughlin R., 2018, SHARED FUTURE FOSTER, P126
Meen G., 2001, ADV URBAN REGIONAL E, DOI [10.1007/978-1-4615-1673-6, DOI 10.1007/978-1-4615-1673-6]
Merlo A, 2015, INT ECON REV, V56, P457, DOI 10.1111/iere.12111
MHCLG (Ministry of Housing Communities and Local Government), 2020, English Housing Survey: headline report 2019-2020
National Assocation of Realtors, 2019, Technical report
National Association of Realtors, 2006, 2006 PROF HOM BUYERS
Novy-Marx R, 2009, REAL ESTATE ECON, V37, P1, DOI 10.1111/j.1540-6229.2009.00232.x
Office of Fair Trading, 2010, HOM BUYING SELL A MA
Palm R, 2001, URBAN GEOGR, V22, P641, DOI 10.2747/0272-3638.22.7.641
Pangallo M, 2018, EPJ DATA SCI, V7, DOI 10.1140/epjds/s13688-018-0176-2
Piazzesi M, 2020, AM ECON REV, V110, P720, DOI 10.1257/aer.20141772
Rae A, 2016, CITIES, V52, P140, DOI 10.1016/j.cities.2015.12.002
Rae A, 2015, HOUSING STUD, V30, P453, DOI 10.1080/02673037.2014.974142
Ribeiro M.T., 2016, 2016 ICML WORKSHOP H
Richardson H, 2012, J REAL ESTATE RES, V34, P515
RICS, 2017, ARTIF INTELL
RWI-Leibniz-Institut fur Wirtschaftsforschung, 2020, REAL EST DAT GERM RW
Seiler MJ, 2012, J REAL ESTATE RES, V34, P211
US Census Bureau, 2018, AM HOUS SURV AHS
van Dijk DW, 2018, REAL ESTATE ECON, V46, P368, DOI 10.1111/1540-6229.12187
Watkins CA, 2001, ENVIRON PLANN A, V33, P2235, DOI 10.1068/a34162
Watkins C, 2008, HOUSING STUD, V23, P163, DOI 10.1080/02673030801893131
WHEATON WC, 1990, J POLIT ECON, V98, P1270, DOI 10.1086/261734
Zhao QY, 2021, J BUS ECON STAT, V39, P272, DOI 10.1080/07350015.2019.1624293
NR 72
TC 0
Z9 0
U1 6
U2 7
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 1753-9269
J9 J EUR REAL ESTATE RE
JI J. Eur. Real Estate Res.
PD OCT 11
PY 2023
VL 16
IS 2
BP 172
EP 199
DI 10.1108/JERER-02-2023-0008
EA JUL 2023
PG 28
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA U2PL2
UT WOS:001027203400001
DA 2024-03-27
ER
PT J
AU Chhabra, M
Das, S
Sarne, D
AF Chhabra, Meenal
Das, Sanmay
Sarne, David
TI Expert-mediated sequential search
SO EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
LA English
DT Article
DE E-commerce; Artificial intelligence; Sequential decision making;
Expert-mediated search
ID BUNDLING INFORMATION GOODS; JOB SEARCH; PRICE DISPERSION; 2-SIDED
MARKETS; BUYER SEARCH; ECONOMICS; SUBSIDIES; PRODUCTS; INTERNET; COSTS
AB This paper studies markets, such as Internet marketplaces for used cars or mortgages, in which consumers engage in sequential search. In particular, we consider the impact of information-brokers (experts) who can, for a fee, provide better information on true values of opportunities. We characterize the optimal search strategy given a price and the terms of service set by the expert, and show how to use this characterization to solve the monopolist expert's service pricing problem. Our analysis enables the investigation of three common pricing schemes (pay-per-use, unlimited subscription, and package pricing) that can be used by the expert. We demonstrate that in settings characteristic of electronic marketplaces, namely those with lower search costs for consumers and lower costs of production of expert services, unlimited subscription schemes are favored. Finally, we show that the platform that connects consumer and experts can improve social welfare by subsidizing the purchase of expert services. The optimal level of subsidy forces the buyer to exactly fully internalize the marginal cost of provision of expert services. In electronic markets, this cost is minimal, so it may be worthwhile for the platform to make the expert freely available to consumers. (C) 2013 Elsevier B.V. All rights reserved.
C1 [Chhabra, Meenal] Virginia Tech, Dept Comp Sci, Blacksburg, VA USA.
[Das, Sanmay] Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 USA.
[Sarne, David] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel.
[Chhabra, Meenal; Das, Sanmay] Rensselaer Polytech Inst, Troy, NY 12181 USA.
[Chhabra, Meenal; Das, Sanmay] Virginia Tech, Blacksburg, VA USA.
C3 Virginia Polytechnic Institute & State University; Washington University
(WUSTL); Bar Ilan University; Rensselaer Polytechnic Institute; Virginia
Polytechnic Institute & State University
RP Das, S (autor correspondiente), Washington Univ, Dept Comp Sci & Engn, St Louis, MO 63130 USA.
EM meenal@cs.vt.edu; sanmay@seas.wustl.edu; sarned@cs.biu.ac.il
FU US-Israel Binational Science Foundation [2008404]; US National Science
Foundation CAREER award [IIS-0952918/1303350]; Israel Science Foundation
[1083/13]; Direct For Computer & Info Scie & Enginr; Div Of Information
& Intelligent Systems [1414452] Funding Source: National Science
Foundation
FX This work was supported by a US-Israel Binational Science Foundation
Grant (2008404), a US National Science Foundation CAREER award
(IIS-0952918/1303350), and an Israel Science Foundation Grant (1083/13).
CR Armstrong M, 2006, RAND J ECON, V37, P668, DOI 10.1111/j.1756-2171.2006.tb00037.x
Atakan AE, 2006, ECONOMETRICA, V74, P667, DOI 10.1111/j.1468-0262.2006.00678.x
Bakos JY, 1997, MANAGE SCI, V43, P1676, DOI 10.1287/mnsc.43.12.1676
Bakos Y, 1999, MANAGE SCI, V45, P1613, DOI 10.1287/mnsc.45.12.1613
Baryla Edward., 2000, Journal of Real Estate Research, V20, P75
Baye MR, 2001, AM ECON REV, V91, P454, DOI 10.1257/aer.91.3.454
Bental B, 2002, J EVOL ECON, V12, P397, DOI 10.1007/s00191-002-0125-9
Bloch F, 2000, INT ECON REV, V41, P93, DOI 10.1111/1468-2354.00056
Brynjolfsson E, 2000, MANAGE SCI, V46, P563, DOI 10.1287/mnsc.46.4.563.12061
Burdett K, 1998, REV ECON DYNAM, V1, P220, DOI 10.1006/redy.1997.0004
Cason TN, 2003, J ECON THEORY, V112, P232, DOI 10.1016/S0022-0531(03)00135-0
Chhabra M., 2011, P 10 INT C AUTONOMOU, P415
Chun YH, 1996, EUR J OPER RES, V92, P135, DOI 10.1016/0377-2217(95)00045-3
Das S, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P947
DIAMOND PA, 1982, J POLIT ECON, V90, P881, DOI 10.1086/261099
Dranove D, 2010, J ECON LIT, V48, P935, DOI 10.1257/jel.48.4.935
Galeotti A, 2009, EUR ECON REV, V53, P417, DOI 10.1016/j.euroecorev.2008.08.003
Geng XJ, 2005, MANAGE SCI, V51, P662, DOI 10.1287/mnsc.1040.0344
Grosfeld-Nir A, 2009, EUR J OPER RES, V197, P667, DOI 10.1016/j.ejor.2007.07.036
Haynes M, 2008, INT J IND ORGAN, V26, P459, DOI 10.1016/j.ijindorg.2007.02.003
Ho LS, 2000, POLICY SCI, V33, P89, DOI 10.1023/A:1004774928116
Janetos A., 1980, BEHAV ECOLOGY SOCIOB, V7, P107
Kang BK, 2005, EUR J OPER RES, V165, P765, DOI 10.1016/j.ejor.2002.12.003
Kephart JO, 2000, COMPUT NETW, V32, P731, DOI 10.1016/S1389-1286(00)00026-8
Lee R., 2009, INTERVIEWING 2 SIDED
Lim C., 2006, DECIS ANAL, V3, P3, DOI DOI 10.1287/DECA.1050.0044
LIPPMAN SA, 1976, ECON INQ, V14, P155, DOI 10.1111/j.1465-7295.1976.tb00386.x
MACQUEEN JB, 1964, MANAGE SCI, V10, P746, DOI 10.1287/mnsc.10.4.746
MCCALL JJ, 1970, Q J ECON, V84, P113, DOI 10.2307/1879403
McMillan J., 1994, HDB GAME THEORY EC A, P905
MILGROM PR, 1981, BELL J ECON, V12, P380, DOI 10.2307/3003562
MONAHAN GE, 1980, OPER RES, V28, P1319, DOI 10.1287/opre.28.6.1319
MONAHAN GE, 1982, J APPL PROBAB, V19, P72, DOI 10.2307/3213917
Mortensen D., 1999, HDB LABOR EC, DOI [10.1016/S1573-4463(99)30025-0, DOI 10.1016/S1573-4463(99)30025-0, https://doi.org/10.1016/S1573-4463(99)30025-0]
Moscarini G, 2001, ECONOMETRICA, V69, P1629, DOI 10.1111/1468-0262.00259
Rysman M, 2009, J ECON PERSPECT, V23, P125, DOI 10.1257/jep.23.3.125
Sahib P., 2002, DEMOGR RES, V6, P355
Sundararajan A, 2004, MANAGE SCI, V50, P1660, DOI 10.1287/mnsc.1040.0291
Terwiesch C, 2004, MANAGE SCI, V50, P145, DOI 10.1287/mnsc.1030.0178
WEITZMAN ML, 1979, ECONOMETRICA, V47, P641, DOI 10.2307/1910412
Wiegmann DD, 2010, J MATH BIOL, V60, P543, DOI 10.1007/s00285-009-0274-7
Wilson KE, 2011, EUR J OPER RES, V215, P218, DOI 10.1016/j.ejor.2011.05.045
WRIGHT R, 1986, J POLIT ECON, V94, P38, DOI 10.1086/261362
NR 43
TC 14
Z9 15
U1 1
U2 45
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0377-2217
EI 1872-6860
J9 EUR J OPER RES
JI Eur. J. Oper. Res.
PD MAY 1
PY 2014
VL 234
IS 3
BP 861
EP 873
DI 10.1016/j.ejor.2013.10.033
PG 13
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA AA9NI
UT WOS:000331419800027
DA 2024-03-27
ER
PT J
AU Zhai, MF
Wang, XY
Zhao, XJ
AF Zhai, Mengfan
Wang, Xinyue
Zhao, Xijie
TI The importance of online customer reviews characteristics on
remanufactured product sales: Evidence from the mobile phone market on
Amazon.com
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Online consumer reviews; Remanufactured products; Natural language
processing; Formal characteristics; Content characteristics; Emotional
characteristics
ID WORD-OF-MOUTH; NEGATIVE REVIEWS; HOTEL BOOKING; INFORMATION; INTENTION;
EMOTIONS; DYNAMICS; BEHAVIOR; QUALITY
AB Remanufactured products have emerged as an important option for achieving sustainable development, which is crucial to the success of the circular economy. Consequently, it is necessary to investigate the influence of online customer reviews on remanufactured product sales, an aspect that has not been well understood in current research. In this study, we employed natural language processing methodologies, analyzing over 50,000 reviews from 2020 to 2021 on Amazon.com to explore the impacts of online customer reviews on remanufactured product sales. The findings indicate that the formal characteristics of online customer reviews, that is, review length and review valence have positive influences on remanufactured product sales. Furthermore, the content characteristics of online customer reviews, including the volume and valence of product usability, product service quality, and product cost performance, positively promote remanufactured product sales. Additionally, this study reveals three emotional characteristics of online customer reviews that significantly influence remanufactured product sales. Specifically, contentment and surprise positively affect remanufactured product sales, while anger negatively affects remanufactured product sales. The findings of this study provide useful insights for remanufactured product sellers to manage online customer reviews and develop marketing strategies.
C1 [Zhai, Mengfan; Zhao, Xijie] North China Univ Water Resources & Elect Power, Sch Management & Econ, Zhengzhou 450046, Peoples R China.
[Wang, Xinyue] Guangdong Univ Finance & Econ, Sch Informat Sci, Guangzhou 510320, Peoples R China.
[Wang, Xinyue] Sch Informat Sci, 21 Luntou St, Guangzhou, Peoples R China.
C3 North China University of Water Resources & Electric Power; Guangdong
University of Finance & Economics
RP Wang, XY (autor correspondiente), Sch Informat Sci, 21 Luntou St, Guangzhou, Peoples R China.
EM zhaimengfan@ncwu.edu.cn; 20231056@gdufe.edu.cn; 13410061570@163.com
FU High -Level Talent Research Project of the North China University of
Water Resources and Electric Power [202210007]; Henan University
Philosophy and Social Science Innovation Team Funding Project
[2019-CXTD-12, 2024-CXTD-10]
FX This work was supported by the High -Level Talent Research Project of
the North China University of Water Resources and Electric Power
(202210007) , and Henan University Philosophy and Social Science
Innovation Team Funding Project (2019-CXTD-12; 2024-CXTD-10) .
CR Abbasi GA, 2022, J CLEAN PROD, V359, DOI 10.1016/j.jclepro.2022.132137
Abbey JD, 2015, PROD OPER MANAG, V24, P488, DOI 10.1111/poms.12238
Alyahya M, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103172
Berger J, 2010, MARKET SCI, V29, P815, DOI 10.1287/mksc.1090.0557
Cai XW, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103141
Cambria E, 2014, IEEE COMPUT INTELL M, V9, P48, DOI 10.1109/MCI.2014.2307227
Chatterjee S, 2019, DECIS SUPPORT SYST, V119, P14, DOI 10.1016/j.dss.2019.02.008
Chaudhuri N, 2021, DECIS SUPPORT SYST, V149, DOI 10.1016/j.dss.2021.113622
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chintagunta PK, 2010, MARKET SCI, V29, P944, DOI 10.1287/mksc.1100.0572
CNNIC, 2022, The 51th Statistical Report on Internet Development in China
Colasante A, 2021, J CLEAN PROD, V329, DOI 10.1016/j.jclepro.2021.129774
Delre SA, 2023, J BUS RES, V156, DOI 10.1016/j.jbusres.2022.113329
Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, DOI 10.48550/ARXIV.1810.04805]
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Elwalda A, 2016, COMPUT HUM BEHAV, V56, P306, DOI 10.1016/j.chb.2015.11.051
Filieri R, 2021, INT J CONTEMP HOSP M, V33, P199, DOI 10.1108/IJCHM-05-2020-0402
Neto JQF, 2016, INT J PROD ECON, V171, P371, DOI 10.1016/j.ijpe.2015.02.006
Gopinath S, 2014, MARKET SCI, V33, P241, DOI 10.1287/mksc.2013.0820
Guo JP, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101891
Hazen BT, 2012, INT J PROD ECON, V135, P781, DOI 10.1016/j.ijpe.2011.10.011
Hu N, 2014, DECIS SUPPORT SYST, V57, P42, DOI 10.1016/j.dss.2013.07.009
Huang N, 2016, J CONSUM PSYCHOL, V26, P474, DOI 10.1016/j.jcps.2016.03.001
Jang S, 2021, J BUS RES, V130, P583, DOI 10.1016/j.jbusres.2019.09.027
Kalyan K.S., 2021, arXiv
Kaushik K, 2018, J RETAIL CONSUM SERV, V45, P21, DOI 10.1016/j.jretconser.2018.08.002
Khan FH, 2016, APPL SOFT COMPUT, V39, P140, DOI 10.1016/j.asoc.2015.11.016
Kim D, 2023, J RETAIL CONSUM SERV, V74, DOI 10.1016/j.jretconser.2023.103409
Kuo YF, 2009, COMPUT HUM BEHAV, V25, P887, DOI 10.1016/j.chb.2009.03.003
Li CY, 2018, COMPUT HUM BEHAV, V84, P272, DOI 10.1016/j.chb.2018.03.009
Li KL, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102107
Liang TP, 2019, INFORM MANAGE-AMSTER, V56, P70, DOI 10.1016/j.im.2018.07.002
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Lovett MJ, 2013, J MARKETING RES, V50, P427, DOI 10.1509/jmr.11.0458
Ludwig S, 2013, J MARKETING, V77, P87, DOI 10.1509/jm.11.0560
Luo RL, 2022, INT J PROD ECON, V245, DOI 10.1016/j.ijpe.2022.108408
Majumder MG, 2022, J BUS RES, V150, P147, DOI 10.1016/j.jbusres.2022.06.012
Mauri AG, 2013, INT J HOSP MANAG, V34, P99, DOI 10.1016/j.ijhm.2013.02.012
Minnema A, 2016, J RETAILING, V92, P253, DOI 10.1016/j.jretai.2016.03.001
Mirzaei T, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2020.103404
Moore GC, 1991, INFORM SYST RES, V2, P192, DOI 10.1287/isre.2.3.192
Morgan DL, 1996, ANNU REV SOCIOL, V22, P129, DOI 10.1146/annurev.soc.22.1.129
Mudambi SM, 2010, MIS QUART, V34, P185
Park DH, 2008, ELECTRON COMMER R A, V7, P386, DOI 10.1016/j.elerap.2007.11.004
Pashchenko Y, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103011
Reimers N, 2020, Arxiv, DOI arXiv:2004.09813
Reimers N, 2019, Arxiv, DOI [arXiv:1908.10084, DOI 10.48550/ARXIV.1908.10084]
Ren J, 2019, EUR J INFORM SYST, V28, P272, DOI 10.1080/0960085X.2018.1524419
Richins ML, 1997, J CONSUM RES, V24, P127, DOI 10.1086/209499
Rose J. M., 2004, Journal of Information Systems, V18, P53, DOI 10.2308/jis.2004.18.1.53
Salehan M, 2016, DECIS SUPPORT SYST, V81, P30, DOI 10.1016/j.dss.2015.10.006
Schwarz N., 2012, International Handbook of Survey Methodology, P18
See-To EWK, 2014, COMPUT HUM BEHAV, V31, P182, DOI 10.1016/j.chb.2013.10.013
Setia P, 2013, MIS QUART, V37, P565, DOI 10.25300/MISQ/2013/37.2.11
Singhal D, 2019, INT J PROD RES, V57, P7289, DOI 10.1080/00207543.2019.1598590
Tereyagoglu N, 2016, SPRING SER SUP CHA M, V3, P19, DOI 10.1007/978-3-319-30094-8_2
Nguyen TV, 2020, EUR J OPER RES, V281, P543, DOI 10.1016/j.ejor.2019.08.015
Tseng ML, 2015, APPL SOFT COMPUT, V28, P411, DOI 10.1016/j.asoc.2014.11.048
Turney PD, 2006, COMPUT LINGUIST, V32, P379, DOI 10.1162/coli.2006.32.3.379
Ullah R, 2016, DECIS SUPPORT SYST, V81, P41, DOI 10.1016/j.dss.2015.10.007
Valentini F, 2021, Arxiv, DOI [arXiv:2104.06474, 10.48550/arXiv.2104.06474, DOI 10.48550/ARXIV.2104.06474]
van der Heijden H, 2004, MIS QUART, V28, P695, DOI 10.2307/25148660
Wagar BM, 2004, PSYCHOL REV, V111, P67, DOI 10.1037/0033-295X.111.1.67
Wang MH, 2015, J ELECTRON COMMER RE, V16, P72
Wang Q, 2022, COMPUT HUM BEHAV, V133, DOI 10.1016/j.chb.2022.107272
Wang X, 2022, J MARKETING, V86, P155, DOI 10.1177/00222429211047822
Wang YC, 2016, INT J PROD ECON, V181, P460, DOI 10.1016/j.ijpe.2015.08.031
Wang YQ, 2021, INT J HOSP MANAG, V95, DOI 10.1016/j.ijhm.2021.102895
Wright DB, 2011, BEHAV RES METHODS, V43, P8, DOI 10.3758/s13428-010-0044-x
Ye QA, 2011, COMPUT HUM BEHAV, V27, P634, DOI 10.1016/j.chb.2010.04.014
Yin DZ, 2021, MIS QUART, V45, P1059, DOI 10.25300/MISQ/2021/15363
Yin DZ, 2014, MIS QUART, V38, P539, DOI 10.25300/MISQ/2014/38.2.10
Yin WP, 2021, Arxiv, DOI [arXiv:2106.09449, 10.48550/arXiv.2106.09449, DOI 10.48550/ARXIV.2106.09449]
Zhang WH, 2012, EXPERT SYST APPL, V39, P10283, DOI 10.1016/j.eswa.2012.02.166
Zhao XY, 2015, INT J CONTEMP HOSP M, V27, P1343, DOI 10.1108/IJCHM-12-2013-0542
Zheng LL, 2021, J BUS RES, V135, P226, DOI 10.1016/j.jbusres.2021.06.038
Zhong N, 2020, MARKET SCI, V39, P827, DOI 10.1287/mksc.2019.1212
Zhou QJ, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2020.102424
Zhou WQ, 2016, J MANAGE INFORM SYST, V33, P202, DOI 10.1080/07421222.2016.1172460
NR 79
TC 0
Z9 0
U1 8
U2 8
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD MAR
PY 2024
VL 77
AR 103677
DI 10.1016/j.jretconser.2023.103677
EA DEC 2023
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA EG6N2
UT WOS:001137809100001
DA 2024-03-27
ER
PT J
AU Mogaji, E
Soetan, TO
Kieu, TA
AF Mogaji, Emmanuel
Soetan, Taiwo O.
Kieu, Tai Anh
TI The implications of artificial intelligence on the digital marketing of
financial services to vulnerable customers
SO AUSTRALASIAN MARKETING JOURNAL
LA English
DT Article
DE AI; Digital marketing; Financial services; Vulnerable customers
ID SOCIAL MEDIA
AB Artificial intelligence (AI) is rapidly transforming digital marketing practices. While the extant literature extensively covers AI applications that generally benefit businesses and customers, there is scant research on AI deployments that exacerbate problems for financially vulnerable customers. These customers have limited access to financial systems, services or technologies. To rectify this research deficit, this paper describes the challenges confronting businesses as they attempt to integrate AI into the digital marketing of their financial services. Ultimately, Al-enabled digital marketing is not as simple as collecting big data and using analytical algorithms; the technology may not always help businesses target their customers more effectively. This paper examines the relationships between AI, digital marketing, and financial services in relation to vulnerable customers, highlighting key implications in the collection, processing, and delivery of information, as well as the importance of human connection for optimal customer experience and engagement with financial services providers. Understanding ethical implications, as well as data and modelling challenges, is necessary for the successful deployment of AI. This study provides a theoretical framework to financial services providers, AI developers, marketers, policymakers, and academics, aiding the understanding of the precarious conditions facing vulnerable customers, and the ways in which they can more effectively be reached.
C1 [Mogaji, Emmanuel] Univ Greenwich, London, England.
[Soetan, Taiwo O.] Red River Coll, Winnipeg, MB, Canada.
[Kieu, Tai Anh] Univ Econ & Finance, Ho Chi Minh City, Vietnam.
C3 University of Greenwich
RP Mogaji, E (autor correspondiente), Univ Greenwich, London, England.
EM e.o.mogaji@greenwich.ac.uk
RI Soetan, Taiwo/ADH-7388-2022; Mogaji, Emmanuel/B-8900-2014; Soetan, Taiwo
O./HMV-3285-2023; Kieu, Tai Anh/I-4795-2018
OI Mogaji, Emmanuel/0000-0003-0544-4842; Soetan, Taiwo
O./0000-0002-4270-8213; Kieu, Tai Anh/0000-0002-0238-5958
CR Agarwal R, 2002, INFORM SYST RES, V13, P168, DOI 10.1287/isre.13.2.168.84
Albers-Miller N.D., 1999, J SERV MARK, V13, P390, DOI DOI 10.1108/08876049910282682
Alzahrani H., 2016, GLOBAL J COMPUT SCI, V16, P1
Black JS, 2020, BUS HORIZONS, V63, P215, DOI 10.1016/j.bushor.2019.12.001
Boerman SC, 2017, J ADVERTISING, V46, P363, DOI 10.1080/00913367.2017.1339368
Brockmann EN, 2002, GROUP ORGAN MANAGE, V27, P436, DOI 10.1177/1059601102238356
Butenko E.D, 2018, FINANC CREDIT, V24, P143
Canhoto AI, 2020, BUS HORIZONS, V63, P183, DOI 10.1016/j.bushor.2019.11.003
Castillo J, 2002, J MANAGE INQUIRY, V11, P46, DOI 10.1177/1056492602111018
CBA, 2020, DAIL IQ
CMA, 2019, CONSUMER VULNERABILI
Coppack Martin, 2015, 8 FCA
Costa A., 2015, Innovations: Technology, Governance, Globalization, V10, P53
Cozzi L., 2018, POPULATION ACCESS EL
Czarnecka B, 2020, INT J BANK MARK, V38, P756, DOI 10.1108/IJBM-07-2019-0249
Davenport TH, 2018, HARVARD BUS REV, V96, P108
de Ruyter K, 2018, AUSTRALAS MARK J, V26, P199, DOI 10.1016/j.ausmj.2018.07.003
Dimitrieska S., 2018, Entrepreneurship, V6, P298
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Epstein SL, 2015, ARTIF INTELL, V221, P36, DOI 10.1016/j.artint.2014.12.006
Gabor D, 2017, NEW POLIT ECON, V22, P423, DOI 10.1080/13563467.2017.1259298
Gökerik M, 2018, ASIA PAC J MARKET LO, V30, P1222, DOI 10.1108/APJML-10-2017-0257
Grover P., 2017, GLOBAL J FLEXIBLE SY, V18, P203, DOI [10.1007/s40171-017-0159-3, DOI 10.1007/S40171-017-0159-3]
Gupta R. K., 2017, JK Science, V19, P191
Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
HAYESROTH B, 1995, ARTIF INTELL, V72, P329, DOI 10.1016/0004-3702(94)00004-K
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Kihn M., 2016, CUSTOMER JOURNEY ANA
Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
Lacasse R.M., 2016, International Scientific Conference on Digital Intelligence, P1
Leong C, 2017, INT J INFORM MANAGE, V37, P92, DOI 10.1016/j.ijinfomgt.2016.11.006
Lui A, 2018, INF COMMUN TECHNOL L, V27, P267, DOI 10.1080/13600834.2018.1488659
Metcalf L, 2019, CALIF MANAGE REV, V61, P84, DOI 10.1177/0008125619862256
Mitchell M, 2019, INFORMATION, V10, DOI 10.3390/info10020051
Mogaji E, 2019, FINTECH ART INT LAW
Mogaji E, 2020, ADV THE PRAC EMER MA, P137, DOI 10.1007/978-3-030-24374-6_10
Mogaji E, 2019, TRAVEL BEHAV SOC, V14, P21, DOI 10.1016/j.tbs.2018.09.004
Mogaji E, 2017, J PROD BRAND MANAG, V26, P531, DOI 10.1108/JPBM-07-2016-1285
Mogaji E, 2016, COGENT BUS MANAG, V3, DOI 10.1080/23311975.2016.1223389
Mugrauer A., 2019, MARKETING MANAGERS A
Nadler A, 2018, CRIT STUD MEDIA COMM, V35, P151, DOI 10.1080/15295036.2017.1387279
Nasser L., 2019, INFLUENCE AI DIGITAL
O'Connor GE, 2019, J BUS RES, V100, P421, DOI 10.1016/j.jbusres.2018.12.033
Overgoor G, 2019, CALIF MANAGE REV, V61, P156, DOI 10.1177/0008125619859318
Pan YH, 2016, ENGINEERING, V2, P409, DOI 10.1016/J.ENG.2016.04.018
Paschen J, 2020, BUS HORIZONS, V63, P403, DOI 10.1016/j.bushor.2020.01.003
Poppleton A., 2019, RETAIL BANKS ARE PRE
Russell S.J., 2010, MALAYSIA, V3rd
Salampasis D, 2018, HANDBOOK OF BLOCKCHAIN, DIGITAL FINANCE, AND INCLUSION, VOL 2: CHINATECH, MOBILE SECURITY, DISTRIBUTED LEDGER, AND BLOCKCHAIN, P451, DOI 10.1016/B978-0-12-812282-2.00018-8
Saura J.R., 2019, Organizational Transformation and Management Innovation in the Fourth Industrial Revolution, P86, DOI DOI 10.4018/978-1-5225-7074-5.CH005
Silver L., 2018, INTERNET CONNECTIVIT
Sterner J., 2017, ARTIFICIAL INTELLIGE
Sun TQ, 2019, GOV INFORM Q, V36, P368, DOI 10.1016/j.giq.2018.09.008
Theodoridis P.K, 2019, STRATEGIC INNOVATIVE
Treleaven P., 2017, Journal of Financial Transformation, V45, P14
Ukpabi DC, 2018, EUROMED ACAD BUS CON, P1339
van Esch P, 2019, BUS HORIZONS, V62, P729, DOI 10.1016/j.bushor.2019.07.004
van Esch P, 2019, COMPUT HUM BEHAV, V90, P215, DOI 10.1016/j.chb.2018.09.009
Wayne T., 2020, Research Agenda Working Papers, V2020, P40
Webster FE, 2013, J ACAD MARKET SCI, V41, P389, DOI 10.1007/s11747-013-0331-z
Westpac, 2019, CUSTOMER VULNERABILI
Wirtz BW, 2019, INT J PUBLIC ADMIN, V42, P596, DOI 10.1080/01900692.2018.1498103
World Bank, 2018, FINANCIAL INCLUSION
Yurdakul D, 2017, MARKETING THEOR, V17, P289, DOI 10.1177/1470593117704265
NR 65
TC 61
Z9 62
U1 12
U2 71
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1441-3582
EI 1839-3349
J9 AUSTRALAS MARK J
JI Australas. Mark. J.
PD AUG
PY 2021
VL 29
IS 3
BP 235
EP 242
DI 10.1016/j.ausmj.2020.05.003
PG 8
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA UJ5KG
UT WOS:000691323200005
DA 2024-03-27
ER
PT J
AU Jin, KY
Zhong, ZZ
Zhao, EY
AF Jin, Keyan
Zhong, Zoe Ziqi
Zhao, Elena Yifei
TI Sustainable Digital Marketing Under Big Data: An AI Random Forest Model
Approach
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Big Data; Business; Engineering management; Artificial intelligence;
Social networking (online); Optimization; Technological innovation;
Artificial intelligence (AI); big data; random forest model (RFM);
social media; sustainable digital marketing
ID ARTIFICIAL-INTELLIGENCE
AB Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0-150 encompasses 17% of the population, whereas the range of 150-300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies.
C1 [Jin, Keyan] Univ Granada, Dept Quantitat Methods Econ & Business, Ceuta 51001, Spain.
[Zhong, Zoe Ziqi] London Sch Econ & Polit Sci, Dept Management, London WC2A 2AE, England.
[Zhao, Elena Yifei] Univ Southern Calif, Annenberg Sch Commun & Journalism, Los Angeles, CA 90089 USA.
C3 University of Granada; University of London; London School Economics &
Political Science; University of Southern California
RP Zhong, ZZ (autor correspondiente), London Sch Econ & Polit Sci, Dept Management, London WC2A 2AE, England.
EM kjin@correo.ugr.es; z.zhong6@lse.ac.uk; zhaoyife@usc.edu
OI Zhong, Zoe Ziqi/0000-0002-3919-9999
FU China Scholarship Council
FX No Statement Available
CR Al-Emran M, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102383
Albahri AS, 2022, COMPLEX INTELL SYST, V8, P1781, DOI 10.1007/s40747-021-00503-w
Amiri AM, 2023, J SMALL BUS ENTERP D, V30, P621, DOI 10.1108/JSBED-04-2022-0206
Andronie M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020751
Bai JW, 2022, PATTERN RECOGN, V122, DOI 10.1016/j.patcog.2021.108331
Balyan AK, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22165986
Behl A, 2022, MANAGE DECIS, V60, P368, DOI 10.1108/MD-01-2020-0121
Bracarense N, 2022, PAC ASIA J ASSOC INF, V14, P136, DOI 10.17705/1pais.14209
Butuner R, 2023, EUR FOOD RES TECHNOL, V249, P1303, DOI 10.1007/s00217-023-04214-z
Dash G, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126735
Demirbaga U, 2022, IEEE T NETW SERV MAN, V19, P3896, DOI 10.1109/TNSM.2022.3204851
Diez-Martin F, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11102839
Eleonora Z, 2023, QUAL-ACCESS SUCCESS, V24, P360, DOI 10.47750/QAS/24.192.43
Foresti R, 2020, ENGINEERING-PRC, V6, P835, DOI 10.1016/j.eng.2019.11.014
Gao W., 2022, Secur. Commun. Netw., P1
Ghosh A, 2022, GEOCARTO INT, V37, P9663, DOI 10.1080/10106049.2021.2022016
Hancock JT, 2020, J BIG DATA-GER, V7, DOI 10.1186/s40537-020-00369-8
He B, 2023, TUNN UNDERGR SP TECH, V133, DOI 10.1016/j.tust.2022.104979
Islam U, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14148374
Jablonka KM, 2020, CHEM REV, V120, P8066, DOI 10.1021/acs.chemrev.0c00004
Kang XH, 2020, J INTELL FUZZY SYST, V39, P1131, DOI 10.3233/JIFS-192032
Kastouni MZ, 2022, J KING SAUD UNIV-COM, V34, P2758, DOI 10.1016/j.jksuci.2020.11.024
Khanbaghi M, 2020, ENERGIES, V13, DOI 10.3390/en13194997
Khare V. K., 2023, Anvesak UGC Care Group 1 J., V53, P107
Kiguchi M, 2022, APPL SOFT COMPUT, V118, DOI 10.1016/j.asoc.2022.108491
Kim J, 2022, INTERNET RES, V32, P1003, DOI 10.1108/INTR-11-2020-0651
Kongar Elif, 2021, IEEE Engineering Management Review, V49, P133, DOI 10.1109/EMR.2021.3055036
Koo DM, 2022, INTERNET RES, V32, P967, DOI 10.1108/INTR-04-2022-684
Kuzior A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13158145
Lee M, 2021, INT J CONTEMP HOSP M, V33, P2117, DOI 10.1108/IJCHM-06-2020-0587
Li J, 2019, TEH VJESN, V26, P1670, DOI 10.17559/TV-20190603165825
Li Z., 2020, J. Comput., V31, P256
Liu Q., 2023, Front. Bus., Econ. Manage., V9, P168
Low S, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12135402
Lu JZ, 2022, IEEE SYST J, V16, P5465, DOI 10.1109/JSYST.2021.3106195
Marinakis V, 2020, ENERGIES, V13, DOI 10.3390/en13071555
Munsch A, 2021, J GLOB SCHOLARS MARK, V31, P10
Nagendra NP, 2023, IEEE T ENG MANAGE, V70, P1099, DOI 10.1109/TEM.2022.3159451
Ngiam KY, 2019, LANCET ONCOL, V20, pE262, DOI 10.1016/S1470-2045(19)30149-4
Nica E., 2021, J. Self-Governance Manag. Econ., V9, P35, DOI [10.22381/jsme9320213, DOI 10.22381/JSME9320213]
Ong AKS, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142316041
Paniagua C, 2021, IEEE SYST J, V15, P1149, DOI 10.1109/JSYST.2020.2993323
Peng YZ, 2022, J INNOV KNOWL, V7, DOI 10.1016/j.jik.2022.100198
Rakic S, 2023, APPL SYST INNOV, V6, DOI 10.3390/asi6050089
Saura JR, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12031003
Leguina JR, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9111822
Salih Ageed Z., 2021, Qubahan Academic Journal, V1, P29, DOI [10.48161/qaj.v1n2a46, DOI 10.48161/QAJ.V1N2A46]
Samara D, 2020, J HOSP TOUR TECHNOL, V11, P343, DOI 10.1108/JHTT-12-2018-0118
Satpathi A, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15032786
Saura JR, 2023, J SMALL BUS MANAGE, V61, P1278, DOI 10.1080/00472778.2021.1955127
Shankar V, 2022, INT J RES MARK, V39, P541, DOI 10.1016/j.ijresmar.2021.09.005
Sharma S, 2022, IEEE T ENG MANAGE, V69, P3546, DOI 10.1109/TEM.2022.3148341
Shaw S., 2021, Econ.,Manage. Financial Markets, V16, P106, DOI 10.22381/emfm16220217
Sinha Moumita, 2020, UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, P65, DOI 10.1145/3386392.3397600
Sisodia D, 2022, IETE TECH REV, V39, P789, DOI 10.1080/02564602.2021.1915892
Stergiou CL, 2021, IEEE INTERNET THINGS, V8, P5164, DOI 10.1109/JIOT.2020.3033131
Sun L, 2020, J STRUCT ENG, V146, DOI 10.1061/(ASCE)ST.1943-541X.0002535
Thangam Dhanabalan, 2023, Climate and Energy, P11, DOI 10.1002/gas.22329
Wang J, 2020, J INTERNET TECHNOL, V21, P393, DOI 10.3966/160792642020032102008
Yadav S, 2023, IEEE T ENG MANAGE, V70, P1215, DOI 10.1109/TEM.2022.3177188
Yousaf Z, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13105715
Zhou J, 2022, TUNN UNDERGR SP TECH, V124, DOI 10.1016/j.tust.2022.104494
Zhou YY, 2022, ACTA AGR SCAND B-S P, V72, P284, DOI 10.1080/09064710.2021.2008482
Ziakis C, 2023, INFORMATION, V14, DOI 10.3390/info14120664
NR 64
TC 0
Z9 0
U1 3
U2 3
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 3566
EP 3579
DI 10.1109/TEM.2023.3348991
PG 14
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA IR4M6
UT WOS:001168043600001
DA 2024-03-27
ER
PT J
AU Rani, TS
Garg, U
Kalyani, P
AF Rani, T. Suchitra
Garg, Umang
Kalyani, P.
TI Artificial Intelligence and its Role in Transforming Marketing and
Impact on Consumer Perception
SO PACIFIC BUSINESS REVIEW INTERNATIONAL
LA English
DT Article
DE Artificial Intelligence; Consumer Buying; Transforming Marketing;
Chatbots; Marketing Automation
AB With every changing product and service landscape the customer needs and their expectations are also evolving. In terms of quick delivery and quick response to customer queries an organization is left with no choice but to adapt to the latest technologies like Artificial Intelligence (AI). Artificial Intelligence is the capacity of machines to recognize, learn and make decision from its surrounding environment. This paper is an attempt to identify the ways how artificial intelligence is transforming marketing. It also aims to study the impact of awareness of AI on perception of use of AI with reference to different uses of AI during the customer journey. The select sample belongs to the area of Hyderabad and Secunderabad. Correlation and regression results showed that impact of awareness of AI on perception of use of AI with reference to voice search was found to be having significant influence. However, it was found that there was no impact of awareness of AI on perception of other four uses in the study. AI is one among the emerging technologies which is still in the introduction stage and to some extent an inscrutable event though AI is already impacting the customers lives in a big way.
C1 [Rani, T. Suchitra; Garg, Umang; Kalyani, P.] Amity Global Business Sch, Hyderabad, India.
RP Rani, TS (autor correspondiente), Amity Global Business Sch, Hyderabad, India.
CR Amado A, 2018, EUR RES MANAG BUS EC, V24, P1, DOI 10.1016/j.iedeen.2017.06.002
[Anonymous], EC APPL INFORM, V25, P28
Avinaash M., 2018, P INT J PURE APPL MA, V119, P1881
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
eia, EC APPL INF YEARS 25, DOI [10.35219/eai158404094, DOI 10.35219/EAI158404094]
Kim KY, 2014, KSII T INTERNET INF, V8, P567, DOI 10.3837/tiis.2014.02.014
Lessmann S., 2019, INFORM SCIENCES, P15
mapr.com, MAP R HOM PAG
Shahid M. Z., 2019, GLOBAL J MANAGEMENT, V19, P26
smartinsights.com, US
Soni N., 2019, Journal of Business Research, P1
NR 11
TC 0
Z9 0
U1 7
U2 25
PU PACIFIC INST MANAGEMENT
PI RAJASTHAN
PA PACIFIC HILLS, PRATAP NAGAR EXTENSION, AIR PORT RD, UDAIPUR, RAJASTHAN,
313 001, INDIA
SN 0974-438X
J9 PAC BUS REV INT
JI Pac. Bus. Rev. Int.
PD JUN
PY 2022
VL 14
IS 12
BP 1
EP 7
PG 7
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 5A5RM
UT WOS:000862944700001
DA 2024-03-27
ER
PT J
AU Kaiser, C
Ahuvia, A
Rauschnabel, PA
Wimble, M
AF Kaiser, Carolin
Ahuvia, Aaron
Rauschnabel, Philipp A.
Wimble, Matt
TI Social media monitoring: What can marketers learn from Facebook brand
photos?
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Brand love; Monitoring; User-generated content; Social media; Machine
learning; AI; Artificial intelligence
ID WORD-OF-MOUTH; GENETIC ALGORITHM; FEATURE-SELECTION; NEURAL-NETWORKS;
ONLINE; MODEL; MOTIVATIONS; BEHAVIOR; PICTURE; NARCISSISM
AB Users upload > 350 million photos per day to Facebook. While considerable research has explored text-based user-generated content on social media, research on photos is still in its early stages. This paper uses a sample of 44,765 Facebook photos from 503 Facebook users in the United States and Germany to determine the degree to which photos play an integral role in people's social media communications. The analysis shows that uploading brand photos (i.e., photos containing a brand name or logo) is related to brand love, brand loyalty, and word-of-mouth (WOM) endorsement of the brand in question. We then code a subsample of these photos for content and train a powerful hybrid machine learning algorithm combining genetic search and artificial neural networks. The resulting algorithm is able to predict users' brand love, brand loyalty, and WOM endorsement from the content of their brand photos posted on Facebook. Finally, we discuss the implications for social media marketing, in particular social media monitoring.
C1 [Ahuvia, Aaron] Univ Michigan, Fairlane Ctr South, Dearborn Coll Business, 19000 Hubbard Dr, Dearborn, MI 48128 USA.
[Rauschnabel, Philipp A.] Univ Bundeswehr Munchen, Professorship Digital Mkt & Media Innovat, Fak Betriebswirtschaft, Werner Heisenberg Weg 39, D-85579 Neubiberg, Germany.
[Kaiser, Carolin] Nuremberg Inst Market Decis, Nordwestring 101, D-90419 Nurnberg, Germany.
[Wimble, Matt] Suffolk Univ, Sawyer Sch Business, 73 Tremont St, Boston, MA 02108 USA.
C3 University of Michigan System; University of Michigan; Bundeswehr
University Munich; Nuremberg Institute for Market Decisions; Suffolk
University
RP Rauschnabel, PA (autor correspondiente), Univ Bundeswehr Munchen, Professorship Digital Mkt & Media Innovat, Fak Betriebswirtschaft, Werner Heisenberg Weg 39, D-85579 Neubiberg, Germany.
EM Carolin.Kaiser@nim.org; Ahuvia@umich.edu; philipp.rauschnabel@gmail.com;
mwimble@suffolk.edu
RI Rauschnabel, Philipp/ABB-8212-2020; Ahuvia, Aaron/N-6189-2018
OI Rauschnabel, Philipp/0000-0003-2188-6747; Ahuvia,
Aaron/0000-0002-1796-3933
FU NIM - Nuremberg Institute for Market Decisions; Carolin Kaiser, of NIM
FX Funding for this project was provided by NIM - Nuremberg Institute for
Market Decisions (formerly GfK Verein). Carolin Kaiser, of NIM, was a
full participant in all aspects of this research.
CR Ahuvia AC, 2015, REV MARKET RES, V12, P121, DOI 10.1108/S1548-643520150000012005
Aleixandre M, 2004, SENSOR ACTUAT B-CHEM, V103, P122, DOI 10.1016/j.snb.2004.04.044
Algesheimer R, 2005, J MARKETING, V69, P19, DOI 10.1509/jmkg.69.3.19.66363
Alvarez-Milán A, 2018, J BUS RES, V92, P61, DOI 10.1016/j.jbusres.2018.07.017
Iniesta-Bonillo MA, 2016, J BUS RES, V69, P5002, DOI 10.1016/j.jbusres.2016.04.071
[Anonymous], 1981, MEANING THINGS DOMES
Bagozzi RP, 2017, MARKET LETT, V28, P1, DOI 10.1007/s11002-016-9406-1
Batra R, 2012, J MARKETING, V76, P1, DOI 10.1509/jm.09.0339
Becker S., 2016, Conceptualizing Germany's Energy Transition, P21, DOI [DOI 10.1057/978-1-137-50593-4_3, 10.1057/978-1-137-50593-4, DOI 10.4324/9781315725185]
Belk RW, 2013, J CONSUM RES, V40, P477, DOI 10.1086/671052
Boley BB, 2013, TOURISM MANAGE, V37, P27, DOI 10.1016/j.tourman.2012.11.020
Bourlai Elli., 2014, P 2014 ACM C WEB SCI, DOI [10.1145/2615569.2615697, DOI 10.1145/2615569.2615697]
Brodie RJ, 2013, J BUS RES, V66, P105, DOI 10.1016/j.jbusres.2011.07.029
Brodie RJ, 2011, J SERV RES-US, V14, P283, DOI 10.1177/1094670511415523
Calder B. J., 2018, Customer Engagement Marketing, P221, DOI [10.1007/978-3-319-61985-9_10, DOI 10.1007/978-3-319-61985-9_10]
Chalfen Richard., 1987, SNAPSHOT VERSIONS LI
Chen Y, 2015, ACSR ADV COMPUT, V13, P264
Cheung CMK, 2012, DECIS SUPPORT SYST, V53, P218, DOI 10.1016/j.dss.2012.01.015
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
Cui G, 2012, INT J ELECTRON COMM, V17, P39, DOI 10.2753/JEC1086-4415170102
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Delen D, 2018, J BUS RES, V90, P186, DOI 10.1016/j.jbusres.2018.05.013
Durrant A, 2011, VISUAL STUD, V26, P113, DOI 10.1080/1472586X.2011.571887
EFRON B, 1983, J AM STAT ASSOC, V78, P316, DOI 10.2307/2288636
Ellison NB, 2007, J COMPUT-MEDIAT COMM, V12, P1143, DOI 10.1111/j.1083-6101.2007.00367.x
Erkan I, 2016, COMPUT HUM BEHAV, V61, P47, DOI 10.1016/j.chb.2016.03.003
Felix R, 2017, J BUS RES, V70, P118, DOI 10.1016/j.jbusres.2016.05.001
Feng H, 2014, NEUROCOMPUTING, V129, P409, DOI 10.1016/j.neucom.2013.09.018
Fish KE, 2004, J BUS RES, V57, P79, DOI 10.1016/S0148-2963(02)00287-4
Geisser S, 1993, PREDICTIVE INFERENCE
Hawkins DM, 2003, J CHEM INF COMP SCI, V43, P579, DOI 10.1021/ci025626i
Hennig-Thurau T, 2003, INT J ELECTRON COMM, V8, P51, DOI 10.1080/10864415.2003.11044293
Hennig-Thurau T, 2010, J SERV RES-US, V13, P311, DOI 10.1177/1094670510375460
Holland J.H., 1992, CONTROL ARTIFICIAL I
Hollebeek L, 2011, J STRATEG MARK, V19, P555, DOI 10.1080/0965254X.2011.599493
Hollebeek LD, 2011, J MARKET MANAG-UK, V27, P785, DOI 10.1080/0267257X.2010.500132
Huang CM, 2013, INT J PSYCHOL, V48, P334, DOI 10.1080/00207594.2011.649285
Hum NJ, 2011, COMPUT HUM BEHAV, V27, P1828, DOI 10.1016/j.chb.2011.04.003
Ibrahim Y., 2015, Journal of Media Practice, V16, P42, DOI [DOI 10.1080/14682753.2015.1015800, 10.1080/14682753.2015.1015800.]
Jalilvand MR, 2012, MARK INTELL PLAN, V30, P460, DOI 10.1108/02634501211231946
KAISER C, 2009, IADIS INT J WWW INTE, V6, P63
Kaiser C, 2012, INTERNET RES, V22, P275, DOI 10.1108/10662241211235653
Kapidzic S, 2013, CYBERPSYCH BEH SOC N, V16, P14, DOI 10.1089/cyber.2012.0143
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
Kim D, 2013, J BUS RES, V66, P1243, DOI 10.1016/j.jbusres.2012.02.019
King RA, 2014, J INTERACT MARK, V28, P167, DOI 10.1016/j.intmar.2014.02.001
Kozinets RV, 2002, J CONSUM RES, V29, P20, DOI 10.1086/339919
Kumar V, 2016, J MARKETING RES, V53, P497, DOI 10.1509/jmr.15.0044
Kwok L, 2013, CORNELL HOSP Q, V54, P84, DOI 10.1177/1938965512458360
LACHENBR.PA, 1968, TECHNOMETRICS, V10, P1, DOI 10.2307/1266219
LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310
LEARDI R, 1992, J CHEMOMETR, V6, P267, DOI 10.1002/cem.1180060506
Lee E, 2015, CYBERPSYCH BEH SOC N, V18, P552, DOI 10.1089/cyber.2015.0157
Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491, DOI 10.1109/TKDE.2005.66
Liu N, 2014, PR ELECTROMAGN RES S, P959
Lo IS, 2011, TOURISM MANAGE, V32, P725, DOI 10.1016/j.tourman.2010.06.001
Miller AD, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P347
Morgan EM, 2010, COMPUT HUM BEHAV, V26, P1405, DOI 10.1016/j.chb.2010.04.017
Moro S, 2016, J BUS RES, V69, P3341, DOI 10.1016/j.jbusres.2016.02.010
Muñiz AM, 2011, BUS HORIZONS, V54, P209, DOI 10.1016/j.bushor.2011.01.002
Muntinga DG, 2011, INT J ADVERT, V30, P13, DOI 10.2501/IJA-30-1-013-046
Oreski S, 2012, EXPERT SYST APPL, V39, P12605, DOI 10.1016/j.eswa.2012.05.023
Pansari A, 2017, J ACAD MARKET SCI, V45, P294, DOI 10.1007/s11747-016-0485-6
Park C, 2003, INT MARKET REV, V20, P534, DOI 10.1108/02651330310498771
Pfeil U, 2006, J COMPUT-MEDIAT COMM, V12, P88, DOI 10.1111/j.1083-6101.2006.00316.x
Rauschnabel P.A., 2012, Advances in Advertising Research, VIII, P153, DOI DOI 10.1007/978-3-8349-4291-3_12
Rauschnabel PA, 2019, J RETAIL CONSUM SERV, V49, P43, DOI 10.1016/j.jretconser.2019.03.004
Raymer ML, 2000, IEEE T EVOLUT COMPUT, V4, P164, DOI 10.1109/4235.850656
Reguera-Alvarado N, 2016, J BUS RES, V69, P4685, DOI 10.1016/j.jbusres.2016.04.016
ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519
Russel S., 2010, ARTIF INTELL
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Seder JP, 2012, SOC PSYCHOL PERS SCI, V3, P407, DOI 10.1177/1948550611424968
Sheldon P., 2019, DARK SIDE SOCIAL MED
Sheldon P, 2016, COMPUT HUM BEHAV, V58, P89, DOI 10.1016/j.chb.2015.12.059
SIEDLECKI W, 1989, PATTERN RECOGN LETT, V10, P335, DOI 10.1016/0167-8655(89)90037-8
Singh JP, 2017, J BUS RES, V70, P346, DOI 10.1016/j.jbusres.2016.08.008
Smith L.R., 2012, J INTERCULTURAL COMM, V41, P279, DOI DOI 10.1080/17475759.2012.728771
Stefanone MA, 2011, CYBERPSYCH BEH SOC N, V14, P41, DOI 10.1089/cyber.2010.0049
STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x
Tan F., 2007, Journal of Information Science Technology, V4, P3
Thelwall M, 2016, J ASSOC INF SCI TECH, V67, P2575, DOI 10.1002/asi.23620
Van House N., 2005, CHI 05, P1853, DOI [DOI 10.1145/1056808.1057039, 10.1145/1056808.1057039.]
WALLENDORF M, 1988, J CONSUM RES, V14, P531, DOI 10.1086/209134
Walther JB, 2008, HUM COMMUN RES, V34, P28, DOI 10.1111/j.1468-2958.2007.00312.x
Wang SS, 2010, COMPUT HUM BEHAV, V26, P226, DOI 10.1016/j.chb.2009.10.001
Wang X., 2009, Proc. 3rd international Workshop on Data Mining and Audience Intelligence for Advertising, P18
Watson S. W, 2006, ED493049 ERIC
White Leanne., 2010, TOURISM INFORM VISUA, P115
Wong NY, 1998, PSYCHOL MARKET, V15, P423, DOI 10.1002/(SICI)1520-6793(199808)15:5<423::AID-MAR2>3.0.CO;2-9
Yi-Wen Fan, 2012, International Journal of Electronic Business Management, V10, P175
Yoon G.-M., 2012, ADV INFORM SCI SERVI, V4, P7
Zhao SY, 2008, COMPUT HUM BEHAV, V24, P1816, DOI 10.1016/j.chb.2008.02.012
NR 94
TC 29
Z9 31
U1 9
U2 77
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD SEP
PY 2020
VL 117
BP 707
EP 717
DI 10.1016/j.jbusres.2019.09.017
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA MW2RD
UT WOS:000556889900067
DA 2024-03-27
ER
PT J
AU Koonsanit, K
Nishiuchi, N
AF Koonsanit, Kitti
Nishiuchi, Nobuyuki
TI Predicting Final User Satisfaction Using Momentary UX Data and Machine
Learning Techniques
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE user experience; UX; UX evaluation; satisfaction; prediction; machine
learning
ID MANAGEMENT
AB User experience (UX) evaluation investigates how people feel about using products or services and is considered an important factor in the design process. However, there is no comprehensive UX evaluation method for time-continuous situations during the use of products or services. Because user experience changes over time, it is difficult to discern the relationship between momentary UX and episodic or cumulative UX, which is related to final user satisfaction. This research aimed to predict final user satisfaction by using momentary UX data and machine learning techniques. The participants were 50 and 25 university students who were asked to evaluate a service (Experiment I) or a product (Experiment II), respectively, during usage by answering a satisfaction survey. Responses were used to draw a customized UX curve. Participants were also asked to complete a final satisfaction questionnaire about the product or service. Momentary UX data and participant satisfaction scores were used to build machine learning models, and the experimental results were compared with those obtained using seven built machine learning models. This study shows that participants' momentary UX can be understood using a support vector machine (SVM) with a polynomial kernel and that momentary UX can be used to make more accurate predictions about final user satisfaction regarding product and service usage.
C1 [Koonsanit, Kitti; Nishiuchi, Nobuyuki] Tokyo Metropolitan Univ, Grad Sch Syst Design, Dept Comp Sci, Tokyo 1910065, Japan.
C3 Tokyo Metropolitan University
RP Koonsanit, K (autor correspondiente), Tokyo Metropolitan Univ, Grad Sch Syst Design, Dept Comp Sci, Tokyo 1910065, Japan.
EM koonsanit-kitti@ed.tmu.ac.jp; nnishiuc@tmu.ac.jp
OI Koonsanit, Kitti/0000-0002-9968-2309
FU JSPS KAKENHI [JP20K12511]
FX This research was funded by JSPS KAKENHI, grant number JP20K12511.
CR Akinbi A., 2020, SN Comput. Sci, V1, P272, DOI DOI 10.1007/S42979-020-00285-X
Angamuthu B, 2015, BVIMSR J MANAG RES, V7, P43
Badran O., 2018, J MANAG INF DECIS SC, V21, P1
Balasubramoniam Vignesh, 2013, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), V2, P1214
Barari M, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101985
Batista G. E. A. P. A., 2004, ACM SIGKDD EXPL NEWS, V6, P20, DOI [10.1145/1007730.1007735, DOI 10.1145/1007730.1007735]
Beleites C, 2013, ANAL CHIM ACTA, V760, P25, DOI 10.1016/j.aca.2012.11.007
Ben-Hur A, 2010, METHODS MOL BIOL, V609, P223, DOI 10.1007/978-1-60327-241-4_13
Bolger N, 2003, ANNU REV PSYCHOL, V54, P579, DOI 10.1146/annurev.psych.54.101601.145030
Cahyana N, 2019, 2019 5TH INTERNATIONAL CONFERENCE ON SCIENCE ININFORMATION TECHNOLOGY (ICSITECH), P217, DOI [10.1109/icsitech46713.2019.8987499, 10.1109/ICSITech46713.2019.8987499]
Carneiro J, 2014, COMM COM INF SC, V430, P368
Siebert LC, 2021, INT J ENERGY SECT MA, V15, P743, DOI 10.1108/IJESM-10-2018-0007
Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
Dokic I., 2017, EC ANAL, V50, P50
Feng L, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11082432
Gharaibeh NK, 2015, INT J ADV COMPUT SC, V6, P1
Hashizume A, 2016, INT J COMPUT SCI ISS, V13, P86, DOI [10.20943/01201605.8693, DOI 10.20943/01201605.8693]
Hassan RS, 2015, PROC ECON FINANC, V23, P563, DOI 10.1016/S2212-5671(15)00513-4
Haykin S.S., 2009, NEURAL NETWORKS LEAR, V10
He HB, 2008, IEEE IJCNN, P1322, DOI 10.1109/IJCNN.2008.4633969
James G, 2013, SPRINGER TEXTS STAT, V103, P59, DOI 10.1007/978-1-4614-7138-7_3
Karapanos E., 2010, Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems, P4075, DOI 10.1145/1753846.1754105
Kaul D., 2017, REV PROFESSIONAL MAN, V15, P55, DOI DOI 10.20968/RPM/2017/V15/I2/163914
Khondoker M, 2016, STAT METHODS MED RES, V25, P1804, DOI 10.1177/0962280213502437
Kinha Y, 2020, EASY GUIDE CHOOSE RI
Klotins E, 2011, USABILITY USER EXPER
Koonsanit Kitti, 2020, 2020 IEEE Region 10 Conference (TENCON), P561, DOI 10.1109/TENCON50793.2020.9293912
Korber Moritz., 2013, Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI'13, P194, DOI DOI 10.1145/2516540.2516555
Kujala S, 2011, INTERACT COMPUT, V23, P473, DOI 10.1016/j.intcom.2011.06.005
Kumar S, 2019, J BIG DATA-GER, V6, DOI 10.1186/s40537-019-0224-1
Kurosu M., 2016, LNCS, V9731, P331, DOI [10.1007/978-3-319-39510-4_31, DOI 10.1007/978-3-319-39510-4_31]
Law ELC, 2014, INT J HUM-COMPUT ST, V72, P526, DOI 10.1016/j.ijhcs.2013.09.006
Lemaître G, 2017, J MACH LEARN RES, V18
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Longadge R, 2013, ARXIV13051707, P1707, DOI DOI 10.48550/ARXIV.1305.1707
Marti P, 2016, ACSIS-ANN COMPUT SCI, V8, P1647, DOI 10.15439/2016F302
Matsuda Y, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18113978
Meng M., 2015, ADV PETROLEUM EXPLOR, V10, P72, DOI DOI 10.3968/7830
Ng A. Y., 1997, P 14 INT C MACH LEAR, V97, P245, DOI [DOI 10.5555/645526.657119, DOI 10.1007/S13398-014-0173-7.2]
Nguyen Hien M., 2011, International Journal of Knowledge Engineering and Soft Data Paradigms, V3, P4, DOI 10.1504/IJKESDP.2011.039875
Oppong S.H., 2013, ASIAN J MANAGEMENT S, V2, P202, DOI DOI 10.7748/NR2004.07.12.1.7.C5927
Paper D., 2020, Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, P137, DOI DOI 10.1007/978-1-4842-5373-1
Pedregosa F., 2011, J Mach Learn Res, V12, P2825
Precourt G, 2014, J ADVERTISING RES, V54, P124, DOI 10.2501/JAR-54-2-124-125
Rahimi R, 2017, J TRAVEL TOUR MARK, V34, P40, DOI 10.1080/10548408.2015.1130108
Raikwal J.S, 2012, INT J COMPUT APPL, V50, P2
Raschka S., 2018, COMPUT RES REPOS, V1811, P12808, DOI DOI 10.1109/TPAMI.1986.4767851
Roto V., 2011, Dagstuhl Seminar on Demarcating User Experience, page, P12, DOI DOI 10.4230/DAGSEMPROC.10373.1
Sánchez-Adame LM, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10228199
Santos MS, 2018, IEEE COMPUT INTELL M, V13, P59, DOI 10.1109/MCI.2018.2866730
Schilling K, 2012, J MED LIBR ASSOC, V100, P258, DOI 10.3163/1536-5050.100.4.007
Schölkopf B, 2000, NEURAL COMPUT, V12, P1207, DOI 10.1162/089976600300015565
Senechal T, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOP (ICCVW), P10, DOI 10.1109/ICCVW.2015.11
Shankar V, 2006, J INTERACT MARK, V20, P2, DOI 10.1002/dir.20057
Spool J, IS DESIGN METRICALLY
Stover-Wright E, 2013, J REHABIL ADM, V37, P103
Subramanian J, 2013, CONTEMP CLIN TRIALS, V36, P636, DOI 10.1016/j.cct.2013.06.011
Sulaiman S.M., 2018, CUSTOMER RELATIONSHI, V2, P692, DOI [10.5220/0008892606920698, DOI 10.5220/0008892606920698]
Tao F., 2014, INT J BUS SOC SCI, V5, P255, DOI [10.30845/ijbss, DOI 10.30845/IJBSS]
Taufik M., 2021, IOP Conference Series: Materials Science and Engineering, V1115, DOI 10.1088/1757-899X/1115/1/012040
Torres-Valencia C, 2017, J MULTIMODAL USER IN, V11, P9, DOI 10.1007/s12193-016-0222-y
V??n?nen-Vainio-Mattila K., 2010, P 6 NORD C HUM COMP, P521, DOI DOI 10.1145/1868914.1868973
Vall-Llosera L, 2020, AXIOMS, V9, DOI 10.3390/axioms9010021
Wawre S.V., 2016, Int. J. Sci. Res, V5, P819
Zhang ML, 2005, 2005 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2, P718
NR 65
TC 6
Z9 6
U1 4
U2 17
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD DEC
PY 2021
VL 16
IS 7
BP 3136
EP 3156
DI 10.3390/jtaer16070171
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA XZ4SN
UT WOS:000737643200001
OA gold
DA 2024-03-27
ER
PT J
AU Oh, YK
Yi, J
AF Oh, Yun Kyung
Yi, Jisu
TI Asymmetric effect of feature level sentiment on product rating: an
application of bigram natural language processing (NLP) analysis
SO INTERNET RESEARCH
LA English
DT Article
DE Online consumer review; Bigram NLP analysis; Feature level sentiment
analysis; Big data analytics
ID CUSTOMER SATISFACTION; PERFORMANCE; REVIEWS; QUALITY; SALES
AB Purpose The evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings. Design/methodology/approach This study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings. Findings The results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors. Originality/value This study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.
C1 [Oh, Yun Kyung] Dongduk Womens Univ, Dept Business Adm, Seoul, South Korea.
[Yi, Jisu] Gachon Univ, Coll Business, Seongnam, South Korea.
C3 Dongduk Women's University; Gachon University
RP Yi, J (autor correspondiente), Gachon Univ, Coll Business, Seongnam, South Korea.
EM yunk.oh13@gmail.com; yijisu@outlook.com
RI Oh, Yun Kyung/ABD-2530-2021
OI Oh, Yun Kyung/0000-0003-4759-2026
CR ANDERSON EW, 1994, J MARKETING, V58, P53, DOI 10.2307/1252310
Arbore A, 2009, J RETAIL CONSUM SERV, V16, P271, DOI 10.1016/j.jretconser.2009.02.002
Bhakkad A., 2013, International Journal of Computer Applications, V68, P9
Bi JW, 2020, TOURISM MANAGE, V77, DOI 10.1016/j.tourman.2019.104006
Bi JW, 2019, TOURISM MANAGE, V70, P460, DOI 10.1016/j.tourman.2018.09.010
Braga I.A., 2009, 14 PORT C ART INT NE, P489
Büschken J, 2020, MARKET SCI, V39, P727, DOI 10.1287/mksc.2019.1214
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Du RY, 2015, J MARKETING, V79, P29, DOI 10.1509/jm.12.0459
Falk T, 2010, J ACAD MARKET SCI, V38, P288, DOI 10.1007/s11747-009-0152-2
Fan ZP, 2017, J BUS RES, V74, P90, DOI 10.1016/j.jbusres.2017.01.010
Filieri R, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106527
Fishbein M., 1977, BELIEF ATTITUDE INTE, V5, P177
Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
Hu MQ, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P755
JOHNSON MD, 1984, J CONSUM RES, V11, P741, DOI 10.1086/209010
KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185
Kano N, 1984, J JAPANESE SOC QUALI, V41, P39, DOI [10.20684/quality.14.2_147, DOI 10.20684/QUALITY.14.2_147]
Liu B., 2012, SYNTHESIS LECT HUMAN, V5, P1, DOI [DOI 10.2200/S00416ED1V01Y201204HLT016, DOI 10.1007/978-3-031-02145-9]
Long Chong., 2010, P 23 INT C COMPUTATI, P766
Luo Y, 2019, INT J HOSP MANAG, V80, P144, DOI 10.1016/j.ijhm.2019.02.008
Matzler K, 2004, IND MARKET MANAG, V33, P271, DOI 10.1016/S0019-8501(03)00055-5
Mittal V, 1998, J MARKETING, V62, P33, DOI 10.2307/1251801
Nielsen F.A., 2011, MSM, V2011, P93, DOI DOI 10.1016/J.KN0SYS.2015.06.015
Oh Yun Kyung, 2020, [Journal of Distribution Science, 유통과학연구], V18, P65
Oliver RL, 1997, J RETAILING, V73, P311, DOI 10.1016/S0022-4359(97)90021-X
Rao AR, 2005, J MARKETING RES, V42, P401, DOI 10.1509/jmkr.2005.42.4.401
Schouten K, 2016, IEEE T KNOWL DATA EN, V28, P813, DOI 10.1109/TKDE.2015.2485209
Ting SC, 2002, TOTAL QUAL MANAGE, V13, P547, DOI 10.1080/09544120220149331
Tirunillai S, 2012, MARKET SCI, V31, P198, DOI 10.1287/mksc.1110.0682
Tsao HY, 2019, ONLINE INFORM REV, V43, P283, DOI 10.1108/OIR-11-2017-0307
Wakamiya S, 2019, J MED INTERNET RES, V21, DOI 10.2196/12783
Yi J, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102519
Yi J, 2022, INTERNET RES, V32, P606, DOI 10.1108/INTR-08-2020-0478
NR 34
TC 6
Z9 6
U1 5
U2 50
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1066-2243
J9 INTERNET RES
JI Internet Res.
PD MAY 9
PY 2022
VL 32
IS 3
BP 1023
EP 1040
DI 10.1108/INTR-11-2020-0649
EA JUL 2021
PG 18
WC Business; Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science; Telecommunications
GA 0Z5VN
UT WOS:000679421500001
DA 2024-03-27
ER
PT J
AU Alsiehemy, A
AF Alsiehemy, Ali
TI Emergence of Digital Marketing in Current Scenario and Implementation of
AI to Improve the Productivity of a Concern
SO PACIFIC BUSINESS REVIEW INTERNATIONAL
LA English
DT Article
DE Artificial Intelligence (AI); Digital Marketing; AI Based Automated
Programs; Search Engines; Social Media Sites
ID ARTIFICIAL-INTELLIGENCE
AB Businesses have been on the rise in current decades as an outcome of digital progressions. The promotion has achieved a phase in its evolution where it is essential to modify to digitalization. While it seems to be a boost for marketing, every AI-based computerised programs and platforms reduce the burden of traditional advertising and personalization operations. In many cases, the systems used for online marketing include algorithms for determining the optimum combinations, while in others, businesses take the initiative to build and deploy unique methods in-house. The authors' goal is to summarize the prevailing status of AI in marketing efforts and facilitate implementations of a smart marketing approach to increase a site's exposure using keywords. This article also focuses on the various benefits of AI in Digital Marketing. Along with all these, the author discussed how Artificial Intelligence is used in today's digital marketing scenario to increase the productivity of any company.
C1 [Alsiehemy, Ali] Univ Tabuk, Dept Mkt, Tabuk, Saudi Arabia.
C3 University of Tabuk
RP Alsiehemy, A (autor correspondiente), Univ Tabuk, Dept Mkt, Tabuk, Saudi Arabia.
EM a.alsiehemy@ut.edu.sa
CR Dzyabura D, 2019, MARKET SCI, V38, P417, DOI 10.1287/mksc.2018.1144
Fahimnia B, 2015, INT J PROD ECON, V162, P101, DOI 10.1016/j.ijpe.2015.01.003
Hajek P, 2017, KNOWL-BASED SYST, V128, P139, DOI 10.1016/j.knosys.2017.05.001
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Huang MH, 2017, J ACAD MARKET SCI, V45, P906, DOI 10.1007/s11747-017-0545-6
Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
Kwok R, 2019, NATURE, V567, P133, DOI 10.1038/d41586-019-00746-1
Liao T, 2015, INFORM COMMUN SOC, V18, P310, DOI 10.1080/1369118X.2014.989252
Misra K, 2019, MARKET SCI, V38, P226, DOI 10.1287/mksc.2018.1129
Mohamadi A, 2016, J FAC FOR-ISTANB UNI, V66, P683, DOI 10.17099/jffiu.75819
Montes GA, 2019, TECHNOL FORECAST SOC, V141, P354, DOI 10.1016/j.techfore.2018.11.010
Rouhani S, 2016, J ENTERP INF MANAG, V29, P19, DOI 10.1108/JEIM-12-2014-0126
Seo Y, 2015, J HYDROL, V520, P224, DOI 10.1016/j.jhydrol.2014.11.050
Seranmadevi R., 2019, Management Science Letters, V9, P33, DOI [https://doi.org/10.5267/j.msl.2018.11.002, DOI 10.5267/J.MSL.2018.11.002]
Solaiman SM, 2017, ARTIF INTELL LAW, V25, P155, DOI 10.1007/s10506-016-9192-3
Zhang HJ, 2017, IEEE T IND INFORM, V13, P520, DOI 10.1109/TII.2016.2605629
NR 16
TC 0
Z9 0
U1 13
U2 14
PU PACIFIC INST MANAGEMENT
PI RAJASTHAN
PA PACIFIC HILLS, PRATAP NAGAR EXTENSION, AIR PORT RD, UDAIPUR, RAJASTHAN,
313 001, INDIA
SN 0974-438X
J9 PAC BUS REV INT
JI Pac. Bus. Rev. Int.
PD JAN
PY 2023
VL 15
IS 7
BP 19
EP 27
PG 9
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA N4OB8
UT WOS:001036814100003
DA 2024-03-27
ER
PT J
AU Alt, R
Zimmermann, HD
AF Alt, Rainer
Zimmermann, Hans-Dieter
TI Towards AI application marketplaces-an interview with Dorian Selz
SO ELECTRONIC MARKETS
LA English
DT Article
DE Artificial intelligence; cognitive search; AI marketplace; information
objects
AB This interview with the CEO from Squirro reports on how a search engine in the enterprise context evolves with artificial intelligence (AI) towards a cognitive search engine and ultimately towards an electronic marketplace for information objects. Using several examples, Dorian Selz describes the opportunities of Squirro's low-code in creating user-specific information objects from internal and external data sources as well as in deriving probabilities and projections. This not only has the potential to improve existing business processes, but also sheds light on new predictive capabilities. The example shows potential elements of future AI marketplaces, such as models, app directories and connectors.
C1 [Alt, Rainer] Univ Leipzig, Leipzig, Germany.
[Zimmermann, Hans-Dieter] Eastern Switzerland Univ Appl Sci, St Gallen, Switzerland.
C3 Leipzig University
RP Alt, R (autor correspondiente), Univ Leipzig, Leipzig, Germany.; Zimmermann, HD (autor correspondiente), Eastern Switzerland Univ Appl Sci, St Gallen, Switzerland.
EM rainer.alt@uni-leipzig.de; hansdieter.zimmermann@ost.ch
RI Zimmermann, Hans-Dieter/P-4702-2018; Alt, Rainer/U-6769-2018
OI Zimmermann, Hans-Dieter/0000-0002-6672-3311; Alt,
Rainer/0000-0002-6395-0658
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR Alt R, 2021, ELECTRON MARK, V31, P233, DOI 10.1007/s12525-021-00489-w
Emmott S., 2021, GARTNER MAGIC QUADRA
Selz D, 2020, ELECTRON MARK, V30, P57, DOI 10.1007/s12525-019-00393-4
NR 3
TC 1
Z9 1
U1 3
U2 11
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD MAR
PY 2022
VL 32
IS 1
BP 139
EP 143
DI 10.1007/s12525-021-00516-w
EA FEB 2022
PG 5
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1F1AS
UT WOS:000751234400001
OA hybrid
DA 2024-03-27
ER
PT J
AU Kapoor, R
Kapoor, K
AF Kapoor, Rashmeet
Kapoor, Kush
TI Transition from traditional to digital marketing: a study of evolution
of E-marketing in the Indian hotel industry
SO WORLDWIDE HOSPITALITY AND TOURISM THEMES
LA English
DT Article
DE Digitalization; Artificial intelligence; Social media marketing; Digital
marketing; Traditional marketing; E-marketing tools
AB Purpose
The study aims at analyzing the adoption and preference of E-Marketing tools in five-star hotels in India. This paper also explores the scope of artificial intelligence and the challenges with regards to its application.
Design/methodology/approach
A qualitative approach is adopted for this research, wherein the data has been collected through conducting one on one telephonic interviews (some in questionnaire format) and a roundtable conference with the general managers and marketing communication managers of 30 New Delhi/National Capital Region (NCR) hotels respectively to understand the transition from traditional to digital marketing era and how are they using various social media marketing tools.
Findings
This study aims to inform how digitalization has benefitted the industry in various aspects and its comparison to the traditional marketing methods. It also discovers the future of artificial intelligence in the Indian hospitality space.
Practical implications
This study aims to help managerial decision-making in the application of various E-marketing tools and strategies, suggesting the right mix of both traditional and digital marketing platforms.
Originality/value
Arguably this is one of a kind study, as there has been no such research done specifically aiming at the five-star hotels of the Indian market. The findings will help the industry explore and enhance their digital presence by suggesting the appropriate mix of both traditional and digital approaches and can be used as a good source for further exploring the perspective of digitalization by academicians as well.
C1 [Kapoor, Rashmeet] Vedatya Inst, Gurgaon, India.
[Kapoor, Kush] Roseate Hotels & Resorts, Gurgaon, India.
RP Kapoor, R (autor correspondiente), Vedatya Inst, Gurgaon, India.
EM rashmeet.kapoor@vedatya.ac.in; kush.kapoor@roseatehotels.com
OI Kapoor, Rashmeet/0000-0002-6454-5486
CR [Anonymous], 2019, The Economic Times
Arya A., 2016, IMPORTANT IS DIGITAL
Bagley E., 2014, DIGITAL DIRECTION 2
Baidya S., 2020, 2H19 COUNTRY REPORT
Barry C., 2011, PUTTING SOCIAL MEDIA
Chang T., 2018, DIGITAL TRANSFORMATI
Dentsu Aegis Network, 2020, DIGITAL ADVERTISING
Gupta R., 2019, CHANGING SALES MARKE
Hospitality Bizz India, 2018, ARTIFICIAL INTELLIGE
IBEF, 2019, DIG AD IND GROW 32 T
International Data Corporation, 2018, IMPR PROD FAST TIM M
Ivanov S., 2017, Revista Turismo & Desenvolvimento, P1501
Jaganmohan M., 2019, SHARE MEDIA CHANNELS
Kaushik P.C., 2017, INT RES J MANAGEMENT, V4, P61
Keelery S., 2020, DIGITAL ADVERTISING
Malhotra N.K., 2009, MARKETING RES APPL O
McCaskill A., 2015, NTR CITATION
Naik S, 2019, INT J INNOVATIVE RES
SAMALA N, 2020, J TOURISM FUTURES
SETIAWAN Iwan., 2017, MARKETING 4 0
Shejale P., 2019, TIPS PERFECT DIGITAL
Tata Consultancy Service, 2017, GETT SMART SECT 13 G
The Economic Times, 2018, ARTIFICIAL INTELLIGE
Tripadvisor, 2013, TRIPBAROMETER TRIPAD
Visser M, 2018, DIGITAL MARKETING FU, P14
Wali B., 2017, TEDX TALKS
Yang L, 2020, DIGITAL TRANSFORMATION IN BUSINESS AND SOCIETY: THEORY AND CASES, P211, DOI 10.1007/978-3-030-08277-2_13
NR 27
TC 8
Z9 8
U1 10
U2 46
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1755-4217
EI 1755-4225
J9 WORLDW HOSP TOUR THE
JI Worldw. Hosp. Tour. Themes
PD JUL 6
PY 2021
VL 13
IS 2
SI SI
BP 199
EP 213
DI 10.1108/WHATT-10-2020-0124
EA MAY 2021
PG 15
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA TN5FU
UT WOS:000657678700001
DA 2024-03-27
ER
PT J
AU Tchelidze, CL
AF Tchelidze, Candidate Lasha
TI POTENTIAL AND SKILL REQUIREMENTS OF ARTIFICIAL INTELLIGENCE IN DIGITAL
MARKETING
SO QUALITY-ACCESS TO SUCCESS
LA English
DT Article
DE Artificial intelligence; Digitalization; Required skills; Marketer
skills; Marketing and technologies; Digital skills
AB Technological development has contributed to development of artificial intelligence. Self-learning machines have becoming more and more popular in the world. Investments, on development of artificial intelligence, are on the rise in various countries. This particular study emphasizes limitless potential of this technology. Self-learning machines have been integrated into different fields. Various digital algorithms are implemented in digital marketing as well. According to the results, this technology facilitates process to learn online consumer behavior and attitudes. Study highlights importance of creativity, mathematical skills, analytical skills, basic understanding of technologies and knowledge of communication in humans. These skills have been stated as the requirements of artificial intelligence for digital marketers, in order to exploit the technological advancement and promote prolific marketing campaigns.
C1 [Tchelidze, Candidate Lasha] Int Black Sea Univ, Tbilisi, Georgia.
C3 International Black Sea University
RP Tchelidze, CL (autor correspondiente), Int Black Sea Univ, Tbilisi, Georgia.
EM lasha.chelo@gmail.com
CR Akdeniz C., 2016, ARTIFICIAL INTELLIGE
Anderson G., 2005, FUNDAMENTALS ED RES, P109
Andre Q., 2018, Customer Needs and Solutions, V5, P28, DOI 10.1007/s40547-017-0085-8
[Anonymous], 2017, COMPETITIVE STRATEGY
[Anonymous], THESIS
Borana J., 2016, Science [ETEBMS-2016], V5
Bozeman B, 2000, RES POLICY, V29, P627, DOI 10.1016/S0048-7333(99)00093-1
Braun V., 2012, APA handbook of research methods in psychology, Vol. 2: Research designs: Quantitative, qualitative, neuropsychological, P57, DOI [10.1007/978-981-10-2779-6_103-1, DOI 10.1007/978-981-10-5251-4_103, DOI 10.1037/13620-004, https://doi.org/10.1037/13620-004]
Buchner A.G., 1998, SIGMOD Record, V27, P54, DOI DOI 10.1145/306101.306124
Cellan-Jones Rory., 2014, BBC NEWS 1202
Cockburn Iain M., 2018, W24449 NAT BUR EC RE
Dyer J, 2015, FORBES, V196, P90
Eberhard M, 2007, 21 CENTURY ELECT CAR, P1
Forbes Technology Council, 2018, 14 WAYS AI WILL BEN
Geraci RM, 2007, ZYGON, V42, P961, DOI 10.1111/j.1467-9744.2007.00883.x
Harris M. C., 2010, Q2AMEDIA, P4
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huggins R., 2011, COMPETITION COMPETIT, P81
Jones M.T., 2015, ARTIF INTELL, P1
Kose U., 2017, ARXIV170402114
Kulkarni P., 2015, ARTIF INTELL, P8
Kumar V, 2011, J MARKETING, V75, P16, DOI 10.1509/jmkg.75.1.16
Lane P.J., 1998, STRATEGIC MANAGEMENT, V19, P2
Lu HM, 2018, MOBILE NETW APPL, V23, P368, DOI 10.1007/s11036-017-0932-8
Makridakis S, 2017, FUTURES, V90, P46, DOI 10.1016/j.futures.2017.03.006
McWilliams A., 2010, J MANAGEMENT
Pham-Gia K., 2011, RADICAL INNOVATION O, P17
Porter M., 1998, The Competitive Advantage: Creating and Sustaining Superior Performance (2e ed.)
Radosevic S., 1999, INT TECHNOLOGY TRANS, P5
Ruan Z., 2019, Digital marketing in the artificial intelligence and machine learning age
Siau K., 2017, 12 ANN MIDW ASS INF, V48, P18
Stern J., 2017, ACCELERATING MPI RED, P1
Sterne J., 2017, Artificial intelligence for marketing: practical applications
van Laar E, 2017, COMPUT HUM BEHAV, V72, P577, DOI 10.1016/j.chb.2017.03.010
Wahab S., 2012, International Business Research, V5, P61, DOI DOI 10.5539/IBR.V5N1P61
Warwick Kevin, 2013, ARTIF INTELL, P13
NR 36
TC 0
Z9 0
U1 14
U2 70
PU SOC ROMANA PENTRU ASIGURAREA CALITATII
PI BUCHAREST
PA STR VASILE PARVAN NR 14, SECTOR 1, POSTAL CODE 010 216, BUCHAREST,
00000, ROMANIA
SN 1582-2559
EI 2668-4861
J9 QUAL-ACCESS SUCCESS
JI Qual.-Access Success
PD OCT
PY 2019
VL 20
SU 3
BP 73
EP 78
PG 6
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA JN3OZ
UT WOS:000496810200010
DA 2024-03-27
ER
PT J
AU Tong, SL
Luo, XM
Xu, B
AF Tong, Siliang
Luo, Xueming
Xu, Bo
TI Personalized mobile marketing strategies
SO JOURNAL OF THE ACADEMY OF MARKETING SCIENCE
LA English
DT Article
DE Mobile marketing; Mobile personalization; Mobile marketing mix;
Artificial intelligence
ID INTENTIONS; PROMOTIONS; FRAMEWORK; CONSUMERS; COMMERCE; CHANNELS;
ISSUES; MEDIA; PRICE
AB The prevalence of mobile usage data has provided unprecedented insights into customer hyper-context information and brings ample opportunities for practitioners to design more pertinent marketing strategies and timely targeted campaigns. Granular unstructured mobile data also stimulate new research frontiers. This paper integrates the traditional marketing mix model to develop a framework of personalized mobile marketing strategies. The framework incorporates personalization into the center of mobile product, mobile place, mobile price, mobile promotion, and mobile prediction. Extant studies in mobile marketing are reviewed under the proposed framework, and promising topics about personalized mobile marketing are discussed for future research.
C1 [Tong, Siliang] Temple Univ, Fox Sch Business, Philadelphia, PA 19122 USA.
[Luo, Xueming] Temple Univ, Fox Sch Business, Global Ctr Big Data & Mobile Analyt, Philadelphia, PA 19122 USA.
[Xu, Bo] Fudan Univ, Sch Management, Shanghai, Peoples R China.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple
University; Pennsylvania Commonwealth System of Higher Education
(PCSHE); Temple University; Fudan University
RP Xu, B (autor correspondiente), Fudan Univ, Sch Management, Shanghai, Peoples R China.
EM tug76173@temple.edu; luoxm@temple.edu; bxu@fudan.edu.cn
CR Adweek, 2018, ADWEEK
Aguirre E, 2015, J RETAILING, V91, P34, DOI 10.1016/j.jretai.2014.09.005
Andrews M, 2016, J INTERACT MARK, V34, P15, DOI 10.1016/j.intmar.2016.03.004
Andrews M, 2016, MARKET SCI, V35, P218, DOI 10.1287/mksc.2015.0905
[Anonymous], 2018, THEMANIFEST
[Anonymous], 2018, DIGITAL TRENDS
[Anonymous], 2018, Quartz
[Anonymous], 2019, NUMB MOB PHON US WOR
[Anonymous], 2019, MOBILE MARKETER
[Anonymous], 2018, HUBSPOT
[Anonymous], 2018, BUSINESSOFAPPS
[Anonymous], 2017, Harvard Business Review Manager's Handbook: The 17 Skills Leaders Need to Stand Out
[Anonymous], 2018, RETAILDIVE
[Anonymous], 2019, Businesswire
Arora S, 2017, J MARKETING, V81, P62, DOI 10.1509/jm.15.0205
Balasubramanian S, 2002, J ACAD MARKET SCI, V30, P348, DOI 10.1177/009207002236910
Bart Y, 2014, J MARKETING RES, V51, P270, DOI 10.1509/jmr.13.0503
Barwise P., 2002, J INTERACT MARK, V16, P14
Bluecorona, 2018, 61 MOB MARK STAT 201
Castelo N., 2019, WORKING PAPER
Ciechanowski L, 2019, FUTURE GENER COMP SY, V92, P539, DOI 10.1016/j.future.2018.01.055
Danaher PJ, 2015, J MARKETING RES, V52, P710, DOI 10.1509/jmr.13.0341
de Haan E, 2018, J MARKETING, V82, P1, DOI 10.1509/jm.17.0113
Dickinger A, 2008, J INTERACT MARK, V22, P23, DOI 10.1002/dir.20115
Doyle M., 2018, 6 WAYS MEASURE ONLIN
Dubé JP, 2017, MARKET SCI, V36, P944, DOI 10.1287/mksc.2017.1042
Economides N, 2017, MARKET SCI, V36, P815, DOI 10.1287/mksc.2017.1027
Einav L, 2014, AM ECON REV, V104, P489, DOI 10.1257/aer.104.5.489
eMarketer, 2019, WHATS SUDD GROWTH TI
Fast Company, 2018, FAST CO
Fong N, 2019, J MARKETING RES, V56, P310, DOI 10.1177/0022243718817513
Fong NM, 2015, J MARKETING RES, V52, P726, DOI 10.1509/jmr.14.0229
Ghose A., 2019, MANAGEMENT SCI ARTIC
Ghose A, 2019, INFORM SYST RES, V30, P154, DOI 10.1287/isre.2018.0792
Ghose A, 2014, MANAGE SCI, V60, P1470, DOI 10.1287/mnsc.2014.1945
Ghose A, 2013, INFORM SYST RES, V24, P613, DOI 10.1287/isre.1120.0453
Ghose A, 2011, MANAGE SCI, V57, P1671, DOI 10.1287/mnsc.1110.1350
Gill M, 2017, J MARKETING, V81, P45, DOI 10.1509/jm.16.0149
Grewal D, 2018, J MARKETING, V82, P102, DOI 10.1509/jm.17.0277
Grewal D, 2017, J RETAILING, V93, P1, DOI 10.1016/j.jretai.2016.12.008
Grewal D, 2016, J INTERACT MARK, V34, P3, DOI 10.1016/j.intmar.2016.03.003
Grewal L., 2019, J MARKETING RES
Gurau C, 2009, J CONSUM MARK, V26, P496, DOI 10.1108/07363760911001556
Ho SY, 2018, MIS QUART, V42, P757, DOI 10.25300/MISQ/2018/14083
Hofacker CF, 2016, J INTERACT MARK, V34, P25, DOI 10.1016/j.intmar.2016.03.001
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Hui SK, 2013, J MARKETING, V77, P1, DOI 10.1509/jm.11.0436
Investopedia, 2018, Mobile Advertising Definition
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Kim SJ, 2015, J INTERACT MARK, V31, P28, DOI 10.1016/j.intmar.2015.05.004
Kübler R, 2018, J MARKETING, V82, P20, DOI 10.1509/jm.16.0140
Kumar V., 2018, J ACAD MARKET SCI, P1, DOI DOI 10.1007/s11747-018-0613-6
Lamberton C, 2016, J MARKETING, V80, P146, DOI 10.1509/jm.15.0415
Leung E, 2018, J MARKETING RES, V55, P818, DOI 10.1177/0022243718818423
Li CX, 2017, MARKET SCI, V36, P762, DOI 10.1287/mksc.2017.1044
Luo XM, 2014, MANAGE SCI, V60, P1738, DOI 10.1287/mnsc.2013.1836
McCarthy E.Jerome., 1964, BASIC MARKETING MANA, VSecond
Melumad S, 2019, J MARKETING RES, V56, P259, DOI 10.1177/0022243718815429
Nysveen H, 2005, J ACAD MARKET SCI, V33, P330, DOI 10.1177/0092070305276149
Provost F, 2015, INFORM SYST RES, V26, P243, DOI 10.1287/isre.2015.0576
Ransbotham S., 2019, MARKETING SCI
Shankar V, 2016, J INTERACT MARK, V34, P37, DOI 10.1016/j.intmar.2016.03.002
Shankar V, 2010, J INTERACT MARK, V24, P111, DOI 10.1016/j.intmar.2010.02.006
Shankar V, 2009, J INTERACT MARK, V23, P118, DOI 10.1016/j.intmar.2009.02.002
Shen H, 2016, J MARKETING RES, V53, P745, DOI 10.1509/jmr.14.0563
Spaid BI, 2014, J MARKET THEORY PRAC, V22, P73, DOI 10.2753/MTP1069-6679220105
Statista, 2018, CHART MOB E COMM IS
The Verge, 2019, THE VERGE
Vargo SL, 2004, J MARKETING, V68, P1, DOI 10.1509/jmkg.68.1.1.24036
Vlachos Pavlos A., 2008, Journal of Services Marketing, V22, P280, DOI 10.1108/08876040810881687
Wang F., 2019, J ACAD MARKETING SCI
Wang Q, 2018, INFORM SYST RES, V29, P273, DOI 10.1287/isre.2017.0735
Wang RJH, 2015, J RETAILING, V91, P217, DOI 10.1016/j.jretai.2015.01.002
Xu H, 2012, INFORM SYST RES, V23, P1342, DOI 10.1287/isre.1120.0416
Xu J, 2014, J MARKETING, V78, P97, DOI 10.1509/jm.13.0198
Xu KQ, 2017, MANAGE SCI, V63, P1469, DOI 10.1287/mnsc.2015.2406
Zinrelo, 2018, TIER LOYALT PROGR WH
Zubcsek PP, 2017, J MARKETING, V81, P109, DOI 10.1509/jm.15.0215
NR 78
TC 112
Z9 128
U1 36
U2 254
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0092-0703
EI 1552-7824
J9 J ACAD MARKET SCI
JI J. Acad. Mark. Sci.
PD JAN
PY 2020
VL 48
IS 1
SI SI
BP 64
EP 78
DI 10.1007/s11747-019-00693-3
EA OCT 2019
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA KQ2KE
UT WOS:000490889400001
DA 2024-03-27
ER
PT J
AU Liu, X
AF Liu, Xia
TI Analyzing the impact of user-generated content on B2B Firms' stock
performance: Big data analysis with machine learning methods
SO INDUSTRIAL MARKETING MANAGEMENT
LA English
DT Article
DE Big data; User-generated content; LDA; Sentiment analysis; Machine
learning
ID AUTOMATED CONTENT-ANALYSIS; WORD-OF-MOUTH; SENTIMENT ANALYSIS; DATA
ANALYTICS; TEXT ANALYSIS; REVIEWS; MODEL; PANEL; MANAGEMENT; COMMUNITY
AB Marketing scholars are interested in the big data of user-generated content (UGC) from social media platforms. However, the majority of current UGC studies have been conducted in the business-to-consumer (B2C) context. To fill the knowledge gap in business-to-business (B2B) research, we investigate whether UGC has differential impacts on stock performance for B2B and B2C firms by using big data. We collect a large dataset of 84 million tweets from 20.3 million Twitter accounts and 8 years of stock data for 407 companies from the S&P500 index. The results from machine learning methods are transformed into a monthly panel data. We conduct fixed effects model on the panel data. We find that UGC has a significant impact on firms' stock performance and that its impact on stock performance is much stronger among B2C firms than among B2B firms. While consumers' positive sentiment does not play a significant role in stock performance, consumers' negative sentiment and WOM significantly impact stock prices.
C1 [Liu, Xia] Rowan Univ, William G Rohrer Coll Business, Dept Mkt, 201 Mull Hill Rd, Glassboro, NJ 08028 USA.
[Liu, Xia] Rowan Univ, William G Rohrer Coll Business, BIS, 201 Mull Hill Rd, Glassboro, NJ 08028 USA.
C3 Rowan University; Rowan University
RP Liu, X (autor correspondiente), Rowan Univ, William G Rohrer Coll Business, Dept Mkt, 201 Mull Hill Rd, Glassboro, NJ 08028 USA.; Liu, X (autor correspondiente), Rowan Univ, William G Rohrer Coll Business, BIS, 201 Mull Hill Rd, Glassboro, NJ 08028 USA.
EM liul@rowan.edu
CR Aksoy L, 2008, J MARKETING, V72, P105, DOI 10.1509/jmkg.72.4.105
Allison P., 2008, FIXED EFFECTS REGRES
[Anonymous], 6 INT ADV COMP C IAC
[Anonymous], 2010, IFIP ANN C DAT APPL
[Anonymous], 2018, NUMBER MONTHLY ACTIV
Armbrust M, 2015, SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P1383, DOI 10.1145/2723372.2742797
Bird S., 2009, NATURAL LANGUAGE PRO
Bjeladinovic S, 2018, ENTERP INF SYST-UK, V12, P1202, DOI 10.1080/17517575.2018.1446102
Blei DM, 2014, ANNU REV STAT APPL, V1, P203, DOI 10.1146/annurev-statistics-022513-115657
Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Boyd D, 2012, INFORM COMMUN SOC, V15, P662, DOI 10.1080/1369118X.2012.678878
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Cameron AC, 2010, MICROECONOMETRICS US, V2
Chintagunta P, 2016, MARKET SCI, V35, P341, DOI 10.1287/mksc.2016.0996
Chintagunta PK, 2010, MARKET SCI, V29, P944, DOI 10.1287/mksc.1100.0572
Choi I, 2001, J INT MONEY FINANC, V20, P249, DOI 10.1016/S0261-5606(00)00048-6
Chu Z, 2012, IEEE T DEPEND SECURE, V9, P811, DOI 10.1109/TDSC.2012.75
Culotta A, 2016, MARKET SCI, V35, P343, DOI 10.1287/mksc.2015.0968
Engelberg J, 2018, J ACCOUNT ECON, V66, P415, DOI 10.1016/j.jacceco.2018.08.005
Fornell C, 2006, J MARKETING, V70, P3, DOI 10.1509/jmkg.2006.70.1.3
George G, 2016, ACAD MANAGE J, V59, P1493, DOI 10.5465/amj.2016.4005
George G, 2014, ACAD MANAGE J, V57, P321, DOI 10.5465/amj.2014.4002
Goh KY, 2013, INFORM SYST RES, V24, P88, DOI 10.1287/isre.1120.0469
Grimmer J, 2013, POLIT ANAL, V21, P267, DOI 10.1093/pan/mps028
Grün B, 2011, J STAT SOFTW, V40, P1
HAUSMAN JA, 1978, ECONOMETRICA, V46, P1251, DOI 10.2307/1913827
Heimerl F, 2014, P ANN HICSS, P1833, DOI 10.1109/HICSS.2014.231
Heinonen K, 2011, J CONSUM BEHAV, V10, P356, DOI 10.1002/cb.376
Heng Y, 2018, J RETAIL CONSUM SERV, V42, P161, DOI 10.1016/j.jretconser.2018.02.006
Hennig-Thurau T, 2015, J ACAD MARKET SCI, V43, P375, DOI 10.1007/s11747-014-0388-3
Hewett K, 2016, J MARKETING, V80, P1, DOI 10.1509/jm.15.0033
Homburg C, 2015, J MARKETING RES, V52, P629, DOI 10.1509/jmr.11.0448
Hsiao C, 2007, TEST-SPAIN, V16, P1, DOI 10.1007/s11749-007-0046-x
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Iliev R, 2015, LANG COGN, V7, P265, DOI 10.1017/langcog.2014.30
Izogo Ernest Emeka, 2017, TQM Journal, V29, P19, DOI 10.1108/TQM-10-2014-0089
Jin SAA, 2014, J ADVERTISING, V43, P181, DOI 10.1080/00913367.2013.827606
Jungherr A, 2014, J COMMUN, V64, P239, DOI 10.1111/jcom.12087
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
Kim AJ, 2016, COMPUT HUM BEHAV, V58, P98, DOI 10.1016/j.chb.2015.12.047
Lacy S, 2015, J MASS COMMUN Q, V92, P791, DOI 10.1177/1077699015607338
Lansdall-Welfare T, 2017, P NATL ACAD SCI USA, V114, pE457, DOI 10.1073/pnas.1606380114
Lee TY, 2011, J MARKETING RES, V48, P881, DOI 10.1509/jmkr.48.5.881
Li XY, 2018, IEEE T INF FOREN SEC, V13, P1917, DOI 10.1109/TIFS.2018.2806925
Lilien GL, 2016, INT J RES MARK, V33, P543, DOI 10.1016/j.ijresmar.2016.01.003
Liu X, 2017, J ADVERTISING, V46, P236, DOI 10.1080/00913367.2017.1297273
Liu X, 2016, MARKET SCI, V35, P363, DOI 10.1287/mksc.2015.0972
Lu Y., 2009, FDN STAT NATURAL LAN, P131
Luo XM, 2009, MARKET SCI, V28, P148, DOI 10.1287/mksc.1080.0389
Malthouse EC, 2017, J ADVERTISING, V46, P227, DOI 10.1080/00913367.2017.1299653
Mankad S, 2016, SERV SCI, V8, P124, DOI 10.1287/serv.2016.0126
Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
McCallum A. K., 2002, MALLET MACHINE LEARN
Meng XR, 2016, J MACH LEARN RES, V17
Minnick K, 2017, J CORP FINANC, V44, P233, DOI 10.1016/j.jcorpfin.2017.04.001
Morstatter F, 2016, PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, P533, DOI 10.1109/ASONAM.2016.7752287
Netzer O, 2008, MARKET SCI, V27, P185, DOI 10.1287/mksc.1070.0294
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Nunez-Mir GC, 2016, METHODS ECOL EVOL, V7, P1262, DOI 10.1111/2041-210X.12602
Pustejovsky J., 2012, NATURAL LANGUAGE ANN
Ravi K, 2015, KNOWL-BASED SYST, V89, P14, DOI 10.1016/j.knosys.2015.06.015
Saboo AR, 2016, MIS QUART, V40, P911, DOI 10.25300/MISQ/2016/40.4.06
Schwartz B., 2012, High Performance MySQL: Optimization, Backups, and Replication
Shuai CY, 2017, APPL ENERG, V187, P310, DOI 10.1016/j.apenergy.2016.11.029
Sivarajah U, 2017, J BUS RES, V70, P263, DOI 10.1016/j.jbusres.2016.08.001
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Trilling D, 2018, COMMUN METHODS MEAS, V12, P158, DOI 10.1080/19312458.2018.1447655
Uotila J, 2009, STRATEGIC MANAGE J, V30, P221, DOI 10.1002/smj.738
van der Meer TGLA, 2016, PUBLIC RELAT REV, V42, P952, DOI 10.1016/j.pubrev.2016.09.001
Varian H.R., 2014, BUS ECON, V49, P27
Varian HR, 2014, J ECON PERSPECT, V28, P3, DOI 10.1257/jep.28.2.3
Varol O, 2017, P INT AAAI C WEB SOC, DOI DOI 10.48550/ARXIV.1703.03107
Wang YC, 2018, INFORM MANAGE-AMSTER, V55, P64, DOI 10.1016/j.im.2017.04.001
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Wooldridge JM, 2010, ECONOMETRIC ANALYSIS OF CROSS SECTION AND PANEL DATA, 2ND EDITION, P1
Wu PJ, 2018, TELEMAT INFORM, V35, P237, DOI 10.1016/j.tele.2017.11.004
Zhang ZF, 2018, NEUROCOMPUTING, V275, P1407, DOI 10.1016/j.neucom.2017.09.080
NR 78
TC 71
Z9 72
U1 17
U2 100
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0019-8501
EI 1873-2062
J9 IND MARKET MANAG
JI Ind. Mark. Manage.
PD APR
PY 2020
VL 86
DI 10.1016/j.indmarman.2019.02.021
PG 10
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LG2LA
UT WOS:000527937700004
DA 2024-03-27
ER
PT J
AU Pillai, R
Sivathanu, B
AF Pillai, Rajasshrie
Sivathanu, Brijesh
TI Adoption of artificial intelligence (AI) for talent acquisition in
IT/ITeS organizations
SO BENCHMARKING-AN INTERNATIONAL JOURNAL
LA English
DT Article
DE Artificial intelligence; TOE; TTF; Talent acquisition; Adoption; IT;
ITeS; PLS-SEM
ID TASK-TECHNOLOGY FIT; SUPPLY CHAIN MANAGEMENT; SOCIAL COGNITIVE THEORY;
BUSINESS INTELLIGENCE; ACCEPTANCE MODEL; MOBILE BANKING; BIG DATA;
PLS-SEM; DETERMINANTS; SMES
AB Purpose Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition. Design/methodology/approach This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM. Findings This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition. Practical implications This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition. Originality/value This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.
C1 [Pillai, Rajasshrie] Pune Inst Business Management, Dept Management, Pune, Maharashtra, India.
[Sivathanu, Brijesh] Sri Balaji Univ, Dept Management, Pune, Maharashtra, India.
RP Pillai, R (autor correspondiente), Pune Inst Business Management, Dept Management, Pune, Maharashtra, India.
EM rajasshrie1@gmail.com
RI S, BRIJESH/AAQ-4753-2021; Pillai, Rajasshrie/GRO-0859-2022
OI Sivathanu, Dr. Brijesh/0000-0003-2505-9140
CR Aayog Niti, 2018, National Strategy for Artificial Intelligence
Ahmadi Hossein., 2015, Journal of Soft Computing and Decision Support Systems, V2, P26
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Ajzen I., 1985, SSSP SPRINGER SERIES, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2]
Al-Qirim N., 2006, International Journal of Internet and Enterprise Management, V4, P293, DOI 10.1504/IJIEM.2006.011042
Al-Qirim N, 2007, ELECTRON COMMER R A, V6, P462, DOI 10.1016/j.elerap.2007.02.012
Alaiad A., 2016, IEEE T PROFESSIONAL, V60, P1
Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
Alam MGR, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0160366
Alam SS, 2011, J BUS ECON MANAG, V12, P375, DOI 10.3846/16111699.2011.576749
Alatawi F., 2012, GOV WORKSHOP, V2, P1
Albert Edward Tristram, 2019, Strategic HR Review, V18, P215, DOI 10.1108/SHR-04-2019-0024
Allen DG, 2007, J APPL PSYCHOL, V92, P1696, DOI 10.1037/0021-9010.92.6.1696
Awa H.O., 2010, International Journal of Business and Management, V5, P184
Awa HO, 2016, COGENT BUS MANAG, V3, DOI 10.1080/23311975.2016.1196571
Awa HO, 2017, J ENTERP INF MANAG, V30, P893, DOI 10.1108/JEIM-03-2016-0079
Awa HO, 2017, BOTTOM LINE, V30, P2, DOI 10.1108/BL-12-2016-0044
Awa HO, 2016, INFORM TECHNOL PEOPL, V29, P901, DOI 10.1108/ITP-03-2015-0068
Awa HO, 2015, J ENTERP INF MANAG, V28, P326, DOI 10.1108/JEIM-10-2013-0073
Baig MI, 2019, INFORM PROCESS MANAG, V56, DOI 10.1016/j.ipm.2019.102095
Balaid Ali, 2014, 2014 8th Malaysian Software Engineering Conference (MySEC), P201, DOI 10.1109/MySec.2014.6986014
Bandura A, 2001, MEDIA PSYCHOL, V3, P265, DOI 10.1207/S1532785XMEP0303_03
Beechler S, 2009, J INT MANAG, V15, P273, DOI 10.1016/j.intman.2009.01.002
Belanche D, 2019, IND MANAGE DATA SYST, V119, P1411, DOI 10.1108/IMDS-08-2018-0368
Benlian A, 2011, DECIS SUPPORT SYST, V52, P232, DOI 10.1016/j.dss.2011.07.007
Bersin J., 2018, TALENT TRENDS TECHNO
Bogle S., 2012, INT J INFORM SCI TEC, V2, P1, DOI DOI 10.5121/IJIST.2012.2301
Bugg K., 2015, Library Leadership Management, V29
Bughin J., 2017, JOBS LOST JOBS GAINE, P1
Burton-Jones A, 2013, INFORM SYST RES, V24, P632, DOI 10.1287/isre.1120.0444
Chan FTS, 2013, INT J PROD RES, V51, P1196, DOI 10.1080/00207543.2012.693961
Charlier Robert, 2017, Artificial Intelligence in HR: a No-brainer
Cheng YM, 2020, BENCHMARKING, V27, P1591, DOI 10.1108/BIJ-05-2019-0208
Chong AYL, 2012, EXPERT SYST APPL, V39, P8645, DOI 10.1016/j.eswa.2012.01.201
Collings D., 2013, HUMAN RESOURCE MANAG, V23, P305
Cruz-Jesus Frederico, 2018, Trends and Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing (AISC 745), P1094, DOI 10.1007/978-3-319-77703-0_106
Cruz-Jesus F, 2019, COMPUT IND, V109, P1, DOI 10.1016/j.compind.2019.03.007
Daradkeh MK, 2019, INFORM TECHNOL PEOPL, V32, P668, DOI 10.1108/ITP-10-2017-0359
Das R, 2018, BENCHMARKING, V25, P1213, DOI 10.1108/BIJ-09-2016-0143
Dastin J., 2018, Reuters9 October
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Deloitte, 2017, 2018 HR TECHN DISR
Demers E., 2001, Review of Accounting Studies, V6, P331
Depietro R., 1990, The process of technological innovation, P151
Derous E, 2016, INT J SELECT ASSESS, V24, P1, DOI 10.1111/ijsa.12123
Dhamija P, 2020, TQM J, V32, P869, DOI 10.1108/TQM-10-2019-0243
Dishaw MT, 1999, INFORM MANAGE-AMSTER, V36, P9, DOI 10.1016/S0378-7206(98)00101-3
Dries N, 2013, HUM RESOUR MANAGE R, V23, P272, DOI 10.1016/j.hrmr.2013.05.001
Dubey R, 2018, INT J LOGIST MANAG, V29, P485, DOI 10.1108/IJLM-02-2017-0039
DWIVEDI YK, 2019, INT J INFORM MANAGEM
El-Haddadeh R, 2020, INFORM SYST FRONT, V22, P985, DOI 10.1007/s10796-019-09912-2
Erro-Garcés A, 2021, BENCHMARKING, V28, P1858, DOI 10.1108/BIJ-12-2018-0444
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gangwar H, 2014, J ENTERP INF MANAG, V27, P488, DOI 10.1108/JEIM-08-2012-0047
Gerrard Michael, 2010, ACIS 2010 P 21 AUSTR
Ghobakhloo M, 2011, IND MANAGE DATA SYST, V111, P1238, DOI 10.1108/02635571111170785
GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
Goodman E., 2017, HARVARD BUSINESS REV
GROVER V, 1993, DECISION SCI, V24, P603, DOI 10.1111/j.1540-5915.1993.tb01295.x
Gu V., 2020, INT J PRODUCTIVITY P
Guide VDR, 2015, J OPER MANAG, V37, pV, DOI 10.1016/S0272-6963(15)00056-X
Gupta A, 2016, ISPRS ANN PHOTO REM, V3, P263, DOI 10.5194/isprsannals-III-3-263-2016
Hair J.F., 2016, A premier on partial least squares structural equation modeling (PLS-SEM), V2nd
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
Hameed MA, 2012, INFORM MANAGE-AMSTER, V49, P218, DOI 10.1016/j.im.2012.05.002
Han S, 2018, IND MANAGE DATA SYST, V118, P618, DOI 10.1108/IMDS-05-2017-0214
Henderson D, 2012, INT J ACCOUNT INF SY, V13, P109, DOI 10.1016/j.accinf.2012.02.001
Hossain MA, 2017, INFORM TECHNOL PEOPL, V30, P785, DOI 10.1108/ITP-06-2016-0133
Howard M.C., 2018, INFORM MANAGEMENT, P1
Hsu CL, 2016, TECHNOL FORECAST SOC, V108, P42, DOI 10.1016/j.techfore.2016.04.012
Hsu CW, 2017, TECHNOL ANAL STRATEG, V29, P1089, DOI 10.1080/09537325.2016.1269160
Huang G., 2002, Benchmarking, V9, P357, DOI [10.1108/14635770210442699, DOI 10.1108/14635770210442699]
Huang TL, 2015, ELECTRON COMMER RES, V15, P269, DOI 10.1007/s10660-014-9163-2
Hungund S, 2019, BENCHMARKING, V26, P1451, DOI 10.1108/BIJ-05-2018-0127
Ifinedo P, 2011, INT J INF TECH DECIS, V10, P731, DOI 10.1142/S0219622011004543
Ivanov S.H., 2017, Artificial Intelligence and Service Automation by Travel, Tourism and Hospitality Companies -A Cost -Benefit Analysis
Jia Q, 2016, INT CON DISTR COMP S, P690, DOI 10.1109/ICDCS.2016.94
Jia Q, 2017, COMPUT HUM BEHAV, V67, P95, DOI 10.1016/j.chb.2016.10.022
Kapoor KK, 2018, INFORM SYST FRONT, V20, P531, DOI 10.1007/s10796-017-9810-y
Kashi K, 2016, J ORG COMP ELECT COM, V26, P203, DOI 10.1080/10919392.2016.1194055
Khemthong S., 2006, J BUSINESS SYSTEMS G, V1, P51, DOI [10.15209/jbsge.v1i2.74, DOI 10.15209/JBSGE.V1I2.74]
Kin TongYoon., 2005, Employee Relations, V27, P103, DOI DOI 10.1108/01425450510569337
Kinder T, 2000, TECHNOVATION, V20, P461, DOI 10.1016/S0166-4972(00)00017-1
Kumar S, 2019, BENCHMARKING, V26, P2188, DOI 10.1108/BIJ-08-2018-0251
Lai YY, 2018, INT J LOGIST MANAG, V29, P676, DOI 10.1108/IJLM-06-2017-0153
Landers RN., 2016, Social media in employee selection and recruitment: Theory, practice, and current challenges, DOI DOI 10.1007/978-3-319-29989-1
Laumer S, 2010, INTEGR SER INFORM SY, V28, P63, DOI 10.1007/978-1-4419-6108-2_4
Lee JN, 2004, INFORM SYST RES, V15, P110, DOI 10.1287/isre.1040.0013
Lin HX, 2020, J HOSP MARKET MANAG, V29, P530, DOI 10.1080/19368623.2020.1685053
Lin JCC, 2007, BEHAV INFORM TECHNOL, V26, P507, DOI 10.1080/01449290600740843
Lin TC, 2008, INFORM MANAGE-AMSTER, V45, P410, DOI 10.1016/j.im.2008.06.004
Lin WS, 2012, INT J HUM-COMPUT ST, V70, P498, DOI 10.1016/j.ijhcs.2012.01.006
Low CY, 2011, IND MANAGE DATA SYST, V111, P1006, DOI 10.1108/02635571111161262
Maciag G.A., 2000, National Underwrite, V104, P19
Mårtensson J, 2016, ANN INTENSIVE CARE, V6, DOI 10.1186/s13613-016-0108-x
Masood T, 2019, ROBOT CIM-INT MANUF, V58, P181, DOI 10.1016/j.rcim.2019.02.003
McCarthy J., 1955, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
McDonald K, 2017, CHANG CONT MANAG, P87
McGill TJ, 2009, COMPUT EDUC, V52, P496, DOI 10.1016/j.compedu.2008.10.002
McGoven S., 2018, The new age: artificial intelligence for human resource opportunities and functions
Melanthiou Y., 2015, J TRANSNATIONAL MANA, V20, P31, DOI DOI 10.1080/15475778.2015.998141
Michaels E., 2001, WAR TALENT
Moghaddam H., 2015, Journal of Global Business Advancement, V8, P305, DOI DOI 10.1504/JGBA.2015.071331
Muduli A, 2020, BENCHMARKING, V27, P1615, DOI 10.1108/BIJ-07-2019-0312
Nair P., 2017, RISE AI RECRUITER IS
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Oliveira T., 2011, Literature Review of Information Technology Adoption Models at Firm Level
Oliveira T, 2019, INT J INFORM MANAGE, V49, P1, DOI 10.1016/j.ijinfomgt.2019.02.009
Oliveira T, 2014, INFORM MANAGE-AMSTER, V51, P497, DOI 10.1016/j.im.2014.03.006
Oliveira T, 2010, IND MANAGE DATA SYST, V110, P1337, DOI 10.1108/02635571011087428
Onik M.M., 2018, P SMART CIT S SCS 20, DOI [10.1049/CP.2018.1371, DOI 10.1049/CP.2018.1371]
Osmonbekov T, 2010, J BUS RES, V63, P217, DOI 10.1016/j.jbusres.2009.01.012
Park JY, 2020, J SPORT REHABIL, V29, P93, DOI 10.1123/jsr.2017-0216
Phahlane M.M., 2017, MULTIDIMENSIONAL FRA
Phahlane MM, 2017, 2017 CONFERENCE ON INFORMATION COMMUNICATION TECHNOLOGY AND SOCIETY (ICTAS)
Phillips-Wren G, 2016, J DECIS SYST, V25, P450, DOI 10.1080/12460125.2016.1187398
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Premkumar G, 1999, OMEGA-INT J MANAGE S, V27, P467, DOI 10.1016/S0305-0483(98)00071-1
Puklavec B, 2018, IND MANAGE DATA SYST, V118, P236, DOI 10.1108/IMDS-05-2017-0170
Qu WG, 2011, J BUS RES, V64, P997, DOI 10.1016/j.jbusres.2010.11.023
Quaddus M, 2007, EUR J INFORM SYST, V16, P202, DOI 10.1057/palgrave.ejis.3000671
Queiroz MM, 2021, BENCHMARKING, V28, P1761, DOI 10.1108/BIJ-12-2018-0435
Qureshi M.A., 2018, INT J TECHNOL ENHANC, V1, P1, DOI [10.1504/IJTEL.2018.10017608, DOI 10.1504/IJTEL.2018.10017608]
Radhika R., 2016, J MULTIDISCIPLINARY, V3, P78
Rahayu R, 2015, WORLD CONFERENCE ON TECHNOLOGY, INNOVATION AND ENTREPRENEURSHIP, P142, DOI 10.1016/j.sbspro.2015.06.423
Rahman M., 2017, J ENTERPRISE INFORM, V31, P247
Rahman M, 2018, J ENTERP INF MANAG, V31, P247, DOI 10.1108/JEIM-05-2017-0066
Ramdani B, 2013, J SMALL BUS ENTERP D, V20, P735, DOI 10.1108/JSBED-12-2011-0035
Ringle C.M., 2005, Smart PLS 2.0
Rogers, 2003, DIFFUSION INNOVATION
Rogers E., 2005, Diffusion of Innovations, V4th ed., DOI DOI 10.1007/978-3-642-79868-9_2
Sabherwal R., 2006, INFORM SYSTEM SUCCES
Salam MA, 2021, BENCHMARKING, V28, P1697, DOI 10.1108/BIJ-12-2018-0428
Salleh K., 2018, PACIS P, P211
Sen S., 2018, AI AUTOMATION HR IMP
Shaltoni AM, 2017, J BUS IND MARK, V32, P1009, DOI 10.1108/JBIM-06-2016-0122
Shih KH, 2010, INT J MOB COMMUN, V8, P257, DOI 10.1504/IJMC.2010.032974
Singh N, 2018, BENCHMARKING, V25, P3459, DOI 10.1108/BIJ-08-2017-0215
Sivathanu B, 2019, INF RESOUR MANAG J, V32, P52, DOI 10.4018/IRMJ.2019040103
Sivathanu B, 2019, J SCI TECHNOL POLICY, V10, P143, DOI 10.1108/JSTPM-07-2017-0033
SONY M, 2019, PROD PLAN CONTR 1115, DOI DOI 10.1108/BIJ-09-2018-0284
Strohmeier S., 2007, Human Resources Management Review, V17, P19, DOI 10.1016/j.hrmr.2006.11.002
Sun J, 2017, INT J INFORM MANAGE, V37, P400, DOI 10.1016/j.ijinfomgt.2017.04.010
Tam C, 2016, INT J BANK MARK, V34, P434, DOI 10.1108/IJBM-11-2014-0169
Tavana M, 2019, BENCHMARKING, V27, P81, DOI 10.1108/BIJ-04-2019-0178
Teo TSH, 2008, J MANAGE INFORM SYST, V25, P99, DOI 10.2753/MIS0742-1222250303
Thomas M, 2016, INFORM SYST FRONT, V18, P693, DOI 10.1007/s10796-015-9556-3
Thong J., 1991, J MANAGEMNET INFORAM, V15, P187
Tong DYK, 2009, IND MANAGE DATA SYST, V109, P281, DOI 10.1108/02635570910930145
Tornatzky L.G. Fleischer., 1990, The Process of Technology Innovation
Tsai MC, 2010, INFORM MANAGE-AMSTER, V47, P255, DOI 10.1016/j.im.2010.05.001
Tsou HT, 2015, INT J INFORM MANAGE, V35, P1, DOI 10.1016/j.ijinfomgt.2014.09.001
Tung LL, 2005, J STRATEGIC INF SYST, V14, P417, DOI 10.1016/j.jsis.2005.06.001
Tussyadiah I., 2018, CONSUMER EVALUATION, DOI [10.1007/978-3-319-72923-7, DOI 10.1007/978-3-319-72923-7]
Tussyadiah I, 2019, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2019, P359, DOI 10.1007/978-3-030-05940-8_28
Upadhyay Ashwani Kumar, 2018, Strategic HR Review, V17, P255, DOI 10.1108/SHR-07-2018-0051
van Esch P, 2019, COMPUT HUM BEHAV, V90, P215, DOI 10.1016/j.chb.2018.09.009
Vanduhe VZ, 2020, IEEE ACCESS, V8, P21473, DOI 10.1109/ACCESS.2020.2966179
Venkatesh V., 2009, MIS Q, P425
Virdyananto A., 2017, 2016 INT C INFORM TE, P1
Wamba SF, 2016, PROD PLAN CONTROL, V27, P979, DOI 10.1080/09537287.2016.1167981
Wang SL, 2019, BEHAV INFORM TECHNOL, V38, P974, DOI 10.1080/0144929X.2019.1626486
Wang YS, 2013, INT J INFORM MANAGE, V33, P199, DOI 10.1016/j.ijinfomgt.2012.09.004
Wang YY, 2018, INT J INFORM MANAGE, V38, P7, DOI 10.1016/j.ijinfomgt.2017.07.003
Wu WW, 2011, EXPERT SYST APPL, V38, P15057, DOI 10.1016/j.eswa.2011.05.039
Wymer S. A., 2005, Electronic Markets, V15, P438, DOI 10.1080/10196780500303151
Xia BS, 2014, BENCHMARKING, V21, P300, DOI 10.1108/BIJ-08-2012-0050
Yadegaridehkordi E, 2020, ELECTRON COMMER R A, V40, DOI 10.1016/j.elerap.2019.100921
Yang H, 2017, IND MANAGE DATA SYST, V117, P68, DOI 10.1108/IMDS-01-2016-0017
Yang ZJ, 2015, COMPUT HUM BEHAV, V45, P254, DOI 10.1016/j.chb.2014.12.022
Yoon TE, 2013, COMPUT HUM BEHAV, V29, P772, DOI 10.1016/j.chb.2012.12.003
You T., 2019, 2019 TALENT ACQUISIT
Yuan YF, 2010, INFORM MANAGE-AMSTER, V47, P125, DOI 10.1016/j.im.2009.12.004
Zafar H, 2013, HUM RESOUR MANAGE R, V23, P105, DOI 10.1016/j.hrmr.2012.06.010
Zhou T, 2010, COMPUT HUM BEHAV, V26, P760, DOI 10.1016/j.chb.2010.01.013
Zhu K, 2006, EUR J INFORM SYST, V15, P601, DOI 10.1057/palgrave.ejis.3000650
NR 177
TC 67
Z9 71
U1 45
U2 205
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1463-5771
EI 1758-4094
J9 BENCHMARKING
JI Benchmarking
PD NOV 9
PY 2020
VL 27
IS 9
BP 2599
EP 2629
DI 10.1108/BIJ-04-2020-0186
EA AUG 2020
PG 31
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA PB7BX
UT WOS:000562876300001
DA 2024-03-27
ER
PT J
AU Zheng, TX
Wu, FR
Law, R
Qiu, QH
Wu, R
AF Zheng, Tianxiang
Wu, Feiran
Law, Rob
Qiu, Qihang
Wu, Rong
TI Identifying unreliable online hospitality reviews with biased user-given
ratings: A deep learning forecasting approach
SO INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT
LA English
DT Article
DE Online customer review; Review reliability; Review rating prediction;
Deep learning; Information quality
ID BIG DATA; SENTIMENT ANALYSIS; SOCIAL MEDIA; SATISFACTION; HELPFULNESS;
IMPACT; PERCEPTIONS; ANALYTICS; BEHAVIOR; PHOTOS
AB This study considers the review reliability problem by identifying biased user-given ratings through rating prediction on the basis of the textual content. Deep learning approaches were introduced to investigate the textual review and validate the effect of rating prediction using a dataset collected from Yelp. The definition of "biased rating" was clarified and influenced the matching rules. The approach obtains high performance on a total of 1,000,000 reviews for prediction, with user-given ratings as the benchmark. Using the revealed biased ratings, unreliable reviews were detected by combining the results of several deep learning kernels. Findings shed light on understanding review quality by distinguishing biased ratings and unreliable reviews that may cause inconsistency and ambiguity to readers. Hence, theoretical and managerial areas for social media analytics are enriched on the basis of online review meta-data in hospitality and tourism.
C1 [Zheng, Tianxiang; Wu, Feiran] Jinan Univ, Shenzhen Tourism Coll JNU UF Int Joint Lab Inform, 6 Qiaocheng East Ave, Shenzhen 518053, Guangdong, Peoples R China.
[Law, Rob] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, TST East, Kowloon, 17 Sci Museum Rd, Hong Kong 999077, Peoples R China.
[Qiu, Qihang] Adam Mickiewicz Univ, Fac Human Geog & Planning, Krygowskiego 10, PL-61680 Poznan, Poland.
[Wu, Rong] Guangdong Univ Technol, Sch Architecture & Urban Planning, 729 Dongfeng East Rd, Guangzhou 510090, Guangdong, Peoples R China.
C3 Jinan University; Hong Kong Polytechnic University; Adam Mickiewicz
University; Guangdong University of Technology
RP Wu, R (autor correspondiente), Guangdong Univ Technol, Sch Architecture & Urban Planning, 729 Dongfeng East Rd, Guangzhou 510090, Guangdong, Peoples R China.
EM zheng_tx@jnu.edu.cn; wufeiran@stu2018.jnu.edu.cn; rob.law@polyu.edu.hk;
qihang.qiu@amu.edu.pl; wurong5@mail2.sysu.edu.cn
RI Law, Rob/Y-3608-2019; Qiu, Qihang/GMW-4485-2022
OI Law, Rob/0000-0001-7199-3757; Qiu, Qihang/0000-0002-7821-8670
FU Special Funds of High-level University Construction Program of Guangdong
Province [88018052]
FX This work was partially supported by the Special Funds of High-level
University Construction Program of Guangdong Province under Grant No.
88018052.
CR Alaei AR, 2019, J TRAVEL RES, V58, P175, DOI 10.1177/0047287517747753
Antonio N, 2018, INT J CONTEMP HOSP M, V30, P3574, DOI 10.1108/IJCHM-05-2017-0302
Bengio Y, 2004, J MACH LEARN RES, V5, P1089
Berezina K, 2016, J HOSP MARKET MANAG, V25, P1, DOI 10.1080/19368623.2015.983631
Calheiros AC, 2017, J HOSP MARKET MANAG, V26, P675, DOI 10.1080/19368623.2017.1310075
Cheng MM, 2019, INT J HOSP MANAG, V76, P58, DOI 10.1016/j.ijhm.2018.04.004
Chollet F., 2017, Deep learning with Python
Costa A, 2019, J RETAIL CONSUM SERV, V47, P272, DOI 10.1016/j.jretconser.2018.12.006
Duan WJ, 2016, CORNELL HOSP Q, V57, P282, DOI 10.1177/1938965515620483
Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010
Ganu G, 2012, INFORM SYST, V38, P1, DOI 10.1016/j.is.2012.03.001
Gavilan D, 2018, TOURISM MANAGE, V66, P53, DOI 10.1016/j.tourman.2017.10.018
Gössling S, 2019, CURR ISSUES TOUR, V22, P1744, DOI 10.1080/13683500.2018.1455171
Gursoy D, 2019, INT J HOSP MANAG, V76, P53, DOI 10.1016/j.ijhm.2018.06.003
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
Hu N, 2019, TOURISM MANAGE, V72, P417, DOI 10.1016/j.tourman.2019.01.002
Hu N, 2012, DECIS SUPPORT SYST, V52, P674, DOI 10.1016/j.dss.2011.11.002
Kirilenko AP, 2018, J TRAVEL RES, V57, P1012, DOI 10.1177/0047287517729757
Kuhzady S, 2019, TOUR ANAL, V24, P69, DOI [10.3727/108354219x15458295631972, 10.3727/108354219X15458295631972]
Kwok L, 2017, INT J CONTEMP HOSP M, V29, P307, DOI 10.1108/IJCHM-11-2015-0664
Law R, 2019, ANN TOURISM RES, V75, P410, DOI 10.1016/j.annals.2019.01.014
Lei Xue, 2015, 2015 23rd International Conference on Geoinformatics. Proceedings, P1, DOI 10.1109/GEOINFORMATICS.2015.7378578
Levy SE, 2013, CORNELL HOSP Q, V54, P49, DOI 10.1177/1938965512464513
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
Liu L., 2009, Encyclopedia of Database Systems, P532, DOI [DOI 10.1007/978-0-387-39940-9, 10.1007/978-0-387-39940-9_565, DOI 10.1007/978-0-387-39940-9_565]
Liu XW, 2018, TOURISM MANAGE, V66, P38, DOI 10.1016/j.tourman.2017.11.005
Liu Y, 2019, TOURISM MANAGE, V71, P337, DOI 10.1016/j.tourman.2018.10.004
Ma YF, 2018, INT J HOSP MANAG, V71, P120, DOI 10.1016/j.ijhm.2017.12.008
Mellinas JP, 2019, TOURISM MANAGE, V71, P421, DOI 10.1016/j.tourman.2018.10.034
Powers D. M. W., 2020, J MACH LEARN TECHNOL, V2, P37, DOI [10.48550/arXiv.2010.16061, 10.9735/2229-3981, DOI 10.9735/2229-3981]
Ren J, 2019, EUR J INFORM SYST, V28, P272, DOI 10.1080/0960085X.2018.1524419
Schuckert M, 2016, ASIA PAC J TOUR RES, V21, P259, DOI 10.1080/10941665.2015.1029954
Shin S, 2019, J TRAVEL RES, V58, P579, DOI 10.1177/0047287518768456
Sijoria C, 2019, J HOSP MARKET MANAG, V28, P1, DOI 10.1080/19368623.2018.1497564
Singh JP, 2017, J BUS RES, V70, P346, DOI 10.1016/j.jbusres.2016.08.008
Starosta K, 2019, TOURISM, V67, P405
Wang X, 2019, INT J HOSP MANAG, V77, P438, DOI 10.1016/j.ijhm.2018.08.007
Wen L, 2019, TOURISM ECON, V25, P309, DOI 10.1177/1354816618768317
Wijnhoven F, 2014, DECIS SUPPORT SYST, V59, P262, DOI 10.1016/j.dss.2013.12.005
Xiang Z, 2018, INF TECHNOL TOUR, V18, P43, DOI 10.1007/s40558-017-0098-z
Xiang Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/j.tourman.2016.10.001
Xiang Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/j.ijhm.2014.10.013
Xie KL, 2014, INT J HOSP MANAG, V43, P1, DOI 10.1016/j.ijhm.2014.07.007
Xie X, 2019, SOFT COMPUT, V23, P599, DOI 10.1007/s00500-017-2904-0
Zhang BR, 2020, ASIA PAC J TOUR RES, V25, P323, DOI 10.1080/10941665.2019.1709876
Zhang K, 2019, TOURISM MANAGE, V75, P595, DOI 10.1016/j.tourman.2019.07.002
Zhang YL, 2016, ROUTL ADV HETEROD EC, V23, P13, DOI 10.1016/j.tourman.2015.09.001
Zhao YB, 2019, INT J HOSP MANAG, V76, P111, DOI 10.1016/j.ijhm.2018.03.017
Zhu DH, 2019, INT J HOSP MANAG, V77, P178, DOI 10.1016/j.ijhm.2018.06.028
NR 49
TC 27
Z9 28
U1 7
U2 84
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0278-4319
EI 1873-4693
J9 INT J HOSP MANAG
JI Int. J. Hosp. Manag.
PD JAN
PY 2021
VL 92
AR 102658
DI 10.1016/j.ijhm.2020.102658
PG 9
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA PB7ZJ
UT WOS:000596534500006
DA 2024-03-27
ER
PT J
AU Rezaei, F
Vanani, IR
Jafari, A
Kakavand, S
AF Rezaei, Faridoddin
Vanani, Iman Raeesi
Jafari, Amirhosein
Kakavand, Sanaz
TI Identification of Influential Factors and Improvement of Hotel Online
User-Generated Scores: A Prescriptive Analytics Approach
SO JOURNAL OF QUALITY ASSURANCE IN HOSPITALITY & TOURISM
LA English
DT Article; Early Access
DE hospitality management; natural language processing; online customer
reviews; prescriptive analytics; service quality; machine learning
ID WORD-OF-MOUTH; CUSTOMER SATISFACTION; SERVICE QUALITY; BIG DATA; SOCIAL
MEDIA; MODERATING ROLE; SENTIMENT ANALYSIS; BUDGET HOTELS; REVIEWS;
HOSPITALITY
AB The information obtained from customers' feedback can help hotel managers improve their provided services in a targeted manner according to customers' expectations. Besides, other customers consider online hotel scores an efficient tool for quickly evaluating the quality of hotels' services. Therefore, a higher online score indicates customer satisfaction and would lead to more bookings, price acceptance, and higher financial performance. In this article, we extracted the shortcomings related to hotel attributes utilizing a novel methodology that comprises machine learning algorithms, text mining, and a combination of customers' comments and scores. Then we examined the quantitative effect of fixing these problems on hotels' online scores. Furthermore, considering the origin of the problems, the cost required for fixing them, and the quantitative effect of solving them on improving the hotels' online scores, we provided some prescriptions for hotel managers as the last phase of business analytics. This model and its resulting prescriptions can be used to increase hotels' online scores significantly by improving service quality at the lowest cost. Finally, to describe the most important attributes, we used The Nordic European School of thought and classified them based on the technical and functional dimensions of Gronroos' service quality model.
C1 [Rezaei, Faridoddin; Vanani, Iman Raeesi; Jafari, Amirhosein; Kakavand, Sanaz] Allameh Tabatabai Univ, Fac Management & Accounting, Tehran, Iran.
C3 Allameh Tabataba'i University
RP Vanani, IR (autor correspondiente), Allameh Tabatabai Univ, Fac Management & Accounting, Tehran, Iran.
EM imanraeesi@atu.ac.ir
RI Jafari, Amirhosein/B-7375-2016; Raeesi Vanani, Iman/GQP-7444-2022
OI Raeesi Vanani, Iman/0000-0001-8324-9896; jafari,
amirhosein/0000-0002-3547-2118; Rezaei, Faridoddin/0000-0003-1973-6424
CR Aggarwal S, 2020, J HOSP TOUR MANAG, V45, P580, DOI 10.1016/j.jhtm.2020.10.009
Albayrak T, 2015, TOURISM MANAGE, V46, P43, DOI 10.1016/j.tourman.2014.06.009
Ali F, 2016, J HOSP MARKET MANAG, V25, P449, DOI 10.1080/19368623.2015.1019172
Amado A, 2018, EUR RES MANAG BUS EC, V24, P1, DOI 10.1016/j.iedeen.2017.06.002
Ananth M., 1992, Cornell Hotel and Restaurant Administration Quarterly, V33, P12, DOI 10.1177/001088049203300403
Anderson C, 2012, IMPACT SOCIAL MEDIA
Anderson SW, 2009, M&SOM-MANUF SERV OP, V11, P52, DOI 10.1287/msom.1070.0193
[Anonymous], 2014, MEASURING CUSTOMER S, DOI DOI 10.15308/SINTEZA-2014-788-795
Araque O, 2017, EXPERT SYST APPL, V77, P236, DOI 10.1016/j.eswa.2017.02.002
Assaf AG, 2012, INT J HOSP MANAG, V31, P642, DOI 10.1016/j.ijhm.2011.08.008
Au N., 2009, Information and Communication Technologies in Tourism 2009, P73
BABAKUS E, 1992, HEALTH SERV RES, V26, P767
Baka V, 2016, TOURISM MANAGE, V53, P148, DOI 10.1016/j.tourman.2015.09.004
Banerjee S, 2017, DECIS SUPPORT SYST, V96, P17, DOI 10.1016/j.dss.2017.01.006
Berezina K, 2016, J HOSP MARKET MANAG, V25, P1, DOI 10.1080/19368623.2015.983631
Berger C.R., 1974, Hum. Commun. Res., V1, P99, DOI DOI 10.1111/J.1468-2958.1975.TB00258.X
Bharwani S, 2013, INT J CONTEMP HOSP M, V25, P823, DOI 10.1108/IJCHM-05-2012-0065
Boehmke B., 2019, HANDS ON MACHINE LEA, DOI DOI 10.1201/9780367816377
Bonn M. A., 2007, Journal of Travel Research, V45, P345, DOI 10.1177/0047287506295947
Boo S, 2018, J TRAVEL TOUR MARK, V35, P895, DOI 10.1080/10548408.2018.1445066
Brazyte K, 2017, J QUAL ASSUR HOSP TO, V18, P282, DOI 10.1080/1528008X.2016.1230033
Brownlee J., 2019, GENERATIVE ADVERSARI
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Canito J, 2018, COMPUT IND, V99, P1, DOI 10.1016/j.compind.2018.03.018
Casaló LV, 2015, INT J HOSP MANAG, V49, P28, DOI 10.1016/j.ijhm.2015.05.005
Centuori SM, 2016, BBA-MOL CELL BIOL L, V1861, P663, DOI 10.1016/j.bbalip.2016.04.006
Chatterjee S, 2020, INT J HOSP MANAG, V85, DOI 10.1016/j.ijhm.2019.102356
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chittiprolu V, 2021, INT J CULT TOUR HOSP, V15, P131, DOI 10.1108/IJCTHR-02-2020-0050
Chua BL, 2015, INT J HOSP MANAG, V44, P131, DOI 10.1016/j.ijhm.2014.10.012
Connelly BL, 2011, J MANAGE, V37, P39, DOI 10.1177/0149206310388419
De Pelsrnacker P, 2018, INT J HOSP MANAG, V72, P47, DOI 10.1016/j.ijhm.2018.01.003
Devesa M, 2010, TOURISM MANAGE, V31, P547, DOI 10.1016/j.tourman.2009.06.006
Dortyol IT, 2014, INT J CONTEMP HOSP M, V26, P470, DOI 10.1108/IJCHM-11-2012-0211
Duverger P, 2013, J TRAVEL RES, V52, P465, DOI 10.1177/0047287513478498
Ekiz E, 2012, J HOSP TOUR TECHNOL, V3, P96, DOI 10.1108/17579881211248817
Ekiz EH, 2011, INT J CONTEMP HOSP M, V23, P327, DOI 10.1108/09596111111122514
Filieri R, 2016, ANN TOURISM RES, V58, P46, DOI 10.1016/j.annals.2015.12.019
FORNELL C, 1992, J MARKETING, V56, P6, DOI 10.2307/1252129
Fuchs G, 2011, TOURISM MANAGE, V32, P266, DOI 10.1016/j.tourman.2010.01.012
Ganguli S, 2011, INT J BANK MARK, V29, P168, DOI 10.1108/02652321111107648
Gao BJ, 2018, TOURISM MANAGE, V65, P176, DOI 10.1016/j.tourman.2017.10.007
Gavilan D, 2018, TOURISM MANAGE, V66, P53, DOI 10.1016/j.tourman.2017.10.018
Geetha M, 2017, TOURISM MANAGE, V61, P43, DOI 10.1016/j.tourman.2016.12.022
Grimes M., 2012, GLOBAL CONSUMERS TRU
GRONROOS C, 1984, EUR J MARKETING, V18, P36, DOI 10.1108/EUM0000000004784
Gronroos C., 1990, Service management and marketing, V27
Gronroos C., 1988, REV BUS, V9, P10, DOI [DOI 10.1016/S1474-4422(12)70307-1, DOI 10.1111/J.1945-1474.1988.TB00329.X]
Gu B, 2014, PROD OPER MANAG, V23, P570, DOI 10.1111/poms.12043
Gu HM, 2008, INT J HOSP MANAG, V27, P337, DOI 10.1016/j.ijhm.2007.10.005
Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
Hagerty J., 2016, PLANNING GUIDE DATA
Han J.W., 2007, data mining concepts and technology
Haywood-Farmer J., 1988, INT J OPER PROD MAN, V8, P19, DOI DOI 10.1108/EB054839
He W, 2017, ONLINE INFORM REV, V41, P921, DOI 10.1108/OIR-07-2016-0201
Hobday RA, 2019, J HOSP INFECT, V103, pE23, DOI 10.1016/j.jhin.2019.04.003
Huber F., 2001, J PROD BRAND MANAG, V10, P160, DOI 10.1108/10610420110395403
Hudson S, 2013, J TRAVEL TOUR MARK, V30, P156, DOI 10.1080/10548408.2013.751276
Hyndman RJ, 2006, INT J FORECASTING, V22, P679, DOI 10.1016/j.ijforecast.2006.03.001
Jang SH, 2018, AUSTRALAS MARK J, V26, P23, DOI 10.1016/j.ausmj.2018.02.001
Ji P, 2019, IEEE T SYST MAN CY-S, V49, P1993, DOI 10.1109/TSMC.2018.2875163
Kim H, 2015, TOURISM MANAGE, V49, P29, DOI 10.1016/j.tourman.2015.02.004
Kim K, 2017, TECHNOL FORECAST SOC, V123, P362, DOI 10.1016/j.techfore.2017.01.001
Kim WG, 2015, INT J HOSP MANAG, V44, P165, DOI 10.1016/j.ijhm.2014.10.014
Kirilenko AP, 2018, J TRAVEL RES, V57, P1012, DOI 10.1177/0047287517729757
KLEIN B, 1981, J POLIT ECON, V89, P615, DOI 10.1086/260996
Konstantinov AV, 2021, KNOWL-BASED SYST, V222, DOI 10.1016/j.knosys.2021.106993
Kunz W, 2017, J SERV MARK, V31, P161, DOI 10.1108/JSM-10-2016-0352
Ladhari R, 2015, INT J HOSP MANAG, V46, P36, DOI 10.1016/j.ijhm.2015.01.010
Lai IKW, 2018, INT J CONTEMP HOSP M, V30, P114, DOI 10.1108/IJCHM-08-2016-0408
Lee HM, 2020, APPL ACOUST, V162, DOI 10.1016/j.apacoust.2020.107206
Lee J, 2020, INT J HOSP MANAG, V91, DOI 10.1016/j.ijhm.2020.102669
Lee PJ, 2018, TELEMAT INFORM, V35, P436, DOI 10.1016/j.tele.2018.01.001
Lehtinen U., 1991, SERV IND J, V11, P287, DOI [10.1080/02642069100000047, DOI 10.1080/02642069100000047]
Lepenioti K, 2020, INT J INFORM MANAGE, V50, P57, DOI 10.1016/j.ijinfomgt.2019.04.003
Leung D, 2013, J TRAVEL TOUR MARK, V30, P3, DOI 10.1080/10548408.2013.750919
Li CY, 2017, TOURISM MANAGE, V62, P42, DOI 10.1016/j.tourman.2017.03.009
Li HY, 2013, ASIA PAC J TOUR RES, V18, P784, DOI 10.1080/10941665.2012.708351
Liat CB, 2017, J GLOB MARK, V30, P42
Lima ACES, 2015, APPL MATH COMPUT, V270, P756, DOI 10.1016/j.amc.2015.08.059
Lin PMC, 2018, J CHINA TOUR RES, V14, P123, DOI 10.1080/19388160.2018.1455613
Litvin SW, 2008, TOURISM MANAGE, V29, P458, DOI 10.1016/j.tourman.2007.05.011
Liu L, 2016, J ECON MANAGE STRAT, V25, P32, DOI 10.1111/jems.12131
Mao ZX, 2018, INT J HOSP MANAG, V74, P189, DOI 10.1016/j.ijhm.2018.05.002
Mattila A. S., 2001, Cornell Hotel and Restaurant Administration Quarterly, V42, P73, DOI 10.1016/S0010-8804(01)81012-0
Mauri AG, 2013, INT J HOSP MANAG, V34, P99, DOI 10.1016/j.ijhm.2013.02.012
McAfee A, 2012, HARVARD BUS REV, V90, P60
Merlo EM, 2011, AFR J BUS MANAGE, V5, P8410
Miguéis VL, 2016, LECT NOTES BUS INF P, V247, P155, DOI 10.1007/978-3-319-32689-4_12
Miyoung Jeong, 2008, Journal of Hospitality & Leisure Marketing, V17, P121, DOI 10.1080/10507050801978265
Moro S, 2018, INT J CULT TOUR HOSP, V12, P420, DOI 10.1108/IJCTHR-04-2018-0051
Nicolau JL, 2020, INT J HOSP MANAG, V87, DOI 10.1016/j.ijhm.2020.102497
Nie RX, 2020, INT J HOSP MANAG, V88, DOI 10.1016/j.ijhm.2020.102495
Nieto-Garcia M, 2019, INT J HOSP MANAG, V77, P583, DOI 10.1016/j.ijhm.2018.09.002
Nieto-García M, 2017, INT J HOSP MANAG, V62, P67, DOI 10.1016/j.ijhm.2016.12.006
Ögüt H, 2012, SERV IND J, V32, P197, DOI 10.1080/02642069.2010.529436
Olbrich R, 2011, INT J ELECTRON COMM, V16, P15, DOI 10.2753/JEC1086-4415160202
Padma P, 2020, INT J HOSP MANAG, V84, DOI 10.1016/j.ijhm.2019.102318
Pallesen S, 2016, SCAND J HOSP TOUR, V16, P243, DOI 10.1080/15022250.2015.1074938
Pantouvakis A, 2012, TOTAL QUAL MANAG BUS, V23, P177, DOI 10.1080/14783363.2012.647846
PARASURAMAN A, 1994, J MARKETING, V58, P111, DOI 10.1177/002224299405800109
PARASURAMAN A, 1985, J MARKETING, V49, P41, DOI 10.2307/1251430
Pedregosa F., 2020, FEATURE EXTRACTION
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Phillips P, 2017, J TRAVEL RES, V56, P235, DOI 10.1177/0047287516636481
Pizam A, 2016, INT J CONTEMP HOSP M, V28, P2, DOI 10.1108/IJCHM-04-2015-0167
Prentice C, 2013, INT J CONTEMP HOSP M, V25, P49, DOI 10.1108/09596111311290219
Qi MW, 2017, BUILD ENVIRON, V111, P1, DOI 10.1016/j.buildenv.2016.09.027
Qu H., 2000, J QUALITY ASSURANCE, V1, P65, DOI [https://doi.org/10.1300/J162v01n03_04, DOI 10.1300/J162V01N03_04]
Radojevic T, 2015, TOURISM MANAGE, V51, P13, DOI 10.1016/j.tourman.2015.04.002
Ren LP, 2016, INT J HOSP MANAG, V52, P13, DOI 10.1016/j.ijhm.2015.09.009
Ren LP, 2015, J QUAL ASSUR HOSP TO, V16, P45, DOI 10.1080/1528008X.2015.966299
Richard M.D., 1993, J SERV MARK, V7, P59
Robbins R, 2021, TOUR HOSP RES, V21, P144, DOI 10.1177/1467358420961544
Ryu K, 2010, J HOSP TOUR RES, V34, P310, DOI 10.1177/1096348009350624
Sayfuddin ATM, 2021, INT J HOSP MANAG, V99, DOI 10.1016/j.ijhm.2021.103065
Schröer C, 2021, PROCEDIA COMPUT SCI, V181, P526, DOI 10.1016/j.procs.2021.01.199
Schuckert M, 2016, ASIA PAC J TOUR RES, V21, P259, DOI 10.1080/10941665.2015.1029954
Cantallops AS, 2014, INT J HOSP MANAG, V36, P41, DOI 10.1016/j.ijhm.2013.08.007
SHAPIRO C, 1983, Q J ECON, V98, P659, DOI 10.2307/1881782
Sharda R., 2018, BUSINESS INTELLIGENC, V4th
Shin SI, 2017, TELEMAT INFORM, V34, P449, DOI 10.1016/j.tele.2016.09.005
Sigala M., 2019, Big data and innovation in tourism, travel, and hospitality
Sim J, 2006, J QUAL ASSUR HOSP TO, V7, P1, DOI 10.1300/J162v07n03_01
Sohrabi B., 2018, Journal of Cyberspace Studies, V2, P103, DOI [DOI 10.22059/JCSS.2017.239847, DOI 10.22059/JCSS.2017.239847.1009]
Soifer I, 2021, J QUAL ASSUR HOSP TO, V22, P539, DOI 10.1080/1528008X.2020.1814935
Song C, 2020, KNOWL-BASED SYST, V194, DOI 10.1016/j.knosys.2020.105572
Sparks BA, 2016, TOURISM MANAGE, V53, P74, DOI 10.1016/j.tourman.2015.09.011
Sparks BA, 2011, TOURISM MANAGE, V32, P1310, DOI 10.1016/j.tourman.2010.12.011
Stringam BB, 2012, J QUAL ASSUR HOSP TO, V13, P187, DOI 10.1080/1528008X.2012.692281
Sun KA, 2013, INT J HOSP MANAG, V35, P68, DOI 10.1016/j.ijhm.2013.05.008
Thi NV, 2022, J QUAL ASSUR HOSP TO, V23, P913, DOI 10.1080/1528008X.2021.1920550
Torres EN, 2015, INT J HOSP MANAG, V50, P77, DOI 10.1016/j.ijhm.2015.07.012
Torres EN, 2013, INT J CONTEMP HOSP M, V25, P642, DOI 10.1108/IJCHM-Dec-2011-0228
Vermeulen IE, 2009, TOURISM MANAGE, V30, P123, DOI 10.1016/j.tourman.2008.04.008
Viglia G, 2016, INT J CONTEMP HOSP M, V28, P2035, DOI 10.1108/IJCHM-05-2015-0238
Wang L, 2020, TOURISM MANAGE, V76, DOI 10.1016/j.tourman.2019.103961
WESTBROOK RA, 1991, J CONSUM RES, V18, P84, DOI 10.1086/209243
Xiang Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/j.tourman.2016.10.001
Xiang Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/j.ijhm.2014.10.013
Xie KL, 2014, INT J HOSP MANAG, V43, P1, DOI 10.1016/j.ijhm.2014.07.007
Xu X, 2017, INT J INFORM MANAGE, V37, P673, DOI 10.1016/j.ijinfomgt.2017.06.004
Xu X, 2016, INT J HOSP MANAG, V55, P57, DOI 10.1016/j.ijhm.2016.03.003
Xu X, 2015, INT J HOSP MANAG, V49, P105, DOI 10.1016/j.ijhm.2015.06.003
Yang Y, 2019, J TRAVEL RES, V58, P1274, DOI 10.1177/0047287518815984
Yang Y, 2018, TOURISM MANAGE, V67, P248, DOI 10.1016/j.tourman.2018.01.015
Ye QA, 2011, COMPUT HUM BEHAV, V27, P634, DOI 10.1016/j.chb.2010.04.014
Ye Q, 2009, INT J HOSP MANAG, V28, P180, DOI 10.1016/j.ijhm.2008.06.011
Zhang XF, 2016, ONLINE INFORM REV, V40, P119, DOI 10.1108/OIR-04-2015-0101
Zhang YL, 2016, ROUTL ADV HETEROD EC, V23, P13, DOI 10.1016/j.tourman.2015.09.001
Zhang ZQ, 2011, INT J CONTEMP HOSP M, V23, P972, DOI 10.1108/09596111111167551
Zhao XY, 2015, INT J CONTEMP HOSP M, V27, P1343, DOI 10.1108/IJCHM-12-2013-0542
Zhao YB, 2019, INT J HOSP MANAG, V76, P111, DOI 10.1016/j.ijhm.2018.03.017
Zheng XY, 2018, J INTELL INF SYST, V51, P557, DOI 10.1007/s10844-018-0496-5
Zhou LQ, 2014, INT J HOSP MANAG, V38, P1, DOI 10.1016/j.ijhm.2013.12.004
NR 155
TC 0
Z9 0
U1 3
U2 19
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1528-008X
EI 1528-0098
J9 J QUAL ASSUR HOSP TO
JI J. Qual. Assur. Hosp. Tour.
PD 2022 NOV 28
PY 2022
DI 10.1080/1528008X.2022.2146620
EA NOV 2022
PG 40
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA 6Q8OI
UT WOS:000891868800001
DA 2024-03-27
ER
PT J
AU Cheng, XS
Cohen, J
Mou, J
AF Cheng, Xusen
Cohen, Jason
Mou, Jian
TI AI-ENABLED TECHNOLOGY INNOVATION IN E-COMMERCE
SO JOURNAL OF ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE AI-enabled technology; E-commerce; Digital economy
ID BIG DATA ANALYTICS; ARTIFICIAL-INTELLIGENCE
AB Recently, advanced digital/internet-based technology has become more prevalent and advanced to play a dominant role in e-commerce. Among them, AI-driven technology innovation in e-commerce plays an important role for its development. There is research potential to discuss how AI-driven technology innovation can benefit the digital economy, as typified by e-commerce, and how it can contribute to the digital transformation of companies in traditional industries. This special issue expands our understanding of organizational and customer intentions and behavior toward AI, such as privacy issues, the perceived benefits and risks of AI-driven technology innovations in e-commerce and building long-term trust relationships between users and AI.
C1 [Cheng, Xusen] Renmin Univ China, Sch Informat, Beijing, Peoples R China.
[Cohen, Jason] Univ Witwatersrand, Sch Business Sci, Johannesburg, South Africa.
[Mou, Jian] Pusan Natl Univ, Sch Business, 2 Busandaehak Ro 63 Beon Gil, Pusan 46241, South Korea.
C3 Renmin University of China; University of Witwatersrand; Pusan National
University
RP Mou, J (autor correspondiente), Pusan Natl Univ, Sch Business, 2 Busandaehak Ro 63 Beon Gil, Pusan 46241, South Korea.
EM xusen.cheng@ruc.edu.cn; jason.cohen@wits.ac.za; jian.mou@pusan.ac.kr
FU National Natural Science Foundation of China [72271236, 72061147005];
School of Interdisciplinary Studies at Renmin University of China;
Metaverse Research Center at Renmin University of China
[2020K2A9A2A1110432911, FY2023]
FX The special issue editors take this opportunity to thank all the authors
for their interest in the topic and for all the reviewers constructive
comments and recommendations to the authors. We are also grateful to the
Editor in Chief of JECR, Professor Melody Kiang, for giving us this
opportunity and for her support and guidance in developing the special
issue. We would like to thank the National Natural Science Foundation of
China (Grant No. 72271236 ; 72061147005) , the School of
Interdisciplinary Studies at Renmin University of China, the Metaverse
Research Center at Renmin University of China, and the framework of the
international cooperation program managed by the National Research
Foundation of Korea (2020K2A9A2A1110432911, FY2023) for providing
funding for part of this research.
CR Agrawal A., 2022, POWER PREDICTION DIS
Akter S, 2016, INT J PROD ECON, V182, P113, DOI 10.1016/j.ijpe.2016.08.018
Akter S, 2016, ELECTRON MARK, V26, P173, DOI 10.1007/s12525-016-0219-0
Amankwah-Amoah J, 2021, EUR MANAG J, V39, P179, DOI 10.1016/j.emj.2020.09.002
Beckers J, 2021, J RETAIL CONSUM SERV, V62, DOI 10.1016/j.jretconser.2021.102645
Benlian A, 2020, INFORM SYST J, V30, P1010, DOI 10.1111/isj.12243
Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
Chan J, 2021, INT J CONTEMP HOSP M, V33, P1668, DOI 10.1108/IJCHM-09-2020-1013
Chen XH, 2021, TECHNOL FORECAST SOC, V166, DOI 10.1016/j.techfore.2021.120645
Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
Cheng XS, 2022, INTERNET RES, V32, P496, DOI 10.1108/INTR-08-2020-0460
Cheng XS, 2020, BRIT J EDUC TECHNOL, V51, P1653, DOI 10.1111/bjet.13018
Chinchanachokchai S, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102528
Cichy P, 2021, MIS QUART, V45, P1863, DOI 10.25300/MISQ/2021/14165
Davenport TH, 2018, HARVARD BUS REV, V96, P108
Gang R, 2019, INFORM PROCESS MANAG, V56, P1425, DOI 10.1016/j.ipm.2018.04.003
Gao SQ, 2020, J MED INTERNET RES, V22, DOI 10.2196/16649
George G, 2020, J MANAGE STUD, V57, P1754, DOI 10.1111/joms.12634
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Hasija A, 2022, J BUS LOGIST, V43, P388, DOI 10.1111/jbl.12301
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Iansiti M., 2020, COMPETING AGE STRATE
Kawasaki T, 2022, TRANSPORT POLICY, V115, P88, DOI 10.1016/j.tranpol.2021.10.023
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Lukinskiy V, 2023, INT J INFORM MANAGE, V68, DOI 10.1016/j.ijinfomgt.2022.102567
Luo JM, 2021, INT J HOSP MANAG, V98, DOI 10.1016/j.ijhm.2021.103032
Mena C, 2022, J BUS RES, V138, P77, DOI 10.1016/j.jbusres.2021.08.064
Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
Rana NP, 2022, EUR J INFORM SYST, V31, P364, DOI 10.1080/0960085X.2021.1955628
Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
Wang XH, 2021, INT J PROD RES, V59, P3841, DOI 10.1080/00207543.2020.1752949
Wongkitrungrueng A, 2020, J BUS RES, V117, P543, DOI 10.1016/j.jbusres.2018.08.032
Zhang D, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102304
Zhang Q, 2021, COMPLEX INTELL SYST, V7, P439, DOI 10.1007/s40747-020-00212-w
Zhu TQ, 2022, IEEE T KNOWL DATA EN, V34, P2824, DOI 10.1109/TKDE.2020.3014246
Zuiderwijk A, 2021, GOV INFORM Q, V38, DOI 10.1016/j.giq.2021.101577
NR 36
TC 1
Z9 1
U1 34
U2 46
PU CALIFORNIA STATE UNIV
PI LONG BEACH
PA COLL BUSINESS, LONG BEACH, CA 90840 USA
SN 1526-6133
EI 1938-9027
J9 J ELECTRON COMMER RE
JI J. Electron. Commer. Res.
PD FEB
PY 2023
VL 24
IS 1
SI SI
BP 1
EP 6
PG 6
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA A8RC7
UT WOS:000957725600001
DA 2024-03-27
ER
PT J
AU Liu, WD
She, XS
AF Liu, Wei-Dong
She, Xi-Shui
TI Application of Computer Vision on E-Commerce Platforms and Its Impact on
Sales Forecasting
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Bidirection Attention Mechanism; BiLSTM; Computer Vision; E -commerce
Platform; ResNet-101
AB In today's digital age, the e -commerce industry continues to grow and flourish. The widespread application of computer vision technology has brought revolutionary changes to e -commerce platforms. Extracting image features from e -commerce platforms using deep learning techniques is of paramount importance for predicting product sales. Deep learning -based computer vision models can automatically learn image features without the need for manual feature extractors. By employing deep learning techniques, key features such as color, shape, and texture can be effectively extracted from product images, providing more representative and diverse data for sales prediction models. This study proposes the use of ResNet-101 as an image feature extractor, enabling the automatic learning of rich visual features to provide high -quality image representations for subsequent analysis. Furthermore, a bidirectional attention mechanism is introduced to dynamically capture correlations between different modalities, facilitating the fusion of multimodal features.
C1 [Liu, Wei-Dong] Dongbei Univ Finance & Econ, Dalian, Peoples R China.
[She, Xi-Shui] Fengjia Univ, Taichung, Taiwan.
C3 Dongbei University of Finance & Economics
RP Liu, WD (autor correspondiente), Dongbei Univ Finance & Econ, Dalian, Peoples R China.
CR Abdullah A. S., 2023, Anbar Journal of Engineering Sciences, V14
Cai WW, 2021, MOB INF SYST, V2021, DOI 10.1155/2021/5568208
Chaudhuri A, 2018, IEEE INT CONF BIG DA, P1728, DOI 10.1109/BigData.2018.8622259
Cheng L, 2020, IEEE T SERV COMPUT, V13, P368, DOI 10.1109/TSC.2019.2906203
Durga BK, 2022, COMPUT ELECTR ENG, V104, DOI 10.1016/j.compeleceng.2022.108384
Efat MIA, 2022, ANN OPER RES, DOI 10.1007/s10479-022-04838-6
Ekambaram V, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P3110, DOI 10.1145/3394486.3403362
Erdelyi C, 2019, Using Computer Vision Techniques to Play an Existing Video Game
Fan QY, 2021, J PHYS CHEM C, V125, P27615, DOI 10.1021/acs.jpcc.1c08970
Giri C, 2022, FORECASTING-BASEL, V4, P565, DOI 10.3390/forecast4020031
Guo S, 2019, IEEE INT CONF COMP V, P3113, DOI 10.1109/ICCVW.2019.00377
Jain V, 2022, Journal of Student Research, V11
Karunasingha DSK, 2022, INFORM SCIENCES, V585, P609, DOI 10.1016/j.ins.2021.11.036
Kumar B., 2023, 2023 INT C SUST COMP
Li JB, 2022, INT CONF ACOUST SPEE, P8007, DOI 10.1109/ICASSP43922.2022.9747085
Liu X, 2022, SCI PROGRAMMING-NETH, V2022, DOI 10.1155/2022/4000171
Liu ZW, 2016, PROC CVPR IEEE, P1096, DOI 10.1109/CVPR.2016.124
Mehmood I, 2019, IEEE INTERNET THINGS, V6, P9246, DOI 10.1109/JIOT.2019.2896151
Ning EH, 2024, NEURAL NETWORKS, V169, P532, DOI 10.1016/j.neunet.2023.11.003
Ning X, 2024, INFORM FUSION, V102, DOI 10.1016/j.inffus.2023.102033
Papadopoulos SI, 2022, INT J MULTIMED INF R, V11, P717, DOI 10.1007/s13735-022-00262-5
Rights JD, 2023, MULTIVAR BEHAV RES, V58, P340, DOI 10.1080/00273171.2021.1985948
Senapati B., 2023, 2023 IEEE INT C EL I
Sheela T., 2022, 2022 INT C COMP COMM
Shetty Sharath Kariya, 2022, International Journal of Advanced Operations Management, P366, DOI 10.1504/IJAOM.2022.127864
Singh A, 2022, MICROPROCESS MICROSY, V95, DOI 10.1016/j.micpro.2022.104681
Singla P, 2022, EARTH SCI INFORM, V15, P291, DOI 10.1007/s12145-021-00723-1
Soni N, 2020, PROCEDIA COMPUT SCI, V167, P2200, DOI 10.1016/j.procs.2020.03.272
Thivakaran T., 2022, 2022 4 INT C INV RES
Wu DH, 2023, COMPUT ELECTRON AGR, V205, DOI 10.1016/j.compag.2023.107622
Xu W, 2023, DECIS SUPPORT SYST, V172, DOI 10.1016/j.dss.2023.113984
Yang C, 2021, Journal of Physics: Conference Series
Yang TB, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3554729
Zhang C., 2021, Journal of Jilin University Science Edition, V59, P609
Zhang X, 2023, J CLOUD COMPUT-ADV S, V12, DOI 10.1186/s13677-023-00390-1
Zhang Y., 2021, Journal of Jilin University: Science Edition, V59, P643, DOI DOI 10.13413/J.CNKI.JDXBLXB.2020152
Zhao L, 2023, J RETAIL CONSUM SERV, V74, DOI 10.1016/j.jretconser.2023.103379
NR 37
TC 0
Z9 0
U1 0
U2 0
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PY 2024
VL 36
IS 1
AR 336848
DI 10.4018/JOEUC.336848
PG 20
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA LF6P2
UT WOS:001185409200001
OA gold
DA 2024-03-27
ER
PT J
AU Vasquez, FGZ
Poveda, DAM
Mora, DPM
AF Vasquez, Freddy Giovanni Zuniga
Poveda, Diego Alejandro Mora
Mora, Diego Patricio Molina
TI THE IMPORTANCE OF ARTIFICIAL INTELLIGENCE IN MARKETING PROCESS
COMMUNICATIONS
SO VIVAT ACADEMIA
LA English
DT Article
DE marketing; artificial intelligence; AI; digital marketing
ID INTERNET; THINGS
AB These days it is no longer unusual to talk about marketing, and what is involved in its use within organizations, we know that it deals with every possible interaction between companies and people, and why not say it, marketing allows achieve organizational objectives by creating increasingly adaptable and intelligent experiences for customers, for this it is necessary to deploy three types of capabilities: creative, analytical and technological, in the use of these capabilities is where employment intervenes of artificial intelligence; the correct application of this, allows the optimization of resources and reduction of costs; but above all it has a transcendental impact for clients, since it makes it easier to anticipate their needs and offer solutions to them, even before they look for them through predictive analysis; or, using cookies, deep learning techniques and the use of chatbot, data can be obtained from various sources of information to create advertising content that is as personalized as possible. This article aims to carry out an exhaustive bibliographic review on this subject, based on information that has been published in scientific databases, which allows obtaining a reference framework on the importance of the use of artificial intelligence in marketing, which affirms that the use of AI in current marketing is vital for the evolution, adaptability and survival of organizations in this new world of digital transformation 4.0.
C1 [Vasquez, Freddy Giovanni Zuniga; Mora, Diego Patricio Molina] Technol Super Univ Spain, Guayaquil, Ecuador.
[Poveda, Diego Alejandro Mora] Tech Univ Ambato, Ambato, Ecuador.
C3 Universidad Tecnica de Ambato
RP Vasquez, FGZ (autor correspondiente), Technol Super Univ Spain, Guayaquil, Ecuador.
EM freddy.zuniga@iste.edu.ec; da.mora@uta.edu.ec; diego.molina@iste.edu.ec
CR Abashidze I, 2016, MANAG SYST PROD ENG, V24, P217, DOI 10.2478/mspe-01-04-2016
Alcaide J. C, 2019, CUSTOMER EXPERIENCE
Amor-Bravo E. M., 2019, MARKETING CUARTA REV
Arenal-Laza C., 2019, ENTORNO INFORMACION
Bededetti A. M., 2020, MARKETING REDES SOCI
Boden M.A., 2017, Inteligencia Artificial
Bova T., 2019, ARPA
Castillo-Romero J. A., 2019, BIG DATA
Cuervo Sanchez C. A., 2021, ADRESEARCH REV INT I, V24, DOI 10.7263/adresic-024-02
Devang V., 2019, Annals of "Dunarea de Jos" University of Galati: Fascicle I, Economics and Applied Informatics, V25, P28, DOI [DOI 10.35219/EAI158404094, 10.35219/eai158404094]
Edwards B., 2020, Marketing en movimiento
García Mendoza Carmen, 2021, Universidad y Sociedad, V13, P526
Giraldez G., 2021, MARKETING DIGITAL QU
Gonzales-Onate C., 2019, NEGOCIO PUBLICITARIO
Grandinetti R, 2020, INNOV MARKET, V16, P91, DOI 10.21511/im.16(2).2020.08
Jones VK., 2018, J BRAND STRATEGY, V7, P233
Joyanes-Aguilar L., 2021, INTERNET COSAS FUTUR
Jyng L. W., 2020, INT J PSYCHOSOCIAL R, V24, P1118
Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
Markuleta-Arrula M., 2020, MARKETING ONLINE EST
Martinez A., 2020, Pro Sciences, V4, P36, DOI [10.29018/issn.2588-1000vol4iss30.2020pp36-47, DOI 10.29018/ISSN.2588-1000VOL4ISS30.2020PP36-47]
Martinez L., 2019, COMUNICACION PERSUAS
Martinez S., 2021, EMPATIA DIGITAL INTE
Mercade-Mele P., 2017, SPANISH J MARKETING, V21, P54, DOI [DOI 10.1016/J.SJME.2017.04.003, 10.1016/j.sjme.2017.04.003]
Munoz-Barros F. D., 2020, COMM081PO TECNICAS V
Nguyen B, 2017, J MARKET MANAG-UK, V33, P1, DOI 10.1080/0267257X.2016.1257542
Ostos-Mota G., 2021, OPORTUNIDADES PARTIC
Overgoor G, 2019, CALIF MANAGE REV, V61, P156, DOI 10.1177/0008125619859318
Paniagua-Martin F., 2021, LENGUAJES MARCAS SIS
Paschen J, 2019, J BUS IND MARK, V34, P1410, DOI 10.1108/JBIM-10-2018-0295
Pereda-Marin S., 2018, DIRECCION GESTION RE
Ramirez-Hauncher A., 2019, MOBILE MARKETING
Recuenco A., 2020, SCIENDO, V23, P299, DOI [http://dx.doi.org/10.17268/sciendo.2020.036, DOI 10.17268/SCIENDO.2020.036]
Rodriguez-Fenandez L., 2021, PROPAGANDA DIGITAL C
Rouhiainen L., 2021, INTELIGENCIA ARTIFIC
Rouhiainen L, 2018, Inteligencia artificial
Sainz de Vicuna Ancin J. M., 2021, PLAN MARKETING DIGIT
Sainz de Vicuna Ancin J. M., 2020, PLAN MARKETING PRACT
Salazar-Garcia I., 2020, HARVARD DEUSTO MARKE, P42
Sanchez-Monasterio M., 2018, HARVARD DEUSTO BUSIN, P62
Setiawan I., 2022, MARKETING 5 0 VERSIO
Silva-Guerra H., 2021, MARKETING INT AM LAT
Stengel R., 2021, GUERRAS INFORM
Stone M, 2020, BOTTOM LINE, V33, P183, DOI 10.1108/BL-03-2020-0022
Turletti P., 2018, El ROI de marketing y ventas: calculo y utilidad nuevo estandar de rendimiento
Villaseca-Morales D., 2021, SILICON VALLEY TU NE
Viteri Luque F., 2018, RECIMUNDO: Revista Cientifica de La Investigacion y El Conocimiento, V2, P764
Yegin T., 2020, EKEV AKAD DERGISI, V81, P489, DOI 10.17753/Ekev1340
Zamora P. A., 2017, UNIANDES EPISTEME RE, P312
NR 49
TC 0
Z9 0
U1 37
U2 54
PU UNIV COMPLUTENSE MADRID, SERVICIO PUBLICACIONES
PI MADRID
PA CIUDAD UNIV, OBISPO TREJO 3, MADRID, 28040, SPAIN
SN 1575-2844
J9 VIVAT ACAD
JI Vivat Acad.
PY 2023
IS 156
BP 19
EP 37
DI 10.15178/va.2023.e1474
PG 19
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA J8TU4
UT WOS:001012302500001
DA 2024-03-27
ER
PT J
AU Zhou, LC
AF Zhou, Lichun
TI Product advertising recommendation in e-commerce based on deep learning
and distributed expression
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Deep learning; E-commerce; Distributed expression; Advertising
recommendation
ID CONSUMER
AB With the advent of Internet big data era, recommendation system has become a hot research topic of information selection. This paper studies the application of deep learning and distributed expression technology in e-commerce product advertising recommendation. In this paper, firstly, from the semantic level of advertising, we build a similarity network based on the theme distribution of advertising, and then build a deep learning model framework for advertising click through rate prediction. Finally, we propose an improved recommendation algorithm based on recurrent neural network and distributed expression. Aiming at the particularity of the recommendation algorithm, this paper improves the traditional recurrent neural network, and introduces a time window to control the hidden layer data transfer of the recurrent neural network. The experimental results show that the improved recurrent neural network model based on time window is superior to the traditional recurrent neural network model in the accuracy of recommendation system. The complexity of calculation is reduced and the accuracy of recommendation system is improved.
C1 [Zhou, Lichun] Shangqiu Normal Univ, Sch Media & Commun, Shangqiu 476000, Henan, Peoples R China.
C3 Shangqiu Normal University
RP Zhou, LC (autor correspondiente), Shangqiu Normal Univ, Sch Media & Commun, Shangqiu 476000, Henan, Peoples R China.
EM zhoulc666@163.com
CR Adhikary A, 2014, PROC ECON FINANC, V11, P230, DOI 10.1016/S2212-5671(14)00191-9
Ardila D, 2019, NAT MED, V25, P954, DOI 10.1038/s41591-019-0447-x
[Gong Xueqing 宫学庆], 2013, [华东师范大学学报. 自然科学版, Journal of East China Normal University. Natural Science], P70
Hao T, 2018, MULTIMED TOOLS APPL, V77, P22173, DOI 10.1007/s11042-018-5705-2
Jeong HJ, 2013, INT J ADVERT, V32, P539, DOI 10.2501/IJA-32-4-539-558
Khan S, 2018, MECH SYST SIGNAL PR, V107, P241, DOI 10.1016/j.ymssp.2017.11.024
Lange T, 2015, J LAW MED ETHICS, V43, P878, DOI 10.1111/jlme.12326
Lee-Wingate SN, 2013, INT J PHARM HEALTHC, V7, P265, DOI 10.1108/IJPHM-05-2013-0025
Li MX, 2019, INT J OPHTHALMOL-CHI, V12, P1012, DOI 10.18240/ijo.2019.06.22
[林奕欧 Lin Yiou], 2017, [电子科技大学学报, Journal of University of Electronic Science and Technology of China], V46, P913
Minarro-Giménez JA, 2014, STUD HEALTH TECHNOL, V205, P584, DOI 10.3233/978-1-61499-432-9-584
Moeskops P, 2018, NEUROIMAGE-CLIN, V17, P251, DOI 10.1016/j.nicl.2017.10.007
Morgan FW, 1997, J ADVERTISING, V26, P63, DOI 10.1080/00913367.1997.10673523
Nair R, 2017, IBM J RES DEV, V61, DOI 10.1147/JRD.2017.2709198
Park J, 2011, IEEE MULTIMEDIA, V18, P78, DOI 10.1109/MMUL.2010.6
[孙毅 Sun Yi], 2019, [电力系统自动化, Automation of Electric Power Systems], V43, P183
Vandecasteele F, 2017, ACM T MULTIM COMPUT, V13, DOI 10.1145/3092834
Wei J, 2017, EXPERT SYST APPL, V69, P29, DOI 10.1016/j.eswa.2016.09.040
Williams GW, 2017, J INT FOOD AGRIBUSIN, V30, P1
[余文喆 Yu Wenzhe], 2013, [华东师范大学学报. 自然科学版, Journal of East China Normal University. Natural Science], P46
Zhang HJ, 2020, NEURAL COMPUT APPL, V32, P4519, DOI [10.1007/s00521-018-3691-y, 10.1007/s00521-018-3579-x]
[朱斌 Zhu Bin], 2018, [计算机辅助设计与图形学学报, Journal of Computer-Aided Design & Computer Graphics], V30, P1778
NR 22
TC 43
Z9 44
U1 10
U2 65
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD JUN
PY 2020
VL 20
IS 2
SI SI
BP 321
EP 342
DI 10.1007/s10660-020-09411-6
EA APR 2020
PG 22
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LK9BZ
UT WOS:000528429900001
DA 2024-03-27
ER
PT J
AU Liang, TP
Li, YW
Yen, NS
Hsu, SM
Banker, S
AF Liang, Ting-Peng
Li, Yu-Wen
Yen, Nai-Shing
Hsu, Shen-Mou
Banker, Sachin
TI HOW DIGITAL ASSISTANTS EVOKE SOCIAL CLOSENESS: AN FMRI INVESTIGATION
SO JOURNAL OF ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Digital assistants; Personalization; Anthropomorphization; Functional
magnetic resonance imaging (fMRI); Electronic commerce
ID CORTICAL MIDLINE STRUCTURES; WEB PERSONALIZATION; PREFRONTAL CORTEX;
EGOCENTRIC BIAS; SELF-DISCLOSURE; TRUST; INFORMATION; PERCEPTION;
DISTANCE; BRAIN
AB The growing popularity of digital assistants (from Microsoft's Clippy to Amazon's Alexa) is changing how consumers acquire information and make decisions. Often embodied in anthropomorphized forms, digital assistants (DAs) are designed to serve consumers by suggesting relevant products to simplify purchasing decisions. In this work, we aim to understand how consumers evaluate social relationships with different types of DAs and their subsequent effects on purchasing. Our findings show that consumers judge DAs as being more socially close both when DAs are anthropomorphized and when they provide higher-quality recommendations. Evidence from fMRI indicated that both recommendation quality and anthropomorphization fostered greater feelings of social closeness by recruiting similar brain mechanisms involved in mental simulation (i.e., inferior frontal gyms and cortical midline structures). Although anthropomorphized DAs were evaluated as more socially close, they did not facilitate increased purchase interest, suggesting that stimulation of neural reward networks is also necessary for driving greater purchasing.
C1 [Liang, Ting-Peng] Natl Sun Yat Sen Univ, Elect Commerce Res Ctr, 70 Lien Hai Rd, Kaohsiung 80424, Taiwan.
[Li, Yu-Wen] Wenzao Ursuline Univ Languages, Dept Digital Content Applicat & Management, 900 Mintsu 1st Rd, Kaohsiung 807, Taiwan.
[Yen, Nai-Shing] Natl Chengchi Univ, Dept Psychol, Taipei, Taiwan.
[Hsu, Shen-Mou] Natl Taiwan Univ, Image Ctr Integrated Body Mind & Culture Res, Taipei, Taiwan.
[Banker, Sachin] Univ Utah, Dept Mkt Eccles Sch Business, Salt Lake City, UT 84112 USA.
C3 National Sun Yat Sen University; National Chengchi University; National
Taiwan University; Utah System of Higher Education; University of Utah
RP Li, YW (autor correspondiente), Wenzao Ursuline Univ Languages, Dept Digital Content Applicat & Management, 900 Mintsu 1st Rd, Kaohsiung 807, Taiwan.
EM tpliang@mail.nsysu.edu.tw; yuwen@mail.wzu.edu.tw; nsy@nccu.edu.tw;
smhsu@nccu.edu.tw; sachin.banker@eccles.utah.edu
RI li, yuwen/HGU-6435-2022; liang, ting/JFB-4960-2023; Li, yu/HHZ-5236-2022
FU Ministry of Science and Technology, Taiwan ROC
FX Funding of this research was provided by the Ministry of Science and
Technology, Taiwan ROC.
CR Aggarwal P, 2012, J CONSUM RES, V39, P307, DOI 10.1086/662614
Ahn SJ, 2011, J ADVERTISING, V40, P93, DOI 10.2753/JOA0091-3367400207
Aljukhadar M, 2010, INT J ELECTRON COMM, V14, P103, DOI 10.2753/JEC1086-4415140404
[Anonymous], 1987, INTENTIONAL STANCE
Arioli M, 2018, HUM BRAIN MAPP, V39, P1412, DOI 10.1002/hbm.23930
Baek EC, 2017, PSYCHOL SCI, V28, P851, DOI 10.1177/0956797617695073
Bapna R, 2017, MIS QUART, V41, P115, DOI 10.25300/MISQ/2017/41.1.06
Bargh JA, 1999, AM PSYCHOL, V54, P462, DOI 10.1037/0003-066x.54.7.462
Bartra O, 2013, NEUROIMAGE, V76, P412, DOI 10.1016/j.neuroimage.2013.02.063
Behrens TEJ, 2009, SCIENCE, V324, P1160, DOI 10.1126/science.1169694
Benbasat Izak, 2010, P 31 INT C INF SYST
Bente G, 2008, HUM COMMUN RES, V34, P287, DOI 10.1111/j.1468-2958.2008.00322.x
Binzel C, 2013, J DEV ECON, V103, P99, DOI 10.1016/j.jdeveco.2013.01.009
Bogardus ES, 1947, SOCIOMETRY, V10, P306, DOI 10.2307/2785570
Buccino G, 2004, NEURON, V42, P323, DOI 10.1016/S0896-6273(04)00181-3
Bues M, 2017, PSYCHOL MARKET, V34, P157, DOI 10.1002/mar.20981
Camerer C, 2005, J ECON LIT, V43, P9, DOI 10.1257/0022051053737843
Cavanna AE, 2006, BRAIN, V129, P564, DOI 10.1093/brain/awl004
Charness G, 2008, J ECON BEHAV ORGAN, V68, P29, DOI 10.1016/j.jebo.2008.03.001
Chartrand TL, 2013, ANNU REV PSYCHOL, V64, P285, DOI 10.1146/annurev-psych-113011-143754
Chartrand TL, 1999, J PERS SOC PSYCHOL, V76, P893, DOI 10.1037/0022-3514.76.6.893
De Keyzer F., 2015, Journal of Interactive Advertising, V15, P124, DOI DOI 10.1080/15252019.2015.1082450
DeBruine LM, 2002, P ROY SOC B-BIOL SCI, V269, P1307, DOI 10.1098/rspb.2002.2034
DeBruine LM, 2005, P ROY SOC B-BIOL SCI, V272, P919, DOI 10.1098/rspb.2004.3003
Delgado MR, 2004, CEREB CORTEX, V14, P1022, DOI 10.1093/cercor/bhh062
Dijksterhuis A., 2007, Social psychology and the unconscious: The automaticity of higher mental processes, P51
Dimoka A, 2012, MIS QUART, V36, P811
Dimoka A, 2012, MIS QUART, V36, P679
Dimoka A, 2011, INFORM SYST RES, V22, P687, DOI 10.1287/isre.1100.0284
Dimoka A, 2011, NEUROIMAGE, V54, pS263, DOI 10.1016/j.neuroimage.2010.05.007
Dimoka A, 2010, MIS QUART, V34, P373
Eddington KM, 2007, J COGNITIVE NEUROSCI, V19, P1152, DOI 10.1162/jocn.2007.19.7.1152
Emery NJ, 2000, NEUROSCI BIOBEHAV R, V24, P581, DOI 10.1016/S0149-7634(00)00025-7
Farb NAS, 2010, EMOTION, V10, P25, DOI 10.1037/a0017151
FIEDLER K, 1995, PERS SOC PSYCHOL B, V21, P525, DOI 10.1177/0146167295215010
Frith CD, 2007, CURR BIOL, V17, pR724, DOI 10.1016/j.cub.2007.05.068
Fudge JL, 2002, J NEUROSCI, V22, P10078
Gallese V, 1998, TRENDS COGN SCI, V2, P493, DOI 10.1016/S1364-6613(98)01262-5
Gong L, 2007, HUM COMMUN RES, V33, P163, DOI 10.1111/j.1468-2958.2007.00295.x
Gounaris SpirosP., 2007, J RELATIONSHIP MARKE, V6, P63, DOI DOI 10.1300/J366V06N01_05
Grimm S, 2009, HUM BRAIN MAPP, V30, P2617, DOI 10.1002/hbm.20693
Groenewegen HJ, 2000, PROG BRAIN RES, V126, P3
Gusnard DA, 2001, P NATL ACAD SCI USA, V98, P4259, DOI 10.1073/pnas.071043098
Haruno M, 2006, J NEUROPHYSIOL, V95, P948, DOI 10.1152/jn.00382.2005
Heatherton TF, 2006, SOC COGN AFFECT NEUR, V1, P18, DOI 10.1093/scan/nsl001
Heiser M, 2003, EUR J NEUROSCI, V17, P1123, DOI 10.1046/j.1460-9568.2003.02530.x
Holzwarth M, 2006, J MARKETING, V70, P19, DOI 10.1509/jmkg.70.4.19
Hong Y., 2016, MIS Q FORTHCOMING FO
Iacoboni M, 1999, SCIENCE, V286, P2526, DOI 10.1126/science.286.5449.2526
Iacoboni M, 2009, ANNU REV PSYCHOL, V60, P653, DOI 10.1146/annurev.psych.60.110707.163604
Jin S.A., 2009, Journal of Interactive Advertising, V10, P51, DOI DOI 10.1080/15252019.2009.10722162
JOHN OP, 1994, J PERS SOC PSYCHOL, V66, P206, DOI 10.1037/0022-3514.66.1.206
Johnson SC, 2005, J COGNITIVE NEUROSCI, V17, P1897, DOI 10.1162/089892905775008607
Karmarkar UR, 2015, J MARKETING RES, V52, P467, DOI 10.1509/jmr.13.0488
Keeling K, 2010, J BUS RES, V63, P793, DOI 10.1016/j.jbusres.2008.12.015
Kelley WM, 2002, J COGNITIVE NEUROSCI, V14, P785, DOI 10.1162/08989290260138672
Kervyn N, 2012, J CONSUM PSYCHOL, V22, P166, DOI 10.1016/j.jcps.2011.09.006
Knutson B, 2008, NEURON, V58, P814, DOI 10.1016/j.neuron.2008.05.018
Knutson B, 2008, NEUROREPORT, V19, P509, DOI 10.1097/WNR.0b013e3282f85c01
Knutson B, 2007, NEURON, V53, P147, DOI 10.1016/j.neuron.2006.11.010
Komiak SYX, 2006, MIS QUART, V30, P941
KRUEGER J, 1994, J PERS SOC PSYCHOL, V67, P596, DOI 10.1037/0022-3514.67.4.596
Laurenceau JP, 1998, J PERS SOC PSYCHOL, V74, P1238, DOI 10.1037/0022-3514.74.5.1238
Lee EJ, 2004, COMPUT HUM BEHAV, V20, P779, DOI 10.1016/j.chb.2003.11.005
Liang T., 2009, Em: PACIS 2009 Proceedings, P73
Liang TP, 2006, J MANAGE INFORM SYST, V23, P45, DOI 10.2753/MIS0742-1222230303
Liang TP, 2009, PAC ASIA J ASSOC INF, V1, pI
Liviatan I, 2008, J EXP SOC PSYCHOL, V44, P1256, DOI 10.1016/j.jesp.2008.04.007
Macrae CN, 2004, CEREB CORTEX, V14, P647, DOI 10.1093/cercor/bhh025
Mahy CEV, 2014, DEV COGN NEUROS-NETH, V9, P68, DOI 10.1016/j.dcn.2014.01.002
Miller DT, 1998, EUR J SOC PSYCHOL, V28, P475, DOI 10.1002/(SICI)1099-0992(199805/06)28:3<475::AID-EJSP881>3.0.CO;2-M
Moon Y, 2000, J CONSUM RES, V26, P323, DOI 10.1086/209566
Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
NASS C, 1995, INT J HUM-COMPUT ST, V43, P223, DOI 10.1006/ijhc.1995.1042
Nass C, 1997, J APPL SOC PSYCHOL, V27, P864, DOI 10.1111/j.1559-1816.1997.tb00275.x
Nass C, 2001, J EXP PSYCHOL-APPL, V7, P171, DOI 10.1037//1076-898X.7.3.171
Nass Clifford Ivar, 2005, WIRED SPEECH VOICE A
Northoff G, 2004, TRENDS COGN SCI, V8, P102, DOI 10.1016/j.tics.2004.01.004
Northoff G, 2008, TRENDS COGN SCI, V12, P259, DOI 10.1016/j.tics.2008.04.007
Northoff G, 2006, NEUROIMAGE, V31, P440, DOI 10.1016/j.neuroimage.2005.12.002
Ochsner KN, 2004, J COGNITIVE NEUROSCI, V16, P1746, DOI 10.1162/0898929042947829
OLDFIELD RC, 1971, NEUROPSYCHOLOGIA, V9, P97, DOI 10.1016/0028-3932(71)90067-4
Olmstead K, 2017, ASSESSMENT ANAL HAND
Park RE, 1924, J APPL SOCIOL, V8, P339
PARKER EB, 1978, CONTEMP SOCIOL, V7, P32, DOI 10.2307/2065899
Pavlou PA, 2007, MIS QUART, V31, P105
Perez S., 2018, TECHCRUNCH
Phan KL, 2004, NEUROIMAGE, V21, P768, DOI 10.1016/j.neuroimage.2003.09.072
Qiu LY, 2009, J MANAGE INFORM SYST, V25, P145, DOI 10.2753/MIS0742-1222250405
Riedl R, 2017, L N INF SYST ORGAN, V21, P1, DOI 10.1007/978-3-319-48755-7
Riedl R., 2016, Fundamentals of NeuroIS: Information Systems and the Brain, DOI DOI 10.1007/978-3-662-45091-8
Riedl R., 2011, 32 INT C INFORM SYST
Riedl R, 2014, J ASSOC INF SYST, V15, pI
Ries ML, 2007, J INT NEUROPSYCH SOC, V13, P450, DOI 10.1017/S1355617707070488
ROSS L, 1977, J EXP SOC PSYCHOL, V13, P279, DOI 10.1016/0022-1031(77)90049-X
Schilbach L, 2010, NAT REV NEUROSCI, V11, DOI 10.1038/nrn2805-c1
Schultz W, 2015, PHYSIOL REV, V95, P853, DOI 10.1152/physrev.00023.2014
Sherwood CC, 2011, P NATL ACAD SCI USA, V108, P13029, DOI 10.1073/pnas.1016709108
Tam KY, 2006, MIS QUART, V30, P865
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Tesser A., 1988, SELF EVALUATION MAIN, V21, P181
Trope Y, 2007, J CONSUM PSYCHOL, V17, P83, DOI 10.1016/S1057-7408(07)70013-X
Wang LC, 2007, J MARKETING, V71, P143, DOI 10.1509/jmkg.71.3.143
Waytz A, 2010, J PERS SOC PSYCHOL, V99, P410, DOI 10.1037/a0020240
Yuan LY, 2019, J MANAGE INFORM SYST, V36, P450, DOI 10.1080/07421222.2019.1598691
NR 105
TC 2
Z9 2
U1 0
U2 14
PU CALIFORNIA STATE UNIV
PI LONG BEACH
PA COLL BUSINESS, LONG BEACH, CA 90840 USA
SN 1526-6133
EI 1938-9027
J9 J ELECTRON COMMER RE
JI J. Electron. Commer. Res.
PD NOV
PY 2021
VL 22
IS 4
BP 285
EP 304
PG 20
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA XN6KS
UT WOS:000729612100002
DA 2024-03-27
ER
PT J
AU Ma, LY
Sun, BH
AF Ma, Liye
Sun, Baohong
TI Machine learning and AI in marketing - Connecting computing power to
human insights
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE Artificial intelligence (AI); Machine learning; Digital marketing; Big
data; Unstructured data; Tracking data; Network; Prediction;
Interpretation; Marketing theory
ID DISCRETE-CHOICE MODELS; VARIATIONAL INFERENCE; CUSTOMER; TEXT;
INFORMATION; PRODUCT
AB Artificial intelligence (AI) agents driven by machine learning algorithms are rapidly transforming the business world, generating heightened interest from researchers. In this paper, we review and call for marketing research to leverage machine learning methods. We provide an overview of common machine learning tasks and methods, and compare them with statistical and econometric methods that marketing researchers traditionally use. We argue that machine learning methods can process large-scale and unstructured data, and have flexible model structures that yield strong predictive performance. Meanwhile, such methods may lack model transparency and interpretability. We discuss salient AI-driven industry trends and practices, and review the still nascent academic marketing literature which uses machine learning methods. More importantly, we present a unified conceptual framework and a multi-faceted research agenda. From five key aspects of empirical marketing research: method, data, usage, issue, and theory, we propose a number of research priorities, including extending machine learning methods and using them as core components in marketing research, using the methods to extract insights from large-scale unstructured, tracking, and network data, using them in transparent fashions for descriptive, causal, and prescriptive analyses, using them to map out customer purchase journeys and develop decision-support capabilities, and connecting the methods to human insights and marketing theories. Opportunities abound for machine learning methods in marketing, and we hope our multi-faceted research agenda will inspire more work in this exciting area. (c) 2020 Elsevier B.V. All rights reserved.
C1 [Ma, Liye] Univ Maryland, Robert H Smith Sch Business, College Pk, MD 20742 USA.
[Sun, Baohong] Cheung Kong Grad Sch Business Amer, New York, NY USA.
C3 University System of Maryland; University of Maryland College Park
RP Ma, LY (autor correspondiente), Univ Maryland, Robert H Smith Sch Business, College Pk, MD 20742 USA.
EM liyema@rhsmith.umd.edu; bhsun@ckgsb.edu.cn
CR ALTMAN NS, 1992, AM STAT, V46, P175, DOI 10.2307/2685209
Amos B., 2016, CMU SCH COMPUTER SCI, V6
[Anonymous], 2015, Deep learning, DOI 10.1038/nature14539
[Anonymous], 2016, DEEP LEARNING
[Anonymous], 2018, WORKING PAPER
[Anonymous], MACH LEARN
[Anonymous], 2012, ARXIV12063278
Ansari A, 2018, MARKET SCI, V37, P987, DOI 10.1287/mksc.2018.1113
Athey S, 2016, P NATL ACAD SCI USA, V113, P7353, DOI 10.1073/pnas.1510489113
Bahdanau D., 2014, NEURAL MACHINE TRANS
Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
BERRY S, 1995, ECONOMETRICA, V63, P841, DOI 10.2307/2171802
BERRY ST, 1994, RAND J ECON, V25, P242, DOI 10.2307/2555829
Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114
Blei DM, 2017, J AM STAT ASSOC, V112, P859, DOI 10.1080/01621459.2017.1285773
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Braun M, 2013, MARKET SCI, V32, P753, DOI 10.1287/mksc.2013.0802
Braun M, 2010, J AM STAT ASSOC, V105, P324, DOI 10.1198/jasa.2009.tm08030
Breiman L, 1996, MACH LEARN, V24, P49
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Chakraborty I, 2019, WORKING PAPER
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Chiong KX, 2019, MANAGE SCI, V65, P256, DOI 10.1287/mnsc.2017.2928
Chung TS, 2016, J ACAD MARKET SCI, V44, P66, DOI 10.1007/s11747-015-0441-x
Chung TS, 2009, MARKET SCI, V28, P52, DOI 10.1287/mksc.1080.0371
Cohn DA, 1996, J ARTIF INTELL RES, V4, P129, DOI 10.1613/jair.295
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
Cui DP, 2005, MARKET SCI, V24, P595, DOI 10.1287/mksc.1050.0123
Dai Z., 2019, ARXIV190102860
Daria Dzyabura, 2019, WORKING PAPER
de Haan E, 2018, J MARKETING, V82, P1, DOI 10.1509/jm.17.0113
Dong YX, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P135, DOI 10.1145/3097983.3098036
Drucker H, 1997, ADV NEUR IN, V9, P155
Dzyabura D, 2011, MARKET SCI, V30, P801, DOI 10.1287/mksc.1110.0660
Erdem T, 1996, MARKET SCI, V15, P1, DOI 10.1287/mksc.15.1.1
Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P226
Evgeniou T, 2005, MARKET SCI, V24, P415, DOI 10.1287/mksc.1040.0100
Evgeniou T, 2007, MARKET SCI, V26, P805, DOI 10.1287/mksc.1070.0291
Feng F, 2018, WORKING PAPER
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Frost C, 2000, J R STAT SOC A STAT, V163, P173, DOI 10.1111/1467-985X.00164
Grimes Seth, 2008, UNSTRUCTURED DATA 80
Guo T, 2018, WORKING PAPER
Hartmann J, 2019, WORKING PAPER
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
Hauser JR, 2010, J MARKETING RES, V47, P485, DOI 10.1509/jmkr.47.3.485
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647
Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50, DOI 10.1145/312624.312649
Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427
Huang DL, 2016, MARKET SCI, V35, P445, DOI 10.1287/mksc.2015.0946
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Huang MH, 2019, CALIF MANAGE REV, V61, P43, DOI 10.1177/0008125619863436
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Jacobs BJD, 2016, MARKET SCI, V35, P389, DOI 10.1287/mksc.2016.0985
Jordan MI, 1999, MACH LEARN, V37, P183, DOI 10.1023/A:1007665907178
Kakatkar C, 2019, INT J RES MARK, V36, P117, DOI 10.1016/j.ijresmar.2018.10.001
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Kawaf F, 2019, INT J RES MARK, V36, P169, DOI 10.1016/j.ijresmar.2018.11.002
Ke GL, 2017, ADV NEUR IN, V30
Klostermann J, 2018, INT J RES MARK, V35, P538, DOI 10.1016/j.ijresmar.2018.08.002
Koller D., 2009, Probabilistic Graphical Models: Principles and Techniques
LeCun Y., 1995, HDB BRAIN THEORY NEU, V3361, P1995, DOI [10.5555/303568.303704, DOI 10.5555/303568.303704]
Lee TY, 2011, J MARKETING RES, V48, P881, DOI 10.1509/jmkr.48.5.881
Lewis D.D., SIGIR 94, P3
Li H., 2019, WORKING PAPER
Li HS, 2014, J MARKETING RES, V51, P40, DOI 10.1509/jmr.13.0050
Li SB, 2011, J MARKETING RES, V48, P683, DOI 10.1509/jmkr.48.4.683
Li X, 2019, INT J RES MARK, V36, P216, DOI 10.1016/j.ijresmar.2019.02.004
Lin CF, 2002, IEEE T NEURAL NETWOR, V13, P464, DOI 10.1109/72.991432
Lippmann R. P., 1987, IEEE ASSP Magazine, V4, P4, DOI 10.1145/44571.44572
Lipton Z. C., 2016, COMMUN ACM, DOI DOI 10.1145/3233231
Liu L, 2018, Working Paper
Liu X, 2018, MARKET SCI, V37, P855, DOI 10.1287/mksc.2018.1106
Ma L., 2019, Working paper
Ma LY, 2015, MARKET SCI, V34, P627, DOI 10.1287/mksc.2015.0912
Malik N, 2019, WORKING PAPER
McCarthy J., 1955, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
McCulloch Warren S., 1943, BULL MATH BIOPHYS, V5, P115, DOI 10.1007/BF02459570
Mikolov T., 2013, ARXIV, DOI DOI 10.48550/ARXIV.1301.3781
Misra K., 2018, Dynamic online pricing with incomplete information using multi-armed bandit experiments
Mitchell T., MACHINE LEARNING, P2
Mullainathan S, 2017, J ECON PERSPECT, V31, P87, DOI 10.1257/jep.31.2.87
Netzer O, 2008, MARKET SCI, V27, P185, DOI 10.1287/mksc.1070.0294
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Ng ICL, 2017, INT J RES MARK, V34, P3, DOI 10.1016/j.ijresmar.2016.11.003
Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191
Quinlan J. R., 1986, Machine Learning, V1, P81, DOI 10.1007/BF00116251
Radford A., 2018, THE OPEN
Rafieian O, 2018, WORKING PAPER
Romov P., 2015, P 2015 INT ACM REC S, P1, DOI DOI 10.1145/2813448.2813510
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Rust RT, 2020, INT J RES MARK, V37, P15, DOI 10.1016/j.ijresmar.2019.08.002
Rust RT, 2014, MARKET SCI, V33, P206, DOI 10.1287/mksc.2013.0836
Schwartz EM, 2017, MARKET SCI, V36, P500, DOI 10.1287/mksc.2016.1023
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Selvaraju RR, 2017, IEEE I CONF COMP VIS, P618, DOI 10.1109/ICCV.2017.74
Simonyan K., 2014, 13126034 ARXIV, P1
STONE CJ, 1977, ANN STAT, V5, P595, DOI 10.1214/aos/1176343886
Sun BH, 2006, J INTERACT MARK, V20, P82, DOI 10.1002/dir.20069
Sun BH, 2011, J MARKETING RES, V48, P72, DOI 10.1509/jmkr.48.1.72
Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
Tang J, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P1067, DOI 10.1145/2736277.2741093
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Toubia O, 2017, MARKET SCI, V36, P1, DOI 10.1287/mksc.2016.0994
Trusov M, 2016, MARKET SCI, V35, P405, DOI 10.1287/mksc.2015.0956
Tsochantaridis I, 2005, J MACH LEARN RES, V6, P1453
Turing A., 1950, MIND, VLIX
Vapnik V., 1998, STAT LEARNING THEORY
Vaswani A, 2017, ADV NEUR IN, V30
Vermeer SAM, 2019, INT J RES MARK, V36, P492, DOI 10.1016/j.ijresmar.2019.01.010
Wager S, 2018, J AM STAT ASSOC, V113, P1228, DOI 10.1080/01621459.2017.1319839
Watkins C. J. C. H., 1989, THESIS
WATKINS CJCH, 1992, MACH LEARN, V8, P279, DOI 10.1007/BF00992698
Wu QA, 2010, INFORM RETRIEVAL, V13, P254, DOI 10.1007/s10791-009-9112-1
Yang Y, 2019, WORKING PAPER
Yoganarasimhan H, 2020, MANAGE SCI, V66, P1045, DOI 10.1287/mnsc.2018.3255
Zhang M., 2019, WORKING PAPER
Zhang S, 2017, WORKING PAPER
Zhang ZF, 2017, PROC CVPR IEEE, P4352, DOI 10.1109/CVPR.2017.463
Zhu X., 2005, Time-sensitive Dirichlet process mixture models
NR 123
TC 138
Z9 151
U1 67
U2 299
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD SEP
PY 2020
VL 37
IS 3
BP 481
EP 504
DI 10.1016/j.ijresmar.2020.04.005
PG 24
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA OH1NT
UT WOS:000582339000004
DA 2024-03-27
ER
PT J
AU Akbarabadi, M
Hosseini, M
AF Akbarabadi, Mina
Hosseini, Monireh
TI Predicting the helpfulness of online customer reviews: The role of title
features
SO INTERNATIONAL JOURNAL OF MARKET RESEARCH
LA English
DT Article
DE machine learning; online customer reviews; review helpfulness prediction
ID USER; CONTRIBUTE
AB Nowadays, many people refer to online customer reviews that are available on most shopping websites to make a better purchase decision. An automated review helpfulness prediction model can help the websites to rank reviews based on their level of helpfulness. This study examines the effect of review title features on predicting the helpfulness of online reviews. Moreover, a new method is proposed to categorize action verbs in a review text. Text, reviewer, readability, and title features are the four main categories that are used in this article. We examine our proposed prediction model on two real-life Amazon datasets using machine learning techniques. The results show a promising performance of the model. However, feature importance analysis reveals the low importance of title features in the predictive model. It means that the title characteristics cannot be a powerful determinant of online review helpfulness. The results of this study can be beneficial to both buyers and website owners to have a deep insight into online reviews helpfulness.
C1 [Akbarabadi, Mina; Hosseini, Monireh] KN Toosi Univ Technol, Tehran, Iran.
C3 K. N. Toosi University of Technology
RP Hosseini, M (autor correspondiente), KN Toosi Univ Technol, Ind Engn Fac, IT Dept, 17 Pardis Ave,Molla Sadra St,Vanak Sq 470, Tehran 1999143344, Iran.
EM hosseini@kntu.ac.ir
CR Ahmad SN, 2016, INT J ELECTRON COMM, V20, P76, DOI 10.1080/10864415.2016.1061471
[Anonymous], 2002, P ACL 02 WORKSH EFF
[Anonymous], 1963, MEASUREMENT READABIL
[Anonymous], 2007, P 9 INT C ELECT COMM
[Anonymous], P 8 INT C STAT AN TE
Bickart B., 2001, J INTERACT MARK, V15, P31, DOI [10.1002/dir.1014, DOI 10.1002/DIR.1014]
Bloom B. S., 1956, Taxonomy of educational objectives: Handbook I: Cognitive domain
Cao Q, 2011, DECIS SUPPORT SYST, V50, P511, DOI 10.1016/j.dss.2010.11.009
Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
Das B., 2013, 2013 IEEE 13 INT C D
Genc-Nayebi N, 2017, J SYST SOFTWARE, V125, P207, DOI 10.1016/j.jss.2016.11.027
Goh KY, 2013, INFORM SYST RES, V24, P88, DOI 10.1287/isre.1120.0469
He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
Hong H, 2017, DECIS SUPPORT SYST, V102, P1, DOI 10.1016/j.dss.2017.06.007
Hu YH, 2017, INFORM MANAGE-AMSTER, V54, P728, DOI 10.1016/j.im.2016.12.009
Huang AH, 2015, COMPUT HUM BEHAV, V48, P17, DOI 10.1016/j.chb.2015.01.010
Huitt W. G., 2011, BLOOM ALS TAXONOMY C
Kim S.-M., 2006, EMP METH NAT LANG PR
Krishnamoorthy S, 2015, EXPERT SYST APPL, V42, P3751, DOI 10.1016/j.eswa.2014.12.044
Lee PJ, 2018, TELEMAT INFORM, V35, P436, DOI 10.1016/j.tele.2018.01.001
Lee S, 2014, EXPERT SYST APPL, V41, P3041, DOI 10.1016/j.eswa.2013.10.034
Liu Jingjing, 2007, P 2007 JOINT C EMPIR, P334
Liu YF, 2008, ISPD'08: PROCEEDINGS OF THE 2008 ACM INTERNATIONAL SYMPOSIUM ON PHYSICAL DESIGN, P15
Ma YF, 2018, INT J HOSP MANAG, V71, P120, DOI 10.1016/j.ijhm.2017.12.008
Malik MSI, 2018, INFORM PROCESS MANAG, V54, P88, DOI 10.1016/j.ipm.2017.09.004
Mudambi SM, 2010, MIS QUART, V34, P185
Muller A.C., 2016, Introduction to machine learning with Python: A guide for data scientists
Ngo-Ye TL, 2017, EXPERT SYST APPL, V71, P98, DOI 10.1016/j.eswa.2016.11.029
Ngo-Ye TL, 2014, DECIS SUPPORT SYST, V61, P47, DOI 10.1016/j.dss.2014.01.011
Park DH, 2007, INT J ELECTRON COMM, V11, P125, DOI 10.2753/JEC1086-4415110405
Qazi A, 2016, COMPUT HUM BEHAV, V58, P75, DOI 10.1016/j.chb.2015.12.028
Siering M, 2018, DECIS SUPPORT SYST, V108, P1, DOI 10.1016/j.dss.2018.01.004
Singh JP, 2017, J BUS RES, V70, P346, DOI 10.1016/j.jbusres.2016.08.008
Stanny CJ, 2016, EDUC SCI, V6, DOI 10.3390/educsci6040037
Wan Y, 2015, ELECTRON MARK, V25, P313, DOI 10.1007/s12525-015-0186-x
Zhang RC, 2012, WORLD WIDE WEB, V15, P117, DOI 10.1007/s11280-011-0127-3
Zhang Y, 2018, ELECTRON COMMER R A, V27, P1, DOI 10.1016/j.elerap.2017.10.008
NR 37
TC 26
Z9 26
U1 7
U2 51
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1470-7853
EI 2515-2173
J9 INT J MARKET RES
JI Int. J. Market Res.
PD MAY
PY 2020
VL 62
IS 3
BP 272
EP 287
DI 10.1177/1470785318819979
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LQ8KZ
UT WOS:000535248500003
DA 2024-03-27
ER
PT J
AU Kushwaha, AK
Kumar, P
Kar, AK
AF Kushwaha, Amit Kumar
Kumar, Prashant
Kar, Arpan Kumar
TI What impacts customer experience for B2B enterprises on using AI-enabled
chatbots? Insights from Big data analytics
SO INDUSTRIAL MARKETING MANAGEMENT
LA English
DT Article
DE Artificial intelligence; Chatbots; Big data analytics; Customer
experience; Social media analytics; Service quality
ID ARTIFICIAL-INTELLIGENCE; DECISION-MAKING; VIRTUAL-REALITY; SERVICE
EXPERIENCE; TRUST; SYSTEMS; COMMITMENT; EVOLUTION; ADOPTION; SUCCESS
AB Many B2B firms have widely accepted AI-based chatbots to provide human-like service interaction at different customer touchpoints in recent years. One of the objectives behind introducing this technology is to provide an enhanced, live channel Customer Experience (CX) all round the clock. Researchers have focused on delivering the CX by improvising the chatbot's internal algorithm, giving limited attention to CX theories from management literature, which leaves a gap. With the proposed paper, we have investigated the influencing factors of AI-based chatbots from the lens of CX theories for B2B firms. In this paper, a model for organizing CX has been proposed using the diffusion of innovation theory, trust commitment theory, information systems success model, and Hoffman & Novak's flow model for the computer-mediated environment and verified using the social media data. The methodology used for this study is the social media analytics-based content analysis method (sentiment analysis, hierarchical clustering, topic modeling) for data preparation, followed by lasso and ridge regression for model verification. The results suggest that CX in B2B enterprises using chatbots is influenced by these bots' overall system design, customers' ability to use technology, and customer trust towards brand and system.
C1 [Kushwaha, Amit Kumar; Kumar, Prashant; Kar, Arpan Kumar] Indian Inst Technol Delhi, Dept Management Studies, New Delhi, India.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
Technology (IIT) - Delhi
RP Kar, AK (autor correspondiente), Indian Inst Technol Delhi, Dept Management Studies, New Delhi, India.
EM arpankar@dms.iitd.ac.in
RI Kar, Arpan Kumar/B-9999-2009; Kumar, Prashant/AAG-9851-2019
OI Kar, Arpan Kumar/0000-0003-4186-4887; Kumar,
Prashant/0000-0001-9160-5811; KUSHWAHA, AMIT KUMAR/0000-0002-5537-1250
CR Adamopoulos P, 2018, INFORM SYST RES, V29, P612, DOI 10.1287/isre.2017.0768
Akter S, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102387
Alter S, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2021.102392
[Anonymous], 1962, DIFFUSION INNOVATION
Aswani R, 2018, INT J INFORM MANAGE, V38, P107, DOI 10.1016/j.ijinfomgt.2017.07.005
Bag S, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120420
Baird A, 2012, J MANAGE INFORM SYST, V29, P293, DOI 10.2753/MIS0742-1222290210
Balakrishnan J, 2021, PSYCHOL MARKET, V38, P643, DOI 10.1002/mar.21462
Barbuceanu M, 2004, AI MAG, V25, P47
Barnum C. M., 2020, USABILITY TESTING ES
Bedoui A, 2020, COMPUT STAT DATA AN, V145, DOI 10.1016/j.csda.2020.106917
Benbasat I, 2005, Journal of the Association for Information Systems, V6, P4, DOI [DOI 10.17705/1JAIS.00065, 10.17705/1jais.00065]
Berry LL, 2006, ACAD MANAGE PERSPECT, V20, P43, DOI 10.5465/AMP.2006.20591004
Bleier A, 2019, J MARKETING, V83, P98, DOI 10.1177/0022242918809930
Borges AFS, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102225
Boyd DE, 2019, J BUS RES, V100, P590, DOI 10.1016/j.jbusres.2018.06.007
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Canhoto AI, 2020, BUS HORIZONS, V63, P183, DOI 10.1016/j.bushor.2019.11.003
Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
Chae B, 2015, INT J PROD ECON, V165, P247, DOI 10.1016/j.ijpe.2014.12.037
Chang JW, 2016, COMPUT IND, V78, P29, DOI 10.1016/j.compind.2015.10.007
Cheetham W, 2007, AI MAG, V28, P89
Chehreghani MH, 2009, DECIS SUPPORT SYST, V47, P374, DOI 10.1016/j.dss.2009.04.002
Chiu YT, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102379
Collins C, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102383
Dale R., 2001, Computational Linguistics, V27, P602, DOI [DOI 10.1162/COLI.2000.27, 10.1162/coli.2000.27]
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
De Keyser A, 2020, J SERV RES-US, V23, P433, DOI 10.1177/1094670520928390
DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60
Demlehner Q, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102317
Duan YQ, 2019, INT J INFORM MANAGE, V48, P63, DOI 10.1016/j.ijinfomgt.2019.01.021
Dubey R, 2021, INT J PROD RES, V59, P1586, DOI 10.1080/00207543.2020.1865583
Dwivedi YK, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2020.102168
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
Dwivedi YK, 2017, GOV INFORM Q, V34, P211, DOI 10.1016/j.giq.2017.03.001
Dwivedi YK, 2015, INFORM SYST FRONT, V17, P143, DOI 10.1007/s10796-014-9500-y
Dwivedi YogeshK., 2015, MARKETING REV, V15, P289, DOI [DOI 10.1111/JAN.13030, 10.1362/146934715X14441363377999, DOI 10.1362/146934715X14441363377999]
EY Global, 2017, CHATB CAN ENG YOUR C
Farrokhi A, 2020, IND MARKET MANAG, V91, P257, DOI 10.1016/j.indmarman.2020.09.015
Forum W. E., 2020, CHATBOTS RESET FRAME
Frow P, 2007, J BRAND MANAG, V15, P89, DOI 10.1057/palgrave.bm.2550120
Garbarino E, 1999, J MARKETING, V63, P70, DOI 10.2307/1251946
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Geva H, 2019, MIS QUART, V43, P501, DOI 10.25300/MISQ/2019/14346
González-Carrasco I, 2019, INFORM SCIENCES, V485, P319, DOI 10.1016/j.ins.2019.02.030
Grewal D, 2018, J MARKET EDUC, V40, P85, DOI 10.1177/0273475318755838
Griffis SE, 2012, J OPER MANAG, V30, P282, DOI 10.1016/j.jom.2012.02.002
Grover P, 2022, ANN OPER RES, V308, P177, DOI 10.1007/s10479-020-03683-9
Grover P, 2019, TECHNOL FORECAST SOC, V145, P438, DOI 10.1016/j.techfore.2018.09.009
Güçdemir H, 2015, IND MANAGE DATA SYST, V115, P1022, DOI 10.1108/IMDS-01-2015-0027
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Hakanen T, 2017, IND MARKET MANAG, V63, P167, DOI 10.1016/j.indmarman.2016.10.011
Hastie T., 2005, ELEMENTS STAT LEARNI, V27, P83, DOI DOI 10.1007/978-0-387-84858-7
Hernandez B, 2009, IND MARKET MANAG, V38, P338, DOI 10.1016/j.indmarman.2007.12.002
Hoyer WD, 2020, J INTERACT MARK, V51, P57, DOI 10.1016/j.intmar.2020.04.001
Hu Q, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102250
Iankova S, 2019, IND MARKET MANAG, V81, P169, DOI 10.1016/j.indmarman.2018.01.001
Kao A., 2007, Natural Language Processing and Text Mining, DOI DOI 10.1007/978-1-84628-754-1
Kar AK, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102205
Kar AK, 2021, INFORM SYST FRONT, V23, P1341, DOI 10.1007/s10796-020-10045-0
Kauffmann E, 2020, IND MARKET MANAG, V90, P523, DOI 10.1016/j.indmarman.2019.08.003
Klaus P, 2020, J SERV MARK, V34, P389, DOI 10.1108/JSM-01-2019-0043
Kumar P, 2023, INFORM SYST FRONT, V25, P2197, DOI [10.1007/s10796-021-10136-6, 10.1109/TNSE.2021.3089435]
Kumar S., 2021, Int. J. Inf. Manag. Data Insights, V1, P100008, DOI DOI 10.1016/J.JJIMEI.2021.100008
Kushwaha Amit Kumar, 2020, Responsible Design, Implementation and Use of Information and Communication Technology. 19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12066), P456, DOI 10.1007/978-3-030-44999-5_38
Kushwaha A. K., 2020, IFIP ADV INFORM COMM, P16, DOI 10.1007/978-3-030-64849-7_3
Kushwaha A. K., 2020, IFIP ADV INF COMMUN, P249, DOI [10.1007/978-3-030-64849-7_22, DOI 10.1007/978-3-030-64849-7_22]
Kushwaha A.K., 2021, International Journal of Information Management Data Insights, V1, P100017, DOI DOI 10.1016/J.JJIMEI.2021.100017
Lai JY, 2009, IND MARKET MANAG, V38, P263, DOI 10.1016/j.indmarman.2008.01.002
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Lenney P, 2009, IND MARKET MANAG, V38, P553, DOI 10.1016/j.indmarman.2008.12.020
Marakanon L., 2017, Kasetsart Journal of Social Sciences, V38, P24, DOI [10.1016/j.kjss.2016.08.012, DOI 10.1016/J.KJSS.2016.08.012]
Martin J, 2015, J RETAIL CONSUM SERV, V25, P81, DOI 10.1016/j.jretconser.2015.03.008
McKinsey, 2019, Driving Impact at Scale from Automation and AI
Mendling J, 2018, COMMUN ASSOC INF SYS, V43, P297, DOI 10.17705/1CAIS.04319
MIRVIS PH, 1991, ACAD MANAGE REV, V16, P636, DOI 10.2307/258925
Mithas S, 2011, MIS QUART, V35, P237
Mogaji E, 2021, AUSTRALAS MARK J, V29, P235, DOI 10.1016/j.ausmj.2020.05.003
MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
Nanji A., 2019, CONSUMERS FEEL BRAND
Nguyen J, 2020, KNOWL-BASED SYST, V190, DOI 10.1016/j.knosys.2019.105308
Nishant R, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102104
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
Panda S, 2020, IND MARKET MANAG, V91, P246, DOI 10.1016/j.indmarman.2020.09.011
Paschen J, 2020, BUS HORIZONS, V63, P403, DOI 10.1016/j.bushor.2020.01.003
Pillai R, 2022, PROD PLAN CONTROL, V33, P1517, DOI 10.1080/09537287.2021.1882689
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
Prentic C, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102186
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Puccinelli NM, 2009, J RETAILING, V85, P15, DOI 10.1016/j.jretai.2008.11.003
Rathore AK, 2017, DECIS ANAL, V14, P229, DOI 10.1287/deca.2017.0355
Robinson S, 2020, J BUS RES, V116, P366, DOI 10.1016/j.jbusres.2019.08.038
Roy S, 2019, IND MARKET MANAG, V82, P52, DOI 10.1016/j.indmarman.2019.02.017
Shareef MA, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120334
Smelser N.J., 2001, STAT SOC BEHAV SC, V11, DOI DOI 10.1016/B0-08-043076-7/03379-9
Steinhoff L, 2019, J ACAD MARKET SCI, V47, P369, DOI 10.1007/s11747-018-0621-6
STEUER J, 1992, J COMMUN, V42, P73, DOI 10.1111/j.1460-2466.1992.tb00812.x
Suh KS, 2005, MIS QUART, V29, P673
Sung E, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102382
Taylor SA, 2020, IND MARKET MANAG, V87, P256, DOI 10.1016/j.indmarman.2019.10.004
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Tiwary NK, 2021, J BUS RES, V131, P121, DOI 10.1016/j.jbusres.2021.03.028
Trivedi J, 2019, J INTERNET COMMER, V18, P91, DOI 10.1080/15332861.2019.1567188
Varian HR, 2014, J ECON PERSPECT, V28, P3, DOI 10.1257/jep.28.2.3
Vriens M, 2019, INT J MARKET RES, V61, P130, DOI 10.1177/1470785318810106
Yang XX, 2018, IEEE T RELIAB, V67, P885, DOI 10.1109/TR.2018.2847353
Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
Yim CK, 2012, J MARKETING, V76, P121, DOI 10.1509/jm.11.0205
Zhang D, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102304
NR 110
TC 56
Z9 57
U1 49
U2 208
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0019-8501
EI 1873-2062
J9 IND MARKET MANAG
JI Ind. Mark. Manage.
PD OCT
PY 2021
VL 98
BP 207
EP 221
DI 10.1016/j.indmarman.2021.08.011
EA SEP 2021
PG 15
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA WI5ZH
UT WOS:000708437700017
DA 2024-03-27
ER
PT J
AU Wijaya, DR
Paramita, NLPSP
Uluwiyah, A
Rheza, M
Zahara, A
Puspita, DR
AF Wijaya, Dedy Rahman
Paramita, Ni Luh Putu Satyaning Pradnya
Uluwiyah, Ana
Rheza, Muhammad
Zahara, Annisa
Puspita, Dwi Rani
TI Estimating city-level poverty rate based on e-commerce data with machine
learning
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Big data; E-commerce; Machine learning; Poverty rate estimation
ID SUPPORT VECTOR REGRESSION; ARTIFICIAL NEURAL-NETWORK; ALGORITHMS;
PREDICTION; FRAMEWORK; SENTIMENT
AB There are many big data sources in Indonesia, for example, data from social media, financial transactions, transportation, call detail records, and e-commerce. These types of data have been considered as potential resources to complement periodic surveys and censuses to monitor development indicators such as poverty levels. Data from e-commerce in particular could potentially represent the real expenditure of households, better complying with the formal calculation of the poverty line than other datasets. The contribution of this research is to propose a framework for poverty rate estimation based on e-commerce data using machine learning algorithms. The influence of items and aspects in e-commerce data was investigated in conjunction with poverty rate estimation. The experimental result showed that e-commerce data could potentially be used as a proxy for calculating city-level poverty rates. It was also found that cars and motorbikes are the two most significant items for poverty prediction in Indonesia.
C1 [Wijaya, Dedy Rahman] Telkom Univ, Sch Appl Sci, Bandung, Indonesia.
[Paramita, Ni Luh Putu Satyaning Pradnya] Inst Teknol Sepuluh Nopember, Stat Dept, Surabaya, Indonesia.
[Uluwiyah, Ana] Stat Indonesia BPS, Educ & Training Ctr, Jakarta, Indonesia.
[Rheza, Muhammad; Zahara, Annisa] United Nat Global Pulse, Pulse Lab Jakarta, Jakarta, Indonesia.
[Puspita, Dwi Rani] Univ Indonesia, Inst Econ & Social Res, Jakarta, Indonesia.
C3 Telkom University; Institut Teknologi Sepuluh Nopember; Statistics
Indonesia; University of Indonesia
RP Wijaya, DR (autor correspondiente), Telkom Univ, Sch Appl Sci, Bandung, Indonesia.
EM dedyrw@tass.telkomuniversity.ac.id; pradnya@statistika.its.ac.id;
auluwiyah@bps.go.id; muhammad.rheza@un.or.id; anissa.zahara@un.or.id;
dwirani.puspa@ui.ac.id
RI Wijaya, Dedy Rahman/P-2905-2015
OI Wijaya, Dedy Rahman/0000-0003-0351-7331; , Ana/0000-0002-3149-6657;
Paramita, Ni Luh Putu Satyaning Pradnya/0000-0001-6478-085X
FU Pulse Lab Jakarta; Government of Indonesia
FX This work was supported by Pulse Lab Jakarta (PLJ), which is a joint
initiative of the United Nations and the Government of Indonesia.
CR Akib K., 2018, E COMMERCE INDONESIA
Babenko B., 2017, 31 C NEUR INF PROC S, P1, DOI 10.1109/vppc.2005.1554579
Baranyi J, 1999, INT J FOOD MICROBIOL, V48, P159, DOI 10.1016/S0168-1605(99)00035-5
Barbancho AM, 2011, INT CONF ACOUST SPEE, P377
Blumenstock J, 2015, SCIENCE, V350, P1073, DOI 10.1126/science.aac4420
BPS-Statistics Indonesia, 2018, KEM DAN KET
Braithwaite A, 2016, CONFLICT MANAG PEACE, V33, P45, DOI 10.1177/0738894214559673
Brown G, 2012, J MACH LEARN RES, V13, P27
Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199
Deeplearning4j Development Team, 2017, DEEPLEARNING4J OPENS
Du J, 2017, PATTERN RECOGN, V63, P149, DOI 10.1016/j.patcog.2016.10.003
Fund U. N. P, 2014, POP POV
Greff K, 2017, IEEE T NEUR NET LEAR, V28, P2222, DOI 10.1109/TNNLS.2016.2582924
Guan W, 2018, OPTICAL MEMORY NEURA, V27, P272, DOI [10.3103/s1060992x18040094, DOI 10.3103/S1060992X18040094]
Guo KH, 2012, ADV MATER RES-SWITZ, V403-408, P3805, DOI 10.4028/www.scientific.net/AMR.403-408.3805
Hariyanto, 2017, INT CONF INFORM COMM, P241, DOI 10.1109/ICTS.2017.8265677
Hinton G, 2012, IEEE SIGNAL PROC MAG, V29, P82, DOI 10.1109/MSP.2012.2205597
Indonesia B.-S., 2018, PERS PEND MISK MEN K
Indonesia B.-S, 2018, NAT SOC EC SURV
Jean N, 2016, SCIENCE, V353, P790, DOI 10.1126/science.aaf7894
Jiang SC, 2017, EXPERT SYST APPL, V82, P216, DOI 10.1016/j.eswa.2017.04.017
Kalman B. L., 1992, IJCNN International Joint Conference on Neural Networks (Cat. No.92CH3114-6), P578, DOI 10.1109/IJCNN.1992.227257
KipkosgeiLagat, 2018, AM J THEORETICAL APP, V7, P67, DOI [10.11648/j.ajtas.20180702.13, DOI 10.11648/J.AJTAS.20180702.13]
Lee S, 2017, ELECTRON COMMER R A, V26, P35, DOI 10.1016/j.elerap.2017.09.006
Li JD, 2018, ACM COMPUT SURV, V50, DOI 10.1145/3136625
Li YX, 2018, MULTIMED TOOLS APPL, V77, P897, DOI 10.1007/s11042-016-4332-z
Liu XJ, 2020, NEUROCOMPUTING, V397, P393, DOI 10.1016/j.neucom.2019.08.108
Målqvist M, 2015, ARCH DIS CHILD, V100, pS5, DOI 10.1136/archdischild-2013-305722
Mellander C, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0139779
Mirzaei A, 2020, NEUROCOMPUTING, V383, P396, DOI 10.1016/j.neucom.2019.12.017
Mustakim M., 2016, JURNAL ILMU KOMPUTER, V9, P1, DOI [10.21609/jiki.v9i1.287, DOI 10.21609/JIKI.V9I1.287]
Naguib IA, 2012, SPECTROCHIM ACTA A, V86, P515, DOI 10.1016/j.saa.2011.11.003
Njuguna C, 2017, J BUS RES, V70, P318, DOI 10.1016/j.jbusres.2016.08.005
OECD, 2018, POV RAT IND
Ou W, 2018, ELECTRON COMMER R A, V32, P13, DOI 10.1016/j.elerap.2018.10.003
Pandey SM, 2018, AAAI CONF ARTIF INTE, P7793
Perez A., 2017, Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Pokhriyal N, 2017, P NATL ACAD SCI USA, V114, pE9783, DOI 10.1073/pnas.1700319114
Portugal I, 2018, EXPERT SYST APPL, V97, P205, DOI 10.1016/j.eswa.2017.12.020
Rout JK, 2018, ELECTRON COMMER RES, V18, P181, DOI 10.1007/s10660-017-9257-8
Shirzad A, 2014, KSCE J CIV ENG, V18, P941
Steele JE, 2017, J R SOC INTERFACE, V14, DOI 10.1098/rsif.2016.0690
Tian F, 2016, ELECTRON COMMER R A, V16, P66, DOI 10.1016/j.elerap.2015.10.003
Tseng KK, 2018, ELECTRON COMMER RES, V18, P65, DOI 10.1007/s10660-017-9272-9
United Nations, 2015, MILL DEV GOALS REP
Vapnik V, 1998, STAT LEARNING THEORY, V3
Vincent OR, 2017, ELECTRON COMMER R A, V25, P59, DOI 10.1016/j.elerap.2017.08.002
Wang L, 2019, J THEOR BIOL, V461, P230, DOI 10.1016/j.jtbi.2018.10.029
Wang YR, 2018, ELECTRON COMMER R A, V29, P1, DOI 10.1016/j.elerap.2018.03.003
Wijaya D. R., 2016, International Review on Computers and Software, V11, P659, DOI [10.15866/irecos.v11i8.9425, DOI 10.15866/IRECOS.V11I8.9425, DOI 10.15866/IREC0S.V11I8.9425]
Wijaya DR, 2020, IEEE ACCESS, V8, P33944, DOI 10.1109/ACCESS.2020.2974982
Wijaya DR, 2019, COMPUT ELECTRON AGR, V157, P305, DOI 10.1016/j.compag.2019.01.001
Yang S, 2021, ELECTRON COMMER RES, V21, P727, DOI 10.1007/s10660-019-09337-8
Yu LP, 2003, ANN NY ACAD SCI, V1005, P1, DOI 10.1196/annals.1288.002
Zhang W, 2018, ELECTRON COMMER R A, V31, P12, DOI 10.1016/j.elerap.2018.08.001
NR 55
TC 17
Z9 17
U1 7
U2 37
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD MAR
PY 2022
VL 22
IS 1
SI SI
BP 195
EP 221
DI 10.1007/s10660-020-09424-1
EA JUN 2020
PG 27
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA ZN0VD
UT WOS:000541207900001
DA 2024-03-27
ER
PT J
AU Laurell, C
Sandström, C
Berthold, A
Larsson, D
AF Laurell, Christofer
Sandstrom, Christian
Berthold, Adam
Larsson, Daniel
TI Exploring barriers to adoption of Virtual Reality through Social Media
Analytics and Machine Learning - An assessment of technology, network,
price and trialability
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Virtual reality; Oculus rift; HTC vive; Social media analytics; Machine
learning; Adoption
ID INFORMATION-TECHNOLOGY; EXPERIENCE
AB This paper aims to assess how diffusion of Virtual Reality (VR) technology is taking place and identify potential barriers to increased adoption. This is done by utilising Social Media Analytics to collect a data set covering an empirical material of 6044 user-generated content concerning the market-leading VR headsets Oculus Rift and HTC Vive, and machine learning to identify critical barriers to adoption. Our findings suggest that there is a lack of sufficient technological performance of these headsets and that more applications are required for this technology to take off. We contribute to literature on VR by providing a systematic assessment of current barriers to adoption while also pointing out implications for marketing.
C1 [Laurell, Christofer] Stockholm Sch Econ, Inst Res, Box 6501, SE-11383 Stockholm, Sweden.
[Laurell, Christofer; Sandstrom, Christian] Jonkoping Int Business Sch, Box 1026, SE-55111 Jonkoping, Sweden.
[Sandstrom, Christian] Chalmers Univ Technol, Sci & Technol Studies, SE-41296 Gothenburg, Sweden.
[Sandstrom, Christian] Ratio Inst, POB 3203, SE-10364 Stockholm, Sweden.
[Berthold, Adam; Larsson, Daniel] Chalmers Univ Technol, Vera Sandbergs Alle 8B, SE-41296 Gothenburg, Sweden.
C3 Stockholm School of Economics; Jonkoping University; Chalmers University
of Technology; Chalmers University of Technology
RP Laurell, C (autor correspondiente), Stockholm Sch Econ, Inst Res, Box 6501, SE-11383 Stockholm, Sweden.
EM christofer.laurell@hhs.se; christian.sandstrom@chalmers.se
OI Sandstrom, Christian/0000-0002-8625-8744
FU Jan Wallander and Tom Hedelius Foundation; Tore Browaldh Foundation;
Marianne and Marcus Wallenberg Foundation, Sweden
FX This work was supported by the Jan Wallander and Tom Hedelius Foundation
and Tore Browaldh Foundation; and Marianne and Marcus Wallenberg
Foundation, Sweden.
CR Adner R, 2016, STRATEGIC MANAGE J, V37, P625, DOI 10.1002/smj.2363
Amend J. M., 2016, GM USES VIRTUAL WORL
Arthur WB, 1996, HARVARD BUS REV, V74, P100
Berg LP, 2017, VIRTUAL REAL-LONDON, V21, P1, DOI 10.1007/s10055-016-0293-9
BMW, 2017, VIRT OCH AUGM REAL
Brooker P, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951716658060
Carr C., 2016, MAKING SENSE VIRTUAL, V38, P68
Charron JP, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00800
Christensen Clayton M., 1997, INNOVATORS DILEMMA N
ClassVR, 2017, RED COSTS MIL TRAIN
Geissinger A, 2020, TECHNOL FORECAST SOC, V155, DOI 10.1016/j.techfore.2018.06.012
Gleasure R, 2016, J ASSOC INF SYST, V17, P708, DOI 10.17705/1jais.00439
Griffin T., 2017, 2017 TTRA INT C
Gronstedt A., 2016, TD: Talent Development, V70, P54
Huang YC, 2016, INT J TOUR RES, V18, P116, DOI 10.1002/jtr.2038
IKEA, 2017, VIRT REAL MAG
Jung Timothy., 2016, INFORM COMMUNICATION, P621, DOI DOI 10.1007/978-3-319-28231-2_45
Karpf D, 2012, INFORM COMMUN SOC, V15, P639, DOI 10.1080/1369118X.2012.665468
Kumar S, 2016, INT CON ADV INFO NET, P973, DOI 10.1109/AINA.2016.177
Lau K.W., 2016, ADV ADVERTISING RES, V6, P75
Laurell C, 2019, TECHNOL FORECAST SOC, V141, P117, DOI 10.1016/j.techfore.2018.07.015
Laurell C, 2017, TECHNOL FORECAST SOC, V125, P58, DOI 10.1016/j.techfore.2017.05.038
Lee B. Y., 2017, VIRTUAL REALITY IS G
Lin JHT, 2017, COMPUT HUM BEHAV, V72, P350, DOI 10.1016/j.chb.2017.02.057
Magnusson J., 2014, ENTERPRISE SYSTEM PL
Meissner M., 2017, J BUS RES, DOI [10.1016/j.jbusres.2017.09, DOI 10.1016/J.JBUSRES.2017.09]
OBrien J. M., 2016, RACE MAKE VIRTUAL RE
Oculus, 2016, OC TOUCH LAUNCH TOD
OSKARSSON C, 1994, TECHNOL ANAL STRATEG, V6, P3, DOI 10.1080/09537329408524149
Rogers, 2003, DIFFUSION INNOVATION
Schilling M.A., 2010, Strategic Management of Technological Innovation, V3rd
Shin D, 2018, COMPUT HUM BEHAV, V78, P64, DOI 10.1016/j.chb.2017.09.012
Steam, 2017, STEAM HARDW SOFTW SU
Stieglitz S, 2014, BUS INFORM SYST ENG+, V6, P89, DOI 10.1007/s12599-014-0315-7
TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144
Van Kerrebroeck H, 2017, COMPUT HUM BEHAV, V77, P437, DOI 10.1016/j.chb.2017.07.019
Van Kerrebroeck H, 2017, VIRTUAL REAL-LONDON, V21, P177, DOI 10.1007/s10055-017-0306-3
van Zoonen W, 2016, COMPUT HUM BEHAV, V63, P132, DOI 10.1016/j.chb.2016.05.028
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
NR 40
TC 55
Z9 56
U1 6
U2 51
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD JUL
PY 2019
VL 100
BP 469
EP 474
DI 10.1016/j.jbusres.2019.01.017
PG 6
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA IC4NL
UT WOS:000470942500042
DA 2024-03-27
ER
PT J
AU Lei, ZZ
AF Lei, Zhizhong
TI Research and analysis of deep learning algorithms for investment
decision support model in electronic commerce
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Deep learning; Investment; E-commerce; Decision support model
ID NEURAL-NETWORKS; SYSTEM; RECOGNITION
AB In order to improve the accuracy of e-commerce decision-making, this paper proposes an investment decision-making support model in e-commerce based on deep learning calculation to support the company. Investment decision-making system is not only an important means of enterprise investment and financing, but also an important way for investors to make profits. It also plays an important role in macroeconomic regulation, resource allocation and other aspects. This paper takes investment data related to Internet and e-commerce business as the research object, studies the theory and method of investment decision-making quality evaluation at home and abroad, and puts forward a prediction model of company decision-making quality evaluation based on deep learning algorithm, aiming at providing decision support for investors. Then a neural network investment quality evaluation model is constructed, including model structure, parameters and algorithm design. The experimental data are input into training, and the data processing process and prediction results are displayed. Experiments show that the evaluation indexes of prediction model is mainly used to judge the quality of investment of Internet or commercial enterprises. Based on this deep learning model, various index data of enterprises are analyzed, which can assist investors in decision-making.
C1 [Lei, Zhizhong] Liaoning Univ, Sch Econ, Shenyang 110036, Liaoning, Peoples R China.
C3 Liaoning University
RP Lei, ZZ (autor correspondiente), Liaoning Univ, Sch Econ, Shenyang 110036, Liaoning, Peoples R China.
EM leisheng9960@163.com
CR [Anonymous], 2016, BIOMED RES INT
Aouadni I, 2017, ANN OPER RES, V256, P3, DOI 10.1007/s10479-016-2154-z
Barros P, 2017, NEUROCOMPUTING, V253, P104, DOI 10.1016/j.neucom.2017.01.096
Böttiger Y, 2018, EUR J CLIN PHARMACOL, V74, P365, DOI 10.1007/s00228-017-2391-3
Chen MC, 2018, ACTA MATH APPL SIN-E, V34, P398, DOI 10.1007/s10255-018-0740-3
Chong E, 2017, EXPERT SYST APPL, V83, P187, DOI 10.1016/j.eswa.2017.04.030
Ghadai S, 2018, COMPUT AIDED GEOM D, V62, P263, DOI 10.1016/j.cagd.2018.03.024
Ghasemi F, 2018, DRUG DISCOV TODAY, V23, P1784, DOI 10.1016/j.drudis.2018.06.016
Ifenthaler D, 2014, TECHNOL KNOWL LEARN, V19, P241, DOI 10.1007/s10758-014-9224-6
Kharazmi P, 2018, J MED SYST, V42, DOI 10.1007/s10916-017-0885-2
Kong HF, 2019, IEEE ACCESS, V7, P65174, DOI 10.1109/ACCESS.2019.2917545
Kraus M, 2017, DECIS SUPPORT SYST, V104, P38, DOI 10.1016/j.dss.2017.10.001
Nweke HF, 2018, EXPERT SYST APPL, V105, P233, DOI 10.1016/j.eswa.2018.03.056
Sanfey AG, 2003, SCIENCE, V300, P1755, DOI 10.1126/science.1082976
Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
Shameer K, 2017, BRIEF BIOINFORM, V18, P105, DOI 10.1093/bib/bbv118
Slezak D, 2018, INFORM SCIENCES, V451, P112, DOI 10.1016/j.ins.2018.04.026
Sultana A, 2017, PEER PEER NETW APPL, V10, P1113, DOI 10.1007/s12083-016-0465-0
Venkateswara H, 2017, IEEE SIGNAL PROC MAG, V34, P117, DOI 10.1109/MSP.2017.2740460
Xiao GL, 2018, INT J PROD RES, V56, P1389, DOI 10.1080/00207543.2017.1328140
Yang YX, 2018, J THORAC DIS, V10, pS867, DOI 10.21037/jtd.2018.02.57
Yau Kok-Lim Alvin, 2017, ACM Computing Surveys, V50, DOI 10.1145/3068287
Zhao FF, 2018, COGN COMPUT, V10, P296, DOI 10.1007/s12559-017-9511-3
NR 23
TC 8
Z9 8
U1 2
U2 20
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD JUN
PY 2020
VL 20
IS 2
SI SI
BP 275
EP 295
DI 10.1007/s10660-019-09389-w
EA NOV 2019
PG 21
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LK9BZ
UT WOS:000494384900001
DA 2024-03-27
ER
PT J
AU Esangbedo, CO
Zhang, JX
Esangbedo, MO
Kone, SD
Xu, L
AF Esangbedo, Caroline Olufunke
Zhang, Jingxiao
Esangbedo, Moses Olabhele
Kone, Seydou Dramane
Xu, Lin
TI The role of industry-academia collaboration in enhancing educational
opportunities and outcomes under the digital driven Industry 4.0
SO JOURNAL OF INFRASTRUCTURE POLICY AND DEVELOPMENT
LA English
DT Article
DE Industry 4.0; industry-academia collaboration; artificial intelligence;
patent development; curriculum; research and development; SEM
ID KNOWLEDGE TRANSFER; OPEN-INNOVATION; UNIVERSITY; CHINA; INTERMEDIARIES;
PERSPECTIVE; POLICY
AB We studied the role of industry-academic collaboration (IAC) in the enhancement of educational opportunities and outcomes under the digital driven Industry 4.0 using research and development, the patenting of products/knowledge, curriculum development, and artificial intelligence as proxies for IAC. Relevant conceptual, theoretical, and empirical literature were reviewed to provide a background for this research. The investigator used mainly principal (primary) data from a sample of 230 respondents. The primary statistics were acquired through a questionnaire. The statistics were evaluated using the structural equation model (SEM) and Stata version 13.0 as the statistical software. The findings indicate that the direct total effect of Artificial intelligence (Aint) on educational opportunities (EduOp) is substantial (Coef. 0.2519916) and statistically significant (p < 0.05), implying that changes in Aint have a pronounced influence on EduOp. Additionally, considering the indirect effects through intermediate variables, Research and Development (Res_dev) and Product Patenting (Patenting) play crucial roles, exhibiting significant indirect effects on EduOp. Res_dev exhibits a negative indirect effect (Coef = -0.009969, p = 0.000) suggesting that increased research and development may dampen the impact of Aint on EduOp against a priori expectation while Patenting has a positive indirect effect (Coef = 0.146621, p = 0.000), indicating that innovation, as reflected by patenting, amplifies the effect of Aint on EduOp. Notably, Curriculum development (Curr_dev) demonstrates a remarkable positive indirect effect (Coef = 0.8079605, p = 0.000) underscoring the strong role of current development activities in enhancing the influence of Aint on EduOp. The study contributes to knowledge on the effective deployment of artificial intelligence, which has been shown to enhance educational opportunities and outcomes under the digital driven Industry 4.0 in the study area.
C1 [Esangbedo, Caroline Olufunke; Zhang, Jingxiao; Kone, Seydou Dramane] Changan Univ, Sch Econ & Management, Xian 710000, Peoples R China.
[Esangbedo, Moses Olabhele] Xuzhou Univ Technol, Sch Management Engn, Xuzhou 221018, Peoples R China.
[Xu, Lin] Northwest Univ, Sch Foreign Languages, Xian 710069, Peoples R China.
C3 Chang'an University; Xuzhou University of Technology; Northwest
University Xi'an
RP Zhang, JX (autor correspondiente), Changan Univ, Sch Econ & Management, Xian 710000, Peoples R China.
EM zhangjingxiao964@126.com
FU National Social Science Fund projects of China [20BJY010]; National
Social Science Fund Post-financing projects of China [19FJYB017]; China
Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund
[71942006]; China Qinghai Natural Science Foundation [2020-JY-736]; List
of Key Science and Technology Projects in China's Transportation
Industry in the 2018 International Science and Technology Cooperation
Project [2018-GH-006, 2019-MS5-100]; Emerging Engineering Education
Research and Practice Project of Ministry of Education of China
[E-GKRWJC20202914]; Higher Education Teaching Reform Project in Shaanxi
Province, China [19BZ016]; Humanities and Social Sciences Research
Project of the Ministry of Education of China [21XJA752003]; British
Council ("Integrated Built Environment Teaching & Learning in the Joint
Curriculum Development amid Digital-Driven Industry 4.0 among China);
International Education Research Program of Chang'an University, China
[300108221113]; Going Global Partnership: UK-China-ASEAN, Education
Partnership Initiative - British Council; National Natural Science
Foundation of China [72074191]
FX This research is supported by the National Social Science Fund projects
of China (No.20BJY010) ; the National Social Science Fund Post-financing
projects of China (No.19FJYB017) ; the China Sichuan-Tibet Railway Major
Fundamental Science Problems Special Fund (No.71942006) ; the China
Qinghai Natural Science Foundation (No.2020-JY-736) ; the List of Key
Science and Technology Projects in China's Transportation Industry in
the 2018 International Science and Technology Cooperation Project
(No.2018-GH-006 and No.2019-MS5-100) ; the Emerging Engineering
Education Research and Practice Project of Ministry of Education of
China (No.E-GKRWJC20202914) ; the Higher Education Teaching Reform
Project in Shaanxi Province, China (No.19BZ016) ; the Humanities and
Social Sciences Research Project of the Ministry of Education of China
(21XJA752003) ; the Going Global Partnership: UK-China-ASEAN, Education
Partnership Initiative funded by British Council ("Integrated Built
Environment Teaching & Learning in the Joint Curriculum Development amid
Digital-Driven Industry 4.0 among China, Vietnam, and UK") ; the
International Education Research Program of Chang'an University, China,
2022 (No. 300108221113) ; and the National Natural Science Foundation of
China (No. 72074191) .
CR Abrahams L, 2010, The African Journal of Information and Communication (AJIC), V10, DOI [10.23962/10539/19768, DOI 10.23962/10539/19768]
Agrawal A, 2001, INT J MANAG REV, V3, P285, DOI 10.1111/1468-2370.00069
Aiello F, 2019, INT REV APPL ECON, V33, P813, DOI 10.1080/02692171.2019.1608917
Aishath MA, 2019, International Journal of Computer Sciences and Engineering, V7, P1778, DOI [10.26438/ijcse/v7i5.17781790, DOI 10.26438/IJCSE/V7I5.17781790]
Albahari A, 2017, TECHNOL FORECAST SOC, V116, P13, DOI 10.1016/j.techfore.2016.11.012
Alexander A, 2020, J TECHNOL TRANSFER, V45, P371, DOI 10.1007/s10961-018-9685-1
Ankrah S, 2015, SCAND J MANAG, V31, P387, DOI 10.1016/j.scaman.2015.02.003
[Anonymous], 2010, CHIN STAT YB
López SF, 2015, SERV BUS, V9, P637, DOI 10.1007/s11628-014-0243-0
Fischer BB, 2019, TECHNOL FORECAST SOC, V145, P330, DOI 10.1016/j.techfore.2018.05.001
Gann DM, 2005, RES POLICY, V34, P122, DOI 10.1016/j.respol.2004.10.001
George D., 2021, IBM SPSS STAT 27 STE, V17th, DOI DOI 10.4324/9781003205333
GRILICHES Z, 1990, J ECON LIT, V28, P1661, DOI 10.3386/w3301
Guimon J., 2013, PROMOTING U IND COLL
Hair J.F., 2011, INT ENCY STAT SCI, P904, DOI [DOI 10.1007/978-3-642-04898-2_395, 10.1007/978-3-642-04898-2_395]
He Y, 2017, How China is Preparing for an AI-powered Future
Homma H.N., 2008, INNOVATION TECHNIQUE, P432, DOI [10.1007/978-1-4020-8739-4_76, DOI 10.1007/978-1-4020-8739-4_76]
Hong J, 2015, TECHNOL ANAL STRATEG, V27, P1068, DOI 10.1080/09537325.2015.1060310
Hong W, 2008, RES POLICY, V37, P580, DOI 10.1016/j.respol.2007.12.008
Hou BJ, 2019, EUR J INNOV MANAG, V22, P717, DOI 10.1108/EJIM-09-2018-0195
Hou BJ, 2019, TECHNOL ANAL STRATEG, V31, P692, DOI 10.1080/09537325.2018.1543868
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Hughes A, 2012, CAMB J ECON, V36, P723, DOI 10.1093/cje/bes017
Jing M, 2017, Science & Research
Khachoo Q, 2018, J ECON BUS, V97, P39, DOI 10.1016/j.jeconbus.2018.03.002
Kim YC, 2019, RES POLICY, V48, P813, DOI 10.1016/j.respol.2018.11.007
Kollo T, 2005, Advanced Multivariate Statistics with Matrices, P98, DOI DOI 10.1007/1-4020-3419-9
Koomsap P, 2019, ADV TRANSDISCIPL ENG, V10, P75, DOI 10.3233/ATDE190110
Kusmin KL, 2018, INTERACT DES ARCHIT, P28
Laursen K, 2004, RES POLICY, V33, P1201, DOI 10.1016/j.respol.2004.07.004
Leydesdorff L, 2004, J AM SOC INF SCI TEC, V55, P991, DOI 10.1002/asi.20045
Li T, 2020, P 2020 ASEE VIRTUAL
Liefner I, 2016, SCI PUBL POLICY, V43, P849, DOI 10.1093/scipol/scw002
Liu XL, 2017, SCI PUBL POLICY, V44, P656, DOI 10.1093/scipol/scw092
Luan CJ, 2010, SCIENTOMETRICS, V84, P53, DOI 10.1007/s11192-010-0194-8
Lucietto A, 2021, School of Engineering Education Faculty Publications
Manyika J., 2017, JOBS LOST JOBS GAINE
Mears L, 2011, J INTELL MANUF, V22, P693, DOI 10.1007/s10845-009-0329-z
Mgaiwa SJ, 2021, SAGE OPEN, V11, DOI 10.1177/21582440211006709
Miller K, 2018, R&D MANAGE, V48, P7, DOI 10.1111/radm.12228
Ministry of Education, 2019, Annual Report on the National Higher Education
Ministry of Science and Technology China, 2020, Report of the Ministry of Science and Technology of the People's Republic of China on the Development of Industry-Academia Collaboration in 2020
Motohashi Kazuyuki, 2006, [JOURNAL OF TECHNOLOGY INNOVATION, 기술혁신연구], V14, P49
Motohashi K, 2012, TECHNOL SOC, V34, P149, DOI 10.1016/j.techsoc.2012.02.006
Mukherji N, 2021, J TECHNOL TRANSFER, V46, P223, DOI 10.1007/s10961-019-09770-9
Munyoki J., 2011, BUSINESS ADM MANAGEM, V1, P163
Murphy R, 2020, Artificial intelligence applications to support K-12 teachers and teaching: A review of promising applications, challenges, and risks, DOI [10.7249/pe315, DOI 10.7249/PE315, 10.7249/PE315]
Nagaoka S, 2010, HBK ECON, V2, P1083, DOI 10.1016/S0169-7218(10)02009-5
O'Dwyer M, 2023, J TECHNOL TRANSFER, V48, P900, DOI 10.1007/s10961-022-09932-2
Payne M., 2007, Project Manager Today
Perkmann M, 2007, INT J MANAG REV, V9, P259, DOI 10.1111/j.1468-2370.2007.00225.x
Perkmann M, 2013, RES POLICY, V42, P423, DOI 10.1016/j.respol.2012.09.007
Perkmann M, 2011, R&D MANAGE, V41, P202, DOI 10.1111/j.1467-9310.2011.00637.x
Polt W., 2001, SCI PUBL POLICY, V284, P247, DOI [10.3152/147154301781781453, DOI 10.3152/147154301781781453]
Ponomariov B, 2013, J TECHNOL TRANSFER, V38, P749, DOI 10.1007/s10961-013-9301-3
Preacher KJ, 2004, BEHAV RES METH INS C, V36, P717, DOI 10.3758/BF03206553
Prudhomme D, Patents and Innovation in China and Hong Kong, P29, DOI [10.1017/9781108163583.004, DOI 10.1017/9781108163583.004]
Rahm D, 2000, University-Industry R&D Collaboration in the United States, the United Kingdom, and Japan, DOI [10.1007/978-94-015-9574-2, DOI 10.1007/978-94-015-9574-2]
Rosell C, 2009, RES POLICY, V38, P1, DOI 10.1016/j.respol.2008.07.014
Ryan S, 2021, The U.S., China, and Artificial Intelligence Competition Factors
Scandura A, 2016, RES POLICY, V45, P1907, DOI 10.1016/j.respol.2016.06.009
Smith K., 2005, Oxford handbook of innovation
Tessema B.S., 2017, Education Journal, V6, P87, DOI DOI 10.11648/J.EDU.20170602.13
Thorndike RM, 1995, APPL PSYCH MEAS, V19, P303, DOI 10.1177/014662169501900308
Tsai KelleeS., 2015, State Capitalism, Institutional Adaptation, and the Chinese Miracle, P1, DOI DOI 10.1017/CBO9781139962858
TUCKER LR, 1973, PSYCHOMETRIKA, V38, P1, DOI 10.1007/bf02291170
United Nations Educational Scientific and Cultural Organization (UNESCO), 2019, ICT in Education: A Global Outlook
Vedaraman S, WIPO Lectures
Wang C, 2017, SCI PUBL POLICY, V44, P354, DOI 10.1093/scipol/scw068
Weagle D, 2019, Innov. Solutions for Energy Transitions, V158, P1135, DOI [10.1016/j.egypro.2019.01.279.l, DOI 10.1016/J.EGYPRO.2019.01.279.L]
West S. G., 2012, Handbook of structural equation modeling, P209, DOI DOI 10.1016/J.SURFCOAT.2015.05.023
Xie J, 2003, STRUCT EQU MODELING, V10, P566, DOI 10.1207/S15328007SEM1004_5
Yixin Zheng, 2021, E3S Web of Conferences, V235, DOI 10.1051/e3sconf/202123501012
Zhang LX, 2018, BMJ-BRIT MED J, V360, DOI 10.1136/bmj.j5910
Ziegler WL, 1983, P 20 ANN COMPUTER PE, DOI [10.1145/800030.800626, DOI 10.1145/800030.800626]
NR 75
TC 0
Z9 0
U1 3
U2 3
PU ENPRESS PUBL LLC
PI TUSTIN
PA 14701 MYFORD RD, STE B-1, TUSTIN, CA, UNITED STATES
SN 2572-7923
EI 2572-7931
J9 J INFRASTRUCT POLICY
JI J. Infrastruct. Policy Dev.
PY 2023
VL 8
IS 1
AR 2569
DI 10.24294/jipd.v8i1.2569
PG 32
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA FJ6U6
UT WOS:001145448200011
OA gold
DA 2024-03-27
ER
PT J
AU Li, YY
Xie, Y
AF Li, Yiyi
Xie, Ying
TI Is a Picture Worth a Thousand Words? An Empirical Study of Image Content
and Social Media Engagement
SO JOURNAL OF MARKETING RESEARCH
LA English
DT Article
DE image processing; natural language processing; social media analytics;
user-generated content; visual marketing
ID OF-MOUTH; INFORMATION; FORMAT; MODEL
AB Are social media posts with pictures more popular than those without? Why do pictures with certain characteristics induce higher engagement than some other pictures? Using data sets of social media posts about major airlines and sport utility vehicle brands collected from Twitter and Instagram, the authors empirically examine the influence of image content on social media engagement. After accounting for selection bias on the inclusion of image content, the authors find a significant and robust positive mere presence effect of image content on user engagement in both product categories on Twitter. They also find that high-quality and professionally shot pictures consistently lead to higher engagement on both platforms for both product categories. However, the effect of colorfulness varies by product category, while the presence of human face and image-text fit can induce higher user engagement on Twitter but not on Instagram. These findings shed light on how to improve social media engagement using image content.
C1 [Li, Yiyi] Univ Texas Arlington, Coll Business, Mkt, Arlington, TX 76019 USA.
[Xie, Ying] Univ Texas Dallas, Naveen Jindal Sch Management, Mkt, Richardson, TX 75083 USA.
C3 University of Texas System; University of Texas Arlington; University of
Texas System; University of Texas Dallas
RP Li, YY (autor correspondiente), Univ Texas Arlington, Coll Business, Mkt, Arlington, TX 76019 USA.
EM yiyi.li@uta.edu; ying.xie@utdallas.edu
RI LI, yi/HKO-0480-2023
CR Adam Vavrek, 2012, SKYWORD 0606
Alex Casalboni, 2016, GOOGLE VISION API IM
Benjamin Rey, 2014, WISELYTICS 0305
Berger J, 2014, J CONSUM PSYCHOL, V24, P586, DOI 10.1016/j.jcps.2014.05.002
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
Bruce NI, 2017, J MARKETING RES, V54, P202, DOI 10.1509/jmr.14.0117
Buechel EC, 2018, J CONSUM PSYCHOL, V28, P40, DOI 10.1002/jcpy.1013
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chintagunta PK, 2010, MARKET SCI, V29, P944, DOI 10.1287/mksc.1100.0572
Chung CM, 2006, MARKET LETT, V17, P4
Cyr D, 2009, MIS QUART, V33, P539
eMarketer, 2011, WHAT ENC FAC ENG
eMarketer, 2018, US SOC NETW US EMARK
FINN A, 1988, J MARKETING RES, V25, P168, DOI 10.2307/3172648
Gans Joshua S., 2017, 2889388 U TOR
Goldfarb A, 2011, MARKET SCI, V30, P389, DOI 10.1287/mksc.1100.0583
Hagtvedt H, 2008, J MARKETING RES, V45, P379, DOI 10.1509/jmkr.45.3.379
HAUSMAN J, 1984, ECONOMETRICA, V52, P909, DOI 10.2307/1911191
HECKLER SE, 1992, J CONSUM RES, V18, P475, DOI 10.1086/209275
HOUSTON MJ, 1987, J MARKETING RES, V24, P359, DOI 10.2307/3151383
Kumar A, 2016, J MARKETING, V80, P7, DOI 10.1509/jm.14.0249
Larry Loeb, 2016, INFORMATIONWEEK
Lee YH, 1999, J CONSUM RES, V26, P156, DOI 10.1086/209557
Mary Meeker, 2016, INTERNET TRENDS 2016
Moore Sarah G., 2017, J ASS CONSUMER RES, V2, P22945
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Patricio Robles, 2016, ECONSULTANCY 0308
Peters K, 2013, J INTERACT MARK, V27, P281, DOI 10.1016/j.intmar.2013.09.007
Pieters R, 2004, J MARKETING, V68, P36, DOI 10.1509/jmkg.68.2.36.27794
Pumawirawan N, 2015, J INTERACT MARK, V31, P17, DOI 10.1016/j.intmar.2015.05.001
Rainer W., 2008, ECONOMETRIC ANAL COU
ROSENBAUM PR, 1983, BIOMETRIKA, V70, P41, DOI 10.1093/biomet/70.1.41
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Seshadri Tirunillai, 2012, MARKET SCI, V31, P19
Statista, 2019, MOST POP MOB SOC NET
Stuart EA, 2010, STAT SCI, V25, P1, DOI 10.1214/09-STS313
Szegedy C, 2016, PROC CVPR IEEE, P2818, DOI 10.1109/CVPR.2016.308
Tan C., 2014, P 52 ANN M ASS COMP
Wedel M, 2015, MARKET SCI, V34, P134, DOI 10.1287/mksc.2014.0882
Xiao L, 2014, MARKET SCI, V33, P338, DOI 10.1287/mksc.2013.0837
Zhang S, 2017, WORKING PAPER
NR 42
TC 263
Z9 304
U1 82
U2 502
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2437
EI 1547-7193
J9 J MARKETING RES
JI J. Mark. Res.
PD FEB
PY 2020
VL 57
IS 1
BP 1
EP 19
DI 10.1177/0022243719881113
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA KB9GB
UT WOS:000506793900001
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Cherednichenko, O
Ivashchenko, O
Lincenyi, M
Kovác, M
AF Cherednichenko, Olga
Ivashchenko, Oksana
Lincenyi, Marcel
Kovac, Marian
TI INFORMATION TECHNOLOGY FOR INTELLECTUAL ANALYSIS OF ITEM DESCRIPTIONS IN
E-COMMERCE
SO ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
LA English
DT Article
DE Information Technology; e-Commerce; Product Matching; Text Processing;
Model; Artificial Intelligence
ID DIGITALIZATION; SYSTEMS
AB E-commerce is experiencing a robust surge, propelled by the worldwide digital transformation and the mutual advantages accrued by both consumers and merchants. The integration of information technologies has markedly augmented the efficacy of digital enterprise, ushering in novel prospects and shaping innovative business paradigms. Nonetheless, adopting information technology is concomitant with risks, notably concerning safeguarding personal data. This substantiates the significance of research within the domain of artificial intelligence for e-commerce, with particular emphasis on the realm of recommender systems. This paper is dedicated to the discourse surrounding the construction of information technology tailored for processing textual descriptions pertaining to commodities within the e-commerce landscape. Through a qualitative analysis, we elucidate factors that mitigate the risks inherent in unauthorized data access. The cardinal insight discerned is that the apt utilization of product matching technologies empowers the formulation of recommendations devoid of entailing customers' personal data or vendors' proprietary information. A meticulously devised structural model of this information technology is proffered, delineating the principal functional components essential for processing textual data found within electronic trading platforms. Central to our exposition is the exploration of the product comparison predicated on textual depictions. The resolution of this challenge stands to enhance the efficiency of product searches and facilitate product juxtaposition and categorization. The prospective implementation of the propounded information technology, either in its entirety or through its constituent elements, augurs well for sellers, enabling them to improve a pricing strategy and heightened responsiveness to market sales trends. Concurrently, it streamlines the procurement journey for buyers by expediting the identification of requisite goods within the intricate milieu of e-commerce platforms.
C1 [Cherednichenko, Olga; Ivashchenko, Oksana; Lincenyi, Marcel; Kovac, Marian] Bratislava Univ Econ & Management, Furdekova 16, Bratislava 85104, Slovakia.
RP Cherednichenko, O (autor correspondiente), Bratislava Univ Econ & Management, Furdekova 16, Bratislava 85104, Slovakia.
EM olga.cherednichenko@vsemba.sk; oksana.ivashchenko@vsemba.sk;
marcel.lincenyi@vsemba.sk; marian.kovac@vsemba.sk
FU EU NextGenerationEU through the Recovery and Resilience Plan for
Slovakia [09I03-03-V01-00078, 09I03-03-V01-00080]
FX This research is funded by the EU NextGenerationEU through the Recovery
and Resilience Plan for Slovakia under the project No.
09I03-03-V01-00078 and the project No. 09I03-03-V01-00080
CR Ahmed M, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9081295
Akritidis L, 2019, Arxiv, DOI [arXiv:1903.04276, 10.1007/s10462-020-09807-8, DOI 10.1007/S10462-020-09807-8]
Akritidis L, 2018, 2018 INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA)
Andonov A, 2021, TEM J, V10, P1558, DOI 10.18421/TEM104-09
[Anonymous], 2023, Digital transformation: Market insights report
[Anonymous], 2019, HIST AM REC ALG
Appel A. P., 2022, ARXIV
Bharadiya J. P., 2023, Int. J. Comput. (IJC), V48, P123
Bizer Christian, 2019, Datenbank-Spektrum, V19, P127, DOI 10.1007/s13222-019-00313-y
bright data, 2023, About us
Caputo A, 2021, J BUS RES, V123, P489, DOI 10.1016/j.jbusres.2020.09.053
Chatterjee S, 2015, IEEE INT ADV COMPUT, P393, DOI 10.1109/IADCC.2015.7154737
Cherednichenko O., 2023, ECONOMICS CULTURE, V20, P77, DOI [10.2478/jec-2023-0007, DOI 10.2478/JEC-2023-0007]
Crescenzi V, 2021, Arxiv, DOI arXiv:2101.11259
Dou W., 2023, SOFT TARGET ENHANCED, DOI [10.1609/aaai.v37i4.25544, DOI 10.1609/AAAI.V37I4.25544]
Foxcroft J., 2021, CANADIAN C, DOI [10.21428/594757db.08c5079, DOI 10.21428/594757DB.08C5079]
Gopalakrishnan V., 2012, P 21 ACM INT C INFOR, P605, DOI 10.1145/2396761.2396839
Guru S, 2020, DECISION-INDIA, V47, P137, DOI 10.1007/s40622-020-00241-x
Jamra RK, 2020, INT CONF ELECT ENG, P142, DOI 10.1109/ICELTICS50595.2020.9315437
Kannan Anitha, 2011, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11, P404, DOI DOI 10.1145/2020408.2020474
Konda P, 2016, PROC VLDB ENDOW, V9, P1581, DOI 10.14778/3007263.3007314
Konda PradapVenkatramanan., 2018, Magellan: Toward building entity matching management systems
Kopcke Hanna., 2012, P 15 INT C EXTENDING, P545
Korovkinas K, 2019, BALT J MOD COMPUT, V7, P47, DOI 10.22364/bjmc.2019.7.1.04
Kumar D. R., 2019, INT J RES ANALYTICAL, V6, P276
Li YL, 2020, Arxiv, DOI arXiv:2004.00584
Lukasik Szymon, 2021, Computational Science - ICCS 2021. 21st International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12743), P92, DOI 10.1007/978-3-030-77964-1_8
Magnolia Market, 2023, US
McKinsey and company, 2013, RETAILERS CAN KEEP C
Mikolov T., 2013, ARXIV, DOI DOI 10.48550/ARXIV.1301.3781
Mikolov T, 2013, Advances in neural information processing systems, DOI DOI 10.48550/ARXIV.1310.4546
Mudgal S, 2018, INT CONF MANAGE DATA, P19, DOI 10.1145/3183713.3196926
Nigam P, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2876, DOI 10.1145/3292500.3330759
Oxylabs, 2023, US
Peeters Ralph, 2020, WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics, P195, DOI 10.1145/3405962.3405964
Peeters R., 2020, SMALL, V745, P2
Peeters R, 2023, Arxiv, DOI [arXiv:2301.09521, 10.48550/arXiv.2301.09521, DOI 10.48550/ARXIV.2301.09521]
Primpeli A, 2021, LECT NOTES COMPUT SC, V12922, P182, DOI 10.1007/978-3-030-88361-4_11
Primpeli A, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P3101, DOI 10.1145/3340531.3412781
Primpeli A, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P381, DOI 10.1145/3308560.3316609
Ristoski P, 2018, SEMANT WEB, V9, P707, DOI 10.3233/SW-180300
Shah K., 2018, P 2018 C ASS COMPUTA, V3, P8, DOI [DOI 10.18653/V1/N18-3002, 10.18653/v1/N18-3002]
Singh AK, 2019, INT J ADV COMPUT SC, V10, P305
Smartproxy, 2023, US
Smith B, 2017, IEEE INTERNET COMPUT, V21, P12, DOI 10.1109/MIC.2017.72
Statista Market Insights, 2023, COMMERCE RETAIL SALE
Strauss O, 2019, WEBIST: PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES, P242, DOI 10.5220/0008069402420249
Taher G., 2021, International Journal of Academic Research in Accounting, Finance and Management Sciences, V11, DOI [https://doi.org/10.6007/IJARAFMS/v11-i1/8987, DOI 10.6007/IJARAFMS/V11-I1/8987]
Vaska S, 2021, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.539363
Wachsmuth H, 2015, LECT NOTES COMPUT SC, V9383, P1, DOI 10.1007/978-3-319-25741-9
Wachsmuth Henning, 2013, Computational Linguistics and Intelligent Text Processing. 14th International Conference, CICLing 2013. Proceedings, P38, DOI 10.1007/978-3-642-37247-6_4
Wang P, 2021, PROC INT CONF DATA, P1296, DOI 10.1109/ICDE51399.2021.00116
Wang T., 2022, ARXIV, DOI [10.48550/arXiv.2205.05889, DOI 10.48550/ARXIV.2205.05889]
Winters-Hilt S, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-S7-S18
Yao YK, 2013, J COMPUT, V8, P2632, DOI 10.4304/jcp.8.10.2632-2639
Ye C, 2022, KNOWL-BASED SYST, V251, DOI 10.1016/j.knosys.2022.109033
Zheng XS, 2022, TRANSFORM BUS ECON, V21, P772
Zuo Zhen, 2020, KDD WORKSH IND REC S
NR 58
TC 0
Z9 0
U1 5
U2 5
PU ENTERPRENEURSHIP & SUSTAINABILITY CENTER
PI VILNUS
PA M K CIURLIONIO STR 86A, VILNUS, 03100, LITHUANIA
SN 2345-0282
J9 ENTREP SUSTAIN ISS
JI Entrepreneurship. Sustain.
PD SEP
PY 2023
VL 11
IS 1
BP 178
EP 190
DI 10.9770/jesi.2023.11.1(10)
PG 13
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA X8HW3
UT WOS:001100803400009
OA gold
DA 2024-03-27
ER
PT J
AU Zhou, C
Li, H
Zhang, LL
Ren, YF
AF Zhou, Chi
Li, He
Zhang, Linlin
Ren, Yufei
TI Optimal Recommendation Strategies for AI-Powered E-Commerce Platforms: A
Study of Duopoly Manufacturers and Market Competition
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE recommendation strategy; platform operations; duopoly competition;
pricing strategies; product substitutability
ID PRICE-COMPETITION; CHANNEL; COOPERATION; SYSTEM
AB Artificial intelligence-powered recommendation systems have gained popularity as a tool to enhance user experience and boost sales. Platforms often need to make decisions about which seller to recommend and the strength of the recommendation when conducting recommendations. Therefore, it is necessary to explore the recommendation strategy of the platform in the case of duopoly competition. We develop a game model where two competing manufacturers sell products through an agency contract on a common platform, and they can decide whether or not to provide recommendations to the manufacturers. Our highlight lies in the endogenous recommendation strength of the platform. The findings suggest that it is optimal for the platform to offer recommendation services when the commission rate is high. The platform also prefers to only recommend one manufacturer in the market with low or high competition, but it prefers to recommend both manufacturers in moderately competitive markets. From the view of manufacturers, they can benefit from the recommendation service as long as the commission rate is not too low. Moreover, recommending only one manufacturer consistently yields stronger recommendations compared to recommending multiple manufacturers. However, the impact of recommendation on prices is influenced by the commission rate and product substitutability. These results have significant implications for platform decision making and provide valuable insights into the trade-offs involved in the development of recommendation systems.
C1 [Zhou, Chi; Li, He] Tianjin Univ Technol, Sch Management, Tianjin 300384, Peoples R China.
[Zhang, Linlin] Beijing Technol & Business Univ, Sch E Commerce & Logist, Beijing 100048, Peoples R China.
[Ren, Yufei] Univ Minnesota Duluth, Labovitz Sch Business & Econ, Duluth, MN 55812 USA.
C3 Tianjin University of Technology; Beijing Technology & Business
University; University of Minnesota System; University of Minnesota
Duluth; University of Minnesota Twin Cities; University of Minnesota
Hospital
RP Li, H (autor correspondiente), Tianjin Univ Technol, Sch Management, Tianjin 300384, Peoples R China.; Zhang, LL (autor correspondiente), Beijing Technol & Business Univ, Sch E Commerce & Logist, Beijing 100048, Peoples R China.
EM czhou@tju.edu.cn; lh300384@163.com; zhanglinlin@btbu.edu.cn;
yren@d.umn.edu
RI Zhang, Linlin/HGE-7116-2022
OI Zhang, Linlin/0000-0003-4553-448X; Zhou, Chi/0000-0002-3267-6107
FU Tianjin Philosophy and Social Science Planning
FX This work is supported by the Tianjin Philosophy and Social Science
Planning Project (No. TJGL22-013), the Innovation Centre for Digital
Business and Capital Development of Beijing Technology and Business
University (No. SZSK202209).
CR Adomavicius G, 2018, INFORM SYST RES, V29, P84, DOI 10.1287/isre.2017.0703
Anderson EG, 2014, INFORM SYST RES, V25, P152, DOI 10.1287/isre.2013.0505
Cai GS, 2012, MARKET SCI, V31, P172, DOI 10.1287/mksc.1110.0688
Chen PP, 2020, OMEGA-INT J MANAGE S, V94, DOI 10.1016/j.omega.2019.07.001
CHOI SC, 1991, MARKET SCI, V10, P271, DOI 10.1287/mksc.10.4.271
Choi TM, 2020, EUR J OPER RES, V284, P1031, DOI 10.1016/j.ejor.2020.01.049
Ding Y, 2018, OMEGA-INT J MANAGE S, V77, P58, DOI 10.1016/j.omega.2017.05.007
Dogan O, 2023, J THEOR APPL EL COMM, V18, P831, DOI 10.3390/jtaer18020043
Ettl M, 2020, M&SOM-MANUF SERV OP, V22, P461, DOI 10.1287/msom.2018.0756
Ghose A, 2007, INFORM SYST RES, V18, P300, DOI 10.1287/isre.1070.0130
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
Huang H, 2016, INT J PROD ECON, V178, P12, DOI 10.1016/j.ijpe.2016.04.022
Jena SK, 2014, INT J PROD ECON, V156, P346, DOI 10.1016/j.ijpe.2014.06.018
Jeuland AP, 2008, MARKET SCI, V27, P52, DOI 10.1287/mksc.1070.0332
Lee D, 2021, MANAGE SCI, V67, P524, DOI 10.1287/mnsc.2019.3546
Li G, 2019, TRANSPORT RES E-LOG, V130, P108, DOI 10.1016/j.tre.2019.08.014
Li LS, 2018, INFORM SYST RES, V29, P1003, DOI 10.1287/isre.2017.0765
Lu QH, 2018, EUR J OPER RES, V264, P1074, DOI 10.1016/j.ejor.2017.07.002
Mantin B, 2014, PROD OPER MANAG, V23, P1937, DOI 10.1111/poms.12203
Martínez-de-Albéniz V, 2022, M&SOM-MANUF SERV OP, V24, P2367, DOI 10.1287/msom.2022.1105
Shen YL, 2019, PROD OPER MANAG, V28, P1173, DOI 10.1111/poms.12977
SPENCE M, 1976, AM ECON REV, V66, P407
Tang F, 2023, EUR J OPER RES, V310, P397, DOI 10.1016/j.ejor.2023.03.020
Taylor TA, 2018, M&SOM-MANUF SERV OP, V20, P704, DOI 10.1287/msom.2017.0678
Tsunoda Y, 2021, PROD OPER MANAG, V30, P4219, DOI 10.1111/poms.13518
Wang RH, 2022, IEEE T ENG MANAGE, V69, P2260, DOI [10.1155/2020/2676210, 10.1109/TEM.2020.3007347]
Wei J, 2020, EUR J OPER RES, V280, P508, DOI 10.1016/j.ejor.2019.07.048
Wu H, 2015, PROD OPER MANAG, V24, P1768, DOI 10.1111/poms.12363
Yoon N, 2021, J THEOR APPL EL COMM, V16, P1912, DOI 10.3390/jtaer16050107
Yu J, 2022, J THEOR APPL EL COMM, V17, P1769, DOI 10.3390/jtaer17040089
Zha Y, 2023, MARKET SCI, V42, P659, DOI 10.1287/mksc.2022.1397
Zhang JQ, 2019, EUR J OPER RES, V272, P928, DOI 10.1016/j.ejor.2018.07.023
Zhang T, 2023, INT J PROD RES, V61, P3829, DOI 10.1080/00207543.2021.1884308
Zhen XP, 2022, EUR J OPER RES, V299, P168, DOI 10.1016/j.ejor.2021.06.030
Zhou B., 2022, MARK SCI, V41, P1
Zhou C, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3262318
Zhou C, 2022, ELECTRON COMMER R A, V53, DOI 10.1016/j.elerap.2022.101144
Zhu XX, 2016, EUR J OPER RES, V248, P856, DOI 10.1016/j.ejor.2015.07.053
NR 38
TC 1
Z9 1
U1 23
U2 35
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD JUN
PY 2023
VL 18
IS 2
BP 1086
EP 1106
DI 10.3390/jtaer18020055
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA K2SY0
UT WOS:001015001000001
OA gold, Green Published
DA 2024-03-27
ER
PT J
AU Dhote, S
Vichoray, C
Pais, R
Baskar, S
Shakeel, PM
AF Dhote, Sunita
Vichoray, Chandan
Pais, Rupesh
Baskar, S.
Shakeel, P. Mohamed
TI Hybrid geometric sampling and AdaBoost based deep learning approach for
data imbalance in E-commerce
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE E-commerce; Deep learning; AdaBoost; Data imbalance; Geometric analysis
ID PREDICTION
AB Presently, significance of deep learning techniques starts to overlook the world of E-commerce with their endless customizable online shopping experience to the users. Though huge data is streaming constantly during online commerce, data imbalance problem is still unaddressed due to insufficient analytical algorithms to handle huge datasets for smooth outliers. This leads to high congestion in the network as well as the extraordinary cost problem during online commerce. The foremost objective of this work is to resolve the classification task of imbalance data and churn rate using hybrid geometric sampling and AdaBoost based deep learning classification approach that uses diverse solution to provide a balance among prediction, accuracy, precision, specificity, sensitivity, and usability of data in E-commerce. This proposed solution helps to reduce the data imbalance problem and prediction of churn as well as non-churn customers in E-commerce web links. The experimental analysis has been carried out for the proposed algorithm in accordance with conventional techniques to check the practicability of the algorithm in real time practice.
C1 [Dhote, Sunita; Vichoray, Chandan; Pais, Rupesh] Shri Ramdeobaba Coll Engn & Management, Dept Management Technol, Nagpur, Maharashtra, India.
[Baskar, S.] Karpagam Acad Higher Educ, Dept Elect & Commun Engn, Coimbatore, Tamil Nadu, India.
[Shakeel, P. Mohamed] Univ Tekn Malaysia Melaka, Fac Informat & Commun Technol, Melaka, Malaysia.
C3 Rashtrasant Tukadoji Maharaj Nagpur University; Shri Ramdeobaba College
of Engineering & Management; Karpagam Academy of Higher Education
(KAHE); University Teknikal Malaysia Melaka
RP Dhote, S (autor correspondiente), Shri Ramdeobaba Coll Engn & Management, Dept Management Technol, Nagpur, Maharashtra, India.
EM sunitadhote@outlook.com
RI Pais, Rupesh/AAB-3101-2021; Vichoray, Chandan/AAD-4273-2021; S,
Baskar/R-6346-2017; Mohamed Shakeel, Pethuraj/P-4135-2019
OI Vichoray, Chandan/0000-0002-2072-735X; S, Baskar/0000-0003-3570-3059;
CR Aydin F., 2019, ARXIV PREPRINT ARXIV
Barbado R, 2019, INFORM PROCESS MANAG, V56, P1234, DOI 10.1016/j.ipm.2019.03.002
Berger P, 2019, IEEE INTELL SYST, V34, P44, DOI 10.1109/MIS.2019.2895788
Dolfen Paul, 2019, W25610 NAT BUR EC
Fader PS, 2005, APPL STOCH MODEL BUS, V21, P461, DOI 10.1002/asmb.592
Gao SL, 2019, 2019 4TH ASIA-PACIFIC CONFERENCE ON INTELLIGENT ROBOT SYSTEMS (ACIRS 2019), P155, DOI [10.1109/acirs.2019.8935950, 10.1109/ACIRS.2019.8935950]
Golwalkar Y. V, 2019, U.S. Patent Application, Patent No. [10/169,806, 10169806]
Goolsbee AD, 2018, AEA PAP P, V108, P488, DOI 10.1257/pandp.20181038
Lafkihi M, 2019, TRANSPORT RES E-LOG, V125, P348, DOI 10.1016/j.tre.2019.03.021
Leung KH, 2018, EXPERT SYST APPL, V91, P386, DOI 10.1016/j.eswa.2017.09.026
Liu DR, 2020, EXPERT SYST, V37, DOI 10.1111/exsy.12511
Nolan J, 2019, COMPUTER SCI INFORM, V2, P1
Patil Megharani, 2019, Smart Intelligent Computing and Applications. Proceedings of the Second International Conference on SCI 2018. Smart Innovation, Systems and Technologies (SIST 105), P197, DOI 10.1007/978-981-13-1927-3_20
Pei Z, 2019, IND MARKET MANAG, V76, P12, DOI 10.1016/j.indmarman.2018.06.013
Raj ED, 2017, NEUROCOMPUTING, V219, P412, DOI 10.1016/j.neucom.2016.09.036
Shaikh Javed R., 2019, Applications of Computing, Automation and Wireless Systems in Electrical Engineering. Proceedings of MARC 2018. Lecture Notes in Electrical Engineering (LNEE 553), P551, DOI 10.1007/978-981-13-6772-4_48
Sivasankar E, 2019, INT J BUSINESS INFOR, V31, P1, DOI DOI 10.1504/IJBIS.2019.099524
Sohaib O, 2019, COMPUT IND ENG, V132, P47, DOI 10.1016/j.cie.2019.04.020
Sun J, 2018, ROUTL CONTEMP CHINA, P1, DOI 10.4324/9781315626758
Wang C, 2019, NAT MICROBIOL, V18, P1, DOI DOI 10.1142/S1469026819500032
NR 20
TC 43
Z9 44
U1 5
U2 44
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD JUN
PY 2020
VL 20
IS 2
SI SI
BP 259
EP 274
DI 10.1007/s10660-019-09383-2
PG 16
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LM3OZ
UT WOS:000532161800001
DA 2024-03-27
ER
PT J
AU Cao, YL
Shao, Y
Zhang, HX
AF Cao, Yali
Shao, Yue
Zhang, Hongxia
TI Study on early warning of E-commerce enterprise financial risk based on
deep learning algorithm
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Financial risks; E-commerce enterprises; Deep learning; Early warning
AB With the development trend of economic progress, the capital business of e-commerce enterprises has become complicated. The financial risk of listed companies is a problem that needs to be paid attention to. The financial risk of e-commerce companies is a complex and gradual process, and its unique reasons may be many. E-commerce companies are facing financial risks or difficulties, and bankruptcy and liquidation are also increasing. Financial risk has seriously affected e-commerce companies and society. As a result, the early warning methods of financial risks have been constantly improved. With the arrival of the new economic era in the era of knowledge economy, the early warning of financial risks in e-commerce companies has become a hot issue in the financial management of e-commerce companies. Based on the deep learning algorithm, this paper studies from the perspective of establishing the financial early warning model based on deep learning and constructing the financial risk early warning mechanism of e-commerce companies, and analyzes and forecasts the financial risks of listed companies. Through the construction of financial security early warning system, crisis signals can be diagnosed as soon as possible, and crisis signals can be prevented and solved timely and effectively.
C1 [Cao, Yali] Beijing Technol & Business Univ, Business Sch, Beijing 100048, Peoples R China.
[Shao, Yue] Univ Int Business & Econ, Business Sch, Beijing 100029, Peoples R China.
[Zhang, Hongxia] Zhejiang Agr & Forestry Univ, Jiyang Coll, Zhuji 311800, Peoples R China.
C3 Beijing Technology & Business University; University of International
Business & Economics; Zhejiang A&F University
RP Zhang, HX (autor correspondiente), Zhejiang Agr & Forestry Univ, Jiyang Coll, Zhuji 311800, Peoples R China.
EM zhanghxia1982@163.com
RI Liu, Jing/IQX-0664-2023
FU Collaborative Innovation Center of State Owned Assets Management of BTBU
[GZGL-KFKT-2019-02]; project of The Research Foundation for Youth
Scholars of Beijing Technology and Business University
[PXM2019_014213_000007]; Planning of philosophy and social sciences in
Zhejiang province [20NDJC220YB]
FX This work was supported by the opening project of Collaborative
Innovation Center of State Owned Assets Management of BTBU (No.
GZGL-KFKT-2019-02), the project of The Research Foundation for Youth
Scholars of Beijing Technology and Business University (No.
PXM2019_014213_000007) and Planning of philosophy and social sciences in
Zhejiang province (No. 20NDJC220YB).
CR Bologna P, 2015, J FINANC SERV RES, V47, P81, DOI 10.1007/s10693-013-0180-4
Cheng Z, 2016, BUILD ENVIRON, V108, P85, DOI 10.1016/j.buildenv.2016.08.019
Denglin L., 2016, RES MODERN STATE OWN, V18, P89
Frost J, 2014, REV INT ECON, V22, P722, DOI 10.1111/roie.12124
Furong L., 2019, FINANCE ACCOUNTING S, V222, P77
Gan Wei, 2017, T CHINA ELECTROTECHN, V32, P11
Giudici P, 2017, QUANT FINANC, V17, P1995, DOI 10.1080/14697688.2017.1357968
Haiyan W, 2016, CHINA EC TRADE, V21, P236
Huilin Y, 2016, FINANCE ACCOUNTING S, V20, P49
Iazzolino G, 2013, APPL ECON LETT, V20, P1310, DOI 10.1080/13504851.2013.806771
Jobst AA, 2013, INT REV FINANC ANAL, V28, P112, DOI 10.1016/j.irfa.2013.01.005
Keith JL, 2014, J BUS ETHICS, V130, P1
Korol T, 2013, ECON MODEL, V31, P22, DOI 10.1016/j.econmod.2012.11.017
Lei Z, 2016, CHINA COLLECTIVE EC, V12, P108
Lodi G, 2015, PHYS EDUC, V2, P1379
Ma Y, 2014, CHINA WORLD ECON, V22, P64, DOI 10.1111/cwe.12092
Oet MV, 2013, J BANK FINANC, V37, P4510, DOI 10.1016/j.jbankfin.2013.02.016
Sha L, 2015, FINANCE ACCOUNTING N, V11, P106
Sun W, 2015, J INFORM COMPUTATION, V12, P601
Wenping Z, 2015, J FINANCE ACCOUNTING, V23, P68
Xiaoying S, 2015, ELECT DESIGN ENG, V23, P58
Yang Xiutuan Lu, 2015, FINANCE ACCOUNTING, V10, P73
Yanyan T, 2015, FINANCE ACCOUNTING N, V2, P45
Ying Z, 2015, COOPERATIVE EC SCI T, V4, P134
Yingchao Du, 2015, ENTERPRISE REFORM MA, V9, P123
Zhongxi T, 2015, ENTERPRISE REFORM MA, V23, P139
NR 26
TC 24
Z9 25
U1 19
U2 116
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD MAR
PY 2022
VL 22
IS 1
SI SI
BP 21
EP 36
DI 10.1007/s10660-020-09454-9
EA JAN 2021
PG 16
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA ZN0VD
UT WOS:000605860500001
DA 2024-03-27
ER
PT J
AU Pessanha, GRG
Soares, EA
AF Gomes Pessanha, Gabriel Rodrigo
Soares, Eduardo Almeida
TI JUST ONE POST? FORECASTS OF DAILY SALES OF BEAUTY AND COSMETICS RETAIL
COMPANIES BASED ON THE INFLUENCE OF SOCIAL MEDIA
SO REVISTA BRASILEIRA DE MARKETING
LA English
DT Article
DE Social media; Images; Artificial intelligence; Sales forecasting;
Digital marketing; Digital influencer
ID WORD-OF-MOUTH; MANAGEMENT JUDGMENT; IMPACT; PURCHASE; INFORMATION;
PRODUCT; ENGAGEMENT; TWITTER; BRANDS; IDENTIFICATION
AB Objective: To study the relevance of Instagram posts in the construction of forecasting models for the variation of daily sales revenues for retail companies in the beauty and cosmetics sector.
Methodology: Time series of daily sales between the years 2017 and 2019 of 10 retail companies in the beauty and cosmetics sector were considered. Methods based on machine learning were used and the forecasting models were increased with numerical variables from the official profile of the company, from the posting made by the contracted digital influencer and the characteristics of the images posted by the digital influencer were included in the models.
Relevance and Originality: The study is innovative, as it goes beyond qualitative reflections on the theme and provides empirical evidence regarding the impacts on forecast accuracy from the inclusion of social media variables. A data fusion strategy (numerics and images) was also presented to forecast daily sales of retail companies in the beauty and cosmetics sector.
Main results: The models proved to be efficient in forecasting and the importance of the likes and engagement variables reinforces the idea that the identification and social reference generated by the ID are important aspects in the purchase decision process. It was found that the images are responsible for adding exclusive attributes that help in forecasting and understanding the patterns of the sales series.
Theoretical and methodological contributions:The study showed in a promising way the efficiency of methods based on machine learning in forecasting sales from Instagram data, especially with regard to the incorporation and extraction of image data.
C1 [Gomes Pessanha, Gabriel Rodrigo] Fed Univ Alfenas UNIFAL, Inst Appl Social Sci ICSA, Business, Varginha, MG, Brazil.
[Soares, Eduardo Almeida] Univ Lancaster, Lancaster, England.
C3 Universidade Federal de Alfenas; Lancaster University
RP Pessanha, GRG (autor correspondiente), Fed Univ Alfenas UNIFAL, Inst Appl Social Sci ICSA, Business, Varginha, MG, Brazil.
EM gabriel.pessanha@unifal-mg.edu.br; e.almeidasoares@lancaster.ac.uk
RI Soares, Eduardo/AAU-8358-2020
OI Soares, Eduardo/0000-0002-2634-8270
CR Agnihotri R, 2016, IND MARKET MANAG, V53, P172, DOI 10.1016/j.indmarman.2015.09.003
Alalwan AA, 2018, INT J INFORM MANAGE, V42, P65, DOI 10.1016/j.ijinfomgt.2018.06.001
Alalwan AA, 2017, TELEMAT INFORM, V34, P1177, DOI 10.1016/j.tele.2017.05.008
Araujo T, 2017, INT J ADVERT, V36, P496, DOI 10.1080/02650487.2016.1173765
Arora A, 2019, J RETAIL CONSUM SERV, V49, P86, DOI 10.1016/j.jretconser.2019.03.012
Asur S., 2010, Proceedings 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), P492, DOI 10.1109/WI-IAT.2010.63
Audrezet A, 2020, J BUS RES, V117, P557, DOI 10.1016/j.jbusres.2018.07.008
Bakhshi S, 2014, 32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014), P965, DOI 10.1145/2556288.2557403
BUCKLAND ST, 1993, J APPL ECOL, V30, P478, DOI 10.2307/2404188
Casalo LV, 2020, J BUS RES, V117, P510, DOI 10.1016/j.jbusres.2018.07.005
Chen IF, 2017, NEURAL COMPUT APPL, V28, P2633, DOI 10.1007/s00521-016-2215-x
Childers CC, 2019, J CURR ISS RES AD, V40, P258, DOI 10.1080/10641734.2018.1521113
Cui RM, 2018, PROD OPER MANAG, V27, P1749, DOI 10.1111/poms.12707
De Veirman M, 2017, INT J ADVERT, V36, P798, DOI 10.1080/02650487.2017.1348035
De Vries NJ, 2014, J BRAND MANAG, V21, P495, DOI 10.1057/bm.2014.18
Dhanesh GS, 2019, PUBLIC RELAT REV, V45, P40, DOI 10.1016/j.pubrev.2019.03.002
Djafarova E, 2017, COMPUT HUM BEHAV, V68, P1, DOI 10.1016/j.chb.2016.11.009
Tien DH, 2019, ASIA PAC MANAG REV, V24, P238, DOI 10.1016/j.apmrv.2018.06.003
Erkan I, 2016, COMPUT HUM BEHAV, V61, P47, DOI 10.1016/j.chb.2016.03.003
Fahimnia A, 2019, ARXIV PREPRINT ARXIV
Felix R, 2017, J BUS RES, V70, P118, DOI 10.1016/j.jbusres.2016.05.001
Fildes R, 2007, INTERFACES, V37, P570, DOI 10.1287/inte.1070.0309
Fildes R, 2019, INT J FORECASTING, V35, P144, DOI 10.1016/j.ijforecast.2017.12.006
Fuchs C., 2017, SOCIAL MEDIA CRITICA
Gensler S, 2013, J INTERACT MARK, V27, P242, DOI 10.1016/j.intmar.2013.09.004
Gillon K, 2014, COMMUN ASSOC INF SYS, V34, P287
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Goodwin P, 2002, OMEGA-INT J MANAGE S, V30, P127, DOI 10.1016/S0305-0483(01)00062-7
Gruhl D., 2005, P 11 ACM SIGKDD INT, P78, DOI DOI 10.1145/1081870.1081883
Hajli MN, 2014, INT J MARKET RES, V56, P387, DOI 10.2501/IJMR-2014-025
Hajli N, 2015, TECHNOL FORECAST SOC, V94, P350, DOI 10.1016/j.techfore.2015.01.012
Highfield Tim, 2015, First Monday, V20, DOI 10.5210/fm.v20i1.5563
Huang T, 2014, EUR J OPER RES, V237, P738, DOI 10.1016/j.ejor.2014.02.022
Hudson S, 2016, INT J RES MARK, V33, P27, DOI 10.1016/j.ijresmar.2015.06.004
Hughes C, 2019, J MARKETING, V83, P78, DOI 10.1177/0022242919854374
Hwang KJ, 2018, COMPUT HUM BEHAV, V87, P155, DOI 10.1016/j.chb.2018.05.029
Hyndman RobJ., 2014, Foresight: The International Journal of Applied Forecasting, P42
Jiménez-Castillo D, 2019, INT J INFORM MANAGE, V49, P366, DOI 10.1016/j.ijinfomgt.2019.07.009
Jin SV, 2018, COMPUT HUM BEHAV, V79, P154, DOI 10.1016/j.chb.2017.10.033
Johnston R., 2009, ADM PRODUCAO, V2
Jordan MI, 2015, SCIENCE, V349, P255, DOI 10.1126/science.aaa8415
Kambhampati S., 2014, P INT AAAI C WEB SOC, V8
Kim E, 2021, COMMUN RES, V48, P717, DOI 10.1177/0093650218782819
Kim N, 2018, INT J INFORM MANAGE, V39, P38, DOI 10.1016/j.ijinfomgt.2017.10.006
Kourentzes N, 2016, INT J PROD ECON, V181, P145, DOI 10.1016/j.ijpe.2015.09.011
Kremer M, 2016, MANAGE SCI, V62, P2745, DOI 10.1287/mnsc.2015.2259
Kulkarni G, 2012, DECIS SUPPORT SYST, V52, P604, DOI 10.1016/j.dss.2011.10.017
Kumar A, 2016, J MARKETING, V80, P7, DOI 10.1509/jm.14.0249
Lahuerta-Otero E, 2016, COMPUT HUM BEHAV, V64, P575, DOI 10.1016/j.chb.2016.07.035
Lassen NB, 2014, IEEE INT ENTERP DIST, P81, DOI 10.1109/EDOC.2014.20
Lawrence M, 2006, INT J FORECASTING, V22, P493, DOI 10.1016/j.ijforecast.2006.03.007
Lee D, 2018, MANAGE SCI, V64, P5105, DOI 10.1287/mnsc.2017.2902
Lin HC, 2018, BUS HORIZONS, V61, P431, DOI 10.1016/j.bushor.2018.01.010
Lipizzi C, 2015, INT J INFORM MANAGE, V35, P490, DOI 10.1016/j.ijinfomgt.2015.04.001
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Lou C., 2019, Journal of Interactive Advertising, V19, P58, DOI DOI 10.1080/15252019.2018.1533501
Lu BZ, 2016, COMPUT HUM BEHAV, V56, P225, DOI 10.1016/j.chb.2015.11.057
Luo RB, 2012, GIGASCIENCE, V1, DOI 10.1186/2047-217X-1-18
More Jyoti Sunil, 2019, Applied Computing and Informatics, V15, P102, DOI 10.1016/j.aci.2017.11.001
Oliva R, 2009, PROD OPER MANAG, V18, P138, DOI 10.1111/j.1937-5956.2009.01003.x
Ramanathan R, 2010, MANAGE DECIS, V48, P1493, DOI 10.1108/00251741011090298
Ramanathan U, 2011, EXPERT SYST APPL, V38, P5544, DOI 10.1016/j.eswa.2010.10.082
Rosario AB, 2016, J MARKETING RES, V53, P297, DOI 10.1509/jmr.14.0380
Schivinski B., 2014, J MARK COMMUN, V22, P1, DOI DOI 10.1080/13527266.2013.871323
Schouten AP, 2020, INT J ADVERT, V39, P258, DOI 10.1080/02650487.2019.1634898
Shiau WL, 2018, INT J INFORM MANAGE, V43, P52, DOI 10.1016/j.ijinfomgt.2018.06.006
Simonyan K, 2015, Arxiv, DOI [arXiv:1409.1556, DOI 10.48550/ARXIV.1409.1556]
Sokolova K, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.01.011
Suykens JAK, 1999, NEURAL PROCESS LETT, V9, P293, DOI 10.1023/A:1018628609742
Syntetos AA, 2016, EUR J OPER RES, V252, P1, DOI 10.1016/j.ejor.2015.11.010
Trapero JR, 2015, J OPER RES SOC, V66, P299, DOI 10.1057/jors.2013.174
Welbourne DJ, 2016, PUBLIC UNDERST SCI, V25, P706, DOI 10.1177/0963662515572068
NR 72
TC 0
Z9 0
U1 16
U2 56
PU UNIV NOVE JULHO
PI SAO PAULO
PA AV FRANCISCO MATARAZZO 612, AGUA BRANCA, SAO PAULO, C05001-100, BRAZIL
SN 2177-5184
J9 REV BRASIL MARK
JI Rev. Brasil. Mark.
PD OCT-DEC
PY 2021
VL 20
IS 4
BP 241
EP 267
DI 10.5585/remark.v20i4.17914
PG 27
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA XN9IQ
UT WOS:000729811400001
OA gold
DA 2024-03-27
ER
PT J
AU Pang, H
Zhang, WK
AF Pang, He
Zhang, Wukang
TI RETRACTADO: Decision support model of e-commerce strategic planning
enhanced by machine learning (Retracted Article)
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article; Retracted Publication
DE E-commerce strategic planning; Decision support; Machine learning;
Intelligent decision
AB The effectiveness of e-commerce strategic planning determines whether e-commerce can achieve the expected goals and performance. Technology adoption has become the main research idea for research organizations to adopt e-commerce, but the study of strategic planning process is still in its infancy. Construct a supervised machine learning model with strong generalization ability to solve the decision support problem of e-commerce strategic planning more effectively. A decision support method integrating rough set and support vector machine (SVM) is proposed, which is based on a three-stage SVM hybrid model. Decision support method, e-commerce strategic planning crisis early warning decision model is based on parallel meta-heuristic strategy and FKNN. Experiments show that the intelligent decision-making methods proposed in this paper can obtain good performance, and achieve the expected results and objectives, and promote the further development of the machine learning method itself, which has certain theoretical significance and practical application value.
C1 [Pang, He; Zhang, Wukang] XiAn Univ Finance & Econ, Sch Econ, Xian 710100, Shaanxi, Peoples R China.
C3 Xi'an University of Finance & Economics
RP Pang, H (autor correspondiente), XiAn Univ Finance & Econ, Sch Econ, Xian 710100, Shaanxi, Peoples R China.
EM panghecandy@126.com; zhangwukang@xaufe.edu.cn
CR Andersson H, 2020, INT J MED INFORM, V133, DOI 10.1016/j.ijmedinf.2019.103975
Arnolds IV, 2018, ANN OPER RES, V263, P453, DOI 10.1007/s10479-017-2485-4
Babita P., 2018, J HEALTHC ENG, V2018
Bai, 2017, J COMPUT THEOR NANOS, V14, P189, DOI [10.1166/jctn.2017.6147, DOI 10.1166/JCTN.2017.6147]
Bai, 2017, C E CA, V42, P1460
Caradot N, 2018, J HYDROINFORM, V20, P1131, DOI 10.2166/hydro.2018.217
Costin Y, 2019, J ENTERP CULT, V27, P471, DOI 10.1142/S0218495819500171
Crick JM, 2020, J BUS RES, V113, P158, DOI 10.1016/j.jbusres.2018.11.033
Dutta G, 2019, COMPUT IND ENG, V128, P985, DOI 10.1016/j.cie.2018.06.033
Dutta G, 2018, COMPUT IND ENG, V124, P36, DOI 10.1016/j.cie.2018.07.016
Farani AY, 2019, APPL ECON, V51, P2326, DOI 10.1080/00036846.2018.1543940
Friedman Y, 2018, INNOV-ORGAN MANAG, V20, P61, DOI 10.1080/14479338.2017.1369141
Griffin M, 2016, BIG DATA-US, V4, P60, DOI 10.1089/big.2015.0043
Hanchen Zhou, 2019, Journal of Physics: Conference Series, V1237, DOI 10.1088/1742-6596/1237/2/022002
Hu N, 2016, LAND USE POLICY, V57, P356, DOI 10.1016/j.landusepol.2016.06.004
Lin SJ, 2017, INT J FUZZY SYST, V19, P659, DOI 10.1007/s40815-016-0196-7
Mao, 2019, J INTELL FUZZY SYST, V37, P1
Menon Karan, 2018, International Journal of Entrepreneurship and Small Business, V33, P575
Murça MCR, 2018, TRANSPORT RES C-EMER, V97, P301, DOI 10.1016/j.trc.2018.10.020
Peng PF, 2016, ITM WEB CONF, V7, DOI 10.1051/itmconf/20160706003
Ranjan S, 2017, INT J ADV MANUF TECH, V88, P369, DOI 10.1007/s00170-016-8770-6
Raval V, 2018, J BUS ETHICS, V150, P741, DOI 10.1007/s10551-016-3199-2
Shafi K, 2017, IEEE COMPUT INTELL M, V12, P42, DOI 10.1109/MCI.2016.2627670
Sirén C, 2019, J BUS RES, V101, P555, DOI 10.1016/j.jbusres.2018.11.025
Tian L, 2018, PROD OPER MANAG, V27, P1595, DOI 10.1111/poms.12885
Vazquez P, 2018, J BUS ETHICS, V150, P691, DOI 10.1007/s10551-016-3171-1
Wyness L., 2019, J SMALL BUS ENTERP D, V31, P183, DOI [10.1080/08276331.2018.1493338, DOI 10.1080/08276331.2018.1493338]
Yan MR, 2018, KNOWL MAN RES PRACT, V16, P173, DOI 10.1080/14778238.2018.1442994
Zhang WG, 2019, MANAGE DECIS, V57, P2414, DOI 10.1108/MD-05-2018-0538
Zheng Y, 2020, NEURAL COMPUT APPL, V32, P1869, DOI 10.1007/s00521-019-04567-1
NR 30
TC 0
Z9 0
U1 9
U2 34
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD AUG
PY 2023
VL 21
IS SUPPL 1
SU 1
BP 11
EP 11
DI 10.1007/s10257-021-00506-7
EA FEB 2021
PG 1
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA T4NO4
UT WOS:000613583600003
DA 2024-03-27
ER
PT J
AU Islam, M
Al Mamun, A
Afrin, S
Quaosar, GMAA
Uddin, MA
AF Islam, Muhaiminul
Al Mamun, Abdullah
Afrin, Samina
Quaosar, G. M. Azmal Ali
Uddin, Md Aftab
TI Technology Adoption and Human Resource Management Practices: The Use of
Artificial Intelligence for Recruitment in Bangladesh
SO SOUTH ASIAN JOURNAL OF HUMAN RESOURCE MANAGEMENT
LA English
DT Article
DE Actual use; artificial intelligence; Bangladesh; human resource
professionals; intention to use; recruiting talents; UTAUT; SEM-PLS
ID INFORMATION-TECHNOLOGY; BEHAVIORAL INTENTION; UNIFIED THEORY; UTAUT
MODEL; ACCEPTANCE; BANKING; SYSTEM; STUDENTS; IMPACT; SMES
AB Artificial intelligence (AI) is now considered indispensable in undertaking operational activities, especially in the area of human resource analytics. However, in practice, the rate of the adoption of such modern algorithms in organisations is still in its early stages. Consequently, the primary objective of this study is to identify the main antecedents of the adoption of Al-based technologies in recruitment, using the lens of the unified theory of acceptance and use of technology (UTAUT) model, alongside perceived credibility and moderating variables, in the context of an emerging nation in South Asia, namely Bangladesh. Data were collected from 283 human resource professionals employed in different manufacturing and service firms in Bangladesh through the administration of a questionnaire, which was analysed by applying PLS-SEM. The outcomes of the study show that all the direct hypothesised relationships were found to be significant, apart from the extended variable of perceived credibility. However, no moderating effect of gender or firm size was found in any of the hypothesised propositions. Finally, policy implications and recommendations for future researchers are proposed.
C1 [Islam, Muhaiminul] Univ Dhaka, Dept Org Strategy & Leadership, Dhaka, Bangladesh.
[Al Mamun, Abdullah; Afrin, Samina; Uddin, Md Aftab] Univ Chittagong, Dept Human Resource Management, Chattogram 4331, Bangladesh.
[Quaosar, G. M. Azmal Ali] Comilla Univ, Dept Management Studies, Cumilla, Bangladesh.
C3 University of Dhaka; University of Chittagong; Comilla University
RP Uddin, MA (autor correspondiente), Univ Chittagong, Dept Human Resource Management, Chattogram 4331, Bangladesh.
EM mdaftabuddin@cu.ac.bd
RI Mamun, Abdullah Al/ABC-7588-2021; Uddin, Md. Aftab/E-5896-2017
OI Mamun, Abdullah Al/0000-0001-9042-8163; Quaosar, G. M. Azmal
Ali/0000-0001-9125-7378; Uddin, Md. Aftab/0000-0002-9101-7451; Islam,
Muhaiminul/0000-0002-5927-3762
CR Absar M., 2012, Indian Journal Of Industrial Relations, V47, P436
Adikaram AS, 2021, S ASIAN J HUM RE MAN, V8, P192, DOI 10.1177/23220937211018021
Ahmadi H, 2018, TECHNOL FORECAST SOC, V134, P124, DOI 10.1016/j.techfore.2018.05.021
Al Mamun Abdullah, 2022, International Journal of Technology and Human Interaction, V18, P1, DOI 10.4018/IJTHI.299070
Alam M.S., 2019, J. Manag. Res., V6, P1
Alam M.S., 2020, Bus Perspect Rev, V2, P15, DOI [10.38157/business-perspective-review.v2i2.122, DOI 10.38157/BUSINESS-PERSPECTIVE-REVIEW.V2I2.122]
Albert Edward Tristram, 2019, Strategic HR Review, V18, P215, DOI 10.1108/SHR-04-2019-0024
Alhammadi A., 2015, DETERMINANTS CLOUD C
Alshamaila Y, 2013, J ENTERP INF MANAG, V26, P250, DOI 10.1108/17410391311325225
Argyris YA, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102347
Azim MT, 2019, MANAG RES REV, V42, P837, DOI 10.1108/MRR-08-2018-0286
Babu Kudrat-E-Khuda, 2021, International Journal of Public Law and Policy, V7, P319
Barrane FZ, 2018, INT J INNOV TECHNOL, V15, DOI 10.1142/S0219877019500044
Bhalgat, 2019, EXPLORATION ARTIFICI
Bhatiasevi V, 2016, INFORM DEV, V32, P799, DOI 10.1177/0266666915570764
Black JS, 2020, BUS HORIZONS, V63, P215, DOI 10.1016/j.bushor.2019.12.001
BRISLIN RW, 1970, J CROSS CULT PSYCHOL, V1, P185, DOI 10.1177/135910457000100301
Byrne B, 2010, INTERNATIONAL HANDBOOK OF PSYCHOLOGY IN EDUCATION, P3
Chin W. W., 2010, HDB PARTIAL LEAST SQ, P655, DOI DOI 10.1007/978-3-540-32827-8_29
Cohen J., 1977, STAT POWER ANAL EHAV
Cruz-Jesus Frederico, 2018, Trends and Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing (AISC 745), P1094, DOI 10.1007/978-3-319-77703-0_106
Cukurova M, 2020, INT J ARTIF INTELL E, V30, P205, DOI 10.1007/s40593-019-00188-w
Dangi N, 2020, J ASIA BUS STUD, V14, P599, DOI 10.1108/JABS-12-2019-0364
Dey T., 2020, IMPLEMENTATION HRIS
Dijkstra TK, 2015, MIS QUART, V39, P297, DOI 10.25300/MISQ/2015/39.2.02
Dwivedi YK, 2017, GOV INFORM Q, V34, P211, DOI 10.1016/j.giq.2017.03.001
El-Gohary H, 2012, TOURISM MANAGE, V33, P1256, DOI 10.1016/j.tourman.2011.10.013
Fan L, 2019, ASIA PAC EDUC REV, V20, P101, DOI 10.1007/s12564-018-9572-3
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Geetha R., 2018, INT J MECH ENG TECHN, V9, P63, DOI DOI 10.34218/IJMET.12.9.2021.005
Ghalandari K., 2012, MIDDLE EAST J SCI RE, V12, P801, DOI DOI 10.5829/idosi.mejsr.2012.12.6.2536
Gudergan SP, 2008, J BUS RES, V61, P1238, DOI 10.1016/j.jbusres.2008.01.012
Hair J.F., 2009, Multivariate data analysis
Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
Hemalatha A., 2021, 2021 INT C ART INT S, P60
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Hossin MS., 2021, Int Fellowship J of Interdiscip Res, V1, P66
ICTD, 2019, NAT STRAT ART INT BA
Iqbal FM., 2018, Asian J Soc Manag Stud, V5, P102
Isaac O, 2019, ASIA PAC MANAG REV, V24, P335, DOI 10.1016/j.apmrv.2018.12.003
Jacobsen D.I., 2018, Public Organization Review, V18, P111
Jahanshahi D, 2020, CASE STUD TRANSP POL, V8, P1212, DOI 10.1016/j.cstp.2020.08.002
Jilani MMAK, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19052752
Kaba B, 2014, J ASSOC INF SCI TECH, V65, P1662, DOI 10.1002/asi.23069
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Khan IU, 2022, J INTERNET COMMER, V21, P183, DOI 10.1080/15332861.2021.1882749
Kmail AB, 2015, PROC INT C TOOLS ART, P620, DOI 10.1109/ICTAI.2015.95
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015010101
Lisa A.K., 2021, THESIS UMEA U SWEDEN
Mahmood M., 2015, South Asian Journal of Human Resources Management, V2, P171, DOI DOI 10.1177/2322093715599481
Mahmood M, 2019, MANAGE DECIS, V57, P741, DOI 10.1108/MD-07-2017-0707
Mahmood M, 2015, ASIA PAC J HUM RESOU, V53, P331, DOI 10.1111/1744-7941.12057
McGovern S., 2018, NEW AGE ARTIFICIAL I
Mehrotra S., 2022, METAMORPHOSIS-AFR, V21, P31, DOI [10.1177/09726225211066220, DOI 10.1177/09726225211066220]
MUDULI A, 2020, BENCHMARKING
Nisha N, 2019, J GLOB INF MANAG, V27, P19, DOI 10.4018/JGIM.2019010102
OConnor S. W., 2020, ARTIF INTELL
Onaolapo Sodiq, 2018, Interdisciplinary Journal of e-Learning and Learning Objects, V14, P92, DOI 10.28945/4085
Palau-Saumell R, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11041210
Palshikar G. K., 2018, ADV ANAL APPL, P141, DOI [10.1007/978-981-13-1208-3_13, DOI 10.1007/978-981-13-1208-3_13]
Pan Y, 2022, INT J HUM RESOUR MAN, V33, P1125, DOI 10.1080/09585192.2021.1879206
Patel C, 2019, J BUS RES, V103, P397, DOI 10.1016/j.jbusres.2017.11.007
Patil P, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102144
Pillai R, 2020, BENCHMARKING, V27, P2599, DOI 10.1108/BIJ-04-2020-0186
Quaosar G.M. A. A., 2018, International Business Research, V11, P133, DOI DOI 10.5539/IBR.V11N4P133
Rabiul MK, 2021, INT J HOSP MANAG, V92, DOI 10.1016/j.ijhm.2020.102712
Rajan CA, 2015, IIMB MANAG REV, V27, P105, DOI 10.1016/j.iimb.2015.04.008
Ramayah T, 2016, TELEMAT INFORM, V33, P150, DOI 10.1016/j.tele.2015.06.007
Rubel MRB, 2020, S ASIAN J HUM RE MAN, V7, P9, DOI 10.1177/2322093719875567
Salah OH, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0243355
Salleh KA, 2019, PROCEDIA COMPUT SCI, V164, P168, DOI 10.1016/j.procs.2019.12.169
Samat MF., 2020, ASIAN J UNI EDU, V16, P220
Sarstedt M, 2011, ADV INT MARKETING, V22, P195, DOI 10.1108/S1474-7979(2011)0000022012
Saunders Mark., 2009, Research Methods for Business Students
Sen S., 2018, AIHR DIGITAL
Senarathna I, 2018, AUSTRALAS J INF SYST, V22
Sharma A., 2018, AI REINVENTED HIRING
Shiferaw Kirubel Biruk, 2019, Informatics in Medicine Unlocked, V17, P26, DOI 10.1016/j.imu.2019.100182
Soleimani M, 2022, INT J KNOWL MANAG, V18, DOI 10.4018/IJKM.290022
Suki NM, 2017, INT J MANAG EDUC-OXF, V15, P528, DOI 10.1016/j.ijme.2017.10.002
Tambe P, 2019, CALIF MANAGE REV, V61, P15, DOI 10.1177/0008125619867910
Tsourela M., 2015, J HIGH TECHNOLOGY MA, V26, P124, DOI [10.1016/j.hitech.2015.09.003, DOI 10.1016/J.HITECH.2015.09.003]
Uddin M., 2020, J OPEN INNOV-TECHNOL, V6, P2, DOI DOI 10.3390/JOITMC6010002
Uddin M.A., 2021, ESSENTIALS MACHINE L, P211
Uddin MA, 2020, EUR J INNOV MANAG, V23, P877, DOI 10.1108/EJIM-06-2019-0168
Upadhyay Ashwani Kumar, 2018, Strategic HR Review, V17, P255, DOI 10.1108/SHR-07-2018-0051
van Esch P, 2019, COMPUT HUM BEHAV, V90, P215, DOI 10.1016/j.chb.2018.09.009
Vardarlier P., 2020, DIGITAL BUSINESS STR, P355
Vedapradha R., 2019, J Serv Sci Manag, V12, P382
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Wang J, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10051654
Wang YS, 2003, INT J SERV IND MANAG, V14, P501, DOI 10.1108/09564230310500192
WEF, 2018, IS ART INT REPL JOBS
Yu CS, 2012, J ELECTRON COMMER RE, V13, P104
NR 97
TC 7
Z9 7
U1 16
U2 46
PU SAGE PUBLICATIONS INDIA PVT LTD
PI NEW DELHI
PA B-1-I-1 MOHAN CO-OPERATIVE INDUSTRIAL AREA, MATHURA RD, POST BAG NO 7,
NEW DELHI 110 044, INDIA
SN 2322-0937
EI 2349-5790
J9 S ASIAN J HUM RE MAN
JI South Asian J. Hum. Resour. Manag.
PD DEC
PY 2022
VL 9
IS 2
SI SI
BP 324
EP 349
DI 10.1177/23220937221122329
EA OCT 2022
PG 26
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 6G0WS
UT WOS:000863950400001
DA 2024-03-27
ER
PT J
AU Guo, LN
AF Guo, Lina
TI Cross-border e-commerce platform for commodity automatic pricing model
based on deep learning
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Deep learning; Cross-border e-commerce; Automatic image description;
Pricing model
ID SYSTEM
AB With the innovation of information technology and the rise of the Internet economy, cross-border e-commerce has grown up to be an important means and strategy for enterprises to seek rapid development. This paper proposes a model that fuses CNN (Convolutional Neural Network) and attention mechanism to encode image features, and selects the image features of commodities. A 5-layer CNN without a fully connected layer is constructed to initially extract image features, and then a set of attention mechanism strategies is designed. This strategy is used to select the image features that have the greatest impact when generating words at different times. Considering the characteristics of quantitative indicators of the pricing model, this paper transforms this evaluation process of consumers into price perception. Corresponding mathematical model is set up to improve and expand the original probability unit model. The consumer selection model is utilized to obtain a prediction of product market share, and a nonlinear constraint programming is established to determine the optimal price. The strategy takes into account the changed market shares of consumer characteristics and product quality evaluation results. In the two-layer hybrid channel supply chain model, retailers and manufacturers all use third-party platforms when they achieve maximum benefits; when price cross-elasticity coefficients and third-party platform usage fees are independent variables of influencing factors, retailers are dispersed on CNN to get the most profit under the pricing strategy. Similarly, when the unit product tax difference is the independent variable of the influencing factors, the manufacturer is also the most profitable under the CNN decentralized pricing strategy.
C1 [Guo, Lina] Xinxiang Univ, Sch Management, Xinxiang 453000, Henan, Peoples R China.
C3 Xinxiang University
RP Guo, LN (autor correspondiente), Xinxiang Univ, Sch Management, Xinxiang 453000, Henan, Peoples R China.
EM xxxygln@163.com
RI N'Dri, Amoin Bernadine/IWD-7811-2023
FU Research project of Henan science and technology think tank in 2020
(hnkjzk-2020-46c): Research on the optimization of rural industrial
structure in Henan Province from the perspective of urban-rural
integration development
FX This work was supported by the Research project of Henan science and
technology think tank in 2020 (hnkjzk-2020-46c): Research on the
optimization of rural industrial structure in Henan Province from the
perspective of urban-rural integration development.
CR Agrawal DR, 2017, INT TAX PUBLIC FINAN, V24, P903, DOI 10.1007/s10797-016-9422-3
Hanna N., 2016, Journal of Innovation Management, V4, P4
Hong Jin Young, 2018, [The Journal of International Trade & Commerce, 무역연구], V14, P179, DOI 10.16980/jitc.14.2.201804.179
Hu R., 2016, WORLD CUSTOMS J, V10, P65
Kristof G., 2018, CENTRAL EUR EC J, V3, P53
Leung KH, 2019, INT J PROD RES, V57, P6528, DOI 10.1080/00207543.2019.1566674
Li F, 2017, AM J IND BUSINESMA, V7, P581, DOI DOI 10.4236/AJIBM.2017.75043
Li G, 2019, ELECTRON COMMER RES, V19, P779, DOI 10.1007/s10660-019-09334-x
Liu AH, 2017, IEEE AERO EL SYS MAG, V32, P4, DOI 10.1109/MAES.2017.150104
Qiao PL, 2018, WIRELESS PERS COMMUN, V103, P847, DOI 10.1007/s11277-018-5481-3
ROSENBLATT F, 1959, ACTA PSYCHOL, V15, P296, DOI 10.1016/S0001-6918(59)80143-8
Ruiz M, 2018, CHINA STUDIES REV, V6, P133, DOI [10.18002/sin.v1i6.5492, DOI 10.18002/SIN.V1I6.5492]
Tian M, 2019, EKOLOJI, V28, P2861
Ting Bai, 2016, [Asia Marketing Journal, 아시아마케팅저널], V18, P63
Wang FF, 2019, ELECTRON COMMER RES, V19, P863, DOI 10.1007/s10660-019-09368-1
Wang P., 2018, Mod. Econ, V9, P1665, DOI DOI 10.4236/ME.2018.910105
Wang W., 2016, OPEN JOURNAL OF BUSI, V04, P500, DOI DOI 10.4236/OJBM.2016.43053
Wang W., 2016, MOD EC, V7, P880, DOI [10.4236/me.2016.78091, DOI 10.4236/ME.2016.78091]
Yang N., 2017, REV FACULTAD INGENIE, V32, P490
Zhang XX, 2018, J ELECTRON COMMER OR, V16, P53, DOI 10.4018/JECO.2018100104
NR 20
TC 17
Z9 17
U1 9
U2 94
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD MAR
PY 2022
VL 22
IS 1
SI SI
BP 1
EP 20
DI 10.1007/s10660-020-09449-6
EA NOV 2020
PG 20
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA ZN0VD
UT WOS:000588875200001
DA 2024-03-27
ER
PT J
AU Melnychenko, S
Volosovych, S
Baraniuk, Y
AF Melnychenko, Svitlana
Volosovych, Svitlana
Baraniuk, Yurii
TI DOMINANT IDEAS OF FINANCIAL TECHNOLOGIES IN DIGITAL BANKING
SO BALTIC JOURNAL OF ECONOMIC STUDIES
LA English
DT Article
DE digital banking; financial technologies; cloud services; blockchain; big
data; artificial intelligence; biometrics
AB The purpose of the research is the definition of the dominant ideas of financial technologies in digital banking. The methods of theoretical generalization, qualitative, quantitative and correlation analysis, causality tests, description and explanation are used, which made it possible to establish the relationship between the volume of investments in financial technologies and the performance of the banking system, identify the areas of application of financial technologies in the activities of the bank, determine the dominant ideas of financial technologies in digital banking and to uncover the factors and prospects of intensifying the use of financial technologies in digital banking in Ukraine. Results of the research are to substantiate the impact of artificial intelligence, biometrics, cloud services, big data, blockchain and open banking services on digital banking. Due to financial technologies in digital banking, it is possible to generate and store large amounts of data, simultaneously analyze and apply the results of their analysis, provide personalized banking services, perform the functions of central storage of information about the client of financial and non-financial nature, which facilitates the effective investment and credit decision-making, as well as improving the level of information security of banking operations. Practical implications. Financial services markets are transformed by the impact of financial technologies. Development of financial technology instruments by non-banking institutions necessitates the identification of opportunities for their use in banks.The set of financial technologies used by banksforms the digital banking system, the development level of which is the main competitive advantage of the bank in the business environment. Digital banking is characterized by the continuity and security of banking services, which provide the consumer with the ability to receive them online anywhere around the clock, personalization of banking services, digital authentication of users and digitization of banking transactions with the replacement of paperwork. The use of financial technologies in digital banking enables to automate customer segmentation processes, reduce costs on payment transactions, optimize accounting, financial and tax accounting, improve customer service and expand your customer base while maximizing revenue in certain business segments. Value/originalliy. The basic spheres of the use of financial technologies in digital banking, as well as the factors and prospects of intensifying the use of their instruments in Ukraine are revealed. The main areas of use of financial technologies in digital banking are customer behavior analysis, transaction monitoring, customer identification and segmentation, fraud management, banking services personification, risk assessment and regulatory compliance, customer response analysis, process automation, financial advice, investment decision-making, trade facilitation, syndicated loan services, and P2P transfers. The prospects for developing financial technology tools in digital banking include strengthening the interaction between regulators, banks and financial technology companies, the increased use of biometrics, the development of neo-banking and open banking services.
C1 [Melnychenko, Svitlana; Volosovych, Svitlana; Baraniuk, Yurii] Kyiv Natl Univ Trade & Econ, Kiev, Ukraine.
C3 State University of Trade & Economics
RP Melnychenko, S (autor correspondiente), Kyiv Natl Univ Trade & Econ, Kiev, Ukraine.
EM melnichenko@knteu.kiev.ua; volosovich_sv@ukr.net;
baraniukyurii@gmail.com
RI Svitlana, Volosovych/AFL-9941-2022; Baraniuk, Yurii/AAF-2487-2020;
Melnychenko, Svitlana/N-9535-2016
OI Svitlana, Volosovych/0000-0003-3143-7582; Baraniuk,
Yurii/0000-0003-1289-2248; Melnychenko, Svitlana/0000-0002-5162-6324
CR [Anonymous], 2019, FINT GAIN FAV B2B PA
Bainazarov N., 2018, RUSBASE
Baranovskyi OI, 2018, FINANC CREDIT ACT, V3, P350
Binance, 2019, 10 WAYS BLOCKCH IMPR
Debonis J., 2017, BANKING CLOUD WHY BA
Euro area statistics, 2019, BANKS BAL SHEET
FinTech Futures, 2014, 6 REAS WHY CLOUD COM
Five Degrees Solutions, 2019, DIG BANK ANYWH ANYT
Hontar A., 2017, VESTNYK VOLZHSKOHO U, V4, P1
Ifrim O., 2019, PAYPERS
KPMG, 2019, PULS FINT 2018 END O
KPMG, 2018, PULS FINT BIANN GLOB
Mazaraki A., 2018, UKRAINIAN EC GROWTH, P310
Mazaraki A., 2015, EC ANN, V3-4, P42
Melnyk T, 2019, BALT J ECON STUD, V5, P148, DOI 10.30525/2256-0742/2019-5-4-148-154
Mnykh Ye., 2011, KONTROL SFERI INNOVA
National Bank of Ukraine, 2018, FACEBOOK NEWS
Rysin M., 2017, VISNYK UNIVERSYTETU, V3, P71
Scheele J., 2018, GLOBALSIGN BLOG
Springfield C., 2018, IMPACT CLOUD COMPUTI
Thakkar D., 2019, BAYOMETRIC
W.UP, 2018, MACH LEARN BANK RIS
Williams-Grut O., 2015, Business Insider
NR 23
TC 3
Z9 6
U1 8
U2 91
PU BALTIC JOURNAL ECONOMIC STUDIES
PI RIGA
PA VALDEKU IELA 62-156, RIGA, LV-1058, LATVIA
SN 2256-0742
EI 2256-0963
J9 BALT J ECON STUD
JI Balt. J. Econ. Stud.
PY 2020
VL 6
IS 1
BP 92
EP 99
DI 10.30525/2256-0742/2020-6-1-92-99
PG 8
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA KV5JN
UT WOS:000520519100013
OA Green Submitted, gold
DA 2024-03-27
ER
PT J
AU Hartmann, J
Heitmann, M
Schamp, C
Netzer, O
AF Hartmann, Jochen
Heitmann, Mark
Schamp, Christina
Netzer, Oded
TI The Power of Brand Selfies
SO JOURNAL OF MARKETING RESEARCH
LA English
DT Article
DE user-generated content; social media; image analysis; deep learning;
natural language processing; interpretable machine learning
ID WORD-OF-MOUTH; SOCIAL MEDIA; MENTAL SIMULATION; IMPACT
AB Smartphones have made it nearly effortless to share images of branded experiences. This research classifies social media brand imagery and studies user response. Aside from packshots (standalone product images), two types of brand-related selfie images appear online: consumer selfies (featuring brands and consumers' faces) and an emerging phenomenon the authors term "brand selfies" (invisible consumers holding a branded product). The authors use convolutional neural networks to identify these archetypes and train language models to infer social media response to more than a quarter-million brand-image posts (185 brands on Twitter and Instagram). They find that consumer-selfie images receive more sender engagement (i.e., likes and comments), whereas brand selfies result in more brand engagement, expressed by purchase intentions. These results cast doubt on whether conventional social media metrics are appropriate indicators of brand engagement. Results for display ads are consistent with this observation, with higher click-through rates for brand selfies than for consumer selfies. A controlled lab experiment suggests that self-reference is driving the differential response to selfie images. Collectively, these results demonstrate how (interpretable) machine learning helps extract marketing-relevant information from unstructured multimedia content and that selfie images are a matter of perspective in terms of actual brand engagement.
C1 [Hartmann, Jochen] Univ Hamburg, Fac Business Adm, Hamburg, Germany.
[Heitmann, Mark] Univ Hamburg, Fac Business Adm, Mkt & Customer Insight, Hamburg, Germany.
[Schamp, Christina] Vienna Univ Econ & Business, Digital Mkt & Behav Insights, Vienna, Austria.
[Netzer, Oded] Columbia Univ, Columbia Business Sch, Business, New York, NY 10027 USA.
C3 University of Hamburg; University of Hamburg; Vienna University of
Economics & Business; Columbia University
RP Hartmann, J (autor correspondiente), Univ Hamburg, Fac Business Adm, Hamburg, Germany.
EM jochen.hartmann@uni-hamburg.de
RI Hartmann, Jochen/IUN-2216-2023
OI Hartmann, Jochen/0000-0002-1178-8708
FU German Research Foundation (DFG) [HE 6703/1-2]
FX The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This work
was supported by the German Research Foundation (DFG) (grant number HE
6703/1-2).
CR Adams Peter., 2019, MARKETING DIVE 0717
Akpinar E, 2017, J MARKETING RES, V54, P318, DOI 10.1509/jmr.13.0350
[Anonymous], 2020, Most popular social networks worldwide as of April 2020, ranked by number of active users
Aribarg A, 2020, J MARKETING RES, V57, P20, DOI 10.1177/0022243719879711
Bakhshi S, 2014, 32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014), P965, DOI 10.1145/2556288.2557403
Barasch A, 2018, J CONSUM RES, V44, P1220, DOI 10.1093/jcr/ucx112
BOWER GH, 1979, J RES PERS, V13, P420, DOI 10.1016/0092-6566(79)90005-9
BURNKRANT RE, 1995, J CONSUM RES, V22, P17, DOI 10.1086/209432
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chung J, 2022, J CONSUM RES, V48, P817, DOI 10.1093/jcr/ucab034
Chung JS, 2017, PROC CVPR IEEE, P3444, DOI 10.1109/CVPR.2017.367
Colicev A, 2018, J MARKETING, V82, P37, DOI 10.1509/jm.16.0055
Colicev Anatoli., 2021, REAL VALUE FACEBOOK
Dean A, 2017, SPRINGER TEXTS STAT, P1, DOI 10.1007/978-3-319-52250-0
Debevec K., 1992, J CONSUM PSYCHOL, V1, P83, DOI DOI 10.1016/S1057-7408(08)80046-0
Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
Dzyabura D, 2021, J MARKETING, V85, P44, DOI 10.1177/0022242921996661
Elder RS, 2022, J CONSUM PSYCHOL, V32, P293, DOI 10.1002/jcpy.1242
Elder RS, 2012, J CONSUM RES, V38, P988, DOI 10.1086/661531
Escalas JE, 2007, J CONSUM RES, V33, P421, DOI 10.1086/510216
Feehan Blair., 2021, RIVAL IQ 0216
Glum J., 2015, INT BUSINESS TIMES
Godes D, 2004, MARKET SCI, V23, P545, DOI 10.1287/mksc.1040.0071
Grewal L, 2019, J MARKETING RES, V56, P197, DOI 10.1177/0022243718821960
Grewal R, 2020, J MARKETING RES, V57, P985, DOI 10.1177/0022243720965237
Guido Gianluigi., 2018, J ADVERTISING RES, V58, P1
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
Herhausen D, 2019, J MARKETING, V83, P1, DOI 10.1177/0022242918822300
Hughes C, 2019, J MARKETING, V83, P78, DOI 10.1177/0022242919854374
John LK, 2017, J MARKETING RES, V54, P144, DOI 10.1509/jmr.14.0237
Johnson GA, 2017, J MARKETING RES, V54, P867, DOI 10.1509/jmr.15.0297
Kane G.C., 2016, MIT SLOAN MANAGE REV
Li YY, 2020, J MARKETING RES, V57, P1, DOI 10.1177/0022243719881113
Liu L, 2020, MARKET SCI, V39, P669, DOI 10.1287/mksc.2020.1226
Liu Y, 2020, ABS190711692 CORR
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Manning CD, 2020, P NATL ACAD SCI USA, V117, P30046, DOI 10.1073/pnas.1907367117
McFerran B, 2010, J CONSUM PSYCHOL, V20, P306, DOI 10.1016/j.jcps.2010.06.017
Meeker Mary., 2016, KLEINER PERKINS 0531
Melumad S, 2020, J MARKETING, V84, P28, DOI 10.1177/0022242920912732
MeyersLevy J, 1996, J CONSUM RES, V22, P408, DOI 10.1086/209458
Moe WW, 2017, INT SER OPER RES MAN, V254, P483, DOI 10.1007/978-3-319-56941-3_16
Moe WW, 2017, J PROD INNOVAT MANAG, V34, P697, DOI 10.1111/jpim.12405
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Ordenes FV, 2019, J CONSUM RES, V45, P988, DOI 10.1093/jcr/ucy032
Pendlebury Ty., 2016, CNET
Pennebaker JW., 2015, LIWC 2015 OPERATORS
Petit O, 2022, J RETAILING, V98, P277, DOI 10.1016/j.jretai.2021.04.003
Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879
Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Selvaraju RR, 2017, IEEE I CONF COMP VIS, P618, DOI 10.1109/ICCV.2017.74
Sheldon P, 2016, COMPUT HUM BEHAV, V58, P89, DOI 10.1016/j.chb.2015.12.059
Simonyan K, 2015, Arxiv, DOI [arXiv:1409.1556, DOI 10.48550/ARXIV.1409.1556]
Statista, 2021, MOST POP DAT APPS WO
Symons CS, 1997, PSYCHOL BULL, V121, P371, DOI 10.1037/0033-2909.121.3.371
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
To RN, 2021, J CONSUM RES, V48, P123, DOI 10.1093/jcr/ucaa063
Vicinitas, 2018, 2018 RES 100000000 T
Xiao L, 2014, MARKET SCI, V33, P338, DOI 10.1287/mksc.2013.0837
Yosinski J, 2014, ADV NEUR IN, V27
Zhang Mengxia., 2021, Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp, DOI DOI 10.2139/SSRN.3108288
Zhang S., 2017, AIRBNB PROPERTY DEMA, DOI [10.2139/ssrn.2976021, DOI 10.2139/SSRN.2976021]
Zhao M, 2011, J MARKETING RES, V48, P827, DOI 10.1509/jmkr.48.5.827
NR 66
TC 38
Z9 41
U1 54
U2 279
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2437
EI 1547-7193
J9 J MARKETING RES
JI J. Mark. Res.
PD DEC
PY 2021
VL 58
IS 6
SI SI
BP 1159
EP 1177
AR 00222437211037258
DI 10.1177/00222437211037258
EA OCT 2021
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA WX9LI
UT WOS:000709525700001
OA Green Published, Green Accepted, hybrid
DA 2024-03-27
ER
PT J
AU Nanduri, J
Jia, YT
Oka, A
Beaver, J
Liu, YW
AF Nanduri, Jay
Jia, Yuting
Oka, Anand
Beaver, John
Liu, Yung-Wen
TI Microsoft Uses Machine Learning and Optimization to Reduce E-Commerce
Fraud
SO INFORMS JOURNAL ON APPLIED ANALYTICS
LA English
DT Article
DE e-commerce fraud; fraud protection; knowledge graph; machine learning;
dynamic prospective control; dynamic programming; multiple-party
decisions
AB Many merchants conduct their businesses through e-commerce. One major challenge in tackling e-commerce fraud results from dynamic fraud patterns, which can degrade the detection power of risk models and can lead to them failing to detect fraud that has emerging unrecognized patterns. The problem is further exacerbated by the conventional decision frameworks that ignore the follow-up decisions made by other associated parties (e.g., payment-instrument-issuing banks and manual review agents). Microsoft developed a new fraud-management system (FMS) that effectively tackles these two challenges. It keeps features used by the machine learning (ML) risk models up to date by using real-time archiving, dynamic risk tables, and knowledge graphs. The FMS uses customized long-term and short-term sequential ML models to detect both historical and emerging fraud patterns. It also makes rapid real-time optimal decisions using a dynamic programming approach to optimize the long-term profit by taking into account the aforementioned multiple-party decisions. After implementing these innovations over a two-year period (2016-2018), Microsoft reduced its fraud loss by 0.52%, thus generating $75 million in additional savings; reduced the incorrect fraud rejection rate by 1.38%; and improved its bank authorization rate by 7.7 percentage points. The result was many millions of dollars in additional revenue. These innovations simultaneously prevent fraud and increase bank acceptance. In April 2019, Microsoft launched Microsoft Dynamics 365 Fraud Protection, a cloud-based service available for all e-commerce merchants.
C1 [Nanduri, Jay; Jia, Yuting; Oka, Anand; Beaver, John; Liu, Yung-Wen] Microsoft Corp, Dynam 365 Fraud Protect, Redmond, WA 98052 USA.
C3 Microsoft
RP Nanduri, J (autor correspondiente), Microsoft Corp, Dynam 365 Fraud Protect, Redmond, WA 98052 USA.
EM jay.nanduri@microsoft.com; yutjia@microsoft.com; anoka@microsoft.com;
johnbea@microsoft.com; yungliu@microsoft.com
CR [Anonymous], 2018, 2018 TRUE COST FRAUD
[Anonymous], 2001, INTRO GRAPH THEORY
Bertsekas D. P., 2005, DYNAMIC PROGRAMMING, VI
Cowan R, 2004, J ECON DYN CONTROL, V28, P1557, DOI 10.1016/j.jedc.2003.04.002
eMarketer, 2018, WORLDW RET EC SAL EM
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Geffner H., 1998, WORKING NOTES FALL A, P61
Jia Y, 2019, U.S. Patent, Patent No. [US20190066109, 20190066109]
Jia Y, 2019, U.S. Patent, Patent No. [US20190087821, 20190087821]
Li J, 2018, MICROSOFT J APPL RES, V10, P14
Lin Z-Z, 1998, 9825 U MICH
Nanduri J, 2020, J SYSTEMS SCI INFORM
Puterman ML., 1994, Markov decision processes: discrete stochastic dynamic programming, V1
SMALLWOOD RD, 1973, OPER RES, V21, P1071, DOI 10.1287/opre.21.5.1071
Venkatesan R, 2016, NEUROCOMPUTING, V207, P310, DOI 10.1016/j.neucom.2016.05.006
NR 15
TC 17
Z9 19
U1 11
U2 61
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 2644-0865
EI 2644-0873
J9 INFORMS J APPL ANAL
JI INFORMS J. Appl. Anal.
PD JAN-FEB
PY 2020
VL 50
IS 1
BP 64
EP 79
DI 10.1287/inte.2019.1017
PG 16
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA KF8EC
UT WOS:000509469100007
DA 2024-03-27
ER
PT J
AU Yang, Y
AF Yang, Ying
TI RETRACTADO: Research on the optimization of the supplier intelligent
management system for cross-border e-commerce platforms based on machine
learning (Retracted Article)
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article; Retracted Publication
DE Machine learning; Cross-border e-commerce; System optimization
AB At present, with the continuous development of the intelligent system, it is used in many industries. In e-commerce industry, the intelligent system has also been used, especially in supplier management. Based on the machine learning theory, this paper studies the optimization of the supplier management intelligent system of cross-border e-commerce platforms. Based on the wisdom algorithm and machine learning perspective, the optimization of cross-border e-commerce platform supplier credit system is studied in this paper. Firstly, the calculation of the traditional supplier credit evaluation is optimized by introducing the decision matrix algorithm of the difference matrix and the cloud model evaluation method. Then a multi-objective joint decision model of supplier selection and order allocation is established, and the multi-objective evolutionary algorithm combined with actual examples is applied to verify the effectiveness and feasibility of the algorithm and model. Finally, the decision makers' preferences are integrated into the intelligent decision-making, and the cloud model evaluation method is adopted. The rough set and gray relational analysis mathematical tools are used to construct the procurement supply evaluation system. The research results show that the comparison of the three general indicators of the procurement supply chain can be obtained through the cloud model evaluation calculation, which indirectly reflects the preference decision weights of the three objective functions of the cross-border e-commerce supplier selection and order allocation multi-objective optimization model. This indicates that the procurement supply evaluation system constructed in this paper has achieved the purpose of scientific evaluation and selection of suppliers, and has played a theoretical reference role for supplier management of cross-border e-commerce platform.
C1 [Yang, Ying] Jilin Univ, Northeast Asian Studies Coll, Changchun 130012, Peoples R China.
C3 Jilin University
RP Yang, Y (autor correspondiente), Jilin Univ, Northeast Asian Studies Coll, Changchun 130012, Peoples R China.
EM BertDrewsS@yahoo.com
RI N'Dri, Amoin Bernadine/IWD-7811-2023
CR Ahmed RR, 2015, EUR J SCI RES, V39, P130
Feng X., 2015, BUS EC, V10, P78
Gan ST, 2017, VALUE ENG, V64, P369
Ghose A, 2009, MIS QUART, V33, P263
Kim DJ, 2012, INF SYST E-BUS MANAG, V10, P219, DOI 10.1007/s10257-010-0136-2
Lamba K, 2017, PROD PLAN CONTROL, V28, P877, DOI 10.1080/09537287.2017.1336787
Lin FT, 2015, INF SYST E-BUS MANAG, V13, P267, DOI 10.1007/s10257-014-0268-x
Luo BJ, 2013, INF SYST E-BUS MANAG, V11, P141, DOI 10.1007/s10257-011-0182-4
Pappas IO, 2018, INF SYST E-BUS MANAG, V16, P479, DOI 10.1007/s10257-018-0377-z
Rong DI., 2017, TECHNOECONOMICS MANA, V7, P14
Sanderson J., 2015, HEALTH TECHNOL ASSES, V3, P12
Shan SN, 2017, J HUBEI CORRESP U, V36, P230
Vaidya K, 2016, INFORM SYST FRONT, V18, P333, DOI 10.1007/s10796-014-9536-z
Wang YY, 2018, J HEILONGJIANG VOCAT, V10, P21
Yang LJ, 2016, CHINA BUS MARK, V5, P42
NR 15
TC 11
Z9 11
U1 5
U2 77
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD DEC
PY 2020
VL 18
IS 4
SI SI
BP 851
EP 870
DI 10.1007/s10257-019-00402-1
PG 20
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA PA8KC
UT WOS:000595877400023
DA 2024-03-27
ER
PT J
AU Blomster, M
Koivumäki, T
AF Blomster, Miikka
Koivumaki, Timo
TI Exploring the resources, competencies, and capabilities needed for
successful machine learning projects in digital marketing
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article
DE Case study; Machine learning; Organizational capabilities;
Agile-Stage-Gate; Digital marketing
ID AGILE SOFTWARE-DEVELOPMENT; DYNAMIC CAPABILITIES; STAGE-GATE;
ARTIFICIAL-INTELLIGENCE; COMPETITIVE ADVANTAGE; MANAGEMENT; PERFORMANCE;
MODEL; INNOVATION; ANALYTICS
AB This study aimed to explore the organizational resources, competencies, and capabilities needed for the successful implementation of machine learning development projects for digital marketing operations in marketing organizations. The structure of the machine learning development project was investigated via the Agile-Stage-Gate model to identify the workflow, tasks, and roles of the marketing management and development teams during the project. With the accomplished project illustration, the necessary resources, competencies, and capabilities were identified. The findings suggest that marketing organizations' capability to understand and refine data by taking into the notion the impact of the marketing environment is the most crucial competence of machine learning development projects because it forms a solid base for algorithm execution and successful project implementation for marketing purposes. Marketing organizations must develop rigorous business processes and management procedures to support data governance and thus provide suitable data for machine learning purposes. Personnel's understanding of the data's characteristics and capabilities for running successful machine learning projects were also seen as key competencies for marketing organizations.
C1 [Blomster, Miikka] Oulu Univ Appl Sci, Business Sch, POB 222, Oulu 90101, Finland.
[Koivumaki, Timo] Univ Oulu, Oulu Business Sch, POB 8000, Oulu 90014, Finland.
C3 University of Oulu; University of Oulu
RP Blomster, M (autor correspondiente), Oulu Univ Appl Sci, Business Sch, POB 222, Oulu 90101, Finland.
EM miikka.blomster@oamk.fi; timo.koivumaki@oulu.fi
OI Koivumaki, Timo/0000-0002-9559-6913; Blomster,
Miikka/0000-0002-5928-6635
FU Oulu University of Applied Sciences; University of Oulu
FX This study was funded by Oulu University of Applied Sciences and the
University of Oulu. No external funding exists.
CR Ambrosini V, 2009, INT J MANAG REV, V11, P29, DOI 10.1111/j.1468-2370.2008.00251.x
AMIT R, 1993, STRATEGIC MANAGE J, V14, P33, DOI 10.1002/smj.4250140105
Annosi MC, 2020, J BUS RES, V110, P554, DOI 10.1016/j.jbusres.2018.05.011
Ansari A, 2018, MARKET SCI, V37, P987, DOI 10.1287/mksc.2018.1113
Baden-Fuller C, 2020, IND MARKET MANAG, V89, P105, DOI 10.1016/j.indmarman.2019.11.008
Balducci B, 2018, J ACAD MARKET SCI, V46, P557, DOI 10.1007/s11747-018-0581-x
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Barney J. B., 1986, Management Science, V32, P1231, DOI 10.1287/mnsc.32.10.1231
Barney JB, 2001, ACAD MANAGE REV, V26, P41, DOI 10.2307/259393
Barreto I, 2010, J MANAGE, V36, P256, DOI 10.1177/0149206309350776
Batt PJ, 2012, IND MARKET MANAG, V41, P379, DOI 10.1016/j.indmarman.2012.02.002
Baxendale S, 2015, J RETAILING, V91, P235, DOI 10.1016/j.jretai.2014.12.008
Beverland M, 2010, IND MARKET MANAG, V39, P56, DOI 10.1016/j.indmarman.2008.09.005
Bini SA, 2018, J ARTHROPLASTY, V33, P2358, DOI 10.1016/j.arth.2018.02.067
Blazquez D, 2018, TECHNOL FORECAST SOC, V130, P99, DOI 10.1016/j.techfore.2017.07.027
Bughin J., 2017, Artificial Intelligence the Next Digital Frontier
Camisón C, 2014, J BUS RES, V67, P2891, DOI 10.1016/j.jbusres.2012.06.004
Capriati M, 2022, ECON INNOV NEW TECH, V31, P447, DOI 10.1080/10438599.2020.1823674
Capron L, 1999, J MARKETING, V63, P41, DOI 10.2307/1251944
Castanon, 2019, DATA SCI
Chern CC, 2015, INF SYST E-BUS MANAG, V13, P445, DOI 10.1007/s10257-014-0265-0
Colicev A, 2019, INT J RES MARK, V36, P100, DOI 10.1016/j.ijresmar.2018.09.005
COLLIS DJ, 1994, STRATEGIC MANAGE J, V15, P143, DOI 10.1002/smj.4250150910
Coombs JE, 2006, R&D MANAGE, V36, P421, DOI 10.1111/j.1467-9310.2006.00444.x
Cooper R.G., 2013, PDMA HDB NEW PRODUCT, P3
COOPER RG, 1990, BUS HORIZONS, V33, P44, DOI 10.1016/0007-6813(90)90040-I
Cooper RG, 2016, IND MARKET MANAG, V59, P167, DOI 10.1016/j.indmarman.2016.10.006
Cooper RobertG., 2001, Winning at New Products: Accelerating the Process from Idea to Launch, V3rd
Creswell J. W., 2018, Qualitative inquiry research design: choosing among five approaches, V4th ed.
Damanpour F, 2009, J MANAGE STUD, V46, P650, DOI 10.1111/j.1467-6486.2008.00814.x
Day G.S., 2010, PROFITING CUSTOMER V
Day GS, 2008, MIT SLOAN MANAGE REV, V49, P43
Day GS, 2016, CALIF MANAGE REV, V58, P59, DOI 10.1525/cmr.2016.58.4.59
Day GS, 2014, J ACAD MARKET SCI, V42, P27, DOI 10.1007/s11747-013-0348-3
Day GS, 2011, J MARKETING, V75, P183, DOI 10.1509/jmkg.75.4.183
Denzin N.K., 1994, HDB QUALITATIVE RES, V2nd
Dubois A, 2010, IND MARKET MANAG, V39, P129, DOI 10.1016/j.indmarman.2009.08.003
Eisenhardt KM, 2000, STRATEGIC MANAGE J, V21, P1105, DOI 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557
Eisenhardt KM, 1999, SLOAN MANAGE REV, V40, P65
Erevelles S, 2016, J BUS RES, V69, P897, DOI 10.1016/j.jbusres.2015.07.001
Fecher F, 2020, J BUS RES, V110, P567, DOI 10.1016/j.jbusres.2018.05.039
Galende J, 2003, RES POLICY, V32, P715, DOI 10.1016/S0048-7333(02)00082-3
Gandomi A, 2015, INT J INFORM MANAGE, V35, P137, DOI 10.1016/j.ijinfomgt.2014.10.007
GRANT RM, 1991, CALIF MANAGE REV, V33, P114, DOI 10.2307/41166664
Grant RM, 1996, STRATEGIC MANAGE J, V17, P109, DOI 10.1002/smj.4250171110
Guest G, 2006, FIELD METHOD, V18, P59, DOI 10.1177/1525822X05279903
Guo T, 2020, MARKET SCI, V39, P516, DOI 10.1287/mksc.2019.1181
Helfat CE, 2003, STRATEGIC MANAGE J, V24, P997, DOI 10.1002/smj.332
Hirt R., 2017, DESIGNING DIGITAL TR, P55
Höppner S, 2020, EUR J OPER RES, V284, P920, DOI 10.1016/j.ejor.2018.11.072
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Hunt SD, 2020, IND MARKET MANAG, V89, P129, DOI 10.1016/j.indmarman.2019.07.004
Hyde K.F., 2000, QUAL MARK RES, V3, P82, DOI DOI 10.1108/13522750010322089
Inan GG, 2015, PROCD SOC BEHV, V210, P310, DOI 10.1016/j.sbspro.2015.11.371
Järvinen J, 2015, IND MARKET MANAG, V50, P117, DOI 10.1016/j.indmarman.2015.04.009
Joo M, 2014, MANAGE SCI, V60, P56, DOI 10.1287/mnsc.2013.1741
Kachouie R, 2018, EUR J MARKETING, V52, P1007, DOI 10.1108/EJM-10-2016-0588
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Karlström D, 2006, EMPIR SOFTW ENG, V11, P203, DOI 10.1007/s10664-006-6402-8
Karlström D, 2005, IEEE SOFTWARE, V22, P43, DOI 10.1109/MS.2005.59
Katkalo VS, 2010, IND CORP CHANGE, V19, P1175, DOI 10.1093/icc/dtq026
Kietzmann J, 2020, EUR J MARKETING, V54, P473, DOI 10.1108/EJM-01-2020-0007
Kuehl N, 2020, ELECTRON MARK, V30, P351, DOI 10.1007/s12525-019-00351-0
Lawrence ND, 2019, DATA SCI DIGITAL SYS, P1
Lawrence ND, 2017, DATA READINESS LEVEL, P1
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Li HS, 2020, J MARKETING RES, V57, P1019, DOI 10.1177/0022243720954376
Li HS, 2014, J MARKETING RES, V51, P40, DOI 10.1509/jmr.13.0050
Luksha P, 2008, STRATEG ENTREP J, V2, P269, DOI 10.1002/sej.57
Ma L, 2019, IMAGE NETWORK INTERE
Ma LY, 2020, INT J RES MARK, V37, P481, DOI 10.1016/j.ijresmar.2020.04.005
MacCormack A, 2003, J PROD INNOVAT MANAG, V20, P217, DOI 10.1111/1540-5885.2003004
Maijanen P, 2017, J MEDIA BUS STUD, V14, P146, DOI 10.1080/16522354.2017.1290025
Makadok R, 2001, STRATEGIC MANAGE J, V22, P387, DOI 10.1002/smj.158
Martínez A, 2020, EUR J OPER RES, V281, P588, DOI 10.1016/j.ejor.2018.04.034
Masoudinia F, 2020, INT J ELECTRON, V107, P1177, DOI 10.1080/00207217.2020.1726484
Mikalef P, 2018, INF SYST E-BUS MANAG, V16, P547, DOI 10.1007/s10257-017-0362-y
Miles M. B., 1994, Qualitative Data Analysis: An Expanded Sourcebook, V2nd ed., DOI DOI 10.1016/0149-7189(96)88232-2
Mitchell T. M., 1997, MACH LEARN
Mol MJ, 2009, J BUS RES, V62, P1269, DOI 10.1016/j.jbusres.2009.01.001
Moorman C, 2016, J MARKETING, V80, P6, DOI 10.1509/jm.15.0423
Nenonen S, 2019, J ACAD MARKET SCI, V47, P617, DOI 10.1007/s11747-019-00643-z
Nerur S, 2007, COMMUN ACM, V50, P79, DOI 10.1145/1226736.1226739
O'Connor GC, 2013, J PROD INNOVAT MANAG, V30, P209, DOI 10.1111/j.1540-5885.2012.00996.x
O'Reilly CA, 2013, ACAD MANAGE PERSPECT, V27, P324, DOI 10.5465/amp.2013.0025
Palmer Adrian, 2006, J RELATIONSHIP MARKE, V4, P1, DOI DOI 10.1300/J366V04N03_01
Pavlou PA, 2011, DECISION SCI, V42, P239, DOI 10.1111/j.1540-5915.2010.00287.x
Perri 6., 2012, PRINCIPLES METHODOLO
PETERAF MA, 1993, STRATEGIC MANAGE J, V14, P179, DOI 10.1002/smj.4250140303
Petrillo A, 2018, BUS PROCESS MANAG J, V24, P128, DOI 10.1108/BPMJ-03-2016-0071
Piekkari R, 2010, IND MARKET MANAG, V39, P109, DOI 10.1016/j.indmarman.2008.04.017
Polat V., 2015, J BUS STUD Q, V7, P1, DOI [10.4018/978-1-4666-8751-6.ch093, DOI 10.4018/978-1-4666-8751-6.CH093]
Portugal I, 2018, EXPERT SYST APPL, V97, P205, DOI 10.1016/j.eswa.2017.12.020
Proserpio D, 2020, MARKET LETT, V31, P393, DOI 10.1007/s11002-020-09538-4
Rafieian O, 2021, MARKET SCI, V40, P193, DOI 10.1287/mksc.2020.1235
Ribeiro B., 2017, International Journal of Machine Intelligence and Sensory Signal Processing, P32, DOI DOI 10.1504/IJMISSP.2017.088173
Ortega MJR, 2010, J BUS RES, V63, P1273, DOI 10.1016/j.jbusres.2009.09.007
Rust RT, 2020, INT J RES MARK, V37, P15, DOI 10.1016/j.ijresmar.2019.08.002
Saldana J., 2016, CODING MANUAL QUALIT
Schmidt J, 2019, NPJ COMPUT MATER, V5, DOI 10.1038/s41524-019-0221-0
Schoemaker PJH, 2018, CALIF MANAGE REV, V61, P15, DOI 10.1177/0008125618790246
SCHROEDER R., 1989, RES MANAGEMENT INNOV, P107
Silverman D., 2001, Interpreting qualitative data
Stadler C, 2013, ORGAN SCI, V24, P1782, DOI 10.1287/orsc.1120.0810
Stettina CJ, 2015, INT J PROJ MANAG, V33, P140, DOI 10.1016/j.ijproman.2014.03.008
Tang HK, 1998, TECHNOVATION, V18, P297, DOI 10.1016/S0166-4972(98)00009-1
Teece D, 2003, HDB KNOWLEDGE MANAGE, V2
Teece DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/smj.640
Teece DJ, 2018, J MANAGE ORGAN, V24, P359, DOI 10.1017/jmo.2017.75
Teece DJ, 2017, CAMB J ECON, V41, P693, DOI 10.1093/cje/bew063
Teece DJ, 2016, EUR ECON REV, V86, P202, DOI 10.1016/j.euroecorev.2015.11.006
Teece DJ, 2014, ACAD MANAGE PERSPECT, V28, P328, DOI 10.5465/amp.2013.0116
Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Thummadi B. V., 2011, Proceedings of the 2011 Agile Conference, P67, DOI 10.1109/AGILE.2011.29
Trusov M, 2016, MARKET SCI, V35, P405, DOI 10.1287/mksc.2015.0956
Urban G, 2020, MIT SLOAN MANAGE REV, V61, P71
Vermeer SAM, 2019, INT J RES MARK, V36, P492, DOI 10.1016/j.ijresmar.2019.01.010
Wamba SF, 2017, J BUS RES, V70, P356, DOI 10.1016/j.jbusres.2016.08.009
Wan ZY, 2021, IEEE T SOFTWARE ENG, V47, P1331, DOI 10.1109/TSE.2019.2921343
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
WERNERFELT B, 1984, STRATEGIC MANAGE J, V5, P171, DOI 10.1002/smj.4250050207
Williams L, 2003, COMPUTER, V36, P39, DOI 10.1109/MC.2003.1204373
Winter SG, 2003, STRATEGIC MANAGE J, V24, P991, DOI 10.1002/smj.318
WOLFE RA, 1994, J MANAGE STUD, V31, P405, DOI 10.1111/j.1467-6486.1994.tb00624.x
Yang CS, 2015, INF SYST E-BUS MANAG, V13, P403, DOI 10.1007/s10257-014-0266-z
Yang CC, 2009, INT J PROD ECON, V122, P4, DOI 10.1016/j.ijpe.2009.03.016
Ye WJ, 2021, INF SYST E-BUS MANAG, V19, P107, DOI 10.1007/s10257-020-00482-4
Yin R.K., 1989, Case Study Research: Design and Research
Zahra SA, 2002, ACAD MANAGE REV, V27, P185, DOI 10.2307/4134351
Zahra SA, 2006, J MANAGE STUD, V43, P917, DOI 10.1111/j.1467-6486.2006.00616.x
NR 131
TC 7
Z9 7
U1 8
U2 67
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD MAR
PY 2022
VL 20
IS 1
BP 123
EP 169
DI 10.1007/s10257-021-00547-y
EA NOV 2021
PG 47
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 0D5EU
UT WOS:000722832900001
DA 2024-03-27
ER
PT J
AU Dwivedi, YK
Balakrishnan, J
Baabdullah, AM
Das, R
AF Dwivedi, Yogesh K.
Balakrishnan, Janarthanan
Baabdullah, Abdullah M.
Das, Ronnie
TI Do chatbots establish "humanness" in the customer purchase journey? An
investigation through explanatory sequential design
SO PSYCHOLOGY & MARKETING
LA English
DT Article
DE AI humanness; chatbots; customer journey; elaboration likelihood model
(ELM); marketing automation; recommendation intention
ID ELABORATION LIKELIHOOD MODEL; ARTIFICIAL-INTELLIGENCE;
BEHAVIORAL-RESEARCH; EXPERIENCE; SATISFACTION; VARIABLES; STEREOTYPES;
MECHANISMS; PERSUASION; COMPETENCE
AB Chatbots incorporate various behavioral and psychological marketing elements to satisfy customers at various stages of their purchase journey. This research follows the foundations of the Elaboration Likelihood Model (ELM) and examines how cognitive and peripheral cues impact experiential dimensions, leading to chatbot user recommendation intentions. The study introduced warmth and competence as mediating variables in both the purchase and postpurchase stages, utilizing a robust explanatory sequential mixed-method research design. The researchers tested and validated the proposed conceptual model using a 3 x 3 factorial design, collecting 354 responses in the purchase stage and 286 responses in the postpurchase stage. In the second stage, they conducted in-depth qualitative interviews (Study 2) to gain further insights into the validity of the experimental research (Study 1). The results obtained from Study 1 revealed that "cognitive cues" and "competence" significantly influence recommendation intentions among chatbot users. On the other hand, "peripheral cues" and warmth significantly contribute to positive experiences encountered during the purchase stage. The researchers further identified 69 thematic codes through exploratory research, providing a deeper understanding of the variables. Theoretically, this study extends the ELM by introducing new dimensions to human-machine interactions at the heart of digital transformation. From a managerial standpoint, the study emphasizes the significance of adding a "humanness" element in chatbot development to create more engaging and positive customer experiences actively.
C1 [Dwivedi, Yogesh K.] Swansea Univ, Digital Futures Sustainable Business & Soc Res Grp, Dept Business, Sch Management, Swansea, Wales.
[Dwivedi, Yogesh K.] Pune & Symbiosis Int Deemed Univ, Symbiosis Inst Business Management, Dept Management, Pune, Maharashtra, India.
[Balakrishnan, Janarthanan] Natl Inst Technol Tiruchirappalli, Dept Management Studies, Tiruchirappalli, Tamil Nadu, India.
[Baabdullah, Abdullah M.] King Abdulaziz Univ, Fac Econ & Adm, Dept Management Informat Syst, Jeddah, Saudi Arabia.
[Das, Ronnie] Audencia Business Sch, Dept Mkt, Nantes, France.
[Dwivedi, Yogesh K.] Swansea Univ, Digital Futures Sustainable Business & Soc Res Grp, Sch Management, Bay Campus, Swansea SA1 8EN, Wales.
C3 Swansea University; Symbiosis International University; Symbiosis
Institute of Business Management (SIBM) Pune; National Institute of
Technology (NIT System); National Institute of Technology
Tiruchirappalli; King Abdulaziz University; Audencia; Swansea University
RP Dwivedi, YK (autor correspondiente), Swansea Univ, Digital Futures Sustainable Business & Soc Res Grp, Sch Management, Bay Campus, Swansea SA1 8EN, Wales.
EM y.k.dwivedi@swansea.ac.uk
RI Baabdullah, Abdullah M./AAX-8282-2020; Balakrishnan,
Janarthanan/H-9687-2012; Dwivedi, Yogesh Kumar/A-5362-2008
OI Dwivedi, Yogesh Kumar/0000-0002-5547-9990
CR Akdim K, 2023, INT J CONTEMP HOSP M, V35, P2816, DOI 10.1108/IJCHM-12-2020-1406
Al-Ansi A, 2019, INT J HOSP MANAG, V83, P210, DOI 10.1016/j.ijhm.2018.10.017
Apperly IA, 2012, Q J EXP PSYCHOL, V65, P825, DOI 10.1080/17470218.2012.676055
Areni CS, 2003, PSYCHOL MARKET, V20, P349, DOI 10.1002/mar.10077
Ayyildiz AY, 2022, TECHNOL SOC, V70, DOI 10.1016/j.techsoc.2022.101995
Babbar S, 2008, INT J OPER PROD MAN, V28, P804, DOI 10.1108/01443570810895267
BAGOZZI RP, 1991, ADMIN SCI QUART, V36, P421, DOI 10.2307/2393203
Balakrishnan J, 2023, J SUSTAIN TOUR, V31, P2506, DOI 10.1080/09669582.2021.1995398
Balakrishnan J, 2024, ANN OPER RES, V333, P653, DOI 10.1007/s10479-021-04049-5
Balakrishnan J, 2021, PSYCHOL MARKET, V38, P643, DOI 10.1002/mar.21462
Banik S, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102739
Baron-Cohen S., 1995, MINDBLINDNESS ESSAY, DOI DOI 10.7551/MITPRESS/4635.001.0001
Barta S, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103149
Belanche D, 2021, PSYCHOL MARKET, V38, P2357, DOI 10.1002/mar.21532
Belanche D, 2020, SERV IND J, V40, P203, DOI 10.1080/02642069.2019.1672666
Belanche D, 2021, ELECTRON MARK, V31, P477, DOI 10.1007/s12525-020-00432-5
Belk R, 2021, SERV IND J, V41, P860, DOI 10.1080/02642069.2020.1727892
Bennett AM, 2012, J CONSUM PSYCHOL, V22, P199, DOI 10.1016/j.jcps.2011.10.005
BENTLER PM, 1990, PSYCHOL BULL, V107, P238, DOI 10.1037/0033-2909.107.2.238
Bian XM, 2020, J BRAND MANAG, V27, P438, DOI 10.1057/s41262-020-00189-4
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Bolton RN, 2018, J SERV MANAGE, V29, P776, DOI 10.1108/JOSM-04-2018-0113
Borau S, 2021, PSYCHOL MARKET, V38, P1052, DOI 10.1002/mar.21480
Braun V., 2006, Qualitative Research in Psychology, V3, P77, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA]
Calder BJ, 2009, J INTERACT MARK, V23, P321, DOI 10.1016/j.intmar.2009.07.002
Cao XY, 2017, COMPUT HUM BEHAV, V73, P403, DOI 10.1016/j.chb.2017.03.060
Chang RCS, 2018, COMPUT HUM BEHAV, V84, P194, DOI 10.1016/j.chb.2018.02.025
Chang YP, 2019, J ACAD MARKET SCI, V47, P1027, DOI 10.1007/s11747-019-00632-2
Chang Y, 2023, COMPUT HUM BEHAV, V139, DOI 10.1016/j.chb.2022.107544
Chen CJ, 2022, ASIA PAC BUS REV, V28, P380, DOI 10.1080/13602381.2021.1872912
Chen JS, 2021, INT J RETAIL DISTRIB, V49, P1512, DOI 10.1108/IJRDM-08-2020-0312
Chen J, 2023, IEEE T INTELL TRANSP, V24, P10067, DOI 10.1109/TITS.2023.3269794
Chen Q, 2023, INTERNET RES, V33, P2205, DOI 10.1108/INTR-09-2021-0686
Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
Cheung CMK, 2012, DECIS SUPPORT SYST, V54, P461, DOI 10.1016/j.dss.2012.06.008
Chopra IP, 2022, TOUR RECREAT RES, DOI 10.1080/02508281.2022.2088007
Clement J, 2007, J MARKET MANAG-UK, V23, P917, DOI 10.1362/026725707X250395
Pinochet LHC, 2018, INNOV MANAG REV, V15, P303, DOI 10.1108/INMR-05-2018-0028
Crandall CS, 2011, PERS SOC PSYCHOL B, V37, P1488, DOI 10.1177/0146167211411723
Cyr D, 2018, INFORM MANAGE-AMSTER, V55, P807, DOI 10.1016/j.im.2018.03.009
de Kervenoael R, 2020, TOURISM MANAGE, V78, DOI 10.1016/j.tourman.2019.104042
Djelassi S, 2018, DECIS SUPPORT SYST, V111, P38, DOI 10.1016/j.dss.2018.04.004
Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
Dwivedi YK, 2023, PSYCHOL MARKET, V40, P750, DOI 10.1002/mar.21767
Dwivedi YK, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102542
Dwivedi YK, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2020.102168
Epley N., 2018, Journal of the Association for Consumer Research, V3, P591
Fadel KJ, 2009, INT J KNOWL MANAG, V5, P26, DOI 10.4018/jkm.2009062902
Fan H, 2023, J BUS RES, V156, DOI 10.1016/j.jbusres.2022.113526
Fan XJ, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101986
Fiske ST, 2007, TRENDS COGN SCI, V11, P77, DOI 10.1016/j.tics.2006.11.005
Flavián C, 2022, J SERV MANAGE, V33, P293, DOI 10.1108/JOSM-10-2020-0378
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Grewal D, 2020, J RETAILING, V96, P3, DOI 10.1016/j.jretai.2020.02.002
Hassenzahl M, 2006, BEHAV INFORM TECHNOL, V25, P91, DOI 10.1080/01449290500330331
Henseler J, 2017, J ADVERTISING, V46, P178, DOI 10.1080/00913367.2017.1281780
Heung VCS, 2012, INT J HOSP MANAG, V31, P1167, DOI 10.1016/j.ijhm.2012.02.004
Hill J, 2015, COMPUT HUM BEHAV, V49, P245, DOI 10.1016/j.chb.2015.02.026
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
Hu LT, 1998, PSYCHOL METHODS, V3, P424, DOI 10.1037/1082-989X.3.4.424
Hu P, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106727
Hu Q, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102250
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Huang R, 2020, J BUS RES, V121, P616, DOI 10.1016/j.jbusres.2020.04.034
Jiang Y, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107485
Judd CM, 2005, J PERS SOC PSYCHOL, V89, P899, DOI 10.1037/0022-3514.89.6.899
Kar AK, 2023, INFORM SYST FRONT, V25, P1351, DOI 10.1007/s10796-021-10219-4
Kaur P, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102091
Kelley H.H., 1967, NEBRASKA S MOTIVATIO
Kerr G, 2015, J ADVERTISING RES, V55, P390, DOI 10.2501/JAR-2015-001
Klaus P, 2013, INT J MARKET RES, V55, P227, DOI 10.2501/IJMR-2013-021
Kong M., 2007, MANAG SERV QUAL, V17, P275, DOI DOI 10.1108/09604520710744308
Konya-Baumbach E, 2023, COMPUT HUM BEHAV, V139, DOI 10.1016/j.chb.2022.107513
Kushwaha AK, 2021, IND MARKET MANAG, V98, P207, DOI 10.1016/j.indmarman.2021.08.011
Kushwaha AK, 2021, INFORM SYST FRONT, DOI 10.1007/s10796-021-10184-y
Lazzeri N, 2018, APPL SCI-BASEL, V8, DOI 10.3390/app8020302
Lee J, 2016, INT J INFORM MANAGE, V36, P360, DOI 10.1016/j.ijinfomgt.2016.01.001
Liu BJ, 2018, CYBERPSYCH BEH SOC N, V21, P625, DOI 10.1089/cyber.2018.0110
Loupiac P, 2019, INT J RETAIL DISTRIB, V48, P92, DOI 10.1108/IJRDM-07-2018-0146
Loureiro SMC, 2024, INT J HUM-COMPUT INT, V40, P782, DOI 10.1080/10447318.2022.2124346
Lu CC, 2019, INT J INFORM MANAGE, V47, P101, DOI 10.1016/j.ijinfomgt.2018.12.020
Lu ZL, 2000, J EXP PSYCHOL HUMAN, V26, P1534, DOI 10.1037//0096-1523.26.5.1534
Maar D, 2023, J SERV MANAGE, V34, P208, DOI 10.1108/JOSM-06-2021-0237
MacKenzie SB, 2012, J RETAILING, V88, P542, DOI 10.1016/j.jretai.2012.08.001
Maier C, 2023, INT J INFORM MANAGE, V70, DOI 10.1016/j.ijinfomgt.2023.102625
Martini MC, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0146310
McCrudden MT, 2019, J MIX METHOD RES, V13, P381, DOI 10.1177/1558689818762576
McLean G, 2019, COMPUT HUM BEHAV, V99, P28, DOI 10.1016/j.chb.2019.05.009
Mishra A, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2021.102413
Mishra R, 2021, INT J CONSUM STUD, V45, P147, DOI 10.1111/ijcs.12617
Mogaji E, 2021, TELEMAT INFORM, V65, DOI 10.1016/j.tele.2021.101711
Moriuchi E, 2021, J STRATEG MARK, V29, P375, DOI 10.1080/0965254X.2020.1740766
Moriuchi E, 2019, PSYCHOL MARKET, V36, P489, DOI 10.1002/mar.21192
Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
Nguyen QN, 2022, COMPUT HUM BEHAV, V128, DOI 10.1016/j.chb.2021.107093
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Papadimitriou D, 2018, J HOSP TOUR RES, V42, P503, DOI 10.1177/1096348015584443
PARASURAMAN A, 1988, J RETAILING, V64, P12
Petty R., 1986, Communication and persuasion: Central and peripheral routes to attitude change, DOI [DOI 10.1016/S0065-2601(08)60214-2, 10.1016/S0065-2601(08)60214-2]
Pizzi G, 2023, PSYCHOL MARKET, V40, P1372, DOI 10.1002/mar.21813
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Portney LG., 2009, Foundations of Clinical Research: Applications to Practice, V892
PREMACK D, 1978, BEHAV BRAIN SCI, V1, P515, DOI 10.1017/S0140525X00076512
Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
Qin XB, 2019, J ADVERTISING, V48, P338, DOI 10.1080/00913367.2019.1652122
Quach S, 2022, J ACAD MARKET SCI, V50, P1299, DOI 10.1007/s11747-022-00845-y
Rapp A, 2021, INT J HUM-COMPUT ST, V151, DOI 10.1016/j.ijhcs.2021.102630
Ren XY, 2018, J SERV MARK, V32, P223, DOI 10.1108/JSM-04-2017-0133
Martín SS, 2011, PSYCHOL MARKET, V28, P145, DOI 10.1002/mar.20384
Schepers J, 2022, J SERV RES-US, V25, P565, DOI 10.1177/10946705221107704
Schmeichel B.J., 2018, Self-Regulation and Self-Control, P310, DOI [10.4324/9781315175775-10, DOI 10.4324/9781315175775-10]
Shahab MH, 2021, INT J CONSUM STUD, V45, P664, DOI 10.1111/ijcs.12658
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shi S, 2020, INT J INFORM MANAGE, V50, P325, DOI 10.1016/j.ijinfomgt.2019.09.001
Shin D, 2022, NEW MEDIA SOC, V24, P2680, DOI 10.1177/1461444821993801
Shin D, 2018, COMPUT HUM BEHAV, V78, P64, DOI 10.1016/j.chb.2017.09.012
Sirdeshmukh D, 2002, J MARKETING, V66, P15, DOI 10.1509/jmkg.66.1.15.18449
Smid SC, 2020, STRUCT EQU MODELING, V27, P131, DOI 10.1080/10705511.2019.1577140
Söderlund M, 2021, EUR J MARKETING, V55, P94, DOI 10.1108/EJM-09-2019-0748
Soderlund M, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102401
Sridevi G., 2022, INT J INFORM MANAGEM, V2
Todd PM, 2003, J ECON PSYCHOL, V24, P143, DOI 10.1016/S0167-4870(02)00200-3
Tsohou A, 2015, COMPUT SECUR, V52, P128, DOI 10.1016/j.cose.2015.04.006
Tueanrat Y, 2021, J BUS RES, V125, P336, DOI 10.1016/j.jbusres.2020.12.028
Van den Broeck E, 2019, COMPUT HUM BEHAV, V98, P150, DOI 10.1016/j.chb.2019.04.009
Votto A M., 2021, International Journal of Information Management Data Insights, V1
Walker C, 2019, INT J QUAL METH, V18, DOI 10.1177/1609406919834379
Wang CC, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103325
WEINER B, 1985, PSYCHOL REV, V92, P548, DOI 10.1037/0033-295X.92.4.548
Yang SC, 2006, PSYCHOL MARKET, V23, P429, DOI 10.1002/mar.20120
Yoganathan V, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104309
Yoo CW, 2017, TECHNOL FORECAST SOC, V123, P330, DOI 10.1016/j.techfore.2016.10.071
Youn S, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106721
Zha XJ, 2018, COMPUT HUM BEHAV, V79, P227, DOI 10.1016/j.chb.2017.10.038
Zhang X, 2019, TOTAL QUAL MANAG BUS, V30, P579, DOI 10.1080/14783363.2017.1317587
Zhang Y, 1996, PSYCHOL MARKET, V13, P531, DOI 10.1002/(SICI)1520-6793(199609)13:6<531::AID-MAR1>3.3.CO;2-V
Zhou XY, 2019, J CONSUM RES, V45, P953, DOI 10.1093/jcr/ucy012
NR 139
TC 1
Z9 1
U1 79
U2 105
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0742-6046
EI 1520-6793
J9 PSYCHOL MARKET
JI Psychol. Mark.
PD NOV
PY 2023
VL 40
IS 11
BP 2244
EP 2271
DI 10.1002/mar.21888
EA AUG 2023
PG 28
WC Business; Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA T8XT9
UT WOS:001050655700001
OA hybrid
DA 2024-03-27
ER
PT J
AU Dhillon, PS
Aral, S
AF Dhillon, Paramveer S.
Aral, Sinan
TI Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
SO MARKETING SCIENCE
LA English
DT Article
DE machine learning; deep learning; natural language processing; digital
marketing; user profiling
ID ONLINE; PATH
AB In recent years, there has been significant interest in understanding users' online content consumption patterns. But the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret, and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern data sets used by digital marketers. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods.
C1 [Dhillon, Paramveer S.] Univ Michigan, Sch Informat, Ann Arbor, MI 48109 USA.
[Aral, Sinan] MIT, MIT Sloan Sch Management, Cambridge, MA 02142 USA.
C3 University of Michigan System; University of Michigan; Massachusetts
Institute of Technology (MIT)
RP Dhillon, PS (autor correspondiente), Univ Michigan, Sch Informat, Ann Arbor, MI 48109 USA.
EM dhillonp@umich.edu; sinan@mit.edu
OI /0000-0002-0994-9488; Aral, Sinan/0000-0002-2762-058X
CR Ansari A, 2003, J MARKETING RES, V40, P131, DOI 10.1509/jmkr.40.2.131.19224
Bahdanau D, 2016, Arxiv, DOI [arXiv:1409.0473, DOI 10.48550/ARXIV.1409.0473]
Bengio Y, 2001, ADV NEUR IN, V13, P932
Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
Bird Steven., 2006, P 21 INT C COMP LING, P69
Blei D. M., 2006, P 23 INT C MACH LEAR, P113, DOI DOI 10.1145/1143844.1143859
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Bodapati AV, 2008, J MARKETING RES, V45, P77, DOI 10.1509/jmkr.45.1.77
Bruce NI, 2008, MARKET SCI, V27, P659, DOI 10.1287/mksc.1070.0317
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Chang Jonathan, 2009, ADV NEURAL INFORM PR, P288, DOI DOI 10.5555/2984093.2984126
Charlin L., 2015, P 9 ACM C REC SYST, P155, DOI DOI 10.1145/2792838.2800174
DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
Dew R, 2020, J MARKETING RES, V57, P55, DOI 10.1177/0022243719874047
Dhillon P., 2011, P NIPS, P199
Dhillon PS, 2015, J MACH LEARN RES, V16, P3035
Dhillon PS, 2012, P 29 INT C MACHINE L, P1551
Dube Jean-pierreSanjog Misra., 2017, Scalable Price Targeting
Eckart C, 1936, PSYCHOMETRIKA, V1, P211, DOI 10.1007/BF02288367
Farias VF, 2019, MANAGE SCI, V65, P3131, DOI 10.1287/mnsc.2018.3092
Goldfarb A, 2011, MARKET SCI, V30, P389, DOI 10.1287/mksc.1100.0583
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Gopalan P, 2015, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, P326
Guadagni PM, 2008, MARKET SCI, V27, P29, DOI 10.1287/mksc.1070.0331
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Hui L., 2020, Evaluation of neural architectures trained with square loss vs cross-entropy in classification tasks. arXiv
Hui SK, 2009, J CONSUM RES, V36, P478, DOI 10.1086/599046
King DB, 2015, ACS SYM SER, V1214, P1
Koren Y, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P447
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Lee DD, 2001, ADV NEUR IN, V13, P556
Liu J, 2018, MARKET SCI, V37, P930, DOI 10.1287/mksc.2018.1112
Mairal J, 2010, J MACH LEARN RES, V11, P19
Martens D, 2016, MIS QUART, V40, P869, DOI 10.25300/MISQ/2016/40.4.04
Mela C.F, 2018, HARVARD BUS REV, V30, P1
Mikolov T., 2013, ARXIV, DOI DOI 10.48550/ARXIV.1301.3781
Mnih Andriy, 2007, ADV NEURAL INFORM PR, P1257, DOI DOI 10.1145/1390156.1390267
Moe WW, 2004, MANAGE SCI, V50, P326, DOI 10.1287/mnsc.1040.0153
Moe WW, 2003, J CONSUM PSYCHOL, V13, P29, DOI 10.1207/153276603768344762
Montgomery AL, 2004, MARKET SCI, V23, P579, DOI 10.1287/mksc.1040.0073
Murphy KP, 2012, MACHINE LEARNING: A PROBABILISTIC PERSPECTIVE, P1
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Paszke A, 2019, ADV NEUR IN, V32
Pennington J., EMPIRICAL METHODS NA, P1532, DOI [DOI 10.3115/V1/D14-1162, 10.3115/v1/D14-1162]
Perlich C, 2014, MACH LEARN, V95, P103, DOI 10.1007/s10994-013-5375-2
Sarwar B.M., 2002, P 5 INT C COMP INF T, V50, P158
Schwartz H., 2013, P 7 INT AAAI C WEBL, P583, DOI DOI 10.1609/ICWSM.V7I1.14442
Sinnenberg L, 2017, AM J PUBLIC HEALTH, V107, pE1, DOI 10.2105/AJPH.2016.303512
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Trusov M, 2016, MARKET SCI, V35, P405, DOI 10.1287/mksc.2015.0956
Urban GL, 2014, MARKET SCI, V33, P27, DOI 10.1287/mksc.2013.0803
Xavier Glorot, 2011, 14 INT C ARTIF INTEL, P315, DOI DOI 10.1002/ECS2.1832
Xiong L., 2010, SDM, P211
Yoganarasimhan H, 2020, MANAGE SCI, V66, P1045, DOI 10.1287/mnsc.2018.3255
NR 55
TC 6
Z9 6
U1 7
U2 75
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD NOV-DEC
PY 2021
VL 40
IS 6
BP 1059
EP 1080
DI 10.1287/mksc.2021.1293
PG 23
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA XN0MD
UT WOS:000729208200004
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Dellaert, BGC
Shu, SB
Arentze, TA
Baker, T
Diehl, K
Donkers, B
Fast, NJ
Häubl, G
Johnson, H
Karmarkar, UR
Oppewal, H
Schmitt, BH
Schroeder, J
Spiller, SA
Steffel, M
AF Dellaert, Benedict G. C.
Shu, Suzanne B.
Arentze, Theo A.
Baker, Tom
Diehl, Kristin
Donkers, Bas
Fast, Nathanael J.
Haubl, Gerald
Johnson, Heidi
Karmarkar, Uma R.
Oppewal, Harmen
Schmitt, Bernd H.
Schroeder, Juliana
Spiller, Stephen A.
Steffel, Mary
TI Consumer decisions with artificially intelligent voice assistants
SO MARKETING LETTERS
LA English
DT Article
DE Artificial intelligence; Voice assistants; Consumer decision-making;
Consumer dialogs; Digital marketing; Consumer models
ID SEARCH; CHOICES; ADVICE; OTHERS; AGENTS; MIND
AB Consumers are widely adopting Artificially Intelligent Voice Assistants (AIVAs). AIVAs now handle many different everyday tasks and are also increasingly assisting consumers with purchasing decisions, making AIVAs a rich topic for marketing researchers. We develop a series of propositions regarding how consumer decision-making processes may change when moved from traditional online purchase environments to AI-powered voice-based dialogs, in the hopes of encouraging further academic thinking and research in this rapidly developing, high impact area of consumer-firm interaction. We also provide suggestions for marketing managers and policymakers on points to pay attention to when they respond to the proposed effects of AIVAs on consumer decisions.
C1 [Dellaert, Benedict G. C.; Donkers, Bas] Erasmus Univ, Erasmus Sch Econ, Rotterdam, Netherlands.
[Dellaert, Benedict G. C.; Oppewal, Harmen] Monash Univ, Monash Business Sch, Caulfield, Australia.
[Shu, Suzanne B.] Cornell Univ, Dyson Sch Appl Econ & Management, Ithaca, NY USA.
[Arentze, Theo A.] Eindhoven Univ Technol, Dept Built Environm, Eindhoven, Netherlands.
[Baker, Tom] Univ Penn, Sch Law, Philadelphia, PA 19104 USA.
[Diehl, Kristin; Fast, Nathanael J.] Univ Southern Calif, Marshall Sch Business, Los Angeles, CA 90007 USA.
[Haubl, Gerald] Univ Alberta, Alberta Sch Business, Edmonton, AB, Canada.
[Johnson, Heidi] Financial Hlth Network, Washington, DC USA.
[Karmarkar, Uma R.] Univ Calif San Diego, Rady Sch Management, San Diego, CA USA.
[Karmarkar, Uma R.] Univ Calif San Diego, Sch Global Policy & Strategy, San Diego, CA USA.
[Schmitt, Bernd H.] Columbia Univ, Columbia Business Sch, New York, NY USA.
[Schroeder, Juliana] Univ Calif Berkeley, Haas Sch Business, Berkeley, CA 94720 USA.
[Spiller, Stephen A.] UCLA, Anderson Sch Management, Los Angeles, CA USA.
[Steffel, Mary] Northeastern Univ, DAmore McKim Sch Business, Boston, MA 02115 USA.
C3 Erasmus University Rotterdam; Erasmus University Rotterdam - Excl
Erasmus MC; Monash University; Cornell University; Eindhoven University
of Technology; University of Pennsylvania; University of Southern
California; University of Alberta; University of California System;
University of California San Diego; University of California System;
University of California San Diego; Columbia University; University of
California System; University of California Berkeley; University of
California System; University of California Los Angeles; Northeastern
University
RP Dellaert, BGC (autor correspondiente), Erasmus Univ, Erasmus Sch Econ, Rotterdam, Netherlands.; Dellaert, BGC (autor correspondiente), Monash Univ, Monash Business Sch, Caulfield, Australia.
EM dellaert@ese.eur.nl; suzanne.shu@cornell.edu; T.A.Arentze@tue.nl;
tombaker@law.upernedu; kdiehl@marshall.usc.edu; denkers@ese.eur.nl;
nathanael.fast@marshall.usc.edu; gerald.haeubl@ualberta.ca;
hjohnson@finhealthnetwork.org; ukarmarkar@ucsd.edu;
harmen.oppewal@monash.edu; bhs1@gsb.columbia.edu;
jschroeder@berkeley.edu; Stephen.spiller@anderson.ucla.edu;
m.steffel@neu.edu
RI Baker, Tom/JFK-7280-2023; Spiller, Stephen A/A-2208-2012; Oppewal,
Harmen/C-1457-2013; Dellaert, Benedict/D-1020-2010
OI Oppewal, Harmen/0000-0002-5221-2043; Shu, Suzanne/0000-0002-6187-3177;
Baker, Tom/0000-0002-0876-2312; Dellaert, Benedict/0000-0003-4637-1192
CR ALBA JW, 1987, J CONSUM RES, V13, P411, DOI 10.1086/209080
[Anonymous], 1993, ADAPTIVE DECISION MA, DOI DOI 10.1017/CBO9781139173933
Arentze TA, 2015, TRANSPORT SCI, V49, P577, DOI 10.1287/trsc.2013.0513
Baker T, 2018, IOWA LAW REV, V103, P713
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Castelo Noah., 2019, Journal of the Association for Consumer Research, V4, P217, DOI DOI 10.1086/703462
Diehl K, 2003, J CONSUM RES, V30, P56, DOI 10.1086/374698
Diehl K, 2005, J MARKETING RES, V42, P313, DOI 10.1509/jmkr.2005.42.3.313
Epley N, 2013, NEBR SYM MOTIV, V60, P127, DOI 10.1007/978-1-4614-6959-9_6
Fast NJ, 2020, CURR OPIN PSYCHOL, V33, P172, DOI 10.1016/j.copsyc.2019.07.039
Ge X, 2015, J CONSUM PSYCHOL, V25, P245, DOI 10.1016/j.jcps.2014.09.003
Giddens CL, 2013, J VOICE, V27, DOI 10.1016/j.jvoice.2012.12.010
Griffin JG, 2010, J MARKETING RES, V47, P323, DOI 10.1509/jmkr.47.2.323
Häubl G, 2010, MARKET SCI, V29, P438, DOI 10.1287/mksc.1090.0525
Harvey N, 1997, ORGAN BEHAV HUM DEC, V70, P117, DOI 10.1006/obhd.1997.2697
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Kim SY, 2019, MARKET LETT, V30, P1, DOI 10.1007/s11002-019-09485-9
Lieberman A, 2020, CURR OPIN PSYCHOL, V31, P16, DOI 10.1016/j.copsyc.2019.06.022
Loewenstein G, 2011, AM ECON REV, V101, P423, DOI 10.1257/aer.101.3.423
Lucas GM, 2014, COMPUT HUM BEHAV, V37, P94, DOI 10.1016/j.chb.2014.04.043
Mathur Arunesh, 2019, Proceedings of the ACM on Human-Computer Interaction, V3, DOI 10.1145/3359183
Meissner M, 2020, J BUS RES, V111, P163, DOI 10.1016/j.jbusres.2019.01.008
Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
Munz K., 2019, NOT SO EASY LISTENIN
NEDUNGADI P, 1990, J CONSUM RES, V17, P263, DOI 10.1086/208556
Newman DT, 2020, ORGAN BEHAV HUM DEC, V160, P149, DOI 10.1016/j.obhdp.2020.03.008
Raveendhran R., 2019, HUMANS JUDGE A UNPUB
Raveendhran R, 2019, CAMB HANDB PSYCHOL, P921
Ryan RM, 2000, AM PSYCHOL, V55, P68, DOI 10.1037/0003-066X.55.1.68
Sawchak MW, 2017, ANTITRUST LAW J, V81, P903
Schroeder J, 2016, J EXP PSYCHOL GEN, V145, P1427, DOI 10.1037/xge0000214
Schroeder J, 2015, PSYCHOL SCI, V26, P877, DOI 10.1177/0956797615572906
Schuller B, 2013, COMPUT SPEECH LANG, V27, P4, DOI 10.1016/j.csl.2012.02.005
Shu SB, 2008, J BEHAV DECIS MAKING, V21, P352, DOI 10.1002/bdm.593
Simon D, 2016, PSYCHOL SCI, V27, P1588, DOI 10.1177/0956797616666501
Slovic Paul., 1972, Oregon Research Institute Research Monograph, V12, P10, DOI DOI 10.1037/E310462005-001
Soman D, 2003, MANAGE SCI, V49, P1229, DOI 10.1287/mnsc.49.9.1229.16574
Steffel M, 2018, J CONSUM RES, V44, P1015, DOI 10.1093/jcr/ucx080
Steffel M, 2016, ORGAN BEHAV HUM DEC, V135, P32, DOI 10.1016/j.obhdp.2016.04.006
Usta M, 2011, J MARKETING RES, V48, P403, DOI 10.1509/jmkr.48.2.403
Wang SS, 2015, REV GEN PSYCHOL, V19, P393, DOI 10.1037/gpr0000056
West PM, 1996, J CONSUM RES, V23, P120, DOI 10.1086/209471
Xiao B, 2007, MIS QUART, V31, P137
Yalcin ÖN, 2018, BIOL INSPIR COGN ARC, V26, P20, DOI 10.1016/j.bica.2018.07.010
NR 44
TC 40
Z9 40
U1 20
U2 145
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0923-0645
EI 1573-059X
J9 MARKET LETT
JI Mark. Lett.
PD DEC
PY 2020
VL 31
IS 4
SI SI
BP 335
EP 347
DI 10.1007/s11002-020-09537-5
EA AUG 2020
PG 13
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA OT6PT
UT WOS:000560285300001
OA Green Published, hybrid, Green Submitted
DA 2024-03-27
ER
PT J
AU Filieri, R
Lin, ZB
Li, YL
Lu, XQ
Yang, XW
AF Filieri, Raffaele
Lin, Zhibin
Li, Yulei
Lu, Xiaoqian
Yang, Xingwei
TI Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human
Intelligence Approach
SO JOURNAL OF SERVICE RESEARCH
LA English
DT Article
DE emotion; emotion detection; online customer reviews; machine learning;
thematic analysis; service robot
ID BIG DATA; CONSUMER-BEHAVIOR; EXPERIENCE; ANALYTICS; TOURISM;
NETNOGRAPHY; HOSPITALITY; FIELD
AB Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers' intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories.
C1 [Filieri, Raffaele] Audencia Business Sch, Dept Mkt, 8 Route Joneliere, F-44312 Nantes, Pays De Ia Loir, France.
[Lin, Zhibin; Li, Yulei] Univ Durham, Business Sch, Mill Hill Lane, Durham, England.
[Lu, Xiaoqian] Jimei Univ, Sch Business Adm, Xiamen, Peoples R China.
[Yang, Xingwei] Queens Univ, Smith Sch Business, Kingston, ON, Canada.
C3 Audencia; Durham University; Jimei University; Queens University -
Canada
RP Filieri, R (autor correspondiente), Audencia Business Sch, Dept Mkt, 8 Route Joneliere, F-44312 Nantes, Pays De Ia Loir, France.; Lu, XQ (autor correspondiente), Jimei Univ, Sch Business Adm, Xiamen, Peoples R China.
EM raffaele.filieri@audencia.com; xiaoqianlu@jmu.edu.cn
RI Paleja, Heer/IQT-1538-2023; xiaoqian, Lu/JED-5231-2023; Lin,
Zhibin/ACD-8628-2022; Lin, Zhibin/R-7541-2018; Filieri,
Raffaele/AAK-2553-2021
OI Lin, Zhibin/0000-0001-5575-2216; Lin, Zhibin/0000-0001-5575-2216; Li,
Yulei/0000-0003-3579-6179; Filieri, Raffaele/0000-0002-3534-8547; lu,
xiaoqian/0000-0001-6276-0142
CR Akdim K, 2023, INT J CONTEMP HOSP M, V35, P2816, DOI 10.1108/IJCHM-12-2020-1406
Alba-Juez Laura, 2019, EMOTION DISCOURSE
Antons D, 2018, J SERV RES-US, V21, P17, DOI 10.1177/1094670517738373
Bagozzi RP, 1999, J ACAD MARKET SCI, V27, P184, DOI 10.1177/0092070399272005
Bartneck C, 2007, AI SOC, V21, P217, DOI 10.1007/s00146-006-0052-7
Belanche D, 2020, SERV IND J, V40, P203, DOI 10.1080/02642069.2019.1672666
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Bolukbasi T., 2021, ARXIV PREPRINT ARXIV
Boughorbel S, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0177678
Bougie R, 2003, J ACAD MARKET SCI, V31, P377, DOI 10.1177/0092070303254412
Boyatzis R.E., 1998, Transforming qualitative information: Thematic analysis and code development
Braun V., 2006, Qualitative Research in Psychology, V3, P77, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA]
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Cabibihan JJ, 2013, INT J SOC ROBOT, V5, P593, DOI 10.1007/s12369-013-0202-2
Caic M, 2018, J SERV MANAGE, V29, P178, DOI 10.1108/JOSM-07-2017-0179
Carroll BA, 2006, MARKET LETT, V17, P79, DOI 10.1007/s11002-006-4219-2
Chuah SHW, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102551
COLBY BN, 1989, CONTEMP SOCIOL, V18, P957, DOI 10.2307/2074241
D'Acunto D, 2021, INT J CONTEMP HOSP M, V33, P2773, DOI 10.1108/IJCHM-11-2020-1329
de Kervenoael R, 2020, TOURISM MANAGE, V78, DOI 10.1016/j.tourman.2019.104042
Dhaoui C, 2017, J CONSUM MARK, V34, P480, DOI 10.1108/JCM-03-2017-2141
EKMAN P, 1992, COGNITION EMOTION, V6, P169, DOI 10.1080/02699939208411068
Erevelles S, 2016, J BUS RES, V69, P897, DOI 10.1016/j.jbusres.2015.07.001
Fereday J., 2006, INT J QUAL METH, V5, P80, DOI [https://doi.org/10.1177/160940690600500107, DOI 10.1177/160940690600500107, 10.1177/160940690600500107]
Filieri R, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104291
Filieri R, 2021, PSYCHOL MARKET, V38, P431, DOI 10.1002/mar.21440
Filieri R, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106527
Filieri R, 2015, J BUS RES, V68, P1261, DOI 10.1016/j.jbusres.2014.11.006
Gaind B., 2019, Global Journal of Engineering Science and Researches, P78, DOI 1901.08458.pdf
Gonzlez-Carvajal S., 2020, ARXIV PREPRINT ARXIV
Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
Hayes JL, 2021, J ADVERTISING, V50, P81, DOI 10.1080/00913367.2020.1809576
Heinonen K, 2018, J SERV MARK, V32, P657, DOI 10.1108/JSM-08-2017-0294
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Ivanov S, 2020, TOURISM ECON, V26, P1065, DOI 10.1177/1354816619879583
Izard C., 1977, HUMAN EMOTIONS, DOI 10.1007/978-1-4899-2209-0
Jörling M, 2019, J SERV RES-US, V22, P404, DOI 10.1177/1094670519842334
Kanda T, 2004, HUM-COMPUT INTERACT, V19, P61, DOI 10.1207/s15327051hci1901&2_4
Kozinets RV, 2002, J MARKETING RES, V39, P61, DOI 10.1509/jmkr.39.1.61.18935
Kubler RV, 2020, J INTERACT MARK, V50, P136, DOI 10.1016/j.intmar.2019.08.001
Kulviwat S, 2007, PSYCHOL MARKET, V24, P1059, DOI 10.1002/mar.20196
Laros FJM, 2005, J BUS RES, V58, P1437, DOI 10.1016/j.jbusres.2003.09.013
LaValley MP, 2008, CIRCULATION, V117, P2395, DOI 10.1161/CIRCULATIONAHA.106.682658
Lazarus R. S., 1991, Emotion and adaptation
Lee D, 2018, MANAGE SCI, V64, P5105, DOI 10.1287/mnsc.2017.2902
Lee S, 2011, J BUS RES, V64, P1195, DOI 10.1016/j.jbusres.2011.06.022
Li DJ, 2010, INT J SOC ROBOT, V2, P175, DOI 10.1007/s12369-010-0056-9
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
MacDorman KF, 2009, AI SOC, V23, P485, DOI 10.1007/s00146-008-0181-2
MATTHEWS BW, 1975, BIOCHIM BIOPHYS ACTA, V405, P442, DOI 10.1016/0005-2795(75)90109-9
McLeay F, 2021, J SERV RES-US, V24, P104, DOI 10.1177/1094670520933354
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Mirzaalian F, 2019, J HOSP TOUR TECHNOL, V10, P764, DOI 10.1108/JHTT-08-2018-0078
Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
Murphy J, 2017, EUR J TOUR RES, V15, P104
Ordenes FV, 2014, J SERV RES-US, V17, P278, DOI 10.1177/1094670514524625
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Plutchik R., 1980, EMOTION THEORY RES E, DOI DOI 10.1016/B978-0-12-558701-3.50007-7
Pumawirawan N, 2015, J INTERACT MARK, V31, P17, DOI 10.1016/j.intmar.2015.05.001
Rau PLP, 2009, COMPUT HUM BEHAV, V25, P587, DOI 10.1016/j.chb.2008.12.025
Richins ML, 1997, J CONSUM RES, V24, P127, DOI 10.1086/209499
Robinson S, 2020, J BUS RES, V116, P366, DOI 10.1016/j.jbusres.2019.08.038
Romani S, 2012, INT J RES MARK, V29, P55, DOI 10.1016/j.ijresmar.2011.07.001
RUSSELL JA, 1977, J RES PERS, V11, P273, DOI 10.1016/0092-6566(77)90037-X
Ryoo JH, 2021, J MARKETING, V85, P70, DOI 10.1177/0022242920937703
Saravia E, 2018, 2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2018), P3687
SMITH CA, 1993, COGNITION EMOTION, V7, P233, DOI 10.1080/02699939308409189
SPIGGLE S, 1994, J CONSUM RES, V21, P491, DOI 10.1086/209413
Stahl BC, 2016, ROBOT AUTON SYST, V86, P152, DOI 10.1016/j.robot.2016.08.018
Statista, 2022, AM DAT CREAT CONS ST
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Tung VWS, 2018, INT J CONTEMP HOSP M, V30, P2680, DOI 10.1108/IJCHM-06-2017-0322
Turja T, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103220
Tussyadiah I.P., 2018, Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference, P308, DOI DOI 10.1007/978-3-319-72923-7_24
Tussyadiah IP, 2020, ANN TOURISM RES, V81, DOI 10.1016/j.annals.2020.102886
Ullah R, 2016, DECIS SUPPORT SYST, V81, P41, DOI 10.1016/j.dss.2015.10.007
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Vermeer SAM, 2019, INT J RES MARK, V36, P492, DOI 10.1016/j.ijresmar.2019.01.010
Watson L, 2007, EUR J MARKETING, V41, P487, DOI 10.1108/03090560710737570
Wirtz J., 2021, Eur. Bus. Rev., V29, P909
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Wong A., 2004, MANAG SERV QUAL, V14, P365, DOI 10.1108/09604520410557976
Xiang Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/j.ijhm.2014.10.013
Yang Zhilin, 2019, ARXIV ABS190608237
Ye F, 2022, J SERV RES-US, V25, P301, DOI 10.1177/1094670520975143
Yin DZ, 2014, MIS QUART, V38, P539, DOI 10.25300/MISQ/2014/38.2.10
Yu CE, 2020, J HOSP MARKET MANAG, V29, P22, DOI 10.1080/19368623.2019.1592733
NR 92
TC 29
Z9 30
U1 56
U2 253
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1094-6705
EI 1552-7379
J9 J SERV RES-US
JI J. Serv. Res.
PD NOV
PY 2022
VL 25
IS 4
SI SI
BP 614
EP 629
AR 10946705221103937
DI 10.1177/10946705221103937
EA MAY 2022
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5I4NZ
UT WOS:000805270400001
OA Green Accepted
DA 2024-03-27
ER
PT J
AU Zhang, H
Fan, LJ
Chen, M
Qiu, C
AF Zhang, Hui
Fan, Lijun
Chen, Min
Qiu, Chen
TI The Impact of SIPOC on Process Reengineering and Sustainability of
Enterprise Procurement Management in E-Commerce Environments Using Deep
Learning
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Deep Learning; E-Commerce; Enterprise Procurement Management Process;
SIPOC Model
AB In order to better promote the healthy and long-term development of enterprise procurement management process, under the background of e-commerce environment, suppliers-inputs-process-outputs-customers (SIPOC) model, deep learning, and related theories of enterprise procurement management are expounded and proposed. Then, D electric power enterprise is studied as a sample. After understanding the current situation of procurement management of the enterprise, there are a series of problems in the enterprise, such as complex process, and no correlation between procurement management process and overall strategic planning. Finally, through the analysis of the early warning indicators of the enterprise by the deep learning algorithm, the procurement management process has caused certain risks to the financial management level of the enterprise, and the procurement management process of the enterprise needs to be adjusted. The material record and consumption scheme of the enterprise is optimized by using the SIPOC organizational system model.
C1 [Zhang, Hui] Pai Chai Univ, Daejeon, South Korea.
[Fan, Lijun] Hunan Univ Technol & Business, Sch Business Adm, Changsha, Peoples R China.
[Chen, Min] Wenzhou Univ, Sch Business, Wenzhou, Peoples R China.
[Qiu, Chen] Wuhan Univ, Sch Econ & Management, Wuhan, Peoples R China.
C3 Pai Chai University; Hunan University of Technology & Business; Wenzhou
University; Wuhan University
RP Chen, M (autor correspondiente), Wenzhou Univ, Sch Business, Wenzhou, Peoples R China.
RI Chen, Min/AAD-4064-2019
OI Fan, Lijun/0009-0000-4432-8604
FU Philosophy and Social Science Project of Zhejiang Province
[21NDJC144YB]; Wenzhou Philosophy and Social Science Planning
[21wsk169]; second batch of industry-University Collaborative Education
Program of The Higher Education Department of the Ministry of Education
[202102586003]
FX This research was supported by the project of "Executives' Dynamic
Relationship Network, System Differences, and Chinese Enterprises'
Internationalization Strategy: Internal Mechanism and Empirical Test"
(Grant No.: 21NDJC144YB) (2021 Philosophy and Social Science Project of
Zhejiang Province). This research was supported by the project of
"Research on the dynamic evolution mechanism of network public opinion
of public emergencies and the ability of local governments to cope with
them"(Grant No.: 21wsk169)(Wenzhou Philosophy and Social Science
Planning in 2021). This research was supported by the second batch of
industry-University Collaborative Education Program of The Higher
Education Department of the Ministry of Education in 2021 with project
name of "Smart Enterprise Business Data Analysis and Operation Decision
Teaching Ability Improvement Teacher Training Project" (Grant no.
202102586003).
CR Ancarani A., 2005, Journal of Purchasing and Supply Management, V11, P232, DOI 10.1016/j.pursup.2005.12.004
Cerqueus T, 2022, ELECTRON COMMER RES, V22, P513, DOI 10.1007/s10660-020-09438-9
Deng LR, 2022, EMERG MARK FINANC TR, V58, P3034, DOI 10.1080/1540496X.2021.2025047
Haidong Zhong, 2021, International Journal of Technology, Policy and Management, V21, P140, DOI 10.1504/IJTPM.2021.116514
He Y, 2020, IEEE COMMUN LETT, V24, P2221, DOI 10.1109/LCOMM.2020.3005947
Lei ZZ, 2020, ELECTRON COMMER RES, V20, P275, DOI 10.1007/s10660-019-09389-w
Liu AJ, 2022, J THEOR APPL EL COMM, V17, P69, DOI 10.3390/jtaer17010004
[刘程军 Liu Chengjun], 2019, [地理科学, Scientia Geographica Sinica], V39, P1719
Liu F, 2020, IEEE T NEUR NET LEAR, V31, P5588, DOI 10.1109/TNNLS.2020.2973293
Liu Y, 2020, IND MARKET MANAG, V87, P18, DOI 10.1016/j.indmarman.2020.02.027
Liu ZC, 2022, IEEE T POWER ELECTR, V37, P8767, DOI 10.1109/TPEL.2022.3153797
Meng FQ, 2021, KSII T INTERNET INF, V15, P4028, DOI 10.3837/tiis.2021.11.009
Pochynok N., 2021, TECHNOLOGY AUDIT PRO, V4, P60, DOI [10.15587/2706-5448.2021.238858, DOI 10.15587/2706-5448.2021.238858]
Sitar-Taut DA, 2020, INFORM TECHNOL MANAG, V21, P131, DOI 10.1007/s10799-019-00309-w
Westland JC, 2020, ELECTRON COMMER RES, V20, P1, DOI 10.1007/s10660-020-09407-2
Wu XX, 2021, INFORM SOFTWARE TECH, V133, DOI 10.1016/j.infsof.2021.106530
Wu ZA, 2020, IEEE T CLOUD COMPUT, V8, P508, DOI 10.1109/TCC.2017.2700842
Xia HS, 2021, ELECTRON MARK, V31, P295, DOI 10.1007/s12525-020-00435-2
Yang WD, 2021, INFORM SCIENCES, V570, P526, DOI 10.1016/j.ins.2021.05.009
Zheng W, 2021, IEEE T RELIAB, V70, P1658, DOI 10.1109/TR.2021.3118026
Zheng WF, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12073416
Zheng WF, 2021, IEEE ACCESS, V9, P91476, DOI 10.1109/ACCESS.2021.3074937
Zhu MX, 2021, ENERGY REP, V7, P2703, DOI 10.1016/j.egyr.2021.05.009
NR 23
TC 29
Z9 29
U1 13
U2 55
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PY 2022
VL 34
IS 8
AR 70
DI 10.4018/JOEUC.306270
PG 17
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA 4I9HG
UT WOS:000850882800002
OA gold
DA 2024-03-27
ER
PT J
AU Trivedi, SK
Patra, P
Srivastava, PR
Zhang, JZ
Zheng, LJ
AF Trivedi, Shrawan Kumar
Patra, Pradipta
Srivastava, Praveen Ranjan
Zhang, Justin Zuopeng
Zheng, Leven J.
TI What prompts consumers to purchase online? A machine learning approach
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article; Early Access
DE Consumer; E-commerce; Purchase intention; Machine learning; Artificial
intelligence; Feature selection
ID BIG DATA ANALYTICS; E-COMMERCE; ARTIFICIAL-INTELLIGENCE; RELATIONSHIP
MANAGEMENT; ENERGY-CONSUMPTION; RANDOM FOREST; INTENTION; TRUST; IMPACT;
ADOPTION
AB With e-commerce emerging as a prominent mode of purchasing, there is a pressing need for businesses across the globe to understand online consumer purchase behavior and, in particular, their purchase intention. Information on purchase behavior provides valuable insights for designing marketing activities to reach wider target audiences, promote greater customer involvement, and achieve higher investment returns. This research builds a novel algorithm for predicting the purchase intention of e-commerce website users. The dataset for the study was publically available online. Under-sampling was used to remove the imbalance in the dataset, and two-stage feature selection was applied to identify the most important consumer characteristics. Then, the greedy search and the wrapper methods were used to generate a dataset comprising the five most relevant features. Subsequently, an improved machine learning model was proposed based on stacking well-known classifiers and compared against state-of-the-art Machine Learning classifiers using various measures to evaluate its performance. Our results showed that the proposed algorithm returned the best overall accuracies for 50-50, 66-34, and 80-20 splits of the dataset. It also outperformed other classifiers in extant literature. Our findings help e-commerce sites offer their users predictive and personalized recommendations.
C1 [Trivedi, Shrawan Kumar] Rajiv Gandhi Inst Petr Technol, Amethi, India.
[Patra, Pradipta] Indian Inst Management, Sirmaur, Himachal Prades, India.
[Srivastava, Praveen Ranjan] Indian Inst Management Rohtak, Rohtak, Haryana, India.
[Zhang, Justin Zuopeng] Univ North Florida, Coggin Coll Business, Dept Management, Jacksonville, FL 32224 USA.
[Zheng, Leven J.] Hong Kong Metropolitan Univ, Ho Man Tin, Hong Kong, Peoples R China.
C3 Indian Institute of Management (IIM System); Indian Institute of
Management Sirmaur; Indian Institute of Management (IIM System); Indian
Institute of Management Rohtak; State University System of Florida;
University of North Florida; Hong Kong Metropolitan University
RP Zheng, LJ (autor correspondiente), Hong Kong Metropolitan Univ, Ho Man Tin, Hong Kong, Peoples R China.
EM strivedi@rgipt.ac.in; pradipta.patra@iimsirmaur.ac.in;
praveen.ranjan@iimrohtak.ac.in; justin.zhang@unf.edu; lzheng@hkmu.edu.hk
RI Zhang, Justin/M-9298-2019
OI Zhang, Justin/0000-0002-4074-9505; Srivastava, Praveen
Ranjan/0000-0001-7467-5500
CR Abdou D, 2020, J GLOB INF MANAG, V28, P38, DOI 10.4018/JGIM.2020100103
Aggawal R., 2017, COMMON PITFALLS STAT, DOI [10.4103/picr.PICR_87_17, DOI 10.4103/PICR.PICR_87_17]
Ahluwalia P, 2020, J GLOB INF MANAG, V28, P1, DOI 10.4018/JGIM.2020010101
Akram U, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102669
Akroush MN, 2015, BUS PROCESS MANAG J, V21, P1353, DOI 10.1108/BPMJ-02-2015-0022
Akter S, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa3
Akter S, 2016, ELECTRON MARK, V26, P173, DOI 10.1007/s12525-016-0219-0
Al-Hasan A, 2021, J GLOB INF MANAG, V29, P194, DOI 10.4018/JGIM.20210401.oa1
Alfonso V., 2021, BIS Bulletin, P36
Ali M, 2021, J GLOB INF MANAG, V29, P46, DOI 10.4018/JGIM.2021050103
Alismaili SZ, 2020, J GLOB INF MANAG, V28, P73, DOI 10.4018/JGIM.2020040104
Aljarah I, 2018, COGN COMPUT, V10, P478, DOI 10.1007/s12559-017-9542-9
Alt R, 2019, ELECTRON MARK, V29, P143, DOI 10.1007/s12525-019-00353-y
Amoroso DL., 2016, ENCY E COMMERCE DEV, P1610, DOI [10.4018/978-1-4666-9787-4.ch114, DOI 10.4018/978-1-4666-9787-4.CH114]
Ariffin SK, 2018, J RES INTERACT MARK, V12, P309, DOI 10.1108/JRIM-11-2017-0100
Baati Karim, 2020, IFIP INT C ARTIFICIA, P43
Bag S, 2019, J BUS RES, V94, P408, DOI 10.1016/j.jbusres.2017.11.031
Balgomera K., 2022, J BUSINESS MANAGEMEN, V4, P100
Bawack RE, 2022, ELECTRON MARK, V32, P297, DOI 10.1007/s12525-022-00537-z
Belgiu M, 2016, ISPRS J PHOTOGRAMM, V114, P24, DOI 10.1016/j.isprsjprs.2016.01.011
Boroon L, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa34
BROWN M., 2003, EUR J MARKETING, V37, P1666, DOI DOI 10.1108/03090560310495401
Cai J, 2018, NEUROCOMPUTING, V300, P70, DOI 10.1016/j.neucom.2017.11.077
Chaffey, 2022, ECOMMERCE ECOMMERCE
Changchit C, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa51
Chaudhary Archana, 2016, Information Processing in Agriculture, V3, P215, DOI 10.1016/j.inpa.2016.08.002
Chaudhuri N, 2021, DECIS SUPPORT SYST, V149, DOI 10.1016/j.dss.2021.113622
Chen MY, 2013, ELECTRON COMMER RES, V13, P1, DOI 10.1007/s10660-013-9104-5
Chen N, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102344
Chen YF, 2016, ELECTRON COMMER RES, V16, P355, DOI 10.1007/s10660-015-9206-3
Cheng GP, 2021, J THEOR APPL EL COMM, V16, P1025, DOI 10.3390/jtaer16040058
Cheng LC, 2021, J GLOB INF MANAG, V29, P61, DOI 10.4018/JGIM.2021030104
Cheng Y, 2022, J PROD BRAND MANAG, V31, P252, DOI 10.1108/JPBM-05-2020-2907
Chevalier, 2022, STAT 1286420 CONSUME
Chou S, 2015, INTERNET RES, V25, P542, DOI 10.1108/IntR-01-2014-0006
Cisco Systems, 2019, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update 2017-2022
Cision, 2022, NEWS REL GLOB E COMM
Dastane O., 2020, J ASIAN BUSINESS STR, V10, P142, DOI DOI 10.18488/JOURNAL.1006.2020.101.142.158
Dwivedi YK, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2020.102168
Erlangga H., 2021, Turkish Journal of Computer and Mathematics Education (TURCOMAT), V12, P3672, DOI DOI 10.17762/TURCOMAT.V12I3.1648
Esmeli R, 2021, ELECTRON MARK, V31, P697, DOI 10.1007/s12525-020-00448-x
Faraoni M, 2019, BRIT FOOD J, V121, P574, DOI 10.1108/BFJ-04-2018-0216
Feng QL, 2015, WATER-SUI, V7, P1437, DOI 10.3390/w7041437
Gallego-Gomez C, 2021, J GLOB INF MANAG, V29, P103, DOI 10.4018/JGIM.2021010106
Gholami R, 2021, J GLOB INF MANAG, V29, P166, DOI 10.4018/JGIM.2021030109
Gholami R, 2020, J GLOB INF MANAG, V28, P21, DOI 10.4018/JGIM.2020100102
Guo L, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1984, DOI 10.1145/3292500.3330670
Guo YY, 2020, J GLOB INF MANAG, V28, P52, DOI 10.4018/JGIM.2020040103
Hamami F, 2021, AIP CONF PROC, V2329, DOI 10.1063/5.0043452
Haponik, 2021, BEST MACHINE LEARNIN
Harzevili NS, 2018, APPL SOFT COMPUT, V69, P516, DOI 10.1016/j.asoc.2018.04.020
Hong L, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa44
Hung WH, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa5
Ibrahim O., 2018, NEW MODEL CUSTOMER P
Islam M, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa52
Kabir MT, 2019, IEEE WCNC, DOI 10.1109/wcnc.2019.8885804
Kaur S, 2017, J INTERNET COMMER, V16, P343, DOI 10.1080/15332861.2017.1347861
Kim JB, 2012, ELECTRON COMMER RES, V12, P125, DOI 10.1007/s10660-012-9089-5
Kondrateva G, 2020, J GLOB INF MANAG, V28, P1, DOI 10.4018/JGIM.2020070101
Kumar, 2021, E COMMERCE MACHINE L
Kumar A, 2018, INT J RETAIL DISTRIB, V46, P247, DOI 10.1108/IJRDM-08-2017-0161
Kumar A, 2019, NEURAL COMPUT APPL, V31, P877, DOI 10.1007/s00521-017-3047-z
KUMAR UD, 2017, BUSINESS ANAL SCI DA
Lam HY, 2021, J GLOB INF MANAG, V29, P1, DOI 10.4018/JGIM.2021050101
Laxman L. K. P., 2021, HDB RES INNOVATION D, P1
Li C, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa46
Li Q, 2018, ELECTRON COMMER RES, V18, P693, DOI 10.1007/s10660-018-9299-6
Li Q, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), P1048, DOI 10.1109/ICDMW.2015.172
Liao SH, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101621
Lim YJ, 2016, PROC ECON FINANC, V35, P401, DOI 10.1016/S2212-5671(16)00050-2
Lin JB, 2021, ELECTRON COMMER R A, V50, DOI 10.1016/j.elerap.2021.101103
Ling C, 2019, IEEE ACCESS, V7, P112963, DOI 10.1109/ACCESS.2019.2935121
Lissitsa S, 2021, ELECTRON COMMER RES, V21, P545, DOI 10.1007/s10660-019-09381-4
Liu QH, 2021, J GLOB INF MANAG, V29, P26, DOI 10.4018/JGIM.2021050102
Liu YM, 2020, J GLOB INF MANAG, V28, P17, DOI 10.4018/JGIM.2020070102
Lu BZ, 2016, COMPUT HUM BEHAV, V56, P225, DOI 10.1016/j.chb.2015.11.057
Ma SZ, 2021, J GLOB INF MANAG, V29, P86, DOI 10.4018/JGIM.20210901.oa6
Meghani, 2018, ARTICLE LEADERBOARD
Meng L, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102733
Mokryn O, 2019, ELECTRON COMMER R A, V34, DOI 10.1016/j.elerap.2019.100836
Mou J, 2020, J GLOB INF MANAG, V28, P167, DOI 10.4018/JGIM.2020010109
Mursalin M, 2017, NEUROCOMPUTING, V241, P204, DOI 10.1016/j.neucom.2017.02.053
Nagy S, 2021, AMFITEATRU ECON, V23, P155, DOI 10.24818/EA/2021/56/155
Nair-Ghaswalla Amrita, 2021, NEWS WHY BUILDING TR
Namogoo, 2021, BLOG CONS BEH PSYCH
Nanduri J, 2020, INFORMS J APPL ANAL, V50, P64, DOI 10.1287/inte.2019.1017
Narang N., 2020, EPRA INT J MULTIDISC, V6, P58
Necula SC, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10061955
Netti K., 2015, NOVEL METHOD MINIMIS, DOI [10.1109/ICCIC.2015.7435801, DOI 10.1109/ICCIC.2015.7435801]
Chau NT, 2021, J GLOB INF MANAG, V29, P44, DOI 10.4018/JGIM.2021010103
Noviantoro T., 2021, Rev Bus Account Financ, V1, P130
Omigie NO, 2020, J GLOB INF MANAG, V28, P36, DOI 10.4018/JGIM.2020070103
Panda, 2020, ART COMP INS FIRMS L
Pandya J., 2015, C5 0 ALGORITHM IMPRO
Patel, 2021, M GLOBAL IDENTITY RE
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
Potempa A., 2015, CLASSIFICATION E CUS, DOI [10.7148/2015-0594, DOI 10.7148/2015-0594]
Rahman MS, 2020, J GLOB INF MANAG, V28, P39, DOI 10.4018/JGIM.2020010103
Rehman IH., 2021, J GLOBAL INFORM MANA, V29, P1
Sakar CO, 2019, NEURAL COMPUT APPL, V31, P6893, DOI 10.1007/s00521-018-3523-0
Santo PE, 2022, BALT J MANAG, V17, P56, DOI 10.1108/BJM-04-2021-0140
SCCG, 2022, FEAT ART TRUST INBL
Sengupta S, 2020, J GLOB INF MANAG, V28, P131, DOI 10.4018/JGIM.2020040107
Sethi R.S., 2018, Academy of Marketing Studies Journal, V22, P1
Shankar A, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa19
Shaouf A, 2016, COMPUT HUM BEHAV, V60, P622, DOI 10.1016/j.chb.2016.02.090
Shareef MA, 2019, INFORM TECHNOL PEOPL, V32, P1345, DOI 10.1108/ITP-05-2018-0241
Shaw, 2022, BLOG ECOMMERCE MACHI
Shihab MR, 2019, ELECTRON COMMER RES, V19, P159, DOI 10.1007/s10660-018-9294-y
Shin D, 2021, J GLOB INF MANAG, V29, P77, DOI 10.4018/JGIM.2021030105
Siknun GP, 2016, PROCEDIA ENVIRON SCI, V33, P332, DOI 10.1016/j.proenv.2016.03.084
Singh, 2019, VIEWS WEBS TRUST DEF
Soleimani M, 2022, INF SYST E-BUS MANAG, V20, P57, DOI 10.1007/s10257-021-00545-0
Song PY, 2020, TEH VJESN, V27, P1467, DOI 10.17559/TV-20200808113807
Srivastava PR, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa23
Statista, 2022, FOR 1262881 MOB APP
Statista, 2022, STAT 607716 WORLDW A
Statista, 2022, STAT 251666 NUMB DIG
Statista, 2022, STAT 617136 DIG POP
Statista, 2021, TOP 846 AM DOSSIERKE
Sumarliah E, 2022, RES J TEXT APPAR, V26, P90, DOI 10.1108/RJTA-11-2020-0121
Sun Y, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa32
Talukder MS, 2020, J GLOB INF MANAG, V28, P74, DOI 10.4018/JGIM.2020100105
Ballestar MT, 2019, REV MANAG SCI, V13, P589, DOI 10.1007/s11846-018-0316-x
Trivedi SK, 2019, KNOWL INF SYST, V60, P1427, DOI 10.1007/s10115-018-1271-1
Trivedi SK, 2018, ELECTRON LIBR, V36, P590, DOI 10.1108/EL-08-2017-0182
Trivedi SK, 2014, APPL COMPUT REV, V14, P53, DOI 10.1145/2600617.2600622
Trivedi SK, 2013, IEEE INT C COMPUT, P1153, DOI 10.1109/CSE.2013.171
Vali H, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa7
Varsha PS, 2021, J GLOB INF MANAG, V29, P221, DOI 10.4018/JGIM.20210701.oa10
Ventre I, 2020, J INT CONSUM MARK, V32, P287, DOI 10.1080/08961530.2020.1712293
Verhagen T, 2018, ELECTRON COMMER RES, V18, P537, DOI 10.1007/s10660-017-9270-y
Wu M. Y., 2022, ARTICLES BUILD TRUST
Xiao L, 2021, J GLOB INF MANAG, V29, P138, DOI 10.4018/JGIM.20210901.oa8
Yang GJ, 2021, J GLOB INF MANAG, V29, P55, DOI 10.4018/JGIM.20210901.oa4
Yaseen A., 2021, LAHORE GARRISON U RE, V5, P62, DOI [10.54692/lgurjcsit.2021.0502209, DOI 10.54692/LGURJCSIT.2021.0502]
Yim MYC, 2017, J INTERACT MARK, V39, P89, DOI 10.1016/j.intmar.2017.04.001
Zhang C, 2021, J GLOB INF MANAG, V29, P103, DOI 10.4018/JGIM.2021050105
Zhang D, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102304
Zhao SK, 2020, J GLOB INF MANAG, V28, P121, DOI 10.4018/JGIM.2020010107
Zheng BC, 2018, IEEE ICCE
Zhu BAK, 2022, IEEE ACCESS, V10, P36637, DOI 10.1109/ACCESS.2021.3054044
NR 142
TC 3
Z9 3
U1 24
U2 64
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD 2022 NOV 9
PY 2022
DI 10.1007/s10660-022-09624-x
EA NOV 2022
PG 37
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 6A2XW
UT WOS:000880523000002
DA 2024-03-27
ER
PT J
AU Nguyen, N
Johnson, J
Tsiros, M
AF Nguyen, Nguyen
Johnson, Joseph
Tsiros, Michael
TI Unlimited Testing: Let's Test Your Emails with AI
SO MARKETING SCIENCE
LA English
DT Article
DE email marketing; machine learning; text mining; emotion detection; NLP;
deep learning
AB Testing email marketing effectiveness is an active research area because email remains an important channel for customer acquisition and retention. Email open rates are a key measure of campaign effectiveness. Scholars identify three predictors of open rates: recipients' characteristics, headline characteristics, and sending time. The industry-favored A/B testing has three drawbacks: it takes hours, depletes lists available for main campaigns, and limits testable email versions because of sample size and power requirements. These limitations continue to motivate researchers to build and improve open rate prediction models. Although they reduce testing time, models developed in marketing use only recipients' past open rates as predictors. By contrast, models in computer science typically use only email headline characteristics as predictors. Consequently, current models' open rate prediction errors are high. The authors address the limitations of both literature streams and use all three predictors and machine learning to build an email open rate predictor (EMOP) based on their universal emotion detector (UED). They test EMOP on data from four brands and set state-of-the-art prediction results. Experimental validation shows that EMOP can pick the best headline from a set of professionally generated headlines. Also, UED ranked second at the SemEval 2018 Task 1 E-c competition as of January 5, 2023.
C1 [Nguyen, Nguyen; Johnson, Joseph; Tsiros, Michael] Univ Miami, Miami Herbert Business Sch, Coral Gables, FL 33124 USA.
C3 University of Miami
RP Johnson, J (autor correspondiente), Univ Miami, Miami Herbert Business Sch, Coral Gables, FL 33124 USA.
EM nnguyenlethanh@mbs.miami.edu; jjohnson@bus.miami.edu; tsiros@miami.edu
OI Nguyen, Nguyen/0000-0002-8174-4065
CR Alhuzali H, 2021, PREPRINT
Ameer I, 2023, EXPERT SYST APPL, V213, DOI 10.1016/j.eswa.2022.118534
[Anonymous], 2022, Statista
[Anonymous], 2016, STATISTA
[Anonymous], 2016, Statista
Balakrishnan R, 2014, IEEE INT CONF BIG DA, P579, DOI 10.1109/BigData.2014.7004277
Baziotis Christos., 2018, P 12 INT WORKSHOP SE, P245, DOI DOI 10.18653/V1/S18-1037
Blattberg RC, 2009, DATABASE MARKETING A
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Bonfrer A, 2009, MARKET SCI, V28, P251, DOI 10.1287/mksc.1080.0393
Boyd R, 2022, DEV PSYCHOMETRIC PRO, DOI DOI 10.13140/RG.2.2.23890.43205
Chakraborty I, 2022, J MARKETING RES, V59, P600, DOI 10.1177/00222437211052500
Chatterjee A., 2019, P 13 INT WORKSHOP SE, P39, DOI DOI 10.18653/V1/S19-2005
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Chochlakis G, 2022, PREPRINT
Clark K, 2020, PREPRINT
Cortiz D, 2021, PREPRINT
Demszky D, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P4040
Devlin J., 2018, ARXIV
Dorogush AV, 2018, PREPRINT
Duppada V, 2018, P 12 INT WORKSH SEM, P1
Fei H, 2020, AAAI CONF ARTIF INTE, V34, P7692
Feit EM, 2019, MARKET SCI, V38, P1038, DOI 10.1287/mksc.2019.1194
Fong NM, 2017, MANAGE SCI, V63, P2353, DOI 10.1287/mnsc.2016.2447
Hanson W, 2000, PRINCIPLES INTERNET
Hartmann J, 2022, EMOTION ENGLISH DIST
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
HOLBROOK MB, 1980, J ADVERTISING RES, V20, P53
Jaidka K, 2018, WEBSCI'18: PROCEEDINGS OF THE 10TH ACM CONFERENCE ON WEB SCIENCE, P177, DOI 10.1145/3201064.3201071
Joulin A, 2017, P 15 C EUR CHAPT ASS, V2, P427, DOI [10.48550/arxiv.1607.01759, DOI 10.48550/ARXIV.1607.01759]
Kant N, 2018, PREPRINT
Ke G, 2019, LIGHTGBM HIGHLY EFFI
Kim Y, 2014, ACL ANTHOLOGY
Kristensen E, 2022, ECOMMERCE REVENUE EN
Kumar V, 2014, J MARKETING RES, V51, P403, DOI 10.1509/jmr.13.0169
Li J, 2021, J MARKETING, V85, P123, DOI 10.1177/0022242920959043
Liu Y, 2019, PREPRINT
Loshchilov I, 2019, P INT C LEARN REPR
Manning CD, 2008, INTRO INFORM RETRIEV
McCorkle S, 2015, EMAIL MARKETING IS S
McInnes L., 2018, arXiv, DOI 10.21105/joss.00861
McInnes L, 2017, INT CONF DAT MIN WOR, P33, DOI 10.1109/ICDMW.2017.12
Meisheri H, 2018, P 12 INT WORKSH SEM, P291, DOI [DOI 10.18653/V1/S18-1043, 10.18653/v1/S18-1043]
Mikolov T., 2013, ARXIV, DOI DOI 10.48550/ARXIV.1301.3781
Neuendorf K. A., 2017, CONTENT ANAL GUIDEBO, DOI [DOI 10.4135/9781071802878, 10.4135/9781071802878]
Ogilvy D., 1983, Ogilvy on Advertising
Ohman E, 2018, P 9 WORKSH COMP APPR, P24
Padilla N, 2021, J MARKETING RES, V58, P981, DOI 10.1177/00222437211032938
Park JiHo., 2018, Proceedings of The 12th International Workshop on Semantic Evaluation, P264
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI DOI 10.3115/V1/D14-1162
Poria S, 2014, KNOWL-BASED SYST, V69, P108, DOI 10.1016/j.knosys.2014.06.011
Qiang JP, 2022, IEEE T KNOWL DATA EN, V34, P1427, DOI 10.1109/TKDE.2020.2992485
Radford A., 2019, OPENAI BLOG, V1, P1, DOI DOI 10.18653/V1/P19-1195
Raffel C, 2020, PREPRINT
Reimers N, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P567
ROSSITER JR, 1981, J ADVERTISING RES, V21, P63
Sahni NS, 2018, MARKET SCI, V37, P236, DOI 10.1287/mksc.2017.1066
Sahni NS, 2017, MANAGE SCI, V63, P2688, DOI 10.1287/mnsc.2016.2450
Sanh Victor., 2020, PREPRINT
Saravia E, 2018, 2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2018), P3687
Schuster M, 1997, IEEE T SIGNAL PROCES, V45, P2673, DOI 10.1109/78.650093
Schwartz EM, 2017, MARKET SCI, V36, P500, DOI 10.1287/mksc.2016.1023
Shi X, 2021, PREPRINT
Shwartz-Ziv R, 2021, PREPRINT
Sinha M, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P531, DOI 10.1145/3159652.3159683
Strapparava C, 2007, P 4 INT WORKSH SEM E, P70, DOI DOI 10.3115/1621474.1621487
Tellis GJ, 2019, J MARKETING, V83, P1, DOI 10.1177/0022242919841034
Vaswani A., 2017, ADV NEURAL INFORM PR, P6000
Wang A., 2018, PREPRINT
Wells W., 1992, Advertising: Principles and practice, V2nd
WILBUR WJ, 1994, J INFORM SCI, V20, P270, DOI 10.1177/016555159402000405
Williams P, 2002, J CONSUM RES, V28, P636, DOI 10.1086/338206
Wood SN, 2015, J ROY STAT SOC C, V64, P139, DOI 10.1111/rssc.12068
Xu P, 2020, PREPRINT
Yang Z, 2020, ARXIV
Ying W, 2019, P 5 WORKSH NOIS US G, P316, DOI DOI 10.18653/V1/D19-5541
Zhang M, 2014, SIGIR'14: PROCEEDINGS OF THE 37TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P73, DOI 10.1145/2600428.2609599
Zhou D, 2016, P 2016 C EMP METH NA, P638, DOI DOI 10.18653/V1/D16-1061
NR 79
TC 0
Z9 0
U1 45
U2 48
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD MAR-APR
PY 2024
VL 43
IS 2
DI 10.1287/mksc.2021.0126
EA JUL 2023
PG 22
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LM6E7
UT WOS:001044243600001
DA 2024-03-27
ER
PT J
AU Bhattacharya, C
Sinha, M
AF Bhattacharya, Chandrima
Sinha, Manish
TI Role of Artificial Intelligence in Banking for Leveraging Customer
Experience
SO AUSTRALASIAN ACCOUNTING BUSINESS AND FINANCE JOURNAL
LA English
DT Article
DE Artificial Intelligence; Digital Banking; Chatbots; Customer Experience
AB Purpose: In light of digital advancements, banks need to create customer experiences that strengthen loyalty and trust. For establishing a strong digital banking base, it is crucial for banks to make their processes efficient and fast. The purpose of the paper is to analyze the efficacy of banking functions on implementing Artificial Intelligence for enhancing customer engagement and improving customer satisfaction. It targets banks in metropolitan cities of India having tech-savvy customers, leading a fast-paced life who desire personalization and expect faultless and seamless services.Methodology: The study focusses on front, middle and back-office banking processes. The data for middle and back-office processes is collected through 10 interviews of senior officials and head of IT team in major banks. Literature review and theoretical research is carried out for various international and Indian banks with respect to the integration of AI to improve customer interactions and internal banking processes. For understanding the front-office user experience with AI-Banking, data has been gathered through a survey regarding usage of Chatbots on online banking platforms. A quantitative analysis using the Relative Importance Index reveals major use-cases ranked by customers. Spearman correlation is applied to find the relationship between the two most popular use-cases.Findings: The research paper reveals banking features integrated with AI. Chatbot use-cases on banking platforms are ranked based on customer experience. It is proved that there is a positive correlation (0.247) between the two most popular use-cases. The paper proposes IT Architecture and best practices for the Practical/Theoretical implications: Based on the complete picture of AI integration with banking operations, evolving Indian banks could focus on the most popular use-cases to attract customers. A comparison with the features developed for various banks may provide a way for growth in the digital banking sector. The correlation between Chatbot use-cases may benefit the established Indian banks to further expand business.Originality/value: Implementation of AI in banking is identified for Indian Banks. It is proved that if a person uses Chatbot for assistance in customer service, they are likely to use Chatbot for recommendation regarding offers and discounts.
C1 [Bhattacharya, Chandrima; Sinha, Manish] Symbiosis Int Univ, Symbiosis Ctr Management & Human Resource Dev, SIU, Pune, India.
C3 Symbiosis International University; Symbiosis Centre for Management &
Human Resource Development (SCMHRD)
RP Bhattacharya, C (autor correspondiente), Symbiosis Int Univ, Symbiosis Ctr Management & Human Resource Dev, SIU, Pune, India.
EM chandrima994@gmail.com; manish_sinha@scmhrd.edu
CR Agarwal P., 2019, ADV SCI ENG TECHNOLO, DOI [10.1109/ICASET.2019.8714305, DOI 10.1109/ICASET.2019.8714305]
[Anonymous], 2016, HADOOP BANKING GAME
Belanche D, 2019, IND MANAGE DATA SYST, V119, P1411, DOI 10.1108/IMDS-08-2018-0368
Bhandari A., 2020, BUILD YOUR OWN OPTIC
Biswas S., 2020, BANK FUTURE CAN BANK
Euart J., 2020, GLOBAL VIEW FINANCIA
Gallego-Gomez C, 2020, INT J ENTERP INF SYS, V16, P20, DOI 10.4018/IJEIS.2020070102
Indriasari Elisa, 2019, 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI), P863, DOI 10.1109/IIAI-AAI.2019.00175
Königstorfer F, 2020, J BEHAV EXP FINANC, V27, DOI 10.1016/j.jbef.2020.100352
Korobov G., 2020, SAAS VS PREMISE DIGI
McBride T., 2019, REALIZING OPPORTUNIT
Sandu N, 2019, INT CONF INFO TECH, DOI 10.1109/ithet46829.2019.8937382
Sumathi N., 2017, 2 INT C COMPUTING CO, DOI [10.1109/ICCCT2.2017.7972246, DOI 10.1109/ICCCT2.2017.7972246]
Trivedi J, 2019, J INTERNET COMMER, V18, P91, DOI 10.1080/15332861.2019.1567188
Vaganova O., 2019, HUMANITIES SOCIAL SC, V7, P786, DOI [10.18510/hssr.2019.7599, DOI 10.18510/HSSR.2019.7599]
NR 15
TC 1
Z9 1
U1 17
U2 41
PU UNIV WOLLONGONG
PI WOLLONGONG
PA NORTHFIELDS AVE, WOLLONGONG, NSW 2522, AUSTRALIA
SN 1834-2000
EI 1834-2019
J9 AUSTRALAS ACCOUNT BU
JI Australas. Account. Bus. Financ. J.
PY 2022
VL 16
IS 5
BP 89
EP 105
PG 17
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 7Y1LH
UT WOS:000914649300007
DA 2024-03-27
ER
PT J
AU Kull, AJ
Romero, M
Monahan, L
AF Kull, Alexander J.
Romero, Marisabel
Monahan, Lisa
TI How may I help you? Driving brand engagement through the warmth of an
initial chatbot message
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Artificial intelligence; Brand relationships; Brand-self distance;
Digital marketing; Stereotype content model; Virtual agent
ID ARTIFICIAL-INTELLIGENCE; SOCIAL MEDIA; CONSUMERS; AGENTS; STEREOTYPES;
ENCOUNTERS; NONPROFITS; FRAMEWORK; ATTITUDES; BEHAVIOR
AB Despite the growing number of brands that rely on chatbots to address customer service inquiries that once required human intervention, academics and practitioners are only beginning to acknowledge the role of chatbots in brand-building activities. Chatbots can initiate online conversations, thereby often serving as a consumer's first brand impression. However, little is known about how managers can strategically tailor a chatbot's initial message to foster consumer-brand connections and, ultimately, engagement. Three studies demonstrate that when chatbots initiate a conversation using a warm (vs. competent) message, brand engagement increases, as assessed using both computerized text analysis and traditional scale measures. Brand-self distance mediates this effect, such that a warm (vs. competent) initial chatbot message makes consumers feel closer to the brand. Further, the authors identify brand affiliation as a theoretically relevant moderator. This research thus offers managers insight into how initial chatbot messages can attract and engage consumers.
C1 [Kull, Alexander J.] Univ San Diego, Sch Business, 5998 Alcala Pk, San Diego, CA 92110 USA.
[Romero, Marisabel] Colorado State Univ, Coll Business, 1201 Campus Delivery, Ft Collins, CO 80523 USA.
[Monahan, Lisa] Meredith Coll, Sch Business, 3800 Hillsborough St, Raleigh, NC 27607 USA.
C3 University of San Diego; Colorado State University
RP Kull, AJ (autor correspondiente), Univ San Diego, Sch Business, 5998 Alcala Pk, San Diego, CA 92110 USA.
EM akull@sandiego.edu; mari.romero_lopez@colostate.edu;
lamonahan@meredith.edu
RI hajibabaei, hossein/HFB-7762-2022
CR Aaker J, 2010, J CONSUM RES, V37, P224, DOI 10.1086/651566
Aggarwal P, 2004, J CONSUM RES, V31, P87, DOI 10.1086/383426
Aggarwal P, 2007, J CONSUM RES, V34, P468, DOI 10.1086/518544
Allport G. W., 1954, The nature of prejudice
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Bernritter SF, 2016, J INTERACT MARK, V33, P27, DOI 10.1016/j.intmar.2015.10.002
Bloomfield EF, 2020, SCI COMMUN, V42, P369, DOI 10.1177/1075547020927017
Booth RJ, 2015, LINGUISTIC INQUIRY A
Brandtzaeg P. B., 2018, Interactions, V25, P38, DOI [DOI 10.1145/3236669, 10.1145/3236669, https://doi.org/10.1145/3236669]
Brodie RJ, 2013, J BUS RES, V66, P105, DOI 10.1016/j.jbusres.2011.07.029
Brodie RJ, 2011, J SERV RES-US, V14, P252, DOI 10.1177/1094670511411703
CACIOPPO JT, 1982, J PERS SOC PSYCHOL, V42, P116, DOI 10.1037/0022-3514.42.1.116
Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
Candello H, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P3476, DOI 10.1145/3025453.3025919
Canhoto AI, 2020, BUS HORIZONS, V63, P183, DOI 10.1016/j.bushor.2019.11.003
Chan J., 2019, SINGLE GRAIN 0111
Choi WJ, 2013, J MARKETING, V77, P96, DOI 10.1509/jm.11.0544
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Computer Generated Solutions, 2019, CHATB DEL SPEED CONS
Corti K, 2016, COMPUT HUM BEHAV, V58, P431, DOI 10.1016/j.chb.2015.12.039
Currás-Pérez R, 2009, J BUS ETHICS, V89, P547, DOI 10.1007/s10551-008-0016-6
Davenport TH, 2018, HARVARD BUS REV, V96, P108
DeWall CN, 2009, J PERS SOC PSYCHOL, V96, P729, DOI 10.1037/a0014634
EPAM, 2017, CHATB ADD CUST TOUCH
Fiske ST, 2002, J PERS SOC PSYCHOL, V82, P878, DOI 10.1037//0022-3514.82.6.878
Fiske ST, 2007, TRENDS COGN SCI, V11, P77, DOI 10.1016/j.tics.2006.11.005
Fournier S, 1998, J CONSUM RES, V24, P343, DOI 10.1086/209515
Fournier S, 2013, J CONSUM PSYCHOL, V23, P253, DOI 10.1016/j.jcps.2013.01.004
Franzen K, 2020, ARE BLOGS RELIABLE S
Fung P, 2018, LECT NOTES COMPUT SC, V9624, P173, DOI 10.1007/978-3-319-75487-1_14
Gnewuch Ulrich, 2017, P INT C INF SYST ICI, V6, P4046
Goodman JK, 2018, J CONSUM RES, V45, P365, DOI 10.1093/jcr/ucy010
Gretry A, 2017, J BUS RES, V74, P77, DOI 10.1016/j.jbusres.2017.01.012
Gronroos C., 1990, Service Management and Marketing: Managing the Moments of Truth in Service Competition
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
Hayes A. F., 2018, REGRESSION BASED APP
Hofstede G., 1997, CULTURES ORG SOFTWAR
Hofstede Insights, 2021, COUNTR COMP
Hollebeek L, 2011, J STRATEG MARK, V19, P555, DOI 10.1080/0965254X.2011.599493
Hollebeek LD, 2011, J MARKET MANAG-UK, V27, P785, DOI 10.1080/0267257X.2010.500132
Holtgraves TM, 2007, COMPUT HUM BEHAV, V23, P2163, DOI 10.1016/j.chb.2006.02.017
Holzwarth M, 2006, J MARKETING, V70, P19, DOI 10.1509/jmkg.70.4.19
Hsieh SH, 2016, J INTERACT MARK, V33, P13, DOI 10.1016/j.intmar.2015.10.001
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huang R, 2020, J BUS RES, V121, P616, DOI 10.1016/j.jbusres.2020.04.034
Hur JD, 2015, J CONSUM RES, V42, P340, DOI 10.1093/jcr/ucv017
Johnen M, 2019, J ACAD MARKET SCI, V47, P858, DOI 10.1007/s11747-019-00661-x
Jones E.E., 1990, INTERPERSONAL PERCEP
Joshi N., 2018, FORBES
Jovic D., 2020, SMALLBIZGENIUS 1120
Kees J, 2017, J ADVERTISING, V46, P141, DOI 10.1080/00913367.2016.1269304
Kervyn N, 2014, SOC COGNITION, V32, P256, DOI 10.1521/soco.2014.32.3.256
Kervyn N, 2012, J CONSUM PSYCHOL, V22, P166, DOI 10.1016/j.jcps.2011.09.006
Kim J, 2021, J BUS RES, V130, P552, DOI 10.1016/j.jbusres.2019.09.043
Kirmani A, 2017, J MARKETING, V81, P103, DOI 10.1509/jm.15.0369
Köhler CF, 2011, J MARKETING, V75, P93, DOI 10.1509/jm.75.2.93
Kolbl Z, 2019, J BUS RES, V104, P614, DOI 10.1016/j.jbusres.2018.12.060
Kull AJ, 2016, INT J RES MARK, V33, P77, DOI 10.1016/j.ijresmar.2015.04.005
Kumar V, 2021, J BUS RES, V125, P864, DOI 10.1016/j.jbusres.2020.01.007
Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
Leite I, 2013, INT J HUM-COMPUT ST, V71, P250, DOI 10.1016/j.ijhcs.2012.09.005
Lemmink J, 1998, INT J RES MARK, V15, P505, DOI 10.1016/S0167-8116(98)00016-0
LEVENSON RW, 1992, J PERS SOC PSYCHOL, V63, P234, DOI 10.1037/0022-3514.63.2.234
Li XS, 2019, J CONSUM RES, V45, P973, DOI 10.1093/jcr/ucy016
Li YC, 2019, COMMUN REP, V32, P15, DOI 10.1080/08934215.2018.1545859
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Luo J. T., 2016, J SERV MARK, V20, P112
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Macinnis D.J., 2009, Handbook of Brand Relationships
MACRAE CN, 1994, J PERS SOC PSYCHOL, V66, P37, DOI 10.1037/0022-3514.66.1.37
Makarius EE, 2020, J BUS RES, V120, P262, DOI 10.1016/j.jbusres.2020.07.045
Marinova D, 2017, J SERV RES-US, V20, P29, DOI 10.1177/1094670516679273
Marketing Science Institute, 2020, RES PRIOR 2020 2022
Martínez-López FJ, 2013, IND MARKET MANAG, V42, P489, DOI 10.1016/j.indmarman.2013.03.001
Misal D., 2018, ANALYTICS INDIA 0907
Murtarelli G, 2021, J BUS RES, V129, P927, DOI 10.1016/j.jbusres.2020.09.018
Nguyen M.-H., 2020, BUSINESS INSIDER
Nielsen J., 2011, LONG USERS STAY WEB
Pansari A, 2017, J ACAD MARKET SCI, V45, P294, DOI 10.1007/s11747-016-0485-6
Park C.W., 1985, Research in Consumer Behavior, V1, P201
Park CW, 2013, J CONSUM PSYCHOL, V23, P229, DOI 10.1016/j.jcps.2013.01.002
Paulssen M, 2014, EUR J MARKETING, V48, P964, DOI 10.1108/EJM-11-2011-0657
Pennebaker JW., 2015, LIWC 2015 OPERATORS
Phillips C., 2018, CHATBOTS MAGAZINE
Rafaeli A, 2017, J SERV RES-US, V20, P91, DOI 10.1177/1094670516679275
Robinson S, 2020, J BUS RES, V116, P366, DOI 10.1016/j.jbusres.2019.08.038
Russell AMT, 2008, EUR J SOC PSYCHOL, V38, P1193, DOI 10.1002/ejsp.539
Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
Schroll R, 2018, J CONSUM RES, V45, P648, DOI 10.1093/jcr/ucy014
Sela A, 2012, J CONSUM RES, V39, P644, DOI 10.1086/664972
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shum HY, 2018, FRONT INFORM TECH EL, V19, P10, DOI 10.1631/FITEE.1700826
Sirianni NJ, 2013, J MARKETING, V77, P108, DOI 10.1509/jm.11.0485
Statista Research Department, 2021, DIG LEAD GEN AD SPEN
Steele I., 2018, CHATBOT DOS DONTS TH
Stephen A. T., 2015, 15110 MARK SCI I
Stokburger-Sauer N, 2012, INT J RES MARK, V29, P406, DOI 10.1016/j.ijresmar.2012.06.001
Trivedi J, 2019, J INTERNET COMMER, V18, P91, DOI 10.1080/15332861.2019.1567188
Tsai WC, 2002, J APPL PSYCHOL, V87, P1001, DOI 10.1037//0021-9010.87.5.1001
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
van Doorn J, 2010, J SERV RES-US, V13, P253, DOI 10.1177/1094670510375599
Verma A., 2019, ENTREPRENEUR
Vora I., 2018, TOP 5 FUNDAMENTAL RU
Wan J., 2015, Strong Brands, Strong Relationships, P119
Wang XH, 2018, J BUS RES, V84, P89, DOI 10.1016/j.jbusres.2017.11.011
Wang Z, 2017, J CONSUM RES, V43, P787, DOI 10.1093/jcr/ucw062
Willis J, 2006, PSYCHOL SCI, V17, P592, DOI 10.1111/j.1467-9280.2006.01750.x
Wojciszke B, 1998, PERS SOC PSYCHOL B, V24, P1251, DOI 10.1177/01461672982412001
Wu JT, 2017, J MARKET MANAG-UK, V33, P61, DOI 10.1080/0267257X.2016.1233132
Ybarra O, 2001, MOTIV EMOTION, V25, P85, DOI 10.1023/A:1010633908298
NR 110
TC 59
Z9 60
U1 56
U2 234
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD OCT
PY 2021
VL 135
BP 840
EP 850
DI 10.1016/j.jbusres.2021.03.005
EA JUL 2021
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA TY1UF
UT WOS:000683570800018
DA 2024-03-27
ER
PT J
AU Arpaci, I
AF Arpaci, Ibrahim
TI What drives students' online self-disclosure behaviour on social media?
A hybrid SEM and artificial intelligence approach
SO INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS
LA English
DT Article
DE social media; self-disclosure; trust; artificial intelligence; machine
learning
ID PRIVACY CALCULUS; TRUST; COMMERCE; ASSOCIATIONS; INTENTION; LOCATION;
ADOPTION; PARADOX; IMPACT
AB This study investigated drivers of the online self-disclosure behaviour on social media by employing a complementary structural equation modelling (SEM) and artificial intelligence approach. The study developed a theoretical model based on the 'theory of planned behaviour' (TPB) and 'communication privacy management' (CPM) theory. The predictive model was validated by employing a multi-analytical approach based on the data obtained from 300 undergraduate students. The model focused on the role of security, privacy, and trust perceptions in predicting the attitudes toward the selfie-posting behaviour. The results suggested that privacy and security are significantly associated with the trust, which explains a significant amount of the variance in the attitudes. Consistently, results of the machine-learning classification algorithms suggested that attributes of the security, privacy, and trust could predict the attitudes with an accuracy of more than 61%% in most cases. Further, mediation analysis results indicated that privacy has no direct effect, but an indirect effect on the attitudes. These findings suggested a trade-off between the privacy concerns and perceived benefits of the actual behaviour.
C1 [Arpaci, Ibrahim] Tokat Gaziosmanpasa Univ, Dept Comp Educ & Instruct Technol, Fac Educ, TR-60250 Tokat, Turkey.
C3 Gaziosmanpasa University
RP Arpaci, I (autor correspondiente), Tokat Gaziosmanpasa Univ, Dept Comp Educ & Instruct Technol, Fac Educ, TR-60250 Tokat, Turkey.
EM ibrahim.arpaci@gop.edu.tr
RI ARPACI, Ibrahim/AAC-2389-2019
OI ARPACI, Ibrahim/0000-0001-6513-4569
CR Aiken KD, 2006, J ACAD MARKET SCI, V34, P308, DOI 10.1177/0092070304271004
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
[Anonymous], 2005, HDB ATTITUDES
[Anonymous], SEM CONFIRMATORY FAC
Arpaci I, 2019, COMPUT HUM BEHAV, V90, P181, DOI 10.1016/j.chb.2018.09.005
Arpaci I, 2016, COMPUT HUM BEHAV, V58, P150, DOI 10.1016/j.chb.2015.12.067
Arpaci I, 2015, CYBERPSYCH BEH SOC N, V18, P602, DOI 10.1089/cyber.2015.0243
Baek YM, 2014, COMPUT HUM BEHAV, V38, P33, DOI 10.1016/j.chb.2014.05.006
BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
Barry CT, 2017, PSYCHOL POP MEDIA CU, V6, P48, DOI 10.1037/ppm0000089
Barth S, 2017, TELEMAT INFORM, V34, P1038, DOI 10.1016/j.tele.2017.04.013
Beldad A, 2010, COMPUT HUM BEHAV, V26, P857, DOI 10.1016/j.chb.2010.03.013
Chen JV, 2017, INT J MOB COMMUN, V15, P329, DOI 10.1504/IJMC.2017.083465
Chou YHD, 2018, INT J MOB COMMUN, V16, P117
De Wolf R, 2014, COMPUT HUM BEHAV, V35, P444, DOI 10.1016/j.chb.2014.03.010
DeCew J., 1997, The pursuit of privacy: Law, ethics, and the rise of technology
Dhir A, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00815
Dinev T, 2006, INFORM SYST RES, V17, P61, DOI 10.1287/isre.1060.0080
Fogel J, 2009, COMPUT HUM BEHAV, V25, P153, DOI 10.1016/j.chb.2008.08.006
Frampton BD, 2013, COMPUT HUM BEHAV, V29, P2257, DOI 10.1016/j.chb.2013.05.006
Frye NE, 2010, COMPUT HUM BEHAV, V26, P1120, DOI 10.1016/j.chb.2010.03.016
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Hallam C, 2017, COMPUT HUM BEHAV, V68, P217, DOI 10.1016/j.chb.2016.11.033
Halpern D, 2016, PERS INDIV DIFFER, V97, P98, DOI 10.1016/j.paid.2016.03.019
Joinson AN, 2010, HUM-COMPUT INTERACT, V25, P1, DOI 10.1080/07370020903586662
Kim HS, 2018, INT J MOB COMMUN, V16, P135
Kisekka V, 2013, COMPUT HUM BEHAV, V29, P2722, DOI 10.1016/j.chb.2013.07.023
Lauer TW, 2007, INT J INF SECUR, V6, P323, DOI 10.1007/s10207-007-0028-8
Liang H, 2016, NEW MEDIA SOC
McCullagh K, 2008, INF COMMUN TECHNOL L, V17, P3, DOI 10.1080/13600830801886984
McKnight DH, 2002, J STRATEGIC INF SYST, V11, P297, DOI 10.1016/S0963-8687(02)00020-3
Mesch GS, 2012, COMPUT HUM BEHAV, V28, P1471, DOI 10.1016/j.chb.2012.03.010
Nissenbaum H, 1998, LAW PHILOS, V17, P559
Olivero N, 2004, J ECON PSYCHOL, V25, P243, DOI 10.1016/S0167-4870(02)00172-1
Petronio S., 2002, BOUNDARY PRIVACY DIA
Qiu L, 2015, COMPUT HUM BEHAV, V52, P443, DOI 10.1016/j.chb.2015.06.032
Ridgway JL, 2016, CYBERPSYCH BEH SOC N, V19, P2, DOI 10.1089/cyber.2015.0433
Roca Juan Carlos, 2009, Information Management & Computer Security, V17, P96, DOI 10.1108/09685220910963983
Rong K, 2018, INT J MOB COMMUN, V16, P247, DOI 10.1504/IJMC.2018.10003824
Smith HJ, 2011, MIS QUART, V35, P989
Sobel M.E., 1982, SOCIOLOGICAL METHODO, P290, DOI [DOI 10.2307/270723, 10.2307/270723]
Taddei S, 2013, COMPUT HUM BEHAV, V29, P821, DOI 10.1016/j.chb.2012.11.022
Tan X, 2018, INT J MOB COMMUN, V16, P286, DOI 10.1504/IJMC.2018.10004210
Tufekci Z, 2008, B SCI TECHNOL SOC, V28, P20, DOI DOI 10.1177/0270467607311484
Weber Rolf H., 2009, Computer Law and Security Report, V25, P522, DOI 10.1016/j.clsr.2009.09.002
Westin A.F., 1967, PRIVACY FREEDOM
Wood JT., 2015, INTERPERSONAL COMMUN
NR 47
TC 17
Z9 17
U1 6
U2 50
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 1470-949X
EI 1741-5217
J9 INT J MOB COMMUN
JI Int. J. Mob. Commun.
PY 2020
VL 18
IS 2
BP 229
EP 241
PG 13
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA KW4DH
UT WOS:000521115300005
DA 2024-03-27
ER
PT J
AU Ren, XC
He, J
Huang, ZL
AF Ren, Xiaocong
He, Jun
Huang, Zilong
TI RETRACTADO: An empirical study on the behavior of e-commerce strategic
planning based on deep learning algorithm (Retracted Article)
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article; Retracted Publication
DE E-commerce; Key success factors; Evaluation; Structural equation model;
Deep learning algorithm; Data sparse
AB On the basis of large-scale literature research, the evaluation and element model for the successful implementation of e-commerce are established, and the key elements (customer, strategy, leadership, technology) and the evaluation elements (system quality, system quality, information quality, service quality) affect the success of e-commerce. First, learn the effective features of the items from the content data through deep learning in advance, and then transform the learned features into the learning task of the collaborative filtering target, and add balance and no relevant constraints to the e-commerce strategic planning behavior values of users and items, using alternating optimization algorithms to learn the value of e-commerce strategic planning behavior and fine-tuning the deep network, and finally get the compact and informative e-commerce strategic planning behavior value of users and items, effectively solving the data sparse problem and cold start in the collaborative filtering algorithm problem. Secondly, the combination of conceptual model and structural equation model has innovated research methods and introduced structural equation model method, which effectively handles the complex relationship between multi-dimensional variables and revises and verifies the hypothetical model. Through path analysis, the interaction and influence between key success factors, success evaluation factors, and successful implementation of e-commerce are explored, and useful attempts are made to expand relevant research data analysis methods.
C1 [Ren, Xiaocong; He, Jun; Huang, Zilong] Liaoning Univ, Sch Econ, Shenyang 110036, Liaoning, Peoples R China.
C3 Liaoning University
RP Huang, ZL (autor correspondiente), Liaoning Univ, Sch Econ, Shenyang 110036, Liaoning, Peoples R China.
EM appilerr@163.com; danleerr@163.com; beresun@163.com
FU Program of Liaoning Social Science Planning Fund Project: Research on
the Reform and Development of Urban Public Utilities PPP in Northeast
China [L19CJL004]
FX This work is supported by the Program of Liaoning Social Science
Planning Fund Project: Research on the Reform and Development of Urban
Public Utilities PPP in Northeast China (Grant No.L19CJL004).
CR Aringhieri R, 2018, OPER RES PERSPECT, V5, P22, DOI 10.1016/j.orp.2017.12.001
Bai, 2017, C E CA, V42, P1460
Chen, 2018, PAPER ASIA, V1, P14
Chen C., 2015, ADV J FOOD SCI TECHN, V7, P977, DOI [10.19026/ajfst.7.2546, DOI 10.19026/AJFST.7.2546]
Dhote S, 2020, ELECTRON COMMER RES, V20, P259, DOI 10.1007/s10660-019-09383-2
Ding K, 2017, J INTELL MANUF, V28, P1303, DOI 10.1007/s10845-015-1051-7
Guo, 2017, B TECNICO TECH B, V55, P352
Hamamoto M, 2019, ENERG POLICY, V125, P286, DOI 10.1016/j.enpol.2018.10.042
Jipp M, 2016, J COGN ENG DECIS MAK, V10, P138, DOI 10.1177/1555343416637517
Menning L, 2017, J INFECT DIS, V216, pS24, DOI 10.1093/infdis/jix059
Mita-Arancibia, 2015, 5 REV FUNDACI N EDUC, V18, P15, DOI [10.4321/S2014-98322015000100004, DOI 10.4321/S2014-98322015000100004]
Mocanu E, 2019, IEEE T SMART GRID, V10, P3698, DOI 10.1109/TSG.2018.2834219
Myint K, 2016, MALAYS J MED SCI, V23, P49
Qu BY, 2015, SECUR COMMUN NETW, V8, P68, DOI 10.1002/sec.755
Ting Dai, 2016, International Journal of Web Portals, V8, P13, DOI 10.4018/IJWP.2016010102
Vandecasteele F, 2017, ACM T MULTIM COMPUT, V13, DOI 10.1145/3092834
Wang SY, 2019, WASTE MANAGE, V87, P33, DOI 10.1016/j.wasman.2019.01.038
Wu, 2015, AFR J BUS MANAGE, V9, P3
Wu DH, 2020, BIOSYST ENG, V189, P150, DOI 10.1016/j.biosystemseng.2019.11.017
Yang GL, 2018, ENG AGR-JABOTICABAL, V38, P783, DOI [10.1590/1809-4430-eng.agric.v38n5p783-796/2018, 10.1590/1809-4430-Eng.Agric.v38n5p783-796/2018]
Yang YT, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10030933
Zhang Chichen, 2017, Wei Sheng Yan Jiu, V46, P755
Zhang H., 2016, INT TECHNOL MANAG, V7, P61
Zhou Huilin, 2017, Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, V34, P173, DOI 10.7507/1001-5515.201608055
최종근, 2016, [Journal of Korea Academia-Industrial cooperation Society, 한국산학기술학회논문지], V17, P538, DOI 10.5762/KAIS.2016.17.5.538
NR 25
TC 0
Z9 0
U1 2
U2 30
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD AUG
PY 2023
VL 21
IS SUPPL 1
SU 1
BP 9
EP 9
DI 10.1007/s10257-021-00504-9
EA JAN 2021
PG 1
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA T4NO4
UT WOS:000608670900002
DA 2024-03-27
ER
PT J
AU Abubakar, AM
AF Abubakar, A. Mohammed
TI Using hybrid SEM - artificial intelligence Approach to examine the nexus
between boreout, generation, career, life and job satisfaction
SO PERSONNEL REVIEW
LA English
DT Article
DE Job satisfaction; Quantitative; Nigeria; Service industry; Life
satisfaction; Advanced statistical; Career satisfaction; Boreout
ID WORK VALUES; NEURAL-NETWORK; EMPLOYEES; WORKPLACE; BOREDOM; ANTECEDENTS;
PERFORMANCE; OUTCOMES; CONSERVATION; INCIVILITY
AB Purpose Boreout is a psychological state of intense boredom and apathy. Characterized by the absence of mental stimuli (i.e. menial tasks) required to keep employees conscious about their environment, and this incessant decline in mental stimuli may turn employees into "professional zombies." The diversity in work needs and preferences across generations has become a key organizational factor, generational differences have been studied in Western countries, not much information is available about generational cohorts and satisfaction (i.e. career, life and job satisfaction) in developing countries. The purpose of this paper is to provide more insights on these phenomena. Design/methodology/approach Drawing upon conservation of resources theory, this paper examines the potential effects of boreout on important job outcomes (i.e. career, life and job satisfaction) conditioned by generation (Gen-Xers and Gen-Yers) in the service industry. Data analyses with Artificial Intelligence technique (i.e. artificial neural network) and structural equation modeling were conducted with data collated from Nigerian service employees. Findings Results revealed that boreout has a negative impact on career, life and job satisfaction. The hypothesized relationships were significantly moderated by generation cohorts as Gen-Xers and Gen-Yers were found to be distinct cohorts. Originality/value This paper advocates that non-western organizations should avoid utmost service standardization and rigid stylization of work processes and procedures.
C1 [Abubakar, A. Mohammed] Antalya Bilim Univ, Coll Business & Social Sci, Antalya, Turkey.
C3 Antalya Bilim University
RP Abubakar, AM (autor correspondiente), Antalya Bilim Univ, Coll Business & Social Sci, Antalya, Turkey.
EM mohammed.abubakar@antalya.edu.tr
RI ABUBAKAR, A Mohammed/L-7214-2018
OI ABUBAKAR, A Mohammed/0000-0002-1163-0185
CR Abubakar A.M., 2018, INT J OCCUPATIONAL S, V24, P1
Abubakar A.M., 2016, J MANAGEMENT DEV, V41, P53
Abubakar AM, 2018, J MANAG DEV, V37, P226, DOI 10.1108/JMD-06-2017-0207
Abubakar AM, 2017, TOUR MANAG PERSPECT, V23, P129, DOI 10.1016/j.tmp.2017.06.001
Aiken LS., 1991, MULTIPLE REGRESSION
ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
Bagozzi R.P., 1994, PRINCIPLE MARKETING, P386
Bakker A. B., 2008, Career Development International, V13, P209, DOI [DOI 10.1108/13620430810870476, 10.1108/13620430810870476]
Bakker AB, 2010, J PERS PSYCHOL, V9, P3, DOI 10.1027/1866-5888/a000006
Batt R., 2002, Human Resource Management Journal, V12, P14
Biggs S., 2011, Generational intelligence: A critical approach to age relations
Broadbridge A.M., 2007, CAREER DEV INT, V12, P523
Bruursema K, 2011, WORK STRESS, V25, P93, DOI 10.1080/02678373.2011.596670
Burke R.J., 2001, CROSS CULTURAL MANAG, V8, P117, DOI DOI 10.1108/13527600110797254
Carstensen LL, 2006, SCIENCE, V312, P1913, DOI 10.1126/science.1127488
Cavanaugh MA, 2000, J APPL PSYCHOL, V85, P65, DOI 10.1037/0021-9010.85.1.65
Cennamo L, 2008, J MANAGE PSYCHOL, V23, P891, DOI 10.1108/02683940810904385
Chan D, 2009, STATISTICAL AND METHODOLOGICAL MYTHS AND URBAN LEGENDS: DOCTRINE, VERITY AND FABLE IN THE ORGANIZATIONAL AND SOCIAL SCIENCES, P309
Chuang NK, 2011, J HOSP MARKET MANAG, V20, P551, DOI 10.1080/19368623.2011.570642
Conway JM, 2010, J BUS PSYCHOL, V25, P325, DOI 10.1007/s10869-010-9181-6
Costanza DP, 2012, J BUS PSYCHOL, V27, P375, DOI 10.1007/s10869-012-9259-4
D'Amato A, 2008, J MANAGE PSYCHOL, V23, P929, DOI 10.1108/02683940810904402
Dahling JJ, 2010, PERS INDIV DIFFER, V48, P574, DOI 10.1016/j.paid.2009.12.009
DeMeuse K., 2001, Journal of Managerial Issues, V13, P102
DIENER E, 1985, J PERS ASSESS, V49, P71, DOI 10.1207/s15327752jpa4901_13
Erdogan B, 2018, J VOCAT BEHAV, V107, P233, DOI 10.1016/j.jvb.2018.05.003
Fajana S., 2009, PERSONALFUHRUNG, V7, P80
Fisher CD, 1998, J ORGAN BEHAV, V19, P503, DOI 10.1002/(SICI)1099-1379(199809)19:5<503::AID-JOB854>3.0.CO;2-9
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Fry R., 2016, THIS YEAR MILLENNIAL
GREENHAUS JH, 1990, ACAD MANAGE J, V33, P64, DOI 10.5465/256352
Gursoy D, 2008, INT J HOSP MANAG, V27, P448, DOI 10.1016/j.ijhm.2007.11.002
Gursoy D, 2011, INT J HOSP MANAG, V30, P783, DOI 10.1016/j.ijhm.2010.10.009
Hackman J.R. e., 1980, Work redesign
Halbesleben JRB, 2013, J APPL PSYCHOL, V98, P492, DOI 10.1037/a0032242
Harris L.C., 2002, J SERV RES-US, V4, P163, DOI DOI 10.1177/1094670502004003001
Harris LC, 2006, J ACAD MARKET SCI, V34, P543, DOI 10.1177/0092070306287324
Haykin S., 2001, Neural Networks: A Comprehensive Foundation
HOBFOLL SE, 1989, AM PSYCHOL, V44, P513, DOI 10.1037/0003-066X.44.3.513
Hobfoll SE, 2011, J OCCUP ORGAN PSYCH, V84, P116, DOI 10.1111/j.2044-8325.2010.02016.x
Ineme KM, 2016, J HIGH EDUC POLICY M, V38, P664, DOI 10.1080/1360080X.2016.1211976
Inglehart R., 1997, MODERNIZATION POSTMO, DOI DOI 10.1515/9780691214429
Jackson A., 2005, BETTER ED BADLY PAID
Janssen O, 2000, J OCCUP ORGAN PSYCH, V73, P287, DOI 10.1348/096317900167038
Joyner T., 2000, SecuredLender, V56, P64
Karatepe O. M., 2010, FIU Hospitality Review, V28, P48
Karatepe OM, 2015, INT J CONTEMP HOSP M, V27, P1254, DOI 10.1108/IJCHM-01-2014-0028
Karatepe OM, 2014, INT J HOSP MANAG, V36, P14, DOI 10.1016/j.ijhm.2013.08.003
Kass Steven J, 2003, J Atten Disord, V7, P83, DOI 10.1177/108705470300700202
Kline R. B., 2015, PRINCIPLES PRACTICE
Kong HY, 2012, INT J HOSP MANAG, V31, P76, DOI 10.1016/j.ijhm.2011.03.002
Kooij DTAM, 2011, J ORGAN BEHAV, V32, P197, DOI 10.1002/job.665
Kupperschmidt B R, 2000, Health Care Manag (Frederick), V19, P65
Lancaster L., 2002, GENERATIONS COLLIDE
Lapierre LM, 2018, J ORGAN BEHAV, V39, P385, DOI 10.1002/job.2234
Lee C, 2010, INT J HOSP MANAG, V29, P344, DOI 10.1016/j.ijhm.2009.08.008
Lemire L, 1999, PUBLIC PERS MANAGE, V28, P375, DOI 10.1177/009102609902800305
Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
Lepine JA, 2005, ACAD MANAGE J, V48, P764, DOI 10.2307/20159696
Loukidou L, 2009, INT J MANAG REV, V11, P381, DOI 10.1111/j.1468-2370.2009.00267.x
Macey WH, 2008, IND ORGAN PSYCHOL-US, V1, P3, DOI 10.1111/j.1754-9434.2007.0002.x
May DR, 2004, J OCCUP ORGAN PSYCH, V77, P11, DOI 10.1348/096317904322915892
McKevitt D, 2017, INT J PROJ MANAG, V35, P1673, DOI 10.1016/j.ijproman.2017.07.010
MIKULAS WL, 1993, PSYCHOL REC, V43, P3
Mncwango B., 2016, PUBLIC ATTITUDES WOR
Modderman A.J.T., 2017, THESIS
Morris SA, 2004, IND MANAGE DATA SYST, V104, P129, DOI 10.1108/02635570410522099
NEAR JP, 1984, SOC INDIC RES, V15, P351, DOI 10.1007/BF00351444
Netemeyer RG, 1997, J MARKETING, V61, P85, DOI 10.2307/1251791
Ng ESW, 2010, J BUS PSYCHOL, V25, P281, DOI 10.1007/s10869-010-9159-4
Ng TWH, 2005, PERS PSYCHOL, V58, P367, DOI 10.1111/j.1744-6570.2005.00515.x
Owoyemi O.A., 2011, EUROPEAN J EC FINANC, V30, P65
Park J, 2012, INT J HOSP MANAG, V31, P1195, DOI 10.1016/j.ijhm.2012.02.007
Parry E, 2011, INT J MANAG REV, V13, P79, DOI 10.1111/j.1468-2370.2010.00285.x
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Purohit B.K., 2010, LEVERAGING INTEGRATI
Romero Norma., 2017, Journal of Behavior, Health Social Issues, V9, P1, DOI [DOI 10.1016/J.JBHSI.2017.08.001, DOI 10.1016/J.JBHSI.2017.08]
Rothlin P., 2008, Boreout! Overcoming workplace demotivation
Russo M, 2016, J POSIT PSYCHOL, V11, P173, DOI 10.1080/17439760.2015.1025424
SCHUMAN H, 1989, AM SOCIOL REV, V54, P359, DOI 10.2307/2095611
Schwartz SH, 2000, J CROSS CULT PSYCHOL, V31, P465, DOI 10.1177/0022022100031004003
Schwartz ShalomS., 1995, WORLD PSYCHOL, V1, P91
Shalley CE, 2009, ACAD MANAGE J, V52, P489, DOI 10.5465/AMJ.2009.41330806
Sheldon K.M., 2018, J HAPPINESS STUD, P1
Sirgy MJ, 2018, APPL RES QUAL LIFE, V13, P229, DOI 10.1007/s11482-017-9509-8
Smola KW, 2002, J ORGAN BEHAV, V23, P363, DOI 10.1002/job.147
Stock RM, 2016, J BUS RES, V69, P4259, DOI 10.1016/j.jbusres.2016.02.037
Stock RM, 2015, J PROD INNOVAT MANAG, V32, P574, DOI 10.1111/jpim.12239
Tetjesen S., 2007, Career Development International, V12, P504, DOI DOI 10.1108/13620430710821994
Tulgan B., 1995, Managing generation x: How to bring out the best in young talent
Twenge JM, 2010, J MANAGE, V36, P1117, DOI 10.1177/0149206309352246
van der Heijden GAH, 2012, EUR J WORK ORGAN PSY, V21, P349, DOI 10.1080/1359432X.2011.578824
Van Dyne L, 2002, J ORGAN BEHAV, V23, P57, DOI 10.1002/job.127
Walker D.D., 2009, THESIS
Westerman J.W., 2007, Career Development International, V12, P150, DOI DOI 10.1108/13620430710733631
Wilk SL, 2005, J APPL PSYCHOL, V90, P917, DOI 10.1037/0021-9010.90.5.917
Yavas U, 2013, J HOSP MARKET MANAG, V22, P375, DOI 10.1080/19368623.2012.667596
Zacher H, 2009, PSYCHOL AGING, V24, P487, DOI 10.1037/a0015425
Zhao K., 2015, J STAT COMPUT SIM, V86, P1
Zhao XY, 2011, INT J HOSP MANAG, V30, P46, DOI 10.1016/j.ijhm.2010.04.010
NR 100
TC 14
Z9 14
U1 7
U2 45
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0048-3486
EI 1758-6933
J9 PERS REV
JI Pers. Rev.
PD NOV 19
PY 2019
VL 49
IS 1
BP 67
EP 86
DI 10.1108/PR-06-2017-0180
PG 20
WC Industrial Relations & Labor; Psychology, Applied; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA KC0ZL
UT WOS:000506916800001
DA 2024-03-27
ER
PT J
AU Deng, GK
Zhang, JY
Xu, Y
AF Deng, Guangkuan
Zhang, Jianyu
Xu, Ying
TI How e-commerce platforms build channel power: the role of AI resources
and market-based assets
SO JOURNAL OF BUSINESS & INDUSTRIAL MARKETING
LA English
DT Article
DE Channel power; AI resources; Market-based assets; Intraplatform
competition; E-commerce platform
ID ARTIFICIAL-INTELLIGENCE; INFORMATION-TECHNOLOGY; COMPETITIVE ADVANTAGE;
INFLUENCE STRATEGIES; MEASUREMENT ERROR; FIRM RESOURCES; PERFORMANCE;
BUSINESS; COMMITMENT; VIEW
AB PurposeConsidering the emergence of e-commerce platforms and their integration into marketing channels, this paper aims to investigate how artificial intelligence (AI) resources - both technological and human - possessed by e-commerce platforms can enhance their channel power by acquiring market-based assets (relational and intellectual). Design/methodology/approachBased on resource-based theory and resource orchestration theory, the authors developed a framework tested using survey data gathered from the sellers, which incorporated six key variables: the e-commerce platform's AI technology resources and human resources, rational and intellectual market-based assets, intraplatform competition and channel power. The analyses are performed using the regression analysis technique. FindingsThe empirical findings indicate that both technological and human AI resources are crucial in building channel power. In addition, market-based assets serve as a mediator in this relationship, while intraplatform competition moderates the effect of intellectual market-based assets on channel power negatively. Originality/valueThis study contributes to the existing literature by exploring how e-commerce platforms' AI resources affect their channel power. The results offer valuable guidance to managers and researchers on optimizing AI resources to improve channel power.
C1 [Deng, Guangkuan; Xu, Ying] Southwest Univ Sci & Technol, Sch Econ & Management, Mianyang, Peoples R China.
[Zhang, Jianyu] Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu, Peoples R China.
C3 Southwest University of Science & Technology - China; Southwestern
University of Finance & Economics - China
RP Deng, GK (autor correspondiente), Southwest Univ Sci & Technol, Sch Econ & Management, Mianyang, Peoples R China.
EM styzdgk@126.com; xncdzjy@126.com; 976394681@qq.com
RI G.K., Deng/AAZ-7371-2020
OI G.K., Deng/0000-0002-5071-463X
FU Doctoral Project of Southwest University of Science and Technology
[21sx7109, 22sx7112]
FX The authors would like to thank the Editor-in-Chief, Dr Wesley Johnston,
and anonymous reviewers for providing valuable insights and constructive
comments. Funding: This research was funded by the Doctoral Project of
Southwest University of Science and Technology (Grant No. 21sx7109,
22sx7112).
CR Aghaie S, 2022, EUR J MARKETING, V56, P1556, DOI 10.1108/EJM-02-2021-0087
Aguirre E, 2015, J RETAILING, V91, P34, DOI 10.1016/j.jretai.2014.09.005
ANDERSON JC, 1994, J MARKETING, V58, P1, DOI 10.2307/1251912
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Armstrong M, 2006, RAND J ECON, V37, P668, DOI 10.1111/j.1756-2171.2006.tb00037.x
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Bharadwaj AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983
Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
Brown JR, 1995, J RETAILING, V71, P363, DOI 10.1016/0022-4359(95)90019-5
Byrd TA, 2000, J MANAGE INFORM SYST, V17, P167
Cennamo C, 2013, STRATEGIC MANAGE J, V34, P1331, DOI 10.1002/smj.2066
Chadwick C, 2015, STRATEGIC MANAGE J, V36, P360, DOI 10.1002/smj.2217
Chakravarty A, 2014, J MARKETING, V78, P1
Clauss T, 2019, IND MARKET MANAG, V77, P75, DOI 10.1016/j.indmarman.2018.12.005
Co HC, 1998, INT J OPER PROD MAN, V18, P87, DOI 10.1108/01443579810192925
Crook TR, 2007, J OPER MANAG, V25, P546, DOI 10.1016/j.jom.2006.05.008
Crook TR, 2008, STRATEGIC MANAGE J, V29, P1141, DOI 10.1002/smj.703
Das TK, 2000, J MANAGE, V26, P31, DOI 10.1016/S0149-2063(99)00037-9
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Deng GK, 2021, INT MARKET REV, V38, P736, DOI 10.1108/IMR-01-2019-0026
Dentchev NA, 2017, BUS SOC, V56, P527, DOI 10.1177/0007650315586179
Dimitrova BV, 2020, J MARK CHANNELS, V26, P60, DOI 10.1080/1046669X.2019.1646187
Dong MC, 2019, J ACAD MARKET SCI, V47, P1064, DOI 10.1007/s11747-018-0614-5
DWYER FR, 1987, J MARKETING, V51, P11, DOI 10.2307/1251126
Eisenmann T, 2006, HARVARD BUS REV, V84, P92
ELANSARY AI, 1972, J MARKETING RES, V9, P47, DOI 10.2307/3149605
Ellram LM, 2019, IND MARKET MANAG, V79, P36, DOI 10.1016/j.indmarman.2019.03.007
Enholm IM, 2022, INFORM SYST FRONT, V24, P1709, DOI 10.1007/s10796-021-10186-w
Feng C, 2020, IND MARKET MANAG, V91, P41, DOI 10.1016/j.indmarman.2020.08.003
Ferrier WJ, 2001, ACAD MANAGE J, V44, P858, DOI 10.5465/3069419
FORNELL C, 1981, J MARKETING RES, V18, P382, DOI 10.2307/3150980
FRAZIER GL, 1991, J MARKETING, V55, P52, DOI 10.2307/1252203
French J.R. B., 1959, The bases of social power
GASKI JF, 1985, J MARKETING RES, V22, P130, DOI 10.2307/3151359
GASKI JF, 1984, J MARKETING, V48, P9, DOI 10.2307/1251326
Guan Y, 2019, DECIS SUPPORT SYST, V118, P58, DOI 10.1016/j.dss.2019.01.003
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
Hagiu A, 2014, MIT SLOAN MANAGE REV, V55, P92
Hair J.F., 2009, Multivariate data analysis
Hakansson H., 1995, DEV RELATIONSHIPS BU
Helfat C., 2007, DYNAMIC CAPABILITIES
Hill J, 2015, COMPUT HUM BEHAV, V49, P245, DOI 10.1016/j.chb.2015.02.026
HUNT SD, 1974, J MARKETING RES, V11, P186, DOI 10.2307/3150557
JACCARD J, 1995, PSYCHOL BULL, V117, P348, DOI 10.1037/0033-2909.117.2.348
Jain M, 2014, IND MARKET MANAG, V43, P312, DOI 10.1016/j.indmarman.2013.09.001
JAWORSKI BJ, 1993, J MARKETING, V57, P53, DOI 10.2307/1251854
Johnston WJ, 2018, J ACAD MARKET SCI, V46, P674, DOI 10.1007/s11747-017-0564-3
Keeling K, 2010, J BUS RES, V63, P793, DOI 10.1016/j.jbusres.2008.12.015
Koehler J., 2018, EUROPEAN BUSINESS MA, V4, P55, DOI DOI 10.11648/J.EBM.20180402.12
KUMAR N, 1995, J MARKETING RES, V32, P348, DOI 10.2307/3151986
Kumar V, 2016, J MARKETING, V80, P108, DOI 10.1509/jm.80.5.1
Li P., 2019, MANAG REV, V31, P103
Li SY, 2021, IND MARKET MANAG, V98, P105, DOI 10.1016/j.indmarman.2021.07.015
Lin WL, 2020, CORP SOC RESP ENV MA, V27, P1761, DOI 10.1002/csr.1923
Liu WP, 2018, IND MARKET MANAG, V73, P7, DOI 10.1016/j.indmarman.2018.01.006
Liu Y., 2022, J CONT MARKETING SCI, V5, P81
Mao MS, 2019, INFORM SCIENCES, V471, P269, DOI 10.1016/j.ins.2018.07.029
Melville N, 2004, MIS QUART, V28, P283
Mikalef P, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2021.101596
MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
Mustak M, 2021, J BUS RES, V124, P389, DOI 10.1016/j.jbusres.2020.10.044
Palmatier RW., 2014, Marketing Channel Strategy, V8th
Park SH, 2001, STRATEGIC MANAGE J, V22, P455, DOI 10.1002/smj.167
Penrose E. T., 1959, The Theory of the Growth of the Firm
PODSAKOFF PM, 1986, J MANAGE, V12, P531, DOI 10.1177/014920638601200408
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Powell TC, 1997, STRATEGIC MANAGE J, V18, P375, DOI 10.1002/(SICI)1097-0266(199705)18:5<375::AID-SMJ876>3.3.CO;2-Z
Preacher KJ, 2004, BEHAV RES METH INS C, V36, P717, DOI 10.3758/BF03206553
Preacher KJ, 2006, J EDUC BEHAV STAT, V31, P437, DOI 10.3102/10769986031004437
Rahman M, 2022, J BUS RES, V138, P422, DOI 10.1016/j.jbusres.2021.08.023
Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
Ray G, 2004, STRATEGIC MANAGE J, V25, P23, DOI 10.1002/smj.366
Rindfleisch A, 1997, J MARKETING, V61, P30, DOI 10.2307/1252085
Robbins S., 2000, Management, V7th
Roson Roberto., 2005, J NETWORK EC, V4, P142, DOI DOI 10.2202/1446-9022.1070
Rutskiy V., 2021, ARTIF INTELL, P895
Santos J.R.Q., 1999, Journal of Extension, V37, DOI DOI 10.1080/09500690802187025
Shankar V, 2018, J RETAILING, V94, pVI, DOI 10.1016/S0022-4359(18)30076-9
Siemieniako D, 2018, IND MARKET MANAG, V70, P90, DOI 10.1016/j.indmarman.2017.08.013
Siering M, 2018, DECIS SUPPORT SYST, V107, P52, DOI 10.1016/j.dss.2018.01.002
Sirmon DG, 2007, ACAD MANAGE REV, V32, P273, DOI 10.2307/20159292
Sirmon DG, 2011, J MANAGE, V37, P1390, DOI 10.1177/0149206310385695
Srivastava RK, 2001, J MANAGE, V27, P777, DOI 10.1016/S0149-2063(01)00123-4
Srivastava RK, 1998, J MARKETING, V62, P2, DOI 10.2307/1251799
Steinhoff L, 2021, AUSTRALAS MARK J, V29, P111, DOI 10.1016/j.ausmj.2020.07.003
Thomas LDW, 2014, ACAD MANAGE PERSPECT, V28, P198, DOI 10.5465/amp.2011.0105
Van Alstyne MW, 2016, HARVARD BUS REV, V94, P54
van Esch P, 2019, COMPUT HUM BEHAV, V90, P215, DOI 10.1016/j.chb.2018.09.009
Varadarajan R, 2020, IND MARKET MANAG, V89, P89, DOI 10.1016/j.indmarman.2020.03.003
Vishnoi SK., 2018, Indian Journal of Economics and Business, V17, P167
Wade M, 2004, MIS QUART, V28, P107
Watson GF, 2015, J RETAILING, V91, P546, DOI 10.1016/j.jretai.2015.04.002
WERNERFELT B, 1984, STRATEGIC MANAGE J, V5, P171, DOI 10.1002/smj.4250050207
Wirth N, 2018, INT J MARKET RES, V60, P435, DOI 10.1177/1470785318776841
Wodecki A., 2019, ARTIF INTELL, P133, DOI DOI 10.1007/978-3-319-91596-8_3
Xiao D, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106876
Xu Y, 2023, NANKAI BUS REV NT, V14, P249, DOI 10.1108/NBRI-06-2021-0041
Yang DF, 2013, TOTAL QUAL MANAG BUS, V24, P707, DOI 10.1080/14783363.2013.776763
[张传栋 Zhang Chuandong], 2020, [实验力学, Journal of Experimental Mechanics], V35, P1
Zhang T., 2017, MANAG REV, V29, P134
Zhang T., 2010, Management World, V7, P119
Zhang T, 2017, J BUS IND MARK, V32, P1217, DOI 10.1108/JBIM-12-2016-0286
Zhuang G., 2004, EUROPEAN J MARKING, V38, P675
Zhuang GJ, 2010, IND MARKET MANAG, V39, P137, DOI 10.1016/j.indmarman.2008.07.002
NR 106
TC 1
Z9 1
U1 41
U2 51
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 0885-8624
EI 2052-1189
J9 J BUS IND MARK
JI J. Bus. Ind. Mark.
PD FEB 13
PY 2024
VL 39
IS 2
BP 173
EP 188
DI 10.1108/JBIM-11-2022-0497
EA JUL 2023
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HM9C2
UT WOS:001020025600001
DA 2024-03-27
ER
PT J
AU Han, QW
Lucas, C
Aguiar, E
Macedo, P
Wu, ZZ
AF Han, Qiwei
Lucas, Carolina
Aguiar, Emila
Macedo, Patricia
Wu, Zhenze
TI Towards privacy-preserving digital marketing: an integrated framework
for user modeling using deep learning on a data monetization platform
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Deep learning; Digital marketing; Data monetization; Data privacy;
Hyperbolic embeddings; Federated learning
ID ECONOMICS
AB This paper presents a novel approach to privacy-preserving user modeling for digital marketing campaigns using deep learning techniques on a data monetization platform, which enables users to maintain control over their personal data while allowing marketers to identify suitable target audiences for their campaigns. The system comprises of several stages, starting with the use of representation learning on hyperbolic space to capture the latent user interests across multiple data sources with hierarchical structures. Next, Generative Adversarial Networks are employed to generate synthetic user interests from these embeddings. To ensure the privacy of user data, a Federated Learning technique is implemented for decentralized user modeling training, without sharing data with marketers. Lastly, a targeting strategy based on recommendation system is constructed to leverage the learned user interests for identifying the optimal target audience for digital marketing campaigns. Overall, the proposed approach provides a comprehensive solution for privacy-preserving user modeling for digital marketing.
C1 [Han, Qiwei; Lucas, Carolina; Aguiar, Emila; Macedo, Patricia; Wu, Zhenze] Nova Sch Business & Econ, Carcavelos, Portugal.
C3 Universidade Nova de Lisboa
RP Han, QW (autor correspondiente), Nova Sch Business & Econ, Carcavelos, Portugal.
EM qiwei.han@novasbe.pt; 44364@novasbe.pt; 44993@novasbe.pt;
44359@novasbe.pt; 44524@novasbe.pt
RI Han, Qiwei/HKN-5336-2023
OI Han, Qiwei/0000-0002-6044-4530
FU FCT|FCCN (b-on); Fundacao para a Ciencia e a Tecnologia
[UIDB/00124/2020, UIDP/00124/2020, PINFRA/22209/2016]; POR Lisboa and
POR Norte [PINFRA/22209/2016]
FX Open access funding provided by FCT|FCCN (b-on). This work was funded by
Fundacao para a Ciencia e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020
and Social Sciences DataLab-PINFRA/22209/2016), POR Lisboa and POR Norte
(Social Sciences DataLab, PINFRA/22209/2016).
CR Acquisti A, 2016, J ECON LIT, V54, P442, DOI 10.1257/jel.54.2.442
Agrawal R, 2000, SIGMOD REC, V29, P439, DOI 10.1145/335191.335438
Aiolfi S, 2021, INT J RETAIL DISTRIB, V49, P1089, DOI 10.1108/IJRDM-10-2020-0410
Aivodji Ulrich Matchi, 2019, 2019 IEEE Security and Privacy Workshops (SPW). Proceedings, P175, DOI 10.1109/SPW.2019.00041
AlexPof, 2021, POINCAREKMEANS K MEA, P11
Balazevic I., 2019, Advances in Neural Information Processing Systems, V32
Balazevic I, 2019, ADV NEUR IN, V32
Baowaly MK, 2019, J AM MED INFORM ASSN, V26, P228, DOI 10.1093/jamia/ocy142
Bataineh AS, 2016, PROCEDIA COMPUT SCI, V83, P472, DOI 10.1016/j.procs.2016.04.211
Behera RK, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.03.026
Cheng Y, 2020, COMMUN ACM, V63, P33, DOI 10.1145/3387107
Choi Edward., 2017, Proceedings of the 2nd Machine Learning for Healthcare Conference, volume 68 of Proceedings of Machine Learning Research, P286
Choi H, 2019, MARKET SCI, V38, P948, DOI 10.1287/mksc.2019.1197
Drainakis G, 2020, IEEE INT SYM N C A, DOI 10.1109/nca51143.2020.9306745
Evans DS, 2009, J ECON PERSPECT, V23, P37, DOI 10.1257/jep.23.3.37
Fan SK, 2015, BIG DATA RES, V2, P28, DOI 10.1016/j.bdr.2015.02.006
Faroukhi AZ, 2020, J BIG DATA-GER, V7, DOI 10.1186/s40537-019-0281-5
Fiore U, 2019, INFORM SCIENCES, V479, P448, DOI 10.1016/j.ins.2017.12.030
Geiping J., 2020, Advances in Neural Information Processing Systems, V33, P16937
Goldfarb A, 2011, MANAGE SCI, V57, P57, DOI 10.1287/mnsc.1100.1246
Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
Guo YT, 2020, PROC INT CONF PARAL, DOI 10.1145/3404397.3404410
Hunter Tatum, 2023, Washington Post
Jain Priyank, 2016, Journal of Big Data, V3, DOI 10.1186/s40537-016-0059-y
Jenkings R., 2012, DRUM
KAIROUZ P, 2021, MACH LEARN, V14, P1, DOI DOI 10.1561/2200000083
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Ke TT, 2023, MANAGE SCI, V69, P4389, DOI 10.1287/mnsc.2022.4614
Kemppainen L., 2018, J BUSINESS MODELS, V6, P79, DOI [10.5278/ojs.jbm.v6i3.2053, DOI 10.5278/OJS.JBM.V6I3.2053]
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Lambrecht A, 2014, MARKET LETT, V25, P331, DOI 10.1007/s11002-014-9310-5
Li Q, 2021, INT J DIGIT EARTH, V14, P661, DOI 10.1080/17538947.2020.1865466
Li T, 2020, IEEE SIGNAL PROC MAG, V37, P50, DOI 10.1109/MSP.2020.2975749
Lin SY, 2022, DATA TECHNOL APPL, V56, P720, DOI 10.1108/DTA-09-2021-0260
Liu C-H., 2015, NATL GOVERNANCE AGE, P10
Martin KD, 2017, J ACAD MARKET SCI, V45, P135, DOI 10.1007/s11747-016-0495-4
McMahan Brendan, 2017, COLLABORATIVE MACHIN
Mehta S, 2021, INFORM SYST RES, V32, P1281, DOI 10.1287/isre.2021.1027
Meta Business Help Center, BEST PRACT CHOOS CAT
Moore E., 2017, Artificial intelligence and statistics, P1273, DOI DOI 10.48550/ARXIV.1602.05629
Najjar MS., 2013, MIS Q EXEC, V4, P213
Nickel M, 2017, ADV NEUR IN, V30
Ofulue J., 2022, MANAGEMENT REV Q, DOI [10.1007/s11301-022-00309-1, DOI 10.1007/S11301-022-00309-1]
Park N, 2018, PROC VLDB ENDOW, V11, P1071, DOI 10.14778/3231751.3231757
Parvinen P, 2020, COMMUN ASSOC INF SYS, V47, P25, DOI 10.17705/1CAIS.04702
Peukert C, 2022, MARKET SCI, V41, P318, DOI 10.1287/mksc.2021.1339
Radford A., 2015, ARXIV
Ray J, 2020, INFORM SYST RES, V31, P1, DOI 10.1287/isre.2019.0872
Rieke N, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-00323-1
Silva S, 2019, I S BIOMED IMAGING, P270, DOI [10.1109/ISBI.2019.8759317, 10.1109/isbi.2019.8759317]
Singhal K., 2021, Adv. Neural Inf. Process. Syst., V34, P11220
Taylor CR, 2004, RAND J ECON, V35, P631, DOI 10.2307/1593765
Tran LV, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P609, DOI 10.1145/3336191.3371850
Visconti R.M., 2017, BIG DATA ANALYICS, P345
Wu JZ, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P957, DOI 10.1145/3442381.3449926
Xu L, 2019, ADV NEUR IN, V32
Yang P, 2019, ACM T INTEL SYST TEC, V10, DOI 10.1145/3339474
Yang Timothy, 2018, APPL FEDERATED LEARN
Yang WS, 2019, LECT NOTES COMPUT SC, V11514, P18, DOI 10.1007/978-3-030-23551-2_2
Yoon J, 2020, IEEE J BIOMED HEALTH, V24, P2378, DOI 10.1109/JBHI.2020.2980262
Zhang HJ, 2020, NEURAL COMPUT APPL, V32, P4519, DOI [10.1007/s00521-018-3691-y, 10.1007/s00521-018-3579-x]
Zhang K, 2019, PROCEDIA COMPUT SCI, V147, P400, DOI 10.1016/j.procs.2019.01.256
NR 62
TC 0
Z9 0
U1 11
U2 18
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD SEP
PY 2023
VL 23
IS 3
SI SI
BP 1701
EP 1730
DI 10.1007/s10660-023-09713-5
EA JUN 2023
PG 30
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA S4MI7
UT WOS:001004920300001
OA hybrid, Green Published
DA 2024-03-27
ER
PT J
AU Guercini, S
AF Guercini, Simone
TI Marketing automation and the scope of marketers' heuristics
SO MANAGEMENT DECISION
LA English
DT Article
DE Marketing automation; Scope of heuristics; In depth interviews;
Marketers' decision-making models; AI in marketing
ID ARTIFICIAL-INTELLIGENCE; DECISION; RIGOR
AB Purpose - This paper examines the relationship between marketing automation emergence and the marketers' use of heuristics in their decision-making processes. Heuristics play a role for the integration of human decision-making models and automation in augmentation processes, particularly in marketing where automation is widespread.Design/methodology/approach - This study analyzes qualitative data about the impact of marketing automation on the scope of heuristics in decision-making models, and it is based on evidence collected from interviews with twenty-two experienced marketers.Findings - Marketers make extensive use of heuristics to manage their tasks. While the adoption of new automatic marketing tools modify the task environment and field of use of traditional decision-making models, the adoption of heuristics rules with a different scope is essential to defining inputs, interpreting/evaluating outputs and control the marketing automation system.Originality/value - The paper makes a contribution to research on the relationship between marketing automation and decision-making models. In particular, it proposes the results of in-depth interviews with senior decision makers to assess the impact of marketing automation on the scope of heuristics as decision-making models adopted by marketers.
C1 [Guercini, Simone] Univ Florence, Dept Econ & Management, Florence, Italy.
C3 University of Florence
RP Guercini, S (autor correspondiente), Univ Florence, Dept Econ & Management, Florence, Italy.
EM simone.guercini@unifi.it
OI Guercini, Simone/0000-0002-7542-6984
FU The author thanks two anonymous referees for useful comments to previous
draft of this paper.
FX The author thanks two anonymous referees for useful comments to previous
draft of this paper.
CR Agrawal A., 2018, Prediction machines: the simple economics of artificial intelligence
Alpaydin, 2016, MACHINE LEARNING NEW
Armstrong JS, 2015, J BUS RES, V68, P1717, DOI 10.1016/j.jbusres.2015.03.031
Artinger F, 2015, J ORGAN BEHAV, V36, pS33, DOI 10.1002/job.1950
Bagshaw A., 2015, J DIRECT DATA DIGITA, V17, P84
Bingham CB, 2019, STRATEG ENTREP J, V13, P121, DOI 10.1002/sej.1312
Brock JKU, 2019, CALIF MANAGE REV, V61, P110, DOI 10.1177/1536504219865226
Brynjolfsson E, 2014, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
Bucklin R.E., 1998, MARKET LETT, V9, P235, DOI [10.1023/A:1008047504898, DOI 10.1023/A:1008047504898]
Bucklin RE, 2002, MARKET LETT, V13, P245, DOI 10.1023/A:1020231107662
Camiciottoli BC, 2014, EUR J MARKETING, V48, P1092, DOI 10.1108/EJM-12-2011-0770
Cui DP, 2005, MARKET SCI, V24, P595, DOI 10.1287/mksc.1050.0123
Daugherty P. R., 2018, Human + machine: Reimagining work in the age of AI
Dubois A, 2002, J BUS RES, V55, P553, DOI 10.1016/S0148-2963(00)00195-8
Dubois A, 2014, J BUS RES, V67, P1277, DOI 10.1016/j.jbusres.2013.03.036
Eisenhardt KM, 2016, ACAD MANAGE J, V59, P1113, DOI 10.5465/amj.2016.4004
EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557
Gartner, 2022, MARK TECHN 2022 5 IN
Gigerenzer G., 2004, Handbook of Judgment and Decision Making, P62
Gigerenzer G., 2022, STAY SMART SMART WOR
Gigerenzer G., 1999, SIMPLE HEURISTICS MA
Gigerenzer G, 2021, SYNTHESE, V198, P3547, DOI 10.1007/s11229-019-02296-5
Gigerenzer G, 2011, ANNU REV PSYCHOL, V62, P451, DOI 10.1146/annurev-psych-120709-145346
Gigerenzer G, 2009, TOP COGN SCI, V1, P107, DOI 10.1111/j.1756-8765.2008.01006.x
Gioia D.A., 2004, Renewing Research Practice, P97, DOI DOI 10.1177/1094428112452151
Gioia DA, 2013, ORGAN RES METHODS, V16, P15, DOI 10.1177/1094428112452151
Green KC, 2015, J BUS RES, V68, P1678, DOI 10.1016/j.jbusres.2015.03.026
Guercini S., 2019, Mind and Society, V18, P191, DOI DOI 10.1007/S11299-019-00221-4
Guercini S., 2023, MICROMACROMARKETING, V32, P239
Guercini S., 2022, MIND SOC, P1
Guercini S, 2022, IND MARKET MANAG, V105, P380, DOI 10.1016/j.indmarman.2022.06.019
Guercini S, 2020, J INT MANAG, V26, DOI 10.1016/j.intman.2020.100782
Guercini S, 2015, IND MARKET MANAG, V48, P26, DOI 10.1016/j.indmarman.2015.03.008
Guercini S, 2012, J RES MARK ENTREP, V14, P199, DOI 10.1108/14715201211271410
Haesevoets T, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106730
Heimbach I, 2015, BUS INFORM SYST ENG+, V57, P129, DOI 10.1007/s12599-015-0370-8
Hirt M.Wilmott., 2014, McKinsey Quarterly, V5, P1
Hoffman DL, 2022, J MARKETING, V86, P1, DOI 10.1177/00222429211061636
Huang MH, 2022, J RETAILING, V98, P209, DOI 10.1016/j.jretai.2021.03.001
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
Johnson CD, 2021, ACAD MANAGE PERSPECT, V35, P292, DOI 10.5465/amp.2017.0159
Katsikopoulos K, 2019, COGN PROCESS, V20, P269, DOI 10.1007/s10339-019-00914-1
Katsikopoulos KV, 2020, CLASSIFICATION IN THE WILD, P1
Kelleher JD, 2019, MIT PRESS ESSENT, P1
Langley A, 2011, RES METHOD STRAT MAN, V6, P201, DOI 10.1108/S1479-8387(2011)0000006007
Leyer M, 2021, BUS HORIZONS, V64, P711, DOI 10.1016/j.bushor.2021.02.026
Lincoln YS., 1985, Naturalistic inquiry
Little J.D., 2001, UC BERK 5 INV CHOIC
Loock M, 2015, J BUS RES, V68, P2027, DOI 10.1016/j.jbusres.2015.02.016
Luan SH, 2019, ACAD MANAGE J, V62, P1735, DOI 10.5465/amj.2018.0172
Maanen J.V., 2011, Tales of the Field: On Writing Ethnography, VSecond
March J.G., 1994, PRIMER DECISION MAKI
Mari A., 2019, RISE MACHINE LEARNIN, DOI [10.5167/uzh-197751, DOI 10.5167/UZH-197751]
Martignon L, 1999, MIND MACH, V9, P565, DOI 10.1023/A:1008313020307
Mehta P, 2022, PSYCHOL MARKET, V39, P2013, DOI 10.1002/mar.21716
Mero J, 2022, J BUS RES, V145, P583, DOI 10.1016/j.jbusres.2022.03.026
Merton R.K., 1956, The focused interview
Newell A., 1981, HEURISTIC GEORGE POL
Paschen U, 2020, BUS HORIZONS, V63, P147, DOI 10.1016/j.bushor.2019.10.004
Picone PM, 2021, FAM BUS REV, V34, P12, DOI 10.1177/0894486520985630
Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
Ritter T, 2020, IND MARKET MANAG, V86, DOI 10.1016/j.indmarman.2019.11.019
Rosenberg N., 1994, EXPLORING BLACK BOX
Rust RT, 2021, J MARKETING, V85, P21, DOI 10.1177/0022242921995173
Rusthollkarhu S, 2022, IND MARKET MANAG, V104, P241, DOI 10.1016/j.indmarman.2022.04.014
Shepherd DA, 2015, J MANAGE, V41, P11, DOI 10.1177/0149206314541153
SIMON HA, 1995, ARTIF INTELL, V77, P95, DOI 10.1016/0004-3702(95)00039-H
Sinyard DB, 2020, J SMALL BUS MANAGE, V58, P1221, DOI 10.1111/jsbm.12451
Skiera B, 2013, MARKET SCI, V32, P213, DOI 10.1287/mksc.1120.0735
Smith A., 2020, CONSUMER BEHAV ANAL
Stone M, 2020, BOTTOM LINE, V33, P183, DOI 10.1108/BL-03-2020-0022
Sull Donald., 2015, SIMPLE RULES THRIVE
Tang PM, 2022, ACAD MANAGE J, V65, P1019, DOI 10.5465/amj.2020.1516
Teodorescu MHM, 2021, MIS QUART, V45, P1483, DOI 10.25300/MISQ/2021/16535
Todd P. M., 2012, Ecological Rationality: Intelligence in the World
Todd P M., 2012, ABC Research Group, Ecological rationality: Intelligence in the world, P487, DOI DOI 10.1093/ACPROF:OSO/9780195315448.001.0001
Zellner A., 2001, SIMPLICITY INFERENCE, P242
NR 78
TC 0
Z9 0
U1 14
U2 14
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 0025-1747
EI 1758-6070
J9 MANAGE DECIS
JI Manag. Decis.
PD SEP 5
PY 2023
VL 61
IS 13
BP 295
EP 320
DI 10.1108/MD-07-2022-0909
EA SEP 2023
PG 26
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA X4MF9
UT WOS:001072491800001
OA hybrid
DA 2024-03-27
ER
PT J
AU Parise, S
Guinan, PJ
Kafka, R
AF Parise, Salvatore
Guinan, Patricia J.
Kafka, Ron
TI Solving the crisis of immediacy: How digital technology can transform
the customer experience
SO BUSINESS HORIZONS
LA English
DT Article
DE Digital marketing; Augmented reality; Mobile apps; Video conferencing;
Remote expert; Virtual concierge; Digital assistant; Omnichannel;
Touchpoints
ID SOCIAL COMMERCE; TELEPRESENCE; ENVIRONMENTS; REALITY
AB Marketers are currently facing a 'crisis of immediacy' challenge: how to meet consumers' need to receive content, expertise, and personalized solutions in real time during their shopping experience. Today's digital technologies such as video conferencing, location-based mobile apps, and augmented reality provide a highly personalized and immersive environment that allows for interactivity and rich information exchange between the brand and consumer. We conducted in-depth interviews with over 35 retailers, large-scale surveys with international shoppers, and pilot projects with stores and banking institutions to study how companies are leveraging digital technologies to transform the customer experience. Our findings show that there are two main technology-based models that organizations are deploying to support customers' immediate needs: the remote expert and the digital assistant. We provide company examples of both models, as well as when they are most appropriate and success factors to inform managers. (C) 2016 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.
C1 [Parise, Salvatore; Guinan, Patricia J.] Babson Coll, Babson Hall, Babson Pk, MA 02457 USA.
[Kafka, Ron] Cisco Syst, San Francisco Bay Area, CA USA.
C3 Babson College; Cisco Systems Inc
RP Parise, S (autor correspondiente), Babson Coll, Babson Hall, Babson Pk, MA 02457 USA.
EM sparise@babson.edu; guinan@babson.edu; rkafka@cisco.com
CR [Anonymous], DEMYSTIFYING ONLINE
Biggs J., 2013, TechCrunch
Cisco, 2015, CISC REM EXP SMART S
Cisco, 2013, Catch and keep digital shoppers
Dhebar A, 2013, BUS HORIZONS, V56, P199, DOI 10.1016/j.bushor.2012.11.004
Eroglu SA, 2001, J BUS RES, V54, P177, DOI 10.1016/S0148-2963(99)00087-9
Gilbert J., 2014, TRY MAKEUP GENIUS IN
Graeber C., 2013, TRENDS 2013 5 TRENDS
Gunn M., 2011, INFORM WEEK
HubSpot, 2011, MARK FACT VS MARK FA
Indvik L., 2012, MASHABLE
Klein LR, 2003, J INTERACT MARK, V17, P41, DOI 10.1002/dir.10046
Kohler T, 2011, MIS QUART, V35, P773
Kurylko D. T., 2015, AUTOMOTIVE NEWS
Lapowsky I., 2013, ALEX ANI IS PIONEERI
Leggett K, 2013, TECHRADAR AD D PROS
Liang TP, 2011, INT J ELECTRON COMM, V16, P69, DOI 10.2753/JEC1086-4415160204
McPartlin S., 2012, Understanding how US online shoppers are reshaping the retail experience
Mehrabian A., 1974, APPROACH ENV PSYCHOL, V12
Minkara O., 2013, COBROWSING CUSTOMER
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
Scholz J, 2016, BUS HORIZONS, V59, P149, DOI 10.1016/j.bushor.2015.10.003
Shaul B., 2014, ADWEEK
Suh KS, 2006, BEHAV INFORM TECHNOL, V25, P99, DOI 10.1080/01449290500330398
Suh KS, 2005, MIS QUART, V29, P673
Wisely R., 2014, LINCOLNS CONCIERGE P
Zaso A., 2013, MOBILE CHECKOUTS ALE
Zhang H, 2015, INFORM MANAGE-AMSTER, V52, P468, DOI 10.1016/j.im.2015.01.008
Zhang H, 2014, INFORM MANAGE-AMSTER, V51, P1017, DOI 10.1016/j.im.2014.07.005
[No title captured]
NR 30
TC 137
Z9 160
U1 7
U2 295
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD JUL-AUG
PY 2016
VL 59
IS 4
BP 411
EP 420
DI 10.1016/j.bushor.2016.03.004
PG 10
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA DQ1KW
UT WOS:000378960900007
DA 2024-03-27
ER
PT J
AU Ray, A
Bala, PK
AF Ray, Arghya
Bala, Pradip Kumar
TI User generated content for exploring factors affecting intention to use
travel and food delivery services
SO INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT
LA English
DT Article
DE Natural-Language-Processing (NLP); Online-Food-Delivery-Services (OFDs);
Online-Travel-Agency-Services (OTAs); Qualitative study;
Structural-Equation-Modeling (SEM); User-Generated-Content (UGC)
ID PERCEIVED RISK; ONLINE; ADOPTION; QUALITY; SATISFACTION; CONSUMPTION;
AGENCIES; MEDIA; MODEL; TRUST
AB Customer reviews/comments on product-websites and on social-media pages can serve as great information sources for both customers and service-providers. For exploring the drivers of usage intention in context of Online-Food-Delivery services (OFDs) and Online-Travel-Agency services (OTAs), traditional-based (qualitative or quantitative or mixed-method) approaches may not be enough. This study utilizes a multi-method approach comprising of both traditional and Natural-Language-Processing (NLP)-based approaches. This study has captured the emic-perspectives using qualitative semi-structured-interviews and etic-perspectives using NLPbased analysis of extant literature. The conceptual model developed from the model of online-decision making stance, was tested quantitatively using survey data and by NLP-based approach using online user reviews. The path-model was tested using Structural-Equation-Modeling (SEM). Results of this study reveal that price benefits and trust-in-service are major predictors of customer's usage intention in OFD and OTA contexts. The study concludes with the various implications, limitations and future directions.
C1 [Ray, Arghya; Bala, Pradip Kumar] Indian Inst Management Ranchi, 5th Floor,Audrey House Campus,Meurs Rd, Ranchi 834008, Jharkhand, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
Management Ranchi
RP Ray, A (autor correspondiente), Indian Inst Management Ranchi, 5th Floor,Audrey House Campus,Meurs Rd, Ranchi 834008, Jharkhand, India.
EM arghya.ray16fpm@iimranchi.ac.in; pkbala@iimranchi.ac.in
RI RAY, ARGHYA/N-3898-2017; Ray, Arghya/GXH-3304-2022
OI RAY, ARGHYA/0000-0001-5836-0621
CR Aggarwal GC., 2012, MINING TEXT DATA, pU577
Alalwan AA, 2020, INT J INFORM MANAGE, V50, P28, DOI 10.1016/j.ijinfomgt.2019.04.008
Alvesson M, 1996, LEADERSHIP QUART, V7, P455, DOI 10.1016/S1048-9843(96)90002-8
[Anonymous], 2009, J MARKETING
[Anonymous], 2018, BIMS J MANAG, V3, P58
[Anonymous], 2019, INT J HOSP MANAGE
[Anonymous], 2018, DEV BUSINESS SIMULAT
[Anonymous], 1995, QUANTITATIVE APPL SO
[Anonymous], 2013, ENCY CROSS CULTURAL
[Anonymous], 2018, ESERVICES REPORT 201
[Anonymous], 2018, INF PROCESS MANAGE
[Anonymous], 2017, ADV INTELLIGENT SYST
Arnett DB, 2003, J RETAILING, V79, P161, DOI 10.1016/S0022-4359(03)00036-8
Augustin K.I., 2018, RUSS J AGR SOCIOECON, V11, DOI [10.18551/rjoas.2018-11.26, DOI 10.18551/RJOAS.2018-11.26]
Baabdullah AM, 2019, INT J INFORM MANAGE, V44, P38, DOI 10.1016/j.ijinfomgt.2018.09.002
Blei DM, 2010, J ACM, V57, DOI 10.1145/1667053.1667056
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Braun V., 2019, HDB RES METHODS HLTH
Brill E, 1995, COMPUT LINGUIST, V21, P543
Cassel C, 1999, J APPL STAT, V26, P435, DOI 10.1080/02664769922322
Chai L.T., 2019, Journal of Marketing Advances and Practices, V1, P62, DOI DOI 10.1515/9783110420388-004
Chalabi A., 2007, Patent, Patent No. 7184948
Chang YW, 2019, TOURISM MANAGE, V71, P187, DOI 10.1016/j.tourman.2018.08.026
Charmaz K., 2012, The SAGE Handbook of Interview Research: The Complexity of the Craft, DOI 10.4135/9781452218403.n25
Chatterjee S, 2019, DECIS SUPPORT SYST, V119, P14, DOI 10.1016/j.dss.2019.02.008
Chen CF, 2010, SERV IND J, V30, P2081, DOI 10.1080/02642060903191108
Cho M, 2019, INT J HOSP MANAG, V77, P108, DOI 10.1016/j.ijhm.2018.06.019
Correa JC, 2019, J RETAIL CONSUM SERV, V46, P45, DOI 10.1016/j.jretconser.2018.05.002
Cox C, 2009, J HOSP MARKET MANAG, V18, P743, DOI 10.1080/19368620903235753
Creswell JW., 2014, Research design: qualitative, quantitative, and mixed methods approaches, V4th ed.
Daly J., 1997, PUBLIC HLTH RES METH
DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
El-Masri M, 2017, ETR&D-EDUC TECH RES, V65, P743, DOI 10.1007/s11423-016-9508-8
Elvandari Cecilia Desvita Ratna, 2018, Journal of Industrial and Information Technology in Agriculture, V1, P1
Fang YH, 2012, COMPUT HUM BEHAV, V28, P1790, DOI 10.1016/j.chb.2012.04.019
Feldman R, 2007, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Filieri R, 2015, TOURISM MANAGE, V51, P174, DOI 10.1016/j.tourman.2015.05.007
Fondevila-Gascón JF, 2019, FUTURE INTERNET, V11, DOI 10.3390/fi11030080
Gao LL, 2017, INFORM SYST FRONT, V19, P525, DOI 10.1007/s10796-015-9611-0
Gosling SD, 2003, J RES PERS, V37, P504, DOI 10.1016/S0092-6566(03)00046-1
Hair J.F., 2009, Multivariate data analysis
Hair JF, 2013, LONG RANGE PLANN, V46, P1, DOI 10.1016/j.lrp.2013.01.001
He Z, 2019, INT J PROD ECON, V215, P61, DOI 10.1016/j.ijpe.2018.05.008
Hopkins DJ, 2010, AM J POLIT SCI, V54, P229, DOI 10.1111/j.1540-5907.2009.00428.x
Hu F, 2020, INT J HOSP MANAG, V84, DOI 10.1016/j.ijhm.2019.102317
Huang CM, 2010, BMC MED INFORM DECIS, V10, DOI 10.1186/1472-6947-10-57
Huang J, 2017, INT J HOSP MANAG, V66, P117, DOI 10.1016/j.ijhm.2017.07.003
IBM Watson, PERS INS
Jang S, 2019, INT J HOSP MANAG, V78, P27, DOI 10.1016/j.ijhm.2018.11.010
Jedin MA L, 2017, 4INT C E COMM ICOEC, V2, P7
John O. P., 1990, Handbook of personality theory and research, P66
Kim DJ, 2008, DECIS SUPPORT SYST, V44, P544, DOI 10.1016/j.dss.2007.07.001
Krippendorff K., 2018, Content analysis: An introduction to its methodology
Kunimoto R., 2014, INT J INNOV MANAG, V5, P417, DOI [https://doi.org/10.7763/IJIMT.2014.V5.551, DOI 10.7763/IJIMT.2014.V5.551]
Lee D, 2020, J INT FIN MANAG ACC, V31, P139, DOI 10.1111/jifm.12111
Lerman Kristina., 2010, P 4 INT AAAI C WEBLO, P90
Luminet O, 2001, PSYCHOTHER PSYCHOSOM, V70, P254, DOI 10.1159/000056263
Maimaiti M, 2018, EUR J CLIN NUTR, V72, P1282, DOI 10.1038/s41430-018-0191-1
Neuendorf K. A., 2017, CONTENT ANAL GUIDEBO, DOI [DOI 10.4135/9781071802878, 10.4135/9781071802878]
Patton M. Q., 1990, Qualitative research and evaluation methods, V2nd
Permatasari Anggraeni, 2018, International Journal of Business and Globalisation, V21, P74
Pigatto G, 2017, BRIT FOOD J, V119, P639, DOI 10.1108/BFJ-05-2016-0207
Ray A, 2020, KNOWL PROCESS MANAG, V27, P63, DOI 10.1002/kpm.1623
Ray A, 2019, J RETAIL CONSUM SERV, V51, P221, DOI 10.1016/j.jretconser.2019.05.025
Ray A, 2019, INT J INFORM MANAGE, V47, P140, DOI 10.1016/j.ijinfomgt.2019.01.015
Roh M, 2019, INT J INFORM MANAGE, V47, P262, DOI 10.1016/j.ijinfomgt.2018.09.017
Saga R, 2016, ARTIF LIFE ROBOT, V21, P155, DOI 10.1007/s10015-016-0270-0
Segura MA, 2019, DATA BRIEF, V25, DOI 10.1016/j.dib.2019.104007
Sell K., 2016, Open Journal of Nursing, V6, P213, DOI DOI 10.4236/OJN.2016.63022
Siering M, 2018, DECIS SUPPORT SYST, V107, P52, DOI 10.1016/j.dss.2018.01.002
Simon T, 2015, INT J INFORM MANAGE, V35, P609, DOI 10.1016/j.ijinfomgt.2015.07.001
StatisticsSolutions, 2019, TEST ASS
Tecichert T., 2020, BR FOOD J, V1
Tsang NKF, 2010, J TRAVEL TOUR MARK, V27, P306, DOI 10.1080/10548401003744743
Vaismoradi M, 2013, NURS HEALTH SCI, V15, P398, DOI 10.1111/nhs.12048
van Riel ACR, 2004, TOTAL QUAL MANAG BUS, V15, P475, DOI 10.1080/1478336042000183578
Vijayarani S., 2015, INT J COMPUT SCI COM, V5, P7, DOI DOI 10.1016/J.PR0CS.2013.05.286
Wang SY, 2018, TRANSPORT RES A-POL, V117, P58, DOI 10.1016/j.tra.2018.08.014
WESTBROOK RA, 1991, J CONSUM RES, V18, P84, DOI 10.1086/209243
Xu J., 2018, Topic Modeling with LSA, PLSA, LDA lda2Vec
Yeo VCS, 2017, J RETAIL CONSUM SERV, V35, P150, DOI 10.1016/j.jretconser.2016.12.013
Yuan Y., 2018, CONTINUOUS USAGE INT, DOI [10.1007/ s.I.0257-018-0372-4., DOI 10.1007/S.10257-018-0372-4]
ZionMarketResearch, 2018, GLOBAL ONLINE TRAVEL
NR 83
TC 53
Z9 56
U1 16
U2 129
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0278-4319
EI 1873-4693
J9 INT J HOSP MANAG
JI Int. J. Hosp. Manag.
PD JAN
PY 2021
VL 92
AR 102730
DI 10.1016/j.ijhm.2020.102730
PG 12
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA PB7ZJ
UT WOS:000596534500005
DA 2024-03-27
ER
PT J
AU Rodgers, W
Nguyen, T
AF Rodgers, Waymond
Tam Nguyen
TI Advertising Benefits from Ethical Artificial Intelligence Algorithmic
Purchase Decision Pathways
SO JOURNAL OF BUSINESS ETHICS
LA English
DT Article
DE Ethical considerations; Digital marketing; AI Algorithms
ID INTERNET SHOPPING BEHAVIOR; PRIOR KNOWLEDGE; INFORMATION OVERLOAD;
ELECTRONIC CHANNELS; PLANNED BEHAVIOR; USER ACCEPTANCE; ONLINE;
CONSUMERS; TECHNOLOGY; IMPACT
AB Artificial intelligence (AI) has dramatically changed the way organizations communicate, understand, and interact with their potential consumers. In the context of this trend, the ethical considerations of advertising when applying AI should be the core question for marketers. This paper discusses six dominant algorithmic purchase decision pathways that align with ethical philosophies for online customers when buying a product/goods. The six ethical positions include: ethical egoism, deontology (i.e., rule-based), relativist, utilitarianism, virtue ethics, and ethics of care (i.e., stakeholders' perspective). Furthermore, this paper launches an "intelligent advertising" AI theme by examining its present and future as well as identifying the key phases of intelligent advertising. Several research questions are offered to guide future research on intelligent advertising to benefit ethical AI decision-making. Finally, several areas that can be widely applied to ethical intelligent advertising are suggested for future research.
C1 [Rodgers, Waymond] Univ Texas El Paso, Univ Hull, 500 W Univ Ave, El Paso, TX 79968 USA.
[Tam Nguyen] Univ Da Nang, Univ Econ, Da Nang, Vietnam.
[Tam Nguyen] Univ Hull, Cottingham Rd, Kingston Upon Hull HU6 7RX, N Humberside, England.
C3 University of Texas System; University of Texas El Paso; University of
Danang; University of Hull
RP Rodgers, W (autor correspondiente), Univ Texas El Paso, Univ Hull, 500 W Univ Ave, El Paso, TX 79968 USA.
EM W.Rodgers@hull.ac.uk; tamntm@due.edu.vn
OI Rodgers, Waymond/0000-0003-4349-5667
CR Abbasi A, 2018, MIS QUART, V42, P427, DOI 10.25300/MISQ/2018/13239
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Ajzen I., 1985, SSSP SPRINGER SERIES, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2]
[Anonymous], 2009, ETHICAL BEGINNINGS P
Bakpayev M, 2022, AUSTRALAS MARK J, V30, P90, DOI 10.1016/j.ausmj.2020.04.002
Barnett SB, 2017, J CONSUM RES, V44, P160, DOI 10.1093/jcr/ucw083
Batra R, 2016, J MARKETING, V80, P122, DOI 10.1509/jm.15.0419
Bellman S, 1999, COMMUN ACM, V42, P32, DOI 10.1145/322796.322805
BETTMAN JR, 1980, J CONSUM RES, V7, P234, DOI 10.1086/208812
Bettman JR, 1998, J CONSUM RES, V25, P187, DOI 10.1086/209535
Bhatnagar A, 2000, COMMUN ACM, V43, P98, DOI 10.1145/353360.353371
Bhattachrayya S.K., 2004, European Business Review, V16, P128, DOI DOI 10.1108/09555340410524238
Biswas D, 2004, J INTERACT MARK, V18, P30, DOI 10.1002/dir.20010
Bolstad W. M., 2016, Introduction to Bayesian Statistics, DOI DOI 10.1002/9780470181188
Broder Andrei Z, 2008, Computational advertising and recommender systems, P1
Brown Juanita., 1992, J ACAD MARKET SCI, V20, P235
Bucklin RE, 2002, MARKET LETT, V13, P245, DOI 10.1023/A:1020231107662
Busch O., 2016, PROGRAMMATIC ADVERTI
Butler P., 1998, EUR MANAG J, V16, P600
Campbell BM., 1969, ACTA SOCIOL, DOI 10.1177/000169937401700206
Chen G, 2019, J ADVERTISING, V48, P347, DOI 10.1080/00913367.2019.1654421
Chen YM, 2018, J RETAIL CONSUM SERV, V41, P281, DOI 10.1016/j.jretconser.2017.04.004
Chen YC, 2009, ELECTRON COMMER R A, V8, P48, DOI 10.1016/j.elerap.2008.09.001
Cho C.-H., 2018, DIGITAL MARKETING 4
Cho YC., 2015, INT J MANAGEMENT INF, V19, P21, DOI DOI 10.19030/IJMIS.V19I1.9086
Choudhury V, 2008, MIS QUART, V32, P179
Constantinides E, 2004, INTERNET RES, V14, P111, DOI 10.1108/10662240410530835
Court D., 2009, MCKIN Q, V3, P96
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Deng SS, 2019, J ADVERTISING, V48, P356, DOI 10.1080/00913367.2019.1652121
Deng SS, 2019, INFORM MANAGE-AMSTER, V56, P536, DOI 10.1016/j.im.2018.09.013
Dorn C, 2010, LECT NOTES COMPUT SC, V6336, P327, DOI 10.1007/978-3-642-15618-2_23
EDWARDS W, 1954, PSYCHOL BULL, V51, P380, DOI 10.1037/h0053870
Engel BRD., 1990, CONSUMER BEHAV
Engel J, 1968, CONSUMER BEHAV
Erdem T, 2004, J CONSUM RES, V31, P191, DOI 10.1086/383434
Fishbein M., 1967, Readings in Attitude Theory and Measurement
Fishbein M., 1980, Nebraska Symposium on Motivation, 1979, P65
Foss K, 2011, ORGAN STUD, V32, P683, DOI 10.1177/0170840611407017
Frankish Keith, 2014, CAMBRIDGE HDB ARTIFI, DOI 10.1017/CBO9781139046855
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
George JF, 2004, INTERNET RES, V14, P198, DOI 10.1108/10662240410542634
Gupta A, 2004, INT J ELECTRON COMM, V8, P131, DOI 10.1080/10864415.2004.11044302
Gupta A, 2017, J RETAIL CONSUM SERV, V36, P1, DOI 10.1016/j.jretconser.2016.12.012
Hatton Chandler, 2017, Strategy & Leadership, V45, P25, DOI 10.1108/SL-02-2017-0017
He Dehua, 2008, Tsinghua Science and Technology, V13, P287, DOI 10.1016/S1007-0214(08)70046-4
Heeter C, 2000, Journal of Interactive Advertising, V1, P3, DOI DOI 10.1080/15252019.2000.10722040
HERR PM, 1989, J CONSUM RES, V16, P67, DOI 10.1086/209194
Howard J., 1969, Journal of the American Statistical Association
Hsu MH, 2006, INT J HUM-COMPUT ST, V64, P889, DOI 10.1016/j.ijhcs.2006.04.004
Huang MH, 2000, INT J INFORM MANAGE, V20, P337, DOI 10.1016/S0268-4012(00)00027-X
Huang MX, 2013, ONLINE INFORM REV, V37, P927, DOI 10.1108/OIR-07-2012-0117
IAB, 2014, PROGR EV DAT TECHN A
Jiang Z., 2019, JOURNALISM COMMUNICA, V5, P56
Johnson B., 2000, ADVERTISING AGE, V71, P19
Johnson DG, 2015, J BUS ETHICS, V127, P707, DOI 10.1007/s10551-014-2180-1
Johnson EJ, 2003, J MARKETING, V67, P62, DOI 10.1509/jmkg.67.2.62.18615
Jordan MI, 2015, SCIENCE, V349, P255, DOI 10.1126/science.aaa8415
Kaplan A, 2020, BUS HORIZONS, V63, P37, DOI 10.1016/j.bushor.2019.09.003
Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
Karimi S., 2013, PURCHASE DECISION PR
Karimi S, 2018, J BUS RES, V91, P71, DOI 10.1016/j.jbusres.2018.05.038
Karimi S, 2015, DECIS SUPPORT SYST, V77, P137, DOI 10.1016/j.dss.2015.06.004
Kietzmann J, 2018, J ADVERTISING RES, V58, P263, DOI 10.2501/JAR-2018-035
Kim J, 2005, J FASH MARK MANAG, V9, P106, DOI 10.1108/13612020510586433
Kirsten M., 2019, J BUS ETHICS, DOI 10.1007/s10551-019-04213-9
KIRSTEN M, 2018, J BUS ETHICS
Kotler P., 2006, PRINCIPLES MARKETING
Koufaris M, 2002, INFORM SYST RES, V13, P205, DOI 10.1287/isre.13.2.205.83
Langley A, 1999, ACAD MANAGE REV, V24, P691, DOI 10.2307/259349
Laohapensang O, 2009, J FASH MARK MANAG, V13, P501, DOI 10.1108/13612020910991367
LeBlanc RonaldP., 2001, MARK INTELL PLAN, V19, P500
Lee HY, 2007, TOURISM MANAGE, V28, P886, DOI 10.1016/j.tourman.2006.04.013
Lee HK, 2020, INT J CULT POLICY, V26, P544, DOI 10.1080/10286632.2019.1577401
Li H., 2019, J ADVERTISING ED, V23, P100
Li HR, 2019, J ADVERTISING, V48, P333, DOI 10.1080/00913367.2019.1654947
Li HR, 2003, J CONSUM PSYCHOL, V13, P395, DOI 10.1207/S15327663JCP1304_07
Li HR, 2002, J ADVERTISING, V31, P37, DOI 10.1080/00913367.2002.10673665
Lian JW, 2008, COMPUT HUM BEHAV, V24, P48, DOI 10.1016/j.chb.2007.01.002
Liao B., 2017, CONT COMMUNICATIONS, V7, P97
Limayem M, 2000, IEEE T SYST MAN CY A, V30, P421, DOI 10.1109/3468.852436
Liu X., 2003, Electronic Commerce Research and Applications, V2, P229, DOI DOI 10.1016/S1567-4223(03)00027-9
Liu YP, 2002, J ADVERTISING, V31, P53, DOI 10.1080/00913367.2002.10673685
Liu YT, 2018, IEEE T FUZZY SYST, V26, P338, DOI 10.1109/TFUZZ.2017.2659764
Lombard M., 2001, Journal of Interactive Advertising, V1, P56, DOI [DOI 10.1080/15252019.2001.10722051, 10.1080/15252019.2001.10722051]
Malthouse EC, 2019, J ADVERTISING, V48, P366, DOI 10.1080/00913367.2019.1652123
McGaughey R.E., 1998, J MARKETING THEORY P, V6, P1, DOI DOI 10.1080/10696679.1998.11501800
McMillan SJ, 2002, J ADVERTISING, V31, P29, DOI 10.1080/00913367.2002.10673674
Mick DG, 2004, J BUS ETHICS, V52, P207, DOI 10.1023/B:BUSI.0000035906.74034.d4
Moon B. J., 2004, International Journal of Internet Marketing and Advertising, V1, P104, DOI 10.1504/IJIMA.2004.003692
Pascual-Miguel FJ, 2015, J BUS RES, V68, P1550, DOI 10.1016/j.jbusres.2015.01.050
Paul J, 2016, J RETAIL CONSUM SERV, V29, P123, DOI 10.1016/j.jretconser.2015.11.006
Pavlou PA, 2006, MIS QUART, V30, P115
Pavlou PA., 2015, NEW MEANINGS MARKETI
Pavlou PA, 2007, MIS QUART, V31, P105
PAYNE JW, 1988, J EXP PSYCHOL LEARN, V14, P534, DOI 10.1037/0278-7393.14.3.534
Petro G., 2018, Forbes
Punj G, 2012, PSYCHOL MARKET, V29, P791, DOI 10.1002/mar.20564
Qin XB, 2019, J ADVERTISING, V48, P338, DOI 10.1080/00913367.2019.1652122
Rahman O., 2018, International Journal of Fashion Design, Technology and Education, V11, P287, DOI DOI 10.1080/17543266.2018.1429498
Ratchford BT, 2003, J MARKETING RES, V40, P193, DOI 10.1509/jmkr.40.2.193.19221
Rehman SU, 2019, J GLOB ENTREP RES, V9, DOI 10.1186/s40497-019-0166-2
ROBERTS JH, 1991, J MARKETING RES, V28, P429, DOI 10.2307/3172783
Roca Juan Carlos, 2009, Information Management & Computer Security, V17, P96, DOI 10.1108/09685220910963983
Rodgers W, 2001, J BUS ETHICS, V31, P355, DOI 10.1023/A:1010777917540
Rodgers W., 2006, Process thinking: six pathways to successful decision making
Rodgers W., 2016, DECISION MAKING PERS
Rodgers W., 1997, THROUGHPUT MODELING
Rodgers W., 2020, IEEE T ENG MANAGE
Rodgers W, 2021, J BUS RES, V126, P401, DOI 10.1016/j.jbusres.2020.12.039
Rodgers W, 2019, J WORLD BUS, V54, DOI 10.1016/j.jwb.2019.101012
Rodgers W, 2019, ACCOUNT FORUM, V43, P220, DOI 10.1080/01559982.2019.1605871
Rodgers W, 2015, J BUS ETHICS, V131, P871, DOI 10.1007/s10551-014-2152-5
Rodgers W, 2010, J BUS ETHICS, V91, P83, DOI 10.1007/s10551-009-0069-1
ROSENBERG MJ, 1956, J ABNORM SOC PSYCH, V53, P367, DOI 10.1037/h0044579
RUSSELL STUART J., 2020, Artificial Intelligence: A Modern Approach
Sachdeva R., 2015, The IUP Journal of Marketing Management, V14, P7
Saldivar AAF, 2016, IEEE C EVOL COMPUTAT, P5317, DOI 10.1109/CEC.2016.7748366
Shim S, 2001, J RETAILING, V77, P397, DOI 10.1016/S0022-4359(01)00051-3
Sicilia M, 2010, ELECTRON COMMER R A, V9, P183, DOI 10.1016/j.elerap.2009.03.004
Sismeiro C, 2004, J MARKETING RES, V41, P306, DOI 10.1509/jmkr.41.3.306.35985
Solomon M., 2006, Consumer Behaviour: A European Perspective: A European Perspective, V3rd ed
Soto-Acosta P, 2014, ONLINE INFORM REV, V38, P543, DOI 10.1108/OIR-01-2014-0008
Tandon Urvashi, 2019, Journal of Information Technology Case and Application Research, V21, P13, DOI 10.1080/15228053.2019.1609779
TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144
TVERSKY A, 1981, SCIENCE, V211, P453, DOI 10.1126/science.7455683
van Esch P, 2021, J ADVERTISING, V50, P63, DOI 10.1080/00913367.2020.1832939
Varadarajan R, 2010, J INTERACT MARK, V24, P96, DOI 10.1016/j.intmar.2010.02.004
Vijayasarathy LR, 2004, INFORM MANAGE-AMSTER, V41, P747, DOI 10.1016/j.im.2003.08.011
Wang YC, 2017, INT J INFORM MANAGE, V37, P179, DOI 10.1016/j.ijinfomgt.2015.11.005
Waymond Rodgers., 2020, Artificial intelligence in a throughput model: Some major algorithms
Xia L, 2002, J CONSUM PSYCHOL, V12, P265, DOI 10.1207/S15327663JCP1203_08
Xu YZ, 2020, AUSTRALAS MARK J, V28, P189, DOI 10.1016/j.ausmj.2020.03.005
NR 134
TC 15
Z9 16
U1 63
U2 213
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0167-4544
EI 1573-0697
J9 J BUS ETHICS
JI J. Bus. Ethics
PD JUL
PY 2022
VL 178
IS 4
SI SI
BP 1043
EP 1061
DI 10.1007/s10551-022-05048-7
EA FEB 2022
PG 19
WC Business; Ethics
WE Social Science Citation Index (SSCI)
SC Business & Economics; Social Sciences - Other Topics
GA 2X6TF
UT WOS:000754336400002
OA Green Published, hybrid
DA 2024-03-27
ER
PT J
AU Simkova, N
Smutny, Z
AF Simkova, Nikola
Smutny, Zdenek
TI Business E-NeGotiAtion: A Method Using a Genetic Algorithm for Online
Dispute Resolution in B2B Relationships
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE alternative dispute resolution; B2B relationships; genetic algorithm;
artificial intelligence; embedded single-case study; negotiation;
e-commerce
ID DESIGN SCIENCE RESEARCH; ARTIFICIAL-INTELLIGENCE; DECISION-SUPPORT;
EXPERT-SYSTEM; ANGER
AB An opportunity to resolve disputes as an out-of-court settlement through computer-mediated communication is usually easier, faster, and cheaper than filing an action in court. Artificial intelligence and law (AI & Law) research has gained importance in this area. The article presents a design of the E-NeGotiAtion method for assisted negotiation in business to business (B2B) relationships, which uses a genetic algorithm for selecting the most appropriate solution(s). The aim of the article is to present how the method is designed and contribute to knowledge on online dispute resolution (ODR) with a focus on B2B relationships. The evaluation of the method consisted of an embedded single-case study, where participants from two countries simulated the realities of negotiation between companies. For comparison, traditional negotiation via e-mail was also conducted. The evaluation confirms that the proposed E-NeGotiAtion method quickly achieves solution(s), approaching the optimal solution on which both sides can decide, and also very importantly, confirms that the method facilitates negotiation with the partner and creates a trusted result. The evaluation demonstrates that the proposed method is economically efficient for parties of the dispute compared to negotiation via e-mail. For a more complicated task with five or more products, the E-NeGotiAtion method is significantly more suitable than negotiation via e-mail for achieving a resolution that favors one side or the other as little as possible. In conclusion, it can be said that the proposed method fulfills the definition of the dual-task of ODR-it resolves disputes and builds confidence.
C1 [Simkova, Nikola] Masaryk Univ, Fac Informat, Botanicka 68a, Brno 60200, Czech Republic.
[Smutny, Zdenek] Prague Univ Econ & Business, Fac Informat & Stat, W. Churchill Sq. 1938-4, Prague 13067, Czech Republic.
C3 Masaryk University Brno; Prague University of Economics & Business
RP Smutny, Z (autor correspondiente), Prague Univ Econ & Business, Fac Informat & Stat, W. Churchill Sq. 1938-4, Prague 13067, Czech Republic.
EM simkova@fi.muni.cz; zdenek.smutny@vse.cz
RI Smutny, Zdenek/L-7498-2016; Simkova, Nikola/V-8092-2017
OI Smutny, Zdenek/0000-0002-6646-2991; Simkova, Nikola/0000-0002-6390-1872
FU Faculty of Informatics and Statistics, Prague University of Economics
and Business [IP400040, F4/1/2019]
FX The paper was processed with support from an institutional fund IP400040
for long-term conceptual development of science and research and
internal grant F4/1/2019 at the Faculty of Informatics and Statistics,
Prague University of Economics and Business.
CR Abrahams B, 2010, FRONT ARTIF INTEL AP, V212, P297, DOI 10.3233/978-1-60750-577-8-297
Abrahams B, 2012, GROUP DECIS NEGOT, V21, P3, DOI 10.1007/s10726-010-9189-3
Almotairi Khaled., 2020, Acta Informatica Pragensia, V9, P184, DOI DOI 10.18267/J.AIP.141
Anagnostopoulou D, 2018, EUR BUS LAW REV, V29, P975
Andrade F, 2010, LECT NOTES ARTIF INT, V6284, P5
[Anonymous], 2013, DIR 2013 11 EU EUR P
[Anonymous], 2013, REG EU 524 2013 EUR
[Anonymous], 2020, BUS TO BUS E COMM MA
Cáceres E, 2008, FRONT ARTIF INTEL AP, V189, P78, DOI 10.3233/978-1-58603-952-3-78
Carneiro D, 2017, FUTURE GENER COMP SY, V66, P171, DOI 10.1016/j.future.2016.05.019
Carneiro D, 2014, ARTIF INTELL REV, V41, P211, DOI 10.1007/s10462-011-9305-z
Carneiro D, 2013, KNOWL INF SYST, V36, P789, DOI 10.1007/s10115-012-0563-0
Carneiro D, 2013, NEUROCOMPUTING, V109, P16, DOI 10.1016/j.neucom.2012.03.024
Cermak R, 2020, ACTA INFORM PRAG, V9, P132, DOI [10.18267/j.aip.137, DOI 10.18267/J.AIP.137]
Cobb-Clark DA, 2012, ECON LETT, V115, P11, DOI 10.1016/j.econlet.2011.11.015
Cortes P., 2010, ONLINE DISPUTE RESOL, DOI [10.4324/9780203847756, DOI 10.4324/9780203847756]
Cortes P., 2014, MAASTRICHT J EUR COM, V21, P14, DOI [10.1177/1023263X1402100102, DOI 10.1177/1023263X1402100102]
Cortés P, 2011, INT J LAW INFORM TEC, V19, P1, DOI 10.1093/ijlit/eaq011
Costa N.M.S., 2010, P 12 INT C ENT INF S, P103
Dantas TET, 2021, SUSTAIN PROD CONSUMP, V26, P213, DOI 10.1016/j.spc.2020.10.005
Doong H.S., 2009, P 42 ANN HAW INT C S, P1
Dresch A, 2015, DESIGN SCI RES METHO
EISENHARDT KM, 1991, ACAD MANAGE REV, V16, P620, DOI 10.2307/258921
El Jelali S, 2015, ARTIF INTELL LAW, V23, P1, DOI 10.1007/s10506-015-9162-1
Elziny AA, 2016, AIN SHAMS ENG J, V7, P57, DOI 10.1016/j.asej.2015.05.002
European Commission, 2013, FLASH EUR, V359
European Commission, 2012, BUS TO BUS ALT DISP, V347
European Commission, 2013, DIR EUR PARL COUNC A
European Commission, 2015, CONS COND SCOR CONS
Eurostat, 2014, INT PURCH IND
Fuchs C, 2017, INT J COMMUN-US, V11, P2431
Gabuth Y, 2008, EUR ECON REV, V52, P259, DOI 10.1016/j.euroecorev.2007.04.004
Gerring J, 2004, AM POLIT SCI REV, V98, P341
Goldberg D. E., 1989, GENETIC ALGORITHMS S, V27, DOI 10.5860/CHOICE.27-0936
Gotti M., 2014, LAW CULTURE VISUAL S, P631, DOI [10.1007/978-90-481-9322-6_28, DOI 10.1007/978-90-481-9322-6_28]
Grand View Research,, 2020, B2C E COMM MARK SIZ
Gudauskas R, 2015, INT J COMPUT COMMUN, V10, P667, DOI 10.15837/ijccc.2015.5.2028
Gupta H, 2021, J CLEAN PROD, V295, DOI 10.1016/j.jclepro.2021.126253
Gurnakova J., 2013, ROZHODOVANIE PROFESI
Hevner A, 2010, INTEGR SER INFORM SY, V22, P1, DOI 10.1007/978-1-4419-5653-8
Hynek J., 2008, GENETICKE ALGORITMY
Johannesson P., 2014, An introduction to design science, DOI [10.1007/978-3-319-10632-8, DOI 10.1007/978-3-319-10632-8]
Johnson NA, 2009, DECIS SUPPORT SYST, V46, P673, DOI 10.1016/j.dss.2008.10.007
Johnson NA, 2009, DECIS SUPPORT SYST, V46, P660, DOI 10.1016/j.dss.2008.10.008
Katsh E., 2007, INT REV LAW COMPUTER, V21, P97, DOI [10.1080/13600860701492096, DOI 10.1080/13600860701492096]
Kersten G, 1999, GROUP DECIS NEGOT, V8, P251, DOI 10.1023/A:1008657921819
KOLODNER JL, 1989, COGNITIVE SCI, V13, P507, DOI 10.1207/s15516709cog1304_2
Kumar M., 2011, TRUST TECHNOLOGY B2B
Lai HC, 2010, ELECTRON COMMER R A, V9, P537, DOI 10.1016/j.elerap.2010.03.005
Lavrakas P.J., 2008, Encyclopedia of survey research methods Vols, V1-0, DOI DOI 10.4135/9781412963947
Lazansky J., 2001, ARTIFICIAL INTELLIGE, V3
Lin WL, 2020, IND MARKET MANAG, V89, P61, DOI 10.1016/j.indmarman.2019.12.009
Lodder A.R., 2003, P WORKSH ONL DISP RE
Loutocky P., 2017, MIMOSOUDNI RESENI SP
Mania K., 2015, INT COMPAR JURISPRUD, V1, P76, DOI DOI 10.1016/J.ICJ.2015.10.006
MCCARTY LT, 1977, HARVARD LAW REV, V90, P837, DOI 10.2307/1340132
Mitchell M., 1999, INTRO GENETIC ALGORI
Mitterhofer R., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), P610, DOI 10.1109/HICSS.2012.334
Muecke N., 2008, P 5 INT WORKSH ONL D, V430, P62
Oskamp A., 1995, Artificial Intelligence and Law, V3, P209, DOI 10.1007/BF00872531
Peffers K, 2007, J MANAGE INFORM SYST, V24, P45, DOI 10.2753/MIS0742-1222240302
Richter N., 2011, P FRONTIERS ED C, P3
Rogers N.H., 2013, DESIGNING SYSTEMS PR
Rule C., 2003, Online Dispute Resolution for Business: B2B, Ecommerce, Consumer, Employment, Insurance, and Other Commercial Conflicts
Rule C, 2020, ANNU REV LAW SOC SCI, V16, P277, DOI 10.1146/annurev-lawsocsci-101518-043049
Sampani C, 2021, INF COMMUN TECHNOL L, V30, P235, DOI 10.1080/13600834.2021.1875539
Sánchez-Anguix V, 2013, INFORM SCIENCES, V222, P25, DOI 10.1016/j.ins.2010.11.018
Saraf B, 2013, COMPUT LAW SECUR REV, V29, P403, DOI 10.1016/j.clsr.2013.05.004
Sawyer S., 2006, REMEMBRANCE ROB KLIN, V223, DOI [10.1007/978-0-387-37876-3_4, DOI 10.1007/978-0-387-37876-3_4]
Schultz T, 2001, ONLINE DISPUTE RESOL
SEDLAR Matin, 2014, Psychologie a jeji kontexty, V5, P89
Simkova N., 2017, SLOV EC ASS M SEAM 2, V29
Simková N, 2017, P INT CONF STRAT MAN, P120
Simkova N, 2016, SCHRIFTEN INFORMATIK, V45, P303
Simková N, 2015, SCHRIFTEN INFORMATIK, V44, P247
Sivanandam S., 2008, INTRO GENETIC ALGORI, DOI [DOI 10.1007/978-3-540-73190-0, 10.1007/978-3-540-73190-0_7]
Smutny Z, 2016, J INF SCI, V42, P681, DOI 10.1177/0165551515608731
Sudzina F, 2014, PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON FINANCE AND BANKING, P394
Tan YH, 2000, DECIS SUPPORT SYST, V29, P389, DOI 10.1016/S0167-9236(00)00085-3
Van Kleef GA, 2010, J EXP SOC PSYCHOL, V46, P753, DOI 10.1016/j.jesp.2010.03.013
Walsham G., 1997, Information Systems and Qualitative Research. Proceedings of the IFIP TC8 WG 8.2 International Conference on Information Systems and Qualitative Research, P466
Walton N, 2021, TECHNOL FORECAST SOC, V166, DOI 10.1016/j.techfore.2021.120576
Waterman D. A., 1986, Expert Systems, V3, P212, DOI 10.1111/j.1468-0394.1986.tb00203.x
WATSON D, 1988, J PERS SOC PSYCHOL, V54, P1063, DOI 10.1037/0022-3514.54.6.1063
Wieringa R. J., 2014, STAKEHOLDER GOAL ANA, DOI DOI 10.1007/978-3-662-43839-8
Winter R, 2008, EUR J INFORM SYST, V17, P470, DOI 10.1057/ejis.2008.44
Xu ZC, 2008, 2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11, P3966, DOI 10.1109/CCDC.2008.4598076
Xu ZC, 2009, KNOWL-BASED SYST, V22, P18, DOI 10.1016/j.knosys.2008.04.009
Yin R.K., 2016, QUALITATIVE RES STAR
Zeleznikow J, 2003, FRONT ARTIF INTEL AP, V106, P21
NR 90
TC 6
Z9 6
U1 6
U2 41
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD AUG
PY 2021
VL 16
IS 5
BP 1186
EP 1216
DI 10.3390/jtaer16050067
PG 31
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA SX6MW
UT WOS:000665317600001
OA gold
DA 2024-03-27
ER
PT J
AU Wodecki, A
AF Wodecki, Andrzej
TI THE RESERVE PRICE OPTIMIZATION FOR PUBLISHERS ON REAL-TIME BIDDING
ON-LINE MARKETPLACES WITH TIME-SERIES FORECASTING
SO FOUNDATIONS OF MANAGEMENT
LA English
DT Article
DE online marketing; real-time bidding; reserve price optimization; machine
learning; forecasting
AB Today's Internet marketing ecosystems are very complex, with many competing players, transactions concluded within milliseconds, and hundreds of different parameters to be analyzed in the decision-making process. In addition, both sellers and buyers operate under uncertainty, without full information about auction results, purchasing preferences, and strategies of their competitors or suppliers. As a result, most market participants strive to optimize their trading strategies using advanced machine learning algorithms. In this publication, we propose a new approach to determining reserve-price strategies for publishers, focusing not only on the profits from individual ad impressions, but also on maximum coverage of advertising space. This strategy combines the heuristics developed by experienced RTB consultants with machine learning forecasting algorithms like ARIMA, SARIMA, Exponential Smoothing, and Facebook Prophet. The paper analyses the effectiveness of these algorithms, recommends the best one, and presents its implementation in real environment. As such, its results may form a basis for a competitive advantage for publishers on very demanding online advertising markets.
C1 [Wodecki, Andrzej] Warsaw Univ Technol, Fac Management, Warsaw, Poland.
C3 Warsaw University of Technology
RP Wodecki, A (autor correspondiente), Warsaw Univ Technol, Fac Management, Warsaw, Poland.
EM andrzej.wodecki@pw.edu.pl
OI Wodecki, Andrzej/0000-0002-9077-3191
FU Europejski Fundusz Rozwoju Regionalnego (European Regional Development
Fund) [RPMA.01.02.00-14-9523/17]
FX This work was supported by Europejski Fundusz Rozwoju Regionalnego
(European Regional Development Fund), grant number:
RPMA.01.02.00-14-9523/17.
CR [Anonymous], 2020, PROPHET
[Anonymous], 2020, AD EXCHANGE HIST REP
[Anonymous], 2020, Exponential Smoothing Algorithm
[Anonymous], 2020, AUTOREGRESSIVE INTEG
[Anonymous], 2020, PROPHET DIAGNOSTICS
Austin A., 2019, PROGRAMMATIC MARKETI
Austin D, 2016, PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016), P528, DOI 10.1109/DSAA.2016.32
Brownlee J., 2020, Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning (Machine Learning Mastery)
Brownlee J., 2018, Introduction to Time Series Forecasting With Python
Cai H, 2017, WSDM'17: PROCEEDINGS OF THE TENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P661, DOI 10.1145/3018661.3018702
Chapman P., 2004, CRISP DM 1 0
Choi H, 2019, INT J MOL SCI, V20, DOI 10.3390/ijms20010087
Hipel K., 1994, Dev. Water Sci., V45, P419
Li JJ, 2018, IEEE T COMPUT SOC SY, V5, P583, DOI 10.1109/TCSS.2018.2831234
Li JJ, 2017, IEEE SYS MAN CYBERN, P1256, DOI 10.1109/SMC.2017.8122785
Lu JW, 2019, PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), P1633, DOI 10.1145/3357384.3358027
Makridakis S, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0194889
MYERSON RB, 1981, MATH OPER RES, V6, P58, DOI 10.1287/moor.6.1.58
Radovanovic A., 2012, P 21ST INT C WORLD W, P91
Ren K, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P363, DOI 10.1145/3292500.3330870
RILEY JG, 1981, AM ECON REV, V71, P381
Wang J., 2017, ARXIV 1610 03013
Wang Yuchen., 2016, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, P115
Wu W.C.-H., 2015, P 21TH ACM SIGKDD IN, P1305
Wu W, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P2526, DOI 10.1145/3219819.3220066
Yang C, 2020, SIGNAL TRANSDUCT TAR, V5, DOI 10.1038/s41392-020-00325-1
NR 26
TC 2
Z9 2
U1 0
U2 3
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 2080-7279
EI 2300-5661
J9 FOUND MANAGE
JI Found. Manag.
PD JAN
PY 2020
VL 12
IS 1
DI 10.2478/fman-2020-0013
PG 14
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA OV7BB
UT WOS:000592359400001
OA gold, Green Submitted
DA 2024-03-27
ER
PT J
AU Allil, K
AF Allil, Kamaal
TI Integrating AI-driven marketing analytics techniques into the classroom:
pedagogical strategies for enhancing student engagement and future
business success
SO JOURNAL OF MARKETING ANALYTICS
LA English
DT Article; Early Access
DE AI-driven marketing analytics; Marketing education; Pedagogical
strategies; Curriculum integration; Classroom activities;
Industry-academia collaboration
ID ARTIFICIAL-INTELLIGENCE; MANAGEMENT; EVOLUTION; ISSUES; SKILLS; MODEL;
WORLD
AB This paper outlines a practical pedagogical framework for integrating AI-driven analytics into marketing education, tailored to equip students for the fast-evolving industry. Central to this approach is an iterative model that adapts teaching strategies to keep pace with technological advancements and industry demands. The framework emphasizes practical application, steering curriculum development towards the inclusion of AI tools like machine learning and predictive analytics, and crafting experiential learning opportunities. A focused examination of current teaching methods reveals gaps and introduces actionable solutions for fostering analytical skills essential for the AI-enhanced marketing landscape. The model not only advocates for a balance between theory and practice but also addresses challenges such as resource accessibility and the necessity of ethical considerations in AI education. By promoting interdisciplinary collaboration and continual curriculum refreshment, the paper positions the model as an essential blueprint for nurturing future marketing professionals capable of leveraging AI analytics for strategic decision-making. The conclusion calls for academia-industry partnerships to further enrich marketing education and underscores the importance of this framework in preparing students for successful careers in AI-driven marketing.
C1 [Allil, Kamaal] Univ Hertfordshire, Hertfordshire Business Sch, Hatfield, England.
C3 University of Hertfordshire
RP Allil, K (autor correspondiente), Univ Hertfordshire, Hertfordshire Business Sch, Hatfield, England.
EM k.allil@herts.ac.uk
OI Allil, Kamaal/0000-0001-5466-6519
CR Abadi M., 2016, arXiv, DOI DOI 10.48550/ARXIV.1603.04467
ACM, 2018 ACM Code of Ethics and Professional Conduct
Ahmad K., 2020, Data-driven artificial intelligence in education: A comprehensive review
Al-Fudail M, 2008, COMPUT EDUC, V51, P1103, DOI 10.1016/j.compedu.2007.11.004
Allen M.J., 2004, ASSESSING ACAD PROGR
AMA, 2023, AMA Statement of Ethics
Nguyen A, 2023, EDUC INF TECHNOL, V28, P4221, DOI 10.1007/s10639-022-11316-w
Anicic KP, 2017, IEEE T EDUC, V60, P191, DOI 10.1109/TE.2016.2633959
[Anonymous], 2015, FALLING SHORT COLL L
Ballou B., 2018, J ACCOUNTING ED, V44, P14, DOI DOI 10.1016/J.JACCEDU.2018.05.003
Barbeito-Caamaño A, 2020, CORP SOC RESP ENV MA, V27, P2831, DOI 10.1002/csr.2006
Hernández-Lara AB, 2019, COMPUT HUM BEHAV, V92, P600, DOI 10.1016/j.chb.2018.03.001
Bhutoria A., 2022, Computers and Education: Artificial Intelligence, V3
Bird E., 2020, European Parliamentary Research Service, P634
Bird S, 2009, NATURAL LANGUAGE PRO, DOI DOI 10.5555/1717171
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
Bonilla-Chaves EF, 2023, BEHAV SCI-BASEL, V13, DOI 10.3390/bs13030244
Bonk C., 1998, J ACCOUNTING ED, V16, P261, DOI [10.1016/S0748-5751(98)00012-8, DOI 10.1016/S0748-5751(98)00012-8]
Bose I, 2009, EUR J OPER RES, V195, P1, DOI 10.1016/j.ejor.2008.04.006
Boud D., 2000, STUD CONTIN EDUC, V22, P151, DOI [https://doi.org/10.1080/713695728, DOI 10.1080/713695728]
Broby D., 2022, Journal of Finance and Data Science, V8, DOI DOI 10.1016/J.JFDS.2022.05.003
Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
Chaffey D., 2012, Digital marketing: strategy, implementation and practice, V5th
Chandrasekaran S, 2013, INT J ENG EDUC, V29, P1109
Chollet F., 2021, Deep Learning With Python, V2nd ed
Clayton PR, 2019, J EDUC BUS, V94, P57, DOI 10.1080/08832323.2018.1502142
Colwell J., 2008, 2008 ANN C EXPOSITIO
Cope B, 2021, EDUC PHILOS THEORY, V53, P1229, DOI 10.1080/00131857.2020.1728732
Crittenden WF, 2019, J MARKET EDUC, V41, P5, DOI 10.1177/0273475318820895
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Di Gregorio A, 2019, EUR MANAG J, V37, P251, DOI 10.1016/j.emj.2019.03.004
Elhajjar S., 2021, Marketing Education Review, V31, P2
Ferrell O.C., 2020, MARKETING ED REV, V30, P3, DOI [DOI 10.1080/10528008.2020.1718510, 10.1080/10528008.2020.1718510]
Foxall GR, 2017, BEHAV ANALYST, V40, P309, DOI 10.1007/s40614-017-0127-4
Fuglseth AM, 2014, COMPUT HUM BEHAV, V40, P161, DOI 10.1016/j.chb.2014.07.040
García-Marzá D, 2005, J BUS ETHICS, V57, P209, DOI 10.1007/s10551-004-8202-7
GDPR, 2016, REGULATION EU 201667
Geron A., 2022, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
Goldberg J, 2014, INT J ENG EDUC, V30, P6
Gudeta D, 2022, COGENT EDUC, V9, DOI 10.1080/2331186X.2022.2030076
Hair J., 2020, Essentials of Business Research Methods. Essentials of Business Research Methods, DOI [DOI 10.4324/9781315716862, 10.4324/9780429203374, DOI 10.4324/9780429203374]
Harambasic S., 2023, P FEB ZAGREB INT ODY, P204
Hargittai E, 2012, SOC SCI COMPUT REV, V30, P95, DOI 10.1177/0894439310397146
Hartmann J., 2023, Artificial intelligence in marketing
Hastie T., 2009, ELEMENTS STAT LEARNI, P219, DOI [10.1007/978-0-387-84858-7_7, DOI 10.1007/978-0-387-84858-7]
Heaton J., 2018, Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning
Herreid C.F., 2007, Start with a story: the case study method of teaching college science
Hill A., 2007, New Directions for Teaching and Learning, P33, DOI [DOI 10.1002/TL.296, 10.1002/t1.296, DOI 10.1002/T1.296]
Hirschberg J, 2015, SCIENCE, V349, P261, DOI 10.1126/science.aaa8685
Hochheiser H, 2007, INT J HUM-COMPUT INT, V23, P339, DOI 10.1080/10447310701702717
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Iacobucci D, 2019, J MARK ANAL, V7, P152, DOI 10.1057/s41270-019-00059-2
Imran R, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100805
Johnson DW, 2009, EDUC RESEARCHER, V38, P365, DOI 10.3102/0013189X09339057
Jongbloed B, 2002, HIGH EDUC, V44, P413, DOI 10.1023/A:1019825803857
Kelleher J.D., 2020, Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies
Kim J, 2022, EDUC INF TECHNOL, V27, P6069, DOI 10.1007/s10639-021-10831-6
Kitchenham A, 2008, J TRANSFORM EDUC, V6, P104, DOI 10.1177/1541344608322678
Kohavi R, 2002, COMMUN ACM, V45, P45, DOI 10.1145/545151.545177
Kotler Philip., 1967, MARK MANAG
Kurtzke S, 2021, J MARKET EDUC, V43, P298, DOI 10.1177/02734753211042404
Lamberton C, 2016, J MARKETING, V80, P146, DOI 10.1509/jm.15.0415
Lattuca LR., 2001, Creating interdisciplinarity: Interdisciplinary research and teaching among college and university faculty, DOI [10.2307/j.ctv167563f, DOI 10.2307/J.CTV167563F]
LAVIDGE RJ, 1961, J MARKETING, V25, P59, DOI 10.2307/1248516
LeClair D., 2018, Marketing Education Review, V28, P6, DOI DOI 10.1080/10528008.2017.1421050
Li T, 2007, J MARKET EDUC, V29, P25, DOI 10.1177/0273475306297380
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
Liu B., 2012, SYNTHESIS LECT HUMAN, V5, P1, DOI [DOI 10.2200/S00416ED1V01Y201204HLT016, DOI 10.1007/978-3-031-02145-9]
Liu Y., 2018, MARKETING ED REV, V28, P14, DOI DOI 10.1080/10528008.2017.1421048
Lutz C, 2019, HUM BEHAV EMERG TECH, V1, P141, DOI 10.1002/hbe2.140
Maheshwari S, 2021, INT J PROD RES, V59, P1875, DOI 10.1080/00207543.2020.1793011
Malik KM, 2023, EDUC INF TECHNOL, V28, P3581, DOI 10.1007/s10639-022-11350-8
Mandinach EB, 2013, EDUC RESEARCHER, V42, P30, DOI 10.3102/0013189X12459803
Mangalaraj G, 2023, J COMPUT INFORM SYST, V63, P11, DOI 10.1080/08874417.2021.2015726
Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
Martin KE, 2015, MIS Q EXEC, V14, P67
Miranda SM, 2003, INFORM SYST RES, V14, P87, DOI 10.1287/isre.14.1.87.14765
Mittelstadt BD, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951716679679
Ngai EWT, 2009, EXPERT SYST APPL, V36, P2592, DOI 10.1016/j.eswa.2008.02.021
Ngai EWT, 2022, J BUS RES, V145, P35, DOI 10.1016/j.jbusres.2022.02.049
Nicol DJ, 2006, STUD HIGH EDUC, V31, P199, DOI 10.1080/03075070600572090
PEDRO F., 2019, Artificial intelligence in education: Challenges and opportunities for sustainable development
Pirker J., 2014, P 2014 C INNOVATION
Plangger K, 2022, J ACAD MARKET SCI, V50, P1125, DOI 10.1007/s11747-022-00906-2
Prince M, 2004, J ENG EDUC, V93, P223, DOI 10.1002/j.2168-9830.2004.tb00809.x
Ramaswamy S, 2018, PROCEDIA COMPUT SCI, V140, P170, DOI 10.1016/j.procs.2018.10.326
Reddy YM, 2010, ASSESS EVAL HIGH EDU, V35, P435, DOI 10.1080/02602930902862859
Rohm AJ, 2019, J MARKET EDUC, V41, P47, DOI 10.1177/0273475318798086
Rooks RN., 2022, International Journal for the Scholarship of Teaching and Learning, V16, P1, DOI [10.20429/ijsotl.2022.160208, DOI 10.20429/IJSOTL.2022.160208]
Rosenboom A., 2023, The Future of Responsible Management Education: University Leadership and the Digital Transformation Challenge
Rozo H., 2019, J TECHNOLOGY SCI ED, V9, P308, DOI DOI 10.3926/JOTSE.652
Russell S, 2022, Artificial intelligence: A modern approach, V4th
Salminen J, 2022, J INTERACT MARK, V57, P517, DOI 10.1177/10949968221095556
Sanchez-Prieto Jose Carlos, 2020, Learning and Collaboration Technologies. Designing, Developing and Deploying Learning Experiences. 7th International Conference, LCT 2020. Held as Part of the 22nd HCI International Conference, HCII 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12205), P292, DOI 10.1007/978-3-030-50513-4_22
Schlee RP, 2017, J MARKET EDUC, V39, P69, DOI 10.1177/0273475317712765
Shmueli G, 2011, MIS QUART, V35, P553
Siedlok F, 2014, INT J MANAG REV, V16, P194, DOI 10.1111/ijmr.12016
Sokhanvar Z, 2021, STUD EDUC EVAL, V70, DOI 10.1016/j.stueduc.2021.101030
Srinivasa KG., 2022, Learning, teaching, and assessment methods for contemporary learners: pedagogy for the digital generation, DOI [10.1007/978-981-19-6734-4, DOI 10.1007/978-981-19-6734-4]
Sung B, 2020, ASIA PAC J MARKET LO, V32, P1089, DOI 10.1108/APJML-04-2019-0227
Tan S., 2023, Learning intelligence: Innovative and digital transformative learning strategies: Cultural and social engineering perspectives
Thontirawong P., 2021, Marketing Education Review, V31, P58, DOI [10.1080/10528008.2021.1871849, DOI 10.1080/10528008.2021.1871849]
Vanslambrouck S, 2018, INTERNET HIGH EDUC, V36, P33, DOI 10.1016/j.iheduc.2017.09.002
Verma S., 2021, INT J INFORM MANAGE, V1, DOI [10.1016/j.jjimei.2020.100002, DOI 10.1016/J.JJIMEI.2020.100002]
Vlacic B, 2021, J BUS RES, V128, P187, DOI 10.1016/j.jbusres.2021.01.055
Weathers D., 2019, Marketing Education Review, V29, P266, DOI DOI 10.1080/10528008.2019.1673664
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Wernli D., 2023, Implementing interdisciplinarity in research-intensive universities
Wiliam D., 1998, ASSESS EDUC, V5, P7, DOI DOI 10.1080/0969595980050102
Wilson EJ., 2018, Marketing Education Review, V28, P52, DOI [DOI 10.1080/10528008.2017, DOI 10.1080/10528008.2017.1419431]
Yogish D., 2019, INT C RECENT TRENDS, DOI [10.1007/978-981-13-9187-3_53, DOI 10.1007/978-981-13-9187-3_53]
Younan M., 2021, 2021 17 INT COMPUTER, P101
Zhang CM, 2021, J IND INF INTEGR, V23, DOI 10.1016/j.jii.2021.100224
Zulfiqar S, 2018, COGENT EDUC, V5, DOI 10.1080/2331186X.2018.1424678
NR 114
TC 0
Z9 0
U1 4
U2 4
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 2050-3318
EI 2050-3326
J9 J MARK ANAL
JI J. Market. Anal.
PD 2024 JAN 27
PY 2024
DI 10.1057/s41270-023-00281-z
EA JAN 2024
PG 27
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA FX7V7
UT WOS:001149226200001
DA 2024-03-27
ER
PT J
AU Sands, S
Ferraro, C
Demsar, V
Chandler, G
AF Sands, Sean
Ferraro, Carla
Demsar, Vlad
Chandler, Garreth
TI False idols: Unpacking the opportunities and challenges of falsity in
the context of virtual influencers
SO BUSINESS HORIZONS
LA English
DT Article
DE Influencer marketing; Virtual influencers; Social media marketing;
Falsity; Artificial intelligence
ID TECHNOLOGY; RESPONSES; IDENTITY; PURCHASE; AVATARS
AB Influencer marketing has become a dominant and targeted means for brands to connect with consumers, but it also brings risks associated with influencer transgression and reputation damage. In recent years, virtual influencers have gained popularity and given rise to falsity, or artificially created and manipulated influencers that are revolutionizing the field of influencer marketing. A virtual influ-encer is an entitydhumanlike or notdthat is autonomously controlled by artificial intelligence and visually presented as an interactive, real-time rendered being in a digital environment. As brands increasingly seek to engage virtual influencers to connect with and sell to audiences, we take a step back and discuss the opportu-nities and challenges they present for firms and managers. To help marketers un-derstand this emerging field, we first document the rise of virtual influencers. Then, we discuss consumer reactions to virtual influencers before unpacking their associated opportunities and challenges for brands and marketers. Finally, we conclude with an overview of implications and future considerations.(c) 2022 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.
C1 [Sands, Sean; Ferraro, Carla; Demsar, Vlad] Swinburne Univ Technol, John St, Hawthorn 3122, Australia.
[Chandler, Garreth] Evolved Grp, L2-405 Little Bourke St, Melbourne, Australia.
C3 Swinburne University of Technology
RP Sands, S (autor correspondiente), Swinburne Univ Technol, John St, Hawthorn 3122, Australia.
EM ssands@swin.edu.au; cferraro@swin.edu.au; vdemsar@swin.edu.au;
garreth.chandler@theevolvedgroup.com
CR Araque J., 2021, BUSINESS INSIDER
Arsenyan J, 2021, INT J HUM-COMPUT ST, V155, DOI 10.1016/j.ijhcs.2021.102694
Baklanov N., 2019, HYPE AUDITOR 1114
Balis J., 2022, HARVARD BUS REV
Bradley S, 2020, DRUM 0320
Caldwell G., 2019, GLOBAL COSMETIC 0625
Campbell C, 2022, J ADVERTISING, V51, P22, DOI 10.1080/00913367.2021.1909515
Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
Campbell C, 2015, BUS HORIZONS, V58, P599, DOI 10.1016/j.bushor.2015.06.003
Chitrakorn K, 2021, VOGUE BUSINESS 0804
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Cook J., 2020, HUFFINGTON POST
Cooney B., 2021, DEXERTO 0914
Cusumano K, 2019, THE MAGAZINE
Dans Enrique., 2019, FORBES
Demsar V, 2021, J MARKET MANAG-UK, V37, P1058, DOI 10.1080/0267257X.2021.1900335
Dill-Shackleford KE, 2016, SOC PERSONAL PSYCHOL, V10, P634, DOI 10.1111/spc3.12274
Downie M, 2008, J SOC PSYCHOL, V148, P523, DOI 10.3200/SOCP.148.5.523-534
Edwards C, 2019, COMPUT HUM BEHAV, V90, P357, DOI 10.1016/j.chb.2018.08.027
Feine J, 2019, INT J HUM-COMPUT ST, V132, P138, DOI 10.1016/j.ijhcs.2019.07.009
Felnhofer A, 2019, INT J HUM-COMPUT ST, V132, P45, DOI 10.1016/j.ijhcs.2019.07.010
Florian A, 2021, THE CNN
Gnewuch, 2017, P INT C INF SYST I
Grewal D, 2017, J RETAILING, V93, P1, DOI 10.1016/j.jretai.2016.12.008
Guthrie S., 2020, INFLUENCER MARKETING, P271
Handley L., 2020, CNBC
Hanus MD, 2015, INT J HUM-COMPUT ST, V84, P33, DOI 10.1016/j.ijhcs.2015.07.004
Harding S., 2019, THE VERDICT
Harding S., 2019, MINDSHARE TRENDS REP
Haslam N, 2006, PERS SOC PSYCHOL REV, V10, P252, DOI 10.1207/s15327957pspr1003_4
Hiort A, 2021, VIRTUAL INFLUENCERS
Hobbs R., 2021, STYLUS 0527
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Hwang KJ, 2018, COMPUT HUM BEHAV, V87, P155, DOI 10.1016/j.chb.2018.05.029
Jackson Lauren Michele, 2018, New Yorker
Jerger C, 2017, J SERV RES-US, V20, P362, DOI 10.1177/1094670517728339
Kietzmann J, 2020, BUS HORIZONS, V63, P135, DOI 10.1016/j.bushor.2019.11.006
Kim J., 2021, J INTERACTIVE ADVERT, V21, P141, DOI DOI 10.1080/15252019.2021.2001273
Klein Matt., 2020, Forbes
Kramer S. D., 2018, IMPACT INFLUENCER MA
LivePerson, 2021, RBS CREAT PERS MOB E
Lorenz T., 2018, The Atlantic
Mani Z, 2018, J PROD INNOVAT MANAG, V35, P780, DOI 10.1111/jpim.12463
Moustakas E., 2020, P 2020 INT C CYB SEC, P1
Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
Naumann S., 2021, OMD 0527
Nguyen H., 2021, SAMM
Pan Z, 2017, J MANAGE INFORM SYST, V34, P71, DOI 10.1080/07421222.2017.1296747
Price R., 2016, Business Insider
Rasmussen M., 2021, 5 INNOVATIVE COMMERC
Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
Sands S, 2020, EUR MANAG J, V38, P45, DOI 10.1016/j.emj.2019.10.001
Seymour M., 2020, P 53 HAW INT C SYST, DOI [10.24251/HICSS.2020.574, DOI 10.24251/HICSS.2020.574]
Seymour M, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P1748
Seymour M, 2018, J ASSOC INF SYST, V19, P953, DOI 10.17705/1jais.00515
Shechtman N., 2003, P SIGCHI C HUM FACT, P281, DOI [10.1145/642611.642661, DOI 10.1145/642611.642661]
Sheehan K., 2009, Journal of Interactive Advertising, V9, P40, DOI DOI 10.1080/15252019.2009.10722154
Söderlund M, 2021, EUR J MARKETING, V55, P94, DOI 10.1108/EJM-09-2019-0748
Statista, 2021, NUMB BRAND SPONS INF
Stein JP, 2020, INT J HUM-COMPUT ST, V142, DOI 10.1016/j.ijhcs.2020.102463
Stewart R., 2019, DRUM 1107
Teh C., 2021, Business Insider
Thomas VL, 2021, J ADVERTISING, V50, P11, DOI 10.1080/00913367.2020.1810595
Time Staff, 2018, TIME
Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
Wagman K. B, 2020, FIXING SOCIAL M 0330
Wiese E, 2020, INT J HUM-COMPUT ST, V133, P1, DOI 10.1016/j.ijhcs.2019.08.002
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Wong R., 2018, ADWEEK
NR 69
TC 35
Z9 35
U1 124
U2 354
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD NOV-DEC
PY 2022
VL 65
IS 6
BP 777
EP 788
DI 10.1016/j.bushor.2022.08.002
EA OCT 2022
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5J6EN
UT WOS:000869133900008
DA 2024-03-27
ER
PT J
AU Pillai, R
Sivathanu, B
Dwivedi, YK
AF Pillai, Rajasshrie
Sivathanu, Brijesh
Dwivedi, Yogesh K.
TI Shopping intention at AI-powered automated retail stores (AIPARS)
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE TRAM; PLS-SEM; Perceived enjoyment; Customization; Interactivity;
Artificial intelligence-powered automated; retail stores
ID INTEGRATING TECHNOLOGY READINESS; IMAGE INTERACTIVITY TECHNOLOGY; ONLINE
PURCHASE INTENTION; ARTIFICIAL-INTELLIGENCE; CONSUMER ACCEPTANCE;
AUGMENTED REALITY; USER ACCEPTANCE; VIRTUAL-REALITY; BIG DATA; INTRINSIC
MOTIVATION
AB Artificial Intelligence (AI) is transforming the way retail stores operate. AI-Powered Automated Retail Stores are the next revolution in physical retail. Consumers are facing fully automated technology in these retail stores. Therefore, it is necessary to scrutinize the antecedents of consumers' intention to shop at AI-Powered Automated Retail Stores. This study delves into this area to find the predictors of consumers' intention to shop at AI-Powered Automated Retail Stores. It extends the technology readiness and acceptance model by the addition of AI context specific constructs such as Perceived Enjoyment, Customization and Interactivity from the present literature. The proposed model is tested by surveying 1250 consumers & the data is analyzed using the PLS-SEM technique and empirically validated. The outcome of the study reveals that Innovativeness and Optimism of consumers affect the perceived ease and perceived usefulness. Insecurity negatively affects the perceived usefulness of AI-powered automated retail stores. Perceived ease of use, perceived usefulness, perceived enjoyment, customization and interactivity are significant predictors of shopping intention of consumers in AI-powered automated stores. This research presents insightful academic and managerial implications in the domain of retailing and technology in retail.
C1 [Pillai, Rajasshrie] Pune Inst Business Management, Pune, Maharashtra, India.
[Sivathanu, Brijesh] Sri Balaji Univ, Pune, Maharashtra, India.
[Dwivedi, Yogesh K.] Swansea Univ, Emerging Markets Res Ctr, Sch Management, Swansea, W Glam, Wales.
C3 Swansea University
RP Pillai, R (autor correspondiente), Pune Inst Business Management, Pune, Maharashtra, India.
EM rajasshrie1@gmail.com; brij.jesh2002@gmail.com; ykdwivedi@gmail.com
RI S, BRIJESH/AAQ-4753-2021; Dwivedi, Yogesh Kumar/A-5362-2008; Pillai,
Rajasshrie/GRO-0859-2022
OI Dwivedi, Yogesh Kumar/0000-0002-5547-9990; Sivathanu, Dr.
Brijesh/0000-0003-2505-9140
CR Adapa S, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101901
Agrebi S, 2015, J RETAIL CONSUM SERV, V22, P16, DOI 10.1016/j.jretconser.2014.09.003
Akroush MN, 2015, BUS PROCESS MANAG J, V21, P1353, DOI 10.1108/BPMJ-02-2015-0022
Aladwani AM, 2002, INFORM MANAGE-AMSTER, V39, P467, DOI 10.1016/S0378-7206(01)00113-6
Alaiad A, 2017, IEEE T PROF COMMUN, V60, P4, DOI 10.1109/TPC.2016.2632822
Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
Alexander B, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2019.101913
Alexandra S., 2018, THESE RETAILERS USE
Ali H., 2015, International Journal for Infonomics (IJI), V8, P1029, DOI [DOI 10.20533/IJI.1742.4712.2015.0121, 10.20533/iji.1742.4712.2015.0121]
[Anonymous], 2018, GLOB POW RET 2018 TR
Armstrong J.S., 1977, J MARK RES, V14
Ashraf AR, 2015, IDEAS IN MARKETING: FINDING THE NEW AND POLISHING THE OLD, P826
Asia I., 2019, AI SPENDING RISE PRE
Bai W., 2017, T MACH LEARN ARTIF I, V5, P51, DOI [10.14738/tmlai.53.3390, DOI 10.14738/TMLAI.53.3390]
Ballantine PW, 2005, INT J RETAIL DISTRIB, V33, P461, DOI 10.1108/09590550510600870
Basgoze P., 2015, J SCI RES INNOVATIVE, V2, P26
Bauer C, 2018, PROCEDIA COMPUT SCI, V141, P503, DOI 10.1016/j.procs.2018.10.135
Begley S., 2019, AUTOMATION RETAIL EX
Beklemysheva A., 2018, GROCERY CHAINS USING
Berman B, 2019, BUS HORIZONS, V62, P75, DOI 10.1016/j.bushor.2018.08.006
Bhatia R., 2018, INDIAN RETAILERS EMB
Bhutani A., 2019, GLOB MARK INSIGHT, V1
Brill T.M., 2018, Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial intelligence applications
Bruner GC, 2005, J BUS RES, V58, P553, DOI 10.1016/j.jbusres.2003.08.002
Bughin J., 2017, Artificial Intelligence: The next digital frontier?
Bulmer S, 2018, J RETAIL CONSUM SERV, V42, P107, DOI 10.1016/j.jretconser.2018.01.016
Buyle R, 2018, MEDIA COMMUN-LISBON, V6, P127, DOI 10.17645/mac.v6i4.1679
Cambre M. A., 1985, Journal of Educational Computing Research, V1, P37, DOI 10.2190/FK5L-092H-T6YB-PYBA
Chantal T., 2018, SHOPPERS PREFER RETA
Chebat JC, 2014, J RETAIL CONSUM SERV, V21, P610, DOI 10.1016/j.jretconser.2014.04.010
Chen L., 2004, EUR MANAG J, V22, P74, DOI DOI 10.1016/J.EMJ.2003.11.014
Chen MF, 2018, INTERNET RES, V28, P351, DOI 10.1108/IntR-03-2017-0099
Chen MY, 2013, ELECTRON COMMER RES, V13, P1, DOI 10.1007/s10660-013-9104-5
Chen SC, 2013, CYBERPSYCH BEH SOC N, V16, P604, DOI 10.1089/cyber.2012.0606
Chen SC, 2009, IND MANAGE DATA SYST, V109, P1248, DOI 10.1108/02635570911002306
Chen SJ, 2003, INT J SERV IND MANAG, V14, P556, DOI 10.1108/09564230310500228
Chen YF, 2014, INT J TECHNOL HUM IN, V10, P19, DOI 10.4018/ijthi.2014010102
Chen YM, 2018, J RETAIL CONSUM SERV, V41, P281, DOI 10.1016/j.jretconser.2017.04.004
Chin WW, 2008, J MARKET THEORY PRAC, V16, P287, DOI 10.2753/MTP1069-6679160402
Chiu HC, 2011, ELECTRON COMMER R A, V10, P268, DOI 10.1016/j.elerap.2010.07.002
Chiu Y.-T.H., 2012, J SERV MANAG ISS MAN, V23, P479, DOI [10.1108/09564231211260431, DOI 10.1108/09564231211260431]
Cho H, 2009, INT J RETAIL DISTRIB, V37, P389, DOI 10.1108/09590550910954892
Cho YC., 2015, INT J MANAGEMENT INF, V19, P21, DOI DOI 10.19030/IJMIS.V19I1.9086
Chong AYL, 2012, DECIS SUPPORT SYST, V53, P34, DOI 10.1016/j.dss.2011.12.001
Chopra K, 2019, INT J RETAIL DISTRIB, V47, P331, DOI 10.1108/IJRDM-11-2018-0251
Chris A., 2018, WALMART ISSUED PATEN
Chung N, 2015, COMPUT HUM BEHAV, V50, P588, DOI 10.1016/j.chb.2015.02.068
Cowan K, 2019, J BUS RES, V100, P483, DOI 10.1016/j.jbusres.2018.10.063
Das Samantak, 2017, THINK INDIA
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
Demirkan H, 2014, J RETAIL CONSUM SERV, V21, P860, DOI 10.1016/j.jretconser.2014.02.012
Dhamdhere P., 2016, 9 EXAMPLES INNOVATIV
Driediger F, 2019, J RETAIL CONSUM SERV, V48, P224, DOI 10.1016/j.jretconser.2019.02.005
Duan YQ, 2019, INT J INFORM MANAGE, V48, P63, DOI 10.1016/j.ijinfomgt.2019.01.021
Dubey R, 2018, INT J LOGIST MANAG, V29, P485, DOI 10.1108/IJLM-02-2017-0039
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
Dwivedi YK, 2017, GOV INFORM Q, V34, P211, DOI 10.1016/j.giq.2017.03.001
Dwivedi YK, 2016, GOV INFORM Q, V33, P174, DOI 10.1016/j.giq.2015.06.003
Elizabeth H., 2018, BEST GROCERY LIST AP
Erdogmus N, 2011, PROCD SOC BEHV, V24, DOI 10.1016/j.sbspro.2011.09.131
Etemad-Sajadi R, 2016, COMPUT HUM BEHAV, V61, P227, DOI 10.1016/j.chb.2016.03.045
Evanschitzky H, 2015, J PROD INNOVAT MANAG, V32, P459, DOI 10.1111/jpim.12241
Fan TJ, 2015, INT J PROD ECON, V159, P117, DOI 10.1016/j.ijpe.2014.10.004
Fishbein M., 1975, Beliefs, Attitude, Intention and Behavior: An Introduction to Theory and Research
Fornell C., 1981, J MARK RES, V18
Fu SH, 2018, INT J INFORM MANAGE, V40, P88, DOI 10.1016/j.ijinfomgt.2018.01.013
Garaus M, 2016, J BUS RES, V69, P3687, DOI 10.1016/j.jbusres.2016.03.030
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Godoe P., 2012, J EUROPEAN PSYCHOL S, V3, DOI [10.5334/jeps.aq, DOI 10.5334/JEPS.AQ, DOI 10.1037/E537242013-005]
GUADAGNOLI E, 1988, PSYCHOL BULL, V103, P265, DOI 10.1037/0033-2909.103.2.265
Gudergan SP, 2008, J BUS RES, V61, P1238, DOI 10.1016/j.jbusres.2008.01.012
Guide VDR, 2015, J OPER MANAG, V37, pV, DOI 10.1016/S0272-6963(15)00056-X
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Ha S, 2009, J BUS RES, V62, P565, DOI 10.1016/j.jbusres.2008.06.016
Hair JF, 2017, LONG RANGE PLANNING
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
Han S.L., 2018, 2018 GLOB MARK C TOK, P271
Han S, 2018, IND MANAGE DATA SYST, V118, P618, DOI 10.1108/IMDS-05-2017-0214
Hausman AV, 2009, J BUS RES, V62, P5, DOI 10.1016/j.jbusres.2008.01.018
Hengstler M, 2016, TECHNOL FORECAST SOC, V105, P105, DOI 10.1016/j.techfore.2015.12.014
Henseler J, 2010, STRUCT EQU MODELING, V17, P82, DOI 10.1080/10705510903439003
Ho T.H. L., 2014, International Journal of Business and Management, V9, P145, DOI DOI 10.5539/IJBM.V9N5P145
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
Hong W., 2002, E SERVICE NEW DIR TH, V6, P21
Huang TL, 2015, ELECTRON COMMER RES, V15, P269, DOI 10.1007/s10660-014-9163-2
Huang YM, 2012, COMPUT EDUC, V58, P273, DOI 10.1016/j.compedu.2011.08.008
Huilgol M., 2017, COMING SOON AMAZON G
IBM, 2019, COM AI REV RET CONS
Ibm, 2017, SMART SHOPP PLAYB ST
Inman JJ, 2017, J RETAILING, V93, P7, DOI 10.1016/j.jretai.2016.12.006
Ivanov Stanislav Hristov, 2017, ADOPTION ROBOTS SERV
Jackson TW., 2014, INT J MARK STUD, V6, P15, DOI [10.5539/ijms.v6n2p15, DOI 10.5539/IJMS.V6N2P15]
Jin C, 2013, INFORM MANAGE-AMSTER, V50, P162, DOI 10.1016/j.im.2013.03.002
Joo J, 2013, COMPUT HUM BEHAV, V29, P2512, DOI 10.1016/j.chb.2013.06.002
Kahlert M., 2016, UNDERSTANDING CUSTOM
Kahn B.E., 2018, Journal of the Association for Consumer Research, Volume, V3, P255
Kalinic Z, 2016, INF SYST E-BUS MANAG, V14, P367, DOI 10.1007/s10257-015-0287-2
Kallweit K, 2014, J RETAIL CONSUM SERV, V21, P268, DOI 10.1016/j.jretconser.2014.02.002
Kang JH, 2018, ASIA PAC J TOUR RES, V23, P95, DOI 10.1080/10941665.2017.1410190
Kesari B, 2016, J RETAIL CONSUM SERV, V31, P22, DOI 10.1016/j.jretconser.2016.03.005
Kim H.-Y., 2017, International Journal of Fashion Design, Technology and Education, V10, P26, DOI [10.1080/17543266.2016.1177737, DOI 10.1080/17543266.2016.1177737]
Kim JB, 2012, ELECTRON COMMER RES, V12, P125, DOI 10.1007/s10660-012-9089-5
Kim T., 2018, TOP MOBILE APPS BARG
Kim T, 2019, INT J SPORT MARK SPO, V20, P109, DOI [10.1108/IJSMS-06-2017-0050, 10.1108/ijsms-06-2017-0050]
Kimberly W., 2016, ARTIFICIAL INTELLIGE
KOTLER Philip., 2012, Marketing. Decimocuarta edicion
Kshetri N, 2018, IT PROF, V20, P73, DOI 10.1109/MITP.2018.043141672
Kumar A., 2013, International Journal of Mobile Marketing, V8, P23
Kuo K.-M., 2013, BMC MED INFORM DECIS, V2, P2, DOI [10.1016/j.sbspro.2011.09.131, DOI 10.1016/J.SBSPRO.2011.09.131.]
Lai YY, 2018, INT J LOGIST MANAG, V29, P676, DOI 10.1108/IJLM-06-2017-0153
Lam SY, 2008, J INTERACT MARK, V22, P19, DOI 10.1002/dir.20119
Laukkanen T., 2016, PAC AS C INF SYST PA
Lee H, 2018, SOC BEHAV PERSONAL, V46, P683, DOI 10.2224/sbp.6693
Lee HH, 2006, INT J RETAIL DISTRIB, V34, P621, DOI 10.1108/09590550610675949
Lee I, 2015, BUS HORIZONS, V58, P431, DOI 10.1016/j.bushor.2015.03.008
Lee J., 2002, J SERV MARK, V16, P553
Lee KC, 2008, COMPUT HUM BEHAV, V24, P88, DOI 10.1016/j.chb.2007.01.018
Lee W. K., 2017, INT J MOB COMMUN, V16, P1, DOI DOI 10.1504/IJMC.2018.10006137
Lee YE, 2004, INT J ELECTRON COMM, V8, P79, DOI 10.1080/10864415.2004.11044299
Li R, 2017, J RETAIL CONSUM SERV, V34, P19, DOI 10.1016/j.jretconser.2016.09.003
Liao S.S., 2005, COMMUN ASS INF SYST, V15, DOI [10.17705/1CAIS.01528, DOI 10.17705/1CAIS.01528]
Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
Liljander V, 2006, J RETAIL CONSUM SERV, V13, P177, DOI 10.1016/j.jretconser.2005.08.004
Lin CH, 2007, PSYCHOL MARKET, V24, P641, DOI 10.1002/mar.20177
Lin JSC, 2011, MANAG SERV QUAL, V21, P424, DOI 10.1108/09604521111146289
Liu F., 2018, RES BUSINESS MODEL I, V248, P285, DOI [10.2991/icsser-18.2018.67, DOI 10.2991/ICSSER-18.2018.67]
Liu F, 2019, INT J INFORM MANAGE, V45, P107, DOI 10.1016/j.ijinfomgt.2018.09.004
Lu CC, 2019, INT J INFORM MANAGE, V47, P101, DOI 10.1016/j.ijinfomgt.2018.12.020
Lundberg E., 2017, COMPETE EFFECTIVELY, V76
Magrath V, 2013, J FASH MARK MANAG, V17, P98, DOI 10.1108/13612021311305164
Mani Z, 2018, J PROD INNOVAT MANAG, V35, P780, DOI 10.1111/jpim.12463
Mann MK, 2015, IDEAS IN MARKETING: FINDING THE NEW AND POLISHING THE OLD, P752
Marhefka SL, 2019, TELEMED E-HEALTH, V25, P511, DOI 10.1089/tmj.2018.0066
Martens M, 2017, INT J INNOV TECHNOL, V14, DOI 10.1142/S021987701750033X
Morosan C, 2014, INT J CONTEMP HOSP M, V26, P246, DOI 10.1108/IJCHM-11-2012-0221
Mukerjee h.S., 2019, Business Perspectives and Research, V7, P1, DOI DOI 10.1177/2278533718800118
Müller-Seitz G, 2009, J RETAIL CONSUM SERV, V16, P31, DOI 10.1016/j.jretconser.2008.08.002
Natarajan T, 2017, J RETAIL CONSUM SERV, V37, P8, DOI 10.1016/j.jretconser.2017.02.010
Niemeier S., 2013, RESHAPING RETAIL WHY, DOI [10.1002/9781119207979, DOI 10.1002/9781119207979]
Niemela M., 2017, MONITORING ACCEPTANC, P225, DOI [10.1145/302979, DOI 10.1145/3029798.3038333]
Nilsson Nils, 2010, QUEST ARTIFICIAL INT, DOI DOI 10.1017/CBO9780511819346
Nunnally J., 1978, PSYCHOMETRIC METHODS
Ogonowski A, 2014, J RETAIL CONSUM SERV, V21, P482, DOI 10.1016/j.jretconser.2014.03.004
Olsson T, 2013, PERS UBIQUIT COMPUT, V17, P287, DOI 10.1007/s00779-011-0494-x
Pantano E, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102096
Pantano E, 2019, J RETAIL CONSUM SERV, V49, P297, DOI 10.1016/j.jretconser.2019.01.019
Pantano E, 2017, J RETAIL CONSUM SERV, V38, P81, DOI 10.1016/j.jretconser.2017.05.011
Pantano E, 2014, INT J INFORM MANAGE, V34, P344, DOI 10.1016/j.ijinfomgt.2014.03.002
Pantano E, 2012, J RETAIL CONSUM SERV, V19, P279, DOI 10.1016/j.jretconser.2012.02.002
Papagiannidis S, 2014, J RETAIL CONSUM SERV, V21, P877, DOI 10.1016/j.jretconser.2014.02.010
Parasuraman A, 2015, J SERV RES-US, V18, P59, DOI 10.1177/1094670514539730
Parasuraman A., 2001, TECHNO READY MARKETI
Parasuraman W, 2000, Journal of Service Research, V2, P307, DOI [10.1177/109467050024001, DOI 10.1177/109467050024001]
Park S.R., 2012, ROLE INTERACTIVITY I
Pierdicca R, 2015, IEEE INT CONF MULTI
Piller FT, 2004, INT J COMPUT INTEG M, V17, P583, DOI 10.1080/0951192042000273140
PODSAKOFF PM, 1986, J MANAGE, V12, P531, DOI 10.1177/014920638601200408
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Rahman O, 2016, J RETAIL CONSUM SERV, V28, P155, DOI 10.1016/j.jretconser.2015.09.008
Rahman SA, 2017, TECHNOL FORECAST SOC, V118, P236, DOI 10.1016/j.techfore.2017.01.027
Rana NP, 2016, COMPUT HUM BEHAV, V59, P265, DOI 10.1016/j.chb.2016.02.019
Rese A, 2017, TECHNOL FORECAST SOC, V124, P306, DOI 10.1016/j.techfore.2016.10.010
Rezaei S, 2016, J INTERNET COMMER, V15, P408, DOI 10.1080/15332861.2016.1256749
Ringle C.M., 2005, Smart PLS 2.0
Rojas-mendez J.I., 2015, MARKETING TRANSITION, DOI [10.1007/978-3-319-18687-0, DOI 10.1007/978-3-319-18687-0.]
Roy SK, 2017, TECHNOL FORECAST SOC, V124, P257, DOI 10.1016/j.techfore.2016.09.022
Saarijärvi H, 2014, J RETAIL CONSUM SERV, V21, P26, DOI 10.1016/j.jretconser.2013.06.009
San-Martín S, 2015, J RETAIL CONSUM SERV, V23, P1, DOI 10.1016/j.jretconser.2014.11.001
Shamy NE, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P4139
Shariff A, 2017, NAT HUM BEHAV, V1, P694, DOI 10.1038/s41562-017-0202-6
Shin S., 2014, Journal of Applied Business Research, V30, P1615, DOI [10.19030/jabr.v30i6.8873, DOI 10.19030/JABR.V30I6.8873]
Silbert S., 2018, SARAH SILBERT WHAT I
Sivathanu B, 2019, INF RESOUR MANAG J, V32, P27, DOI 10.4018/IRMJ.2019070102
Song SoYoung., 2017, Modeling the consumer acceptance of retail service robots (Doctoral dissertation, University of Tennessee)
Speicher M., 2017, J P ACM INTERACT MOB, V1, P1, DOI DOI 10.1145/3130967
Srinivasan SS, 2002, J RETAILING, V78, P41, DOI 10.1016/S0022-4359(01)00065-3
STEUER J, 1992, J COMMUN, V42, P73, DOI 10.1111/j.1460-2466.1992.tb00812.x
Tamilmani K, 2019, INT J INFORM MANAGE, V46, P222, DOI 10.1016/j.ijinfomgt.2019.01.008
Thamizhvanan A, 2013, J INDIAN BUS RES, V5, P17, DOI 10.1108/17554191311303367
Tom S., 2018, THIS AMAZON GO INSPI
Tsai JM, 2019, INT J INFORM MANAGE, V49, P34, DOI 10.1016/j.ijinfomgt.2019.03.003
Tsikriktsis N, 2004, J SERV RES-US, V7, P42, DOI 10.1177/1094670504266132
Tsou HT, 2015, INT J INFORM MANAGE, V35, P1, DOI 10.1016/j.ijinfomgt.2014.09.001
Tussyadiah I., 2012, INFORM COMMUNICATION, DOI [10.1007/978-3-7091-1142-0, DOI 10.1007/978-3-7091-1142-0]
Tussyadiah I, 2019, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2019, P359, DOI 10.1007/978-3-030-05940-8_28
van der Heijden H, 2004, MIS QUART, V28, P695, DOI 10.2307/25148660
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Voropanova E, 2015, INT REV RETAIL DISTR, V25, P529, DOI 10.1080/09593969.2015.1089304
Wagner G, 2017, J RETAIL CONSUM SERV, V34, P278, DOI 10.1016/j.jretconser.2016.01.010
Walczuch R, 2007, INFORM MANAGE-AMSTER, V44, P206, DOI 10.1016/j.im.2006.12.005
Wang YS, 2013, INT J INFORM MANAGE, V33, P199, DOI 10.1016/j.ijinfomgt.2012.09.004
Wang YY, 2018, INT J INFORM MANAGE, V38, P7, DOI 10.1016/j.ijinfomgt.2017.07.003
Wu WY, 2015, SOC BEHAV PERSONAL, V43, P85, DOI 10.2224/sbp.2015.43.1.85
Wünderlich NV, 2015, J SERV MARK, V29, P442, DOI 10.1108/JSM-01-2015-0040
Yang HE, 2009, PROD PLAN CONTROL, V20, P370, DOI 10.1080/09537280902843755
Yang H, 2018, J SENSORS, V2018, DOI 10.1155/2018/6464036
Yang H, 2017, IND MANAGE DATA SYST, V117, P68, DOI 10.1108/IMDS-01-2016-0017
Yeh YS, 2009, ONLINE INFORM REV, V33, P1066, DOI 10.1108/14684520911011016
Yeo VCS, 2017, J RETAIL CONSUM SERV, V35, P150, DOI 10.1016/j.jretconser.2016.12.013
NR 203
TC 142
Z9 146
U1 57
U2 230
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD NOV
PY 2020
VL 57
AR 102207
DI 10.1016/j.jretconser.2020.102207
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA NT5NP
UT WOS:000572987800012
OA Green Accepted
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Hartmann, J
Huppertz, J
Schamp, C
Heitmann, M
AF Hartmann, Jochen
Huppertz, Juliana
Schamp, Christina
Heitmann, Mark
TI Comparing automated text classification methods
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE Text classification; Social media; Machine learning; User-generated
content; Sentiment analysis; Natural language processing
ID SENTIMENT; EMOTIONS; OPINION; MODEL
AB Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives. (C) 2018 Elsevier B.V. All rights reserved.
C1 [Hartmann, Jochen; Huppertz, Juliana; Schamp, Christina; Heitmann, Mark] Univ Hamburg, Mkt & Customer Insight, Moorweidenstr 18, D-20148 Hamburg, Germany.
C3 University of Hamburg
RP Hartmann, J (autor correspondiente), Univ Hamburg, Mkt & Customer Insight, Moorweidenstr 18, D-20148 Hamburg, Germany.
EM jochen.hartmann@uni-hamburg.de
RI Hartmann, Jochen/IUN-2216-2023
OI Schamp, Christina/0009-0009-8350-7862; Hartmann,
Jochen/0000-0002-1178-8708
FU German Research Foundation (DFG) research unit 1452, "How Social Media
is Changing Marketing" [HE 6703/1-2]
FX This work was funded by the German Research Foundation (DFG) research
unit 1452, "How Social Media is Changing Marketing", HE 6703/1-2.
CR Aggarwal CC., 2012, MINING TEXT DATA, P77, DOI [10.1007/978-1-4614-3223-4, 10.1007/978-1-4614-3223-4_4]
Akpinar E, 2017, J MARKETING RES, V54, P318, DOI 10.1509/jmr.13.0350
Alberto T. C., 2015, P 14 IEEE INT C MACH
Almeida TA, 2011, DOCENG 2011: PROCEEDINGS OF THE 2011 ACM SYMPOSIUM ON DOCUMENT ENGINEERING, P259
Annett M., 2008, C CAN SOC COMP STUD
[Anonymous], 2009, P 18 ACM C INF KNOWL
[Anonymous], 2009, P 15 ACM SIGKDD INT
[Anonymous], ADV IND
[Anonymous], 2017, MARKETING SCI I WORK
Barasch A, 2014, J MARKETING RES, V51, P286, DOI 10.1509/jmr.13.0238
Bellmann R., 1961, ADAPTIVE CONTROL PRO
Bennett K. P., 2000, SIGKDD Explorations, V2, P1, DOI DOI 10.1145/380995.380999
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Bermingham A, 2010, CIKM 2010
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Boiy Erik, 2007, 11th International Conference on Electronic Publishing. Openness in Digital Publishing: Awareness, Discovery and Access, P349
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Caruana R, 2006, P 23 INT C MACH LEAR, P161, DOI [DOI 10.1145/1143844.1143865, 10.1145/1143844.1143865.]
Cavanaugh LA, 2015, J MARKETING RES, V52, P657, DOI 10.1509/jmr.10.0219
Chang Edward., 2008, Advances in Neural Information Processing Systems, V20, P257
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
Das SR, 2007, MANAGE SCI, V53, P1375, DOI 10.1287/mnsc.1070.0704
DeTienne KB, 2003, ORGAN RES METHODS, V6, P236, DOI 10.1177/1094428103251907
Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361
Domingos P, 2012, COMMUN ACM, V55, P78, DOI 10.1145/2347736.2347755
Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, P148, DOI 10.1145/288627.288651
Efron B, 2016, INST MATH STAT MG, P1, DOI 10.1017/CBO9781316576533
Fang X., 2015, J BIG DATA, V2, P1, DOI DOI 10.1186/S40537-015-0015-2
Felbermayr A, 2016, J INTERACT MARK, V36, P60, DOI 10.1016/j.intmar.2016.05.004
FERNNDEZDELGADO M, 2014, J MACH LEARN RES, V15, P3133
Ghose A, 2012, MARKET SCI, V31, P493, DOI 10.1287/mksc.1110.0700
Go A., 2009, TECH REP, V150, P1, DOI DOI 10.1016/J.SEDGEO.2006.07.004
Hanson NJ, 2019, INT J SPORT PHYSIOL, V14, P196, DOI 10.1123/ijspp.2018-0263
Hennig-Thurau T, 2015, J ACAD MARKET SCI, V43, P375, DOI 10.1007/s11747-014-0388-3
Hewett K, 2016, J MARKETING, V80, P1, DOI 10.1509/jm.15.0033
Homburg C, 2015, J MARKETING RES, V52, P629, DOI 10.1509/jmr.11.0448
Hu MQ, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P755
Huang DL, 2016, MARKET SCI, V35, P445, DOI 10.1287/mksc.2015.0946
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Hutto E.E., 2014, 8 INT C WEBL SOC MED
Joachims T., 1998, P 10 EUR C MACH LEAR, P137, DOI DOI 10.1007/BFB0026683
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Kotziats D., 2015, GROUP INDIVIDUAL LAB, P1
Kuhnen CM, 2012, MANAGE SCI, V58, P1249, DOI 10.1287/mnsc.1110.1490
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Lee D, 2018, MANAGE SCI, V64, P5105, DOI 10.1287/mnsc.2017.2902
Markets and Markets, 2018, TEXT AN MARK COMP
McAuley J, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/2766462.2767755
Mohammad S. M., 2010, P NAACL HLT 2010 WOR, P26
Moraes R, 2013, EXPERT SYST APPL, V40, P621, DOI 10.1016/j.eswa.2012.07.059
Neethu MS., 2013, 2013 4 INT C COMPUTI, P1, DOI [10.1109/ICCCNT.2013.6726818, DOI 10.1109/ICCCNT.2013.6726818]
Netter O., 2016, WORKING PAPER
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Ng AY, 2002, ADV NEUR IN, V14, P841
Nielsen F.A., 2011, MSM, V2011, P93, DOI DOI 10.1016/J.KN0SYS.2015.06.015
Ordenes F. V., 2018, J CONSUM RES, P1
Ordenes FV, 2017, J CONSUM RES, V43, P875, DOI 10.1093/jcr/ucw070
Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79, DOI 10.3115/1118693.1118704
Pang B., 2004, ANN M ASS COMP LING, DOI [DOI 10.3115/1218955.1218990, 10.3115/1218955.1218990]
Pennebaker J. W., 2015, The Development and Psychometric Properties of LIWC2015
Puranam D, 2017, MARKET SCI, V36, P726, DOI 10.1287/mksc.2017.1048
Scholkopf B, 2001, LEARNING KERNELS SUP
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283
Tirunillai S, 2012, MARKET SCI, V31, P198, DOI 10.1287/mksc.1110.0682
Wolpert DH, 1996, NEURAL COMPUT, V8, P1341, DOI 10.1162/neco.1996.8.7.1341
Wu XD, 2008, KNOWL INF SYST, V14, P1, DOI 10.1007/s10115-007-0114-2
Xia R, 2011, INFORM SCIENCES, V181, P1138, DOI 10.1016/j.ins.2010.11.023
Yang Y., 1999, P 22 ANN INT ACM SIG
Yang Z, 2006, ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 2, P861
Yoganarasimhan H., 2018, MANAGE SCI, P1
Zhang YC, 2017, INT J RES MARK, V34, P100, DOI 10.1016/j.ijresmar.2016.07.003
杨云峰, 1999, [西安公路交通大学学报, Journal of Xian Highway University], P67
NR 74
TC 180
Z9 193
U1 23
U2 183
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD MAR
PY 2019
VL 36
IS 1
BP 20
EP 38
DI 10.1016/j.ijresmar.2018.09.009
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HW3WL
UT WOS:000466623300003
OA Green Published, hybrid
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Melumad, S
Meyer, R
AF Melumad, Shiri
Meyer, Robert
TI Full Disclosure: How Smartphones Enhance Consumer Self-Disclosure
SO JOURNAL OF MARKETING
LA English
DT Article
DE natural language processing; self-disclosure; technology; user-generated
content
ID COMPUTER-MEDIATED COMMUNICATION; FACE-TO-FACE; INATTENTIONAL BLINDNESS;
SENSITIVE TOPICS; PC WEB; MOBILE; AWARENESS; IMPACT; PRIVACY; WALKING
AB Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer. This tendency is found in a wide range of domains including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal information in web advertisements. The authors show that this increased willingness to self-disclose on one's smartphone arises from the psychological effects of two distinguishing properties of the device: (1) feelings of comfort that many associate with their smartphone and (2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. The enhancing effect of smartphones on self-disclosure yields several important marketing implications, including the creation of content that is perceived as more persuasive by outside readers. The authors explore implications for how these findings can be strategically leveraged by managers, including how they may generalize to other emerging technologies.
C1 [Melumad, Shiri; Meyer, Robert] Univ Penn, Wharton Sch, Mkt, Philadelphia, PA 19104 USA.
C3 University of Pennsylvania
RP Melumad, S (autor correspondiente), Univ Penn, Wharton Sch, Mkt, Philadelphia, PA 19104 USA.
EM melumad@wharton.upenn.edu; meyerr@wharton.upenn.edu
CR Altman I., 1973, Social penetration: The development of interpersonal relationships
Andrade EB, 2002, ADV CONSUM RES, V29, P350
Antoun C, 2017, PUBLIC OPIN QUART, V81, P280, DOI 10.1093/poq/nfw088
Bak JinYeong., 2014, P 2014 C EMPIRICAL M, P1986
Bak JY., 2012, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, V2, P60
Balani Sairam., 2015, Proceedings of the 33rd Annual Association for Computing Machinery Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA '15, P1373, DOI DOI 10.1145/2702613.2732733
Barak A, 2007, CYBERPSYCHOL BEHAV, V10, P407, DOI 10.1089/cpb.2006.9938
Bowling A, 2005, J PUBLIC HEALTH-UK, V27, P281, DOI 10.1093/pubmed/fdi031
Brockmeyer T, 2015, FRONT PSYCHOL, V6, DOI 10.3389/fpsyg.2015.01564
Buskirk TD, 2014, FIELD METHOD, V26, P322, DOI 10.1177/1525822X14526146
Carver CS, 1981, ATTENTION SELF REGUL
CASTIELLO U, 1990, ACTA PSYCHOL, V73, P195, DOI 10.1016/0001-6918(90)90022-8
CHAIKIN AL, 1976, J COUNS PSYCHOL, V23, P479
Cheever NA, 2014, COMPUT HUM BEHAV, V37, P290, DOI 10.1016/j.chb.2014.05.002
Chen IP, 2011, PERCEPT MOTOR SKILL, V112, P959, DOI 10.2466/22.24.26.PMS.112.3.959-974
Chen PL, 2018, BMC PUBLIC HEALTH, V18, DOI 10.1186/s12889-018-6163-5
Clayton RB, 2015, J COMPUT-MEDIAT COMM, V20, P119, DOI 10.1111/jcc4.12109
COZBY PC, 1973, PSYCHOL BULL, V79, P73, DOI 10.1037/h0033950
DAVIS D, 1975, J EXP SOC PSYCHOL, V11, P381, DOI 10.1016/0022-1031(75)90017-7
Derlega V. J., 1993, SELF DISCLOSURE
DERLEGA VJ, 1977, J SOC ISSUES, V33, P102, DOI 10.1111/j.1540-4560.1977.tb01885.x
Dienlin T., 2014, Media and private life, P105
Faraji-Rad A, 2015, J CONSUM RES, V42, P458, DOI 10.1093/jcr/ucv032
Forgas JP, 2011, J PERS SOC PSYCHOL, V100, P449, DOI 10.1037/a0021129
GIFFORD R, 1988, J ENVIRON PSYCHOL, V8, P177, DOI 10.1016/S0272-4944(88)80008-2
Grewal L, 2019, J MARKETING RES, V56, P791, DOI 10.1177/0022243719834514
HEBERLEIN TA, 1978, AM SOCIOL REV, V43, P447, DOI 10.2307/2094771
Houghton D. J., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), P3480, DOI 10.1109/HICSS.2012.415
Hyman IE, 2010, APPL COGNITIVE PSYCH, V24, P597, DOI 10.1002/acp.1638
Jiang L, 2010, J CONSUM RES, V36, P778, DOI 10.1086/605364
John LK, 2011, J CONSUM RES, V37, P858, DOI 10.1086/656423
Joinson AN, 2001, EUR J SOC PSYCHOL, V31, P177, DOI 10.1002/ejsp.36
JOURARD SM, 1958, J ABNORM SOC PSYCH, V56, P91, DOI 10.1037/h0043357
KIESLER S, 1984, AM PSYCHOL, V39, P1123, DOI 10.1037/0003-066X.39.10.1123
Kim J., 2011, Computer-mediated communication in personal relationships, P156, DOI DOI 10.3726/B12772
King Ed, 2016, KAGGLE
Lin MIB, 2017, ACCIDENT ANAL PREV, V101, P87, DOI 10.1016/j.aap.2017.02.005
Manchanda P, 2006, J MARKETING RES, V43, P98, DOI 10.1509/jmkr.43.1.98
Mavletova A, 2013, SURV RES METHODS-GER, V7, P191
Melumad S, 2019, J MARKETING RES, V56, P259, DOI 10.1177/0022243718815429
Melumad Shiri, 2020, J CONSUMER RES
Miwa Y, 2006, ENVIRON BEHAV, V38, P484, DOI 10.1177/0013916505280084
Moon Y, 2000, J CONSUM RES, V26, P323, DOI 10.1086/209566
Murphy GL, 2016, PSYCHON B REV, V23, P1035, DOI 10.3758/s13423-015-0834-3
Okdie Bradley, 2011, THESIS
Omarzu J, 2000, PERS SOC PSYCHOL REV, V4, P174, DOI 10.1207/S15327957PSPR0402_05
Panova T, 2018, J BEHAV ADDICT, V7, P252, DOI 10.1556/2006.7.2018.49
Pennebaker JW, 2001, CURR DIR PSYCHOL SCI, V10, P90, DOI 10.1111/1467-8721.00123
Pennebaker JW., 2015, LIWC 2015 OPERATORS
Ransbotham S, 2019, MARKET SCI, V38, P773, DOI 10.1287/mksc.2018.1115
Ravichander A, 2018, 19TH ANNUAL MEETING OF THE SPECIAL INTEREST GROUP ON DISCOURSE AND DIALOGUE (SIGDIAL 2018), P253
Rituparna, 2018, KAGGLE
Ruppel EK, 2017, J COMPUT-MEDIAT COMM, V22, P18, DOI 10.1111/jcc4.12179
Sassenberg K, 2005, EUR J SOC PSYCHOL, V35, P361, DOI 10.1002/ejsp.254
Short J., 1976, SOCIAL PSYCHOL TELEC
Skierkowski D, 2012, COMPUT HUM BEHAV, V28, P744, DOI 10.1016/j.chb.2011.11.023
SPEARS R, 1994, COMMUN RES, V21, P427, DOI 10.1177/009365094021004001
Toninelli D, 2016, SURV RES METHODS-GER, V10, P153, DOI 10.18148/srm/2016.v1012.6274
Vahedi Z, 2018, STRESS HEALTH, V34, P347, DOI 10.1002/smi.2805
Veltri GA, 2017, COMPUT HUM BEHAV, V73, P238, DOI 10.1016/j.chb.2017.03.018
Wallace P., 1999, PSYCHOL INTERNET
Walther JB, 1996, COMMUN RES, V23, P3, DOI 10.1177/009365096023001001
Wang YC, 2016, ACM CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW 2016), P74, DOI 10.1145/2818048.2820010
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Weisband S., 1996, Human Factors in Computing Systems. Common Ground. CHI 96 Conference Proceedings, P3, DOI 10.1145/238386.238387
Wells T, 2014, SOC SCI COMPUT REV, V32, P238, DOI 10.1177/0894439313505829
NR 66
TC 52
Z9 61
U1 29
U2 285
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2429
EI 1547-7185
J9 J MARKETING
JI J. Mark.
PD MAY
PY 2020
VL 84
IS 3
BP 28
EP 45
DI 10.1177/0022242920912732
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LH4OS
UT WOS:000528765200002
DA 2024-03-27
ER
PT J
AU Ren, SY
Choi, TM
Lee, KM
Lin, L
AF Ren, Shuyun
Choi, Tsan-Ming
Lee, Ka-Man
Lin, Lei
TI Intelligent service capacity allocation for cross-border-E-commerce
related third-party-forwarding logistics operations: A deep learning
approach
SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
LA English
DT Article
DE Cross-border e-commerce; Multi-product newsvendor; Logistics service
capacity (LSC) allocation; Third-party forwarding logistics (3PFL); Deep
learning
ID SUPPLY CHAIN; SHARING NETWORK; PREDICTION; DEMAND; MODEL; PERFORMANCE;
TECHNOLOGY; BLOCKCHAIN; ALGORITHM; DELIVERY
AB With the rise of "cross-border-e-commerce", the third-party-forwarding-logistics (3PFL) service becomes increasingly popular. Different from the traditional third-party-logistics (3PL) service, the 3PFL company provides forwarding services cost-effectively by consolidating orders from different e-tailers/platforms. The random arrivals of orders create a big challenge. Different from most of the existing studies, a deep learning based one-step integration optimal decision making approach S2SCL(Seq2Seq based CNN-LSTM) is proposed in this paper which intelligently integrates inventory optimization and demand-forecasting process. The Seg2Seq based forecasting architecture, which integrates CNN and LSTM network, is able to model the system dynamics and dependency-relations in varying demand for logistics services. Besides generating the point forecasting results, the proposed approach can quantify demand uncertainty via a dynamic distribution and make optimal decision on logistics service capacity allocation. Through a case-study analysis with real data obtained from a 3PFL company in China's Great Bay Area, we compare the proposed S2SCL with two benchmark models, including a one-step statistics based integration approach ARIMA and a two-step optimization based approach PSO-ELM, for two tasks: (1) point forecasting and (2) optimal logistic service capacity (LSC) allocation. Experimental results show that S2SCL outperforms the two benchmark models in both tasks significantly.
C1 [Ren, Shuyun] Guangdong Univ Technol, Guangzhou, Peoples R China.
[Choi, Tsan-Ming] Hong Kong Polytech Univ, Inst Text & Clothing, Business Div, Hung Hom,Kowloon, Hong Kong, Peoples R China.
[Lee, Ka-Man] Hong Kong Polytech Univ, Fac Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China.
[Lin, Lei] Univ Rochester, Goergen Inst Data Sci, Rochester, NY 14623 USA.
C3 Guangdong University of Technology; Hong Kong Polytechnic University;
Hong Kong Polytechnic University; University of Rochester
RP Lee, KM (autor correspondiente), Hong Kong Polytech Univ, Fac Engn, Hung Hom, Kowloon, Hong Kong, Peoples R China.
EM ckm.lee@polyu.edu.hk
RI Lee, Carman/G-5618-2010; Choi, Tsan-Ming/P-6065-2014; Choi,
Tsan-Ming/HCH-1964-2022
OI Choi, Tsan-Ming/0000-0003-3865-7043; Choi, Tsan-Ming/0000-0003-3865-7043
FU Innovation and Technology Fund (Project: ZM2K); National Natural Science
Foundation of China [71801054]
FX This research is supported by Innovation and Technology Fund (Project:
ZM2K). Shuyun Ren's research is partially supported by National Natural
Science Foundation of China (Project account: 71801054). The author
sincerely thank Wah Tung Tai Company Ltd for providing industrial real
data for this research.
CR [Anonymous], 2015, 150804025 ARXIV
Arabzad SM, 2015, INT J PROD RES, V53, P1038, DOI 10.1080/00207543.2014.938836
Ban GY, 2019, OPER RES, V67, P90, DOI 10.1287/opre.2018.1757
Barenji AV, 2019, TRANSPORT RES E-LOG, V126, P15, DOI 10.1016/j.tre.2019.04.002
Bliese PD, 2020, J MANAGE, V46, P1275, DOI 10.1177/0149206319886909
Chen FY, 2001, EUR J OPER RES, V131, P603, DOI 10.1016/S0377-2217(00)00102-8
Chiu CH, 2016, ANN OPER RES, V240, P489, DOI 10.1007/s10479-013-1386-4
Choi TM, 2019, TRANSPORT RES E-LOG, V131, P139, DOI 10.1016/j.tre.2019.09.019
Choi TM, 2019, TRANSPORT RES E-LOG, V127, P178, DOI 10.1016/j.tre.2019.05.007
Choi TM, 2013, INT J PROD RES, V51, P2593, DOI 10.1080/00207543.2012.737949
Choi TM, 2011, DECIS SUPPORT SYST, V51, P130, DOI 10.1016/j.dss.2010.12.002
Crainic TG, 2016, EUR J OPER RES, V253, P404, DOI 10.1016/j.ejor.2016.02.040
Deutsch Y, 2018, INT J PROD RES, V56, P251, DOI 10.1080/00207543.2017.1395490
Farahani RZ, 2015, INT J PROD RES, V53, P3769, DOI 10.1080/00207543.2014.988889
Fei X, 2011, TRANSPORT RES C-EMER, V19, P1306, DOI 10.1016/j.trc.2010.10.005
Gal Y, 2016, PR MACH LEARN RES, V48
Geoffrey EHinton., 2012, Improving neural networks by preventing co-adaptation of feature detectors
Hao SY, 2019, TRANSPORT RES C-EMER, V107, P287, DOI 10.1016/j.trc.2019.08.005
Holzapfel A, 2018, EUR J OPER RES, V264, P948, DOI 10.1016/j.ejor.2016.09.013
Huang KC, 2007, TRANSPORT RES C-EMER, V15, P235, DOI 10.1016/j.trc.2006.08.006
Janjevic M, 2019, TRANSPORT RES E-LOG, V131, P37, DOI 10.1016/j.tre.2019.09.001
Kim D., 2019, 190109614 ARXIV
Li L, 2015, TRANSPORT RES E-LOG, V75, P115, DOI 10.1016/j.tre.2014.11.003
Li ML, 2020, COMPUTAT GEOSCI, V24, P1815, DOI 10.1007/s10596-019-09925-5
Li SQ, 2019, TRANSPORT RES E-LOG, V122, P463, DOI 10.1016/j.tre.2018.12.007
Li X., 2017, INT J PROD ECON, V215, P144
Lin L, 2018, TRANSPORT RES C-EMER, V97, P258, DOI 10.1016/j.trc.2018.10.011
Lin L, 2018, TRANSPORT RES C-EMER, V92, P323, DOI 10.1016/j.trc.2018.05.012
Lin L, 2014, TRANSPORT RES C-EMER, V43, P158, DOI 10.1016/j.trc.2013.11.018
Lin L, 2014, TRANSPORT RES A-POL, V64, P65, DOI 10.1016/j.tra.2014.03.013
Lin L, 2013, TRANSPORT RES REC, P40, DOI 10.3141/2392-05
Liu N, 2013, MATH PROBL ENG, V2013, DOI 10.1155/2013/738675
Liu WH, 2018, TRANSPORT RES E-LOG, V116, P102, DOI 10.1016/j.tre.2018.05.006
Liu WH, 2015, TRANSPORT RES E-LOG, V83, P65, DOI 10.1016/j.tre.2015.08.009
Liu X., 2015, International Journal of Intelligent Information Systems. Special Issue: Content-based Image Retrieval and Machine Learning, V4, P15, DOI DOI 10.11648/J.IJIIS.S.2015040202.14
Liu XT, 2018, TRANSPORT RES E-LOG, V117, P23, DOI 10.1016/j.tre.2018.01.019
Luo SY, 2019, TRANSPORT RES E-LOG, V131, P68, DOI 10.1016/j.tre.2019.09.013
McAllister R, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4745
Mofidi SS, 2018, TRANSPORT RES E-LOG, V114, P66, DOI 10.1016/j.tre.2018.02.012
Niu BZ, 2019, ELECTRON COMMER RES, V19, P915, DOI 10.1007/s10660-019-09379-y
Ren SY, 2019, INT J PROD ECON, V218, P339, DOI 10.1016/j.ijpe.2019.06.020
Ren SY, 2017, ANN OPER RES, V257, P335, DOI 10.1007/s10479-016-2204-6
Ren SY, 2015, IEEE T SYST MAN CY-S, V45, P411, DOI 10.1109/TSMC.2014.2342194
Shao SJ, 2019, TRANSPORT RES E-LOG, V123, P17, DOI 10.1016/j.tre.2019.01.007
Tang CS, 2019, TRANSPORT RES E-LOG, V129, P1, DOI 10.1016/j.tre.2019.06.004
Tappia E, 2019, EUR J OPER RES, V274, P947, DOI 10.1016/j.ejor.2018.10.048
Tongzon JL, 2009, TRANSPORT RES E-LOG, V45, P186, DOI 10.1016/j.tre.2008.02.004
van der Laan E, 2016, J OPER MANAG, V45, P114, DOI 10.1016/j.jom.2016.05.004
Vlahogianni EI, 2007, COMPUT-AIDED CIV INF, V22, P317, DOI 10.1111/j.1467-8667.2007.00488.x
Wang JW, 2019, TRANSPORT RES C-EMER, V100, P372, DOI 10.1016/j.trc.2019.02.002
Wang YL, 2015, EUR J OPER RES, V247, P685, DOI 10.1016/j.ejor.2015.05.053
Yeming Gong, 2011, Logistics Research, V3, P191, DOI 10.1007/s12159-011-0057-6
Yu Y, 2011, EXPERT SYST APPL, V38, P7373, DOI 10.1016/j.eswa.2010.12.089
NR 53
TC 76
Z9 77
U1 27
U2 322
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1366-5545
J9 TRANSPORT RES E-LOG
JI Transp. Res. Pt. e-Logist. Transp. Rev.
PD FEB
PY 2020
VL 134
AR 101834
DI 10.1016/j.tre.2019.101834
PG 19
WC Economics; Engineering, Civil; Operations Research & Management Science;
Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
Science; Transportation
GA KS7MA
UT WOS:000518489600010
DA 2024-03-27
ER
PT J
AU Deng, GK
Zhang, JY
He, LJ
Xu, Y
AF Deng, Guangkuan
Zhang, Jianyu
He, Lijuan
Xu, Ying
TI Research on the impact of e-commerce platform's AI resources on seller
opportunism: a cultivational governance mechanism
SO NANKAI BUSINESS REVIEW INTERNATIONAL
LA English
DT Article
DE E-commerce platform; AI resource; Cultivational governance mechanism;
Seller opportunism; Xunzi's Philosophy of Humanity
ID VALUE CO-CREATION; ARTIFICIAL-INTELLIGENCE; INFORMATION-TECHNOLOGY;
RELATIONAL GOVERNANCE; COMPETITIVE ADVANTAGE; DIGITAL EMPOWERMENT;
MARKETING CHANNELS; ELECTRONIC MARKETS; ADVERSE SELECTION; MODERATING
ROLE
AB Purpose - Drawing on the wisdom of ancient Chinese philosopher Xunzi, this paper aims to present a novel mechanism for governing opportunism, referred to as "cultivational governance." By examining the role of artificial intelligence (AI) resources possessed by e-commerce platforms, the authors explore how these resources contribute to mitigating seller opportunism. The central hypothesis of this study posits that two distinct types of AI resources, namely, AI technology resources and AI human resources, serve as crucial factors in curbing seller opportunism. Furthermore, the authors propose that platform digital empowerment and value cocreation act as mediating variables linking AI resources to opportunism.
Design/methodology/approach - Based on the resource-based view and resource orchestration theory, the authors developed a framework and tested it using survey data from sellers. This framework encompasses five key variables: e-commerce platform's AI technology resources, AI human resources, platform digital empowerment, value cocreation and seller opportunism. Regression analysis was used for data analysis.
Findings - The empirical results validate the effectiveness of cultivational governance mechanisms, as both AI resources effectively suppress seller opportunism through digital empowerment and value cocreation. Specifically, e-commerce platforms' AI technology resources significantly promote value cocreation and platform digital empowerment, while AI human resources primarily contribute to platform digital empowerment. Although platform digital empowerment encourages value cocreation, its direct impact on reducing seller opportunism was not supported. Notably, value cocreation negatively affects seller opportunism.
Originality/value - The present research mainly contributes to the marketing channel governance literature by introducing a new approach to inhibit opportunism, namely, the cultivational governance mechanism.
C1 [Deng, Guangkuan] Southwest Univ Sci & Technol, Sch Econ & Management, Mianyang, Sichuan, Peoples R China.
[Zhang, Jianyu] SouthWestern Univ Finance & Econ, Sch Business Adm, Chengdu, Peoples R China.
[He, Lijuan] Party Sch CPC Mianyang Comm, Dept Party Hist & Party Bldg, Mianyang, Sichuan, Peoples R China.
[Xu, Ying] Southwest Univ Sci & Technol, Sch Econ & Management, Mianyang, Sichuan, Peoples R China.
C3 Southwest University of Science & Technology - China; Southwestern
University of Finance & Economics - China; Southwest University of
Science & Technology - China
RP Deng, GK (autor correspondiente), Southwest Univ Sci & Technol, Sch Econ & Management, Mianyang, Sichuan, Peoples R China.
EM styzdgk@126.com; xncdzjy@126.com; hljdgk@gmail.com; 976394681@qq.com
RI G.K., Deng/AAZ-7371-2020
OI G.K., Deng/0000-0002-5071-463X
FU Doctoral Project of Southwest University of Science and Technology
[21sx7109, 22sx7112]
FX This research was funded by the Doctoral Project of Southwest University
of Science and Technology (Grant No. 21sx7109, 22sx7112).
CR Amit R, 2001, STRATEGIC MANAGE J, V22, P493, DOI 10.1002/smj.187
ANDERSON JC, 1994, J MARKETING, V58, P1, DOI 10.2307/1251912
Armstrong M, 2006, RAND J ECON, V37, P668, DOI 10.1111/j.1756-2171.2006.tb00037.x
Ba SL, 2002, MIS QUART, V26, P243, DOI 10.2307/4132332
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Bharadwaj AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983
Brislin R.W., 1980, HDB CROSS CULTURAL P, V2, P389, DOI DOI 10.3390/HEALTHCARE6030093
Busser JA, 2018, TOURISM MANAGE, V65, P69, DOI 10.1016/j.tourman.2017.09.014
Byrd TA, 2000, J MANAGE INFORM SYST, V17, P167
Cao Z, 2015, J OPER MANAG, V33-34, P15, DOI 10.1016/j.jom.2014.09.009
Ceccagnoli M, 2012, MIS QUART, V36, P263
Chadwick C, 2015, STRATEGIC MANAGE J, V36, P360, DOI 10.1002/smj.2217
Claro DP, 2010, IND MARKET MANAG, V39, P221, DOI 10.1016/j.indmarman.2009.03.009
Compeau LD, 1998, J BUS RES, V42, P295, DOI 10.1016/S0148-2963(97)00126-4
Crook TR, 2008, STRATEGIC MANAGE J, V29, P1141, DOI 10.1002/smj.703
De Bruyn A, 2020, J INTERACT MARK, V51, P91, DOI 10.1016/j.intmar.2020.04.007
de Oliveira DT, 2017, BUS HORIZONS, V60, P747, DOI 10.1016/j.bushor.2017.07.002
Deng GK, 2021, INT MARKET REV, V38, P736, DOI 10.1108/IMR-01-2019-0026
Dewan S, 2004, J IND ECON, V52, P497, DOI 10.1111/j.0022-1821.2004.00237.x
Dimitrova BV, 2020, J MARK CHANNELS, V26, P60, DOI 10.1080/1046669X.2019.1646187
Dong MC, 2019, J ACAD MARKET SCI, V47, P1064, DOI 10.1007/s11747-018-0614-5
Dong MC, 2015, PROD OPER MANAG, V24, P1657, DOI 10.1111/poms.12247
Eisenmann T, 2006, HARVARD BUS REV, V84, P92
Enholm IM, 2022, INFORM SYST FRONT, V24, P1709, DOI 10.1007/s10796-021-10186-w
Feng C, 2020, IND MARKET MANAG, V91, P41, DOI 10.1016/j.indmarman.2020.08.003
FORNELL C, 1981, J MARKETING RES, V18, P382, DOI 10.2307/3150980
Forsstrom Birgitta., 2005, VALUE COCREATION IND
Fu WH, 2017, J SERV MANAGE, V28, P348, DOI 10.1108/JOSM-10-2015-0347
Grover V, 2012, MIS QUART, V36, P225
Gummesson E., 2010, J BUSINESS MARKET MA, V4, P181, DOI [DOI 10.1007/S12087-010-0044-2, https://doi.org/10.1007/s12087-010-0044-2]
Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
Hagiu A, 2014, MIT SLOAN MANAGE REV, V55, P92
Hagiu A, 2015, MANAGE SCI, V61, P184, DOI 10.1287/mnsc.2014.2042
Hair J.F., 2009, Multivariate data analysis
Handley SM, 2012, J OPER MANAG, V30, P55, DOI 10.1016/j.jom.2011.06.001
Heide JB, 2007, J MARKETING RES, V44, P425, DOI 10.1509/jmkr.44.3.425
Hein Andreas, 2019, Electronic Markets, V29, P503, DOI 10.1007/s12525-019-00337-y
Hirst G, 2009, EUR J WORK ORGAN PSY, V18, P231, DOI 10.1080/13594320802394202
Jap SD, 2003, MANAGE SCI, V49, P1684, DOI 10.1287/mnsc.49.12.1684.25112
Jia F, 2021, J BUS RES, V131, P69, DOI 10.1016/j.jbusres.2021.03.039
Knoblock John., 1994, Xunzi: A Translation and Study of the Complete Works, VIII.
KUMAR N, 1995, J MARKETING RES, V32, P348, DOI 10.2307/3151986
Lacoste S, 2016, IND MARKET MANAG, V52, P151, DOI 10.1016/j.indmarman.2015.05.018
Lenka S, 2017, PSYCHOL MARKET, V34, P92, DOI 10.1002/mar.20975
Leong C, 2016, MIS QUART, V40, P475, DOI 10.25300/MISQ/2016/40.2.11
Lewis G, 2011, AM ECON REV, V101, P1535, DOI 10.1257/aer.101.4.1535
Li SY, 2021, IND MARKET MANAG, V98, P105, DOI 10.1016/j.indmarman.2021.07.015
Liu Y., 2022, J CONT MARKETING SCI, V5, P81
Liu Y, 2010, IND MARKET MANAG, V39, P844, DOI 10.1016/j.indmarman.2009.09.004
Lui SS, 2009, J BUS RES, V62, P1214, DOI 10.1016/j.jbusres.2008.08.003
Lumineau F, 2022, J BUS RES, V149, P85, DOI 10.1016/j.jbusres.2022.05.011
MAINIERO LA, 1986, ADMIN SCI QUART, V31, P633, DOI 10.2307/2392967
McGregor Douglas., 1960, HUMAN SIDE ENTERPRIS
Melville N, 2004, MIS QUART, V28, P283
Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
Nadeem W, 2021, J BUS ETHICS, V169, P421, DOI 10.1007/s10551-019-04314-5
Ngo LV, 2009, IND MARKET MANAG, V38, P45, DOI 10.1016/j.indmarman.2007.11.002
Palmatier RW., 2014, Marketing Channel Strategy, V8th
Parker Geoffrey G., 2016, Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You
Payne AF, 2008, J ACAD MARKET SCI, V36, P83, DOI 10.1007/s11747-007-0070-0
Peng H., 2020, PLATFORM ECOSYSTEM V
Penrose E. T., 1959, The Theory of the Growth of the Firm
PERSKY J, 1995, J ECON PERSPECT, V9, P221, DOI 10.1257/jep.9.2.221
PODSAKOFF PM, 1986, J MANAGE, V12, P531, DOI 10.1177/014920638601200408
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Poppo L, 2002, STRATEGIC MANAGE J, V23, P707, DOI 10.1002/smj.249
Powell TC, 1997, STRATEGIC MANAGE J, V18, P375, DOI 10.1002/(SICI)1097-0266(199705)18:5<375::AID-SMJ876>3.3.CO;2-Z
Preacher KJ, 2004, BEHAV RES METH INS C, V36, P717, DOI 10.3758/BF03206553
PROVAN KG, 1989, ACAD MANAGE J, V32, P202, DOI 10.5465/256427
Ray G, 2004, STRATEGIC MANAGE J, V25, P23, DOI 10.1002/smj.366
Ren SJ, 2015, PROD PLAN CONTROL, V26, P969, DOI 10.1080/09537287.2014.1002022
Rutskiy V., 2021, ARTIF INTELL, P895
Santos J.R.Q., 1999, Journal of Extension, V37, DOI DOI 10.1080/09500690802187025
Shankar V, 2018, J RETAILING, V94, pVI, DOI 10.1016/S0022-4359(18)30076-9
Sheng SB, 2018, J ACAD MARKET SCI, V46, P1014, DOI 10.1007/s11747-018-0582-9
Sirmon DG, 2007, ACAD MANAGE REV, V32, P273, DOI 10.2307/20159292
Sirmon DG, 2011, J MANAGE, V37, P1390, DOI 10.1177/0149206310385695
Stalnaker A, 2016, DAO COMPAN CHIN PHIL, V7, P35, DOI 10.1007/978-94-017-7745-2_2
Sun Q, 2018, J CLEAN PROD, V184, P414, DOI 10.1016/j.jclepro.2018.02.114
Sun Y, 2016, J BUS RES, V69, P3772, DOI 10.1016/j.jbusres.2015.12.069
Sungmoon K, 2011, DAO, V10, P291, DOI 10.1007/s11712-011-9224-3
Tangpong C, 2010, J OPER MANAG, V28, P398, DOI 10.1016/j.jom.2009.12.001
Thomas LDW, 2014, ACAD MANAGE PERSPECT, V28, P198, DOI 10.5465/amp.2011.0105
Thomaz F, 2015, J MARKETING, V79, P63, DOI 10.1509/jm.12.0404
Van Alstyne MW, 2016, HARVARD BUS REV, V94, P54
Vargo SL, 2004, J MARKETING, V68, P1, DOI 10.1509/jmkg.68.1.1.24036
Vargo SL, 2008, J ACAD MARKET SCI, V36, P1, DOI 10.1007/s11747-007-0069-6
Wade M, 2004, MIS QUART, V28, P107
Wang JX, 2022, ELECTRON COMMER RES, V22, P1377, DOI 10.1007/s10660-021-09469-w
Wang MY, 2016, J OPER MANAG, V46, P84, DOI 10.1016/j.jom.2016.07.001
Wang XH, 2013, J BUS IND MARK, V28, P137, DOI 10.1108/08858621311295272
Wathne KH, 2000, J MARKETING, V64, P36, DOI 10.1509/jmkg.64.4.36.18070
Watson GF, 2015, J RETAILING, V91, P546, DOI 10.1016/j.jretai.2015.04.002
WERNERFELT B, 1984, STRATEGIC MANAGE J, V5, P171, DOI 10.1002/smj.4250050207
Williamson O. E., 1975, Markets and hierarchies
Wingreen SC, 2019, ELECTRON COMMER RES, V19, P339, DOI 10.1007/s10660-018-9305-z
Wirth N, 2018, INT J MARKET RES, V60, P435, DOI 10.1177/1470785318776841
Wong A, 2005, J APPL PSYCHOL, V90, P782, DOI 10.1037/0021-9010.90.4.782
Xiao D, 2020, COMPUT IND ENG, V150, DOI 10.1016/j.cie.2020.106876
Yang DF, 2018, J BUS RES, V90, P1, DOI 10.1016/j.jbusres.2018.04.037
Zhang T., 2017, MANAG REV, V29, P134
Zhang T., 2010, Management World, V7, P119
Zhang T, 2017, J BUS IND MARK, V32, P1217, DOI 10.1108/JBIM-12-2016-0286
Zheng HC, 2019, DECIS SUPPORT SYST, V127, DOI 10.1016/j.dss.2019.113118
Zhou W., 2018, CHINESE J MANAGEMENT, V15, P1110
[朱勤 Zhu Qin], 2019, [科学学研究, Studies in Science of Science], V37, P2026
NR 106
TC 0
Z9 0
U1 12
U2 12
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 2040-8749
EI 2040-8757
J9 NANKAI BUS REV NT
JI Nankai Bus. Rev. Int.
PD NOV 23
PY 2023
VL 14
IS 4
BP 720
EP 745
DI 10.1108/NBRI-07-2022-0074
EA SEP 2023
PG 26
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA Z2XL6
UT WOS:001101963200001
DA 2024-03-27
ER
PT J
AU Rahman, M
Ming, TH
Baigh, TA
Sarker, M
AF Rahman, Mahfuzur
Ming, Teoh Hui
Baigh, Tarannum Azim
Sarker, Moniruzzaman
TI Adoption of artificial intelligence in banking services: an empirical
analysis
SO INTERNATIONAL JOURNAL OF EMERGING MARKETS
LA English
DT Article
DE Artificial intelligence; Bank customers; Intention to adopt; PLS-SEM;
Malaysia; G20; G21; L86; 033
ID TECHNOLOGY ACCEPTANCE MODEL; PERCEIVED USEFULNESS; INTERNET BANKING;
CONTINUANCE INTENTION; DISCRIMINANT VALIDITY; SOCIAL-INFLUENCE; USER
ACCEPTANCE; ONLINE BANKING; E-COMMERCE; PLS-SEM
AB Purpose This study aims to understand the importance and challenges of adopting artificial intelligence (AI) in the banking industry in Malaysia and examine the factors that are important in investigating consumers' intention to adopt AI in banking services. Design/methodology/approach The qualitative research was carried out using in-depth interviews from officials in the baking industry to understand the importance and challenges of adopting AI in the banking industry. In the quantitative study, a total of 302 completed questionnaires were received from Malaysian banking customers. The data were analysed using the SmartPLS 3.0 software to identify the important predictors of their intention to adopt AI. Findings The qualitative results reveal that AI is an essential tool for fraud detection and risk prevention. The absence of regulatory requirements, data privacy and security, and lack of relevant skills and IT infrastructure are significant challenges of AI adoption. The quantitative results indicate that attitude towards AI, perceived usefulness, perceived risk, perceived trust, and subjective norms significantly influence intention to adopt AI in banking services while perceived ease of use and awareness do not. The results also show that attitude towards AI significantly mediates the relationship between perceived usefulness and intention to adopt AI in banking services. Practical implications Financial technology (FinTech) is regarded as a critical determinant of strategic planning in the banking industry. While AI provides various disruptive opportunities in the FinTech space in terms of data collection, analysis, safeguarding and streamlining processes, it also poses a sea of threats to incumbent banks. This study provides vital insights for the policymakers of the banking industry to address the challenges of adopting AI in banking. It also provides the important predictors of the bank customers' intention to adopt AI in banking services. Policymakers can devise their strategies to enhance AI adoption considering the facts. Originality/value This study is amongst the pioneer in exploring the importance and potential challenges in implementing AI technology in banking services and identifying the essential factors influencing the intention to adopt AI in Malaysia's banking services.
C1 [Rahman, Mahfuzur] Univ Malaya, Fac Business & Econ, Dept Finance, Kuala Lumpur, Malaysia.
[Ming, Teoh Hui] Univ Malaya, Fac Business & Econ, Kuala Lumpur, Malaysia.
[Baigh, Tarannum Azim] Univ Malaya, Fac Business & Econ, Dept Econ & Adm, Kuala Lumpur, Malaysia.
[Sarker, Moniruzzaman] Univ Southampton, Southampton Malaysia Business Sch, Johor Baharu, Kagawa, Malaysia.
C3 Universiti Malaya; Universiti Malaya; Universiti Malaya
RP Rahman, M (autor correspondiente), Univ Malaya, Fac Business & Econ, Dept Finance, Kuala Lumpur, Malaysia.
EM mahfuzur@um.edu.my; hui85ming@gmail.com; tarannum.azimbaigh@gmail.com;
mrajib.sarker@gmail.com
RI Sarker, Moniruzzaman/ABD-1755-2021; Rahman, Mahfuzur/KFA-7885-2024;
Rahman, Mahfuzur/P-5952-2019
OI Sarker, Moniruzzaman/0000-0003-3595-5838; Rahman,
Mahfuzur/0000-0003-2072-5829; Rahman, Mahfuzur/0000-0003-2072-5829
FU Ungku Aziz Centre for Development Studies (CPDS), Universiti Malaya
[PD010-2018]
FX This work is ostensibly supported by the Ungku Aziz Centre for
Development Studies (CPDS), Universiti Malaya; project PD010-2018.
CR Abdullah D.B., 2017, J SOCIAL SCI HUMANIT, V2, P1
Abe, 2016, FIN CHATB AR TRANSF
Acemoglu D, 2020, J POLIT ECON, V128, P2188, DOI 10.1086/705716
Ajzen I., 1985, SSSP SPRINGER SERIES, DOI [10.1007/978-3-642-69746-3_2, DOI 10.1007/978-3-642-69746-3_2]
Al-Somali SA, 2009, TECHNOVATION, V29, P130, DOI 10.1016/j.technovation.2008.07.004
Alalwan AA, 2016, J ENTERP INF MANAG, V29, P118, DOI 10.1108/JEIM-04-2015-0035
Albaity M, 2019, INT J EMERG MARK, V14, P988, DOI 10.1108/IJOEM-05-2018-0218
Alsajjan B, 2010, J BUS RES, V63, P957, DOI 10.1016/j.jbusres.2008.12.014
Alsheibani S., 2018, PACIS 2018 P, V37
Alzaidi AA, 2018, INT J COMPUT SCI NET, V18, P140
Alzubi M.M., 2017, J INTERNET BANKING C, V22, P1
An S, 2007, J HEALTH COMMUN, V12, P567, DOI 10.1080/10810730701508633
[Anonymous], 2018, Malay Mail
BAGOZZI RP, 1990, J APPL PSYCHOL, V75, P547, DOI 10.1037/0021-9010.75.5.547
Baigh TA, 2021, INT J SOC ECON, V48, P1443, DOI 10.1108/IJSE-02-2021-0085
Bauer R.A., 1967, CONSUMER BEHAV RISK
Belanche D, 2019, IND MANAGE DATA SYST, V119, P1411, DOI 10.1108/IMDS-08-2018-0368
Berger T., 2016, DIGITALIZATION JOBS, P51
Bharadwaj R., 2019, FINTECH NEWS
Bhattacherjee A, 2000, IEEE T SYST MAN CY A, V30, P411, DOI 10.1109/3468.852435
Bitner M.J., 2001, Managing Service Quality, V11, P375, DOI DOI 10.1108/09604520110410584
Bogue R, 2014, IND ROBOT, V41, P119, DOI 10.1108/IR-01-2014-0300
Caffaro F, 2020, J RURAL STUD, V76, P264, DOI 10.1016/j.jrurstud.2020.04.028
Castañeda JA, 2007, INFORM MANAGE-AMSTER, V44, P384, DOI 10.1016/j.im.2007.02.003
Charbaji A., 2003, CORP GOV-OXFORD, V3, P76, DOI DOI 10.1108/14720700310459872
CHIN W. W., STAT STRATEGIES SMAL, P307
Chin WW, 1998, QUANT METH SER, P295
Chong AYL, 2010, INT J BANK MARK, V28, P267, DOI 10.1108/02652321011054963
Chuah SHW, 2018, IND MANAGE DATA SYST, V118, P1597, DOI 10.1108/IMDS-07-2017-0326
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Crews C, 2019, RES TECHNOL MANAGE, V62, P30, DOI 10.1080/08956308.2019.1541725
Crosman P., 2018, AM BANKER, V183, P1
Curran JM, 2007, J MARKET THEORY PRAC, V15, P283, DOI 10.2753/MTP1069-6679150401
Curran JamesM., 2005, J SERV MARK, V19, P103, DOI [10.1108/08876040510591411, DOI 10.1108/08876040510591411]
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Davis FD, 1996, INT J HUM-COMPUT ST, V45, P19, DOI 10.1006/ijhc.1996.0040
DIGFIN, 2020, 10 REAS WATCH MAL VI
EAC Focus, 2017, AI FUT MAL BANK SECT
Elhajjar S, 2020, INT J BANK MARK, V38, P352, DOI 10.1108/IJBM-02-2019-0055
Faqih KMS, 2016, J RETAIL CONSUM SERV, V30, P140, DOI 10.1016/j.jretconser.2016.01.016
Featherman MS, 2003, INT J HUM-COMPUT ST, V59, P451, DOI 10.1016/S1071-5819(03)00111-3
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Fragata A., 2013, J EC BUSINESS MANAGE, V1, P204
Franke G, 2019, INTERNET RES, V29, P430, DOI 10.1108/IntR-12-2017-0515
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Ghazizadeh M, 2012, COGN TECHNOL WORK, V14, P39, DOI 10.1007/s10111-011-0194-3
Goh J., 2020, EDGE MARKET
González-Carrasco I, 2019, INFORM SCIENCES, V485, P319, DOI 10.1016/j.ins.2019.02.030
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
Hair J.F., 2009, Multivariate data analysis
Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Han S, 2018, IND MANAGE DATA SYST, V118, P618, DOI 10.1108/IMDS-05-2017-0214
Hanafizadeh P, 2014, TELEMAT INFORM, V31, P492, DOI 10.1016/j.tele.2013.04.003
Hanafizadeh P, 2014, TELEMAT INFORM, V31, P62, DOI 10.1016/j.tele.2012.11.001
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Howcroft B., 2002, International Journal of Bank Marketing, V20, P111, DOI [10.1108/02652320210424205, DOI 10.1108/02652320210424205]
International Federation of Robotics, 2017, EX SUMM WORLD ROB 20
Jahangir N, 2008, AFR J BUS MANAGE, V2, P32
Jung D, 2018, ELECTRON MARK, V28, P367, DOI 10.1007/s12525-017-0279-9
Juwaheer TD, 2012, WORLD J SCI TECHNOL, V9, P204, DOI 10.1108/20425941211250552
Kaya O., 2019, Artificial intelligence in banking: A lever for profitability with limited implementation to date
KPMG International, 2019, MULT THREAT FRAUD AR
Lee J., 2018, Manuf. Lett., V18, P20, DOI [10.1016/j.mfglet.2018.09.002, DOI 10.1016/J.MFGLET.2018.09.002]
Lin ZB, 2015, TRANSPORT RES E-LOG, V81, P158, DOI 10.1016/j.tre.2015.07.001
Liu Y, 2021, ARTIF INTELL REV, V54, P3517, DOI 10.1007/s10462-020-09929-z
Mallawaarachchi, 2019, IMPORTANCE ARTIFICIA, V1
Marinova D, 2017, J SERV RES-US, V20, P29, DOI 10.1177/1094670516679273
Martins C, 2014, INT J INFORM MANAGE, V34, P1, DOI 10.1016/j.ijinfomgt.2013.06.002
McKnight D. H., 2002, Information Systems Research, V13, P334, DOI 10.1287/isre.13.3.334.81
Meunier S., 2018, INT BANKER
Mohd NoorN.R. A., 2019, Procedia Computer Science, V163, P434, DOI DOI 10.1016/J.PROCS.2019.12.126
Mutahar Ahmed M., 2018, International Journal of Services and Standards, V12, P180
Nadimpalli M., 2017, International Journal of Innovative Research in Science, Engineering and Technology, V6
Nagdev K, 2021, INT J EMERG MARK, V16, P51, DOI 10.1108/IJOEM-05-2018-0263
NEWELL A, 1958, PSYCHOL REV, V65, P151, DOI 10.1037/h0048495
O'Cass A., 2003, J RETAIL CONSUM SERV, V10, P81, DOI DOI 10.1016/S0969-6989(02)00004-8
Paramasivam S., 2019, MICROSOFT MALAYSIA N
Park J., 2016, Advanced Science and Technology Letters, V141, P104, DOI DOI 10.14257/ASTL.2016.141.21
Pavlou P., 2001, AMCIS 2001 P, P159
Pavlou PA, 2007, MIS QUART, V31, P105
Payne EM, 2018, J RES INTERACT MARK, V12, P328, DOI 10.1108/JRIM-07-2018-0087
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879
Rahman M, 2020, QUAL RES FINANC MARK, V12, P599, DOI 10.1108/QRFM-09-2019-0117
Ray S., 2018, HISTORY
Rehman M, 2012, TRANSFORM GOV-PEOPLE, V6, P258, DOI 10.1108/17506161211251263
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Ringle CM, 2014, REV BRASIL MARK, V13, P54, DOI 10.5585/remark.v13i2.2717
Riquelme HE, 2010, INT J BANK MARK, V28, P328, DOI 10.1108/02652321011064872
Roca Juan Carlos, 2009, Information Management & Computer Security, V17, P96, DOI 10.1108/09685220910963983
Royle V.L.F., 2015, INT J INFORM FUTURIS, V3, P1125
Salimon MG, 2017, INT J BANK MARK, V35, P558, DOI 10.1108/IJBM-05-2016-0060
Sarker M, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102354
Sarstedt M., 2017, HDB MARKET RES, DOI DOI 10.1007/978-3-319-05542-8_15-1
Sathye M., 1999, INT J BANK MARK, V17, P324, DOI [10.1108/02652329910305689, DOI 10.1108/02652329910305689]
Schmelzer R., 2019, 5 BENEFITS BANKING I
Schrotenboer, 2019, IMPACT ARTIFICIAL IN
Shanmugam A., 2014, Acad. Res. Int, V5, P236
Singh J, 2017, J SERV RES-US, V20, P3, DOI 10.1177/1094670516681513
Smith T.J., 2015, The Journal of Business Diversity, V15, P39
Strauss A., 1990, Basics of qualitative research: Grounded theory procedures and techniques
Suki Norazah Mohd, 2011, Journal of Information Technology Management, V22, P1
Susanto A, 2016, IND MANAGE DATA SYST, V116, P508, DOI 10.1108/IMDS-05-2015-0195
TAYLOR JW, 1974, J MARKETING, V38, P54, DOI 10.2307/1250198
TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144
Unisys Corporation, 2018, MAL BANK CUST READ E
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Vanneschi L, 2018, EXPERT SYST APPL, V104, P1, DOI 10.1016/j.eswa.2018.03.025
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2000, MIS QUART, V24, P115, DOI 10.2307/3250981
Vijai D. C., 2019, International Journal of Advanced Research, V7, P1581, DOI 10.21474/IJAR01/8987
Walczak Steven, 2016, International Journal of Sociotechnology and Knowledge Development, V8, P1, DOI 10.4018/IJSKD.2016100101
Wamba-Taguimdje SL, 2020, BUS PROCESS MANAG J, V26, P1893, DOI 10.1108/BPMJ-10-2019-0411
Wiljer D, 2019, J MED IMAGING RADIAT, V50, pS8, DOI 10.1016/j.jmir.2019.09.010
Yang HD, 2009, J COMPUT INFORM SYST, V50, P25
Young JE, 2009, INT J SOC ROBOT, V1, P95, DOI 10.1007/s12369-008-0006-y
Yun T.Z., 2019, EDGE MALAYSIA
Zhou T, 2011, INTERNET RES, V21, P527, DOI 10.1108/10662241111176353
NR 124
TC 14
Z9 14
U1 47
U2 183
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 1746-8809
EI 1746-8817
J9 INT J EMERG MARK
JI Int. J. Emerg. Mark.
PD NOV 21
PY 2023
VL 18
IS 10
BP 4270
EP 4300
DI 10.1108/IJOEM-06-2020-0724
EA DEC 2021
PG 31
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA CG5B8
UT WOS:000739401900001
DA 2024-03-27
ER
PT J
AU Aw, ECX
Tan, GWH
Cham, TH
Raman, R
Ooi, KB
AF Aw, Eugene Cheng-Xi
Tan, Garry Wei-Han
Cham, Tat-Huei
Raman, Ramakrishnan
Ooi, Keng-Boon
TI Alexa, what's on my shopping list? Transforming customer experience with
digital voice assistants
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Artificial intelligence; Digital voice assistant; Customer experience;
Parasocial interactions; Smart-shopping perception; PLS-SEM
ID INFORMATION-TECHNOLOGY; SOCIAL-INFLUENCE; ARTIFICIAL-INTELLIGENCE;
ADOPTION; ACCEPTANCE; CONSUMERS; CONTINUANCE; INTERNET; VALIDITY;
IDENTITY
AB Artificial intelligence is disrupting the retail industry. Digital voice assistants as one of the most popular AI technologies are poised to revolutionize consumers' shopping journeys yet we have a sparse understanding of their role in fostering customer experience. The present study seeks to address this issue by proposing and validating a research model encompassing human-like attributes (i.e., perceived anthropomorphism, perceived animacy, and perceived intelligence), technology attributes (i.e., performance expectancy, effort expectancy, and perceived security), and contextual factors (i.e., social influence and facilitating conditions) as the antecedents to continuance usage of digital voice assistants to shop. The effects are facilitated by the formation of several perceptual-based outcomes such as parasocial interactions, smart-shopping perception, and AI-enabled customer experience. Data (n = 411) was collected through an online questionnaire-based survey and analysed using Partial Least Squares Structural Equation Modelling. The results indicated (i) all human-like and technology attributes, except effort expectancy, have a significant impact on parasocial interactions, (ii) perceived intelligence, perceived security, and performance expectancy significantly influence smart-shopping perception, (iii) parasocial interactions and smart-shopping perception foster AI-enabled customer experience, and (iv) AI enabled customer experience and social influence determine the continuance intention to shop using digital voice assistants. Theoretical and practical implications are discussed.
C1 [Aw, Eugene Cheng-Xi; Tan, Garry Wei-Han; Cham, Tat-Huei; Ooi, Keng-Boon] UCSI Univ, UCSI Grad Business Sch, Kuala Lumpur, Malaysia.
[Tan, Garry Wei-Han; Ooi, Keng-Boon] Nanchang Inst Technol, Nanchang, Peoples R China.
[Raman, Ramakrishnan] Symbiosis Int, Symbiosis Inst Business Management Pune, Pune, India.
[Ooi, Keng-Boon] Chang Jung Christian Univ, Tainan, Taiwan.
C3 UCSI University; Nanchang Institute Technology; Symbiosis International
University; Symbiosis Institute of Business Management (SIBM) Pune;
Chang Jung Christian University
RP Cham, TH; Ooi, KB (autor correspondiente), UCSI Univ, UCSI Grad Business Sch, Kuala Lumpur, Malaysia.; Ooi, KB (autor correspondiente), Nanchang Inst Technol, Nanchang, Peoples R China.; Ooi, KB (autor correspondiente), Chang Jung Christian Univ, Tainan, Taiwan.
EM jaysoncham@gmail.com; ooikengboon@gmail.com
RI Aw, Eugene Cheng-Xi/K-8475-2019; Tan Wei Han, Garry/C-6565-2011; Pune,
SIBM/Z-5178-2019; OOI, Keng-Boon/I-4143-2019; Raman,
Ramakrishnan/U-2170-2017
OI Aw, Eugene Cheng-Xi/0000-0001-6712-1171; Tan Wei Han,
Garry/0000-0003-2974-2270; OOI, Keng-Boon/0000-0002-3384-1207; Raman,
Ramakrishnan/0000-0003-3642-6989
CR Alalwan AA, 2016, INT J BANK MARK, V34, P690, DOI 10.1108/IJBM-06-2015-0093
Ameen N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106548
Atkins KG, 2012, INT J RETAIL DISTRIB, V40, P360, DOI 10.1108/09590551211222349
Aw ECX, 2023, INFORM TECHNOL PEOPL, V36, P66, DOI 10.1108/ITP-07-2021-0548
Aw ECX, 2021, J BUS RES, V132, P146, DOI 10.1016/j.jbusres.2021.04.025
Aw ECX, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102328
Aw ECX, 2020, J CONSUM MARK, V37, P895, DOI 10.1108/JCM-10-2019-3474
Aw ECX, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101991
BAGOZZI RP, 1991, ADMIN SCI QUART, V36, P421, DOI 10.2307/2393203
Balakrishnan J, 2021, INFORM SYST FRONT, DOI 10.1007/s10796-021-10203-y
Balakrishnan J, 2024, ANN OPER RES, V333, P653, DOI 10.1007/s10479-021-04049-5
Balakrishnan J, 2021, PSYCHOL MARKET, V38, P643, DOI 10.1002/mar.21462
BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
Bartneck C, 2009, INT J SOC ROBOT, V1, P71, DOI 10.1007/s12369-008-0001-3
Becker L, 2020, J ACAD MARKET SCI, V48, P630, DOI 10.1007/s11747-019-00718-x
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Boon Liat Cheng, 2019, International Journal of Services, Economics and Management, V10, P289
Burgoon JK, 2000, COMPUT HUM BEHAV, V16, P553, DOI 10.1016/S0747-5632(00)00029-7
Buteau E, 2021, COMMUN RES REP, V38, P336, DOI 10.1080/08824096.2021.1980380
Canziani B, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106714
Carter M, 2020, MIS QUART, V44, P983, DOI 10.25300/MISQ/2020/14607
Cham TH, 2022, INT J BANK MARK, V40, P1017, DOI 10.1108/IJBM-06-2021-0283
Cham TH, 2022, INT J TOUR RES, V24, P140, DOI 10.1002/jtr.2489
Chattaraman V, 2019, COMPUT HUM BEHAV, V90, P315, DOI 10.1016/j.chb.2018.08.048
Cheng TCE, 2006, DECIS SUPPORT SYST, V42, P1558, DOI 10.1016/j.dss.2006.01.002
Chuah SHW, 2021, COMPUT HUM BEHAV, V123, DOI 10.1016/j.chb.2021.106870
de Vaus D. A., 2001, RES DESIGN SOCIAL RE
Edwards C, 2019, COMPUT HUM BEHAV, V90, P357, DOI 10.1016/j.chb.2018.08.027
Faul F, 2007, BEHAV RES METHODS, V39, P175, DOI 10.3758/BF03193146
Gao T, 2019, COGNITIVE SCI, V43, DOI 10.1111/cogs.12775
Gaur L, 2021, INT J CONTEMP HOSP M, V33, P4079, DOI 10.1108/IJCHM-11-2020-1246
Gillath O, 2021, COMPUT HUM BEHAV, V115, DOI 10.1016/j.chb.2020.106607
Grover P, 2022, ANN OPER RES, V308, P177, DOI 10.1007/s10479-020-03683-9
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Gutierrez A, 2019, COMPUT HUM BEHAV, V95, P295, DOI 10.1016/j.chb.2018.09.015
Hair J. F., 2017, ADV ISSUES PARTIAL L
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Han S, 2018, IND MANAGE DATA SYST, V118, P618, DOI 10.1108/IMDS-05-2017-0214
Hayllar Will., 2018, The Talking Shop: The Rise of Voice Commerce
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hernández-Ortega B, 2022, J SERV MARK, V36, P725, DOI 10.1108/JSM-07-2020-0286
Hew JJ, 2019, TECHNOL FORECAST SOC, V144, P311, DOI 10.1016/j.techfore.2017.10.007
Hew JJ, 2017, TELEMAT INFORM, V34, P914, DOI 10.1016/j.tele.2017.04.001
HORTON D, 1956, PSYCHIATR, V19, P215, DOI 10.1080/00332747.1956.11023049
Hoyer WD, 2020, J INTERACT MARK, V51, P57, DOI 10.1016/j.intmar.2020.04.001
Hu P., COMPUT HUM BEHAV, V128, P2021
Jain S, 2022, J BUS RES, V139, P701, DOI 10.1016/j.jbusres.2021.10.007
Janssen M, 2018, PUBLIC MANAG REV, V20, P647, DOI 10.1080/14719037.2017.1305689
Ketchen DJ, 2013, LONG RANGE PLANN, V46, P184, DOI 10.1016/j.lrp.2013.01.002
Kim HW, 2007, INT J HUM-COMPUT ST, V65, P511, DOI 10.1016/j.ijhcs.2006.11.009
Klaus P, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102490
Kock N., 2018, WarpPLS User Manual Version 6.0, V6th
Kock N, 2018, INFORM SYST J, V28, P227, DOI 10.1111/isj.12131
Kulviwat S, 2009, J BUS RES, V62, P706, DOI 10.1016/j.jbusres.2007.04.014
Labrecque LI, 2014, J INTERACT MARK, V28, P134, DOI 10.1016/j.intmar.2013.12.003
Lee K, 2020, INFORM SYST FRONT, V22, P563, DOI 10.1007/s10796-019-09975-1
Lee KY, 2021, INTERNET RES, V31, P1899, DOI 10.1108/INTR-06-2020-0327
Leong LY, 2019, EXPERT SYST APPL, V133, P296, DOI 10.1016/j.eswa.2019.05.024
Lim CM, 2011, PSYCHOL MARKET, V28, P763, DOI 10.1002/mar.20411
Linder M., 2020, HUMANIZING DIGITAL 2
McLean G, 2021, J BUS RES, V124, P312, DOI 10.1016/j.jbusres.2020.11.045
McLean G, 2019, COMPUT HUM BEHAV, V99, P28, DOI 10.1016/j.chb.2019.05.009
Meyer C, 2007, HARVARD BUS REV, V85, P116
Mishra A, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2021.102413
Moore S, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102755
Moriuchi E, 2021, PSYCHOL MARKET, V38, P21, DOI 10.1002/mar.21407
Moriuchi E, 2019, PSYCHOL MARKET, V36, P489, DOI 10.1002/mar.21192
Nass C, 2000, COMMUN ACM, V43, P36, DOI 10.1145/348941.348976
Nass Clifford Ivar, 2005, WIRED SPEECH VOICE A
Ng FZX, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102851
Novak TP, 2019, J ACAD MARKET SCI, V47, P216, DOI 10.1007/s11747-018-0608-3
Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
Pitardi V, 2021, PSYCHOL MARKET, V38, P626, DOI 10.1002/mar.21457
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Poushneh A, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102283
Prisbell M., 1980, COMMUN Q, V28, P22, DOI [10.1080/01463378009369372, DOI 10.1080/01463378009369372]
PwC, 2018, CONS INT SER PREP VO
Qiu HL, 2020, J HOSP MARKET MANAG, V29, P247, DOI 10.1080/19368623.2019.1645073
Rabassa V, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121292
Reeves B, 1996, MEDIA EQUATION PEOPL
Rhee CE, 2020, COMPUT HUM BEHAV, V109, DOI [10.1016/j.chb.2020.102659, 10.1016/j.chb.2020.106359]
Risselada H, 2014, J MARKETING, V78, P52, DOI 10.1509/jm.11.0592
Sarstedt M, 2019, AUSTRALAS MARK J, V27, P197, DOI 10.1016/j.ausmj.2019.05.003
Scates, ACC SPEED AR TOP SUC
Seeber I, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103174
Shareef MA, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120334
Sidaoui K, 2020, J SERV MANAGE, V31, P745, DOI 10.1108/JOSM-11-2019-0341
Tassiello V, 2021, PSYCHOL MARKET, V38, P1069, DOI 10.1002/mar.21488
Tew HT, 2022, J COMPUT INFORM SYST, V62, P527, DOI 10.1080/08874417.2020.1858731
Thong JYL, 2006, INT J HUM-COMPUT ST, V64, P799, DOI 10.1016/j.ijhcs.2006.05.001
Upadhyay N, 2022, INT J ENTREP BEHAV R, V28, P1138, DOI 10.1108/IJEBR-01-2021-0052
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2000, MIS QUART, V24, P115, DOI 10.2307/3250981
Venkatesh V, 2022, ANN OPER RES, V308, P641, DOI 10.1007/s10479-020-03918-9
Venkatesh V, 2012, MIS QUART, V36, P157
Vimalkumar M, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106763
Vlacic B, 2021, J BUS RES, V128, P187, DOI 10.1016/j.jbusres.2021.01.055
Wan SM, 2022, J COMPUT INFORM SYST, V62, P1223, DOI 10.1080/08874417.2021.2004564
Wang G., TECHNOL FORECAST SOC, V175
Whang C, 2021, PSYCHOL MARKET, V38, P581, DOI 10.1002/mar.21437
Wong CH, 2015, ONLINE INFORM REV, V39, P762, DOI 10.1108/OIR-05-2015-0152
Wong CH, 2014, IND MANAGE DATA SYST, V114, P1050, DOI 10.1108/IMDS-05-2014-0146
Wong LW, 2020, INT J PROD RES, V58, P2100, DOI 10.1080/00207543.2020.1730463
Xiu-Ming Loh, 2022, Journal of Computer Information Systems, V62, P39, DOI 10.1080/08874417.2019.1688733
Yan LY, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102300
Yang GY, 2022, ANN OPER RES, V308, P703, DOI 10.1007/s10479-020-03602-y
Zhang MY, 2021, INT J CONTEMP HOSP M, V33, P3883, DOI 10.1108/IJCHM-11-2020-1256
Zhou T, 2014, COMPUT HUM BEHAV, V37, P283, DOI 10.1016/j.chb.2014.05.008
NR 109
TC 52
Z9 52
U1 114
U2 256
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD JUL
PY 2022
VL 180
AR 121711
DI 10.1016/j.techfore.2022.121711
EA APR 2022
PG 13
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA 1F4NH
UT WOS:000795144900001
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Zintso, Y
Fedorishina, I
Zaiachkovska, H
Kovalchuk, O
Tyagunova, Z
AF Zintso, Yuliya
Fedorishina, Irina
Zaiachkovska, Halyna
Kovalchuk, Oleh
Tyagunova, Zlata
TI ANALYSIS OF CURRENT TRENDS IN THE USE OF DIGITAL MARKETING FOR THE
SUCCESSFUL PROMOTION OF GOODS AND SERVICES IN UKRAINE
SO FINANCIAL AND CREDIT ACTIVITY-PROBLEMS OF THEORY AND PRACTICE
LA English
DT Article
DE digital marketing; product promotion; trends in digital marketing;
social media; artificial intelligence
AB One of the main trends in the development of most economic processes in the modern world is digitalization. Marketing is no exception, also subject to general trends and focused today on the promotion of products through the Internet, and social networks; it uses cloud technologies and artificial intelligence to develop the processes of master -ing markets. The main trend of modern digital marketing is becoming more and more customer-oriented, which can be achieved in different ways and with the use of different tools. Businesses are forced to adjust to the global trends of digital marketing because at other times they will miss the opportunity to take full advantage of all the opportunities that are present on the market in a timely manner. In this context the importance and role of personalized advertising and the formation of individual approaches for product promotion, in which digital marketing tools, in particular the use of artificial intelligence and promotion through social networks, help as well. Given the above, the purpose of the study is to analyze current trends in the use of digital marketing for the successful promotion of goods and services in an unstable environment, which will allow modern companies to identify areas for improving their financial condition and developing their potential in the context of using various digital marketing tools.The analysis of the advantages and disadvantages of modern digital marketing tools, as well as the specifics of using various digital product promotion tools, is based on a study that lasted in Ukraine in 2023 for 3 months.The methodological basis of the research is the systematization of the experience of leading scientists and the generalization of scientists' views on the problems of the de-velopment of digital marketing. The methods used in the study were general scientific methods of analysis and synthesis, induction and deduction, generalization, systemati-zation, and graphical methods.The scientific novelty of the study is to identify the main trends of modern digital mar-keting and to specify the limitations of its use, which may adversely affect the develop-ment of the company and the prospects for its promotion in the market.
C1 [Zintso, Yuliya] Ivan Franko Natl Univ Lviv, Fac Econ, Dept Mkt, Lvov, Ukraine.
[Fedorishina, Irina] State Univ Trade & Econ, Dept Mkt, Kiev, Ukraine.
[Zaiachkovska, Halyna] Khmelnytskyi Cooperat Trade & Econ Inst, Dept Mkt & Management, Khmelnytskyi, Ukraine.
[Kovalchuk, Oleh] Lutsk Natl Tech Univ, Fac Business & Law, Dept Mkt, Lutsk, Ukraine.
[Tyagunova, Zlata] Khmelnytskyi Cooperat Trade Econ Inst, Dept Mkt & Management, Khmelnytskyi, Ukraine.
C3 Ministry of Education & Science of Ukraine; Ivan Franko National
University Lviv; State University of Trade & Economics; Khmelnytskyi
Cooperative Trade & Economic Institute; Ministry of Education & Science
of Ukraine; Lutsk National Technical University; Khmelnytskyi
Cooperative Trade & Economic Institute
RP Zintso, Y (autor correspondiente), Ivan Franko Natl Univ Lviv, Fac Econ, Dept Mkt, Lvov, Ukraine.
EM yuliya10.04@gmail.com
RI Kovalchuk, Oleh/ABK-3854-2022
OI Kovalchuk, Oleh/0000-0002-0580-8581
CR Abbasi SG, 2021, J PUBLIC AFF, V21, DOI 10.1002/pa.2512
Afonasova MA, 2019, POL J MANAG STUD, V19, P22, DOI 10.17512/pjms.2019.19.2.02
Akter M., 2020, Open Journal of Business and Management, V8, P2696, DOI DOI 10.4236/OJBM.2020.86167
[Anonymous], MAN IT ENG, V8, P321
[Anonymous], 2023, STRAT CHANG DIG MARK
Arora D., 2022, LEADERSHIP STRATEGIE, V18, P119, DOI [10.4018/978-1-6684-3453-6.ch009, DOI 10.4018/978-1-6684-3453-6.CH009]
Asraf M, 2020, ONLINE INFORM REV, V44, P745, DOI 10.1108/OIR-05-2018-0156
Bala M., 2018, INT J MANAGEMENT IT, V8, P321
Cherniaieva O., 2023, Futurity Economics&Law, V3, P4, DOI [10.57125/FEL.2023.03.25.01, DOI 10.57125/FEL2023.03.25.01]
Dey BL, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102057
Girchenko T., 2016, EUROPEAN COOPERATION, V11, P24
Gobble MM, 2018, RES TECHNOL MANAGE, V61, P66, DOI 10.1080/08956308.2018.1495969
Hrynchyshyn Y., 2021, Futurity Economics&Law, V1, P12, DOI [10.57125/FEL2021.06.25.2, DOI 10.57125/FEL2021.06.25.2]
Ihnatenko R, 2022, FINANC CREDIT ACT, V1, P428
Levchenko Y., 2022, Futurity Economics & Law, V2, P22, DOI [10.57125/FEL.2022.12.25.03, DOI 10.57125/FEL.2022.12.25.03]
Li FF, 2021, J ACAD MARKET SCI, V49, P51, DOI 10.1007/s11747-020-00733-3
Pan WR, 2022, J BUS RES, V139, P303, DOI 10.1016/j.jbusres.2021.09.061
Petrescu M, 2021, J MARK ANAL, V9, P155, DOI 10.1057/s41270-021-00129-4
Redjeki F., 2021, International Journal of Science and Society, V3, P40
Sanakuiev M., 2023, Futurity Economics&Law, V3, P16, DOI [10.57125/FEL.2023.03.25.02, DOI 10.57125/FEL.2023.03.25.02]
Saura JR, 2021, IND MARKET MANAG, V98, P161, DOI 10.1016/j.indmarman.2021.08.006
Shahid S, 2022, 3C EMPRESA, V11, P149, DOI 10.17993/3cemp.2022.110149.149-177
Vitsentzatou E, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142215228
Yasmin A., 2015, INT J MANAG SCI BUS, V1, P69
NR 24
TC 0
Z9 0
U1 17
U2 21
PU FINTECHALIANCE
PI Kyiv
PA Highway Kharkivska, bldg 180/21, Kyiv, UKRAINE
SN 2306-4994
EI 2310-8770
J9 FINANC CREDIT ACT
JI Financ. Credit Act.
PY 2023
VL 3
IS 50
BP 174
EP 184
DI 10.55643/fcaptp.3.50.2023.4080
PG 11
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA N2LK2
UT WOS:001035387500014
OA gold
DA 2024-03-27
ER
PT J
AU Darrow, RM
AF Darrow, Ross M.
TI The future of AI is the market
SO JOURNAL OF REVENUE AND PRICING MANAGEMENT
LA English
DT Article
DE Customer Value Chain; Microeconomics; New distribution capability;
Multi-armed bandits; Multi-agent systems; Marketplace; Distribution
artificial intelligence
AB We've made great progress with the current generation of AI, with learning customer preferences embedded in recommender systems to better customize offerings. This is perfect for the New Distribution Capability that IATA is spearheading for the airline industry. To truly make this work, and not just for airlines but for all travel (hotels, rental cars, attractions, concerts, tours, and more), we need to move on from (a) supervised learning built upon masses of data to systems that learn on the fly with little data, and from (b) centralized (even if in the cloud) machine learning to distributed artificial intelligence, and from (c) recommender systems to marketplace approaches.
C1 [Darrow, Ross M.] Sabre Res, Bedford, MA 01730 USA.
RP Darrow, RM (autor correspondiente), Sabre Res, Bedford, MA 01730 USA.
EM ross_darrow@prodigy.net
CR [Anonymous], 2018, INTELLIGENCE ITS ROL
Bacon Tom, 2019, 4 WAYS NDC IS RESHAP
Corea Francesco, 2019, DISTRIBUTED ARTIFICI
Deng H., 2019, RECOMMENDER SYSTEMS
Diamis PeterH., 2020, EXPONENTIAL TECHNOLO
Dinculescu Monica, 2018, INTRO REINFORCEMENT
Domingos P., 2015, THE MASTER ALGORITHM
Eug?nio Oliveira, 2010, NEGOTIATION BASED AP, DOI 1132120/A_Negotiation_Based_Approach_to_Airline_Operations_Recovery
Jordan M. I., 2019, Harvard Data Science Review, V1, P1, DOI [10.1162/99608f92.f06c6e61, DOI 10.1162/99608F92.F06C6E61, 10.1162/99608f92.f06c6-61]
Jordan Michael I, 2018, CLARITY THOUGHT AI
Michael I. Jordan, 2019, SAFFROM ADAPTIVE ALG
Opricovic S, 2004, EUR J OPER RES, V156, P445, DOI [10.1016/S0377-2217(03)00020-1, 10.1016/s0377-2217(03)00020-1]
Richard Ratliff, 2019, AGIFORS REV MAN STUD
Sabre Inc., 2019, EXPL NDC
Sabre Inc., 2019, PREF DRIV AIR SHOPP
Sahni H., 2018, REINFORCEMENT LEARNI
Salimans T., 2017, EVOLUTION STRATEGIES
Shebalov Sergey, 2019, AGIFORS 59 ANN S P
Surmenok Pavel, 2017, CONTEXTUAL BANDITS R
Teixeria ThalesS, 2019, UNLOCKING CUSTOMER V
Veinbergs Jevgenijs, 2019, WHAT SHAPES REVENUE
Vinod B., 2015, ASCEND, V4, P11
White J. M., 2013, Bandit Algorithms for Website Optimization: Developing, Deploying, and Debugging
Wooldridge M., 2009, An introduction to multiagent systems
NR 24
TC 1
Z9 1
U1 2
U2 14
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1476-6930
EI 1477-657X
J9 J REVENUE PRICING MA
JI J. Revenue Pricing Manag.
PD JUN
PY 2021
VL 20
IS 3
SI SI
BP 381
EP 386
DI 10.1057/s41272-021-00321-2
EA MAR 2021
PG 6
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA SE7OG
UT WOS:000629106200002
DA 2024-03-27
ER
PT J
AU Xie, YG
Liang, CY
Zhou, PY
Jiang, L
AF Xie, Yuguang
Liang, Changyong
Zhou, Peiyu
Jiang, Li
TI Exploring the influence mechanism of chatbot-expressed humor on service
satisfaction in online customer service
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Humor; Chatbots; Competence; Entertainment; Social presence; Service
satisfaction
ID BRAND ENGAGEMENT; ANTHROPOMORPHISM; ROBOTS
AB As chatbots gain widespread adoption in online customer service, optimizing their inherent value and enhancing customer satisfaction emerges as an indispensable concern necessitating attention. Therefore, this study focuses on the effect of chatbot-expressed humor on customer service satisfaction from the perspective of emotional expression and constructs three mediating pathways (cognitive, emotional, and social). The results of two online and one laboratory-based experiment show that chatbot-expressed humor can significantly enhance customer service satisfaction. The impact of humor on service satisfaction was mediated by competence, entertainment, and social presence. Moreover, identity disclosure exerts a negative moderating influence on the association between chatbot-expressed humor and both service satisfaction and competence. Through this study, we hope to provide ideas for online service providers to adopt AI strategies and for product designers to improve the design of chatbots.
C1 [Xie, Yuguang; Liang, Changyong; Zhou, Peiyu; Jiang, Li] Hefei Univ Technol, Sch Management, Hefei, Peoples R China.
[Liang, Changyong] Minist Educ, Key Lab Proc Optimizat & Intelligent Decis Making, Hefei, Peoples R China.
C3 Hefei University of Technology
RP Liang, CY (autor correspondiente), Hefei Univ Technol, Sch Management, Hefei, Peoples R China.
EM cyliang@hfut.edu.cn
OI Zhou, Peiyu/0000-0003-2823-5242
FU National Natural Science Foundation of China [72131006]
FX Funding This work was supported by the National Natural
Science Foundation of China (grant number: 72131006) .
CR Andel SA, 2020, COMPUT HUM BEHAV, V113, DOI 10.1016/j.chb.2020.106505
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Barry JM, 2018, J MARKET THEORY PRAC, V26, P158, DOI 10.1080/10696679.2017.1389247
Belanche D, 2021, PSYCHOL MARKET, V38, P2357, DOI 10.1002/mar.21532
CHATTOPADHYAY A, 1990, J MARKETING RES, V27, P466, DOI 10.2307/3172631
Chen JS, 2021, INT J RETAIL DISTRIB, V49, P1512, DOI 10.1108/IJRDM-08-2020-0312
Chen QS, 2019, FRONT PSYCHOL, V10, DOI 10.3389/fpsyg.2019.01578
Cheng XS, 2022, INTERNET RES, V32, P496, DOI 10.1108/INTR-08-2020-0460
Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
Chi OH, 2020, J HOSP MARKET MANAG, V29, P757, DOI 10.1080/19368623.2020.1721394
Chuah SHW, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102551
De Cicco R, 2020, INT J RETAIL DISTRIB, V48, P1213, DOI 10.1108/IJRDM-12-2019-0406
Eisend M, 2022, PSYCHOL MARKET, V39, P962, DOI 10.1002/mar.21634
Eisend M, 2009, J ACAD MARKET SCI, V37, P191, DOI 10.1007/s11747-008-0096-y
Flaherty Karen., 2004, J CURR ISS RES AD, V26, P25
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Green HN, 2022, ACMIEEE INT CONF HUM, P313, DOI 10.1109/HRI53351.2022.9889353
Gulas CS, 2010, J ADVERTISING, V39, P109, DOI 10.2753/JOA0091-3367390408
Gulfraz MB, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103000
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Han ELZB, 2023, INFORM SYST RES, V34, P1296, DOI 10.1287/isre.2022.1179
Hatfield E., 1993, CURR DIR PSYCHOL SCI, V2, P96, DOI [10.1111/1467-8721.ep10770953, DOI 10.1111/1467-8721.EP10770953]
Herrando C, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.712606
Hsu CL, 2023, J RETAIL CONSUM SERV, V71, DOI 10.1016/j.jretconser.2022.103211
Hu Q, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102250
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huang YS, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103044
Jin EJ, 2022, CYBERPSYCH BEH SOC N, V25, P597, DOI 10.1089/cyber.2021.0318
Johanson D.L., 2020, Technol. Mind Behav., V1, P1, DOI [https://doi.org/10.1037/tmb0000021, DOI 10.1037/TMB0000021#SUPPLEMENTAL-MATERIALS]
Kim J, 2022, COMPUT HUM BEHAV, V136, DOI 10.1016/j.chb.2022.107383
Kim SY, 2019, MARKET LETT, V30, P1, DOI 10.1007/s11002-019-09485-9
Kim T, 2020, TELEMAT INFORM, V51, DOI 10.1016/j.tele.2020.101406
Kobel S, 2021, J BUS RES, V132, P260, DOI 10.1016/j.jbusres.2021.04.034
Konya-Baumbach E, 2023, COMPUT HUM BEHAV, V139, DOI 10.1016/j.chb.2022.107513
Li MC, 2023, J RETAIL CONSUM SERV, V71, DOI 10.1016/j.jretconser.2022.103209
Li XG, 2021, COMPUT HUM BEHAV, V118, DOI 10.1016/j.chb.2021.106680
Lima XJ, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2020.102422
Liu BJ, 2018, CYBERPSYCH BEH SOC N, V21, P625, DOI 10.1089/cyber.2018.0110
Liu DW, 2023, TECHNOL SOC, V73, DOI 10.1016/j.techsoc.2023.102244
Liu S, 2022, DECIS SUPPORT SYST, V157, DOI 10.1016/j.dss.2022.113754
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Lussier B, 2017, IND MARKET MANAG, V65, P168, DOI 10.1016/j.indmarman.2017.03.012
Ma YY, 2021, TELEMAT INFORM, V59, DOI 10.1016/j.tele.2021.101562
Mathies C, 2016, J SERV THEOR PRACT, V26, P137, DOI 10.1108/JSTP-09-2014-0187
McLean G, 2021, J BUS RES, V124, P312, DOI 10.1016/j.jbusres.2020.11.045
Merrill K, 2022, COMMUN RES REP, V39, P93, DOI 10.1080/08824096.2022.2045929
Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
Mozafari N, 2022, J SERV MANAGE, V33, P221, DOI 10.1108/JOSM-10-2020-0380
Nguyen TH, 2022, INFORM SYST FRONT, V24, P797, DOI 10.1007/s10796-021-10212-x
Niculescu A, 2013, INT J SOC ROBOT, V5, P171, DOI 10.1007/s12369-012-0171-x
Nikou S, 2019, TELEMAT INFORM, V45, DOI 10.1016/j.tele.2019.101283
Nuruzzaman M, 2020, KNOWL-BASED SYST, V196, DOI 10.1016/j.knosys.2020.105810
Osei-Frimpong K, 2018, TECHNOL FORECAST SOC, V128, P10, DOI 10.1016/j.techfore.2017.10.010
Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
Qiu LY, 2023, MIS QUART, V47, P511, DOI 10.25300/MISQ/2022/16300
Rathee S, 2022, J BUS RES, V139, P44, DOI 10.1016/j.jbusres.2021.09.041
Schanke S, 2021, INFORM SYST RES, V32, P736, DOI 10.1287/isre.2021.1015
SCHWARZ N, 1983, J PERS SOC PSYCHOL, V45, P513, DOI 10.1037/0022-3514.45.3.513
Shao C, 2021, HUM BEHAV EMERG TECH, V3, P978, DOI 10.1002/hbe2.293
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shin H, 2023, INT J CONSUM STUD, V47, P545, DOI 10.1111/ijcs.12849
Shin H, 2020, EUR J MARKETING, V54, P2013, DOI 10.1108/EJM-08-2018-0522
Skjuve M., 2019, Human Technology, V15, P30, DOI [DOI 10.17011/HT/URN.201902201607, 10.17011/ht/urn.201902201607]
Söderlund M, 2018, J RETAIL CONSUM SERV, V42, P55, DOI 10.1016/j.jretconser.2018.01.013
Song H, 2019, INT J HUM-COMPUT INT, V35, P448, DOI 10.1080/10447318.2018.1455126
Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
Speck P.S., 1991, CURRENT ISSUES RES A, V13, P1
Sung E, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102382
Tae M, 2020, ACMIEEE INT CONF HUM, P462, DOI 10.1145/3371382.3378267
Tay BTC, 2016, COMPUT HUM BEHAV, V60, P19, DOI 10.1016/j.chb.2016.01.042
Tsai WHS, 2021, J RES INTERACT MARK, V15, P460, DOI 10.1108/JRIM-12-2019-0200
van Dolen WM, 2008, J ECON PSYCHOL, V29, P160, DOI 10.1016/j.joep.2007.05.001
Warren C., 2016, Journal of Marketing Behavior, V2, P39, DOI [https://doi.org/10.1561/107.00000027, DOI 10.1561/107.00000027]
Wojciszke B, 2009, EUR J SOC PSYCHOL, V39, P973, DOI 10.1002/ejsp.595
Wu CL, 2023, CURR PSYCHOL, V42, P16392, DOI 10.1007/s12144-021-01641-7
Wu RJ, 2022, J BUS RES, V141, P299, DOI 10.1016/j.jbusres.2021.12.037
Xu XA, 2022, ANN TOURISM RES, V95, DOI 10.1016/j.annals.2022.103439
Yang HY, 2022, ANN TOURISM RES, V95, DOI 10.1016/j.annals.2022.103425
Yogeeswaran K, 2016, J HUM-ROBOT INTERACT, V5, P29, DOI 10.5898/JHRI.5.2.Yogeeswaran
Yuan LY, 2019, J MANAGE INFORM SYST, V36, P450, DOI 10.1080/07421222.2019.1598691
Zhang H, 2022, 2022 31ST IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN 2022), P572, DOI 10.1109/RO-MAN53752.2022.9900515
Zhang MY, 2021, INT J CONTEMP HOSP M, V33, P3883, DOI 10.1108/IJCHM-11-2020-1256
Zhou H, 2018, AAAI CONF ARTIF INTE, P730
Zhu DH, 2020, INT J CONTEMP HOSP M, V32, P1367, DOI 10.1108/IJCHM-10-2019-0904
NR 84
TC 1
Z9 1
U1 61
U2 61
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD JAN
PY 2024
VL 76
AR 103599
DI 10.1016/j.jretconser.2023.103599
EA OCT 2023
PG 17
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA Y3MY5
UT WOS:001104355300001
DA 2024-03-27
ER
PT J
AU Kozinets, RV
AF Kozinets, Robert V.
TI Algorithmic branding through platform assemblages: core conceptions and
research directions for a new era of marketing and service management
SO JOURNAL OF SERVICE MANAGEMENT
LA English
DT Article
DE Algorithms; Artificial intelligence; Branding; Brands; Consumer
experience; Digital marketing; Marketing; Platforms; Platform
management; Service platforms; Social media; Social media marketing
ID CONSUMER; AFFORDANCES; EXPERIENCE; COMMUNITY; AGE
AB Purpose Contemporary branding transpires in a complex technological and media environment whose key contextual characteristics remain largely unexplained. The article provides a conceptual understanding of the elements of contemporary branding as they take place using networked platforms and explains them as an increasingly important practice that affects customer and manager experience. Design/methodology/approach This article draws on a variety of recent sources to synthesize a model that offers a more contextualized, comprehensive and up-to-date understanding of how branding has become and is being altered because of the use of branded service platforms and algorithms. Findings Core terminology about technoculture, technocultural fields, platform assemblages, affordances, algorithms and networks of desire set the foundation for a deeper conceptual understanding of the novel elements of algorithmic branding. Algorithmic branding transcended the mere attachment of specific "mythic" qualities to a product or experience and has morphed into the multidimensional process of using media to manage communication. The goal of marketers is now to use engagement practices as well as algorithmic activation, amplification, customization and connectivity to drive consumers deeper into the brand spiral, entangling them in networks of brand-related desire. Practical implications The model has a range of important managerial implications for brand management and managerial relations. It promotes a understanding of platform brands as service brands. It underscores and models the interconnected role that consumers, devices and algorithms, as well as technology companies and their own service brands play in corporate branding efforts. It suggests that consumers might unduly trust these service platforms. It points to the growing importance of platforms' service brands and the consequent surrender of branding power to technology companies. And it also provides a range of important ethical and pragmatic questions that curious marketers, researchers and policy-makers may examine. Originality/value This model provides a fresh look at the important topic of branding today, updating prior conceptions with a comprehensive and contextually grounded model of service platforms and algorithmic branding.
C1 [Kozinets, Robert V.] Univ Southern Calif, Marshall Sch Business, Los Angeles, CA 90007 USA.
C3 University of Southern California
RP Kozinets, RV (autor correspondiente), Univ Southern Calif, Marshall Sch Business, Los Angeles, CA 90007 USA.
EM rkozinets@usc.edu
OI Kozinets, Robert/0000-0003-1724-8259
CR Arvidsson Adam., 2006, BRANDS MEANING VALUE
BERGEN M, 1992, J MARKETING, V56, P1, DOI 10.2307/1252293
Berry LL, 2000, J ACAD MARKET SCI, V28, P128, DOI 10.1177/0092070300281012
Berthon P, 2003, MIT SLOAN MANAGE REV, V44, P49
Brakus JJ, 2009, J MARKETING, V73, P52, DOI 10.1509/jmkg.73.3.52
Breidbach CF, 2020, J SERV MANAGE, V31, P163, DOI 10.1108/JOSM-03-2019-0073
Bruns A., 2008, BLOGS WIKIPEDIA 2 LI
Bucher T, 2017, INFORM COMMUN SOC, V20, P30, DOI 10.1080/1369118X.2016.1154086
Bygstad B, 2016, J INF TECHNOL-UK, V31, P83, DOI 10.1057/jit.2015.13
Carah N, 2020, SOCIOL COMPASS, V14, DOI 10.1111/soc4.12752
Carah N, 2018, MEDIA CULT SOC, V40, P178, DOI 10.1177/0163443718754648
Carah N, 2017, NEW MEDIA SOC, V19, P384, DOI 10.1177/1461444815605463
De Landa Manuel, 2006, A New Philosophy of Society: Assemblage Theory and Social Complexity
Deleuze Gilles., 1983, Anti-Oedipus: Capitalism and Schizophrenia
Field JM, 2018, J SERV MANAGE, V29, P55, DOI 10.1108/JOSM-08-2017-0191
Fischer E, 2004, J BUS VENTURING, V19, P681, DOI 10.1016/j.jbusvent.2003.09.005
Foehr J, 2020, J ASSOC CONSUM RES, V5, P181, DOI 10.1086/707731
Fournier S, 2011, BUS HORIZONS, V54, P193, DOI 10.1016/j.bushor.2011.01.001
FRIESTAD M, 1994, J CONSUM RES, V21, P1, DOI 10.1086/209380
Gäthke J, 2020, J SERV MANAGE, V31, P227, DOI 10.1108/JOSM-05-2019-0151
Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
Hallinan B, 2016, NEW MEDIA SOC, V18, P117, DOI 10.1177/1461444814538646
Hampton KN, 2016, AM BEHAV SCI, V60, P101, DOI 10.1177/0002764215601714
Hoffman DL, 2018, J CONSUM RES, V44, P1178, DOI 10.1093/jcr/ucx105
Justice-Leibrock J., 2013, USER EXPERIENCE LAB
Keiningham TL, 2018, J SERV MANAGE, V29, P2, DOI 10.1108/JOSM-03-2017-0077
Kitchin R, 2017, INFORM COMMUN SOC, V20, P14, DOI 10.1080/1369118X.2016.1154087
Kotliar DM, 2020, NEW MEDIA SOC, V22, P1152, DOI 10.1177/1461444820912535
Kozinets R.V., 2020, Netnography: The essential guide to qualitative socialmedia research, V3e. ed
Kozinets R, 2017, J CONSUM RES, V43, P659, DOI 10.1093/jcr/ucw061
Kozinets RV, 2021, J CONSUM RES, V48, P428, DOI 10.1093/jcr/ucab014
Kozinets RV, 2021, J MARKETING, V85, P156, DOI 10.1177/0022242920972933
Kozinets RV, 2019, J CONSUM RES, V46, P620, DOI 10.1093/jcr/ucz034
Kozinets RV, 2016, J CONSUM RES, V42, P834, DOI 10.1093/jcr/ucv090
Kozinets RV, 2010, J MARKETING, V74, P71, DOI 10.1509/jmkg.74.2.71
Kozinets RV, 2008, J MACROMARKETING, V28, P339, DOI 10.1177/0276146708325382
LEE CS, 2021, ONLINE INFORM REV DO
Logg JM, 2019, ORGAN BEHAV HUM DEC, V151, P90, DOI 10.1016/j.obhdp.2018.12.005
Lury C., 2009, J CULTURAL EXONOMICS, V2, P67, DOI [DOI 10.1080/17530350903064022, 10.1080/17530350903064022]
Lury Celia., 2010, Blowing up the brand: Critical perspectives on promotional culture, P29
Martin KD, 2017, J ACAD MARKET SCI, V45, P135, DOI 10.1007/s11747-016-0495-4
Marwick AE, 2015, PUBLIC CULTURE, V27, P137, DOI 10.1215/08992363-2798379
ONeil C., 2016, Weapons of math destruction: How big data increases inequality and threatens democracy
Perren R, 2018, J MARKETING, V82, P20, DOI 10.1509/jm.14.0250
Scaraboto D, 2013, J CONSUM RES, V39, P1234, DOI 10.1086/668298
Schafer J. Ben, 1999, Proceedings of the 1st ACM conference on Electronic commerce, P158, DOI [DOI 10.1145/336992.337035, 10.1145/336992.337035]
Schau HJ, 2009, J MARKETING, V73, P30, DOI 10.1509/jmkg.73.5.30
Schull N. D., 2012, ADDICTION DESIGN MAC, DOI DOI 10.2307/J.CTT12F4D0
Seaver N, 2017, BIG DATA SOC, V4, DOI 10.1177/2053951717738104
Sidaoui K, 2020, J SERV MANAGE, V31, P745, DOI 10.1108/JOSM-11-2019-0341
Smith AN, 2021, J ACAD MARKET SCI, V49, P258, DOI 10.1007/s11747-020-00736-0
Srinivasan R, 2021, J MARKETING, V85, P74, DOI 10.1177/0022242921997082
Striphas T, 2015, EUR J CULT STUD, V18, P395, DOI 10.1177/1367549415577392
Strong DM, 2014, J ASSOC INF SYST, V15, P53
van Dijck J., 2013, The Culture of Connectivity: A Critical History of Social Media
Wirtz J, 2019, J SERV MANAGE, V30, P452, DOI 10.1108/JOSM-11-2018-0369
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Zeithaml V.A., 2014, Profiting From Services and Solutions: What Product-Centric Firms Need to Know, V1st
NR 58
TC 13
Z9 13
U1 11
U2 64
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1757-5818
EI 1757-5826
J9 J SERV MANAGE
JI J. Serv. Manage.
PD APR 18
PY 2022
VL 33
IS 3
SI SI
BP 437
EP 452
DI 10.1108/JOSM-07-2021-0263
EA NOV 2021
PG 16
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 0M9SZ
UT WOS:000722753500001
DA 2024-03-27
ER
PT J
AU Timoshenko, A
Hauser, JR
AF Timoshenko, Artem
Hauser, John R.
TI Identifying Customer Needs from User-Generated Content
SO MARKETING SCIENCE
LA English
DT Article
DE customer needs; online reviews; machine learning; voice of the customer;
user-generated content; market research; text mining; deep learning;
natural language processing
ID PRODUCT DEVELOPMENT; MANAGEMENT; CONSUMER; DESIGN; MODEL
AB Firms traditionally rely on interviews and focus groups to identify customer needs for marketing strategy and product development. User-generated content (UGC) is a promising alternative source for identifying customer needs. However, established methods are neither efficient nor effective for large UGC corpora because much content is noninformative or repetitive. We propose a machine-learning approach to facilitate qualitative analysis by selecting content for efficient review. We use a convolutional neural network to filter out noninformative content and cluster dense sentence embeddings to avoid sampling repetitive content. We further address two key questions: Are UGC-based customer needs comparable to interview-based customer needs? Do the machine-learning methods improve customer-need identification? These comparisons are enabled by a custom data set of customer needs for oral care products identified by professional analysts using industry-standard experiential interviews. The analysts also coded 12,000 UGC sentences to identify which previously identified customer needs and/or new customer needs were articulated in each sentence. We show that (1) UGC is at least as valuable as a source of customer needs for product development, likely more valuable, compared with conventional methods, and (2) machine-learning methods improve efficiency of identifying customer needs from UGC (unique customer needs per unit of professional services cost).
C1 [Timoshenko, Artem; Hauser, John R.] MIT, MIT Sloan Sch Management, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
C3 Massachusetts Institute of Technology (MIT)
RP Timoshenko, A (autor correspondiente), MIT, MIT Sloan Sch Management, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM artem.timoshenko@sloan.mit.edu; hauser@mit.edu
RI Timoshenko, Artem/IXN-4676-2023; Hauser, John R/O-3046-2019
OI Timoshenko, Artem/0000-0002-5431-2136; /0000-0001-8510-8640
CR Akao Y., 2004, QUALITY FUNCTION DEP
Alam I., 2002, J SERV MARK, V16, P515, DOI DOI 10.1108/08876040210443391
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Baroni M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P238
BROWN SL, 1995, ACAD MANAGE REV, V20, P343, DOI 10.2307/258850
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Chan LK, 2002, EUR J OPER RES, V143, P463, DOI 10.1016/S0377-2217(02)00178-9
Chiu J. P., 2015, ARXIV151108308, DOI [10.1162/tacl_a_00104, DOI 10.1162/TACL_A_00104]
Collobert R, 2011, J MACH LEARN RES, V12, P2493
Colson E, 2016, COMMUNICATION
Corrigan KD, 2013, MARKETING NEWS, P39
Dolnicar S., 2003, J MARKET RES, V11, P5, DOI DOI 10.1016/S1441-3582(03)70123-2
Fader PS, 2012, MARKET SCI, V31, P369, DOI 10.1287/mksc.1120.0715
GREEN PE, 1978, J CONSUM RES, V5, P103, DOI 10.1086/208721
GRIFFIN A, 1993, MARKET SCI, V12, P1, DOI 10.1287/mksc.12.1.1
Griffin A, 2009, J PROD INNOVAT MANAG, V26, P222, DOI 10.1111/j.1540-5885.2009.00347.x
Harris ZS, 1954, WORD, V10, P146, DOI 10.1080/00437956.1954.11659520
HAUSER JR, 1988, HARVARD BUS REV, V66, P63
Herrmann A, 2000, INT J PROD ECON, V66, P77, DOI 10.1016/S0925-5273(99)00114-0
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
Iyyer M, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1681
Jiao JX, 2006, CONCURRENT ENG-RES A, V14, P173, DOI 10.1177/1063293X06068357
Jin J, 2015, ENG APPL ARTIF INTEL, V41, P115, DOI 10.1016/j.engappai.2015.02.006
Kano N, 1984, J JAPANESE SOC QUALI, V41, P39, DOI [10.20684/quality.14.2_147, DOI 10.20684/QUALITY.14.2_147]
Kaulio MA, 1998, TOTAL QUAL MANAGE, V9, P141, DOI 10.1080/0954412989333
Kim DS, 2017, MARKET SCI, V36, P54, DOI 10.1287/mksc.2016.1003
Kim Y, 2014, EMNLP, DOI 10.3115/v1/D14-1181
Kiss T, 2006, COMPUT LINGUIST, V32, P485, DOI 10.1162/coli.2006.32.4.485
Krishnan V, 2001, MANAGE SCI, V47, P1, DOI 10.1287/mnsc.47.1.1.10668
Kuehl N, 2016, LECT NOTES BUS INF P, V247, P187, DOI 10.1007/978-3-319-32689-4_14
Lample G, 2016, P NAACL HLT, V2016, P260, DOI 10.18653/v1/n16-1030
Lee TY, 2011, J MARKETING RES, V48, P881, DOI 10.1509/jmkr.48.5.881
Lei Tao., 2015, EMNLP 2015, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, P1565
Matzler K, 1998, TECHNOVATION, V18, P25, DOI 10.1016/S0166-4972(97)00072-2
McAuley J, 2015, KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2783258.2783381
Mikolov T., 2013, ARXIV, DOI DOI 10.48550/ARXIV.1301.3781
Mikolov T, 2013, Advances in neural information processing systems, DOI DOI 10.48550/ARXIV.1310.4546
Mikulic J, 2011, MANAG SERV QUAL, V21, P46, DOI 10.1108/09604521111100243
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Nguyen T. H., 2015, P 1 WORKSH VECT SPAC, P39, DOI DOI 10.3115/V1/W15-1506
Orme BrianK., 2006, Getting Started with Conjoint Analysis
PARK CW, 1986, J MARKETING, V50, P135, DOI 10.2307/1251291
Peng W, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2089094.2089114
Qian YN, 2001, P 20 ACM INT C INF K, P1241
Le Q, 2014, PR MACH LEARN RES, V32, P1188
Santos C.N., 2014, P COLING 2014 25 INT, P69
Schaffhausen CR, 2016, DESIGN STUD, V44, P1, DOI 10.1016/j.destud.2016.01.002
Schaffhausen CR, 2015, J MECH DESIGN, V137, DOI 10.1115/1.4030161
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Socher R., 2013, EMNLP, P935
Stone RB, 2008, J ENG DESIGN, V19, P489, DOI 10.1080/09544820802286711
SULLIVAN LP, 1986, QUAL PROG, V19, P39
Tai KS, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1556
Tieleman T, 2012, ONLINE LECT NEURAL N
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Ulrich K.T., 2008, Product Design and Development, V4th
Urban GlenL., 1993, Design and Marketing of New Products, V2nd
Wilson TS, 2005, PHIL EDUC, P347, DOI 10.3115/1220575.1220619
Wu HH, 2010, QUAL QUANT, V44, P1139, DOI 10.1007/s11135-009-9267-2
Ying YP, 2006, J MARKETING RES, V43, P355, DOI 10.1509/jmkr.43.3.355
Zahay D, 2004, IND MARKET MANAG, V33, P657, DOI 10.1016/j.indmarman.2003.10.002
NR 61
TC 201
Z9 237
U1 106
U2 741
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD JAN-FEB
PY 2019
VL 38
IS 1
BP 1
EP 20
DI 10.1287/mksc.2018.1123
PG 20
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HM0HF
UT WOS:000459127200001
OA Green Published, Green Submitted
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Kohli, G
AF Kohli, Guneesha
TI PITFALLS OF DIGITIZATION WITH RESPECT TO TRADITIONAL BUSINESSES AND
EMPLOYMENT
SO JIMS8M-THE JOURNAL OF INDIAN MANAGEMENT & STRATEGY
LA English
DT Article
DE Digitization; Retail Apocalypse; E-commerce; Deep Discounting;
Artificial Intelligence; Unemployment
AB From Artificial Intelligence (AI) to E-commerce, Digitization has created possibilities in the business world for companies, entrepreneurs and self-employed workers, for new opportunities and innovation. It has enabled companies to bring perfect products and services reaching larger and remote markets making customers happy by enhancing their daily life. It has enabled remote working which has made free-lancing popular and preferable and made both internal and external communication instant, making action and reaction to opportunities and problems much faster. While there is no end to the benefits of digitization, it also has some pitfalls which need to be worked upon to benefit from it sustainably. E-commerce and deep discounting done by platforms like Amazon and Flipkart have left competing small traditional businesses and retail giants bankrupt and out of business. According to the Geneva-based World Economic Forum (WEF), by 2025 more than half of all current workplace tasks will be taken over by robots as compared to 29% currently. While experts say, that AI will be creating much more jobs than it takes, the key to this is education for humans to overtake technology. This overlooks the rural workforce such as women working in farmlands losing their jobs due to modern machinery, who have limited or no access to high quality education and will take a very long time to catch up with technology to get employed. In this paper, we will be looking at the extent to which digitization has a fected traditional businesses and employment opportunities and what is being done to cope with it.
C1 [Kohli, Guneesha] Malika Int Gurgaon, Footwear & Accessories, Gurgaon, Haryana, India.
RP Kohli, G (autor correspondiente), Malika Int Gurgaon, Footwear & Accessories, Gurgaon, Haryana, India.
CR Bloomberg J., 2018, Digitization, digitalization, and digital transformation: confuse them at your peril
Bughin Jacques., 2018, Why digital strategies fail
Chakraborty S., 2019, DEEP DISCOUNTING NOT
Dash D., 2019, DRAFT RULES ISSUED B
Dominguez A., 2018, DIGITIZATION BUSINES
Frankenfield J., 2020, ARTIFICIAL INTELLIGE
Kumar S., 2019, Advantages and disadvantages of artificial intelligence [WWW Document]
Martin S., 2019, WARNING MACHINES WIL
Picchi A., 2019, STORE CLOSINGS WHO A
Reisinger D., 2019, I EXPERT SAYS AUTOMA
Tandon S., 2019, 12MN INDIAN WOMEN MA
Urie D., 2020, PIER 1 IMPORTS MACYS
NR 12
TC 0
Z9 0
U1 3
U2 26
PU JAGANNATH INT MANAGEMENT SCH
PI NEW DELHI
PA OCF POCKET 9, SECTOR-B, VASANT KUNJ, NEW DELHI, 110 070, INDIA
SN 0973-9335
EI 0973-9343
J9 JIMS8M-J INDIAN MANA
JI JIMS8M-J. Indian Manag. Strategy
PD JAN-MAR
PY 2020
VL 25
IS 1
BP 28
EP 33
DI 10.5958/0973-9343.2020.00004.6
PG 6
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA OH1QR
UT WOS:000582346600004
DA 2024-03-27
ER
PT J
AU Le, TH
Arcodia, C
Novais, MA
Kralj, A
Phan, TC
AF Le, Truc H.
Arcodia, Charles
Novais, Margarida Abreu
Kralj, Anna
Thanh Cong Phan
TI Exploring the multi-dimensionality of authenticity in dining experiences
using online reviews
SO TOURISM MANAGEMENT
LA English
DT Article
DE Authenticity; Restaurant; User-generated content (UGC); Text mining;
Integrated learning; Human-machine learning; Machine learning;
Classification
ID MODEL
AB The quest for authenticity in dining experiences has become increasingly important. This paper explores authenticity dimensions that are of value to customers in dining experiences, and by that gains a multidimensional understanding of authenticity in this context. Following an integrated learning approach using text mining and classification techniques, this paper explores and confirms different dimensions of authenticity by identifying and classifying authenticity judgements in online restaurant reviews. The results suggest that authenticity is a multi-dimensional concept encompassing Authenticity of the Other, Authenticity of the Producer, and Authenticity of the Self as first-level dimensions. Additionally, besides historical and categorical authenticity which have been previously explored in the literature, a new type of authenticity - Deviated Authenticity - emerged as a second-level dimension falling under Authenticity of the Other. This paper enhances existing conceptualisations of authenticity and establishes avenues for exploring the multi-dimensionality of other consumer research concepts using user-generated content.
C1 [Le, Truc H.; Arcodia, Charles; Novais, Margarida Abreu] Griffith Univ, Dept Tourism Sport & Hotel Management, 170 Kessels Rd, Nathan, Qld 4111, Australia.
[Kralj, Anna] Griffith Univ, Dept Tourism Sport & Hotel Management, Parklands Dr, Southport, Qld 4215, Australia.
[Thanh Cong Phan] Griffith Univ, Sch Informat & Commun Technol, Parklands Dr, Southport, Qld 4215, Australia.
C3 Griffith University; Griffith University; Griffith University - Gold
Coast Campus; Griffith University; Griffith University - Gold Coast
Campus
RP Le, TH (autor correspondiente), Griffith Univ, 170 Kessels Rd, Nathan, Qld 4111, Australia.
EM truc.le@griffith.edu.au; c.arcodia@griffith.edu.au;
m.abreunovais@griffith.edu.au; a.kralj@griffith.edu.au;
thanhcong.phan@griffithuni.edu.au
OI Phan, Cong/0000-0002-4715-1610; Abreu Novais,
Margarida/0000-0002-5046-4159; Le, Truc H/0000-0001-6201-1504
FU Griffith Institute for Tourism (GIFT)
FX This work was partially supported by Griffith Institute for Tourism
(GIFT) in 2018 under the Higher Degree by Research Data Collection
Support Scheme. We also sincerely thank Dr Quoc Viet Hung (Henry) Nguyen
-Senior Lecturer and ARC DECRA Fellow at School of Information and
Communication Technology at Griffith University for his guidance in the
analytical approach.
CR Alaei AR, 2019, J TRAVEL RES, V58, P175, DOI 10.1177/0047287517747753
Ansari F, 2018, PROCEDIA MANUF, V23, P117, DOI 10.1016/j.promfg.2018.04.003
Belhassen Y, 2006, ANN TOURISM RES, V33, P853, DOI 10.1016/j.annals.2006.03.009
Beverland MB, 2010, J CONSUM RES, V36, P838, DOI 10.1086/615047
Björk P, 2016, CURR ISSUES TOUR, V19, P1260, DOI 10.1080/13683500.2013.868412
Bojanowski P., 2017, Trans. Assoc. Comput. Ling., V5, P135, DOI DOI 10.1162/TACL_A_00051
Carroll G.R., 2015, EMERGING TRENDS SOCI, DOI DOI 10.1002/9781118900772.ETRDS0020
Carroll GR, 2009, RES ORGAN BEHAV, V29, P255, DOI 10.1016/j.riob.2009.06.003
Chhabra D., 2013, Leisure/Loisir, V37, P361
Chhabra D, 2013, J HERIT TOUR, V8, P145, DOI 10.1080/1743873X.2013.767816
Ciresan D, 2012, PROC CVPR IEEE, P3642, DOI 10.1109/CVPR.2012.6248110
COHEN E, 1988, ANN TOURISM RES, V15, P371, DOI 10.1016/0160-7383(88)90028-X
COHEN E, 1979, SOCIOLOGY, V13, P179, DOI 10.1177/003803857901300203
Demetry D, 2019, ORGAN SCI, V30, P937, DOI 10.1287/orsc.2018.1260
Devlin Jacob, 2019, P N AM CHAPT ASS COM, P4171
Dutton Denis, 2003, OXFORD HDB AESTHETIC, P258
Gilmore JamesH., 2007, Authenticity: What Consumers Really Want
Godinho Bilro R., 2019, 48 ANN EUR MARK ACAD
Grayson K, 2004, J CONSUM RES, V31, P296, DOI 10.1086/422109
Gurney K., 2018, INTRO NEURAL NETWORK
Hall CM, 2007, TOURISM MANAGE, V28, P1139, DOI 10.1016/j.tourman.2006.09.008
Hashimi H, 2015, COMPUT HUM BEHAV, V51, P729, DOI 10.1016/j.chb.2014.10.062
Hsiao YH, 2017, TELEMAT INFORM, V34, P284, DOI 10.1016/j.tele.2016.08.002
Jones S, 2010, J MAT CULT, V15, P181, DOI 10.1177/1359183510364074
Keller G., 2014, BUSINESS STAT AUSTR, V6th
Kim JH, 2020, INT J HOSP MANAG, V85, DOI 10.1016/j.ijhm.2019.102354
Kirilenko AP, 2018, J TRAVEL RES, V57, P1012, DOI 10.1177/0047287517729757
Kolar T, 2010, TOURISM MANAGE, V31, P652, DOI 10.1016/j.tourman.2009.07.010
Kovács B, 2019, REV GEN PSYCHOL, V23, P32, DOI 10.1177/1089268019829469
Kovács B, 2017, SOCIOL SCI, V4, P80, DOI 10.15195/v4.a4
Kovács B, 2014, ORGAN SCI, V25, P458, DOI 10.1287/orsc.2013.0843
Le TH, 2021, CURR ISSUES TOUR, V24, P1640, DOI 10.1080/13683500.2020.1829568
Le TH, 2021, TOUR RECREAT RES, V46, P360, DOI 10.1080/02508281.2020.1805932
Le TH, 2019, TOURISM MANAGE, V74, P258, DOI 10.1016/j.tourman.2019.02.012
Le Truc H., 2020, CAUTHE 2020 20 20 VI
Lehman D.W., 2018, SOCIUS SOCIOLOGICAL, V4
Lopez R., 2019, INT J HOSP MANAG
Ma YF, 2018, INT J HOSP MANAG, V71, P120, DOI 10.1016/j.ijhm.2017.12.008
MACCANNELL D, 1973, AM J SOCIOL, V79, P589, DOI 10.1086/225585
Mandros P, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P355, DOI 10.1145/3097983.3098062
Margarida Abreu Novais, 2018, CAUTHE 2018
Mazutis DD, 2015, J BUS ETHICS, V131, P137, DOI 10.1007/s10551-014-2253-1
Mkono M., 2013, THESIS S CROSS U
Mkono M, 2013, INT J CULT TOUR HOSP, V7, P353, DOI 10.1108/IJCTHR-03-2013-0010
Mkono M, 2012, INT J HOSP MANAG, V31, P387, DOI 10.1016/j.ijhm.2011.06.013
Munoz C.L., 2006, J CONSUM BEHAV, V5, P222, DOI DOI 10.1002/CB.174
Newman GE, 2019, REV GEN PSYCHOL, V23, P8, DOI 10.1037/gpr0000158
Newman GE, 2016, PHILOS COMPASS, V11, P609, DOI 10.1111/phc3.12343
O'Connor K, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0179187
Pang L, 2018, IEEE 3 ADV INF TECH
Pine B.Joseph., 2011, The Experience Economy
Reisinger Y, 2006, ANN TOURISM RES, V33, P65, DOI 10.1016/j.annals.2005.04.003
Rickly-Boyd JM, 2012, ANN TOURISM RES, V39, P269, DOI 10.1016/j.annals.2011.05.003
Rocha RM., 2011, DISABIL REHABIL, V11
Shalizi Cosma Rohilla, 2006, P33
Sidali KL, 2014, BRIT FOOD J, V116, P1692, DOI 10.1108/BFJ-02-2014-0056
Smart P, 2019, SELENIUM SCRAPING NO
Stepchenkova S, 2021, J TRAVEL RES, V60, P401, DOI 10.1177/0047287519899989
Svozil D, 1997, CHEMOMETR INTELL LAB, V39, P43, DOI 10.1016/S0169-7439(97)00061-0
Toral SL, 2018, J TRAVEL RES, V57, P908, DOI 10.1177/0047287517724918
Vásquez C, 2015, FOOD FOODWAYS, V23, P231, DOI 10.1080/07409710.2015.1102483
Wang N, 1999, ANN TOURISM RES, V26, P349, DOI 10.1016/S0160-7383(98)00103-0
Xiang Z, 2018, INF TECHNOL TOUR, V18, P43, DOI 10.1007/s40558-017-0098-z
Xiang Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/j.tourman.2016.10.001
Xu X, 2016, INT J HOSP MANAG, V55, P57, DOI 10.1016/j.ijhm.2016.03.003
Zeng G., 2019, Restaurant chains in China-The dilemma of standardisation versus authenticity
Zhang L, 2018, INT J HOSP MANAG, V68, P115, DOI 10.1016/j.ijhm.2017.10.008
NR 67
TC 19
Z9 21
U1 18
U2 118
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0261-5177
EI 1879-3193
J9 TOURISM MANAGE
JI Tourism Manage.
PD AUG
PY 2021
VL 85
AR 104292
DI 10.1016/j.tourman.2021.104292
EA FEB 2021
PG 15
WC Environmental Studies; Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Social Sciences - Other Topics;
Business & Economics
GA RQ5QZ
UT WOS:000642474700003
DA 2024-03-27
ER
PT J
AU Gerlich, M
AF Gerlich, Michael
TI The Power of Virtual Influencers: Impact on Consumer Behaviour and
Attitudes in the Age of AI
SO ADMINISTRATIVE SCIENCES
LA English
DT Article
DE virtual influencers; consumer behaviour; artificial intelligence;
consumer perception; social media marketing; influencer marketing
ID CELEBRITY ENDORSER; COMMUNICATION
AB In recent years, a new type of influencer has emerged in the field of social media marketing: virtual influencers. Though it is spreading fast, the trend is still new and, therefore, limited research has been conducted on the topic. This study aims to investigate the impact of virtual influencers on customers and whether there is a direct impact on human influencers due to the rise of virtual influencers in the industry. The study employed a questionnaire-based survey method to collect and analyse responses from a sample of 357 participants. The questions focus on trust, credibility, expertise, and contribution to purchase intention by the virtual influencers. The results indicate that customers are increasingly attracted to virtual influencers and that virtual influences are perceived as more trustworthy, credible, and relevant to customers' preferences, leading to an increase in purchase intention. The study also discusses the implications of these findings for managers designing marketing campaigns.
C1 [Gerlich, Michael] SBS Swiss Business Sch, Dept Management, Flughafenstr 3, CH-8302 Zurich, Switzerland.
C3 SBS Swiss Business School
RP Gerlich, M (autor correspondiente), SBS Swiss Business Sch, Dept Management, Flughafenstr 3, CH-8302 Zurich, Switzerland.
EM michael.gerlich@cantab.net
RI Gerlich, Michael/GQA-3552-2022
OI Gerlich, Michael/0000-0003-4033-4403
CR Alboqami H, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2022.103242
Amelina D., 2016, PACIS 2016 Proceedings, P232
Amos C, 2008, INT J ADVERT, V27, P209, DOI 10.1080/02650487.2008.11073052
Anisa Matthews, 2021, VIRTUAL LY BLACK INF
Arsenyan J, 2021, INT J HUM-COMPUT ST, V155, DOI 10.1016/j.ijhcs.2021.102694
Balakrishnan BKPD, 2014, PROCD SOC BEHV, V148, P177, DOI 10.1016/j.sbspro.2014.07.032
Block E, 2021, PUBLIC RELAT INQ, V10, P265, DOI 10.1177/2046147X211026936
Brown D, 2008, INFLUENCER MARKETING
Chaudhuri A, 2001, J MARKETING, V65, P81, DOI 10.1509/jmkg.65.2.81.18255
Chung SY, 2017, PSYCHOL MARKET, V34, P481, DOI 10.1002/mar.21001
Oliveira ABD, 2021, AUSTRALAS J INF SYST, V25, DOI 10.3127/ajis.v25i0.3223
Franke C, 2023, J ADVERTISING, V52, P523, DOI 10.1080/00913367.2022.2154721
Gerlich M., 2023, TRANSNATL MARK J, V11, P131, DOI [10.58262/tmj.v11i1.1010, DOI 10.58262/TMJ.V11I1.1010]
Gerlich Michael., 2022, J DIGITAL SOCIAL MED, V9, P337
HAASE RF, 1983, EDUC PSYCHOL MEAS, V43, P35, DOI 10.1177/001316448304300105
Henson R., 2015, Brain Mapping, P477, DOI [10.1016/B978-0-12-397025-1.00319-5, DOI 10.1016/B978-0-12-397025-1.00319-5, https://doi.org/10.1016/B978-0-12-397025-1.00319-5]
HORTON D, 1956, PSYCHIATR, V19, P215, DOI 10.1080/00332747.1956.11023049
Hovland CI, 1953, Communication and Persuasion, DOI 10.1007/BF02713272
Hsu CL, 2013, INTERNET RES, V23, P69, DOI 10.1108/10662241311295782
Jiménez-Castillo D, 2019, INT J INFORM MANAGE, V49, P366, DOI 10.1016/j.ijinfomgt.2019.07.009
Kádeková Z, 2018, COMMUN TODAY, V9, P90
Kapitan S, 2016, MARKET LETT, V27, P553, DOI 10.1007/s11002-015-9363-0
La Ferle C., 2005, Journal of Current Issues and Research in Advertising, V27, P67, DOI DOI 10.1080/10641734.2005.10505182
Lazarsfeld Paul., 1944, The People's Choice
Liu X, 2012, J MOD APPL STAT METH, V11, P242, DOI 10.22237/jmasm/1335846000
Lou C., 2019, Journal of Interactive Advertising, V19, P58, DOI DOI 10.1080/15252019.2018.1533501
Maria Lewczyk, 2021, WHY BRANDS SHOULD WO
McCormick K, 2016, J RETAIL CONSUM SERV, V32, P39, DOI 10.1016/j.jretconser.2016.05.012
MCCRACKEN G, 1989, J CONSUM RES, V16, P310, DOI 10.1086/209217
Miao F, 2022, J MARKETING, V86, P67, DOI 10.1177/0022242921996646
Moustakas E., 2020, P 2020 INT C CYB SEC, P1
Nowak KL, 2018, REV COMMUN RES, V6, P30, DOI 10.12840/issn.2255-4165.2018.06.01.015
Ralf Hirschmann, 2021, VIRTUAL INFLUENCERS
Sands S, 2022, EUR J MARKETING, V56, P1721, DOI 10.1108/EJM-12-2019-0949
Silva MJD, 2022, SOC NETW ANAL MIN, V12, DOI 10.1007/s13278-022-00966-w
Stein JP, 2022, NEW MEDIA SOC, DOI 10.1177/14614448221102900
Tallarida R.J., 1987, MANUAL PHARM CALCULA
Thomas VL, 2021, J ADVERTISING, V50, P11, DOI 10.1080/00913367.2020.1810595
Torres P, 2019, PSYCHOL MARKET, V36, P1267, DOI 10.1002/mar.21274
Vivek SD, 2012, J MARKET THEORY PRAC, V20, P127, DOI 10.2753/MTP1069-6679200201
Williams C., 2007, J BUSINESS EC RES, V5, P65, DOI DOI 10.19030/JBER.V5I3.2532
NR 41
TC 2
Z9 2
U1 95
U2 105
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3387
J9 ADM SCI
JI Adm. Sci.
PD AUG
PY 2023
VL 13
IS 8
AR 178
DI 10.3390/admsci13080178
PG 21
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA Q2YL1
UT WOS:001056220800001
OA gold
DA 2024-03-27
ER
PT J
AU Rahman, MS
Hossain, MA
Fattah, FAMA
AF Rahman, Muhammad Sabbir
Hossain, Md Afnan
Fattah, Fadi Abdel Muniem Abdel
TI Does marketing analytics capability boost firms' competitive marketing
performance in data-rich business environment?
SO JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT
LA English
DT Article
DE Marketing analytics capability; Artificial intelligence; Holistic
marketing decision; Competitive marketing performance
ID BIG DATA ANALYTICS; RESOURCE-BASED VIEW; ARTIFICIAL-INTELLIGENCE;
INFORMATION-TECHNOLOGY; KNOWLEDGE MANAGEMENT; DECISION-MAKING;
MODERATING ROLE; VALUE CREATION; DYNAMIC CAPABILITIES; SYSTEMS
AB Purpose Few well-documented studies have explained the importance of researching firms' marketing analytics capability (FMAC). In spite of its significance, there is scant attention to conceptualising and empirically investigating FMAC and its consequences in a data-driven business context. Thus, this study aims to develop and test a conceptual model that relates FMAC and its repercussions in the data-rich business environment. Design/methodology/approach This study analysed the data from 250 managers amongst large and medium-sized manufacturing and service-intensive firms. Furthermore, this research performed an empirical study by using operationalised questionnaire survey method to verify the hypotheses and reach its theoretical and managerial implications. Structural equation modelling with maximum-likelihood estimation method was applied to verify the validity of the proposed research model. Findings Multivariate analysis results show that FMAC significantly influences firms' competitive marketing performance (FCMP) with the presence of holistic marketing decision-making (HMDM) as a mediator. Moreover, adoption of artificial intelligence (AAI) enhances the relationship of FMAC-HMDM and FMAC-FCMP linkages. Practical implications This study analyses how FMAC can enhance FCMP and contributes to resource-based views and technological capability theories. From a managerial perspective, guidelines are provided for marketers to adopt advance technologies, such as AI, to optimise FMAC and HMDM to achieve competitive marketing performance. Originality/value Believing that "how to be competitive in marketing performance under data-rich-environment", this research is the first to use the data of a firm manager to facilitate the understanding of FMAC, which provides a new direction for improving marketing performance. In addition, HMDM and AAI are also proposed for firms to optimise FCMP.
C1 [Rahman, Muhammad Sabbir] North South Univ, Dept Mkt & Int Business, Dhaka, Bangladesh.
[Hossain, Md Afnan] Univ Wollongong, Sch Business, Wollongong, NSW, Australia.
[Hossain, Md Afnan] North South Univ, Sch Business & Econ, Dhaka, Bangladesh.
[Fattah, Fadi Abdel Muniem Abdel] ASharqiyah Univ, Coll Business Adm, Ibra, Oman.
C3 North South University (NSU); University of Wollongong; North South
University (NSU)
RP Rahman, MS (autor correspondiente), North South Univ, Dept Mkt & Int Business, Dhaka, Bangladesh.
EM sabbiriiu@gmail.com
RI AbdelFattah, Fadi/L-7441-2014; Hossain, Dr Md Afnan/M-6626-2017; Rahman,
Muhammad/G-3968-2018
OI AbdelFattah, Fadi/0000-0002-4665-4777; Hossain, Dr Md
Afnan/0000-0003-2954-1823; Rahman, Muhammad/0000-0003-1613-7944
FU North South University (NSU)
FX The authors acknowledge North South University (NSU) for awarding
research grant to conduct this research.
CR Aderemi Helen Olubunmi, 2012, International Journal of Business Information Systems, V11, P127, DOI 10.1504/IJBIS.2012.048887
Agarwal Sanjeev., 2003, J SERV MARK, V17, P68, DOI DOI 10.1108/08876040310461282
Ageeva E, 2019, J BUS RES, V98, P15, DOI 10.1016/j.jbusres.2018.12.079
Ahmed E, 2017, COMPUT NETW, V129, P459, DOI 10.1016/j.comnet.2017.06.013
Alsheibani S., 2018, PACIS 2018 P, V37
ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
ANDERSON P, 1990, ADMIN SCI QUART, V35, P604, DOI 10.2307/2393511
Anfer Omar, 2019, New Knowledge in Information Systems and Technologies. Advances in Intelligent Systems and Computing (AISC 931), P633, DOI 10.1007/978-3-030-16184-2_60
[Anonymous], 2016, INT J BUSINESS SOCIA
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Ashrafi A, 2019, INT J INFORM MANAGE, V47, P1, DOI 10.1016/j.ijinfomgt.2018.12.005
Atuahene-Gima K, 2004, J MARKETING, V68, P33, DOI 10.1509/jmkg.68.4.33.42732
Atuahene-Gima K, 2004, ACAD MANAGE J, V47, P583, DOI [10.2307/20159603, 10.5465/20159603]
Aydiner AS, 2019, J BUS RES, V96, P228, DOI 10.1016/j.jbusres.2018.11.028
BACON DR, 1995, EDUC PSYCHOL MEAS, V55, P394, DOI 10.1177/0013164495055003003
BAGOZZI RP, 1991, ADMIN SCI QUART, V36, P421, DOI 10.2307/2393203
BAGOZZI RP, 1981, MULTIVAR BEHAV RES, V16, P323, DOI 10.1207/s15327906mbr1603_4
BARIUS B, 1994, IND MARKET MANAG, V23, P145, DOI 10.1016/0019-8501(94)90016-7
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
Bharadwaj AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983
BHARADWAJ SG, 1993, J MARKETING, V57, P83, DOI 10.2307/1252221
Booth D., 2019, APPL MARKETING ANAL, V4, P214
Bruni DS, 2009, BRIT J MANAGE, V20, pS101, DOI 10.1111/j.1467-8551.2008.00615.x
Brynjolfsson E., 2017, Harvard Business Review, V95, P3
Cacciolatti L, 2016, J BUS RES, V69, P5597, DOI 10.1016/j.jbusres.2016.03.067
Camisón C, 2014, J BUS RES, V67, P2891, DOI 10.1016/j.jbusres.2012.06.004
Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
Cao GM, 2019, IND MARKET MANAG, V76, P72, DOI 10.1016/j.indmarman.2018.08.002
Cao GM, 2017, J ENTERP INF MANAG, V30, P874, DOI 10.1108/JEIM-04-2016-0080
Capron L, 1999, J MARKETING, V63, P41, DOI 10.2307/1251944
Challagalla G, 2014, J MARKETING, V78, P4, DOI 10.1509/jm.12.0314
Chang W, 2010, J BUS RES, V63, P849, DOI 10.1016/j.jbusres.2009.07.003
Chen DQ, 2015, J MANAGE INFORM SYST, V32, P4, DOI 10.1080/07421222.2015.1138364
Choi H, 2015, J PROD INNOVAT MANAG, V32, P233, DOI 10.1111/jpim.12175
Chuang SH, 2004, EXPERT SYST APPL, V27, P459, DOI 10.1016/j.eswa.2004.05.008
Chung W, 2005, J MANAGE INFORM SYST, V21, P57, DOI 10.1080/07421222.2005.11045821
Cleeren K, 2017, J ACAD MARKET SCI, V45, P593, DOI 10.1007/s11747-017-0558-1
Coombs JE, 2006, R&D MANAGE, V36, P421, DOI 10.1111/j.1467-9310.2006.00444.x
Côrte-Real N, 2017, J BUS RES, V70, P379, DOI 10.1016/j.jbusres.2016.08.011
Cruz-Cárdenas J, 2021, J BUS RES, V122, P217, DOI 10.1016/j.jbusres.2020.08.054
Davcik NS, 2016, J BUS RES, V69, P5547, DOI 10.1016/j.jbusres.2016.04.169
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Davenport TH, 2018, HARVARD BUS REV, V96, P108
DAY GS, 1994, J MARKETING, V58, P37, DOI 10.2307/1251915
De Luca LM, 2021, J ACAD MARKET SCI, V49, P790, DOI 10.1007/s11747-020-00739-x
Duan L, 2015, J MANAG ANAL, V2, P1, DOI 10.1080/23270012.2015.1020891
Dubey R, 2019, BRIT J MANAGE, V30, P341, DOI 10.1111/1467-8551.12355
Dutta S, 1999, MARKET SCI, V18, P547, DOI 10.1287/mksc.18.4.547
Duysters G., 2000, J High Technol Manag Res, V11, P75, DOI DOI 10.1016/S1047-8310(00)00022-5
Dwivedi YK, 2015, INFORM SYST FRONT, V17, P143, DOI 10.1007/s10796-014-9500-y
Edgeman R, 2013, CORP GOV-INT J BUS S, V13, P527, DOI 10.1108/CG-06-2013-0073
Eisenhardt KM, 2000, STRATEGIC MANAGE J, V21, P1105, DOI 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
Fethi MD, 2010, EUR J OPER RES, V204, P189, DOI 10.1016/j.ejor.2009.08.003
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
France SL, 2019, EXPERT SYST APPL, V119, P456, DOI 10.1016/j.eswa.2018.11.002
Gatignon H, 1997, J MARKETING RES, V34, P77, DOI 10.2307/3152066
Germann F, 2013, INT J RES MARK, V30, P114, DOI 10.1016/j.ijresmar.2012.10.001
Goodboy AK, 2017, COMMUN RES REP, V34, P68, DOI 10.1080/08824096.2016.1214121
GRANT RM, 1991, CALIF MANAGE REV, V33, P114, DOI 10.2307/41166664
Grant RM, 1996, ORGAN SCI, V7, P375, DOI 10.1287/orsc.7.4.375
Griffith DA, 2010, J WORLD BUS, V45, P217, DOI 10.1016/j.jwb.2009.09.008
Grossman R. L., 2014, J ORG DESIGN, V3, P20, DOI DOI 10.7146/JOD.9799
Gupta M, 2016, INFORM MANAGE-AMSTER, V53, P1049, DOI 10.1016/j.im.2016.07.004
Hair J.F., 2009, Multivariate data analysis
Han JK, 1998, J MARKETING, V62, P30, DOI 10.2307/1252285
Hanssens DM, 2016, J MARKETING, V80, P173, DOI 10.1509/jm.15.0417
He W, 2019, J ENTERP INF MANAG, V32, P152, DOI 10.1108/JEIM-02-2018-0031
Hossain M. A., 2020, TECHNOLOGICAL INNOVA, P117, DOI [10.4018/978-1-5225-9940-1.ch001, DOI 10.4018/978-1-5225-9940-1.CH001]
Hossain MA, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102187
Hossain Tasnim M. Taufique, 2020, Industrial Marketing Management, V87, P225, DOI 10.1016/j.indmarman.2019.12.006
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
Iacobucci D, 2019, J MARK ANAL, V7, P152, DOI 10.1057/s41270-019-00059-2
Jain V, 2016, INT J PROD ECON, V171, P84, DOI 10.1016/j.ijpe.2015.10.024
Jaklic J, 2018, INT J INFORM MANAGE, V43, P305, DOI 10.1016/j.ijinfomgt.2018.08.017
Kamal MM, 2020, INT J INFORM MANAGE, V55, DOI 10.1016/j.ijinfomgt.2020.102206
Keller, 2012, PRENTICE HALL VIDEO
Kenny D.A., 2014, Mediation
Kim N, 2013, J PROD INNOVAT MANAG, V30, P136, DOI 10.1111/j.1540-5885.2012.00992.x
Kumar V, 2008, J MARKETING, V72, P50, DOI 10.1509/jmkg.72.5.50
Kyriazos T.A., 2018, PSYCHOLOGY, V09, P2207, DOI [DOI 10.4236/PSYCH.2018.98126, https://doi.org/10.4236/psych.2018.98126, 10.4236/psych.2018.98126]
Liang XN, 2020, J BUS RES, V109, P545, DOI 10.1016/j.jbusres.2019.01.021
Lindell MK, 2001, J APPL PSYCHOL, V86, P114, DOI 10.1037//0021-9010.86.1.114
Mabert V.A., 2000, PRODUCTION INVENTORY, V41, P52
MacKinnon DP, 2007, ANNU REV PSYCHOL, V58, P593, DOI 10.1146/annurev.psych.58.110405.085542
Mandal S, 2017, J BUS IND MARK, V32, P1021, DOI 10.1108/JBIM-08-2016-0187
Marsh HW, 2014, ANNU REV CLIN PSYCHO, V10, P85, DOI 10.1146/annurev-clinpsy-032813-153700
Martínez-López FJ, 2013, IND MARKET MANAG, V42, P489, DOI 10.1016/j.indmarman.2013.03.001
McEvily SK, 2004, STRATEGIC MANAGE J, V25, P713, DOI 10.1002/smj.425
McNaughton M, 2017, J ENTERP INF MANAG, V30, P701, DOI 10.1108/JEIM-06-2016-0121
Melville N, 2004, MIS QUART, V28, P283
Mikalef P, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.05.004
Moller K., 1987, Journal of Marketing Management, V3, P185, DOI [DOI 10.1080/0267257X.1987.9964038, 10.1080/0267257X.1987.9964038]
NARVER JC, 1990, J MARKETING, V54, P20, DOI 10.2307/1251757
Nason RS, 2018, J MANAGE, V44, P32, DOI 10.1177/0149206315610635
Nath P, 2010, IND MARKET MANAG, V39, P317, DOI 10.1016/j.indmarman.2008.09.001
Nemati HR, 2002, DECIS SUPPORT SYST, V33, P143, DOI 10.1016/S0167-9236(01)00141-5
Newbert SL, 2008, TECHNOVATION, V28, P6, DOI 10.1016/j.technovation.2007.07.002
Nicolae P.A., 2009, ANN U ORADEA EC SCI, V18, P808
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Ogrean, 2017, PALGRAVE HDB MANAGIN, P513
PETERAF MA, 1993, STRATEGIC MANAGE J, V14, P179, DOI 10.1002/smj.4250140303
Petter S, 2013, J MANAGE INFORM SYST, V29, P7, DOI 10.2753/MIS0742-1222290401
Plakoyiannaki E., 2002, J DATABASE MARKETING, V9, P228, DOI DOI 10.1057/PALGRAVE.JDM.3240004
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Pontes HM, 2016, INT J MENT HEALTH AD, V14, P1062, DOI 10.1007/s11469-016-9694-y
Pop N A., 2012, Management Marketing, V7, P631
Qi JY, 2016, INFORM MANAGE-AMSTER, V53, P951, DOI 10.1016/j.im.2016.06.002
Radhakrishnan A, 2008, OMEGA-INT J MANAGE S, V36, P1105, DOI 10.1016/j.omega.2006.06.003
Rahman MS, 2020, J SERV THEOR PRACT, V30, P681, DOI 10.1108/JSTP-04-2020-0077
Rahman MS, 2020, J GLOB INF MANAG, V28, P39, DOI 10.4018/JGIM.2020010103
Rajaraman V, 2014, RESONANCE, V19, P198, DOI 10.1007/s12045-014-0027-9
Ramachandran, 2018, HDB ADV MARKETING ER, P360, DOI DOI 10.4135/9789353287733.N31
Ransbotham S., 2016, MIT Sloan Management Review, V57, P1
Ransbotham S., 2017, Reshaping Business With Artificial Intelligence
Ravichandran T, 2005, J MANAGE INFORM SYST, V21, P237, DOI 10.1080/07421222.2005.11045820
Rossi PE, 2003, MARKET SCI, V22, P304, DOI 10.1287/mksc.22.3.304.17739
Rothaermel FT, 2004, STRATEGIC MANAGE J, V25, P201, DOI 10.1002/smj.376
Ortega MJR, 2010, J BUS RES, V63, P1273, DOI 10.1016/j.jbusres.2009.09.007
Russell S.J., 2003, ARTIF INTELL, V2
Rust RT, 2012, J MARKETING, V76, P47, DOI 10.1509/jm.10.0441
Sabherwal R, 2006, MANAGE SCI, V52, P1849, DOI 10.1287/mnsc.1060.0583
Sanchez R, 2008, RES COMPET-BASED MAN, V4, P3, DOI 10.1016/S1744-2117(08)04001-2
Seddon PB, 2017, INFORM SYST J, V27, P237, DOI 10.1111/isj.12101
Sharma P, 2016, J BUS RES, V69, P5662, DOI 10.1016/j.jbusres.2016.03.074
Sharma R, 2014, EUR J INFORM SYST, V23, P433, DOI 10.1057/ejis.2014.17
Simsek S, 2021, J ENTERP INF MANAG, V34, P718, DOI 10.1108/JEIM-12-2019-0375
Sivarajah U, 2017, J BUS RES, V70, P263, DOI 10.1016/j.jbusres.2016.08.001
Son I, 2014, INFORM MANAGE-AMSTER, V51, P653, DOI 10.1016/j.im.2014.05.006
Song M, 2005, STRATEGIC MANAGE J, V26, P259, DOI 10.1002/smj.450
Song M, 2008, J INT MARKETING, V16, P4, DOI 10.1509/jimk.16.1.4
Song M, 2007, J ACAD MARKET SCI, V35, P18, DOI 10.1007/s11747-006-0005-1
Spanos YE, 2001, STRATEGIC MANAGE J, V22, P907, DOI 10.1002/smj.174
Sterne J., 2017, Artificial intelligence for marketing: practical applications
Syam N, 2018, IND MARKET MANAG, V69, P135, DOI 10.1016/j.indmarman.2017.12.019
Tallon PP, 2000, J MANAGE INFORM SYST, V16, P145, DOI 10.1080/07421222.2000.11518269
Tang Z., 2011, International Journal of Computer Applications, V29, P13
Taylor GA, 2005, J RETAILING, V81, P293, DOI 10.1016/j.jretai.2004.11.004
Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Teng BS, 2007, J MANAGE STUD, V44, P119, DOI 10.1111/j.1467-6486.2006.00645.x
Tippins MJ, 2003, STRATEGIC MANAGE J, V24, P745, DOI 10.1002/smj.337
Trieu VH, 2017, DECIS SUPPORT SYST, V93, P111, DOI 10.1016/j.dss.2016.09.019
Tsai K., 2004, J HIGH TECHNOLOGY MA, V15, P183, DOI 10.1016/j.hitech.2004.03.002
Tucminen M., 1997, Proceedings ofthe Annual Conference ofthe European Marketing Academy, P1220
van Bruggen GH, 1998, MANAGE SCI, V44, P645, DOI 10.1287/mnsc.44.5.645
Vargo SL, 2008, J SERV RES-US, V11, P211, DOI 10.1177/1094670508324260
Verhoef PC, 2003, DECIS SUPPORT SYST, V34, P471, DOI 10.1016/S0167-9236(02)00069-6
Verhoef PC, 2010, J INTERACT MARK, V24, P121, DOI 10.1016/j.intmar.2010.02.009
Verma S, 2017, J ENTERP INF MANAG, V30, P354, DOI 10.1108/JEIM-10-2015-0099
Verona G, 1999, ACAD MANAGE REV, V24, P132, DOI 10.2307/259041
Viaene S, 2013, IT PROF, V15, P12, DOI 10.1109/MITP.2013.93
Vorhies D.W., 1999, EUR J MARKETING, V33, P1171
Wade M, 2004, MIS QUART, V28, P107
Wamba SF, 2017, J BUS RES, V70, P356, DOI 10.1016/j.jbusres.2016.08.009
Wang E, 2007, INT J PROD RES, V45, P2419, DOI 10.1080/00207540601020437
Weber FD, 2019, DIGIT POLICY REGUL G, V21, P264, DOI 10.1108/DPRG-09-2018-0050
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Weerawardena J., 2003, Journal of Strategic Marketing, V11, P15, DOI [DOI 10.1080/0965254032000096766, 10.1080/0965254032000096766]
WERNERFELT B, 1984, STRATEGIC MANAGE J, V5, P171, DOI 10.1002/smj.4250050207
Wilden R, 2015, J ACAD MARKET SCI, V43, P181, DOI 10.1007/s11747-014-0380-y
Wu WW, 2020, TECHNOL ANAL STRATEG, V32, P1098, DOI 10.1080/09537325.2020.1743261
Xu ZN, 2016, J BUS RES, V69, P1562, DOI 10.1016/j.jbusres.2015.10.017
Yang, 2020, INFORM MANAGE-AMSTER, V57
Yonghong Z., 2005, Singapore Management Review, V27, P81
Zahra SA, 2007, J BUS RES, V60, P1070, DOI 10.1016/j.jbusres.2006.12.014
Zhang GQ, 1999, EUR J OPER RES, V116, P16, DOI 10.1016/S0377-2217(98)00051-4
Zhou KZ, 2010, STRATEGIC MANAGE J, V31, P547, DOI 10.1002/smj.830
Zongyang Leng, 2015, Management Decision, V53, P1247, DOI 10.1108/MD-07-2013-0367
이영화, 2007, [Journal of Korea Design Forum, 한국디자인포럼], V16, P303
NR 171
TC 21
Z9 21
U1 16
U2 88
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1741-0398
EI 1758-7409
J9 J ENTERP INF MANAG
JI J. Enterp. Inf. Manag.
PD MAR 8
PY 2022
VL 35
IS 2
BP 455
EP 480
DI 10.1108/JEIM-05-2020-0185
EA APR 2021
PG 26
WC Computer Science, Interdisciplinary Applications; Information Science &
Library Science; Management
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA ZQ1YV
UT WOS:000646305000001
DA 2024-03-27
ER
PT J
AU Lee, YS
Sikora, R
AF Lee, Yoon Sang
Sikora, Riyaz
TI Application of adaptive strategy for supply chain agent
SO INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT
LA English
DT Article
DE Multi-agent systems; Artificial intelligence; E-commerce; Simulation;
Supply chain management
AB With the tremendous increase in the globalization of trade the corresponding supply chains supporting the manufacture, distribution and supply of goods has become extremely complex. Intelligent agents can help with the problem of effective management of these complex supply chains. In this paper we introduce the design, implementation and testing of an intelligent agent for handling procurement, customer sales, and scheduling of production in a stylized supply chain environment. The supply chain environment used in this paper is modeled after the trading agent competition that is held annually to choose the best agent for managing a supply chain. Our supply chain agent, which we call SCMaster, uses dynamic inventory control and various reinforcement learning techniques like Q-learning, Softmax, epsilon-greedy, and sliding window protocol to make our agent adapt dynamically to the changing environment created by competing agents. A multi-agent simulation environment is developed in Java to test the efficacy of our agent design. Two competing agents are created modeled after the winners of past trading agent competitions and are tested against our agent in various experimental designs. Results of simulations show that our agent has better performance compared to the other agents.
C1 [Lee, Yoon Sang] Columbus State Univ, D Abbott Turner Coll Business, 4225 Univ Ave, Columbus, GA 31907 USA.
[Sikora, Riyaz] Univ Texas Arlington, Coll Business, 701 West St, Arlington, TX 76019 USA.
C3 University System of Georgia; Columbus State University; University of
Texas System; University of Texas Arlington
RP Sikora, R (autor correspondiente), Univ Texas Arlington, Coll Business, 701 West St, Arlington, TX 76019 USA.
EM lee_yoon@columbusstate.edu; rsikora@uta.edu
RI N'Dri, Amoin Bernadine/IWD-7811-2023
OI Lee, Yoon Sang/0000-0003-3877-1567
CR Abadie A, 2005, REV ECON STUD, V72, P1, DOI 10.1111/0034-6527.00321
Acquity Group, 2014, UNC SHIFT LANDSC B2B
Adami C, 2016, PHYS LIFE REV, V19, P1, DOI 10.1016/j.plrev.2016.08.015
Alpaydin E, 2014, ADAPT COMPUT MACH LE, P115
Benisch M., 2004, ACM T COMPUT LOG, V4, P29
Benisch M, 2009, ELECTRON COMMER R A, V8, P78, DOI 10.1016/j.elerap.2008.09.005
Benisch Michael., 2004, 5 ACM C ELECT COMMER, P152, DOI DOI 10.1145/988772.988796
Borghetti B, 2006, AAAI, P1857
Burke DA, 2006, AAMAS 2006 WORKSH TR, P29
Chaturvedi A.R., 2006, INFORM SYSTEMS E BUS, V4, P25
Chatzidimitriou KC, 2008, EXPERT SYST APPL, V35, P591, DOI 10.1016/j.eswa.2007.07.050
CHENG F, 2002, MANUFACTURING SERVIC, V4, P114, DOI DOI 10.1287/MS0M.4.2.114.282
Choi TY, 2001, J OPER MANAG, V19, P351, DOI 10.1016/S0272-6963(00)00068-1
Collins J, 2009, ELECTRON COMMER R A, V8, P91, DOI 10.1016/j.elerap.2008.09.004
Collins John., 2006, SUPPLY CHAIN MANAGEM
Eriksson J, 2006, AI COMMUN, V19, P1
Forbes Technology Council, 2016, UPC TECHN 10 TRENDS
Kiekintveld C., 2004, ACM SIGECOM EXCH, V4, P9
Kiekintveld Christopher., 2006, 7 ACM C ELECT COMMER, P208, DOI DOI 10.1145/1134707.1134730
McLaren T., 2004, INF SYST E BUS MANAG, V2, P207
O'Leary DE, 2008, INF SYST E-BUS MANAG, V6, P239, DOI 10.1007/s10257-008-0086-0
Pardoe D, 2009, HANDB INFO SYST, V3, P141
Podobnik V, 2006, LECT NOTES COMPUT SC, V4277, P752
Saar-Tsechansky M, 2015, MIS QUART, V39, pIII
Sibdari Soheil, 2012, International Journal of Operational Research, V14, P121, DOI 10.1504/IJOR.2012.046644
Sikora RT, 2008, DECIS SUPPORT SYST, V46, P101, DOI 10.1016/j.dss.2008.05.005
Sikora RT, 2008, KNOWL-BASED SYST, V21, P800, DOI 10.1016/j.knosys.2008.03.041
Simchi-Levi D., 2008, STRATEGIES CASE STUD
Sutton R. S., 1998, INTRO REINFORCEMENT, V2
VEPSALAINEN APJ, 1987, MANAGE SCI, V33, P1035, DOI 10.1287/mnsc.33.8.1035
Vilalta R, 2002, ARTIF INTELL REV, V18, P77, DOI 10.1023/A:1019956318069
WELLMAN MP, 2006, AAMAS 06 WORKSH GAM
NR 32
TC 4
Z9 5
U1 6
U2 50
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1617-9846
EI 1617-9854
J9 INF SYST E-BUS MANAG
JI Inf. Syst. E-Bus. Manag.
PD MAR
PY 2019
VL 17
IS 1
BP 117
EP 157
DI 10.1007/s10257-018-0378-y
PG 41
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HX6AC
UT WOS:000467485100005
DA 2024-03-27
ER
PT J
AU Nayyar, V
AF Nayyar, Varun
TI The role of marketing analytics in the ethical consumption of online
consumers
SO TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE
LA English
DT Article
DE marketing analytics; consumer privacy; artificial intelligence; online
retailing; mobile apps; marketing mix
ID PLS-SEM; PRIVACY; FUTURE; CONSEQUENCES; KNOWLEDGE; REVIEWS; PRODUCT;
TRUST; STATE; POWER
AB Modern marketing requires a dynamic shift from traditional to digital worlds where technology with data handling significantly needs marketing analytics effectiveness with a customised approach, leading to ethical online consumption for consumers. This current study validated the model developed from the contributions of Davis et al. (2021) and Grewal et al. (2020) by focusing on the effectiveness of marketing analytics. Data was collected from 435 respondents who were either business heads or employees working at a managerial level through a well-drafted questionnaire. Hair et al. (2017) incitation on PLS-SEM robust measuring standards helped in model validation through internal consistency, validity, reliability, multicollinearity, nonlinearity, effect size, HTMT, R2 and finally model fitness. The empirical prediction of this research gives a clear signal to corporate to redesign their present online marketing models by using marketing analytics and then critically analysing the role of consumer privacy, artificial intelligence, and marketing mix while promoting their products or services through web or mobile apps. Finally, to what extent marketing analyses can help in generating a user-friendly interface for online consumers by capturing their personality traits with a cognitive mindset is still critical to understand and may encourage future work.
C1 [Nayyar, Varun] Apeejay Inst Management & Engn Tech Campus, Jalandhar, Punjab, India.
RP Nayyar, V (autor correspondiente), Apeejay Inst Management & Engn Tech Campus, Jalandhar, Punjab, India.
EM varunnayyar90@gmail.com
RI Nayyar, Dr Varun/AAS-8058-2020
OI Nayyar, Dr Varun/0000-0001-5609-9327
CR Agarwal R, 2020, J ACAD MARKET SCI, V48, P9, DOI 10.1007/s11747-019-00692-4
Agrawal A., 2018, Prediction machines: the simple economics of artificial intelligence
Ajayi S, 2023, EUROMED J BUS, V18, P397, DOI 10.1108/EMJB-10-2021-0164
Aljumah AI, 2021, BUS PROCESS MANAG J, V27, P1108, DOI 10.1108/BPMJ-11-2020-0527
Appel G, 2020, J ACAD MARKET SCI, V48, P79, DOI 10.1007/s11747-019-00695-1
Bornschein R, 2020, J PUBLIC POLICY MARK, V39, P135, DOI 10.1177/0743915620902143
BRISLIN RW, 1970, J CROSS CULT PSYCHOL, V1, P185, DOI 10.1177/135910457000100301
Byun KA, 2021, J INTERACT MARK, V55, P81, DOI 10.1016/j.intmar.2021.01.003
Chae I, 2019, J MARKETING RES, V56, P57, DOI 10.1177/0022243718820587
Chen R, 2003, TOTAL QUAL MANAG BUS, V14, P677, DOI 10.1080/1478336032000053825
Chin W, 2020, IND MANAGE DATA SYST, V120, P2161, DOI 10.1108/IMDS-10-2019-0529
COHEN J, 1992, PSYCHOL BULL, V112, P155, DOI 10.1037/0033-2909.112.1.155
Loureiro SMC, 2021, J BUS RES, V129, P911, DOI 10.1016/j.jbusres.2020.11.001
Loureiro SMC, 2021, INT J CONTEMP HOSP M, V33, P3840, DOI 10.1108/IJCHM-09-2020-1032
Cowley S, 2021, J MARKET EDUC, V43, P189, DOI 10.1177/0273475320948570
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Davis B, 2021, J PUBLIC POLICY MARK, V40, P447, DOI 10.1177/07439156211042372
Dzyabura D, 2019, MARKET SCI, V38, P417, DOI 10.1287/mksc.2018.1144
Erevelles S, 2016, J BUS RES, V69, P897, DOI 10.1016/j.jbusres.2015.07.001
Ganguli D., 2021, ANAL INSIGHT 0614
Goldstein DG, 2014, J MARKETING RES, V51, P742, DOI 10.1509/jmr.13.0439
Grewal D, 2020, J ACAD MARKET SCI, V48, P96, DOI 10.1007/s11747-019-00697-z
Grewal L, 2019, J MARKETING RES, V56, P791, DOI 10.1177/0022243719834514
Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
Hair JF, 2019, EUR J MARKETING, V53, P566, DOI 10.1108/EJM-10-2018-0665
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hayes AF, 2015, MULTIVAR BEHAV RES, V50, P1, DOI 10.1080/00273171.2014.962683
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Herhausen D, 2019, J MARKETING, V83, P1, DOI 10.1177/0022242918822300
Hildebrand C, 2019, J ACAD MARKET SCI, V47, P291, DOI 10.1007/s11747-018-0599-0
Hoffman DL, 2018, J CONSUM RES, V44, P1178, DOI 10.1093/jcr/ucx105
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Iacobucci D, 2019, J MARK ANAL, V7, P152, DOI 10.1057/s41270-019-00059-2
Kejriwal M., 2021, Online Social Networks and Media, V24, P100149, DOI [10.1016/j.osnem.2021.100149, DOI 10.1016/J.OSNEM.2021.100149]
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015040101
Kopalle PK, 2021, J PUBLIC POLICY MARK, V40, P453, DOI 10.1177/0743915621999031
Kurtzke S, 2021, J MARKET EDUC, V43, P298, DOI 10.1177/02734753211042404
Langan R, 2019, J MARKET EDUC, V41, P32, DOI 10.1177/0273475318823849
Li Y, 2020, INTERNET RES, V30, P511, DOI 10.1108/INTR-02-2018-0066
Liu X., 2018, Marketing Education Review, V28, P28, DOI [10.1080/10528008.2017.1421049, DOI 10.1080/10528008.2017.1421049]
Loureiro S.M.C., 2022, J PROMOTION MANAGEME, V28, P111, DOI [https://doi.org/10.1080/10496491.2021.1987942, DOI 10.1080/10496491.2021.1987942]
Lourenço CJS, 2020, J INTERACT MARK, V49, P107, DOI 10.1016/j.intmar.2019.10.003
MacKenzie SB, 2012, J RETAILING, V88, P542, DOI 10.1016/j.jretai.2012.08.001
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Mintu-Wimsatt A., 2018, MARKETING ED REV, V28, P1, DOI DOI 10.1080/10528008.2018.1436974
Mondal T., 2022, MATER TODAY-PROC, V1
Nayyar V, 2022, SYST RES BEHAV SCI, V39, P542, DOI 10.1002/sres.2857
Nayyar V, 2020, INT REV PUB NON MARK, V17, P277, DOI 10.1007/s12208-020-00248-1
Okazaki S, 2020, J RETAILING, V96, P458, DOI 10.1016/j.jretai.2020.05.007
Petrescu M, 2020, J CONSUM MARK, V37, P675, DOI 10.1108/JCM-02-2019-3080
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Presthus W, 2018, PROCEDIA COMPUT SCI, V138, P603, DOI 10.1016/j.procs.2018.10.081
Purcarea VL, 2015, PROC ECON FINANC, V26, P1020, DOI 10.1016/S2212-5671(15)00925-9
RAMSEY JB, 1969, J ROY STAT SOC B, V31, P350
Rigdon E., 2017, Marketing: ZFP-Journal of Research and Management, V39, P4, DOI [DOI 10.15358/0344-1369-2017-3-4, 10.15358/0344-1369-2017-3-4]
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Schlee RP, 2017, J MARKET EDUC, V39, P69, DOI 10.1177/0273475317712765
Shiu JY, 2017, TOTAL QUAL MANAG BUS, V28, P746, DOI 10.1080/14783363.2015.1121094
Sivarajah U, 2017, J BUS RES, V70, P263, DOI 10.1016/j.jbusres.2016.08.001
Stocchi L, 2022, J ACAD MARKET SCI, V50, P195, DOI 10.1007/s11747-021-00815-w
Tapscott D., 1995, DIGITAL EC PROMISE P
Thomaz F, 2020, J ACAD MARKET SCI, V48, P43, DOI 10.1007/s11747-019-00704-3
Tong SL, 2020, J ACAD MARKET SCI, V48, P64, DOI 10.1007/s11747-019-00693-3
Wang X, 2021, J RETAILING, V97, P658, DOI 10.1016/j.jretai.2020.12.001
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Wong K.K.-K., 2016, MARKETING B, V26, DOI DOI 10.13140/RG.2.1.1643.0562
Wu KW, 2012, COMPUT HUM BEHAV, V28, P889, DOI 10.1016/j.chb.2011.12.008
NR 69
TC 2
Z9 2
U1 6
U2 24
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1478-3363
EI 1478-3371
J9 TOTAL QUAL MANAG BUS
JI Total Qual. Manag. Bus. Excell.
PD MAY 19
PY 2023
VL 34
IS 7-8
BP 1015
EP 1031
DI 10.1080/14783363.2022.2139676
EA NOV 2022
PG 17
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA C8NJ7
UT WOS:000877443500001
DA 2024-03-27
ER
PT J
AU Al-Emran, M
AlQudah, AA
Abbasi, GA
Al-Sharafi, MA
Iranmanesh, M
AF Al-Emran, Mostafa
AlQudah, Adi Ahmad
Abbasi, Ghazanfar Ali
Al-Sharafi, Mohammed A.
Iranmanesh, Mohammad
TI Determinants of Using AI-Based Chatbots for Knowledge Sharing: Evidence
From PLS-SEM and Fuzzy Sets (fsQCA)
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article; Early Access
DE Chatbots; Behavioral sciences; Artificial intelligence; Mathematical
models; Fuzzy sets; Predictive models; Knowledge engineering; Chatbot;
fuzzy set qualitative comparative analysis (fsQCA); knowledge sharing;
partial least squares-structural equation modeling (PLS-SEM); technology
adoption
ID INFORMATION-TECHNOLOGY; UNIFIED THEORY; ACCEPTANCE; IMPACT; AVOIDANCE;
BEHAVIORS; STANDARDS; STUDENTS; MODEL
AB While adopting chatbots powered by artificial intelligence could enhance knowledge sharing, it also causes challenges due to the "dark side " of these agents. However, research on the factors influencing chatbots for knowledge sharing is lacking. To bridge this gap, we developed the integrated chatbot acceptance-avoidance model, which looks at the positive and negative determinants of using chatbots for knowledge sharing. Through a comprehensive questionnaire survey of 447 students, the research model is evaluated using the partial least squares-structural equation modeling (PLS-SEM), a symmetric approach, and fuzzy set qualitative comparative analysis (fsQCA) as an asymmetric approach. The PLS-SEM results supported the positive role of performance expectancy, effort expectancy, and habit and the negative role of perceived threats in affecting chatbot use for knowledge sharing. Although PLS-SEM results revealed that social influence, facilitating conditions, and hedonic motivation have no impact on chatbot use, the fsQCA analysis revealed that all factors might play a role in shaping the use of chatbots. In addition to the theoretical contributions, the findings provide several managerial implications for universities, instructors, and chatbot developers to help them make insightful decisions and promote the use of chatbots.
C1 [Al-Emran, Mostafa; AlQudah, Adi Ahmad] British Univ Dubai, Fac Engn & IT, Dubai, U Arab Emirates.
[Al-Emran, Mostafa] Dijlah Univ Coll, Dept Comp Tech Engn, Baghdad, Iraq.
[Abbasi, Ghazanfar Ali] King Fahd Univ Petr & Minerals, Dept Management & Mkt, Dhahran 31261, Saudi Arabia.
[Abbasi, Ghazanfar Ali] King Fahd Univ Petr & Minerals, Interdisciplinary Res Ctr Finance & Digital Econ, KFUPM Business Sch, Dhahran 31261, Saudi Arabia.
[Al-Sharafi, Mohammed A.] Univ Teknol Malaysia, Dept Informat Syst, Skudai 81310, Malaysia.
[Al-Sharafi, Mohammed A.] Sunway Univ, Dept Business Analyt, Bandar Sunway 47500, Malaysia.
[Iranmanesh, Mohammad] Edith Cowan Univ, Sch Business & Law, Joondalup, WA 6027, Australia.
C3 Dijlah University College; King Fahd University of Petroleum & Minerals;
King Fahd University of Petroleum & Minerals; Universiti Teknologi
Malaysia; Sunway University; Edith Cowan University
RP Al-Emran, M (autor correspondiente), British Univ Dubai, Fac Engn & IT, Dubai, U Arab Emirates.
EM mustafa.n.alemran@gmail.com; adi.qudah@gmail.com;
ghazanfar.abbasi@hotmail.co.uk; alsharafi@ieee.org;
m.iranmanesh@ecu.edu.au
RI Al-Emran, Mostafa/W-4466-2018; Iranmanesh, Mohammad/G-2321-2012;
Al-Sharafi, Mohammed A./E-1530-2017; ABBASI, GHAZANFAR ALI/Y-9112-2018
OI Al-Emran, Mostafa/0000-0002-5269-5380; Iranmanesh,
Mohammad/0000-0001-6964-6238; Al-Sharafi, Mohammed
A./0000-0003-0726-6031; AlQudah, Adi/0000-0003-3942-5869; ABBASI,
GHAZANFAR ALI/0000-0003-0748-8996
CR Abbasi GA, 2022, J CLEAN PROD, V359, DOI 10.1016/j.jclepro.2022.132137
Abbasi GA, 2022, TECHNOL SOC, V68, DOI 10.1016/j.techsoc.2022.101937
Abu Gharrah A., 2021, Journal of Technology and Science Education, V11, P53, DOI DOI 10.3926/JOTSE.1081
Acosta-maestre H. A., 2021, THESIS NOVA SE U FOR
Agogo D, 2018, EUR J INFORM SYST, V27, P570, DOI 10.1080/0960085X.2018.1435230
Al-Azawei A, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101325
Al-Sharafi MA, 2023, INTERACT LEARN ENVIR, V31, P7491, DOI 10.1080/10494820.2022.2075014
Albanna H, 2022, INT J INFORM MANAGE, V63, DOI 10.1016/j.ijinfomgt.2021.102452
Almahri FAJ, 2020, 2020 THE 6TH IEEE INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT (ICIM 2020), P284, DOI [10.1109/ICIM49319.2020.244712, 10.1109/icim49319.2020.244712]
AlQudah A. A., 2021, PROC INT C EMERG TEC, P969, DOI [10.1007/978-3-030-85990-9_77, DOI 10.1007/978-3-030-85990-9_77]
AlQudah AA, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app112210537
Alt M. A., 2021, STUDIA U, V66, P13, DOI [https://doi.org/10.2478/subboec-2021-0002, DOI 10.2478/SUBBOEC-2021-0002]
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Arpaci I, 2022, TECHNOL SOC, V71, DOI 10.1016/j.techsoc.2022.102120
Atwell E., 2007, Ldv forum, V22, P29
Bagozzi R, 2003, J BEHAV DECIS MAKING, V16, P273, DOI 10.1002/bdm.446
Binsawadi M, 2022, PEERJ COMPUT SCI, V8, DOI 10.7717/peerj-cs.926
Bodea Constanta-Nicoleta, 2021, Research on Project, Programme and Portfolio Management. Integrating Sustainability into Project Management. Lecture Notes in Management and Industrial Engineering (LNMIE), P249, DOI 10.1007/978-3-030-60139-3_17
Boonsiritomachai W., 2017, KASETSART J SOCIAL S, DOI [DOI 10.1016/J.KJSS.2017.10, 10.1016/j.kjss.2017.10.005, DOI 10.1016/J.KJSS.2017.10.005]
Breward M, 2017, INFORM SYST RES, V28, P760, DOI 10.1287/isre.2017.0706
Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
Carpenter D, 2019, COMMUN ASSOC INF SYS, V44, P380, DOI 10.17705/1CAIS.04422
Chatterjee S, 2020, EDUC INF TECHNOL, V25, P3443, DOI 10.1007/s10639-020-10159-7
Chen DQ, 2019, IEEE T ENG MANAGE, V66, P552, DOI 10.1109/TEM.2018.2835461
Chen HL, 2020, J EDUC COMPUT RES, V58, P1161, DOI 10.1177/0735633120929622
Chen Y, 2016, MIS QUART, V40, P205, DOI 10.25300/MISQ/2016/40.1.09
De Nieva JO, 2020, CHIUXID 2020: 6TH INTERNATIONAL ACM IN-COOPERATION HCI AND UX CONFERENCE, P1, DOI 10.1145/3431656.3431657
Douglas E, 2019, J BUS RES, V99, P69, DOI 10.1016/j.jbusres.2019.02.031
Dul J, 2016, ORGAN RES METHODS, V19, P10, DOI 10.1177/1094428115584005
Dwivedi YK, 2020, CURR OPIN PSYCHOL, V36, P13, DOI 10.1016/j.copsyc.2020.03.008
Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
Dwivedi YK, 2017, GOV INFORM Q, V34, P211, DOI 10.1016/j.giq.2017.03.001
Foroughi B, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103158
Foth M, 2012, J PUBLIC HEALTH-HEID, V20, P253, DOI 10.1007/s10389-011-0456-9
Frangoudes Fotos, 2021, Learning and Collaboration Technologies Games and Virtual Environments for Learning. 8th International Conference, LCT 2021. Held as Part of the 23rd HCI International Conference, HCII 2021. Proceedings. Lecture Notes in Computer Science (LNCS 12785), P170, DOI 10.1007/978-3-030-77943-6_11
Fuller CM, 2016, J BUS RES, V69, P3192, DOI 10.1016/j.jbusres.2015.12.008
Gansser OA, 2021, TECHNOL SOC, V65, DOI 10.1016/j.techsoc.2021.101535
Gao H.-L., 2021, FUTURE INTERNET, V13, DOI [10.3390/fi13070162, DOI 10.3390/FI13070162]
Gazulla ED, 2023, EDUC INF TECHNOL, V28, P109, DOI 10.1007/s10639-022-11162-w
Gnewuch U, 2022, BUS INFORM SYST ENG+, V64, P773, DOI 10.1007/s12599-022-00755-x
Hair J. F., 2022, A primer on partial least squares structural equation modeling (PLS-SEM), V3rd
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Huang WJ, 2022, J COMPUT ASSIST LEAR, V38, P237, DOI 10.1111/jcal.12610
Jantavongso S., 2019, WORLD J PEDIATR, V2, P1, DOI DOI 10.14456/JTI.2019.1
Jeng DJF, 2012, COMPUT IND ENG, V62, P819, DOI 10.1016/j.cie.2011.12.016
Joshi H, 2021, PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES (WEBIST), P197, DOI 10.5220/0010718400003058
Juniper, 2018, CHATB DEL 11BN ANN
Kim AJ, 2021, JMIR MHEALTH UHEALTH, V9, DOI 10.2196/26424
Laleye FAA, 2020, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING (SAC'20), P859, DOI 10.1145/3341105.3373936
Li F, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103039
Li L, 2021, ELECTRON MARK, V31, P575, DOI 10.1007/s12525-020-00454-z
Liang HG, 2009, MIS QUART, V33, P71
Liébana-Cabanillas F, 2021, INFORM TECHNOL MANAG, V22, P133, DOI 10.1007/s10799-021-00328-6
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Mcaulay J. R., 2021, 15 CHATBOT STAT 2022
Mehran J, 2020, J TRAVEL TOUR MARK, V37, P112, DOI 10.1080/10548408.2020.1720890
Mikalef Patrick, 2013, J. theor. appl. electron. commer. res., V8, P17, DOI 10.4067/S0718-18762013000100003
Mleczko K., 2021, Multidisciplinary Aspects Prod Eng, V4, P499, DOI [10.2478/mape-2021-0045, DOI 10.2478/MAPE-2021-0045]
Moorthy K, 2019, AUSTRALAS J EDUC TEC, V35, P174, DOI 10.14742/ajet.4432
Nikolopoulou K, 2021, COMPUT EDUC OPEN, V2, DOI 10.1016/j.caeo.2021.100041
Nikolopoulou K, 2020, EDUC INF TECHNOL, V25, P4139, DOI 10.1007/s10639-020-10157-9
Okonkwo C.W., 2021, Comput. Educ.: Artif. Intell., V2, DOI DOI 10.1016/J.CAEAI.2021.100033
Ong R. J., 2021, Journal of Physics: Conference Series, V1755, DOI 10.1088/1742-6596/1755/1/012051
Pagaling GT, 2022, NEUROL SCI, V43, P811, DOI 10.1007/s10072-021-05705-1
Pappas IO, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102310
Pappas IO, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102128
Paulin D, 2011, PROCEEDINGS OF THE 12TH EUROPEAN CONFERENCE ON KNOWLEDGE MANAGEMENT, VOLS 1 AND 2, P752
Peng MH, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13179946
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Ragin C. C., 2008, Config. Comp. Methods, V51, P87, DOI DOI 10.4135/9781452226569.N5
Ragin CC, 2005, SOCIOL METHOD RES, V33, P423, DOI 10.1177/0049124105274499
Rese A, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102176
Rodriguez Cardona D., 2021, P 54 HAW INT C SYST, DOI [10.24251/HICSS.2021.068, DOI 10.24251/HICSS.2021.068]
Schneider CQ, 2010, COMP SOCIOL, V9, P397, DOI 10.1163/156913210X12493538729793
Schuetzler RM, 2020, J MANAGE INFORM SYST, V37, P875, DOI 10.1080/07421222.2020.1790204
Selamat MA, 2021, TECHNOL SOC, V66, DOI 10.1016/j.techsoc.2021.101685
Sitar-Taut DA, 2021, HUM BEHAV EMERG TECH, V3, P366, DOI 10.1002/hbe2.261
Sugumar M., 2020, PEER PEER NETW APPL, V30
Tegos S, 2014, INT J ARTIF INTELL E, V24, P62, DOI 10.1007/s40593-013-0007-3
Varshneya G, 2017, GLOB BUS REV, V18, P478, DOI 10.1177/0972150916668620
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Warkentin M, 2017, J ASSOC INF SYST, V18, P758, DOI 10.17705/1jais.00474
Wei XY, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3140952
Woodside AG, 2014, J BUS RES, V67, P2495, DOI 10.1016/j.jbusres.2014.07.006
YILDIZDURAK H, 2022, EDUC INF TECHNOL, DOI DOI 10.1007/S10639-022-11149
NR 86
TC 26
Z9 26
U1 166
U2 252
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD 2023 JAN 31
PY 2023
DI 10.1109/TEM.2023.3237789
EA JAN 2023
PG 15
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA 8Y6SL
UT WOS:000932825300001
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Wu, YL
Liu, QY
AF Wu, Yingli
Liu, Qiuyan
TI A Novel Deep Learning-Based Visual Search Engine in Digital Marketing
for Tourism E-Commerce Platforms
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE CLIP; CLIP-ItP; Multimodal Data; Top-K Product Recommendation; Tourism
E-Commerce; Visual Search Engine
AB Visual search technology, because of its convenience and high efficiency, is widely used by major tourism e-commerce platforms in product search functions. This study introduces an innovative visual search engine model, namely CLIP-ItP, aiming to thoroughly explore the application potential of visual search in tourism e-commerce. The model is an extension of the CLIP (contrastive languageimage pre-training) framework and is developed through three pivotal stages. Firstly, by training an image feature extractor and a linear model, the visual search engine labels images, establishing an experimental visual search engine. Secondly, CLIP-ItP jointly trains multiple text and image encoders, facilitating the integration of multimodal data, including product image labels, categories, names, and attributes. Finally, leveraging user-uploaded images and jointly selected product attributes, CLIP-ItP provides personalized top-k product recommendations.
C1 [Wu, Yingli] Jiaxing Nanhu Univ, Sch Business Management, Jiaxing, Peoples R China.
[Liu, Qiuyan] Zhejiang Univ Water Resources & Elect Power, Sch Econ & Management, Hangzhou, Peoples R China.
C3 Zhejiang University of Water Resources and Electric Power
RP Liu, QY (autor correspondiente), Zhejiang Univ Water Resources & Elect Power, Sch Econ & Management, Hangzhou, Peoples R China.
FU Ministry of Education's Industry University Cooperation Collaborative
Education Project [230821141307061]; Research and Creation Project of
Zhejiang Provincial Department of Culture and Tourism [2023KYY039];
General Research Project of Zhejiang Provincial Department of Education
[Y202352193]
FX This work was supported by The Ministry of Education's Industry
University Cooperation Collaborative Education Project (Grant No.
230821141307061) , Research and Creation Project of Zhejiang Provincial
Department of Culture and Tourism (Grant No. 2023KYY039) , and General
Research Project of Zhejiang Provincial Department of Education (Grant
No. Y202352193) .
CR Abouelnaga Y, 2016, 2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI), P1192, DOI [10.1109/CSCI.2016.224, 10.1109/CSCI.2016.0225]
Abubakr AG, 2022, J ELECTRON IMAGING, V31, DOI 10.1117/1.JEI.31.5.051604
Anand G, 2021, Applications of Machine Learning
Arumugam S., 2022, AIP Conference Proceedings
Berg T, 2014, PROC CVPR IEEE, P2019, DOI 10.1109/CVPR.2014.259
Chen CK, 2022, NAT BIOMED ENG, V6, P1420, DOI 10.1038/s41551-022-00929-8
Chen M, 2022, ENTERP INF SYST-UK, V16, DOI 10.1080/17517575.2020.1856422
Dagan A, 2023, INFORM RETRIEVAL J, V26, DOI 10.1007/s10791-023-09418-1
Du M., 2022, P 28 ACM SIGKDD C KN
Eswaran A, 2022, 2022 8 INT C ADV COM
Feng ZT, 2022, J GLOB INF MANAG, V30, DOI 10.4018/JGIM.306271
Foping F, 2022, Journal of Computer Vision and Pattern Recognition, V1, P1
Gaafar AS, 2022, INFORM-INT J COMPUT, V46, P21, DOI 10.31449/inf.v46i5.3872
Guang J. Y, 2021, Journal of Jilin University, V59
He GH, 2021, J ORGAN END USER COM, V33, DOI 10.4018/JOEUC.20211101.oa16
Hou J, 2021, J ORGAN END USER COM, V33, DOI 10.4018/JOEUC.20211101.oa13
Kang W, Investigating the potential of gan-based interactive image editing for novel image search
Li Y, 2022, 2022 IEEE 2 INT C PO
Mete B. R, 2019, 2019 3 INT S MULT ST
Monjur M. E. I, 2023, Open Journal of Business and Management, V11, P2389, DOI [10.4236/ojbm.2023.115132, DOI 10.4236/OJBM.2023.115132]
Mustafic F, 2019, 2019 XXVII INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION AND AUTOMATION TECHNOLOGIES (ICAT 2019), DOI 10.1109/icat47117.2019.8939009
Nayeem T. A, 2022, 2022 12 INT C EL COM
Ning X, 2024, INFORM FUSION, V102, DOI 10.1016/j.inffus.2023.102033
Parkhi OM, 2012, PROC CVPR IEEE, P3498, DOI 10.1109/CVPR.2012.6248092
Radford A., 2021, INT C MACH LEARN
Shen CW, 2019, COMPUT HUM BEHAV, V101, P474, DOI 10.1016/j.chb.2018.09.031
Singh D, 2023, ENG APPL ARTIF INTEL, V126, DOI 10.1016/j.engappai.2023.106833
Tian SS, 2024, NEURAL NETWORKS, V169, P307, DOI 10.1016/j.neunet.2023.10.039
Varish N, 2022, MULTIMED TOOLS APPL, V81, P20373, DOI 10.1007/s11042-022-12289-1
Waqas U, 2023, CMC-COMPUT MATER CON, V75, P243, DOI 10.32604/cmc.2023.035716
Wolfe JM, 2021, PSYCHON B REV, V28, P1060, DOI 10.3758/s13423-020-01859-9
Ye SY, 2023, PSYCHOL REP, DOI 10.1177/00332941231172707
Ye SY, 2023, MANAGE DECIS, V61, P77, DOI 10.1108/MD-09-2021-1166
Yu M, 2022, Mathematical Problems in Engineering
Yuan LJ, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.843428
Zhang H, 2022, J ORGAN END USER COM, V34, DOI 10.4018/JOEUC.306270
NR 36
TC 0
Z9 0
U1 0
U2 0
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PY 2024
VL 36
IS 1
AR 340386
DI 10.4018/JOEUC.340386
PG 27
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA LF6P2
UT WOS:001185409200011
OA gold
DA 2024-03-27
ER
PT J
AU Bharti, SS
Prasad, K
Sudha, S
Kumari, V
AF Bharti, Swaraj S.
Prasad, Kanika
Sudha, Shwati
Kumari, Vineeta
TI Customer acceptability towards AI-enabled digital banking: a PLS-SEM
approach
SO JOURNAL OF FINANCIAL SERVICES MARKETING
LA English
DT Article
DE Customer acceptance; Banking industry; Artificial Intelligence;
Technology acceptance model; Structural equation modelling; Partial
least square
ID PERFORMANCE; CHATBOT
AB Artificial Intelligence (AI) has proved its significance in every field and is yet to be explored in the banking sector in India. The study aims to understand the customers' perception of using AI-based technologies in banks. Satisfaction is the first step towards acceptability and retention of customers towards lesser-known technology and automated process implemented in banks. The constructs of the study are referred from the technology acceptance model to define their level of acceptance and are divided into independent and dependent variables. The independent variables are "transparency", "awareness level", "security", "efficiency", "trust", and "social influence", and the dependent variable is "customer satisfaction". Therefore, the structural equation model was developed from the customers' (N = 500) response to retail banks in northern India. The study reveals that trust is the most significant factor for greater customers' satisfaction towards AI-enabled technologies in banks, followed by the customers' awareness level. The security of AI-based banks is the least important contributor to customer satisfaction. Additionally, the control variables, i.e., age and gender, govern the customers' perception. Understanding customer acceptability towards AI-based technology in retail banks is rare in emerging nations such as India. The findings provide insight into the formulation of compliance. It also highlights the regulation applicable to digital banks by the competent authority in India. The paper concludes by stating practical implications for banking authorities and decision-makers to incorporate AI into their system for customer service.
C1 [Bharti, Swaraj S.; Prasad, Kanika; Sudha, Shwati] Natl Inst Technol Jamshedpur, Dept Humanities Social Sci & Management, Jamshedpur 831014, India.
[Kumari, Vineeta] Magadh Univ, PG Dept Commerce, Bodhgaya 824234, India.
C3 National Institute of Technology (NIT System); National Institute of
Technology Jamshedpur
RP Kumari, V (autor correspondiente), Magadh Univ, PG Dept Commerce, Bodhgaya 824234, India.
EM 2020rshs006@nitjsr.ac.in; kprasad.prod@nitjsr.ac.in;
ssudha.hum@nitjsr.ac.in; vidhatamu@gmail.com
RI Kumari, Vineeta/AAD-5154-2021
OI Kumari, Vineeta/0000-0001-8231-1213; Bharti, Swaraj
S/0000-0001-8655-4263
CR Abdulquadri A, 2021, J ENTERP COMMUNITIES, V15, P258, DOI 10.1108/JEC-06-2020-0126
Abikari M, 2023, J FINANC SERV MARK, V28, P691, DOI 10.1057/s41264-022-00172-x
Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Agarwal P, 2022, J SCI TECHNOL POLICY, DOI 10.1108/JSTPM-08-2021-0122
Aitken M, 2021, TECHNOL SOC, V66, DOI 10.1016/j.techsoc.2021.101666
Arif I, 2020, TECHNOL SOC, V61, DOI 10.1016/j.techsoc.2020.101231
Auh S, 2019, J MARKETING RES, V56, P1012, DOI 10.1177/0022243719866408
Banna H, 2021, STUD ECON FINANC, V38, P504, DOI 10.1108/SEF-09-2020-0388
Bawack RE, 2021, J ENTERP INF MANAG, V34, P645, DOI 10.1108/JEIM-07-2020-0284
Belanche D, 2019, IND MANAGE DATA SYST, V119, P1411, DOI 10.1108/IMDS-08-2018-0368
Bellini S, 2017, J RETAIL CONSUM SERV, V36, P164, DOI 10.1016/j.jretconser.2017.02.001
Bhatia A, 2021, QUAL RES FINANC MARK, V13, P632, DOI 10.1108/QRFM-10-2020-0199
Boot A, 2021, J FINANC STABIL, V53, DOI 10.1016/j.jfs.2020.100836
Boustani NM, 2022, J ASIA BUS STUD, V16, P267, DOI 10.1108/JABS-09-2020-0376
Cabrera-Sánchez JP, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101888
Chauhan S, 2022, INT J QUAL SERV SCI, V14, P311, DOI 10.1108/IJQSS-02-2021-0027
Cheng Y, 2022, J PROD BRAND MANAG, V31, P252, DOI 10.1108/JPBM-05-2020-2907
Choinski TC., 2020, ARTIF INTELL, DOI [10.1108/978-1-78973-811-720201002, DOI 10.1108/978-1-78973-811-720201002]
Chong LL, 2021, TECHNOL SOC, V64, DOI 10.1016/j.techsoc.2020.101497
Danyali AA, 2018, J ORGAN CHANGE MANAG, V31, P1226, DOI 10.1108/JOCM-07-2017-0269
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dehnert M, 2022, ELECTRON MARK, V32, P1503, DOI 10.1007/s12525-022-00524-4
Dvoretskaya A., 2021, ROLE FINANCIAL INTER, DOI [10.1007/978-3-030-71337-9_4, DOI 10.1007/978-3-030-71337-9_4]
Egala SB, 2021, INT J BANK MARK, V39, P1420, DOI 10.1108/IJBM-02-2021-0072
Fan H, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2022.102937
Flavián C, 2022, J SERV MANAGE, V33, P293, DOI 10.1108/JOSM-10-2020-0378
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gambhir Bhumika, 2022, Development and Learning in Organizations: An International Journal, V36, P17, DOI 10.1108/DLO-01-2021-0016
Gansser OA, 2021, TECHNOL SOC, V65, DOI 10.1016/j.techsoc.2021.101535
Garcia-Almanza AL., 2013, STUDIES COMPUTATIONA, V427, P633, DOI [10.1007/978-3-642-29694-9_24, DOI 10.1007/978-3-642-29694-9_24]
Guerra-Leal EM, 2023, REV BEHAV FINANCE, V15, P257, DOI 10.1108/RBF-08-2021-0150
Gunawardane G, 2023, J FINANC SERV MARK, V28, P255, DOI 10.1057/s41264-022-00148-x
Gupta A, 2018, INT SYMP EC, V25, P215, DOI 10.1108/S1571-038620180000025010
Gupta R., 2020, 1 INT C CYB EM DIG E, DOI [10.1007/978-3-030-84842-2_5, DOI 10.1007/978-3-030-84842-2_5]
Gupta S, 2023, RES INT BUS FINANC, V66, DOI 10.1016/j.ribaf.2023.101993
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hamdan A., 2020, 4 IND REVOLUTION IMP
Hasal M, 2021, CONCURR COMP-PRACT E, V33, DOI 10.1002/cpe.6426
Jaksic M, 2019, RISK MANAG-UK, V21, P1, DOI 10.1057/s41283-018-0039-y
Ji Y, 2022, PAC-BASIN FINANC J, V72, DOI 10.1016/j.pacfin.2022.101731
Jiménez-Barreto J, 2021, INT J CONTEMP HOSP M, V33, P3860, DOI 10.1108/IJCHM-10-2020-1244
Kamboj S, 2022, INT J BANK MARK, V40, P128, DOI 10.1108/IJBM-10-2020-0534
Kaur B, 2021, RISKS, V9, DOI 10.3390/risks9110209
Kavale K. A., 2020, BLOCKCHAIN ARTIFICIA, DOI [10.4324/9780203052990-10, DOI 10.4324/9780203052990-10]
Khan MTI., 2017, RES INT BUS FINANC, DOI [10.1016/j.techsoc.2021.101822, DOI 10.1016/J.TECHSOC.2021.101822]
Kohnke L, 2023, RELC J, V54, P828, DOI 10.1177/00336882211067054
Ladhari R, 2011, J FINANC SERV MARK, V16, P111, DOI 10.1057/fsm.2011.10
Lappeman J, 2023, J FINANC SERV MARK, V28, P337, DOI 10.1057/s41264-022-00154-z
Lee JC, 2022, INT J BANK MARK, V40, P631, DOI 10.1108/IJBM-08-2021-0394
Lei SI, 2021, INT J CONTEMP HOSP M, V33, P3977, DOI 10.1108/IJCHM-12-2020-1399
Levy S, 2022, INT J BANK MARK, V40, P679, DOI 10.1108/IJBM-08-2021-0383
Li L, 2021, ELECTRON MARK, V31, P575, DOI 10.1007/s12525-020-00454-z
Luo SM, 2022, TECHNOL SOC, V68, DOI 10.1016/j.techsoc.2021.101821
Mainardes EW, 2023, J FINANC SERV MARK, V28, P378, DOI 10.1057/s41264-022-00156-x
Mogaji E, 2022, INT J BANK MARK, V40, P1272, DOI 10.1108/IJBM-09-2021-0440
Nambiar BK, 2023, J FINANC SERV MARK, V28, P58, DOI 10.1057/s41264-022-00139-y
Oino I, 2018, RES INT BUS FINANC, V44, P309, DOI 10.1016/j.ribaf.2017.07.099
Paulet Elisabeth, 2019, Journal of Business Strategy, V41, P19
Payne EHM, 2021, J RES INTERACT MARK, V15, P200, DOI 10.1108/JRIM-12-2020-0252
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Rahman M, 2023, INT J EMERG MARK, V18, P4270, DOI 10.1108/IJOEM-06-2020-0724
Robinson SC, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101421
Rodrigues ARD, 2022, RES INT BUS FINANC, V60, DOI 10.1016/j.ribaf.2022.101616
Seiler V, 2021, RES INT BUS FINANC, V58, DOI 10.1016/j.ribaf.2021.101490
Shankar A, 2020, AUSTRALAS MARK J, V28, P273, DOI 10.1016/j.ausmj.2020.06.008
Singh S, 2020, MANAGE DECIS, V58, P1675, DOI 10.1108/MD-09-2019-1318
Somya G, 2022, RES INT BUS FINANC, V63, DOI 10.1016/j.ribaf.2022.101757
Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
Srivastava S, 2023, J FINANC SERV MARK, V28, P236, DOI 10.1057/s41264-022-00147-y
Subero-Navarro A, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102813
Suhartanto D, 2022, J ISLAMIC MARK, V13, P1958, DOI 10.1108/JIMA-12-2020-0380
Tabesh Pooya, 2022, Journal of Business Strategy, P373, DOI 10.1108/JBS-05-2021-0090
Taneja S, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102418
Tölö E, 2020, J FINANC STABIL, V49, DOI 10.1016/j.jfs.2020.100746
Tsindeliani IA, 2022, J MONEY LAUND CONTRO, V25, P165, DOI 10.1108/JMLC-02-2021-0011
Udoka OS., 2022, INT J ENTREPRENEURSH, V5, P16, DOI [10.52589/IJEBI-FN3AWAFN, DOI 10.52589/IJEBI-FN3AWAFN]
van Tonder E, 2018, J RETAIL CONSUM SERV, V45, P92, DOI 10.1016/j.jretconser.2018.08.011
Vedapradha R, 2021, ASIA PAC J INNOV ENT, V15, P51, DOI 10.1108/APJIE-09-2020-0142
Venkatesan R, 2017, J ACAD MARKET SCI, V45, P289, DOI 10.1007/s11747-016-0513-6
Vieira A., 2017, DIGITAL MARKETPLACES, DOI [10.1007/978-3-662-49275-8_31, DOI 10.1007/978-3-662-49275-8_31]
Wongsansukcharoen J, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102826
Wu CG, 2022, INT J BANK MARK, V40, P68, DOI 10.1108/IJBM-09-2020-0465
Xue MN, 2021, ELECTRON LIBR, V39, P539, DOI 10.1108/EL-07-2020-0207
Yi HT, 2021, J RETAIL CONSUM SERV, V62, DOI 10.1016/j.jretconser.2021.102598
Yun Joo A., 2021, BLOCKCHAIN CRYPTOCUR, DOI [10.1007/978-981-33-6137-9, DOI 10.1007/978-981-33-6137-9]
NR 86
TC 2
Z9 2
U1 10
U2 14
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1363-0539
EI 1479-1846
J9 J FINANC SERV MARK
JI J. Financ. Serv. Mark.
PD DEC
PY 2023
VL 28
IS 4
SI SI
BP 779
EP 793
DI 10.1057/s41264-023-00241-9
EA AUG 2023
PG 15
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA AJ1R6
UT WOS:001049146300001
DA 2024-03-27
ER
PT J
AU Sharma, S
Islam, N
Singh, G
Dhir, A
AF Sharma, Shavneet
Islam, Nazrul
Singh, Gurmeet
Dhir, Amandeep
TI Why Do Retail Customers Adopt Artificial Intelligence (AI) Based
Autonomous Decision-Making Systems?
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Decision making; Cultural differences; Artificial intelligence;
Mathematical models; Uncertainty; Social networking (online); Autonomous
systems; Artificial Intelligence (AI); autonomous decision-making;
covariance-based structural equation modeling (CB-SEM); Hofstede'
s cultural dimensions; unified theory of acceptance and use of
technology (UTAUT) model
ID NATIONAL CULTURAL-VALUES; INFORMATION-TECHNOLOGY; BEHAVIORAL INTENTIONS;
USER ACCEPTANCE; CONSUMERS ACCEPTANCE; MODERATING ROLE; MOBILE BANKING;
HEALTH-CARE; UTAUT MODEL; IMPACT
AB Advancements in Artificial Intelligence (AI) have led to the development of autonomous decision-making processes, allowing customers to delegate decisions and tasks. Such technologies have the potential to alter the retailing landscape. Grounded in the unified theory of acceptance and use of technology and Hofstede's cultural theory, this article investigates customers' adoption of AI-based autonomous decision-making processes by analyzing 454 customer responses using covariance-based structural equation modeling. The results reveal that effort expectancy, performance expectancy, facilitating conditions, and social influence are positively associated with customers' adoption of autonomous decision-making processes. Collectivism strengthened the positive association of social influence with customer attitude, whereas uncertainty avoidance dampened the associations of performance expectancy, effort expectancy, and social influence with attitude. The findings provide useful implications for system developers and managers while providing future researchers with directions to further explore autonomous decision-making processes.
C1 [Sharma, Shavneet; Singh, Gurmeet] Univ South Pacific, Sch Management & Publ Adm, Suva 00679, Fiji.
[Islam, Nazrul] Univ Exeter Business Sch, Dept Management, Exeter EX4 4PU, Devon, England.
[Dhir, Amandeep] Univ Agder, Sch Business & Law, N-4630 Kristiansand, Norway.
C3 University of the South Pacific; University of Agder
RP Islam, N (autor correspondiente), Univ Exeter Business Sch, Dept Management, Exeter EX4 4PU, Devon, England.
EM shavneet.sharma@usp.ac.fj; n.islam@exeter.ac.uk;
gurmeet.singh@usp.ac.fj; amandeep.dhir@uia.no
RI Sharma, Shavneet/ABH-2566-2020; Singh, Gurmeet/Q-7326-2019; Islam,
Nazrul/AAO-9069-2020; Islam, Professor Nazrul/JQX-2250-2023; Dhir,
Amandeep/F-1826-2013
OI Sharma, Shavneet/0000-0001-5523-5091; Singh,
Gurmeet/0000-0003-2931-0670; Islam, Nazrul/0000-0002-9276-8388; Islam,
Professor Nazrul/0000-0003-0515-1134; Dhir, Amandeep/0000-0002-6006-6058
CR Abadi B, 2021, J CLEAN PROD, V279, DOI 10.1016/j.jclepro.2020.123802
Abbasi MS, 2015, J ENTERP INF MANAG, V28, P747, DOI 10.1108/JEIM-12-2014-0124
Ajzen I, 2001, ANNU REV PSYCHOL, V52, P27, DOI 10.1146/annurev.psych.52.1.27
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Alalwan AA, 2020, INT J INFORM MANAGE, V50, P28, DOI 10.1016/j.ijinfomgt.2019.04.008
Ali A, 2020, J FOOD PROD MARK, V26, P581, DOI 10.1080/10454446.2020.1846148
Alsaleh DA, 2019, J RES INTERACT MARK, V13, P119, DOI 10.1108/JRIM-10-2017-0092
Ameen N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106548
Arendt H., 1958, The Human Condition
Behl A, 2021, J GLOB INF MANAG, V29, DOI 10.4018/JGIM.20211101.oa14
Belanche D, 2019, IND MANAGE DATA SYST, V119, P1411, DOI 10.1108/IMDS-08-2018-0368
Blut M, 2020, J ACAD MARKET SCI, V48, P649, DOI 10.1007/s11747-019-00680-8
Botti S, 2004, J PERS SOC PSYCHOL, V87, P312, DOI 10.1037/0022-3514.87.3.312
Carter M, 2020, MIS QUART, V44, P983, DOI 10.25300/MISQ/2020/14607
Cha SS, 2020, INT J CONTEMP HOSP M, V32, P2947, DOI 10.1108/IJCHM-01-2020-0046
Chatterjee S., LNF SYST FRONT, DOI [10.1007/s10796-021-1.0181-1, DOI 10.1007/S10796-021-1.0181-1]
Chatterjee S, 2020, EDUC INF TECHNOL, V25, P3443, DOI 10.1007/s10639-020-10159-7
Chen CF, 2020, ENERGY RES SOC SCI, V69, DOI 10.1016/j.erss.2020.101616
Chen R, 2011, DECIS SUPPORT SYST, V52, P73, DOI 10.1016/j.dss.2011.05.005
Chi OHX, 2022, J TRAVEL RES, V61, P170, DOI 10.1177/0047287520971054
Chopra K, 2019, INT J RETAIL DISTRIB, V47, P331, DOI 10.1108/IJRDM-11-2018-0251
Coeckelbergh M, 2012, ETHICS INF TECHNOL, V14, P53, DOI 10.1007/s10676-011-9279-1
Cui YY, 2022, EUR J MARKETING, V56, P1650, DOI 10.1108/EJM-02-2020-0122
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
de Bellis E, 2020, J RETAILING, V96, P74, DOI 10.1016/j.jretai.2019.12.004
Dhir A, 2021, J CLEAN PROD, V312, DOI 10.1016/j.jclepro.2021.127443
Dhir A, 2021, INT J CONSUM STUD, V45, P1373, DOI 10.1111/ijcs.12659
Dong JQ, 2009, ASIAN J TECHNOL INNO, V17, P129
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
Erjavec J, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102867
Fan WJ, 2020, ANN OPER RES, V294, P567, DOI 10.1007/s10479-018-2818-y
Fernandes T, 2021, J BUS RES, V122, P180, DOI 10.1016/j.jbusres.2020.08.058
Fishbein M., 1980, UNDERSTANDING ATTITU
Ford DP, 2003, IEEE T ENG MANAGE, V50, P8, DOI 10.1109/TEM.2002.808265
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gaur L, 2021, INT J CONTEMP HOSP M, V33, P4079, DOI 10.1108/IJCHM-11-2020-1246
Gupta M, 2019, J BUS RES, V97, P20, DOI 10.1016/j.jbusres.2018.12.018
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Hair J.F., 2009, Multivariate data analysis
Heitmann M, 2007, J MARKETING RES, V44, P234, DOI 10.1509/jmkr.44.2.234
Hofstede G., 1994, Values survey modules manual
Hofstede G., 2011, ONLINE READINGS PSYC, V2, P2307, DOI [10.9707/2307-0919.1014, DOI 10.9707/2307-0919.1014]
Hoftede G., 1980, INT STUD MANAG ORG, P15, DOI DOI 10.1080/00208825.1980.11656300
Hu Q, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102250
Huang F, 2019, COMPUT EDUC, V133, P69, DOI 10.1016/j.compedu.2019.01.012
Hung CL, 2014, INT J INNOV TECHNOL, V11, DOI 10.1142/S0219877014500102
Hwang J, 2019, INT J HOSP MANAG, V81, P94, DOI 10.1016/j.ijhm.2019.03.002
Il Im, 2011, Information & Management, V48, P1, DOI 10.1016/j.im.2010.09.001
Jadil Y, 2021, J BUS RES, V132, P354, DOI 10.1016/j.jbusres.2021.04.052
Kaba B, 2014, J ASSOC INF SCI TECH, V65, P1662, DOI 10.1002/asi.23069
Kapser S, 2020, TRANSPORT RES C-EMER, V111, P210, DOI 10.1016/j.trc.2019.12.016
Kaur P, 2021, TECHNOL FORECAST SOC, V172, DOI 10.1016/j.techfore.2021.121039
Kaye SA, 2020, ACCIDENT ANAL PREV, V137, DOI 10.1016/j.aap.2020.105441
Khanra S, 2020, ENTERP INF SYST-UK, V14, P878, DOI 10.1080/17517575.2020.1812005
Kim D, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120448
Knein E, 2020, J INT MANAG, V26, DOI 10.1016/j.intman.2019.100731
Lai C, 2016, COMPUT HUM BEHAV, V62, P676, DOI 10.1016/j.chb.2016.04.039
Leidner DE, 2006, MIS QUART, V30, P357
Li J, 2019, TOURISM MANAGE, V73, P172, DOI 10.1016/j.tourman.2019.02.006
Lin HX, 2020, J HOSP MARKET MANAG, V29, P530, DOI 10.1080/19368623.2020.1685053
Lin XL, 2020, INT J INFORM MANAGE, V50, P45, DOI 10.1016/j.ijinfomgt.2019.05.004
Ling HC, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102331
Lu J, 2017, COMPUT HUM BEHAV, V75, P130, DOI 10.1016/j.chb.2017.05.002
Lu L, 2019, INT J HOSP MANAG, V80, P36, DOI 10.1016/j.ijhm.2019.01.005
Lv XY, 2021, ANN TOURISM RES, V87, DOI 10.1016/j.annals.2020.103114
McCoy S, 2005, COMMUN ASSOC INF SYS, V15, P211
Mehta A, 2019, COMPUT EDUC, V141, DOI 10.1016/j.compedu.2019.103617
Moriuchi E, 2021, PSYCHOL MARKET, V38, P21, DOI 10.1002/mar.21407
Pan JX, 2019, INT J PROD RES, V57, P5801, DOI 10.1080/00207543.2018.1550272
Park C, 2015, INT MARKET REV, V32, P414, DOI 10.1108/IMR-09-2013-0213
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
Png IPL, 2001, IEEE T ENG MANAGE, V48, P36, DOI 10.1109/17.913164
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Poushneh A, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102283
Raffaghelli JE, 2022, COMPUT EDUC, V182, DOI 10.1016/j.compedu.2022.104468
Roggeveen AL, 2020, J RETAILING, V96, P299, DOI 10.1016/j.jretai.2020.08.0O1
Ruparel N, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101335
Shaikh AA., 2021, Research Anthology on Securing Mobile Technologies and Applications, V14, P39, DOI 10.4018/978-17998-8545-0.ch038
Sharma Rashmini, 2020, International Journal of Information Management, V53, P68, DOI 10.1016/j.ijinfomgt.2020.102116
Sharma R, 2021, PSYCHOL MARKET, V38, P965, DOI 10.1002/mar.21469
Sharma S., 2020, IEEE T ENG MANAGE, DOI DOI 10.1109/TEM.2020.3019033
Sharma S, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102363
Sharma S, 2020, AUSTRALAS J INF SYST, V24, DOI 10.3127/ajis.v24i0.2751
Sharma S, 2021, INT J CULT TOUR HOSP, V15, P227, DOI 10.1108/IJCTHR-03-2020-0064
Sharma S, 2020, J INTERNET COMMER, V19, P62, DOI 10.1080/15332861.2019.1695180
Shavitt S, 2020, J RETAILING, V96, P40, DOI 10.1016/j.jretai.2019.11.009
Shi S, 2021, J TRAVEL RES, V60, P1714, DOI 10.1177/0047287520966395
Shi S, 2020, INT J INFORM MANAGE, V50, P325, DOI 10.1016/j.ijinfomgt.2019.09.001
Shin HH, 2020, INT J CONTEMP HOSP M, V32, P2613, DOI 10.1108/IJCHM-09-2019-0798
Shiu E, 2015, J BUS RES, V68, P534, DOI 10.1016/j.jbusres.2014.09.009
Sia CL, 2004, IEEE T ENG MANAGE, V51, P253, DOI 10.1109/TEM.2004.830859
Singh G, 2021, TECHNOL FORECAST SOC, V173, DOI 10.1016/j.techfore.2021.121094
Singh G, 2021, TQM J, V33, P1753, DOI 10.1108/TQM-07-2020-0163
Smith A, 2009, INT MARKET REV, V26, P580, DOI 10.1108/02651330911001305
Sohn K, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101324
Srite M, 2006, MIS QUART, V30, P679
Srite M, 2006, AUSTRALAS J INF SYST, V14, P5
Straub D., 2004, Communications of the Association for Information Systems, V13, P380, DOI [DOI 10.17705/1CAIS.01324, 10.17705/1CAIS.01324]
Styvén ME, 2020, PSYCHOL MARKET, V37, P724, DOI 10.1002/mar.21334
Sun S, 2020, J HOSP TOUR MANAG, V44, P61, DOI 10.1016/j.jhtm.2020.04.012
Talwar S, 2020, AUSTRALAS MARK J, V28, P286, DOI 10.1016/j.ausmj.2020.06.014
Tam C, 2019, INFORM MANAGE-AMSTER, V56, P356, DOI 10.1016/j.im.2018.07.009
Tamilmani K, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102269
Tan E, 2016, YOUNG CONSUM, V17, P18, DOI 10.1108/YC-07-2015-00537
Tandon A, 2021, J BUS RES, V136, P186, DOI 10.1016/j.jbusres.2021.07.036
Tandon A, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102667
Tandon A, 2021, TECHNOL FORECAST SOC, V171, DOI 10.1016/j.techfore.2021.120931
Tandon A, 2021, INTERNET RES, V31, P782, DOI 10.1108/INTR-11-2019-0455
Tandon A, 2020, COMPUT HUM BEHAV, V113, DOI 10.1016/j.chb.2020.106487
Tandon A, 2020, COMPUT IND, V122, DOI 10.1016/j.compind.2020.103290
Taras V, 2009, J INT MANAG, V15, P357, DOI 10.1016/j.intman.2008.08.005
Tarhini A, 2017, INTERACT LEARN ENVIR, V25, P306, DOI 10.1080/10494820.2015.1122635
Thong JYL, 2011, IEEE T ENG MANAGE, V58, P613, DOI 10.1109/TEM.2010.2058851
Tubadji A, 2021, J ECON ISSUES, V55, P38, DOI 10.1080/00213624.2021.1873046
Tubadji A, 2021, ECON INQ, V59, P1234, DOI 10.1111/ecin.12978
Tubadji A, 2016, LETT SPAT RESOUR SCI, V9, P247, DOI 10.1007/s12076-015-0155-1
Tubadji A, 2012, INT J SOC ECON, V39, P690, DOI 10.1108/03068291211245718
Upadhyay N, 2022, INT J BANK MARK, V40, P960, DOI 10.1108/IJBM-06-2021-0262
Usta M, 2011, J MARKETING RES, V48, P403, DOI 10.1509/jmkr.48.2.403
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Willems K, 2017, TECHNOL FORECAST SOC, V124, P228, DOI 10.1016/j.techfore.2016.10.066
Yip KHT, 2021, ONLINE INFORM REV, V45, P389, DOI 10.1108/OIR-07-2020-0287
Yoo B, 2011, J INT CONSUM MARK, V23, P193, DOI 10.1080/08961530.2011.578059
Yu LT, 2018, ACAD MANAGE J, V61, P2296, DOI 10.5465/amj.2015.0183
Yuen YY, 2015, MARK INTELL PLAN, V33, P292, DOI 10.1108/MIP-08-2013-0126
Zhang Y, 2018, INT J INFORM MANAGE, V40, P76, DOI 10.1016/j.ijinfomgt.2018.01.015
Zhao Y, 2021, BRIT J EDUC TECHNOL, V52, P20, DOI 10.1111/bjet.13002
Zhao YY, 2020, INT J HOSP MANAG, V91, DOI 10.1016/j.ijhm.2020.102683
Zhou M, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101911
Zhu G, 2020, TRANSPORT RES F-TRAF, V73, P80, DOI 10.1016/j.trf.2020.06.004
NR 132
TC 22
Z9 22
U1 34
U2 120
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 1846
EP 1861
DI 10.1109/TEM.2022.3157976
EA APR 2022
PG 16
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA EO4E7
UT WOS:000785780800001
OA Green Accepted
DA 2024-03-27
ER
PT J
AU Ben Saad, S
Choura, F
AF Ben Saad, Sihem
Choura, Fatma
TI Towards better interaction between salespeople and consumers: the role
of virtual recommendation agent
SO EUROPEAN JOURNAL OF MARKETING
LA English
DT Article
DE E-commerce; Digital transformation; Artificial intelligence; Innovation;
Digital technologies; Augmented reality; Virtual recommendation agent
(VRA); Perceived enjoyment; Online impulse buying; Perceived risk;
Gender
ID PLAY ONLINE GAMES; EXAMINING GENDER-DIFFERENCES; PERCEIVED RISK;
ARTIFICIAL-INTELLIGENCE; TECHNOLOGY ACCEPTANCE; DIGITAL TRANSFORMATION;
INTRINSIC MOTIVATION; CUSTOMER EXPERIENCE; ELECTRONIC COMMERCE; PURCHASE
INTENTION
AB PurposeIn the context of a profound digital transformation, the need for social interactivity is becoming fundamental for consumers on e-commerce sites. It allows them to interact with the company in the same way as with salespeople in physical stores. Among the different existing virtual agents used by companies to offer online solid interaction, this study focuses on virtual recommendation agents (VRAs). The purpose of this paper is to investigate the effectiveness of VRA on consumers' psychological states and online impulse buying. Design/methodology/approachAn experimental website was designed for this study. After interacting with VRA, respondents had to take part in a survey. The questionnaire included measures of perception of the VRA, perceived enjoyment, online impulse buying and perceived risk. Structural equation modelling was used to test the research model. FindingsThe results confirm the positive influence of the VRA on perceived enjoyment, which is positively associated with online impulse buying. The effect of the VRA's presence on perceived enjoyment is moderated by gender. Research limitations/implicationsOnly one product category was studied, for which the advice of VRAs is undoubtedly essential. However, this could also be valid for other products, such as technological products, where the consumer's level of expertise may be low. Hence, the authors propose to extend this study to various products for a better generalization of the results. Practical implicationsThis study provides practitioners with relevant findings on the efficiency of VRAs and offers them guidelines to design more interactive commercial websites with higher levels of social interactions. Such interactions should reduce perceived risks and make visitors more confident. This can encourage more traffic and sales, which implies growth in incomes and revenues. Social implicationsThrough this technology, VRAs can create more humanized links between consumers and companies. Originality/valueWorking on VRAs is original as they represent the technology that can replace salespeople. In addition, to the best of the authors' knowledge, this research is the first to test the impact of VRA on online impulse buying. By examining the VRA's set of fundamental capabilities, this study contributes to existing research on how companies should integrate digital technologies in their sales interactions with consumers, which to date has focused on other sales channels such as social media platforms.
C1 [Ben Saad, Sihem] Univ Tunis Carthage, Business Dept, Carthage Business Sch, Tunis, Tunisia.
[Choura, Fatma] Univ Tunis El Manar, Higher Inst Comp Sci, Ariana, Tunisia.
[Choura, Fatma] Univ La Manouba, LIGUE Lab, Manouba, Tunisia.
C3 Universite de Tunis-El-Manar; Universite de la Manouba
RP Ben Saad, S (autor correspondiente), Univ Tunis Carthage, Business Dept, Carthage Business Sch, Tunis, Tunisia.
EM sihem.bensaad87@gmail.com; fatma.choura@isi.utm.tn
CR Agarwal S., 2001, J MARKET THEORY PRAC, V9, P1, DOI 10.1080/10696679.2001
Aggarwal P, 2007, J CONSUM RES, V34, P468, DOI 10.1086/518544
Ahn T, 2007, INFORM MANAGE-AMSTER, V44, P263, DOI 10.1016/j.im.2006.12.008
Akman H, 2019, EUR J MARKETING, V53, P1205, DOI 10.1108/EJM-12-2016-0780
Akrout F., 2010, Les Methodes des Equations Structurelle, V1iere
Al-Natour S, 2005, SIGHCI 2005 P, P4
Algharabat R, 2017, J RETAIL CONSUM SERV, V36, P203, DOI 10.1016/j.jretconser.2017.02.007
[Anonymous], 2002, D CISIONS MARKETING
[Anonymous], 1996, REV FRANCAISE GESTIO
[Anonymous], 2010, MANAGEMENT AVENIR
Anshu K, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102798
Arioli M, 2018, BIOMED RES INT, V2018, DOI 10.1155/2018/4283427
Ayub R., 2018, MARKET FORCES RES J, V13, P1
Azuma RT, 1997, PRESENCE-VIRTUAL AUG, V6, P355, DOI 10.1162/pres.1997.6.4.355
Barakat M A., 2019, Journal of Marketing Management, V7, P120, DOI DOI 10.15640/JMM.V7N1A10
Barari M, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101985
BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
Bartneck C, 2009, INT J SOC ROBOT, V1, P71, DOI 10.1007/s12369-008-0001-3
Bauer R. A., 1960, CONSUMER BEHAV RISK
Beneke J, 2013, J PROD BRAND MANAG, V22, P218, DOI 10.1108/JPBM-02-2013-0262
Benlian A, 2020, INFORM SYST J, V30, P1010, DOI 10.1111/isj.12243
Bianchi I, 2014, ACTA PSYCHOL, V151, P117, DOI 10.1016/j.actpsy.2014.06.003
BIERSTEDT R, 1965, AM SOCIOL REV, V30, P789, DOI 10.2307/2091154
Blazevic V, 2014, J INTERACT MARK, V28, P87, DOI 10.1016/j.intmar.2013.09.003
Bleier A, 2019, J MARKETING, V83, P98, DOI 10.1177/0022242918809930
Blinka L., 2014, Cyberpsychology, V8, P1, DOI DOI 10.5817/CP2014-2-6
Boksberger PE, 2007, J AIR TRANSP MANAG, V13, P90, DOI 10.1016/j.jairtraman.2006.10.003
Bonetti F, 2018, PROGR IS, P119, DOI 10.1007/978-3-319-64027-3_9
Borges A, 2013, INT J RETAIL DISTRIB, V41, P498, DOI 10.1108/IJRDM-02-2012-0014
Bressolles G., 2007, Journal of Customer Behaviour, V6, P37, DOI [10.1362/147539207x198365, DOI 10.1362/147539207X198365]
Brom C., 2009, LECT NOTES COMPUTER, V5773, DOI 10.1007/978-3-642-04380-2_8
CARLSON R, 1971, PSYCHOL BULL, V75, P203
Caru A., 2007, ACR EUROPEAN ADV
Chan TKH, 2017, INFORM MANAGE-AMSTER, V54, P204, DOI 10.1016/j.im.2016.06.001
Chandler J, 2010, J CONSUM PSYCHOL, V20, P138, DOI 10.1016/j.jcps.2009.12.008
Chang CC, 2013, TELEMAT INFORM, V30, P311, DOI 10.1016/j.tele.2012.10.006
Chang CC, 2011, INT J ONLINE MARKET, V1, P29, DOI 10.4018/ijom.2011070103
Chang YW, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102325
Charfi A., 2011, REV FRAN AISE MARKET, V20, P49
Chaudhuri A, 2001, J MARKETING, V65, P81, DOI 10.1509/jmkg.65.2.81.18255
Chen Diane, 2016, Transgend Health, V1, P117, DOI 10.1089/trgh.2016.0009
Chen MF, 2007, FOOD QUAL PREFER, V18, P662, DOI 10.1016/j.foodqual.2006.10.002
Chen YS, 2013, J BUS ETHICS, V114, P489, DOI 10.1007/s10551-012-1360-0
Choi DS, 2004, CYBERPSYCHOL BEHAV, V7, P11, DOI 10.1089/109493104322820066
Cissé-Depardon K, 2009, RECH APPL MARKET-ENG, V24, P43, DOI 10.1177/076737010902400103
Clemons EK, 2009, DECIS SUPPORT SYST, V48, P46, DOI 10.1016/j.dss.2009.05.003
Cohen Andrew., 1994, Assessing Language Ability in the Classroom
Cortez RM, 2020, IND MARKET MANAG, V88, P125, DOI 10.1016/j.indmarman.2020.05.004
Cova B., 2008, DECISIONS MARKETING, V50, P19
Crandall JW, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-017-02597-8
Cyr D, 2013, COMPUT HUM BEHAV, V29, P1358, DOI 10.1016/j.chb.2013.01.050
Dacko SG, 2017, TECHNOL FORECAST SOC, V124, P243, DOI 10.1016/j.techfore.2016.09.032
Daugherty T, 2008, PSYCHOL MARKET, V25, P568, DOI 10.1002/mar.20225
DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
de Almeida SO, 2014, J INTERACT MARK, V28, P196, DOI 10.1016/j.intmar.2014.02.003
De Keyser A, 2020, J SERV RES-US, V23, P433, DOI 10.1177/1094670520928390
de Ruyter A., 2018, Journal of International Affairs, V72, P37
Dholakia R.R., 1999, INT J RETAIL DISTRIB, V27, P154, DOI [10.1108/09590559910268499, DOI 10.1108/09590559910268499]
Dickson PR, 2000, J CONSUM RES, V27, P115, DOI 10.1086/314313
DION K, 1972, J PERS SOC PSYCHOL, V24, P285, DOI 10.1037/h0033731
Dittmar H, 2004, SEX ROLES, V50, P423, DOI 10.1023/B:SERS.0000018896.35251.c7
Doofinder, 2021, US
DOWLING GR, 1994, J CONSUM RES, V21, P119, DOI 10.1086/209386
Du SC, 2014, P NATL ACAD SCI USA, V111, pE1454, DOI 10.1073/pnas.1322355111
DuPuis E.Melanie., 2004, Smoke and Mirrors: The Politics and Culture of Air Pollution
Durand E., 2015, MANAGEMENT AVENIR, V83, P33
Dwivedi A, 2019, EUR J MARKETING, V53, P1176, DOI 10.1108/EJM-09-2016-0511
EAGLY AH, 1987, J PERS SOC PSYCHOL, V53, P451, DOI 10.1037/0022-3514.53.3.451
Eagly AH, 1997, AM PSYCHOL, V52, P1380, DOI 10.1037/0003-066X.52.12.1380.b
EDGINGTON ES, 1985, EDUC ADMIN QUART, V21, P235, DOI 10.1177/0013161X85021003007
Emerson R.M., 1981, SOCIAL PSYCHOL SOCIO
Engeser S, 2008, MOTIV EMOTION, V32, P158, DOI 10.1007/s11031-008-9102-4
Epley N., 2018, Journal of the Association for Consumer Research, V3, P591
Epley N, 2008, SOC COGNITION, V26, P143, DOI 10.1521/soco.2008.26.2.143
Etemad-Sajadi R, 2016, COMPUT HUM BEHAV, V61, P227, DOI 10.1016/j.chb.2016.03.045
Eyal T, 2018, J PERS SOC PSYCHOL, V114, P547, DOI 10.1037/pspa0000115
Featherman M., 2003, 36 ANN HI INT C SYST, DOI 10.1109/hicss.2003.1174433
Featherman MS, 2003, INT J HUM-COMPUT ST, V59, P451, DOI 10.1016/S1071-5819(03)00111-3
Febrilia I., 2021, SOC SCI HUMANIT OPEN, V4, DOI [10.1016/j.ssaho.2021.100182, DOI 10.1016/J.SSAHO.2021.100182]
Ferreira I, 2007, OBES REV, V8, P129, DOI 10.1111/j.1467-789X.2006.00264.x
Figueiredo B, 2016, J CONSUM RES, V43, P509, DOI 10.1093/jcr/ucw038
Filser M., 1996, REV FRAN AISE GESTIO, V110, P90
Filser M., 2002, Decisions Marketing, V28, P13, DOI DOI 10.2307/40592824
Fiore AM, 2005, PSYCHOL MARKET, V22, P669, DOI 10.1002/mar.20079
Floh A, 2013, ELECTRON COMMER R A, V12, P425, DOI 10.1016/j.elerap.2013.06.001
FORNELL C, 1981, J MARKETING RES, V18, P382, DOI 10.2307/3150980
Forsythe SM, 2003, J BUS RES, V56, P867, DOI 10.1016/S0148-2963(01)00273-9
Fryer LK, 2017, COMPUT HUM BEHAV, V75, P461, DOI 10.1016/j.chb.2017.05.045
Fu SH, 2018, INT J INFORM MANAGE, V40, P88, DOI 10.1016/j.ijinfomgt.2018.01.013
Fyrberg A, 2009, J SERV MANAGE, V20, P420, DOI 10.1108/09564230910978511
Gilbert MH, 2011, EUR REV APPL PSYCHOL, V61, P195, DOI 10.1016/j.erap.2011.09.001
Mieres CG, 2006, EUR J MARKETING, V40, P61, DOI 10.1108/03090560610637310
Gray DE, 2003, SOC SCI MED, V56, P631, DOI 10.1016/S0277-9536(02)00059-X
Grewal D, 2020, J RETAILING, V96, P3, DOI 10.1016/j.jretai.2020.02.002
Guo YM, 2009, INFORM SYST J, V19, P369, DOI 10.1111/j.1365-2575.2007.00292.x
Gurviez P., 2002, RECHERCHE APPL MARKE, V17, P41, DOI DOI 10.1177/076737010201700304
Hair J.F., 2009, Multivariate data analysis
Hamari J, 2014, COMPUT HUM BEHAV, V40, P133, DOI 10.1016/j.chb.2014.07.048
Handayani W., 2018, Management Science Letters, V8, P283, DOI DOI 10.5267/J.MSL.2018.4.019
Hashmi FK, 2020, J RELIG HEALTH, V59, P2697, DOI 10.1007/s10943-020-01063-y
Hassan L, 2019, INT J INFORM MANAGE, V46, P151, DOI 10.1016/j.ijinfomgt.2018.12.004
Hassani Naghmeh Sheikh, 2021, International Journal of Business Innovation and Research, V25, P242, DOI 10.1504/IJBIR.2021.115459
Hetzel P, 2002, REV FRANCAISE MARKET, V187, P61
Hirschman E.C., 1986, PERSPECTIVES METHODO, P213
Hirschman E.C., 1989, MEANING CHRISTMAS
Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841
Hoffman DL, 2009, J INTERACT MARK, V23, P23, DOI 10.1016/j.intmar.2008.10.003
Holdack E, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2020.102259
Holzwarth M, 2006, J MARKETING, V70, P19, DOI 10.1509/jmkg.70.4.19
Horst M, 2007, COMPUT HUM BEHAV, V23, P1838, DOI 10.1016/j.chb.2005.11.003
Hostler RE, 2011, INFORM MANAGE-AMSTER, V48, P336, DOI 10.1016/j.im.2011.08.002
Hsu CL, 2007, COMPUT HUM BEHAV, V23, P1642, DOI 10.1016/j.chb.2005.09.001
Hsu CL, 2004, INFORM MANAGE-AMSTER, V41, P853, DOI 10.1016/j.im.2003.08.014
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Huang YC, 2016, INT J TOUR RES, V18, P116, DOI 10.1002/jtr.2038
Hwang YJ, 2007, DECIS SUPPORT SYST, V43, P746, DOI 10.1016/j.dss.2006.12.008
Iacobucci D., 2008, MEDIATION ANAL
Ibarra-Cantu C, 2021, J BUS RES, V134, P70, DOI 10.1016/j.jbusres.2021.05.012
Itani OS, 2020, IND MARKET MANAG, V90, P264, DOI 10.1016/j.indmarman.2020.07.015
Jacobson J, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.03.001
Jacoby Jacob., 1972, ACR SPECIAL VOLUMES, DOI DOI 10.4337/9781843768708.00081
Jafari-Sadeghi V, 2021, J BUS RES, V124, P100, DOI 10.1016/j.jbusres.2020.11.020
Jahan N., 2021, Prince Sultan University Research Review, V5, P85, DOI [DOI 10.1108/PRR-12-2019-0036, 10.1108/PRR-12-2019-0036]
Javornik A, 2016, J RETAIL CONSUM SERV, V30, P252, DOI 10.1016/j.jretconser.2016.02.004
Jeffrey Scott A., 2007, Electronic Commerce Research, V7, P367, DOI 10.1007/s10660-007-9011-8
Jin CH, 2014, COMPUT HUM BEHAV, V39, P29, DOI 10.1016/j.chb.2014.06.022
Johnstone Micael-Lee., 2005, Journal of Consumer Behaviour, V4, P234, DOI DOI 10.1002/CB.11
KAHNEMAN D, 1979, COGNITION, V7, P409, DOI 10.1016/0010-0277(79)90024-6
KAPLAN LB, 1974, J APPL PSYCHOL, V59, P287, DOI 10.1037/h0036657
Kapoor R, 2018, MEASUREMENT, V120, P52, DOI 10.1016/j.measurement.2018.02.008
Kasap Z, 2009, IEEE COMPUT GRAPH, V29, P20, DOI 10.1109/MCG.2009.26
Kätsyri J, 2020, NEUROIMAGE, V204, DOI 10.1016/j.neuroimage.2019.116216
Kauffman RJ, 2001, INT J ELECTRON COMM, V5, P5, DOI 10.1080/10864415.2001.11044222
Kegel LC, 2020, SOC COGN AFFECT NEUR, V15, P347, DOI 10.1093/scan/nsaa039
Kim DJ, 2008, DECIS SUPPORT SYST, V44, P544, DOI 10.1016/j.dss.2007.07.001
Kim J, 2008, J INTERACT MARK, V22, P45, DOI 10.1002/dir.20113
Klerck D, 2007, PSYCHOL MARKET, V24, P171, DOI 10.1002/mar.20157
KOTLER P, 1974, J MARKETING, V38, P20, DOI 10.2307/1249846
KOTLER P, 1974, J RETAILING, V49, P48
Kozinets RV, 2021, J MARKETING, V85, P156, DOI 10.1177/0022242920972933
Kuppelwieser VG, 2021, J BUS RES, V126, P624, DOI 10.1016/j.jbusres.2020.01.042
Laranjo L, 2018, J AM MED INFORM ASSN, V25, P1248, DOI 10.1093/jamia/ocy072
Laroche M, 1996, J BUS RES, V37, P115, DOI 10.1016/0148-2963(96)00056-2
Lee JW, 2019, EUR J MARKETING, V53, P1125, DOI 10.1108/EJM-12-2016-0788
Lee MC, 2010, INT J HUM-COMPUT INT, V26, P601, DOI 10.1080/10447311003781318
Lemoine J.F., 2008, Revue Francaise du Marketing, V217, P45
Lemoine J-F., 2011, Decisions Marketing, V31, P47
Lennon SJ, 2007, PSYCHOL MARKET, V24, P375, DOI 10.1002/mar.20165
Leong LY, 2017, INTERNET RES, V27, P786, DOI 10.1108/IntR-04-2016-0107
Li H., 2001, Journal of Interactive Marketing, V15, P13, DOI [https://doi.org/10.1002/dir.1013, DOI 10.1002/DIR.1013, 10.1002/dir.1013]
Li J, 2015, INT J HUM-COMPUT ST, V77, P23, DOI 10.1016/j.ijhcs.2015.01.001
Li XG, 2021, COMPUT HUM BEHAV, V118, DOI 10.1016/j.chb.2021.106680
Liang CC, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2020.102434
Lin KY, 2011, COMPUT HUM BEHAV, V27, P1152, DOI 10.1016/j.chb.2010.12.009
Lin XL, 2020, INT J INFORM MANAGE, V50, P45, DOI 10.1016/j.ijinfomgt.2019.05.004
Liu YW, 2017, PERS INDIV DIFFER, V104, P224, DOI 10.1016/j.paid.2016.08.011
Lobova SV, 2020, J INTELLECT CAP, V21, P623, DOI 10.1108/JIC-11-2019-0277
Lowry PB, 2013, J ASSOC INF SYST, V14, P617
Luna-Arocas R., 2000, INT J ORG THEORY BEH, V13, P55
LURIGIO AJ, 1985, J PERS SOC PSYCHOL, V48, P1112, DOI 10.1037/0022-3514.48.5.1112
Lusch RF, 2015, MIS QUART, V39, P155
Macedonia M, 2014, MIND BRAIN EDUC, V8, P74, DOI 10.1111/mbe.12047
Maff esoli, 1990, TELOS, V85, P89, DOI DOI 10.3817/0990085089
Mao LL, 2015, J INTEGR AGR, V14, P1542, DOI 10.1016/S2095-3119(15)61039-5
Marschner L, 2015, INT J PSYCHOPHYSIOL, V97, P85, DOI 10.1016/j.ijpsycho.2015.05.007
McCarthy M, 2005, FOOD QUAL PREFER, V16, P435, DOI 10.1016/j.foodqual.2004.08.003
MEREDITH W, 1992, PSYCHOMETRIKA, V57, P289, DOI 10.1007/BF02294510
MEYERSLEVY J, 1991, J MARKETING RES, V28, P84, DOI 10.2307/3172728
Milfont TL, 2010, INT J PSYCHOL RES, V3, P111
Mishra AS, 2015, PSYCHOL MARKET, V32, P1158, DOI 10.1002/mar.20846
Mitchell V.W., 1999, EUR J MARKETING, V33, P163, DOI [10.1108/03090569910249229, DOI 10.1108/03090569910249229]
Mohan G, 2013, EUR J MARKETING, V47, P1711, DOI 10.1108/EJM-03-2011-0110
Molinillo S, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2020.102404
Moncrief WC, 2000, J BUS RES, V49, P245, DOI 10.1016/S0148-2963(99)00019-3
Nghia HT, 2022, ASIA PAC J MARKET LO, V34, P594, DOI 10.1108/APJML-09-2020-0668
Norouzi* N., 2019, S SPATIAL USER INTER, P1, DOI DOI 10.1007/978-3-030-04110-6_1
Novak TP, 2019, J ACAD MARKET SCI, V47, P216, DOI 10.1007/s11747-018-0608-3
Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
Nowak KL, 2018, REV COMMUN RES, V6, P30, DOI 10.12840/issn.2255-4165.2018.06.01.015
O'Keefe DJ, 2003, COMMUN THEOR, V13, P251, DOI 10.1111/j.1468-2885.2003.tb00292.x
Ong EYL, 2011, PERS INDIV DIFFER, V50, P180, DOI 10.1016/j.paid.2010.09.022
Oppenheimer DM, 2009, J EXP SOC PSYCHOL, V45, P867, DOI 10.1016/j.jesp.2009.03.009
Otnes C, 2001, J RETAILING, V77, P111, DOI 10.1016/S0022-4359(00)00047-6
Paavilainen J, 2013, SIMULAT GAMING, V44, P794, DOI 10.1177/1046878113514808
Pandey S, 2018, INT J RETAIL DISTRIB, V46, P323, DOI 10.1108/IJRDM-01-2017-0005
Pantano E., 2014, Procedia Environmental Science, V22, P101, DOI [10.1016/j.proenv.2014.11.010, DOI 10.1016/J.PROENV.2014.11.010]
Pantano E, 2012, J RETAIL CONSUM SERV, V19, P279, DOI 10.1016/j.jretconser.2012.02.002
Pantano Eleonora, 2012, Journal of Technology Management & Innovation, V7, P1
Pantano E, 2010, J RETAIL CONSUM SERV, V17, P200, DOI 10.1016/j.jretconser.2010.03.010
Parboteeah DV, 2009, INFORM SYST RES, V20, P60, DOI 10.1287/isre.1070.0157
Pe-Than EPP, 2014, COMPUT HUM BEHAV, V39, P88, DOI 10.1016/j.chb.2014.06.023
Petrick J. F., 2004, Journal of Travel Research, V42, P397, DOI 10.1177/0047287504263037
Pitt CS, 2020, EUR J MARKETING, V54, P305, DOI 10.1108/EJM-01-2019-0083
POLEGATO R, 1994, J CONSUM AFF, V28, P278, DOI 10.1111/j.1745-6606.1994.tb00852.x
Ponsignon F, 2020, TOUR MANAG PERSPECT, V35, DOI 10.1016/j.tmp.2020.100723
POORTINGA YH, 1987, J CROSS CULT PSYCHOL, V18, P259, DOI 10.1177/0022002187018003001
Pras B., 2009, MARKET FONDEMENTS ME
Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
Pura M., 2005, MANAG SERV QUAL, V15, P509, DOI [10.1108/09604520510634005, DOI 10.1108/09604520510634005]
Qiu LY, 2009, J MANAGE INFORM SYST, V25, P145, DOI 10.2753/MIS0742-1222250405
Ramaswamy VenkatFrancis Gouillart., 2010, POWER COCREATION BUI
Rauschnabel Philipp A., 2016, International Journal of Technology Marketing, V11, P123
Rentschler R., 1996, Accounting, Auditing Accountability Journal, V9, P100, DOI DOI 10.1108/09513579610151971
Rese A, 2017, TECHNOL FORECAST SOC, V124, P306, DOI 10.1016/j.techfore.2016.10.010
Rihova I, 2015, INT J TOUR RES, V17, P356, DOI 10.1002/jtr.1993
Rook DW, 1995, J CONSUM RES, V22, P305, DOI 10.1086/209452
ROOK DW, 1987, J CONSUM RES, V14, P189, DOI 10.1086/209105
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
ROSENTHAL R, 1995, PSYCHOL BULL, V118, P183, DOI 10.1037/0033-2909.118.2.183
Sachs JD, 2019, NAT SUSTAIN, V2, P805, DOI 10.1038/s41893-019-0352-9
Martín SS, 2011, ELECTRON MARK, V21, P267, DOI 10.1007/s12525-011-0074-y
Sarah FH, 2021, J INTERNET COMMER, V20, P25, DOI 10.1080/15332861.2020.1836593
Satyavani B., 2018, J BUSINESS MANAGEMEN, V20, P77, DOI DOI 10.9790/487X-2008047783
Schlosser AE, 2006, J MARKETING, V70, P133, DOI 10.1509/jmkg.70.2.133
Schmidt S, 2019, COMPUT GRAPH-UK, V83, P1, DOI 10.1016/j.cag.2019.06.002
Schmitt N, 1996, PSYCHOL ASSESSMENT, V8, P350, DOI 10.1037/1040-3590.8.4.350
Septianto F, 2019, J BUS RES, V99, P12, DOI 10.1016/j.jbusres.2019.02.021
Shaw N, 2019, INT J INFORM MANAGE, V45, P44, DOI 10.1016/j.ijinfomgt.2018.10.024
SHETH JN, 1991, J BUS RES, V22, P159, DOI 10.1016/0148-2963(91)90050-8
Shiau WL, 2018, INT J INFORM MANAGE, V43, P52, DOI 10.1016/j.ijinfomgt.2018.06.006
Shin DH, 2008, CYBERPSYCHOL BEHAV, V11, P378, DOI 10.1089/cpb.2007.0117
Shin DH, 2011, COMPUT HUM BEHAV, V27, P852, DOI 10.1016/j.chb.2010.11.010
Shin DH, 2010, INT J HUM-COMPUT INT, V26, P317, DOI 10.1080/10447310903575473
Sigala M., 2006, Managing Service Quality: An International Journal, V16, P395, DOI [10.1108/09604520610675720/FULL/PDF, DOI 10.1108/09604520610675720]
Sin, 2020, HUM-COMPUT INTERACT, V45, P1, DOI [https://doi.org/10.1080/07370024.2020.1731696, DOI 10.1080/07370024.2020.1731696]
Sin J, 2020, HUM-COMPUT INTERACT, V35, P481, DOI 10.1080/07370024.2020.1731690
Smith JB, 2007, J MARKET THEORY PRAC, V15, P7, DOI 10.2753/MTP1069-6679150101
Söderlund GBW, 2016, FRONT PSYCHOL, V7, DOI 10.3389/fpsyg.2016.00034
Spence J. T., 1979, Masculinity and femininity: Their psychological dimensions, correlates, and antecedents
STEUER J, 1992, J COMMUN, V42, P73, DOI 10.1111/j.1460-2466.1992.tb00812.x
Sun NY, 2021, COMPUT HUM BEHAV REP, V4, DOI 10.1016/j.chbr.2021.100146
Sweeney JC, 2001, J RETAILING, V77, P203, DOI 10.1016/S0022-4359(01)00041-0
Sweetser P., 2005, COMPUTERS ENTERTAINM, V3, P1
Tam JLM, 2012, MARK INTELL PLAN, V30, P33, DOI 10.1108/02634501211193903
TAYLOR MC, 1982, PSYCHOL BULL, V92, P347, DOI 10.1037/0033-2909.92.2.347
TAYLOR SA, 1994, J RETAILING, V70, P163, DOI 10.1016/0022-4359(94)90013-2
Thaichon P, 2018, J PERS SELL SALES M, V38, P277, DOI 10.1080/08853134.2018.1441718
Trepte S, 2012, COMPUT HUM BEHAV, V28, P832, DOI 10.1016/j.chb.2011.12.003
Tsichla E., 2014, J MARK COMMUN, V22, P563, DOI [10.1080/13527266.2014.903507, DOI 10.1080/13527266.2014.903507]
Tuu HH, 2009, ASIA PAC J MARKET LO, V21, P521, DOI 10.1108/13555850910997571
Uysal E, 2022, J ACAD MARKET SCI, V50, P1153, DOI 10.1007/s11747-022-00856-9
Vaitonyt J., 2021, COMPUT HUM BEHAV, V3
Van den Poel D, 1999, J BUS RES, V45, P249, DOI 10.1016/S0148-2963(97)00236-1
van der Heijden H, 2004, MIS QUART, V28, P695, DOI 10.2307/25148660
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Venkatesh V, 2000, MIS QUART, V24, P115, DOI 10.2307/3250981
Verhoef PC, 2009, J RETAILING, V85, P31, DOI 10.1016/j.jretai.2008.11.001
Viot C., 2014, D CISIONS MARKETING, V65, P45
Vitale G, 2020, J MANAG CONTROL, V31, P123, DOI 10.1007/s00187-020-00298-w
Vorderer P, 2004, COMMUN THEOR, V14, P388, DOI 10.1111/j.1468-2885.2004.tb00321.x
WALSH J, 2000, EUROPEAN MANAGEMENT, V18, P85, DOI [DOI 10.1016/S0263-2373(99)00071-7, 10.1016/S0263-2373(99)00071-7]
Wang FY, 2007, IEEE INTELL SYST, V22, P79, DOI 10.1109/MIS.2007.41
Wang H, 2016, J BUS RES, V69, P3827, DOI 10.1016/j.jbusres.2016.04.007
Wang W, 2019, COMPUT HUM BEHAV, V93, P176, DOI 10.1016/j.chb.2018.12.007
Wei PS, 2014, INTERNET RES, V24, P313, DOI 10.1108/IntR-04-2013-0082
Weigel P, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9245350
Weiser EB, 2000, CYBERPSYCHOL BEHAV, V3, P167, DOI 10.1089/109493100316012
Wells JD, 2011, J ASSOC INF SYST, V12, P32
Wiese E, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.01663
WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115, DOI 10.1017/S0269888900008122
Wu IL, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2020.102099
Wu IL, 2016, INT J INFORM MANAGE, V36, P284, DOI 10.1016/j.ijinfomgt.2015.11.015
Wu J., 2007, Journal of Electronic Commerce Research, V8, P128, DOI DOI 10.4018/JEBR.2012070103
Wu KW, 2015, J RETAIL CONSUM SERV, V25, P36, DOI 10.1016/j.jretconser.2015.03.007
Xiao SH, 2013, INT J MANAG REV, V15, P333, DOI 10.1111/j.1468-2370.2012.00345.x
Xu YR, 2002, IND MANAGE DATA SYST, V102, P442, DOI 10.1108/02635570210445871
Yan ZJ, 2016, INFORM MANAGE-AMSTER, V53, P643, DOI 10.1016/j.im.2016.02.001
Yüksel A, 2007, TOURISM MANAGE, V28, P703, DOI 10.1016/j.tourman.2006.04.025
ZEITHAML VA, 1985, J MARKETING, V49, P64, DOI 10.2307/1251616
Montenegro JLZ, 2019, EXPERT SYST APPL, V129, P56, DOI 10.1016/j.eswa.2019.03.054
Zhang KZK, 2018, INTERNET RES, V28, P522, DOI 10.1108/IntR-12-2016-0377
Zhao F, 2016, INNOVATION, ENTREPRENEURSHIP AND DIGITAL ECOSYSTEMS, P2156
Zhao X., 2011, Recherche et Applications en Marketing, V26, P81, DOI [10.1177/076737011102600105, DOI 10.1177/076737011102600105]
Zheng XB, 2019, INT J INFORM MANAGE, V48, P151, DOI 10.1016/j.ijinfomgt.2019.02.010
Zhou LN, 2013, ELECTRON COMMER R A, V12, P61, DOI 10.1016/j.elerap.2013.02.003
Zhu K, 2006, EUR J INFORM SYST, V15, P601, DOI 10.1057/palgrave.ejis.3000650
NR 277
TC 9
Z9 9
U1 29
U2 89
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0309-0566
EI 1758-7123
J9 EUR J MARKETING
JI Eur. J. Market.
PD FEB 13
PY 2023
VL 57
IS 3
SI SI
BP 858
EP 903
DI 10.1108/EJM-11-2021-0892
EA NOV 2022
PG 46
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 9B9AS
UT WOS:000882762200001
DA 2024-03-27
ER
PT J
AU Deng, YP
Zheng, JY
Khern-Am-Nuai, W
Kannan, K
AF Deng, Yipu
Zheng, Jinyang
Khern-Am-Nuai, Warut
Kannan, Karthik
TI More than the Quantity: The Value of Editorial Reviews for a
User-Generated Content Platform
SO MANAGEMENT SCIENCE
LA English
DT Article
DE natural language processing; editorial review; user-generated content;
difference-in-differences; herding effect
ID WORD-OF-MOUTH; NEGATIVE REVIEWS; FILM-CRITICS; ONLINE; PRODUCT; IMPACT;
INFORMATION; BEHAVIOR; RATINGS; COMMUNITY
AB We investigate an editorial review program for which a review platform supplements user reviews with editorial ones written by professional writers. Specifically, we examine whether and how editorial reviews influence subsequent user reviews (reviews written by noneditor reviewers). A quasiexperiment conducted on a leading review platform in Asia, based on several econometric and natural language processing techniques, yields empirical evidence of an overall positive effect of editorial reviews on subsequent user reviews from the platform's perspective. First, more reviews are provided for restaurants that receive editorial reviews. In addition, these reviews discuss substantive topics while also including a discussion on other topics, leading to a net increase in content length and variety. They also are more neutral in sentiment and are associated with lower rating valences. Further analysis of the mechanism reveals that the subsequent user reviews of the restaurants that receive editorial reviews become more similar to the editorial reviews in regard to topics, sentiment/rating, length, and readability, indicating a herding effect in how to write a review as the main driver of the change in the subsequent reviews. We further empirically isolate this herding effect among long-time reviewers. The findings suggest that review platforms could use an editorial review program not only to boost the quantitative aspect of user reviews but also, to manage the qualitative aspect as well.
C1 [Deng, Yipu] Univ Hong Kong, Fac Business & Econ, Hong Kong, Peoples R China.
[Zheng, Jinyang; Kannan, Karthik] Purdue Univ, Krannert Sch Management, West Lafayette, PA 47907 USA.
[Khern-Am-Nuai, Warut] McGill Univ, Desautels Fac Management, Montreal, PQ H3A IG5, Canada.
C3 University of Hong Kong; Purdue University System; Purdue University;
McGill University
RP Deng, YP (autor correspondiente), Univ Hong Kong, Fac Business & Econ, Hong Kong, Peoples R China.
EM yipudeng@hku.hk; zhengjy@purdue.edu; warut.khern-am-nuai@mcgill.ca;
kkarthik@purdue.edu
RI Kannan, Dr. Karthik/X-8987-2019; Khern-am-nuai, Warut/B-8633-2018
OI Kannan, Dr. Karthik/0000-0001-5438-2460; Khern-am-nuai,
Warut/0000-0002-1028-1593; Deng, Yipu/0000-0003-0924-8689; Zheng,
Jinyang/0000-0001-5028-4193
FU Social Sciences and Humanities Research Council [435-2020-0163]
FX The authors acknowledge financial support fromthe Social Sciences and
Humanities Research Council [Grant 435-2020-0163].
CR Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Autor DH, 2003, J LABOR ECON, V21, P1, DOI 10.1086/344122
Basuroy S, 2003, J MARKETING, V67, P103, DOI 10.1509/jmkg.67.4.103.18692
Bateman PJ, 2011, INFORM SYST RES, V22, P841, DOI 10.1287/isre.1090.0265
Berger J, 2010, MARKET SCI, V29, P815, DOI 10.1287/mksc.1090.0557
Bertrand M, 2004, Q J ECON, V119, P249, DOI 10.1162/003355304772839588
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
Burtch G, 2022, Management Sci.
Burtch G, 2018, MANAGE SCI, V64, P2065, DOI 10.1287/mnsc.2016.2715
Cabral L, 2015, MANAGE SCI, V61, P2052, DOI 10.1287/mnsc.2014.2074
Cai HB, 2009, AM ECON REV, V99, P864, DOI 10.1257/aer.99.3.864
Cao J, 2009, NEUROCOMPUTING, V72, P1775, DOI 10.1016/j.neucom.2008.06.011
Chan J, 2014, MIS QUART, V38, P955, DOI 10.25300/MISQ/2014/38.4.01
Cheema A, 2010, J MARKETING RES, V47, P553, DOI 10.1509/jmkr.47.3.553
Chen Y, 2010, AM ECON REV, V100, P1358, DOI 10.1257/aer.100.4.1358
Chen YB, 2005, MARKET SCI, V24, P218, DOI 10.1287/mksc.1040.0089
Chen YB, 2012, J MARKETING, V76, P116, DOI 10.1509/jm.09.0034
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Cui RM, 2019, MANAGE SCI, V65, P1216, DOI 10.1287/mnsc.2017.2950
Dellarocas C, 2003, MANAGE SCI, V49, P1407, DOI 10.1287/mnsc.49.10.1407.17308
Diestre L, 2020, MANAGE SCI, V66, P3677, DOI 10.1287/mnsc.2019.3386
Eliashberg J, 1997, J MARKETING, V61, P68, DOI 10.2307/1251831
Geetha M, 2017, TOURISM MANAGE, V61, P43, DOI 10.1016/j.tourman.2016.12.022
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
Goes PB, 2014, INFORM SYST RES, V25, P222, DOI 10.1287/isre.2013.0512
Goh KY, 2013, INFORM SYST RES, V24, P88, DOI 10.1287/isre.1120.0469
Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101
Ho YC, 2017, INFORM SYST RES, V28, P626, DOI 10.1287/isre.2017.0694
Hosanagar K, 2014, MANAGE SCI, V60, P805, DOI 10.1287/mnsc.2013.1808
Hu N, 2009, COMMUN ACM, V52, P144, DOI 10.1145/1562764.1562800
Huang N, 2019, MANAGE SCI, V65, P327, DOI 10.1287/mnsc.2017.2944
Hutto C. J., 2014, P INT AAAI C WEB SOC, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
Jiang Y, 2020, INFORM SYST RES, V31, P715, DOI 10.1287/isre.2019.0909
Jin GZ, 2003, Q J ECON, V118, P409, DOI 10.1162/003355303321675428
Khern-am-nuai W, 2018, INFORM SYST RES, V29, P871, DOI 10.1287/isre.2017.0750
Lappas T, 2016, INFORM SYST RES, V27, P940, DOI 10.1287/isre.2016.0674
Lee YJ, 2015, MANAGE SCI, V61, P2241, DOI 10.1287/mnsc.2014.2082
Lee YJ, 2013, J MANAGE INFORM SYST, V30, P213, DOI 10.2753/MIS0742-1222300308
Ma M, 2007, INFORM SYST RES, V18, P42, DOI 10.1287/isre.1070.0113
MANSKI CF, 1993, REV ECON STUD, V60, P531, DOI 10.2307/2298123
MARSH C, 1985, BRIT J POLIT SCI, V15, P51, DOI 10.1017/S0007123400004063
Mayzlin D, 2014, AM ECON REV, V104, P2421, DOI 10.1257/aer.104.8.2421
McKinney V, 2002, INFORM SYST RES, V13, P296, DOI 10.1287/isre.13.3.296.76
Moe WW, 2012, MARKET SCI, V31, P372, DOI 10.1287/mksc.1110.0662
Moe WW, 2011, J MARKETING RES, V48, P444, DOI 10.1509/jmkr.48.3.444
Moffitt Robert A., 2001, Social dynamics, DOI DOI 10.7551/MITPRESS/6294.003.0005
Nair HS, 2010, J MARKETING RES, V47, P883, DOI 10.1509/jmkr.47.5.883
Özpolat K, 2013, INFORM SYST RES, V24, P1100, DOI 10.1287/isre.2013.0489
Pamuru V, 2021, INFORM SYST RES, V32, P950, DOI 10.1287/isre.2021.1004
Qiao D, 2017, P 50 HAW INT C SYST, V7, P1
Ransbotham S, 2019, MARKET SCI, V38, P773, DOI 10.1287/mksc.2018.1115
Reddy SK, 1998, J MARKETING RES, V35, P370, DOI 10.2307/3152034
Schlosser AE, 2005, J CONSUM RES, V32, P260, DOI 10.1086/432235
Shaffer G, 2002, MARKET SCI, V21, P273, DOI 10.1287/mksc.21.3.273.137
Tirole J, 1988, THEORY IND ORG
Xu KQ, 2017, MANAGE SCI, V63, P1469, DOI 10.1287/mnsc.2015.2406
Yazdani E, 2018, MARKET SCI, V37, P838, DOI 10.1287/mksc.2018.1101
Yin DZ, 2016, INFORM SYST RES, V27, P131, DOI 10.1287/isre.2015.0617
Yu Y, 2022, MIS Quart
Zeng XH, 2013, INFORM SYST RES, V24, P71, DOI 10.1287/isre.1120.0464
Zhang JJ, 2012, MANAGE SCI, V58, P892, DOI 10.1287/mnsc.1110.1459
Zhang XQ, 2011, AM ECON REV, V101, P1601, DOI 10.1257/aer.101.4.1601
Zhao WZ, 2015, BMC BIOINFORMATICS, V16, DOI 10.1186/1471-2105-16-S13-S8
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 65
TC 13
Z9 13
U1 32
U2 113
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD SEP
PY 2022
VL 68
IS 9
BP 6865
EP 6888
DI 10.1287/mnsc.2021.4238
EA DEC 2021
PG 24
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA R5JR4
UT WOS:000828467900001
DA 2024-03-27
ER
PT J
AU Economides, AA
Grousopoulou, A
AF Economides, Anastasios A.
Grousopoulou, Amalia
TI Use of mobile phones by male and female Greek students
SO INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS
LA English
DT Article
DE analysis of questionnaire; cell phone; gender difference; handheld
device; higher education; mobile communications; mobile device; mobile
phone; Personal Digital Assistant; PDA; smart phone; user experience
ID INTERNET USE; GENDER; TECHNOLOGY; USAGE; TIME
AB Mobile technology is a continuously growing domain, and research activities regarding its use are quite intensive. A questionnaire regarding the use of mobile devices was developed and distributed to 416 students in it Greek University. There were completed 384 questionnaires. The results revealed that students Use their mobiles mostly for phone calls and Short Message Service (SMS). They also tend to use their mobiles to take photos and activate the reminder. However, they do not deal with many of the devices' operations. They use their mobiles to communicate (telephone, SMS and e-mail) mostly with their boy/girl friend, then with their friends. They use their mobiles mostly at home, then at the University. Also, they consider health issues as the main reason to limit the use of their mobiles. Finally, there was not a statistically significant relationship between genders and their preferences.
C1 [Economides, Anastasios A.; Grousopoulou, Amalia] Univ Macedonia, Dept Informat Syst, Thessaloniki 54006, Greece.
C3 University of Macedonia
RP Economides, AA (autor correspondiente), Univ Macedonia, Dept Informat Syst, Egnatia 156, Thessaloniki 54006, Greece.
EM economid@uom.gr; amalia.grous@hotmail.com
RI Economides, Anastasios A/F-8585-2012
OI Economides, Anastasios A/0000-0001-8056-1024
CR Anderson P., 2004, JISC TECHNOLOGY STAN
Attewell J., 2004, MOBILE TECHNOLOGIES
Auter P. J., 2007, International Journal of Mobile Communications, V5, P139, DOI 10.1504/IJMC.2007.011813
BIMBER B, 2000, SOCIAL SCI Q, V81, P858
Boneva B, 2001, AM BEHAV SCI, V45, P530, DOI 10.1177/00027640121957204
Brenner V, 1997, PSYCHOL REP, V80, P879, DOI 10.2466/pr0.1997.80.3.879
Brown B, 2002, COMP SUPP COMP W SER, P3
Campbell SW, 2006, COMMUN EDUC, V55, P280, DOI 10.1080/03634520600748573
CAMPBELL SW, 2004, P ANN M NAT COMM ASS
Cheung W, 2005, BRIT J EDUC TECHNOL, V36, P237, DOI 10.1111/j.1467-8535.2005.00455.x
COLLIS BA, 1987, J EDUC RES, V81, P17, DOI 10.1080/00220671.1987.10885792
*COMSC, 2007, MOR ON Q WIR SUBSCR
Cutshall R, 2006, EDUC TECHNOL SOC, V9, P112
DIVITINI M, 2002, IEEE WORKSH WMTE 02
DORING N, 2004, SENSE PLACE GLOBAL L, P209
Goodson P, 2001, ARCH SEX BEHAV, V30, P101, DOI 10.1023/A:1002724116437
Green N, 2002, INFORM SOC, V18, P281, DOI 10.1080/01972240290075129
ITU, 2006, WORLD TEL IND
*ITU MIC, 2004, P WORKSH SHAP FUT MO
Jackson LA, 2001, SEX ROLES, V44, P363, DOI 10.1023/A:1010937901821
Jennings S. E., 2001, Journal of Educational Computing Research, V25, P367
Katz J., 2005, A sense of place: The global and the local in mobile communication, P305
Lee H, 2002, INFORM SOC, V18, P235, DOI 10.1080/01972240290075084
Li N, 2007, COMPUT EDUC, V48, P301, DOI 10.1016/j.compedu.2005.01.007
LING R, 2000, ADOPTION MOBILE TELE
LIUKKUNEN K, 2005, P WORLD C ED MULT HY, P1579
Makrakis V, 1996, COMPUT EDUC, V26, P225, DOI 10.1016/0360-1315(95)00085-2
McDonough K, 2006, TURK ONLINE J DISTAN, V7, P153
*MED REP WOM, 2000, MED REP WOM, V28, P1
Mifsud L., 2004, RES USE HANDHELD TEC
Mitra A, 2005, J COMPUT-MEDIAT COMM, V10
Newburger EricC., 1999, COMPUTER USE US
Odell PM, 2000, CYBERPSYCHOL BEHAV, V3, P855, DOI 10.1089/10949310050191836
Ono H, 2003, SOC SCI QUART, V84, P111, DOI 10.1111/1540-6237.t01-1-8401007
Petruk S, 2007, RNA BIOL, V4, P1
Saunders B., 2002, Educational Technology and Society, V5, P1436
Scornavacca E., 2006, International Journal of Mobile Communications, V4, P558
Selwyn N, 2007, BRIT J EDUC TECHNOL, V38, P744, DOI 10.1111/j.1467-8535.2006.00659.x
Sharples M, 2000, COMPUT EDUC, V34, P177, DOI 10.1016/S0360-1315(99)00044-5
Shashaani L, 2001, COMPUT EDUC, V37, P363, DOI 10.1016/S0360-1315(01)00059-8
Shaw LH, 2002, CYBERPSYCHOL BEHAV, V5, P517, DOI 10.1089/109493102321018150
Sismanidis E., 2007, UBIQUITOUS COMPUTING, V2, P27
Smith B.N., 1996, Education, V117, P188
Sugai P., 2007, International Journal of Mobile Communications, V5, P281, DOI 10.1504/IJMC.2007.012395
Switzer S., 2005, ISSUES INFORM SYSTEM, VVI, P127
*TMG, 2005, PRIM MOB TERM RAT
*TMG, 2004, SUP MOB INT TOP 10 M
Triantafillou E., 2008, INT J WEB BASED LEAR, V3, P13
Trifonova A., 2006, International Journal of Mobile Communications, V4, P459, DOI 10.1504/IJMC.2006.008952
Trifonova A., 2004, P IEEE INT C ADV LEA
Trifonova A., 2006, WSEAS Transactions on Advances in Engineering Educatio, V3, P1790
Turel O, 2006, TELECOMMUN POLICY, V30, P314, DOI 10.1016/j.telpol.2005.10.003
Vahey P., 2002, PALM ED PIONEERS PRO
Vasiliou A., 2007, International Journal of Mobile Communications, V5, P423, DOI 10.1504/IJMC.2007.012789
*VNUN, 2006, PICT MESS STILL TOO
Waycott J, 2003, PERS UBIQUIT COMPUT, V7, P30, DOI 10.1007/s00779-002-0211-x
Wei R., 1999, TELEMAT INFORM, V16, P11, DOI [10.1016/S0736-5853(99)00016-7, DOI 10.1016/S0736-5853(99)00016-7]
WILSON T, 2000, INTERNETWEEK, V28, P827
Wireless Intelligence, 2005, WIR INT MARK DAT AN
Zhang YX, 2002, CYBERPSYCHOL BEHAV, V5, P143, DOI 10.1089/109493102753770534
2005, MOBILEDIA NEWS
NR 61
TC 37
Z9 43
U1 0
U2 30
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 1470-949X
EI 1741-5217
J9 INT J MOB COMMUN
JI Int. J. Mob. Commun.
PY 2008
VL 6
IS 6
BP 729
EP 749
DI 10.1504/IJMC.2008.019822
PG 21
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA 384WF
UT WOS:000261774200005
DA 2024-03-27
ER
PT J
AU Paas, LJ
AF Paas, Leonard J.
TI Marketing analytics stages: Demystifying and deploying machine learning
SO INTERNATIONAL JOURNAL OF MARKET RESEARCH
LA English
DT Article
DE big data; machine learning; artificial intelligence; business
applications; data maturity
ID CUSTOMER LIFETIME VALUE; PATTERNS; ACQUISITION; MARKOV; TIME
AB Organisations that develop analytical capabilities can leverage advanced data platforms and cloud-based solutions; they may also experiment with sophisticated machine learning algorithms. But when business analysts or data scientists fail to bridge the gaps among data, analytics, and decision-making, it might imply a premature implementation of complex data analytics. This article aims to derive clear guidelines from management literature to formulate a stepwise approach for deploying marketing analytics with increasing levels of complexity. Furthermore, we demystify the relevant jargon.
C1 [Paas, Leonard J.] Univ Auckland, Business Sch, Auckland, New Zealand.
[Paas, Leonard J.] Univ Auckland, Business Sch, Dept Mkt, Post Private Bag 92019, Auckland 1142, New Zealand.
C3 University of Auckland; University of Auckland
RP Paas, LJ (autor correspondiente), Univ Auckland, Business Sch, Dept Mkt, Post Private Bag 92019, Auckland 1142, New Zealand.
EM Leo.Paas@auckland.ac.nz
OI Paas, Leo/0000-0002-6611-3038
CR Balducci B, 2018, J ACAD MARKET SCI, V46, P557, DOI 10.1007/s11747-018-0581-x
BERGER PD, 1998, J INTERACT MARK, V12, P17
Bhat SA, 2016, INT J BANK MARK, V34, P388, DOI 10.1108/IJBM-11-2014-0160
Birkett A., 2019, 160 DOWNLOADED, V16
Borle S, 2008, MANAGE SCI, V54, P100, DOI 10.1287/mnsc.1070.0746
Bowerman B. L., 2019, BUSINESS STAT ANAL P
Breur T., 2000, J DATABASE MARKETING, V8, P9, DOI [10.1057/palgrave.jdm.3240013, DOI 10.1057/PALGRAVE.JDM.3240013]
Camm J.D., 2019, BUSINESS ANAL, DOI 10.1109/IS48319.2020.9199954
Chapman P., 1999, CRISP DM10 STEP BY S
Chung TS, 2016, J ACAD MARKET SCI, V44, P66, DOI 10.1007/s11747-015-0441-x
Coussement K, 2014, J BUS RES, V67, P2751, DOI 10.1016/j.jbusres.2012.09.024
Davenport T., 2018, DOWNLOADED, V28, P2021
Davenport T.H., 2014, Big data at work
Davenport Thomas., 2017, Competing on Analytics
Ebbes P., 2022, HDB MARKET RES
Fader P., 2022, The customer-base audit: The first step on the journey to customer centricity
Geertsema P., 2022, SSRN DOWNLOADED, V1, P2022
Golder PN, 2023, J MARKETING, V87, P319, DOI 10.1177/00222429221129200
Hahsler M, 2005, J STAT SOFTW, V14
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
HEBDEN JJ, 1974, OXFORD B ECON STAT, V36, P67
HOMMEL G, 1988, BIOMETRIKA, V75, P383, DOI 10.2307/2336190
Huang MH, 2022, J RETAILING, V98, P209, DOI 10.1016/j.jretai.2021.03.001
Hudson E. A., 2019, DOWNLOADED, V17
Jordan MI, 2015, SCIENCE, V349, P255, DOI 10.1126/science.aaa8415
Juhl HJ, 2017, J CONSUM RES, V44, P519, DOI 10.1093/jcr/ucx052
Kamakura W.A., 1991, INT J RES MARK, V8, P329
Kamakura WA, 2003, STAT NEERL, V57, P46, DOI 10.1111/1467-9574.00220
Kamakura WA, 2012, MARKET LETT, V23, P505, DOI 10.1007/s11002-012-9181-6
Kellar G. M., 2012, ACAD MARKETING STUDI, V16, P87
Kerremans M., 2022, BUILDING DIGITAL FUT
Knott A., 2002, J INTERACT MARK, V16, P59, DOI [DOI 10.1002/DIR.10038, 10.1002/dir.10038]
Ko KC, 2023, INT J MARKET RES, V65, P402, DOI 10.1177/14707853231158605
Lee SJ, 2023, NEURAL COMPUT APPL, V35, P10945, DOI 10.1007/s00521-023-08276-8
Leenheer J, 2007, INT J RES MARK, V24, P31, DOI 10.1016/j.ijresmar.2006.10.005
Liebman E, 2019, MIS QUART, V43, P765, DOI 10.25300/MISQ/2019/14750
Lipton ZC, 2018, COMMUN ACM, V61, P36, DOI 10.1145/3233231
Ma LY, 2020, INT J RES MARK, V37, P481, DOI 10.1016/j.ijresmar.2020.04.005
Marchand A, 2020, J RETAILING, V96, P328, DOI 10.1016/j.jretai.2020.01.001
MCFALL J, 1969, J MARKETING, V33, P50, DOI 10.2307/1248673
Paas L. J., 1999, J MARKET FOCUSED MAN, V3, P275, DOI [10.1023/a:1009851006779, DOI 10.1023/A:1009851006779]
Paas L. J., 1999, J DATABASE MARKETING, V6, P321
Paas LJ, 2007, J R STAT SOC A STAT, V170, P955, DOI 10.1111/j.1467-985X.2007.00478.x
Paas LJ, 2021, INT J MARKET RES, V63, P27, DOI 10.1177/1470785320938293
Paas LJ, 1998, J ECON PSYCHOL, V19, P353, DOI 10.1016/S0167-4870(98)00011-7
PAROUSH J, 1965, ECONOMETRICA, V33, P225, DOI 10.2307/1911897
Pauwels K, 2014, It's Not the Size of the Data: It's How You Use it: Smarter Marketing with Analytics and Dashboards
Peterson R., 1992, Journal of The Academy of Marketing Science, V20, P61, DOI [DOI 10.1007/BF02723476, 10.1007/BF02723476]
Prinzie A, 2006, EUR J OPER RES, V170, P710, DOI 10.1016/j.ejor.2004.05.004
Rivera DS, 2015, J DECIS SYST, V24, P73, DOI 10.1080/12460125.2015.994335
Shankar V, 2022, J ACAD MARKET SCI, V50, P1324, DOI 10.1007/s11747-022-00840-3
Sheth J, 2021, J BUS RES, V125, P780, DOI 10.1016/j.jbusres.2020.04.050
Toubia O, 2017, MARKET SCI, V36, P1, DOI 10.1287/mksc.2016.0994
van der Ark LA, 2012, J STAT SOFTW, V48, P1
Verhoef P. C., 2022, CREATING VALUE DATA
Wedel M., 2000, MARKET SEGMENTATION
Yang A.X., 2004, J TARGETING MEASUREM, V13, P50, DOI DOI 10.1057/PALGRAVE.JT.5740131
Zhang G., 2006, International Technology and Innovation Conference, P1710
NR 58
TC 0
Z9 0
U1 5
U2 7
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1470-7853
EI 2515-2173
J9 INT J MARKET RES
JI Int. J. Market Res.
PD NOV
PY 2023
VL 65
IS 6
BP 687
EP 707
DI 10.1177/14707853231191726
EA AUG 2023
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA U5BJ8
UT WOS:001047347200001
DA 2024-03-27
ER
PT J
AU Kim, D
Song, Y
Kim, S
Lee, S
Wu, YQ
Shin, J
Lee, D
AF Kim, Doha
Song, Yeosol
Kim, Songyie
Lee, Sewang
Wu, Yanqin
Shin, Jungwoo
Lee, Daeho
TI How should the results of artificial intelligence be explained to
users?- Research on consumer preferences in user-centered explainable
artificial intelligence
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Explainable artificial intelligence; Explanation interface;
User-centered design; User experience; Interpretability; Conjoint
analysis
ID BLACK-BOX; CONJOINT-ANALYSIS; EXPLANATIONS; CHALLENGES; LIGHT
AB Artificial intelligence (AI) has become part of our everyday lives, and its presence and influence are expected to grow exponentially. Regardless of its expanding impact, the perplexing algorithms and processes that drive AI's decision and output can lead to decreased trust, and thus impede the adoption of future AI services. Explainable AI (XAI) in recommender systems has surfaced as a solution that can help users understand how and why an AI recommended a specific product or service. However, there is no standardized explanation method that satisfies users' preferences and needs. Therefore, the main objective of this study is to explore a unified explanation method that centers around human perspective. This study examines the preference for AI interfaces by inves-tigating the components of user-centered explainability, including scope (global and local) and format (text and visualization). A mixed logit model is used to analyze data collected by a conjoint survey. Results show that local explanation and visualization are preferred, and users dislike lengthy textual interfaces. Our findings incorporate the extraction of monetary value from each attribute.
C1 [Kim, Doha; Song, Yeosol; Kim, Songyie; Lee, Sewang; Wu, Yanqin; Lee, Daeho] Sungkyunkwan Univ, Dept Human AI Interact, 25-2 Sungkyunkwan Ro, Seoul 03063, South Korea.
[Lee, Sewang; Lee, Daeho] Sungkyunkwan Univ, Dept Appl Artificial Intelligence, 25-2 Sungkyunkwan Ro, Seoul 03063, South Korea.
[Lee, Daeho] Sungkyunkwan Univ, Dept Interact Sci, 25-2 Sungkyunkwan Ro, Seoul 03063, South Korea.
[Shin, Jungwoo] Kyung Hee Univ, Dept Ind & Management Syst Engn, 1732 Deogyeong Daero, Yongin 17104, South Korea.
C3 Sungkyunkwan University (SKKU); Sungkyunkwan University (SKKU);
Sungkyunkwan University (SKKU); Kyung Hee University
RP Lee, D (autor correspondiente), Sungkyunkwan Univ, Dept Human AI Interact, 25-2 Sungkyunkwan Ro, Seoul 03063, South Korea.
EM daeho.lee@skku.edu
OI Wu, Yanqin/0000-0001-6759-6353; Kim, Doha/0000-0003-3138-3510; Kim, Song
Yie/0000-0001-8409-5657
FU Ministry of Education of the Republic of Korea; NRF [2020S1A5A8045556,
2020R1F1A1048202]
FX Funding This research was supported by the Ministry of Education of the
Republic of Korea and the NRF (No. 2020S1A5A8045556, 2020R1F1A1048202) .
CR Adadi A, 2018, IEEE ACCESS, V6, P52138, DOI 10.1109/ACCESS.2018.2870052
Allenby GM, 1999, J ECONOMETRICS, V89, P57
Antona B, 2018, APPL ERGON, V68, P12, DOI 10.1016/j.apergo.2017.10.014
Araujo T, 2020, AI SOC, V35, P611, DOI 10.1007/s00146-019-00931-w
Asatiani A, 2020, MIS Q EXEC, V19, P259, DOI 10.17705/2msqe.00037
Bader V, 2019, ORGANIZATION, V26, P655, DOI 10.1177/1350508419855714
Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
Beaudouin V, 2020, Arxiv, DOI [arXiv:2003.07703, DOI 10.2139/SSRN.3559477]
Bond R.R., 2019, HUMAN CTR ARTIFICIAL, P2
Calegari R, 2020, INTELL ARTIF, V14, P7, DOI 10.3233/IA-190036
Champion EM, 2017, DIGIT SCHOLARSH HUM, V32, P25, DOI 10.1093/llc/fqw053
Chung WY, 2022, TELEMAT INFORM, V70, DOI 10.1016/j.tele.2022.101799
Das A., 2020, arXiv
Deeney TA, 2010, READ TEACH, V63, P440, DOI 10.1598/RT.63.6.1
Doshi-Velez Finale, 2018, EXPLAINABLE INTERPRE, P3, DOI [DOI 10.1007/978-3-319-98131-4_1, 10.1007/978-3-319-98131-4_1]
Durán JM, 2021, J MED ETHICS, V47, P329, DOI [10.1136/medethics-2021-107531, 10.1136/medethics-2020-106820]
Ebbers F, 2021, ELECTRON MARK, V31, P411, DOI 10.1007/s12525-020-00447-y
Edwards YD, 2003, J MARKETING RES, V40, P321, DOI 10.1509/jmkr.40.3.321.19233
Fang Q, 2014, IEEE T MULTIMEDIA, V16, P796, DOI 10.1109/TMM.2014.2298216
Ferreira Juliana J., 2020, Design, User Experience, and Usability. Design for Contemporary Interactive Environments. 9th International Conference, DUXU 2020 Held as Part of the 22nd HCI International Conference, HCII 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12201), P56, DOI 10.1007/978-3-030-49760-6_4
Gilpin LH, 2018, PR INT CONF DATA SC, P80, DOI 10.1109/DSAA.2018.00018
GREEN PE, 1978, J CONSUM RES, V5, P103, DOI 10.1086/208721
Gregor S, 1999, MIS QUART, V23, P497, DOI 10.2307/249487
Gryz J, 2021, INTERNET POLICY REV, V10, DOI 10.14763/2021.2.1564
Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
Gunning D., 2021, Appl. AI Lett., DOI [DOI 10.1002/AIL2.61, 10.1002/ail2.61]
Gunning D, 2019, SCI ROBOT, V4, DOI 10.1126/scirobotics.aay7120
Gunning D, 2019, AI MAG, V40, P44, DOI 10.1609/aimag.v40i2.2850
Hair J.F., 2009, Multivariate data analysis
Hohman F, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300809
Holm EA, 2019, SCIENCE, V364, P26, DOI 10.1126/science.aax0162
Huber J, 2001, MARKET LETT, V12, P259, DOI 10.1023/A:1011120928698
Klein A, 2014, TELEMAT INFORM, V31, P410, DOI 10.1016/j.tele.2013.11.006
Ko S, 2023, ENERG POLICY, V173, DOI 10.1016/j.enpol.2022.113385
König PD, 2022, BIG DATA SOC, V9, DOI 10.1177/20539517211069632
Kouki P, 2017, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17), P84, DOI 10.1145/3109859.3109915
Larsson Stefan, 2020, Internet Policy Review, V9, DOI DOI 10.14763/2020.2.1469
Liao QV, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376590
Liao Q Vera, 2021, EXTENDED ABSTRACTS S, P1
Lim BY, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P2119
Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344
Lipton Z.C., 2018, Queue, V16, P31, DOI DOI 10.1145/3236386.3241340
Maeng K, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101327
Martens D, 2014, MIS QUART, V38, P73, DOI 10.25300/MISQ/2014/38.1.04
Miller T, 2017, Arxiv, DOI [arXiv:1712.00547, 10.48550/arXiv.1712.00547]
Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
Mucha H, 2021, EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21), DOI 10.1145/3411763.3451759
Newell S, 2015, J STRATEGIC INF SYST, V24, P3, DOI 10.1016/j.jsis.2015.02.001
Palacio S., 2021, P IEEE CVF INT C COM, P3766
Pedreschi D, 2019, AAAI CONF ARTIF INTE, P9780
Pinotsis DA., 2017, INT REV LAW COMPUTER, V31, P170, DOI [10.1080/13600869.2017.1298499, DOI 10.1080/13600869.2017.1298499]
Rader E, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3173677
Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
Ramon Y, 2021, Arxiv, DOI [arXiv:2107.02624, DOI 10.48550/ARXIV.2107.02624]
Ras G, 2022, J ARTIF INTELL RES, V73, P329
Ren JP, 2021, IEEE SENS J, V21, P25190, DOI 10.1109/JSEN.2021.3104038
ROLLER CM, 1985, READ RES QUART, V20, P437, DOI 10.2307/747853
Ryan M, 1999, SOC SCI MED, V48, P535, DOI 10.1016/S0277-9536(98)00374-8
Samek Wojciech, 2019, Explainable AI: interpreting, explaining and visualizing deep learning, V11700
Sarker Iqbal H, 2021, SN Comput Sci, V2, P160, DOI 10.1007/s42979-021-00592-x
Shin J, 2016, TECHNOL FORECAST SOC, V112, P329, DOI 10.1016/j.techfore.2016.08.004
Smirnova A., 2020, INT C DIG TRANSF GLO, P107
Szymanski M, 2021, IUI '21 - 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P109, DOI 10.1145/3397481.3450662
Train K., 2005, APPL SIMULATION METH, P117, DOI DOI 10.1007/1-4020-3684-1_7
Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1, DOI 10.1017/CBO9780511805271
Ribeiro MT, 2016, Arxiv, DOI arXiv:1606.05386
Venolia G.D., 2003, Proceedings of the SIGCHI conference on Human factors in computing systems, P361
Wachter S., 2018, HARVARD J LAW TECHNO, V31, P841, DOI DOI 10.2139/SSRN.3063289
Waltl B., 2018, Jusletter IT, V4, P1
Wang DD, 2019, I C OPT COMMUN NETW, DOI [10.1109/icocn.2019.8934212, 10.1145/3290605.3300831]
Woo J, 2022, J RETAIL CONSUM SERV, V69, DOI 10.1016/j.jretconser.2022.103084
Yu R, 2019, LEG INF MANAG, V19, P2, DOI 10.1017/S1472669619000021
NR 72
TC 2
Z9 2
U1 40
U2 72
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD MAR
PY 2023
VL 188
AR 122343
DI 10.1016/j.techfore.2023.122343
EA JAN 2023
PG 8
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA C8ZK1
UT WOS:000964734200001
DA 2024-03-27
ER
PT J
AU Moriuchi, E
Landers, VM
Colton, D
Hair, N
AF Moriuchi, Emi
Landers, V. Myles
Colton, Deborah
Hair, Neil
TI Engagement with chatbots versus augmented reality interactive technology
in e-commerce
SO JOURNAL OF STRATEGIC MARKETING
LA English
DT Article
DE Augmented reality interactive technology; chatbots; technology
engagement; e-commerce; consumers; retail
ID SELF-SERVICE; CUSTOMER SATISFACTION; PLS-SEM; ACCEPTANCE; INTENTIONS;
CONSUMERS; ADOPTION; COMPANY; QUALITY; ROLES
AB As competition intensifies in the retail industry organizations are increasingly turning to forms of artificial intelligence as a means of differentiation. E-commerce companies are moving towards integrating technologies such as chatbots and augmented reality interactive technology which have proved to be popular solutions to customer service in the practitioner domain. However, little is known about consumers' attitude and engagement with these emerging technologies when used in a retail environment. A theory-based research model which was designed to uncover the motivational mechanisms needed to provide engagement and effective decision-making processes in this context. Empirical testing conducted with a field study supported the proposed model.
C1 [Moriuchi, Emi; Landers, V. Myles; Colton, Deborah; Hair, Neil] Rochester Inst Technol, E Philip Saunders Coll Business, Rochester, NY 14623 USA.
C3 Rochester Institute of Technology
RP Moriuchi, E (autor correspondiente), Rochester Inst Technol, E Philip Saunders Coll Business, Rochester, NY 14623 USA.
EM emoriuchi@saunders.rit.edu
CR AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
[Anonymous], 2013, UNDERSTANDING AUGMEN
[Anonymous], 2016, PARTIALLY OBSERVED M, DOI DOI 10.1017/CBO9781316471104
[Anonymous], 2004, SERVICES MARKETING P
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Ayanso A, 2015, DECIS SUPPORT SYST, V77, P112, DOI 10.1016/j.dss.2015.06.003
Ayeh JK, 2013, TOURISM MANAGE, V35, P132, DOI 10.1016/j.tourman.2012.06.010
Bagozzi RP, 2012, J ACAD MARKET SCI, V40, P8, DOI 10.1007/s11747-011-0278-x
BETTMAN JR, 1979, J MARKETING, V43, P37, DOI 10.1177/002224297904300205
Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
Caro F, 2019, BUS HORIZONS, V62, P47, DOI 10.1016/j.bushor.2018.08.002
Collier JE, 2015, J BUS RES, V68, P703, DOI 10.1016/j.jbusres.2014.08.001
Coniam D, 2014, TEXT TALK, V34, P545, DOI 10.1515/text-2014-0018
Cook DP, 1999, PROD OPER MANAG, V8, P318, DOI 10.1111/j.1937-5956.1999.tb00311.x
Cronin JJ, 2000, J RETAILING, V76, P193, DOI 10.1016/S0022-4359(00)00028-2
Dabholkar PA, 2002, J ACAD MARKET SCI, V30, P184, DOI 10.1177/0092070302303001
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
de Ruyter K, 2001, INT J SERV IND MANAG, V12, P184, DOI 10.1108/09564230110387542
Doshi-Velez F., 2009, Advances in Neural Information Processing Systems, P477
Eastlick MA, 2012, INT REV RETAIL DISTR, V22, P337, DOI 10.1080/09593969.2012.690775
Faggella D, 2018, 3 DOOR MEDIA
Ferrara E, 2016, COMMUN ACM, V59, P96, DOI 10.1145/2818717
Fliess S, 2004, J BUS RES, V57, P392, DOI 10.1016/S0148-2963(02)00273-4
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
Hair JF, 2017, J ADVERTISING, V46, P163, DOI 10.1080/00913367.2017.1281777
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Huang TL, 2015, ELECTRON COMMER RES, V15, P269, DOI 10.1007/s10660-014-9163-2
Kaczorowska-Spychalska D, 2019, MANAG-POL, V23, P251, DOI 10.2478/manment-2019-0015
Karami AB, 2010, ACMIEEE INT CONF HUM, P155, DOI 10.1109/HRI.2010.5453219
Kim J, 2008, PSYCHOL MARKET, V25, P901, DOI 10.1002/mar.20245
Kim J, 2008, J INTERACT MARK, V22, P45, DOI 10.1002/dir.20113
Larivière B, 2017, J BUS RES, V79, P238, DOI 10.1016/j.jbusres.2017.03.008
Lee HJ, 2015, INT J RETAIL DISTRIB, V43, P676, DOI 10.1108/IJRDM-04-2014-0049
Lis B, 2013, J MEDIA BUS STUD, V10, P41, DOI 10.1080/16522354.2013.11073571
McCarthy J., 2004, TECHNOLOGY EXPERIENC, DOI [10.1145/1015530.1015549, DOI 10.1145/1015530.1015549]
Migge B. H., 2013, THESIS, DOI [10.3929/ethz-a-009976669, DOI 10.3929/ETHZ-A-009976669]
Moriuchi E, 2018, J BUS RES, V82, P381, DOI 10.1016/j.jbusres.2017.07.024
Mugan J, 2017, CHATBOTS THEORY PRAC
O'Brien HL, 2008, J AM SOC INF SCI TEC, V59, P938, DOI 10.1002/asi.20801
Ohta Y., 2014, MIXED REALITY MERGIN
PASK G, 1980, INT J MAN MACH STUD, V13, P357, DOI 10.1016/S0020-7373(80)80002-2
R Vyas, 2017, WHY CHATBOTS CANNOT
Rosenthal V., 2017, FORBES CHATBOT ENGAG
RUST RT, 1994, J ADVERTISING, V23, P71, DOI 10.1080/00913367.1943.10673460
Sanzo M. J., 2003, British Food Journal, V105, P771, DOI 10.1108/00070700310511807
Sivadas E., 2000, INT J RETAIL DISTRIB, V28, P73
Sivaramakrishnan S, 2007, J INTERACT MARK, V21, P60, DOI 10.1002/dir.20075
TAYLOR SA, 1994, J RETAILING, V70, P163, DOI 10.1016/0022-4359(94)90013-2
Waghmare C., 2019, INTRO AZURE BOT SERV, P147
Wakefield KL, 2016, J SERV MARK, V30, P686, DOI 10.1108/JSM-08-2016-0291
Williams JD, 2007, COMPUT SPEECH LANG, V21, P393, DOI 10.1016/j.csl.2006.06.008
Wong K.K.-K., 2013, MARKETING B, V24, P1, DOI DOI 10.1108/EBR-10-2013-0128
NR 56
TC 65
Z9 67
U1 11
U2 84
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0965-254X
EI 1466-4488
J9 J STRATEG MARK
JI J. Strateg. Mark.
PD JUL 4
PY 2021
VL 29
IS 5
BP 375
EP 389
DI 10.1080/0965254X.2020.1740766
EA MAR 2020
PG 15
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA SP5BC
UT WOS:000524440500001
DA 2024-03-27
ER
PT J
AU Sáez-Ortuño, L
Huertas-Garcia, R
Forgas-Coll, S
Puertas-Prats, E
AF Saez-Ortuno, Laura
Huertas-Garcia, Ruben
Forgas-Coll, Santiago
Puertas-Prats, Eloi
TI How can entrepreneurs improve digital market segmentation? A comparative
analysis of supervised and unsupervised learning algorithms
SO INTERNATIONAL ENTREPRENEURSHIP AND MANAGEMENT JOURNAL
LA English
DT Article
DE Digital marketing; Clusters; AI algorithms; Unsupervised algorithms;
Supervised algorithms; XGBoost; K-means
ID MODEL; IMPLEMENTATION
AB The identification of digital market segments to make value-creating propositions is a major challenge for entrepreneurs and marketing managers. New technologies and the Internet have made it possible to collect huge volumes of data that are difficult to analyse using traditional techniques. The purpose of this research is to address this challenge by proposing the use of AI algorithms to cluster customers. Specifically, the proposal is to compare the suitability of supervised algorithms, XGBoost, versus unsupervised algorithms, K-means, for segmenting the digital market. To do so, both algorithms have been applied to a sample of 5 million Spanish users captured between 2010 and 2022 by a lead generation start-up. The results show that supervised learning with this type of data is more useful for segmenting markets than unsupervised learning, as it provides solutions that are better suited to entrepreneurs' commercial objectives.
C1 [Saez-Ortuno, Laura; Huertas-Garcia, Ruben; Forgas-Coll, Santiago] Univ Barcelona, Business Dept, Avda Diagonal 690, Barcelona 08034, Spain.
[Puertas-Prats, Eloi] Univ Barcelona, Maths & Comp Sci Dept, Gran Via 585, Barcelona 08007, Spain.
C3 University of Barcelona; University of Barcelona
RP Sáez-Ortuño, L (autor correspondiente), Univ Barcelona, Business Dept, Avda Diagonal 690, Barcelona 08034, Spain.
EM laurasaez@ub.edu; rhuertas@ub.edu; Santiago.forgas@ub.edu;
epuertas@ub.edu
RI Sáez Ortuño, Laura/HKN-3995-2023; Forgas-Coll, Santiago/R-2620-2017;
Puertas Prats, Eloi/F-9425-2016
OI Sáez Ortuño, Laura/0000-0001-6660-9458; Forgas-Coll,
Santiago/0000-0003-2288-3716; Puertas Prats, Eloi/0000-0001-6292-6448
FU CRUE-CSIC agreement; Springer Nature
FX Open Access funding provided thanks to the CRUE-CSIC agreement with
Springer Nature.
CR Ahsan MM, 2021, TECHNOLOGIES, V9, DOI 10.3390/technologies9030052
Ali I, 2023, INT J INFORM MANAGE, V69, DOI 10.1016/j.ijinfomgt.2022.102510
Alonso-González MJ, 2020, TRANSPORT RES A-POL, V132, P378, DOI 10.1016/j.tra.2019.11.022
Amit R, 2012, MIT SLOAN MANAGE REV, V53, P41
[Anonymous], 2009, The elements of statistical learning, DOI DOI 10.1007/978-0-387-84858-7_7
Arabie P., 1996, CLUSTERING CLASSIFIC
Audretsch DB, 2020, INT ENTREP MANAG J, V16, P373, DOI 10.1007/s11365-020-00648-z
Bala M., 2018, INT J MANAGEMENT IT, V8, P321
Baliouskas P, 2023, INT ENTREP MANAG J, V19, P2037, DOI 10.1007/s11365-022-00810-9
BALL GH, 1967, BEHAV SCI, V12, P153, DOI 10.1002/bs.3830120210
Basu D., 2022, ADV REDOX RES, V5, P100034, DOI [10.1016/j.arres.2022.100034, DOI 10.1016/J.ARRES.2022.100034]
Bishop C. M, 2007, Pattern Recognition and Machine Learning, DOI DOI 10.1007/978-0-387-45528-0
Blewitt ME, 2008, NAT GENET, V40, P663, DOI 10.1038/ng.142
Boone DS, 2002, INT J RES MARK, V19, P287, DOI 10.1016/S0167-8116(02)00080-0
Bremer L, 2017, INT J RES MARK, V34, P516, DOI 10.1016/j.ijresmar.2016.10.001
Buhmann A, 2020, J BUS ETHICS, V163, P265, DOI 10.1007/s10551-019-04226-4
Chakraborty H, 2009, CONTEMP CLIN TRIALS, V30, P71, DOI 10.1016/j.cct.2008.07.008
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Silva FJ, 2013, INT ENTREP MANAG J, V9, P619, DOI 10.1007/s11365-013-0266-3
Dahle Y, 2023, INT ENTREP MANAG J, V19, P1583, DOI 10.1007/s11365-023-00874-1
Dahle Y, 2018, INT ICE CONF ENG
Dayan N, 2021, PROC VLDB ENDOW, V14, P2932, DOI 10.14778/3476311.3476373
Desai V., 2019, International Journal of Trend in Scientific Research and Development, V5, P196, DOI DOI 10.31142/IJTSRD23100
DeSarbo W.S., 1998, Marketing Letters, V9, P115, DOI DOI 10.1023/A:1007997714444
Desarbo WS, 2005, STRATEGIC MANAGE J, V26, P47, DOI 10.1002/smj.431
Eskerod P., 2020, Oxford Research Encyclopedia of Business and Management, P1
Fernández-Delgado M, 2014, J MACH LEARN RES, V15, P3133
GAREY MR, 1978, J ACM, V25, P499, DOI 10.1145/322077.322090
Gonzalez Philippe, 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), P1, DOI 10.1109/ICASSP49357.2023.10097075
Guerola-Navarro V, 2022, INT ENTREP MANAG J, DOI 10.1007/s11365-022-00800-x
Gultom Syawal, 2018, IOP Conference Series: Materials Science and Engineering, V420, DOI 10.1088/1757-899X/420/1/012092
Hartmann J, 2023, INT J RES MARK, V40, P75, DOI 10.1016/j.ijresmar.2022.05.005
Hartmann J, 2019, INT J RES MARK, V36, P20, DOI 10.1016/j.ijresmar.2018.09.009
Henriques J, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9071164
Ibrahim Ibrahim, 2021, Journal of Applied Science and Technology Trends, V2, P10, DOI DOI 10.38094/JASTT20179
Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504
Jain AK, 2010, PATTERN RECOGN LETT, V31, P651, DOI 10.1016/j.patrec.2009.09.011
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
Jordan MI, 2015, SCIENCE, V349, P255, DOI 10.1126/science.aaa8415
Kamthania Deepali, 2018, CIT. Journal of Computing and Information Technology, V26, P57, DOI 10.20532/cit.2018.1003863
Kaufman L., 2009, Finding groups in data: an introduction to cluster analysis, V344
Ke GL, 2017, ADV NEUR IN, V30
Kodinariya T.M., 2013, International Journal, V1, DOI DOI 10.18576/AMIS/100428
Kohavi R., 1998, MACH LEARN, V30, P271, DOI DOI 10.1023/A:1017181826899
Kohonen T, 1996, P IEEE, V84, P1358, DOI 10.1109/5.537105
Kraus S, 2020, INT ENTREP MANAG J, V16, P1023, DOI 10.1007/s11365-020-00635-4
Kuo RJ, 2002, COMPUT OPER RES, V29, P1475, DOI 10.1016/S0305-0548(01)00043-0
Liang YX, 2019, 2019 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND PATTERN RECOGNITION (AIPR 2019), P150, DOI 10.1145/3357254.3357290
Liao YK, 2022, INT ENTREP MANAG J, V18, P1623, DOI 10.1007/s11365-022-00803-8
Likas A, 2003, PATTERN RECOGN, V36, P451, DOI 10.1016/S0031-3203(02)00060-2
Liu JL, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0246306
Liu Y, 2010, MARKET SCI, V29, P880, DOI 10.1287/mksc.1100.0565
LLOYD SP, 1982, IEEE T INFORM THEORY, V28, P129, DOI 10.1109/TIT.1982.1056489
MacQueen J., 1967, P 5 BERKELEY S MATH, P281, DOI DOI 10.1234/12345678
Memarsadeghi N, 2007, INT J COMPUT GEOM AP, V17, P71, DOI 10.1142/S0218195907002252
MILLIGAN GW, 1988, J CLASSIF, V5, P181, DOI 10.1007/BF01897163
Mitchell R, 2017, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.127
Murray PW, 2017, COMPUT IND ENG, V109, P233, DOI 10.1016/j.cie.2017.04.017
Natekin A, 2013, FRONT NEUROROBOTICS, V7, DOI 10.3389/fnbot.2013.00021
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Olhede SC, 2018, PHILOS T R SOC A, V376, DOI 10.1098/rsta.2017.0364
Ordabayeva N, 2022, J MARKETING, V86, P70, DOI 10.1177/00222429221074704
Hung PD, 2019, PROCEEDINGS OF THE 2019 5TH INTERNATIONAL CONFERENCE ON E-BUSINESS AND APPLICATIONS (ICEBA 2019), P100, DOI 10.1145/3317614.3317626
Poongodi M, 2022, INT J SYST ASSUR ENG, V13, P16, DOI 10.1007/s13198-021-01130-x
Raschka S., 2019, PYTHON MACHINE LEARN, V3rd
Ravikumar P, 2010, ANN STAT, V38, P1287, DOI 10.1214/09-AOS691
Sáez-Ortuño L, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100349
Sáez-Ortuño L, 2023, J BUS RES, V158, DOI 10.1016/j.jbusres.2023.113702
Sathya R., 2013, International Journal of Advanced Research in Artificial Intelligence, V2, P34
Sculley David, 2010, P 19 INT C WORLD WID, P1177, DOI [DOI 10.1145/1772690.1772862, 10.1145/1772690.1772862]
SMITH WR, 1956, J MARKETING, V21, P3, DOI 10.2307/1247695
Sommer L, 2011, INT ENTREP MANAG J, V7, P111, DOI 10.1007/s11365-010-0162-z
Stead M, 2007, HEALTH EDUC, V107, P126, DOI 10.1108/09654280710731548
Strehl A, 2003, INFORMS J COMPUT, V15, P208, DOI 10.1287/ijoc.15.2.208.14448
Sujatha S., 2013, INT J ENG RES TECHNO, V2, P1
Syakur MA, 2018, IOP CONF SER-MAT SCI, V336, DOI 10.1088/1757-899X/336/1/012017
Tadelis S, 2016, ANNU REV ECON, V8, P321, DOI 10.1146/annurev-economics-080315-015325
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Tsamados A, 2022, AI SOC, V37, P215, DOI 10.1007/s00146-021-01154-8
TUKEY JW, 1962, ANN MATH STAT, V33, P1, DOI 10.1214/aoms/1177704711
Vassiliadis P, 2009, INT J DATA WAREHOUS, V5, P1, DOI 10.4018/jdwm.2009070101
Wedel M., 2000, MARKET SEGMENTATION
Zamri Nurnadiah, 2022, Procedia Computer Science, P172, DOI 10.1016/j.procs.2022.08.021
Zhou ZH, 2018, NATL SCI REV, V5, P44, DOI 10.1093/nsr/nwx106
NR 85
TC 3
Z9 3
U1 6
U2 9
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1554-7191
EI 1555-1938
J9 INT ENTREP MANAG J
JI Int. Entrep. Manag. J.
PD DEC
PY 2023
VL 19
IS 4
BP 1893
EP 1920
DI 10.1007/s11365-023-00882-1
EA AUG 2023
PG 28
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA AI7J9
UT WOS:001042771900001
OA hybrid
DA 2024-03-27
ER
PT J
AU Bhuiyan, KH
Ahmed, S
Jahan, I
AF Bhuiyan, Kamrul Hasan
Ahmed, Selim
Jahan, Israt
TI Consumer attitude toward using artificial intelligence (AI) devices in
hospitality services
SO JOURNAL OF HOSPITALITY AND TOURISM INSIGHTS
LA English
DT Article; Early Access
DE Consumer attitude; Artificial intelligence; Hospitality; Technology
acceptance; PLS-SEM
ID PLS-SEM; USER ACCEPTANCE; TECHNOLOGY; ROBOTS
AB PurposeThe study investigates the consumer's attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.Design/methodology/approachThis study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.FindingsThe finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.Practical implicationsThis study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.Originality/valueThis study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.
C1 [Bhuiyan, Kamrul Hasan] Univ Glasgow, Sch Interdisciplinary Studies, Glasgow, Scotland.
[Ahmed, Selim] World Univ Bangladesh, Dept Business Adm, Dhaka, Bangladesh.
[Ahmed, Selim] INTI Int Univ, Nilai, Malaysia.
[Jahan, Israt] Univ Glasgow, Sch Social & Polit Sci, Glasgow, Scotland.
C3 University of Glasgow; INTI International University; University of
Glasgow
RP Ahmed, S (autor correspondiente), World Univ Bangladesh, Dept Business Adm, Dhaka, Bangladesh.; Ahmed, S (autor correspondiente), INTI Int Univ, Nilai, Malaysia.
EM selim.ahmed@business.wub.edu.bd
RI Ahmed, Selim/R-3327-2017
OI Ahmed, Selim/0000-0002-0361-6797
CR Al Amin M, 2021, EUR J MANAG BUS ECON, V30, P211, DOI 10.1108/EJMBE-04-2020-0066
Alegre J, 2010, ANN TOURISM RES, V37, P52, DOI 10.1016/j.annals.2009.07.001
Althuizen N, 2018, INFORM SYST J, V28, P879, DOI 10.1111/isj.12172
Ameen N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106548
Anthes G, 2017, COMMUN ACM, V60, P19, DOI 10.1145/3088342
Bappy T.A., 2021, Technology Application in the Tourism and Hospitality Industry of Bangladesh
Bhuiyan KH, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142215043
Chi OH, 2020, J HOSP MARKET MANAG, V29, P757, DOI 10.1080/19368623.2020.1721394
Davenport TH, 2015, HARVARD BUS REV, V93, P59
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
de Kervenoael R, 2020, TOURISM MANAGE, V78, DOI 10.1016/j.tourman.2019.104042
Fam KS, 2023, J HOSP TOUR RES, V47, P257, DOI 10.1177/10963480211014944
Fan AL, 2020, J HOSP MARKET MANAG, V29, P269, DOI 10.1080/19368623.2019.1639095
Fernandez Robin Cristobal, 2014, HCI in Business. First International Conference, HCIB 2014. Held as Part of HCI International 2014. Proceedings: LNCS 8527, P609, DOI 10.1007/978-3-319-07293-7_59
Gani M.O., 2023, International Journal of Spa and Wellness, V6, P54, DOI [10.1080/24721735.2022.2096292, DOI 10.1080/24721735.2022.2096292]
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
Hasan M.D.R., 2019, Journal of Business and Retail Management Research (JBRMR), V14, P20, DOI [10.24052/jbrmr/v14is01/art-03, DOI 10.24052/JBRMR/V14IS01/ART-03]
Henseler J, 2018, QUAL QUANT, V52, P1, DOI 10.1007/s11135-018-0689-6
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Im J, 2017, J HOSP MARKET MANAG, V26, P361, DOI 10.1080/19368623.2017.1248805
Islam Md Zahirul, 2023, Advanced Information Networking and Applications: Proceedings of the 37th International Conference on Advanced Information Networking and Applications (AINA-2023). Lecture Notes in Networks and Systems (655), P446, DOI 10.1007/978-3-031-28694-0_43
Islam Md. M., 2022, INT J TOURISM POLICY, V12, P203, DOI [10.1504/ijtp.2022.122236, DOI 10.1504/IJTP.2022.122236]
Jahan I., 2020, International Journal of Management, V11, P114
Kim HY, 2018, J CONSUM RES, V45, P429, DOI 10.1093/jcr/ucy006
Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
Lalicic L, 2021, J BUS RES, V129, P891, DOI 10.1016/j.jbusres.2020.11.005
Lee B, 2018, J HOSP MARKET MANAG, V27, P61, DOI 10.1080/19368623.2017.1337539
Lim JS, 2022, TECHNOL SOC, V69, DOI 10.1016/j.techsoc.2022.101965
Lin HX, 2020, J HOSP MARKET MANAG, V29, P530, DOI 10.1080/19368623.2020.1685053
Ltd Huawei Technologies Co, 2023, Artificial Intelligence Technology, DOI [10.1007/978-981-19-2879-6_1, DOI 10.1007/978-981-19-2879-6_1]
Lu L, 2019, INT J HOSP MANAG, V80, P36, DOI 10.1016/j.ijhm.2019.01.005
Manrai R, 2023, J FINANC SERV MARK, V28, P1, DOI 10.1057/s41264-021-00134-9
Martin BAS, 2020, J HOSP TOUR MANAG, V44, P108, DOI 10.1016/j.jhtm.2020.06.004
Memon M.A., 2020, J APPL STRUCTURAL EQ, V4, P1, DOI [DOI 10.47263/JASEM.4, 10.47263/JASEM.4(2)01, DOI 10.47263/JASEM.4(2)01]
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Murphy J., 2021, Future of Tourism Marketing, P16
Oborn E, 2011, J HEALTH SERV RES PO, V16, P46, DOI 10.1258/jhsrp.2010.010001
Qiu HL, 2020, J HOSP MARKET MANAG, V29, P247, DOI 10.1080/19368623.2019.1645073
Ross J.R., 2019, 5 Ways Artificial Intelligence Offers Real ROI
Roy P, 2020, GLOB BUS REV, DOI 10.1177/0972150920939753
Russell S., 2010, Artificial intelligence a modern approach, V3rd
Schuetzler RM, 2018, PROCEEDINGS OF THE 51ST ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS), P283
Shani A, 2009, CORNELL HOSP Q, V50, P446, DOI 10.1177/1938965509344294
Shiau WL, 2019, INTERNET RES, V29, P398, DOI 10.1108/IntR-10-2018-0447
Siau K., 2017, 12 ANN MIDW ASS INF, V48, P18
Srite M, 2006, AUSTRALAS J INF SYST, V14, P5
Trejos N., 2016, USA Today, V9
Tussyadiah I.P., 2018, Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference, P308, DOI DOI 10.1007/978-3-319-72923-7_24
Vecchiato R, 2015, TECHNOL FORECAST SOC, V101, P25, DOI 10.1016/j.techfore.2014.08.016
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Wan J., 2015, Strong Brands, Strong Relationships, P119
Wang B, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12010030
NR 54
TC 0
Z9 0
U1 8
U2 8
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 2514-9792
EI 2514-9806
J9 J HOSP TOUR INSIGHTS
JI J. Hosp. Tour. Insights
PD 2024 FEB 16
PY 2024
DI 10.1108/JHTI-08-2023-0551
EA FEB 2024
PG 18
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA IR6R4
UT WOS:001168101200001
DA 2024-03-27
ER
PT J
AU Nilashi, M
Abumalloh, RA
Minaei-Bidgoli, B
Zogaan, WA
Alhargan, A
Mohd, S
Azhar, SNFS
Asadi, S
Samad, S
AF Nilashi, Mehrbakhsh
Abumalloh, Rabab Ali
Minaei-Bidgoli, Behrouz
Zogaan, Waleed Abdu
Alhargan, Ashwaq
Mohd, Saidatulakmal
Azhar, Sharifah Nurlaili Farhana Syed
Asadi, Shahla
Samad, Sarminah
TI Revealing travellers' satisfaction during COVID-19 outbreak: Moderating
role of service quality
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Online customers' reviews; Customers' satisfaction; Machine learning;
Service quality; Survey study
ID USER-GENERATED CONTENT; WORD-OF-MOUTH; CUSTOMER SATISFACTION; BIG DATA;
LEARNING APPROACH; ONLINE RATINGS; SLEEP QUALITY; SOCIAL MEDIA; REVIEWS;
TOURISM
AB User-Generated-Content (UGC) has gained increasing attention as an important indicator of business success in the tourism and hospitality sectors. Previous literature has analyzed travelers' satisfaction through quantitative approaches using questionnaire surveys. Another direction of research has explored the dimensions of satisfaction based on online customers' reviews using the machine learning approach. This study aims to present a new method that combines machine learning and survey-based approaches for customers' satisfaction analysis during the COVID-19 outbreak. In addition, we investigate the moderating role of service quality on the relationship between hotels' performance criteria and customers' satisfaction. To achieve this, the Latent Dirichlet Allocation (LDA) was used for textual data analysis, k-means was used for data segmentation, dimensionality reduction approach was used for the imputation of the missing values, and fuzzy rule-based was used for the prediction of satisfaction level. Following that, a survey-based approach was used to validate the research model by distributing the questionnaire and analyzing the collected data using the Structural Equation Modeling technique. The result of this research presents important contributions from the methodological and practical perspectives in the context of customers' satisfaction in tourism and hospitality during the COVID-19 outbreak. The outcomes of this research confirm the significant influence of the quality of services during the COVID-19 crisis on the relationship between hotel services and travellers' satisfaction.
C1 [Nilashi, Mehrbakhsh; Azhar, Sharifah Nurlaili Farhana Syed] Univ Sains Malaysia, Ctr Global Sustainabil Studies CGSS, Usm Penang 11800, Malaysia.
[Nilashi, Mehrbakhsh; Minaei-Bidgoli, Behrouz] Iran Univ Sci & Technol, Sch Comp Engn, Tehran, Iran.
[Abumalloh, Rabab Ali] Imam Abdulrahman Bin Faisal Univ, Community Coll, Comp Dept, POB 1982, Dammam, Saudi Arabia.
[Zogaan, Waleed Abdu] Jazan Univ, Fac Comp Sci & Informat Technol, Dept Comp Sci, Jazan 45142, Saudi Arabia.
[Alhargan, Ashwaq] Saudi Elect Univ, Coll Comp & Informat, Comp Sci Dept, Riyadh, Saudi Arabia.
[Mohd, Saidatulakmal] Univ Sains, Ctr Global Sustainabil Studies, George Town, Malaysia.
[Mohd, Saidatulakmal] Univ Sains, Sch Social Sci, George Town, Malaysia.
[Asadi, Shahla] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Ctr Software Technol & Management, Bangi 43600, Selangor, Malaysia.
[Samad, Sarminah] Princess Nourah Bint Abdulrahman Univ, Coll Business & Adm, Dept Business Adm, Riyadh, Saudi Arabia.
C3 Universiti Sains Malaysia; Iran University Science & Technology; Imam
Abdulrahman Bin Faisal University; Jazan University; Saudi Electronic
University; Universiti Sains Malaysia; Universiti Sains Malaysia;
Universiti Kebangsaan Malaysia; Princess Nourah bint Abdulrahman
University
RP Nilashi, M (autor correspondiente), Univ Sains Malaysia, Ctr Global Sustainabil Studies CGSS, Usm Penang 11800, Malaysia.
EM nilashidotnet@hotmail.com
RI Samad, Sarminah/AAX-7406-2021; Abumalloh, Rabab/AAD-9682-2022; Zogaan,
Waleed Abdu/JGM-6769-2023; Nilashi, Mehrbakhsh/C-4311-2016;
Minaei-Bidgoli, Behrouz/L-2779-2018; Abumalloh, Rabab/KBQ-4135-2024;
Syed Azhar, Sharifah Nurlaili Farhana/Y-3485-2018; Mohd,
Saidatulakmal/J-1535-2014; Abumalloh, Rabab/C-8963-2017
OI Zogaan, Waleed Abdu/0000-0002-1087-7549; Nilashi,
Mehrbakhsh/0000-0003-0099-8299; Minaei-Bidgoli,
Behrouz/0000-0002-9327-7345; Syed Azhar, Sharifah Nurlaili
Farhana/0000-0002-2987-353X; Mohd, Saidatulakmal/0000-0002-7947-7324;
Abumalloh, Rabab/0000-0003-2805-3764; ALhargan,
Ashwaq/0000-0001-9541-8415
CR Abideen AZ, 2020, J HEALTH RES, V34, P547, DOI 10.1108/JHR-04-2020-0109
Ahani A, 2019, J RETAIL CONSUM SERV, V51, P331, DOI 10.1016/j.jretconser.2019.06.014
Ahani A, 2019, INT J HOSP MANAG, V80, P52, DOI 10.1016/j.ijhm.2019.01.003
Al-Qudah Yousef, 2017, International Journal of Applied Decision Sciences, V10, P175
Alnawas I, 2019, J HOSP MARKET MANAG, V28, P833, DOI 10.1080/19368623.2019.1568339
Amaral F., 2014, INT J STRATEGIC INNO, V1, P137, DOI DOI 10.15556/IJSIM.01.03.002
Anderson M, 2012, ECON J, V122, P957, DOI 10.1111/j.1468-0297.2012.02512.x
Ayeh JK, 2013, TOURISM MANAGE, V35, P132, DOI 10.1016/j.tourman.2012.06.010
Azlan AA, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0233668
Bag S, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120420
Bakar AYA, 2020, ASIAN J PSYCHIATR, V54, DOI 10.1016/j.ajp.2020.102272
Bhatnagar E., 2019, PRABANDHAN INDIAN J, V12, P46
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Botta A, 2008, INT J INTELL SYST, V23, P397, DOI 10.1002/int.20273
Cenni I, 2017, DISCOURSE CONTEXT ME, V16, P22, DOI 10.1016/j.dcm.2017.01.004
Chan I.C. C., 2021, Information and Communication Technologies in Tourism 2021, P549, DOI [10.1007/978-3-030-65785-7_52, DOI 10.1007/978-3-030-65785-7_52]
Chang YC, 2019, INT J INFORM MANAGE, V48, P263, DOI 10.1016/j.ijinfomgt.2017.11.001
Chau M, 2012, MIS QUART, V36, P1189
Chen CF, 2008, TOURISM MANAGE, V29, P1166, DOI 10.1016/j.tourman.2008.02.019
Chen RY, 2018, DECIS SUPPORT SYST, V108, P96, DOI 10.1016/j.dss.2018.02.008
Chow CKW, 2015, J AIR TRANSP MANAG, V47, P39, DOI 10.1016/j.jairtraman.2015.04.003
Christodoulides G, 2012, J ADVERTISING RES, V52, P53, DOI 10.2501/JAR-52-1-053-064
Chua A, 2016, TOURISM MANAGE, V57, P295, DOI 10.1016/j.tourman.2016.06.013
Cox C, 2009, J HOSP MARKET MANAG, V18, P743, DOI 10.1080/19368620903235753
Creswell JW., 2018, Research design: Qualitative, quantitative, and mixed methods approaches, V5th, DOI DOI 10.7748/NR.12.1.82.S2
Cró S, 2017, TOURISM MANAGE, V63, P3, DOI 10.1016/j.tourman.2017.05.009
Dimitriou L, 2008, TRANSPORT RES C-EMER, V16, P554, DOI 10.1016/j.trc.2007.11.003
Dong J, 2014, IFIP ADV INF COMM TE, V445, P238
Evermann J, 2016, J BUS RES, V69, P4565, DOI 10.1016/j.jbusres.2016.03.050
Fan A, 2018, INT J CONTEMP HOSP M, V30, P1508, DOI [10.1108/IJCHM-01-2017-0050, 10.1108/ijchm-01-2017-0050]
Felbermayr A, 2016, J INTERACT MARK, V36, P60, DOI 10.1016/j.intmar.2016.05.004
Fernandes AAR, 2018, J MANAG DEV, V37, P76, DOI 10.1108/JMD-12-2016-0315
Flanagin AJ, 2013, COMPUT HUM BEHAV, V29, P1626, DOI 10.1016/j.chb.2013.02.001
Forgas S, 2010, J AIR TRANSP MANAG, V16, P229, DOI 10.1016/j.jairtraman.2010.01.001
Gavilan D, 2018, TOURISM MANAGE, V66, P53, DOI 10.1016/j.tourman.2017.10.018
Ghasemaghaei M, 2020, J BUS RES, V108, P147, DOI 10.1016/j.jbusres.2019.09.062
Godnov U, 2016, ANN TOURISM RES, V58, P162, DOI 10.1016/j.annals.2016.02.005
González-Rodríguez MR, 2021, J DESTIN MARK MANAGE, V19, DOI 10.1016/j.jdmm.2020.100543
Gour A, 2021, INT J CONTEMP HOSP M, V33, P490, DOI 10.1108/IJCHM-07-2020-0760
Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
Gursoy D, 2020, J HOSP MARKET MANAG, V29, P527, DOI 10.1080/19368623.2020.1788231
Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
Hair JF, 2013, LONG RANGE PLANN, V46, P1, DOI 10.1016/j.lrp.2013.01.001
Hao JX, 2015, TOURISM MANAGE, V48, P231, DOI 10.1016/j.tourman.2014.11.009
Hashem IAT, 2015, INFORM SYST, V47, P98, DOI 10.1016/j.is.2014.07.006
Hoisington, 2018, WHY HOTELIERS CANT I
Hu F, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104320
Hu Y, 2018, INT J HOSP MANAG, V75, P27, DOI 10.1016/j.ijhm.2018.03.004
Huang WJ, 2018, J HOSP MARKET MANAG, V27, P833, DOI 10.1080/19368623.2018.1448315
KHUONG MaiNgoc., 2014, INT J TRADE EC FINAN, V5, P490, DOI [10.7763/IJTEF.2014.V5.421, DOI 10.7763/IJTEF.2014.V5.421]
Knoll J, 2016, INT J ADVERT, V35, P266, DOI 10.1080/02650487.2015.1021898
Lee M, 2020, J HOSP TOUR TECHNOL, V11, P137, DOI 10.1108/JHTT-07-2018-0054
Li HX, 2020, INT J CONTEMP HOSP M, V32, P1713, DOI 10.1108/IJCHM-06-2019-0581
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Loh S., 2003, Information Technology and Tourism, V6, P157, DOI 10.3727/1098305031436980
Lu WL, 2015, J HOSP MARKET MANAG, V24, P119, DOI 10.1080/19368623.2014.907758
Lucini FR, 2020, J AIR TRANSP MANAG, V83, DOI 10.1016/j.jairtraman.2019.101760
Luo LJ, 2021, ONLINE INFORM REV, V45, P614, DOI 10.1108/OIR-05-2020-0186
Mattila AS, 2004, INT J SERV IND MANAG, V15, P134, DOI 10.1108/09564230410532475
Mehraliyev F, 2020, TOURISM MANAGE, V79, DOI 10.1016/j.tourman.2020.104096
Mendes-Filho L., 2009, PACIS 2009 P
Mitra S, 2020, J BUS RES, V114, P213, DOI 10.1016/j.jbusres.2020.04.003
Moon S, 2021, INT J RES MARK, V38, P343, DOI 10.1016/j.ijresmar.2020.08.001
Moon S, 2019, J BUS RES, V102, P83, DOI 10.1016/j.jbusres.2019.05.016
MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
Mou J, 2019, ELECTRON COMMER RES, V19, P749, DOI 10.1007/s10660-019-09338-7
Namukasa J., 2013, TQM Journal, V25, P520, DOI DOI 10.1108/TQM-11-2012-0092
Nieto-Garcia M, 2019, INT J HOSP MANAG, V77, P583, DOI 10.1016/j.ijhm.2018.09.002
Nilashi M, 2021, TELEMAT INFORM, V61, DOI 10.1016/j.tele.2021.101597
Nilashi M, 2019, J CLEAN PROD, V215, P767, DOI 10.1016/j.jclepro.2019.01.012
Nilashi M, 2018, J COMPUT SCI-NETH, V28, P168, DOI 10.1016/j.jocs.2018.09.006
Nilashi M, 2016, ELECTRON COMMER R A, V19, P70, DOI 10.1016/j.elerap.2016.09.003
Nunkoo R, 2020, INT J HOSP MANAG, V91, DOI 10.1016/j.ijhm.2019.102414
Nunkoo R, 2017, INT J CONTEMP HOSP M, V29, P2978, DOI 10.1108/IJCHM-11-2016-0610
Nusair K, 2019, INT J CONTEMP HOSP M, V31, P2691, DOI 10.1108/IJCHM-06-2018-0489
OLIVER RL, 1980, J MARKETING RES, V17, P460, DOI 10.2307/3150499
Pacheco L, 2017, TOUR ANAL, V22, P337, DOI 10.3727/108354217X14955605216087
Padlee SF, 2019, TOUR HOSP MANAG-CROA, V25, P121, DOI 10.20867/thm.25.1.9
Park JH, 2014, COMPUT HUM BEHAV, V31, P1, DOI 10.1016/j.chb.2013.10.002
Radojevic T, 2018, TOURISM MANAGE, V67, P326, DOI 10.1016/j.tourman.2018.02.007
Rauch DA, 2015, INT J CONTEMP HOSP M, V27, P87, DOI 10.1108/IJCHM-06-2013-0254
Ray A, 2021, INT J HOSP MANAG, V92, DOI 10.1016/j.ijhm.2020.102730
Ren LP, 2015, J QUAL ASSUR HOSP TO, V16, P45, DOI 10.1080/1528008X.2015.966299
Rhee HT, 2015, ELECTRON MARK, V25, P211, DOI 10.1007/s12525-014-0161-y
Ritchie BW, 2019, ANN TOURISM RES, V79, DOI 10.1016/j.annals.2019.102812
Rivera MA, 2020, INT J HOSP MANAG, V87, DOI 10.1016/j.ijhm.2020.102528
Román C, 2016, TOURISM MANAGE, V52, P488, DOI 10.1016/j.tourman.2015.08.001
Selvachandran Ganeshsree, 2018, International Journal of Fuzzy Systems Applications, V7, P22, DOI 10.4018/IJFSA.2018010102
Shamim S, 2021, COMPUT HUM BEHAV, V121, DOI 10.1016/j.chb.2021.106777
Sharma PN, 2021, DECISION SCI, V52, P567, DOI 10.1111/deci.12329
Shmueli G, 2016, J BUS RES, V69, P4552, DOI 10.1016/j.jbusres.2016.03.049
Siering M, 2018, DECIS SUPPORT SYST, V107, P52, DOI 10.1016/j.dss.2018.01.002
Sparks BA, 2016, TOURISM MANAGE, V53, P74, DOI 10.1016/j.tourman.2015.09.011
Suhartanto D, 2013, TOUR REV INT, V17, P47, DOI 10.3727/154427213X13649094288106
Taecharungroj V, 2019, TOURISM MANAGE, V75, P550, DOI 10.1016/j.tourman.2019.06.020
Talón-Ballestero P, 2018, TOURISM MANAGE, V68, P187, DOI 10.1016/j.tourman.2018.03.017
Tareq Z., 2015, REV ENERGY TECHNOL P, V2, P29
Tian G, 2021, FOOD QUAL PREFER, V88, DOI 10.1016/j.foodqual.2020.104060
TripAdvisor, 2020, IMPR GUEST CONF TRIP
Tsakiridis NL, 2017, EXPERT SYST APPL, V83, P257, DOI 10.1016/j.eswa.2017.04.026
Tung F., 2013, African Journal of Business Management, V7, P1730, DOI [https://doi.org/10.5897/AJBM10.383, DOI 10.5897/AJBM10.383]
Ukpabi DC, 2018, TOUR MANAG PERSPECT, V28, P251, DOI 10.1016/j.tmp.2018.03.006
Viglia G, 2016, INT J CONTEMP HOSP M, V28, P2035, DOI 10.1108/IJCHM-05-2015-0238
Wan Y, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), P1318, DOI 10.1109/ICDMW.2015.7
Xiang Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/j.ijhm.2014.10.013
Xie K, 2016, INFORM MANAGE-AMSTER, V53, P1034, DOI 10.1016/j.im.2016.06.003
Xu X, 2021, DECIS SUPPORT SYST, V142, DOI 10.1016/j.dss.2020.113467
Yadegaridehkordi E, 2021, TECHNOL SOC, V65, DOI 10.1016/j.techsoc.2021.101528
Yang Y, 2018, J TRAVEL RES, V57, P243, DOI 10.1177/0047287517691153
Yuan H, 2018, ANN OPER RES, V270, P553, DOI 10.1007/s10479-017-2421-7
ZADEH LA, 1983, FUZZY SET SYST, V11, P199, DOI 10.1016/S0165-0114(83)80081-5
Zadeh LA, 1996, FUZZY SETS FUZZY LOG, P775, DOI DOI 10.1145/175247.175255
Zhang N, 2018, BUILD ENVIRON, V137, P198, DOI 10.1016/j.buildenv.2018.04.007
Zhang T, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11123437
Zhang YC, 2021, SUSTAIN ENERGY TECHN, V45, DOI 10.1016/j.seta.2020.100986
Zhao Y, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102205
Zheng TX, 2021, INT J HOSP MANAG, V92, DOI 10.1016/j.ijhm.2020.102658
Zhi TF, 2016, ARCH GERONTOL GERIAT, V65, P211, DOI 10.1016/j.archger.2016.03.023
NR 119
TC 26
Z9 27
U1 9
U2 110
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD JAN
PY 2022
VL 64
AR 102783
DI 10.1016/j.jretconser.2021.102783
EA OCT 2021
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA WI9NN
UT WOS:000708679900003
DA 2024-03-27
ER
PT J
AU Zhang, ZL
Yang, KJ
Zhang, JZ
Palmatier, RW
AF Zhang, Zelin
Yang, Kejia
Zhang, Jonathan Z.
Palmatier, Robert W.
TI Uncovering Synergy and Dysergy in Consumer Reviews: A Machine Learning
Approach
SO MANAGEMENT SCIENCE
LA English
DT Article
DE user-generated content; opinion mining; interaction effects; machine
learning
ID IMPACT; AGREEMENT
AB Massive online text reviews can be a powerful market research tool for understanding consumer experiences and helping firms improve and innovate. This research exploits the rich semantic properties of text reviews and proposes a novel machine learning modeling framework that can reliably and efficiently extract consumer opinions and uncover potential interaction effects across these opinions, thereby identifying hidden and nuanced areas for product and service improvement beyond existingmodeling approaches in this domain. In particular, we develop an opinion extraction and effect estimation framework that allows for uncovering customer opinions' average effects and their interaction effects. Interactions among opinions can be synergistic when the co-occurrence of two opinions yields an effect greater than the sum of two parts, or as what we call dysergistic, when the co-occurrence of two opinions results in dampened effect. We apply themodel in the context of large-scale customer ratings and text reviews for hotels and demonstrate our framework's ability to screen synergy and dysergy effects among opinions. Our model also flexibly and efficiently accommodates a large number of opinions, which provides insights into rare yet potentially important opinions. Themodel can guidemanagers to prioritize joint areas of product and service improvement and innovation by uncovering the most prominent synergistic pairs. Model comparison with extant machine learning approaches demonstrates our improved predictive ability andmanagerial insights.
C1 [Zhang, Zelin] Renmin Univ China, Sch Business, Dept Mkt, Beijing 100872, Peoples R China.
[Yang, Kejia] Mercatus Inc, Beijing 100025, Peoples R China.
[Zhang, Jonathan Z.] Colorado State Univ, Coll Business, Dept Mkt, Ft Collins, CO 80523 USA.
[Palmatier, Robert W.] Univ Washington, Foster Sch Business, Dept Mkt, Seattle, WA 98195 USA.
C3 Renmin University of China; Colorado State University; University of
Washington; University of Washington Seattle
RP Yang, KJ (autor correspondiente), Mercatus Inc, Beijing 100025, Peoples R China.
EM zhangzelin@rmbs.ruc.edu.cn; philipy1219@gmail.com;
jonathan.zhang@colostate.edu; palmatrw@uw.edu
OI zhang, zelin/0000-0002-2182-4303; Yang, Kejia/0000-0002-8121-0186
FU National Natural Science Foundation of China [72072173]
FX The authors acknowledge the support of research funding from the
National Natural Science Foundation of China [Grant 72072173].
CR [Anonymous], MACH LEARN
Ansari A, 2018, MARKET SCI, V37, P987, DOI 10.1287/mksc.2018.1113
Athey S, 2019, ANN STAT, V47, P1148, DOI 10.1214/18-AOS1709
Benjamini Y., 1995, J. R. Stat. Soc. B, V57, P289, DOI DOI 10.1111/J.2517-6161.1995.TB02031.X
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Carmeli CEOGFRN, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P3419, DOI 10.1145/3442381.3450081
COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104
Crump RK, 2009, BIOMETRIKA, V96, P187, DOI 10.1093/biomet/asn055
Dan Pelleg AM, 2000, P 17 INT C MACH LEAR, P727, DOI [10.1007/3-540-44491-2_3, DOI 10.1007/3-540-44491-2_3, DOI 10.1007/3]
Dens N, 2018, INT J MARKET RES, V60, P268, DOI 10.1177/1470785317751333
Devlin J., 2018, ARXIV
Fan JQ, 2008, ANN STAT, V36, P2605, DOI 10.1214/07-AOS504
Fan YY, 2015, ANN STAT, V43, P1243, DOI 10.1214/14-AOS1308
FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619
Gandomi A, 2015, INT J INFORM MANAGE, V35, P137, DOI 10.1016/j.ijinfomgt.2014.10.007
He RD, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P388, DOI 10.18653/v1/P17-1036
Ji YF, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P996, DOI 10.18653/v1/P17-1092
Jindal RP, 2016, J MARKETING, V80, P72, DOI 10.1509/jm.15.0036
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Kiss T, 2006, COMPUT LINGUIST, V32, P485, DOI 10.1162/coli.2006.32.4.485
Kobayashi N, 2020, AAAI CONF ARTIF INTE, V34, P8099
Kocisk T., 2015, Reasoning about entailment with neural attention
Kolluru K, 2020, PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), P3748
LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310
Lee JD, 2016, ANN STAT, V44, P907, DOI 10.1214/15-AOS1371
Li YL, 2019, PROC VLDB ENDOW, V12, P1330, DOI 10.14778/3342263.3342271
Lin ZH, 2014, NAT LANG ENG, V20, P151, DOI 10.1017/S1351324912000307
Loshchilov I., 2017, DECOUPLED WEIGHT DEC
MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
Mittal V, 1998, J MARKETING, V62, P33, DOI 10.2307/1251801
Naik PA, 2003, J MARKETING RES, V40, P375, DOI 10.1509/jmkr.40.4.375.19385
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Popescu A.-M, 2007, NATURAL LANGUAGE PRO, P9, DOI DOI 10.1007/978-1-84628-754-1_2
Reimers N, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P3982
Rendle Steffen, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), P995, DOI 10.1109/ICDM.2010.127
ROBINS JM, 1994, J AM STAT ASSOC, V89, P846, DOI 10.2307/2290910
ROBINSON PM, 1988, ECONOMETRICA, V56, P931, DOI 10.2307/1912705
Ruppert D., 2003, SEMIPARAMETRIC REGRE
Shine BC, 2007, J MARKETING RES, V44, P663, DOI 10.1509/jmkr.44.4.663
Taylor J, 2015, P NATL ACAD SCI USA, V112, P7629, DOI 10.1073/pnas.1507583112
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
van der Laan M.J., 2006, INT J BIOSTAT, V2, P1, DOI DOI 10.2202/1557-4679.1043
Vaswani A, 2017, ADV NEUR IN, V30
Wang J, 2020, PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), P1706
Westreich D, 2010, J CLIN EPIDEMIOL, V63, P826, DOI 10.1016/j.jclinepi.2009.11.020
Wu Z., 2020, P C EMPIRICAL METHOD, P2576, DOI DOI 10.18653/V1/2020.FINDINGS-EMNLP.234
Zhao QY, 2022, J ROY STAT SOC B, V84, P382, DOI 10.1111/rssb.12483
Zhao QY, 2019, J R STAT SOC B, V81, P735, DOI 10.1111/rssb.12327
NR 49
TC 5
Z9 5
U1 28
U2 145
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD APR
PY 2023
VL 69
IS 4
BP 2339
EP 2360
DI 10.1287/mnsc.2022.4443
EA MAY 2022
PG 22
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA Q1CB5
UT WOS:000827170100001
DA 2024-03-27
ER
PT J
AU Alqtati, N
Wilson, JAJ
De Silva, V
AF Alqtati, Nael
Wilson, Jonathan A. J.
De Silva, Varuna
TI Mining Arabic Twitter conversations on health care: a new approach to
analysing Arabic language on social media
SO JOURNAL OF ISLAMIC MARKETING
LA English
DT Article
DE Islamic markets; Twitter; The Muslim consumer; Arabic; Social media
analysis; Digital marketing; Natural language processing; Social media
analytics; Health care; Middle East
ID SENTIMENT ANALYSIS; EMERGING MARKETS; CHALLENGES; ETHICS
AB Purpose This paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through Arabic language and script, in vivo.
Design/methodology/approach Using quantitative and qualitative situational linguistic analyses of Classical Arabic, found in Quranic and religious texts scripts; Modern Standard Arabic, which is commonly used in formal Arabic channels; and dialectical Arabic, which varies hugely from one Arabic country to another: this study analyses rich marketing and consumer messages (tweets) - as a basis for developing an Arabic language social media methodological tool.
Findings Despite the popularity of Arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing Arabic social media conversations have limitations and require further development. Furthermore, due to its unique morphology, developing text understanding capabilities specific to the Arabic language poses challenges.
Practical implications This study demonstrates the application and effectiveness of the proposed methodology on a random sample of Twitter data from Arabic-speaking regions. Furthermore, as Arabic is the language of Islam, the study is of particular importance to Islamic and Muslim geographies, markets and marketing.
Social implications The findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments.
Originality/value To remedy these gaps, this study presents a new methodology and analytical approach to investigating Arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.
C1 [Alqtati, Nael] Qtaticom, London, England.
[Wilson, Jonathan A. J.] Regents Univ London, Provosts Grp, London, England.
[De Silva, Varuna] Loughborough Univ, Inst Digital Technol, London, England.
C3 Loughborough University
RP Wilson, JAJ (autor correspondiente), Regents Univ London, Provosts Grp, London, England.
EM jw@islamicmarketing.co.uk
RI Alqtati, Nael/ABD-9120-2021; Wilson, Jonathan A.J./AAB-2744-2021
OI Alqtati, Nael/0000-0002-4667-4292;
CR Abdul-Mageed M, 2014, COMPUT SPEECH LANG, V28, P20, DOI 10.1016/j.csl.2013.03.001
Aggarwal CC., 2012, MINING TEXT DATA, P77, DOI [10.1007/978-1-4614-3223-4, 10.1007/978-1-4614-3223-4_4]
Agrawal, 2013, INTRO STUDY TIME SER, DOI [10.13140/2.1.2771.8084, DOI 10.13140/2.1.2771.8084]
Akbar YH, 2005, THUNDERBIRD INT BUS, V47, P389, DOI 10.1002/tie.20059
Al-Shalabi R., 2004, Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications (IEEE Cat. No.04EX852), DOI 10.1109/ICTTA.2004.1307875
Al-Twairesh N, 2018, Sentiment analysis of Arabic tweets: feature engineering and a hybrid APPROACH
Alhawarat M, 2015, INT J ADV COMPUT SC, V6, P262
Alrefaie, 2019, LARGEST LIST F ARABI
[Anonymous], 2019, TWITTER
[Anonymous], 2017, INT C SMART CIT INFR
[Anonymous], 2014, Arab Social Media Report
[Anonymous], 2012, TWITTER REACHES HALF
Azam N, 2015, INT CONF SOFT COMP, P79, DOI 10.1109/ISCMI.2015.33
Baccouche A, 2018, IEEE INT SYMP SIGNAL, P382, DOI 10.1109/ISSPIT.2018.8642685
Behdenna S., 2018, J DIGITAL INFORM MAN, V16, P324
Brandenburg, 2013, QUANTITATIVE MARKET
Burgess SM, 2006, INT J RES MARK, V23, P337, DOI 10.1016/j.ijresmar.2006.08.001
DOMO, 2019, DAT NEV SLEEPS 7 0
Duwairi RM, 2014, 2014 5TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS)
Duwairi R, 2014, J INF SCI, V40, P501, DOI 10.1177/0165551514534143
Duwairi RM, 2016, INT CONF INFORM COMM, P127, DOI 10.1109/IACS.2016.7476098
Erickson G., 2017, New methods of market research and analysis
Extra Digital, 2019, PROM AR SOC MED
Gridach Mourad., 2016, WSSANLP 2016, P23
Habash N., 2010, SYNTHESIS LECT HUMAN, V3, P1, DOI DOI 10.2200/S00277ED1V01Y201008HLT010
Hague P.N., 2004, MARKET RES PRACTICE
Hmeidi I, 2015, J INF SCI, V41, P114, DOI 10.1177/0165551514558172
Holes C., 2004, GEORGETOWN CLASSICS
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Jardaneh G, 2019, 2019 IEEE JORDAN INTERNATIONAL JOINT CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY (JEEIT), P596, DOI 10.1109/JEEIT.2019.8717386
Kao A., 2007, Natural Language Processing and Text Mining, P1, DOI [DOI 10.1007/978-1-84628-754-1, 10.1007/978-1-84628-754-1]
Khorsheed MS, 2018, IEEE ACCESS, V6, P36522, DOI 10.1109/ACCESS.2018.2852619
Khosrow-Pour D.B.A., 2009, Encyclopedia of Information Science and Technology, VSecond, DOI [DOI 10.4018/978-1-60566-026-4, 10.4018/978-1-60566-026-4.ch063, DOI 10.4018/978-1-60566-026-4.CH063]
Lages CR, 2015, INT MARKET REV, V32, P52, DOI 10.1108/IMR-12-2014-0374
Lasik-Eyes.co.uk, 2020, LAS EYE SURG COST
Ma JW, 2017, J BUS RES, V74, P139, DOI 10.1016/j.jbusres.2016.10.025
Mills T, 2018, FORBES
Mohammad S., 2013, 2 JOINT C LEX COMP S
Mooi E, 2011, CONCISE GUIDE TO MARKET RESEARCH: THE PROCESS, DATA, AND METHODS USING IBM SPSS STATISTICS, P237, DOI 10.1007/978-3-642-12541-6_9
MrInsights, 2019, GLOB LASIK EYE SURG
Muhammad A.B., 2012, Annotation of Conceptual Co-Reference and Text Mining the Qur'an
Mullen L. A., 2018, Journal of Open Source Software, V3, P655, DOI [10.21105/joss.00655, DOI 10.21105/JOSS.00655]
Nabil Mahmoud, 2015, P 2015 C EMPIRICAL M, P2515, DOI DOI 10.18653/V1/D15-1299
NHS, 2018, LAS EYE SURG LENS SU
NLTK, 2019, 2 ACC TEXT CORP LEX
nltk, 2019, NATURAL LANGUAGE TOO
Nunan D, 2013, INT J MARKET RES, V55, P505, DOI 10.2501/IJMR-2013-015
Pandas, 2019, PYTH DAT AN LIB PAND
Peter J.P., 1987, Consumer behavior, marketing strategy perspectives
Punel A, 2018, J AIR TRANSP MANAG, V73, P67, DOI 10.1016/j.jairtraman.2018.08.004
Python Software Foundation, 2019, REREG EXPR OP PYTH 3
Python Software Foundation, 2019, UN HOWTO PYTH 3 3 7
Python Software Foundation, 2019, IT FUNCT CREAT IT EF
Python Software Foundation, 2019, STRING COMM STRING O
Ranks.nl, 2019, ARABIC
Saad, 2017, PREPROCESS ARABIC TE
Salloum S A., 2017, International Journal of Information Technology and Language Studies, V1, P8
Semiocast, 2011, AR HIGH GROWTH TWITT
Shabbir HA, 2019, J BUS ETHICS, V159, P727, DOI 10.1007/s10551-018-3784-7
SocialBakers, 2019, AI POW SOC MED DIG
Stanford University, 2019, STANF NAT LANG PROC
Tableau Software, 2019, TABLEAU BUSINESS INT
The Social Clinic, 2013, SAUD AR RANKS 1 TWIT
Vijayarani S., 2015, INT J COMPUT SCI COM, V5, P7, DOI DOI 10.1016/J.PR0CS.2013.05.286
Wamda, 2019, TRISKA HAMID
Wankhede S, 2018, PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT), P723, DOI 10.1109/ICICCT.2018.8473277
Weedoo, 2017, TWITTER ARAB WORLD S
Wilson J.A., 2018, HALAL BRANDING
Wilson JAJ, 2011, J BRAND MANAG, V19, P91, DOI 10.1057/bm.2011.7
Wilson JAJ, 2013, J ISLAMIC MARK, V4, P7, DOI 10.1108/17590831311306327
Wilson JAJ, 2011, J ISLAMIC MARK, V2, P28, DOI 10.1108/17590831111115222
Wilson JAJ, 2013, J ISLAMIC MARK, V4, P22, DOI 10.1108/17590831311306336
Wilson Jonathan A. J., 2014, Social Business, V4, P255
Yoon YS, 2018, I C INF COMM TECH CO, P1194, DOI 10.1109/ICTC.2018.8539363
NR 74
TC 1
Z9 1
U1 0
U2 9
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1759-0833
EI 1759-0841
J9 J ISLAMIC MARK
JI J. Islamic Mark.
PD DEC 1
PY 2022
VL 13
IS 12
BP 2649
EP 2671
DI 10.1108/JIMA-12-2020-0355
EA AUG 2021
PG 23
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 6R8EC
UT WOS:000685615500001
DA 2024-03-27
ER
PT J
AU Roelen-Blasberg, T
Habel, J
Klarmann, M
AF Roelen-Blasberg, Tobias
Habel, Johannes
Klarmann, Martin
TI Automated inference of product attributes and their importance from
user-generated content: Can we replace traditional market research?
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE User-generated content; Marketing research automation; Natural language
processing; Conjoint analysis; Satisfaction driver analysis; Involvement
ID CUSTOMER SATISFACTION; TEXT ANALYSIS; ONLINE; PREFERENCES; INFORMATION;
PERFORMANCE; EXPERIENCE
AB User-generated content, particularly online product reviews by customers, provide mar-keters with rich data of customer evaluations of product attributes. This study proposes, benchmarks, and validates a new approach for inferring attribute-level evaluations from user-generated content. Moreover, little is known about whether and when insights from product reviews gained in such a way are consistent with traditional research methods, such as conjoint analysis and satisfaction driver analysis. To provide first insights into this question, the authors apply their approach to a dataset with almost one million product reviews from 52 product categories and run conjoint and satisfaction driver analyses for these categories. Results indicate that the consistency between methods largely varies across product categories. Initial exploratory analyses suggest that consistency might be higher for categories characterized by low experience qualities, high hedonic value, and high customer willingness to post online reviews-but further work will be necessary to validate these findings.(c) 2022 Elsevier B.V. All rights reserved.
C1 [Roelen-Blasberg, Tobias] MARA, Mannheim, Germany.
[Habel, Johannes] Univ Houston, CT Bauer Coll Business, Houston, TX USA.
[Klarmann, Martin] Karlsruhe Inst Technol KIT, Mkt & Sales Res Grp Inst Informat Syst & Mkt IISM, Karlsruhe, Germany.
[Klarmann, Martin] Karlsruhe Inst Technol KIT, Mkt & Sales Res Grp, Zirkel 2,Bldg 20-21, D-76131 Karlsruhe, Germany.
C3 University of Houston System; University of Houston; Helmholtz
Association; Karlsruhe Institute of Technology; Helmholtz Association;
Karlsruhe Institute of Technology
RP Klarmann, M (autor correspondiente), Karlsruhe Inst Technol KIT, Mkt & Sales Res Grp, Zirkel 2,Bldg 20-21, D-76131 Karlsruhe, Germany.
EM martin.klarmann@kit.edu
CR Aerts G, 2017, COMPUT HUM BEHAV, V75, P855, DOI 10.1016/j.chb.2017.06.023
Alavi S, 2021, J PERS SELL SALES M, V41, P301, DOI 10.1080/08853134.2021.1915794
[Anonymous], 2021, LOWE
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Bi JW, 2019, TOURISM MANAGE, V70, P460, DOI 10.1016/j.tourman.2018.09.010
Blair-Goldensohn S., 2008, P WWW 2008 WORKSHOP, V14, P339
Branavan SRK, 2009, J ARTIF INTELL RES, V34, P569, DOI 10.1613/jair.2633
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Büschken J, 2013, MARKET SCI, V32, P533, DOI 10.1287/mksc.2013.0779
Chakraborty I., 2021, J MARKETING RES
Chen L., 2022, STRUCTURAL TOPIC SEN, DOI [10.2139/ssrn.4020651, DOI 10.2139/SSRN.4020651]
Chen YX, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242920913025
Decker R, 2010, INT J RES MARK, V27, P293, DOI 10.1016/j.ijresmar.2010.09.001
Dzyabura D, 2019, MARKET SCI, V38, P88, DOI 10.1287/mksc.2018.1124
Fang Luo, 2011, Proceedings of the 2011 Fourth International Conference on Business Intelligence and Financial Engineering (BIFE 2011), P86, DOI 10.1109/BIFE.2011.101
Feldman R, 2007, IEEE DATA MINING, P469, DOI 10.1109/ICDM.2007.27
Gatti L, 2016, IEEE T AFFECT COMPUT, V7, P409, DOI 10.1109/TAFFC.2015.2476456
GREEN PE, 1990, J MARKETING, V54, P3, DOI 10.2307/1251756
Guo T, 2021, J MARKETING RES, V58, P115, DOI 10.1177/0022243720972106
Habel J, 2015, J ACAD MARKET SCI, V43, P768, DOI 10.1007/s11747-014-0400-y
Hamilton RW, 2017, J MARKETING RES, V54, P540, DOI 10.1509/jmr.14.0012
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
HOLBROOK MB, 1984, J CONSUM RES, V11, P728, DOI 10.1086/209009
Hu MQ, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P755
Huang P, 2009, J MARKETING, V73, P55, DOI 10.1509/jmkg.73.2.55
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Jianxing Y, 2011, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, V1, P1496, DOI DOI 10.1109/CC.2013.6488828
Joung J, 2021, J MECH DESIGN, V143, DOI 10.1115/1.4049865
Joung J, 2021, J MECH DESIGN, V143, DOI 10.1115/1.4048960
Kapferer J.-N., 1993, Psychology and Marketing, V10, P347, DOI [DOI 10.1002/MAR.4220100408, https://doi.org/10.1002/mar.4220100408]
Kivetz R, 2002, J MARKETING RES, V39, P155, DOI 10.1509/jmkr.39.2.155.19084
Kubler R. V., 2017, MARKETING SCI I WORK, V17
LAURENT G, 1985, J MARKETING RES, V22, P41, DOI 10.2307/3151549
Lee TY, 2011, J MARKETING RES, V48, P881, DOI 10.1509/jmkr.48.5.881
Liu B., 2012, SYNTHESIS LECT HUMAN, V5, P1, DOI [DOI 10.2200/S00416ED1V01Y201204HLT016, DOI 10.1007/978-3-031-02145-9]
Lowe, 2021, INV FACT SHEET
Manning CD, 2008, INTRO INFORM RETRIEV
Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
Moe WW, 2012, MARKET SCI, V31, P372, DOI 10.1287/mksc.1110.0662
Moe WW, 2014, SOCIAL MEDIA INTELLIGENCE, P1, DOI 10.1017/CBO9781139381338
Moon S, 2017, INT J RES MARK, V34, P265, DOI 10.1016/j.ijresmar.2016.05.007
NELSON P, 1970, J POLIT ECON, V78, P311, DOI 10.1086/259630
NELSON P, 1974, J POLIT ECON, V82, P729, DOI 10.1086/260231
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Okada EM, 2005, J MARKETING RES, V42, P43, DOI 10.1509/jmkr.42.1.43.56889
Pauwels K, 2009, J SERV RES-US, V12, P175, DOI 10.1177/1094670509344213
Peck J, 2003, J CONSUM RES, V30, P430, DOI 10.1086/378619
Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI DOI 10.3115/V1/D14-1162
Popescu Ana-Maria, 2005, P HUMAN LANGUAGE TEC, P339
Poria S, 2016, KNOWL-BASED SYST, V108, P42, DOI 10.1016/j.knosys.2016.06.009
Qiu GA, 2011, COMPUT LINGUIST, V37, P9, DOI 10.1162/coli_a_00034
Reimann M, 2010, INT J RES MARK, V27, P188, DOI 10.1016/j.ijresmar.2009.10.001
Salton G., 1986, INTRO MODERN INFORM
Schoenmueller V, 2020, J MARKETING RES, V57, P853, DOI 10.1177/0022243720941832
SHAFIR E, 1993, COGNITION, V49, P11, DOI 10.1016/0010-0277(93)90034-S
Simmons JP, 2011, PSYCHOL SCI, V22, P1359, DOI 10.1177/0956797611417632
Smith AK, 1999, J MARKETING RES, V36, P356, DOI 10.2307/3152082
Somprasertsri G, 2008, PROCEEDINGS OF THE 2008 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, P250
Sundaram DS., 1998, Word-of-mouth communications: A motivational analysis
Taddy M, 2013, J AM STAT ASSOC, V108, P755, DOI 10.1080/01621459.2012.734168
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Voss KE, 2003, J MARKETING RES, V40, P310, DOI 10.1509/jmkr.40.3.310.19238
Wang X, 2022, J MARKETING, V86, P155, DOI 10.1177/00222429211047822
Wittink DickR., 1990, Marketing Letters, V1, P113, DOI DOI 10.1007/BF00435295
Wu S, 2012, INT CONF DAT MIN WOR, P874, DOI 10.1109/ICDMW.2012.129
Zhai Z., 2011, P 4 ACM INT C WEB SE, P347, DOI DOI 10.1145/1935826.1935884
Zhang L, 2014, STUD BIG DATA, V1, P1, DOI 10.1007/978-3-642-40837-3_1
Zhang Lei., 2011, P 49 ANN M ASS COMPU, V2[81, P575
Zhang Lei., 2010, Proceedings of the 23rd International Conference on Computational Linguistics: Posters, P1462
Zhuang L., 2006, P 15 ACM INT C INF K, P43, DOI [DOI 10.1145/1183614.1183625, 10.1145/1183614.1183625]
NR 72
TC 2
Z9 2
U1 13
U2 29
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD MAR
PY 2023
VL 40
IS 1
BP 164
EP 188
DI 10.1016/j.ijresmar.2022.04.004
EA MAR 2023
PG 25
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA A0CX5
UT WOS:000951903100001
DA 2024-03-27
ER
PT J
AU van Dieijen, M
Borah, A
Tellis, GJ
Franses, PH
AF van Dieijen, Myrthe
Borah, Abhishek
Tellis, Gerard J.
Franses, Philip Hans
TI Big Data Analysis of Volatility Spillovers of Brands across Social Media
and Stock Markets
SO INDUSTRIAL MARKETING MANAGEMENT
LA English
DT Article
DE User-generated content; Stock market performance; Volatility;
Multivariate GARCH model; Spillover effects; Natural language processing
ID WORD-OF-MOUTH; MULTIVARIATE GARCH MODELS; DYNAMICS; SALES; CAPABILITIES;
UNCERTAINTY; INVESTMENT; REVIEWS; CHATTER; IMPACT
AB Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content.
Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user-generated content in Twitter. The authors discuss the managerial implications.
C1 [van Dieijen, Myrthe] Erasmus Univ, Erasmus Sch Econ ESE, Dept Econometr, Rotterdam, Netherlands.
[Borah, Abhishek] INSEAD, Mkt, Blvd Constance, F-77305 Fontainebleau, France.
[Tellis, Gerard J.] Univ Southern Calif, Mkt & Management & Org, POB 90089-1421, Los Angeles, CA 90007 USA.
[Tellis, Gerard J.] Univ Southern Calif, Ctr Global Innovat, POB 90089-1421, Los Angeles, CA 90007 USA.
[Tellis, Gerard J.] Univ Southern Calif, Amer Enterprise, Marshall Sch Business, POB 90089-1421, Los Angeles, CA 90007 USA.
[Franses, Philip Hans] Erasmus Univ, Appl Econometr, Erasmus Sch Econ ESE, ET-25,Burgemeester Oudlaan 50, NL-3062 PA Rotterdam, Netherlands.
[Franses, Philip Hans] Erasmus Univ, Mkt Res, Erasmus Sch Econ ESE, ET-25,Burgemeester Oudlaan 50, NL-3062 PA Rotterdam, Netherlands.
C3 Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University
Rotterdam; INSEAD Business School; University of Southern California;
University of Southern California; University of Southern California;
Erasmus University Rotterdam; Erasmus University Rotterdam - Excl
Erasmus MC; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus
University Rotterdam
RP Borah, A (autor correspondiente), INSEAD, Mkt, Blvd Constance, F-77305 Fontainebleau, France.
EM vandieijen@ese.eur.nl; abhishek.borah@insead.edu; tellis@usc.edu;
franses@ese.eur.nl
OI Franses, Philip Hans/0000-0002-2364-7777
FU foundation A.A. van Beek-Fonds
FX We thank seminar participants at the International Symposium on
Forecasting in 2014, the European Marketing Academy Conference in 2015,
the Marketing Science Conference in 2015, the Centre for European
Economic Research (ZEW) Conference on the Economics of Information and
Communication Technologies in 2016, and the Marketing Dynamics
Conference in 2016. Myrthe van Dieijen is grateful for the generous
hospitality extended during the visit to the University of Southern
California in facilitating the collaboration on this project. Financial
support from the foundation A.A. van Beek-Fonds for this visit is
gratefully acknowledged.
CR Alom F, 2011, ECON BULL, V31, P1439
Andersen TG, 1998, INT ECON REV, V39, P885, DOI 10.2307/2527343
Andersen TG, 2010, HANDB FINANC, P67, DOI 10.1016/B978-0-444-50897-3.50005-5
Asur S., 2010, WORKING PAPER
Bauwens L, 2006, J APPL ECONOMET, V21, P79, DOI 10.1002/jae.842
Bloom N, 2007, REV ECON STUD, V74, P391, DOI 10.1111/j.1467-937X.2007.00426.x
Bollen J, 2011, J COMPUT SCI-NETH, V2, P1, DOI 10.1016/j.jocs.2010.12.007
BOLLERSLEV T, 1992, J ECONOMETRICS, V52, P5, DOI 10.1016/0304-4076(92)90064-X
Bollevslev T., 1992, ECONOMET REV, V11, P143, DOI 10.1080/07474939208800229
Broyden C. G., 1970, Journal of the Institute of Mathematics and Its Applications, V6, P222
Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chintagunta PK, 2010, MARKET SCI, V29, P944, DOI 10.1287/mksc.1100.0572
Christoffersen P, 2004, MANAGE SCI, V50, P1204, DOI 10.1287/mnsc.1040.0276
Comte F, 2003, J MULTIVARIATE ANAL, V84, P61, DOI 10.1016/S0047-259X(02)00009-X
CUNHA SS, 2008, PRACTICAL FINANCIAL, V2, DOI DOI 10.1371/JOURNAL.PNTD.0000167
Day G.S., 2010, Strategy from the outside in: Profiting from customer value
Day GS, 2011, J MARKETING, V75, P183, DOI 10.1509/jmkg.75.4.183
DAY GS, 1994, J MARKETING, V58, P37, DOI 10.2307/1251915
Dellarocas C, 2007, J INTERACT MARK, V21, P23, DOI 10.1002/dir.20087
Doan T.A., 2013, ESTIMA WINRATS
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Elyasani E., 1998, Journal of Multinational Financial Management, V8, P89
Engle R, 2001, J ECON PERSPECT, V15, P157, DOI 10.1257/jep.15.4.157
ENGLE RF, 1995, ECONOMET THEOR, V11, P122, DOI 10.1017/S0266466600009063
Fama EF, 2004, J ECON PERSPECT, V18, P25, DOI 10.1257/0895330042162430
FLETCHER R, 1970, COMPUT J, V13, P317, DOI 10.1093/comjnl/13.3.317
Franses P. H., 2000, Nonlinear Time Series Models in Empirical Finance
Godes D, 2004, MARKET SCI, V23, P545, DOI 10.1287/mksc.1040.0071
GOLDFARB D, 1970, MATH COMPUT, V24, P23, DOI 10.2307/2004873
Gopinath S, 2014, MARKET SCI, V33, P241, DOI 10.1287/mksc.2013.0820
Hafner C. M., 2004, WORKING PAPER
Hafner CM, 2006, J INT MONEY FINANC, V25, P719, DOI 10.1016/j.jimonfin.2006.04.006
Hennig-Thurau T, 2015, J ACAD MARKET SCI, V43, P375, DOI 10.1007/s11747-014-0388-3
Joshi P, 2011, SAGE OPEN, V1, DOI 10.1177/2158244011413474
KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185
Leahy JV, 1996, J MONEY CREDIT BANK, V28, P64, DOI 10.2307/2077967
Li H, 2008, RES INT BUS FINANC, V22, P247, DOI 10.1016/j.ribaf.2007.06.001
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Luo XM, 2007, J MARKETING, V71, P75, DOI 10.1509/jmkg.71.3.75
Luo XM, 2013, INFORM SYST RES, V24, P146, DOI 10.1287/isre.1120.0462
Luo XM, 2009, MARKET SCI, V28, P148, DOI 10.1287/mksc.1080.0389
McAlister L, 2012, MARKET LETT, V23, P1, DOI 10.1007/s11002-011-9142-5
Mizik N, 2003, J MARKETING, V67, P63, DOI 10.1509/jmkg.67.1.63.18595
Moe WW, 2011, J MARKETING RES, V48, P444, DOI 10.1509/jmkr.48.3.444
Moorman C, 2016, J MARKETING, V80, P6, DOI 10.1509/jm.15.0423
Mu JF, 2018, IND MARKET MANAG, V75, P37, DOI 10.1016/j.indmarman.2018.03.010
Mu JF, 2015, IND MARKET MANAG, V49, P151, DOI 10.1016/j.indmarman.2015.05.003
Nam H, 2014, J MARKETING, V78, P21, DOI 10.1509/jm.12.0151
Onishi H, 2012, INT J RES MARK, V29, P221, DOI 10.1016/j.ijresmar.2011.11.003
Rui HX, 2013, DECIS SUPPORT SYST, V55, P863, DOI 10.1016/j.dss.2012.12.022
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
SHANNO DF, 1970, MATH COMPUT, V24, P647, DOI 10.2307/2004840
Sonnier GP, 2011, MARKET SCI, V30, P702, DOI 10.1287/mksc.1110.0642
Srinivasan S, 2009, J MARKETING RES, V46, P293, DOI 10.1509/jmkr.46.3.293
Tellis GJ, 2006, MARKET SCI, V25, P217, DOI 10.1287/mksc.1050.0178
Tirunillai S, 2012, MARKET SCI, V31, P198, DOI 10.1287/mksc.1110.0682
Trusov M, 2009, J MARKETING, V73, P90, DOI 10.1509/jmkg.73.5.90
Urbig D, 2013, J MANAGE, V39, P985, DOI 10.1177/0149206311416120
Worthington A, 2004, INT J FINANC ECON, V9, P71, DOI 10.1002/ijfe.222
NR 60
TC 15
Z9 16
U1 10
U2 60
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0019-8501
EI 1873-2062
J9 IND MARKET MANAG
JI Ind. Mark. Manage.
PD JUL
PY 2020
VL 88
BP 465
EP 484
DI 10.1016/j.indmarman.2018.12.006
PG 20
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA MQ2ZP
UT WOS:000552766500046
OA Green Published
DA 2024-03-27
ER
PT J
AU Reisenbichler, M
Reutterer, T
Schweidel, DA
Dan, D
AF Reisenbichler, Martin
Reutterer, Thomas
Schweidel, David A.
Dan, Daniel
TI Frontiers: Supporting Content Marketing with Natural Language Generation
SO MARKETING SCIENCE
LA English
DT Article
DE SEO; content marketing; natural language generation; transfer learning
ID SEARCH ENGINE OPTIMIZATION; ARTIFICIAL-INTELLIGENCE
AB Advances in natural language generation (NLG) have facilitated technologies such as digital voice assistants and chatbots. in this research, we demonstrate how NLG can support content marketing by using it to draft content for the landing page of a website in search engine optimization (SEO). Traditional SEO projects rely on hand-crafted content that is both time consuming and costly to produce. To address the costs associated with producing SEO content, we propose a semiautomated methodology using state-of-the-art NLG and demonstrate that the content-writing machine can create unique, human-like SEO content. As part of our research, we demonstrate that although the machine-generated content is designed to perform well in search engines, the role of the human editor remains essential. Comparing the resulting content with human refinement to traditional human-written SEO texts, we find that the revised, machine-generated texts are virtually indistinguishable from those created by SEO experts along a number of human perceptual dimensions. We conduct field experiments in two industries to demonstrate our approach and show that the resulting SEO content outperforms that created by human writers (including SEO experts) in search engine rankings. Additionally, we illustrate how our approach can substantially reduce the production costs associated with content marketing, increasing their return on investment.
C1 [Reisenbichler, Martin; Reutterer, Thomas] Vienna Univ Econ & Business, Dept Mkt, A-1020 Vienna, Austria.
[Schweidel, David A.] Emory Univ, Goizueta Business Sch, Mkt Area, Atlanta, GA 30322 USA.
[Dan, Daniel] Modul Univ, Sch Appl Data Sci, A-1190 Vienna, Austria.
C3 Vienna University of Economics & Business; Emory University
RP Reisenbichler, M (autor correspondiente), Vienna Univ Econ & Business, Dept Mkt, A-1020 Vienna, Austria.
EM martin.reisenbichter@wu.ac.at; thomas.reutterer@wu.ac.at;
dschweidel@emory.edu; daniel.dan@modul.ac.at
RI dan, dan/KEH-7711-2024
OI Dan, Daniel/0000-0002-7251-7899
CR Aaker JL, 1997, J MARKETING RES, V34, P347, DOI 10.2307/3151897
Ascarza E., 2021, HARVARD BUS REV, V99, P48
Azzopardi L, 2018, ACM/SIGIR PROCEEDINGS 2018, P605, DOI 10.1145/3209978.3210027
Bar-Ilan J, 2006, COMPUT NETW, V50, P1448, DOI 10.1016/j.comnet.2005.10.020
Baye MR, 2016, J ECON MANAGE STRAT, V25, P6, DOI 10.1111/jems.12141
Berger J, TEXTANALYZER
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Berman R, 2013, MARKET SCI, V32, P644, DOI 10.1287/mksc.2013.0783
Booth RJ, 2015, LINGUISTIC INQUIRY A
Brown T., 2020, NEURIPS, P1877, DOI 10.48550/arXiv.2005.14165
Brynjolfsson E, 2017, SCIENCE, V358, P1530, DOI 10.1126/science.aap8062
Carnevale M, 2017, INT J RES MARK, V34, P572, DOI 10.1016/j.ijresmar.2017.01.003
Danaher PJ, 2006, J MARKETING RES, V43, P182, DOI 10.1509/jmkr.43.2.182
Dathathri Sumanth, 2020, 8 INT C LEARN REPR I
Davenport TH, 2021, Harvard Business Review
Ghose A, 2019, MANAGE SCI, V65, P1363, DOI 10.1287/mnsc.2017.2991
Google, 2020, OPT YOUR CONT SEARCH
Heaven WD, 2020, OpenAI's New Language Generator GPT-3 Is Shockingly Good-And Completely Mindless
Kamoen N, 2013, SURV RES METHODS-GER, V7, P181
Liu J, 2018, MARKET SCI, V37, P930, DOI 10.1287/mksc.2018.1112
Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
Luh CJ, 2016, ONLINE INFORM REV, V40, P239, DOI 10.1108/OIR-04-2015-0112
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Marchenko OO, 2020, CYBERN SYST ANAL+, V56, P13, DOI 10.1007/s10559-020-00216-x
Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
Pitler E, 2008, P C EMP METH NAT LAN
Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
Radford A., 2018, IMPROVING LANGUAGE U
Radford Alec, 2019, LANGUAGE MODELS ARE
Roberts C, 2010, AM BEHAV SCI, V54, P43, DOI 10.1177/0002764210376310
Rocklage MD, 2018, PSYCHOL SCI, V29, P749, DOI 10.1177/0956797617744797
Salminen J., 2019, P 9 INT C INF SYST T, P1, DOI [10.1145/3361570.3361578, DOI 10.1145/3361570.3361578]
Sheffield JP, 2020, BUS PROF COMMUN Q, V83, P153, DOI 10.1177/2329490619890335
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Vaswani A, 2017, ADV NEUR IN, V30
Wilson HJ, 2017, MIT SLOAN MANAGE REV, V58, P14
NR 36
TC 8
Z9 8
U1 66
U2 154
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD MAY-JUN
PY 2022
VL 41
IS 3
BP 441
EP 452
DI 10.1287/mksc.2022.1354
PG 13
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1T0PR
UT WOS:000804442500002
DA 2024-03-27
ER
PT J
AU Zhao, L
Zhang, ML
Tu, JB
Li, JL
Zhang, Y
AF Zhao, Lu
Zhang, Mingli
Tu, Jianbo
Li, Jialing
Zhang, Yan
TI Can users embed their user experience in user-generated images? Evidence
from JD.com
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE User experience; User -generated images; Deep learning model; Image
semantic feature; e -commerce platform
ID E-COMMERCE; SOCIAL MEDIA; PRODUCT-EXPERIENCE; CUSTOMER LOYALTY;
INFORMATION; IMPACT; CONSUMPTION; CONSUMERS; FEELINGS; SERVICES
AB Nowadays, massive user-generated images (UGIs) are posted online to convey users' experiences with specific brands or products. Thus, this visual information is precious, as it conveys users' actual and subjective feelings about brands and products. Because of the unprecedented quantity of images and the heterogeneity of their content, it is quite challenging for brand marketers and retailers to probe into subjective user experience in largescale UGIs. To address this gap, this study aims to identify the connection between user experience and different image semantic features (i.e. centrality and richness) by using deep learning models. By employing objective data (8963 images) from JD.com and using deep learning algorithms (faster R-CNN), we found that users with positive user experience prefer to generate high-centrality and high-richness pictures. Our study enriches the relevant literature and provides valuable practical implications for brand marketers and e-commerce retailers. Based on findings of this work, relevant stakeholders can understand their users' experience better from objective UGIs and devise corresponding recommendation and service strategies.
C1 [Zhao, Lu] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China.
[Zhang, Mingli] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China.
[Tu, Jianbo] North China Univ Technol, Sch Econ & Management, Beijing, Peoples R China.
[Li, Jialing] Chem Ind Press, Beijing, Peoples R China.
[Zhang, Yan] Beijing Forestry Univ, Beijing, Peoples R China.
C3 Chinese Academy of Sciences; Academy of Mathematics & System Sciences,
CAS; Beihang University; North China University of Technology; Beijing
Forestry University
RP Zhang, ML (autor correspondiente), Beihang Univ, Sch Econ & Management, Beijing, Peoples R China.
EM zhaolu@amss.ac.cn; zhang_ml_ml@126.com; tujianbo19820416@163.com;
jennylee_cip@163.com; zhangyanmkt@outlook.com
RI Li, Jialing/F-4875-2013
OI zhang, yan/0000-0003-1022-8642
FU Humanities and Social Science Fund of Ministry of Education of the
People's Republic of China [21YJC630129]; China Postdoctoral Sci-ence
Foundation [E2909318]
FX Acknowledgement Authors thank Dr. Yu Wang for her great efforts in the
paper proof-reading. We would like to thank Humanities and Social
Science Fund of Ministry of Education of the People's Republic of China,
20YJA630091; Humanities and Social Science Fund of Ministry of Education
of the People's Republic of China, 21YJC630129 and China Postdoctoral
Sci-ence Foundation, E2909318 for funding our research.
CR Abrate G, 2019, TOURISM MANAGE, V74, P224, DOI 10.1016/j.tourman.2019.03.013
Akter S, 2016, ELECTRON MARK, V26, P173, DOI 10.1007/s12525-016-0219-0
Alomari HW, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e03917
[Anonymous], 2020, WIKIPEDIA
[Anonymous], 2009, 9241 210 2010 ERGONO, DOI DOI 10.3403/30388991
[Anonymous], 2022, J RETAILING CONSUM S, V70
Argyris YA, 2020, COMPUT HUM BEHAV, V112, DOI 10.1016/j.chb.2020.106443
Arrigo E, 2018, J RETAIL CONSUM SERV, V44, P170, DOI 10.1016/j.jretconser.2018.06.011
Bakri M, 2020, J SERV MARK, V34, P429, DOI 10.1108/JSM-11-2018-0341
Barger V, 2016, J RES INTERACT MARK, V10, P268, DOI 10.1108/JRIM-06-2016-0065
Basu S, 2018, ELECTRON COMMER R A, V30, P25, DOI 10.1016/j.elerap.2018.05.004
Bechara A, 2005, GAME ECON BEHAV, V52, P336, DOI 10.1016/j.geb.2004.06.010
Belk R, 2014, J BUS RES, V67, P1595, DOI 10.1016/j.jbusres.2013.10.001
BITNER MJ, 1992, J MARKETING, V56, P57, DOI 10.2307/1252042
Bornemann T, 2011, J CONSUM RES, V38, P490, DOI 10.1086/659874
Brubaker PJ, 2018, PUBLIC RELAT REV, V44, P342, DOI 10.1016/j.pubrev.2018.04.010
Bruce NI, 2017, J MARKETING RES, V54, P202, DOI 10.1509/jmr.14.0117
Cai XW, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103141
Chen LCE, 2018, LECT NOTES COMPUT SC, V11211, P833, DOI 10.1007/978-3-030-01234-2_49
Chen N, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102344
com J.D., 2019, INTRO JD
DAFT RL, 1987, MIS QUART, V11, P355, DOI 10.2307/248682
Deng N, 2019, TOUR MANAG PERSPECT, V30, P182, DOI 10.1016/j.tmp.2019.02.016
Dolbec PY, 2013, J RETAILING, V89, P460, DOI 10.1016/j.jretai.2013.06.003
Dydata, 2020, ABOUT US
Feng BY, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106812
Flavián C, 2006, J RETAIL CONSUM SERV, V13, P363, DOI 10.1016/j.jretconser.2005.11.003
Follett J., 2015, DESIGNING EMERGING T, P130
Freeman LC, 2008, J SOC STRUCT, V9, P1
Geise S, 2015, COMMUN THEOR, V25, P46, DOI 10.1111/comt.12048
Giglio S, 2019, TOURISM MANAGE, V72, P306, DOI 10.1016/j.tourman.2018.12.007
Godovykh M, 2021, TOUR REV, V76, P277, DOI 10.1108/TR-01-2020-0025
Goldfarb A, 2011, MARKET SCI, V30, P389, DOI 10.1287/mksc.1100.0583
Gulfraz MB, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103000
Guo MZ, 2020, OMEGA-INT J MANAGE S, V96, DOI 10.1016/j.omega.2019.05.010
Gupta P, 2010, J BUS RES, V63, P1041, DOI 10.1016/j.jbusres.2009.01.015
HASSENZAHL M., 2008, 20 INT C ASS FRANCOP, P11, DOI [10.1145/1512714.1512717, DOI 10.1145/1512714.1512717]
Haugeland IKF, 2022, INT J HUM-COMPUT ST, V161, DOI 10.1016/j.ijhcs.2022.102788
He W, 2017, ONLINE INFORM REV, V41, P921, DOI 10.1108/OIR-07-2016-0201
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
HOLBROOK MB, 1982, J CONSUM RES, V9, P132, DOI 10.1086/208906
Hovy EH, 2015, TEXT SPEECH LANG TEC, V48, P13, DOI 10.1007/978-3-319-08043-7_2
Hu X, 2019, J INF SCI, V45, P16, DOI 10.1177/0165551518762070
Huang J, 2021, J BUS RES, V122, P761, DOI 10.1016/j.jbusres.2020.06.065
Hui H.W., 2010, INT J IMAGE PROCESS, V4, P192
Hussain S, 2021, EXPERT SYST APPL, V170, DOI 10.1016/j.eswa.2020.114545
Jansen BJ, 2009, J AM SOC INF SCI TEC, V60, P2169, DOI 10.1002/asi.21149
Jetter H., 2007, International Open Workshop "User experience - Towards a unified view", P106
Jiang ZH, 2004, J MANAGE INFORM SYST, V21, P111, DOI 10.1080/07421222.2004.11045817
Kaiser C, 2020, J BUS RES, V117, P707, DOI 10.1016/j.jbusres.2019.09.017
Kanani R, 2021, ELECTRON COMMER R A, V48, DOI 10.1016/j.elerap.2021.101059
KATZ D, 1960, PUBLIC OPIN QUART, V24, P163, DOI 10.1086/266945
Kim J, 2021, INT J HOSP MANAG, V92, DOI 10.1016/j.ijhm.2020.102708
Klostermann J, 2018, INT J RES MARK, V35, P538, DOI 10.1016/j.ijresmar.2018.08.002
Kolotylo-Kulkarni M, 2021, J BUS RES, V126, P221, DOI 10.1016/j.jbusres.2020.12.006
Kumari Parveen, 2020, International Journal of Electronic Customer Relationship Management, V12, P191, DOI 10.1504/IJECRM.2020.110037
Laros FJM, 2005, J BUS RES, V58, P1437, DOI 10.1016/j.jbusres.2003.09.013
Lee J, 2008, ELECTRON COMMER R A, V7, P341, DOI 10.1016/j.elerap.2007.05.004
Lee S.W., 2020, MULTIMEDIA TOLLS APP, V80, P625
Li HW, 2023, MANAGE SCI, V69, P5209, DOI 10.1287/mnsc.2022.4616
Lim JS, 2020, COMPUT HUM BEHAV, V108, DOI 10.1016/j.chb.2020.106327
Lim KH, 2000, INFORM SYST RES, V11, P115, DOI 10.1287/isre.11.2.115.11776
Liu M, 2021, J RURAL STUD, V83, P106, DOI 10.1016/j.jrurstud.2021.02.021
Liu W., 2016, PROC EUR C COMPUT VI, P21
Liu XL, 2019, ARTIF INTELL REV, V52, P1089, DOI 10.1007/s10462-018-9641-3
Mikellides B., 2017, COLOUR PSYCHOL EMOTI, Vsecond
Mugge R., 2005, DES J, V8, P38, DOI DOI 10.2752/146069205789331637
Ordenes FV, 2019, J SERV MANAGE, V30, P593, DOI 10.1108/JOSM-08-2019-0254
Palvia P, 2011, DECIS SUPPORT SYST, V51, P657, DOI 10.1016/j.dss.2011.03.006
Park E, 2019, J RETAIL CONSUM SERV, V51, P14, DOI 10.1016/j.jretconser.2019.05.019
Petit O, 2019, J INTERACT MARK, V45, P42, DOI 10.1016/j.intmar.2018.07.004
Pham MT, 1998, J CONSUM RES, V25, P144, DOI 10.1086/209532
Preece J., 2015, INTERACTION DESIGN B, P12
Presi C, 2014, EUR J MARKETING, V48, P1600, DOI 10.1108/EJM-07-2012-0400
Qin JD, 2022, DECIS SUPPORT SYST, V163, DOI 10.1016/j.dss.2022.113859
Quach S, 2022, J BUS RES, V150, P538, DOI 10.1016/j.jbusres.2022.06.046
Ren S., 2016, CVPR
Rose G, 2019, ENVIRON PLANN D, V37, P411, DOI 10.1177/0263775818771080
Schau HJ, 2003, J CONSUM RES, V30, P385
Schiffersrin H.N., 2008, PRODUCT EXPERIENCE
Schulze K., 2010, P 7 INT C METH TECHN, V14
Septianto F, 2018, J RETAIL CONSUM SERV, V44, P1, DOI 10.1016/j.jretconser.2018.05.006
Setchi R, 2019, IEEE T AFFECT COMPUT, V10, P182, DOI 10.1109/TAFFC.2017.2705691
Shahbaznezhad H, 2021, J INTERACT MARK, V53, P47, DOI 10.1016/j.intmar.2020.05.001
Shavitt S., 1989, ADV CONSUM RES, V16, P200
Shavitt S., 1992, J CONSUM PSYCHOL, V1, P337
Shrirao A.S., 2017, INT C CURR TRENDS CO
Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972
Smith AN, 2012, J INTERACT MARK, V26, P102, DOI 10.1016/j.intmar.2012.01.002
SNYDER M, 1985, J PERS SOC PSYCHOL, V49, P586, DOI 10.1037/0022-3514.49.3.586
Son Y, 2023, J RETAIL CONSUM SERV, V71, DOI 10.1016/j.jretconser.2022.103210
Sousa R, 2009, INT J OPER PROD MAN, V29, P834, DOI 10.1108/01443570910977715
Srinivasan SS, 2002, J RETAILING, V78, P41, DOI 10.1016/S0022-4359(01)00065-3
Statista, 2017, ABOUT US
Sun XY, 2019, DECIS SUPPORT SYST, V124, DOI 10.1016/j.dss.2019.113099
VanMeter R, 2018, J INTERACT MARK, V43, P83, DOI 10.1016/j.intmar.2018.03.003
Wakefield LT, 2018, J INTERACT MARK, V41, P44, DOI 10.1016/j.intmar.2017.09.005
Wang M.Y., 2022, BACKGROUND COMPOSITI, DOI [10.2139/ssrn.4044841, DOI 10.2139/SSRN.4044841]
Wang Y, 2022, BEHAV INFORM TECHNOL, V41, P3044, DOI 10.1080/0144929X.2021.1970807
Wang Y, 2022, INFORM TECHNOL PEOPL, V35, P2241, DOI 10.1108/ITP-12-2020-0869
WESTBROOK RA, 1991, J CONSUM RES, V18, P84, DOI 10.1086/209243
Wilcox K, 2009, J MARKETING RES, V46, P247, DOI 10.1509/jmkr.46.2.247
Wu KW, 2016, J RETAIL CONSUM SERV, V28, P17, DOI 10.1016/j.jretconser.2015.08.009
Xia HS, 2020, DECIS SUPPORT SYST, V131, DOI 10.1016/j.dss.2019.113235
Xiao Y, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103013
Xu X, 2017, INT J INFORM MANAGE, V37, P673, DOI 10.1016/j.ijinfomgt.2017.06.004
Yang Y, 2023, ELECTRON COMMER R A, V57, DOI 10.1016/j.elerap.2022.101232
Yoo K. H., 2008, Information Technology and Tourism, V10, P283, DOI 10.3727/109830508788403114
Yu CE, 2020, TOURISM MANAGE, V80, DOI 10.1016/j.tourman.2020.104110
Yu W, 2018, COMPUT VIS IMAGE UND, V169, P40, DOI 10.1016/j.cviu.2018.01.001
Zhang M., 2021, MANAGE SCI
Zhang M.L., 2021, COMPUT HUM BEHAV, V127
Zhang ML, 2020, J BUS RES, V118, P389, DOI 10.1016/j.jbusres.2020.07.008
Zhang ML, 2017, INTERNET RES, V27, P839, DOI 10.1108/IntR-06-2016-0148
Zhang SY, 2022, MANAGE SCI, V68, P5644, DOI 10.1287/mnsc.2021.4175
Zhao L., 2021, INT J PSYCHOL BEHAV, V17, P1
Zhao Y, 2020, ELECTRON COMMER R A, V41, DOI 10.1016/j.elerap.2020.100980
Zhou ZM, 2020, INTERNET RES, V30, P575, DOI 10.1108/INTR-12-2018-0531
Zhu J, 2019, J INTERACT MARK, V46, P101, DOI 10.1016/j.intmar.2018.10.002
NR 119
TC 2
Z9 2
U1 23
U2 26
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD SEP
PY 2023
VL 74
AR 103379
DI 10.1016/j.jretconser.2023.103379
EA JUN 2023
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA K5AW9
UT WOS:001016575200001
DA 2024-03-27
ER
PT J
AU Urban, G
Timoshenko, A
Dhillon, P
Hauser, JR
AF Urban, Glen
Timoshenko, Artem
Dhillon, Paramveer
Hauser, John R.
TI Is Deep Learning a Game Changer for Marketing Analytics?
SO MIT SLOAN MANAGEMENT REVIEW
LA English
DT Article
C1 [Urban, Glen] MIT Sloan Sch Management, Cambridge, MA 02142 USA.
[Timoshenko, Artem] Northwestern Univ, Mkt, Evanston, IL 60208 USA.
[Dhillon, Paramveer] Univ Michigan, Informat, Ann Arbor, MI 48109 USA.
[Hauser, John R.] MIT Sloan Sch Management, Mkt, Cambridge, MA USA.
C3 Massachusetts Institute of Technology (MIT); Northwestern University;
University of Michigan System; University of Michigan; Massachusetts
Institute of Technology (MIT)
RP Urban, G (autor correspondiente), MIT Sloan Sch Management, Cambridge, MA 02142 USA.
RI Hauser, John R/O-3046-2019; Timoshenko, Artem/IXN-4676-2023
OI Timoshenko, Artem/0000-0002-5431-2136
FU Suruga Bank; MIT's Initiative on the Digital Economy
FX We would like to thank Suruga Bank and MIT's Initiative on the Digital
Economy for their funding support, and NerdWallet and Comscore for
providing the data used in the research.
CR [Anonymous], 2015, DEEP LEARNING
Burnap A., 2019, DESIGN EVALUATION PR
Dhillon P., 2019, WORKING PAPER
Geron A., 2017, HANDS ON MACHINE LEA
Liu L., 2019, Working Paper
Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Urban GL, 2009, MIT SLOAN MANAGE REV, V50, P53
NR 8
TC 15
Z9 18
U1 0
U2 16
PU SLOAN MANAGEMENT REVIEW ASSOC, MIT SLOAN SCHOOL MANAGEMENT
PI CAMBRIDGE
PA 77 MASSACHUSETTS AVE, E60-100, CAMBRIDGE, MA 02139-4307 USA
SN 1532-9194
J9 MIT SLOAN MANAGE REV
JI MIT Sloan Manage. Rev.
PD WIN
PY 2020
VL 61
IS 2
BP 71
EP 76
PG 6
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA NW3JD
UT WOS:000574904600013
DA 2024-03-27
ER
PT J
AU Yu, J
Egger, R
AF Yu, Joanne
Egger, Roman
TI Looking behind the scenes at dark tourism: a comparison between academic
publications and user-generated-content using natural language
processing
SO JOURNAL OF HERITAGE TOURISM
LA English
DT Article
DE Dark tourism; Instagram; tourism movements; scatterplot; tourist
experience; natural language processing
ID DEATH; SITES; THANATOURISM; ATTRACTIONS; MOTIVATION; EXPERIENCE;
MANAGEMENT
AB Although scholars have discussed the topic of dark tourism from various angles, little is known from tourists' perspectives. Thus, it remains largely unanswered whether the research agendas in the field of dark tourism also correspond to the reality of tourism or whether relevant topics remain unaddressed. This study uses natural language processing approaches to uncover the differences and similarities in knowledge between tourists and researchers based on academic publications and Instagram posts. The results provide insights into underexplored tourist experiences at various dark places, such as Vienna, Philadelphia, Estonia, and the Czech Republic. The findings also uncover activities rarely considered as a form of dark tourism, such as urban exploration and ghost hunting. By highlighting the differences in perceptions between scholars and tourists, this study points to future research directions and offers insights into what interests tourists to various stakeholders.
C1 [Yu, Joanne] Modul Univ Vienna, Dept Tourism & Serv Management, Vienna, Austria.
[Egger, Roman] Salzburg Univ Appl Sci, Dept Innovat & Management Tourism, Salzburg, Austria.
RP Yu, J (autor correspondiente), Modul Univ Vienna, Dept Tourism & Serv Management, Vienna, Austria.
EM joanne.yu@modul.ac.at
RI Yu, Joanne/P-1427-2019; Egger, Roman/AAS-1732-2020
OI Yu, Joanne/0000-0001-7605-6367; Egger, Roman/0000-0003-4888-6026
CR Abraham V, 2022, J HERIT TOUR, V17, P264, DOI 10.1080/1743873X.2021.1955892
Ashworth G.J., 2002, Journal of International Research in Geographical and Environmental Education, V11, P363, DOI [10.1080/10382040208667504, DOI 10.1080/10382040208667504]
Biran A, 2014, ANN TOURISM RES, V47, P1, DOI 10.1016/j.annals.2014.03.004
Biran A, 2011, ANN TOURISM RES, V38, P820, DOI 10.1016/j.annals.2010.12.001
Bolan P., 2018, SHINING DIGITAL LIGH, P727
Bryce D, 2015, TOURISM MANAGE, V46, P571, DOI 10.1016/j.tourman.2014.08.012
Buda DM, 2014, ANN TOURISM RES, V46, P102, DOI 10.1016/j.annals.2014.03.005
Causevic S, 2011, ANN TOURISM RES, V38, P780, DOI 10.1016/j.annals.2010.12.004
Chronis A, 2012, ANN TOURISM RES, V39, P1797, DOI 10.1016/j.annals.2012.05.028
Cohen EH, 2011, ANN TOURISM RES, V38, P193, DOI 10.1016/j.annals.2010.08.003
Commane G., 2019, Holocaust Studies, P158, DOI [DOI 10.1080/17504902.2018.1472879, 10.1080/17504902.2018.1472879]
Domingo-Carrillo MA, 2020, CURR ISSUES TOUR, V23, P880, DOI 10.1080/13683500.2018.1564738
DUNKLEY R.A., 2007, ASIAN J TOURISM HOSP, V1, P54
Dunkley R, 2011, TOURISM MANAGE, V32, P860, DOI 10.1016/j.tourman.2010.07.011
Egger R., 2022, INFORM COMMUNICATION, P343, DOI DOI 10.1007/978-3-030-94751-4_31
Egger R., 2022, Applied Data Science in Tourism, P85, DOI DOI 10.1007/978-3-030-88389-8_6
Egger R, 2022, TOUR REV, V77, P1234, DOI 10.1108/TR-05-2021-0244
Falagas ME, 2008, FASEB J, V22, P338, DOI 10.1096/fj.07-9492LSF
Farkic J, 2021, ANN TOURISM RES, V90, DOI 10.1016/j.annals.2021.103256
Filep S., 2017, Positive Tourism
Foley M., 1996, International Journal of Heritage Studies, V2, P198, DOI [10.1080/13527259608722175, https://doi.org/10.1080/13527259608722175, DOI 10.1080/13527259608722175]
Gao J, 2016, PROCEEDINGS 2016 IEEE SYMPOSIUM ON SERVICE-ORIENTED SYSTEM ENGINEERING SOSE 2016, P433, DOI 10.1109/SOSE.2016.63
Geru M., 2018, EC APPL INFORM, V24, P5, DOI DOI 10.1109/SOSE.2016.63
Gibbs M, 2015, INFORM COMMUN SOC, V18, P255, DOI 10.1080/1369118X.2014.987152
Hartmann R, 2014, J HERIT TOUR, V9, P166, DOI 10.1080/1743873X.2013.807266
Hauser D, 2022, J DESTIN MARK MANAGE, V24, DOI 10.1016/j.jdmm.2022.100702
Iliev D, 2021, TOURISM GEOGR, V23, P963, DOI 10.1080/14616688.2020.1722215
Isaac RK, 2021, J HERIT TOUR, V16, P363, DOI 10.1080/1743873X.2021.1920963
Isaac RK, 2014, CURR ISSUES TOUR, V17, P164, DOI 10.1080/13683500.2013.776021
Kang EJ, 2012, TOURISM MANAGE, V33, P257, DOI 10.1016/j.tourman.2011.03.004
Kennell J, 2021, J HERIT TOUR, V16, P367, DOI 10.1080/1743873X.2020.1782924
Kessler J.S., 2017, ARXIV PREPRINT ARXIV, P1, DOI [10.48550/arXiv.1703.00565, DOI 10.48550/ARXIV.1703.00565]
Kidron CA, 2013, ANN TOURISM RES, V41, P175, DOI 10.1016/j.annals.2012.12.009
Korstanje ME, 2012, WORLDW HOSP TOUR THE, V4, P8, DOI 10.1108/17554211211198552
Lacanienta A, 2020, J HERIT TOUR, V15, P626, DOI 10.1080/1743873X.2020.1739055
Lagos E., 2015, Tourismos, V10, P153
Lemelin H, 2010, CURR ISSUES TOUR, V13, P477, DOI 10.1080/13683500903406367
Lennon J.J., 2018, The Palgrave handbook of dark tourism studies, P585
Lennon JohnJ., 2000, DARK TOURISM
Light D, 2017, TOURISM MANAGE, V61, P275, DOI 10.1016/j.tourman.2017.01.011
Magee R, 2015, SERV IND J, V35, P898, DOI 10.1080/02642069.2015.1090980
Marrone M, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0242283
Martini A, 2020, CURR ISSUES TOUR, V23, P679, DOI 10.1080/13683500.2018.1518972
Miles S, 2014, J HERIT TOUR, V9, P134, DOI 10.1080/1743873X.2013.871017
Mott C, 2014, ANTIPODE, V46, P229, DOI 10.1111/anti.12033
Mowatt RA, 2011, ANN TOURISM RES, V38, P1410, DOI 10.1016/j.annals.2011.03.012
Munoz CL, 2017, J POLITICAL MARKETIN, V16, P290, DOI 10.1080/15377857.2017.1334254
Neuhofer B, 2021, ANN TOURISM RES, V91, DOI 10.1016/j.annals.2021.103310
Oren G, 2021, TOURISM MANAGE, V82, DOI 10.1016/j.tourman.2020.104194
Podoshen JS, 2013, TOURISM MANAGE, V35, P263, DOI 10.1016/j.tourman.2012.08.002
Podoshen JS, 2011, TOURISM MANAGE, V32, P1332, DOI 10.1016/j.tourman.2011.01.007
Prayag G, 2021, J SUSTAIN TOUR, V29, P1508, DOI 10.1080/09669582.2020.1823398
Raab Nigel A., 2020, The Humanities in Transition from Postmodernism to the Digital Age
Raine R, 2013, INT J CULT TOUR HOSP, V7, P242, DOI 10.1108/IJCTHR-05-2012-0037
Roberts Catherine., 2018, The Palgrave Handbook of Dark Tourism Studies, DOI [DOI 10.1057/978-1-137-47566-4_25, https://doi.org/10.1057/978-1-137-47566-4_25]
Seaton A.V., 1996, International Journal of Heritage Studies, V2, P234, DOI DOI 10.1080/13527259608722178
Sharpley R, 2009, ASPEC TOUR, P3
Sigala M, 2021, J HERIT TOUR, V16, P412, DOI 10.1080/1743873X.2020.1851697
Smith VL, 1998, ANN TOURISM RES, V25, P202, DOI 10.1016/S0160-7383(97)00086-8
Stone P, 2008, ANN TOURISM RES, V35, P574, DOI 10.1016/j.annals.2008.02.003
Stone P, 2013, INT J CULT TOUR HOSP, V7, P307, DOI 10.1108/IJCTHR-06-2013-0039
Stone PR, 2012, ANN TOURISM RES, V39, P1565, DOI 10.1016/j.annals.2012.04.007
Stone PR, 2006, TOURISM, V54, P145
Strange C, 2003, ANN TOURISM RES, V30, P386, DOI 10.1016/S0160-7383(02)00102-0
Sun JJ, 2021, ANN TOURISM RES, V86, DOI 10.1016/j.annals.2020.103087
Turner J, 2021, J MED INTERNET RES, V23, DOI 10.2196/27307
Walter T, 2009, ASPEC TOUR, P39
Waltman L, 2013, J AM SOC INF SCI TEC, V64, P372, DOI 10.1002/asi.22775
Wight AC, 2007, TOURISM MANAGE, V28, P519, DOI 10.1016/j.tourman.2006.03.006
Winter C, 2011, INT J TOUR RES, V13, P164, DOI 10.1002/jtr.806
Wyatt B, 2021, J HERIT TOUR, V16, P433, DOI 10.1080/1743873X.2020.1858087
Yan BJ, 2016, TOURISM MANAGE, V53, P108, DOI 10.1016/j.tourman.2015.09.014
Yang WQ, 2011, TOURISM MANAGE, V32, P949, DOI 10.1016/j.tourman.2010.07.007
Zheng CH, 2020, TOURISM GEOGR, V22, P105, DOI 10.1080/14616688.2019.1618903
NR 74
TC 2
Z9 2
U1 1
U2 7
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1743-873X
EI 1747-6631
J9 J HERIT TOUR
JI J. Herit. Tour.
PD SEP 3
PY 2022
VL 17
IS 5
BP 548
EP 562
DI 10.1080/1743873X.2022.2097011
EA JUL 2022
PG 15
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA 4N4WH
UT WOS:000827544000001
DA 2024-03-27
ER
PT J
AU Philp, M
Jacobson, J
Pancer, E
AF Philp, Matthew
Jacobson, Jenna
Pancer, Ethan
TI Predicting social media engagement with computer vision: An examination
of food marketing on Instagram
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Social media marketing; Consumer engagement; Machine learning; Food;
Processing fluency; Google Vision AI
ID WORD-OF-MOUTH; CUSTOMER ENGAGEMENT; PROCESSING FLUENCY; MESSAGE
STRATEGY; PROTOTYPICALITY; FACEBOOK; POPULARITY; COMPLEXITY; SELECTION;
BEHAVIOR
AB In a crowded social media marketplace, restaurants often try to stand out by showcasing elaborate "Instagrammable" foods. Using an image classification machine learning algorithm (Google Vision AI) on restaurants' Instagram posts, this study analyzes how the visual characteristics of product offerings (i.e., their food) relate to social media engagement. Results demonstrate that food images that are more confidently evaluated by Google Vision AI (a proxy for food typicality) are positively associated with engagement (likes and comments). A followup experiment shows that exposure to typical-appearing foods elevates positive affect, suggesting they are easier to mentally process, which drives engagement. Therefore, contrary to conventional social media practices and food industry trends, the more typical a food appears, the more social media engagement it receives. Using Google Vision AI to identify what product offerings receive engagement presents an accessible method for marketers to understand their industry and inform their social media marketing strategies.
C1 [Philp, Matthew; Jacobson, Jenna] Toronto Metropolitan Univ, Ted Rogers Sch Management, 350 Victoria St, Toronto, ON M5B 2K3, Canada.
[Pancer, Ethan] St Marys Univ, Sobey Sch Business, 903 Robie St, Halifax, NS B3H 3C2, Canada.
C3 Toronto Metropolitan University; Saint Marys University - Canada
RP Philp, M (autor correspondiente), Toronto Metropolitan Univ, Ted Rogers Sch Management, 350 Victoria St, Toronto, ON M5B 2K3, Canada.
EM mphilp@ryerson.ca; jenna.jacobson@ryerson.ca; ethan.pancer@smu.ca
RI Pancer, Ethan/IQW-3081-2023
OI Philp, Matthew/0000-0003-2130-8840; Jacobson, Jenna/0000-0002-1371-1077
FU Social Sciences and Humanities Research Council of Canada
[435-2018-1319, 430-2019-00218, 139286]
FX The authors gratefully acknowledge the help from Ngoc My Vo, Maxwell
Poole, Portchia Sedlezky-Anselmo, Alyssa Counsell, Rishad Habib,
Shengkun Xie, Ali Tezer, and Zhe Zhang in preparing this manuscript.
Additionally, the authors would like to thank the Social Sciences and
Humanities Research Council of Canada (#435-2018-1319, #430-2019-00218,
and #139286) for their financial support.
CR [Anonymous], 2019, Cloud vision api
[Anonymous], 1963, MEASUREMENT READABIL
Bakhshi S, 2014, 32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014), P965, DOI 10.1145/2556288.2557403
BELK RW, 1988, J CONSUM RES, V15, P139, DOI 10.1086/209154
Berger J, 2007, J CONSUM RES, V34, P121, DOI 10.1086/519142
Berger J, 2014, J CONSUM PSYCHOL, V24, P586, DOI 10.1016/j.jcps.2014.05.002
Berger J, 2013, J CONSUM RES, V40, P567, DOI 10.1086/671345
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Berger J, 2011, J MARKETING RES, V48, P869, DOI 10.1509/jmkr.48.5.869
Bernritter SF, 2016, J INTERACT MARK, V33, P27, DOI 10.1016/j.intmar.2015.10.002
Bolin JH, 2014, J EDUC MEAS, V51, P335, DOI 10.1111/jedm.12050
Brown K. V, 2016, GIZMODO
Cao DM, 2021, J BUS RES, V122, P835, DOI 10.1016/j.jbusres.2020.06.025
Carbone L, 2019, THIS IS INSTAGRAM AL
Chall Jeanne Sternlicht, 1995, Readability Revisited: The New Dale-Chall Readability Formula
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chintagunta PK, 2010, MARKET SCI, V29, P944, DOI 10.1287/mksc.1100.0572
De Luca LM, 2021, J ACAD MARKET SCI, V49, P790, DOI 10.1007/s11747-020-00739-x
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Dubay W., 2004, IMPACT INFORM
Eigenraam AW, 2018, J INTERACT MARK, V44, P102, DOI 10.1016/j.intmar.2018.07.002
Fantozzi J, 2017, THE INSIDER
Flesch R, 1948, J APPL PSYCHOL, V32, P221, DOI 10.1037/h0057532
Google, 2019, VISION AI
Gunning R., 1952, TECHNIQUE CLEAR WRIT
Heath C, 2001, J PERS SOC PSYCHOL, V81, P1028, DOI 10.1037//0022-3514.81.6.1028
Heath C., 2005, EXTREME COMP BIASED
HEKKERT P, 1990, BRIT J PSYCHOL, V81, P483, DOI 10.1111/j.2044-8295.1990.tb02374.x
Hitz L, 2019, SPROUT SOCIAL
Hosseini H, 2017, 2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), P101, DOI 10.1109/ICMLA.2017.0-172
Instagram, 2021, 2022 INSTAGRAM TREND
Jaakonmäki R, 2017, PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P1152
Janiszewski C, 2001, J CONSUM RES, V28, P18, DOI 10.1086/321945
Janiszewski C, 2021, J CONSUM PSYCHOL, V31, P633, DOI 10.1002/jcpy.1263
Kaiser C, 2020, J BUS RES, V117, P707, DOI 10.1016/j.jbusres.2019.09.017
Kim C, 2017, PUBLIC RELAT REV, V43, P441, DOI 10.1016/j.pubrev.2017.02.006
Kim S, 2015, INT J HOSP MANAG, V49, P40, DOI 10.1016/j.ijhm.2015.05.006
Klauda SL, 2008, J EDUC PSYCHOL, V100, P310, DOI 10.1037/0022-0663.100.2.310
Kludt A, 2014, EATER
Kong HM, 2021, J BUS RES, V131, P640, DOI 10.1016/j.jbusres.2020.08.021
Landwehr JR, 2013, J MARKETING, V77, P92, DOI 10.1509/jm.11.0286
LANGLOIS JH, 1990, PSYCHOL SCI, V1, P115, DOI 10.1111/j.1467-9280.1990.tb00079.x
Lee S, 2017, BBC
Lepkowska-White E, 2017, J INTERNET COMMER, V16, P323, DOI 10.1080/15332861.2017.1317148
Lewis-Kraus G., 2015, The New York Times Magazine
Li YY, 2020, J MARKETING RES, V57, P1, DOI 10.1177/0022243719881113
Liu L, 2020, MARKET SCI, V39, P669, DOI 10.1287/mksc.2020.1226
Liu X, 2021, J BUS RES, V125, P815, DOI 10.1016/j.jbusres.2019.04.042
Luckerson V., 2015, TIME
MARTINDALE C, 1988, J EXP PSYCHOL HUMAN, V14, P661, DOI 10.1037/0096-1523.14.4.661
Mayer S, 2018, PSYCHOL AESTHET CREA, V12, P399, DOI 10.1037/aca0000187
Mazloom Masoud, 2016, P 24 ACM INT C MULT, P197, DOI DOI 10.1145/2964284.2967210
McCarthy A, 2021, EATER
MCCLELLAND JL, 1981, PSYCHOL REV, V88, P375, DOI 10.1037/0033-295X.88.5.375
MCLAUGHLIN GH, 1969, J READING, V12, P639
Meta for Business, 2013, WHAT INCR CONT SHAR
Mittal A, 2011, CONF REC ASILOMAR C, P723, DOI 10.1109/ACSSC.2011.6190099
Moldovan S, 2011, INT J RES MARK, V28, P109, DOI 10.1016/j.ijresmar.2010.11.003
Moore S., 2020, Consumer Psychology Review, V3, P34, DOI DOI 10.1002/ARCP.1055
Nanne AJ, 2020, J INTERACT MARK, V50, P156, DOI 10.1016/j.intmar.2019.09.003
Needles A., 2013, Cornell Hospitality Reports, V13, P6
Ngai EWT, 2016, J ORGAN END USER COM, V28, P13, DOI 10.4018/JOEUC.2016040102
Noseworthy TJ, 2014, J CONSUM RES, V41, P1108, DOI 10.1086/678301
Pancer E, 2022, J CONSUM PSYCHOL, V32, P336, DOI 10.1002/jcpy.1246
Pancer E, 2019, J CONSUM PSYCHOL, V29, P262, DOI 10.1002/jcpy.1073
Philp M, 2020, J BUS RES, V116, P283, DOI 10.1016/j.jbusres.2020.05.018
Philp M, 2018, MARKET LETT, V29, P101, DOI 10.1007/s11002-018-9447-8
POSNER MI, 1968, J EXP PSYCHOL, V77, P353, DOI 10.1037/h0025953
Reber R, 2004, PERS SOC PSYCHOL REV, V8, P364, DOI 10.1207/s15327957pspr0804_3
Rhodes G, 1996, PSYCHOL SCI, V7, P105, DOI 10.1111/j.1467-9280.1996.tb00338.x
Rietveld R, 2020, J INTERACT MARK, V49, P20, DOI 10.1016/j.intmar.2019.06.003
Selye H., 1975, STRESS LIFE
Shimamura A.P., 2012, Aesthetic Science: Connecting Minds, Brains, and Experience
Simonson I, 2000, J CONSUM RES, V27, P49, DOI 10.1086/314308
SMITH EE, 1974, PSYCHOL REV, V81, P214, DOI 10.1037/h0036351
So KKF, 2021, J BUS RES, V129, P474, DOI 10.1016/j.jbusres.2020.05.054
Sokolov E.N., 1963, PERCEPTION CONDITION
Statista, 2021, MED US INT MIN AUG 2
Statista, 2022, DAIL TIM SPENT SOC N
Stewart J.B., 2016, IT WANTS MORE
Swani K, 2017, J BUS RES, V79, P123, DOI 10.1016/j.jbusres.2017.06.003
Swani K, 2013, J RES INTERACT MARK, V7, P269, DOI 10.1108/JRIM-05-2013-0026
Tafesse W, 2018, J CONSUM MARK, V35, P241, DOI 10.1108/JCM-08-2016-1905
Tubular, 2022, MOST VIEWS OV CREAT
WHITFIELD TWA, 1979, BRIT J PSYCHOL, V70, P65, DOI 10.1111/j.2044-8295.1979.tb02144.x
Winkielman P, 2003, PSYCHOLOGY OF EVALUATION, P189
Winkielman P, 2001, J PERS SOC PSYCHOL, V81, P989, DOI 10.1037//0022-3514.81.6.989
Winkielman P, 2006, PSYCHOL SCI, V17, P799, DOI 10.1111/j.1467-9280.2006.01785.x
NR 88
TC 13
Z9 15
U1 35
U2 98
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD OCT
PY 2022
VL 149
BP 736
EP 747
DI 10.1016/j.jbusres.2022.05.078
EA JUN 2022
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 2F6RY
UT WOS:000813036000016
OA hybrid
DA 2024-03-27
ER
PT J
AU Le, T
Ho, T
Nguyen, VH
Le, HS
AF Le, Thien
Ho, Thanh
Nguyen, Van-Ho
Le, Hoanh-Su
TI How to deeply understand the voice of the customer? A proposal for a
synthesis of techniques for analyzing online reviews in the hospitality
industry
SO JOURNAL OF HOSPITALITY AND TOURISM INSIGHTS
LA English
DT Article; Early Access
DE Voice of the customer; Hospitality management; User-generated content;
Graph model; Topic modeling; Natural language processing
ID TEXT ANALYTICS; EXPERIENCE; MANAGEMENT; MODEL
AB PurposeThis study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.Design/methodology/approachThis study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer's row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.FindingsExperimental results allow analysts and managers to "listen to the customer's voice" accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.Originality/valueThe novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.
C1 [Le, Hoanh-Su] Univ Econ & Law, Ho Chi Minh City, Vietnam.
Vietnam Natl Univ, Ho Chi Minh City, Vietnam.
C3 VNUHCM - University of Economics & Law; Vietnam National University
Hochiminh City
RP Le, HS (autor correspondiente), Univ Econ & Law, Ho Chi Minh City, Vietnam.
EM sulh@uel.edu.vn
RI Ho, Thanh/ACK-8182-2022; Nguyen Van, Ho/HSG-6733-2023
OI Ho, Thanh/0000-0002-9033-3735; Le, Hoanh-Su/0000-0002-3132-2550; Nguyen
Van, Ho/0000-0001-6706-0276
FU University of Economics and Law, Vietnam National University Ho Chi Minh
City, Vietnam
FX This research is funded by University of Economics and Law, Vietnam
National University Ho Chi Minh City, Vietnam.
CR Aguwa C, 2017, KNOWL-BASED SYST, V125, P136, DOI 10.1016/j.knosys.2017.03.019
Alamsyah Andry, 2022, 2022 7th International Workshop on Big Data and Information Security (IWBIS), P45, DOI 10.1109/IWBIS56557.2022.9924920
Alamsyah A, 2021, INFORMATION, V12, DOI 10.3390/info12100413
Balducci B., 2022, Handbook of Research on Customer Loyalty, V107
Barravecchia F, 2022, INT J QUAL RELIAB MA, V39, P1453, DOI 10.1108/IJQRM-07-2021-0217
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Buhalis D, 2008, TOURISM MANAGE, V29, P609, DOI 10.1016/j.tourman.2008.01.005
Cherkasov A., 2021, Marketing Analysis
Das B.K., 2021, Library Philosophy and Practice, P5818
Didimo W, 2020, IEEE ACCESS, V8, P16073, DOI 10.1109/ACCESS.2020.2967974
Fedyk W., 2023, Journal of Environmental Management and Tourism, V14, P442, DOI [10.14505/jemt.14.2(66).14, DOI 10.14505/JEMT.14.2(66).14]
Fisher NI, 2019, APPL STOCH MODEL BUS, V35, P138, DOI 10.1002/asmb.2417
Found P, 2012, INT J LEAN SIX SIG, V3, P251, DOI 10.1108/20401461211282736
Gallagher C, 2019, INT J DATA WAREHOUS, V15, P21, DOI 10.4018/IJDWM.2019100102
GRIFFIN A, 1993, MARKET SCI, V12, P1, DOI 10.1287/mksc.12.1.1
Hocevar M, 2021, INT J TOUR CITIES, V7, P818, DOI 10.1108/IJTC-05-2020-0110
Hu N, 2019, TOURISM MANAGE, V72, P417, DOI 10.1016/j.tourman.2019.01.002
김유신, 2013, [Journal of Intelligence and Information Systems, 지능정보연구], V19, P113, DOI 10.13088/jiis.2013.19.3.113
Laitinen MA, 2018, J LIBR ADM, V58, P394, DOI 10.1080/01930826.2018.1448655
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Lipkin M, 2022, J SERV MARK, V36, P1, DOI 10.1108/JSM-03-2021-0080
Litvin SW, 2008, TOURISM MANAGE, V29, P458, DOI 10.1016/j.tourman.2007.05.011
Marcolin CB, 2021, INT J HOSP MANAG, V94, DOI 10.1016/j.ijhm.2020.102853
Melander L, 2019, BENCHMARKING, V27, P215, DOI 10.1108/BIJ-04-2018-0112
Min H, 2019, INT J HOSP MANAG, V82, P59, DOI 10.1016/j.ijhm.2019.03.017
Müller O, 2016, MIS Q EXEC, V15, P243
Nguyen TTT, 2023, J MARK ANAL, V11, P443, DOI 10.1057/s41270-022-00174-7
Nicholas C.K.W., 2017, P 1 INT C BIG DATA R, P104
Pereira LN, 2022, CURR ISSUES TOUR, V25, P2733, DOI 10.1080/13683500.2021.1999397
Price R, 2016, J INT CONSUM MARK, V28, P92, DOI 10.1080/08961530.2015.1092405
Rajendran S, 2023, BENCHMARKING, V30, P1, DOI 10.1108/BIJ-12-2020-0650
Rajendran S, 2021, INT J LOGIST-RES APP, V24, P473, DOI 10.1080/13675567.2020.1769042
Ramadhani Dian Puteri, 2022, 2022 10th International Conference on Information and Communication Technology (ICoICT), P310, DOI 10.1109/ICoICT55009.2022.9914859
Ramadhani Dian Puteri, 2021, 2021 9th International Conference on Information and Communication Technology (ICoICT), P219, DOI 10.1109/ICoICT52021.2021.9527445
Schuckert M, 2015, J TRAVEL TOUR MARK, V32, P608, DOI 10.1080/10548408.2014.933154
Sezgen E, 2019, J AIR TRANSP MANAG, V77, P65, DOI 10.1016/j.jairtraman.2019.04.001
Sharma GD, 2021, TOUR MANAG PERSPECT, V37, DOI 10.1016/j.tmp.2020.100786
Spoor JM, 2023, J MARK ANAL, V11, P747, DOI 10.1057/s41270-022-00185-4
Stylos N, 2021, INT J CONTEMP HOSP M, V33, P1015, DOI 10.1108/IJCHM-07-2020-0644
Nguyen VH, 2021, J INF PROCESS SYST, V17, P586, DOI 10.3745/JIPS.04.0217
Wang G, 2023, J HOSP TOUR INSIGHTS, V6, P509, DOI 10.1108/JHTI-09-2021-0260
Zhang JR, 2019, J CONSUM MARK, V36, P655, DOI 10.1108/JCM-02-2018-2581
Zobnina M, 2018, WORLDW HOSP TOUR THE, V10, P436, DOI [10.1108/WHATT-03-2018-0020, 10.1108/whatt-03-2018-0020]
NR 43
TC 0
Z9 0
U1 5
U2 5
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 2514-9792
EI 2514-9806
J9 J HOSP TOUR INSIGHTS
JI J. Hosp. Tour. Insights
PD 2024 FEB 2
PY 2024
DI 10.1108/JHTI-07-2023-0460
EA FEB 2024
PG 21
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA GR0G2
UT WOS:001154273700001
DA 2024-03-27
ER
PT J
AU Richter, NF
Tudoran, AA
AF Richter, Nicole Franziska
Tudoran, Ana Alina
TI Elevating theoretical insight and predictive accuracy in business
research: Combining PLS-SEM and selected machine learning algorithms
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Partial least squares-structural equation; modeling (PLS-SEM); Machine
learning (ML); Prediction; Method triangulation; Unified theory of
acceptance and use of; technology (UTAUT)
ID INFORMATION-TECHNOLOGY; BAYESIAN NETWORKS; ACCEPTANCE; PERFORMANCE;
ANALYTICS; SERVICES; QUALITY; MODELS
AB We propose a routine for combining partial least squares-structural equation modeling (PLS-SEM) with selected machine learning (ML) algorithms to exploit the two method's causal-predictive and causal-exploratory capa-bilities. Triangulating these two methods can improve the predictive accuracy of research models, enhance the understanding of relationships, assist in identifying new relationships and therewith contribute to theorizing. We demonstrate the advantages and challenges of triangulating the two methods on an illustrative example along a four-step-routine: (1) Develop a PLS-SEM on a baseline conceptual model and use its standards to assess mea-surement model quality and generate latent variables scores. (2) Apply specific ML algorithms on the extracted data to validate relationships and identify new (linear) relationships that may go beyond the initial hypotheses; similarly, assess model advancements in the form of nonlinearities and interaction effects. (3) Evaluate the theoretical plausibility of alternative models. (4) Integrate alternative models in PLS-SEM and compare these with the baseline model using a recently proposed prediction-oriented test procedure in PLS-SEM.
C1 [Richter, Nicole Franziska] Univ Southern Denmark, Esbjerg, Denmark.
[Tudoran, Ana Alina] Aarhus Univ, Aarhus, Denmark.
C3 University of Southern Denmark; Aarhus University
RP Richter, NF (autor correspondiente), Univ Southern Denmark, Esbjerg, Denmark.
EM nicole@sam.sdu.dk; anat@econ.au.dk
RI Tudoran, Ana/HMU-9691-2023; Richter, Nicole Franziska/AEN-0137-2022
OI Tudoran, Ana/0000-0002-1380-4775; Richter, Nicole
Franziska/0000-0002-1278-176X
CR AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Al-Gahtani SS, 2007, INFORM MANAGE-AMSTER, V44, P681, DOI 10.1016/j.im.2007.09.002
[Anonymous], 2003, Learning Bayesian Networks
Basco R, 2022, J FAM BUS STRATEG, V13, DOI 10.1016/j.jfbs.2021.100457
Becker JM, 2023, INT J CONTEMP HOSP M, V35, P321, DOI 10.1108/IJCHM-04-2022-0474
Becker JM, 2015, MARKET LETT, V26, P643, DOI 10.1007/s11002-014-9299-9
Blut M, 2022, J ASSOC INF SYST, V23, P13, DOI 10.17705/1jais.00719
Boehmke B, 2019, Hands-on machine learning with R, DOI DOI 10.1201/9780367816377
Bollen K. A., 2013, Handbook of causal analysis for social research, P301, DOI DOI 10.1007/978-94-007-6094-3_15
Bouckaert R. R, 2003, C P 20 INT C MACH LE
Bozan K, 2016, P ANN HICSS, P3105, DOI 10.1109/HICSS.2016.391
Bruno A, 2022, PATTERN RECOGN IMAGE, V32, P519, DOI 10.1134/S1054661822030087
Buckler F, 2008, Marketing-Journal of Research in Management, V4, P49
Burton RM, 2011, ORGAN SCI, V22, P1195, DOI 10.1287/orsc.1100.0635
Canhoto AI, 2021, J BUS RES, V131, P441, DOI 10.1016/j.jbusres.2020.10.012
Carvalho E. C. A. d., 2021, Knowledge-Based Systems, V223
Cho G, 2022, MANAGE INT REV, V62, P569, DOI 10.1007/s11575-022-00479-w
Cho G, 2022, STRUCT EQU MODELING, V29, P611, DOI 10.1080/10705511.2022.2057315
Contractor FJ, 2003, J INT BUS STUD, V34, P5, DOI 10.1057/palgrave.jibs.8400003
Danks N., 2017, EVALUATING PREDICTIV
Díez-Mesa F, 2018, TRANSPORT RES A-POL, V118, P1, DOI 10.1016/j.tra.2018.08.012
Druzdzel M. J, 1993, Causality in Bayesian Belief Networks
Elwert F., 2013, Handbook OfCausal Analysis for Social Research, P245, DOI [DOI 10.1007/978-94-007-6094-3_13, DOI 10.1007/978-1-4471-6699-3_13]
Ferreira MB, 2021, J BUS RES, V131, P411, DOI 10.1016/j.jbusres.2020.10.035
FRIEDMAN JH, 1991, ANN STAT, V19, P1, DOI 10.1214/aos/1176347963
Garbe JN, 2009, J INT MANAG, V15, P413, DOI 10.1016/j.intman.2008.10.002
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Gregor S, 2006, MIS QUART, V30, P611
Guenther P, 2023, IND MARKET MANAG, V111, P127, DOI 10.1016/j.indmarman.2023.03.010
Gupta S, 2008, EUR J OPER RES, V190, P818, DOI 10.1016/j.ejor.2007.05.054
Hair J. F., 2022, A primer on partial least squares structural equation modeling (PLS-SEM), V3rd
Hair J. F., 2017, ADV ISSUES PARTIAL L
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hair JF, 2017, J ACAD MARKET SCI, V45, P616, DOI 10.1007/s11747-017-0517-x
Hair JF, 2021, J MARKET THEORY PRAC, V29, P65, DOI 10.1080/10696679.2020.1860683
Hastie T., 2005, ELEMENTS STAT LEARNI, V27, P83, DOI DOI 10.1007/978-0-387-84858-7
Jaeger RG, 1998, HERPETOLOGICA, V54, pS64
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
Joreskog K. G., 1982, Systems under Indirect Observation, Part I, V1, P263
Kenett RS, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19084859
Kohavi R., 1995, INT JOINT C ART INT, DOI DOI 10.1067/MOD.2000.109031
Koller D., 2009, Probabilistic Graphical Models: Principles and Techniques
Kuhn M., 2013, APPL PREDICTIVE MODE, P600, DOI [10.1007/978-1-4614-6849-3, DOI 10.1007/978-1-4614-6849-3]
Kuhn M, 2008, J STAT SOFTW, V28, P1, DOI 10.18637/jss.v028.i05
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Leong LY, 2020, J BUS RES, V110, P24, DOI 10.1016/j.jbusres.2019.11.056
Liengaard BD, 2021, DECISION SCI, V52, P362, DOI 10.1111/deci.12445
Liew BXW, 2023, J PAIN, V24, P426, DOI 10.1016/j.jpain.2022.10.003
Lo PS, 2022, J BUS RES, V147, P325, DOI 10.1016/j.jbusres.2022.04.013
Loh XM, 2022, EXPERT SYST APPL, V205, DOI 10.1016/j.eswa.2022.117659
MacKay DJ, 2003, Information theory, inference, and learning algorithms
Mandhani J, 2020, TRANSPORT RES A-POL, V140, P320, DOI 10.1016/j.tra.2020.08.014
Margaritis D., 2003, Learning Bayesian network model structure from data
McQuarrie A.D.R., 1998, REGRESSION TIME SERI
MITTELSTAEDT RA, 1976, J CONSUM RES, V3, P84, DOI 10.1086/208655
Neapolitan R., 2007, Probabilistic Methods for Financial and Marketing Informatics
Nielsen BB, 2020, J INT BUS STUD, V51, P1478, DOI 10.1057/s41267-020-00372-4
Ooi KB, 2018, EXPERT SYST APPL, V93, P376, DOI 10.1016/j.eswa.2017.10.009
Pearl J., 2021, HDB STRUCTURAL EQUAT, P68, DOI DOI 10.21236/ADA557445
Pearl J, 2000, Causality: Models, Reasoning and Inference, V64
Pearl J, 2012, Arxiv, DOI arXiv:1207.4173
Perl J., 1988, PROBABILISTIC REASON
Provost F., 2013, Data science for business: What you need to know about data mining and data-analytic thinking
Rau M. A., 2012, Searching for variables and models to investigate mediators of learning from multiple representations
Ray A, 2021, J BUS RES, V128, P391, DOI 10.1016/j.jbusres.2021.02.028
Refaeilzadeh, 2009, ENCY DATABASE SYST, DOI [DOI 10.1007/978-1-4899-7993-3_565-2, 10.1007/978-0-387-39940-9_565]
Richter N, 2007, RES GLOB STRATEG MAN, V13, P359, DOI 10.1016/S1064-4857(07)13015-1
Richter NF, 2022, MANAGE INT REV, V62, P449, DOI 10.1007/s11575-022-00475-0
Richter NF, 2016, INT MARKET REV, V33, P376, DOI 10.1108/IMR-04-2014-0148
Richter NF, 2016, EUR MANAG J, V34, P589, DOI 10.1016/j.emj.2016.08.001
Rigdon EE, 2019, PSYCHOMETRIKA, V84, P772, DOI 10.1007/s11336-019-09677-2
Rigdon EE, 2010, REV MARKET RES, V7, P255, DOI 10.1108/S1548-6435(2010)0000007011
Ringle C M., 2022, SmartPLS 4
Ringle CM, 2023, DATA BRIEF, V48, DOI 10.1016/j.dib.2023.109074
Sachan S, 2020, EXPERT SYST APPL, V144, DOI 10.1016/j.eswa.2019.113100
Sanchez-Franco MJ, 2019, INTERNET RES, V29, P478, DOI 10.1108/IntR-12-2017-0531
Sánchez-Franco MJ, 2019, J BUS RES, V101, P499, DOI 10.1016/j.jbusres.2018.12.051
Sarstedt M., 2017, HDB MARKET RES, DOI DOI 10.1007/978-3-319-05542-8_15-1
Sarstedt M, 2023, J MARKET THEORY PRAC, V31, P261, DOI 10.1080/10696679.2022.2056488
Sarstedt M, 2022, PSYCHOL MARKET, V39, P1035, DOI 10.1002/mar.21640
Sarstedt M, 2022, J BUS RES, V138, P398, DOI 10.1016/j.jbusres.2021.08.051
Sarstedt M, 2020, TOURISM ECON, V26, P531, DOI 10.1177/1354816618823921
Schubring S, 2016, J BUS RES, V69, P4604, DOI 10.1016/j.jbusres.2016.03.052
SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136
Scutari M., 2021, Bayesian networks: with examples in R
Scutari M, 2007, The bnlearn Package-Bayesian network structure learning
Scutari M, 2013, ARTIF INTELL MED, V57, P207, DOI 10.1016/j.artmed.2012.12.006
Sener A, 2019, J BUS RES, V99, P87, DOI 10.1016/j.jbusres.2019.01.070
Sharma PN, 2023, EUR J MARKETING, V57, P1662, DOI 10.1108/EJM-08-2020-0636
Sharma PN, 2021, DECISION SCI, V52, P567, DOI 10.1111/deci.12329
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Shmueli G, 2016, J BUS RES, V69, P4552, DOI 10.1016/j.jbusres.2016.03.049
Shmueli G, 2011, MIS QUART, V35, P553
Shmueli G, 2010, STAT SCI, V25, P289, DOI 10.1214/10-STS330
Spirtes P., 2000, CAUSATION PREDICTION
Srivastava A, 2023, IEEE T PARALL DISTR, V34, P1699, DOI 10.1109/TPDS.2023.3244135
Torgo L., 2017, Data mining with R: Learning with case studies, V2nd
Tsamardinos I., 2003, C P 9 ACM SIGKDD INT
Tudoran AA, 2022, ELECTRON MARK, V32, P351, DOI 10.1007/s12525-021-00515-x
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Wood S, 2022, Package 'mgcv'. R package version, 1, 729
Wood S. N., 2017, GEN ADDITIVE MODELS, DOI DOI 10.1201/9781315370279
Yaramakala S, 2005, Fifth IEEE International Conference on Data Mining, Proceedings, P809, DOI 10.1109/ICDM.2005.134
NR 104
TC 0
Z9 0
U1 6
U2 6
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD FEB
PY 2024
VL 173
AR 114453
DI 10.1016/j.jbusres.2023.114453
EA DEC 2023
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA GB3L3
UT WOS:001150157500001
OA hybrid
DA 2024-03-27
ER
PT J
AU Sharma, V
Kapse, M
Poulose, J
Mahajan, Y
AF Sharma, Vinod
Kapse, Manohar
Poulose, Jeanne
Mahajan, Yogesh
TI Robotic dining delight unravelling the key factors driving customer
satisfaction in service robot restaurants using PLS-SEM and ML
SO COGENT BUSINESS & MANAGEMENT
LA English
DT Article
DE robotics; PLS-SEM; machine learning; repeat experience; trust; service
restaurants
ID DELIVERY; REGULARIZATION; TECHNOLOGY; INTENTIONS; MODELS
AB In the past few years there has been a remarkable surge in demand for robot service restaurants. However, as both the technology and the concept of such restaurants are relatively new, there is a limited understanding of how consumers would react to this new change in the service industry. This study focuses on the key factors influencing customer satisfaction and their intention to repeat the experience by using two staged hybrid PLS-SEM and Machine Learning approaches. The finding confirms that perceived enjoyment, speed, and novelty influence customer satisfaction, whereas perceived usefulness has no influence. Additionally, the study uncovers that customer satisfaction and trust positively mediate the relationship and establish the link with repeat experience. The machine learning models (Artificial Neural Network, Support Vector Machines, Random Forest, K-Nearest Neighbors, Elastic Net) predict the intention to repeat the experience of the service robot with an overall model fit of around 57%. We also discussed several new and useful theoretical and practical implications for enhancing the customer experience during the visit to the restaurants.
C1 [Sharma, Vinod; Kapse, Manohar; Mahajan, Yogesh] Symbiosis Int, SCMHRD, Pune, India.
[Poulose, Jeanne] CHRIST, Sch Business & Management, Delhi, India.
C3 Symbiosis International University; Symbiosis Centre for Management &
Human Resource Development (SCMHRD)
RP Mahajan, Y (autor correspondiente), Symbiosis Int, SCMHRD, Pune, India.
EM yogesh_mahajan@scmhrd.edu
RI Sharma, Vinod/E-4283-2018; Poulose, Jeanne/E-4477-2018; Kapse,
Manohar/EVO-8855-2022
OI Sharma, Vinod/0000-0002-0815-8502; Poulose, Jeanne/0000-0002-3370-4026;
Kapse, Manohar/0000-0002-1258-8599
CR Agrawal G., 2023, Indian Journal of Marketing, V53, P8, DOI [https://doi.org/10.17010/ijom/2023/v53/i4/172687, DOI 10.17010/IJOM/2023/V53/I4/172687]
AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1023/A:1022689900470
Ahmed S, 2023, TQM J, V35, P519, DOI 10.1108/TQM-06-2021-0158
Atulkar S, 2017, J RETAIL CONSUM SERV, V39, P23, DOI 10.1016/j.jretconser.2017.06.013
Becker M, 2023, SERV BUS, V17, P137, DOI 10.1007/s11628-022-00499-4
Bello D. C., 1985, Journal of Travel Research, V24, P20, DOI 10.1177/004728758502400104
Bonfanti A, 2023, INT J HOSP MANAG, V109, DOI 10.1016/j.ijhm.2022.103416
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Cepeda-Carrion G, 2019, J KNOWL MANAG, V23, P67, DOI 10.1108/JKM-05-2018-0322
Chen CR, 2023, COGENT BUS MANAG, V10, DOI 10.1080/23311975.2023.2188987
Chi OH, 2023, INT J INFORM MANAGE, V70, DOI 10.1016/j.ijinfomgt.2023.102623
Chin WW, 2003, INFORM SYST RES, V14, P189, DOI 10.1287/isre.14.2.189.16018
Chin WW, 1998, QUANT METH SER, P295
Choi M, 2023, INT J CONTEMP HOSP M, V35, P469, DOI 10.1108/IJCHM-02-2022-0157
Clarke J, 2021, J DESTIN MARK MANAGE, V20, DOI 10.1016/j.jdmm.2021.100605
Correa JC, 2022, COGENT BUS MANAG, V9, DOI 10.1080/23311975.2022.2071099
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
Das A. R., 2022, Vision: The Journal of Business Perspective, p09722629221131101, DOI [https://doi.org/10.1177/09722629221131101, DOI 10.1177/09722629221131101]
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
de Graaf MMA, 2016, INT J SOC ROBOT, V8, P589, DOI 10.1007/s12369-016-0368-5
De Keyser A, 2019, J SERV MANAGE, V30, P156, DOI 10.1108/JOSM-03-2018-0082
Dong P, 2013, TOURISM MANAGE, V36, P541, DOI 10.1016/j.tourman.2012.09.004
El-Said O, 2022, HELIYON, V8, DOI 10.1016/j.heliyon.2022.e08986
El-Said OA, 2021, J HOSP MARKET MANAG, V30, P799, DOI 10.1080/19368623.2021.1888368
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Friedman J, 2010, J STAT SOFTW, V33, P1, DOI 10.18637/jss.v033.i01
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Gaber HR, 2019, COGENT BUS MANAG, V6, DOI 10.1080/23311975.2019.1618431
Go H, 2020, TOUR REV, V75, P625, DOI 10.1108/TR-02-2019-0062
Gupta KP, 2023, YOUNG CONSUM, V24, P331, DOI 10.1108/YC-09-2022-1598
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hair JF, 2019, RAUSP MANAG J, V54, P490, DOI [10.1108/RAUSP-05-2019-0098, 10.1108/rausp-05-2019-0098]
Han J, 2012, MOR KAUF D, P1
Hare C, 2023, POLIT ANAL, V31, P537, DOI 10.1017/pan.2022.24
HARMAN GH, 1967, J PHILOS, V64, P75, DOI 10.2307/2023773
Hastie T., 2005, ELEMENTS STAT LEARNI, V27, P83, DOI DOI 10.1007/978-0-387-84858-7
Hastie T. J., 1986, Generalized additive models
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hwang J, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15076253
Hwang J, 2019, J HOSP TOUR MANAG, V38, P102, DOI 10.1016/j.jhtm.2019.01.004
Jang HW, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12103998
Kamran Q, 2021, COGENT BUS MANAG, V8, DOI 10.1080/23311975.2021.1901643
Kim T, 2023, INT J HOSP MANAG, V108, DOI 10.1016/j.ijhm.2022.103358
Kim Y, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15097563
Kock N, 2012, J ASSOC INF SYST, V13, P546, DOI 10.17705/1jais.00302
Kuhn Max, 2023, CRAN
Larivière B, 2017, J BUS RES, V79, P238, DOI 10.1016/j.jbusres.2017.03.008
Lee C.Joanna., 2011, International Journal of Business and Social Science, V2, P1
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Leung XY, 2023, INT J HOSP MANAG, V108, DOI 10.1016/j.ijhm.2022.103380
McKnight D.H., 2011, ACM T MANAG INF SYST, V2, P1, DOI [10.1145/1985347.1985353, DOI 10.1145/1985347.1985353]
Meyer P, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103173
Moon HY, 2022, INT J CONTEMP HOSP M, V34, P2176, DOI 10.1108/IJCHM-09-2021-1151
Negnevitsky M, 2002, ARTIFICIAL INTELLIGE
Nicodemus KK, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-110
Pai CK, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142013483
Podsakoff PM, 2012, ANNU REV PSYCHOL, V63, P539, DOI 10.1146/annurev-psych-120710-100452
Rasheed HMW, 2023, TECHNOL FORECAST SOC, V192, DOI 10.1016/j.techfore.2023.122555
Ringle C M., 2022, SmartPLS 4
Rodrigues F, 2019, PERCEPT MOTOR SKILL, V126, P949, DOI 10.1177/0031512519863188
Roozen I, 2023, INT J SOC ROBOT, V15, P15, DOI 10.1007/s12369-022-00949-z
Ruiz-Palomino P, 2023, ACAD-REV LATINOAM AD, V36, P269, DOI 10.1108/ARLA-11-2022-0201
Ruiz-Palomino P, 2021, TOUR MANAG PERSPECT, V40, DOI 10.1016/j.tmp.2021.100891
Ryan RM, 2001, ANNU REV PSYCHOL, V52, P141, DOI 10.1146/annurev.psych.52.1.141
Sagi O, 2018, WIRES DATA MIN KNOWL, V8, DOI 10.1002/widm.1249
Scholkopf B, 2001, LEARNING KERNELS SUP, DOI DOI 10.7551/MITPRESS/4175.001.0001
Seo KH, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084431
Seo W., 2022, The Journal of Internet Electronic Commerce Resarch, V22, P25, DOI [https://doi.org/10.37272/JIECR.2022.10.22.5.25, DOI 10.37272/JIECR.2022.10.22.5.25]
Shah TR, 2023, ASIA PAC J MARKET LO, DOI 10.1108/APJML-09-2022-0780
Skandrani H, 2011, QUAL MARK RES, V14, P391, DOI 10.1108/13522751111163227
Song B, 2022, INT J HOSP MANAG, V106, DOI 10.1016/j.ijhm.2022.103278
Strobl C, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-307
Tiwari P, 2022, FIIB BUS REV, DOI 10.1177/23197145221113377
Tiwari P, 2023, BENCHMARKING, V30, P4536, DOI 10.1108/BIJ-06-2022-0392
Tussyadiah IP, 2020, ANN TOURISM RES, V81, DOI 10.1016/j.annals.2020.102886
Tuv E, 2006, STUD FUZZ SOFT COMP, V207, P187
Venables W.N., 2002, Modern Applied Statistics with S Fourth, DOI DOI 10.2307/2685660
Lu VN, 2020, J SERV THEOR PRACT, V30, P361, DOI 10.1108/JSTP-04-2019-0088
Wali AF, 2016, COGENT BUS MANAG, V3, DOI 10.1080/23311975.2016.1183555
Wang QW, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0286034
Wang XV, 2021, J MANUF SYST, V60, P823, DOI 10.1016/j.jmsy.2021.02.005
Wang YW, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103002
Wijesekera A. T., 2023, Journal of Business Management, V1, P174, DOI [https://doi.org/10.47747/jbm.v1i2.1115, DOI 10.47747/JBM.V1I2.1115]
Wirtz J., 2023, Italian Journal of Marketing, V2023, P289, DOI [https://doi.org/10.1007/s43039-023-00076-1, DOI 10.1007/S43039-023-00076-1]
Wirtz J, 2020, ORGAN DYN, V49, DOI 10.1016/j.orgdyn.2019.04.005
Won D, 2023, ASIA PAC J MARKET LO, V35, P1112, DOI 10.1108/APJML-09-2021-0709
Wood SN, 2004, J AM STAT ASSOC, V99, P673, DOI 10.1198/016214504000000980
Wright Marvin N, 2019, CRAN
Xiaoqin Feng, 2019, Journal of Physics: Conference Series, V1237, DOI 10.1088/1742-6596/1237/2/022045
Xie LS, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103448
Xu JJ, 2023, INT J HOSP MANAG, V113, DOI 10.1016/j.ijhm.2023.103523
Yang CF, 2024, INTERNET RES, V34, P563, DOI 10.1108/INTR-02-2022-0120
Yani-de-Soriano M, 2019, EUR J MARKETING, V53, P708, DOI 10.1108/EJM-09-2017-0570
ZEITHAML VA, 1988, J MARKETING, V52, P35, DOI 10.2307/1251263
Zou H, 2005, J R STAT SOC B, V67, P301, DOI 10.1111/j.1467-9868.2005.00503.x
NR 96
TC 0
Z9 0
U1 6
U2 6
PU TAYLOR & FRANCIS AS
PI OSLO
PA KARL JOHANS GATE 5, NO-0154 OSLO, NORWAY
SN 2331-1975
J9 COGENT BUS MANAG
JI Cogent Bus. Manag.
PD DEC 11
PY 2023
VL 10
IS 3
AR 2281053
DI 10.1080/23311975.2023.2281053
PG 21
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA Y8ZH4
UT WOS:001108083800001
OA gold
DA 2024-03-27
ER
PT J
AU Ginzarly, M
Srour, FJ
Roders, AP
AF Ginzarly, Manal
Srour, F. Jordan
Roders, Ana Pereira
TI THE INTERPLAY OF CONTEXT, EXPERIENCE, AND EMOTION AT WORLD HERITAGE
SITES: A QUALITATIVE AND MACHINE LEARNING APPROACH
SO TOURISM CULTURE & COMMUNICATION
LA English
DT Article
DE World Heritage; Machine learning; Visitors' experience; User-generated
content; Heritage values
ID SOCIAL MEDIA DATA; VISITOR EXPERIENCE; CO-CREATION; TOURISM RESEARCH;
PROTECTED AREA; NATIONAL-PARK; SATISFACTION; PERCEPTIONS; INFORMATION;
AUTHENTICITY
AB This study illustrates how user-generated content, posted in the form of heritage site reviews on social media, can serve to reveal the relationship between the cocreated interpretation of World Heritage Sites (WHSs)-in terms of values, tangible and intangible attributes, as well as site visit logistics-and the emotional experience of the site. Two WHSs are taken as a case study. More than 2,000 reviews were retrieved from TripAdvisor and analyzed through the application of a mixed method that integrates qualitative digital ethnography and machine learning. Results show that TripAdvisor reviews capture tourists' emotional reactions from personal encounters with heritage and provide insights into the range of values-including the social, historic, and aesthetical values-that visitors experience when engaging with aspects of the past to associate meanings for the present. Results also show that the relation between experiences gained at WHSs and contextual aspects is not linear; instead, it is a complex one that results from the interaction of different factors and their associated sentiments. We discuss our results by reflecting on their theoretical and practical implications.
C1 [Ginzarly, Manal] Lebanese Amer Univ, Sch Architecture & Design, Beirut, Lebanon.
[Srour, F. Jordan] Lebanese Amer Univ, Adnan Kassar Sch Business, Beirut, Lebanon.
[Roders, Ana Pereira] Delft Univ Technol, Dept Architectural Engn & Technol, Delft, Netherlands.
C3 Lebanese American University; Lebanese American University; Delft
University of Technology
RP Ginzarly, M (autor correspondiente), Lebanese Amer Univ, Sch Architecture & Design, Beirut, Lebanon.
EM manal.ginzarly@lau.edu.lb
RI Srour, F. Jordan/F-3312-2018; Ginzarly, Manal/AAH-1458-2019; Pereira
Roders, Ana/G-3836-2012
OI Srour, F. Jordan/0000-0001-7623-723X; Ginzarly,
Manal/0000-0003-3693-9258; Pereira Roders, Ana/0000-0003-2571-9882
CR Adelman Leslie M., 2000, Curator, V43, P33
Aigner A, 2016, INT J HERIT STUD, V22, P181, DOI 10.1080/13527258.2015.1107615
Alabau-Montoya J, 2020, J HERIT TOUR, V15, P500, DOI 10.1080/1743873X.2019.1692853
Alazaizeh MM, 2019, J TOUR CULT CHANGE, V17, P269, DOI 10.1080/14766825.2018.1455693
Caldito LA, 2015, TOURISM IN RUSSIA: A MANAGEMENT HANDBOOK, P101
[Anonymous], 1972, Convention Concerning the Protection of the World Cultural and Natural Heritage, DOI DOI 10.1111/J.1468-0033.1973.TB02056.X
[Anonymous], 2011, Recommendation on the Historic Urban Landscape, including a glossary of definitions
[Anonymous], 1994, Nara Document on Authenticity
[Anonymous], 2015, Itb world travel trends report 2015/2016
[Anonymous], 2013, THE BURR CHART
[Anonymous], 2005, Making Tourism More Sustainable: A Guide for Policy Makers
Baniya R, 2021, J HERIT TOUR, V16, P632, DOI 10.1080/1743873X.2020.1833892
Baral N, 2017, J SUSTAIN TOUR, V25, P1494, DOI 10.1080/09669582.2017.1291647
Bertacchini F, 2018, EDULEARN PROC, P4155
BITNER MJ, 1992, J MARKETING, V56, P57, DOI 10.2307/1252042
Boccardi G, 2019, HIST ENVIRON POLICY, V10, P4, DOI 10.1080/17567505.2018.1531647
Bonn M. A., 2007, Journal of Travel Research, V45, P345, DOI 10.1177/0047287506295947
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Bruyere B, 2009, ENVIRON MANAGE, V43, P49, DOI 10.1007/s00267-008-9190-7
Bubalo M, 2019, LANDSCAPE URBAN PLAN, V184, P101, DOI 10.1016/j.landurbplan.2019.01.001
Buckley R, 2018, J TRAVEL RES, V57, P563, DOI 10.1177/0047287517713723
Byon KK, 2010, MARK INTELL PLAN, V28, P508, DOI 10.1108/02634501011053595
Cameron C, 2020, PLURAL HIST CULTURE, V8, P17
Campos AC, 2018, CURR ISSUES TOUR, V21, P369, DOI 10.1080/13683500.2015.1081158
Castellanos-Verdugo M, 2016, J CLEAN PROD, V124, P226, DOI 10.1016/j.jclepro.2016.02.126
Caust J, 2017, J CULT HERIT, V27, P1, DOI 10.1016/j.culher.2017.02.004
Cetin G, 2016, CURR ISSUES TOUR, V19, P137, DOI 10.1080/13683500.2014.994595
Chaddad R, 2021, J TOUR CULT CHANGE, V19, P805, DOI 10.1080/14766825.2020.1802470
Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
Cox C, 2009, J HOSP MARKET MANAG, V18, P743, DOI 10.1080/19368620903235753
da Mota VT, 2020, J OUTDOOR REC TOUR, V30, DOI 10.1016/j.jort.2020.100295
Dai TC, 2021, HABITAT INT, V107, DOI 10.1016/j.habitatint.2020.102307
de la Torre M., 2002, ASSESSING VALUES CUL
de la Torre M, 2013, HERIT SOC, V6, P155, DOI 10.1179/2159032X13Z.00000000011
de Rojas C, 2008, TOURISM MANAGE, V29, P525, DOI 10.1016/j.tourman.2007.06.004
Domínguez-Quintero AM, 2020, CURR ISSUES TOUR, V23, P248, DOI 10.1080/13683500.2018.1502261
Dunkel A, 2015, LANDSCAPE URBAN PLAN, V142, P173, DOI 10.1016/j.landurbplan.2015.02.022
Duval M, 2019, INT J HERIT STUD, V25, P1279, DOI 10.1080/13527258.2019.1590449
Ek R, 2008, SCAND J HOSP TOUR, V8, P122, DOI 10.1080/15022250802110091
Excel, TRANSL TEXT DIFF LAN
Falk J.H., 1992, MUSEUM EXPERIENCE
Falk JohnH. Lynn D. Dierking., 2000, Learning From Museums: Visitor Experiences and the Making of Meaning
Freeman C. G., 2017, PARTICIPATORY CULTUR
Gao J, 2020, ASIA PAC J TOUR RES, V25, P1042, DOI 10.1080/10941665.2018.1564340
Gentry K, 2019, INT J HERIT STUD, V25, P1148, DOI 10.1080/13527258.2019.1570964
Gibson Lisanne., 2009, VALUING HIST ENV
Giglio S, 2019, TOURISM MANAGE, V72, P306, DOI 10.1016/j.tourman.2018.12.007
Ginzarly M, 2021, J CULT HERIT MANAG S, V11, P361, DOI 10.1108/JCHMSD-02-2020-0023
Gonzalez PA, 2014, CULT STUD, V28, P359, DOI 10.1080/09502386.2013.789067
Guo Y, 2017, TOURISM MANAGE, V59, P467, DOI 10.1016/j.tourman.2016.09.009
Gursoy D, 2022, J TOUR CULT CHANGE, V20, P311, DOI 10.1080/14766825.2021.1937193
Hand Martin., 2008, MAKING DIGITAL CULTU
Harrison Rodney., 2010, UNDERSTANDING POLITI, P5
Hausmann A, 2018, CONSERV LETT, V11, DOI 10.1111/conl.12343
Heinich N., 2011, The Nordic Journal of Aesthetics, V22, P40, DOI DOI 10.7146/NJA.V22I40-41.5203
HEINICH Nathalie., 2017, VALEURS APPROCHE SOC
Holdgaard N, 2014, DIGIT CREAT, V25, P190, DOI 10.1080/14626268.2014.904364
Hosany S, 2010, J TRAVEL RES, V49, P351, DOI 10.1177/0047287509346859
Huang K, 2019, TOURIST STUD, V19, P549, DOI 10.1177/1468797619850107
Huh J., 2006, Journal of Hospitality & Leisure Marketing, V14, P81, DOI 10.1300/J150v14n03_07
ICOMOS, 2008, ICOMOS CHART INT PRE
Jafari A, 2013, J MARKET MANAG-UK, V29, P1729, DOI 10.1080/0267257X.2013.811095
Jensen Bente, 2013, 2013 Digital Heritage International Congress (DigitalHeritage). Federating the 19th Int'I VSMM, 10th Eurographics GCH, & 2nd UNESCO Memory of the World Conferences, plus special sessions from CAA, Arqueologico 2.0, Space2Place, ICOMOS ICIP & CIPA, EU projects, et al. Proceedings, P311
Kempiak J, 2017, INT J HERIT STUD, V23, P375, DOI 10.1080/13527258.2016.1277776
Khirfan L., 2016, World Heritage, Urban Design and Tourism: Three Cities in the Middle East
Kim JH, 2021, CURR ISSUES TOUR, V24, P304, DOI 10.1080/13683500.2020.1711711
Kim JH, 2018, J TRAVEL RES, V57, P856, DOI 10.1177/0047287517721369
Kim JH, 2010, J TRAVEL TOUR MARK, V27, P780, DOI 10.1080/10548408.2010.526897
Knudsen D. C., 2016, LANDSCAPE TOURISM ME, P1, DOI [10.4324/9781315591407-6, DOI 10.4324/9781315591407-6]
Knutson B. J., 2006, Journal of Hospitality & Leisure Marketing, V15, P31, DOI 10.1300/J150v15n03_03
Kreziak D., 2011, Decisions Marketing, V64, P23
Labadi Sophia., 2010, Heritage and Globalisation. Milton Park
Lam JMS, 2020, J DESTIN MARK MANAGE, V18, DOI 10.1016/j.jdmm.2020.100490
Laurajane Smith., 2006, USES HERITAGE
Laws E., 2007, QUALITY ISSUES HERIT, P61, DOI [10.4324/9780080515489-15, DOI 10.4324/9780080515489-15]
Levin N, 2017, APPL GEOGR, V79, P115, DOI 10.1016/j.apgeog.2016.12.009
Lewis S, 2010, SOC SCI INFORM, V49, P351, DOI 10.1177/0539018410370726
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
Litvin SW, 2008, TOURISM MANAGE, V29, P458, DOI 10.1016/j.tourman.2007.05.011
Liu Y, 2021, HERITAGE-BASEL, V4, P316, DOI 10.3390/heritage4010020
Loader BD, 2011, INFORM COMMUN SOC, V14, P757, DOI 10.1080/1369118X.2011.592648
Martí P, 2019, COMPUT ENVIRON URBAN, V74, P161, DOI 10.1016/j.compenvurbsys.2018.11.001
Mason R., 2002, Assessing the Values of Cultural Heritage, P5
Miller ZD, 2019, ELGAR RES AGENDAS, P53
Minkiewicz J, 2014, J MARKET MANAG-UK, V30, P30, DOI 10.1080/0267257X.2013.800899
Mkono M, 2013, J HOSP TOUR RES, V37, P184, DOI 10.1177/1096348011425502
Mohammadi F, 2021, TOUR REV, V76, P305, DOI 10.1108/TR-10-2019-0425
Munar AM, 2013, TOUR SOC SCI SER, V18, P1, DOI 10.1108/S1571-5043(2013)0000018003
Munar AM, 2014, TOURISM MANAGE, V43, P46, DOI 10.1016/j.tourman.2014.01.012
Munar AM, 2013, SCAND J HOSP TOUR, V13, P1, DOI 10.1080/15022250.2013.764511
Nowacki M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084383
Oh H. M., 2007, Journal of Travel Research, V46, P119, DOI 10.1177/0047287507304039
Owuor I, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9090526
Park E, 2019, TOURISM MANAGE, V74, P99, DOI 10.1016/j.tourman.2019.03.001
Payne AF, 2008, J ACAD MARKET SCI, V36, P83, DOI 10.1007/s11747-007-0070-0
Pine BJ, 1998, HARVARD BUS REV, V76, P97
Ponsignon F, 2020, TOUR MANAG PERSPECT, V35, DOI 10.1016/j.tmp.2020.100723
Poria Y, 2003, ANN TOURISM RES, V30, P238, DOI 10.1016/S0160-7383(02)00064-6
Poria Y, 2013, TOURISM MANAGE, V35, P272, DOI 10.1016/j.tourman.2012.02.011
Prebensen N. K., 2018, Creating experience value in tourism, P1, DOI 10.1079/9781786395030.0001
Radder L., 2015, J APPL BUS RES, V31, P455, DOI [10.19030/jabr.v31i2.9129, DOI 10.19030/JABR.V31I2.9129]
Reichenberger I, 2017, INT J TOUR RES, V19, P629, DOI 10.1002/jtr.2135
Reisinger Y., 2006, Current Issues in Tourism, V9, P481, DOI 10.2167/cit280.0
Rihova I, 2015, INT J TOUR RES, V17, P356, DOI 10.1002/jtr.1993
Roberts H, 2019, URBAN STUD, V56, P818, DOI 10.1177/0042098017748544
González-Rodríguez MR, 2016, INT J CONTEMP HOSP M, V28, P2609, DOI 10.1108/IJCHM-02-2015-0057
Ross D, 2020, CURR ISSUES TOUR, V23, P2811, DOI 10.1080/13683500.2020.1782355
Sáez JA, 2015, INFORM SCIENCES, V291, P184, DOI 10.1016/j.ins.2014.08.051
Sanjbod HS, 2016, HIST ENVIRON POLICY, V7, P177, DOI 10.1080/17567505.2016.1172786
Scarpi D, 2019, TOURISM MANAGE, V71, P389, DOI 10.1016/j.tourman.2018.10.030
Schuckert M, 2015, J TRAVEL TOUR MARK, V32, P608, DOI 10.1080/10548408.2014.933154
Serageldin M., 2000, VALUES HERITAGE CONS, P51
Silberman N., 2012, Heritage and Social Media: Understanding heritage in a participatory culture, P13
Silberman N., 2018, MUSEUM STUDIES APPRO
Sorensen F, 2018, TOUR HOSP EVENT MAN, P1, DOI 10.1007/978-3-319-78553-0_1
Steffan-Dewenter I., 2002, Ecology, V83, P1421, DOI 10.1890/0012-9658(2002)083[1421:SDEOLC]2.0.CO;2
Stephenson J, 2008, LANDSCAPE URBAN PLAN, V84, P127, DOI 10.1016/j.landurbplan.2007.07.003
Stoleriv OM, 2019, VISIT STUD, V22, P192, DOI 10.1080/10645578.2019.1665390
Stuedahl D., 2011, Nordic Museology, V1, p3.14, DOI [10.5617/nm.3142, DOI 10.5617/NM.3142]
Suntikul W, 2016, INT J TOUR RES, V18, P308, DOI 10.1002/jtr.2050
Tan SK, 2014, TOURISM MANAGE, V42, P248, DOI 10.1016/j.tourman.2013.11.008
Tenkanen H, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-18007-4
Thapa Brijesh, 2017, Journal of Ecotourism, V16, P112, DOI 10.1080/14724049.2016.1245737
Timothy DJ, 2018, TOUR MANAG PERSPECT, V25, P177, DOI 10.1016/j.tmp.2017.11.018
Toivonen T, 2019, BIOL CONSERV, V233, P298, DOI 10.1016/j.biocon.2019.01.023
TRIBE J., 2007, CRITICAL TURN TOURIS, P29
UNESCO, 2018, Interpretation of Sites of Memory
UNESCO, 2010, WHC1034COM8EADD UNES
UNESCO, 2019, OP GUID IMPL WORLD H
UNESCO, 2010, WHC1034COM8E UNESCO, P23
van der Duim R., 2007, The critical turn in tourism studies: Innovative research methods, P149
Walden-Schreiner C, 2018, APPL GEOGR, V90, P44, DOI 10.1016/j.apgeog.2017.11.004
Wang D, 2012, J TRAVEL RES, V51, P371, DOI 10.1177/0047287511426341
Wang S, 2017, J TRAVEL TOUR MARK, V34, P680, DOI 10.1080/10548408.2016.1224751
Wang Y, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17124206
Weng LS, 2020, J DESTIN MARK MANAGE, V16, DOI 10.1016/j.jdmm.2020.100431
WTTC, 2020, SUSTAINABLE DEV GOAL
Xu H, 2023, TOUR RECREAT RES, V48, P173, DOI 10.1080/02508281.2021.1913022
Zeng BX, 2014, TOUR MANAG PERSPECT, V10, P27, DOI 10.1016/j.tmp.2014.01.001
Zhang R., 2020, CHINESE HERITAGE SIT, P20
Zhang RR, 2019, TOURISM MANAGE, V74, P212, DOI 10.1016/j.tourman.2019.03.004
Zhu YJ, 2018, INT J HERIT STUD, V24, P181, DOI 10.1080/13527258.2017.1347886
NR 142
TC 1
Z9 1
U1 4
U2 11
PU COGNIZANT COMMUNICATION CORP
PI PUTNAM VALLEY
PA 18 PEEKSKILL HOLLOW RD, PO BOX 37, PUTNAM VALLEY, NY 10579 USA
SN 1098-304X
EI 1943-4146
J9 TOUR CULT COMMUN
JI Tour. Cult. Commun.
PY 2022
VL 22
IS 4
BP 321
EP 340
DI 10.3727/109830421X16345418234065
PG 20
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA D4TX0
UT WOS:000968685300003
DA 2024-03-27
ER
PT J
AU Gruettner, A
Wambsganss, T
Back, A
AF Gruettner, Arne
Wambsganss, Thiemo
Back, Andrea
TI From data to dollar: using the wisdom of an online tipster community to
improve sports betting returns
SO EUROPEAN JOURNAL OF INTERNATIONAL MANAGEMENT
LA English
DT Article
DE data mining; natural language processing; online communities; sports
betting; sports innovation; user-generated content; wisdom-of-crowds
AB With thousands of (online) bookmakers accepting wagers on sporting events, sports betting has become a billion-dollar business worldwide. Therefore, researchers and practitioners have gathered interest in investigating the "wisdom-of-crowds" effect in online tipster communities to predict the outcomes of sports events. We extracted 1,534,041 tips of 3484 tipsters from Blogabet.com and used this user-generated content to investigate whether there is wisdom in online tipster communities that can be used to improve betting returns. We applied state-of-the-art data mining and natural language processing techniques and tested our hypotheses using quantitative research methods. Our results demonstrate that there is indeed wisdom in such online tipster communities that can improve sports betting returns. Tipsters won 3.29% more tips than the implied win probability set by bookmakers and produced averaged yields of 3.97%. We further identified four characteristics that are significant indicators for smarter sub-crowds within the overall crowd of an online tipster community.
C1 [Gruettner, Arne; Wambsganss, Thiemo; Back, Andrea] Univ St Gallen, Inst Informat Management, St Gallen, Switzerland.
C3 University of St Gallen
RP Gruettner, A (autor correspondiente), Univ St Gallen, Inst Informat Management, St Gallen, Switzerland.
EM arne.gruettner@unisg.ch; thiemo.wambsganss@unisg.ch;
andrea.back@unisg.ch
RI Wambsganss, Thiemo/HIK-0071-2022
OI Wambsganss, Thiemo/0000-0002-7440-9357
CR [Anonymous], 2019, About WordNet
Back A, 2020, P 53 HAW INT C SYST
Beretta M, 2019, J PROD INNOVAT MANAG, V36, P5, DOI 10.1111/jpim.12439
Bird S., 2009, NATURAL LANGUAGE PRO
Brown A, 2019, EUR J OPER RES, V272, P1073, DOI 10.1016/j.ejor.2018.07.015
Budescu DV, 2015, MANAGE SCI, V61, P267, DOI 10.1287/mnsc.2014.1909
Byrum J, 2016, MIT SLOAN MANAGE REV, V57, P43
Chen HL, 2014, REV FINANC STUD, V27, P1367, DOI 10.1093/rfs/hhu001
Da Z, 2020, MANAGE SCI, V66, P1847, DOI 10.1287/mnsc.2019.3294
de Oliveira S, 2018, P NATL ACAD SCI USA, V115, P2066, DOI 10.1073/pnas.1717632115
Deutscher Sportwettenverband, 2020, SPORTW DTSCH BRICHT
Elaad G, 2020, FINANC RES LETT, V35, DOI 10.1016/j.frl.2019.09.006
Fayyad U., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P82
Fitzmaurice G. M., 2012, APPL LONGITUDINAL AN, V998
Flesch R, 1943, Teachers college contributions to education
Goldstein D.G., 2014, P 15 ACM C EC COMP A, P1
Gruettner A, 2019, P 25 AM C INF SYST A, P1
Gruettner A, 2020, DATA SET THE WISDOM
Haan M., 2005, Journal of Cultural Economics, V29, P59
Hedeker D., 2006, LONGITUDINAL DATA AN, V451
Hoornaert S, 2017, J PROD INNOVAT MANAG, V34, P580, DOI 10.1111/jpim.12396
Klein M, 2015, DECIS SUPPORT SYST, V78, P39, DOI 10.1016/j.dss.2015.06.005
Krumm J, 2008, IEEE PERVAS COMPUT, V7, P10, DOI 10.1109/MPRV.2008.85
LAIRD NM, 1982, BIOMETRICS, V38, P963, DOI 10.2307/2529876
Lamberson PJ, 2012, MANAGE SCI, V58, P805, DOI 10.1287/mnsc.1110.1441
Levitt SD, 2013, GEORGETOWN LAW J, V101, P581
Ma JF, 2019, DECIS SUPPORT SYST, V117, P113, DOI 10.1016/j.dss.2018.11.003
Maechler M., 2014, CONVERGENCE, V12, P2
Marcus N., 2010, P 15 INT C INT US IN, DOI [10.1145/1719970.1720024, DOI 10.1145/1719970.1720024]
Mollick E, 2016, MANAGE SCI, V62, P1533, DOI 10.1287/mnsc.2015.2207
O'Leary DE, 2017, EUR J OPER RES, V260, P715, DOI 10.1016/j.ejor.2016.12.043
Otterbacher J, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P955
Peeters T, 2018, INT J FORECASTING, V34, P17, DOI 10.1016/j.ijforecast.2017.08.002
Peurala J, 2013, INT SPORTS LAW J, V13, P268, DOI 10.1007/s40318-013-0027-z
Ratten V., 2017, Sports innovation management
Ratten V, 2011, INT ENTREP MANAG J, V7, P57, DOI 10.1007/s11365-010-0138-z
Rhyn M, 2019, P INT C INF SYST ICI, P1
Riedl C, 2013, INT J ELECTRON COMM, V17, P7, DOI 10.2753/JEC1086-4415170301
Schumaker RP, 2016, DECIS SUPPORT SYST, V88, P76, DOI 10.1016/j.dss.2016.05.010
Simmons JP, 2011, J CONSUM RES, V38, P1, DOI 10.1086/658070
Surowiecki James., 2004, WISDOM SHAPES BUSINE
van Dijk H, 2012, ORGAN BEHAV HUM DEC, V119, P38, DOI 10.1016/j.obhdp.2012.06.003
Vermunt JK, 2005, MULTIVAR BEHAV RES, V40, P281, DOI 10.1207/s15327906mbr4003_1
Walther JB, 2007, COMPUT HUM BEHAV, V23, P2538, DOI 10.1016/j.chb.2006.05.002
Wambsganss T, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P1184
Wang R. Y., 1996, Journal of Management Information Systems, V12, P5
Weimer M., 2007, P C REC ADV NAT LANG, P1
Wincent J., 2019, J BUS VENTURING INSI, V11, P1, DOI DOI 10.1016/J.JBVI.2018.E00109
NR 48
TC 0
Z9 0
U1 0
U2 10
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 1751-6757
EI 1751-6765
J9 EUR J INT MANAG
JI Eur. J. Int. Manag.
PY 2021
VL 15
IS 2-3
SI SI
BP 314
EP 338
PG 25
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA QQ7MC
UT WOS:000624704500008
DA 2024-03-27
ER
PT J
AU Manovich, L
AF Manovich, Lev
TI 100 Billion Data Rows per Second: Media Analytics in the Early 21st
Century
SO INTERNATIONAL JOURNAL OF COMMUNICATION
LA English
DT Article
DE machine learning; social media; culture industry; user-generated
content; data science
AB This article describes the newest stage in the development of modern technological media. I call this stage "media analytics." It follows the previous stages of massive reproduction (1500-), broadcasting (1920-), the use of computers for media creation workflows (1981-), the Web as global content creation and distribution network (1993-), and social media platforms (2004-), to name just a few such stages. Unlike other stages, the new stage is not focused on new mechanisms for creation, publishing, or distribution of media, although it also affects these operations. Instead, this new stage is about automatic computational analysis of the content of all online digital media, personal online behaviors and communication, and automatic actions based on this analysis.
C1 [Manovich, Lev] CUNY, New York, NY 10021 USA.
[Manovich, Lev] Cultural Analyt Lab, New York, NY USA.
C3 City University of New York (CUNY) System
RP Manovich, L (autor correspondiente), CUNY, New York, NY 10021 USA.; Manovich, L (autor correspondiente), Cultural Analyt Lab, New York, NY USA.
EM Manovich.lev@gmail.com
OI Lopez Castro, Raquel/0000-0002-3968-9955
CR Ananny M., 2015, AUDITING ALGORITHMS
[Anonymous], 2016, How Search Works
[Anonymous], 2015, NY TIMES
[Anonymous], 1 MONDAY
[Anonymous], 2011, NY TIMES
[Anonymous], 2016, TINEYE 0221
[Anonymous], 2013, CHRONICLE REV
Asay M., 2015, INFOWORLD
Association for Psychological Science, 2015, SCIENCEDAILY
Beaufays F, 2015, NEURAL NETWORKS GOOG
Bordwell David., 1988, CLASSICAL HOLLYWOOD
Chartbeat, 2015, AB
Dey A., 1999, BETTER UNDERSTANDING
Dredge S., 2014, The Guardian
Garfield E, 1997, SCIENTIST, V11, P9
Google, 2016, CONT ID WORKS VID FI
Gupta A., 2013, Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013, P507, DOI DOI 10.1109/ICACCI.2013.6637223
Horkheimer MaxTheodor Adorno., 1969, DIALECTIC ENLIGHTENM
Huss M, 2014, DATA SIZE ESTIMATES
Kaplan Karen., 2015, Los Angeles Times
LeCompte C., 2015, NIEMAN REPORTS
Luckerson V., 2015, TIME
Madrigal A.C., 2014, The Atlantic
MailChimp, 2017, US SEND TIM OPT
Manovich L., 2015, SCI CULTURE SOCIAL C
Manovich L., 2013, SOFTWARE TAKES COMMA
Mencar C., 2013, RESEARCHGATE
Pichai Sundar, 2015, TENSORFLOW SMARTER M
Richter Felix, 2014, STATISTA 0930
Ritzer G, 2010, J CONSUM CULT, V10, P13, DOI 10.1177/1469540509354673
Sanderson M, 2012, P IEEE, V100, P1444, DOI 10.1109/JPROC.2012.2189916
Sawers P., 2014, THE NEXT WEB
Schifanella Rossano, 2015, IMAGE IS WORTH MORE
Serrà J, 2012, SCI REP-UK, V2, DOI 10.1038/srep00521
Twitter, 2017, FOLL TARG TWITT
Vanderbilt T., 2013, Wired
Wiener Janet., 2014, Facebook
Woodie A, 2015, DATANAMI 0209
Zhou R., 2010, P 2010 ACM INT MEAS
NR 39
TC 15
Z9 16
U1 0
U2 6
PU USC ANNENBERG PRESS
PI LOS ANGELES
PA UNIV SOUTHERN CALIFORNIA, KERCKHOFF HALL, 734 W ADAMS BLVD, MC7725, LOS
ANGELES, CA 90089 USA
SN 1932-8036
J9 INT J COMMUN-US
JI Int. J. Commun.
PY 2018
VL 12
BP 473
EP 488
PG 16
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA GF3CG
UT WOS:000431821400034
DA 2024-03-27
ER
PT J
AU Hartmann, MC
Koblet, O
Baer, MF
Purves, RS
AF Hartmann, Maximilian C.
Koblet, Olga
Baer, Manuel F.
Purves, Ross S.
TI Automated motif identification: Analysing Flickr images to identify
popular viewpoints in Europe's protected areas
SO JOURNAL OF OUTDOOR RECREATION AND TOURISM-RESEARCH PLANNING AND
MANAGEMENT
LA English
DT Article
DE Landscape perception; User-generated content; Computer vision; Image
similarity; Places of interest; Social media
ID CULTURAL ECOSYSTEM SERVICES; SOCIAL MEDIA DATA; LANDSCAPE; VISITORS;
TOURISM; FUTURE
AB Visiting landscapes and appreciating them from specific viewpoints is not a new phenomenon. Such so-called motifs were popularised by travel guides and art in the romantic era, and find their contemporary digital twins through images captured in social media. We developed and implemented a conceptual model of motifs, based around spatial clustering, image similarity and the appreciation of a motif by multiple individuals. We identified 119 motifs across Europe, using 2146176 georeferenced Creative Commons Flickr images found in Natura 2000 protected areas. About 65% of motifs contain cultural elements such as castles or bridges. The remaining 35% are natural features, and biased towards coastal elements such as cliffs. Characterisation and localisation of motifs could allow identification of locations subject to increased pressure, and thus disturbance, especially since the visual characteristics of motifs allow managers to explore why sites are being visited. Future work will include methods of calculating image similarity using tags, explore different algorithms for assessing content similarity and study the behaviour of motifs through time.
C1 [Hartmann, Maximilian C.; Koblet, Olga; Baer, Manuel F.; Purves, Ross S.] Univ Zurich, Dept Geog, Winterthurerstr 190, CH-8057 Zurich, Switzerland.
C3 University of Zurich
RP Hartmann, MC (autor correspondiente), Univ Zurich, Dept Geog, Winterthurerstr 190, CH-8057 Zurich, Switzerland.
EM maximilianchristoph.hartmann@geo.uzh.ch
OI Baer, Manuel/0000-0002-9474-3299; Hartmann, Maximilian
Christoph/0000-0002-6013-3820
FU Swiss National Science Foundation [200020E_186389]; Swiss National
Science Foundation (SNF) [200020E_186389] Funding Source: Swiss National
Science Foundation (SNF)
FX Acknowledgments We gratefully acknowledge funding from the Swiss
National Science Foundation (200020E_186389) . We also are very grateful
for the constructive comments of the referees.
CR [Anonymous], 2021, UNWTO WORLD TOUR BAR, V19, P1, DOI [10.18111/wtobarometereng.2021.19.1.3, DOI 10.18111/WTOBAROMETERENG.2021.19.1.1]
[Anonymous], P 8 INT C WEBL SOC M
[Anonymous], 2014, A World That Counts
Arts K, 2015, AMBIO, V44, pS661, DOI 10.1007/s13280-015-0705-1
Bate Jonathan., 2002, SONG EARTH
Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014
Bell C., 2002, Tourism: between place and performance, P21
Beunen R, 2011, J ENVIRON PLANN MAN, V54, P1041, DOI 10.1080/09640568.2010.549034
Bommert A, 2017, COMPUT MATH METHOD M, V2017, DOI 10.1155/2017/7907163
Butler R. W., 2014, Tourism Today, P7
Cooper D, 2011, T I BRIT GEOGR, V36, P89, DOI 10.1111/j.1475-5661.2010.00405.x
Di Minin E, 2015, FRONT ENV SCI-SWITZ, V3, DOI 10.3389/fenvs.2015.00063
EEA, 2021, EUR NAT 2000 SIT
Figueroa-Alfaro RW, 2017, J ENVIRON PLANN MAN, V60, P266, DOI 10.1080/09640568.2016.1151772
Ghermandi A, 2020, TOURISM MANAGE, V77, DOI 10.1016/j.tourman.2019.104002
Ghermandi A, 2016, WATER RES, V105, P297, DOI 10.1016/j.watres.2016.09.009
Ginsberg J, 2009, NATURE, V457, P1012, DOI 10.1038/nature07634
Goodchild MF, 2007, GEOJOURNAL, V69, P211, DOI 10.1007/s10708-007-9111-y
Hadwen Wade L., 2007, Ecological Management & Restoration, V8, P177, DOI 10.1111/j.1442-8903.2007.00364.x
Hampton SE, 2013, FRONT ECOL ENVIRON, V11, P156, DOI 10.1890/120103
Hausmann A, 2018, CONSERV LETT, V11, DOI 10.1111/conl.12343
Hausmann A, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-00858-6
Hays S, 2013, CURR ISSUES TOUR, V16, P211, DOI 10.1080/13683500.2012.662215
Heikinheimo V, 2020, LANDSCAPE URBAN PLAN, V201, DOI 10.1016/j.landurbplan.2020.103845
Heikinheimo V, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/ijgi6030085
Hiltmann A., 2018, AMAZINGAESCHER EIN G
Hu YJ, 2018, GEOGR COMPASS, V12, DOI 10.1111/gec3.12404
Jenkins O. H., 2003, Tourism Geographies, V5, P305, DOI 10.1080/14616680309715
Rota JJ, 2019, GEOGR TIDSSKR-DEN, V119, P69, DOI 10.1080/00167223.2019.1573409
Kahootz M, 2018, SOCIAL MEDIA DEMOGRA
Komossa F, 2021, LANDSCAPE URBAN PLAN, V212, DOI 10.1016/j.landurbplan.2021.104105
Lee H, 2019, ECOL INDIC, V96, P505, DOI 10.1016/j.ecolind.2018.08.035
Leung Y. F., 2018, TOURISM VISITOR MANA, DOI [10.2305/iucn.ch.2018.pag.27.en. https://portals.iucn.org/library/node/47918, DOI 10.2305/IUCN.CH.2018.PAG.27.EN.HTTPS://PORTALS.IUCN.ORG/LIBRARY/NODE/47918]
Lovelace R, 2016, J REGIONAL SCI, V56, P722, DOI 10.1111/jors.12293
Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94
Naidoo R, 2005, ENVIRON DEV ECON, V10, P159, DOI 10.1017/S1355770X0400186X
Nüst D, 2018, PEERJ, V6, DOI 10.7717/peerj.5072
O'Connor N, 2014, J TOUR CULT CHANGE, V12, P1, DOI 10.1080/14766825.2013.862253
Oteros-Rozas E, 2018, ECOL INDIC, V94, P74, DOI 10.1016/j.ecolind.2017.02.009
Pavord A., 2016, LANDSKIPPING PAINTER
Pickering C, 2020, J OUTDOOR REC TOUR, V29, DOI 10.1016/j.jort.2019.100252
Pickering C, 2018, J OUTDOOR REC TOUR, V21, P10, DOI 10.1016/j.jort.2017.11.002
Ramachandram D, 2017, IEEE SIGNAL PROC MAG, V34, P96, DOI 10.1109/MSP.2017.2738401
Richards DR, 2015, ECOL INDIC, V53, P187, DOI 10.1016/j.ecolind.2015.01.034
Rublee E, 2011, IEEE I CONF COMP VIS, P2564, DOI 10.1109/ICCV.2011.6126544
Simmonds C., 2018, The Guardian
Suzman M, 2020, VIRT WORLD DAT FOR O
Tenkanen H, 2017, SCI REP-UK, V7, DOI 10.1038/s41598-017-18007-4
Tieskens KF, 2018, LANDSCAPE URBAN PLAN, V177, P128, DOI 10.1016/j.landurbplan.2018.05.002
Toivonen T, 2019, BIOL CONSERV, V233, P298, DOI 10.1016/j.biocon.2019.01.023
Väisänen T, 2021, CONSERV BIOL, V35, P424, DOI 10.1111/cobi.13704
van Zanten BT, 2016, P NATL ACAD SCI USA, V113, P12974, DOI 10.1073/pnas.1614158113
Wartmann FM, 2021, URBAN FOR URBAN GREE, V64, DOI 10.1016/j.ufug.2021.127261
Wartmann FM, 2019, APPL GEOGR, V113, DOI 10.1016/j.apgeog.2019.102112
NR 54
TC 6
Z9 6
U1 7
U2 26
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2213-0780
EI 2213-0799
J9 J OUTDOOR REC TOUR
JI J. Outdo. Recreat. Tour. Res. Plan.
PD MAR
PY 2022
VL 37
AR 100479
DI 10.1016/j.jort.2021.100479
EA JAN 2022
PG 10
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA 0D6OX
UT WOS:000776113500004
OA hybrid, Green Published
DA 2024-03-27
ER
PT J
AU Salari, N
Liu, S
Shen, ZJM
AF Salari, Nooshin
Liu, Sheng
Shen, Zuo-Jun Max
TI Real-Time Delivery Time Forecasting and Promising in Online Retailing:
When Will Your Package Arrive?
SO M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
LA English
DT Article
DE logistics; online retail; forecasting; machine learning
ID EMERGENCY-DEPARTMENT; SCORING RULES; BIG DATA; PREDICTION; DEMAND
AB Problem definition: Providing fast and reliable delivery services is key to running a successful online retail business. To achieve a better delivery time guarantee policy, we study how to estimate and promise delivery time for new customer orders in real time. Academic/practical relevance: Delivery time promising is critical to managing customer expectations and improving customer satisfaction. Simply overpromising or underpromising is undesirable because of the negative impacts on short-/long-term sales. To the best of our knowledge, we are the first to develop a data-driven framework to predict the distribution of order delivery time and set promised delivery time to customers in a cost-effective way. Methodology: We apply and extend tree-based models to generate distributional forecasts by exploiting the complicated relationship between delivery time and relevant operational predictors. To account for the cost-sensitive decision-making problem structure, we develop a new split rule for quantile regression forests that incorporates an asymmetric loss function in split point selection. We further propose a cost-sensitive decision rule to decide the promised delivery day from the predicted distribution. Results: Our decision rule is proven to be optimal given certain cost structures. Tested on a real-world data set shared from JD.com, our proposed machine learning-based models deliver superior forecasting performance. In addition, we demonstrate that our framework has the potential to provide better promised delivery time in terms of sales, cost, and accuracy as compared with the conventional promised time set by JD.com. Specifically, our simulation results indicate that the proposed delivery time promise policy can improve the sales volume by 6.1% over the current policy. Managerial implications: Through a more accurate estimation of the delivery time distribution, online retailers can strategically set the promised time to maximize customer satisfaction and boost sales. Our data-driven framework reveals the importance of modeling fulfillment operations in delivery time forecasting and integrating the decision-making problemstructure with the forecasting model.
C1 [Salari, Nooshin; Liu, Sheng] Univ Toronto, Rotman Sch Management, Toronto, ON M5S 1A1, Canada.
[Salari, Nooshin] Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94720 USA.
[Shen, Zuo-Jun Max] Univ Hong Kong, Fac Engn, Hong Kong, Peoples R China.
[Shen, Zuo-Jun Max] Univ Hong Kong, Fac Business & Econ, Hong Kong, Peoples R China.
[Shen, Zuo-Jun Max] Univ Calif Berkeley, Coll Engn, Berkeley, CA 94720 USA.
C3 University of Toronto; University of California System; University of
California Berkeley; University of Hong Kong; University of Hong Kong;
University of California System; University of California Berkeley
RP Liu, S (autor correspondiente), Univ Toronto, Rotman Sch Management, Toronto, ON M5S 1A1, Canada.
EM nsalari@mie.utoronto.ca; sheng.liu@rotman.utoronto.ca;
maxshen@berkeley.edu
RI Shen, Zuo-Jun Max/JXM-7549-2024
OI Shen, Zuo-Jun Max/0000-0003-4538-8312; Salari,
Nooshin/0000-0001-6948-8313; Liu, Sheng/0000-0003-2365-6013
CR Acimovic J, 2015, M&SOM-MANUF SERV OP, V17, P34, DOI 10.1287/msom.2014.0505
Ang E, 2016, M&SOM-MANUF SERV OP, V18, P141, DOI 10.1287/msom.2015.0560
Arora S, 2020, PREPRINT, DOI 10.2139/ssrn.3155428.3614760
Ban GY, 2019, OPER RES, V67, P90, DOI 10.1287/opre.2018.1757
Bernard S, 2016, PATTERN RECOGN, V52, P46, DOI 10.1016/j.patcog.2015.10.010
Bourke C, 2008, MACH LEARN, V71, P219, DOI 10.1007/s10994-008-5056-8
Bray RobertL., 2020, Manufacturing Service Operations Management
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Brier G.W., 1950, MON WEATHER REV, V78, P1, DOI DOI 10.1175/1520-0493(1950)0782.0.CO;2
Chan T, 2018, DELIVERY SERVICE CUS
Cohen MC, 2017, OPER RES, V65, P446, DOI 10.1287/opre.2016.1573
Cui R, PREPRINT, DOI 10.2139/ssrn.3563404
Cui RM, 2020, MANAGE SCI, V66, P3879, DOI 10.1287/mnsc.2019.3411
Dong J, 2019, MANAGE SCI, V65, P1969, DOI 10.1287/mnsc.2018.3048
Ferreira KJ, 2016, M&SOM-MANUF SERV OP, V18, P69, DOI 10.1287/msom.2015.0561
Fisher ML, 2019, J MARKETING RES, V56, P732, DOI 10.1177/0022243719849940
Freedman, 2019, WHY LOGISTICS HAVE N
Gneiting T, 2007, J AM STAT ASSOC, V102, P359, DOI 10.1198/016214506000001437
Grushka-Cockayne Y, 2017, OPER RES, V65, P712, DOI 10.1287/opre.2017.1588
Guo X, 2022, MANUFACTURING SERVIC
Ha TM, 1997, IEEE T PATTERN ANAL, V19, P608, DOI 10.1109/34.601248
Hastie T., 2005, ELEMENTS STAT LEARNI, V27, P83, DOI DOI 10.1007/978-0-387-84858-7
Hernández-Orallo J, 2012, J MACH LEARN RES, V13, P2813
Hildebrandt FD, 2022, TRANSPORT SCI, V56, P1058, DOI 10.1287/trsc.2021.1095
Ibrahim R, 2011, OPER RES, V59, P1106, DOI 10.1287/opre.1110.0974
Ibrahim R, 2009, MANAGE SCI, V55, P1729, DOI 10.1287/mnsc.1090.1041
Ibrahim R, 2009, M&SOM-MANUF SERV OP, V11, P397, DOI 10.1287/msom.1080.0223
KOENKER R, 1978, ECONOMETRICA, V46, P33, DOI 10.2307/1913643
Kuhn M., 2013, Applied predictive modeling, V26
Lachiche N., 2003, P 20 INT C MACH LEAR, P416
Lewis DD, 2004, J MACH LEARN RES, V5, P361
MATHESON JE, 1976, MANAGE SCI, V22, P1087, DOI 10.1287/mnsc.22.10.1087
Meinshausen N, 2006, J MACH LEARN RES, V7, P983
Perakis G, 2021, PREPRINT, DOI 10.2139/ssrn.3305039
Rao S, 2011, J OPER MANAG, V29, P692, DOI 10.1016/j.jom.2011.04.001
Shang Y, 2017, OPER RES, V65, P1574, DOI 10.1287/opre.2017.1612
SHEN ZJ, 2020, MANUFACTURING SERVIC
State Post Bureau of the People's Republic of China, 2020, NOT 2019 DEL SERV SA
Sun Y, 2012, ANN EMERG MED, V60, P299, DOI 10.1016/j.annemergmed.2012.03.011
Tsoumakas G, 2011, IEEE T KNOWL DATA EN, V23, P1079, DOI 10.1109/TKDE.2010.164
NR 40
TC 7
Z9 8
U1 10
U2 67
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1523-4614
EI 1526-5498
J9 M&SOM-MANUF SERV OP
JI M&SOM-Manuf. Serv. Oper. Manag.
PD MAY-JUN
PY 2022
VL 24
IS 3
BP 1421
EP 1436
DI 10.1287/msom.2022.1081
EA FEB 2022
PG 16
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA 8N3KE
UT WOS:000803577400001
DA 2024-03-27
ER
PT J
AU Liu, X
Shin, H
Burns, AC
AF Liu, Xia
Shin, Hyunju
Burns, Alvin C.
TI Examining the impact of luxury brand's social media marketing on
customer engagement: Using big data analytics and natural language
processing
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Big data; Luxury brand; Customer engagement; Social media; Twitter
ID VALUE CO-CREATION; CONSUMER ENGAGEMENT; FASHION BRANDS; MOTIVATIONS;
MANAGEMENT; CONTEXT; EQUITY; PASS; AGE
AB This research utilizes big data in investigating the impact of a luxury brand's social media marketing activities on customer engagement. In particular, applying the dual perspective of customer engagement, this research examines the influence of focusing on the entertainment, interaction, trendiness, and customization dimensions of a luxury brand's social media activities on customer engagement with brand-related social media content. Using big data retrieved from a 60-month period on Twitter (July 2012 to June 2017), this paper analyzes 3.78 million tweets from the top 15 luxury brands with the highest number of Twitter followers. The results indicate that focusing on the entertainment, interaction, and trendiness dimensions of a luxury brand's social media marketing efforts significantly increases customer engagement, while focusing on the customization dimension does not. The findings have important implications for the design, delivery, and management of social media marketing for luxury brands to engage customers with social media content.
C1 [Liu, Xia] Rowan Univ, William G Rohrer Coll Business, Dept Mkt & Business Informat Syst, 201 Mullica Hill Rd, Glassboro, NJ 08028 USA.
[Shin, Hyunju] Georgia Southern Univ, Parker Coll Business, Dept Mkt, POB 8154, Statesboro, GA 30450 USA.
[Burns, Alvin C.] Louisiana State Univ, Dept Mkt, EJ Ourso Coll Business, 501 South Quad Dr, Baton Rouge, LA 70803 USA.
C3 Rowan University; University System of Georgia; Georgia Southern
University; Louisiana State University System; Louisiana State
University
RP Liu, X (autor correspondiente), Rowan Univ, William G Rohrer Coll Business, Dept Mkt & Business Informat Syst, 201 Mullica Hill Rd, Glassboro, NJ 08028 USA.
EM liul@rowan.edu; hshin@georgiasouthern.edu; alburns@lsu.edu
RI hajibabaei, hossein/HFB-7762-2022; Shin, Hyunju/AAQ-7023-2021
OI Shin, Hyunju/0000-0002-6338-801X
CR Algesheimer R, 2005, J MARKETING, V69, P19, DOI 10.1509/jmkg.69.3.19.66363
Allison P., 2008, FIXED EFFECTS REGRES
[Anonymous], 2015, NY TIMES
Araujo T, 2015, J ADVERTISING RES, V55, P284, DOI 10.2501/JAR-2015-009
Barger V, 2016, J RES INTERACT MARK, V10, P268, DOI 10.1108/JRIM-06-2016-0065
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Bird S., 2009, NATURAL LANGUAGE PRO
Brodie RJ, 2013, J BUS RES, V66, P105, DOI 10.1016/j.jbusres.2011.07.029
Butler TD, 2016, J STRATEGY MANAG, V9, P322, DOI 10.1108/JSMA-07-2015-0058
Cailleux H, 2009, J BRAND MANAG, V16, P406, DOI 10.1057/bm.2008.50
Cameron AC, 2010, MICROECONOMETRICS US, V2
Campesato O., 2018, REGULAR EXPRESSIONS
Chen H, 2017, J BRAND MANAG, V24, P562, DOI 10.1057/s41262-017-0050-8
Choi E, 2016, J BUS RES, V69, P5827, DOI 10.1016/j.jbusres.2016.04.180
Christodoulides G, 2009, J BRAND MANAG, V16, P395, DOI 10.1057/bm.2008.49
Chu SC, 2011, INT J ADVERT, V30, P47, DOI 10.2501/IJA-30-1-047-075
Colditz JB, 2018, AM J PUBLIC HEALTH, V108, P1009, DOI 10.2105/AJPH.2018.304497
Dauriz L., 2014, Perspectives on Retail Consumer Goods, V3, P26
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Dearden K, 2013, MATERN CHILD NUTR, V9, P143, DOI 10.1111/j.1740-8709.2011.00347.x
Dhaoui C, 2014, J GLOB FASH MARK, V5, P209, DOI 10.1080/20932685.2014.907605
Dobele A., 2007, BUS HORIZONS, V50, P291
Erevelles S, 2016, J BUS RES, V69, P897, DOI 10.1016/j.jbusres.2015.07.001
Foux G., 2006, BRAND STRATEGY, P38
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Golan GJ, 2008, J COMPUT-MEDIAT COMM, V13, P959, DOI 10.1111/j.1083-6101.2008.00426.x
Gummerus J, 2012, MANAG RES REV, V35, P857, DOI 10.1108/01409171211256578
HAUSMAN JA, 1978, ECONOMETRICA, V46, P1251, DOI 10.2307/1913827
Hayes AF, 2007, COMMUN METHODS MEAS, V1, P77, DOI 10.1080/19312450709336664
Heine K, 2014, J GLOB FASH MARK, V5, P223
Hennig-Thurau T, 2015, J ACAD MARKET SCI, V43, P375, DOI 10.1007/s11747-014-0388-3
Hennig-Thurau T, 2010, J SERV RES-US, V13, P311, DOI 10.1177/1094670510375460
Hewett K, 2016, J MARKETING, V80, P1, DOI 10.1509/jm.15.0033
Hofacker CF, 2016, J CONSUM MARK, V33, P89, DOI 10.1108/JCM-04-2015-1399
Hollebeek L, 2011, J STRATEG MARK, V19, P555, DOI 10.1080/0965254X.2011.599493
Hughes MÜ, 2016, J PROD BRAND MANAG, V25, P357, DOI 10.1108/JPBM-09-2015-0970
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Jahn B., 2012, Identitatsbasierte Luxusmarkenfuhrung, DOI [10.1007/978-3-8349-4060-5_14, DOI 10.1007/978-3-8349-4060-5_14]
Janzen B, 2020, RURAL REMOTE HEALTH, V20, DOI 10.22605/RRH5530
Jin SAA, 2012, MARK INTELL PLAN, V30, P687, DOI 10.1108/02634501211273805
Kapferer JN, 2012, BUS HORIZONS, V55, P453, DOI 10.1016/j.bushor.2012.04.002
Kapferer JN, 2009, J BRAND MANAG, V16, P311, DOI 10.1057/bm.2008.51
Kefi H, 2020, J BUS RES, V117, P579, DOI 10.1016/j.jbusres.2018.08.012
Kim AJ, 2012, J BUS RES, V65, P1480, DOI 10.1016/j.jbusres.2011.10.014
Kim AJ, 2010, J GLOB FASH MARK, V1, P164, DOI 10.1080/20932685.2010.10593068
Kim J, 2019, J BUS RES, V99, P422, DOI 10.1016/j.jbusres.2017.10.001
Koivisto E, 2020, J BUS RES, V117, P570, DOI 10.1016/j.jbusres.2018.10.030
Kontu H, 2014, J GLOB FASH MARK, V5, P235
Kunz W, 2017, J SERV MARK, V31, P161, DOI 10.1108/JSM-10-2016-0352
Kwon E.S., 2011, Journal of Interactive Advertising, V12, P4
Lacey R, 2007, J SERV RES-US, V9, P241, DOI 10.1177/1094670506295850
Larivière B, 2013, J SERV MANAGE, V24, P268, DOI 10.1108/09564231311326996
Lee TY, 2011, J MARKETING RES, V48, P881, DOI 10.1509/jmkr.48.5.881
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Liu X, 2020, IND MARKET MANAG, V86, DOI 10.1016/j.indmarman.2019.02.021
Liu X, 2019, J SERV MARK, V33, P369, DOI 10.1108/JSM-02-2018-0049
Liu X, 2017, J ADVERTISING, V46, P236, DOI 10.1080/00913367.2017.1297273
Lu Y., 2009, FDN STAT NATURAL LAN, P131
Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
Muntinga DG, 2011, INT J ADVERT, V30, P13, DOI 10.2501/IJA-30-1-013-046
Nardo M., 2008, OECD STAT WORKING PA, DOI DOI 10.1787/533411815016
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Ng M, 2014, J GLOB FASH MARK, V5, P251
Okonkwo U, 2009, J BRAND MANAG, V16, P302, DOI 10.1057/bm.2009.2
Pentina I, 2018, J ADVERTISING, V47, P55, DOI 10.1080/00913367.2017.1405756
Phan M, 2011, J GLOB FASH MARK, V2, P213, DOI 10.1080/20932685.2011.10593099
Phelps JE, 2004, J ADVERTISING RES, V44, P333, DOI 10.1017/S0021849904040371
PMX Agency, 2017, PMX AG TREND REP LUX
Pustejovsky J., 2012, NATURAL LANGUAGE ANN
Quach S, 2017, J BUS RES, V81, P163, DOI 10.1016/j.jbusres.2017.06.015
Schivinski B, 2016, J ADVERTISING RES, V56, P64, DOI 10.2501/JAR-2016-004
Schwartz B., 2012, High Performance MySQL: Optimization, Backups, and Replication
Sharpe A., 1999, CANADIAN POLICY RES, P44
Shin HJ, 2018, J BUS ETHICS, V151, P1121, DOI 10.1007/s10551-016-3249-9
Shin HJ, 2013, J SERV MARK, V27, P431, DOI 10.1108/JSM-02-2012-0037
Straker K, 2016, J FASH MARK MANAG, V20, P276, DOI 10.1108/JFMM-10-2015-0077
van Doorn J, 2010, J SERV RES-US, V13, P253, DOI 10.1177/1094670510375599
Verhoef PC, 2010, J SERV RES-US, V13, P247, DOI 10.1177/1094670510375461
Vivek SD, 2012, J MARKET THEORY PRAC, V20, P127, DOI 10.2753/MTP1069-6679200201
Vollmer C., 2008, Always on advertising, marketing, and media in an era of consumer control
Wooldridge JM, 2010, ECONOMETRIC ANALYSIS OF CROSS SECTION AND PANEL DATA, 2ND EDITION, P1
NR 81
TC 183
Z9 196
U1 49
U2 437
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD MAR
PY 2021
VL 125
BP 815
EP 826
DI 10.1016/j.jbusres.2019.04.042
EA JAN 2021
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA PY6WB
UT WOS:000612182000067
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Chen, CH
AF Chen, Chi-hsiang
TI Influence of big data analytical capability on new product performance -
the effects of collaboration capability and team collaboration in
high-tech firm
SO CHINESE MANAGEMENT STUDIES
LA English
DT Article
DE Artificial intelligence; High-tech company; New product performance;
Sobel t-test; SEM
ID INTERNAL INTEGRATION; EXTERNAL INTEGRATION; PLANNED BEHAVIOR;
INNOVATION; KNOWLEDGE; STRATEGIES; SUCCESS; IMPACT; BIAS; ANTECEDENTS
AB PurposeAs the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP. Design/methodology/approachThis paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis. FindingsThe analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP. Practical implicationsThe strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company's collaboration and for effective management to improve NPP in the face of global competition. Originality/valueThis study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC.
C1 [Chen, Chi-hsiang] Tamkang Univ, Dept Business Adm, New Taipei, Taiwan.
C3 Tamkang University
RP Chen, CH (autor correspondiente), Tamkang Univ, Dept Business Adm, New Taipei, Taiwan.
EM cch2010@gmail.com
CR Ahmadi H, 2014, J BUS RES, V67, P704, DOI 10.1016/j.jbusres.2013.11.031
AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
Akter S, 2019, INT J INFORM MANAGE, V48, P85, DOI 10.1016/j.ijinfomgt.2019.01.020
Albergaria M, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.10.008
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Bianchi M, 2020, J BUS RES, V110, P538, DOI 10.1016/j.jbusres.2018.05.003
Cai Z, 2016, INT J OPER PROD MAN, V36, P1247, DOI 10.1108/IJOPM-08-2014-0406
Ceccagnoli M, 2012, MIS QUART, V36, P263
Chang WJ, 2016, EUR MANAG J, V34, P282, DOI 10.1016/j.emj.2015.11.008
Chen CH, 2015, CHIN MANAG STUD, V9, P150, DOI 10.1108/CMS-04-2013-0057
Chen CHV, 2022, CHIN MANAG STUD, V16, P551, DOI 10.1108/CMS-02-2021-0037
Chen FQ, 2018, ECON MODEL, V68, P229, DOI 10.1016/j.econmod.2017.07.012
Choi HY, 2022, TECHNOL FORECAST SOC, V182, DOI 10.1016/j.techfore.2022.121802
Choi TM, 2019, TRANSPORT RES E-LOG, V128, P17, DOI 10.1016/j.tre.2019.05.011
Ciampi F, 2021, J BUS RES, V123, P1, DOI 10.1016/j.jbusres.2020.09.023
Cui L, 2018, IND MARKET MANAG, V71, P27, DOI 10.1016/j.indmarman.2017.11.003
da Silva PC, 2021, J CLEAN PROD, V278, DOI 10.1016/j.jclepro.2020.123855
Davenport T. H., 2017, COMPETING ANAL NEW S
De Giovanni P, 2021, EUR J OPER RES, V292, P515, DOI 10.1016/j.ejor.2020.10.049
De Luca LM, 2007, J MARKETING, V71, P95, DOI 10.1509/jmkg.71.1.095
Drees JM, 2013, J MANAGE, V39, P1666, DOI 10.1177/0149206312471391
Durmusoglu SS, 2021, IND MARKET MANAG, V93, P250, DOI 10.1016/j.indmarman.2021.01.014
Fain N, 2011, INT J TECHNOL MANAGE, V56, P92, DOI 10.1504/IJTM.2011.042458
Flynn BB, 2010, J OPER MANAG, V28, P58, DOI 10.1016/j.jom.2009.06.001
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
FORNELL C, 1992, SOCIOL METHOD RES, V20, P334, DOI 10.1177/0049124192020003003
Ganbold O, 2021, J ENTERP INF MANAG, V34, P948, DOI 10.1108/JEIM-10-2019-0332
Gardner HK, 2012, ACAD MANAGE J, V55, P998, DOI 10.5465/amj.2010.0604
Gillis TB, 2019, U CHICAGO LAW REV, V86, P459
Gimenez C, 2005, INT J OPER PROD MAN, V25, P20, DOI 10.1108/01443570510572222
Graham S, 2018, INT J PROD ECON, V197, P283, DOI 10.1016/j.ijpe.2018.01.005
Guo RP, 2019, TECHNOL ANAL STRATEG, V31, P306, DOI 10.1080/09537325.2018.1500688
HAGGETT S, 1994, IND MARKET MANAG, V23, P101, DOI 10.1016/0019-8501(94)90010-8
Helfat CE, 2011, STRATEGIC MANAGE J, V32, P1243, DOI 10.1002/smj.955
Hemmert M, 2019, ASIA PAC J MANAG, V36, P373, DOI 10.1007/s10490-017-9556-6
Horn P, 2014, INT J PROD ECON, V153, P54, DOI 10.1016/j.ijpe.2014.03.020
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Huo BF, 2014, IEEE T ENG MANAGE, V61, P38, DOI 10.1109/TEM.2013.2278543
Isobe T, 2008, ASIA PAC J MANAG, V25, P413, DOI 10.1007/s10490-008-9098-z
Jha AK, 2020, DECIS SUPPORT SYST, V138, DOI 10.1016/j.dss.2020.113382
Khan SZ, 2019, CORP SOC RESP ENV MA, V26, P285, DOI 10.1002/csr.1678
Lapkin S, 2015, NURS EDUC TODAY, V35, P935, DOI 10.1016/j.nedt.2015.03.018
Lee H, 2019, TOURISM MANAGE, V70, P284, DOI 10.1016/j.tourman.2018.08.017
Li BY, 2020, CHIN MANAG STUD, V14, P1, DOI 10.1108/CMS-10-2018-0715
Li DY, 2014, J BUS RES, V67, P2793, DOI 10.1016/j.jbusres.2012.08.007
Li XQ, 2016, ASIA PAC J MANAG, V33, P165, DOI 10.1007/s10490-015-9451-y
Lozada N, 2019, HELIYON, V5, DOI 10.1016/j.heliyon.2019.e02541
Ferreras-Méndez JL, 2021, TECHNOVATION, V108, DOI 10.1016/j.technovation.2021.102325
Luthra S, 2022, TRANSPORT RES E-LOG, V157, DOI 10.1016/j.tre.2021.102582
Malhotra NK, 2006, MANAGE SCI, V52, P1865, DOI 10.1287/mnsc.1060.0597
Martin SL, 2019, J BUS RES, V101, P615, DOI 10.1016/j.jbusres.2019.02.041
McComb EJ, 2017, TOUR HOSP RES, V17, P286, DOI 10.1177/1467358415583738
Mention AL, 2011, TECHNOVATION, V31, P44, DOI 10.1016/j.technovation.2010.08.002
Mikalef P, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.05.004
Newell S, 2008, LONG RANGE PLANN, V41, P33, DOI 10.1016/j.lrp.2007.10.005
Nieves J, 2014, TOURISM MANAGE, V40, P224, DOI 10.1016/j.tourman.2013.06.010
NOVAK TP, 1995, J MARKETING RES, V32, P357, DOI 10.2307/3151987
Nunan D, 2017, J BUS ETHICS, V145, P481, DOI 10.1007/s10551-015-2904-x
Obschonka M, 2015, J BUS PSYCHOL, V30, P773, DOI 10.1007/s10869-014-9385-2
Pika A, 2021, BIG DATA RES, V25, DOI 10.1016/j.bdr.2021.100210
Porcu L, 2020, J BUS RES, V119, P435, DOI 10.1016/j.jbusres.2019.10.049
Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879
Rai A, 2006, MIS QUART, V30, P225
Saleem H, 2020, ASIA PAC BUS REV, V26, P537, DOI 10.1080/13602381.2020.1759300
SAS, 2019, 5 BIG DAT CHALL
Schoenherr T, 2015, J BUS LOGIST, V36, P120, DOI 10.1111/jbl.12082
Schwab K., 2017, 4 IND REVOLUTION
Shamim S, 2020, TECHNOL FORECAST SOC, V161, DOI 10.1016/j.techfore.2020.120315
Siemsen E, 2010, ORGAN RES METHODS, V13, P456, DOI 10.1177/1094428109351241
Sobel M.E., 1986, SOCIOL METHODOL, P159, DOI DOI 10.2307/270922
Srinivasan R, 2018, PROD OPER MANAG, V27, P1849, DOI 10.1111/poms.12746
Stanley GM, 2018, J PROCESS CONTR, V67, P141, DOI 10.1016/j.jprocont.2017.05.009
Stewart KJ, 2006, INFORM SYST RES, V17, P126, DOI 10.1287/isre.1060.0082
Sun B, 2021, EUR J INNOV MANAG, V24, P1162, DOI 10.1108/EJIM-01-2020-0004
Sun XB, 2022, CHIN MANAG STUD, V16, P627, DOI 10.1108/CMS-04-2021-0175
Sundarakani B, 2021, OMEGA-INT J MANAGE S, V102, DOI 10.1016/j.omega.2021.102452
Swanson NR, 2018, CAN J ECON, V51, P695, DOI 10.1111/caje.12336
Tatikonda MV, 2000, IEEE T ENG MANAGE, V47, P74, DOI 10.1109/17.820727
Taufique KMR, 2015, PROC ECON FINANC, V37, P39, DOI 10.1016/S2212-5671(16)30090-9
Thakur-Wernz P, 2020, TECHNOVATION, V90-91, DOI 10.1016/j.technovation.2019.102097
Titah R, 2016, INT J PROD ECON, V181, P34, DOI 10.1016/j.ijpe.2016.01.002
TRPC, 2020, ARTIF INTELL
Truffer B, 2001, ENERG POLICY, V29, P885, DOI 10.1016/S0301-4215(01)00020-9
Upadhyay P, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2020.102100
Wamba SF, 2017, J BUS RES, V70, P356, DOI 10.1016/j.jbusres.2016.08.009
Wan WH, 2021, CHIN MANAG STUD, V15, P843, DOI 10.1108/CMS-07-2020-0282
Wang CX, 2020, RES TRANSP BUS MANAG, V37, DOI 10.1016/j.rtbm.2020.100557
Wang MM, 2019, J ENTERP INF MANAG, V32, P325, DOI 10.1108/JEIM-07-2018-0167
Woo C, 2016, J CLEAN PROD, V112, P483, DOI 10.1016/j.jclepro.2015.05.119
Wu LM, 2020, J COASTAL RES, P314, DOI 10.2112/SI106-073.1
Xiao SF, 2021, J BUS RES, V124, P38, DOI 10.1016/j.jbusres.2020.11.048
Xiaohua Yang, 2007, Asia Pacific Journal of Management, V24, P497, DOI 10.1007/s10490-007-9044-5
Xu S, 2019, J COASTAL RES, P177, DOI 10.2112/SI97-025.1
Yu AY, 2021, EUR J OPER RES, V292, P199, DOI 10.1016/j.ejor.2020.10.011
Yu WT, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120417
Yu YB, 2021, J ENTERP INF MANAG, V34, P460, DOI 10.1108/JEIM-03-2020-0101
Zeng J, 2018, STRATEG ORGAN, V16, P105, DOI 10.1177/1476127017697510
NR 98
TC 1
Z9 1
U1 14
U2 30
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 1750-614X
EI 1750-6158
J9 CHIN MANAG STUD
JI Chin. Manag. Stud.
PD JAN 2
PY 2024
VL 18
IS 1
BP 1
EP 23
DI 10.1108/CMS-02-2022-0053
EA DEC 2022
PG 23
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA DP6M0
UT WOS:000892567000001
DA 2024-03-27
ER
PT J
AU Mor, M
Dalyot, S
Ram, Y
AF Mor, Matan
Dalyot, Sagi
Ram, Yael
TI Who is a tourist? Classifying international urban tourists using machine
learning
SO TOURISM MANAGEMENT
LA English
DT Article
DE Urban tourism; Data science; Machine learning; User-generated content;
Flickr; Social media
ID SOCIAL MEDIA; BIG DATA; DESTINATIONS; NETWORKS; LOCATION; PATTERNS;
BEHAVIOR; PHOTOS
AB A key issue in tourism management relates to the lack of consensus regarding a theoretical and practical definition of the term "tourist." In turn, this results in a range of methods for counting tourists and measuring tourism. This paper presents a novel non-linear model for classifying international tourists in urban settings, based on machine learning classification methods. These methods utilize innovative feature engineering derived from photos posted on the Flickr social media platform combined with the specific urban destination street structure. The data science model that we developed for identifying international tourists produced an overall accuracy of 69% for Manhattan and 94% for Vienna and Prague, offering new tourism indicators such as repeat visits, travel distances, and short stays. The outcome of this study offers a better understanding of travel patterns among international tourists, which could improve international tourism management and promote a more practical and adaptable model for measuring and analyzing international tourism using machine learning and user-generated content.
C1 [Mor, Matan; Dalyot, Sagi] Technion, Dept Mapping & Geoinformat, Civil & Environm Engn Fac, Haifa, Israel.
[Ram, Yael] Ashkelon Acad Coll, Dept Tourism Studies, Ashqelon, Israel.
C3 Technion Israel Institute of Technology
RP Ram, Y (autor correspondiente), Ashkelon Acad Coll, Dept Tourism Studies, Ashqelon, Israel.
EM matan.mor@campus.technion.ac.il; dalyot@technion.ac.il;
ramy@edu.aac.ac.il
RI Ram, Yael/IUN-0198-2023
OI Ram, Yael/0000-0003-2704-0056
CR [Anonymous], 2019, International Tourism Highlights 2019, DOI [10.18111/9789284421152?download=true, DOI 10.18111/9789284421152?DOWNLOAD=TRUE]
Barrington-Leigh C, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0180698
Barros C, 2020, CURR ISSUES TOUR, V23, P1291, DOI 10.1080/13683500.2019.1619674
Bettaieb B, 2020, GEOGR REV JPN SER B, V93, P66, DOI 10.4157/geogrevjapanb.93.66
Boeing G., 2020, REGION, V6, P39, DOI [10.18335/region.v6i3.278, DOI 10.18335/REGION.V6I3.278]
Campello Ricardo J. G. B., 2013, Advances in Knowledge Discovery and Data Mining. 17th Pacific-Asia Conference (PAKDD 2013). Proceedings, P160, DOI 10.1007/978-3-642-37456-2_14
García-Palomares JC, 2015, APPL GEOGR, V63, P408, DOI 10.1016/j.apgeog.2015.08.002
Chen W, 2019, ISPRS INT J GEO-INF, V8, DOI 10.3390/ijgi8120556
Chen YY, 2019, IEEE T INTELL TRANSP, V20, P3049, DOI 10.1109/TITS.2018.2871269
Coles T., 2004, Global tourism, P463
Collins-Kreiner N, 2021, ANN TOURISM RES, V89, DOI 10.1016/j.annals.2020.103076
Cooper CHV, 2015, INT J GEOGR INF SCI, V29, P1293, DOI 10.1080/13658816.2015.1018834
Dogru N, 2018, 2018 15TH LEARNING AND TECHNOLOGY CONFERENCE (L&T), P40, DOI 10.1109/LT.2018.8368509
Dunne G, 2010, INT J TOUR RES, V12, P409, DOI 10.1002/jtr.760
Egger R., 2022, Applied Data Science in Tourism, P85, DOI DOI 10.1007/978-3-030-88389-8_6
Encalada L, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9122317
Giglio S, 2019, TOURISM MANAGE, V72, P306, DOI 10.1016/j.tourman.2018.12.007
Gunter U, 2021, J HOSP TOUR RES, V45, P373, DOI 10.1177/1096348020963689
Hall CM, 2005, CONTEMP GEOGR LEIS T, P159
Hall CM., 2017, Tourism, Public Transport and Sustainable Mobility
Hasnat MM, 2018, TRANSPORT RES C-EMER, V96, P38, DOI 10.1016/j.trc.2018.09.006
Salas-Olmedo MH, 2018, TOURISM MANAGE, V66, P13, DOI 10.1016/j.tourman.2017.11.001
Joo D, 2018, TOURISM MANAGE, V64, P245, DOI 10.1016/j.tourman.2017.08.012
Kadar B, 2014, TOURISM GEOGR, V16, P88, DOI 10.1080/14616688.2013.868029
Kitchin R, 2014, BIG DATA SOC, V1, DOI 10.1177/2053951714528481
Li DY, 2018, CITIES, V74, P249, DOI 10.1016/j.cities.2017.12.012
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
Liu X, 2019, INT CONF CYBER DIST, P166, DOI 10.1109/CyberC.2019.00038
Ma S, 2020, TOURISM MANAGE, V77, DOI 10.1016/j.tourman.2019.104021
Mancini F, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0200565
McCabe S., 2005, Tourist Studies, V5, P85, DOI 10.1177/1468797605062716
McKercher B., 2003, Journal of Travel Research, V42, P159, DOI 10.1177/0047287503254812
Miah SJ, 2017, INFORM MANAGE-AMSTER, V54, P771, DOI 10.1016/j.im.2016.11.011
Mor M., 2020, AGILE GISCIENCE SERI, V1, P1, DOI [10.5194/agile-giss-1-16-2020, DOI 10.5194/AGILE-GISS-1-16-2020]
Mor M., 2020, ISPRS ANN PHOTOGRAMM, V5
Önder I, 2017, TOUR MANAG PERSPECT, V21, P54, DOI 10.1016/j.tmp.2016.11.002
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Peng X, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/ijgi6070216
Popescu A., 2010, P INT AAAI C WEB SOC, V4, P1
Preis T, 2020, ENVIRON PLANN A, V52, P471, DOI 10.1177/0308518X19872772
Ramos-Henríquez JM, 2021, TOURISM MANAGE, V84, DOI 10.1016/j.tourman.2021.104288
ROGERS E. M., 1971, COMMUNICATION INNOVA, DOI DOI 10.2307/2800105
Sánchez EC, 2020, TOUR MANAG PERSPECT, V35, DOI 10.1016/j.tmp.2020.100718
Shapoval V, 2018, J TRAVEL RES, V57, P310, DOI 10.1177/0047287517696960
Sharma MSP, 2022, ACM T KNOWL DISCOV D, V16, DOI 10.1145/3494566
Shih HY, 2006, TOURISM MANAGE, V27, P1029, DOI 10.1016/j.tourman.2005.08.002
Shoval N, 2011, ANN TOURISM RES, V38, P1594, DOI 10.1016/j.annals.2011.02.007
Su SL, 2016, APPL GEOGR, V73, P26, DOI 10.1016/j.apgeog.2016.06.001
Sun SL, 2019, TOURISM MANAGE, V70, P1, DOI 10.1016/j.tourman.2018.07.010
Taecharungroj V, 2019, TOURISM MANAGE, V75, P550, DOI 10.1016/j.tourman.2019.06.020
UNWTO, 2019, METHODOLOGICAL NOTES, DOI [10.18111/9789284420476, DOI 10.18111/9789284420476]
Valls F, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13063159
Vanicek J., 2019, CZECH J TOURISM, V8, P33
VisitBritain, 2017, FORSIGHTS, P149
Vu HQ, 2018, J TRAVEL RES, V57, P399, DOI 10.1177/0047287517692446
Wang J, 2018, CHAOS SOLITON FRACT, V108, P136, DOI 10.1016/j.chaos.2018.01.028
Xiao X, 2020, ISPRS INT J GEO-INF, V9, DOI 10.3390/ijgi9120730
Yang L, 2017, ISPRS INT J GEO-INF, V6, DOI 10.3390/ijgi6110345
Yao Q, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13010094
Zhang K, 2020, ASIA PAC J TOUR RES, V25, P1199, DOI 10.1080/10941665.2020.1838586
Zhang K, 2020, J QUAL ASSUR HOSP TO, V21, P234, DOI 10.1080/1528008X.2019.1653243
Zhang K, 2019, TOURISM MANAGE, V75, P595, DOI 10.1016/j.tourman.2019.07.002
Zhou LN, 2017, NEUROCOMPUTING, V237, P350, DOI 10.1016/j.neucom.2017.01.026
NR 63
TC 5
Z9 5
U1 15
U2 62
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0261-5177
EI 1879-3193
J9 TOURISM MANAGE
JI Tourism Manage.
PD APR
PY 2023
VL 95
AR 104689
DI 10.1016/j.tourman.2022.104689
EA NOV 2022
PG 14
WC Environmental Studies; Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Social Sciences - Other Topics;
Business & Economics
GA 8S0KI
UT WOS:000928277100001
DA 2024-03-27
ER
PT J
AU Melumad, S
Inman, JJ
Pham, MT
AF Melumad, Shiri
Inman, J. Jeffrey
Pham, Michel Tuan
TI Selectively Emotional: How Smartphone Use Changes User-Generated Content
SO JOURNAL OF MARKETING RESEARCH
LA English
DT Article
DE affect; emotion; mobile marketing; natural language processing; social
media; word of mouth
ID WORD-OF-MOUTH; FUZZY-TRACE THEORY; FEELINGS; COMMUNICATION; RELEVANCE;
GIST
AB User-generated content has become ubiquitous and very influential in the marketplace. Increasingly, this content is generated on smartphones rather than personal computers (PCs). This article argues that because of its physically constrained nature, smartphone (vs. PC) use leads consumers to generate briefer content, which encourages them to focus on the overall gist of their experiences. This focus on gist, in turn, tends to manifest as reviews that emphasize the emotional aspects of an experience in lieu of more specific details. Across five studies-two field studies and three controlled experiments-the authors use natural language processing tools and human assessments to analyze the linguistic characteristics of user-generated content. The findings support the thesis that smartphone use results in the creation of content that is less specific and privileges affect-especially positive affect-relative to PC-generated content. The findings also show that differences in emotional content are driven by the tendency to generate briefer content on smartphones rather than user self-selection, differences in topical content, or timing of writing. Implications for research and practice are discussed.
C1 [Melumad, Shiri] Univ Penn, Wharton Sch, Mkt, Philadelphia, PA 19104 USA.
[Inman, J. Jeffrey] Univ Pittsburgh, Katz Grad Sch Business, Mkt, Pittsburgh, PA 15260 USA.
[Pham, Michel Tuan] Columbia Univ, Grad Sch Business, Business, New York, NY 10027 USA.
C3 University of Pennsylvania; Pennsylvania Commonwealth System of Higher
Education (PCSHE); University of Pittsburgh; Columbia University
RP Melumad, S (autor correspondiente), Univ Penn, Wharton Sch, Mkt, Philadelphia, PA 19104 USA.
EM melumad@wharton.upenn.edu; jinman@katz.pitt.edu; tdp4@columbia.edu
RI Pham, Michel Tuan/AAE-5140-2020
CR Arbuckle James L, 2014, AMOS VERSION 23 0 CO
Berger J, 2014, J CONSUM PSYCHOL, V24, P586, DOI 10.1016/j.jcps.2014.05.002
Berger J, 2013, J CONSUM RES, V40, P567, DOI 10.1086/671345
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Bianchi A, 2005, CYBERPSYCHOL BEHAV, V8, P39, DOI 10.1089/cpb.2005.8.39
BRAINERD CJ, 1990, DEV REV, V10, P3, DOI 10.1016/0273-2297(90)90003-M
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chung CMY, 2006, MARKET LETT, V17, P269, DOI 10.1007/s11002-006-8426-7
Deloitte, 2016, COUCH UND CONS SHOPP
Dimitriu R, 2013, PROC INT C ACC, P127
Dodds PS, 2010, J HAPPINESS STUD, V11, P441, DOI 10.1007/s10902-009-9150-9
Dubois D, 2016, J MARKETING RES, V53, P712, DOI 10.1509/jmr.13.0312
East R, 2007, INT J RES MARK, V24, P175, DOI 10.1016/j.ijresmar.2006.12.004
Ghose A, 2013, INFORM SYST RES, V24, P613, DOI 10.1287/isre.1120.0453
Ghose A, 2011, MANAGE SCI, V57, P1671, DOI 10.1287/mnsc.1110.1350
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
Godes D, 2004, MARKET SCI, V23, P545, DOI 10.1287/mksc.1040.0071
Godes D, 2009, MARKET SCI, V28, P721, DOI 10.1287/mksc.1080.0444
Lau-Gesk L, 2009, J CONSUM RES, V36, P585, DOI 10.1086/605297
Ludwig S, 2013, J MARKETING, V77, P87, DOI 10.1509/jm.11.0560
Luminet O, 2000, COGNITION EMOTION, V14, P661, DOI 10.1080/02699930050117666
Metcalfe J, 1999, PSYCHOL REV, V106, P3, DOI 10.1037/0033-295X.106.1.3
Oliva Aude, 2005, P251, DOI 10.1016/B978-012375731-9/50045-8
OSGOOD CE, 1962, AM PSYCHOL, V17, P10, DOI 10.1037/h0045146
PAULHUS DL, 1994, EUR J SOC PSYCHOL, V24, P89, DOI 10.1002/ejsp.2420240107
Pennebaker James W, 2015, LIWC 2015 LINGUISTIC
Pham MT, 2015, ORGAN BEHAV HUM DEC, V131, P81, DOI 10.1016/j.obhdp.2015.08.003
Pham MT, 1998, J CONSUM RES, V25, P144, DOI 10.1086/209532
Pham MT, 2001, J CONSUM RES, V28, P167, DOI 10.1086/322896
Pieters R, 2012, MARKET SCI, V31, P59, DOI 10.1287/mksc.1110.0673
Preacher KJ, 2004, BEHAV RES METH INS C, V36, P717, DOI 10.3758/BF03206553
Raghunathan R, 1999, ORGAN BEHAV HUM DEC, V79, P56, DOI 10.1006/obhd.1999.2838
Reyna VF, 2012, JUDGM DECIS MAK, V7, P332
Rivers SE, 2008, DEV REV, V28, P107, DOI 10.1016/j.dr.2007.11.002
Schellekens GAC, 2010, J CONSUM RES, V37, P207, DOI 10.1086/651240
Shiri Melumad, 2018, WORKING PAPER
Slatcher RB, 2006, PSYCHOL SCI, V17, P660, DOI 10.1111/j.1467-9280.2006.01762.x
Snehasish Banerjee, 2014, P 8 INT C UB INF MAN
Stephen AT, 2008, PSYCHOL SCI, V19, P1051, DOI 10.1111/j.1467-9280.2008.02198.x
Sue Harding, 2007, INT WORKSH ATT COGN
Sundaram DS, 1998, ADV CONSUM RES, V25, P527
ZAJONC RB, 1980, AM PSYCHOL, V35, P151, DOI 10.1037/0003-066X.35.2.151
NR 42
TC 84
Z9 98
U1 16
U2 176
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2437
EI 1547-7193
J9 J MARKETING RES
JI J. Mark. Res.
PD APR
PY 2019
VL 56
IS 2
BP 259
EP 275
DI 10.1177/0022243718815429
PG 17
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HP6CS
UT WOS:000461770900006
DA 2024-03-27
ER
PT J
AU Ghosh, SK
Dey, S
Ghosh, A
AF Ghosh, Swarup Kr
Dey, Sowvik
Ghosh, Anupam
TI Knowledge Generation Using Sentiment Classification Involving Machine
Learning on E-Commerce
SO INTERNATIONAL JOURNAL OF BUSINESS ANALYTICS
LA English
DT Article
DE Concept Link Graph; Deep Neural Network; Maximum Entropy; Naive Bayes;
Sentiment Classification; Text Mining
AB Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have been demonstrated by appropriate features from raw unstructured data followed by data preprocessing steps. Applying best in class social analytics methodology to examine the sentiments embedded with purchaser remarks, encourages both producer and individual customers. Machine learning methods such as Naive Bayes, maximum entropy classification, Deep Neural Networks were used upon the data, extracted from some websites such as Samsung and Apple for sentiment classification. In the online business arena, the application of sentiment classification explores a great opportunity. The subsidy of such an investigation is that associations can apply the proposed social examination framework to exploit the entire social information on the web and therefore improve their proper blueprint promoting strategies corresponding business.
C1 [Ghosh, Swarup Kr] Brainware Univ, Kolkata, India.
[Dey, Sowvik] Brainware Univ, Dept Comp Sci & Engn, Kolkata, India.
[Ghosh, Anupam] Netaji Subhash Engn Coll, Dept Comp Sci & Engn, Kolkata, India.
C3 Netaji Subhash Engineering College Kolkata
RP Ghosh, SK (autor correspondiente), Brainware Univ, Kolkata, India.
OI Ghosh, Dr. Swarup Kr/0000-0002-9312-4189; Ghosh,
Anupam/0000-0003-2166-3957
CR Abbasi A, 2011, IEEE T KNOWL DATA EN, V23, P447, DOI 10.1109/TKDE.2010.110
Acharjya D. P., 2008, International Journal of Artificial Intelligence and Soft Computing, V1, P1, DOI 10.1504/IJAISC.2008.021260
Acharjya DP., 2009, INT J ARTIF INTELL C, V1, P29
Argamon-Engelson Shlomo., 1998, AAAI/ML Workshop on Text Categorization. Wisconsin: AAAI-98, P1
Banea C., 2011, Multilingual Natural Language Processing, V6, P1
Berger AL, 1996, COMPUT LINGUIST, V22, P39
Das T. K., 2014, International Journal of Intelligent Systems Technologies and Applications, V13, P170, DOI 10.1504/IJISTA.2014.065172
Dave K., 2003, P 12 INT C WORLD WID, DOI [DOI 10.1145/775152.775226, 10.1145/775152.775226]
Deng ZH, 2014, EXPERT SYST APPL, V41, P3506, DOI 10.1016/j.eswa.2013.10.056
GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721, DOI 10.1109/TPAMI.1984.4767596
Ghosh SK, 2018, INT J IMAGE GRAPH, V18, DOI 10.1142/S0219467818500080
Go A., 2009, TECH REP, V150, P1, DOI DOI 10.1016/J.SEDGEO.2006.07.004
Godbole Namrata, 2007, P INT C WEBLOGS SOCI, V7/21, P219
Haddi E, 2013, PROCEDIA COMPUT SCI, V17, P26, DOI 10.1016/j.procs.2013.05.005
Liu B., 2005, P 14 INT C WORLD WID, P342, DOI DOI 10.1145/1060745.1060797
Liu WB, 2017, NEUROCOMPUTING, V234, P11, DOI 10.1016/j.neucom.2016.12.038
Ortigosa A, 2014, COMPUT HUM BEHAV, V31, P527, DOI 10.1016/j.chb.2013.05.024
Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79, DOI 10.3115/1118693.1118704
Rathi R, 2018, ARAB J SCI ENG, V43, P4215, DOI 10.1007/s13369-017-2838-y
Rathi R., 2018, International Journal of Fuzzy Systems Applications, V7, P74, DOI 10.4018/IJFSA.2018010106
Raychaudhuri S, 2002, GENOME RES, V12, P203, DOI 10.1101/gr.199701
Raymond Y K L, 2014, DECIS SUPPORT SYST, V65, P80
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Tan LKW, 2011, LECT NOTES COMPUT SC, V7008, P77, DOI 10.1007/978-3-642-24826-9_13
Thelwall M, 2011, J AM SOC INF SCI TEC, V62, P406, DOI 10.1002/asi.21462
Tripathy B. K., 2010, INT J ADV SCI TECHN, V1, P41
Zhang L., 2017, 2016 12 INT C COMP I
NR 27
TC 0
Z9 0
U1 0
U2 12
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 2334-4547
EI 2334-4555
J9 INT J BUS ANAL
JI Int. J. Bus. Anal.
PD APR-JUN
PY 2019
VL 6
IS 2
SI SI
BP 74
EP 90
DI 10.4018/IJBAN.2019040104
PG 17
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA KI4NY
UT WOS:000511328300005
DA 2024-03-27
ER
PT J
AU Sarkar, M
De Bruyn, A
AF Sarkar, Mainak
De Bruyn, Arnaud
TI LSTM Response Models for Direct Marketing Analytics: Replacing Feature
Engineering with Deep Learning
SO JOURNAL OF INTERACTIVE MARKETING
LA English
DT Article
DE Long-short term memory neural network (LSTM); Recurrent neural network
(RNN); Feature engineering; Response model; Panel data; Direct marketing
ID NEURAL-NETWORKS; SELECTION; BEHAVIOR; PREDICTION; DECISIONS
AB In predictive modeling, firms often deal with high-dimensional data that span multiple channels, websites, demographics, purchase types, and product categories. Traditional customer response models rely heavily on feature engineering, and their performance depends on the analyst's domain knowledge and expertise to craft relevant predictors. As the complexity of data increases, however, traditional models grow exponentially complicated. In this paper, we demonstrate that long-short term memory (LSTM) neural networks, which rely exclusively on raw data as input, can predict customer behaviors with great accuracy. In our first application, a model outperforms standard benchmarks. In a second, more realistic application, an LSTM model competes against 271 hand-crafted models that use a wide variety of features and modeling approaches. It beats 269 of them, most by a wide margin. LSTM neural networks are excellent candidates for modeling customer behavior using panel data in complex environments (e.g., direct marketing, brand choices, clickstream data, churn prediction). (C) 2020 Direct Marketing Educational Foundation, Inc. dba Marketing EDGE. All rights reserved.
C1 [Sarkar, Mainak; De Bruyn, Arnaud] ESSEC Business Sch, Ave Bernard Hirsch, F-95000 Cergy, France.
C3 ESSEC Business School
RP Sarkar, M (autor correspondiente), ESSEC Business Sch, Ave Bernard Hirsch, F-95000 Cergy, France.
EM mainak.sarkar@essec.edu; debruyn@essec.edu
CR Agrawal D, 1996, J RETAILING, V72, P383, DOI 10.1016/S0022-4359(96)90020-2
[Anonymous], 1997, Neural Comput., DOI 10.1162/neco.1997.9.8.1735
[Anonymous], 2013, APPL PREDICTIVE MODE
[Anonymous], MACH LEARN
Ascarza E, 2016, J MARKETING RES, V53, P46, DOI 10.1509/jmr.13.0483
Bahdanau D, 2016, Arxiv, DOI [arXiv:1409.0473, DOI 10.48550/ARXIV.1409.0473]
BASU AK, 1995, J MARKETING RES, V32, P204, DOI 10.2307/3152048
BENGIO Y, 1994, IEEE T NEURAL NETWOR, V5, P157, DOI 10.1109/72.279181
Bengio Yoshua, 2012, Neural Networks: Tricks of the Trade. Second Edition: LNCS 7700, P437, DOI 10.1007/978-3-642-35289-8_26
Bitran GR, 1996, MANAGE SCI, V42, P1364, DOI 10.1287/mnsc.42.9.1364
Bjorck N., 2018, ADV NEUR IN, V31, P7705, DOI DOI 10.48550/ARXIV.1806.02375
Blattberg RC., 2008, International Series in Quantitative Marketing
Bult JR, 1995, MARKET SCI, V14, P378, DOI 10.1287/mksc.14.4.378
Cho K., 2014, ARXIV14061078, P1724, DOI [DOI 10.3115/V1/D14-1179,1724-1734, DOI 10.3115/V1/D14-1179]
Chung J., 2014, ARXIV
Colombo R, 1999, J INTERACT MARK, V13, P2, DOI 10.1002/(SICI)1520-6653(199922)13:3<2::AID-DIR1>3.0.CO;2-H
Coussement K, 2013, J BUS RES, V66, P1629, DOI 10.1016/j.jbusres.2012.12.008
De Bruyn A, 2020, J INTERACT MARK, V51, P91, DOI 10.1016/j.intmar.2020.04.007
Donkers B, 2006, J APPL ECONOMET, V21, P549, DOI 10.1002/jae.858
Elsner R, 2004, MARKET SCI, V23, P192, DOI 10.1287/mksc.1040.0063
Fader PS, 2005, J MARKETING RES, V42, P415, DOI 10.1509/jmkr.2005.42.4.415
Friedman J., 2009, R PACKAGE VERSION
Gal Y, 2016, PR MACH LEARN RES, V48
George M, 2013, J RETAILING, V89, P374, DOI 10.1016/j.jretai.2013.05.001
Gers FA, 1999, IEE CONF PUBL, P850, DOI [10.1162/089976600300015015, 10.1049/cp:19991218]
Gonul F, 1998, MANAGE SCI, V44, P1249, DOI 10.1287/mnsc.44.9.1249
Gonul F., 2000, J INTERACT MARK, V14, P2, DOI 10.1016/0014-4886(73)90290-2
Gönül FF, 2006, MARKET SCI, V25, P65, DOI 10.1287/mksc.1050.0136
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Graves A., 2014, ARXIV14105401, DOI DOI 10.3389/NEUR0.12.006.2007
Greff K, 2017, IEEE T NEUR NET LEAR, V28, P2222, DOI 10.1109/TNNLS.2016.2582924
Guadagni PM, 2008, MARKET SCI, V27, P29, DOI 10.1287/mksc.1070.0331
Hinton G, 2013, ADV MACHINE LEARNING
Hinton G. E., 2001, PRODUCTS HIDDEN MARK
Kingma D. P., 2014, ARXIV
Kuhn M., 2019, Feature Engineering and Selection: a Practical Approach for Predictive Models
Lemmens A, 2006, J MARKETING RES, V43, P276, DOI 10.1509/jmkr.43.2.276
Lilien GL., 2013, Principles of Marketing Engineering and Analytics, V3rd
Ling C. X., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining, P73
Liu H., 2018, FEATURE ENG MACHINE
Luong M-T., 2015, ARXIV
Malthouse EC, 1999, J INTERACT MARK, V13, P10, DOI 10.1002/(SICI)1520-6653(199923)13:4<10::AID-DIR2>3.3.CO;2-V
Martínez A, 2020, EUR J OPER RES, V281, P588, DOI 10.1016/j.ejor.2018.04.034
Mayumi Oshiro Thais, 2012, Machine Learning and Data Mining in Pattern Recognition. Proceedings 8th International Conference, MLDM 2012, P154, DOI 10.1007/978-3-642-31537-4_13
Michelucci U., 2018, Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
Ming Y, 2017, IEEE CONF VIS ANAL, P13, DOI 10.1109/VAST.2017.8585721
Moe WW, 2004, J INTERACT MARK, V18, P5, DOI 10.1002/dir.10074
Moe WW, 2004, MANAGE SCI, V50, P326, DOI 10.1287/mnsc.1040.0153
Neslin SA, 2006, J MARKETING RES, V43, P204, DOI 10.1509/jmkr.43.2.204
Netzer O, 2008, MARKET SCI, V27, P185, DOI 10.1287/mksc.1070.0294
Olah C., 2015, UNDERSTANDING LSTM N
Park YH, 2004, MARKET SCI, V23, P280, DOI 10.1287/mksc.1040.0050
Pascanu R, 2013, P MACHINE LEARNING R, V28, P1310
Pointer I., 2019, Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
Roberts M.L., 1999, DIRECT MARKETING MAN, V2nd
Rudin C, 2019, NAT MACH INTELL, V1, P206, DOI 10.1038/s42256-019-0048-x
RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0
Saleh H., 2018, Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning
Schweidel DA, 2013, MARKET SCI, V32, P471, DOI 10.1287/mksc.2013.0781
Shahriari B, 2016, P IEEE, V104, P148, DOI 10.1109/JPROC.2015.2494218
Siami-Namini S, 2019, IEEE INT CONF BIG DA, P3285, DOI 10.1109/BigData47090.2019.9005997
Simester DI, 2006, MANAGE SCI, V52, P683, DOI 10.1287/mnsc.1050.0504
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Tieleman T., 2012, COURSERA NEURAL NETW, V4, P26
van Diepen M, 2009, J MARKETING RES, V46, P120, DOI 10.1509/jmkr.46.1.120
Vaswani A, 2017, ADV NEUR IN, V30
Zheng A., 2018, FEATURE ENG MACHINE
Zou H, 2005, J R STAT SOC B, V67, P301, DOI 10.1111/j.1467-9868.2005.00503.x
NR 68
TC 23
Z9 26
U1 4
U2 38
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1094-9968
EI 1520-6653
J9 J INTERACT MARK
JI J. Interact. Mark.
PD FEB
PY 2021
VL 53
BP 80
EP 95
DI 10.1016/j.intmar.2020.07.002
EA JAN 2022
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA QC5UF
UT WOS:000614900100006
OA hybrid, Green Published
DA 2024-03-27
ER
PT J
AU Lacárcel, FJS
Huete, R
Zerva, K
AF Lacarcel, Francisco Javier S.
Huete, Raquel
Zerva, Konstantina
TI Decoding digital nomad destination decisions through user-generated
content
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Digital nomad; Destination choice; UGC; Natural language processing;
Sentiment analysis; Knowmads
ID SENTIMENT ANALYSIS; WORK; TWITTER; BEHAVIOR
AB Digital nomads are engaged in a complex quest to select their next destination. In this context, user-generated content (UGC) on social media is a pivotal source to glean insights into digital nomads' destination choices. Accordingly, this study investigates the principal topics that influence digital nomad's destination choice. To this end, data-mining techniques are applied to analyze user-generated content (UGC) from the social platform X (former Twitter). Based on the results, we identify a total of 11 topics associated with digital nomads' location preferences that can be grouped into 3 clusters (positive, negative, and neutral). Specifically, we find six positive topics (employment, retirement, gastronomy, co-working, work motivation, culture), one neutral topic (customer service), and four negative topics (connectivity, work hours, visa issues, loneliness). The results suggest that job flexibility, the attraction of travel, and cultural immersion emerge as positive factors influencing destination choice. By contrast, connectivity concerns, visa management, feelings of isolation, and emotional adjustments stand out as considerable impediments for digital nomads. We spotlight the long-term pursuit of quality of life and technological connectivity as the main drivers of digital nomads in their destination choice. The paper concludes with a formulation of 33 research questions related to digital nomad destination decisions to be addressed in further research.
C1 [Lacarcel, Francisco Javier S.] Univ Alicante, Univ Inst Tourism Res, Alicante, Spain.
[Huete, Raquel] Univ Alicante, Dept Sociol 1, Alicante, Spain.
[Zerva, Konstantina] Univ Girona, Girona, Spain.
C3 Universitat d'Alacant; Universitat d'Alacant; Universitat de Girona
RP Lacárcel, FJS (autor correspondiente), Univ Alicante, Univ Inst Tourism Res, Alicante, Spain.
EM francisco@jlacarcel.net; r.huete@ua.es; konstantina.zerva@udg.edu
RI Zerva, Konstantina/I-4774-2018
FU Ministry of Science, Innovation and Universities (Spain)
[PID2020-117459RB-C21]
FX This work was supported by PID2020-117459RB-C21, "The relationship
between lifestyle migration and destinations' tourism dynamics".
CICONIA, Ministry of Science, Innovation and Universities (Spain) .
CR Andino-Frydman A., 2023, Ethnographies of Work, V35, P95, DOI [10.1108/S0277-283320230000035005, DOI 10.1108/S0277-283320230000035005]
Ata S, 2022, ESIC MARK, V53, DOI 10.7200/esicm.53.280
Bhatia A, 2022, J STAT MANAG SYST, V25, P1871, DOI 10.1080/09720510.2021.1995196
Bhavsar H., 2019, Int. Res. J. Eng. Technol. (IRJET), V6, P510
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Borges-Tiago MT, 2019, J BUS RES, V101, P574, DOI 10.1016/j.jbusres.2018.11.011
Bürgel TR, 2023, TECHNOL FORECAST SOC, V186, DOI 10.1016/j.techfore.2022.122135
Burgess S., 2009, ECIS, P417
Burrows John., 2004, COMPANION DIGITAL HU, P323, DOI DOI 10.1002/9780470999875.CH23
Castilla-Polo A, 2023, CURR ISSUES TOUR, V26, P358, DOI 10.1080/13683500.2022.2084717
Chamboko-Mpotaringa M., 2021, Afr. J. Hosp. Tour. Leis., V10, P712, DOI [10.46222/ajhtl.19770720-128, DOI 10.46222/AJHTL.19770720-128]
Cheregi B.F., 2018, Manag. Dyn. Knowl. Econ., V6, P385, DOI [10.25019/MDKE/6.2.06, DOI 10.25019/MDKE/6.2.06]
Chevtaeva E, 2021, J DESTIN MARK MANAGE, V21, DOI 10.1016/j.jdmm.2021.100633
Cobo-Gomez J.C., 2023, Sustain. Technol. Entrep., DOI [10.1016/j.stae.2023.100061, DOI 10.1016/J.STAE.2023.100061]
Cocking B., 2020, Travel J. Travel Media Identities Places Imaginings, P53, DOI [10.1057/978-1-137-59908-7, DOI 10.1057/978-1-137-59908-7]
Cohen SA, 2015, MOBILITIES-UK, V10, P155, DOI 10.1080/17450101.2013.826481
Cook D, 2023, WORLD LEISURE J, V65, P256, DOI 10.1080/16078055.2023.2190608
Cook D, 2020, INF TECHNOL TOUR, V22, P355, DOI 10.1007/s40558-020-00172-4
Cox C, 2009, J HOSP MARKET MANAG, V18, P743, DOI 10.1080/19368620903235753
D'Andrea A, 2006, GLOBAL NOMADS, P1
Dake DK, 2023, EDUC INF TECHNOL, V28, P4629, DOI 10.1007/s10639-022-11349-1
de Almeida MA, 2021, INT C COMP SUPP COOP, P1209, DOI 10.1109/CSCWD49262.2021.9437685
De Ascaniis S, 2011, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2011, P125
Dhaoui C, 2017, J CONSUM MARK, V34, P480, DOI 10.1108/JCM-03-2017-2141
Dylko I, 2012, COMMUN THEOR, V22, DOI 10.1111/j.1468-2885.2012.01409.x
Ehn K, 2022, SOC MEDIA SOC, V8, DOI 10.1177/20563051221084958
Fairclough N., 2003, Analysing discourse: Textual analysis for social research
Felstead A, 2017, NEW TECH WORK EMPLOY, V32, P195, DOI 10.1111/ntwe.12097
Gabrielatos C., 2017, Corpus Linguistics in the South
Girolami M, 2005, DATA MIN KNOWL DISC, V10, P175, DOI 10.1007/s10618-005-0362-2
Gretzel U., 2018, Advances in social media for travel, tourism and hospitality: New perspectives, practice and cases, P147, DOI DOI 10.4324/9781315565736
Gretzel U., 2019, e-Rev. Tour. Res., V16
Guizzo D., 2019, Contrib. Polit. Econ., V38, P12, DOI [10.1093/cpe/bzz014, DOI 10.1093/CPE/BZZ014]
Hall G, 2019, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2019, P437, DOI 10.1007/978-3-030-05940-8_34
Hasan A, 2018, MATH COMPUT APPL, V23, DOI 10.3390/mca23010011
Hensellek S., 2021, The flexible workplace: Coworking and other modern workplace transformations, P195, DOI [https://doi.org/10.1007/978-3-030-62167-4_11, DOI 10.1007/978-3-030-62167-4_11]
Hiremath BN, 2022, J KING SAUD UNIV-COM, V34, P2840, DOI 10.1016/j.jksuci.2020.03.006
Hirschberg J, 2015, SCIENCE, V349, P261, DOI 10.1126/science.aaa8685
Hoffman M., 2010, ADV NEURAL INFORM PR, V23, P856
Holleran M., 2022, City, V26, P831, DOI [10.1080/13604813.2022.2124713, DOI 10.1080/13604813.2022.2124713]
Horrigan D., 2009, Branded Content: A New Model for Driving Tourism Via Film and Branding Strategies
Hung LP, 2023, J ADV COMPUT INTELL, V27, P84, DOI 10.20965/jaciii.2023.p0084
Jacobson J, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121226
Jelodar H, 2019, MULTIMED TOOLS APPL, V78, P15169, DOI 10.1007/s11042-018-6894-4
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
Kaur G., 2017, International Journal of Research - Granthaalayah, V5, P72, DOI DOI 10.29121/GRANTHAALAYAH.V5.I6.2017.1998
Keum H., 2019, J. Travel Res., V58, P864, DOI [10.1007/s40558-020-00173-3, DOI 10.1007/S40558-020-00173-3]
Khan K, 2023, ESIC MARK, V54, DOI 10.7200/esicm.54.291
Kim H, 2018, SOC MEDIA SOC, V4, P1, DOI 10.1177/2056305118772836
Klein AZ, 2021, J MED INTERNET RES, V23, DOI 10.2196/25314
Kong D., 2019, P 27 EUR C INF SYST
Kraus S, 2023, TECHNOL FORECAST SOC, V189, DOI 10.1016/j.techfore.2023.122381
Krippendorff K., 2018, Content analysis: An introduction to its methodology
Kushwaha B. P., 2020, PalArch's Journal of Archaeology of Egypt/Egyptology, V17, P2029
Lehdonvirta V, 2021, POLICY INTERNET, V13, P134, DOI 10.1002/poi3.238
Leon N, 2009, COMPUT IND, V60, P539, DOI 10.1016/j.compind.2009.05.010
Li G, 2014, APPL INTELL, V40, P441, DOI 10.1007/s10489-013-0463-3
Li Nao, 2023, Tourism Tribune, V38, P122, DOI 10.19765/j.cnki.1002-5006.2022.00.026
Li X., 2018, J. Travel Tour. Mark., V35, P1031, DOI [10.26623/topicssenger.v13i3.2328, DOI 10.26623/TOPICSSENGER.V13I3.2328]
Li X, 2021, TOURISM MANAGE, V83, DOI 10.1016/j.tourman.2020.104245
Linvill DL, 2012, PUBLIC RELAT REV, V38, P636, DOI 10.1016/j.pubrev.2012.05.010
Loria S., 2013, ISO 690
Loughran T, 2016, J ACCOUNT RES, V54, P1187, DOI 10.1111/1475-679X.12123
Magano J., 2020, African Journal of Hospitality, Tourism and Leisure, V9, P1
Makimoto TsugioDavid Manners., 1997, DIGITAL NOMAD
Manap K.A., 2013, The 2013 WEI international academic conference proceeding, P52
Mancinelli F, 2020, INF TECHNOL TOUR, V22, P417, DOI 10.1007/s40558-020-00174-2
Manuaba Ida Bagus Kerthyayana, 2023, Procedia Computer Science, P1101, DOI 10.1016/j.procs.2023.10.621
Mariani MM, 2023, TECHNOL FORECAST SOC, V189, DOI 10.1016/j.techfore.2022.122296
Mathew V, 2021, J CONSUM BEHAV, V20, DOI 10.1002/cb.1854
Meyrowitz J, 2008, J COMMUN, V58, P641, DOI 10.1111/j.1460-2466.2008.00406.x
Moravec JW, 2013, HORIZON, V21, P79, DOI 10.1108/10748121311322978
Mouratidis G., 2018, Digital Nomads: Travel, Remote Work and Alternative Lifestyles, DOI [10.1016/j.jdmm.2021.100633, DOI 10.1016/J.JDMM.2021.100633]
Munková D, 2014, ADV INTELL SYST, V231, P67, DOI 10.1007/978-3-319-01466-1_6
Nash C, 2018, LECT NOTES COMPUT SC, V10766, P207, DOI 10.1007/978-3-319-78105-1_25
Nash C, 2021, HUM BEHAV EMERG TECH, V3, P271, DOI 10.1002/hbe2.234
Nayal P., 2022, J. Promot. Manag., V28, P205, DOI [10.1080/10496491.2021.1989541, DOI 10.1080/10496491.2021.1989541]
Nyagadza B., 2023, Sustain. Technol. Entrep., V2, DOI [10.1016/j.stae.2023.100040, DOI 10.1016/J.STAE.2023.100040]
Ogunrinde A, 2022, Esic market, V53, P3, DOI [10.7200/esicm.53.286, DOI 10.7200/ESICM.53.286]
Olga H, 2020, INF TECHNOL TOUR, V22, P335, DOI 10.1007/s40558-020-00177-z
Orel M, 2021, INT J CULT TOUR HOSP, V15, P3, DOI 10.1108/IJCTHR-12-2019-0229
Ozcan S, 2021, TECHNOVATION, V107, DOI 10.1016/j.technovation.2021.102322
Pacheco C, 2023, WORLD LEISURE J, V65, P192, DOI 10.1080/16078055.2022.2156593
Palguna D.S., 2015, ISO 690
Patel Aksh, 2023, Procedia Computer Science, P2459, DOI 10.1016/j.procs.2023.01.221
Pojanapunya P, 2018, CORPUS LINGUIST LING, V14, P133, DOI 10.1515/cllt-2015-0030
Raheja S, 2023, INT J SOFTW INNOV, V11, P26, DOI 10.4018/IJSI.315740
Saura JR, 2024, ANN OPER RES, V333, P849, DOI 10.1007/s10479-022-04776-3
Saura JR, 2023, TECHNOVATION, V119, DOI 10.1016/j.technovation.2021.102447
Rayson P., 2000, P WORKSHOP COMPARING, V9, P1, DOI [DOI 10.3115/1117729.1117730, 10.3115/1117729.1117730, 10.3329/akmmcj.v8i1.31665]
Resnik Philip, 2015, P 2 WORKSH COMP LING, P99, DOI [10.3115/v1/W15-1212, DOI 10.3115/V1/W15-1212]
Ribeiro-Navarrete S, 2021, TECHNOL FORECAST SOC, V167, DOI 10.1016/j.techfore.2021.120681
Rukavina A, 2014, IEEE CONF VIS ANAL, P383
Sann Raksmey, 2023, Consumer Behavior in Tourism and Hospitality, V18, P17, DOI 10.1108/CBTH-04-2022-0084
Saura JR, 2022, J BUS RES, V142, P242, DOI 10.1016/j.jbusres.2021.12.052
Saura JR, 2021, J BUS RES, V129, P271, DOI 10.1016/j.jbusres.2021.03.001
Schlagwein D., 2018, Justifications in Digital Nomadism
Shawkat S., 2021, 2021 7 INT C RES INN, P1, DOI [10.1109/ICRIIS53035.2021.9617008, DOI 10.1109/ICRIIS53035.2021.9617008]
Short JC, 2018, ANNU REV ORGAN PSYCH, V5, P415, DOI 10.1146/annurev-orgpsych-032117-104622
Sibona C, 2020, COMMUN ASSOC INF SYS, V46, P537, DOI 10.17705/1CAIS.04622
Símová T, 2023, WORLD LEISURE J, V65, P175, DOI 10.1080/16078055.2022.2134200
Situmorang Fransisco, 2021, Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan, V5, P1, DOI [10.34013/jk.v5i2.513, DOI 10.34013/JK.V5I2.513]
Stahl CC, 2023, J YOUTH STUD, V26, P925, DOI 10.1080/13676261.2022.2053671
Sutherland Will, 2017, Proceedings of the ACM on Human-Computer Interaction, V1, DOI 10.1145/3134732
Tagscherer F., 2023, Sustainable Technology and Entrepreneurship, V2, P100039, DOI [DOI 10.1016/J.STAE.2023.100039, 10.1016/100039, DOI 10.1016/100039]
Tajbakhsh MS, 2019, INTELL DATA ANAL, V23, P609, DOI 10.3233/IDA-183998
Thompson Beverly Yuen., 2018, International Journal of the Sociology of Leisure, V2, P27, DOI [DOI 10.1007/S41978-018-00030-Y, 10.1007/s41978-018-00030-y]
Thompson BeverlyYuen., 2018, GLOCALISM J CULTURE, V1, P1, DOI DOI 10.12893/GJCPI.2018.1.11
Tomaz E., 2022, The Covid-19 Pandemic and the Future of Working Spaces, P122
Velichety S, 2022, INT J INFORM MANAGE, V64, DOI 10.1016/j.ijinfomgt.2022.102474
Vijayarani S., 2016, ADV COMPUTATIONAL IN, V3, P37
von Zumbusch JSH, 2020, INF TECHNOL TOUR, V22, P439, DOI 10.1007/s40558-020-00182-2
Wang B, 2020, J ASSOC INF SYST, V21, P1379, DOI 10.17705/1jais.00641
Willment N, 2020, INF TECHNOL TOUR, V22, P391, DOI 10.1007/s40558-020-00173-3
Zafar L., 2017, EMSASW@ ESWC
Zhang Y, 2021, J TRAVEL RES, V60, P1469, DOI 10.1177/0047287520951639
Zhao WNX, 2011, LECT NOTES COMPUT SC, V6611, P338, DOI 10.1007/978-3-642-20161-5_34
Zulfikar W.B., 2023, Sci. J. Inf., V10, P25, DOI [10.15294/sji.v10i1.39952, DOI 10.15294/SJI.V10I1.39952]
NR 118
TC 0
Z9 0
U1 17
U2 17
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD MAR
PY 2024
VL 200
AR 123098
DI 10.1016/j.techfore.2023.123098
EA DEC 2023
PG 15
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA FN6G3
UT WOS:001146546000001
OA hybrid
DA 2024-03-27
ER
PT J
AU Mao, D
Yang, HJ
Zhang, SH
Sun, HZ
Wang, XJ
AF Mao, Da
Yang, Huijie
Zhang, Shaohua
Sun, Haozhe
Wang, Xiaojuan
TI Adaptive Behavioral Dynamics in Public Open Spaces During the COVID-19
Pandemic: A Technological Perspective on Urban Resilience
SO JOURNAL OF THE KNOWLEDGE ECONOMY
LA English
DT Article; Early Access
DE Behavioral dynamics; Information annotation technology; Deep learning;
Computer vision; COVID-19 pandemic; Urban resilience; Age-responsive
design
AB The emergence of the COVID-19 pandemic has significantly disrupted urban life, leading to profound changes in the daily routines and behavioral patterns of city residents. This study focuses on a central public open space within a residential area in Xinxiang, China, as a microcosm of urban dynamics during the pandemic. Leveraging an innovative long-term information annotation technology, the research team transformed 3 months of daytime monitoring video data from the early phase of the pandemic into a meticulously annotated dataset containing 115,975 records of spatial behavior. These records were generated through a combination of visual interpretation and spatial positioning techniques. The ensuing comprehensive analysis aimed to discern shifts in residents' behavioral characteristics, particularly focusing on spatial activity indices and densities. This study not only enhances our understanding of evolving dynamics in public open spaces but also provides empirical support for optimizing and transforming such spaces in the post-pandemic era. Key findings include the utility of long-term information annotation technology for rigorous behavioral analysis, nuanced trends in spatial vitality, age-related variations, identification of predominant resident behaviors, and age-dependent preferences for activity areas. This research contributes to urban behavioral dynamics knowledge and underscores the adaptability of communities in the face of unprecedented challenges, informing urban planning and policy decisions in a rapidly evolving world.
C1 [Mao, Da; Yang, Huijie; Zhang, Shaohua; Sun, Haozhe; Wang, Xiaojuan] Henan Inst Sci & Technol, Sch Hort & Landscape Architecture, Xinxiang 453003, Henan, Peoples R China.
[Mao, Da] Henan Prov Engn Ctr Hort Plant Resource Utilizat &, Xinxiang 453003, Henan, Peoples R China.
[Mao, Da; Yang, Huijie; Zhang, Shaohua; Sun, Haozhe; Wang, Xiaojuan] Xinxiang Urban Rural & Landscape Digital Technol E, Xinxiang 453003, Henan, Peoples R China.
C3 Henan Institute of Science & Technology
RP Mao, D (autor correspondiente), Henan Inst Sci & Technol, Sch Hort & Landscape Architecture, Xinxiang 453003, Henan, Peoples R China.; Mao, D (autor correspondiente), Henan Prov Engn Ctr Hort Plant Resource Utilizat &, Xinxiang 453003, Henan, Peoples R China.; Mao, D (autor correspondiente), Xinxiang Urban Rural & Landscape Digital Technol E, Xinxiang 453003, Henan, Peoples R China.
EM maoda@foxmail.com
FU Key Science and Technology Research and Development Program of Henan
Province, China [212102310490]; Key Science and Technology Research and
Development Program of Henan Province, China [222102320233]; Key Science
and Technology Research and Development Program of Henan Province, China
[232102320180]; High-level Talent Research Project of Henan Institute of
Science and Technology [2017028]; Cultivation Plan of Young Backbone
Teachers in Colleges and Universities of Henan Province, China
[2021GGJS118]
FX This research was funded by the following projects: Key Science and
Technology Research and Development Program of Henan Province, China
(212102310490); Key Science and Technology Research and Development
Program of Henan Province, China (222102320233); Key Science and
Technology Research and Development Program of Henan Province, China
(232102320180); High-level Talent Research Project of Henan Institute of
Science and Technology (2017028); and Cultivation Plan of Young Backbone
Teachers in Colleges and Universities of Henan Province, China
(2021GGJS118)
CR Ahmed U., 2022, ACM Trans. Asian Low-Resource Lang. Inf. Process., DOI [10.1145/3551890, DOI 10.1145/3551890]
Akter S., 2021, Journal of Marketing and Consumer Research, V77, P33, DOI [10.7176/jmcr/77-04, DOI 10.7176/JMCR/77-04]
Alabdullah TTY., 2023, Cashflow: Current Advanced Research on Sharia Finance and Economic Worldwide, V3, P1, DOI [10.55047/cashflow.v3i1.791, DOI 10.55047/CASHFLOW.V3I1.791]
Alessi J, 2020, DIABETOL METAB SYNDR, V12, DOI 10.1186/s13098-020-00584-6
Amankwah-Amoah J, 2021, J BUS RES, V136, P602, DOI 10.1016/j.jbusres.2021.08.011
Arthi V, 2021, EXPLOR ECON HIST, V79, DOI 10.1016/j.eeh.2020.101381
Axon S, 2023, ENERGY RES SOC SCI, V102, DOI 10.1016/j.erss.2023.103188
Barchielli B, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph191911929
Barquilla CAM, 2023, SUSTAIN CITIES SOC, V97, DOI 10.1016/j.scs.2023.104782
Basu S, 2021, URBAN FOR URBAN GREE, V57, DOI 10.1016/j.ufug.2020.126959
Bell DJ, 2020, AM PSYCHOL, V75, P919, DOI 10.1037/amp0000673
Cadima M., 2023, Pastoral cosmopolitanism in Edith Wharton's fiction: The world is a Welter
Cai X, 2020, SCI TOTAL ENVIRON, V746, DOI 10.1016/j.scitotenv.2020.140810
Carl J, 2023, J EXERC SCI FIT, V21, P165, DOI 10.1016/j.jesf.2022.12.003
Chen T, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0260244
Chirisa I, 2022, GEOJOURNAL, V87, P815, DOI 10.1007/s10708-020-10281-6
Clemens V, 2020, EUR CHILD ADOLES PSY, V29, P739, DOI 10.1007/s00787-020-01549-w
Collins C, 2022, URBAN FOR URBAN GREE, V70, DOI 10.1016/j.ufug.2022.127543
Cowie H, 2021, CHILD SOC, V35, P62, DOI 10.1111/chso.12430
Dailey SF, 2023, J COUNS DEV, V101, P114, DOI 10.1002/jcad.12450
Delina LL, 2023, CLEAN RESPONS CONSUM, V10, DOI 10.1016/j.clrc.2023.100132
Fiorenzato E, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0246204
Folke C, 2021, AMBIO, V50, P834, DOI 10.1007/s13280-021-01544-8
Gillberg N, 2023, J NURS MANAGE, V2023, DOI 10.1155/2023/6418267
Giri K., 2020, Initial assessment of the impact of COVID-19 on sustainable forest management Asia-Pacific States
Groulx M, 2021, INT J SUST HIGHER ED, V22, P274, DOI 10.1108/IJSHE-03-2020-0086
Hanson LC, 2020, J PALLIAT MED, V23, P1145, DOI 10.1089/jpm.2020.0446
Hashem IAT, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15053916
Hebebrand J, 2022, NEUROSCI BIOBEHAV R, V141, DOI 10.1016/j.neubiorev.2022.104807
Hess DB, 2023, URBAN PLAN, V8, P235, DOI 10.17645/up.v8i2.6404
Himeur Y, 2023, ARTIF INTELL REV, V56, P4929, DOI 10.1007/s10462-022-10286-2
Hsu CC, 2021, ENVIRON SCI POLLUT R, V28, P57386, DOI 10.1007/s11356-021-14499-w
Iliadis L., 2023, ENG APPL NEURAL NETW
Jaatsi M., 2023, Finland. City Society, V35, P77, DOI [10.1111/ciso.12461, DOI 10.1111/CISO.12461]
Kontoangelos K, 2020, PSYCHIAT INVEST, V17, P491, DOI 10.30773/pi.2020.0161
Leahy SM, 2019, FUTURES, V113, DOI 10.1016/j.futures.2019.04.009
Lim WS, 2020, GERIATR GERONTOL INT, V20, P547, DOI 10.1111/ggi.13939
Liu Y, 2021, TRANSPORT RES C-EMER, V131, DOI 10.1016/j.trc.2021.103361
Van Jaarsveld GM, 2020, FRONT PSYCHIATRY, V11, DOI 10.3389/fpsyt.2020.577427
McCunn L. J., 2020, Cities & health, V5, pS223, DOI DOI 10.1080/23748834.2020.1795385
Miao J, 2021, CHINESE SOCIOL REV, V53, P1, DOI 10.1080/21620555.2020.1820860
Moisander J., 2020, Marketing Management, P237, DOI [DOI 10.4324/9780203710807-19, 10.4324/9780203357262-22, DOI 10.4324/9780203357262-22]
Nandy G., 2022, Telehealth security from a user's perspective: Moving beyond COVID-19 and into a new normal
Nhede NT., 2023, African Journal of Governance and Development, V12, P80, DOI [10.36369/2616-9045/2023/v12i1a5, DOI 10.36369/2616-9045/2023/V12I1A5]
O'Brien L, 2023, FORESTRY, DOI 10.1093/forestry/cpad037
Pedrosa AL, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.566212
Rice WL., 2020, J Urban Ecol, V6, DOI DOI 10.1093/JUE/JUAA020
Sainz-Santamaria J, 2023, CITIES, V132, DOI 10.1016/j.cities.2022.104094
Sepe M, 2021, URBAN DES INT, V26, P159, DOI 10.1057/s41289-021-00153-x
Sikorska D, 2020, ENVIRON SCI POLICY, V108, P144, DOI 10.1016/j.envsci.2020.03.007
Sood S., 2020, RHIME, V7, P23
Sovold LE, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.679397
Steere-Williams J., 2020, The filth disease: Typhoid fever and the practices of epidemiology in Victorian England, V49
Tarakini G, 2021, TROP CONSERV SCI, V14, DOI 10.1177/19400829211008088
Thompson S., 2023, Encyclopedia, V3, P1067, DOI [10.3390/encyclopedia3030078, DOI 10.3390/ENCYCLOPEDIA3030078]
Velez G, 2022, TRANSL ISS PSYCH SCI, V8, P269, DOI 10.1037/tps0000326
Venter ZS, 2021, LANDSCAPE URBAN PLAN, V214, DOI 10.1016/j.landurbplan.2021.104175
Wang N., 2023, Graduate thesis, DOI [10.14288/1.0432047, DOI 10.14288/1.0432047]
White G, 2021, CITIES, V110, DOI 10.1016/j.cities.2020.103064
Winfield A, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0283227
Wong M, 2017, INT J ENV RES PUB HE, V14, DOI 10.3390/ijerph14060614
Xie J, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12176751
Yabe T, 2023, NAT COMMUN, V14, DOI 10.1038/s41467-023-37913-y
Yang MW, 2023, FORESTS, V14, DOI 10.3390/f14081559
Zhang L, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app131810306
Zhang YJ, 2020, INT J ENV RES PUB HE, V17, DOI 10.3390/ijerph17186640
Zhao JC, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126548
Zuckerman MK, 2023, AM J BIOL ANTHROPOL, V182, P557, DOI 10.1002/ajpa.24647
NR 68
TC 0
Z9 0
U1 12
U2 12
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1868-7865
EI 1868-7873
J9 J KNOWL ECON
JI J. Knowl. Econ.
PD 2023 NOV 13
PY 2023
DI 10.1007/s13132-023-01591-4
EA NOV 2023
PG 27
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA Y2OU6
UT WOS:001103719300002
DA 2024-03-27
ER
PT J
AU Bigne, E
Ruiz, C
Cuenca, A
Perez, C
Garcia, A
AF Bigne, Enrique
Ruiz, Carla
Cuenca, Antonio
Perez, Carmen
Garcia, Aitor
TI What drives the helpfulness of online reviews? A deep learning study of
sentiment analysis, pictorial content and reviewer expertise for mature
destinations
SO JOURNAL OF DESTINATION MARKETING & MANAGEMENT
LA English
DT Article
DE Perceived helpfulness; Dual-processing theory; User-generated content;
Sentiment analysis; Deep learning; Mature destinations
ID BIG DATA; CUSTOMER EXPERIENCE; IMPACT; QUALITY; TRUST; DETERMINANTS;
TRIPADVISOR; ANALYTICS; IDENTITY; CHOICE
AB Tourist destinations are increasingly affected by travel-related information shared through social media. Drawing on dual-process theories on how individuals process information, this study examines the role of central and peripheral information processing routes in the formation of consumers' perceptions of the helpfulness of online reviews of mature destinations. We carried out a two-step process to address the perceived helpfulness of usergenerated content, a sentiment analysis using advanced machine-learning techniques (deep learning), and a regression analysis. The database was 2023 comments posted on TripAdvisor about two iconic Venetian cultural attractions, St. Mark's Square (an open, free attraction) and the Doge's Palace (which charges an entry fee). Using deep-learning techniques, with logistic regression, we first identified which factors influenced whether a review received a "helpful" vote. Second, we selected those reviews which received at least one helpful vote to identify, through linear regression, the significant determinants of TripAdvisor users' voting behaviour. The results showed that reviewer expertise is influential in both free and paid-for attractions, although the impact of central cues (sentiment polarity, subjectivity, pictorial content) differs for both attractions. Our study suggests that managers should look beyond individual ratings and focus on the sentiment analysis of online reviews, which are shown to be based on the nature of the attraction (free vs. paid-for).
C1 [Bigne, Enrique; Ruiz, Carla; Cuenca, Antonio; Perez, Carmen] Univ Valencia, Fac Econ, Dept Mkt, Av Naranjos S-N, Valencia 46022, Spain.
[Garcia, Aitor] Vicomtech, Parque Cient & Tecnol Gipuzkoa, Donostia San Sebastian 20009, Spain.
C3 University of Valencia
RP Bigne, E (autor correspondiente), Univ Valencia, Fac Econ, Dept Mkt, Av Naranjos S-N, Valencia 46022, Spain.
EM enrique.bigne@uv.es; carla.ruiz@uv.es; antonio.cuenca@uv.es;
carmen.perez-cabanero@uv.es; agarciap@vicomtech.org
RI Bigné, Enrique/D-9287-2015; Pérez-Cabañero, Carmen/AEK-9613-2022
OI Bigné, Enrique/0000-0002-6529-7605; Pérez-Cabañero,
Carmen/0000-0002-6198-3990
FU Spanish Ministry of Science and Innovation, Spain [PID
2019-111195RB-I00]
FX The authors gratefully acknowledge the financial support of Spanish
Ministry of Science and Innovation, Spain project PID 2019-111195RB-I00.
CR Al-Smadi M, 2018, J COMPUT SCI-NETH, V27, P386, DOI 10.1016/j.jocs.2017.11.006
Aldayel HK, 2016, J INF SCI, V42, P782, DOI 10.1177/0165551515610513
Alnawas I, 2019, J HOSP MARKET MANAG, V28, P833, DOI 10.1080/19368623.2019.1568339
Amplayo RK, 2017, DATA KNOWL ENG, V110, P54, DOI 10.1016/j.datak.2017.03.009
[Anonymous], DISCOVERING STAT USI
Baek H, 2012, INT J ELECTRON COMM, V17, P99, DOI 10.2753/JEC1086-4415170204
Bernini C, 2014, CURR ISSUES TOUR, V17, P1, DOI 10.1080/13683500.2012.702737
Bhandari M, 2018, INT J ADVERT, V37, P125, DOI 10.1080/02650487.2017.1349030
Bigné E, 2020, J TRAVEL RES, V59, P742, DOI 10.1177/0047287519859705
Bigne E, 2019, J BUS RES, V101, P447, DOI 10.1016/j.jbusres.2019.01.020
Brun I, 2017, SERV IND J, V37, P317, DOI 10.1080/02642069.2017.1322959
Buhalis D, 2000, TOURISM MANAGE, V21, P97, DOI 10.1016/S0261-5177(99)00095-3
BUTLER RW, 1980, CAN GEOGR-GEOGR CAN, V24, P5, DOI 10.1111/j.1541-0064.1980.tb00970.x
Cambria E, 2014, IEEE COMPUT INTELL M, V9, P48, DOI 10.1109/MCI.2014.2307227
Cao Q, 2011, DECIS SUPPORT SYST, V50, P511, DOI 10.1016/j.dss.2010.11.009
CHAIKEN S, 1980, J PERS SOC PSYCHOL, V39, P752, DOI 10.1037/0022-3514.39.5.752
Chang Y., 2020, TOURISM MANAGE, V80, P104
Cheng YH, 2015, J BUS RES, V68, P883, DOI 10.1016/j.jbusres.2014.11.046
Do HH, 2019, EXPERT SYST APPL, V118, P272, DOI 10.1016/j.eswa.2018.10.003
Eagly A.H., 1993, PSYCHOL ATTITUDES
Euromonitor International, 2019, TOP 100 CIT DEST
Fang B, 2016, TOURISM MANAGE, V52, P498, DOI 10.1016/j.tourman.2015.07.018
Feldman R, 2013, COMMUN ACM, V56, P82, DOI 10.1145/2436256.2436274
Filieri R, 2016, ANN TOURISM RES, V58, P46, DOI 10.1016/j.annals.2015.12.019
Filieri R, 2015, TOURISM MANAGE, V51, P174, DOI 10.1016/j.tourman.2015.05.007
Forman C, 2008, INFORM SYST RES, V19, P291, DOI 10.1287/isre.1080.0193
Fuchs M, 2014, J DESTIN MARK MANAGE, V3, P198, DOI 10.1016/j.jdmm.2014.08.002
Ganzaroli A, 2017, TOURISM MANAGE, V61, P501, DOI 10.1016/j.tourman.2017.03.019
García-Pablos A, 2016, INF TECHNOL TOUR, V16, P45, DOI 10.1007/s40558-015-0047-7
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
He W, 2017, ONLINE INFORM REV, V41, P921, DOI 10.1108/OIR-07-2016-0201
Hlee S, 2021, J VACAT MARK, V27, P61, DOI 10.1177/1356766720950356
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
Hong H, 2017, DECIS SUPPORT SYST, V102, P1, DOI 10.1016/j.dss.2017.06.007
HOYER WD, 1984, J CONSUM RES, V11, P822, DOI 10.1086/209017
Jiménez-Barreto J, 2018, EUR J MANAG BUS ECON, V27, P26, DOI 10.1108/EJMBE-11-2017-0048
Kim D, 2017, TOURISM MANAGE, V63, P439, DOI 10.1016/j.tourman.2017.07.014
Kozak M, 2012, TOURISM MANAGE, V33, P188, DOI 10.1016/j.tourman.2011.03.001
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
Liu B, 2010, CH CRC MACH LEARN PA, P627
Liu ZW, 2015, TOURISM MANAGE, V47, P140, DOI 10.1016/j.tourman.2014.09.020
Ma E, 2018, INT J CONTEMP HOSP M, V30, P3287, DOI 10.1108/IJCHM-10-2017-0704
Manganari EE, 2017, BEHAV INFORM TECHNOL, V36, P674, DOI 10.1080/0144929X.2016.1275807
Marine-Roig E, 2015, J DESTIN MARK MANAGE, V4, P162, DOI 10.1016/j.jdmm.2015.06.004
Marrese-Taylor E, 2014, EXPERT SYST APPL, V41, P7764, DOI 10.1016/j.eswa.2014.05.045
Mikolov T., 2013, Efficient estimation of word representations in vector space
Mudambi SM, 2010, MIS QUART, V34, P185
Osmani A, 2020, EXPERT SYST, V37, DOI 10.1111/exsy.12527
Park DH, 2008, ELECTRON COMMER R A, V7, P386, DOI 10.1016/j.elerap.2007.11.004
Park SW, 2015, ANN TOURISM RES, V50, P67, DOI 10.1016/j.annals.2014.10.007
Petty RE., 1996, Attitudes and Persuasion: Classic and Contemporary Approaches
Phillips P, 2015, TOURISM MANAGE, V50, P130, DOI 10.1016/j.tourman.2015.01.028
Ratchford B.T., 2020, Spanish Journal of Marketing - ESIC, V24, P3, DOI DOI 10.1108/SJME-11-2019-0096
Rojas-Barahona LM, 2016, LANG LINGUIST COMPAS, V10, P701, DOI 10.1111/lnc3.12228
González-Rodríguez MR, 2016, INT J CONTEMP HOSP M, V28, P2609, DOI 10.1108/IJCHM-02-2015-0057
Rudkowsky E, 2018, COMMUN METHODS MEAS, V12, P140, DOI 10.1080/19312458.2018.1455817
Simeon MI, 2017, J HOSP TOUR TECHNOL, V8, P220, DOI 10.1108/JHTT-10-2016-0067
Smith D, 2005, J INTERACT MARK, V19, P15, DOI 10.1002/dir.20041
Sparks BA, 2013, TOURISM MANAGE, V39, P1, DOI 10.1016/j.tourman.2013.03.007
Sun SL, 2017, INFORM FUSION, V36, P10, DOI 10.1016/j.inffus.2016.10.004
Thelwall M, 2010, J AM SOC INF SCI TEC, V61, P2544, DOI 10.1002/asi.21416
Tsai CF, 2020, TOURISM MANAGE, V80, DOI 10.1016/j.tourman.2020.104122
van der Zee E, 2020, CURR ISSUES TOUR, V23, P175, DOI 10.1080/13683500.2018.1491955
Zhang ZQ, 2016, TOURISM MANAGE, V55, P15, DOI 10.1016/j.tourman.2016.01.004
Zhao YB, 2019, INT J HOSP MANAG, V76, P111, DOI 10.1016/j.ijhm.2018.03.017
NR 65
TC 33
Z9 33
U1 13
U2 96
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2212-571X
EI 2212-5752
J9 J DESTIN MARK MANAGE
JI J. Destin. Mark. Manag.
PD JUN
PY 2021
VL 20
AR 100570
DI 10.1016/j.jdmm.2021.100570
EA MAR 2021
PG 10
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA SV0ZL
UT WOS:000663555400006
OA hybrid, Green Published, Green Accepted
DA 2024-03-27
ER
PT J
AU Wang, Y
Luo, LK
Liu, H
AF Wang, Yue
Luo, Linkai
Liu, Hai
TI Bridging the Semantic Gap Between Customer Needs and Design
Specifications Using User-Generated Content
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Semantics; Product design; Natural languages; Machine learning; Portable
computers; Random access memory; User-generated content; Deep learning;
design; front-end design; transfer learning; user-generated content
ID PRODUCT; REQUIREMENTS
AB Although product design has been considered as a collaborative activity, progress in developing methods to facilitate the design process has been hampered by difficulties in building common bases among various stakeholders. Customers may only have general needs of the product in layman's terms instead of the sufficient domain knowledge to identify the product specifications. Thus, there is a great need in design research to translate the expressed needs in natural language to design specifications and bridge this semantic gap. By leveraging the massive amount of online user-generated content, we develop a deep learning-based method to automatically identify product design parameters or product specifications. Specifically, we crawl product review data and the corresponding product metadata from e-commerce websites. A convolutional neural network based solution is provided to map product reviews to product specifications. Experimental results show that the method can be well adapted to the mapping from customer needs in the natural language to product specifications. The method facilitates product design by addressing the semantic gap between general customer needs and detailed product specifications. In addition, the automated mapping greatly improves the efficiency and reduces labor in the design process.
C1 [Wang, Yue; Luo, Linkai] Hang Seng Univ Hong Kong, Dept Supply Chain & Informat Management, Hong Kong, Peoples R China.
[Liu, Hai] Hang Seng Univ Hong Kong, Dept Comp, Hong Kong, Peoples R China.
C3 Hang Seng University of Hong Kong; Hang Seng University of Hong Kong
RP Wang, Y (autor correspondiente), Hang Seng Univ Hong Kong, Dept Supply Chain & Informat Management, Hong Kong, Peoples R China.
EM yuewang@hsu.edu.hk; llk1896@gmail.com; hliu@hsu.edu.hk
OI Wang, Yue/0000-0002-0185-6172
FU Hong Kong Research Grant Council under FDS project [UGC/FDS14/E06/18]
FX This work was supported by Hong Kong Research Grant Council under FDS
project UGC/FDS14/E06/18.
CR Aizawa A, 2003, INFORM PROCESS MANAG, V39, P45, DOI 10.1016/S0306-4573(02)00021-3
[Anonymous], 2015, Deep learning, DOI 10.1038/nature14539
[Anonymous], 2001, MANAGING BUYER SUPPL
[Anonymous], EVALUATION PRECISION
Calantone RJ, 2010, J PROD INNOVAT MANAG, V27, P349, DOI 10.1111/j.1540-5885.2010.00721.x
Chen H., 2006, P 29 ANN INT ACM SIG, P429, DOI [DOI 10.1145/1148170.1148245, 10.1145/1148170.1148245]
Davulcu H, 2006, ENTERPRISE INFORMATION SYSTEMS VII, P215, DOI 10.1007/978-1-4020-5347-4_24
Dorai C, 2003, IEEE MULTIMEDIA, V10, P15, DOI 10.1109/MMUL.2003.1195157
Edunov S, 2018, 2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2018), P489
Erhan D, 2010, J MACH LEARN RES, V11, P625
Felfernig A, 2007, IEEE T ENG MANAGE, V54, P41, DOI 10.1109/TEM.2006.889066
GREEN PE, 1990, J MARKETING, V54, P3, DOI 10.2307/1251756
GRIFFIN A, 1993, MARKET SCI, V12, P1, DOI 10.1287/mksc.12.1.1
Hu T., 2013, P INT ASS SOC DES RE, P526
Jacovi A., 2018, EMNLP WORKSHOP BLACK, P3
jmcauley.ucsd.edu, US
Joulin Armand, 2016, P ACL
Kim Y, 2014, IEEE ASME INT C ADV, P1747, DOI 10.1109/AIM.2014.6878336
Kmetovicz RonaldEugene., 1992, New product development: design and analysis
Mou L., 2016, PROC C EMPIRICAL MET, P478
Nagamachi M., 1989, Kansei Engineering
Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191
Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI DOI 10.3115/V1/D14-1162
Ramaswamy R., 1993, RES ENG DES, V5, P70
Randall T, 2007, MARKET SCI, V26, P268, DOI 10.1287/mksc.1050.0116
Rizzo G, 2017, SEMANT WEB, V8, P667, DOI 10.3233/SW-170276
Sabin D, 1998, IEEE INTELL SYST APP, V13, P42, DOI 10.1109/5254.708432
Smith P.G., 1998, DEV PRODUCTS HALF TI
Suh N.P., 2001, AXIOMATIC DESIGN ADV
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Tseng M. M., 2018, CIRP ENCY CLOPEDIA P
Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
Wang Y, 2018, CIRP ANN-MANUF TECHN, V67, P145, DOI 10.1016/j.cirp.2018.04.018
Wang Y, 2014, CIRP ANN-MANUF TECHN, V63, P129, DOI 10.1016/j.cirp.2014.03.091
Wang Y, 2014, IEEE T ENG MANAGE, V61, P129, DOI 10.1109/TEM.2013.2248729
Wei J, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P6382
Zhang M, 2007, IEEE T ENG MANAGE, V54, P130, DOI 10.1109/TEM.2006.889072
NR 37
TC 17
Z9 18
U1 13
U2 77
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD AUG
PY 2022
VL 69
IS 4
BP 1622
EP 1634
DI 10.1109/TEM.2020.3021698
PG 13
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA 2A3ML
UT WOS:000809409400063
DA 2024-03-27
ER
PT J
AU Matuszelanski, K
Kopczewska, K
AF Matuszelanski, Kamil
Kopczewska, Katarzyna
TI Customer Churn in Retail E-Commerce Business: Spatial and Machine
Learning Approach
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE churn analysis; customer relationship management; topic modelling;
geodemographics
ID RELATIONSHIP MANAGEMENT; BASE ANALYSIS; PREDICTION; REGRESSION;
RETENTION; SELECTION; MODELS; FUTURE; URBAN
AB This study is a comprehensive and modern approach to predict customer churn in the example of an e-commerce retail store operating in Brazil. Our approach consists of three stages in which we combine and use three different datasets: numerical data on orders, textual after-purchase reviews and socio-geo-demographic data from the census. At the pre-processing stage, we find topics from text reviews using Latent Dirichlet Allocation, Dirichlet Multinomial Mixture and Gibbs sampling. In the spatial analysis, we apply DBSCAN to get rural/urban locations and analyse neighbourhoods of customers located with zip codes. At the modelling stage, we apply machine learning extreme gradient boosting and logistic regression. The quality of models is verified with area-under-curve and lift metrics. Explainable artificial intelligence represented with a permutation-based variable importance and a partial dependence profile help to discover the determinants of churn. We show that customers' propensity to churn depends on: (i) payment value for the first order, number of items bought and shipping cost; (ii) categories of the products bought; (iii) demographic environment of the customer; and (iv) customer location. At the same time, customers' propensity to churn is not influenced by: (i) population density in the customer's area and division into rural and urban areas; (ii) quantitative review of the first purchase; and (iii) qualitative review summarised as a topic.
C1 [Matuszelanski, Kamil; Kopczewska, Katarzyna] Univ Warsaw, Fac Econ Sci, PL-00927 Warsaw, Poland.
C3 University of Warsaw
RP Kopczewska, K (autor correspondiente), Univ Warsaw, Fac Econ Sci, PL-00927 Warsaw, Poland.
EM kmatuszelanski@gmail.com; kkopczewska@wne.uw.edu.pl
RI Kopczewska, Katarzyna/I-1339-2019
OI Kopczewska, Katarzyna/0000-0003-1065-1790
CR Achrol RS, 1999, J MARKETING, V63, P146, DOI 10.2307/1252108
de la Llave MA, 2019, SCAND ACTUAR J, P188, DOI 10.1080/03461238.2018.1531781
[Anonymous], 2013, INT C LEARNING REPRE
[Anonymous], 2002, Journal of the Association for Information Systems, DOI [DOI 10.17705/1JAIS.00022, 10.17705/1jais.00022]
Athanassopoulos AD, 2000, J BUS RES, V47, P191, DOI 10.1016/S0148-2963(98)00060-5
Au WH, 2003, IEEE T EVOLUT COMPUT, V7, P532, DOI 10.1109/TEVC.2003.819264
Bach MP, 2021, J RISK FINANC MANAG, V14, DOI 10.3390/jrfm14110544
Bardicchia M., 2020, Digital CRM: Strategies and emerging trends: Building customer relationship in the digital era
Behrens T, 2018, EUR J SOIL SCI, V69, P757, DOI 10.1111/ejss.12687
Bhattacharya CB, 1998, J ACAD MARKET SCI, V26, P31, DOI 10.1177/0092070398261004
Biecek P., 2021, EXPLANATORY MODEL AN
Biecek PL, 2018, J MACH LEARN RES, V19
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Braun T., 2004, INTERACT MARK, V6, P200, DOI [10.1057/palgrave.im.4340284, DOI 10.1057/PALGRAVE.IM.4340284]
Buckinx W, 2005, EUR J OPER RES, V164, P252, DOI 10.1016/j.ejor.2003.12.010
Burez J, 2007, EXPERT SYST APPL, V32, P277, DOI 10.1016/j.eswa.2005.11.037
Caruana R, 2006, P 23 INT C MACH LEAR, P161, DOI [DOI 10.1145/1143844.1143865, 10.1145/1143844.1143865.]
Chen T., 2015, R PACKAGE VERSION, V1, P1, DOI DOI 10.1145/2939672.2939785
Choi DH., 2006, International Journal of Management, V23, P925, DOI 1197221341
Corner S., 2009, JALT TESTING EVALUAT, V13, P20
Dalvi PK, 2016, 2016 SYMPOSIUM ON COLOSSAL DATA ANALYSIS AND NETWORKING (CDAN)
De Caigny A, 2020, INT J FORECASTING, V36, P1563, DOI 10.1016/j.ijforecast.2019.03.029
Montiel MAD, 2020, PAP REG SCI, V99, P1643, DOI 10.1111/pirs.12552
Dick AS., 1994, Journal of the Academy of Marketing Science, V22, P99, DOI [10.1177/0092070394222001, DOI 10.1177/0092070394222001]
Doshi-Velez F., 2017, ARXIV170208608
Felbermayr A, 2016, J INTERACT MARK, V36, P60, DOI 10.1016/j.intmar.2016.05.004
Fridrich M, 2020, ECON COMPUT ECON CYB, V54, P301, DOI 10.24818/18423264/54.4.20.19
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Greenwell BM, 2020, R J, V12, P343
Gregory B., 2018, ARXIV180203396
Hadden J, 2007, COMPUT OPER RES, V34, P2902, DOI 10.1016/j.cor.2005.11.007
Harris R., 2005, GEODEMOGRAPHICS GIS
He RD, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1, P388, DOI 10.18653/v1/P17-1036
Long HV, 2019, COMPUT INTEL NEUROSC, V2019, DOI 10.1155/2019/9252837
Hong L., 2010, P 1 WORKSH SOC MED A, P80, DOI DOI 10.1145/1964858.1964870
Howley T, 2006, BCS CONF SERIES, P209, DOI 10.1007/1-84628-224-1_16
Jahromi AT, 2010, J STRATEG MARK, V18, P587, DOI 10.1080/0965254X.2010.529158
Jha Mithileshwar., 2003, IIMB Management Review, V15, P89
Kaya E, 2018, EPJ DATA SCI, V7, DOI 10.1140/epjds/s13688-018-0165-5
Kilgarriff A., 2000, Wordnet: An electronic lexical database
Kracklauer Alexander., 2001, INT J RETAIL DISTRIB, V29, P515, DOI DOI 10.1108/EUM0000000006288
Kumar SS, 2017, 2017 INTERNATIONAL CONFERENCE ON COMPUTER AND APPLICATIONS (ICCA), P227, DOI 10.1109/COMAPP.2017.8079769
Kursa MB, 2010, J STAT SOFTW, V36, P1, DOI 10.18637/jss.v036.i11
Lee JY, 2013, MARKET SCI, V32, P960, DOI 10.1287/mksc.2013.0796
Li KW, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19061476
Lucini FR, 2020, J AIR TRANSP MANAG, V83, DOI 10.1016/j.jairtraman.2019.101760
Luo L, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5123
Miguéis VL, 2012, EXPERT SYST APPL, V39, P11250, DOI 10.1016/j.eswa.2012.03.073
Minhas AS, 2021, KNOWL-BASED SYST, V218, DOI 10.1016/j.knosys.2021.106883
Mozer MC, 2000, IEEE T NEURAL NETWOR, V11, P690, DOI 10.1109/72.846740
Murthy SK, 1998, DATA MIN KNOWL DISC, V2, P345, DOI 10.1023/A:1009744630224
Nanayakkara S, 2018, PLOS MED, V15, DOI 10.1371/journal.pmed.1002709
Ngai EWT, 2009, EXPERT SYST APPL, V36, P2592, DOI 10.1016/j.eswa.2008.02.021
Nie GL, 2011, EXPERT SYST APPL, V38, P15273, DOI 10.1016/j.eswa.2011.06.028
Nielsen D, 2016, Tree boosting with xgboost-why does xgboost win" every" machine learning competition?
Oliveira V.L.M., 2012, THESIS U PORTO PORTO
Paruelo JM, 1997, ECOL MODEL, V98, P173, DOI 10.1016/S0304-3800(96)01913-8
Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
Sagi O, 2021, INFORM SCIENCES, V572, P522, DOI 10.1016/j.ins.2021.05.055
SCHMITTLEIN DC, 1994, MARKET SCI, V13, P41, DOI 10.1287/mksc.13.1.41
Sharma S, 2021, INT J INDIAN CULT BU, V22, P287, DOI 10.1504/IJICBM.2021.113017
Singleton AD, 2014, PROF GEOGR, V66, P558, DOI 10.1080/00330124.2013.848764
Sithole B.T., 2018, SCH J EC BUS MANAG, V5, P211
Sun T, 2004, J CONSUM MARK, V21, P245, DOI 10.1108/07363760410542156
Suryadi D., 2020, 2020 INT C DATA ANAL, DOI [10.1109/ICDABI51230.2020.9325646, DOI 10.1109/ICDABI51230.2020.9325646]
Tulkens S, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P3182
Verbeke W, 2012, EUR J OPER RES, V218, P211, DOI 10.1016/j.ejor.2011.09.031
Verbeke W, 2011, EXPERT SYST APPL, V38, P2354, DOI 10.1016/j.eswa.2010.08.023
Xiao J, 2016, IEEE INTELL SYST, V31, P37, DOI 10.1109/MIS.2016.16
Yin JH, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P233, DOI 10.1145/2623330.2623715
Zhao Y, 2005, LECT NOTES ARTIF INT, V3584, P300
Zhao YB, 2019, INT J HOSP MANAG, V76, P111, DOI 10.1016/j.ijhm.2018.03.017
NR 72
TC 11
Z9 12
U1 10
U2 52
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD MAR
PY 2022
VL 17
IS 1
BP 165
EP 198
DI 10.3390/jtaer17010009
PG 34
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 0B8QD
UT WOS:000774891800001
OA gold
DA 2024-03-27
ER
PT J
AU Zila, E
Kukacka, J
AF Zila, Eric
Kukacka, Jiri
TI Moment set selection for the SMM using simple machine learning
SO JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION
LA English
DT Article
DE Agent-based model; Machine learning; Simulated method of moments;
Stepwise selection
ID AGENT-BASED MODELS; EMPIRICAL VALIDATION; BAYESIAN-ESTIMATION;
FINANCIAL-MARKETS; SIMULATED MOMENTS; GMM ESTIMATION; ASSET RETURNS;
TIME-SERIES; EXPECTATIONS; DYNAMICS
AB This paper addresses the moment selection issue of the simulated method of moments, an estimation technique commonly applied to intractable agent-based models. We de-velop a simple machine learning extension reducing arbitrariness and automating the mo-ment choice. Two algorithms are proposed: backward stepwise moment elimination and forward stepwise moment selection. The methodology is tested using simulations on a Markov-switching multifractal framework and two popular financial agent-based models with increasing complexity. We find that both algorithms can identify multiple moment sets that outperform all benchmark sets. Moreover, we achieve considerable in-sample es-timation precision gains of up to 66 percent for agent-based models. Finally, an out-of-sample empirical exercise with S & P 500 data strongly supports the practical applicability of our methodology as the estimated models pass the validity test of overidentifying re-strictions. & COPY; 2023 Elsevier B.V. All rights reserved.
C1 [Zila, Eric; Kukacka, Jiri] Czech Acad Sci, Inst Informat Theory & Automat, Vodarenskou Vezi 4, Prague 8, Czech Republic.
[Zila, Eric; Kukacka, Jiri] Charles Univ Prague, Inst Econ Studies, Fac Social Sci, Opletalova 26, Prague 1, Czech Republic.
C3 Czech Academy of Sciences; Institute of Information Theory & Automation
of the Czech Academy of Sciences; Charles University Prague
RP Kukacka, J (autor correspondiente), Czech Acad Sci, Inst Informat Theory & Automat, Vodarenskou Vezi 4, Prague 8, Czech Republic.
EM 10026254@fsv.cuni.cz; jiri.kukacka@fsv.cuni.cz
RI Kukacka, Jiri/J-1974-2014
OI Kukacka, Jiri/0000-0001-8680-2896; Zila, Eric/0000-0002-0504-7479
FU Czech Science Foundation [20-14817S]; Charles University UNCE program
[UNCE/HUM/035]; Cooperatio Program at Charles University, research area
Economics
FX Jiri Kukacka gratefully acknowledges the financial support from the
Czech Science Foundation under the project Linking financial and
economic agent-based models: An econometric approach' [Grant number
20-14817S] and from the Charles University UNCE program [Grant number
UNCE/HUM/035] . This work was supported by the Cooperatio Program at
Charles University, research area Economics.
CR Alfarano S, 2007, EUR PHYS J B, V55, P183, DOI 10.1140/epjb/e2006-00385-4
Alfarano S., 2005, Computational Economics, V26, P19, DOI 10.1007/s10614-005-6415-1
Alfarano S, 2008, J ECON DYN CONTROL, V32, P101, DOI 10.1016/j.jedc.2006.12.014
Alfarano S, 2006, PHYSICA A, V370, P38, DOI 10.1016/j.physa.2006.04.018
Altonji JG, 1996, J BUS ECON STAT, V14, P353, DOI 10.2307/1392447
[Anonymous], 1983, COMPUT PERFORM
Bargigli L, 2020, J ECON INTERACT COOR, V15, P413, DOI 10.1007/s11403-018-0217-8
Boivin J, 2006, REV ECON STAT, V88, P445, DOI 10.1162/rest.88.3.445
Brock WA, 1998, J ECON DYN CONTROL, V22, P1235, DOI 10.1016/S0165-1889(98)00011-6
Calvet L. E., 2004, Journal of Financial Econometrics, V2, P49, DOI [10.1093/jjfinec/nbh003, DOI 10.1093/JJFINEC/NBH003, https://doi.org/10.1093/jjfinec/nbh003]
Carrasco M, 2002, J BUS ECON STAT, V20, P482, DOI 10.1198/073500102288618621
Chen SH, 2012, KNOWL ENG REV, V27, P187, DOI 10.1017/S0269888912000136
Chen SY, 2022, COMPUT ECON, V59, P687, DOI 10.1007/s10614-021-10106-9
Chen ZX, 2018, COMPUT ECON, V52, P711, DOI 10.1007/s10614-016-9638-4
Cont R., 2007, LONG MEMORY EC, DOI [10.1007/978-3-540-34625-8_10, DOI 10.1007/978-3-540-34625-8_10]
Cont R, 2001, QUANT FINANC, V1, P223, DOI [10.1088/1469-7688/1/2/304, 10.1080/713665670]
DAY RH, 1990, J ECON BEHAV ORGAN, V14, P299, DOI 10.1016/0167-2681(90)90061-H
Dieci R, 2018, HANDB COMPUT ECON, V4, P257, DOI 10.1016/bs.hescom.2018.03.002
DUFFIE D, 1993, ECONOMETRICA, V61, P929, DOI 10.2307/2951768
Fabretti A, 2013, J ECON INTERACT COOR, V8, P277, DOI 10.1007/s11403-012-0096-3
Fagiolo Giorgio, 2019, Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives, P763, DOI DOI 10.1007/978
Farmer JD, 2002, J ECON BEHAV ORGAN, V49, P149, DOI 10.1016/S0167-2681(02)00065-3
Franke R, 2018, J ECON INTERACT COOR, V13, P201, DOI 10.1007/s11403-016-0181-0
Franke R, 2016, J ECON INTERACT COOR, V11, P1, DOI 10.1007/s11403-014-0140-6
Franke R, 2015, N AM J ECON FINANC, V31, P126, DOI 10.1016/j.najef.2014.11.001
Franke R, 2012, J ECON DYN CONTROL, V36, P1193, DOI 10.1016/j.jedc.2011.10.004
Franke R, 2011, COMPUT ECON, V38, P53, DOI 10.1007/s10614-010-9238-7
Franke R, 2009, J EMPIR FINANC, V16, P804, DOI 10.1016/j.jempfin.2009.06.006
Gatti DD, 2020, J ECON BEHAV ORGAN, V178, P875, DOI 10.1016/j.jebo.2020.07.023
Ghonghadze J, 2016, J EMPIR FINANC, V37, P1, DOI 10.1016/j.jempfin.2016.02.002
Gilli M, 2003, COMPUT STAT DATA AN, V42, P299, DOI 10.1016/S0167-9473(02)00214-1
Grazzini J, 2017, J ECON DYN CONTROL, V77, P26, DOI 10.1016/j.jedc.2017.01.014
Grazzini J, 2015, J ECON DYN CONTROL, V51, P148, DOI 10.1016/j.jedc.2014.10.006
Grazzini J, 2012, JASSS-J ARTIF SOC S, V15, DOI 10.18564/jasss.1929
HAMILTON JD, 1989, ECONOMETRICA, V57, P357, DOI 10.2307/1912559
HANSEN LP, 1982, ECONOMETRICA, V50, P1029, DOI 10.2307/1912775
HANSEN LP, 1982, ECONOMETRICA, V50, P1269, DOI 10.2307/1911873
Hastie T., 2005, ELEMENTS STAT LEARNI, V27, P83, DOI DOI 10.1007/978-0-387-84858-7
Hong H, 1999, J FINANC, V54, P2143, DOI 10.1111/0022-1082.00184
Jalali MRH., 2015, Using the Method of Simulated Moments (MSM) for Identification of Dynamic Models
Jang TS, 2021, J ECON BEHAV ORGAN, V182, P493, DOI 10.1016/j.jebo.2019.01.017
Jang TS, 2016, METROECONOMICA, V67, P76, DOI 10.1111/meca.12091
KIRMAN A, 1993, Q J ECON, V108, P137, DOI 10.2307/2118498
Kirman A., 1991, MONEY FINANCIAL MARK, P354
KOCHERLAKOTA NR, 1990, J MONETARY ECON, V26, P285, DOI 10.1016/0304-3932(90)90024-X
Kukacka J, 2021, J ECON BEHAV ORGAN, V192, P324, DOI 10.1016/j.jebo.2021.10.007
Kukacka J, 2020, J ECON DYN CONTROL, V113, DOI 10.1016/j.jedc.2020.103855
Lamperti F, 2018, J ECON DYN CONTROL, V90, P366, DOI 10.1016/j.jedc.2018.03.011
LEE BS, 1991, J ECONOMETRICS, V47, P197, DOI 10.1016/0304-4076(91)90098-X
Lee JS, 2015, JASSS-J ARTIF SOC S, V18, DOI 10.18564/jasss.2897
Lux T, 2008, J BUS ECON STAT, V26, P194, DOI 10.1198/073500107000000403
Lux T, 2023, STUD NONLINEAR DYN E, V27, P423, DOI 10.1515/snde-2021-0052
Lux T, 2022, ECONOMET STAT, V21, P69, DOI 10.1016/j.ecosta.2020.03.001
Lux T, 2022, COMPUT ECON, V60, P451, DOI 10.1007/s10614-021-10155-0
Lux T, 2018, HANDB COMPUT ECON, V4, P437, DOI 10.1016/bs.hescom.2018.02.003
Lux T, 2018, J ECON DYN CONTROL, V91, P391, DOI 10.1016/j.jedc.2018.01.021
Lux T, 2009, HANDB FINANC, P161, DOI 10.1016/B978-012374258-2.50007-5
MANDELBROT BB, 1974, J FLUID MECH, V62, P331, DOI 10.1017/S0022112074000711
Mandes A, 2017, J ECON INTERACT COOR, V12, P469, DOI 10.1007/s11403-016-0173-0
Manzan S, 2007, J ECON BEHAV ORGAN, V64, P111, DOI 10.1016/j.jebo.2006.08.005
Mao C.S., 1990, HYPOTHESIS TESTING F
MCFADDEN D, 1989, ECONOMETRICA, V57, P995, DOI 10.2307/1913621
PAKES A, 1989, ECONOMETRICA, V57, P1027, DOI 10.2307/1913622
Platt D, 2022, COMPUT ECON, V59, P599, DOI 10.1007/s10614-021-10095-9
Platt D, 2020, J ECON DYN CONTROL, V113, DOI 10.1016/j.jedc.2020.103859
Salle I, 2014, COMPUT ECON, V44, P507, DOI 10.1007/s10614-013-9406-7
Schmitt N, 2017, QUANT FINANC, V17, P1187, DOI 10.1080/14697688.2016.1267391
Schmitt N, 2017, J EVOL ECON, V27, P1041, DOI 10.1007/s00191-017-0504-x
Schwartz I., 2022, TIME IS LIMITED ROAD
Tibshirani R, 1996, J ROY STAT SOC B, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x
Tubbenhauer T, 2021, J ECON DYN CONTROL, V131, DOI 10.1016/j.jedc.2021.104231
Vandin A, 2022, J ECON DYN CONTROL, V143, DOI 10.1016/j.jedc.2022.104458
Winker P., 2007, J. Econ. Interact. Coord., P125, DOI [10.1007/s11403-007-0020-4, DOI 10.1007/S11403-007-0020-4, 10.1007/s11403-007-0020-4(2,4)]
Zegadlo Piotr, 2021, Computational Science - ICCS 2021. 21st International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12744), P316, DOI 10.1007/978-3-030-77967-2_27
Zhang W, 2023, IEEE T NEUR NET LEAR, V34, P2170, DOI 10.1109/TNNLS.2021.3106777
NR 75
TC 0
Z9 0
U1 4
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-2681
EI 1879-1751
J9 J ECON BEHAV ORGAN
JI J. Econ. Behav. Organ.
PD AUG
PY 2023
VL 212
BP 366
EP 391
DI 10.1016/j.jebo.2023.05.040
EA JUN 2023
PG 26
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA L1SZ6
UT WOS:001021137800001
DA 2024-03-27
ER
PT J
AU Odugbesan, JA
Aghazadeh, S
Al Qaralleh, RE
Sogeke, OS
AF Odugbesan, Jamiu Adetola
Aghazadeh, Sahar
Al Qaralleh, Rawan Enad
Sogeke, Olukunle Samuel
TI Green talent management and employees' innovative work behavior: the
roles of artificial intelligence and transformational leadership
SO JOURNAL OF KNOWLEDGE MANAGEMENT
LA English
DT Article
DE Green talent management; Innovative work behavior; Sustainable
competitive advantage; Transformational leadership; Artificial
intelligence; PLS-SEM
ID EMOTIONAL INTELLIGENCE; MEDIATING ROLE; DIGITALIZATION; SUSTAINABILITY;
PERCEPTIONS; RESOURCE; IMPACT; ENGAGEMENT; FAIRNESS; ROBOTICS
AB Purpose This study aims to investigate the significance of an emerging concept - green talent management (TM) and its influence on employees' innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions. Design/methodology/approach Two hundred and thirty-five structured questionnaires were administered to the academic staff in five universities located in Northern Cyprus, and the data was analyzed using partial least square structural equation modeling with the aid of WarpPLS (7.0). Findings This study provides evidences that green hard and soft TM exerts significant influence on employees' innovative work behavior. Similarly, transformational leadership and artificial intelligence were confirmed to have a significant impact on employees' innovative work behavior. Moreover, the study found transformational leadership and artificial intelligence to significantly moderate the relationship between green hard TM and employees' innovative work behavior. Research limitations/implications The study provides theoretical and managerial implications of findings that will assist the leaders in higher educational institutions in harnessing the potential of green TM in driving their employees' innovative work behavior toward the achievement of sustainable competitive advantage in the market where they operate. Originality/value The attention of researchers in the recent time has been on the way to address the challenge facing organizational leaders on how to develop and retain employee that will contribute to the sustainability of their organization toward the achievement of sustainable competitive advantage in the market they operate. Meanwhile, the studies exploring these concerns are limited. In view of this, this study investigates the significance of an emerging concept - green talent management and its influence on employees' innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.
C1 [Odugbesan, Jamiu Adetola; Aghazadeh, Sahar] Cyprus West Univ, Dept Business Adm, Mersin, Turkey.
[Al Qaralleh, Rawan Enad] Cyprus Int Univ, Dept Business Adm, Nicosia, North Cyprus, Turkey.
[Sogeke, Olukunle Samuel] Oxford Coll Canada, Oxford, NS, Canada.
C3 Cyprus International University
RP Odugbesan, JA (autor correspondiente), Cyprus West Univ, Dept Business Adm, Mersin, Turkey.
EM odugbesanadetola@gmail.com
CR Abdeldayem M.M., 2020, International Journal of Scientific and Technology Research, V9, P3867
Abdul Latif F.D., 2019, J. Adv. Res. Bus. Mark. Supply Chain Manag, V3, P10
Abraham R, 1999, GENET SOC GEN PSYCH, V125, P209
Adeosun O.T., 2020, RAJAGIRI MANAG J, V14, P107, DOI DOI 10.1108/RAMJ-05-2020-0016
Adetola OJ., 2021, PAKISTAN J COMMERCE, V15, P240
Afsar B., 2018, PERSPECTIVES INNOVAT, V18, P25, DOI [10.15208/pieb.2018.3, DOI 10.15208/PIEB.2018.3, https://doi.org/10.15208/pieb.2018.3]
Afsar B, 2020, EUR J INNOV MANAG, V23, P402, DOI 10.1108/EJIM-12-2018-0257
Agbaeze K.E., 2017, JEB, V4, P135, DOI [DOI 10.31014/AIOR.1992.02.04.162, 10.31014/aior.1992.02.04.162]
Alfawaire F, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13158445
Alzgool M., 2019, Management Science Letters, V9, P2073
Anlesinya A, 2019, AFR J ECON MANAG STU, V10, P440, DOI 10.1108/AJEMS-12-2018-0371
[Anonymous], J APPL PSYCHOL, V94, P1128
Antikainen M, 2018, PROC CIRP, V73, P45, DOI 10.1016/j.procir.2018.04.027
Auranzeb B., 2016, Ind. Eng. Lett., V6, P49
Bamel U, 2022, J INTELLECT CAP, V23, P375, DOI 10.1108/JIC-05-2020-0142
Barnewold L, 2020, INT J MIN SCI TECHNO, V30, P747, DOI 10.1016/j.ijmst.2020.07.003
Barney JB, 2020, BUS SOC, V59, P203, DOI 10.1177/0007650318796792
Bass B.M., 2000, MULTIFACTOR LEADERSH
Bass M.B., 1994, IMPROVING ORGANISATI
Bednall TC, 2018, BRIT J MANAGE, V29, P796, DOI 10.1111/1467-8551.12275
Bel R., 2010, GLOBAL BUSINESS ORG, V29, P47, DOI [10.1002/joe, DOI 10.1002/JOE]
Bhardwaj Garima, 2020, 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM). Proceedings, P47, DOI 10.1109/ICCAKM46823.2020.9051544
Bin Saeed B, 2019, ECON RES-EKON ISTRAZ, V32, P254, DOI 10.1080/1331677X.2018.1556108
Bos-Nehles AC, 2019, INT J HUM RESOUR MAN, V30, P2661, DOI 10.1080/09585192.2017.1380680
Brougham D, 2018, J MANAGE ORGAN, V24, P239, DOI 10.1017/jmo.2016.55
Bui Linh Thi Thu, 2018, International Review of Public Administration, V23, P17
CLARKSON MBE, 1995, ACAD MANAGE REV, V20, P92, DOI 10.2307/258888
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Coley LS, 2012, J BUS IND MARK, V27, P611, DOI 10.1108/08858621211273565
Contreras F, 2020, COGENT BUS MANAG, V7, DOI 10.1080/23311975.2020.1812926
Contreras TorresF., 2012, Canadian Center of Science and Education, DOI [DOI 10.5539/ASS.V13N9P9, 10.5539/ass.v13n9p9]
Crossan MM, 2010, J MANAGE STUD, V47, P1154, DOI [10.1111/J.1467-6486.2009.00880.X, 10.1111/j.1467-6486.2009.00880.x]
de Jong J, 2010, CREAT INNOV MANAG, V19, P23, DOI 10.1111/j.1467-8691.2010.00547.x
de Jong JPJ, 2007, EUR J INNOV MANAG, V10, P41, DOI 10.1108/14601060710720546
Deery M, 2015, INT J CONTEMP HOSP M, V27, P453, DOI 10.1108/IJCHM-12-2013-0538
Dess GG, 2000, ORGAN DYN, V28, P18, DOI 10.1016/S0090-2616(00)88447-8
Dodd MD, 2016, J COMMUN MANAG, V20, P289, DOI 10.1108/JCOM-12-2015-0095
Farndale E, 2021, HUM RESOUR MANAGE-US, V60, P259, DOI 10.1002/hrm.22038
Fawehinmi O, 2020, INT J MANPOWER, V41, P879, DOI 10.1108/IJM-07-2019-0347
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Freeman RE, 2020, BUS SOC, V59, P213, DOI 10.1177/0007650318773750
Gardas BB, 2019, J CLEAN PROD, V229, P850, DOI 10.1016/j.jclepro.2019.05.018
Garg V, 2018, 2018 INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTATIONAL ENGINEERING (ICACE), P113, DOI 10.1109/ICACE.2018.8686988
GEISSER S, 1974, BIOMETRIKA, V61, P101, DOI 10.2307/2334290
Gumusluoglu L, 2009, J BUS RES, V62, P461, DOI 10.1016/j.jbusres.2007.07.032
Haenlein M, 2021, J BUS RES, V124, P405, DOI 10.1016/j.jbusres.2020.10.042
Hair J.F., 2009, Multivariate data analysis
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
HATER JJ, 1988, J APPL PSYCHOL, V73, P695, DOI 10.1037/0021-9010.73.4.695
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hlsheger U.R., 2009, TEAM L
Hughes C, 2019, CHANG CONT MANAG, P61
Janssen O, 2000, J OCCUP ORGAN PSYCH, V73, P287, DOI 10.1348/096317900167038
Jia Q., 2018, P INT C EL BUS, P106
Jung DI, 2003, LEADERSHIP QUART, V14, P525, DOI 10.1016/S1048-9843(03)00050-X
Khaki I., 2017, Int. Lett. Soc. Humanist. Sci, V79, P16, DOI [10.18052/www.scipress.com/ILSHS.79.16, DOI 10.18052/WWW.SCIPRESS.COM/ILSHS.79.16]
Khan M.J., 2012, Pakistan Journal of Social and Clinical Psychology, V9, P17
Kline R. B., 2005, PRINCIPLES PRACTICES, V2nd
Knott PJ, 2015, MANAGE DECIS, V53, P1806, DOI 10.1108/MD-08-2014-0525
Kock N, 2020, DATA ANAL PERSPECT J, V1, P1
Kock N, 2014, INT J E-COLLAB, V10, P1, DOI 10.4018/ijec.2014010101
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Kock N, 2012, J ASSOC INF SYST, V13, P546, DOI 10.17705/1jais.00302
Kör B, 2016, SPRINGERPLUS, V5, DOI 10.1186/s40064-016-3556-8
Korzilius H, 2017, INT J INTERCULT REL, V56, P13, DOI 10.1016/j.ijintrel.2016.11.001
KREJCIE RV, 1970, EDUC PSYCHOL MEAS, V30, P607, DOI 10.1177/001316447003000308
LAWSHE CH, 1975, PERS PSYCHOL, V28, P563, DOI 10.1111/j.1744-6570.1975.tb01393.x
Lee IC, 2017, J INTELLECT CAP, V18, P807, DOI 10.1108/JIC-11-2016-0117
Li C, 2020, PSYCHOL RES BEHAV MA, V13, P105, DOI 10.2147/PRBM.S236876
Li X, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120479
Mahlasela S, 2020, ELECTR J INF SYS DEV, V86, DOI 10.1002/isd2.12126
Mahmood M, 2019, MANAGE DECIS, V57, P741, DOI 10.1108/MD-07-2017-0707
Mahmood Z., 2020, International Journal of Scientific and Technology Research, P3653
Malik MAR, 2015, J ORGAN BEHAV, V36, P59, DOI 10.1002/job.1943
Masood M, 2017, NURS INQ, V24, DOI 10.1111/nin.12188
Masood T, 2020, COMPUT IND, V115, DOI 10.1016/j.compind.2019.07.002
Moguluwa SC, 2021, CUST AGRONEGOCIO, V17, P64
Mohajan H.K., 2017, Annals of Spiru Haret University. Economic Series, V17, P59, DOI [DOI 10.26458/1746, 10.26458/1746]
Mumford MD, 2002, LEADERSHIP QUART, V13, P705, DOI 10.1016/S1048-9843(02)00158-3
Muthuveloo R., 2013, American Journal of Economics and Business Administration, V5, P15, DOI [10.3844/ajebasp.2013.15.21, DOI 10.3844/AJEBASP.2013.15.21]
Ndolo F.M., 2017, INT J MANAGEMENT COM, V5, P580
Nirino N, 2022, J INTELLECT CAP, V23, P420, DOI 10.1108/JIC-02-2020-0038
Nwosu B, 2016, WORLDW HOSP TOUR THE, V8, P235, DOI 10.1108/WHATT-11-2015-0049
Ogbeibu S, 2022, J INTELLECT CAP, V23, P27, DOI 10.1108/JIC-01-2021-0016
Ogbeibu S, 2021, SUSTAIN PROD CONSUMP, V26, P275, DOI 10.1016/j.spc.2020.10.007
Ogbeibu S, 2021, J BUS RES, V124, P422, DOI 10.1016/j.jbusres.2020.10.060
Olaka L.O.M., 2018, IOSR J BUSINESS MANA, V20, P31
Oluwatayo J.A., 2012, Journal of Educational and Social Research, V2, P391, DOI DOI 10.5901/JESR.2012.V2N2.391
Opoku MA, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11226273
Parveen N.A., 2019, International Journal of Research and Analytical Reviews (IJRAR), V6, P513
Pawar BS, 1997, ACAD MANAGE REV, V22, P80, DOI 10.2307/259225
Plessis L.D., 2015, J APPL BUS RES, V31, P1767, DOI [10.19030/jabr.v31i5.9390, DOI 10.19030/JABR.V31I5.9390]
Premnath E., 2020, J XIAN U ARCHITECTUR, V11, P1193
Rana A.H., 2013, SCI INT, V25, P655
Reuvers M., 2008, LEADERSHIP INNOVATIO, V17, P227, DOI [DOI 10.1111/J.1467-8691.2008.00487.X, 10.1111/j.1467-8691.2008.00487.x]
Riaz S, 2018, ADM SCI, V8, DOI 10.3390/admsci8030046
Ruby MP., 2018, INT J PURE APPL MATH, V119, P1891
Salvi A, 2021, J BUS RES, V124, P437, DOI 10.1016/j.jbusres.2020.10.025
Sarc R, 2019, WASTE MANAGE, V95, P476, DOI 10.1016/j.wasman.2019.06.035
Sarra B, 2020, J INTELLECT CAP, V21, P1153, DOI 10.1108/JIC-01-2020-0012
Schutte N., 2017, J INNOV RES ED MANAG, V1, P8
Siegling AB, 2014, PERS INDIV DIFFER, V65, P65, DOI 10.1016/j.paid.2014.01.049
Son J, 2020, INT J HUM RESOUR MAN, V31, P2188, DOI 10.1080/09585192.2018.1443955
Stoldt J, 2018, PROC CIRP, V72, P179, DOI 10.1016/j.procir.2018.03.100
STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x
Taherdoost H., 2016, International Journal of Academic Research in Management, DOI [DOI 10.2139/SSRN.3205040, 10.2139/ssrn.3205040]
Tambe P, 2019, CALIF MANAGE REV, V61, P15, DOI 10.1177/0008125619867910
Wamba-Taguimdje SL, 2020, BUS PROCESS MANAG J, V26, P1893, DOI 10.1108/BPMJ-10-2019-0411
West M.A., 1990, INNOVATION CREATIVIT, P265
Widodo W., 2020, Manag. Sci. Lett, V10, P2175, DOI DOI 10.5267/J.MSL.2020.3.019
Wikström PA, 2010, SUSTAIN DEV, V18, P99, DOI 10.1002/sd.449
Wu J., 2016, ADV INT MANAGEMENT, V29, P279
Yawalkar M.V.V., 2019, Int J Res Anal Rev, V6, P20
Yukl G., 2006, Leadership in organizations, V6th
Zhou WQ, 2018, COGENT BUS MANAG, V5, DOI 10.1080/23311975.2018.1471770
NR 116
TC 22
Z9 22
U1 74
U2 212
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1367-3270
EI 1758-7484
J9 J KNOWL MANAG
JI J. Knowl. Manag.
PD MAR 6
PY 2023
VL 27
IS 3
BP 696
EP 716
DI 10.1108/JKM-08-2021-0601
EA APR 2022
PG 21
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA 9N3YK
UT WOS:000795998800001
DA 2024-03-27
ER
PT J
AU Kastius, A
Schlosser, R
AF Kastius, Alexander
Schlosser, Rainer
TI Dynamic pricing under competition using reinforcement learning
SO JOURNAL OF REVENUE AND PRICING MANAGEMENT
LA English
DT Article
DE Dynamic pricing; Competition; Reinforcement learning; E-commerce; Price
collusion
ID MANAGEMENT
AB Dynamic pricing is considered a possibility to gain an advantage over competitors in modern online markets. The past advancements in Reinforcement Learning (RL) provided more capable algorithms that can be used to solve pricing problems. In this paper, we study the performance of Deep Q-Networks (DQN) and Soft Actor Critic (SAC) in different market models. We consider tractable duopoly settings, where optimal solutions derived by dynamic programming techniques can be used for verification, as well as oligopoly settings, which are usually intractable due to the curse of dimensionality. We find that both algorithms provide reasonable results, while SAC performs better than DQN. Moreover, we show that under certain conditions, RL algorithms can be forced into collusion by their competitors without direct communication.
C1 [Kastius, Alexander; Schlosser, Rainer] Univ Potsdam, Hasso Plattner Inst, Potsdam, Germany.
C3 University of Potsdam
RP Kastius, A (autor correspondiente), Univ Potsdam, Hasso Plattner Inst, Potsdam, Germany.
EM alexander.kastius@hpi.de; rainer.schlosser@hpi.de
OI Schlosser, Rainer/0000-0002-6627-4026
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR [Anonymous], Source Code
Bondoux N, 2020, J REVENUE PRICING MA, V19, P332, DOI 10.1057/s41272-020-00228-4
Calvano E, 2020, AM ECON REV, V110, P3267, DOI 10.1257/aer20190623
DENBOER AV, 2015, SURVEYS OPER RES MAN, V20, P1, DOI DOI 10.1016/J.SORMS.2015.03.001
Espeholt L, 2018, PR MACH LEARN RES, V80
Ghasemkhani A, 2018, IEEE INT CONF COMM
Giannoccaro I, 2002, INT J PROD ECON, V78, P153, DOI 10.1016/S0925-5273(00)00156-0
Gosavi A, 2004, MACH LEARN, V55, P5, DOI 10.1023/B:MACH.0000019802.64038.6c
Gosavi A, 2002, IIE TRANS, V34, P729
Haarnoja T, 2018, PR MACH LEARN RES, V80
Hessel F.N, 2018, P AAAI C ART INT, V32
Kephart JeffreyO., 2000, ICML, P463
Kim BG, 2016, IEEE T SMART GRID, V7, P2187, DOI 10.1109/TSG.2015.2495145
Kingma D. P., 2014, ARXIV
Könönen V, 2006, INT J INTELL SYST, V21, P73, DOI 10.1002/int.20121
Kutschinski E, 2003, J ECON DYN CONTROL, V27, P2207, DOI 10.1016/S0165-1889(02)00122-7
Lillicrap TP., 2015, CONTINUOUS CONTROL D
Maestre R, 2019, ADV INTELL SYST, V868, P120, DOI 10.1007/978-3-030-01054-6_8
Mnih V, 2016, PR MACH LEARN RES, V48
Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
Rana R, 2014, OMEGA-INT J MANAGE S, V47, P116, DOI 10.1016/j.omega.2013.10.004
Schlosser R, 2019, J REVENUE PRICING MA, V18, P451, DOI 10.1057/s41272-019-00206-5
Schlosser R, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P705, DOI 10.1145/3219819.3219833
Silver D., 2016, PROC 4 TH INT C LEAR, P1
Teh YW, 2017, ADV NEUR IN, V30
van Hasselt H, 2016, AAAI CONF ARTIF INTE, P2094
Vengerov D, 2008, FUTURE GENER COMP SY, V24, P687, DOI 10.1016/j.future.2008.02.012
Wang Ziyu, 2016, 2016 33rd International Conference on International Conference on Machine Learning (ICML), DOI 10.48550/arXiv.1511.06581
WATKINS CJCH, 1992, MACH LEARN, V8, P279, DOI 10.1007/BF00992698
Ye P, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P932, DOI 10.1145/3219819.3219830
NR 30
TC 17
Z9 17
U1 8
U2 25
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1476-6930
EI 1477-657X
J9 J REVENUE PRICING MA
JI J. Revenue Pricing Manag.
PD FEB
PY 2022
VL 21
IS 1
BP 50
EP 63
DI 10.1057/s41272-021-00285-3
EA FEB 2021
PG 14
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA ZA0VQ
UT WOS:000622677300001
OA hybrid
DA 2024-03-27
ER
PT J
AU Syrdal, HA
Myers, S
Sen, S
Woodroof, PJ
McDowell, WC
AF Syrdal, Holly A.
Myers, Susan
Sen, Sandipan
Woodroof, Parker J.
McDowell, William C.
TI Influencer marketing and the growth of affiliates: The effects of
language features on engagement behavior
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Social media engagement; Influencer affiliate marketing; Elaboration
likelihood model; Micro-influencers; Big data; Text mining; Linguistic
cues; Natural language processing
ID ELABORATION LIKELIHOOD MODEL; SOCIAL MEDIA ENGAGEMENT; MESSAGE APPEALS;
TEXT ANALYSIS; ONLINE; POPULARITY; PERSUASION; EXPERTISE; ATTITUDE;
WORDS
AB Although most major brands are utilizing affiliate marketing programs, potential drivers of engagement with influencer affiliate marketing content have yet to be explored. To address this gap, the authors apply the Elaboration Likelihood Model to propose that linguistic characteristics of the text within influencers' affiliate marketing posts motivate either peripheral or central route processing, which in turn impacts behavioral in-teractions with the content. To empirically test these relationships, text mining and natural language processing are used to construct a large dataset of influencers' affiliate marketing posts from their Instagram feeds. The analysis reveals certain linguistic styles can enhance engagement, while others negatively impact these behav-iors. In addition to advancing understanding of influencer affiliate marketing and social media engagement, the findings offer important insights for both brands and influencers participating in affiliate marketing.
C1 [Syrdal, Holly A.] Texas State Univ, Dept Marketing, San Marcos, TX USA.
[McDowell, William C.] Texas State Univ, McCoy Coll Business, San Marcos, TX 78666 USA.
[Myers, Susan] Univ Cent Arkansas, Dept Marketing & Management, Marketing, Conway, AR USA.
[Sen, Sandipan] Southeast Missouri State Univ, Dept Marketing, Marketing, Girardeau, MO USA.
[Woodroof, Parker J.] Univ Alabama Birmingham, Dept Marketing Ind Distribut & Econ MIDE, Marketing, Birmingham, AL USA.
C3 Texas State University System; Texas State University San Marcos; Texas
State University System; Texas State University San Marcos; University
of Central Arkansas; University of Alabama System; University of Alabama
Birmingham
RP McDowell, WC (autor correspondiente), Texas State Univ, McCoy Coll Business, San Marcos, TX 78666 USA.
EM holly.syrdal@txstate.edu; smyers@uca.edu; ssen@semo.edu;
woodroof@uab.edu; billmcdowell@txstate.edu
OI Woodroof, Parker/0000-0003-3642-6057
CR Abdelahady M. H., 2020, J TRAVEL TOUR MARK, V4
Adams R. L., 2017, TECHNOL FORECAST SOC
Akpinar E, 2017, J MARKETING RES, V54, P318, DOI 10.1509/jmr.13.0350
Aleti T, 2019, J INTERACT MARK, V48, P17, DOI 10.1016/j.intmar.2019.03.003
Allison TH, 2017, J BUS VENTURING, V32, P707, DOI 10.1016/j.jbusvent.2017.09.002
Angst CM, 2009, MIS QUART, V33, P339
Antoniadis I, 2019, INT J RETAIL DISTRIB, V47, P957, DOI 10.1108/IJRDM-09-2018-0195
Association of National Advertisers, 2018, ADV LOV INFL MARK AN
Belk RW, 1998, J CONSUM RES, V25, P218, DOI 10.1086/209536
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Beverland MB, 2010, J CONSUM RES, V36, P838, DOI 10.1086/615047
Bhattacherjee A, 2006, MIS QUART, V30, P805
Borges-Tiago MT, 2023, J BUS RES, V157, DOI 10.1016/j.jbusres.2022.113606
Brear David, 2008, International Journal of Electronic Finance, V2, P1, DOI 10.1504/IJEF.2008.016881
Breves P, 2021, INT J ADVERT, V40, P1209, DOI 10.1080/02650487.2021.1881237
Breves PL, 2019, J ADVERTISING RES, V59, P440, DOI 10.2501/JAR-2019-030
Chatterjee D, 2002, MIS QUART, V26, P65, DOI 10.2307/4132321
Chong M., 2020, J APPL BUSINESS EC, V22, P81, DOI [DOI 10.33423/JABE.V22I10.3714, https://doi.org/10.33423/jabe.v22i10.3714]
Chung SY, 2017, PSYCHOL MARKET, V34, P481, DOI 10.1002/mar.21001
Cortez RM, 2020, IND MARKET MANAG, V89, P642, DOI 10.1016/j.indmarman.2019.03.012
COX DS, 1988, J CONSUM RES, V15, P111, DOI 10.1086/209149
Crymble L., 2019, MEET BREADCRUMBS S A
Dallal A., 2022, 52 AFFILIATE MARKETI
Davis SW, 2019, J BUS RES, V100, P150, DOI 10.1016/j.jbusres.2019.01.071
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Djafarova E, 2017, COMPUT HUM BEHAV, V68, P1, DOI 10.1016/j.chb.2016.11.009
Dolan R, 2019, EUR J MARKETING, V53, P2213, DOI 10.1108/EJM-03-2017-0182
Drenik G., 2022, Forbes
Dyer Richard., 2004, Heavenly Bodies: Film Stars and Society
Edelman B., 2013, HDB MARKET DESIGN
Edelman B., 2009, 08072 HARV BUS SCH
Edelman B, 2015, J MARKETING RES, V52, P1, DOI 10.1509/jmr.13.0472
Ehlers K., 2021, MICROINFLUENCERS SMA
Farrell JR, 2022, J ADVERTISING RES, V62, P35, DOI 10.2501/JAR-2021-017
Feng Y, 2021, INT J ADVERT, V40, P972, DOI 10.1080/02650487.2020.1824751
Forrester, 2016, NETW HELP DRIV AFF M
Fox AK, 2021, J CURR ISS RES AD, V42, P294, DOI 10.1080/10641734.2020.1763521
Fox PB, 2010, INT J E-BUS RES, V6, P1, DOI 10.4018/jebr.2010100901
Fredrickson BL, 2001, EMO SOC BEH, P123
Gallaugher JM, 2001, INFORM MANAGE, V38, P473, DOI 10.1016/S0378-7206(00)00083-5
Geyser W., 2022, WHAT IS AFFILIATE MA
Geyser W., 2021, What is an influencer - Social media influencers defined
Geyser W., 2021, The State of Influencer Marketing 2021: Benchmark Report
Geyser W., 2022, TOP INFLUENCER AFFIL
Gilliland DI, 2013, J BUS RES, V66, P2650, DOI 10.1016/j.jbusres.2012.06.005
Gregori N, 2014, J TRAVEL RES, V53, P196, DOI 10.1177/0047287513491333
Hagen L, 2016, GOV INFORM Q, V33, P783, DOI 10.1016/j.giq.2016.07.006
Hallur, 2022, 75 SHOCK AFF MARK ST
Hasan M., 2020, SOC MARK ADV C NOV V
Hayes M., 2022, WHAT IS AFFILIATE MA
Hier K., 2022, VOLLUM
Hoffman DL, 2000, HARVARD BUS REV, V78, P179
HOMER PM, 1990, J ADVERTISING, V19, P30, DOI 10.1080/00913367.1990.10673178
Hosman L.A., 2002, PERSUASION HDB DEV T, P371, DOI DOI 10.4135/9781412976046.N19
Hovland CI, 1953, Communication and Persuasion, DOI 10.1007/BF02713272
Howe A., 2021, TAPFILIATE
Hughes C, 2019, J MARKETING, V83, P78, DOI 10.1177/0022242919854374
Hung P., 2022, E COMMERCE TRENDS 20
Hwong YL, 2017, COMPUT HUM BEHAV, V68, P480, DOI 10.1016/j.chb.2016.11.068
Instagram, 2022, DATA POLICY
Instagram, 2021, DATA POLICY
Jin SV, 2019, J BRAND MANAG, V26, P522, DOI 10.1057/s41262-019-00151-z
John LK, 2017, J MARKETING RES, V54, P144, DOI 10.1509/jmr.14.0237
Jordan KN, 2019, P NATL ACAD SCI USA, V116, P3476, DOI 10.1073/pnas.1811987116
Kacewicz E, 2014, J LANG SOC PSYCHOL, V33, P125, DOI 10.1177/0261927X13502654
Kemp E, 2012, INT J ADVERT, V31, P339, DOI 10.2501/IJA-31-2-339-353
Ki CW, 2019, PSYCHOL MARKET, V36, P905, DOI 10.1002/mar.21244
Kusumasondjaja S, 2018, ASIA PAC J MARKET LO, V30, P1135, DOI 10.1108/APJML-10-2017-0267
Lafferty BA, 1999, J BUS RES, V44, P109, DOI 10.1016/S0148-2963(98)00002-2
Lee CH, 2019, IND MANAGE DATA SYST, V119, P1492, DOI 10.1108/IMDS-08-2018-0370
Lee D, 2018, MANAGE SCI, V64, P5105, DOI 10.1287/mnsc.2017.2902
Lee E, 2015, CYBERPSYCH BEH SOC N, V18, P552, DOI 10.1089/cyber.2015.0157
Lee I, 2012, BUS HORIZONS, V55, P441, DOI 10.1016/j.bushor.2012.04.001
Leung FF, 2022, J ACAD MARKET SCI, V50, P226, DOI 10.1007/s11747-021-00829-4
Li J, 2017, J APPL PSYCHOL, V102, P1075, DOI 10.1037/apl0000217
Lie S. A. M., 2022, CURR CONTENTS, V4, P362
LORD KR, 1995, J ADVERTISING, V24, P73, DOI 10.1080/00913367.1995.10673469
Lou C., 2019, Journal of Interactive Advertising, V19, P58, DOI DOI 10.1080/15252019.2018.1533501
Mangiò F, 2022, BUS HORIZONS, V65, P765, DOI 10.1016/j.bushor.2022.07.001
Mathur Arunesh, 2018, Proceedings of the ACM on Human-Computer Interaction, V2, DOI 10.1145/3274388
McShane L, 2019, J BUS-BUS MARK, V26, P1, DOI 10.1080/1051712X.2019.1565132
Mehta A, 2006, J ADVERTISING RES, V46, P49, DOI 10.2501/S0021849906060065
Mersey RD, 2010, J MEDIA RES STUD, V7, P39, DOI 10.1080/16522354.2010.11073506
Michalik M., 2019, PRACE NAUKOWE U EKON, V63
Miehling M., 2010, P 9 EUR C INF WARF S
Moran G, 2020, J PROD BRAND MANAG, V29, P533, DOI 10.1108/JPBM-09-2018-2014
Moulard JG, 2016, PSYCHOL MARKET, V33, P421, DOI 10.1002/mar.20888
Myers S, 2024, J MARKET THEORY PRAC, V32, P43, DOI 10.1080/10696679.2022.2093224
Newman DS, 2016, CLIN SUPERV, V35, P287, DOI 10.1080/07325223.2016.1218811
Newman ML, 2003, PERS SOC PSYCHOL B, V29, P665, DOI 10.1177/0146167203029005010
Oberlo, 2022, TOP INFL MARK PLATF
OHANIAN R, 1990, J ADVERTISING, V19, P39, DOI 10.1080/00913367.1990.10673191
Olbrich Rainer, 2019, International Journal of Internet Marketing and Advertising, V13, P47
Papatla P, 2002, J ADVERTISING, V31, P69, DOI 10.1080/00913367.2002.10673677
Parhankangas A, 2017, J BUS VENTURING, V32, P215, DOI 10.1016/j.jbusvent.2016.11.001
Pennebaker J. W., 2015, DEV PSYCHOMETRIC PRO, DOI [10.15781/T29G6Z, DOI 10.15781/T29G6Z]
Pennebaker J. W., 2015, Linguistic inquiry and word count: LIWC2015
Pennebaker JW, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0115844
Pentina I, 2018, J ADVERTISING, V47, P55, DOI 10.1080/00913367.2017.1405756
Petty R.E., 1986, The Elaboration Likelihood Model of Persuasion, DOI DOI 10.1007/978-1-4612-4964-1_1
PETTY RE, 1983, J CONSUM RES, V10, P135, DOI 10.1086/208954
Rangel, 2021, BRANDS GUID INFL AFF
Rastas, 2022, US AFF MARK STAT
Refersion Marketing, 2021, ULT GUID AFF MARK TR
Ren SN, 2023, J BUS RES, V156, DOI 10.1016/j.jbusres.2022.113476
Rietveld R, 2020, J INTERACT MARK, V49, P20, DOI 10.1016/j.intmar.2019.06.003
Ryan RM, 2000, AM PSYCHOL, V55, P68, DOI 10.1037/0003-066X.55.1.68
Sabate F, 2014, EUR MANAG J, V32, P1001, DOI 10.1016/j.emj.2014.05.001
Santora J., 2022, 12 TYPES INFLUENCERS
Schultz CD, 2017, ELECTRON COMMER R A, V26, P23, DOI 10.1016/j.elerap.2017.09.005
Segev S., 2023, J PROMOT MANAG, V29, P125, DOI [10.1080/10496491.2022.2108189, DOI 10.1080/10496491.2022.2108189]
Sehl K., 2022, ENGAGEMENT RATE CALC
Shahbaznezhad H, 2021, J INTERACT MARK, V53, P47, DOI 10.1016/j.intmar.2020.05.001
Shasha Teng, 2015, International Journal of Internet Marketing and Advertising, V9, P233
Sheth J, 2021, J BUS RES, V125, P780, DOI 10.1016/j.jbusres.2020.04.050
Sheth J, 2021, J MARKET THEORY PRAC, V29, P3, DOI 10.1080/10696679.2020.1860679
Statista, Daily time spent on social networking by internet users worldwide from 2012 to 2022
Statista, 2022, AFF MARK SPEND US 20
Subrahmanyam K, 2008, J APPL DEV PSYCHOL, V29, P420, DOI 10.1016/j.appdev.2008.07.003
Swani K, 2020, MARKET LETT, V31, P279, DOI 10.1007/s11002-020-09518-8
Swani K, 2017, J BUS RES, V79, P123, DOI 10.1016/j.jbusres.2017.06.003
Swani K, 2017, IND MARKET MANAG, V62, P77, DOI 10.1016/j.indmarman.2016.07.006
Swaygroup, 2021, INFLUENCER MARKETING
Syrdal H.A., 2016, 2016 SMA P, P74
Syrdal HA, 2018, J MARKET THEORY PRAC, V26, P4, DOI 10.1080/10696679.2017.1389243
Tafesse W, 2018, J CONSUM MARK, V35, P241, DOI 10.1108/JCM-08-2016-1905
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Tausczik YR, 2010, J LANG SOC PSYCHOL, V29, P24, DOI 10.1177/0261927X09351676
Taylor CR, 2021, J MARKET THEORY PRAC, V29, P51, DOI 10.1080/10696679.2020.1860681
Taylor CR, 2020, INT J ADVERT, V39, P889, DOI 10.1080/02650487.2020.1822104
Toma CL, 2015, J LANG SOC PSYCHOL, V34, P25, DOI 10.1177/0261927X14554484
Turley LW, 1997, J ADVERTISING, V26, P39, DOI 10.1080/00913367.1997.10673534
Tuten T.L. Soloman., 2015, Social media marketing, V2nd
Vaishnav B, 2023, J BUS RES, V157, DOI 10.1016/j.jbusres.2022.113564
van Asperen M, 2018, INT J HOSP TOUR ADM, V19, P78, DOI 10.1080/15256480.2017.1305313
VanMeter R, 2018, J INTERACT MARK, V43, P83, DOI 10.1016/j.intmar.2018.03.003
Voorveld HAM, 2019, J ADVERTISING, V48, P14, DOI 10.1080/00913367.2019.1588808
Vrontis D, 2021, INT J CONSUM STUD, V45, P617, DOI 10.1111/ijcs.12647
WOOD W, 1988, PERS SOC PSYCHOL B, V14, P172, DOI 10.1177/0146167288141017
Woodroof PJ, 2020, J PROD BRAND MANAG, V29, P675, DOI 10.1108/JPBM-05-2019-2362
Xiao M, 2018, J MEDIA BUS STUD, V15, P188, DOI 10.1080/16522354.2018.1501146
Xu WA, 2018, COMPUT HUM BEHAV, V89, P199, DOI 10.1016/j.chb.2018.07.041
Zhang J, 2020, J BUS IND MARK, V35, P721, DOI 10.1108/JBIM-06-2018-0190
NR 143
TC 2
Z9 2
U1 43
U2 79
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD AUG
PY 2023
VL 163
AR 113875
DI 10.1016/j.jbusres.2023.113875
EA APR 2023
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA F8IM0
UT WOS:000984730100001
DA 2024-03-27
ER
PT J
AU Han, WJ
Ozdemir, O
Agarwal, S
AF Han, Wenjia
Ozdemir, Ozgur
Agarwal, Shivam
TI Linking social media marketing to restaurant performance - the
moderating role of advertising expenditure
SO JOURNAL OF HOSPITALITY AND TOURISM INSIGHTS
LA English
DT Article; Early Access
DE Social media marketing; Restaurant performance; Sales revenue; Audience
engagement; Consumer sentiment; Advertising expenditure; Web scraping;
Natural language processing
ID ONLINE REVIEWS; FINANCIAL PERFORMANCE; IMPACT; SERVICE; GRATIFICATIONS;
SATISFACTION; ENGAGEMENT; BEHAVIOR; INDUSTRY; LOYALTY
AB PurposeBuilt upon customer engagement marketing theory and uses and gratification theory, this study examines the link between individual social media marketing (SMM) performance indicators and restaurant sales performance at the firm level. Moreover, the study investigates the moderating effect of advertising expenditure on this proposed relationship.Design/methodology/approachRandom effect regression models were developed in Stata to examine the associations between SMM performance indicators, advertising expenditure, and restaurant firm revenue. Twelve years of SMM data from brands' Facebook pages were collected with a web scraper built in Python. Natural language processing was used to analyze the sentiment of user-generated content (UGC).FindingsThe results suggest that restaurant annual sales revenue increases as the volume of brand posts, "like"s, "share"s and positive comments on restaurants' Facebook pages increase. However, the total number of comments and the number of negative comments show non-significant associations with revenue. Firm advertising expenditure negatively moderates the relationships between sales revenue and the number of "like"s, "share"s, total comments and positive comments.Practical implicationsRestaurants benefit from making frequent posts on SNSs. Promotions that motivate online users to "like", share, and comment on brand posts should be implemented. Firms with limited advertising budgets are encouraged to actively create buzz on SNSs due to evidenced stronger effects of UGC on sales performance than large advertisers.Originality/valueThis research bridges the gap by studying the effects of individual SMM performance indicators on restaurant financial outcomes. The findings support the effectiveness of SMM; and, for the first time, demonstrate that SMM could generate a more profound impact for firms with low advertising budgets.
C1 [Han, Wenjia] Purdue Univ, Doermer Sch Business, Dept Hospitality & Tourism Management, Ft Wayne, IN 46805 USA.
[Ozdemir, Ozgur] Univ Nevada, Las Vegas, NV USA.
[Agarwal, Shivam] Florida Int Univ, Chapman Sch Business, North Miami, FL USA.
C3 Nevada System of Higher Education (NSHE); University of Nevada Las
Vegas; State University System of Florida; Florida International
University
RP Han, WJ (autor correspondiente), Purdue Univ, Doermer Sch Business, Dept Hospitality & Tourism Management, Ft Wayne, IN 46805 USA.
EM han758@pfw.edu; ozgur.ozdemir@unlv.edu; sagar009@fiu.edu
OI Han, Wenjia/0000-0002-9739-9294
CR Alalwan AA, 2017, TELEMAT INFORM, V34, P1177, DOI 10.1016/j.tele.2017.05.008
Aluri A, 2016, CORNELL HOSP Q, V57, P250, DOI 10.1177/1938965515615685
Ampountolas A, 2019, J HOSP TOUR INSIGHTS, V2, P75, DOI 10.1108/JHTI-07-2018-0040
Ananda AS, 2019, ASIA PAC J MARKET LO, V31, P398, DOI 10.1108/APJML-03-2018-0115
Baber R, 2023, J HOSP TOUR INSIGHTS, V6, P2298, DOI 10.1108/JHTI-06-2022-0270
Book LA, 2016, J TRAVEL RES, V55, P993, DOI 10.1177/0047287515606810
Cawley C., 2021, TECH CO 0128
Chan NL, 2011, J TRAVEL TOUR MARK, V28, P345, DOI 10.1080/10548408.2011.571571
Choi EK, 2016, J HOSP MARKET MANAG, V25, P771, DOI 10.1080/19368623.2016.1100102
Choi S, 2018, INT J CONTEMP HOSP M, V30, P2586, DOI 10.1108/IJCHM-04-2017-0195
Chowdhury EK, 2022, BUS SOC REV, V127, P437, DOI 10.1111/basr.12219
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Denny's Corporation, 2021, DENN CORP INV REL SE
Dhaoui C, 2017, J CONSUM MARK, V34, P480, DOI 10.1108/JCM-03-2017-2141
Eggert A, 2014, J SERV RES-US, V17, P23, DOI 10.1177/1094670513485823
Etlinger S., 2011, A Framework for Social Analytics
Gambetti A, 2022, INT J CONTEMP HOSP M, V34, P3300, DOI 10.1108/IJCHM-09-2021-1206
Gao BJ, 2020, J TRAVEL TOUR MARK, V37, P396, DOI 10.1080/10548408.2020.1767261
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
Han H, 2018, J QUAL ASSUR HOSP TO, V19, P66, DOI [10.1080/1528008x.2017.1331153, 10.1080/1528008X.2017.1331153]
Han S, 2021, CORNELL HOSP Q, V62, P89, DOI 10.1177/1938965520973587
Harmeling CM, 2017, J ACAD MARKET SCI, V45, P312, DOI 10.1007/s11747-016-0509-2
Ho JL, 2022, INT J CONTEMP HOSP M, V34, P4206, DOI 10.1108/IJCHM-12-2021-1469
Hovland C., 1957, ORDER PRESENTATION P
Hu N, 2008, INFORM TECHNOL MANAG, V9, P201, DOI 10.1007/s10799-008-0041-2
Hua N, 2014, INT J HOSP MANAG, V36, P137, DOI 10.1016/j.ijhm.2013.08.012
Hua N, 2013, INT J CONTEMP HOSP M, V25, P723, DOI 10.1108/IJCHM-Aug-2012-0138
Hutto C. J., 2014, P INT AAAI C WEB SOC, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
Katz E., 1959, STUDIES PUBLIC COMMU, V2, P1
Kaur W, 2019, TELEMAT INFORM, V39, P25, DOI 10.1016/j.tele.2018.12.005
Kim D, 2018, INT J HOSP MANAG, V68, P89, DOI 10.1016/j.ijhm.2017.09.015
Kim HB, 2005, TOURISM MANAGE, V26, P549, DOI 10.1016/j.tourman.2004.03.010
Kim S, 2015, INT J HOSP MANAG, V49, P40, DOI 10.1016/j.ijhm.2015.05.006
Koh Y, 2009, INT J HOSP MANAG, V28, P289, DOI 10.1016/j.ijhm.2008.10.001
Kumar A, 2016, J MARKETING, V80, P7, DOI 10.1509/jm.14.0249
Kumar V, 2013, MARKET SCI, V32, P194, DOI 10.1287/mksc.1120.0768
Kwok L, 2022, CORNELL HOSP Q, V63, P528, DOI 10.1177/19389655211028143
Li FF, 2021, J ACAD MARKET SCI, V49, P51, DOI 10.1007/s11747-020-00733-3
Li J, 2021, TOURISM ECON, V27, P3, DOI 10.1177/1354816619867807
Lipsman A, 2012, J ADVERTISING RES, V52, P40, DOI 10.2501/JAR-52-1-040-052
Lovett John., 2011, Social Media Metrics Secrets
Madanoglu M, 2011, J RETAILING, V87, P406, DOI 10.1016/j.jretai.2011.02.003
McDonald's Corporation, 2016, US, P10
Meta, 2023, MET REP 4 QUART FULL
Michael Luca., 2016, Harvard Business School NOM Unit Working Paper 12-016
Michopoulou E, 2019, INT J HOSP MANAG, V76, P308, DOI 10.1016/j.ijhm.2018.05.019
Muntinga DG, 2011, INT J ADVERT, V30, P13, DOI 10.2501/IJA-30-1-013-046
Murdough Chris., 2009, J INTERACTIVE ADVERT, V10, P94, DOI DOI 10.1080/15252019.2009.10722165
Myers D., 2015, J COMPUT SCI COLL, V31, P83
Nazlan NH, 2018, J CONSUM BEHAV, V17, P449, DOI 10.1002/cb.1731
Needles A., 2013, IN PRESS
Omondi-Ochieng Peter, 2019, Journal of Economics, Finance and Administrative Science, V24, P327
Ou YC, 2017, J ACAD MARKET SCI, V45, P336, DOI 10.1007/s11747-016-0477-6
Ozdemir O, 2020, INT J CONTEMP HOSP M, V32, P2925, DOI 10.1108/IJCHM-03-2020-0167
Paniagua J, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12208660
Phillips P, 2017, J TRAVEL RES, V56, P235, DOI 10.1177/0047287516636481
Rhou Y, 2014, INT J HOSP MANAG, V39, P41, DOI 10.1016/j.ijhm.2014.02.001
Ruggiero T.E., 2000, MASS COMMUNICATION S, V3, P3, DOI [https://doi.org/10.1207/S15327825MCS0301_02, DOI 10.1207/S15327825MCS0301_02, 10.1207/S15327825MCS0301_02]
Ryu K, 2012, INT J CONTEMP HOSP M, V24, P200, DOI 10.1108/09596111211206141
Simonetti A, 2022, INT J CONTEMP HOSP M, V34, P2049, DOI 10.1108/IJCHM-09-2021-1091
Srinivasan SS, 2002, J RETAILING, V78, P41, DOI 10.1016/S0022-4359(01)00065-3
U.S. Securities and Exchange Commission, 2001, CF ACC INT GUID
US Census Bureau, 2018, FRANCH AM NOT JUST F
van Doorn J, 2017, MARKET LETT, V28, P607, DOI 10.1007/s11002-017-9433-6
Viglia G, 2016, INT J CONTEMP HOSP M, V28, P2035, DOI 10.1108/IJCHM-05-2015-0238
Wang YQ, 2021, INT J HOSP MANAG, V95, DOI 10.1016/j.ijhm.2021.102895
Xie K, 2017, CORNELL HOSP Q, V58, P240, DOI 10.1177/1938965516686109
ZAJONC RB, 1968, J PERS SOC PSYCHOL, V9, P1, DOI 10.1037/h0025848
NR 68
TC 2
Z9 2
U1 14
U2 16
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2514-9792
EI 2514-9806
J9 J HOSP TOUR INSIGHTS
JI J. Hosp. Tour. Insights
PD 2023 AUG 15
PY 2023
DI 10.1108/JHTI-03-2023-0217
EA AUG 2023
PG 19
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA O8HP4
UT WOS:001046166700001
DA 2024-03-27
ER
PT J
AU Luri, I
Schau, HJ
Ghosh, B
AF Luri, Ignacio
Schau, Hope Jensen
Ghosh, Bikram
TI Metaphor-Enabled Marketplace Sentiment Analysis
SO JOURNAL OF MARKETING RESEARCH
LA English
DT Article; Early Access
DE market metaphors; marketplace sentiments; automated text analysis;
natural language processing; sentiment analysis
ID CONCEPTUAL METAPHOR; QUALITATIVE DATA; PUBLIC DISCOURSE; TEXT ANALYSIS;
PERCEPTIONS; CONSUMERS; SCIENCE; CRISIS; WORDS; NEWS
AB Textual data require an analytical trade-off between breadth and depth. Automated approaches locate patterns across large swaths of data points but sacrifice qualitative insight because they are not well equipped to deal with context-determined ways to express meaning, like figurative language. To strengthen the power of automated text analysis, researchers seek hybrid methodologies that combine computer-augmented analysis with sociocultural researcher insights based on qualitative textual interpretation. This article demonstrates a new method, which the authors term metaphor-enabled marketplace sentiment analysis (MEMSA). Building on existing automated text analysis methodologies linking word lists to sentiments, MEMSA adds metaphors that associate topics with sentiments across domains. Using MEMSA, researchers can leverage the sentiment potential of these located metaphors and scale insights to the level of big textual data by employing a dictionary approach enhanced by a specific and useful linguistic property of metaphors: their predictable structure in text (something is something else). This article shows that metaphors add associative detail to sentiments, revealing the targets and sources of sentiments that underlie the associations. Understanding nuanced market sentiments enables marketers to identify sentiment-based trends embedded in market discourse, so they can better formulate, target, position, and communicate value propositions for products and services.
C1 [Luri, Ignacio] DePaul Univ, Driehaus Coll Business, Mkt, Chicago, IL 60604 USA.
[Schau, Hope Jensen] Univ Calif Irvine, Paul Merage Sch Business, Mkt, Irvine, CA USA.
[Ghosh, Bikram] Univ Arizona, Eller Sch Management, Mkt, Tucson, AZ USA.
C3 DePaul University; University of California System; University of
California Irvine; University of Arizona
RP Luri, I (autor correspondiente), DePaul Univ, Driehaus Coll Business, Mkt, Chicago, IL 60604 USA.
EM i.luri@depaul.edu; schauh@uci.edu; bghosh@arizona.edu
OI Ghosh, Bikram/0000-0002-8691-7762
CR Alaparthi S, 2021, J MARK ANAL, V9, P118, DOI 10.1057/s41270-021-00109-8
Alberto PL, 2012, SOC HIST, V37, P261, DOI 10.1080/03071022.2012.701052
Allahverdyan AE, 2013, PHYS REV E, V88, DOI 10.1103/PhysRevE.88.062804
Amernic Je., 2009, Critical Perspectives on Accounting, V20, P875, DOI DOI 10.1016/J.CPA.2009.06.004
Andelic N, 2019, BRIT J SOC PSYCHOL, V58, P630, DOI 10.1111/bjso.12303
Ang SH, 2006, J ADVERTISING, V35, P39, DOI 10.1080/00913367.2006.10639226
Angela Fahrni, 2008, S AFF LANG HUM MACH, DOI [10.5167/uzh-8810, DOI 10.5167/UZH-8810]
Anthony L, 2005, 2005 IEEE INTERNATIONAL PROFESSIONAL COMMUNICATION CONFERENCE (IPCC), P729
Appel O, 2015, ACTA POLYTECH HUNG, V12, P87, DOI 10.12700/APH.12.3.2015.3.6
ARNDT J, 1985, J MARKETING, V49, P11, DOI 10.2307/1251612
Balahur A., 2009, Proceeding WOMSA, V9, P1
Basiri ME, 2020, IEEE T COMPUT SOC SY, V7, P113, DOI 10.1109/TCSS.2019.2951326
Berenson Alonit., 2013, Journalism and Mass Communication, V19, P240
Berger J, 2022, AM PSYCHOL, V77, P525, DOI 10.1037/amp0000882
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Bernthal MJ, 2005, J CONSUM RES, V32, P130, DOI 10.1086/429605
Bickes H, 2014, DISCOURSE SOC, V25, P424, DOI 10.1177/0957926514536956
BLACKBURN MJ, 1993, MATER DESIGN, V14, P19, DOI 10.1016/0261-3069(93)90041-S
Boeynaems A, 2017, INT J COMMUN-US, V11, P2861
Bollen J, 2011, J COMPUT SCI-NETH, V2, P1, DOI 10.1016/j.jocs.2010.12.007
Boyd RL, 2016, CONSUMER PSYCHOLOGY IN A SOCIAL MEDIA WORLD, P222
Brown David W., 2017, Clinton-Trump Corpus
Budd K, 2019, ORGANIZATION, V26, P802, DOI 10.1177/1350508418812569
Burgers C, 2016, COMMUN THEOR, V26, P410, DOI 10.1111/comt.12096
Capelli S, 2009, PSYCHOL MARKET, V26, P1079, DOI 10.1002/mar.20313
Cardillo ER, 2012, NEUROIMAGE, V59, P3212, DOI 10.1016/j.neuroimage.2011.11.079
Cariola L.A., 2019, ICAME Journal, V38, P5, DOI DOI 10.2478/ICAME-2014-0001
Chapman C, 2020, J MARKETING, V84, P26, DOI 10.1177/0022242919886882
Charteris-Black J, 2004, CORPUS APPROACHES TO CRITICAL METAPHOR ANALYSIS, P243
CORBIN J, 1990, Z SOZIOL, V19, P418, DOI 10.1007/BF00988593
Cornelissen JP, 2008, ORGAN STUD, V29, P7, DOI 10.1177/0170840607086634
Cornelissen JP, 2011, ORGAN STUD, V32, P1701, DOI 10.1177/0170840611425729
Cornelissen JP, 2003, PSYCHOL MARKET, V20, P209, DOI 10.1002/mar.10068
Coulter RA, 2001, J ADVERTISING, V30, P1
David O, 2016, CONSTR FRAMES, V8, P214, DOI 10.1075/cf.8.2.04dav
Deignan A, 2008, CAMB HANDB PSYCHOL, P280
Delbaere M, 2019, MARKETING THEOR, V19, P391, DOI 10.1177/1470593118796697
Devika MD, 2016, PROCEDIA COMPUT SCI, V87, P44, DOI 10.1016/j.procs.2016.05.124
Edward Loper, 2002, arXiv, DOI DOI 10.48550/ARXIV.CS/0205028
Efrat R., 2006, Theoretical inquiries in Law, V7, P365
Erik Cambria, 2017, A Practical Guide to Sentiment Analysis
Firth, 1957, STUDIES LINGUISTIC A, P10
Fournier S, 1998, J CONSUM RES, V24, P343, DOI 10.1086/209515
Gadekallu T., 2019, Sentiment Analysis and Knowledge Discovery in Contemporary Business, P77
Gentner D, 2008, CAMB HANDB PSYCHOL, P109
Gibbs RW, 2011, DISCOURSE PROCESS, V48, P529, DOI 10.1080/0163853X.2011.606103
Giesler M, 2016, J CONSUM RES, V43, P497, DOI 10.1093/jcr/ucw047
Giesler M, 2014, J CONSUM RES, V41, P840, DOI 10.1086/677842
Gopaldas A, 2014, J CONSUM RES, V41, P995, DOI 10.1086/678034
Grady J., 1997, PhD dissertation
Gray JH, 1998, QUAL QUANT, V32, P419, DOI 10.1023/A:1004357719066
Gries Stefan Th., 2004, INT J CORPUS LINGUIS, V9, P97
Grimmer J, 2013, POLIT ANAL, V21, P267, DOI 10.1093/pan/mps028
Gunter B, 2014, INT J MARKET RES, V56, P231, DOI 10.2501/IJMR-2014-014
Henry PC, 2005, J CONSUM RES, V31, P766, DOI 10.1086/426610
Hirschman E.C., 2007, Marketing Theory, VI7, P227, DOI DOI 10.1177/1470593107080343
Homburg C, 2015, J MARKETING RES, V52, P629, DOI 10.1509/jmr.11.0448
Hovy D, 2021, LANG LINGUIST COMPAS, V15, DOI 10.1111/lnc3.12432
Huang SC, 2019, J PERS SOC PSYCHOL, V117, P697, DOI 10.1037/pspa0000164
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Humphreys A, 2014, J CONSUM RES, V41, P877, DOI 10.1086/677905
Humphreys A, 2013, J CONSUM RES, V40, P773, DOI 10.1086/672358
Humphreys A, 2010, J MARKETING, V74, P1
HUNT SD, 1995, J BUS RES, V33, P81, DOI 10.1016/0148-2963(94)00057-L
Hutto C. J., 2014, P INT AAAI C WEB SOC, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
Hynes RichardM., 2008, Fla. L. Rev, V60, P1
Jackle S., 2017, POTENTIAL PEDOTPSS H, P1, DOI [DOI 10.1145/3097286.3097298, 10.1145/3097286.3097298]
Jacobsen E., 2004, POLITICS FOOD, P59
James RichardE., 2002, Washburn Law Journal, V42, P143
Jeroen Wittink, 2011, P C ORG KNOWL LEARN
Ji QH, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0239050
Kern FG, 2018, J MIX METHOD RES, V12, P166, DOI 10.1177/1558689816651032
Koenig A., 2013, Academy of Management Proceedings, V2013, P13417
Kövecses Zoltán, 2010, DELTA, V26, P739
Kovecses Zoltan., 1997, Selected Papers on Theoretical and Applied Linguistics, V11, P33
Kurzer Paulette., 2007, Journal of Public Policy, V27, P103, DOI DOI 10.1017/S0143814X07000657
Lakoff G., 1980, Metaphors we live by
Lakoff G, 2006, COGN LINGUIST RES, V34, P185, DOI 10.1515/9783110199901.185
Landau MJ, 2014, SOC PERSONAL PSYCHOL, V8, P463, DOI 10.1111/spc3.12125
Lee SWS, 2014, J EXP SOC PSYCHOL, V54, P61, DOI 10.1016/j.jesp.2014.04.007
Leech NL, 2007, SCHOOL PSYCHOL QUART, V22, P557, DOI 10.1037/1045-3830.22.4.557
Lemon LL, 2020, QUAL REP, V25, P604
Liu B, 2011, DATA CENTRIC SYST AP, P459, DOI 10.1007/978-3-642-19460-3_11
Lönneker-Rodman B, 2008, LANG RESOUR EVAL, V42, P293, DOI 10.1007/s10579-008-9073-9
Louie Andre, 2022, FinancesOnline
MacDonald E, 2023, REV SOC ECON, V81, P363, DOI 10.1080/00346764.2021.1912381
Machiels CJA, 2017, J RETAIL CONSUM SERV, V37, P195, DOI 10.1016/j.jretconser.2017.02.009
MACINNIS DJ, 1987, J CONSUM RES, V13, P473, DOI 10.1086/209082
Mars MM, 2023, J ACAD MARKET SCI, V51, P418, DOI 10.1007/s11747-022-00904-4
McQuarrie EF, 1999, J CONSUM RES, V26, P37, DOI 10.1086/209549
Meire M, 2019, J MARKETING, V83, P21, DOI 10.1177/0022242919873903
Melumad S, 2019, J MARKETING RES, V56, P259, DOI 10.1177/0022243718815429
Michael Erard, 2012, research report
Mikolov T, 2013, Advances in neural information processing systems, DOI DOI 10.48550/ARXIV.1310.4546
Miles C, 2014, MARKETING THEOR, V14, P3, DOI 10.1177/1470593113506433
Mimoun L, 2022, J CONSUM RES, V48, P1073, DOI 10.1093/jcr/ucab043
Mithen Steven., 1996, The Pre-History of the Mind
Mohammad S., 2016, P 5 JOINT C LEXICAL, P23, DOI [10.18653/v1/S16-2003, DOI 10.18653/V1/S16-2003]
Gil MM, 2019, COMPLUT J ENGL STUD, V27, P223, DOI 10.5209/cjes.63865
Natow RS, 2020, QUAL RES, V20, P160, DOI 10.1177/1468794119830077
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Nicolas G., 2019, PSYARXIV, DOI DOI 10.31234/OSF.IO/AFM8K
O'Malley L, 2008, MARKETING THEOR, V8, P167, DOI 10.1177/1470593108089203
O'Mara-Shimek M, 2015, DISCOURSE COMMUN, V9, P103, DOI 10.1177/1750481314556047
Oberlechner T, 2004, BRIT J SOC PSYCHOL, V43, P133, DOI 10.1348/014466604322916024
Ordenes FV, 2017, J CONSUM RES, V43, P875, DOI 10.1093/jcr/ucw070
Paulo M, 2022, EXPERT SYST APPL, V207, DOI 10.1016/j.eswa.2022.117974
Peñaloza L, 2011, J CONSUM RES, V38, P743, DOI 10.1086/660116
Pennebaker J. W., 2015, The Development and Psychometric Properties of LIWC2015
Puschmann C, 2018, SOC MEDIA SOC, V4, DOI 10.1177/2056305118797724
Ramteke J, 2016, 2016 INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT), VOL 1, P107
Rindfleisch A., 1996, BUS HORIZONS, V39, P3
Ryshina-Pankova M, 2010, MOD LANG J, V94, P181, DOI 10.1111/j.1540-4781.2010.01016.x
Salminen J, 2019, J BUS RES, V101, P203, DOI 10.1016/j.jbusres.2019.04.018
Salonova T, 2020, LAB-RUSS REV SOC RES, V12, P5, DOI 10.25285/2078-1938-2020-12-3-5-25
Sandlin JA, 2009, J CONSUM CULT, V9, P79, DOI 10.1177/1469540508099703
Santa Ana O, 1999, DISCOURSE SOC, V10, P191, DOI 10.1177/0957926599010002004
Scaraboto D, 2013, J CONSUM RES, V39, P1234, DOI 10.1086/668298
Schmitt R, 2005, QUAL REP, V10, P358
Semino E, 2008, METAPHOR IN DISCOURSE, P1
Shayegan Mohammad Javad, 2021, International Journal of Web Based Communities, V17, P149, DOI 10.1504/IJWBC.2021.116641
Shimizu Toshihiro., 2010, Osaka Keidai Ronshu (Journal of Osaka University of Economics), V61, P105
Soanes CatherineAngus Stevenson., 2004, CONCISE OXFORD ENGLI
Sobande F, 2020, MARKETING THEOR, V20, P65, DOI 10.1177/1470593119847250
SPIGGLE S, 1994, J CONSUM RES, V21, P491, DOI 10.1086/209413
Statista, 2023, Big Data
Steen G, 1999, AMST STUD THEORY HIS, V175, P57
Steen G, 2007, CULT LENG REPRESENT, V5, P9
Stefanowitsch A, 2006, TRENDS LINGUIST-STUD, V171, P63
Stickles E, 2016, CONSTR FRAMES, V8, P166, DOI 10.1075/cf.8.2.03sti
Straehle C, 1999, DISCOURSE SOC, V10, P67, DOI 10.1177/0957926599010001004
Sundar A, 2014, J MARKETING, V78, P138, DOI 10.1509/jm.13.0253
Sweet E, 2018, INT J HEALTH SERV, V48, P495, DOI 10.1177/0020731418776580
Tausczik YR, 2010, J LANG SOC PSYCHOL, V29, P24, DOI 10.1177/0261927X09351676
Toubia O, 2017, MARKET SCI, V36, P1, DOI 10.1287/mksc.2016.0994
Turner Mark., 1987, DEATH IS MOTHER BEAU
Uma V, 2019, Sentiment Analysis and Knowledge Discovery in Contemporary Business, P15
Weber K, 2005, POETICS, V33, P227, DOI 10.1016/j.poetic.2005.09.011
Wiebe J. M., 1994, Computational Linguistics, V20, P233
Wu SX, 2019, EXPERT SYST APPL, V116, P285, DOI 10.1016/j.eswa.2018.09.024
Young L, 2012, POLIT COMMUN, V29, P205, DOI 10.1080/10584609.2012.671234
Zaltman Gerald., 2008, MARKETING METAPHORIA
Zammarchi Gianpaolo., 2021, Cladag 2021 Book of Abstracts and Short Papers, P436
NR 143
TC 0
Z9 0
U1 43
U2 43
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2437
EI 1547-7193
J9 J MARKETING RES
JI J. Mark. Res.
PD 2023 OCT 9
PY 2023
DI 10.1177/00222437231191526
EA OCT 2023
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA T8SU1
UT WOS:001080636900001
DA 2024-03-27
ER
PT J
AU Nair, K
Gupta, R
AF Nair, Kiran
Gupta, Ruchi
TI Application of AI technology in modern digital marketing environment
SO WORLD JOURNAL OF ENTREPRENEURSHIP MANAGEMENT AND SUSTAINABLE DEVELOPMENT
LA English
DT Article
DE Artificial intelligence; Content curation; Propensity modeling; Machine
learning; Predictive analytics; Lead scoring; Dynamic pricing
AB Purpose - The purpose of this paper is to explore the various application of artificial intelligence (AI) to social media and digital advertising professionals and agencies to specialize to an advanced degree and maintain collaboration and creativity to bring a better return on investment.
Design/methodology/approach - Digital marketers are still oblivious to the importance of AI application, while some others simply do not know how to implement it. AI is currently acting as a significant disruption in digital and social media marketing worldwide.
Findings - Based on the literature review, the paper identifies the various AI applications in the field of digital media marketing.
Originality/value - This paper can serve as a useful guide for social media marketers to implement AI applications to impact digital marketing strategies better.
C1 [Nair, Kiran] Abu Dhabi Sch Management, Mkt, Abu Dhabi, U Arab Emirates.
[Gupta, Ruchi] Shaheed Bhagat Singh Coll, New Delhi, India.
RP Nair, K (autor correspondiente), Abu Dhabi Sch Management, Mkt, Abu Dhabi, U Arab Emirates.
EM kirannairs@hotmail.com; Ruchigupta.sbsc@gmail.com
RI Nair, Kiran S/J-4898-2017; nair, kiran/T-9054-2018
OI nair, kiran/0000-0001-9046-1713
CR Basu, 2020, MANAGEMENT ACCOUNTAN, V55, P1130
Blut M, 2020, J ACAD MARKET SCI, V48, P649, DOI 10.1007/s11747-019-00680-8
Bulearca M., 2010, Global Business Management Research, V2, P237
Csordas A., 2020, PAPERS EC INFORM, V12, P3, DOI [10.7160/aol.2020.120101, DOI 10.7160/AOL.2020.120101]
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Drummond C, 2020, EUR J MARKETING, V54, P1247, DOI 10.1108/EJM-02-2019-0183
Erokhina T.B., 2018, EUROPEAN RES STUDIES, V21, P861
Feng YY, 2018, REC MANAG J, V28, P62, DOI 10.1108/RMJ-09-2016-0022
Geng RB, 2020, IND MANAGE DATA SYST, V120, P464, DOI 10.1108/IMDS-05-2019-0270
Jarek K, 2019, CENT EUR BUS REV, V8, P46, DOI 10.18267/j.cebr.213
Kaczorowska-Spychalska D, 2019, MANAG-POL, V23, P251, DOI 10.2478/manment-2019-0015
Kendall L, 2020, INFORM SYST FRONT, V22, P585, DOI 10.1007/s10796-020-10015-6
Li J, 2020, INT J STRATEG PROP M, V24, P90, DOI 10.3846/ijspm.2019.11535
Curiel CP, 2020, COMMUN SOC-SPAIN, V33, P175, DOI 10.15581/003.33.2.175-192
Ballestar MT, 2019, REV MANAG SCI, V13, P589, DOI 10.1007/s11846-018-0316-x
NR 15
TC 11
Z9 13
U1 34
U2 117
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 2042-5961
EI 2042-597X
J9 WORLD J ENTREP MANAG
JI World J. Entrep. Manag. Sustain. Dev.
PD JUL 26
PY 2021
VL 17
IS 3
BP 318
EP 328
DI 10.1108/WJEMSD-08-2020-0099
EA JAN 2021
PG 11
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA TP4ED
UT WOS:000613691000001
DA 2024-03-27
ER
PT J
AU Li, XL
Ching, AT
AF Li, Xinlong
Ching, Andrew T.
TI How Does a Firm Adapt in a Changing World? The Case of Prosper
Marketplace
SO MARKETING SCIENCE
LA English
DT Article; Early Access
DE choice modeling; electronic commerce; machine learning; structural
models
ID DYNAMIC-MODELS; HETEROGENEITY; BRAND; DRIFT
AB We propose a generalized revealed preference approach to infer how a firm adapts to a changing environment and provide a step-by-step guide to explain how to implement it in general. To illustrate this new approach, we apply it to Prosper, which is a peer-to-peer lending platform. We develop a structural model, in which Prosper uses an adaptive learning algorithm to continuously update its predictive models about borrowers' and lenders' behavior as more data become available and uses these updated models to help assign loan ratings over time. To infer which adaptive learning algorithm Prosper may adopt, we consider a set of algorithms motivated by the machine learning literature. For each algorithm, we use observed Prosper loan-rating decisions to estimate the structural parameters of Prosper's objective function. By comparing the goodness-of-fit of these algorithm-specific models, we find that Prosper most likely uses an ensemble algorithm, which selects past observations based on their economic conditions. We conduct counterfactual experiments to shed light on: (i) How does an exclusive focus on either accurately reporting loan risk or expected current revenue influence Prosper's decision making? (ii) What is the value of adaptive learning for Prosper? (iii) Is there any potential for Prosper to improve its current adaptive learning algorithm?
C1 [Li, Xinlong] Nanyang Technol Univ, Nanyang Business Sch, Singapore 639798, Singapore.
[Ching, Andrew T.] Johns Hopkins Univ, Carey Business Sch, Baltimore, MD 21202 USA.
C3 Nanyang Technological University; Johns Hopkins University
RP Ching, AT (autor correspondiente), Johns Hopkins Univ, Carey Business Sch, Baltimore, MD 21202 USA.
EM xinlong.li@ntu.edu.sg; andrew.ching@jhu.edu
RI Li, Xinlong/IWV-1261-2023
OI Ching, Andrew/0000-0002-0907-284X
FU Nanyang Technological University
FX Financial support from Nanyang Technological University [Startup Grant]
is gratefully acknowledged.
CR Aguirregabiria V, 2020, REV IND ORGAN, V56, P203, DOI 10.1007/s11151-019-09722-5
[Anonymous], 1993, Bounded Rationality in Macroeconomics
Bajari P, 2007, ECONOMETRICA, V75, P1331, DOI 10.1111/j.1468-0262.2007.00796.x
Balakrishnan S, 2008, J MACH LEARN RES, V9, P313
Chan T, 2022, Marketing Sci., V41, P795
Chauvet M, 1995, An econometric characterization of business cycle dynamics with factor structure and regime switching
Chauvet M, 2008, J BUS ECON STAT, V26, P42, DOI 10.1198/073500107000000296
Ching AT, 2010, INT ECON REV, V51, P1175, DOI 10.1111/j.1468-2354.2010.00615.x
Cogley T, 2005, REV ECON DYNAM, V8, P528, DOI 10.1016/j.red.2005.02.001
Crespo F, 2005, FUZZY SET SYST, V150, P267, DOI 10.1016/j.fss.2004.03.028
Cunningham S, 2014, Default rates at Lending Club & Prosper: When loans go bad
Dew R, 2020, J MARKETING RES, V57, P55, DOI 10.1177/0022243719874047
Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361
Doraszelski U, 2018, AM ECON REV, V108, P565, DOI 10.1257/aer.20160177
Erdemy T, 2003, QME-QUANT MARK ECON, V1, P5, DOI 10.1023/A:1023536326497
Evans George W., 2013, RETHINKING EXPECTATI, P68
Evans GW, 2020, Oxford Research Encyclopedia of Economics and Finance, DOI [10.1093/acrefore/9780190625979.013.508, DOI 10.1093/ACREFORE/9780190625979.013.508]
Forman G., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P252, DOI 10.1145/1148170.1148216
Freedman S.M., 2011, NBER working paper (16855)
Friedman N, 1997, MACH LEARN, V29, P131, DOI 10.1023/A:1007465528199
Fu RS, 2021, INFORM SYST RES, V32, P72, DOI 10.1287/isre.2020.0990
Gama J, 2004, LECT NOTES ARTIF INT, V3171, P286
Hamilton JD, 1996, J APPL ECONOM, V11, P573, DOI 10.1002/(SICI)1099-1255(199609)11:5<573::AID-JAE413>3.0.CO;2-T
Hansen LP, 2007, AM ECON REV, V97, P1, DOI 10.1257/aer.97.2.1
Hitsch GJ, 2006, MARKET SCI, V25, P25, DOI 10.1287/mksc.1050.0140
Hoens TR, 2012, PROG ARTIF INTELL, V1, P89, DOI 10.1007/s13748-011-0008-0
Huang GF, 2019, MANAGE SCI, V65, P5556, DOI 10.1287/mnsc.2018.3197
Huang YF, 2022, J MARKETING RES, V59, P411, DOI 10.1177/00222437211068527
Humpherys SL, 2011, DECIS SUPPORT SYST, V50, P585, DOI 10.1016/j.dss.2010.08.009
Iyer R, 2016, MANAGE SCI, V62, P1554, DOI 10.1287/mnsc.2015.2181
Jeon J, 2022, RAND J ECON, V53, P226, DOI 10.1111/1756-2171.12406
Kawai K, 2022, J POLIT ECON, V130, P1585, DOI 10.1086/718984
Kelly MG, 1999, P 5 ACM SIGKDD INT C, P367, DOI [10.1145/312129.312285, DOI 10.1145/312129.312285]
Kim CJ, 1998, REV ECON STAT, V80, P188, DOI 10.1162/003465398557447
Klinkenberg R., 2004, Intelligent Data Analysis, V8, P281
Koren Y, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P447
Kozlowski J, 2020, J POLIT ECON, V128, P2839, DOI 10.1086/707735
Liechty JC, 2005, MARKET SCI, V24, P285, DOI 10.1287/mksc.1040.0088
Lin MF, 2016, MANAGE SCI, V62, P1393, DOI 10.1287/mnsc.2015.2206
Lin MF, 2013, MANAGE SCI, V59, P17, DOI 10.1287/mnsc.1120.1560
Liu XY, 2020, REV IND ORGAN, V56, P281, DOI 10.1007/s11151-019-09733-2
NELDER JA, 1972, J R STAT SOC SER A-G, V135, P370, DOI 10.2307/2344614
Pechenizkiy M., 2010, ACM SIGKDD Explor. Newsl., V11, P109
Schlimmer J. C., 1986, Machine Learning, V1, P317, DOI 10.1023/A:1022810614389
Sornette D., 2004, Phys. Today, V57, P78
Sriram S, 2006, MARKET SCI, V25, P440, DOI 10.1287/mksc.1050.0188
Tsymbal A., 2004, PROBLEM CONCEPT DRIF
Viaene S, 2002, J RISK INSUR, V69, P373, DOI 10.1111/1539-6975.00023
Wang H., 2003, P 9 ACM SIGKDD INT C, P226, DOI [DOI 10.1145/956750.956778, 10.1145/956750.956778]
Wei ZY, 2017, MANAGE SCI, V63, P4236, DOI 10.1287/mnsc.2016.2531
Yao S, 2012, J MARKETING RES, V49, P822, DOI 10.1509/jmr.11.0009
Zhang JJ, 2012, MANAGE SCI, V58, P892, DOI 10.1287/mnsc.1110.1459
NR 52
TC 0
Z9 0
U1 11
U2 11
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD 2023 DEC 27
PY 2023
DI 10.1287/mksc.2022.0198
EA DEC 2023
PG 22
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA DU5B8
UT WOS:001134596200001
DA 2024-03-27
ER
PT J
AU Chon, MG
Kim, S
AF Chon, Myoung-Gi
Kim, Seonwoo
TI Dealing with the COVID-19 crisis: Theoretical application of social
media analytics in government crisis management
SO PUBLIC RELATIONS REVIEW
LA English
DT Article
DE Government crisis management; Social media analytics; Machine learning;
Attribution theory; COVID-19
ID COMMUNICATIVE ACTION; SITUATIONAL THEORY; MOTIVATION
AB Little theory-grounded research addresses how to use social media strategically in government public relations through machine learning. To fill this gap, we propose a way to optimize social media analytics to manage issues and crises by using the framework of attribution theory to analyze 360,861 tweets. In particular, we examined the attribution of crisis responsibility related to the spread of COVID-19 and its relations to the negative emotions of U.S. citizens on Twitter for six months (from January 20 to June 30, 2020). The results of this study showed that social media analytics is a valid tool to monitor how the spread of COVID-19 evolved from an issue to a crisis for the Trump administration. In addition, the federal government's lack of response and inability to handle the outbreak led to citizens' engagement and amplification of negative tweets that blamed the Trump White House. Theoretical and practical implications of the results are discussed.
C1 [Chon, Myoung-Gi; Kim, Seonwoo] Auburn Univ, Sch Commun & Journalism, 237 Tichenor Hall, Auburn, AL 36830 USA.
[Chon, Myoung-Gi; Kim, Seonwoo] Louisiana State Univ, Manship Sch Mass Commun, Journalism Bldg, Baton Rouge, LA 70830 USA.
C3 Auburn University System; Auburn University; Louisiana State University
System; Louisiana State University
RP Chon, MG (autor correspondiente), Auburn Univ, Sch Commun & Journalism, 237 Tichenor Hall, Auburn, AL 36830 USA.
EM mzc0113@auburn.edu; kseonw1@lsu.edu
RI Kim, Seonwoo/KDO-4303-2024
OI Kim, Seon-Woo/0000-0002-3023-9587
CR Agresti A., 2019, An introduction to categorical data analysis, V3rd ed.
Austin L, 2012, J APPL COMMUN RES, V40, P188, DOI 10.1080/00909882.2012.654498
Avery EJ, 2017, PUBLIC RELAT REV, V43, P468, DOI 10.1016/j.pubrev.2017.02.018
Borra E, 2014, ASLIB J INFORM MANAG, V66, P262, DOI 10.1108/AJIM-09-2013-0094
Broom G.M., 2012, Cutlip and Center's effective public relations, V11th
Buckwalter W, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0136589
Chang CC, 2022, HEALTH COMMUN, V37, P476, DOI 10.1080/10410236.2020.1852698
Cheng Y, 2018, SOCIAL MEDIA AND CRISIS COMMUNICATION, P9
Cheng Y, 2018, J CONTING CRISIS MAN, V26, P58, DOI 10.1111/1468-5973.12130
Choi Y, 2009, J PUBLIC RELAT RES, V21, P198, DOI 10.1080/10627260802557506
Chon MG, 2020, J MASS COMMUN Q, V97, P72, DOI 10.1177/1077699019835896
Chon MG, 2019, ASIAN J COMMUN, V29, P424, DOI 10.1080/01292986.2019.1649438
Claeys AS, 2016, J PUBLIC RELAT RES, V28, P232, DOI 10.1080/1062726X.2016.1261703
Collinson S., 2020, THE CNN
Comfort LK, 2020, PUBLIC ADMIN REV, V80, P616, DOI 10.1111/puar.13252
Coombs WT, 2007, CORP REPUT REV, V10, P163, DOI 10.1057/palgrave.crr.1550049
Coombs W.T., 2014, RES J I PUBLIC RELAT, V1, P1
Coombs W. T., 2017, SOCIAL MEDIA CRISIS, DOI [10.4324/9781315749068, DOI 10.4324/9781315749068]
Coombs W.T., 2019, ONGOING CRISIS COMMU, V5th
Coombs WT, 2016, J PUBLIC RELAT RES, V28, P120, DOI 10.1080/1062726X.2016.1167479
Coombs WT, 2015, BUS HORIZONS, V58, P141, DOI 10.1016/j.bushor.2014.10.003
Coombs WT, 2012, PUBLIC RELAT REV, V38, P408, DOI 10.1016/j.pubrev.2012.04.004
Crowley Michael, 2020, The New York Times
Diamond L., 2020, The Atlantic
Elflein J., 2021, CORONAVIRUS COVID 19
Fashion Revolution, 2020, The Fashion Transparency Index 2020
Fearn-Banks K., 2016, Crisis communications: A casebook approach
Fiske S.T., 1991, Social cognition, V2nd ed.
Freberg K, 2012, PUBLIC RELAT REV, V38, P416, DOI 10.1016/j.pubrev.2012.01.008
Grunig J.E., 1992, EXCELLENCE PUBLIC RE, P31, DOI DOI 10.4324/9780203812303
Grunig J. E., 2009, PARADIGMS GLOBAL PUB
Grunig L.A., 2002, EXCELLENT PUBLIC REL
Guo L., 2016, POWER INFORM NETWORK, P3
Hadi TA, 2016, DISASTER MED PUBLIC, V10, P775, DOI 10.1017/dmp.2016.39
Heath R.L., 2005, Encyclopedia of public relations
Hilbe J. M., 2011, NEGATIVE BINOMIAL RE
Jeong Y, 2019, JOURNALISM, V20, P1593, DOI 10.1177/1464884917737212
Jin Y, 2014, COMMUN RES, V41, P74, DOI 10.1177/0093650211423918
Kieser A, 2015, ACAD MANAG ANN, V9, P143, DOI 10.1080/19416520.2015.1011853
Kim DKD, 2020, WORLD MED HEALTH POL, V12, P398, DOI 10.1002/wmh3.363
Kim JN, 2013, J PUBLIC RELAT RES, V25, P1, DOI 10.1080/1062726X.2012.723276
Kim JN, 2011, J COMMUN, V61, P120, DOI 10.1111/j.1460-2466.2010.01529.x
LAZARUS RS, 1991, AM PSYCHOL, V46, P819, DOI 10.1037/0003-066X.46.8.819
Liu B.F., 2007, Journal of Public Relations Research, V19, P377
Macnamara J., 2018, INT J LISTENING, V32, P1, DOI [10.1080/10904018.2017, DOI 10.1080/10904018.2017, DOI 10.1080/10904018.2017.1375076]
Macnamara J, 2016, J PUBLIC RELAT RES, V28, P146, DOI 10.1080/1062726X.2016.1228064
Maier D, 2018, COMMUN METHODS MEAS, V12, P93, DOI 10.1080/19312458.2018.1430754
Manning CD, 2014, PROCEEDINGS OF 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, P55, DOI 10.3115/v1/p14-5010
Peters C, 2020, VOX 0608
Phillips Amber., 2020, Washington Post
Renfro W.L., 1993, Issues management in strategic planning
Schumaker E., 2020, ABCNEWS
Shaver K.G., 2016, An introduction to attribution processes, DOI DOI 10.4324/9781315536019
Shin KA, 2016, ASIAN J COMMUN, V26, P76, DOI 10.1080/01292986.2015.1083597
So J, 2019, COMMUN RES, V46, P663, DOI 10.1177/0093650216679536
Socher R., 2013, EMNLP, P935
TAE KIM， SUNG, 2006, [Korean Journal of Journalism & Communication Studies, 한국언론학보], V50, P175
Taylor M, 2007, PUBLIC RELAT REV, V33, P140, DOI 10.1016/j.pubrev.2006.11.017
Tsugawa S., 2015, Proceedings of the 2015 ACM on Conference on Online Social Networks, P151, DOI [DOI 10.1145/2817946.2817962, 10.1145/2817946.2817962]
Turner MM, 2007, PUBLIC RELAT REV, V33, P114, DOI 10.1016/j.pubrev.2006.11.013
Wang Y, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106568
WEINER B, 1985, PSYCHOL REV, V92, P548, DOI 10.1037/0033-295X.92.4.548
Yang F, 2021, COMMUN Q, V69, P341, DOI 10.1080/01463373.2021.1951789
Yum JY, 2015, J BUS TECH COMMUN, V29, P159, DOI 10.1177/1050651914560570
Zhu Z, 2020, INT J DISAST RISK RE, V50, DOI 10.1016/j.ijdrr.2020.101717
NR 65
TC 13
Z9 13
U1 7
U2 58
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0363-8111
EI 1873-4537
J9 PUBLIC RELAT REV
JI Public Relat. Rev.
PD SEP
PY 2022
VL 48
IS 3
AR 102201
DI 10.1016/j.pubrev.2022.102201
EA APR 2022
PG 10
WC Business; Communication
WE Social Science Citation Index (SSCI)
SC Business & Economics; Communication
GA 1P6FF
UT WOS:000802101800001
PM 35469268
OA Green Published
DA 2024-03-27
ER
PT J
AU Kantanantha, N
Awichanirost, J
AF Kantanantha, Nantachai
Awichanirost, Jiaranai
TI Analyzing and forecasting online tour bookings using Google Analytics
metrics
SO JOURNAL OF REVENUE AND PRICING MANAGEMENT
LA English
DT Article
DE Data analysis; Google Analytics; Machine learning; Forecasting; Tourism;
Online tour bookings
AB An essential part of business operation for tourism industry is revenue management, i.e., how to sell the right tour package, to the right customers, at the right time, at the right price through the most appropriate and cost-effective channels. In today's world, the internet has revolutionized many business operations in the tourism industry which plays an important role in Thailand's GDP. Most tour operators utilize websites as the main channel to build relationships with customers. Thus, website performance measurement is an important strategic factor for online marketing. The objectives of this research were to identify factors contributing from Google Analytics metrics to online bookings and to forecast online bookings using those impactful factors. Several machine learning models namely artificial neural network (ANN), support vector regression, and random forest, were proposed to forecast online bookings using the mean absolute percentage error (MAPE) as the criterion for comparison. It was found that there were three Google Analytics metrics that contributed to online bookings, which were the sessions from referral, unique returning users, and the average session duration. In addition, the ANN model provided the highest accuracy result with a MAPE of 11.39%. The framework from this research can be applied to other online companies to forecast their online bookings, which is an important part of revenue management since accurate forecasts can help companies to achieve their goals.
C1 [Kantanantha, Nantachai; Awichanirost, Jiaranai] Chulalongkorn Univ, Fac Engn, Dept Ind Engn, Bangkok, Thailand.
C3 Chulalongkorn University
RP Kantanantha, N (autor correspondiente), Chulalongkorn Univ, Fac Engn, Dept Ind Engn, Bangkok, Thailand.
EM nantachai.k@chula.ac.th
CR Abraham A., 2005, HDB MEASURING SYSTEM, DOI [10.1002/0471497398.mm421, DOI 10.1002/0471497398.MM421]
Basheer IA, 2000, J MICROBIOL METH, V43, P3, DOI 10.1016/S0167-7012(00)00201-3
Bekavac I, 2015, CROAT OPER RES REV, V6, P373, DOI 10.17535/crorr.2015.0029
Boehmke B, 2019, Hands-on machine learning with R, DOI DOI 10.1201/9780367816377
Burby J., 2007, Web analytics definitions
Chen R, 2015, APPL SOFT COMPUT, V26, P435, DOI 10.1016/j.asoc.2014.10.022
Chen W, 2017, IEICE T INF SYST, VE100D, P190, DOI 10.1587/transinf.2016EDP7152
Claveria O, 2015, INT J TOUR RES, V17, P492, DOI 10.1002/jtr.2016
Feng YY, 2019, PROCEDIA COMPUT SCI, V162, P628, DOI 10.1016/j.procs.2019.12.032
Hu Z., 2013, SCI WORLD J
Kelleher J.D., 2020, Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies
Knoema,, 2021, THAIL CONTR TRAV TOU
Milano R, 2011, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2011, P471
Napagoda, 2013, INT J SCI TECHNOLOGY, V2, P170
Omidvar, 2011, ARXIV PREPRINT ARXIV
Phumchusri N, 2020, J REVENUE PRICING MA, V19, P8, DOI 10.1057/s41272-019-00221-6
Plaza B, 2011, TOURISM MANAGE, V32, P477, DOI 10.1016/j.tourman.2010.03.015
Statista, 2021 NUMB INT US WOR
Teixeira JP, 2012, PROC TECH, V5, P445, DOI 10.1016/j.protcy.2012.09.049
NR 19
TC 1
Z9 1
U1 1
U2 15
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1476-6930
EI 1477-657X
J9 J REVENUE PRICING MA
JI J. Revenue Pricing Manag.
PD JUN
PY 2022
VL 21
IS 3
BP 354
EP 365
DI 10.1057/s41272-021-00338-7
EA JUN 2021
PG 12
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 1J8SL
UT WOS:000661782700001
DA 2024-03-27
ER
PT J
AU Zhang, MX
Luo, L
AF Zhang, Mengxia
Luo, Lan
TI Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant
Survival? Evidence from Yelp
SO MANAGEMENT SCIENCE
LA English
DT Article
DE user-generated content; photos; word of mouth; analytic modeling;
computer vision; age analysis; text mining; Yelp; business survival;
restaurants
ID SMALL BUSINESS SURVIVAL; WORD-OF-MOUTH; FIRM SURVIVAL; PRODUCT; QUALITY;
IMPACT; MODEL; ELICITATION; PERFORMANCE; PREDICTORS
AB Despite the substantial economic impact of the restaurant industry, large-scale empirical research on restaurant survival has been sparse. We investigate whether consumer-posted photos can serve as a leading indicator of restaurant survival above and beyond reviews, firm characteristics, competitive landscape, and macroconditions. We employ machine learning techniques to extract features from 755,758 photos and 1,121,069 reviews posted on Yelp between 2004 and 2015 for 17,719 U.S. restaurants. We also collect data on restaurant characteristics (e.g., cuisine type, price level) and competitive landscape as well as entry and exit (if applicable) time from each restaurant's Yelp/ Facebook page, own website, or Google search engine. Using a predictive XGBoost algorithm, we find that consumer-posted photos are strong predictors of restaurant survival. Interestingly, the informativeness of photos (e.g., the proportion of food photos) relates more to restaurant survival than do photographic attributes (e.g., composition, brightness). Additionally, photos carry more predictive power for independent, young or mid-aged, and medium-priced restaurants. Assuming that restaurant owners possess no knowledge about future photos and reviews, photos can predict restaurant survival for up to three years, whereas reviews are only predictive for one year. We further employ causal forests to facilitate the interpretation of our predictive results. Among photo content variables, the proportion of food photos has the largest positive association with restaurant survival, followed by proportions of outside and interior photos. Among others, the proportion of photos with helpful votes also positively relates to restaurant survival.
C1 [Zhang, Mengxia] Western Univ, Mkt, Ivey Business Sch, London, ON N6A 3K7, Canada.
[Luo, Lan] Univ Southern Calif, Mkt, Los Angeles, CA 90089 USA.
C3 Western University (University of Western Ontario); University of
Southern California
RP Zhang, MX (autor correspondiente), Western Univ, Mkt, Ivey Business Sch, London, ON N6A 3K7, Canada.
EM mezhang@ivey.ca; lluo@marshall.usc.edu
OI , Lan/0000-0003-1002-8237; Zhang, Mengxia/0000-0002-4901-0635
CR Alonso AD, 2013, J HOSP MARKET MANAG, V22, P547, DOI 10.1080/19368623.2012.671562
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Arnheim Rudolf., 1965, Art and visual perception: A psychology of the creative eye
Ataman MB, 2010, J MARKETING RES, V47, P866, DOI 10.1509/jmkr.47.5.866
Athey S., 2019, Estimating Treatment Effects with Causal Forests: An Application, V5, P37
Athey S, 2019, ANN STAT, V47, P1148, DOI 10.1214/18-AOS1709
Athey S, 2016, P NATL ACAD SCI USA, V113, P7353, DOI 10.1073/pnas.1510489113
AUDRETSCH DB, 1995, REV ECON STAT, V77, P97, DOI 10.2307/2109995
Audretsch DB, 2000, REV IND ORGAN, V16, P1, DOI 10.1023/A:1007824501527
Barasch A, 2018, J CONSUM RES, V44, P1220, DOI 10.1093/jcr/ucx112
Barasch A, 2017, PSYCHOL SCI, V28, P1056, DOI 10.1177/0956797617694868
BATES T, 1995, SMALL BUS ECON, V7, P377, DOI 10.1007/BF01302738
Bates T, 1998, J BUS VENTURING, V13, P113, DOI 10.1016/S0883-9026(97)00066-9
BATES T, 1990, REV ECON STAT, V72, P551, DOI 10.2307/2109594
Bekkar M., 2013, J Inf Eng Appl, V3
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Boden RJ, 2000, J BUS VENTURING, V15, P347, DOI 10.1016/S0883-9026(98)00004-4
Bradley AP, 1997, PATTERN RECOGN, V30, P1145, DOI 10.1016/S0031-3203(96)00142-2
Brodersen Kay H., 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), P3121, DOI 10.1109/ICPR.2010.764
Brüggen EC, 2011, J MARKETING, V75, P71, DOI 10.1509/jmkg.75.5.71
Bujisic M, 2014, INT J CONTEMP HOSP M, V26, P1270, DOI 10.1108/IJCHM-04-2013-0162
CARROLL GR, 1983, SOC SCI RES, V12, P303, DOI 10.1016/0049-089X(83)90022-4
Chang Jonathan, 2009, ADV NEURAL INFORM PR, P288, DOI DOI 10.5555/2984093.2984126
Chen T., 2018, R Document
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Chernozhukov V, 2018, ECONOMET J, V21, pC1, DOI 10.1111/ectj.12097
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Clarke RT, 2007, HYDROL EARTH SYST SC, V11, P408, DOI 10.5194/hess-11-408-2007
COOPER AC, 1994, J BUS VENTURING, V9, P371, DOI 10.1016/0883-9026(94)90013-2
Diehl K, 2016, J PERS SOC PSYCHOL, V111, P119, DOI 10.1037/pspa0000055
Dzyabura D, 2019, NES WORKING PAPER SE, DOI [10.2139/ssrn.3209307, DOI 10.2139/SSRN.3209307]
Dzyabura D, 2021, J MARKETING, V85, P44, DOI 10.1177/0022242921996661
Dzyabura D, 2011, MARKET SCI, V30, P801, DOI 10.1287/mksc.1110.0660
Fawcett T., 2004, MACH LEARN, V31, P1
Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010
Fellbaum C., 1998, WORDNET
Fong C, 2018, ANN APPL STAT, V12, P156, DOI 10.1214/17-AOAS1101
Freedman M. J., 2007, IPTPS, V5, P1
Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451
Fritsch M, 2006, REV IND ORGAN, V28, P285, DOI 10.1007/s11151-006-0018-4
Getz D, 2006, TOURISM MANAGE, V27, P146, DOI 10.1016/j.tourman.2004.08.002
Ghose A, 2012, MARKET SCI, V31, P493, DOI 10.1287/mksc.1110.0700
Greene W., 2004, The Econometrics Journal, V7, P98, DOI [DOI 10.1111/J.1368-423X.2004.00123.X, 10.1111/j.1368-423X.2004.00123.x]
Guo T, 2021, J MARKETING RES, V58, P115, DOI 10.1177/0022243720972106
Haapanen M, 2009, APPL ECON, V41, P2449, DOI 10.1080/00036840802360278
HANLEY JA, 1982, RADIOLOGY, V143, P29, DOI 10.1148/radiology.143.1.7063747
Hartmann J, 2021, J MARKETING RES, V58, P1159, DOI 10.1177/00222437211037258
Hauser JR, 2014, J PROD INNOVAT MANAG, V31, P17, DOI 10.1111/jpim.12077
Hirano K., 2005, APPL BAYESIAN MODELI, P73
Hollenbeck B, 2018, J MARKETING RES, V55, P636, DOI 10.1177/0022243718802844
Huang DL, 2016, MARKET SCI, V35, P445, DOI 10.1287/mksc.2015.0946
Hutto C. J., 2014, P INT AAAI C WEB SOC, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
Hyun SS, 2010, CORNELL HOSP Q, V51, P251, DOI 10.1177/1938965510363264
Isola Phillip, 2011, ADV NEURAL INFORM PR, P2429, DOI DOI 10.1167/12.9.1082
KALLEBERG AL, 1991, ACAD MANAGE J, V34, P136, DOI 10.5465/256305
Kalnins A, 2004, MANAGE SCI, V50, P1716, DOI 10.1287/mnsc.1040.0220
Kalnins A, 2013, AM ECON J-MICROECON, V5, P157, DOI 10.1257/mic.5.3.157
Kamshad KM, 1994, J ECON MANAGE STRAT, V3, P521, DOI 10.1111/j.1430-9134.1994.00521.x
Ko EE, 2020, PROC 49 EUR MARKETIN
Kotsiantis S., 2006, GESTS International Transactions on Computer Science and Engineering, V30, P25
Krages B., 2005, Photography: the art of composition
Kreitler H., 1972, Psychology of the arts
KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694
Lafontaine F, 2019, J ECON MANAGE STRAT, V28, P175, DOI 10.1111/jems.12289
Larsen V, 2004, J CONSUM RES, V31, P102, DOI 10.1086/383427
Lee D, 2019, FOCUSED CONCEPT MINE
Li X, 2019, INT J RES MARK, V36, P216, DOI 10.1016/j.ijresmar.2019.02.004
Li YY, 2020, J MARKETING RES, V57, P1, DOI 10.1177/0022243719881113
Liu L, 2020, MARKET SCI, V39, P669, DOI 10.1287/mksc.2020.1226
Liu X, 2019, J MARKETING RES, V56, P918, DOI 10.1177/0022243719866690
Liu Y, 2017, J MARKETING, V81, P30, DOI 10.1509/jm.15.0535
Liu Y, 2006, J MARKETING, V70, P74, DOI 10.1509/jmkg.70.3.74
Lundberg SM, 2017, ADV NEUR IN, V30
Lundberg SM, 2020, NAT MACH INTELL, V2, P56, DOI 10.1038/s42256-019-0138-9
Malik N, 2020, DYNAMIC ANAL BEAUTY
McGrath T, 2017, SOCIAL LISTENING MEE
Mela CF, 1997, J MARKETING RES, V34, P248, DOI 10.2307/3151862
MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748
Molnar C, 2021, Interpretable machine learning e a guide for making black box models explainable
Montabone S, 2010, IMAGE VISION COMPUT, V28, P391, DOI 10.1016/j.imavis.2009.06.006
Montoya R, 2010, MARKET SCI, V29, P909, DOI 10.1287/mksc.1100.0570
Morgan SL, 2015, ANAL METHOD SOC RES, P1
Mudambi SM, 2010, MIS QUART, V34, P185
Narang U., 2019, Working Paper
Netzer O, 2019, J MARKETING RES, V56, P960, DOI 10.1177/0022243719852959
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Newman D., 2010, HUMAN LANGUAGE TECHN, P100
Nishiyama M, 2011, PROC CVPR IEEE, P33, DOI 10.1109/CVPR.2011.5995539
Parsa H. G., 2005, Cornell Hotel and Restaurant Administration Quarterly, V46, P304, DOI 10.1177/0010880405275598
Parsa HG, 2015, CORNELL HOSP Q, V56, P80, DOI 10.1177/1938965514551959
Parsa H. G., 2011, Journal of Foodservice Business Research, V14, P360, DOI 10.1080/15378020.2011.625824
Parsa HG, 2010, EMERGING ASPECTS RED, V1, P16
Pauwels K, 2002, J MARKETING RES, V39, P421, DOI 10.1509/jmkr.39.4.421.19114
Pearl J, 2014, J CAUSAL INFERENCE, V2, P109, DOI 10.1515/jci-2014-0017
Puranam D, 2017, MARKET SCI, V36, P726, DOI 10.1287/mksc.2017.1048
Rafieian O, 2021, MARKET SCI, V40, P193, DOI 10.1287/mksc.2020.1235
Rajaram P, 2021, PREPRINT, DOI [10.2139/ssrn.3496039, DOI 10.2139/SSRN.3496039]
Röder M, 2015, WSDM'15: PROCEEDINGS OF THE EIGHTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P399, DOI 10.1145/2684822.2685324
ROSENBAUM PR, 1983, BIOMETRIKA, V70, P41, DOI 10.1093/biomet/70.1.41
RUBIN DB, 1973, BIOMETRICS, V29, P159, DOI 10.2307/2529684
Ryu K, 2012, INT J CONTEMP HOSP M, V24, P200, DOI 10.1108/09596111211206141
Seiffert C, 2008, PROC INT C TOOLS ART, P445, DOI 10.1109/ICTAI.2008.59
Sulek J. M., 2004, Cornell Hotel and Restaurant Administration Quarterly, V45, P235, DOI 10.1177/0010880404265345
Takahashi K, 2016, 2016 IEEE SECOND INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM), P444, DOI 10.1109/BigMM.2016.41
Timoshenko A, 2019, MARKET SCI, V38, P1, DOI 10.1287/mksc.2018.1123
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Tirunillai S, 2012, MARKET SCI, V31, P198, DOI 10.1287/mksc.1110.0682
Toubia O, 2017, MARKET SCI, V36, P1, DOI 10.1287/mksc.2016.0994
Troncoso I., 2020, The Role of Profile Pictures in Online Labor Markets (October 12, 2020)
Tveterås R, 2000, SMALL BUS ECON, V14, P65, DOI 10.1023/A:1008189411442
Vogel D., 1986, Persuasion and the Role of Visual Presentation Support: The UM/3 M Study
Wadhera D, 2014, EAT BEHAV, V15, P132, DOI 10.1016/j.eatbeh.2013.11.003
WAGNER J, 1994, J IND ECON, V42, P141, DOI 10.2307/2950486
Wooldridge J.M., 2012, INTRO ECONOMETRICS M, DOI DOI 10.1198/JASA.2006.S154
Xiao L, 2014, MARKET SCI, V33, P338, DOI 10.1287/mksc.2013.0837
Yoganarasimhan H, 2020, MANAGE SCI, V66, P1045, DOI 10.1287/mnsc.2018.3255
Zhang Shunyuan., 2018, How Much is an Image Worth? The Impact of Professional versus Amateur Airbnb Property Images on Property Demand
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 118
TC 11
Z9 12
U1 53
U2 146
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD JAN
PY 2023
VL 69
IS 1
BP 25
EP 50
DI 10.1287/mnsc.2022.4359
EA APR 2022
PG 26
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA P9RQ1
UT WOS:000827162300001
DA 2024-03-27
ER
PT J
AU Nanne, AJ
Antheunis, ML
van der Lee, CG
Postma, EO
Wubben, S
van Noort, G
AF Nanne, Annemarie J.
Antheunis, Marjolijn L.
van der Lee, Chris G.
Postma, Eric O.
Wubben, Sander
van Noort, Guda
TI The Use of Computer Vision to Analyze Brand-Related User Generated Image
Content
SO JOURNAL OF INTERACTIVE MARKETING
LA English
DT Article
DE Visual brand-related UGC; Computer vision; Pre-trained computer vision;
Image mining; Automated content analysis
ID SOCIAL MEDIA; CONSUMERS; EWOM
AB With the increasing popularity of visual-oriented social media platforms, the prevalence of visual brand-related User Generated Content (UGC) have increased. Monitoring such content is important as this visual brand-related UGC can have a large influence on a brand's image and hence provides useful opportunities to observe brand performance (e.g., monitoring trends and consumer segments). The current research discusses the application of computer vision for marketing practitioners and researchers and examines the usability of three different pre-trained ready-to-use computer vision models (i.e., YOLOV2, Google Cloud Vision, and Clarifai) to analyze visual brand-related UGC automatically. A 3-step approach was adopted in which 1) a database of 21,738 Instagram pictures related to 24 different brands was constructed, 2) the images were processed by the three different computer vision models, and 3) a label evaluation procedure was conducted with a sample of the labels (object names) outputted by the models. The results of the label evaluation procedure are quantitatively assessed and complemented with four concrete examples of how the output of computer vision can be used to analyze visual brand-related UGC. Results show that computer vision can yield various marketing insights. Moreover, we found that the three tested computer vision models differ in applicability. Google Cloud Vision is more accurate in object detection, whereas Clarifai provides more useful labels to interpret the portrayal of a brand. YOLOV2 did not prove to be useful to analyze visual brand-related UGC. Results and implications of the findings for marketers and marketing scholars will be discussed. (C) 2019 Direct Marketing Educational Foundation, Inc. dba Marketing EDGE. All rights reserved.
C1 [Nanne, Annemarie J.; Antheunis, Marjolijn L.; van der Lee, Chris G.; Wubben, Sander] Tilburg Univ, Tilburg Ctr Cognit & Commun, Tilburg Sch Humanities & Digital Sci, Dept Commun & Cognit, Tilburg, Netherlands.
[Postma, Eric O.] Tilburg Univ, Tilburg Sch Humanities & Digital Sci, Dept Cognit Sci & Artificial Intelligence, Tilburg, Netherlands.
[van Noort, Guda] Univ Amsterdam, Fac Social & Behav Sci, Amsterdam Sch Commun Res, Amsterdam, Netherlands.
C3 Tilburg University; Tilburg University; University of Amsterdam
RP Nanne, AJ (autor correspondiente), Warandelaan 2, NL-5037 AB Tilburg, Netherlands.
EM a.j.nanne@uvt.nl
RI Nanne, Annemarie/KEH-0543-2024
OI van Noort, Guda/0000-0002-6314-1455; Nanne,
Annemarie/0000-0001-7094-6502
CR [Anonymous], 2011, TEXTS COMPUT SCI
[Anonymous], MM 16 AMST NETH
[Anonymous], ICMLA CANC MEX
[Anonymous], REC ADS SOC MED
[Anonymous], 2018, J MED INTERNET RES
[Anonymous], IEEA VVS LECC IT
[Anonymous], 1982, VISION COMPUTATIONAL
[Anonymous], HUMANIZE TOK JAP
Bakhshi S, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0117148
Bianco S, 2017, NEUROCOMPUTING, V245, P23, DOI 10.1016/j.neucom.2017.03.051
Chari S, 2016, PSYCHOL MARKET, V33, P1071, DOI 10.1002/mar.20941
Chau PYK, 2000, J ORG COMP ELECT COM, V10, P1
Chen T, 2016, P 22 ACM SIGKDD INT
Cheng G, 2017, P IEEE, V105, P1865, DOI 10.1109/JPROC.2017.2675998
Chu SC, 2011, INT J ADVERT, V30, P47, DOI 10.2501/IJA-30-1-047-075
de Vries L, 2012, J INTERACT MARK, V26, P83, DOI 10.1016/j.intmar.2012.01.003
Droulers O., 2015, Journal of Applied Business Research, V31, P1403, DOI [10.19030/jabr.v31i4.9326, DOI 10.19030/JABR.V31I4.9326]
Erkan I, 2016, COMPUT HUM BEHAV, V61, P47, DOI 10.1016/j.chb.2016.03.003
Fan WG, 2014, COMMUN ACM, V57, P74, DOI 10.1145/2602574
Gensler S, 2013, J INTERACT MARK, V27, P242, DOI 10.1016/j.intmar.2013.09.004
Guo YD, 2016, LECT NOTES COMPUT SC, V9907, P87, DOI 10.1007/978-3-319-46487-9_6
Hennig-Thurau T, 2013, J INTERACT MARK, V27, P237, DOI 10.1016/j.intmar.2013.09.005
Hernández-Méndez J, 2015, COMPUT HUM BEHAV, V50, P618, DOI 10.1016/j.chb.2015.03.017
Hollenbeck CR, 2012, INT J RES MARK, V29, P395, DOI 10.1016/j.ijresmar.2012.06.002
Ismagilova E, 2016, LECT NOTES COMPUT SC, V9844, P354, DOI 10.1007/978-3-319-45234-0_32
Joseph RK, 2016, CRIT POL ECON S ASIA, P1
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Lin TY, 2014, LECT NOTES COMPUT SC, V8693, P740, DOI 10.1007/978-3-319-10602-1_48
Lin XL, 2016, COMPUT HUM BEHAV, V63, P264, DOI 10.1016/j.chb.2016.05.002
McDonald M., 2012, MARKET SEGMENTATION, V4th
Pew Research Center, 2018, Teens, social media technology 2018
Pew Research Center, 2018, Social media fact sheet
Redmon J, 2016, PROC CVPR IEEE, P779, DOI 10.1109/CVPR.2016.91
Serrano Shanna., 2018, J PROMOTIONAL COMMUN, V6, P72
Steffan-Dewenter I., 2002, Ecology, V83, P1421, DOI 10.1890/0012-9658(2002)083[1421:SDEOLC]2.0.CO;2
Thompson Laure, 2018, COLING
Tous R, 2018, MULTIMED TOOLS APPL, V77, P27123, DOI 10.1007/s11042-018-5910-z
Tran K, 2016, IEEE COMPUT SOC CONF, P434, DOI 10.1109/CVPRW.2016.61
van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
van Noord N, 2015, IEEE SIGNAL PROC MAG, V32, P46, DOI 10.1109/MSP.2015.2406955
Vilnai-Yavetz I, 2015, J INTERACT MARK, V32, P53, DOI 10.1016/j.intmar.2015.05.002
Wilson E.O., 1975, P1
Zeiler MD, 2014, LECT NOTES COMPUT SC, V8689, P818, DOI 10.1007/978-3-319-10590-1_53
NR 44
TC 28
Z9 31
U1 14
U2 69
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1094-9968
EI 1520-6653
J9 J INTERACT MARK
JI J. Interact. Mark.
PD MAY
PY 2020
VL 50
BP 156
EP 167
DI 10.1016/j.intmar.2019.09.003
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LR8HL
UT WOS:000535937100010
OA Green Published
DA 2024-03-27
ER
PT J
AU Berger, J
Moe, WW
Schweidel, DA
AF Berger, Jonah
Moe, Wendy W.
Schweidel, David A.
TI What Holds Attention? Linguistic Drivers of Engagement
SO JOURNAL OF MARKETING
LA English
DT Article
DE digital marketing; natural language processing; online content;
automated textual analysis; content consumption; digital engagement;
emotion
ID WORD-OF-MOUTH; SOCIAL MEDIA; POSITIVE EMOTIONS; INFORMATION; AROUSAL;
CONCRETENESS; UNCERTAINTY; INCREASES; DIRECTIONS; CERTAINTY
AB From advertisers and marketers to salespeople and leaders, everyone wants to hold attention. They want to make ads, pitches, presentations, and content that captivates audiences and keeps them engaged. But not all content has that effect. What makes some content more engaging? A multimethod investigation combines controlled experiments with natural language processing of 600,000 reading sessions from over 35,000 pieces of content to examine what types of language hold attention and why. Results demonstrate that linguistic features associated with processing ease (e.g., concrete or familiar words) and emotion both play an important role. Rather than simply being driven by valence, though, the effects of emotional language are driven by the degree to which different discrete emotions evoke arousal and uncertainty. Consistent with this idea, anxious, exciting, and hopeful language holds attention while sad language discourages it. Experimental evidence underscores emotional language's causal impact and demonstrates the mediating role of uncertainty and arousal. The findings shed light on what holds attention; illustrate how content creators can generate more impactful content; and, as shown in a stylized simulation, have important societal implications for content recommendation algorithms.
C1 [Berger, Jonah] Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA.
[Moe, Wendy W.] Univ Maryland, Smith Sch Business, College Pk, MD 20742 USA.
[Schweidel, David A.] Emory Univ, Goizueta Business Sch, Atlanta, GA 30322 USA.
C3 University of Pennsylvania; University System of Maryland; University of
Maryland College Park; Emory University
RP Berger, J (autor correspondiente), Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA.
EM jberger@wharton.upenn.edu; wmoe@umd.edu; dschweidel@emory.edu
OI Moe, Wendy/0009-0004-5999-6591
CR Alter AL, 2009, PERS SOC PSYCHOL REV, V13, P219, DOI 10.1177/1088868309341564
[Anonymous], 2021, WALL STR J
Ansari A, 2000, J MARKETING RES, V37, P363, DOI 10.1509/jmkr.37.3.363.18779
Berger J, 2020, J MARKETING, V84, P1, DOI 10.1177/0022242919873106
Berger J, 2018, PSYCHOL SCI, V29, P1178, DOI 10.1177/0956797618759465
Berger J, 2014, J CONSUM PSYCHOL, V24, P586, DOI 10.1016/j.jcps.2014.05.002
Berger J, 2012, J MARKETING RES, V49, P192, DOI 10.1509/jmr.10.0353
Berger J, 2011, J MARKETING RES, V48, P869, DOI 10.1509/jmkr.48.5.869
Berger J, 2011, PSYCHOL SCI, V22, P891, DOI 10.1177/0956797611413294
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Brysbaert M, 2014, BEHAV RES METHODS, V46, P904, DOI 10.3758/s13428-013-0403-5
Cavanaugh LA, 2015, J MARKETING RES, V52, P657, DOI 10.1509/jmr.10.0219
Chen Z, 2017, J CONSUM RES, V44, P613, DOI 10.1093/jcr/ucx055
Chou WYS, 2020, AM J PUBLIC HEALTH, V110, pS273, DOI 10.2105/AJPH.2020.305905
Cole Jeffrey I., 2017, RES REPORT
COLTHEART M, 1981, Q J EXP PSYCHOL-A, V33, P497, DOI 10.1080/14640748108400805
Connell L, 2012, COGNITION, V125, P452, DOI 10.1016/j.cognition.2012.07.010
Cramer H, 2015, CHI 2015: PROCEEDINGS OF THE 33RD ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, P2231, DOI 10.1145/2702123.2702360
Dos Santos HL, 2022, J EXP PSYCHOL GEN, V151, P1833, DOI 10.1037/xge0001171
Du RY, 2021, J MARKETING, V85, P163, DOI 10.1177/0022242920969198
Dubois D, 2016, J MARKETING RES, V53, P712, DOI 10.1509/jmr.13.0312
EASTERBROOK JA, 1959, PSYCHOL REV, V66, P183, DOI 10.1037/h0047707
ELLSWORTH PC, 1988, MOTIV EMOTION, V12, P271, DOI 10.1007/BF00993115
Faraji-Rad A, 2017, J CONSUM RES, V44, P1, DOI 10.1093/jcr/ucw073
FERREIRA F, 1991, J MEM LANG, V30, P210, DOI 10.1016/0749-596X(91)90004-4
Firth J, 2019, WORLD PSYCHIATRY, V18, P119, DOI 10.1002/wps.20617
Fossen BL, 2019, MARKET SCI, V38, P274, DOI 10.1287/mksc.2018.1139
Gabielkov M, 2016, SIGMETRICS/PERFORMANCE 2016: PROCEEDINGS OF THE SIGMETRICS/PERFORMANCE JOINT INTERNATIONAL CONFERENCE ON MEASUREMENT AND MODELING OF COMPUTER SCIENCE, P179, DOI [10.1145/2896377.2901462, 10.1145/2964791.2901462]
GILHOOLY KJ, 1980, BEHAV RES METH INSTR, V12, P395, DOI 10.3758/BF03201693
GRAESSER AC, 1994, PSYCHOL REV, V101, P371, DOI 10.1037/0033-295X.101.3.371
Grant Packard, 2024, Journal of Consumer Research, V51, DOI [10.1093/jcr/ucad013, DOI 10.1093/JCR/UCAD013]
Griskevicius V, 2010, EMOTION, V10, P190, DOI 10.1037/a0018421
Hayes Andrew F., 2017, INTRO MEDIATION MODE
Heilman KM, 1997, J NEUROPSYCH CLIN N, V9, P439, DOI 10.1176/jnp.9.3.439
HIDI S, 1990, REV EDUC RES, V60, P549, DOI 10.3102/00346543060004549
John Koetsier, 2020, Forbes
Julia McCoy, 2019, WHY CONTENT MARKETIN
JUST MA, 1980, PSYCHOL REV, V87, P329, DOI 10.1037/0033-295X.87.4.329
Kanuri VK, 2018, J MARKETING, V82, P89, DOI 10.1177/0022242918805411
Keltner D., 2010, The Handbook of Social Psycholgoy, V5th, P312, DOI DOI 10.1002/9780470561119.SOCPSY001009
Kim H, 2010, J CONSUM RES, V36, P983, DOI 10.1086/644763
Kim HS, 2016, MEDIA PSYCHOL, V19, P614, DOI 10.1080/15213269.2015.1090907
Kincaid JP, 1975, Technical report, DOI DOI 10.21236/ADA006655
KINTSCH W, 1980, POETICS, V9, P87, DOI 10.1016/0304-422X(80)90013-3
Kool W, 2010, J EXP PSYCHOL GEN, V139, P665, DOI 10.1037/a0020198
Lai LD, 2014, SOC INFLUENCE, V9, P289, DOI 10.1080/15534510.2013.847859
Lerner JS, 2000, COGNITION EMOTION, V14, P473, DOI 10.1080/026999300402763
Lerner JS, 2001, J PERS SOC PSYCHOL, V81, P146, DOI 10.1037//0022-3514.81.1.146
Lohtia R, 2003, J ADVERTISING RES, V43, P410, DOI 10.1017/S0021849903030459
Lorenz-Spreen P, 2019, NAT COMMUN, V10, DOI 10.1038/s41467-019-09311-w
Luangrath AW, 2017, J CONSUM PSYCHOL, V27, P98, DOI 10.1016/j.jcps.2016.05.002
MacInnis DJ, 2005, J MARKETING, V69, P1, DOI 10.1509/jmkg.69.1.1.55513
Mather M, 2007, PERSPECT PSYCHOL SCI, V2, P33, DOI 10.1111/j.1745-6916.2007.00028.x
Moe WW, 2012, MARKET SCI, V31, P372, DOI 10.1287/mksc.1110.0662
Moe WW, 2006, J INTERACT MARK, V20, P34, DOI 10.1002/dir.20054
Mohammad S. M., 2017, P 6 JOINT C LEXICAL, P65, DOI DOI 10.18653/V1/S17-1007
Moore SG, 2015, J CONSUM RES, V42, P30, DOI 10.1093/jcr/ucv003
Newberry C., 2022, FACEBOOK ALGORITHM W
Öhman A, 2001, PSYCHOL REV, V108, P483, DOI 10.1037//0033-295X.108.3.483
Packard G, 2023, J CONSUM RES, V50, P645, DOI 10.1093/jcr/ucad006
Packard G, 2021, J CONSUM RES, V47, P787, DOI 10.1093/jcr/ucaa038
Packard G, 2017, J MARKETING RES, V54, P572, DOI 10.1509/jmr.15.0248
Paetzold G.H., 2016, Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL:HLT), P435, DOI DOI 10.18653/V1/N16-1050
Pennebaker J. W., 2015, The Development and Psychometric Properties of LIWC2015
Pennebaker J. W., 2015, Linguistic inquiry and word count: LIWC2015
Peters K, 2013, J INTERACT MARK, V27, P281, DOI 10.1016/j.intmar.2013.09.007
Pezzuti T, 2021, J INTERACT MARK, V53, P32, DOI 10.1016/j.intmar.2020.06.005
Pham MT, 2004, J CONSUM PSYCHOL, V14, P360
Pitler E, 2008, P 2008 C EMP METH NA, P186
Pogacar Ruth., 2017, J PERS SOC PSYCHOL, P263
Raghunathan R, 1999, ORGAN BEHAV HUM DEC, V79, P56, DOI 10.1006/obhd.1999.2838
Ransbotham S, 2019, MARKET SCI, V38, P773, DOI 10.1287/mksc.2018.1115
Schraw G, 2001, EDUC PSYCHOL REV, V13, P23, DOI 10.1023/A:1009004801455
SCHRAW G, 1995, J READING BEHAV, V27, P1, DOI 10.1080/10862969509547866
Schwarm S. E., 2005, P 43 ANN M ASS COMPU, P523, DOI DOI 10.3115/1219840.1219905
Schweidel DA, 2016, J MARKETING, V80, P1, DOI 10.1509/jm.15.0258
Sismeiro C, 2004, J MARKETING RES, V41, P306, DOI 10.1509/jmkr.41.3.306.35985
SMITH CA, 1985, J PERS SOC PSYCHOL, V48, P813, DOI 10.1037/0022-3514.48.4.813
SPREEN O, 1966, J VERB LEARN VERB BE, V5, P459, DOI 10.1016/S0022-5371(66)80061-0
Stieglitz S, 2013, J MANAGE INFORM SYST, V29, P217, DOI 10.2753/MIS0742-1222290408
Teeny J, 2020, J CONSUM PSYCHOL, V30, P429, DOI 10.1002/jcpy.1151
Tiedens LZ, 2001, J PERS SOC PSYCHOL, V81, P973, DOI 10.1037//0022-3514.81.6.973
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Toubia O, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2011695118
VILCASSIM NJ, 1991, J MARKETING RES, V28, P29, DOI 10.2307/3172724
Vuilleumier P, 2005, TRENDS COGN SCI, V9, P585, DOI 10.1016/j.tics.2005.10.011
Ward Adrian F., 2022, J CONSUM PSYCHOL, DOI [10.1002/jcpy.1321, DOI 10.1002/JCPY.1321]
Warriner AB, 2013, BEHAV RES METHODS, V45, P1191, DOI 10.3758/s13428-012-0314-x
Weary G, 1997, J PERS SOC PSYCHOL, V73, P839, DOI 10.1037/0022-3514.73.4.839
Winkielman P, 2001, J PERS SOC PSYCHOL, V81, P989, DOI 10.1037//0022-3514.81.6.989
Yan SY, 2022, J MARKETING RES, V59, P1002, DOI 10.1177/00222437221076160
Yi X, 2014, PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14), P113, DOI 10.1145/2645710.2645724
Yin DZ, 2017, J MARKETING RES, V54, P447, DOI 10.1509/jmr.13.0379
Zipf GK, 1950, J CLIN PSYCHOL, V6, P306
NR 94
TC 9
Z9 9
U1 108
U2 172
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2429
EI 1547-7185
J9 J MARKETING
JI J. Mark.
PD SEP
PY 2023
VL 87
IS 5
BP 793
EP 809
DI 10.1177/00222429231152880
EA MAY 2023
PG 17
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA N8ID5
UT WOS:000982800800001
OA hybrid
DA 2024-03-27
ER
PT J
AU Rawashdeh, A
AF Rawashdeh, Awni
TI A deep learning-based SEM-ANN analysis of the impact of AI-based audit
services on client trust
SO JOURNAL OF APPLIED ACCOUNTING RESEARCH
LA English
DT Article; Early Access
DE Artificial inelegance; Client trust; Audit firms; Satisfaction; Auditing
ID NEURAL-NETWORK ANALYSIS; CUSTOMER SATISFACTION; PERCEIVED VALUE;
QUALITY; LOYALTY; PERCEPTIONS; TECHNOLOGY; INTENTIONS; MODEL; IMAGE
AB PurposeThe advent of technology has propelled audit firms to incorporate AI-based audit services, bringing the relationship between audit clients and firms into sharper focus. Nonetheless, the understanding of how AI-based audit services affect this relationship remains sparse. This study strives to probe how an audit client's satisfaction with AI-based audit services influences their trust in audit firms. Identifying the variables affecting this trust, the research aspires to gain a deeper comprehension of the implications of AI-based audit services on the auditor-client relationship, ultimately aiming to boost client satisfaction and cultivate trust.Design/methodology/approachA conceptual framework has been devised, grounded in the client-company relationship model, to delineate the relationship between perceived quality, perceived value, attitude and satisfaction with AI-based audit services and their subsequent impact on trust in audit firms. The research entailed an empirical investigation employing Facebook ads, gathering 288 valid responses for evaluation. The structural equation method, utilized in conjunction with SPSS and Amos statistical applications, verified the reliability and overarching structure of the scales employed to measure these elements. A hybrid multi-analytical technique of structural equation modeling and artificial neural networks (SEM-ANN) was deployed to empirically validate the collated data.FindingsThe research unveiled a significant and positive relationship between perceived value and client satisfaction, trust and attitude towards AI-based audit services, along with the link between perceived quality and client satisfaction. The findings suggest that a favorable attitude and perceived quality of AI-based audit services could enhance satisfaction, subsequently augmenting perceived value and client trust. By focusing on the delivery of superior-quality services that fulfill clients' value expectations, firms may amplify client satisfaction and trust.Research limitations/implicationsFurther inquiries are required to appraise the influence of advanced technology adoption within audit firms on client trust-building mechanisms. Moreover, an understanding of why the impact of perceived quality on perceived value proves ineffectual in the context of audit client trust-building warrants further exploration. In interpreting the findings of this study, one should consider the inherent limitations of the empirical analysis, inclusive of the utilization of Facebook ads as a data-gathering tool.Practical implicationsThe research yielded insightful theoretical and practical implications that can bolster audit clients' trust in audit firms amid technological advancements within the audit landscape. The results imply that audit firms should contemplate implementing trust-building mechanisms by creating value and influencing clients' stance towards AI-based audit services to establish trust, particularly when vying with competing firms. As technological evolutions impinge on trustworthiness, audit firms must prioritize clients' perceived value and satisfaction.Originality/valueTo the researcher's best knowledge, no previous study has scrutinized the impact of satisfaction with AI-based audit services on cultivating audit client trust in audit firms, in contrast to past research that has focused on the auditors' trust in the audit client. To bridge these gaps, this study employs a comprehensive and integrative theoretical model.
C1 [Rawashdeh, Awni] Appl Sci Private Univ, Dept Accounting, Amman, Jordan.
RP Rawashdeh, A (autor correspondiente), Appl Sci Private Univ, Dept Accounting, Amman, Jordan.
EM drxbrl@yahoo.com
FU Applied Science Private University
FX The author would like to express their gratitude to Applied Science
Private University for their valuable contributions and support
throughout the research process.
CR Abbasi GA, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0247582
Afsar B., 2014, Journal of Economics and Business Research, V20, P7
Ahmad H., 2022, Uncertain Supply Chain Management, V10, P1037
Al Abdulrazak RM, 2017, SOC BUS REV, V12, P320, DOI 10.1108/SBR-03-2017-0014
Al-Sayyed S.M., 2021, Accounting, V7, P281, DOI [10.5267/j.ac.2020.12.003, DOI 10.5267/J.AC.2020.12.003]
Ali F, 2015, TOURISM, V63, P37
Ali Z.M., 2019, International Journal of Accounting, V4, P53
Alles MG, 2020, J INF SYST, V34, P109, DOI 10.2308/isys-52633
Amerongen NV, 2022, MANAG AUDIT J, V37, P358, DOI 10.1108/MAJ-02-2021-3039
ANDERSON EW, 1994, J MARKETING, V58, P53, DOI 10.2307/1252310
[Anonymous], 2022, Facebook
Aschauer E, 2016, BEHAV RES ACCOUNT, V29, P19, DOI 10.2308/bria-51654
Ashaari MA, 2021, TECHNOL FORECAST SOC, V173, DOI 10.1016/j.techfore.2021.121119
Babayeva A., 2020, The effects of digitalization on auditing: a study investigating the benefits and challenges of digitalization on the audit profession
Bakarich KM, 2021, J EMERG TECHNOL ACCO, V18, P27, DOI 10.2308/JETA-19-11-20-47
Balliet D, 2013, PSYCHOL BULL, V139, P1090, DOI 10.1037/a0030939
Barcelos E.M. B., 2015, AUST J BASIC APPL SC, V9, P429
Barr-Pulliam D, 2022, J INT FIN MANAG ACC, V33, P337, DOI 10.1111/jifm.12148
Basso A., 2001, P 3 ACM C EL COMM
Behn B.K., 1997, ACCOUNT HORIZ, V11, P7
Berraies S, 2017, INT J BANK MARK, V35, P1018, DOI 10.1108/IJBM-09-2016-0137
Billari F., 2016, 1 INT C ADV RES METH
Blunch N., 2012, INTRO STRUCTURAL EQU, VSecond, DOI DOI 10.4135/9781446249345
Brazel JF, 2014, BEHAV RES ACCOUNT, V26, P131, DOI 10.2308/bria-50630
Chen DQ, 2018, J BUS ETHICS, V153, P1083, DOI 10.1007/s10551-016-3366-5
Cheung KY, 2022, COGENT BUS MANAG, V9, DOI 10.1080/23311975.2022.2085366
Choi E.J., 2013, INT J SMART HOME, V7, P239, DOI [10.14257/ijsh.2013.7.1.22, DOI 10.14257/IJSH.2013.7.1.22]
CIALDINI RB, 1981, ANNU REV PSYCHOL, V32, P357, DOI 10.1146/annurev.ps.32.020181.002041
Collier J.E., 2020, APPL STRUCTURAL EQUA, V1st, DOI [10.4324/9781003018414, DOI 10.4324/9781003018414]
Cooper LA, 2019, ACCOUNT HORIZ, V33, P15, DOI 10.2308/acch-52466
D'Agostino R. B., 1986, GOODNESS OF FIT TECH, V68
Delgado-Ballester E, 2003, INT J MARKET RES, V45, P35
Didyasarin H., 2017, AU-GSB e-Journal, V10, P167
Dodgson MK, 2020, ACCOUNT REV, V95, P89, DOI 10.2308/accr-52556
DONALDSON T, 1995, ACAD MANAGE REV, V20, P65, DOI 10.5465/AMR.1995.9503271992
Doney PM, 1997, J MARKETING, V61, P35, DOI 10.2307/1251829
Du WY, 2018, J STRATEGIC INF SYST, V27, P296, DOI 10.1016/j.jsis.2018.07.001
Eagly A. H., 1993, PSYCHOL ATTITUDES
Eastlick MA, 2006, J BUS RES, V59, P877, DOI 10.1016/j.jbusres.2006.02.006
El-Adly MI, 2019, J RETAIL CONSUM SERV, V50, P322, DOI 10.1016/j.jretconser.2018.07.007
Ennew CT, 1999, J BUS RES, V46, P121, DOI 10.1016/S0148-2963(98)00016-2
Faul F, 2007, BEHAV RES METHODS, V39, P175, DOI 10.3758/BF03193146
Flores F., 1998, Business Ethics Quarterly, V8, P205, DOI [10.2307/3857326, DOI 10.2307/3857326, https://doi.org/10.2307/3857326]
Garbarino E, 1999, J MARKETING, V63, P70, DOI 10.2307/1251946
García-Fernández J, 2018, SPORT MANAG REV, V21, P250, DOI 10.1016/j.smr.2017.07.003
Getty J. M., 1994, Journal of Hospitality & Leisure Marketing, V2, P3, DOI 10.1300/J150v02n03_02
Harber M, 2020, J INT FIN MANAG ACC, V31, P215, DOI 10.1111/jifm.12115
Hasmayni B., 2018, IOSR Journal of Humanities and Social Science (IOSR-JHSS), V23, P65
Hayek A.F., 2022, Sustainable Development through Data Analytics and Innovation, P45
Haykin S. S., 2001, KALMAN FILTERING NEU
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Holbrook M.B., 1999, Consumer value: A framework for analysis and research, P1, DOI DOI 10.4324/9780203010679
Hong-Youl H., 2012, African Journal of Business Management, V6, P6745
Iglesias O, 2020, J BUS ETHICS, V163, P151, DOI 10.1007/s10551-018-4015-y
Ismail I, 2006, MANAG AUDIT J, V21, P738, DOI 10.1108/02686900610680521
Javed MK, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.594132
Kerler W.A., 2005, The Effects of Auditors' Trust in Client Management on Auditors
Kerler WA, 2009, J BUS ETHICS, V85, P109, DOI 10.1007/s10551-008-9752-x
Keshavarz Y, 2018, INT J TOUR CITIES, V4, P220, DOI 10.1108/IJTC-09-2017-0044
Kim C, 2008, J ELECTRON COMMER OR, V6, P1, DOI 10.4018/jeco.2008070101
Kokina J, 2017, J EMERG TECHNOL ACCO, V14, P115, DOI 10.2308/jeta-51730
Kondasani RKR, 2015, INT J HEALTH CARE Q, V28, P452, DOI 10.1108/IJHCQA-01-2015-0008
Konuk FA, 2018, J RETAIL CONSUM SERV, V43, P304, DOI 10.1016/j.jretconser.2018.04.011
Kopp L., 2003, Presentation, Scholl of Accountancy Seminar Series
Kumar R., 2015, Auditing Principles and Practice, V3
LAMBIN J.-J., 2012, Market-driven management: strategic and operational marketing
Lapierre J, 1999, J BUS RES, V45, P235, DOI 10.1016/S0148-2963(97)00223-3
Lee CY, 2015, J ENTERP INF MANAG, V28, P680, DOI 10.1108/JEIM-07-2014-0077
Leong LY, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2020.103416
Leong LY, 2019, EXPERT SYST APPL, V133, P296, DOI 10.1016/j.eswa.2019.05.024
Lewin JE, 1997, J BUS RES, V39, P23, DOI 10.1016/S0148-2963(96)00152-X
Lien CH, 2015, ASIA PAC MANAG REV, V20, P210, DOI 10.1016/j.apmrv.2015.03.005
Louwers T.J., 2018, Auditing assurance services, V7
Manita R, 2020, TECHNOL FORECAST SOC, V150, DOI 10.1016/j.techfore.2019.119751
Mansouri H., 2022, Spanish Journal of Marketing - ESIC, V26, P267, DOI [DOI 10.1108/SJME-09-2021-0176, 10.1108/sjme-09-2021-0176]
McDougall GordonH. G., 2000, Journal of Services Marketing, V14, P392, DOI [10.1108/08876040010340937, DOI 10.1108/08876040010340937]
McMurrian R.C., 2016, Journal of Business Economics Research, V14, P83, DOI 10.19030/jber.v4i11.2710
Merle A, 2008, RECH APPL MARKET-ENG, V23, P27, DOI 10.1177/205157070802300303
Mitnick B.M., 2015, Agency Theory, P1
Munoko I, 2020, J BUS ETHICS, V167, P209, DOI 10.1007/s10551-019-04407-1
Mustikarini A, 2022, MEDITARI ACCOUNT RES, V30, P267, DOI 10.1108/MEDAR-11-2020-1062
Myers D.G., 1994, Exploring Social Psychology
Nicholson CY, 2001, J ACAD MARKET SCI, V29, P3, DOI 10.1177/0092070301291001
Noordin NA, 2022, J RISK FINANC MANAG, V15, DOI 10.3390/jrfm15080339
Öhman P, 2012, MANAG AUDIT J, V27, P477, DOI 10.1108/02686901211227995
Oliver R., 2014, SATISFACTION BEHAV P
Oliver RL, 1997, J RETAILING, V73, P311, DOI 10.1016/S0022-4359(97)90021-X
Özkan P, 2020, INT J BANK MARK, V38, P384, DOI 10.1108/IJBM-03-2019-0096
Patterson P, 2016, J SERV MARK, V30, P485, DOI 10.1108/JSM-05-2016-0190
QUICK Reiner., 2008, Auditing, Trust and Governance - Developing Regulation in Europe
Rahman S., 2020, IOP C SERIES EARTH E
Rama, 2022, Vision-The Journal of Business Perspective, DOI [10.1177/09722629221107229, DOI 10.1177/09722629221107229]
Ranaweera C., 2003, J TARGETING MEASUREM, V12, P82, DOI [DOI 10.1057/PALGRAVE.JT.5740100, DOI 10.1057/5740100, 10.1057/5740100]
Rawashdeh A., 2022, Journal of Positive School Psychology, V57, P9832
Rawashdeh A., 2023, Int. J. Data Netw. Sci, V7, P25, DOI [10.5267/j.ijdns.2022.12.010, DOI 10.5267/J.IJDNS.2022.12.010]
Rawashdeh A, 2023, DECISION SCI LETT, V12, P255, DOI 10.5267/dsl.2023.1.009
Rempel K., 1985, Psychiatry the State of the Art, P305
Resnik L., 2018, IEEE Journal of Translational Engineering in Health and Medicine, V7, P1
Robinson, 2000, SAE Technical Paper
Saleem A., 2015, Global Journal of Management and Business Research, V15, P21
Salijeni G, 2019, ACCOUNT BUS RES, V49, P95, DOI 10.1080/00014788.2018.1459458
Samukri S., 2022, IRE Journals, V5, P13
Sanchez-Cartas JM, 2022, IEEE ACCESS, V10, P10575, DOI 10.1109/ACCESS.2022.3144390
Schirmer N, 2018, J STRATEG MARK, V26, P298, DOI 10.1080/0965254X.2016.1240214
Schmitz J, 2019, AUST ACCOUNT REV, V29, P331, DOI 10.1111/auar.12286
Schneider D, 2022, SOCIOL METHOD RES, V51, P108, DOI 10.1177/0049124119882477
Seethamraju R, 2023, AUST J MANAGE, V48, P780, DOI 10.1177/03128962221108440
Shan SJ, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph182010624
Singh J, 2000, J ACAD MARKET SCI, V28, P150, DOI 10.1177/0092070300281014
Smith JB, 1997, J MARKETING, V61, P3, DOI 10.2307/1252186
Stern MJ, 2014, FIELD METHOD, V26, P284, DOI 10.1177/1525822X13519561
Susanti V., 2020, International Review of Management and Marketing, V10, P19, DOI [10.32479/irmm.9017, DOI 10.32479/IRMM.9017]
Taber KS, 2018, RES SCI EDUC, V48, P1273, DOI 10.1007/s11165-016-9602-2
Tung F., 2013, African Journal of Business Management, V7, P1730, DOI [https://doi.org/10.5897/AJBM10.383, DOI 10.5897/AJBM10.383]
Tyrinopoulos Y, 2008, TRANSPORT POLICY, V15, P260, DOI 10.1016/j.tranpol.2008.06.002
Vanhamme J., 2004, Revue francaise du marketing, V197, P41
Williams B., 2010, Journal of Emergency Primary Health Care (JEPHC), V8, P1, DOI [10.33151/ajp.8.3.93, DOI 10.33151/AJP.8.3.93]
Wu YL, 2018, INTERNET RES, V28, P74, DOI 10.1108/IntR-08-2016-0250
Zaheer A, 1998, ORGAN SCI, V9, P141, DOI 10.1287/orsc.9.2.141
ZEITHAML VA, 1988, J MARKETING, V52, P2, DOI 10.2307/1251446
Zia A., 2022, PSU Research Review, V6, P90, DOI [10.1108/PRR-07-2020-0023, DOI 10.1108/PRR-07-2020-0023]
NR 121
TC 1
Z9 1
U1 4
U2 4
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
ENGLAND
SN 0967-5426
EI 1758-8855
J9 J APPL ACCOUNT RES
JI J. Appl. Account. Res.
PD 2023 SEP 1
PY 2023
DI 10.1108/JAAR-10-2022-0273
EA SEP 2023
PG 29
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA DP9H8
UT WOS:001133374000001
DA 2024-03-27
ER
PT J
AU Sadiq, MW
Akhtar, MW
Huo, CH
Zulfiqar, S
AF Sadiq, Muhammad Waqas
Akhtar, Muhammad Waheed
Huo, Chunhui
Zulfiqar, Salman
TI ChatGPT-powered chatbot as a green evangelist: an innovative path toward
sustainable consumerism in E-commerce
SO SERVICE INDUSTRIES JOURNAL
LA English
DT Article
DE CGGE; consumer equilibrium; green purchase intentions; brand credibility
ID ARTIFICIAL-INTELLIGENCE; BRAND CREDIBILITY; IMPACT; EQUILIBRIUM;
INFORMATION; TECHNOLOGY; ACCEPTANCE; PRODUCTS; SERVICES; BEHAVIOR
AB The purpose of the current article is to propose the construction of ChatGPT as a green evangelist (CGGE) and to develop and validate the CGGE scale using two independent studies. Study 1 mainly adopted exploratory factor analysis to test whether the twenty items of the CGGE construct and the eight items of consumer equilibrium can represent these constructs statistically via exploratory factor analysis. Furthermore, through Study 2, this study primarily tested the convergent and discriminant validity of CGGE and consumer equilibrium. Finally, further analysis explains the relationship between CGGE and consumer equilibrium through green purchase intentions at different levels of brand credibility, including high and low levels, using structural equational modeling. Study 1 showed that the initial twenty items of CGGE are appropriately loaded on four factors and the eight items of consumer equilibrium are appropriately loaded on single factors. Study 2 demonstrated that CGGE could significantly predict consumer equilibrium through green purchase intentions at different brand credibility levels. This article contributes to the advancement of the unified theory of acceptance and use of technology (UTAUT) and research and provides a valuable tool for future empirical research on ChatGPT.
C1 [Sadiq, Muhammad Waqas; Huo, Chunhui] Liaoning Univ, Fac Econ, Business Sch, Shenyang, Peoples R China.
[Sadiq, Muhammad Waqas] COMSATS Univ Islamabad, Dept Management Sci, Sahiwal Campus, Islamabad, Pakistan.
[Akhtar, Muhammad Waheed] Liaoning Univ, Fac Econ, Sunwah Int Business Sch, Shenyang, Peoples R China.
[Akhtar, Muhammad Waheed] Int Univ Rabat, Rabat Business Sch, Rabat, Morocco.
[Huo, Chunhui; Zulfiqar, Salman] Liaoning Univ Shenyang, Asia Australia Business Coll, Fac Econ, Shenyang, Peoples R China.
C3 Liaoning University; COMSATS University Islamabad (CUI); Liaoning
University; Universite Internationale de Rabat
RP Huo, CH (autor correspondiente), Liaoning Univ, Fac Econ, Business Sch, Shenyang, Peoples R China.; Huo, CH (autor correspondiente), Liaoning Univ Shenyang, Asia Australia Business Coll, Fac Econ, Shenyang, Peoples R China.
EM huoch@lnu.edu.cn
RI Sadiq, Muhammad Waqas/AAT-1563-2021; Akhtar, Muhammad
Waheed/AAQ-1051-2021; Zulfiqar, Salman/HKO-6125-2023
OI Sadiq, Muhammad Waqas/0000-0003-3331-0702; Akhtar, Muhammad
Waheed/0000-0002-7579-5720;
FU National Planning Office of Philosophy and Social Sciences of China
[21BGL047]; National Social Science Foundation of China
FX The authors acknowledge the support provided by the National Social
Science Foundation of China (No. 21BGL047) for this study.
CR Abbasi M, 2016, WASTE MANAGE, V56, P13, DOI 10.1016/j.wasman.2016.05.018
Abu ZayyadH M., 2021, J MARK COMMUN, V27, P510, DOI DOI 10.1080/13527266.2020.1728565
Abu-Taieh EM., 2022, Journal of Open Innovation: Technology, Market, and Complexity, V8, P120, DOI [DOI 10.3390/JOITMC8030120, https://doi.org/10.3390/joitmc8030120]
AbuShanab E., 2007, J SYSTEMS INFORM TEC, V9, P78, DOI DOI 10.1108/13287260710817700
Ahmad W, 2020, J CLEAN PROD, V267, DOI 10.1016/j.jclepro.2020.122053
Aksah H., 2023, International Journal of Global Optimization and Its Application, V2, P12, DOI [10.56225/ijgoia.v2i1.161, DOI 10.56225/IJGOIA.V2I1.161]
Alamanda D. T., 2021, INT J APPL BUSINESS, V6, P35
Aleedy M, 2019, INT J ADV COMPUT SC, V10, P60
Ameen N, 2023, SERV IND J, V43, P125, DOI 10.1080/02642069.2023.2185934
Anabel T. W. V., 2022, J LANGUAGES LANGUAGE, V10, P118, DOI [https://doi.org/10.33394/jollt.v10i2.4994, DOI 10.33394/JOLLT.V10I2.4994]
ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
Appolloni A, 2022, ECOL ECON, V193, DOI 10.1016/j.ecolecon.2021.107311
Armitage CJ, 2000, PERS SOC PSYCHOL B, V26, P1421, DOI 10.1177/0146167200263009
Avató JL, 2022, ENERGIES, V15, DOI 10.3390/en15155423
Bae JH, 2021, ENERGY, V230, DOI 10.1016/j.energy.2021.120726
Baidoo-Anu D., 2023, Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
Bangsa AB, 2020, J CLEAN PROD, V245, DOI 10.1016/j.jclepro.2019.118902
Beerbaum Dr D. O., 2023, GENERATIVE ARTIFICIA
Bendary N., 2018, Exploring the extension of unified theory of acceptance and use of technology, UTAUT2, factors effect on perceived usefulness and ease of use on mobile commerce in Egypt
Berger J, 2019, J CONSUM BEHAV, V18, P233, DOI 10.1002/cb.1760
Bertoldi P, 2022, ENERG BUILDINGS, V264, DOI 10.1016/j.enbuild.2022.112075
Biswas A., 2016, J ADV MANAGEMENT SCI, V4, P211, DOI [DOI 10.12720/JOAMS.4.3.211-215, 10.12720/joams.4.3.211-215]
Biswas S., 2023, FUNCTION CHAT GPT SO
Biswas S., 2023, IMPORTANCE CHAT GPT
Biswas SS, 2023, ANN BIOMED ENG, V51, P1126, DOI 10.1007/s10439-023-03171-8
Bumett J, 2007, LANDSCAPE URBAN PLAN, V83, P29, DOI 10.1016/j.landurbplan.2007.09.003
Campbell J, 2014, INT J HOSP MANAG, V42, P39, DOI 10.1016/j.ijhm.2014.05.014
Cardoso A. G., 2023, IMPORTANCE TECHNOLOG
Chen CC, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10030854
Chen H, 2021, CHEMSUSCHEM, V14, P4123, DOI 10.1002/cssc.202100652
Chen J, 2017, INT J INNOV COMPUT I, V13, P85
Chen YS, 2012, MANAGE DECIS, V50, P502, DOI 10.1108/00251741211216250
Cheng Y, 2022, J PROD BRAND MANAG, V31, P252, DOI 10.1108/JPBM-05-2020-2907
Cheung GW, 2023, ASIA PAC J MANAG, DOI 10.1007/s10490-023-09880-x
Chiemeke S., 2011, EDUC RES-UK, V2, P1719
Chin IsaS M., 2020, Journal of Marketing Communications, V26, P896, DOI [10.1080/13527266.2019.1604561, DOI 10.1080/13527266.2019.1604561]
Chopdar PK, 2018, COMPUT HUM BEHAV, V86, P109, DOI 10.1016/j.chb.2018.04.017
Connell J, 2018, QUAL LIFE RES, V27, P1893, DOI 10.1007/s11136-018-1847-y
Dajani D., 2016, Journal of Comparative International Management, V19, P121
Das KP, 2023, ENERGY NEXUS, V9, DOI 10.1016/j.nexus.2022.100167
Dawson JF, 2014, J BUS PSYCHOL, V29, P1, DOI 10.1007/s10869-013-9308-7
Denton FT, 2011, ATLANTIC ECON J, V39, P261, DOI 10.1007/s11293-011-9276-0
Diao J., 2023, CELL REP, V42, P1
Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
Elahi K., 2022, 10 EUR S EC THEOR VI
Erdem T, 2004, J CONSUM RES, V31, P191, DOI 10.1086/383434
Escursell S, 2021, J CLEAN PROD, V280, DOI 10.1016/j.jclepro.2020.124314
Falcao D, 2022, INT J CONSUM STUD, V46, P1718, DOI 10.1111/ijcs.12803
Flavian C, 2021, SERV IND J, V41, P853, DOI 10.1080/02642069.2021.1989177
Forgas-Coll S, 2023, SERV IND J, V43, P154, DOI 10.1080/02642069.2022.2163995
George A. S., 2023, PARTNERS U INT INNOV, V1, P91, DOI [https://doi.org/10.5281/ZENODO.7855594, DOI 10.5281/ZENODO.7855594, 10.5281/zenodo.7855594]
Ghosal I., 2022, FUTURE WORK BUSINESS, P89
Gómez-Llanos E, 2020, SCI TOTAL ENVIRON, V721, DOI 10.1016/j.scitotenv.2020.137743
Gray-Hawkins M., 2020, Journal of Self-Governance and Management Economics, V8, P19, DOI DOI 10.22381/JSME8420202
Guo WS, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103353
Gürlek M, 2021, SERV IND J, V41, P734, DOI 10.1080/02642069.2021.1929930
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hair JF, 2017, J ACAD MARKET SCI, V45, P616, DOI 10.1007/s11747-017-0517-x
HAO S, 2023, SERV IND J, P1
Henseler J, 2017, INT SER QUAN MARKET, P361, DOI 10.1007/978-3-319-53469-5_12
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Howitt P, 2014, J ECON DYN CONTROL, V39, P37, DOI 10.1016/j.jedc.2013.11.003
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Huang YC, 2014, MARK INTELL PLAN, V32, P250, DOI 10.1108/MIP-10-2012-0105
Huang YS, 2021, SERV IND J, V41, P964, DOI 10.1080/02642069.2021.1957845
Ihemezie EJ., 2018, International Journal of Academic Research in Business Social Sciences, DOI [10.6007/IJARBSS/v8-i9/4647, DOI 10.6007/IJARBSS/V8-I9/4647, https://doi.org/10.6007/IJARBSS/v8-i9/4647]
Ikumoro A., 2019, INT J ACAD RES BUSIN, V9, P205, DOI [DOI 10.6007/IJARBSS/V9-I11/6544, 10.6007/ijarbss/v9i11/6544, DOI 10.6007/IJARBSS/V9I11/6544]
Indriani I a D., 2019, INT J MULTICULTURAL, V6, P627, DOI [10.18415/ijmmu.v6i2.706, DOI 10.18415/IJMMU.V6I2.706]
Jeng SP, 2016, J AIR TRANSP MANAG, V55, P1, DOI 10.1016/j.jairtraman.2016.04.005
Jiang H., 2023, ADV MULTIMED, P2023
Joshi Y, 2019, ECOL ECON, V159, P235, DOI 10.1016/j.ecolecon.2019.01.025
Joshi Y, 2017, SUSTAIN PROD CONSUMP, V10, P110, DOI 10.1016/j.spc.2017.02.002
Kachamas Pornpimon, 2019, International Journal of Machine Learning and Computing, V9, P91, DOI 10.18178/ijmlc.2019.9.1.770
Kalla D., 2023, Int. J. Innov. Sci. Res. Technol, V8, P827
Kan MQ, 2022, RESOUR CONSERV RECY, V180, DOI 10.1016/j.resconrec.2022.106156
Khan S., 2020, P 2020 4 INT S COMP, P1
Khatimah H., 2014, American-Eurasian Journal of Sustainable Agriculture, V8, P34
Khrais LT, 2020, FUTURE INTERNET, V12, DOI 10.3390/fi12120226
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015040101
Kumar P, 2021, EUR J MARKETING, V55, P2037, DOI 10.1108/EJM-10-2019-0808
Kyriazos T.A., 2018, PSYCHOLOGY, V09, P2207, DOI [DOI 10.4236/PSYCH.2018.98126, https://doi.org/10.4236/psych.2018.98126, 10.4236/psych.2018.98126]
Lasuin C. A., 2014, Malaysian Journal of Business and Economics, V1, P1
Lee CT, 2022, INTERNET RES, V32, P1329, DOI 10.1108/INTR-01-2021-0030
Lee S, 2020, J GLOB FASH MARK, V11, P232, DOI 10.1080/20932685.2020.1752766
Liao MC, 2022, J IND ECOL, V26, P164, DOI 10.1111/jiec.13214
Lim PK, 2021, INTERNET RES, V31, P497, DOI 10.1108/INTR-03-2020-0165
Lin CJ, 2016, SOC ENTERP J, V12, P281, DOI 10.1108/SEJ-02-2016-0004
Lin JB, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.11.001
Lubis A. R., 2016, EXPERT J MARKETING, V4, P20
Mariani MM, 2023, J BUS RES, V161, DOI 10.1016/j.jbusres.2023.113838
Mashaabi M., 2022, ARXIV
McGee R. W., 2023, IS CHATGPT BIASED CO
Migliore M, 2019, ENVIRON ENG MANAG J, V18, P2297
Ming L, 2022, M&SOM-MANUF SERV OP, V24, P1437, DOI 10.1287/msom.2022.1083
Mogyoros A, 2023, J INTELLET PROP LAW, V18, P367, DOI 10.1093/jiplp/jpad029
Mook A., 2023, WORLD SOC CONVERGENC
Moran D, 2020, CLIM POLICY, V20, pS28, DOI 10.1080/14693062.2018.1551186
Musa H.M., 2013, Natural Environment, V1, P17, DOI [https://doi.org/10.12966/ne.06.02.2013, DOI 10.12966/NE.06.02.2013, 10.12966/ne.06.02.2013]
Ngai EWT, 2021, ELECTRON COMMER R A, V50, DOI 10.1016/j.elerap.2021.101098
Northey G, 2022, INT J BANK MARK, V40, P1182, DOI 10.1108/IJBM-09-2021-0439
Nyumba TO, 2018, METHODS ECOL EVOL, V9, P20, DOI 10.1111/2041-210X.12860
Olan F, 2021, IEEE T ENG MANAGE, DOI 10.1109/TEM.2021.3083536
Park HJ, 2020, J BUS RES, V117, P623, DOI 10.1016/j.jbusres.2018.08.025
Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Popescu G.H., 2015, Economics, Management and Financial Markets; Woodside, V10, P80
Porumb VA, 2020, J CLEAN PROD, V272, DOI 10.1016/j.jclepro.2020.122080
Prakash AV, 2023, SERV IND J, V43, P642, DOI 10.1080/02642069.2023.2166493
Praneetvatakul S, 2022, FRONT SUSTAIN FOOD S, V5, DOI 10.3389/fsufs.2021.704233
Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
Rabby F., 2021, ACAD MARKETING STUDI, V25, P1
Rahi S., 2018, MANAGEMENT SCI LETT, V8, P173, DOI [DOI 10.5267/J.MSL.2018.1.001, 10.5267/j.msl.2018.1.001]
Ramaswamy S, 2018, PROCEDIA COMPUT SCI, V140, P170, DOI 10.1016/j.procs.2018.10.326
Raubenheimer J.E., 2004, South African Journal of Industrial Psychology, V30, P59, DOI 10.4102/sajip.v30i4.168
Razmus W, 2022, PSYCHOL MARKET, V39, P2096, DOI 10.1002/mar.21714
Richter NF, 2016, EUR MANAG J, V34, P589, DOI 10.1016/j.emj.2016.08.001
Sadiq MW, 2022, FRONT PUBLIC HEALTH, V10, DOI 10.3389/fpubh.2022.984848
Sadiq W, 2020, MARK MANAG INNOV, P149, DOI 10.21272/mmi.2020.1-12
Shafeeg A, 2023, Indonesian Journal of Computer Science, DOI 10.33022/ijcs.v12i1.3146
Sheeraz M., 2012, INT J ACAD RES BUSIN, V2, P1
Singh L, 2023, INT J METH PSYCH RES, V32, DOI 10.1002/mpr.1936
Sreen N, 2018, J RETAIL CONSUM SERV, V41, P177, DOI 10.1016/j.jretconser.2017.12.002
Stichnothe H, 2022, INT J LIFE CYCLE ASS, V27, P1270, DOI 10.1007/s11367-022-02106-0
STIGLITZ JE, 1979, AM ECON REV, V69, P339
Suki NM, 2015, PROC ECON FINANC, V37, P262, DOI 10.1016/S2212-5671(16)30123-X
Sun HL, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.856663
Surameery N.M.S., 2023, Int. J. Inform. Technol. Comput. Eng., V3, P17, DOI DOI 10.55529/IJITC.31.17.22
Teo C. B.-C., 2016, J INT BUSINESS EC EN, V1, P915
Testa F, 2015, BUS STRATEG ENVIRON, V24, P252, DOI 10.1002/bse.1821
Thowfeek M. H., 2020, SOLID STATE TECHNOL, V63, P6400
Trujillo-Ortiz A., 2006, KMO KAISER MEYER OLK
Tukker A, 2006, J IND ECOL, V10, P159, DOI 10.1162/jiec.2006.10.3.159
Vashisht Vipul, 2020, Micro-Electronics and Telecommunication Engineering. Proceedings of 3rd ICMETE 2019. Lecture Notes in Networks and Systems (LNNS 106), P683, DOI 10.1007/978-981-15-2329-8_69
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Vitezic V, 2021, SERV IND J, V41, P926, DOI 10.1080/02642069.2021.1974406
Wang H, 2022, SUSTAIN PROD CONSUMP, V32, P198, DOI 10.1016/j.spc.2022.04.015
Wang S.-M., 2023, REV INTEGRATIVE BUSI, V12, P71
Wang S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132111975
Wang YY, 2020, INT J PROD RES, V58, P7471, DOI 10.1080/00207543.2020.1765040
West A., 2018, The Business Management Review, V9, P321, DOI DOI 10.15341/JBE(2155-7950)/10.09.2018/005
Wilson M, 2016, J PROD BRAND MANAG, V25, P394, DOI 10.1108/JPBM-09-2015-0972
Worthington RL, 2006, COUNS PSYCHOL, V34, P806, DOI 10.1177/0011000006288127
Xie H, 2023, ECON RES-EKON ISTRAZ, V36, P230, DOI 10.1080/1331677X.2022.2117718
XIONG Y, 2022, DISCRETE DYN NAT SOC, P1
Yabe M., 2013, MULTIFUNCTIONAL AGR, P21
Yaqub R, 2016, INT T ELECTR ENERGY, V26, P1570, DOI 10.1002/etep.2167
Zhang LM, 2022, ENERG BUILDINGS, V256, DOI 10.1016/j.enbuild.2021.111677
Zhang XY, 2021, INT J HOSP MANAG, V94, DOI 10.1016/j.ijhm.2021.102888
Zhang Z, 2013, ACM TRANS MANAG INF, V3, DOI 10.1145/2407740.2407744
Zhou S, 2023, DIGIT HEALTH, V9, DOI 10.1177/20552076231155679
Zhuang WC, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.644020
NR 152
TC 0
Z9 0
U1 24
U2 24
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0264-2069
EI 1743-9507
J9 SERV IND J
JI Serv. Ind. J.
PD MAR 11
PY 2024
VL 44
IS 3-4
BP 173
EP 217
DI 10.1080/02642069.2023.2278463
EA NOV 2023
PG 45
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HI0F2
UT WOS:001115573500001
DA 2024-03-27
ER
PT J
AU Tudoran, AA
AF Tudoran, Ana Alina
TI A machine learning approach to identifying decision-making styles for
managing customer relationships
SO ELECTRONIC MARKETS
LA English
DT Article
DE Satisficers; Maximizers; Decision-making; Clickstreams; Machine
learning; E-commerce
ID INFORMATION SEARCH; WEB SITE; MAXIMIZERS; EXPERIENCE; MODEL;
CLICKSTREAM; SATISFICERS; DIFFICULTY; ENVIRONMENTS; TENDENCY
AB Decision-making styles have been studied in non-situational settings using the classical survey instrument. This study proposes a novel methodology for identifying decision-making styles in a real-world purchasing situation using only behavioral data and machine learning. We base our analysis on a two-week sample of 1,347,854 clickstream sessions from an e-commerce company and extract a series of parameters to infer the search goal, strategy, and decision difficulty. We implement a range of unsupervised algorithms, and we identify and validate three internally stable classes of decision-makers. One category corresponds to the classical style of satisficers; the other two subcategorize the maximisers' classical style. The customer's entry channel preferences and movement patterns provide compelling support for the style's predictive validity. This study contributes to research and practice by proposing a new methodology to recognize the customer decision style in the e-commerce setting.
C1 [Tudoran, Ana Alina] Aarhus Univ, Sch Business & Social Sci, Fuglesangs Alle, DK-8210 Aarhus, Denmark.
C3 Aarhus University
RP Tudoran, AA (autor correspondiente), Aarhus Univ, Sch Business & Social Sci, Fuglesangs Alle, DK-8210 Aarhus, Denmark.
EM anat@econ.au.dk
RI Tudoran, Ana/HMU-9691-2023
OI Tudoran, Ana/0000-0002-1380-4775
CR Anderl E, 2016, J RETAILING, V92, P185, DOI 10.1016/j.jretai.2015.10.001
Anderson CJ, 2003, PSYCHOL BULL, V129, P139, DOI 10.1037//0033-2909.129.1.139
Ariely D, 2000, J CONSUM RES, V27, P233, DOI 10.1086/314322
Arunachalam D, 2018, EXPERT SYST APPL, V111, P11, DOI 10.1016/j.eswa.2018.03.007
Basu S, 2018, ELECTRON COMMER R A, V30, P25, DOI 10.1016/j.elerap.2018.05.004
BLASHFIELD RK, 1991, J CLASSIF, V8, P277
Blattberg R.C., 2008, INT SERIES QUANTITAT, V18
Brannon DC, 2017, MARKET LETT, V28, P651, DOI 10.1007/s11002-017-9440-7
Brock G, 2008, J STAT SOFTW, V25, P1, DOI 10.18637/jss.v025.i04
Broniarczyk SM, 2014, J CONSUM PSYCHOL, V24, P608, DOI 10.1016/j.jcps.2014.05.003
Buchta C., 2020, CRAN REPOSITORY
Bucklin RE, 2009, J INTERACT MARK, V23, P35, DOI 10.1016/j.intmar.2008.10.004
Card Stuart K., 1983, The Psychology of Human-Computer Interaction
Carrillat FA, 2011, MARKET LETT, V22, P283, DOI 10.1007/s11002-010-9125-y
Chabris ChristopherF., 2008, Measuring Intertemporal Preferences Using Response Times
Cheek NN, 2016, JUDGM DECIS MAK, V11, P126
Cheng JQ, 2018, ACTA PSYCHOL, V190, P199, DOI 10.1016/j.actpsy.2018.08.002
Chowdhury TG, 2009, MARKET LETT, V20, P155, DOI 10.1007/s11002-008-9063-0
Cottrell M, 2006, NEURAL NETWORKS, V19, P762, DOI 10.1016/j.neunet.2006.05.018
Dalal DK, 2015, J BEHAV DECIS MAKING, V28, P437, DOI 10.1002/bdm.1859
de Haan E, 2018, J MARKETING, V82, P1, DOI 10.1509/jm.17.0113
DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1, DOI 10.1111/j.2517-6161.1977.tb01600.x
Diab DL, 2008, JUDGM DECIS MAK, V3, P364
Ding AWX, 2015, INFORM SYST RES, V26, P339, DOI 10.1287/isre.2015.0568
Dolnicar S., 2018, Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful, DOI [DOI 10.1007/978-981-10-8818-6_2, 10.1007/978-981-10-8818-6_2]
Dolnicar S, 2010, MARKET LETT, V21, P83, DOI 10.1007/s11002-009-9083-4
Fu WT, 2007, HUM-COMPUT INTERACT, V22, P355
Furner CP, 2017, ELECTRON MARK, V27, P211, DOI 10.1007/s12525-016-0233-2
Genewein T, 2015, FRONT ROBOT AI, DOI 10.3389/frobt.2015.00027
Ghose A, 2013, INFORM SYST RES, V24, P613, DOI 10.1287/isre.1120.0453
GOWER JC, 1971, BIOMETRICS, V27, P857, DOI 10.2307/2528823
Hair J.F., 2009, Multivariate data analysis
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Hennig C, 2007, COMPUT STAT DATA AN, V52, P258, DOI 10.1016/j.csda.2006.11.025
HUBERT L, 1985, J CLASSIF, V2, P193, DOI 10.1007/BF01908075
Iwanaga J, 2019, ELECTRON COMMER R A, V37, DOI 10.1016/j.elerap.2019.100877
Iyengar SS, 2006, PSYCHOL SCI, V17, P143, DOI 10.1111/j.1467-9280.2006.01677.x
Jaccard P., 1912, New Phytologist, V11, P37, DOI [10.1111/j.1469-8137.1912.tb05611.x, DOI 10.1111/J.1469-8137.1912.TB05611.X]
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Karimi S, 2018, J BUS RES, V91, P71, DOI 10.1016/j.jbusres.2018.05.038
Karimi S, 2015, DECIS SUPPORT SYST, V77, P137, DOI 10.1016/j.dss.2015.06.004
Kendall MG, 1938, BIOMETRIKA, V30, P81, DOI 10.1093/biomet/30.1-2.81
Klein LR, 1998, J BUS RES, V41, P195, DOI 10.1016/S0148-2963(97)00062-3
Kohonen T., 1998, Neurocomputing, V21, P1, DOI 10.1016/S0925-2312(98)00030-7
KRUSKAL WH, 1952, J AM STAT ASSOC, V47, P583, DOI 10.1080/01621459.1952.10483441
Kullback S., 1959, Information theory and statistics
LABAND DN, 1991, ECON INQ, V29, P497, DOI 10.1111/j.1465-7295.1991.tb00842.x
Lai L, 2010, JUDGM DECIS MAK, V5, P164
Leisch F., 2018, CRAN REPOSITORY
Li HS, 2014, J MARKETING RES, V51, P40, DOI 10.1509/jmr.13.0050
Lingras P, 2005, INFORM SCIENCES, V172, P215, DOI 10.1016/j.ins.2004.12.007
Ma JJ, 2014, J CONSUM RES, V41, P71, DOI 10.1086/674977
Mandel N, 2002, J CONSUM RES, V29, P235, DOI 10.1086/341573
Mao W, 2016, J CONSUM PSYCHOL, V26, P66, DOI 10.1016/j.jcps.2015.04.007
MARTINETZ TM, 1993, IEEE T NEURAL NETWOR, V4, P558, DOI 10.1109/72.238311
McClure SM, 2004, SCIENCE, V306, P503, DOI 10.1126/science.1100907
Misuraca R, 2015, PERS INDIV DIFFER, V85, P111, DOI 10.1016/j.paid.2015.04.043
Moe WW, 2003, J CONSUM PSYCHOL, V13, P29, DOI 10.1207/153276603768344762
Montgomery AL, 2004, MARKET SCI, V23, P579, DOI 10.1287/mksc.1040.0073
Moyano-Díaz E, 2020, REV CES PSICOL, V13, P18, DOI 10.21615/cesp.13.1.2
Nakayama M, 2010, ELECTRON MARK, V20, P251, DOI 10.1007/s12525-010-0041-z
NELSON P, 1974, J POLIT ECON, V82, P729, DOI 10.1086/260231
Nenkov GY, 2008, JUDGM DECIS MAK, V3, P371
Nottorf F, 2014, ELECTRON COMMER R A, V13, P45, DOI 10.1016/j.elerap.2013.07.004
Olander F., 1975, UTILITY PROBABILITY, P297, DOI [10.1007/978-94-010-1834-0_18, DOI 10.1007/978-94-010-1834-0_18]
Parker AM, 2007, JUDGM DECIS MAK, V2, P342
Payne John W., 1993, ADAPTIVE DECISION MA
PAYNE JW, 1988, J EXP PSYCHOL LEARN, V14, P534, DOI 10.1037/0278-7393.14.3.534
Peña JM, 1999, PATTERN RECOGN LETT, V20, P1027, DOI 10.1016/S0167-8655(99)00069-0
Pirolli P, 1999, PSYCHOL REV, V106, P643, DOI 10.1037/0033-295X.106.4.643
Polman E, 2010, J BEHAV DECIS MAKING, V23, P179, DOI 10.1002/bdm.647
RAND WM, 1971, J AM STAT ASSOC, V66, P846, DOI 10.2307/2284239
Schellong D., 2016, 24 EUROPEAN C INFORM
Schwartz B, 2002, J PERS SOC PSYCHOL, V83, P1178, DOI 10.1037//0022-3514.83.5.1178
Schwartz B., 2004, The paradox of choice: Why more is less
SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136
SHEHU E, 2020, J MARKETING RES PRES
Shocker AD., 1991, MARKET LETT, V2, P181, DOI [10.1007/BF02404071, DOI 10.1007/BF02404071, DOI 10.1007/BF00554125]
Simon HA, 1955, Q J ECON, V69, P99, DOI 10.2307/1884852
Sismeiro C, 2004, J MARKETING RES, V41, P306, DOI 10.1509/jmkr.41.3.306.35985
Sokal, 1973, NUMERICAL TAXONOMY P
SOKAL ROBERT R., 1962, TAXON, V11, P33, DOI 10.2307/1217208
SPROLES GB, 1986, J CONSUM AFF, V20, P267, DOI 10.1111/j.1745-6606.1986.tb00382.x
van Erven T, 2014, IEEE T INFORM THEORY, V60, P3797, DOI 10.1109/TIT.2014.2320500
Vesanto J, 2000, IEEE T NEURAL NETWOR, V11, P586, DOI 10.1109/72.846731
Vogrincic-Haselbacher C, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.642641
Weathers D, 2006, J INTERACT MARK, V20, P41, DOI 10.1002/dir.20060
Weaver K, 2015, J CONSUM PSYCHOL, V25, P372, DOI 10.1016/j.jcps.2014.10.003
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Wilcoxon F., 1992, Biometrics Bulletin, P196, DOI [10.1007/978-1-4612-4380-9_16, DOI 10.2307/3001968, 10.2307/3001968]
Yadav MS, 2014, J MARKETING, V78, P20, DOI 10.1509/jm.12.0020
Yang L, 2015, J MARKETING RES, V52, P166, DOI 10.1509/jmr.13.0288
Zhang J, 2006, J MANAGE INFORM SYST, V23, P71, DOI 10.2753/MIS0742-1222230304
NR 93
TC 3
Z9 4
U1 2
U2 30
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD MAR
PY 2022
VL 32
IS 1
BP 351
EP 374
DI 10.1007/s12525-021-00515-x
EA JAN 2022
PG 24
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1F1AS
UT WOS:000741582400001
DA 2024-03-27
ER
PT J
AU Ramadan, ZB
AF Ramadan, Zahy B.
TI "Alexafying" shoppers: The examination of Amazon's captive relationship
strategy
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Amazon; Alexa; Retailing; E-commerce; AI; Customer journey
ID BRAND ADDICTION; LOCK-IN; CUSTOMER SATISFACTION; ENGAGEMENT PLATFORMS;
CONSUMER EXPERIENCE; SELF; SERVICE; ONLINE; TECHNOLOGIES; LOVE
AB The virtual assistants' market is drastically growing and is expected to reach $2.1 billion by 2020. Nonetheless, the quick expansion and high penetration of e-retailers' AI ecosystem into the shopper's journey is still under researched in the extant literature. Amazon's Alexa in particular has been fast proliferating into the customer's journey, favoring the development of captive audiences given this new ambient environment. Through a mixed methodology using both qualitative and quantitative approaches, this study examines Amazon's captive relationship strategy on shoppers, brands and competing retailers. The research findings show that Amazon's AI relationship strategy with its customers is based on forming a multi-faceted identity for the AI that would later on facilitate a captive situation that would lead to an addictive relationship. This study is amongst the first to examine the rapid development of e-retailers' AI ecosystem into the shopper's journey. Taking the pioneering case of Amazon's Alexa powered devices, this research presents a working framework upon which scholars and practitioners alike could base their future studies and strategies on in the fast-growing field of interactive voice assistants and AI led conversations.
C1 [Ramadan, Zahy B.] Lebanese Amer Univ, POB 13-5053, Beirut 11022801, Lebanon.
C3 Lebanese American University
RP Ramadan, ZB (autor correspondiente), Lebanese Amer Univ, POB 13-5053, Beirut 11022801, Lebanon.
EM zahy.ramadan@lau.edu.lb
RI Ramadan, Zahy/O-2521-2016
OI Ramadan, Zahy/0000-0001-8368-3617
CR Aaker JL, 1997, J MARKETING RES, V34, P347, DOI 10.2307/3151897
Abdul-Ghani E, 2011, J BUS RES, V64, P1060, DOI 10.1016/j.jbusres.2010.10.009
Ahuvia A.C., 2009, HDB BRAND RELATIONSH, P342, DOI DOI 10.4324/9781315703886
Ahuvia AC, 2005, J CONSUM RES, V32, P171, DOI 10.1086/429607
Albert N, 2008, J BUS RES, V61, P1062, DOI 10.1016/j.jbusres.2007.09.014
Amazon, 2019, ALL THINGS ALEXA ALE
Andersen PH, 2006, IND MARKET MANAG, V35, P522, DOI 10.1016/j.indmarman.2004.10.010
[Anonymous], 2020, CAPTIVE
[Anonymous], 2010, SATISFACTION BEHAV P, DOI DOI 10.4324/9781315700892
[Anonymous], 2014, HDB RES RETAILER CON
[Anonymous], 2009, J BUS IND MARKET
ANTONUCCI TC, 2017, INNOV AGING, V1
ARON A, 1995, J PERS SOC PSYCHOL, V69, P1102, DOI 10.1037/0022-3514.69.6.1102
ARTHUR WB, 1989, ECON J, V99, P116, DOI 10.2307/2234208
BAGOZZI RP, 1991, J PERS SOC PSYCHOL, V61, P98, DOI 10.1037/0022-3514.61.1.98
Barnes DC, 2015, J BUS RES, V68, P433, DOI 10.1016/j.jbusres.2014.06.005
Batra R, 2012, J MARKETING, V76, P1, DOI 10.1509/jm.09.0339
Bauer H., 2009, Proceedings of the American Marketing Association summer educators conference, V15, P252
Belk RW, 2003, J CONSUM RES, V30, P326, DOI 10.1086/378613
BELK RW, 1988, J CONSUM RES, V15, P139, DOI 10.1086/209154
Benady D., 2014, How Technology is Changing Marketing
Binns R, 2019, THEY IT AMAZONS CRM
Bitner MJ, 2002, ACAD MANAGE EXEC, V16, P96, DOI 10.5465/AME.2002.8951333
Boyatzis R.E., 1998, Transforming qualitative information: Thematic analysis and code development
Braun V., 2006, Qualitative Research in Psychology, V3, P77, DOI [10.1191/1478088706qp063oa, DOI 10.1191/1478088706QP063OA]
Breidbach CF, 2017, J SERV THEOR PRACT, V27, P761, DOI 10.1108/JSTP-04-2016-0071
Breidbach CF, 2014, MANAG SERV QUAL, V24, P592, DOI 10.1108/MSQ-08-2013-0158
Brill TM, 2019, J MARKET MANAG-UK, V35, P1401, DOI 10.1080/0267257X.2019.1687571
Brodie RJ, 2019, J SERV RES-US, V22, P173, DOI 10.1177/1094670519827385
Brown T. A., 1993, TESTING STRUCTURAL E, P136, DOI DOI 10.1177/0049124192021002005
Bryman A., 2006, Qualitative Research, V6, P97, DOI [10.1177/1468794106058877, DOI 10.1177/1468794106058877, https://doi.org/10.1177/1468794106058877]
Bryman A., 2015, Business research methods, V4th
Caddy B, 2019, ALEXA WHAT WILL YOU
Canbek G.N., 2016, INT J HUMAN SCI, V13, P592, DOI DOI 10.14687/IJHS.V13I1.3549
Carpenter JM, 2008, J RETAIL CONSUM SERV, V15, P358, DOI 10.1016/j.jretconser.2007.08.003
Carroll BA, 2006, MARKET LETT, V17, P79, DOI 10.1007/s11002-006-4219-2
Chung H, 2017, DIGIT INVEST, V22, pS15, DOI 10.1016/j.diin.2017.06.010
Columbus L., 2019, FORBES
Cui CC, 2018, J BUS RES, V87, P118, DOI 10.1016/j.jbusres.2018.02.028
Dabbous A, 2019, TECHNOL FORECAST SOC, V149, DOI 10.1016/j.techfore.2019.119775
Daye D., 2016, Igniting brand growth via emotional connections
De Wulf K, 2001, J MARKETING, V65, P33, DOI 10.1509/jmkg.65.4.33.18386
Diallo MF, 2017, INT MARKET REV, V34, P536, DOI 10.1108/IMR-07-2014-0224
Edelman DC, 2015, HARVARD BUS REV, V93, P90
Escalas JE, 2005, J CONSUM RES, V32, P378, DOI 10.1086/497549
Escalas JE, 2003, J CONSUM PSYCHOL, V13, P339, DOI 10.1207/S15327663JCP1303_14
Farah M.F., 2020, INT J WEB BASED COMM
Farah MF, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101973
Farah MF, 2019, J RETAIL CONSUM SERV, V48, P136, DOI 10.1016/j.jretconser.2019.02.016
Farah MF, 2017, INT J BANK MARK, V35, P254, DOI 10.1108/IJBM-05-2016-0067
FEHR B, 1991, J PERS SOC PSYCHOL, V60, P425, DOI 10.1037/0022-3514.60.3.425
Fetscherin M., 2020, BRAND RELATIONSHIP P
Ford RC, 2001, CALIF MANAGE REV, V44, P39, DOI 10.2307/41166110
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Fournier S, 1998, J CONSUM RES, V24, P343, DOI 10.1086/209515
Fournier S, 2013, J CONSUM PSYCHOL, V23, P253, DOI 10.1016/j.jcps.2013.01.004
Gordon S., 2018, EVERYTHING WORKS AMA
Grewal D, 2017, J RETAILING, V93, P1, DOI 10.1016/j.jretai.2016.12.008
Harmeling CM, 2017, J ACAD MARKET SCI, V45, P312, DOI 10.1007/s11747-016-0509-2
Harrison MP, 2012, J MARKET THEORY PRAC, V20, P391, DOI 10.2753/MTP1069-6679200403
Harvey S, 2018, CULTIVATING CAPTIVE
Hollebeek LD, 2019, J ACAD MARKET SCI, V47, P161, DOI 10.1007/s11747-016-0494-5
Holt DB, 1997, J CONSUM RES, V23, P326, DOI 10.1086/209487
Isen AM, 2005, MOTIV EMOTION, V29, P297, DOI 10.1007/s11031-006-9019-8
Itani OS, 2019, INT J HOSP MANAG, V80, P78, DOI 10.1016/j.ijhm.2019.01.014
Jiang L, 2013, J SERV MANAGE, V24, P191, DOI 10.1108/09564231311323962
Joreskog K., 1993, LISREL 8
Kallweit K, 2014, J RETAIL CONSUM SERV, V21, P268, DOI 10.1016/j.jretconser.2014.02.002
KARAPANOS E, 2013, USER EXPERIENCE TIME
Klaus P, 2020, J SERV MARK, V34, P389, DOI 10.1108/JSM-01-2019-0043
Kleine SS, 1995, J CONSUM RES, V22, P327, DOI 10.1086/209454
KOEHLER J, 2016, EUROPEAN BUSINESS MA, V4, P55
Kumar V, 2016, J ACAD MARKET SCI, V44, P24, DOI 10.1007/s11747-015-0426-9
Lee HJ, 2016, COMPUT HUM BEHAV, V60, P322, DOI 10.1016/j.chb.2016.02.051
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
LIEBOWITZ SJ, 1995, J LAW ECON ORGAN, V11, P205
Lischer B, 2015, PSYCHOL BRAND PERSON
Lopatovska I, 2018, CHIIR'18: PROCEEDINGS OF THE 2018 CONFERENCE ON HUMAN INFORMATION INTERACTION & RETRIEVAL, P265, DOI 10.1145/3176349.3176868
Loureiro S., 2013, Customer-Centric Marketing Strategies, P414, DOI [DOI 10.4018/978-1-4666-2524-2.CH020, 10.4018/978-1-4666-2524-2.ch020]
Lusch RF, 2014, SERVICE-DOMINANT LOGIC: PREMISES, PERSPECTIVES, POSSIBILITIES, P1
McAlexander JH, 2002, J MARKETING, V66, P38, DOI 10.1509/jmkg.66.1.38.18451
Meuter ML, 2000, J MARKETING, V64, P50, DOI 10.1509/jmkg.64.3.50.18024
Mitchell A.A., 1979, Advances in Consumer Research, V6, P191
Mival Oli, 2004, Personification technologies: Developing artificial companions for older people
Mohammed S, 2019, DID AMAZON BUILD ITS
Mollen A, 2010, J BUS RES, V63, P919, DOI 10.1016/j.jbusres.2009.05.014
Monin B, 2003, J PERS SOC PSYCHOL, V85, P1035, DOI 10.1037/0022-3514.85.6.1035
Morgan-Thomas A, 2020, J BUS RES, V121, P713, DOI 10.1016/j.jbusres.2020.03.042
Mrad M, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102089
Mrad M, 2020, J BUS RES, V113, P399, DOI 10.1016/j.jbusres.2019.09.023
Mrad M, 2018, QUAL MARK RES, V21, P18, DOI 10.1108/QMR-06-2016-0050
Mrad M, 2017, EUR J MARKETING, V51, P1938, DOI 10.1108/EJM-10-2016-0571
Msaed C, 2017, J PROD BRAND MANAG, V26, P102, DOI 10.1108/JPBM-06-2015-0915
Murray KB, 2007, J CONSUM RES, V34, P77, DOI 10.1086/513048
Neuman W., 2006, SOCIAL RES METHODS Q
Nieroda ME, 2018, J BUS RES, V89, P159, DOI 10.1016/j.jbusres.2018.04.024
Nunnally J., 1978, PSYCHOMETRIC METHODS
Obst P, 2010, J COMMUNITY APPL SOC, V20, P525, DOI 10.1002/casp.1067
Orel FD, 2014, J RETAIL CONSUM SERV, V21, P118, DOI 10.1016/j.jretconser.2013.07.002
Orlikowski WJ, 2015, MIS QUART, V39, P201
Park CW, 2010, J MARKETING, V74, P1, DOI 10.1509/jmkg.74.6.1
Phillips S, 2018, FUTURE MARKET RES FU
Porte N., 2018, INTELLIGENT SELF SER
Purington A., 2017, P 2017 CHI C HUM FAC, DOI 10.1145/3027063.3053246
Rafiq M, 2013, J MARKET MANAG-UK, V29, P494, DOI 10.1080/0267257X.2012.737356
Ramadan Z., 2021, PSYCHOL MARKET, P1
Ramadan Zahy B., 2019, International Journal of Web Based Communities, V15, P327
Ramadan Zahy B., 2017, International Journal of Web Based Communities, V13, P364
Ramadan Z, 2019, MARK INTELL PLAN, V37, P660, DOI 10.1108/MIP-11-2018-0490
Ramadan Z, 2018, MARK INTELL PLAN, V36, P93, DOI 10.1108/MIP-06-2017-0100
Ramadan ZB, 2019, J BRAND MANAG, V26, P705, DOI 10.1057/s41262-019-00163-9
Rappaport SD, 2007, J ADVERTISING RES, V47, P135, DOI 10.2501/S0021849907070158
Ray M.L., 1973, NEW MODELS MASS COMM, P146
Reimann M, 2012, J CONSUM PSYCHOL, V22, P128, DOI 10.1016/j.jcps.2011.11.003
Scott SV, 2014, MIS QUART, V38, P873, DOI 10.25300/MISQ/2014/38.3.11
Smith A, 2017, WHAT AMAZON KEY TEAC
Statt N, 2018, AMAZONS RETAIL REVOL
Thomson M, 2005, J CONSUM PSYCHOL, V15, P77, DOI 10.1207/s15327663jcp1501_10
Turk V, 2016, NEW SCI, V232, P16, DOI 10.1016/S0262-4079(16)32318-1
Vargo SL, 2016, J ACAD MARKET SCI, V44, P5, DOI 10.1007/s11747-015-0456-3
Veloutsou C, 2020, ELECTRON COMMER R A, V39, DOI 10.1016/j.elerap.2019.100901
Verleye K, 2014, J SERV RES-US, V17, P68, DOI 10.1177/1094670513494015
Vivek SD, 2012, J MARKET THEORY PRAC, V20, P127, DOI 10.2753/MTP1069-6679200201
Wajid A, 2019, MARK INTELL PLAN, V37, P271, DOI 10.1108/MIP-07-2018-0241
Wallace E, 2014, J PROD BRAND MANAG, V23, P33, DOI 10.1108/JPBM-06-2013-0326
Wiggers Kyle, 2020, venture beat
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Woodard B, 2006, J ADVERTISING RES, V46, P353, DOI 10.2501/S0021849906060442
Yang CF, 2015, PROCEDIA MANUF, V3, P3391, DOI 10.1016/j.promfg.2015.07.529
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 130
TC 25
Z9 25
U1 17
U2 80
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD SEP
PY 2021
VL 62
AR 102610
DI 10.1016/j.jretconser.2021.102610
EA MAY 2021
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA TZ7AK
UT WOS:000684621800008
DA 2024-03-27
ER
PT J
AU Chatterjee, S
Goyal, D
Prakash, A
Sharma, J
AF Chatterjee, Swagato
Goyal, Divesh
Prakash, Atul
Sharma, Jiwan
TI Exploring healthcare/health-product ecommerce satisfaction: A text
mining and machine learning application
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Health-product ecommerce; Text mining; Sentiment; Emotion; Customer
satisfaction; Online reviews
ID WORD-OF-MOUTH; CUSTOMER SATISFACTION; ACCESSIBILITY-DIAGNOSTICITY;
CONSEQUENCES; EXPERIENCE; EMOTIONS; BEHAVIOR; IMPACT; SALES
AB In the digital era, online channels have become an inevitable part of healthcare services making healthcare/ health-product e-commerce an important area of study. However, the reflections of customer-satisfaction and their difference in various subgroups of this industry is still unexplored. Additionally, extant literature has majorly focused on consumer surveys for customer-satisfaction research ignoring the huge data available online. The current study fills these gaps. With 186,057 reviews on 619 e-commerce firms from 29 subcategories of healthcare/health-product industry posted in a review-website between 2008 and 2018, we used text-mining, machine-learning and econometric techniques to find which core and augmented service aspects and which emotions are more important in which service contexts in terms of reflecting and predicting customer satisfaction. Our study contributes towards the healthcare/health-product marketing and services literature in suggesting an automated and machine-learning-based methodology for insight generation. It also helps healthcare/ health-product e-commerce managers in better e-commerce service design and delivery.
C1 [Chatterjee, Swagato] Indian Inst Technol, Vinod Gupta Sch Management, Kharagpur 721302, W Bengal, India.
[Goyal, Divesh; Prakash, Atul; Sharma, Jiwan] Indian Inst Technol, Kharagpur 721302, W Bengal, India.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
Technology (IIT) - Kharagpur; Indian Institute of Technology System (IIT
System); Indian Institute of Technology (IIT) - Kharagpur
RP Chatterjee, S (autor correspondiente), Indian Inst Technol, Vinod Gupta Sch Management, Kharagpur 721302, W Bengal, India.
EM swagato@vgsom.iitkgp.ac.in
RI Chatterjee, Swagato/JYP-6161-2024; Chatterjee, Swagato/JYP-6158-2024
OI Chatterjee, Swagato/0000-0002-3907-3528
CR Ahani A, 2019, J RETAIL CONSUM SERV, V51, P331, DOI 10.1016/j.jretconser.2019.06.014
Ahmad SR, 2019, INTELL DATA ANAL, V23, P159, DOI 10.3233/IDA-173763
Ajorlou A, 2018, MANAGE SCI, V64, P971, DOI 10.1287/mnsc.2016.2657
ANDERSON EW, 1993, MARKET SCI, V12, P125, DOI 10.1287/mksc.12.2.125
ANDERSON EW, 1994, J MARKETING, V58, P53, DOI 10.2307/1252310
[Anonymous], 2019, J SERV THEOR PRACT, DOI DOI 10.1108/JSTP-10-2018-0229
Brill E, 1995, COMPUT LINGUIST, V21, P543
Byrd ET, 2016, TOURISM MANAGE, V52, P19, DOI 10.1016/j.tourman.2015.06.009
Cavanaugh LA, 2016, COGNITION EMOTION, V30, P1430, DOI 10.1080/02699931.2015.1070119
Chang KC, 2015, IND MANAGE DATA SYST, V115, P332, DOI 10.1108/IMDS-09-2014-0265
Chatterjee S, 2020, TOURISM MANAGE, V80, DOI 10.1016/j.tourman.2020.104108
Chatterjee S, 2019, DECIS SUPPORT SYST, V119, P14, DOI 10.1016/j.dss.2019.02.008
Cheung CMK, 2012, DECIS SUPPORT SYST, V53, P218, DOI 10.1016/j.dss.2012.01.015
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Cohen JB, 2006, J CONSUM RES, V33, P1, DOI 10.1086/504121
Dang Y, 2010, IEEE INTELL SYST, V25, P46, DOI 10.1109/MIS.2009.105
Nguyen DQ, 2016, AI COMMUN, V29, P409, DOI 10.3233/AIC-150698
Davis JI, 2011, EMOTION, V11, P438, DOI 10.1037/a0021783
del Bosque IR, 2008, ANN TOURISM RES, V35, P551, DOI 10.1016/j.annals.2008.02.006
Dholakia RR, 2010, INT J RETAIL DISTRIB, V38, P482, DOI 10.1108/09590551011052098
Duan W, 2008, J RETAILING, V84, P233, DOI 10.1016/j.jretai.2008.04.005
Ekinci Y., 2003, J RETAIL CONSUM SERV, V10, P201, DOI DOI 10.1016/S0969-6989(02)00008-5
Fabricant S.M., 1993, PSYCHOL MARKET, V10, P531, DOI DOI 10.1002/MAR.4220100606
Feldman R, 2007, TEXT MINING HDB ADV
Feldman R, 2013, COMMUN ACM, V56, P82, DOI 10.1145/2436256.2436274
Filieri R, 2016, ANN TOURISM RES, V58, P46, DOI 10.1016/j.annals.2015.12.019
Garbarino E, 1999, J MARKETING, V63, P70, DOI 10.2307/1251946
Grewal R, 2010, J MARKETING RES, V47, P612, DOI 10.1509/jmkr.47.4.612
Hasford J, 2016, J BUS RES, V69, P1234, DOI 10.1016/j.jbusres.2015.09.009
Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
Ho-Dac NN, 2013, J MARKETING, V77, P37, DOI 10.1509/jm.11.0011
Hotho Andreas., 2005, J. Lang. Technol. Comput. Linguist, V20, P19, DOI [DOI 10.21248/JLCL.20.2005.68, 10.21248/jlcl.20.2005.68, DOI 10.1111/j.1365-2621.1978.tb09773.x]
Ladhari R, 2007, PSYCHOL MARKET, V24, P1085, DOI 10.1002/mar.20195
Laros FJM, 2005, J BUS RES, V58, P1437, DOI 10.1016/j.jbusres.2003.09.013
Lazar MM, 2011, NEW FEMININITIES: POSTFEMINISM, NEOLIBERALISM AND SUBJECTIVITY, P37
Lynch JG, 2006, J CONSUM RES, V33, P25, DOI 10.1086/504129
Manner C., 2017, J CONSUMER SATISFACT, V30, P23
Martensen A, 2000, TOTAL QUAL MANAGE, V11, pS544
Matzler K, 2008, J TRAVEL RES, V46, P403, DOI 10.1177/0047287507312401
Mohammad SM, 2013, COMPUT INTELL-US, V29, P436, DOI 10.1111/j.1467-8640.2012.00460.x
Mostafa MM, 2013, EXPERT SYST APPL, V40, P4241, DOI 10.1016/j.eswa.2013.01.019
Mouwen A, 2015, TRANSPORT RES A-POL, V78, P1, DOI 10.1016/j.tra.2015.05.005
Mowen JC, 2007, J BUS RES, V60, P590, DOI 10.1016/j.jbusres.2006.06.007
Ng JHY, 2019, PATIENT EDUC COUNS, V102, P790, DOI 10.1016/j.pec.2018.11.013
Oh HaeMoon Oh HaeMoon, 1999, International Journal of Hospitality Management, V18, P67, DOI 10.1016/S0278-4319(98)00047-4
Pappas IO, 2014, INT J RETAIL DISTRIB, V42, P187, DOI 10.1108/IJRDM-03-2012-0034
Park JH, 2014, COMPUT HUM BEHAV, V31, P1, DOI 10.1016/j.chb.2013.10.002
Popescu A.-M, 2007, NATURAL LANGUAGE PRO, P9, DOI DOI 10.1007/978-1-84628-754-1_2
Posselt T, 2005, J INTERACT MARK, V19, P35, DOI 10.1002/dir.20048
Ravald A., 1996, EUR J MARKETING, V30, P19, DOI 10.1108/03090569610106626
Salehan M, 2016, DECIS SUPPORT SYST, V81, P30, DOI 10.1016/j.dss.2015.10.006
SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
Sandars J, 2009, MED TEACH, V31, P362, DOI 10.1080/01421590802204403
Sharp John, 2011, Front Health Serv Manage, V28, P29
Siering M, 2018, DECIS SUPPORT SYST, V107, P52, DOI 10.1016/j.dss.2018.01.002
Soderlund M, 1998, INT J SERV IND MANAG, V9, P169, DOI 10.1108/09564239810210532
Szymanski DM, 2000, J RETAILING, V76, P309, DOI 10.1016/S0022-4359(00)00035-X
Taboada M, 2011, COMPUT LINGUIST, V37, P267, DOI 10.1162/COLI_a_00049
TAYLOR SA, 1994, J RETAILING, V70, P163, DOI 10.1016/0022-4359(94)90013-2
Vaidyanathan R, 2000, J BUS PSYCHOL, V14, P605, DOI 10.1023/A:1022942330911
Vijayarani S., 2015, INT J COMPUT SCI COM, V5, P7, DOI DOI 10.1016/J.PR0CS.2013.05.286
Wang WM, 2019, J ENG DESIGN, V30, P445, DOI 10.1080/09544828.2019.1642460
WESTBROOK RA, 1991, J CONSUM RES, V18, P84, DOI 10.1086/209243
Xu JJ, 2013, MIS QUART, V37, P777, DOI 10.25300/MISQ/2013/37.3.05
Xu X, 2020, J BUS RES, V106, P196, DOI 10.1016/j.jbusres.2018.07.022
Ye Q, 2009, EXPERT SYST APPL, V36, P6527, DOI 10.1016/j.eswa.2008.07.035
You YF, 2016, J INTERNET COMMER, V15, P274, DOI 10.1080/15332861.2016.1212314
Zeithaml VA, 2002, J ACAD MARKET SCI, V30, P362, DOI 10.1177/009207002236911
NR 68
TC 54
Z9 54
U1 16
U2 108
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD JUL
PY 2021
VL 131
BP 815
EP 825
DI 10.1016/j.jbusres.2020.10.043
EA MAY 2021
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA SE4AK
UT WOS:000652015200015
DA 2024-03-27
ER
PT J
AU Jansa, T
Wattanacharoensil, W
Kolar, T
AF Jansa, Tadeja
Wattanacharoensil, Walanchalee
Kolar, Tomaz
TI Computer supported analysis of Thailand's imagery on Pinterest
SO CURRENT ISSUES IN TOURISM
LA English
DT Article
DE Visual UGC; destination image; Pinterest; Thailand; computer vision;
visual content analysis
ID ONLINE DESTINATION IMAGE; SEMIOTIC ANALYSIS; TOURISM
AB This paper reports findings and introduces an innovative methodological approach deployed to explore how complex visual imagery on Pinterest represents Thailand and how commercial and private users differ in this respect. For this purpose, visual content analysis of 300 images was performed in which computer vision labelling and a subsequent manual thematic analysis were deployed during a two-step procedure. The obtained findings reveal that significant differences exist among both types of users regarding some frequently depicted themes (architecture, animals) and regarding textual, graphic and substantive content. These differences in turn inform the theoretical and practical implications for improved communication on social media. The limitations of the study are also discussed.
C1 [Jansa, Tadeja] Jozef Stefan Inst, Ljubljana, Slovenia.
[Wattanacharoensil, Walanchalee] Mahidol Univ Int Coll, Salaya, Nakhon Pathom, Thailand.
[Kolar, Tomaz] Univ Ljubljana, Fac Econ, Ljubljana, Slovenia.
C3 Slovenian Academy of Sciences & Arts (SASA); Jozef Stefan Institute;
Mahidol University; University of Ljubljana
RP Kolar, T (autor correspondiente), Univ Ljubljana, Fac Econ, Ljubljana, Slovenia.
EM tomaz.kolar@ef.uni-lj.si
RI Wattanacharoensil, Walanchalee/GOE-6250-2022; Wattanacharoensil,
Walanchalee/Y-5505-2019
OI Wattanacharoensil, Walanchalee/0000-0003-2242-7622
CR [Anonymous], 2017, 2017 26th international conference on computer communication and networks (ICCCN), DOI 10.1109/ICCCN.2017.8038465
Bauer M.W., 2000, QUALITATIVE RES TEXT, P19, DOI DOI 10.4135/9781849209731
Bell P., 2001, Handbook of Visual Analysis, P10, DOI [DOI 10.4135/9780857020062, 10.4135/9780857020062.n2]
Camprubí R, 2013, CURR ISSUES TOUR, V16, P203, DOI 10.1080/13683500.2012.733358
Chen SH, 2017, LECT NOTES ARTIF INT, V10191, P651, DOI 10.1007/978-3-319-54472-4_61
Deng N, 2018, TOURISM MANAGE, V65, P267, DOI 10.1016/j.tourman.2017.09.010
Gartner W. C., 1993, Journal of Travel & Tourism Marketing, V2, P191
Ghazali RM, 2013, TOUR SOC SCI SER, V18, P73, DOI 10.1108/S1571-5043(2013)0000018007
Gleeson K., 2012, Visual methods in psychology: Using and interpreting images in qualitative research, P314, DOI DOI 10.4324/F9781351032063-3631
Govers R., 2004, Information Technology and Tourism, V7, P73, DOI 10.3727/1098305054517327
Gunn C.A., 1972, Vacationscape: Designing tourist regions
Haenssle H A, 2019, Ann Oncol, V30, p130e, DOI 10.1093/annonc/mdy520
Henkel R., 2006, Asia Pacific Journal of Tourism Research, V11, P269, DOI 10.1080/10941660600753299
Herring SC, 2010, INTERNATIONAL HANDBOOK OF INTERNET RESEARCH, P233, DOI 10.1007/978-1-4020-9789-8_14
Hunter WC, 2016, TOURISM MANAGE, V54, P221, DOI 10.1016/j.tourman.2015.11.012
Krishnamurthy S., 2008, Journal of Interactive Advertising, V8, P1, DOI DOI 10.1080/15252019.2008.10722137
KWAK H, 2016, P 10 INT AAAI C WEB
Lomborg S, 2014, INFORM SOC, V30, P256, DOI 10.1080/01972243.2014.915276
Lu WL, 2015, J HOSP MARKET MANAG, V24, P119, DOI 10.1080/19368623.2014.907758
Mak AHN, 2017, TOURISM MANAGE, V60, P280, DOI 10.1016/j.tourman.2016.12.012
Michaelidou N, 2013, J TRAVEL RES, V52, P789, DOI 10.1177/0047287513481272
Michelson K, 2016, DISCOURSE COMMUN, V10, P235, DOI 10.1177/1750481315623893
Minazzi R., 2015, Social Media Marketing in Tourism and Hospitality
Munar A.M., 2010, CIBEM WORKING PAPER
Munar AM, 2014, TOURISM MANAGE, V43, P46, DOI 10.1016/j.tourman.2014.01.012
Munar AM, 2011, INT J CULT TOUR HOSP, V5, P291, DOI 10.1108/17506181111156989
PAN B, 2006, ANN C TRAV TOUR RES, P16
Phillips BJ, 2014, INT J ADVERT, V33, P633, DOI 10.2501/IJA-33-4-633-655
Picazo P, 2019, J VACAT MARK, V25, P3, DOI 10.1177/1356766717736350
Rittichainuwat B. N., 2001, Cornell Hotel and Restaurant Administration Quarterly, V42, P82, DOI 10.1177/0010880401422008
ROBINSON S, 2017, EXPLORING CLOUD VISI
Rose Gillian., 2001, VISUAL METHODOLOGIES
ROWETT P, 2015, PINTEREST CAN DRIVE
Silverman D., 2013, Doing qualitative research: A practical, DOI DOI 10.3917/DS.293.0349
Song SG, 2016, J TRAVEL TOUR MARK, V33, P687, DOI 10.1080/10548408.2016.1167384
Stepchenkova S, 2013, TOURISM MANAGE, V36, P590, DOI 10.1016/j.tourman.2012.08.006
Tamajón LG, 2017, CURR ISSUES TOUR, V20, P33, DOI 10.1080/13683500.2015.1073229
Tapachai N., 2000, Journal of Travel Research, V39, P37, DOI 10.1177/004728750003900105
TSANG SH, 2018, REV INCEPTION V3 1ST
2018, PINTEREST AUDIENCE D
NR 40
TC 4
Z9 6
U1 2
U2 25
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1368-3500
EI 1747-7603
J9 CURR ISSUES TOUR
JI Curr. Issues Tour.
PD AUG 2
PY 2020
VL 23
IS 15
SI SI
BP 1833
EP 1839
DI 10.1080/13683500.2019.1631761
EA JUN 2019
PG 7
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA MN0JP
UT WOS:000473231000001
DA 2024-03-27
ER
PT J
AU Xie, JH
Liu, X
Zeng, DDJ
Fang, X
AF Xie, Jiaheng
Liu, Xiao
Zeng, Daniel Dajun
Fang, Xiao
TI UNDERSTANDING MEDICATION NONADHERENCE FROM SOCIAL MEDIA: A
SENTIMENT-ENRICHED DEEP LEARNING APPROACH
SO MIS QUARTERLY
LA English
DT Article
DE Sentiment-enriched deep learning; reason mining; social media analytics;
health risk analytics; medication nonadherence
ID BIG DATA; DESIGN SCIENCE; ADHERENCE; ANALYTICS; WORD; PERSPECTIVE;
EXTRACTION; FRAMEWORK; FEATURES; SUPPORT
AB Medication nonadherence (MNA) can lead to serious health ramifications and costs U.S. healthcare systems $290 billion annually. Understanding the reasons underlying patients' MNA is thus an urgent goal for researchers, practitioners, and the pharmaceuticalindustry in order to mitigate negative health and economic consequences. In recent years, patient engagement on social media sites has soared, making it a cost-efficient and rich information source that can complement prior survey studies and deepen the understanding of MNA. Yet these data remain untapped in existing MNA studies because of technical challenges such as long texts, decision-making based on negative sentiment, varied patient vocabulary, and the scarcity of relevant information. For this study, we developed a sentiment-enriched deep learning method (SEDEL) to address these challenges and extract reasons for MNA. We evaluated SEDEL using 53,180 reviews concerning180 drugs and achieved a precision of 89.25%, a recall of 88.48%, and an F1 score of 88.86%. SEDEL significantly outperformed state-of-the-art baseline models. We identified nine categories of MNA reasons, which were verified by domain experts. This study contributes to IS research by devising a novel deep-learning-based approach for reason mining and by providing direct implications for the health industry and for practitioners regarding the design of interventions
C1 [Xie, Jiaheng; Fang, Xiao] Univ Delaware, Dept Accounting & Management Informat Syst, Lerner Coll Business & Econ, Newark, DE 19716 USA.
[Liu, Xiao] Arizona State Univ, Dept Informat Syst, WP Carey Sch Business, Tempe, AZ 85287 USA.
[Zeng, Daniel Dajun] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China.
[Zeng, Daniel Dajun] Univ Chinese Acad Sci, Beijing, Peoples R China.
C3 University of Delaware; Arizona State University; Arizona State
University-Tempe; Chinese Academy of Sciences; Institute of Automation,
CAS; Chinese Academy of Sciences; University of Chinese Academy of
Sciences, CAS
RP Xie, JH (autor correspondiente), Univ Delaware, Dept Accounting & Management Informat Syst, Lerner Coll Business & Econ, Newark, DE 19716 USA.
EM jxie@udel.edu; Xiao.Liu.10@asu.edu; dajun.zeng@ia.ac.cn; xfang@udel.edu
RI Xie, Jiaheng/AAU-2194-2021
OI Xie, Jiaheng/0000-0002-4992-498X; Zeng, Daniel
Dajun/0000-0002-9046-222X; Fang, Xiao/0000-0002-9429-5748
FU National Key Research and Development Program of China [2020AAA0103405];
National Natural Science Foundation of China [71621002, 62071467];
Strategic Priority Research Program of the Chinese Academy of Sciences
[XDA27030100]
FX This work was supported in part by the National Key Research and
Development Program of China under Grant 2020AAA0103405, the National
Natural Science Foundation of China under Grants 71621002 and 62071467,
as well as the Strategic Priority Research Program of the Chinese
Academy of Sciences under Grant XDA27030100.
CR Abbasi A, 2018, MIS QUART, V42, P427, DOI 10.25300/MISQ/2018/13239
Abbasi A, 2012, MIS QUART, V36, P1293
Abbasi A, 2010, MIS QUART, V34, P435
Abbasi A, 2008, MIS QUART, V32, P811
Aczon Melissa., 2017, DYNAMIC MORTALITY RI
[Anonymous], 2013, INFORM SYST RES, DOI DOI 10.1287/isre.1120.0461
[Anonymous], 2010, International Journal of Computer, Electrical, Automation, Control and Information Engineering
Arazy O, 2016, INFORM SYST RES, V27, P792, DOI 10.1287/isre.2016.0647
Baesens B., 2014, MIS QUART, V38, P629, DOI DOI 10.5121/IJGCA.2012.3203
Bardhan I., 2017, MIS QUART, P333
Baroni M, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P238
Basili R, 2003, COMPUT LINGUIST, V29, P655, DOI 10.1162/089120103322753374
Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
Bj_orne J., 2013, P 2 JOINT C LEXICAL, P651
Blackman NJM, 2000, STAT MED, V19, P723, DOI 10.1002/(SICI)1097-0258(20000315)19:5<723::AID-SIM379>3.0.CO;2-A
Bosworth HB, 2011, AM HEART J, V162, P412, DOI 10.1016/j.ahj.2011.06.007
Brisson L, 2015, INT CONF RES CHAL, P187, DOI 10.1109/RCIS.2015.7128879
Chan W., 2015, DEEP RECURRENT NEURA
Chen HC, 2012, MIS QUART, V36, P1165
Chen T, 2017, EXPERT SYST APPL, V72, P221, DOI 10.1016/j.eswa.2016.10.065
Chen ZY, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P347
Cheng JJ, 2016, APPL INTELL, V45, P429, DOI 10.1007/s10489-016-0768-0
Chiu J., 2015, T ASS COMPUTATIONAL, V4, P357
Chiu J.P., 2016, Named entity recognition with bidirectional lstm-cnns, V4, P357
Chollet F., 2018, KERAS PYTHON DEEP LE
Chorowski J, 2019, IEEE-ACM T AUDIO SPE, V27, P2041, DOI 10.1109/TASLP.2019.2938863
Chowdhury MFM., 2013, P 7 INT WORKSH SEM E, V2, P351
CRAIN S, 1987, LANGUAGE, V63, P522, DOI 10.2307/415004
Deng S., 2018, LSE Business Review
Devlin J., 2018, ARXIV
Dredze M., 2012, P 25 INT C NEUR INF
Dunbar SB, 2008, J CARDIOVASC NURS, V23, P258, DOI 10.1097/01.JCN.0000305093.20012.b8
Eickholt J, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-43
Fang Q, 2015, IEEE T MULTIMEDIA, V17, P2281, DOI 10.1109/TMM.2015.2491019
Fu TJ, 2012, ACM T INFORM SYST, V30, DOI 10.1145/2382438.2382443
Garten J., 2015, Proceedings of NAACL-HLT, P95
Gellad W.F., 2009, REV BARRIERS MEDICAT
Gellad WF, 2011, AM J GERIATR PHARMAC, V9, P11, DOI 10.1016/j.amjopharm.2011.02.004
Goh JM, 2016, MIS QUART, V40, P247, DOI 10.25300/MISQ/2016/40.1.11
Goldberg Y., 2014, arXiv preprint arXiv:1402.3722
Goldman DP, 2007, JAMA-J AM MED ASSOC, V298, P61, DOI 10.1001/jama.298.1.61
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Gregor S, 2013, MIS QUART, V37, P337, DOI 10.25300/MISQ/2013/37.2.01
Hai Z, 2014, IEEE T KNOWL DATA EN, V26, P623, DOI 10.1109/TKDE.2013.26
Hamilton Z., 2017, Proc. of the 31st Int. Conf. on Neural Information Processing Systems (NIPS), P1024, DOI DOI 10.48550/ARXIV.1706.02216
Harding E., 2015, Healthcare
Hart KL, 2020, J AFFECT DISORDERS, V260, P366, DOI 10.1016/j.jad.2019.09.043
Hasan Kazi Saidul, 2014, P 2014 C EMPIRICAL M, P751, DOI [10.3115/v1/D14-1083, DOI 10.3115/V1/D14-1083]
Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
Horne B, 2017, P INT AAAI C WEB SOC, V11
Hugtenburg JG, 2013, PATIENT PREFER ADHER, V7, P675, DOI 10.2147/PPA.S29549
Hutto C. J., 2014, P INT AAAI C WEB SOC, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
Jeyapriya A, 2015, 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEMS (ICECS), P548, DOI 10.1109/ECS.2015.7124967
Ju Z., 2011, 2011 5 INT C BIOINF
Kalchbrenner N, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P655, DOI 10.3115/v1/p14-1062
Karahanna E, 2018, MIS QUART, V42, P737, DOI 10.25300/MISQ/2018/11492
Khoury MJ, 2014, SCIENCE, V346, P1054, DOI 10.1126/science.aaa2709
Kim JD, 2003, BIOINFORMATICS, V19, pi180, DOI 10.1093/bioinformatics/btg1023
Kim S.M., 2006, P COLINGACL MAIN C P, P483, DOI DOI 10.3115/1273073.1273136
Kim T, 2019, PROC CVPR IEEE, P12448, DOI 10.1109/CVPR.2019.01274
Kitchens B, 2018, J MANAGE INFORM SYST, V35, P540, DOI 10.1080/07421222.2018.1451957
Krousel-Wood M, 2009, AM J MANAG CARE, V15, P59
Lample G, 2016, P NAACL HLT, V2016, P260, DOI 10.18653/v1/n16-1030
Lau RYK, 2012, MIS QUART, V36, P1239
Lazer D, 2014, SCIENCE, V343, P1203, DOI 10.1126/science.1248506
Lee KJ, 2004, J BIOMED INFORM, V37, P436, DOI 10.1016/j.jbi.2004.08.012
Levy O, 2014, ADV NEUR IN, V27
Li HY, 2015, IEEE DATA MINING, P241, DOI 10.1109/ICDM.2015.28
Liang D., 2016, AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
Liu Pengfei, 2015, P 2015 C EMP METH NA, P1433
Liu Q, 2016, AAAI CONF ARTIF INTE, P2986
Luo XM, 2017, MIS QUART, V41, P371, DOI 10.25300/MISQ/2017/41.2.03
Ma XZ, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1064
Mai F, 2018, J MANAGE INFORM SYST, V35, P19, DOI 10.1080/07421222.2018.1440774
Marcum ZA, 2013, JAMA-J AM MED ASSOC, V309, P2105, DOI 10.1001/jama.2013.4638
Marrese-Taylor E, 2014, EXPERT SYST APPL, V41, P7764, DOI 10.1016/j.eswa.2014.05.045
Maulik U, 2002, IEEE T PATTERN ANAL, V24, P1650, DOI 10.1109/TPAMI.2002.1114856
McHorney CA, 2007, CURR MED RES OPIN, V23, P3137, DOI 10.1185/030079907X242890
Mikolov T., 2013, ARXIV, DOI DOI 10.48550/ARXIV.1301.3781
Mikolov T., 2013, P ADV NEUR INF PROC, V26, P3111, DOI DOI 10.5555/2999792.2999959
Okazaki N., 2007, Crfsuite: a fast implementation of conditional random fields (crfs)
Onishi T, 2018, P 2018 EMNLP WORKSH, P32, DOI DOI 10.18653/V1/W18-5908
Ordóñez FJ, 2016, SENSORS-BASEL, V16, DOI 10.3390/s16010115
Patra B G., 2014, Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), P370
Pennington J., 2014, P 2014 C EMP METH NA, P1532, DOI DOI 10.3115/V1/D14-1162
Pew Internet Research, 2013, 61 AM AD LOOK ONL HL
Popov A, 2016, LECT NOTES ARTIF INT, V9883, P68, DOI 10.1007/978-3-319-44748-3_7
Poria S, 2016, KNOWL-BASED SYST, V108, P42, DOI 10.1016/j.knosys.2016.06.009
Rai A, 2017, MIS QUART, V41, pIII
Rao K, 2015, INT CONF ACOUST SPEE, P4225, DOI 10.1109/ICASSP.2015.7178767
Ruths D, 2014, SCIENCE, V346, P1063, DOI 10.1126/science.346.6213.1063
Saboo AR, 2016, MIS QUART, V40, P911, DOI 10.25300/MISQ/2016/40.4.06
Sorensen K, 2012, BMC PUBLIC HEALTH, V12, DOI 10.1186/1471-2458-12-80
Stieglitz S, 2013, J MANAGE INFORM SYST, V29, P217, DOI 10.2753/MIS0742-1222290408
Sun BJ, 2011, ACM T INFORM SYST, V29, DOI 10.1145/1961209.1961215
Tai KS, 2015, PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1, P1556
Tamblyn R, 2014, ANN INTERN MED, V160, P441, DOI 10.7326/M13-1705
Tang D, 2015, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, P1422
Tang DY, 2014, PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P1555
Traverso G, 2015, NATURE, V519, pS19, DOI 10.1038/519S19a
Turian J, 2010, ACL 2010: 48TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P384
Wallace BC, 2014, J AM MED INFORM ASSN, V21, P1098, DOI 10.1136/amiajnl-2014-002711
Wang S, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P167, DOI 10.1145/2872427.2883086
Wang W., 2016, INT J MACH LEARN CYB, P1053
Wang X., 2016, PROC 26 INT C COMPUT, P2428
Weidenbacher HJ, 2015, PATIENT PREFER ADHER, V9, P327, DOI 10.2147/PPA.S74531
Williams Joni L Strom, 2014, Diabetes Manag (Lond), V4, P29
Xie JH, 2017, THER ADV DRUG SAF, V8, P375, DOI 10.1177/2042098617729318
Xiong SF, 2016, INFORM SCIENCES, V367, P689, DOI 10.1016/j.ins.2016.07.002
Yang D., 2016, P 2016 C N AM CHAPTE, P1480, DOI DOI 10.18653/V1/N16-1174
Zhang DS, 2016, J MANAGE INFORM SYST, V33, P456, DOI 10.1080/07421222.2016.1205907
Zhang L, 2014, STUD BIG DATA, V1, P1, DOI 10.1007/978-3-642-40837-3_1
Zheng S, 2015, IEEE I CONF COMP VIS, P1529, DOI 10.1109/ICCV.2015.179
Zhou C, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1247715.1247718
Zhou Peng, 2016, P 26 INT C COMP LING
NR 115
TC 9
Z9 9
U1 22
U2 118
PU SOC INFORM MANAGE-MIS RES CENT
PI MINNEAPOLIS
PA UNIV MINNESOTA-SCH MANAGEMENT 271 19TH AVE SOUTH, MINNEAPOLIS, MN 55455
USA
SN 0276-7783
J9 MIS QUART
JI MIS Q.
PD MAR
PY 2022
VL 46
IS 1
BP 341
EP 372
DI 10.25300/MISQ/2022/15336
PG 32
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA 0R8YJ
UT WOS:000785872600009
DA 2024-03-27
ER
PT J
AU Lin, MS
Liang, Y
Xue, JX
Pan, B
Schroeder, A
AF Lin, Michael S.
Liang, Yun
Xue, Joanne X.
Pan, Bing
Schroeder, Ashley
TI Destination image through social media analytics and survey method
SO INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
LA English
DT Article
DE Tourism destination image (TDI); Survey; Social media analytics; Textual
analysis; Image analysis; Machine learning
ID BIG DATA; BEHAVIORAL INTENTIONS; TOURISM DESTINATION; LEARNING-MODEL;
HOSPITALITY; DEMAND; EXPERIENCE; REVIEWS; PHOTOS
AB Purpose - Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey.
Design/methodology/approach - This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods.
Findings - The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data.
Originality/value - This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data's underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.
C1 [Lin, Michael S.] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China.
[Liang, Yun; Pan, Bing; Schroeder, Ashley] Penn State Univ, Dept Recreat Pk & Tourism Management, University Pk, PA 16802 USA.
[Xue, Joanne X.] Penn State Univ, Sch Hospitality Management, University Pk, PA USA.
C3 Hong Kong Polytechnic University; Pennsylvania Commonwealth System of
Higher Education (PCSHE); Pennsylvania State University; Pennsylvania
State University - University Park; Pennsylvania Commonwealth System of
Higher Education (PCSHE); Pennsylvania State University; Pennsylvania
State University - University Park
RP Pan, B (autor correspondiente), Penn State Univ, Dept Recreat Pk & Tourism Management, University Pk, PA 16802 USA.
EM bup83@psu.edu
RI Pan, Bing/F-4871-2012
OI Pan, Bing/0000-0002-4094-2495; Xue, Xunyue/0000-0003-2635-6518; Liang,
Yun/0000-0003-1451-0872; /0000-0001-5335-322X
FU Happy Valley Adventure Bureau [208653]
FX The authors would like to thank Happy Valley Adventure Bureau for
supporting the visitor intercept survey project with OSP No. 208653.
CR Afshardoost M, 2020, TOURISM MANAGE, V81, DOI 10.1016/j.tourman.2020.104154
Aiden E., 2014, Uncharted: Big data as a lens on human culture
ALBERS PC, 1988, ANN TOURISM RES, V15, P134, DOI 10.1016/0160-7383(88)90076-X
Baloglu S, 1999, ANN TOURISM RES, V26, P868, DOI 10.1016/S0160-7383(99)00030-4
Biemer PP, 2010, PUBLIC OPIN QUART, V74, P817, DOI 10.1093/poq/nfq058
Bigné JE, 2001, TOURISM MANAGE, V22, P607, DOI 10.1016/S0261-5177(01)00035-8
Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
Callegaro M., 2018, PALGRAVE HDB SURVEY, P175, DOI [10.1007/978-3-319-54395-6_23, DOI 10.1007/978-3-319-54395-6_23]
Chang S, 2018, TOURISM MANAGE, V64, P55, DOI 10.1016/j.tourman.2017.08.004
Chen CF, 2007, TOURISM MANAGE, V28, P1115, DOI 10.1016/j.tourman.2006.07.007
Choi S, 2007, TOURISM MANAGE, V28, P118, DOI 10.1016/j.tourman.2006.03.002
Crompton J. L., 1979, Annals of Tourism Research, V6, P408, DOI 10.1016/0160-7383(92)90128-C
Day J., 2002, Journal of Vacation Marketing, V8, P177, DOI 10.1177/135676670200800207
Deng N, 2018, TOURISM MANAGE, V65, P267, DOI 10.1016/j.tourman.2017.09.010
Du X., 2018, ANN INT S INFORM MAN, P54
Echtner C. M., 1993, Journal of Travel Research, V31, P3, DOI 10.1177/004728759303100402
ehrek R., 2010, P LREC 2010 WORKSH N, P45, DOI DOI 10.13140/2.1.2393.1847
Fakeye P. C., 1991, Journal of Travel Research, V30, P10, DOI 10.1177/004728759103000202
Gallarza MG, 2002, ANN TOURISM RES, V29, P56, DOI 10.1016/S0160-7383(01)00031-7
GARTNER WB, 1993, J BUS VENTURING, V8, P231, DOI 10.1016/0883-9026(93)90029-5
Gitelson R. J., 1994, Journal of Travel & Tourism Marketing, V3, P59, DOI 10.1300/J073v03n03_04
Google Cloud Vision, 2017, REL NOT
Gurung D.J., 2017, INT J HOSPITALITY TO, V10
HANYU K, 1993, J ENVIRON PSYCHOL, V13, P161, DOI 10.1016/S0272-4944(05)80148-3
Hu Y. Z., 1993, Journal of Travel Research, V32, P25, DOI 10.1177/004728759303200204
Jiang QY, 2022, CRIT REV FOOD SCI, V62, P4669, DOI 10.1080/10408398.2021.1878099
Kim H, 2015, TOURISM MANAGE, V49, P29, DOI 10.1016/j.tourman.2015.02.004
Kirilenko AP, 2021, TOURISM MANAGE, V83, DOI 10.1016/j.tourman.2020.104241
Krippendorff K, 2004, HUM COMMUN RES, V30, P411, DOI 10.1093/hcr/30.3.411
Lai K, 2016, J TRAVEL RES, V55, P1065, DOI 10.1177/0047287515619693
Lee CK, 2005, ANN TOURISM RES, V32, P839, DOI 10.1016/j.annals.2004.11.006
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
Li X, 2017, TOURISM MANAGE, V59, P57, DOI 10.1016/j.tourman.2016.07.005
Liu MTC, 2021, ASIA PAC J MARKET LO, V33, P373, DOI 10.1108/APJML-08-2019-0477
Ma SH, 2020, J TRAVEL RES, V59, P352, DOI 10.1177/0047287519839157
MacKay KJ, 1997, ANN TOURISM RES, V24, P537, DOI 10.1016/S0160-7383(97)00011-X
Mariani M, 2018, INT J CONTEMP HOSP M, V30, P3514, DOI 10.1108/IJCHM-07-2017-0461
Marine-Roig E, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11123392
Marine-Roig E, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9081425
Marriott, 2015, MEET PEOPL HEART MAR
Milman A., 1995, Journal of Travel Research, V33, P21, DOI 10.1177/004728759503300304
Neuendorf K. A., 2002, CONTENT ANAL GUIDEBO
Pan B, 2012, J HOSP TOUR TECHNOL, V3, P196, DOI 10.1108/17579881211264486
Pan B, 2011, ANN TOURISM RES, V38, P132, DOI 10.1016/j.annals.2010.06.004
Park SB, 2020, TOURISM MANAGE, V80, DOI 10.1016/j.tourman.2020.104127
Phelps A., 1986, Tourism Management, V7, P168, DOI 10.1016/0261-5177(86)90003-8
Richards DR, 2018, ECOSYST SERV, V31, P318, DOI 10.1016/j.ecoser.2017.09.004
Ruths D, 2014, SCIENCE, V346, P1063, DOI 10.1126/science.346.6213.1063
Sahin S, 2011, ANATOLIA, V22, P69, DOI 10.1080/13032917.2011.556222
Scarles C, 2010, ANN TOURISM RES, V37, P905, DOI 10.1016/j.annals.2010.02.001
Stepchenkova S, 2008, TOURISM MANAGE, V29, P548, DOI 10.1016/j.tourman.2007.06.003
Stepchenkova S, 2014, ANN TOURISM RES, V45, P46, DOI 10.1016/j.annals.2013.12.004
Stepchenkova S, 2013, TOURISM MANAGE, V36, P590, DOI 10.1016/j.tourman.2012.08.006
Stylidis D, 2020, INT J TOUR RES, V22, P604, DOI 10.1002/jtr.2359
Stylidis D, 2017, TOURISM MANAGE, V58, P184, DOI 10.1016/j.tourman.2016.10.014
Taecharungroj V, 2021, INT J TOUR RES, V23, P417, DOI 10.1002/jtr.2415
Tasci A.D. A., 2007, Journal of Vacation Marketing, V13, P359, DOI DOI 10.1177/1356766707081012
Tussyadiah IP, 2009, ANN TOURISM RES, V36, P24, DOI 10.1016/j.annals.2008.10.001
US Census Bureau, 2019, Quick facts
Waldherr A, 2017, SOC SCI COMPUT REV, V35, P427, DOI 10.1177/0894439316643050
Wang CY, 2010, J TRAVEL TOUR MARK, V27, P829, DOI 10.1080/10548408.2010.527249
Wang RW, 2020, J DESTIN MARK MANAGE, V18, DOI 10.1016/j.jdmm.2020.100512
Wenger A., 2008, Journal of Vacation Marketing, V14, P169, DOI [https://doi.org/10.1177/1356766707087525, DOI 10.1177/1356766707087525]
Whitaker S., 2014, 1440 FRB
Woosnam KM, 2020, J SUSTAIN TOUR, V28, P917, DOI 10.1080/09669582.2019.1708920
Xiang Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/j.tourman.2016.10.001
Xiang Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/j.ijhm.2014.10.013
Yeh SS, 2012, ADV HOSP LEISURE, V8, P167, DOI 10.1108/S1745-3542(2012)0000008013
Zhang K, 2019, TOURISM MANAGE, V75, P595, DOI 10.1016/j.tourman.2019.07.002
Zhang XW, 2020, ANN TOURISM RES, V84, DOI 10.1016/j.annals.2020.102999
NR 70
TC 33
Z9 35
U1 11
U2 89
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0959-6119
EI 1757-1049
J9 INT J CONTEMP HOSP M
JI Int. J. Contemp. Hosp. Manag.
PY 2021
VL 33
IS 6
SI SI
BP 2219
EP 2238
DI 10.1108/IJCHM-08-2020-0861
EA JUN 2021
PG 20
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA ZB7CY
UT WOS:000660419600001
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Aggarwal, S
Gour, A
AF Aggarwal, Shikha
Gour, Alekh
TI Peeking inside the minds of tourists using a novel web analytics
approach
SO JOURNAL OF HOSPITALITY AND TOURISM MANAGEMENT
LA English
DT Article
DE Web analytics; Online reviews; Tourism analytics; Machine learning;
Sentiment analysis
ID SOCIAL MEDIA; BIG DATA; ECOTOURISM EXPERIENCES; SENTIMENT ANALYSIS;
ONLINE REVIEWS; HOSPITALITY; SATISFACTION; CLASSIFICATION; TWITTER;
TRAVEL
AB In the era of social media and travel websites, tourists increasingly post reviews about their travel experiences. These posts influence the travel plans and decisions of new tourists and hence; their content is important for businesses and governments. The purpose of this study is to propose a model to investigate tourists' perceptions and underlying reasons for posting respective content on travel websites. A web analytics-based approach is proposed to conduct in-depth analyses of the data extracted from such websites and generate meaningful in-sights. A combination of sentiment analysis and topic modeling through the Latent Dirichlet Allocation and machine learning algorithm is used for analyzing the data. The application of the proposed model is illustrated in the case of Goa, India. Findings reveal that the lack of cleanliness, safety, parking, price, facilities, and services are pertinent topics leading to negative sentiments. These topics also corroborate the social and economic events that took place during the period of study. Further clustering of tourist destinations across Goa reveals the presence of similar dissatisfiers. The above findings can be used for deciding the strategy for remedial action by relevant stakeholders. Moreover, the model can automatically classify and analyze future reviews in real-time.
C1 [Aggarwal, Shikha] Goa Inst Management, Operat Management, Sattari 403505, Goa, India.
[Gour, Alekh] Goa Inst Management, Dept Healthcare Management & Big Data Analyt, Sattari 403505, Goa, India.
C3 Goa Institute of Management; Goa Institute of Management
RP Aggarwal, S (autor correspondiente), Goa Inst Management, Operat Management, Sattari 403505, Goa, India.; Gour, A (autor correspondiente), Goa Inst Management, Dept Healthcare Management & Big Data Analyt, Sattari 403505, Goa, India.
EM sa.shikhaaggarwal@gmail.com; alekh_g@yahoo.co.in
CR [Anonymous], 2018, The Guardian
[Anonymous], 2016, The Hindu
[Anonymous], 2012, N.Y. TIMES
[Anonymous], 2017, TIMES IND
Arasli H, 2014, PROCD SOC BEHV, V109, P1416, DOI 10.1016/j.sbspro.2013.12.645
Bondzi-Simpson A, 2019, INT J CONTEMP HOSP M, V31, P998, DOI [10.1108/ijchm-12-2017-0820, 10.1108/IJCHM-12-2017-0820]
Calheiros AC, 2017, J HOSP MARKET MANAG, V26, P675, DOI 10.1080/19368623.2017.1310075
Catal C, 2017, APPL SOFT COMPUT, V50, P135, DOI 10.1016/j.asoc.2016.11.022
Cheng MM, 2019, INT J HOSP MANAG, V76, P58, DOI 10.1016/j.ijhm.2018.04.004
Chhetri A, 2017, TOURISM GEOGR, V19, P398, DOI 10.1080/14616688.2016.1253765
Chuang J., 2012, P INT WORK OKK
Coghlan A, 2012, TOUR RECREAT RES, V37, P123, DOI 10.1080/02508281.2012.11081697
Devesa M, 2010, TOURISM MANAGE, V31, P547, DOI 10.1016/j.tourman.2009.06.006
Dhaoui C, 2017, J CONSUM MARK, V34, P480, DOI 10.1108/JCM-03-2017-2141
Filieri R, 2015, TOURISM MANAGE, V51, P174, DOI 10.1016/j.tourman.2015.05.007
Fränti P, 2018, APPL INTELL, V48, P4743, DOI 10.1007/s10489-018-1238-7
Fuchs M, 2014, J DESTIN MARK MANAGE, V3, P198, DOI 10.1016/j.jdmm.2014.08.002
Gan QW, 2017, J QUAL ASSUR HOSP TO, V18, P465, DOI [10.1080/1528008x.2016.1250243, 10.1080/1528008X.2016.1250243]
Ganzaroli A, 2017, TOURISM MANAGE, V61, P501, DOI 10.1016/j.tourman.2017.03.019
Geetha M, 2017, TOURISM MANAGE, V61, P43, DOI 10.1016/j.tourman.2016.12.022
Goa Tourism, 2019, TOURS TAT REL DEP T
Guerreiro J, 2020, J HOSP TOUR MANAG, V43, P269, DOI 10.1016/j.jhtm.2019.07.001
Gunn C.A., 2002, TOURISM PLANNING BAS
He W, 2017, ONLINE INFORM REV, V41, P921, DOI 10.1108/OIR-07-2016-0201
Heide M, 2009, EUR J MARKETING, V43, P305, DOI 10.1108/03090560910935442
Vu HQ, 2019, TOURISM MANAGE, V75, P435, DOI 10.1016/j.tourman.2019.06.011
IBEF, 2020, INDIATOURIM HOSPI A
Ivanov S, 2019, J HOSP TOUR TECHNOL, V10, P489, DOI 10.1108/JHTT-08-2018-0087
James TL, 2017, EXPERT SYST APPL, V71, P479, DOI 10.1016/j.eswa.2016.11.004
Kim J, 2015, J TRAVEL RES, V54, P419, DOI 10.1177/0047287514550100
Kim K, 2017, TECHNOL FORECAST SOC, V123, P362, DOI 10.1016/j.techfore.2017.01.001
Kirilenko AP, 2019, TOURISM MANAGE, V72, P400, DOI 10.1016/j.tourman.2019.01.001
Krawczyk M, 2016, INF TECHNOL TOUR, V16, P23, DOI 10.1007/s40558-015-0033-0
Kunz W, 2017, J SERV MARK, V31, P161, DOI 10.1108/JSM-10-2016-0352
Laing C, 2017, J DESTIN MARK MANAGE, V6, P184, DOI 10.1016/j.jdmm.2016.03.006
Leung D, 2013, J TRAVEL TOUR MARK, V30, P3, DOI 10.1080/10548408.2013.750919
Levy SE, 2013, CORNELL HOSP Q, V54, P49, DOI 10.1177/1938965512464513
Li HY, 2013, ASIA PAC J TOUR RES, V18, P784, DOI 10.1080/10941665.2012.708351
Li JJ, 2018, TOURISM MANAGE, V68, P301, DOI 10.1016/j.tourman.2018.03.009
Lu WL, 2012, TOURISM MANAGE, V33, P702, DOI 10.1016/j.tourman.2011.08.003
Maier D, 2018, COMMUN METHODS MEAS, V12, P93, DOI 10.1080/19312458.2018.1430754
Marine-Roig E, 2015, J DESTIN MARK MANAGE, V4, P162, DOI 10.1016/j.jdmm.2015.06.004
Mauri AG, 2013, INT J HOSP MANAG, V34, P99, DOI 10.1016/j.ijhm.2013.02.012
Menner T., 2016, Information and Communication Technologies in Tourism 2016, P411
Misirlis N, 2018, INT J INFORM MANAGE, V38, P270, DOI 10.1016/j.ijinfomgt.2017.10.005
Mohammad S.M., 2013, NRC TECHNICAL REPORT
Moro S, 2018, INT J CONTEMP HOSP M, V30, P343, DOI 10.1108/IJCHM-07-2016-0340
Neal JD, 2008, J TRAVEL RES, V47, P53, DOI 10.1177/0047287507312434
Neidhardt J, 2017, INF TECHNOL TOUR, V17, P101, DOI 10.1007/s40558-017-0079-2
Nilashi M, 2017, COMPUT IND ENG, V109, P357, DOI 10.1016/j.cie.2017.05.016
nltk, 2019, NATURAL LANGUAGE TOO
Pan HF, 2020, J HOSP TOUR MANAG, V43, P179, DOI 10.1016/j.jhtm.2020.04.005
Pantano E, 2013, J HOSP TOUR TECHNOL, V4, P211, DOI 10.1108/JHTT-02-2013-0005
Pearce PL, 2017, J HOSP TOUR MANAG, V30, P55, DOI 10.1016/j.jhtm.2017.01.004
Perles-Ribes JF, 2020, TOURISM ECON, V26, P764, DOI 10.1177/1354816619838413
Philander K, 2016, INT J HOSP MANAG, V55, P16, DOI 10.1016/j.ijhm.2016.02.001
Pokryshevskaya EB, 2017, INT J HOSP MANAG, V67, P1, DOI 10.1016/j.ijhm.2017.07.009
Rossetti M, 2016, INF TECHNOL TOUR, V16, P5, DOI 10.1007/s40558-015-0035-y
Ruhanen L, 2019, J HOSP TOUR MANAG, V39, P110, DOI 10.1016/j.jhtm.2019.03.006
Seeler S, 2019, J HOSP TOUR MANAG, V41, P80, DOI 10.1016/j.jhtm.2019.09.009
Shahrivar RB., 2012, Journal of Travel and Tourism Research (Online), V12, P61, DOI DOI 10.18510/IJTHR.2017.415
Shoval N, 2018, J TRAVEL RES, V57, P3, DOI 10.1177/0047287517691155
Sievert C., 2014, P WORKSH INT LANG LE, P63, DOI [DOI 10.3115/V1/W14-3110, 10.3115/v1/W14-3110]
Sigala M., 2019, Big data and innovation in tourism, travel, and hospitality
Sigala M, 2018, TOUR MANAG PERSPECT, V25, P151, DOI 10.1016/j.tmp.2017.12.003
Sigala M, 2016, HANDBOOK OF MANAGING AND MARKETING TOURISM EXPERIENCES, P85
Sigala Marianna, 2015, Asia Pacific Journal of Information Systems, V25, P179, DOI 10.14329/apjis.2015.25.1.179
Singh T, 2016, PROCEDIA COMPUT SCI, V89, P549, DOI 10.1016/j.procs.2016.06.095
Song H, 2008, TOURISM MANAGE, V29, P203, DOI 10.1016/j.tourman.2007.07.016
Song HY, 2012, ANN TOURISM RES, V39, P1653, DOI 10.1016/j.annals.2012.05.023
Sotiriadis MD, 2013, ELECTRON COMMER RES, V13, P103, DOI 10.1007/s10660-013-9108-1
Sparks BA, 2011, TOURISM MANAGE, V32, P1310, DOI 10.1016/j.tourman.2010.12.011
Srihadi TF, 2016, TOUR MANAG PERSPECT, V19, P32, DOI 10.1016/j.tmp.2016.03.005
Strannegård L, 2012, ANN TOURISM RES, V39, P1995, DOI 10.1016/j.annals.2012.06.006
Sun LJ, 2017, TRANSPORT RES C-EMER, V77, P49, DOI 10.1016/j.trc.2017.01.013
Tosun C., 2007, International Journal of Tourism Research, V9, P87, DOI 10.1002/jtr.595
Trauer B, 2005, TOURISM MANAGE, V26, P481, DOI 10.1016/j.tourman.2004.02.014
Travel & Associate T., 2019, TRAVEL TOURISM ASS G
Trip Advisor, 2019, EXPLORE GOA
Tussyadiah IP, 2014, J TRAVEL RES, V53, P543, DOI 10.1177/0047287513513172
Vermeulen IE, 2009, TOURISM MANAGE, V30, P123, DOI 10.1016/j.tourman.2008.04.008
Walls AR, 2011, INT J HOSP MANAG, V30, P10, DOI 10.1016/j.ijhm.2010.03.008
Williamson D, 2009, J HOSP TOUR MANAG, V16, P55, DOI 10.1375/jhtm.16.1.55
Wu CHJ, 2009, INT J HOSP MANAG, V28, P586, DOI 10.1016/j.ijhm.2009.03.008
Xiang Z., 2016, Information and Communication Technologies in Tourism 2016, P383, DOI DOI 10.1007/978-3-319-28231-2_28
Xiang Z, 2017, TOURISM MANAGE, V58, P51, DOI 10.1016/j.tourman.2016.10.001
Xiang Z, 2015, INT J HOSP MANAG, V44, P120, DOI 10.1016/j.ijhm.2014.10.013
Yang Y, 2015, INT J HOSP MANAG, V47, P14, DOI 10.1016/j.ijhm.2015.02.008
Yoo K.-H., 2016, EXPLORING TRIPADVISO
Yüksel A, 2017, TOURISM MANAGE, V59, P376, DOI 10.1016/j.tourman.2016.08.003
Zhao YB, 2019, INT J HOSP MANAG, V76, P111, DOI 10.1016/j.ijhm.2018.03.017
NR 91
TC 18
Z9 18
U1 7
U2 43
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1447-6770
EI 1839-5260
J9 J HOSP TOUR MANAG
JI J. Hosp. Tour. Manag.
PD DEC
PY 2020
VL 45
BP 580
EP 591
DI 10.1016/j.jhtm.2020.10.009
PG 12
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA PB1EW
UT WOS:000596073300021
DA 2024-03-27
ER
PT J
AU Wang, TC
AF Wang, Tzu-Chien
TI Deep Learning-Based Prediction and Revenue Optimization for Online
Platform User Journeys
SO QUANTITATIVE FINANCE AND ECONOMICS
LA English
DT Article
DE customer journeys managements; e-commerce platform; deep learning;
optimization methods
AB In today's digital landscape, businesses must allocate online resources efficiently. Data-driven AI methods are increasingly adopted for customer journey management. This study enhances existing frameworks with three key propositions, integrating deep learning and optimization to create a three-step revenue optimization model using online customer data. First, we apply K-means clustering to analyze online user data, constructing a behavior model. Then, convolutional neural networks (CNN) and long short-term memory (LSTM) networks predict user behavior and conversion values from sequential data. Finally, the heuristic algorithm optimizes revenue within budget constraints based on conversions. From an academic perspective, our study provides an empirical, theory-grounded model for service and marketing management. Technologically, we identify three key findings: stacking LSTM with CNN effectively processes sequential online user data, outperforming traditional machine learning methods; optimization methods and decision trees improve model interpretability and address marketing attribution challenges by understanding user behavior and channel impacts; and traditional integer programming models fall short in solving high-dimensional online channel planning problems, necessitating heuristic algorithms. Our model aids companies in setting online channel standards and budgets, offering valuable insights and practical guidance to decision-makers.
C1 [Wang, Tzu-Chien] Natl Taiwan Univ, Dept Business Adm, Taipei, Taiwan.
C3 National Taiwan University
RP Wang, TC (autor correspondiente), Natl Taiwan Univ, Dept Business Adm, Taipei, Taiwan.
EM d08741009@ntu.edu.tw
CR Birant D., 2011, KNOWLEDGE ORIENTED A, P91, DOI [10.5772/13683, DOI 10.5772/13683]
Chang CJ, 2002, Rev Bus Inf Syst, V6, P27, DOI [10.19030/rbis.v6i1.4575, DOI 10.19030/RBIS.V6I1.4575]
de Haan E, 2016, INT J RES MARK, V33, P491, DOI 10.1016/j.ijresmar.2015.12.001
DODIN B, 1991, EUR J OPER RES, V52, P267, DOI 10.1016/0377-2217(91)90162-O
Edvardsson B, 2005, INT J SERV IND MANAG, V16, P107, DOI 10.1108/09564230510587177
Grewal D, 2020, J RETAILING, V96, P3, DOI 10.1016/j.jretai.2020.02.002
Han DL, 2010, EUR J OPER RES, V200, P800, DOI 10.1016/j.ejor.2009.02.001
He BL, 2014, PROCEDIA COMPUT SCI, V31, P423, DOI 10.1016/j.procs.2014.05.286
Huang CY, 2007, J AM SOC INF SCI TEC, V58, P1988, DOI 10.1002/asi.20669
Klein R, 2020, EUR J OPER RES, V284, P397, DOI 10.1016/j.ejor.2019.06.034
Kraus M, 2020, EUR J OPER RES, V281, P628, DOI 10.1016/j.ejor.2019.09.018
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Sarkar M, 2021, J INTERACT MARK, V53, P80, DOI 10.1016/j.intmar.2020.07.002
Strauss AK, 2018, EUR J OPER RES, V271, P375, DOI 10.1016/j.ejor.2018.01.011
Sun CS, 2022, INFORM SYST RES, V33, P429, DOI 10.1287/isre.2021.1071
NR 15
TC 0
Z9 0
U1 1
U2 1
PU AMER INST MATHEMATICAL SCIENCES-AIMS
PI SPRINGFIELD
PA PO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES
SN 2573-0134
J9 QUANT FINANC ECON
JI Quant. Financ. Econ.
PY 2024
VL 8
IS 1
BP 1
EP 28
DI 10.3934/QFE.2024001
PG 28
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA FV7J7
UT WOS:001148691800001
OA gold
DA 2024-03-27
ER
PT J
AU Bharadwaj, N
Ballings, M
Naik, PA
Moore, M
Arat, MM
AF Bharadwaj, Neeraj
Ballings, Michel
Naik, Prasad A.
Moore, Miller
Arat, Mustafa Murat
TI A New Livestream Retail Analytics Framework to Assess the Sales Impact
of Emotional Displays
SO JOURNAL OF MARKETING
LA English
DT Article
DE deep learning; emotions; face detection; livestream e-commerce;
salesperson effectiveness
ID EXPRESSIONS; SERVICE; CUSTOMER; CONTAGION; EMPLOYEES
AB At the intersection of technology and marketing, this study develops a framework to unobtrusively detect salespeople's faces and simultaneously extract six emotions: happiness, sadness, surprise, anger, fear, and disgust. The authors analyze 99,451 sales pitches on a livestream retailing platform and match them with actual sales transactions. Results reveal that each emotional display, including happiness, uniformly exhibits a negative U-shaped effect on sales over time. The maximum sales resistance appears in the middle rather than at the beginning or end of sales pitches. Taken together, the results show that in one-to-many screen-mediated communications, salespeople should sell with a straight face. In addition, the authors derive closed-form formulae for the optimal allocation of the presence of a face and emotional displays over the presentation span. In contrast to the U-shaped effects, the optimal face presence wanes at the start, gradually builds to a crescendo, and eventually ebbs. Finally, the study shows how to objectively rank salespeople and circumvent biases in performance appraisals, thereby making novel contributions to people analytics. This research integrates new types of data and methods, key theoretical insights, and important managerial implications to inform the expanding opportunity that livestream e-commerce presents to marketers to create, communicate, deliver, and capture value.
C1 [Bharadwaj, Neeraj] Univ Tennessee, Haslam Coll Business, Mkt, Knoxville, TN 37996 USA.
[Ballings, Michel] Univ Tennessee, Haslam Coll Business, Business Analyt, Knoxville, TN USA.
[Naik, Prasad A.] Univ Calif Davis, Grad Sch Management, Mkt, Davis, CA 95616 USA.
[Moore, Miller; Arat, Mustafa Murat] Univ Tennessee, Haslam Coll Business, Business Analyt & Stat, Knoxville, TN USA.
C3 University of Tennessee System; University of Tennessee Knoxville;
University of Tennessee System; University of Tennessee Knoxville;
University of California System; University of California Davis;
University of Tennessee System; University of Tennessee Knoxville
RP Bharadwaj, N (autor correspondiente), Univ Tennessee, Haslam Coll Business, Mkt, Knoxville, TN 37996 USA.
EM nbharadwaj@utk.edu; michel.ballings@utk.edu; panaik@ucdavis.edu;
miller.moore@utk.edu; marat@vols.utk.edu
RI Ballings, Michel/C-1016-2013
OI Naik, Prasad/0000-0002-0606-6479
FU Haslam College of Business; UC Davis research grant; Class of 1989
Endowment
FX The author(s) received the following financial support for the research,
authorship, and/or publication of this article: The first two authors
received research support from the Haslam College of Business, and the
third author acknowledges funding received from UC Davis research grants
and the Class of 1989 Endowment.
CR [Anonymous], 1988, CURRENT ISSUES RES A
Arriaga O., 2017, IMAGE CAPTIONING CLA
Ataman MB, 2008, MARKET SCI, V27, P1036, DOI 10.1287/mksc.1080.0358
Bagozzi RP, 1999, J ACAD MARKET SCI, V27, P184, DOI 10.1177/0092070399272005
Balducci B, 2018, J ACAD MARKET SCI, V46, P557, DOI 10.1007/s11747-018-0581-x
BERLYNE DE, 1970, PERCEPT PSYCHOPHYS, V8, P279, DOI 10.3758/BF03212593
Bharadwaj N, 2020, IND MARKET MANAG, V90, P106, DOI 10.1016/j.indmarman.2020.07.002
Bharadwaj N, 2020, J INTERACT MARK, V50, P17, DOI 10.1016/j.intmar.2019.09.002
BROWN CS, 1994, J ORGAN BEHAV MANAGE, V14, P55
CALDER BJ, 1980, J MARKETING RES, V17, P173, DOI 10.2307/3150928
Cheshin A, 2018, ORGAN BEHAV HUM DEC, V144, P97, DOI 10.1016/j.obhdp.2017.10.002
Crivelli C, 2018, TRENDS COGN SCI, V22, P388, DOI 10.1016/j.tics.2018.02.006
Eicoff Alvin., 1995, Direct Marketing Through Broadcast Media: TV, Radio, Cable, Informercials, Home Shopping, and More
Eng Ilene, 2019, BANK BRANCH UNVEILS
Fridlund A.J., 1994, HUMAN FACIAL EXPRESS
Friesen E., 1978, Manual for the facial action coding system, V3
GATIGNON H, 1987, J MARKETING RES, V24, P247, DOI 10.2307/3151635
Gountas S, 2007, J BUS RES, V60, P81, DOI 10.1016/j.jbusres.2006.09.022
Grandey AA, 2011, J SERV RES-US, V14, P397, DOI 10.1177/1094670511410304
Greenwald M., 2020, FORBES
Grewal D, 2020, J ACAD MARKET SCI, V48, P1, DOI 10.1007/s11747-019-00711-4
Hamilton R, 2021, J MARKETING, V85, P68, DOI 10.1177/0022242920908227
Hardy Melissa A., 1993, REGRESSION DUMMY VAR, P64
Hareli S, 2009, EMOTION, V9, P378, DOI 10.1037/a0015958
Heerdink MW, 2019, COGNITION EMOTION, V33, P563, DOI 10.1080/02699931.2018.1476324
Hennig-Thurau T, 2006, J MARKETING, V70, P58, DOI 10.1509/jmkg.70.3.58
Hu Ye, 2021, CUSTOMER NEEDS SOLUT, V8, P27
Kadic-Maglajlic S, 2017, J BUS RES, V80, P53, DOI 10.1016/j.jbusres.2017.06.023
Kidwell B, 2021, J MARKETING, V85, P141, DOI 10.1177/0022242921999603
Lam SK, 2017, J SERV RES-US, V20, P12, DOI 10.1177/1094670516679271
Lee YH, 2010, J MARKETING RES, V47, P1151, DOI 10.1509/jmkr.47.6.1151
Li X, 2019, INT J RES MARK, V36, P216, DOI 10.1016/j.ijresmar.2019.02.004
Liu X, 2018, J MARKETING, V82, P86, DOI 10.1509/jm.16.0048
Luo XM, 2021, J MARKETING, V85, P14, DOI 10.1177/0022242920956676
Marinova D, 2018, J MARKETING RES, V55, P178, DOI 10.1509/jmr.15.0243
McDuff D, 2020, J ADVERTISING RES, V60, P370, DOI 10.2501/JAR-2020-027
McKee R., 1997, STORY SUBSTANCE STRU
Miao F, 2022, J MARKETING, V86, P67, DOI 10.1177/0022242921996646
Naik PA, 2005, MARKET SCI, V24, P25, DOI 10.1287/mksc.1040.0083
Naik PA, 1998, MARKET SCI, V17, P214, DOI 10.1287/mksc.17.3.214
Oblander ES, 2020, MARKET LETT, V31, P125, DOI 10.1007/s11002-020-09525-9
Pugh SD, 2001, ACAD MANAGE J, V44, P1018, DOI 10.5465/3069445
Rockwater, 2020, CAN US REPL CHIN 63B
Scarantino A, 2018, PHILOS SCI, V85, P1038
Scarantino A, 2017, PSYCHOL INQ, V28, P165, DOI 10.1080/1047840X.2017.1328951
Scarantino A, 2017, PSYCHOL INQ, V28, P217, DOI 10.1080/1047840X.2017.1345227
Sethuraman R, 2011, J MARKETING RES, V48, P457, DOI 10.1509/jmkr.48.3.457
Siders MA, 2001, ACAD MANAGE J, V44, P570, DOI 10.5465/3069371
Stephens DL, 1996, J BUS RES, V37, P193, DOI 10.1016/S0148-2963(96)00069-0
Teixeira T, 2012, J MARKETING RES, V49, P144, DOI 10.1509/jmr.10.0207
Van Kleef G.A., 2016, INTERPERSONAL DYNAMI
Van Kleef GA, 2015, J APPL PSYCHOL, V100, P1124, DOI 10.1037/apl0000003
Van Kleef GA, 2010, ADV EXP SOC PSYCHOL, V42, P45, DOI 10.1016/S0065-2601(10)42002-X
Van Kleef GA, 2009, ACAD MANAGE J, V52, P562, DOI 10.5465/AMJ.2009.41331253
Van Kleef GA, 2009, CURR DIR PSYCHOL SCI, V18, P184, DOI 10.1111/j.1467-8721.2009.01633.x
Viola P, 2001, PROC CVPR IEEE, P511, DOI 10.1109/cvpr.2001.990517
Wang L, 2018, ACAD MANAGE J, V61, P1050, DOI 10.5465/amj.2015.0460
Wang Z, 2017, J CONSUM RES, V43, P787, DOI 10.1093/jcr/ucw062
Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
Zetlin Minda, 2017, DO YOU SMILE TOO MUC
Zoltners AA, 2008, J PERS SELL SALES M, V28, P115, DOI 10.2753/PSS0885-3134280201
NR 61
TC 37
Z9 39
U1 55
U2 310
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2429
EI 1547-7185
J9 J MARKETING
JI J. Mark.
PD JAN
PY 2022
VL 86
IS 1
SI SI
BP 27
EP 47
AR 00222429211013042
DI 10.1177/00222429211013042
EA SEP 2021
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA XM3TG
UT WOS:000704310700001
DA 2024-03-27
ER
PT J
AU Hudima, T
Trehub, O
Kamyshanskyi, V
AF Hudima, Tetiana
Trehub, Oleksandr
Kamyshanskyi, Vladyslav
TI INTERNATIONAL DIGITAL TRADE & DIGITAL ECONOMY AGREEMENTS: CHALLENGES AND
PROSPECTS FOR UKRAINE
SO FINANCIAL AND CREDIT ACTIVITY-PROBLEMS OF THEORY AND PRACTICE
LA English
DT Article
DE digital agreement; digital trade; foreign economic policy; foreign
economic; relations; artificial intelligence; cross-border data flows;
personal data protection; e-commerce; electronic document management;
technology
AB The article examines the prospects for the digital transformation of Ukraine's foreign economic policy in the context of international digital trade/economy agreements, especially the Digital Trade Agreement between the United Kingdom of Great Britain and Northern Ireland and Ukraine. The economic and legal effectiveness of these agreements in the process of digital transformation of foreign economic policy (foreign trade) in Ukraine and at the international level is proven. To increase their effectiveness and accelerate implementation, the political and legal framework is of great importance. Under other circumstances, the realisation of such agreements will be complicated by the need to bring domestic legislation in line with their provisions, as the case of Ukraine shows. It is substantiated that the implementation of the Digital Trade Agreement between the United Kingdom of Great Britain and Northern Ireland and Ukraine is complicated by the lack of national legislation regarding electronic document management in cross-border trade; the slow pace of implementation of international legislation, for example, on personal data protection; the existence of differences in the legislative approaches of the European Union and the United Kingdom of Great Britain and Northern Ireland regarding certain issues of trade in goods and services etc. It is emphasized that certain norms of the agreement are declarative and that it does not address some important issues that can be of great value for cross-border trade in the future. The expediency of Ukraine's constant participation in international cooperation in order to modernize foreign economic and national economic policy, taking into account the requirements of international documents and current challenges, is reasoned. In this context, constant monitoring, study of foreign experience and analysis of the consequences of resolving such important issues as cross-border transfer of information by electronic means, localization of data and entities that provide their transfer, non-discrimination, etc. are vital.
C1 [Hudima, Tetiana; Trehub, Oleksandr; Kamyshanskyi, Vladyslav] Natl Acad Sci Ukraine, State Org V Mamutov Inst Econ & Legal Res, Kiev, Ukraine.
C3 National Academy of Sciences Ukraine
RP Hudima, T (autor correspondiente), Natl Acad Sci Ukraine, State Org V Mamutov Inst Econ & Legal Res, Kiev, Ukraine.
EM tsgudima@gmail.com
RI Trehub, Oleksandr/IWE-0080-2023; Hudima, Tetiana/AAV-6450-2021;
Kamyshanskyi, Vladyslav/JDV-9858-2023
OI Trehub, Oleksandr/0000-0003-0660-5783; Hudima,
Tetiana/0000-0003-1509-5180; Kamyshanskyi, Vladyslav/0000-0003-4220-8339
CR AA EU&Ukr, Association Agreement between the European Union and its Member States, of the one part, and Ukraine, of the other part
Aaronson SA, 2023, OXFORD REV ECON POL, V39, P98, DOI 10.1093/oxrep/grac046
Chaisse J, 2023, WORLD TRADE REV, V22, P73, DOI 10.1017/S1474745622000337
Cheung P, 2023, The ASEAN Digital Economy: Towards an Integrated Regional Framework, P210, DOI [10.4324/9781003308751, DOI 10.4324/9781003308751]
Ciuriak D., 2022, SSRN Electronic Journal, DOI [10.2139/ssrn.4217903, DOI 10.2139/SSRN.4217903]
Ciuriak D, 2018, SSRN Electronic Journal, DOI [10.2139/ssrn.3110785, DOI 10.2139/SSRN.3110785]
DEPA, Digital Economy Partnership Agreement
Elsig M., 2021, Big Data and Global Trade Law, P42, DOI [10.1017/9781108919234.004, DOI 10.1017/9781108919234.004]
EM, 2022, Explanatory Memorandum on the Digita l Trade Agreement between the United Kingdom o f Great Britain and Northern Ireland and Ukraine
Fay R, 2021, SSRN Electronic Journal, DOI [10.2139/ssrn.3875736, DOI 10.2139/SSRN.3875736]
Gao H, 2018, LEG ISS ECON INTEGR, V45, P47
Government of Canada, 2023, CanadaUkraine FTA modernization: Summary of negotiated outcomes
Greenberg E, 2023, Papayaglobal
H DTA, 2023, Historic Digital Trade Agreement between Ukraine and United Kingdom enhances economic support
Honey S., 2021, Addressing Impediments to Digital Trade, P217
Hudima T, 2023, FINANC CREDIT ACT, V3, P398, DOI 10.55643/fcaptp.3.50.2023.4039
Kono K., 2023, NATO Cooperative Cyber Defence Centre o f Excellence, V37
Lim J. Z., 2022, Impact of Digital Economy Agreements on ASEAN Development: Estimates from a CGE Model, P30
Lopez Gonzalez J, 2018, OECD Trade Policy Papers, V217, DOI [10.1787/1BD89C9A-EN, DOI 10.1787/1BD89C9A-EN]
Mitchell F., 2020, ARTNeT Working Paper Series, P191
NZ export, 2021, New Zealand's export performance over the past decade
ODL PDP, 2022, Council of Europe Project "Supporting Implementation of the European Human Rights Standards in Ukraine
Rakesh V., 2022, PRIV S 2022 DAT PROT, P105
RKD, 2023, Recommendation for a Council decision authorising the opening of negotiations for digita l trade disciplines with the Republic of Korea and with Singapore
S vs NZ, Singapore vs New Zealand
SADEA, Singapore-Australia Digital Economy Agreement
Schrems, 2020, Data Protection Commissioner v Facebook Ireland Limited and Maximillian Schrems
Sheng L., 2022, Big Tech Firms and International Relations: The Role of the Nation-State in New Forms of Power, P93, DOI [10.1007/978-981-19-3682-1_4, DOI 10.1007/978-981-19-3682-1_4]
Soprana M., 2021, Trade L. & Dev., V13, P143
Suslenko V, 2022, FINANC CREDIT ACT, V1, P62
U&C FTA, Ukraine and Canada will update the Free Trade Agreement
UK-Ukraine DTA, 2023, CS Ukraine 2/2023
UK-Ukraine DTA AE, 2023, UK-Ukraine DTA: agreement explainer. Promotional material
Vlassis A., 2022, Analytical report
NR 34
TC 0
Z9 0
U1 0
U2 0
PU FINTECHALIANCE
PI Kyiv
PA Highway Kharkivska, bldg 180/21, Kyiv, UKRAINE
SN 2306-4994
EI 2310-8770
J9 FINANC CREDIT ACT
JI Financ. Credit Act.
PY 2023
VL 5
IS 52
BP 449
EP 460
DI 10.55643/fcaptp.5.52.2023.4139
PG 12
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA AH8W7
UT WOS:001117676000022
DA 2024-03-27
ER
PT J
AU Luo, XM
Lu, XH
Li, J
AF Luo, Xueming
Lu, Xianghua
Li, Jing
TI When and How to Leverage E-commerce Cart Targeting: The Relative and
Moderated Effects of Scarcity and Price Incentives with a Two-Stage
Field Experiment and Causal Forest Optimization
SO INFORMATION SYSTEMS RESEARCH
LA English
DT Article
DE e-commerce; digital; scarcity; incentives; machine learning; causal
random forest
ID EMPIRICAL-ANALYSIS; RESOURCE SCARCITY; SHOPPING GOALS; CONSUMER PRICE;
PROMOTION; IMPACT; PRODUCT; MODEL; SALES; UNCERTAINTY
AB The rise of online shopping cart-tracking technologies enables new opportunities for e-commerce cart targeting (ECT). However, practitioners might target shoppers who have short-listed products in their digital carts without fully considering how ECT designs interact with consumer mindsets in online shopping stages. This paper develops a conceptual model of ECT that addresses the question of when (with versus without carts) and how to target (scarcity versus price promotion). Our ECT model is grounded in the consumer goal stage theory of deliberative or implemental mindsets and supported by a two-stage field experiment involving more than 22,000 mobile users. The results indicate that ECT has a substantial impact on consumer purchases, inducing a 29.9% higher purchase rate than e-commerce targeting without carts. Moreover, this incremental impact is moderated: the ECT design with a price incentive amplifies the impact, but the same price incentive leads to ineffective e-commerce targeting without carts. By contrast, a scarcity message attenuates the impact but significantly boosts purchase responses to targeting without carts. Interestingly, the costless scarcity nudge is approximately 2.3 times more effective than the costly price incentive in the early shopping stage without carts, whereas a price incentive is 11.4 times more effective than the scarcity message in the late stage with carts. We also leverage a causal forest algorithm that can learn purchase response heterogeneity to develop a practical scheme of optimizing ECT. Our model and findings empower managers to prudently target consumer shopping interests embedded in digital carts to capitalize on new opportunities in e-commerce.
C1 [Luo, Xueming] Temple Univ, Fox Sch Business, Philadelphia, PA 19122 USA.
[Lu, Xianghua] Fudan Univ, Sch Management, Shanghai 200433, Peoples R China.
[Li, Jing] Nanjing Univ, Sch Business, Nanjing 210093, Jiangsu, Peoples R China.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple
University; Fudan University; Nanjing University
RP Luo, XM (autor correspondiente), Temple Univ, Fox Sch Business, Philadelphia, PA 19122 USA.
EM xueming.luo@temple.edu; lxhua@fudan.edu.cn; aaronleejane@gmail.com
OI Li, Jing/0000-0002-2242-1987
FU National Natural Science Foundation of China [71422006, 71531006,
71872050]
FX X. Lu thanks the National Natural Science Foundation of China [Grants
71422006, 71531006, and 71872050] for financial support.
CR Alba JW, 1999, J CONSUM RES, V26, P99, DOI 10.1086/209553
Andrews M, 2016, MARKET SCI, V35, P218, DOI 10.1287/mksc.2015.0905
Ansari A, 2003, J MARKETING RES, V40, P131, DOI 10.1509/jmkr.40.2.131.19224
Aydinli A, 2014, J MARKETING, V78, P80, DOI 10.1509/jm.12.0338
Balachander S, 2009, MANAGE SCI, V55, P1623, DOI 10.1287/mnsc.1090.1056
BLATTBERG RC, 1995, MARKET SCI, V14, pG122, DOI 10.1287/mksc.14.3.G122
Blattberg RobertC., 1990, Sales Promotion: Concepts, Methods, and Strategies
Brehm J.W., 1972, RESPONSES LOSS FREED
Bridges E, 2008, J BUS RES, V61, P309, DOI 10.1016/j.jbusres.2007.06.017
Büttner OB, 2015, EUR J MARKETING, V49, P170, DOI 10.1108/EJM-01-2012-0044
Cao ZK, 2018, INFORM SYST RES, V29, P567, DOI 10.1287/isre.2017.0772
Chan JCF, 2010, IEEE T ENG MANAGE, V57, P365, DOI 10.1109/TEM.2009.2034255
Chandon P, 2000, J MARKETING, V64, P65, DOI 10.1509/jmkg.64.4.65.18071
Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906
Close AG, 2010, J BUS RES, V63, P986, DOI 10.1016/j.jbusres.2009.01.022
Devaraj S, 2002, INFORM SYST RES, V13, P316, DOI 10.1287/isre.13.3.316.77
Dimoka A, 2012, MIS QUART, V36, P395
Ding AWX, 2015, INFORM SYST RES, V26, P339, DOI 10.1287/isre.2015.0568
Drèze X, 2003, J INTERACT MARK, V17, P8, DOI 10.1002/dir.10063
Du XM, 2017, INFORM SYST RES, V28, P711, DOI 10.1287/isre.2017.0724
Dubé JP, 2017, MARKET SCI, V36, P944, DOI 10.1287/mksc.2017.1042
Egeln L.S., 2012, ATLANTIC MARKETING J, V1, P1
Fang Z, 2015, INFORM SYST RES, V26, P552, DOI 10.1287/isre.2015.0586
Farahat Ayman., 2012, WWW 2012, P111, DOI DOI 10.1145/2187836.2187852
FOLKES VS, 1993, J CONSUM RES, V20, P467, DOI 10.1086/209362
Fong NM, 2015, J MARKETING RES, V52, P726, DOI 10.1509/jmr.14.0229
Garcia K, 2018, BRANDS KNOW THEYRE B
Ghose A, 2009, MIS QUART, V33, P263
Gondar Town, 2018, RECORDED DATA NUMBER
Haans H, 2011, J RETAIL CONSUM SERV, V18, P389, DOI 10.1016/j.jretconser.2011.05.001
Ho SY, 2011, INFORM SYST RES, V22, P660, DOI 10.1287/isre.1090.0262
Jedidi K, 1999, MARKET SCI, V18, P1, DOI 10.1287/mksc.18.1.1
Jung Y, 2009, COMPUT HUM BEHAV, V25, P123, DOI 10.1016/j.chb.2008.07.011
KALWANI MU, 1992, J MARKETING RES, V29, P90, DOI 10.2307/3172495
Kopalle PK, 1999, MARKET SCI, V18, P317, DOI 10.1287/mksc.18.3.317
Koufaris M, 2002, INFORM SYST RES, V13, P205, DOI 10.1287/isre.13.2.205.83
Kristofferson K, 2017, J CONSUM RES, V43, P683, DOI 10.1093/jcr/ucw056
Lal R, 1997, MARKET SCI, V16, P60, DOI 10.1287/mksc.16.1.60
Lambrecht A, 2013, J MARKETING RES, V50, P561, DOI 10.1509/jmr.11.0503
Lee L, 2006, J CONSUM RES, V33, P60, DOI 10.1086/504136
Leong C, 2016, MIS QUART, V40, P475, DOI 10.25300/MISQ/2016/40.2.11
Li CX, 2017, MARKET SCI, V36, P762, DOI 10.1287/mksc.2017.1044
Luo XM, 2014, MANAGE SCI, V60, P1738, DOI 10.1287/mnsc.2013.1836
Manchanda P, 2006, J MARKETING RES, V43, P98, DOI 10.1509/jmkr.43.1.98
Mandel N, 2002, J CONSUM RES, V29, P235, DOI 10.1086/341573
McCall M, 2009, J PROD BRAND MANAG, V18, P461, DOI 10.1108/10610420910989785
Mehta R, 2016, J CONSUM RES, V42, P767, DOI 10.1093/jcr/ucv051
Mela CF, 1998, J MARKETING RES, V35, P250, DOI 10.2307/3151852
Miyazaki AD, 2009, J PUBLIC POLICY MARK, V28, P71, DOI 10.1509/jppm.28.1.71
Mochon D, 2017, J MARKETING RES, V54, P306, DOI 10.1509/jmr.15.0409
Moe WW, 2004, MANAGE SCI, V50, P326, DOI 10.1287/mnsc.1040.0153
Novak T.P., 1997, Interval Research Corporation, V31, P1
Parboteeah DV, 2009, INFORM SYST RES, V20, P60, DOI 10.1287/isre.1070.0157
Pavlou PA, 2007, MIS QUART, V31, P105
Reczek RW, 2014, J CONSUM RES, V41, P1065, DOI 10.1086/678052
RICHINS ML, 1992, J CONSUM RES, V19, P303, DOI 10.1086/209304
Rossi PE, 1996, MARKET SCI, V15, P321, DOI 10.1287/mksc.15.4.321
Roux C, 2015, J CONSUM RES, V42, P615, DOI 10.1093/jcr/ucv048
Rutz OJ, 2011, MARKET SCI, V30, P789, DOI 10.1287/mksc.1110.0647
Sahni NS, 2015, QME-QUANT MARK ECON, V13, P203, DOI 10.1007/s11129-015-9159-9
Shah AK, 2012, SCIENCE, V338, P682, DOI 10.1126/science.1222426
Sherman L., 2001, Journal of Interactive Marketing, V15, P60, DOI [DOI 10.1002/DIR.1011, 10.1002/dir.1011]
Song J, 2005, MANAGE SCI, V51, P1219, DOI 10.1287/mnsc.1050.0427
Song TT, 2017, INFORM MANAGE-AMSTER, V54, P1072, DOI 10.1016/j.im.2017.03.003
Stock A, 2005, MANAGE SCI, V51, P1181, DOI 10.1287/mnsc.1050.0381
Subramani M, 2001, INFORM SYST RES, V12, P135, DOI 10.1287/isre.12.2.135.9698
Swait J., 2000, J RETAIL CONSUM SERV, V7, P77, DOI [10.1016/S0969-6989(99)00012-0, DOI 10.1016/S0969-6989(99)00012-0]
Tam KY, 2005, INFORM SYST RES, V16, P271, DOI 10.1287/isre.1050.0058
Trope Y, 2003, PSYCHOL REV, V110, P403, DOI 10.1037/0033-295X.110.3.403
VanderMeer D, 2012, MIS QUART, V36, P479
Venkatesh V, 2017, MIS QUART, V41, P83, DOI 10.25300/MISQ/2017/41.1.05
Wager S, 2018, J AM STAT ASSOC, V113, P1228, DOI 10.1080/01621459.2017.1319839
Wang WQ, 2009, MIS QUART, V33, P293
Xu KQ, 2017, MANAGE SCI, V63, P1469, DOI 10.1287/mnsc.2015.2406
Xu LZ, 2012, INFORM SYST RES, V23, P1284, DOI 10.1287/isre.1120.0425
Yao S, 2011, MARKET SCI, V30, P447, DOI 10.1287/mksc.1100.0626
Zhu M, 2015, J MARKETING RES, V52, P13, DOI 10.1509/jmr.13.0451
NR 77
TC 29
Z9 32
U1 17
U2 155
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7047
EI 1526-5536
J9 INFORM SYST RES
JI Inf. Syst. Res.
PD DEC
PY 2019
VL 30
IS 4
BP 1203
EP 1227
DI 10.1287/isre.2019.0859
PG 25
WC Information Science & Library Science; Management
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science; Business & Economics
GA JX6HZ
UT WOS:000503834900006
DA 2024-03-27
ER
PT J
AU Li, JJ
Wu, LR
Qi, JY
Zhang, YX
Wu, ZY
Hu, SB
AF Li, Jinjie
Wu, Lianren
Qi, Jiayin
Zhang, Yuxin
Wu, Zhiyan
Hu, Shuaibo
TI Determinants Affecting Consumer Trust in Communication With AI Chatbots:
The Moderating Effect of Privacy Concerns
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Consumer Trust; AI Chatbots; E-Commerce; Privacy Concerns; Service
Communication
ID ARTIFICIAL-INTELLIGENCE; PERCEIVED RISKS; BUILDING TRUST; TECHNOLOGY;
AGENT
AB This paper summarized the factors that influence consumers' trust in AI chatbots and divided it into chatbot-related factors (expertise, anthropomorphism, responsiveness, and ease of use), companyrelated factors (perceived risk, brand trust, human support), and consumer-related factors (privacy concerns). This research attempts to explore the mechanism of human-AI chatbots trust formation and answer the question of how to promote consumers' trust in AI chatbots. The results found that the chatbot-related factors (expertise, responsiveness, and anthropomorphism) positively affect consumers' trust in chatbots. The company-related factor (brand trust) positively affects consumers' trust in chatbots, and perceived risk negatively affect consumers' trust in chatbots. Privacy concerns have a moderating effect on company-related factors. This study helps deepen the understanding of human-AI chatbots communication trust, constructs a basic model of human-AI chatbots trust, and provides insights for e-commerce enterprises to improve chatbots and enhance consumer trust.
C1 [Li, Jinjie] Guangzhou Univ, Sch Journalism & Commun, Guangzhou, Peoples R China.
[Wu, Lianren; Qi, Jiayin] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China.
[Zhang, Yuxin; Hu, Shuaibo] Shanghai Univ Int Business & Econ, Inst Artificial Intelligence & Change Management, Shanghai, Peoples R China.
[Wu, Zhiyan] Shanghai Univ Int Business & Econ, Sch Business, Shanghai, Peoples R China.
C3 Guangzhou University; Guangzhou University; Shanghai University of
International Business & Economics; Shanghai University of International
Business & Economics
RP Wu, LR; Qi, JY (autor correspondiente), Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou, Peoples R China.
EM lianrenwu@suibe.edu.cn; qijiayin@139.com
RI Wu, Lianren/IAN-7864-2023
OI Wu, Lianren/0000-0001-7886-6494
FU National Natural Science Foundation of China [72274119]
FX This work was supported by National Natural Science Foundation of China
[grant number: 72274119] .
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Ahn J, 2022, J BUS RES, V141, P50, DOI 10.1016/j.jbusres.2021.12.007
Ameen N, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.106976
Aoki N, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2020.101490
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Arm Treasure Data, 2019, Ai vs. Human customer service: Survey data shows when consumers prefer a bot
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Ba SL, 2010, INFORM SYST RES, V21, P423, DOI 10.1287/isre.1100.0282
Baker AL, 2018, ACM T INTERACT INTEL, V8, DOI 10.1145/3181671
Balakrishnan J, 2024, ANN OPER RES, V333, P653, DOI 10.1007/s10479-021-04049-5
Balakrishnan J, 2021, PSYCHOL MARKET, V38, P643, DOI 10.1002/mar.21462
Bansal G, 2015, EUR J INFORM SYST, V24, P624, DOI 10.1057/ejis.2014.41
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Brachten F, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102375
Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Chao CY, 2016, COMPUT HUM BEHAV, V64, P117, DOI 10.1016/j.chb.2016.06.031
Chen JS, 2021, INT J RETAIL DISTRIB, V49, P1512, DOI 10.1108/IJRDM-08-2020-0312
Chen Q, 2022, J BUS RES, V145, P552, DOI 10.1016/j.jbusres.2022.02.088
Chen QQ, 2021, IND MANAGE DATA SYST, V121, P2722, DOI 10.1108/IMDS-12-2020-0761
Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
Cheng XS, 2022, INTERNET RES, V32, P496, DOI 10.1108/INTR-08-2020-0460
Cheng Y, 2022, J PROD BRAND MANAG, V31, P252, DOI 10.1108/JPBM-05-2020-2907
Chi OH, 2021, COMPUT HUM BEHAV, V118, DOI 10.1016/j.chb.2021.106700
Chintalapati S, 2022, INT J MARKET RES, V64, P38, DOI 10.1177/14707853211018428
Choi TR, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101628
Ciechanowski L, 2019, FUTURE GENER COMP SY, V92, P539, DOI 10.1016/j.future.2018.01.055
Corritore CL, 2003, INT J HUM-COMPUT ST, V58, P737, DOI 10.1016/S1071-5819(03)00041-7
Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
Delgado-Ballester E., 2004, EUR J MARKETING, V38, P573, DOI [10.1108/03090560410529222, DOI 10.1108/03090560410529222]
Diederich S, 2020, BUS INFORM SYST ENG+, V62, P193, DOI 10.1007/s12599-020-00639-y
Fan H, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2022.102937
Folstad A., 2020, Quality and User Experience, V5, P1, DOI [10.1007/s41233-020-00033-2, DOI 10.1007/S41233-020-00033-2]
Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Graef R, 2021, ELECTRON MARK, V31, P319, DOI 10.1007/s12525-020-00420-9
Haesevoets T, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106730
Hansen JM, 2018, COMPUT HUM BEHAV, V80, P197, DOI 10.1016/j.chb.2017.11.010
Hasan R, 2021, J BUS RES, V131, P591, DOI 10.1016/j.jbusres.2020.12.012
Ho CC, 2010, COMPUT HUM BEHAV, V26, P1508, DOI 10.1016/j.chb.2010.05.015
Ho TH, 2020, INT J HOSP MANAG, V87, DOI 10.1016/j.ijhm.2020.102501
Holtgraves TM, 2007, COMPUT HUM BEHAV, V23, P2163, DOI 10.1016/j.chb.2006.02.017
Huang R, 2022, TELEMAT INFORM, V70, DOI 10.1016/j.tele.2022.101811
Huang SYB, 2022, COMPUT HUM BEHAV, V129, DOI 10.1016/j.chb.2021.107027
Jain S, 2022, J BUS RES, V139, P701, DOI 10.1016/j.jbusres.2021.10.007
Jang M, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106747
Jin SV, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101644
Kang JY, 2014, J BUS ETHICS, V125, P253, DOI 10.1007/s10551-013-1916-7
Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
Kasilingam DL, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101280
Kim W, 2023, J ADVERTISING, V52, P504, DOI 10.1080/00913367.2022.2043795
Koschate-Fischer N., 2015, Schmalenbach Business Review, V67, P171, DOI [10.1007/BF03396873, DOI 10.1007/BF03396873]
Kushwaha AK, 2021, IND MARKET MANAG, V98, P207, DOI 10.1016/j.indmarman.2021.08.011
Lankton NK, 2015, J ASSOC INF SYST, V16, P880, DOI 10.17705/1jais.00411
Lee M, 2022, J CONSUM BEHAV, V21, P842, DOI 10.1002/cb.2043
Li JJ, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph182212254
Liew TW, 2021, TELEMAT INFORM, V65, DOI 10.1016/j.tele.2021.101721
Liu KF, 2022, COMPUT HUM BEHAV, V127, DOI 10.1016/j.chb.2021.107026
Lou C, 2022, INT J ADVERT, V41, P655, DOI 10.1080/02650487.2021.1951510
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Martínez-López FJ, 2021, J ORGAN END USER COM, V33, P70, DOI 10.4018/JOEUC.20210701.oa4
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
McDonnell M, 2019, INTERACT COMPUT, V31, P116, DOI 10.1093/iwc/iwz007
Mikalef P, 2022, EUR J INFORM SYST, V31, P257, DOI 10.1080/0960085X.2022.2026621
Mogaji E, 2021, TELEMAT INFORM, V65, DOI 10.1016/j.tele.2021.101711
Moriuchi E, 2021, J STRATEG MARK, V29, P375, DOI 10.1080/0965254X.2020.1740766
Mostafa RB, 2022, EUR J MARKETING, V56, P1748, DOI 10.1108/EJM-02-2020-0084
Mozafari N, 2022, J SERV MANAGE, V33, P221, DOI 10.1108/JOSM-10-2020-0380
Mu JF, 2015, IND MARKET MANAG, V49, P151, DOI 10.1016/j.indmarman.2015.05.003
Mudofi L. N. H., 2022, Interdisciplinary Social Studies, V2, P532, DOI [10.55324/iss.v1i5.122, DOI 10.55324/ISS.V1I5.122]
Nordheim CB, 2019, INTERACT COMPUT, V31, P317, DOI 10.1093/iwc/iwz022
Pagani M, 2017, J INTERACT MARK, V37, P133, DOI 10.1016/j.intmar.2016.10.001
Park N, 2021, COMPUT HUM BEHAV, V121, DOI 10.1016/j.chb.2021.106795
Pitardi V, 2021, PSYCHOL MARKET, V38, P626, DOI 10.1002/mar.21457
Pizzi G, 2021, J BUS RES, V129, P878, DOI 10.1016/j.jbusres.2020.11.006
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Rajaobelina L, 2021, PSYCHOL MARKET, V38, P2339, DOI 10.1002/mar.21548
Rapp A, 2021, INT J HUM-COMPUT ST, V151, DOI 10.1016/j.ijhcs.2021.102630
Rese A, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102176
Rousseau DM, 1998, ACAD MANAGE REV, V23, P393, DOI 10.5465/AMR.1998.926617
Roy R, 2021, J BUS RES, V126, P23, DOI 10.1016/j.jbusres.2020.12.051
Sarkar S, 2020, INT J INFORM MANAGE, V50, P286, DOI 10.1016/j.ijinfomgt.2019.08.008
Schaefer K. E., 2012, P HUM FACT ERG SOC A, V56, P1548, DOI [10.1177/1071181312561308, DOI 10.1177/1071181312561308]
Schanke S, 2021, INFORM SYST RES, V32, P736, DOI 10.1287/isre.2021.1015
Seeger AM., 2017, Proceedings of the Sixteenth Annual Pre-ICIS Workshop on HCI Research in MIS Vol, V10
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shim HR, 2020, J ORGAN END USER COM, V32, P62, DOI 10.4018/JOEUC.2020010104
Shumanov M, 2021, COMPUT HUM BEHAV, V117, DOI 10.1016/j.chb.2020.106627
Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
Stoeckli E, 2020, ELECTRON MARK, V30, P369, DOI 10.1007/s12525-019-00359-6
Thiebes S, 2021, ELECTRON MARK, V31, P447, DOI 10.1007/s12525-020-00441-4
Toader DC, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12010256
Trivedi J, 2019, J INTERNET COMMER, V18, P91, DOI 10.1080/15332861.2019.1567188
Van den Broeck E, 2019, COMPUT HUM BEHAV, V98, P150, DOI 10.1016/j.chb.2019.04.009
Wang CL, 2021, INT J INFORM MANAGE, V61, DOI 10.1016/j.ijinfomgt.2021.102401
Wang XQ, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102535
Wei YH, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2021.102838
Wu L., 2022, J ORGAN END USER COM, V34, P1, DOI [10.4018/JOEUC.308819, DOI 10.4018/JOEUC.308819]
Wu LR, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su131911128
Yadav J, 2022, BEHAV INFORM TECHNOL, V41, P1286, DOI 10.1080/0144929X.2021.1872705
Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
Youn S, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106721
Zarouali B, 2017, COMPUT HUM BEHAV, V69, P157, DOI 10.1016/j.chb.2016.11.050
NR 105
TC 2
Z9 2
U1 96
U2 96
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PY 2023
VL 35
IS 1
AR 328089
DI 10.4018/JOEUC.328089
PG 24
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA W5HI7
UT WOS:001091931200001
OA gold
DA 2024-03-27
ER
PT J
AU Díaz-Pacheco, A
Guerrero-Rodríguez, R
Alvarez-Carmona, MA
Rodríguez-González, AY
Aranda, R
AF Diaz-Pacheco, Angel
Guerrero-Rodriguez, Rafael
Alvarez-Carmona, Miguel A.
Rodriguez-Gonzalez, Ansel Y.
Aranda, Ramon
TI Quantifying differences between UGC and DMO's image content on Instagram
using deep learning
SO INFORMATION TECHNOLOGY & TOURISM
LA English
DT Article; Early Access
DE Deep learning; Destination image; Destination marketing organizations;
Social networks; Mexico
ID DESTINATION IMAGE; PICTORIAL ANALYSIS; VISIT; MODEL
AB In the tourism industry, the implementation of effective strategies to promote destinations is considered of utmost importance. Taking advantage of social media, Destination Management Organizations (DMOs) have embraced these platforms as direct channels of communication with potential visitors. However, it remains unclear to what extent these efforts work to effectively construct the desired image and influence visitors' behavior. In order to explore this phenomenon, this study proposes a comparison of destination images within Instagram, used by both DMOs and visitors (user generated content). Thus, a deep-learning method is presented to automatically compute differences between destination images. Four destinations were selected from Mexico (two urban destinations and two beach destinations). The findings suggest that the images of urban destinations share more significant similarities, particularly in dimensions related to culture, tourist infrastructure, and natural resources when compared to beach destinations. Conversely, the images of beach destinations tend to converge on dimensions such as sun and sand, gastronomy, and entertainment, while differing in aspects related to tourist infrastructure and eco-tourism offerings. It is worth noting that these results underscore the importance of tailoring marketing strategies to the unique characteristics of each destination, taking into account the divergences and similarities in the perceptions of potential visitors.
C1 [Diaz-Pacheco, Angel] Univ Guanajuato, Dept Ingn Elect, Campus Irapuato Salamanca,Carretera Salamanca Vall, Guanajuato 36787, Mexico.
[Guerrero-Rodriguez, Rafael] Univ Guanajuato, Div Ciencias Econ Adm, Guanajuato 36000, Mexico.
[Alvarez-Carmona, Miguel A.] Ctr Invest Matemat, Unidad Monterrey, Parque Invest & Innovac Tecnol PIIT, Km 10 Autopista Aeropuerto,Parque Invest & Innovac, Apodaca 66628, Mexico.
[Rodriguez-Gonzalez, Ansel Y.] Ctr Invest Cient & Educ Super Ensenada, Andador 10,110 Ciudad Conocimiento, Tepic 63173, Mexico.
[Aranda, Ramon] Ctr Invest Matemat, Unidad Merida, Parque Cient & Tecnol Yucatan PCTY,Carretera Sierr, Merida 97302, Mexico.
[Alvarez-Carmona, Miguel A.; Rodriguez-Gonzalez, Ansel Y.; Aranda, Ramon] Consejo Nacl Humanidades Ciencias & Tecnol CONAHCY, Ave Insurgentes Sur 1582, Mexico City 03940, Mexico.
C3 Universidad de Guanajuato; Universidad de Guanajuato; CICESE - Centro de
Investigacion Cientifica y de Educacion Superior de Ensenada
RP Aranda, R (autor correspondiente), Ctr Invest Matemat, Unidad Merida, Parque Cient & Tecnol Yucatan PCTY,Carretera Sierr, Merida 97302, Mexico.; Aranda, R (autor correspondiente), Consejo Nacl Humanidades Ciencias & Tecnol CONAHCY, Ave Insurgentes Sur 1582, Mexico City 03940, Mexico.
EM angel.diaz@ugto.mx; r.guerrero-rodriguez@ugto.mx;
miguel.alvarez@cimat.mx; ansel@cicese.edu.mx; arac@cimat.mx
RI Rodríguez González, Ansel Y./D-9778-2018; Pacheco,
Admilson/HJI-9294-2023; Aranda, Ramon/KBA-3487-2024; Guerrero,
Rafael/AES-8683-2022
OI Rodríguez González, Ansel Y./0000-0001-9971-0237; Pacheco,
Admilson/0000-0002-3635-827X; Guerrero, Rafael/0000-0001-8576-1172;
Carlos, Hugo/0000-0002-1610-6921; Alvarez Carmona, Miguel
Angel/0000-0003-4421-5575; Diaz-Pacheco, Angel/0000-0002-5978-0377
CR Alvarez-Carmona MA, 2023, PROCES LENG NAT, P425, DOI 10.26342/2023-71-34
Alvarez-Carmona MA, 2022, J KING SAUD UNIV-COM, V34, P10125, DOI 10.1016/j.jksuci.2022.10.010
[Anonymous], 2018, UNWTO World Tourism Barometer, DOI [DOI 10.18111/WTOBAROMETERENG.2020.18.1.1, DOI 10.18111/WTOBAROMETERENG]
Arefieva V, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104318
Baloglu S, 1999, ANN TOURISM RES, V26, P868, DOI 10.1016/S0160-7383(99)00030-4
Baumann F, 2018, Destination image through digital photography. Instagram as a data collector for UGC analysis
Beerli A, 2004, ANN TOURISM RES, V31, P657, DOI 10.1016/j.annals.2004.01.010
Bernkopf D, 2019, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2019, P145, DOI 10.1007/978-3-030-05940-8_12
Blanco-Moreno S, 2023, Spanish J Mark-ESIC, DOI [10.1108/SJME-06-2022-0134, DOI 10.1108/SJME-06-2022-0134]
Bramwell B, 1996, ANN TOURISM RES, V23, P201, DOI 10.1016/0160-7383(95)00061-5
Khoa BT, 2021, 2021 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS), P677, DOI 10.1109/IEMTRONICS52119.2021.9422610
Carvache-Franco M, 2023, J QUAL ASSUR HOSP TO, V24, P146, DOI 10.1080/1528008X.2021.2024780
Chan CS, 2018, ASIAN GEOGR, V35, P143, DOI 10.1080/10225706.2018.1527235
Choi S, 2007, TOURISM MANAGE, V28, P118, DOI 10.1016/j.tourman.2006.03.002
Couret C, 2016, Curso internacional de la omt en argentina excelencia en la gestion turistica: El turista digital
Cuomo MT, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120345
Gálvez-Rodríguez MD, 2020, TOUR MANAG PERSPECT, V34, DOI 10.1016/j.tmp.2020.100636
Deng N, 2019, TOUR MANAG PERSPECT, V30, P182, DOI 10.1016/j.tmp.2019.02.016
Diaz-Pacheco A, 2022, Como ves, V289, P28
Diaz-Pacheco A, 2023, INT J INTERACT MULTI, DOI 10.9781/ijimai.2023.10.003
Díaz-Pacheco A, 2023, J KING SAUD UNIV-COM, V35, DOI 10.1016/j.jksuci.2023.101746
Diaz-Pacheco A, 2022, J EXP THEOR ARTIF IN, DOI 10.1080/0952813X.2022.2153276
Egger R., 2022, INFORM COMMUNICATION, P343, DOI DOI 10.1007/978-3-030-94751-4_31
Egger R, 2022, TOUR REV, V77, P1234, DOI 10.1108/TR-05-2021-0244
Ensafi F., 2023, Res J Manag Rev, V8, P1
Feizollah A, 2021, J BIG DATA-GER, V8, DOI 10.1186/s40537-021-00463-5
Fellbaum C., 1998, WORDNET
Frías DM, 2008, TOURISM MANAGE, V29, P163, DOI 10.1016/j.tourman.2007.02.020
Gallarza MG, 2002, ANN TOURISM RES, V29, P56, DOI 10.1016/S0160-7383(01)00031-7
Gartner W. C., 1993, Journal of Travel & Tourism Marketing, V2, P191
Gonzalez-Uribe EG., 2021, Red Int Investig Compet, V15, P1508
Gorji AS, 2023, ANATOLIA, V34, P144, DOI 10.1080/13032917.2021.2001665
Govers R., 2003, Information Technology and Tourism, V6, P13, DOI 10.3727/109830503108751199
Grosspietsch M, 2006, TOURISM MANAGE, V27, P225, DOI 10.1016/j.tourman.2004.08.005
Gunn ClareA., 1997, VACATIONSCAPE
Hauser D, 2022, J DESTIN MARK MANAGE, V24, DOI 10.1016/j.jdmm.2022.100702
Huang GI, 2023, TOURISM MANAGE, V95, DOI 10.1016/j.tourman.2022.104686
Huang XY, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14020953
Iglesias-Sánchez PP, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12072793
Inversini A, 2012, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2012, P483
Jiménez-Barreto J, 2020, TOURISM MANAGE, V79, DOI 10.1016/j.tourman.2020.104101
Kolodziej M, 2020, IEEE SENS J, V20, P15213, DOI 10.1109/JSEN.2020.3012404
Kotler P., 2016, Marketing for Hospitality and Tourism
Kuhzady S, 2019, TOUR ANAL, V24, P43, DOI [10.3727/108354219X15458295631954, 10.3727/108354219x15458295631954]
Lalicic L, 2020, J DESTIN MARK MANAGE, V16, DOI 10.1016/j.jdmm.2019.03.004
Langner R, 2010, ACTA PSYCHOL, V133, P64, DOI 10.1016/j.actpsy.2009.10.001
Lee E, 2023, TOUR MANAG PERSPECT, V47, DOI 10.1016/j.tmp.2023.101114
Li J., 2015, e-Review of Tourism Research, V12, P191
Liu MTC, 2021, ASIA PAC J MARKET LO, V33, P373, DOI 10.1108/APJML-08-2019-0477
Lojo A, 2020, J TRAVEL TOUR MARK, V37, P495, DOI 10.1080/10548408.2020.1785370
Mak AHN, 2017, TOURISM MANAGE, V60, P280, DOI 10.1016/j.tourman.2016.12.012
Marine-Roig E, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11123392
Marine-Roig E, 2018, TOURISM MANAGE, V68, P236, DOI 10.1016/j.tourman.2018.03.020
Matos N., 2012, Dos Algarves. A Multidisciplinary E-journal, P101
Nixon L., 2022, Information and Communication Technologies in Tourism 2022, P174, DOI [10.1007/978-3-030-94751-4_16, DOI 10.1007/978-3-030-94751-4_16]
Nixon L, 2017, ENTER2017 ETOURISM C
Nixon LJ, 2019, e-Rev Tour Res, V17
Nixon L, 2023, SPR PROC BUS ECON, P204, DOI 10.1007/978-3-031-25752-0_23
Olmos FG, 2021, Los mexicanos usan mas de 10 redes sociales al dia
Padilla R, 2020, INT CONF SYST SIGNAL, P237, DOI [10.1109/iwssip48289.2020.9145130, 10.1109/IWSSIP48289.2020.9145130]
Palau-Saumell R, 2016, J TRAVEL TOUR MARK, V33, P949, DOI 10.1080/10548408.2015.1075456
Park H, 2021, The China Tourism Forum 2019
Pedersen T, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P1024
González-Rodríguez MR, 2016, INT J CONTEMP HOSP M, V28, P2609, DOI 10.1108/IJCHM-02-2015-0057
Sanchez S. A., 2020, IOP Conference Series: Materials Science and Engineering, V844, DOI 10.1088/1757-899X/844/1/012024
SECTUR, 2021, Compendio estadistico del turismo en mexico 2020
Sewak M., 2018, Practical Convolutional Neural Networks: Implement advanced deep learning models using Python
Shankar Vaishaal, 2021, P IEEECVF INT C COMP, P9661
Song HQ, 2013, J TRAVEL TOUR MARK, V30, P253, DOI 10.1080/10548408.2013.774919
Song SG, 2016, J TRAVEL TOUR MARK, V33, P687, DOI 10.1080/10548408.2016.1167384
Song SB, 2021, IND MANAGE DATA SYST, V121, P1375, DOI 10.1108/IMDS-12-2019-0667
Stepchenkova S, 2013, TOURISM MANAGE, V36, P590, DOI 10.1016/j.tourman.2012.08.006
Sun GX, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12040606
Tang Y, 2013, PROC VLDB ENDOW, V7, P313, DOI 10.14778/2732240.2732249
Toumeh B., 2022, Exploitation of digital transformation technologies in smart tourism destinations: facts and challenges, P124
Tugores-Ques J, 2023, J VACAT MARK, V29, P54, DOI 10.1177/13567667221078246
Tussyadiah IP, 2010, TOURISM AND VISUAL CULTURE, VOL 2: METHODS AND CASES, P156, DOI 10.1079/9781845936112.0156
Wang RW, 2020, J DESTIN MARK MANAGE, V18, DOI 10.1016/j.jdmm.2020.100512
Wu Z, 1994, arXiv, DOI [10.48550/arXiv.cmp-lg/9406033, DOI 10.48550/ARXIV.CMP-LG/9406033]
Xiao X, 2022, TOURISM MANAGE, V93, DOI 10.1016/j.tourman.2022.104585
Yang S, 2021, J HOSP TOUR RES, V45, P1417, DOI 10.1177/10963480211015361
Yu JN, 2021, ANN TOURISM RES, V89, DOI 10.1016/j.annals.2021.103204
Yuan Y, 2015, IEEE T NEUR NET LEAR, V26, P2222, DOI 10.1109/TNNLS.2014.2359471
Zhang K, 2020, ASIA PAC J TOUR RES, V25, P1199, DOI 10.1080/10941665.2020.1838586
Zhou BL, 2018, IEEE T PATTERN ANAL, V40, P1452, DOI 10.1109/TPAMI.2017.2723009
NR 85
TC 0
Z9 0
U1 17
U2 17
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1098-3058
EI 1943-4294
J9 INF TECHNOL TOUR
JI Inf. Technol. Tour.
PD 2024 JAN 4
PY 2024
DI 10.1007/s40558-023-00282-9
EA JAN 2024
PG 37
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA EA2M4
UT WOS:001136115200001
DA 2024-03-27
ER
PT J
AU Xie, JH
Zhang, Z
Liu, X
Zeng, D
AF Xie, Jiaheng
Zhang, Zhu
Liu, Xiao
Zeng, Daniel
TI Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach
Using Similarity Network-Based Deep Learning
SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
LA English
DT Article
DE Computational design science; deep learning; social media analytics;
health IT; HealthTech; opioid addiction; addiction treatment
AB Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.
C1 [Xie, Jiaheng] Univ Delaware, Lerner Coll Business & Econ, Dept Accounting & MIS, Newark, DE USA.
[Zhang, Zhu; Zeng, Daniel] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China.
[Zeng, Daniel] Univ Chinese Acad Sci, Beijing, Peoples R China.
[Zhang, Zhu; Zeng, Daniel] Shenzhen Artificial Intelligence & Data Sci Res I, Shenzhen, Guangdong, Peoples R China.
[Liu, Xiao] Arizona State Univ, Dept Informat Syst, Tempe, AZ USA.
C3 University of Delaware; Chinese Academy of Sciences; Institute of
Automation, CAS; Chinese Academy of Sciences; University of Chinese
Academy of Sciences, CAS; Arizona State University; Arizona State
University-Tempe
RP Xie, JH (autor correspondiente), Alfred Lerner Coll Business & Econ, 303 Alfred Lerner Hall, Newark, DE 19716 USA.
EM jxie@udel.edu; zhu.zhang@ia.ac.cn; xiao.hu.10@asu.edu;
dajun.zeng@ia.ac.cn
RI Xie, Jiaheng/AAU-2194-2021
OI Xie, Jiaheng/0000-0002-4992-498X
FU Ministry of Science and Technology of China [2020AAA0108401,
2017YFC0820105, 2019QY(Y)0101, 2020AAA0103405]; Ministry of Health of
China [2017ZX10303401-002]; National Natural Science Foundation of China
[71621002, 72074209, 71974187, 71472175]; National Science Foundation
[1228509]
FX This work was supported in part by the following grants: Grant Nos.
2020AAA0108401, 2017YFC0820105, 2019QY(Y)0101, and 2020AAA0103405 from
the Ministry of Science and Technology of China, Grant No.
2017ZX10303401-002 from the Ministry of Health of China, and Grant Nos.
71621002, 72074209, 71974187, and 71472175 from the National Natural
Science Foundation of China. Part of the experiments were run on the El
Gato supercomputer that was supported by the National Science Foundation
under Grant No. 1228509.
CR Abbasi A, 2018, MIS QUART, V42, P427, DOI 10.25300/MISQ/2018/13239
Abbasi A, 2012, MIS QUART, V36, P1293
Abbasi A, 2010, MIS QUART, V34, P435
Abbasi A, 2008, MIS QUART, V32, P811
Agarwal R, 2014, INFORM SYST RES, V25, P443, DOI 10.1287/isre.2014.0546
Andrilla CHA, 2017, ANN FAM MED, V15, P359, DOI 10.1370/afm.2099
[Anonymous], 2015, P WORKSHOP NOISY USE
[Anonymous], 2017, UND COST OP CRIS
[Anonymous], 2013, INFORM SYST RES, DOI DOI 10.1287/isre.1120.0461
[Anonymous], 2011, Results from the 2010 National Survey on Drug Use and Health: Summary of National Findings
[Anonymous], 2017, INT WORLD WIDE WEB C
Aral S, 2011, MANAGE SCI, V57, P1623, DOI 10.1287/mnsc.1110.1421
Baesens B, 2016, MIS QUART, V40, P807, DOI 10.25300/MISQ/2016/40:4.03
Bojanowski P., 2016, CORR
Bojko MJ, 2016, J SUBST ABUSE TREAT, V66, P37, DOI 10.1016/j.jsat.2016.03.003
Bughin J., 2010, McKinsey Quarterly, V4, P1
Capurro D, 2014, J MED INTERNET RES, V16, P213, DOI 10.2196/jmir.2679
Chen H, 2017, MIS QUART, V41, P1
Chen HC, 2012, MIS QUART, V36, P1165
dos Santos CN, 2014, PR MACH LEARN RES, V32, P1818
Espinosa-Anke L, 2018, 7 AUGMENTING WORD EM, P2653
Flynn PM, 2003, J SUBST ABUSE TREAT, V25, P177, DOI 10.1016/S0740-5472(03)00125-9
Goh JM, 2016, MIS QUART, V40, P247, DOI 10.25300/MISQ/2016/40.1.11
Gregor S, 2013, MIS QUART, V37, P337, DOI 10.25300/MISQ/2013/37.2.01
Hassamal S, 2017, J PSYCHIATR PRACT, V23, P221, DOI 10.1097/PRA.0000000000000231
He YY, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P1429, DOI 10.1145/2872427.2874816
Hemmatian F, 2019, ARTIF INTELL REV, V52, P1495, DOI 10.1007/s10462-017-9599-6
Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
Hutchinson E, 2014, ANN FAM MED, V12, P128, DOI 10.1370/afm.1595
Jeyapriya A, 2015, 2015 2ND INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEMS (ICECS), P548, DOI 10.1109/ECS.2015.7124967
Kallinikos J, 2014, INFORM SYST RES, V25, P817, DOI 10.1287/isre.2014.0544
Karahanna E, 2018, MIS QUART, V42, P737, DOI 10.25300/MISQ/2018/11492
Kitchens B, 2018, J MANAGE INFORM SYST, V35, P540, DOI 10.1080/07421222.2018.1451957
Knudsen HK, 2011, EVAL PROGRAM PLANN, V34, P375, DOI 10.1016/j.evalprogplan.2011.02.004
Krieger Pharm D.C, 2018, WHAT ARE OPIOIDS WHY
Lau RYK, 2012, MIS QUART, V36, P1239
Li S, 2015, INFORM PROCESS MANAG, V51, P58, DOI 10.1016/j.ipm.2014.08.005
Liu B., 2012, SYNTHESIS LECT HUMAN, V5, P1, DOI [DOI 10.2200/S00416ED1V01Y201204HLT016, DOI 10.1007/978-3-031-02145-9]
Livingston JD, 2018, SUBST USE MISUSE, V53, P344, DOI 10.1080/10826084.2017.1325376
Luo WJ, 2014, IEEE DATA MINING, P380, DOI 10.1109/ICDM.2014.14
Ma BJ, 2016, INFORM PROCESS MANAG, V52, P430, DOI 10.1016/j.ipm.2015.10.004
Mai F, 2018, J MANAGE INFORM SYST, V35, P19, DOI 10.1080/07421222.2018.1440774
McKenna RM, 2017, DRUG ALCOHOL DEPEN, V179, P87, DOI 10.1016/j.drugalcdep.2017.06.028
Nguyen K.A, 2016, ARXIV PREPRINT ARXIV
NIDA, 2018, OV NAT I DRUG AB NID
Pew Research, 2009, 61 AM AD LOOK ONL HL
Qiu GA, 2011, COMPUT LINGUIST, V37, P9, DOI 10.1162/coli_a_00034
Qu M, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P997, DOI 10.1145/3097983.3098185
Rai A, 2017, MIS QUART, V41, pIII
Rogers AH, 2020, PAIN MED, V21, P670, DOI 10.1093/pm/pnz062
RUBENSTEIN H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657
Saboo AR, 2016, MIS QUART, V40, P911, DOI 10.25300/MISQ/2016/40.4.06
Sha Y, 2017, PROCEDIA COMPUT SCI, V108, P48, DOI 10.1016/j.procs.2017.05.106
Sharma A, 2017, CURR PSYCHIAT REP, V19, DOI 10.1007/s11920-017-0783-9
Simpson SS, 2018, JMIR PUBLIC HLTH SUR, V4, P200, DOI 10.2196/publichealth.7726
Smith C.A, AMIA S AM MED INF AS, P682
Stieglitz S, 2013, J MANAGE INFORM SYST, V29, P217, DOI 10.2753/MIS0742-1222290408
Stumbo SP, 2017, J SUBST ABUSE TREAT, V73, P47, DOI 10.1016/j.jsat.2016.11.003
Tang Q, 2012, J MANAGE INFORM SYST, V29, P41, DOI 10.2753/MIS0742-1222290203
Trusov M, 2010, J MARKETING RES, V47, P643, DOI 10.1509/jmkr.47.4.643
Xie K, 2015, J MANAGE INFORM SYST, V32, P204, DOI 10.1080/07421222.2015.1063297
Yao ZJ, 2018, WSDM'18: PROCEEDINGS OF THE ELEVENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, P673, DOI 10.1145/3159652.3159703
Zhang DS, 2016, J MANAGE INFORM SYST, V33, P456, DOI 10.1080/07421222.2016.1205907
Zhang L, 2014, STUD BIG DATA, V1, P1, DOI 10.1007/978-3-642-40837-3_1
Zhang W, MIS QUART
Zhang YT, 2016, IEEE T KNOWL DATA EN, V28, P2793, DOI 10.1109/TKDE.2016.2591008
NR 66
TC 11
Z9 11
U1 11
U2 87
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0742-1222
EI 1557-928X
J9 J MANAGE INFORM SYST
JI J. Manage. Inform. Syst.
PD JAN 2
PY 2021
VL 38
IS 1
BP 166
EP 195
DI 10.1080/07421222.2021.1870388
PG 30
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA RH2KN
UT WOS:000636054300008
DA 2024-03-27
ER
PT J
AU Jiang, PJ
AF Jiang, Pingjun
TI Automated bidding vs manual bidding strategies in search engine
marketing: a keyword efficiency perspective
SO JOURNAL OF MARKETING ANALYTICS
LA English
DT Article
DE Keyword research; Keyword efficiency; Manual bidding; Automated bidding;
AI in marketing; Search engine marketing; Data envelopment analysis
ID SPONSORED SEARCH; PORTFOLIO THEORY; PERFORMANCE; SELECTION
AB We utilize data envelopment analysis to evaluate and compare the pricing efficiency of keywords in the Google-sponsored search markets, specifically in relation to manual bidding strategies and automated bidding strategies. Two totally different sets of efficiency scores are obtained from Google Ads by using extensive data from a company in the online apparel retailing industry. Contrary to the big buzz in the industry, the automated bidding strategy does not improve the average efficiency of keywords. Manual bidding rewards efficiency for keywords more productive of transactions, revenue, and clicks. Automated bidding rewards efficiency for keywords more on cost per click, bounce rate, and E-commerce conversion rate. Automated bidding increases efficiency scores with apparel keywords consisting of words of "color" and "quality attributes." Manual bidding has high-efficiency scores with keywords including words "promotion related," "gender," and "style attributes." Manual bidding works better for modified match types.
C1 [Jiang, Pingjun] La Salle Univ, Sch Business, Dept Mkt, Philadelphia, PA 19141 USA.
C3 La Salle University
RP Jiang, PJ (autor correspondiente), La Salle Univ, Sch Business, Dept Mkt, Philadelphia, PA 19141 USA.
EM jiang@lasalle.edu
CR Abrams Z, 2007, EC'07: PROCEEDINGS OF THE EIGHTH ANNUAL CONFERENCE ON ELECTRONIC COMMERCE, P272
Adler N, 2001, EUR J OPER RES, V132, P260, DOI 10.1016/S0377-2217(00)00150-8
Agarwal A., 2008, SSRN eLibrary
Aggarwal G, 2019, LECT NOTES COMPUT SC, V11920, P17, DOI 10.1007/978-3-030-35389-6_2
Anil R., 2022, ARXIV
Ayanso A., 2022, INT J E-BUS RES, V18, P1
Ayanso A, 2015, DECIS SUPPORT SYST, V70, P42, DOI 10.1016/j.dss.2014.11.009
Ayanso A, 2013, DECISION SCI, V44, P877, DOI 10.1111/deci.12038
Balseiro S, 2017, PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'17), P15, DOI 10.1145/3038912.3052682
BANKER RD, 1984, MANAGE SCI, V30, P1078, DOI 10.1287/mnsc.30.9.1078
Borgs C., 2007, P 16 INT C WORLD WID, P531, DOI DOI 10.1145/1242572.1242644
Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
CARDOZO RN, 1985, J MARKETING, V49, P113, DOI 10.2307/1251438
Chaffey D., 2022, DIGITAL MARKETING EX, DOI [10.4324/9781003009498, DOI 10.4324/9781003009498]
CHARNES A, 1978, EUR J OPER RES, V2, P429, DOI 10.1016/0377-2217(78)90138-8
DEKLUYVER CA, 1984, EUR J OPER RES, V16, P152, DOI 10.1016/0377-2217(84)90069-9
Deng Y, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P3965, DOI 10.1145/3442381.3450052
Deng Y, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P1796, DOI 10.1145/3366423.3380249
Desai PS, 2014, MARKET SCI, V33, P485, DOI 10.1287/mksc.2013.0834
DEVINNEY TM, 1985, J MARKETING, V49, P107, DOI 10.2307/1251437
Dhar V, 2010, INFORM SYST RES, V21, P760, DOI 10.1287/isre.1100.0315
Duffy DL, 2004, J CONSUM MARK, V21, P356, DOI 10.1108/07363760410549177
Gattoufi S., 2004, SOCIO-ECON PLAN SCI, V38, P141, DOI [10.1016/S0038-0121(03)00022-3, DOI 10.1016/S0038-0121(03)00022-3]
Geng T., 2021, AUTOMATED BIDDING BU
Ghose A., 2008, PROCEEDING INT C WEB, P241
Ghose A., 2008, Proc. of WWW'08, P219
Ghose A., ADKDD 08 P 2 INT WOR, P18
Ghose A., 2008, SSRN eLibrary
Gönsch J, 2017, EUR J OPER RES, V263, P337, DOI 10.1016/j.ejor.2017.05.033
Gupta S, 2016, J BUS RES, V69, P5671, DOI 10.1016/j.jbusres.2016.02.042
HOLTHAUSEN DM, 1982, MANAGE SCI, V28, P487, DOI 10.1287/mnsc.28.5.487
Jagpal HS., 1982, Mark. Sci, V1, P79
Jagpal S., 2008, FUSION PROFIT MARKET, DOI [10.1093/acprof:oso/9780195371055.001.0001, DOI 10.1093/ACPROF:OSO/9780195371055.001.0001]
Jang S, 2022, J BUS RES, V140, P459, DOI 10.1016/j.jbusres.2021.11.015
Jansen Bernard J., 2009, International Journal of Internet Marketing and Advertising, V5, P74, DOI 10.1504/IJIMA.2009.021951
Jansen Bernard J., 2008, International Journal of Electronic Business, V6, P114, DOI 10.1504/IJEB.2008.018068
Jansen BJ, 2017, J RES INTERACT MARK, V11, P142, DOI 10.1108/JRIM-07-2016-0073
Jansen BJ, 2012, ELECTRON COMMER RES, V12, P429, DOI 10.1007/s10660-012-9101-0
Jansen BJ, 2011, J ELECTRON COMMER RE, V12, P1
Järvinen J, 2015, IND MARKET MANAG, V50, P117, DOI 10.1016/j.indmarman.2015.04.009
Jenkins L, 2003, EUR J OPER RES, V147, P51, DOI 10.1016/S0377-2217(02)00243-6
Jiang P., 2018, INT J ELECT BUSINESS, V14, P273, DOI [10.1504/IJEB.2018.095980, DOI 10.1504/IJEB.2018.095980]
KAMAKURA WA, 1988, J CONSUM RES, V15, P289, DOI 10.1086/209167
Kim AJ, 2021, J BUS RES, V130, P539, DOI 10.1016/j.jbusres.2019.09.049
Kim J, 2023, J BUS RES, V160, DOI 10.1016/j.jbusres.2023.113819
Kitts B., 2004, Electronic Markets, V14, P186, DOI 10.1080/1019678042000245119
Klapdor S, 2014, J INTERACT MARK, V28, P285, DOI 10.1016/j.intmar.2014.07.001
Kleine A, 2004, OMEGA-INT J MANAGE S, V32, P17, DOI 10.1016/j.omega.2003.09.002
Li Haitao, 2023, arXiv
Lu XH, 2014, J MANAGE INFORM SYST, V30, P299, DOI 10.2753/MIS0742-1222300411
Ma LY, 2020, INT J RES MARK, V37, P481, DOI 10.1016/j.ijresmar.2020.04.005
Mantrala Murali., 2002, Handbook of Marketing
Markowitz H, 1952, J FINANC, V7, P77, DOI 10.1111/j.1540-6261.1952.tb01525.x
Mokaya Brian O., 2010, SEARCH ENGINE ADVERT
Mukherjee P, 2014, ELECTRON COMMER R A, V13, P139, DOI 10.1016/j.elerap.2014.01.001
Özlük Ö, 2007, J REVENUE PRICING MA, V6, P347, DOI 10.1057/palgrave.rpm.5160110
Park S, 2012, INT J CULT TOUR HOSP, V6, P340, DOI 10.1108/17506181211265077
Regelson M., 2006, Proceedings of the Second Workshop on Sponsored Search Auctions, V9623
Ren K, 2018, IEEE T KNOWL DATA EN, V30, P645, DOI 10.1109/TKDE.2017.2775228
Richardson M., 2007, P 16 INT C WORLD WID, P521, DOI DOI 10.1145/1242572.1242643
Rutz O.J., 2007, SSRN eLibrary
Rutz OJ, 2011, MARKET SCI, V30, P646, DOI 10.1287/mksc.1110.0635
Ryals L, 2007, J MARKET MANAG-UK, V23, P991, DOI 10.1362/026725707X250430
Schultz CD, 2020, INFORM RETRIEVAL J, V23, P117, DOI 10.1007/s10791-019-09368-7
Symitsi E, 2022, EUR J OPER RES, V303, P767, DOI 10.1016/j.ejor.2022.03.006
Truong V, 2019, AUSTRALAS J INF SYST, V23
Vragov R, 2020, ELECTRON COMMER R A, V41, DOI 10.1016/j.elerap.2020.100959
Vragov R, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.10.001
Yang C, 2012, 2012 INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICE COMPUTING (CSC), P104, DOI [10.1109/CSC.2012.23, 10.1109/CECNet.2012.6201627]
Yang S, 2014, MARKET SCI, V33, P134, DOI 10.1287/mksc.2013.0812
Yang YW, 2022, DECIS SUPPORT SYST, V163, DOI 10.1016/j.dss.2022.113843
Yun JT, 2020, J ADVERTISING, V49, P446, DOI 10.1080/00913367.2020.1795757
Zhang D, 2009, LECT NOTES COMPUT SC, V5766, P380, DOI 10.1007/978-3-642-04417-5_44
Zhang XQ, 2011, MANAGE SCI, V57, P1703, DOI 10.1287/mnsc.1110.1408
NR 74
TC 0
Z9 0
U1 0
U2 0
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 2050-3318
EI 2050-3326
J9 J MARK ANAL
JI J. Market. Anal.
PD MAR
PY 2024
VL 12
IS 1
SI SI
BP 25
EP 41
DI 10.1057/s41270-023-00260-4
EA OCT 2023
PG 17
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA IX8O1
UT WOS:001169118700002
DA 2024-03-27
ER
PT J
AU Fildes, R
Kolassa, S
Ma, SH
AF Fildes, Robert
Kolassa, Stephan
Ma, Shaohui
TI Post-script-Retail forecasting: Research and practice
SO INTERNATIONAL JOURNAL OF FORECASTING
LA English
DT Article
DE COVID-19; Disruption; Structural change; Instability; Omni-retailing;
Online retail; Machine learning
ID M5
AB This note updates the 2019 review article "Retail forecasting: Research and practice"in the context of the COVID-19 pandemic and the substantial new research on machinelearning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.(c) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
C1 [Fildes, Robert] Univ Management Sch, Lancaster Ctr Mkt Analyt & Forecasting, Lancaster, England.
[Kolassa, Stephan] SAP Switzerland, Zurich, Switzerland.
[Ma, Shaohui] Nanjing Audit Univ, Sch Business, Nanjing 211815, Peoples R China.
C3 Nanjing Audit University
RP Fildes, R (autor correspondiente), Univ Management Sch, Lancaster Ctr Mkt Analyt & Forecasting, Lancaster, England.
EM r.fildes@lancaster.ac.uk
OI Fildes, Robert/0000-0002-5918-7098; Kolassa,
Stephan/0000-0001-9393-0765; Ma, Shaohui/0000-0002-5264-6343
FU National Natural Science Foundation of China; [72072092]
FX Acknowledgments The third author acknowledges the support of the
National Natural Science Foundation of China under grant no. 72072092.
CR Alexandrov A., 2021, FORESIGHT INT J APPL, V62, P8
Ali ÖG, 2020, INT J FORECASTING, V36, P1389, DOI 10.1016/j.ijforecast.2020.02.003
Alicke K., 2019, SUPPLY CHAIN MANAGEM, V23, P40
Alicke K, 2020, SUPPLY CHAIN MANAGEM, V24, P40
Antipov EA, 2020, J REVENUE PRICING MA, V19, P355, DOI 10.1057/s41272-020-00236-4
Beckers J, 2022, GEOGR ANAL, V54, P274, DOI 10.1111/gean.12285
Boone T, 2021, FORESIGHT INT J APPL, V61, P20
Caro F, 2020, M&SOM-MANUF SERV OP, V22, P47, DOI 10.1287/msom.2019.0824
Fildes R, 2022, INT J FORECASTING, V38, P1283, DOI 10.1016/j.ijforecast.2019.06.004
Fildes R, 2021, INT J FORECASTING, V37, P1047, DOI 10.1016/j.ijforecast.2021.04.007
Frasquet M, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102374
Huang T, 2019, EUR J OPER RES, V279, P459, DOI 10.1016/j.ejor.2019.06.011
Huber J, 2020, INT J FORECASTING, V36, P1420, DOI 10.1016/j.ijforecast.2020.02.005
Januschowski T., 2018, Foresight: The International Journal of Applied Forecasting, V51, P42
Kharfan M, 2021, ANN OPER RES, V303, P159, DOI 10.1007/s10479-020-03666-w
Kolassa S, 2016, FORESIGHT INT J APPL, V42, P44
Kolassa S, 2022, INT J FORECASTING, V38, P1562, DOI 10.1016/j.ijforecast.2021.08.006
Ma SH, 2021, EUR J OPER RES, V288, P111, DOI 10.1016/j.ejor.2020.05.038
Ma SH, 2017, EUR J OPER RES, V260, P680, DOI 10.1016/j.ejor.2016.12.032
Martínez-de-Albéniz V, 2020, PROD OPER MANAG, V29, P2508, DOI 10.1111/poms.13238
Montero-Manso P, 2021, INT J FORECASTING, V37, P1632, DOI 10.1016/j.ijforecast.2021.03.004
Nikolopoulos K, 2021, EUR J OPER RES, V290, P99, DOI 10.1016/j.ejor.2020.08.001
Pereira MM, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102165
Punia S, 2020, COMPUT IND ENG, V149, DOI 10.1016/j.cie.2020.106796
Ren SY, 2020, ANN OPER RES, V291, P761, DOI 10.1007/s10479-019-03148-8
Schaer O, 2019, INT J FORECASTING, V35, P197, DOI 10.1016/j.ijforecast.2018.03.005
Seaman B, 2022, INT J FORECASTING, V38, P1468, DOI 10.1016/j.ijforecast.2021.06.002
Spiliotis E., 2022, INT J FORECASTING, V38, P1346
Steinker S, 2017, PROD OPER MANAG, V26, P1854, DOI 10.1111/poms.12721
Stephens D., 2021, RESURRECTING RETAIL
Ulrich M, 2021, EUR J OPER RES, V294, P831, DOI 10.1016/j.ejor.2019.11.029
van Steenbergen RM, 2020, DECIS SUPPORT SYST, V139, DOI 10.1016/j.dss.2020.113401
Wolpert D. H., 1997, IEEE Transactions on Evolutionary Computation, V1, P67, DOI 10.1109/4235.585893
Wolters J, 2021, J RETAILING, V97, P726, DOI 10.1016/j.jretai.2021.01.003
NR 34
TC 3
Z9 3
U1 13
U2 41
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0169-2070
EI 1872-8200
J9 INT J FORECASTING
JI Int. J. Forecast.
PD OCT-DEC
PY 2022
VL 38
IS 4
SI SI
BP 1319
EP 1324
DI 10.1016/j.ijforecast.2021.09.012
EA OCT 2022
PG 6
WC Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5U0AG
UT WOS:000876216700003
PM 36217499
OA Green Published, Green Accepted
DA 2024-03-27
ER
PT J
AU Qi, M
Shi, YY
Qi, YZ
Ma, CX
Yuan, R
Wu, D
Shen, ZJ
AF Qi, Meng
Shi, Yuanyuan
Qi, Yongzhi
Ma, Chenxin
Yuan, Rong
Wu, Di
Shen, Zuo-Jun (Max)
TI A Practical End-to-End Inventory Management Model with Deep Learning
SO MANAGEMENT SCIENCE
LA English
DT Article
DE end-to-end decision-making; inventory management; deep learning;
e-commerce
ID PROPENSITY SCORE; POLICY
AB We investigate a data-driven multiperiod inventory replenishment problem with uncertain demand and vendor lead time (VLT) with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations without any prior assumptions on the distributions of the demand and the VLT. By conducting a series of thorough numerical experiments using real data from one of the leading e-commerce companies, we demonstrate the advantages of the proposed E2E model over conventional PTO frameworks. We also conduct a field experiment with JD.com, and the results show that our new algorithm reduces holding cost, stockout cost, total inventory cost, and turnover rate substantially compared with JD's current practice. For the supply chain management industry, our E2E model shortens the decision process and provides an automatic inventory management solution with the possibility to generalize and scale. The concept of E2E, which uses the input information directly for the ultimate goal, can also be useful in practice for other supply chain management circumstances.
C1 [Qi, Meng] Cornell Univ, SC Johnson Coll Business, Ithaca, NY 14853 USA.
[Shi, Yuanyuan] Univ Calif San Diego, Dept Elect & Comp Engn, San Diego, CA 92161 USA.
[Qi, Yongzhi] JD Com Smart Supply Chain Y, Mountain View, CA 94043 USA.
[Ma, Chenxin; Yuan, Rong; Wu, Di] JD Com Silicon Valley Res Ctr, Mountain View, CA 94043 USA.
[Shen, Zuo-Jun (Max)] Univ Calif Berkeley, Coll Engn, Berkeley, CA 94720 USA.
[Shen, Zuo-Jun (Max)] Univ Hong Kong, Fac Engn, Pokfulam, Hong Kong, Peoples R China.
[Shen, Zuo-Jun (Max)] Univ Hong Kong, Fac Business & Econ, Pokfulam, Hong Kong, Peoples R China.
C3 Cornell University; University of California System; University of
California San Diego; University of California System; University of
California Berkeley; University of Hong Kong; University of Hong Kong
RP Shen, ZJ (autor correspondiente), Univ Calif Berkeley, Coll Engn, Berkeley, CA 94720 USA.; Shen, ZJ (autor correspondiente), Univ Hong Kong, Fac Engn, Pokfulam, Hong Kong, Peoples R China.; Shen, ZJ (autor correspondiente), Univ Hong Kong, Fac Business & Econ, Pokfulam, Hong Kong, Peoples R China.
EM mq56@cornell.edu; yyshi@eng.ucsd.edu; qiyongzhi1@jd.com;
chenxin.ma@jd.com; rongyuan.exe@gmail.com; di.wu@jd.com;
shen@ieor.berkeley.edu
RI Shen, Zuo-Jun Max/JXM-7549-2024
OI Shen, Zuo-Jun Max/0000-0003-4538-8312
CR Ambrogioni L, 2017, PREPRINT
Ban GY, 2019, OPER RES, V67, P90, DOI 10.1287/opre.2018.1757
Bengio Y, 1997, INT J NEURAL SYST, V8, P433, DOI 10.1142/S0129065797000422
Bertsimas D, 2020, MANAGE SCI, V66, P1025, DOI 10.1287/mnsc.2018.3253
Böse JH, 2017, PROC VLDB ENDOW, V10, P1694
Choromanska A, 2015, JMLR WORKSH CONF PRO, V38, P192
Chu LY, 2008, OPER RES LETT, V36, P110, DOI 10.1016/j.orl.2007.04.010
Donti P, 2017, ADV NEUR IN, V31, P5484
EHRHARDT R, 1984, OPER RES, V32, P121, DOI 10.1287/opre.32.1.121
Elmachtoub AN, 2022, MANAGE SCI, V68, P9, DOI 10.1287/mnsc.2020.3922
Fan CY, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2527, DOI 10.1145/3292500.3330662
Friedman J., 2009, ELEMENTS STAT LEARNI, DOI DOI 10.1007/978-0-387-84858-7
Gallego G, 2001, MANAGE SCI, V47, P1344, DOI 10.1287/mnsc.47.10.1344.10261
IGNALL E, 1969, MANAGE SCI, V15, P284, DOI 10.1287/mnsc.15.5.284
Iida T, 2006, M&SOM-MANUF SERV OP, V8, P407, DOI 10.1287/msom.1060.0116
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
KAPLAN RS, 1970, MANAGE SCI, V16, P491, DOI 10.1287/mnsc.16.7.491
Kawaguchi K., 2016, ADV NEURAL INFORM PR, P586
Ke GL, 2017, ADV NEUR IN, V30
Koenker R, 2005, QUANTILE REGRESSION, DOI DOI 10.1017/CBO9780511754098
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Levi R, 2007, MATH OPER RES, V32, P284, DOI 10.1287/moor.1060.0205
Levine S, 2016, J MACH LEARN RES, V17
Liyanage LH, 2005, OPER RES LETT, V33, P341, DOI 10.1016/j.orl.2004.08.003
Muharremoglu A, 2008, OPER RES, V56, P1089, DOI 10.1287/opre.1080.0620
Oroojlooyjadid A, 2020, IISE TRANS, V52, P444, DOI 10.1080/24725854.2019.1632502
Paszke Adam, 2017, NEURIPS WORKSH
ROSENBAUM PR, 1983, BIOMETRIKA, V70, P41, DOI 10.1093/biomet/70.1.41
Rubin DB, 2006, STAT SCI, V21, P206, DOI 10.1214/088342306000000259
SHEATHER SJ, 1991, J ROY STAT SOC B, V53, P683
Snyder L, 2011, FUNDAMENTALS SUPPLY
Srivastava N, 2014, J MACH LEARN RES, V15, P1929
Toktay LB, 2001, MANAGE SCI, V47, P1268, DOI 10.1287/mnsc.47.9.1268.9787
VEINOTT AF, 1965, MANAGE SCI, V12, P206, DOI 10.1287/mnsc.12.3.206
Wang K, 2011, IEEE I CONF COMP VIS, P1457, DOI 10.1109/ICCV.2011.6126402
Wang T, 2012, M&SOM-MANUF SERV OP, V14, P472, DOI 10.1287/msom.1120.0387
Wen R, 2017, PREPRINT
Zhu KJ, 2004, NAV RES LOG, V51, P633, DOI 10.1002/nav.10124
Zipkin PH, 2000, FDN INVENTORY MANAGE
NR 39
TC 7
Z9 7
U1 16
U2 16
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD FEB
PY 2023
VL 69
IS 2
BP 759
EP 773
DI 10.1287/mnsc.2022.4564
PG 15
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA DL6C5
UT WOS:001132227500003
DA 2024-03-27
ER
PT J
AU Rodrigues, VF
Policarpo, LM
da Silveira, DE
Righi, RD
da Costa, CA
Barbosa, JLV
Antunes, RS
Scorsatto, R
Arcot, T
AF Rodrigues, Vinicius Facco
Policarpo, Lucas Micol
da Silveira, Diorgenes Eugenio
Righi, Rodrigo da Rosa
da Costa, Cristiano Andre
Barbosa, Jorge Luis Victoria
Antunes, Rodolfo Stoffel
Scorsatto, Rodrigo
Arcot, Tanuj
TI Fraud detection and prevention in e-commerce: A systematic literature
review
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE E-commerce; Fraud detection; Fraud prevention; Machine learning;
Systematic literature review
ID TRANSACTIONS
AB The high volume of money involved in e-commerce transactions draws the attention of fraudsters, which makes fraud prevention and detection techniques of high importance. Current surveys and reviews on fraud systems focuses mainly on financial-specific domains or general areas, leaving e-commerce aside. In this context, this article presents a systematic literature review on fraud detection and prevention for e-commerce systems. Our methodology involved searching for articles published in the last six years into four different literature databases. The search of articles employs a search string composed of the following keywords: purchase, buy, transactions, fraud prevention, fraud detection, e-commerce, web commerce, online store, real-time, and stream. We apply six filtering criteria to remove irrelevant articles. The methodology resulted in 64 articles, which we carefully analyzed to answer five research questions. Our contribution appears in the updated perception of fraud types, computational methods for fraud detection and prevention, as well as the employed domains. To the best of our knowledge, this is the first survey on combining prevention and detection of e-commerce frauds, linking also architectural insights, artificial intelligence methods, and open challenges and gaps in the research area. The study main findings demonstrate that from 64 articles, only five focus on the fraud prevention problem, and credit card fraud is the most explored fraud type. In addition, current literature lacks studies that propose strategies for detecting fraudsters and automated bots in real-time.
C1 [Rodrigues, Vinicius Facco; Policarpo, Lucas Micol; da Silveira, Diorgenes Eugenio; Righi, Rodrigo da Rosa; da Costa, Cristiano Andre; Barbosa, Jorge Luis Victoria; Antunes, Rodolfo Stoffel] Univ Vale Rio dos Sinos, Appl Comp Program, Sao Leopoldo, RS, Brazil.
[Scorsatto, Rodrigo; Arcot, Tanuj] DELL Eldorado Do Sul, Eldorado Do Sul, RS, Brazil.
C3 Universidade do Vale do Rio dos Sinos (Unisinos)
RP Rodrigues, VF (autor correspondiente), Univ Vale Rio dos Sinos, Appl Comp Program, Sao Leopoldo, RS, Brazil.
EM vfrodrigues@unisinos.br; lmpolicarpo@unisinos.br;
diorgeneses@unisinos.br; rrrighi@unisinos.br; cac@unisinos.br;
jbarbosa@unisinos.br; rsantunes@unisinos.br; rodrigo.scorsatto@dell.com;
tanuj.arcot@dell.com
RI Barbosa, Jorge/L-9389-2013; Rodrigues, Vinicius Facco/V-4035-2017
OI Barbosa, Jorge/0000-0002-0358-2056; Rodrigues, Vinicius
Facco/0000-0001-6129-0548; da Rosa Righi, Rodrigo/0000-0001-5080-7660;
Micol Policarpo, Lucas/0000-0003-0270-4661
FU Dell Inc. via the 7th Amendment to the Technical and Scientific
Cooperation; [01/2017]
FX This work was support by Dell Inc. via the 7th Amendment to the
Technical and Scientific Cooperation Agreement No. 01/2017 - Information
Technology Innovation Support Law - Brazilian Government. The authors
would like to thank Dell Inc. for financing this research project.
CR Abakarim Y, 2018, INT CONF INTELL SYS, DOI 10.1145/3289402.3289530
Abdallah A, 2016, J NETW COMPUT APPL, V68, P90, DOI 10.1016/j.jnca.2016.04.007
Abdullah K, 2020, CONSUM COMM NETWORK
Adewumi AO, 2017, INT J SYST ASSUR ENG, V8, P937, DOI 10.1007/s13198-016-0551-y
Ahmed M, 2016, FUTURE GENER COMP SY, V55, P278, DOI 10.1016/j.future.2015.01.001
Ali I., 2020, 2020 IEEE 23 INT MUL, P1, DOI 10.1109/INMIC50486.2020.9318202
Anowar F, 2020, IEEE SYS MAN CYBERN, P3551, DOI [10.1109/SMC42975.2020.9283136, 10.1109/smc42975.2020.9283136]
Armel A., 2019, 2019 5 INT C OPTIMIZ, P1, DOI [10.1109/icoa.2019.8727610, DOI 10.1109/ICOA.2019.8727610]
Artikis A., 2017, P 11 ACM INT C DISTR, P249, DOI [10.1145/3093742.3093912, DOI 10.1145/3093742.3093912]
Aziz A., 2021, INT J SCI PROG RES, V79, P31
Baesens B, 2021, DECIS SUPPORT SYST, V150, DOI 10.1016/j.dss.2021.113492
Banerjee R, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3174196
Behera TK, 2015, 2015 SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING AND COMMUNICATION ENGINEERING ICACCE 2015, P494, DOI 10.1109/ICACCE.2015.33
Biolchini J., 2005, Systematic review in software engineering(05)
Branco B, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P3101, DOI 10.1145/3394486.3403361
Cao SS, 2019, PROC VLDB ENDOW, V12, P2082, DOI 10.14778/3352063.3352126
Carcillo F, 2018, INFORM FUSION, V41, P182, DOI 10.1016/j.inffus.2017.09.005
Carminati M, 2018, ACM T PRIV SECUR, V21, DOI 10.1145/3178370
Carneiroa N, 2017, DECIS SUPPORT SYST, V95, P91, DOI 10.1016/j.dss.2017.01.002
Carta S, 2019, J INF SECUR APPL, V46, P13, DOI 10.1016/j.jisa.2019.02.007
Chakravorti S., 2003, Review of Network Economics, V2
Chilaka U.L., 2019, CONIC RES ENG J, V3, P456
Correia I., 2015, Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, P181, DOI [DOI 10.1145/2675743, 10.1145/2675743]
Dai Y, 2016, IEEE TRUST BIG, P1644, DOI [10.1109/TrustCom.2016.251, 10.1109/TrustCom.2016.0253]
Dal Pozzolo A, 2018, IEEE T NEUR NET LEAR, V29, P3784, DOI 10.1109/TNNLS.2017.2736643
de Sá AGC, 2018, ENG APPL ARTIF INTEL, V72, P21, DOI 10.1016/j.engappai.2018.03.011
Dornadula VN, 2019, PROCEDIA COMPUT SCI, V165, P631, DOI 10.1016/j.procs.2020.01.057
El Orche A, 2019, PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON NETWORKING, INFORMATION SYSTEMS & SECURITY (NISS19), DOI 10.1145/3320326.3320369
El Orche A, 2018, 2018 IEEE 5TH INTERNATIONAL CONGRESS ON INFORMATION SCIENCE AND TECHNOLOGY (IEEE CIST'18), P143, DOI 10.1109/CIST.2018.8596486
Forough J, 2021, APPL SOFT COMPUT, V99, DOI 10.1016/j.asoc.2020.106883
Gourisaria M. K, 2020, P IEEE 17 IND COUNC, P1, DOI [10.1109/INDICON49873.2020.9342462, DOI 10.1109/INDICON49873.2020.9342462]
Guo QY, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P616, DOI 10.1145/3308558.3313533
Guthrie C, 2020, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102570
Jhangiani R, 2019, 2019 IEEE 10TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON), P135, DOI 10.1109/UEMCON47517.2019.8992993
Jing C, 2019, LECT NOTES COMPUT SC, V11598, P588, DOI 10.1007/978-3-030-32101-7_34
Kamaruddin S, 2016, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATICS AND ANALYTICS (ICIA' 16), DOI 10.1145/2980258.2980319
Kitchenham B., 2007, GUIDELINES PERFORMIN
Kumar V., 2012, INT J COMPUTERS TECH, V3, P447
Laurens R., 2019, ICC 2019 2019 IEEE I, P1, DOI DOI 10.1109/ICC.2019.8761144
Laurens R., 2017, GLOBECOM 2017 2017 I, P1
Laurens R, 2016, IEEE GLOB COMM CONF
Li Z, 2019, PROC INT CONF DATA, P1898, DOI 10.1109/ICDE.2019.00205
Ling C, 2018, 2018 5TH IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (IEEE CSCLOUD 2018) / 2018 4TH IEEE INTERNATIONAL CONFERENCE ON EDGE COMPUTING AND SCALABLE CLOUD (IEEE EDGECOM 2018), P75, DOI 10.1109/CSCloud/EdgeCom.2018.00022
Liu C, 2020, KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P3035, DOI 10.1145/3394486.3403354
Liu Y, 2021, 2021 2ND INTERNATIONAL CONFERENCE ON E-COMMERCE AND INTERNET TECHNOLOGY (ECIT 2021), P213, DOI 10.1109/ECIT52743.2021.00054
Lucas Y, 2020, FUTURE GENER COMP SY, V102, P393, DOI 10.1016/j.future.2019.08.029
Mack C, 2012, J MICRO-NANOLITH MEM, V11, DOI 10.1117/1.JMM.11.2.020101
Misra S, 2020, PROCEDIA COMPUT SCI, V167, P254, DOI 10.1016/j.procs.2020.03.219
Mittal S, 2019, 2019 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2019), P320, DOI [10.1109/CONFLUENCE.2019.8776925, 10.1109/confluence.2019.8776925]
Molloy I, 2017, LECT NOTES COMPUT SC, V9603, P22, DOI 10.1007/978-3-662-54970-4_2
dos Santos MVM, 2016, ADV INTELL SYST COMP, V448, P461, DOI 10.1007/978-3-319-32467-8_41
Mrozek P, 2020, INT CONF UTIL CLOUD, P426, DOI 10.1109/UCC48980.2020.00067
Najadat H, 2020, INT CONF INFORM COMM, P204, DOI 10.1109/ICICS49469.2020.239524
Patil Suraj, 2018, Procedia Computer Science, V132, P385, DOI 10.1016/j.procs.2018.05.199
Pourhabibi T, 2020, DECIS SUPPORT SYST, V133, DOI 10.1016/j.dss.2020.113303
Puh M, 2019, 2019 42ND INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), P1250, DOI [10.23919/MIPRO.2019.8757212, 10.23919/mipro.2019.8757212]
Raghavan P, 2019, PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND KNOWLEDGE ECONOMY (ICCIKE' 2019), P335, DOI [10.1109/ICCIKE47802.2019.9004231, 10.1109/iccike47802.2019.9004231]
Rai A.K., 2020, MACHINE LEARNING IMA, P369
Rajeshwari U, 2016, PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON APPLIED AND THEORETICAL COMPUTING AND COMMUNICATION TECHNOLOGY (ICATCCT), P439, DOI 10.1109/ICATCCT.2016.7912039
Randhawa K, 2018, IEEE ACCESS, V6, P14277, DOI 10.1109/ACCESS.2018.2806420
Sadgali I, 2019, PROCEDIA COMPUT SCI, V148, P45, DOI 10.1016/j.procs.2019.01.007
Sadgali I, 2019, 4TH INTERNATIONAL CONFERENCE ON SMART CITY APPLICATIONS (SCA' 19), DOI 10.1145/3368756.3369082
Sadineni Praveen Kumar, 2020, 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), P659, DOI 10.1109/I-SMAC49090.2020.9243545
Saia R, 2019, FUTURE GENER COMP SY, V93, P18, DOI 10.1016/j.future.2018.10.016
Saia R, 2017, LECT NOTES COMPUT SC, V10394, P464, DOI 10.1007/978-3-319-64701-2_34
Saia R, 2015, 2015 7TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT (IC3K), P496
Shiyang Xuan, 2018, Computational Data and Social Networks. 7th International Conference, CSoNet 2018. Proceedings: Lecture Notes in Computer Science (LNCS 11280), P343, DOI 10.1007/978-3-030-04648-4_29
Smiles J. Anita, 2020, Proceedings of the 3rd International Conference on Intelligent Sustainable Systems (ICISS 2020), P376, DOI 10.1109/ICISS49785.2020.9316080
Song Y, 2022, REV MANAG SCI, V16, P65, DOI 10.1007/s11846-020-00438-5
Sudha C., 2021, P 2021 2 INT C COMPU, DOI [10.1109/ICCAKM50778.2021.9357709, DOI 10.1109/ICCAKM50778.2021.9357709]
Taha AA, 2020, IEEE ACCESS, V8, P25579, DOI 10.1109/ACCESS.2020.2971354
Tran LTT, 2021, J RETAIL CONSUM SERV, V58, DOI 10.1016/j.jretconser.2020.102287
Van Vlasselaer V, 2015, DECIS SUPPORT SYST, V75, P38, DOI 10.1016/j.dss.2015.04.013
Wang SH, 2017, LECT NOTES ARTIF INT, V10536, P241, DOI 10.1007/978-3-319-71273-4_20
Webb GI, 2016, DATA MIN KNOWL DISC, V30, P964, DOI 10.1007/s10618-015-0448-4
Xuan SY, 2018, IEEE INT C NETW SENS
Yang QH, 2015, L N INST COMP SCI SO, V142, P98, DOI 10.1007/978-3-319-16050-4_9
Yin SJ, 2020, INT CONF DAT MIN WOR, P875, DOI 10.1109/ICDMW51313.2020.00125
Zamini M, 2018, 2018 9TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATIONS (IST), P486, DOI 10.1109/ISTEL.2018.8661129
Zhang XW, 2008, PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON ELECTRONIC COMMERCE AND SECURITY, P743, DOI 10.1109/ISECS.2008.37
Zhao J, 2016, DECIS SUPPORT SYST, V86, P109, DOI 10.1016/j.dss.2016.04.003
Zhao PH, 2019, IEEE ACCESS, V7, P108703, DOI 10.1109/ACCESS.2019.2933247
Zojaji Z, 2016, Arxiv, DOI [arXiv:1611.06439, DOI 10.48550/ARXIV.1611.06439]
NR 83
TC 5
Z9 5
U1 24
U2 51
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD NOV-DEC
PY 2022
VL 56
AR 101207
DI 10.1016/j.elerap.2022.101207
EA OCT 2022
PG 19
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 5X6VK
UT WOS:000878737000002
DA 2024-03-27
ER
PT J
AU Li, WF
Chen, HC
Nunamaker, JF
AF Li, Weifeng
Chen, Hsinchun
Nunamaker, Jay F., Jr.
TI Identifying and Profiling Key Sellers in Cyber Carding Community:
AZSecure Text Mining System
SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
LA English
DT Article
DE carding community; cybersecurity; deep learning; fraud detection; online
deception; social media analytics; topic modeling; underground economy
ID SECURITY; WEB; NETWORKS; REVIEWS
AB The past few years have witnessed millions of credit/debit cards flowing through the underground economy and ultimately causing significant financial loss. Examining key underground economy sellers has both practical and academic significance for cybercrime forensics and criminology research. Drawing on social media analytics, we have developed the AZSecure text mining system for identifying and profiling key sellers. The system identifies sellers using sentiment analysis of customer reviews and profiles sellers using topic modeling of advertisements. We evaluated the AZSecure system on eight international underground economy forums. The system significantly outperformed all benchmark machine-learning methods on identifying advertisement threads, classifying customer review sentiments, and profiling seller characteristics, with an average F-measure of about 80 percent to 90 percent. In our case study, we identified the famous carder, Rescator, who was affiliated with the Target breach, and captured important seller characteristics in terms of product type, payment options, and contact channels. Our research leverages social media analytics to probe into the underground economy in order to help law enforcement target key sellers and prevent future fraud. It also contributes to our understanding of the use of information technology in detecting deception in online systems.
C1 [Li, Weifeng] Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA.
[Li, Weifeng] Univ Arizona, Artificial Intelligence Lab, Tucson, AZ 85721 USA.
[Chen, Hsinchun] Eller Coll Management, Dept Management Informat Syst, Tucson, AZ USA.
[Chen, Hsinchun] Eller Coll Management, Dept Management Informat Syst, Management & Technol, Tucson, AZ USA.
[Nunamaker, Jay F., Jr.] Univ Arizona, MIS Comp Sci & Commun, Tucson, AZ 85721 USA.
[Nunamaker, Jay F., Jr.] Univ Arizona, Ctr Management Informat, Tucson, AZ 85721 USA.
[Nunamaker, Jay F., Jr.] Univ Arizona, Natl Ctr Border Secur & Immigrat, Tucson, AZ 85721 USA.
C3 University of Arizona; University of Arizona; University of Arizona;
University of Arizona; University of Arizona
RP Li, WF (autor correspondiente), Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA.; Li, WF (autor correspondiente), Univ Arizona, Artificial Intelligence Lab, Tucson, AZ 85721 USA.
EM weifengli@email.arizona.edu; hchen@eller.arizona.edu;
jnunamaker@cmi.arizona.edu
OI Li, Weifeng/0000-0002-2105-3596
FU National Science Foundation [SES-1314631, DUE-1303362]
FX This work was supported in part by the National Science Foundation under
Grant no. SES-1314631 and DUE-1303362.
CR Abbasi A, 2014, 2014 IEEE JOINT INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (JISIC), P56, DOI 10.1109/JISIC.2014.18
Abbasi A, 2008, J MANAGE INFORM SYST, V25, P49, DOI 10.2753/MIS0742-1222250103
Abhishek V., Examining the Impact of Contextual Ambiguity on Search Advertising Keyword Performance: A Topic Model Approach
Aggarwal R, 2013, MIS QUART, V37, P1093, DOI 10.25300/MISQ/2013/37.4.05
[Anonymous], 2003, P C N AM CHAPT ASS C
[Anonymous], 2011, J COMPUT SCI-NETH, DOI DOI 10.1016/j.jocs.2010.12.007
Aral S., 2011, Content and context: Identifying the impact of qualitative information on consumer choice
Aral S, 2011, MANAGE SCI, V57, P1623, DOI 10.1287/mnsc.1110.1421
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Bao Y, 2014, MANAGE SCI, V60, P1371, DOI 10.1287/mnsc.2014.1930
Benjamin V, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI), P85, DOI 10.1109/ISI.2015.7165944
Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
Chen D., 2013, LEARNING NEW FACTS K
Chu B., 2010, EXAMINING CREATION D
Chung W, 2005, J MANAGE INFORM SYST, V21, P57, DOI 10.1080/07421222.2005.11045821
De Graaf D., 2012, DIGITAL FORENSICS CY, V114, P302
Derrick DC, 2013, ACM TRANS MANAG INF, V4, DOI 10.1145/2499962.2499967
Fallmann H, 2010, LECT NOTES COMPUT SC, V6201, P101, DOI 10.1007/978-3-642-14215-4_6
Fossi M., 2009, The Journal of Financial Services Technology, V3, P77
Fuller CM, 2013, GROUP DECIS NEGOT, V22, P117, DOI 10.1007/s10726-012-9300-z
Herley C, 2010, ECONOMICS OF INFORMATION SECURITY AND PRIVACY, P33, DOI 10.1007/978-1-4419-6967-5_3
Holt TJ, 2010, CRIM JUSTICE STUD, V23, P33, DOI 10.1080/14786011003634415
Holt TJ, 2013, GLOB CRIME, V14, P155, DOI 10.1080/17440572.2013.787925
Holt TJ, 2012, INT J CYBER CRIMINOL, V6, P891
Holt TJ, 2013, SOC SCI COMPUT REV, V31, P165, DOI 10.1177/0894439312452998
Jensen ML, 2013, J MANAGE INFORM SYST, V30, P293, DOI 10.2753/MIS0742-1222300109
Kaspersky Lab, 2015, SEC
Krebs B., 2014, KrebsonSecurity
Lau RYK, 2014, IEEE COMPUT INTELL M, V9, P31, DOI 10.1109/MCI.2013.2291689
Lau RYK, 2012, MIS QUART, V36, P1239
Li X. D., 2009, 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS 2009), P1, DOI 10.1109/PEITS.2009.5407060
Lu Y, 2010, J COMPUT INFORM SYST, V51, P31
Ma X, 2013, J MANAGE INFORM SYST, V30, P279, DOI 10.2753/MIS0742-1222300310
Mahmood MA, 2010, MIS QUART, V34, P431
McCallum A. K., 2002, MALLET MACHINE LEARN
Motoyama M., P 2011 ACM SIGCOMM C, P71, DOI DOI 10.1145/2068816.2068824
Nunamaker J. F. Jr., 1990, Journal of Management Information Systems, V7, P89
Nunamaker JE, 2011, J MANAGE INFORM SYST, V28, P17, DOI 10.2753/MIS0742-1222280102
Odabas M., 2015, EC SOCIOLOGY ONLINE
Peretti K.K., 2008, SANTA CLARA COMPUT H, V25, P375
Png IPL, 2009, J MANAGE INFORM SYST, V26, P97, DOI 10.2753/MIS0742-1222260205
Radianti Jaziar, 2010, Proceedings of the 2010 Fourth International Conference on Emerging Security Information, Systems and Technologies (SECURWARE), P189, DOI 10.1109/SECURWARE.2010.38
Romano NC, 2003, J MANAGE INFORM SYST, V19, P213
Sack W, 2000, J MANAGE INFORM SYST, V17, P73
Singh PV, 2014, INFORM SYST RES, V25, P35, DOI 10.1287/isre.2013.0509
Socher R., 2013, EMNLP, P935
Taboada M, 2011, COMPUT LINGUIST, V37, P267, DOI 10.1162/COLI_a_00049
United States Department of Homeland Security, 2014, US SECR SERV ARR ON
United States Department of Justice, 2013, 5 IND NEW JERS LARG
Verizon, 2014, VERIZON BUS J, V2014, P1
Wang QH, 2012, LECT NOTES BUS INF P, V108, P143
Wu L, 2013, INFORM SYST RES, V24, P30, DOI 10.1287/isre.1120.0465
Yip M., 2013, Proceedings of the 5th Annual ACM Web Science Conference, P453
Zhang X., 2013, 2013 SIGBPS Workshop on Business Processes and Service, P106
Zhang X, 2015, INFORM SYST FRONT, V17, P1239, DOI 10.1007/s10796-015-9567-0
Ziming Zhao, 2012, Computer Security - ESORICS 2012. Proceedings 17th European Symposium on Research in Computer Security, P877, DOI 10.1007/978-3-642-33167-1_50
NR 56
TC 52
Z9 57
U1 8
U2 146
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0742-1222
EI 1557-928X
J9 J MANAGE INFORM SYST
JI J. Manage. Inform. Syst.
PY 2016
VL 33
IS 4
BP 1059
EP 1086
DI 10.1080/07421222.2016.1267528
PG 28
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA EM2RA
UT WOS:000395162100007
DA 2024-03-27
ER
PT J
AU Wang, KL
Chen, ZH
Cheng, L
Zhu, PY
Shi, J
Bian, ZY
AF Wang, Kailai
Chen, Zhenhua
Cheng, Long
Zhu, Pengyu
Shi, Jian
Bian, Zheyong
TI Integrating spatial statistics and machine learning to identify
relationships between e-commerce and distribution facilities in Texas,
US
SO TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
LA English
DT Article
DE E-Commerce; Warehouses and Distribution Centers (W&DCs); Spatial
analysis; Logistics sprawl; Gradient Boosting Decision Trees (GBDT)
ID BLOCKCHAIN; SECURE
AB This paper proposes a novel analytical framework that integrates spatial statistics and machine learning techniques to identify relationships between e-commerce and distribution facilities. The framework includes centrographic analysis, global and local spatial association measurements, and a recently popularized interpretable machine learning approach - gradient boosting decision trees (GBDT) - to analyze warehousing location choices. We apply this framework to ZIP Codes Business Patterns data from 2003 to 2016 in three large metropolitan areas in Texas, US (i.e., Dallas-Fort Worth, Austin, and Houston). The thematic maps reveal the spatial clustering of areas with higher e-commerce activity but lower logistics facility coverage. It is worth noting that we do not observe logistics sprawl in the study region. The GBDT results show that industrial activities and transportation network accessibility are key factors influencing warehousing location choices. We also find that the relationship between warehouses and e-commerce establishments is weaker in Houston, a major maritime gateway for goods entering and leaving, as compared to Dallas-Fort Worth and Austin. Implications for local freight transportation planners and decisionmakers are discussed.
C1 [Wang, Kailai; Bian, Zheyong] Univ Houston, Dept Construct Management, Houston, TX USA.
[Chen, Zhenhua] Ohio State Univ, Knowlton Sch Architecture, City & Reg Planning, Columbus, OH USA.
[Cheng, Long] Southeast Univ, Jiangsu Key Lab Urban ITS, Nanjing, Peoples R China.
[Cheng, Long] Univ Ghent, Dept Geog, Ghent, Belgium.
[Zhu, Pengyu] Hong Kong Univ Sci & Technol, Div Publ Policy, Hong Kong, Peoples R China.
[Shi, Jian] Univ Houston, Dept Engn Technol, Houston, TX USA.
C3 University of Houston System; University of Houston; University System
of Ohio; Ohio State University; Southeast University - China; Ghent
University; Hong Kong University of Science & Technology; University of
Houston System; University of Houston
RP Cheng, L (autor correspondiente), Southeast Univ, Jiangsu Key Lab Urban ITS, Nanjing, Peoples R China.
EM kwang43@central.uh.edu; chen.7172@osu.edu; long.cheng@ugent.be;
pengyuzhu@ust.hk; jshi23@central.uh.edu; zbian2@central.uh.edu
RI Wang, Kailai/HKF-3153-2023
OI Wang, Kailai/0000-0002-5597-6823; Cheng, Long/0000-0003-3720-4093
FU University of Houston-National Research University Fund (NRUF);
[R0507403]
FX This study was partially supported by the University of Houston-National
Research University Fund (NRUF) (Project Number: R0507403) .
CR Akter S., 2022, INTERNET THINGS CYBE
Anderson C, 2023, INFORM ORGAN-UK, V33, DOI 10.1016/j.infoandorg.2023.100455
Ayvaz S, 2021, EXPERT SYST APPL, V173, DOI 10.1016/j.eswa.2021.114598
Nguyen BL, 2020, CMC-COMPUT MATER CON, V65, P87, DOI 10.32604/cmc.2020.011599
Deepa N, 2021, J AMB INTEL HUM COMP, V12, P4877, DOI 10.1007/s12652-020-01911-5
Dua D., 2019, UCI MACHINE LEARNING
Erlingsson U, 2014, CCS'14: PROCEEDINGS OF THE 21ST ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1054, DOI 10.1145/2660267.2660348
Ghayvat H, 2022, IEEE J BIOMED HEALTH, V26, P1937, DOI 10.1109/JBHI.2021.3097237
Gorkhali A, 2020, J MANAG ANAL, V7, P321, DOI 10.1080/23270012.2020.1801529
Hasan H.R., 2020, IEEE ACCESS, V8
He DB, 2015, MULTIMEDIA SYST, V21, P49, DOI 10.1007/s00530-013-0346-9
Issa W, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560816
Janosi Andras, 1988, UCI Machine Learning Repository
Jayabalan J, 2022, J PARALLEL DISTR COM, V164, P152, DOI 10.1016/j.jpdc.2022.03.009
Jia N, 2020, SECUR COMMUN NETW, V2020, DOI 10.1155/2020/8872853
Kahn Michael, 1993, UCI Machine Learning Repository
Khan MA, 2020, IEEE ACCESS, V8, P52018, DOI 10.1109/ACCESS.2020.2980739
Laghari AA, 2022, ARCH COMPUT METHOD E, V29, P1395, DOI 10.1007/s11831-021-09622-6
Liu L, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12010103
Merabet F, 2020, PEER PEER NETW APPL, V13, P439, DOI 10.1007/s12083-019-00782-8
Miyachi K, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102535
Moradi A., 2021, 2021 55 ASILOMAR C S, P790
Nakamoto S, 2008, BITCOIN PEER TO PEER
Pasdar A, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3567582
Paul M, 2023, ICT EXPRESS, V9, P571, DOI 10.1016/j.icte.2023.02.007
Rahman MA, 2021, IEEE INTERNET THINGS, V8, P15847, DOI 10.1109/JIOT.2021.3051080
Roy Chanday, 2022, 2022 7th International Conference on Computer Science and Engineering (UBMK), P462, DOI 10.1109/UBMK55850.2022.9919573
Shen M, 2019, IEEE INTERNET THINGS, V6, P7702, DOI 10.1109/JIOT.2019.2901840
Tang XY, 2022, IEEE WIREL COMMUN, V29, P14, DOI 10.1109/MWC.003.2100598
Wu F, 2019, IEEE SYST J, V13, P2830, DOI 10.1109/JSYST.2018.2876226
Wu F, 2017, MULTIMEDIA SYST, V23, P195, DOI 10.1007/s00530-015-0476-3
Yu RY, 2022, J NETW COMPUT APPL, V207, DOI 10.1016/j.jnca.2022.103483
Zhang P., 2022, IEEE T DEPENDABLE SE
Zhao J., 2023, IEEE Trans. Cloud Comput., P1
Zhu LH, 2021, IEEE INTERNET THINGS, V8, P12106, DOI 10.1109/JIOT.2021.3060764
NR 35
TC 0
Z9 0
U1 13
U2 13
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0965-8564
EI 1879-2375
J9 TRANSPORT RES A-POL
JI Transp. Res. Pt. A-Policy Pract.
PD JUL
PY 2023
VL 173
AR 103696
DI 10.1016/j.tra.2023.103696
EA MAY 2023
PG 17
WC Economics; Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Transportation
GA Q1DX0
UT WOS:001055001900001
DA 2024-03-27
ER
PT J
AU Agnihotri, R
AF Agnihotri, Raj
TI Social media, customer engagement, and sales organizations: A research
agenda
SO INDUSTRIAL MARKETING MANAGEMENT
LA English
DT Article
DE Social media; customer engagement; business markets; sales performance;
AI; social media analytics; social media pitfalls
ID TECHNOLOGY USE; SERVICE BEHAVIORS; PERFORMANCE; IMPACT; USAGE; ONLINE;
CHAIN; CAPABILITIES; ANTECEDENTS; SALESPEOPLE
AB Organizations continue to make investments in social media with the hope that it will help their sales force in improving engagement with customers. The academic literature on social media use in business markets has supported the growth and utilization of such technology; however, much more work is needed. This article, building upon the recent scholarly advances and considering a managerial perspective, offers suggestions to guide future academic research examining the links between social media use and customer engagement within the B2B sales domain. Several research questions are presented under the four broad topics, namely utility of social media technologies, context matters, social media pitfalls, and futuristic social media applications.
C1 [Agnihotri, Raj] Iowa State Univ, Ivy Coll Business, Ames, IA 50011 USA.
C3 Iowa State University
RP Agnihotri, R (autor correspondiente), Iowa State Univ, Ivy Coll Business, Ames, IA 50011 USA.
EM Raj2@iastate.edu
RI Agnihotri, Raj/AAN-9991-2021
OI Agnihotri, Raj/0000-0002-2008-5908
CR Agnihotri R., 2020, HDB RES SALES
Agnihotri R, 2017, J BUS RES, V81, P144, DOI 10.1016/j.jbusres.2017.08.021
Agnihotri R, 2016, IND MARKET MANAG, V53, P172, DOI 10.1016/j.indmarman.2015.09.003
Agnihotri R, 2012, J PERS SELL SALES M, V32, P333, DOI 10.2753/PSS0885-3134320304
Aharony N, 2015, ONLINE INFORM REV, V39, P26, DOI 10.1108/OIR-08-2014-0177
Ahearne M, 2008, MANAGE SCI, V54, P671, DOI 10.1287/mnsc.1070.0783
Ahearne M, 2007, J ACAD MARKET SCI, V35, P603, DOI 10.1007/s11747-006-0013-1
Ahearne M, 2010, J PERS SELL SALES M, V30, P111, DOI 10.2753/PSS0885-3134300202
Alavi Shirin, 2016, International Journal of Management Practice, V9, P56
Alwagait E, 2015, COMPUT HUM BEHAV, V51, P1092, DOI 10.1016/j.chb.2014.09.028
Ancillai C, 2019, IND MARKET MANAG, V82, P293, DOI 10.1016/j.indmarman.2019.01.002
Andriole SJ, 2010, COMMUN ACM, V53, P67, DOI 10.1145/1859204.1858225
[Anonymous], 2010, HARV BUS REV
Antheunis ML, 2015, SOCIETIES, V5, P399, DOI 10.3390/soc5020399
Atsmon Y., 2012, MCKIN Q, V4, P50
Autry CW, 2008, J BUS LOGIST, V29, P157, DOI 10.1002/j.2158-1592.2008.tb00073.x
Ayyagari R, 2011, MIS QUART, V35, P831
Bachrach DG, 2017, IND MARKET MANAG, V62, P88, DOI 10.1016/j.indmarman.2016.08.001
Baehr C, 2010, IEEE T PROF COMMUN, V53, P358, DOI 10.1109/TPC.2010.2077491
Bolander W, 2015, J MARKETING, V79, P1, DOI 10.1509/jm.14.0444
Bowden JLH, 2009, J MARKET THEORY PRAC, V17, P63, DOI 10.2753/MTP1069-6679170105
Brodie RJ, 2013, J BUS RES, V66, P105, DOI 10.1016/j.jbusres.2011.07.029
Pinho JC, 2013, J RES INTERACT MARK, V7, P216, DOI 10.1108/JRIM-May-2012-0020
Chu SC, 2011, INT J ADVERT, V30, P47, DOI 10.2501/IJA-30-1-047-075
Chuang SH, 2020, IND MARKET MANAG, V84, P202, DOI 10.1016/j.indmarman.2019.06.012
Colicev A, 2018, J MARKETING, V82, P37, DOI 10.1509/jm.16.0055
Collins R.H., 1984, The Journal of Personal Selling and Sales Management, V4, P58, DOI DOI 10.1080/08853134.1984.10754372
Cousins PD, 2006, J OPER MANAG, V24, P851, DOI 10.1016/j.jom.2005.08.007
Culnan MJ, 2010, MIS Q EXEC, V9, P243
de Vries L, 2017, J MARKETING, V81, P1, DOI 10.1509/jm.15.0178
Dinner IM, 2014, J MARKETING RES, V51, P527, DOI 10.1509/jmr.11.0466
Ellison N, 2006, J COMPUT-MEDIAT COMM, V11
Ellison NB, 2007, J COMPUT-MEDIAT COMM, V12, P1143, DOI 10.1111/j.1083-6101.2007.00367.x
Fan WG, 2014, COMMUN ACM, V57, P74, DOI 10.1145/2602574
Fang E, 2015, J MARKETING RES, V52, P407, DOI 10.1509/jmr.13.0165
Foltean FS, 2019, J BUS RES, V104, P563, DOI 10.1016/j.jbusres.2018.10.047
Gilfoil D.M., 2012, INT J MANAGEMENT INF, V16, P69
Grant RM, 1996, ORGAN SCI, V7, P375, DOI 10.1287/orsc.7.4.375
Gray PH, 2011, MIS QUART, V35, P629
Grewal Rajdeep., 2015, Customer Needs and Solutions, V2, P193, DOI [10.1007/s40547-015-0040-5, DOI 10.1007/S40547-015-0040-5]
Guesalaga R, 2016, IND MARKET MANAG, V54, P71, DOI 10.1016/j.indmarman.2015.12.002
Hakansson H., 1995, DEV RELATIONSHIPS BU
Hanna R, 2011, BUS HORIZONS, V54, P265, DOI 10.1016/j.bushor.2011.01.007
Hartmann NN, 2018, J MARKETING, V82, P1, DOI 10.1509/jm.16.0268
Hennig-Thurau T, 2010, J SERV RES-US, V13, P311, DOI 10.1177/1094670510375460
Holliman G, 2014, J RES INTERACT MARK, V8, P269, DOI 10.1108/JRIM-02-2014-0013
Hunter GK, 2007, J MARKETING, V71, P16, DOI 10.1509/jmkg.71.1.16
Iankova S, 2019, IND MARKET MANAG, V81, P169, DOI 10.1016/j.indmarman.2018.01.001
Itani OS, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101980
Itani OS, 2017, IND MARKET MANAG, V66, P64, DOI 10.1016/j.indmarman.2017.06.012
Järvinen J, 2016, IND MARKET MANAG, V54, P164, DOI 10.1016/j.indmarman.2015.07.002
Kalra A, 2017, J PERS SELL SALES M, V37, P332, DOI 10.1080/08853134.2017.1391100
Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
Kaput M., 2019, WHAT IS ARTIFICIAL I
Kelley H.H., 1987, ATTRIBUTION SOCIAL I
Kovac M, 2016, SOCIAL MEDIA WORKS B
Krause DR, 2007, J OPER MANAG, V25, P528, DOI 10.1016/j.jom.2006.05.007
Kumar B, 2020, IND MARKET MANAG, V85, P126, DOI 10.1016/j.indmarman.2019.10.002
Kumar V, 2016, J MARKETING RES, V53, P497, DOI 10.1509/jmr.15.0044
Kumar V, 2013, MARKET SCI, V32, P194, DOI 10.1287/mksc.1120.0768
Kumar V, 2010, J SERV RES-US, V13, P297, DOI 10.1177/1094670510375602
Lacoste S, 2016, IND MARKET MANAG, V54, P33, DOI 10.1016/j.indmarman.2015.12.010
Lawson B, 2008, J OPER MANAG, V26, P446, DOI 10.1016/j.jom.2007.10.001
Li F, 2021, J OPER RES SOC, V72, P1432, DOI 10.1080/01605682.2020.1718561
Lilien GL, 2016, INT J RES MARK, V33, P543, DOI 10.1016/j.ijresmar.2016.01.003
Liu D, 2016, REV GEN PSYCHOL, V20, P369, DOI 10.1037/gpr0000091
Lu CY, 2020, J ADVERTISING RES, V60, P394, DOI 10.2501/JAR-2020-004
Maier C, 2015, EUR J INFORM SYST, V24, P447, DOI 10.1057/ejis.2014.3
Malita L, 2011, PROCEDIA COMPUT SCI, V3, DOI 10.1016/j.procs.2010.12.123
Martechadvisor.com, 2018, IMP ART INT SOC MED
Michaelidou N, 2011, IND MARKET MANAG, V40, P1153, DOI 10.1016/j.indmarman.2011.09.009
Min S, 2008, J BUS LOGIST, V29, P283, DOI 10.1002/j.2158-1592.2008.tb00079.x
Minsky L., 2016, HARVARD BUSINESS REV, V8
Moore JN, 2015, J MARKET THEORY PRAC, V23, P1, DOI 10.1080/10696679.2015.980163
Nonaka I., 2003, Knowledge Management Research & Practice, V1, P2, DOI 10.1057/palgrave.kmrp.8500001
Ogilvie J, 2018, IND MARKET MANAG, V75, P55, DOI 10.1016/j.indmarman.2018.03.007
Palmatier RW, 2006, J MARKETING, V70, P136, DOI 10.1509/jmkg.70.4.136
Pansari A, 2017, J ACAD MARKET SCI, V45, P294, DOI 10.1007/s11747-016-0485-6
Rapp A, 2013, J ACAD MARKET SCI, V41, P547, DOI 10.1007/s11747-013-0326-9
Rapp A, 2008, J PERS SELL SALES M, V28, P335, DOI 10.2753/PSS0885-3134280401
Rutz OJ, 2017, J MARKETING RES, V54, P885, DOI 10.1509/jmr.14.0186
Salo J, 2017, IND MARKET MANAG, V66, P115, DOI 10.1016/j.indmarman.2017.07.013
Sashi CM, 2012, MANAGE DECIS, V50, P253, DOI 10.1108/00251741211203551
Schuldt B. A., 2015, ACAD MARKETING STUDI, V19, P230
Sheth JN, 2011, J MARKETING, V75, P166, DOI 10.1509/jmkg.75.4.166
Singh J, 2019, J PERS SELL SALES M, V39, P2, DOI 10.1080/08853134.2018.1557525
Statista, 2019, LEAS COUNTR BAS NUMB
Stephen AT, 2012, J MARKETING RES, V49, P624, DOI 10.1509/jmr.09.0401
Sundaram S, 2007, J ACAD MARKET SCI, V35, P101, DOI 10.1007/s11747-006-0010-4
Tarafdar M, 2015, INFORM SYST J, V25, P103, DOI 10.1111/isj.12042
TATA SP, 1994, J COUNS PSYCHOL, V41, P280
Trainor KJ, 2014, J BUS RES, V67, P1201, DOI 10.1016/j.jbusres.2013.05.002
Vieira VA, 2019, J ACAD MARKET SCI, V47, P1085, DOI 10.1007/s11747-019-00687-1
Villena VH, 2011, J OPER MANAG, V29, P561, DOI 10.1016/j.jom.2010.09.001
Vivek SD, 2012, J MARKET THEORY PRAC, V20, P127, DOI 10.2753/MTP1069-6679200201
Wang WYC, 2016, IND MARKET MANAG, V54, P4, DOI 10.1016/j.indmarman.2015.12.004
Wang Z, 2017, J INTERACT MARK, V39, P15, DOI [10.13393/j.cnki.issn.1672-948X.2017.03.004, 10.1016/j.intmar.2017.02.004]
Xie RD, 2020, LASER OPTOELECTRON P, V57, DOI 10.3788/LOP57.050003
Zeng D, 2010, IEEE INTELL SYST, V25, P13, DOI 10.1109/MIS.2010.151
NR 99
TC 70
Z9 75
U1 13
U2 79
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0019-8501
EI 1873-2062
J9 IND MARKET MANAG
JI Ind. Mark. Manage.
PD OCT
PY 2020
VL 90
BP 291
EP 299
DI 10.1016/j.indmarman.2020.07.017
PG 9
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA OD5GF
UT WOS:000579880200024
DA 2024-03-27
ER
PT J
AU Mandal, S
Maiti, A
AF Mandal, Supriyo
Maiti, Abyayananda
TI Network promoter score (NePS): An indicator of product sales in
E-commerce retailing sector
SO ELECTRONIC MARKETS
LA English
DT Article
DE E-commerce; Product sales; Review network; Network promoter score; Deep
learning technique
ID WORD-OF-MOUTH; ONLINE CONSUMER REVIEWS; HELPFULNESS
AB E-commerce companies want to predict their future product sales from the current customers' feedback to frame a better business strategy. However, the conventional way of analyzing rating activities or quality and sentiment of reviews, volume of sales, or product prices is not enough for establishing a strong regression between these parameters and future product sales. Most of the existing works ignore the heterogeneous positional and influential effects of individual customer reviews and ratings. For the realization of these effects, we use review network i.e., a bipartite network between customers and products based on the customers' review activities. In this paper, we present a concept named Network Promoter Score (NePS) based on the reliability, positional influence of each customer in the network. In-depth experiments on online review datasets show that NePS emerges as a strong indicator of product sales and can be remarkably futuristic compared to the existing parameters. Furthermore, we propose a predictive modeling technique to estimate the product sales of a company based on NePS.
C1 [Mandal, Supriyo; Maiti, Abyayananda] Indian Inst Technol Patna, Dept Comp Sci & Engn, Bihta 801106, Bihar, India.
C3 Indian Institute of Technology (IIT) - Patna
RP Mandal, S (autor correspondiente), Indian Inst Technol Patna, Dept Comp Sci & Engn, Bihta 801106, Bihar, India.
EM supriyo.pcs17@iitp.ac.in; abyaym@iitp.ac.in
OI Mandal, Supriyo/0000-0002-1310-7291
CR Abbasimehr H, 2020, COMPUT IND ENG, V143, DOI 10.1016/j.cie.2020.106435
Allison PD., 1998, MULTIPLE REGRESSION
Archak N, 2011, MANAGE SCI, V57, P1485, DOI 10.1287/mnsc.1110.1370
Baek H, 2017, ELECTRON COMMER R A, V22, P13, DOI 10.1016/j.elerap.2017.02.001
Baek H, 2012, INT J ELECTRON COMM, V17, P99, DOI 10.2753/JEC1086-4415170204
Bickart B., 2001, J INTERACT MARK, V15, P31, DOI [10.1002/dir.1014, DOI 10.1002/DIR.1014]
Brynjolfsson E, 2003, MANAGE SCI, V49, P1580, DOI 10.1287/mnsc.49.11.1580.20580
Chevalier J, 2003, QME-QUANT MARK ECON, V1, P203, DOI 10.1023/A:1024634613982
Chevalier JA, 2006, J MARKETING RES, V43, P345, DOI 10.1509/jmkr.43.3.345
Chintagunta PK, 2010, MARKET SCI, V29, P944, DOI 10.1287/mksc.1100.0572
Chua AYK, 2015, J ASSOC INF SCI TECH, V66, P354, DOI 10.1002/asi.23180
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
Colladon AF, 2019, DECIS SUPPORT SYST, V123, DOI 10.1016/j.dss.2019.113075
Das SR, 2007, MANAGE SCI, V53, P1375, DOI 10.1287/mnsc.1070.0704
Dellarocas C, 2007, J INTERACT MARK, V21, P23, DOI 10.1002/dir.20087
Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001
DuBay WH., 2004, PRINCIPLES READABILI
Elkahky A, 2015, PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW 2015), P278, DOI 10.1145/2736277.2741667
Eslami SP, 2018, J RETAIL CONSUM SERV, V45, P74, DOI 10.1016/j.jretconser.2018.08.003
Eslami SP, 2018, DECIS SUPPORT SYST, V113, P32, DOI 10.1016/j.dss.2018.06.012
Fang B, 2016, TOURISM MANAGE, V52, P498, DOI 10.1016/j.tourman.2015.07.018
Filieri R, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106527
Filieri R, 2018, INFORM MANAGE-AMSTER, V55, P956, DOI 10.1016/j.im.2018.04.010
Fisher NI, 2019, APPL STOCH MODEL BUS, V35, P138, DOI 10.1002/asmb.2417
Forman C, 2008, INFORM SYST RES, V19, P291, DOI 10.1287/isre.1080.0193
Fowdur L., 2009, JOHNSON SCH RES PAPE, V2209, DOI 10.2139/ssrn.1407520
Fu DP, 2018, ELECTRON COMMER RES, V18, P457, DOI 10.1007/s10660-017-9266-7
Gang R, 2019, INFORM PROCESS MANAG, V56, P1425, DOI 10.1016/j.ipm.2018.04.003
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
Godes D, 2005, MARKET LETT, V16, P415, DOI 10.1007/s11002-005-5902-4
Godes D, 2004, MARKET SCI, V23, P545, DOI 10.1287/mksc.1040.0071
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
Hansen DL, 2020, Analyzing Social Media Networks with NodeXL
He RN, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16), P507, DOI 10.1145/2872427.2883037
Hong H, 2017, DECIS SUPPORT SYST, V102, P1, DOI 10.1016/j.dss.2017.06.007
Hu N, 2014, DECIS SUPPORT SYST, V57, P42, DOI 10.1016/j.dss.2013.07.009
Kang Y, 2019, ACM TRANS MANAG INF, V10, DOI 10.1145/3365538
Karimi S, 2017, DECIS SUPPORT SYST, V96, P39, DOI 10.1016/j.dss.2017.02.001
Korfiatis N, 2012, ELECTRON COMMER R A, V11, P205, DOI 10.1016/j.elerap.2011.10.003
Korneta P, 2018, INT J MANAG ECON, V54, P136, DOI 10.2478/ijme-2018-0013
Li L, 2020, NEURAL COMPUT APPL, V32, P4387, DOI 10.1007/s00521-018-3865-7
Li XL, 2019, INFORM MANAGE-AMSTER, V56, P172, DOI 10.1016/j.im.2018.04.007
Lin CY, 2017, ELECTRON MARK, V27, P225, DOI 10.1007/s12525-017-0262-5
Liu B, 2010, CH CRC MACH LEARN PA, P627
Liu QH, 2019, ELECTRON COMMER RES, V19, P521, DOI 10.1007/s10660-018-9312-0
Malik MSI, 2020, SOFT COMPUT, V24, P13913, DOI 10.1007/s00500-020-04767-1
Mandal S, 2019, STUD COMPUT INTELL, V813, P169, DOI 10.1007/978-3-030-05414-4_14
Mandal S, 2020, APPL INTELL, V50, P1955, DOI 10.1007/s10489-020-01643-1
Maslowska E, 2017, DECIS SUPPORT SYST, V98, P1, DOI 10.1016/j.dss.2017.03.010
McAuley J, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/2766462.2767755
Netzer O, 2012, MARKET SCI, V31, P521, DOI 10.1287/mksc.1120.0713
Newman MEJ., 2010, NETWORKS INTRO, DOI DOI 10.1093/ACPROF:OSO/9780199206650.001.0001
Ni JM, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P188
Owen R., 2019, MARKETING WISDOM, P17
Qiu JT, 2015, ELECTRON COMMER RES, V15, P427, DOI 10.1007/s10660-015-9191-6
Reichheld FF, 2003, HARVARD BUS REV, V81, P46
Rossi PE, 2001, J AM STAT ASSOC, V96, P20, DOI 10.1198/016214501750332668
Salehan M, 2016, DECIS SUPPORT SYST, V81, P30, DOI 10.1016/j.dss.2015.10.006
Siering M, 2018, DECIS SUPPORT SYST, V108, P1, DOI 10.1016/j.dss.2018.01.004
Stouthuysen K, 2018, ELECTRON COMMER R A, V27, P23, DOI 10.1016/j.elerap.2017.11.002
Sun XY, 2019, DECIS SUPPORT SYST, V124, DOI 10.1016/j.dss.2019.113099
WERBOS PJ, 1990, P IEEE, V78, P1550, DOI 10.1109/5.58337
Wu Y, 2016, PROCEEDINGS OF THE NINTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM'16), P153, DOI 10.1145/2835776.2835837
Xue JL, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2020.103324
Zhang LH, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2141, DOI 10.1145/3097983.3098117
Zhang Zhu., 2006, CIKM '06, P51, DOI [10.1145/1183614.1183626, DOI 10.1145/1183614.1183626]
NR 66
TC 5
Z9 5
U1 0
U2 16
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD SEP
PY 2022
VL 32
IS 3
SI SI
BP 1327
EP 1349
DI 10.1007/s12525-021-00503-1
EA FEB 2022
PG 23
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 6G4VT
UT WOS:000749980200001
DA 2024-03-27
ER
PT J
AU Butt, A
Ahmad, H
Muzaffar, A
Ali, F
Shafique, N
AF Butt, Asad
Ahmad, Hassan
Muzaffar, Asif
Ali, Fayaz
Shafique, Nouman
TI WOW, the make-up AR app is impressive: a comparative study between China
and South Korea
SO JOURNAL OF SERVICES MARKETING
LA English
DT Article
DE AR content quality; AR environment embedding; AR satisfaction; AR
interactivity; AR enjoyment; AR customization; ISS; TAM; E-commerce;
Digitalization; Customer experience; Artificial intelligence; Technology
and service; Behavioral insight
ID AUGMENTED REALITY; VIRTUAL-REALITY; SERVICE QUALITY; CUSTOMER
SATISFACTION; PERCEIVED ENJOYMENT; PURCHASE INTENTION;
SOCIAL-INTERACTION; MOBILE INTERNET; SYSTEMS SUCCESS; TECHNOLOGY
AB Purpose Consumers today actively participate in online purchasing experiences. As a result, it is critical to comprehend the behavioral aspects of novel technology usage, such as augmented reality (AR). AR apps enable beauty companies to create and design more immersive experience services. This study aims to highlight consumers' perspectives on their continued desire to use AR app services. Design/methodology/approach A comparative study between China and South Korea was conducted with sample sizes of 458 and 315, respectively. Smart PLS was used for analysis. Findings The findings suggest that AR apps influence innovative consumers in China and South Korea to be satisfied with and continue to use such services. Previous research on technology acceptance model, information system success, AR and artificial intelligence (AI)-context-specific variables supported the findings. Practical implications This study contributes to the development of AR apps for beauty brands, as such technology revolutionizes how beauty brands work and grow. As a result, AR apps can pave the way for brands to provide an immersive experience to their customers. Originality/value The current study contributes to AR and AI drivers in the context of beauty brands by using novel technologies such as AR. AR integration with AI-context-specific variables indicates that consumers in China and South Korea are innovative and accept such technologies when purchasing beauty products online.
C1 [Butt, Asad] Univ Cent Punjab, Fac Management Studies, Lahore, Pakistan.
[Ahmad, Hassan] Univ Okara, Okara, Pakistan.
[Muzaffar, Asif] Birmingham City Univ, Birmingham City Business Sch, Birmingham, W Midlands, England.
[Ali, Fayaz] Shenzhen Univ, Coll Management, Res Inst Business Analyt & Supply Chain Managemen, Shenzhen, Peoples R China.
[Shafique, Nouman] Gomal Univ, Dera Ismail Khan, Pakistan.
C3 University of Central Punjab; Birmingham City University; Shenzhen
University; Gomal University
RP Ahmad, H (autor correspondiente), Univ Okara, Okara, Pakistan.
EM hassaan1214@hotmail.com
RI Butt, Asad Hassan/ABB-5692-2020
OI Butt, Asad Hassan/0000-0003-4718-4508; Ahmad, Hassan/0000-0003-3925-3956
CR Abdullah F, 2016, COMPUT HUM BEHAV, V63, P75, DOI 10.1016/j.chb.2016.05.014
Ahmad H, 2020, SMART-J BUS MANAG ST, V16, P49, DOI 10.5958/2321-2012.2020.00016.0
Al-dweeri RM, 2019, IND MANAGE DATA SYST, V119, P902, DOI 10.1108/IMDS-12-2017-0598
Al-Fraihat D, 2020, COMPUT HUM BEHAV, V102, P67, DOI 10.1016/j.chb.2019.08.004
Alalwan AA, 2018, TECHNOL SOC, V55, P100, DOI 10.1016/j.techsoc.2018.06.007
Alha K, 2019, COMPUT HUM BEHAV, V93, P114, DOI 10.1016/j.chb.2018.12.008
[Anonymous], 2019, RNZ 1001
[Anonymous], 2020, Forbes
Asgari Omid, 2015, [Journal of Distribution Science, 유통과학연구], V13, P45
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Bartik AW., 2020, W26989 NBER, DOI [DOI 10.3386/W26989, 10.3386/w26989]
Benoit S, 2017, J SERV MARK, V31, P527, DOI 10.1108/JSM-10-2016-0353
Bhutani A., 2018, GLOBAL AI RETAIL MAR
Black W., 2019, The Great Facilitator: Reflections on the Contributions of Joseph F. Hair, P121
Bonetti F., 2019, International Journal of Technology Marketing, V13, P260
Butt AH, 2021, PSYCHOL MARKET, V38, P1014, DOI 10.1002/mar.21487
Cabero-Almenara J, 2019, HELIYON, V5, DOI 10.1016/j.heliyon.2019.e01597
Camilleri A. C., 2019, 13 ANN INT TECHN ED
Carlson J, 2018, J SERV MARK, V32, P83, DOI 10.1108/JSM-02-2017-0059
Chin W. W., 2010, HDB PARTIAL LEAST SQ, P655, DOI DOI 10.1007/978-3-540-32827-8_29
Chung N, 2018, J TRAVEL RES, V57, P627, DOI 10.1177/0047287517708255
Chung N, 2015, COMPUT HUM BEHAV, V50, P588, DOI 10.1016/j.chb.2015.02.068
Chung N, 2009, BEHAV INFORM TECHNOL, V28, P549, DOI 10.1080/01449290802506562
Daily C., 2020, Nile's Journey: from Treasure to Trash
DeLone W. H., 2016, Found Trends Inf Syst, V1, P1, DOI [10.1561/9781680831436, DOI 10.1561/2900000005, 10.1561/2900000005]
DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
Dieck MCT, 2018, CURR ISSUES TOUR, V21, P154, DOI 10.1080/13683500.2015.1070801
export.gov, 2019, KOR E COMM
Fagan Mary, 2012, Campus-Wide Information Systems, V29, P117, DOI 10.1108/10650741211212368
Fang JM, 2019, TELEMAT INFORM, V39, P75, DOI 10.1016/j.tele.2019.01.006
Fang YH, 2017, J SERV MARK, V31, P574, DOI 10.1108/JSM-07-2016-0256
Freeze RD., 2019, J Inform Syst Educ, V21, P4
Genç R, 2018, PROGR IS, P109, DOI 10.1007/978-3-319-64027-3_8
Ghazali E, 2019, INFORM TECHNOL PEOPL, V32, P646, DOI 10.1108/ITP-07-2017-0216
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Heller J, 2021, J SERV RES-US, V24, P84, DOI 10.1177/1094670520933692
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
Hilken T, 2020, J ACAD MARKET SCI, V48, P143, DOI 10.1007/s11747-019-00688-0
Hilken T, 2017, J ACAD MARKET SCI, V45, P884, DOI 10.1007/s11747-017-0541-x
Holdack E., 2020, J RETAIL CONSUM SERV, DOI 10.1016/j.jretconser.2020.102259
Hollebeek LD, 2021, J SERV RES-US, V24, P3, DOI 10.1177/1094670520975110
Hsu J.W., 2017, OREAL GLAMS NEW RETA
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Huang TL, 2015, ELECTRON COMMER RES, V15, P269, DOI 10.1007/s10660-014-9163-2
Hwangbo H, 2017, MOB INF SYST, V2017, DOI 10.1155/2017/4738340
Ismagilova E, 2019, INT J INFORM MANAGE, V47, P88, DOI 10.1016/j.ijinfomgt.2019.01.004
Jang Y, 2019, TELEMAT INFORM, V42, DOI 10.1016/j.tele.2019.101239
Javornik A, 2016, DIS 2016: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON DESIGNING INTERACTIVE SYSTEMS, P871, DOI 10.1145/2901790.2901881
Jiang Y, 2021, INF RESOUR MANAG J, V34, P41, DOI 10.4018/IRMJ.2021070103
Jung T, 2015, TOURISM MANAGE, V49, P75, DOI 10.1016/j.tourman.2015.02.013
Kang JYM, 2014, INT J CLOTH SCI TECH, V26, P486, DOI 10.1108/IJCST-05-2013-0055
Kaushik AK, 2015, J SERV MARK, V29, P406, DOI 10.1108/JSM-08-2014-0276
Keiningham T, 2017, J SERV MARK, V31, P148, DOI 10.1108/JSM-09-2016-0337
Kim DJ, 2012, INFORM SYST FRONT, V14, P409, DOI 10.1007/s10796-010-9267-8
Kim K, 2015, COMPUT HUM BEHAV, V48, P695, DOI 10.1016/j.chb.2015.02.011
Lee CH, 2018, TELEMAT INFORM, V35, P1958, DOI 10.1016/j.tele.2018.06.008
Lee J, 2021, INT J HUM-COMPUT INT, V37, P534, DOI 10.1080/10447318.2020.1834730
Lee J, 2019, TELEMAT INFORM, V39, P37, DOI 10.1016/j.tele.2018.12.006
Lee S, 2015, COMPUT HUM BEHAV, V51, P336, DOI 10.1016/j.chb.2015.04.049
Legner C, 2017, BUS INFORM SYST ENG+, V59, P301, DOI 10.1007/s12599-017-0484-2
Leong LY, 2017, INTERNET RES, V27, P786, DOI 10.1108/IntR-04-2016-0107
Li T, 2019, J DESTIN MARK MANAGE, V12, P15, DOI 10.1016/j.jdmm.2019.02.003
Li Y, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103197
Liberatore MJ, 2021, VIRTUAL REAL-LONDON, V25, P773, DOI 10.1007/s10055-020-00492-0
Lim A., 2018, AS WATSON OREAL PART
Lim KC, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9132718
Liu D, 2017, MIS QUART, V41, P1011
Lou L, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9071186
Manis KT, 2019, J BUS RES, V100, P503, DOI 10.1016/j.jbusres.2018.10.021
Marinova D, 2017, J SERV RES-US, V20, P29, DOI 10.1177/1094670516679273
McLean G, 2019, COMPUT HUM BEHAV, V101, P210, DOI 10.1016/j.chb.2019.07.002
McLeay F, 2021, J SERV RES-US, V24, P104, DOI 10.1177/1094670520933354
Min S, 2019, J TRAVEL TOUR MARK, V36, P770, DOI 10.1080/10548408.2018.1507866
Moorhouse N, 2018, PROGR IS, P133, DOI 10.1007/978-3-319-64027-3_10
Mudzana Taurayi, 2017, Electronic Journal of Information Systems Evaluation, V20, P24
Murali S, 2016, J RETAIL CONSUM SERV, V30, P67, DOI 10.1016/j.jretconser.2016.01.001
Nanji A., 2019, 7 WAYS BRANDS ARE US
Naranjo-Zolotov M, 2019, GOV INFORM Q, V36, P536, DOI 10.1016/j.giq.2019.05.009
Nunnally J.C., 1967, PSYCHOMETRIC THEORY, V3rd, P248
Orel FD, 2014, J RETAIL CONSUM SERV, V21, P118, DOI 10.1016/j.jretconser.2013.07.002
Ostrom AL, 2021, J SERV RES-US, V24, P329, DOI 10.1177/10946705211021915
Otto AS, 2020, J ACAD MARKET SCI, V48, P543, DOI 10.1007/s11747-019-00657-7
Pai FY, 2011, TECHNOL FORECAST SOC, V78, P650, DOI 10.1016/j.techfore.2010.11.007
Pantano E, 2017, J RETAIL CONSUM SERV, V38, P81, DOI 10.1016/j.jretconser.2017.05.011
Parasuraman A., 1998, J BUS IND MARK, V13, P309, DOI DOI 10.1108/08858629810226636
Parekh P, 2020, VIS COMPUT IND BIOME, V3, DOI 10.1186/s42492-020-00057-7
Pillai R, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102207
Poushneh A, 2018, J RETAIL CONSUM SERV, V41, P169, DOI 10.1016/j.jretconser.2017.12.010
Poushneh A, 2017, J RETAIL CONSUM SERV, V34, P229, DOI 10.1016/j.jretconser.2016.10.005
Prentice C, 2020, J HOSP MARKET MANAG, V29, P739, DOI 10.1080/19368623.2020.1722304
Rafaeli A, 2017, J SERV RES-US, V20, P91, DOI 10.1177/1094670516679275
Rauschnabel PA, 2019, J RETAIL CONSUM SERV, V49, P43, DOI 10.1016/j.jretconser.2019.03.004
Rauschnabel PA, 2018, PSYCHOL MARKET, V35, P557, DOI 10.1002/mar.21106
Rese A, 2017, TECHNOL FORECAST SOC, V124, P306, DOI 10.1016/j.techfore.2016.10.010
Rigdon EE, 2012, LONG RANGE PLANN, V45, P341, DOI 10.1016/j.lrp.2012.09.010
Robertson N, 2016, J SERV MARK, V30, P88, DOI 10.1108/JSM-07-2014-0263
Rouibah K, 2016, ELECTRON COMMER R A, V19, P33, DOI 10.1016/j.elerap.2016.07.001
Roy SK, 2016, SERV IND J, V36, P239, DOI 10.1080/02642069.2016.1186657
Sakhdari K, 2016, TECHNOL INNOV MANAG, V6, P5
Schaarschmidt M, 2017, J SERV MARK, V31, P704, DOI 10.1108/JSM-04-2016-0145
Scholl-Grissemann U, 2022, SERV IND J, V42, P823, DOI 10.1080/02642069.2020.1819252
Scholz J, 2016, BUS HORIZONS, V59, P149, DOI 10.1016/j.bushor.2015.10.003
Shalimov AP, 2019, PHYTOKEYS, P1, DOI 10.3897/phytokeys.133.37773
Sharma SK, 2019, INT J INFORM MANAGE, V44, P65, DOI 10.1016/j.ijinfomgt.2018.09.013
Sheng XJ, 2014, J SERV MARK, V28, P460, DOI 10.1108/JSM-04-2013-0098
Silva RR, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.02200
Spielmann N, 2018, J BUS RES, V88, P255, DOI 10.1016/j.jbusres.2018.03.037
Statista, 2020, E COMM REP 2020
Steenkamp JBEM, 2019, J INT MARKETING, V27, P1, DOI 10.1177/1069031X18811289
STEUER J, 1992, J COMMUN, V42, P73, DOI 10.1111/j.1460-2466.1992.tb00812.x
Talukder MS, 2019, IND MANAGE DATA SYST, V119, P170, DOI 10.1108/IMDS-01-2018-0009
Tenenhaus M, 2005, COMPUT STAT DATA AN, V48, P159, DOI 10.1016/j.csda.2004.03.005
Thamizhvanan A, 2013, J INDIAN BUS RES, V5, P17, DOI 10.1108/17554191311303367
Thomas A., 2020, AUGMENTED REALITY AR
Tsai JM, 2019, INT J INFORM MANAGE, V49, P34, DOI 10.1016/j.ijinfomgt.2019.03.003
van Esch P, 2019, J RETAIL CONSUM SERV, V49, P35, DOI 10.1016/j.jretconser.2019.03.002
van Pinxteren MME, 2019, J SERV MARK, V33, P507, DOI 10.1108/JSM-01-2018-0045
Veeramootoo N, 2018, GOV INFORM Q, V35, P161, DOI 10.1016/j.giq.2018.03.004
Vinzi V.E., 2010, Handbook of partial least squares, P47, DOI [10.1007/978-3-540-32827, DOI 10.1007/978-3-540-32827-8_3, 10.1007/978-3-540-32827-8_3]
Wang XL, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-94273-7
Wang XH, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12051998
Wang YC, 2017, J BUS RES, V70, P287, DOI 10.1016/j.jbusres.2016.08.002
WBR Insights, 2018, HER OR IS US AUGM VI
Whitler K., 2016, How artificial intelligence is changing the retail experience for consumers
Wu HC, 2017, J HOSP TOUR RES, V41, P904, DOI 10.1177/1096348014525638
Yeo VCS, 2017, J RETAIL CONSUM SERV, V35, P150, DOI 10.1016/j.jretconser.2016.12.013
Yeoh W, 2010, J COMPUT INFORM SYST, V50, P23
Yusuf AS, 2018, J SERV MARK, V32, P493, DOI 10.1108/JSM-01-2017-0031
Zaki M, 2019, J SERV MARK, V33, P429, DOI 10.1108/JSM-01-2019-0034
Zeithaml V.A., 2000, CONCEPTUAL FRAMEWORK
Zhang WH, 2017, MANAGE DECIS, V55, P1749, DOI 10.1108/MD-04-2016-0236
Zhao L, 2012, DECIS SUPPORT SYST, V52, P645, DOI 10.1016/j.dss.2011.10.022
Zheng XB, 2019, INT J INFORM MANAGE, V48, P151, DOI 10.1016/j.ijinfomgt.2019.02.010
Zhou V., 2019, 3 TRENDS COSMETICS A
Ziaullah M, 2017, BEHAV INFORM TECHNOL, V36, P85, DOI 10.1080/0144929X.2016.1196503
NR 138
TC 19
Z9 19
U1 24
U2 111
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0887-6045
J9 J SERV MARK
JI J. Serv. Mark.
PD JAN 13
PY 2022
VL 36
IS 1
SI SI
BP 73
EP 88
DI 10.1108/JSM-12-2020-0508
EA NOV 2021
PG 16
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA YM3NX
UT WOS:000723029400001
OA Green Accepted
DA 2024-03-27
ER
PT J
AU Killoran, JB
AF Killoran, John B.
TI Writing for Robots: Search Engine Optimization of Technical
Communication Business Web Sites
SO TECHNICAL COMMUNICATION
LA English
DT Article
DE technical communication businesses; Web sites; search engine
optimization; hyperlinks; titles
AB Purpose: This article explores how businesses offering technical communication services used search engine optimization techniques to attract prospective clients to their business Web sites.
Method: The study draws on a survey of 240 principals of these businesses, brief interviews with half of them, analyses of their sites, and tallies of inbound links to their sites.
Results: The interviews and analyses reveal how businesses oriented their sites not only to a human audience of prospective clients but also to an audience of search engines. Businesses that reported search engines to be more helpful in directing traffic to their sites had sites that, in comparison with those of their less successful peers, featured longer home page titles and received more inbound links.
Conclusion: Though search engine optimization techniques can increase Web site traffic, technical communication businesses varied widely in how extensively and expertly they used such techniques.
C1 Long Isl Univ, Dept English, Greenvale, NY 11548 USA.
C3 Long Island University Post
RP Killoran, JB (autor correspondiente), Long Isl Univ, Dept English, Brooklyn Campus, Greenvale, NY 11548 USA.
EM john.killoran@liu.edu
CR [Anonymous], TOP 20 SIT ENG
Bhargava R., 2006, 5 rules of social media optimization (SMO)
Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X
Burns E., 2007, TOP 10 SEARCH PROVID
Burns E., 2007, US SEARCH ENGINE RAN
Dawson A, 2006, J DOC, V62, P307, DOI 10.1108/00220410610666484
Eaton A, 2008, TECH COMMUN-STC, V55, P111
Evans MP, 2007, INTERNET RES, V17, P21, DOI 10.1108/10662240710730470
Flanagin AJ, 2000, HUM COMMUN RES, V26, P618, DOI 10.1111/j.1468-2958.2000.tb00771.x
FORTUNATO S, 2006, 14 INT WORLD WID C E
*GOOGL, 2009, WEBM GUID
*GOOGL, 2009, GOOGL BAS
*GOOGL, 2009, CHANG YOUR SIT TITL
*GOOGL, 2009, CORP INF TECHN OV
*GOOGL, 2009, KEYW STUFF
HANSELL S, 2007, NY TIMES 0603
Jansen BJ, 2006, INFORM PROCESS MANAG, V42, P1075, DOI 10.1016/j.ipm.2005.09.003
Killoran JB, 2010, TECH COMMUN-STC, V57, P137
Killoran JB, 2009, IEEE T PROF COMMUN, V52, P254, DOI 10.1109/TPC.2009.2025309
Ledford J.L., 2008, SEO: Search Engine Optimization Bible
LOHR S, 2006, NY TIMES 0409
Madden M., 2007, Digital footprints: Online identity management and search in the age of transparency
Malaga RA, 2007, J ELECTRON COMMER OR, V5, P68, DOI 10.4018/jeco.2007070105
Moran M., 2006, Search engine marketing
*OFF GOOGL BLOG, 2008, WE KNEW WEB WAS BIG
*OFF GOOGL WEBM CE, 2007, IMPR SNIPP MET DESCR
*OFF GOOGL WEBM CE, 2007, DISC YOUR LINKS
Pflughoeft KA, 2003, DECISION SCI, V34, P467, DOI 10.1111/j.1540-5414.2003.02539.x
*SEMPO, 2007, RES SUMM SEMPOS IN H
SEMPO, 2008, STAT SEARCH ENG MARK
SHERMAN C, 2006, GETTING NY TIMES MOR
*STC, 2007, STCS SAL DAT
*STC, 2009, ONL BUYERS GUID CONS
STC Consulting and Independent Contracting SIG, 2005, STC CONS IND CONTR S
STCa, 2004, INTERCOM, V51, P14
SULLIVAN D, 2005, SEARCHS LONG TAIL
SULLIVAN D, 2002, DEATH META TAG
SULLIVAN D, 2002, META TAGS REVISITED
SULLIVAN D, 2004, OVERTURE SHIFTING DE
Sweeney S., 2008, 101 Ways to Promote Your Tourism Business WebSite: Filled with Proven Internet Marketing Tips, Tools, and Techniques to Get Visitors to Your Site and Your Destination, V2nd
Vaughan Liwen, 2007, J COMPUTER MEDIATED, V12
*WEBVISIBLE NIELS, 2006, MEAS WEB SIT AB DRIV
*WEBVISIBLE NIELS, 2009, GREAT DIV
Zhang J, 2005, INFORM PROCESS MANAG, V41, P665, DOI 10.1016/j.ipm.2003.12.001
ZHANG J, 2005, INFORM PROCESSING MA, V41, P697
[No title captured]
NR 46
TC 12
Z9 13
U1 9
U2 39
PU SOC TECHNICAL COMMUNICATION
PI FAIRFAX
PA 9401 LEE HIGHWAY, STE 300, FAIRFAX, VA 22031 USA
SN 0049-3155
J9 TECH COMMUN-STC
JI Tech. Commun.
PD MAY
PY 2010
VL 57
IS 2
BP 161
EP 181
PG 21
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA 602WA
UT WOS:000278168500003
DA 2024-03-27
ER
PT J
AU Lukyanenko, R
Parsons, J
Wiersma, YF
Maddah, M
AF Lukyanenko, Roman
Parsons, Jeffrey
Wiersma, Yolanda F.
Maddah, Mahed
TI EXPECTING THE UNEXPECTED: EFFECTS OF DATA COLLECTION DESIGN CHOICES ON
THE QUALITY OF CROWDSOURCED USER-GENERATED CONTENT
SO MIS QUARTERLY
LA English
DT Article
DE Crowdsourcing; user-generated content; citizen science; information
systems design; information quality; information completeness;
information accuracy; information precision; discovery; supervised
machine learning
ID CITIZEN SCIENCE; SOCIAL MEDIA; INFORMATION; SYSTEMS; KNOWLEDGE;
TECHNOLOGIES; PERCEPTIONS; FOUNDATION; CHALLENGES; INSTANCES
AB As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness, and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choices for information quality in crowdsourced user-generated content.
C1 [Lukyanenko, Roman] HEC Montreal, Dept Informat Technol, Montreal, PQ H3T 2A7, Canada.
[Parsons, Jeffrey] Mem Univ Newfoundland, Fac Business Adm, St John, NF A1B 3X5, Canada.
[Wiersma, Yolanda F.] Mem Univ Newfoundland, Dept Biol, St John, NF A1B 3X5, Canada.
[Maddah, Mahed] Suffolk Univ, Sawyer Business Sch, Boston, MA 02108 USA.
C3 Universite de Montreal; HEC Montreal; Memorial University Newfoundland;
Memorial University Newfoundland; Suffolk University
RP Lukyanenko, R (autor correspondiente), HEC Montreal, Dept Informat Technol, Montreal, PQ H3T 2A7, Canada.
EM roman.lukyanenko@hec.ca; jeffreyp@mun.ca; ywiersma@mun.ca;
mmaddah@suffolk.edu
RI Parsons, Jeffrey/AAF-3380-2020; Andrea Simões Braga,
Francisco/GRS-0157-2022
OI Parsons, Jeffrey/0000-0002-4819-2801
FU Natural Sciences and Engineering Research Council of Canada; Social
Sciences and Humanities Research Council of Canada; GEOIDE Network of
Centres of Excellence Canada; Institute for Data Valorisation (IVADO);
Memorial University's Harris Centre
FX We wish to thank Daria Lukyanenko for her assistance in conducting the
field and laboratory experiments. We also wish to thank the Natural
Sciences and Engineering Research Council of Canada, the Social Sciences
and Humanities Research Council of Canada, GEOIDE Network of Centres of
Excellence Canada, The Institute for Data Valorisation (IVADO), and
Memorial University's Harris Centre for providing funding in support of
this project. Finally, we wish to thank the anonymous contributors of
sightings to the NL Nature project.
CR Abbasi A, 2018, MIS QUART, V42, P427, DOI 10.25300/MISQ/2018/13239
Allen G, 2010, INFORM SYST RES, V21, P56, DOI 10.1287/isre.1080.0189
Andriole SJ, 2010, COMMUN ACM, V53, P67, DOI 10.1145/1859204.1858225
[Anonymous], 1977, Treatise on Basic Philosophy: Ontology I: The Furniture of the World
Batini C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541883
BELL BF, 1981, J BIOL EDUC, V15, P213, DOI 10.1080/00219266.1981.9654381
BERLIN B, 1966, SCIENCE, V154, P273, DOI 10.1126/science.154.3746.273
Bishop C. M., 2007, J. Electron. Imaging, V16, P049901, DOI [10.1117/1.2819119, DOI 10.1117/1.2819119]
Boakes EH, 2010, PLOS BIOL, V8, DOI 10.1371/journal.pbio.1000385
Bonney R, 2009, BIOSCIENCE, V59, P977, DOI 10.1525/bio.2009.59.11.9
Brabham DC, 2013, MIT PRESS ESSENT, P1
Browne GJ, 2002, INFORM MANAGE-AMSTER, V39, P625, DOI 10.1016/S0378-7206(02)00014-9
Brynjolfsson E, 2016, MIS QUART, V40, P941, DOI 10.25300/MISQ/2016/40.4.07
Burgess HK, 2017, BIOL CONSERV, V208, P113, DOI 10.1016/j.biocon.2016.05.014
Burton-Jones A, 2017, INFORM SYST RES, V28, P468, DOI 10.1287/isre.2017.0702
Cardone G, 2013, IEEE COMMUN MAG, V51, P112, DOI 10.1109/MCOM.2013.6525603
Chittilappilly AI, 2016, IEEE T KNOWL DATA EN, V28, P2246, DOI 10.1109/TKDE.2016.2555805
Clarke R, 2016, INFORM SYST RES, V27, P365, DOI 10.1287/isre.2016.0631
Clarkson BD, 2016, ECOL MANAG RESTOR, V17, P180, DOI 10.1111/emr.12229
Clery D, 2011, SCIENCE, V333, P173, DOI 10.1126/science.333.6039.173
Clow D., 2011, P 1 INT C LEARNING A, P34, DOI DOI 10.1145/2090116.2090121
Cooper A. K., 2011, P 25 INT CART C PAR
Culnan MJ, 2010, MIS Q EXEC, V9, P243
Daniel F, 2018, ACM COMPUT SURV, V51, DOI 10.1145/3148148
Deng XF, 2016, MIS QUART, V40, P279, DOI 10.25300/MISQ/2016/40.2.01
Devillers R, 2010, T GIS, V14, P387, DOI 10.1111/j.1467-9671.2010.01212.x
Dewan S, 2012, INFORM SYST RES, V23, P1056, DOI 10.1287/isre.1110.0405
Doan A, 2011, COMMUN ACM, V54, P86, DOI 10.1145/1924421.1924442
Engel Sarah R., 2002, American Entomologist, V48, P164
Erickson L., 2012, P 19 AM C INF SYST S
Fielden MA, 2015, CAN ENTOMOL, V147, P737, DOI 10.4039/tce.2015.5
Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504
Galloway AWE, 2006, WILDLIFE SOC B, V34, P1425, DOI 10.2193/0091-7648(2006)34[1425:TROCSA]2.0.CO;2
Gao GD, 2015, MIS QUART, V39, P565, DOI 10.25300/MISQ/2015/39.3.03
Garcia-Molina H, 2016, IEEE T KNOWL DATA EN, V28, P901, DOI 10.1109/TKDE.2016.2518669
Gath-Morad M, 2017, LECT NOTES ARTIF INT, V10051, P189, DOI 10.1007/978-3-319-51957-9_11
Gebauer J, 2006, J ASSOC INF SYST, V7, P122, DOI 10.17705/1jais.00084
Geurts P, 2006, MACH LEARN, V63, P3, DOI 10.1007/s10994-006-6226-1
Gigerenzer G., 1999, SIMPLE HEURISTICS MA
Gilovich T., 2002, HEURISTICS BIASES PS
Goes PB, 2014, MIS QUART, V38, pIII
GOOD PI, 2001, RESAMPLING METHODS P
Goodchild MF, 2007, GEOJOURNAL, V69, P211, DOI 10.1007/s10708-007-9111-y
Goodchild MF, 2010, INT J DIGIT EARTH, V3, P231, DOI 10.1080/17538941003759255
Goodman JK, 2017, J CONSUM RES, V44, P196, DOI 10.1093/jcr/ucx047
Gray ML, 2016, ACM CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING (CSCW 2016), P134, DOI 10.1145/2818048.2819942
Hackshaw A, 2008, EUR RESPIR J, V32, P1141, DOI 10.1183/09031936.00136408
Harnad S., 2005, Handbook of categorization in cognitive science, P20, DOI [10.1016/b978008044612-7/50056-1, DOI 10.1016/B978008044612-7/50056-1]
Hartig T, 2016, SCIENCE, V352, P938, DOI 10.1126/science.aaf3759
Hemsley J., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), P3928, DOI 10.1109/HICSS.2012.580
Hivon J., 2017, TRANSFORM GOV-PEOPLE, V11, P99, DOI [DOI 10.1108/TG-12-2015-0053, 10.1108/TG-12-2015-0053]
Ipeirotis Panagiotis G., 2010, In: Proceedings of the ACM SIGKDD Workshop on Human Computation. ACM: Washington DC, USA, P64, DOI [DOI 10.1145/1837885.1837906, 10.1145/1837885.1837906]
Johnson SL, 2014, MIS QUART, V38, P795, DOI 10.25300/MISQ/2014/38.3.08
Kallinikos J, 2014, INFORM SYST RES, V25, P817, DOI 10.1287/isre.2014.0544
Khatib F, 2011, NAT STRUCT MOL BIOL, V18, P1175, DOI 10.1038/nsmb.2119
Kilian D., 2008, ECT NEWS NETWORK
Kosmala M, 2016, FRONT ECOL ENVIRON, V14, P551, DOI 10.1002/fee.1436
Krogh B., 1996, Proceedings of the Twenty-Ninth Hawaii International Conference on System Sciences, P242, DOI 10.1109/HICSS.1996.495405
LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310
Larsen KR, 2016, MIS QUART, V40, P529, DOI 10.25300/MISQ/2016/40.3.01
Lassaline M. E., 1992, PERCEPTS CONCEPTS CA, V93, P328
Lee Y., 2006, JOURNEY DATA QUALITY
Lee YW, 2003, J MANAGE INFORM SYST, V20, P93, DOI 10.1080/07421222.2003.11045770
Leo L, 2002, COMMUN ACM, V45, P211, DOI [10.1145/505248.506010, DOI 10.1145/505248.506010]
Leonardi PM, 2011, MIS QUART, V35, P147
Levy M, 2017, COMMUN ASSOC INF SYS, V40
Lewandowski E, 2015, CONSERV BIOL, V29, P713, DOI 10.1111/cobi.12481
Li GL, 2016, IEEE T KNOWL DATA EN, V28, P2296, DOI 10.1109/TKDE.2016.2535242
Louv R., 2012, CITIZEN SCI PUBLIC P
Love J, 2017, EUR J INFORM SYST, V26, P315, DOI 10.1057/s41303-017-0036-3
Lukyanenko Roman, 2016, Scandinavian Journal of Information Systems, V28, P37
Lukyanenko R, 2019, EUR J INFORM SYST, V28, P68, DOI 10.1080/0960085X.2018.1488567
Lukyanenko R, 2017, J ASSOC INF SYST, V18, P297, DOI 10.17705/1jais.00456
Lukyanenko R, 2016, CONSERV BIOL, V30, P447, DOI 10.1111/cobi.12706
Lukyanenko R, 2014, INFORM SYST RES, V25, P669, DOI 10.1287/isre.2014.0537
Madnick S.E., 2009, ACM J DATA INF QUAL, V1, P1
Majchrzak A, 2011, COMMUN ACM, V54, P125, DOI 10.1145/1924421.1924449
Markus ML, 2008, J ASSOC INF SYST, V9, P609
Martinez W.L., 2004, Exploratory Data Analysis with MATLAB: Computer Science and Data Analysis Series
Mayden RL, 2002, FISH FISH, V3, P171, DOI 10.1046/j.1467-2979.2002.00086.x
McKinley DC, 2017, BIOL CONSERV, V208, P15, DOI 10.1016/j.biocon.2016.05.015
Meijer Albert, 2009, Electronic Journal of e-Government, V7, P99
MERVIS CB, 1981, ANNU REV PSYCHOL, V32, P89, DOI 10.1146/annurev.ps.32.020181.000513
Newman G, 2012, FRONT ECOL ENVIRON, V10, P298, DOI 10.1890/110294
Norman DA., 1983, MENTAL MODELS, P7
Nov O, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0090375
Olivé A, 2007, CONCEPTUAL MODELLING IN INFORMATION SYSTEMS ENGINEERING, P319, DOI 10.1007/978-3-540-72677-7_20
Ordenes FV, 2014, J SERV RES-US, V17, P278, DOI 10.1177/1094670514524625
Ou GB, 2007, PATTERN RECOGN, V40, P4, DOI 10.1016/j.patcog.2006.04.041
Parreiras FS, 2010, DATA KNOWL ENG, V69, P1194, DOI 10.1016/j.datak.2010.07.009
Parsons J, 2000, ACM T DATABASE SYST, V25, P228, DOI 10.1145/357775.357778
Parsons J, 2011, NATURE, V471, P37, DOI 10.1038/471037a
Petter S, 2013, J MANAGE INFORM SYST, V29, P7, DOI 10.2753/MIS0742-1222290401
Pin-Shan Chen P., 1976, ACM Transactions on Database Systems, V1, P9, DOI 10.1145/320434.320440
Prestopnik NR, 2015, COMPUT HUM BEHAV, V52, P492, DOI 10.1016/j.chb.2015.05.051
Provost F., 2013, Data science for business: What you need to know about data mining and data-analytic thinking
Prpic J, 2015, BUS HORIZONS, V58, P77, DOI 10.1016/j.bushor.2014.09.005
Pultar E, 2009, T GIS, V13, P85, DOI 10.1111/j.1467-9671.2009.01157.x
Rai A., 2016, MIS quarterly, V40, piii
Ramaswami A, 2016, SCIENCE, V352, P940, DOI 10.1126/science.aaf7160
REDMAN T.C., 1996, DATA QUALITY INFORM
ROSCH E, 1976, COGNITIVE PSYCHOL, V8, P382, DOI 10.1016/0010-0285(76)90013-X
Rosemann M, 2008, MIS QUART, V32, P1
Saghafi A., 2016, THESIS
Samuel BM, 2018, MIS QUART, V42, P1187, DOI 10.25300/MISQ/2018/13255
Sekhavat Yoones A., 2012, Conceptual Modeling. Proceedings 31st International Conference, ER 2012, P102, DOI 10.1007/978-3-642-34002-4_8
Shalaginov A, 2016, ADV INTELL SYST COMP, V420, P199, DOI 10.1007/978-3-319-27221-4_17
Shankaranarayanan G., 2017, J DATA INFORM QUALIT
Shanks G, 2008, MIS QUART, V32, P553
Sheng V. S., 2008, P 14 ACM SIGKDD INT, P614, DOI DOI 10.1145/1401890.1401965
Silvertown J, 2009, TRENDS ECOL EVOL, V24, P467, DOI 10.1016/j.tree.2009.03.017
Solomon SR, 2009, J MOD APPL STAT METH, V8, P448, DOI 10.22237/jmasm/1257034080
Sommer R, 2010, P IEEE S SECUR PRIV, P305, DOI 10.1109/SP.2010.25
Stelzer A, 2016, TRANSPORT RES E-LOG, V89, P259, DOI 10.1016/j.tre.2015.05.010
Strong DM, 1997, COMMUN ACM, V40, P103, DOI 10.1145/253769.253804
Sui D., 2012, CROWDSOURCING GEOGRA, P65
Sullivan BL, 2009, BIOL CONSERV, V142, P2282, DOI 10.1016/j.biocon.2009.05.006
Surowiecki J., 2005, WISDOM CROWDS
Svensson G, 2015, PROCEEDINGS OF THE 2009 ACADEMY OF MARKETING SCIENCE (AMS) ANNUAL CONFERENCE, P1
Swanson A, 2015, SCI DATA, V2, DOI 10.1038/sdata.2015.26
TANAKA JW, 1991, COGNITIVE PSYCHOL, V23, P457, DOI 10.1016/0010-0285(91)90016-H
Tax SS, 1998, SLOAN MANAGE REV, V40, P75
Tilly R, 2017, BUS INFORM SYST ENG+, V59, P3, DOI 10.1007/s12599-016-0459-8
TREVINO LK, 1992, ACAD MANAGE J, V35, P38, DOI 10.5465/256472
Tucker AL, 2003, CALIF MANAGE REV, V45, P55, DOI 10.2307/41166165
TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Vincent M, 2014, COMPUT STAT DATA AN, V71, P771, DOI 10.1016/j.csda.2013.06.004
Voss C.A., 2004, J SERV RES-US, V6, P212, DOI 10.1177/1094670503260120
Wallis G, 1999, TRENDS COGN SCI, V3, P22, DOI 10.1016/S1364-6613(98)01261-3
Wand Y, 1996, COMMUN ACM, V39, P86, DOI 10.1145/240455.240479
Wang R. Y., 1996, Journal of Management Information Systems, V12, P5
WANG RY, 1995, DECIS SUPPORT SYST, V13, P349, DOI 10.1016/0167-9236(93)E0050-N
Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175
Wells JD, 2011, MIS QUART, V35, P373
Wiggins A, 2011, 44 HAWAII INT C SYST
WIGGINS A., 2013, Data management guide for public participation in scientific research
Wiggins M, 2011, TLS-TIMES LIT SUPPL, P14
Wuest T, 2016, PROD MANUF RES, V4, P23, DOI 10.1080/21693277.2016.1192517
NR 139
TC 41
Z9 52
U1 7
U2 145
PU SOC INFORM MANAGE-MIS RES CENT
PI MINNEAPOLIS
PA UNIV MINNESOTA-SCH MANAGEMENT 271 19TH AVE SOUTH, MINNEAPOLIS, MN 55455
USA
SN 0276-7783
J9 MIS QUART
JI MIS Q.
PD JUN
PY 2019
VL 43
IS 2
BP 623
EP +
DI 10.25300/MISQ/2019/14439
PG 37
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA IJ4RQ
UT WOS:000475891800012
DA 2024-03-27
ER
PT J
AU Li, J
McCrary, R
AF Li, Jia
McCrary, Rachel
TI Consumer communications and current events: a cross-cultural study of
the change in consumer response to company social media posts due to the
COVID-19 pandemic
SO JOURNAL OF MARKETING ANALYTICS
LA English
DT Article
DE COVID-19; Consumer communications; Social media marketing;
Cross-cultural study; Machine learning; Sentiment analysis; Regression
discontinuity analysis
ID REACTANCE
AB The COVID-19 pandemic has changed the lives of consumers in virtually every nation. Based upon the theory of psychological reactance and psychoevolutionary theory of emotion, we hypothesize how such lifestyle changes affect consumers perceiving and responding to companies' communications messages. The theories also suggest that consumers in different cultures may respond to COVID-19 differently. To test our hypotheses, we implemented a Python scraper to collect companies' Instagram posts pre- and during the COVID-19 lockdown. A machine learning algorithm was applied on the collected post photos to automatically identify certain photo characteristics, such as indoor versus outdoor, and with a single person versus many people; a text mining and sentiment analysis was implemented on the collected post captions to identify the salient emotion each caption exhibited, such as joy and anticipation. After that, we conducted a regression discontinuity analysis of photo characteristics or caption emotion on number of likes or comments to identify consumers' response change due to the COVID-19 pandemic. The estimation results supported our hypotheses and suggested tactics that could improve consumer communications effectiveness in this changed time. Viewing COVID-19 as an example of a current event in the ever-changing world, this paper suggests that such events could impact consumer response and behavior, and that companies' marketing and advertising strategies should be responsive to such events.
C1 [Li, Jia] Wake Forest Univ, Sch Business, 1834 Wake Forest Rd, Winston Salem, NC 27106 USA.
[McCrary, Rachel] Capco Consulting, 128 S Tryon St, Charlotte, NC 28202 USA.
C3 Wake Forest University
RP Li, J (autor correspondiente), Wake Forest Univ, Sch Business, 1834 Wake Forest Rd, Winston Salem, NC 27106 USA.
EM lijia@wfu.edu; rachellynn.mccrary@gmail.com
CR Aaker JL, 1997, J CONSUM RES, V24, P315, DOI 10.1086/209513
Ahmadi I, 2022, J INT MARKETING, V30, P28, DOI 10.1177/1069031X211037590
ALBERT S, 1970, J PERS SOC PSYCHOL, V15, P265, DOI 10.1037/h0029430
[Anonymous], 1962, The emotions: facts, theories, and a new model
Belk RW, 1997, ADV CONSUM RES, V24, P24
Bor J, 2014, EPIDEMIOLOGY, V25, P729, DOI 10.1097/EDE.0000000000000138
Brehm J. W., 1966, THEORY PSYCHOL REACT
Busse MR, 2010, MARKET SCI, V29, P268, DOI 10.1287/mksc.1090.0516
Chon, 1990, RULES HOPE, DOI [10.1007/978-1-4613-9674-1, DOI 10.1007/978-1-4613-9674-1]
Chun H. E, 2007, I HOPE THEREFORE I C
CLEE MA, 1980, J CONSUM RES, V6, P389, DOI 10.1086/208782
de Mello G., 2005, Inside consumption: Consumer motives, goals, and desires, P44
DiNardo J, 2004, Q J ECON, V119, P1383, DOI 10.1162/0033553042476189
Emmerling R.J., 2003, ISSUES EMOTIONAL INT, V1, P1
Figlio K, 2009, PSYCHOANAL CULT SOC, V14, P24, DOI 10.1057/pcs.2008.44
Fitzsimons GJ, 2004, MARKET SCI, V23, P82, DOI 10.1287/mksc.1030.0033
Hall E. T., 1966, HIDDEN DIMENSION, V609
Hofstede G., 1980, CULTURES CONSEQUENCE, DOI DOI 10.1016/S0005-7967(02)00184-5
Hofstede G., 1991, Cultures and organizations, DOI [10.31826/9781463236984-toc, DOI 10.1016/S0005-7967(02)00184-5]
Hsu Tiffany., 2020, The NY Times
Izard CE., 1980, CROSS CULTURAL PERSP, P23
Kivetz R, 2005, J CONSUM RES, V31, P725, DOI 10.1086/426606
Kwon M., 2021, J ASS CONSUMER RES
LAZARUS RS, 1991, AM PSYCHOL, V46, P819, DOI 10.1037/0003-066X.46.8.819
Levav J, 2009, J CONSUM RES, V36, P600, DOI 10.1086/599556
McCracken Grant., 1990, Culture and Consumption: New Approaches to the Symbolic Character of Consumer Goods and Activities
Mendelson L, 2020, STAY TOP STAY HOME A
Meyers-Levy J, 2007, J CONSUM RES, V34, P174, DOI 10.1086/519146
Miceli MariaCristiano Castelfranchi Andrew Ortony., 2015, Expectancy and emotion
Miyamoto Y, 2018, J PERS SOC PSYCHOL, V115, P427, DOI 10.1037/pspi0000133
Mohammad SM, 2013, COMPUT INTELL-US, V29, P436, DOI 10.1111/j.1467-8640.2012.00460.x
Moon YS, 2000, J ADVERTISING, V29, P51, DOI 10.1080/00913367.2000.10673603
National Center for Immunization and Respiratory Diseases & Centers for Disease Control and Prevention, 2020, SOC DIST
Petrescu M, 2021, J MARK ANAL, V9, P155, DOI 10.1057/s41270-021-00129-4
Plutchik R., 1988, Emotions and Psychopathology, P1, DOI [DOI 10.1007/978-1-4757-1987-1_1, 10.1007/978-1-4757-1987-1_1]
Sheth J, 2021, J MARKET THEORY PRAC, V29, P3, DOI 10.1080/10696679.2020.1860679
Statista.com, 2020, COUNTR MOST INST US
THISTLETHWAITE DL, 1960, J EDUC PSYCHOL, V51, P309, DOI 10.1037/h0044319
Tucker C, 2014, 2420920 ROM SCH MAN
WICKLUND RA, 1970, J PERS SOC PSYCHOL, V14, P8, DOI 10.1037/h0028619
NR 40
TC 6
Z9 6
U1 3
U2 25
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 2050-3318
EI 2050-3326
J9 J MARK ANAL
JI J. Market. Anal.
PD JUN
PY 2022
VL 10
IS 2
SI SI
BP 173
EP 183
DI 10.1057/s41270-021-00138-3
EA NOV 2021
PG 11
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 1L4FV
UT WOS:000716233500001
OA Bronze
DA 2024-03-27
ER
PT J
AU Liberali, G
Ferecatu, A
AF Liberali, Gui
Ferecatu, Alina
TI Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models
SO MARKETING SCIENCE
LA English
DT Article
DE machine learning; electronic commerce; website morphing; multiarmed
bandits; hidden Markov models; HMMs; retailing
ID CUSTOMER; SEARCH; CHOICE; USAGE; PATH
AB Websites are created to help visitors take an action, such as making a purchase or a donation. As visitors browse various web pages, they may take rapid steps toward the action or may bounce away. Websites that can adapt to match such consumer dynamics perform better. However, assessing visitors??? changing distance to the action, at each click, and adapting to it in real time is challenging because of the sheer number of design elements that are found in websites, that combine exponentially. We solve this problem by matching latent states to web page designs, combining recent advances in multiarmed bandit (MAB), website morphing, and hidden Markov models (HMM) literature. We develop a novel dynamic program to explicitly model the trade-off firms face between nudging a visitor to later states along the funnel, and maximizing immediate reward given current estimates of purchase probabilities. We use an HMM to assess visitors??? states in real time, and couple it with an MAB model to learn the effectiveness of each design ?? state combination. We provide a proof of concept in two applications. First, we conduct a field study on the Master of Business Administration website of a major university. Second, we implement our algorithm on a cloud server and test it on an experimental online store.
C1 [Liberali, Gui; Ferecatu, Alina] Erasmus Univ, Rotterdam Sch Management, NL-3062 PA Rotterdam, Netherlands.
C3 Erasmus University Rotterdam; Erasmus University Rotterdam - Excl
Erasmus MC
RP Liberali, G (autor correspondiente), Erasmus Univ, Rotterdam Sch Management, NL-3062 PA Rotterdam, Netherlands.
EM liberali@rsm.nl; ferecatu@rsm.nl
RI Ferecatu, Alina/AAE-3170-2022; Liberali, Gui/B-6969-2017
OI Ferecatu, Alina/0000-0003-0161-0487; Liberali, Gui/0000-0003-3354-9270
FU Erasmus Research Institute of Management; Erasmus Corporate Marketing
and Communication; Erasmus Centre for Optimization of Online
Experiments; HyperMorphing Technologies; Sentia.com
FX This work was supported by the Erasmus Research Institute of Management,
the Erasmus Corporate Marketing and Communication, the Erasmus Centre
for Optimization of Online Experiments (http://www.erim.eur.nl/ecode),
HyperMorphing Technologies, and Sentia.com.
CR Abhishek V, 2012, PREPRINT, DOI 10.2139/ssrn.2158421
Alter AL, 2009, PERS SOC PSYCHOL REV, V13, P219, DOI 10.1177/1088868309341564
Ascarza E, 2017, J MARKETING RES, V54, P347, DOI 10.1509/jmr.15.0442
Ascarza E, 2013, MARKET SCI, V32, P570, DOI 10.1287/mksc.2013.0786
Baardman L, 2019, PREPRINT, DOI 10.2139/ssrn.3346642
Berman R., 2018, p-Hacking and False Discovery in A/B Testing, DOI DOI 10.2139/SSRN.3204791
Blake T, 2015, ECONOMETRICA, V83, P155, DOI 10.3982/ECTA12423
Brysbaert M, 2014, BEHAV RES METHODS, V46, P904, DOI 10.3758/s13428-013-0403-5
Chakravarti A, 2003, J CONSUM RES, V30, P244, DOI 10.1086/376803
Chakravarti A, 2006, J MARKETING RES, V43, P642, DOI 10.1509/jmkr.43.4.642
Cubitt R. P., 1998, Experimental Economics, V1, P115, DOI 10.1007/BF01669298
Ephraim Y, 2002, IEEE T INFORM THEORY, V48, P1518, DOI 10.1109/TIT.2002.1003838
Gelman A., 2013, Bayesian Data Analysis, Vthird
Gittins J, 2011, Multi-Armed Bandit Allocation Indices
Hallak A, 2015, PREPRINT
Hauser JR, 2014, MANAGE SCI, V60, P1594, DOI 10.1287/mnsc.2014.1961
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Ho TH, 2006, MARKET SCI, V25, P260, DOI 10.1287/mksc.1050.0158
Jansen BJ, 2008, INFORM PROCESS MANAG, V44, P1251, DOI 10.1016/j.ipm.2007.07.015
Kour G, 2019, BIORXIV, Patent No. 621300
Krishnamurthy V, 2001, IEEE T SIGNAL PROCES, V49, P2893, DOI 10.1109/78.969499
Krishnamurthy V, 1999, INT CONF ACOUST SPEE, P2865, DOI 10.1109/ICASSP.1999.761360
Krishnamurthy V, 2009, MATH OPER RES, V34, P287, DOI 10.1287/moor.1080.0371
Lambrecht A, 2013, J MARKETING RES, V50, P561, DOI 10.1509/jmr.11.0503
Li HS, 2020, J MARKETING RES, V57, P1019, DOI 10.1177/0022243720954376
Li HS, 2014, J MARKETING RES, V51, P40, DOI 10.1509/jmr.13.0050
Li SB, 2011, J MARKETING RES, V48, P683, DOI 10.1509/jmkr.48.4.683
Lin S, 2015, MARKET SCI, V34, P1, DOI 10.1287/mksc.2014.0868
Ma LY, 2015, MARKET SCI, V34, P627, DOI 10.1287/mksc.2015.0912
Matzkin RL, 2007, HBK ECON, V2, P5307, DOI 10.1016/S1573-4412(07)06073-4
Montgomery AL, 2004, MARKET SCI, V23, P579, DOI 10.1287/mksc.1040.0073
Montoya R, 2010, MARKET SCI, V29, P909, DOI 10.1287/mksc.1100.0570
Netzer O, 2019, HETEROGENEITY HMMS A
Netzer O, 2008, MARKET SCI, V27, P185, DOI 10.1287/mksc.1070.0294
Padilla N, 2021, J MARKETING RES, V58, P981, DOI 10.1177/00222437211032938
Romero J, 2013, J INTERACT MARK, V27, P185, DOI 10.1016/j.intmar.2013.04.003
Rosch E., 1978, Cognition and categorization, P27
Ryzhov IO, 2009, ADPRL: 2009 IEEE SYMPOSIUM ON ADAPTIVE DYNAMIC PROGRAMMING AND REINFORCEMENT LEARNING, P137
Schellekens GAC, 2010, J CONSUM RES, V37, P207, DOI 10.1086/651240
Schwartz EM, 2017, MARKET SCI, V36, P500, DOI 10.1287/mksc.2016.1023
Schwartz EM, 2014, MARKET SCI, V33, P188, DOI 10.1287/mksc.2013.0825
Scott SL, 2010, APPL STOCH MODEL BUS, V26, P639, DOI 10.1002/asmb.874
Shi SW, 2014, MARKET SCI, V33, P871, DOI 10.1287/mksc.2014.0872
Stüttgen P, 2012, MARKET SCI, V31, P878, DOI 10.1287/mksc.1120.0732
Thompson WR, 1933, BIOMETRIKA, V25, P285, DOI 10.1093/biomet/25.3-4.285
Toubia O, 2013, MANAGE SCI, V59, P613, DOI 10.1287/mnsc.1120.1570
Trope Y, 2010, PSYCHOL REV, V117, P440, DOI 10.1037/a0018963
Urban GL, 2014, MARKET SCI, V33, P27, DOI 10.1287/mksc.2013.0803
Vakratsas D, 1999, J MARKETING, V63, P26, DOI 10.2307/1251999
van der Lans R, 2008, MARKET SCI, V27, P922, DOI 10.1287/mksc.1070.0327
Vernade C, 2017, PREPRINT
Yang L, 2018, J MARKETING RES, V55, P783, DOI 10.1177/0022243718817004
Yang L, 2015, J MARKETING RES, V52, P166, DOI 10.1509/jmr.13.0288
NR 53
TC 2
Z9 2
U1 15
U2 71
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD JUL-AUG
PY 2022
VL 41
IS 4
BP 341
EP 366
DI 10.1287/mksc.2021.1346
EA FEB 2022
PG 27
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 3L5RD
UT WOS:000804419400001
OA Green Published
DA 2024-03-27
ER
PT J
AU Qin, ZW
Tang, XC
Jiao, Y
Zhang, F
Xu, Z
Zhu, HT
Ye, JP
AF Qin, Zhiwei (Tony)
Tang, Xiaocheng
Jiao, Yan
Zhang, Fan
Xu, Zhe
Zhu, Hongtu
Ye, Jieping
TI Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning
SO INFORMS JOURNAL ON APPLIED ANALYTICS
LA English
DT Article
DE ride-hailing marketplace; order dispatching; reinforcement learning;
data-driven decision making
AB Order dispatching is instrumental to the marketplace engine of a large-scale ride-hailing platform, such as the DiDi platform, which continuously matches passenger trip requests to drivers at a scale of tens of millions per day. Because of the dynamic and stochastic nature of supply and demand in this context, the ride-hailing order-dispatching problem is challenging to solve for an optimal solution. Added to the complexity are considerations of system response time, reliability, and multiple objectives. In this paper, we describe how our approach to this optimization problem has evolved from a combinatorial optimization approach to one that encompasses a semi-Markov decision-process model and deep reinforcement learning. We discuss the various practical considerations of our solution development and real-world impact to the business.
C1 [Qin, Zhiwei (Tony); Tang, Xiaocheng; Jiao, Yan] DiDi Labs, Mountain View, CA 94043 USA.
[Zhang, Fan; Xu, Zhe; Zhu, Hongtu; Ye, Jieping] Didi Chuxing, Beijing 100193, Peoples R China.
RP Qin, ZW (autor correspondiente), DiDi Labs, Mountain View, CA 94043 USA.
EM qinzhiwei@didiglobal.com; xiaochengtang@didiglobal.com;
yanjiao@didiglobal.com; feymanzhangfan@didiglobal.com;
xuzhejesse@didiglobal.com; zhuhongtu@didiglobal.com;
yejieping@didiglobal.com
RI Tang, Xiaocheng/AAE-4870-2019; Qin, Zhiwei (Tony)/F-3201-2014
OI Qin, Zhiwei (Tony)/0000-0001-5383-4816
CR ALBUS J S, 1971, Mathematical Biosciences, V10, P25, DOI 10.1016/0025-5564(71)90051-4
Alonso-Mora J, 2017, P NATL ACAD SCI USA, V114, P462, DOI 10.1073/pnas.1611675114
[Anonymous], 2018, ADV NEURAL INFORM PR
Bailey W. A. Jr., 1987, 1987 Winter Simulation Conference Proceedings, P838, DOI 10.1145/318371.318705
Baird III L. C., 1993, TECHNICAL REPORT
Bello I., 2017, P 5 INT C LEARN REPR
Boutilier C, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4674
Brodsky I., 2018, H3: Uber's Hexagonal Hierarchical Spatial Index
Hales TC, 2001, DISCRETE COMPUT GEOM, V25, P1, DOI 10.1007/s004540010071
Holler J, 2019, IEEE DATA MINING, P1090, DOI 10.1109/ICDM.2019.00129
Jindal I, 2018, IEEE INT CONF BIG DA, P1417, DOI 10.1109/BigData.2018.8622481
Kuhn HW., 1955, NAV RES LOG, V2, P83, DOI [DOI 10.1002/NAV.3800020109, DOI 10.1002/NAV.20053, 10.1002/nav.3800020109]
Kümmel M, 2016, PROCEDIA COMPUT SCI, V83, P163, DOI 10.1016/j.procs.2016.04.112
Li MN, 2019, WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), P983, DOI 10.1145/3308558.3313433
Lopes PAC, 2019, J PARALLEL DISTR COM, V130, P50, DOI 10.1016/j.jpdc.2019.03.014
Miao F, 2016, IEEE T AUTOM SCI ENG, V13, P463, DOI 10.1109/TASE.2016.2529580
Mnih V, 2015, NATURE, V518, P529, DOI 10.1038/nature14236
National Bureau of Statistics of China, 2019, NAT EC PERF MAINT AP
Oda T, 2018, IEEE INFOCOM SER, P2708, DOI 10.1109/INFOCOM.2018.8485988
Sahr K., 2011, Arch. Fotogram. Kartogr. i Teledetekcji, V22, P363
Schulman John, 2017, ARXIV E PRINTS
Shou ZY, 2020, TRANSPORT RES C-EMER, V111, P91, DOI 10.1016/j.trc.2019.12.005
Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
Sutton R. S., 1988, Machine Learning, V3, P9, DOI 10.1023/A:1022633531479
Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
Sutton RS, 1999, ARTIF INTELL, V112, P181, DOI 10.1016/S0004-3702(99)00052-1
Tang XC, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1780, DOI 10.1145/3292500.3330724
Tsitsiklis JN, 1996, MACH LEARN, V22, P59, DOI 10.1007/BF00114724
Uher V, 2019, SYMMETRY-BASEL, V11, DOI 10.3390/sym11060731
University of Michigan Center for Sustainable Studies, 2019, US CIT FACTSH
van Hasselt H, 2016, AAAI CONF ARTIF INTE, P2094
Verma T, 2017, P I C AUTOMAT PLAN S, P409
Vinyals O., 2015, ADV NEURAL INFORM PR, P2692, DOI [DOI 10.1016/J.NEUNET.2014.09.003, DOI 10.48550/ARXIV.1506.03134]
Wang ZD, 2018, IEEE DATA MINING, P617, DOI 10.1109/ICDM.2018.00077
Ward, 2020, STOCHASTIC SYSTEMS, V10, P29, DOI DOI 10.1287/STSY.2019.0037
Xu Z, 2018, KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, P905, DOI 10.1145/3219819.3219824
Yan CW, 2020, NAV RES LOG, V67, P705, DOI 10.1002/nav.21872
Zhang LY, 2017, KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2151, DOI 10.1145/3097983.3098138
NR 38
TC 53
Z9 60
U1 17
U2 92
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 2644-0865
EI 2644-0873
J9 INFORMS J APPL ANAL
JI INFORMS J. Appl. Anal.
PD SEP-OCT
PY 2020
VL 50
IS 5
BP 272
EP 286
DI 10.1287/inte.2020.1047
PG 15
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA NV9XN
UT WOS:000574664700002
DA 2024-03-27
ER
PT J
AU Pal, S
Biswas, B
Gupta, R
Kumar, A
Gupta, S
AF Pal, Shounak
Biswas, Baidyanath
Gupta, Rohit
Kumar, Ajay
Gupta, Shivam
TI Exploring the factors that affect user experience in mobile-health
applications: A text-mining and machine-learning approach
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Mobile health; Service-dominant logic; Machine learning; Text analytics;
Proportional-odds logit
ID SERVICE-DOMINANT LOGIC; MHEALTH SERVICES; ADOPTION; QUALITY; CARE;
VALIDATION; MANAGEMENT; FRAMEWORK; COUNTRY; APPS
AB Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.
C1 [Pal, Shounak] PricewaterhouseCoopers Pvt Ltd, Bengaluru, India.
[Biswas, Baidyanath] Dublin City Univ, DCU Business Sch, Enterprise & Innovat Grp, Dublin, Ireland.
[Gupta, Rohit] Indian Inst Management, Operat Management Area, Ranchi, India.
[Kumar, Ajay] EMLYON Business Sch, AIM Res Ctr Artificial Intelligence Value Creat, Ecully, France.
[Gupta, Shivam] NEOMA Business Sch, Dept Informat Syst, Supply Chain Management & Decis Support, Reims, France.
C3 Dublin City University; Indian Institute of Management (IIM System);
Indian Institute of Management Ranchi; emlyon business school
RP Kumar, A (autor correspondiente), EMLYON Business Sch, AIM Res Ctr Artificial Intelligence Value Creat, Ecully, France.
EM shounak.iiml@gmail.com; baidyanath.biswas@dcu.ie;
rohit.gupta@iimranchi.ac.in; akumar@em-lyon.com;
shivam.gupta@neoma-bs.fr
RI Gupta, Shivam/JNE-3789-2023; Gupta, Shivam/R-2996-2016; Biswas,
Baidyanath/GRR-8008-2022
OI Gupta, Shivam/0000-0002-2714-4958; Biswas,
Baidyanath/0000-0002-0609-3530
CR Ågerfalk PJ, 2020, EUR J INFORM SYST, V29, P203, DOI 10.1080/0960085X.2020.1771968
Agresti A., 2002, CATEGORICAL DATA ANA, DOI [DOI 10.1002/0471249688, 10.1002/0471249688]
Akter S, 2013, ELECTRON MARK, V23, P29, DOI 10.1007/s12525-012-0091-5
Akter S, 2013, INFORM MANAGE-AMSTER, V50, P181, DOI 10.1016/j.im.2013.03.001
Alam MZ, 2020, TECHNOL SOC, V61, DOI 10.1016/j.techsoc.2020.101255
Alam MZ, 2020, INT J INFORM MANAGE, V50, P128, DOI 10.1016/j.ijinfomgt.2019.04.016
Ashraf AR, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2021.102318
Azad-Khaneghah P, 2021, DISABIL REHABIL-ASSI, V16, P712, DOI 10.1080/17483107.2019.1701103
Balta M, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120785
Barrett M, 2015, MIS QUART, V39, P135
Biduski D, 2020, COMPUT HUM BEHAV, V104, DOI 10.1016/j.chb.2019.106169
Birkmeyer S, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2021.102351
Biswas B, 2020, DECIS SUPPORT SYST, V135, DOI 10.1016/j.dss.2020.113324
Biswas M, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0258050
Chang IC, 2016, TELEMAT INFORM, V33, P401, DOI 10.1016/j.tele.2015.09.007
Chatterjee S, 2023, J ENTERP INF MANAG, V36, P22, DOI 10.1108/JEIM-06-2021-0252
Chatterjee S, 2021, J BUS RES, V131, P815, DOI 10.1016/j.jbusres.2020.10.043
Chiu WS, 2021, INFORM TECHNOL PEOPL, V34, P978, DOI 10.1108/ITP-09-2019-0463
Cho H, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101429
Cho J, 2016, INT J MED INFORM, V87, P75, DOI 10.1016/j.ijmedinf.2015.12.016
Veríssimo JMC, 2018, J BUS RES, V89, P442, DOI 10.1016/j.jbusres.2017.12.026
Dahl AJ, 2021, J BUS RES, V125, P840, DOI 10.1016/j.jbusres.2019.02.025
Dwivedi YK, 2016, GOV INFORM Q, V33, P174, DOI 10.1016/j.giq.2015.06.003
Frey A, 2019, MIS Q EXEC, V18, P143, DOI 10.17705/2msqe.00013
Gao Y., 2015, IND MANAGE DATA SYST
Ghose A., 2021, MIS QUART
Gimpel H, 2021, ELECTRON MARK, V31, P765, DOI 10.1007/s12525-020-00455-y
Grover P, 2021, J ENTERP INF MANAG, V34, P1893, DOI 10.1108/JEIM-07-2020-0304
Hashemi H, 2022, J BUS RES, V149, P795, DOI 10.1016/j.jbusres.2022.05.082
Hoque R, 2017, INT J MED INFORM, V101, P75, DOI 10.1016/j.ijmedinf.2017.02.002
Jannati N, 2021, INT J MED INFORM, V151, DOI 10.1016/j.ijmedinf.2021.104474
Kim DJ, 2012, INFORM SYST FRONT, V14, P409, DOI 10.1007/s10796-010-9267-8
Kim KH, 2019, INT J INFORM MANAGE, V46, P187, DOI 10.1016/j.ijinfomgt.2018.12.008
Kim M, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102494
Kraus S, 2021, J BUS RES, V123, P557, DOI 10.1016/j.jbusres.2020.10.030
Lamprinos I, 2016, INT J MED INFORM, V91, P31, DOI 10.1016/j.ijmedinf.2016.04.006
Li JY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102054
Lim S, 2011, INT J MED INFORM, V80, pE189, DOI 10.1016/j.ijmedinf.2011.08.007
Lim WM, 2022, ELECTRON MARK, V32, P1463, DOI 10.1007/s12525-021-00486-z
Lorca-Cabrera J, 2021, INT J MED INFORM, V145, DOI 10.1016/j.ijmedinf.2020.104310
Maglio PP, 2008, J ACAD MARKET SCI, V36, P18, DOI 10.1007/s11747-007-0058-9
Malik N, 2022, INT J MANPOWER, V43, P334, DOI 10.1108/IJM-03-2021-0173
McKinley CJ, 2014, COMPUT HUM BEHAV, V37, P107, DOI 10.1016/j.chb.2014.04.023
Miró J, 2021, JMIR MHEALTH UHEALTH, V9, DOI 10.2196/26471
Müller O, 2016, EUR J INFORM SYST, V25, P289, DOI 10.1057/ejis.2016.2
Nouri R, 2018, J AM MED INFORM ASSN, V25, P1089, DOI 10.1093/jamia/ocy050
Nusairat N., 2021, INT J DATA NETWORK S, V5, P547
Osei-Frimpong K, 2018, TECHNOL FORECAST SOC, V126, P14, DOI 10.1016/j.techfore.2016.04.009
Pal S, 2018, SIGMIS-CPR'18: PROCEEDINGS OF THE 2018 ACM SIGMIS CONFERENCE ON COMPUTERS AND PEOPLE RESEARCH, P18, DOI 10.1145/3209626.3209704
Paramita N, 2021, INT J MANAGEMENT FIN, V2, P1
Peltier JW, 2020, J BUS RES, V121, P724, DOI 10.1016/j.jbusres.2020.03.020
Pennebaker J. W., 2015, The Development and Psychometric Properties of LIWC2015
Pop OM, 2018, J SERV MANAGE, V29, P593, DOI 10.1108/JOSM-02-2017-0041
Prakash AV, 2021, HEALTH POLICY TECHN, V10, DOI 10.1016/j.hlpt.2021.100573
Rahimi R, 2021, INT J MED INFORM, V155, DOI 10.1016/j.ijmedinf.2021.104571
Rodríguez-Priego N, 2022, J BUS RES, V140, P546, DOI 10.1016/j.jbusres.2021.11.022
Russo G, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11051315
Sadegh SS, 2018, INT J MED INFORM, V112, P123, DOI 10.1016/j.ijmedinf.2018.01.003
Salehan M, 2016, DECIS SUPPORT SYST, V81, P30, DOI 10.1016/j.dss.2015.10.006
SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
Schutze H., 2008, Introduction to information retrieval, V39, P234
Sengupta P, 2021, J BUS RES, V137, P538, DOI 10.1016/j.jbusres.2021.08.035
Shah A. M., 2021, Quality and Quantity, V55, P173, DOI DOI 10.1007/S11135-020-00999-3
Sheth JN, 2020, IND MARKET MANAG, V87, P312, DOI 10.1016/j.indmarman.2019.10.012
Shirazi F, 2021, TECHNOL FORECAST SOC, V167, DOI 10.1016/j.techfore.2021.120665
Stieglitz S, 2013, J MANAGE INFORM SYST, V29, P217, DOI 10.2753/MIS0742-1222290408
Stocchi L, 2022, J ACAD MARKET SCI, V50, P195, DOI 10.1007/s11747-021-00815-w
Suroso J. S, 2021, J THEORETICAL APPL I, V99, P3923
Tan A.-H., 1999, Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases, P65
Tan HY, 2020, INFORM TECHNOL PEOPL, V33, P1403, DOI 10.1108/ITP-01-2019-0039
Tartaglione A M., 2018, The TQM Journal
Tran TP, 2021, J BUS RES, V125, P239, DOI 10.1016/j.jbusres.2020.12.029
Tripathi S, 2022, INFORM SYST FRONT, V24, P1971, DOI 10.1007/s10796-021-10217-6
Tsiotsou RH, 2022, J BUS RES, V145, P49, DOI 10.1016/j.jbusres.2022.02.050
Vargo S. L., 2009, Service Science, V1, P32, DOI 10.1287/serv.1.1.32
Vargo SL, 2004, J MARKETING, V68, P1, DOI 10.1509/jmkg.68.1.1.24036
Vargo SL, 2008, IND MARKET MANAG, V37, P254, DOI 10.1016/j.indmarman.2007.07.004
Vargo SL, 2008, J ACAD MARKET SCI, V36, P1, DOI 10.1007/s11747-007-0069-6
Verma S, 2020, J BUS RES, V118, P253, DOI 10.1016/j.jbusres.2020.06.057
Wakefield D, 2018, PALLIATIVE MED, V32, P1288, DOI 10.1177/0269216318783919
Wang C, 2021, HEALTHCARE-BASEL, V9, DOI 10.3390/healthcare9030357
Webster P, 2020, LANCET, V395, P1180, DOI 10.1016/S0140-6736(20)30818-7
Wu IL, 2011, DECIS SUPPORT SYST, V51, P587, DOI 10.1016/j.dss.2011.03.003
Wu P, 2022, BMC HEALTH SERV RES, V22, DOI 10.1186/s12913-021-07339-7
Yakubu H, 2021, TECHNOL FORECAST SOC, V171, DOI 10.1016/j.techfore.2021.120983
Yang HZ, 2022, COMPUT HUM BEHAV, V136, DOI 10.1016/j.chb.2022.107346
Yang XS, 2021, J TELEMED TELECARE, V27, P3, DOI 10.1177/1357633X19856746
Zhang MD, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121303
Zhao Y, 2018, INT J INFORM MANAGE, V43, P342, DOI 10.1016/j.ijinfomgt.2017.08.006
NR 89
TC 8
Z9 8
U1 16
U2 75
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD FEB
PY 2023
VL 156
AR 113484
DI 10.1016/j.jbusres.2022.113484
EA DEC 2022
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 7A9MB
UT WOS:000898769800017
PM 36475057
OA hybrid, Green Published
DA 2024-03-27
ER
PT J
AU Aweisi, A
Arora, D
Emby, R
Rehman, M
Tanev, G
Tanev, S
AF Aweisi, Abdulla
Arora, Daman
Emby, Renee
Rehman, Madiha
Tanev, George
Tanev, Stoyan
TI Using Web Text Analytics to Categorize the Business Focus of Innovative
Digital Health Companies
SO TECHNOLOGY INNOVATION MANAGEMENT REVIEW
LA English
DT Article
DE Digital health sector; topic modeling algorithm; market offer; value
proposition; machine learning; web analytics
AB Categorizing the market focus of larger samples of companies can be a tedious and time-consuming process for both researchers and business analysts interested in developing insights about emerging business sectors. The objective of this article is to suggest a text analytics approach to categorizing the application areas of companies operating in the digital health sector based on the information provided on their websites. More specifically, we apply topic modeling on a collection of text documents, including information collected from the websites of a sample of 100 innovative digital health companies. The topic model helps in grouping the companies offering similar types of market offers. It enables identifying the companies that are most highly associated with each of the topics. In addition, it allows identifying some of the emerging themes that are discussed online by the companies, as well as their specific market offers. The results will be of interest to aspiring technology entrepreneurs, organizations supporting new ventures, and business accelerators interested to enhance their services to new venture clients. The development, operationalization, and automation of the company categorization process based on publicly available information is a methodological contribution that opens the opportunity for future applications in research and business practice.
C1 [Aweisi, Abdulla] TechBrew Robot, Salmon Arm, BC, Canada.
[Arora, Daman] Carleton Univ, TIM Program, Appl Business Analyt Degree, Ottawa, ON, Canada.
[Emby, Renee] Shared Serv Canada, Ottawa, ON, Canada.
[Tanev, George] Export Dev Canada, Ottawa, ON, Canada.
[Tanev, Stoyan] Carleton Univ, Sprott Sch Business, Technol Innovat Management TIM Program, Ottawa, ON, Canada.
[Tanev, Stoyan] Univ Southern Denmark SDU, Fac Engn, Innovat & Design Engn Sect, Odense, Denmark.
C3 Carleton University; Carleton University; University of Southern Denmark
RP Aweisi, A (autor correspondiente), TechBrew Robot, Salmon Arm, BC, Canada.
OI Tanev, Stoyan/0000-0002-9895-5416
CR Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
Hannigan TR, 2019, ACAD MANAG ANN, V13, P586, DOI 10.5465/annals.2017.0099
Hecking T., 2018, TOPIC MODELLING EMPI
Johnson MW, 2008, HARVARD BUS REV, V86, P50
Mamosian H., 2018, P ISPIM CONN FUK C
Taney S, 2015, EXPERT SYST APPL, V42, P7582, DOI 10.1016/j.eswa.2015.06.006
Tang JT, 2012, J BUS VENTURING, V27, P77, DOI 10.1016/j.jbusvent.2010.07.001
Wulfovich S., 2020, DIGITAL HLTH ENTREPR
NR 8
TC 2
Z9 2
U1 0
U2 5
PU CARLETON UNIV GRAPHIC SERVICES
PI OTTAWA
PA DUNTON TOWER RM 2122, 1125 COLONEL BY DR, OTTAWA, ON K1A 5B6, CANADA
SN 1927-0321
J9 TECHNOL INNOV MANAG
JI Technol. Innov. Manag. Rev.
PY 2021
VL 11
IS 7-8
BP 65
EP 78
DI 10.22215/timreview/1457
PG 14
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA WQ8ZW
UT WOS:000714099900006
OA gold
DA 2024-03-27
ER
PT J
AU Miller, T
Niu, J
AF Miller, T.
Niu, J.
TI An assessment of strategies for choosing between competitive
marketplaces
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Double auction; Mechanism design; CAT game; Reinforcement learning;
Marketplace selection; Learning; JCAT
AB Traders that operate in markets with multiple competing marketplaces must often choose with which marketplace they will trade. These choices encourage marketplaces to seek competitive advantages against each other by adjusting various parameters, such as the price they charge, or how they match buyers and sellers. Traders can take advantage of this competition to improve utility. However, appropriate strategies must be used to decide with which marketplace a trader should shout. In this paper, we assess several different solutions to the problem of marketplace selection by running simulations of double auctions using the JCAT platform. The parameter spaces of these strategies are explored to find the best performing strategies. Results indicate that the softmax strategy is the most successful at maximising trader profit and global allocative efficiency in both adaptive and non-adaptive markets. The epsilon-decreasing strategy performs well in adaptive markets, while also showing greater stability in its parameter space than softmax. All marketplace selection strategies outperform the random marketplace selection strategy. (C) 2011 Elsevier B.V. All rights reserved.
C1 [Miller, T.] Univ Melbourne, Dept Comp Sci & Software Engn, Parkville, Vic 3010, Australia.
[Niu, J.] CUNY City Coll, CAISS, New York, NY 10031 USA.
C3 University of Melbourne; City University of New York (CUNY) System; City
College of New York (CUNY)
RP Miller, T (autor correspondiente), Univ Melbourne, Dept Comp Sci & Software Engn, Parkville, Vic 3010, Australia.
EM tmiller@unimelb.edu.au; jniu@gc.cuny.edu
RI Niu, Jinzhong/AAE-4375-2020
OI Niu, Jinzhong/0000-0003-0313-1543; Miller, Tim/0000-0003-4908-6063
CR Brafman RI, 2003, J MACH LEARN RES, V3, P213, DOI 10.1162/153244303765208377
Cai K., 2008, P 10 WORKSH AG MED E
FRIEDMAN D, 1993, SANTE FE I STUDIES S, P3
Ganchev K, 2010, COMMUN ACM, V53, P99, DOI 10.1145/1735223.1735247
GODE DK, 1993, J POLIT ECON, V101, P119, DOI 10.1086/261868
Greenwald A.R., 1999, Agent Mediated Electronic Commerce (IJCAI Workshop), P1
Ladley D., 2005, P IJCAI 05 WORKSH TR
Niu J., 2007, P AAAI 07 WORKSH TRA
Niu J., 2008, Proceedings of the 7th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2008), P1649
Niu JZ, 2010, AUTON AGENT MULTI-AG, V21, P172, DOI 10.1007/s10458-009-9110-0
Rochet JC, 2003, J EUR ECON ASSOC, V1, DOI 10.1162/154247603322493212
Shi B., 2010, Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1, V1, P857
Sohn J., 2009, P 1 C AUCT MARK MECH, P40
Sutton R. S., 1998, INTRO REINFORCEMENT, V2
WHITTLE, 1988, J APPL PROBABILITY A, V25, P287
Widrow B., 1960, IRE WESCON Conv. Rec, DOI DOI 10.21236/AD0241531
NR 16
TC 13
Z9 14
U1 0
U2 21
PU ELSEVIER SCIENCE BV
PI AMSTERDAM
PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD JAN-FEB
PY 2012
VL 11
IS 1
SI SI
BP 14
EP 23
DI 10.1016/j.elerap.2011.07.009
PG 10
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA 895VI
UT WOS:000300524400003
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Pranata, I
Susilo, W
AF Pranata, Ilung
Susilo, Willy
TI Are the most popular users always trustworthy? The case of Yelp
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Electronic commerce; Ratings; Reputation system; Trust; Trust circle;
Data mining; Machine learning; Clustering; Yelp
ID ONLINE CONSUMER REVIEWS; MODERATING ROLE; REPUTATION; TRUST; SYSTEMS;
PRODUCT
AB Consumer ratings play a pivotal role in making purchase decision and are now part of daily decision making. Yet, there always be a concern on the credibility of these ratings. Numerous incidents have occurred in the past where businesses gave incentives to the raters to provide fraud and non-credible reviews. We, as average users, tend to believe recommendations given by people with whom we have close relationship, such as family or friends. In the absence of people that we can inherently trust, we tend to consider ratings that come from popular raters more seriously. This is particularly true in the online environments where many raters are unknown to us. However, we can never be sure how trustworthy these popular raters are when providing their ratings and reviews. This paper investigates the credibility of the most popular users in giving trustworthy ratings on a popular consumer reviews platform Yelp. We begin by identifying and grouping the most popular users. We then collect all ratings of the businesses that this group of users has rated. Endogenous statistical techniques are employed to determine the trustworthiness of each popular user's rating and to discount the unfair ratings. By analyzing and comparing the rating given by each popular user with the computed business' trust rating, we collect statistics that found the most popular users are not always trustworthy in providing their ratings and their percentage of rating trustworthiness. (C) 2016 Elsevier B.V. All rights reserved.
C1 [Pranata, Ilung] Univ Newcastle, Callaghan, NSW, Australia.
[Susilo, Willy] Univ Wollongong, Wollongong, NSW, Australia.
C3 University of Newcastle; University of Wollongong
RP Pranata, I (autor correspondiente), Univ Newcastle, Callaghan, NSW, Australia.
EM Ilung.Pranata@newcastle.edu.au; wsusilo@uow.edu.au
RI Susilo, Willy/A-3724-2008; Pranata, Ilung/H-2364-2014
OI Susilo, Willy/0000-0002-1562-5105; Pranata, Ilung/0000-0003-2555-3472
FU University of Newcastle, Australia through QA research grant; University
of Newcastle, Australia through NSG research grant
FX The authors would like to acknowledge the support given by the
University of Newcastle, Australia in this research through 2015 QA and
NSG research grants.
CR Abdul-Rahman A., 2000, 33 IEEE ANN HAW INT
[Anonymous], P 3 INT C PEER TO PE
[Anonymous], INT C COMP SCI ENG 2
[Anonymous], What is R?
Avery C, 1999, AM ECON REV, V89, P564, DOI 10.1257/aer.89.3.564
Chung CY, 2013, DECIS SUPPORT SYST, V55, P314, DOI 10.1016/j.dss.2013.01.020
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001
Entrust, 2000, CONC TRUST NETW SEC
Gambetta D., 2000, Can We Trust Trust? Trust: Making and Breaking Cooperative Relations
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
HAMBY DM, 1995, HEALTH PHYS, V68, P195, DOI 10.1097/00004032-199502000-00005
HAMPEL FR, 1974, J AM STAT ASSOC, V69, P383, DOI 10.2307/2285666
Hankin L., 2007, THESIS
Hoffman FO, 1983, RADIOLOGICAL ASSESSM
Josang A, 2001, INT J UNCERTAIN FUZZ, V9, P279, DOI 10.1016/S0218-4885(01)00083-1
Josang A, 2006, C P 29 AUSTR COMP SC
Josang A, 2002, 15 BLED EL COMM C
Josang A, 2007, DECIS SUPPORT SYST, V43, P618, DOI 10.1016/j.dss.2005.05.019
Kamvar S., 2003, 12 ACM INT C WORLD W
Kamvar SD, 2003, 12 INT C WORLD WID W
KASS RE, 1995, J AM STAT ASSOC, V90, P773, DOI 10.1080/01621459.1995.10476572
Korfiatis N, 2012, ELECTRON COMMER R A, V11, P205, DOI 10.1016/j.elerap.2011.10.003
Leys C, 2013, J EXP SOC PSYCHOL, V49, P764, DOI 10.1016/j.jesp.2013.03.013
Li XX, 2008, INFORM SYST RES, V19, P456, DOI 10.1287/isre.1070.0154
Liu SY, 2014, COMPUT INTELL-US, V30, P316, DOI 10.1111/j.1467-8640.2012.00464.x
Luca M., 2011, WORKING PAPER
Luca M., 2015, WORKING PAPER
MacQueen J., 1967, PROC 5 BERKELEY S MA, P281
MILLER J, 1991, Q J EXP PSYCHOL-A, V43, P907, DOI 10.1080/14640749108400962
Mohan A, 2008, 6 ANN C PRIV SEC TRU
Moses A., 2012, FAKE ONLINE REV MEAN
MUI L, 2002, 35 HAW INT C SYST SC
Noorian Z., 2011, 10 INT C AUT AG MULT
Park DH, 2007, INT J ELECTRON COMM, V11, P125, DOI 10.2753/JEC1086-4415110405
PELLEG D, 2000, 17 INT C MACH LEARN
Pinyol I, 2013, ARTIF INTELL REV, V40, P1, DOI 10.1007/s10462-011-9277-z
Piskorski M.J., 2011, HARV BUS REV
Pranata I., 2013, 2013 IEEE ACIS 12 IN
Pranata I., 2013, INT C MOB WIR MIDDL
Pranata I., 2011, ACM INT C MAN EM DIG
Pranata I., 2012, International Journal of Computer and Information Technology, V1, P44
Pranata I, 2013, ACM T INTERNET TECHN, V12, DOI 10.1145/2461321.2461323
Qian X., 2014, IEEE T KNOWL DATA EN, V26
Resnick P, 2006, EXP ECON, V9, P79, DOI 10.1007/s10683-006-4309-2
Rosaci D., 2011, WOA ANN M HON HAW
Sabater J., 2001, 4 INT WORKSH DEC FRA
Schlosser AE, 2011, J CONSUM PSYCHOL, V21, P226, DOI 10.1016/j.jcps.2011.04.002
Teacy WTL, 2006, AUTON AGENT MULTI-AG, V12, P183, DOI 10.1007/s10458-006-5952-x
Tucker T., 2011, The Elon Journal of Undergraduate Research in Communications, V2, P37
Wang ZM, 2010, BE J ECON ANAL POLI, V10, DOI 10.2202/1935-1682.2523
Wu P.F., 2011, 32 INT C INF SYST IC
Xiong L, 2004, IEEE T KNOWL DATA EN, V16, P843, DOI 10.1109/TKDE.2004.1318566
Yang X., 2012, 18 ACM SIGKDD INT C
Yelp, 2015, YELP DAT CHALL
Zhang J, 2008, ELECTRON COMMER R A, V7, P330, DOI 10.1016/j.elerap.2008.03.001
Zhu F, 2010, J MARKETING, V74, P133, DOI 10.1509/jmkg.74.2.133
NR 57
TC 14
Z9 18
U1 4
U2 51
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD NOV-DEC
PY 2016
VL 20
BP 30
EP 41
DI 10.1016/j.elerap.2016.09.005
PG 12
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA EI9JS
UT WOS:000392824700003
DA 2024-03-27
ER
PT J
AU Safara, F
AF Safara, Fatemeh
TI A Computational Model to Predict Consumer Behaviour During COVID-19
Pandemic
SO COMPUTATIONAL ECONOMICS
LA English
DT Article
DE Coronavirus disease (COVID-19); Machine learning; E-commerce; Consumer
behavior; Prediction model; Bagging; Boosting
AB The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.
C1 [Safara, Fatemeh] Islamic Azad Univ, Dept Comp Engn, Islamshahr Branch, Islamshahr, Iran.
C3 Islamic Azad University
RP Safara, F (autor correspondiente), Islamic Azad Univ, Dept Comp Engn, Islamshahr Branch, Islamshahr, Iran.
EM fsafara@iiau.ac.ir
RI safara, fatemeh/B-1308-2012
OI safara, fatemeh/0000-0003-3513-3789
CR Agrawal S., 2018, 2018 INT C SMART COM, P1
Ahmadipour M, 2020, CIRC SYST SIGNAL PR, V39, P4067, DOI 10.1007/s00034-020-01355-4
Barua S., 2020, Understanding Coronanomics: The economic implications of the coronavirus (COVID-19) pandemic, DOI DOI 10.2139/SSRN.3566477
Change P. M, 2020, COVID 19 PANDEMIC MA, P3, DOI 10.1016/j.jretai.2020.04.002
Chinchilla LDC, 2016, PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, P623, DOI 10.1109/ASONAM.2016.7752301
Ebru D.Arslan, 2019, P INT S PROD RES 201, P29
Ivanov D, 2020, INT J PROD RES, V58, P3252, DOI 10.1080/00207543.2019.1634850
Khodabandehlou S., 2017, J SYSTEMS INFORM TEC
Kucharski AJ, 2020, LANCET INFECT DIS, V20, P553, DOI 10.1016/S1473-3099(20)30144-4
McDonald N, 2019, TRAFFIC INJ PREV, V20, P764, DOI 10.1080/15389588.2019.1651930
Nicola M, 2020, INT J SURG, V78, P185, DOI 10.1016/j.ijsu.2020.04.018
Pantano E, 2020, J BUS RES, V116, P209, DOI 10.1016/j.jbusres.2020.05.036
Patil Megharani, 2019, Smart Intelligent Computing and Applications. Proceedings of the Second International Conference on SCI 2018. Smart Innovation, Systems and Technologies (SIST 105), P197, DOI 10.1007/978-981-13-1927-3_20
Qiu JT, 2015, ELECTRON COMMER RES, V15, P427, DOI 10.1007/s10660-015-9191-6
Souri Alireza, 2017, Journal of Service Science Research, V9, P219, DOI 10.1007/s12927-017-0011-5
Souri A, 2020, SOFT COMPUT, V24, P17111, DOI 10.1007/s00500-020-05003-6
Souri A, 2019, INFORM TECHNOL PEOPL, V32, P1591, DOI 10.1108/ITP-02-2018-0109
Souri A, 2019, KYBERNETES, V48, P407, DOI 10.1108/K-02-2018-0092
Ballestar MT, 2019, REV MANAG SCI, V13, P589, DOI 10.1007/s11846-018-0316-x
Vanneschi L, 2018, EXPERT SYST APPL, V104, P1, DOI 10.1016/j.eswa.2018.03.025
Wang EP, 2020, FOOD SECUR, V12, P739, DOI 10.1007/s12571-020-01092-1
Wu XQ, 2021, ELECTRON COMMER RES, V21, P689, DOI 10.1007/s10660-019-09339-6
Yoon J, 2021, COMPUT ECON, V57, P247, DOI 10.1007/s10614-020-10054-w
Zeng M, 2019, ELECTRON MARK, V29, P263, DOI 10.1007/s12525-018-0311-8
Zhang J, 2021, COMPUT ECON, V57, P1237, DOI 10.1007/s10614-020-10013-5
NR 25
TC 30
Z9 31
U1 10
U2 90
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0927-7099
EI 1572-9974
J9 COMPUT ECON
JI Comput. Econ.
PD APR
PY 2022
VL 59
IS 4
SI SI
BP 1525
EP 1538
DI 10.1007/s10614-020-10069-3
EA NOV 2020
PG 14
WC Economics; Management; Mathematics, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Mathematics
GA 1B7WC
UT WOS:000586344300001
PM 33169049
OA Bronze, Green Published
DA 2024-03-27
ER
PT J
AU Han, LT
Fang, JM
Zheng, QQ
George, BT
Liao, MY
Hossin, MA
AF Han, Lintong
Fang, Jiaming
Zheng, Qiqi
George, Benjamin T.
Liao, Miyan
Hossin, Md. Altab
TI Unveiling the effects of livestream studio environment design on sales
performance: A machine learning exploration
SO INDUSTRIAL MARKETING MANAGEMENT
LA English
DT Article
DE Livestreaming eCommerce; Studio environment; Video analysis; Visual
complexity; Visual coherence; Machine learning
ID PROCESSING FLUENCY; PURCHASE INTENTION; R PACKAGE; AESTHETICS;
COMPLEXITY; PREFERENCE; INVOLVEMENT
AB Livestreaming eCommerce companies invest a significant amount of time and resources in setting up their livestream studios. However, these companies struggle to determine how the studio environment impacts sales, leading to uncertainty regarding the return on investment in their livestream studios. To address this critical yet under-studied issue, we examine how the dual features of the B2B livestream studio environment, namely visual complexity and coherence, impact sales performance. We leveraged machine learning and deep learning algorithms to evaluate the visual complexity and coherence of the B2B livestream studio setting. We then developed the econometric model to determine how these factors affect sales during livestreaming events. Our analysis drew on a dataset of 954 B2B livestreaming events across 8 industry categories. Our study highlights the adverse effects of visual complexity and coherence on sales performance. Notably, high-involvement products are found to be more affected by visual complexity and coherence compared to low-involvement products.
C1 [Han, Lintong; Fang, Jiaming; Zheng, Qiqi; Liao, Miyan] Univ Elect Sci & Technol China, Sch Management & Econ, 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China.
[George, Benjamin T.] Univ Toledo, John & Lillian Neff Coll Business & Innovat, 2801 W Bancroft, Toledo, OH 43606 USA.
[Hossin, Md. Altab] Chengdu Univ, Sch Innovat & Entrepreneurship, 2025 Chengluo Ave, Chengdu 610106, Sichuan, Peoples R China.
[Fang, Jiaming] 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China.
C3 University of Electronic Science & Technology of China; University
System of Ohio; University of Toledo; Chengdu University
RP Fang, JM (autor correspondiente), 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China.
EM jmfang@uestc.edu.cn
OI Fang, Jiaming/0000-0002-1806-8017
FU Humanities and Social Science Fund of the Ministry of Education of China
[23YJA630023]
FX The authors express their appreciation for the research sponsorship
received from the Humanities and Social Science Fund of the Ministry of
Education of China (23YJA630023) .
CR Baack DW, 2016, IND MARKET MANAG, V55, P169, DOI 10.1016/j.indmarman.2015.10.001
Barnes SJ, 2022, TOURISM MANAGE, V90, DOI 10.1016/j.tourman.2021.104487
BERLYNE DE, 1970, PERCEPT PSYCHOPHYS, V8, P279, DOI 10.3758/BF03212593
Bettman JR, 1998, J CONSUM RES, V25, P187, DOI 10.1086/209535
Chang X, 2022, J BUS IND MARK, V37, P78, DOI 10.1108/JBIM-07-2020-0346
Charrad M, 2014, J STAT SOFTW, V61, P1
Chen CC, 2018, TELEMAT INFORM, V35, P293, DOI 10.1016/j.tele.2017.12.003
Chen H, 2022, ELECTRON COMMER R A, V53, DOI 10.1016/j.elerap.2022.101150
Chen L., 2021, China Finance and Economic Review, V10, P102
Chen ZH, 2022, J BUS RES, V148, P252, DOI 10.1016/j.jbusres.2022.04.043
Cheng FF, 2019, COMPUT HUM BEHAV, V101, P394, DOI 10.1016/j.chb.2018.08.015
Chuang SH, 2020, IND MARKET MANAG, V84, P202, DOI 10.1016/j.indmarman.2019.06.012
Corsaro D, 2011, IND MARKET MANAG, V40, P919, DOI 10.1016/j.indmarman.2011.06.028
de Jong A, 2021, IND MARKET MANAG, V93, P1, DOI 10.1016/j.indmarman.2020.12.004
Eroglu SA, 2003, PSYCHOL MARKET, V20, P139, DOI 10.1002/mar.10064
Fang JM, 2019, INT J ELECTRON COMM, V23, P557, DOI 10.1080/10864415.2019.1655206
FORNELL C, 1987, J MARKETING RES, V24, P337, DOI 10.2307/3151381
Gao XY, 2021, ELECTRON COMMER R A, V49, DOI 10.1016/j.elerap.2021.101087
Geng RS, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.697382
Gul FA, 2020, J BUS RES, V111, P52, DOI 10.1016/j.jbusres.2020.01.035
Guo LY, 2021, INTERNET RES, V31, P1718, DOI 10.1108/INTR-02-2020-0078
Hu X, 2022, J RETAIL CONSUM SERV, V67, DOI 10.1016/j.jretconser.2022.102975
Im H, 2010, J RES INTERACT MARK, V4, P280, DOI 10.1108/17505931011092808
IMedia, 2023, The number of overseas viewers increased by 186%, and "station live broadcast" emerged on Ali International Station
Ismail K, 2019, Why B2B marketers should be live streaming more
Joo E, 2023, J RES INTERACT MARK, V17, P754, DOI 10.1108/JRIM-02-2022-0037
KAPLAN R, 1989, ENVIRON BEHAV, V21, P509, DOI 10.1177/0013916589215001
KAPLAN S, 1987, ENVIRON BEHAV, V19, P3, DOI 10.1177/0013916587191001
Kearns S, 2022, What is B2B marketing: definition, strategy, and trends
Koh B, 2022, DECIS SUPPORT SYST, V160, DOI 10.1016/j.dss.2022.113820
Kotler P., 2010, PRINCIPLES MARKETING
Kumar DS, 2017, J SERV MARK, V31, P556, DOI 10.1108/JSM-01-2016-0021
Kwon J, 2022, IND MARKET MANAG, V104, P212, DOI 10.1016/j.indmarman.2022.03.011
Liao M., 2023, Journal of Business Research, V167, P1
Lin IY, 2016, J SERV MARK, V30, P692, DOI 10.1108/JSM-08-2015-0258
Liu L, 2023, INFORM PROCESS MANAG, V60, DOI 10.1016/j.ipm.2022.103259
Luo MR, 2001, COLOR RES APPL, V26, P340, DOI 10.1002/col.1049
Mayer S, 2018, PSYCHOL AESTHET CREA, V12, P399, DOI 10.1037/aca0000187
McDonald R, 1995, J SOC DYERS COLOUR, V111, P376
Meire M, 2022, IND MARKET MANAG, V106, P292, DOI 10.1016/j.indmarman.2022.09.006
Mosteller J, 2014, J BUS RES, V67, P2486, DOI 10.1016/j.jbusres.2014.03.009
Mou J, 2020, IND MANAGE DATA SYST, V120, P567, DOI 10.1108/IMDS-05-2019-0280
Nagle F, 2020, ROY SOC OPEN SCI, V7, DOI 10.1098/rsos.191487
NASAR JL, 1987, J AM PLANN ASSOC, V53, P499, DOI 10.1080/01944368708977139
Orth UR, 2014, J RETAILING, V90, P524, DOI 10.1016/j.jretai.2014.05.004
Orth UR, 2014, J SERV RES-US, V17, P296, DOI 10.1177/1094670514529606
Özkul E, 2020, COLOR RES APPL, V45, P1217, DOI 10.1002/col.22560
Paakki M, 2019, FOOD QUAL PREFER, V76, P81, DOI 10.1016/j.foodqual.2019.04.004
Park HJ, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101934
Park S, 2012, MARKET SCI, V31, P567, DOI 10.1287/mksc.1120.0718
Peng LF, 2019, INFORM MANAGE-AMSTER, V56, P317, DOI 10.1016/j.im.2018.11.007
Pieters R, 2010, J MARKETING, V74, P48, DOI 10.1509/jmkg.74.5.48
Radder L, 2008, J FASH MARK MANAG, V12, P232, DOI 10.1108/13612020810874908
Nia MR, 2020, J RES INTERACT MARK, V14, P357, DOI 10.1108/JRIM-11-2018-0147
Rosen DE, 2004, J BUS RES, V57, P787, DOI 10.1016/S0148-2963(02)00353-3
Sample KL, 2020, J ACAD MARKET SCI, V48, P405, DOI 10.1007/s11747-019-00684-4
Santos S, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120379
Saraee E, 2020, COMPUT VIS IMAGE UND, V195, DOI 10.1016/j.cviu.2020.102949
Scarpi D, 2021, IND MARKET MANAG, V93, P578, DOI 10.1016/j.indmarman.2020.09.006
Shi CM, 2021, EURASIP J WIREL COMM, V2021, DOI 10.1186/s13638-021-01910-w
Shukla M, 2021, INT J E-BUS RES, V17, P73, DOI 10.4018/IJEBR.2021100105
Storme M, 2015, J CONSUM MARK, V32, P432, DOI 10.1108/JCM-10-2014-1187
Tayson H, 2023, How to market your business with video livestreaming
Tofighi D, 2011, BEHAV RES METHODS, V43, P692, DOI 10.3758/s13428-011-0076-x
Toth Z, 2022, IND MARKET MANAG, V104, P226, DOI 10.1016/j.indmarman.2022.04.019
Wang ST, 2023, ELECTRON COMMER R A, V57, DOI 10.1016/j.elerap.2022.101231
Wongkitrungrueng A, 2020, J BUS RES, V117, P543, DOI 10.1016/j.jbusres.2018.08.032
Xie CW, 2022, TOURISM MANAGE, V91, DOI 10.1016/j.tourman.2022.104513
Yeh YS, 2014, INFORM MANAGE-AMSTER, V51, P995, DOI 10.1016/j.im.2014.06.005
Yu CP, 2020, IND MARKET MANAG, V90, P481, DOI 10.1016/j.indmarman.2019.05.001
Yuan CL, 2021, IND MARKET MANAG, V92, P101, DOI 10.1016/j.indmarman.2020.11.008
ZAICHKOWSKY JL, 1985, J CONSUM RES, V12, P341, DOI 10.1086/208520
Zhang ML, 2022, COMPUT HUM BEHAV, V127, DOI 10.1016/j.chb.2021.107052
Zhang Y, 2020, J BUS RES, V112, P170, DOI 10.1016/j.jbusres.2020.02.034
NR 74
TC 0
Z9 0
U1 2
U2 2
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0019-8501
EI 1873-2062
J9 IND MARKET MANAG
JI Ind. Mark. Manage.
PD FEB
PY 2024
VL 117
BP 161
EP 172
DI 10.1016/j.indmarman.2023.12.021
EA JAN 2024
PG 12
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA GQ4Q1
UT WOS:001154125900001
DA 2024-03-27
ER
PT J
AU Nikseresht, A
Shokouhyar, S
Tirkolaee, EB
Nikookar, E
Shokoohyar, S
AF Nikseresht, Ali
Shokouhyar, Sajjad
Tirkolaee, Erfan Babaee
Nikookar, Ethan
Shokoohyar, Sina
TI An intelligent decision support system for warranty claims forecasting:
Merits of social media and quality function deployment
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Warranty claims prediction; Social media analytics; Quality function
deployment; Time-frequency analysis; Deep learning; Deep ensemble random
vector functional link
ID MONEY-BACK GUARANTEE; ENGINEERING CHARACTERISTICS; REGRESSION-MODEL;
OPTIMIZATION; DESIGN; QFD
AB This work develops a novel approach based on Machine Learning (ML)-assisted Quality Function Deployment (QFD) to sift the gold from the stone. It includes Time-Varying Filter-based Empirical Mode Decomposition (TVFEMD), Deep Ensemble Random Vector Functional Link (DE-RVFL), and a Bayesian optimization algorithm for optimizing the shaped DE-RVFLTVF-EMD hyperparameters. This approach makes it possible for the proposed methods to be dynamic enough to deal with the data's volatility, complexity, uncertainty, and ambiguity. It is demonstrated that incorporating TVF-EMD to provide time-frequency analysis along DE-RVFL, and goal-oriented social media analytics boosts the performance of out-of-sample predictions statistically and compensates for the "warranty data maturation" effect. The proposed algorithm's Root Mean Square Error (RMSE) decreases by 23.37%-88.76% relative to other benchmark cutting-edge models. This study contributes significantly to the services management community. Using the proposed methodology, managers could create plans for warranty claims strategies that reduce inventory levels and waste while optimizing customer satisfaction, advocacy, and revenues. These merits provide incentives and support for policymakers to adopt advanced technologies, such as the ones developed and implemented in the current study, in warranty claims forecasting to improve accuracy and efficiency.
C1 [Nikseresht, Ali] Georgia Inst Technol, Sch Comp Sci, Atlanta, GA 30332 USA.
[Shokouhyar, Sajjad] Australian Inst Business, Dept Supply Chain & Operat Management, 1 King William St, Adelaide, SA 5000, Australia.
[Tirkolaee, Erfan Babaee] Istinye Univ, Dept Ind Engn, Istanbul, Turkiye.
[Tirkolaee, Erfan Babaee] Yuan Ze Univ, Dept Ind Engn & Management, Taoyuan, Taiwan.
[Tirkolaee, Erfan Babaee] Lebanese Amer Univ, Dept Ind & Mech Engn, Byblos, Lebanon.
[Nikookar, Ethan] Univ Wollongong, Ctr Supply ChainResearch, Sch Business, Wollongong, NS, Australia.
[Shokoohyar, Sina] Seton Hall Univ, Stillman Sch Business, Dept Comp & Decis Sci, S Orange, NJ USA.
C3 University System of Georgia; Georgia Institute of Technology;
Australian Institute of Business; Istinye University; Yuan Ze
University; Lebanese American University; University of Wollongong;
Seton Hall University
RP Shokouhyar, S (autor correspondiente), Australian Inst Business, Dept Supply Chain & Operat Management, 1 King William St, Adelaide, SA 5000, Australia.; Tirkolaee, EB (autor correspondiente), Istinye Univ, Dept Ind Engn, Istanbul, Turkiye.
EM Ali.Nikseresht@gatech.edu; sajjad.shokouhyar@aib.edu.au;
erfan.babaee@istinye.edu.tr; Ethan_nikookar@uow.edu.au;
sina.shokoohyar@shu.edu
OI Nikookar, Ethan/0000-0002-4626-367X
CR Aaronson D, 2022, INT J FORECASTING, V38, P567, DOI 10.1016/j.ijforecast.2021.04.001
Abedin MZ, 2023, RES INT BUS FINANC, V65, DOI 10.1016/j.ribaf.2023.101913
Abedin MZ, 2021, ANN OPER RES, DOI 10.1007/s10479-021-04420-6
Abrahams AS, 2012, DECIS SUPPORT SYST, V54, P87, DOI 10.1016/j.dss.2012.04.005
Adeh IM, 2017, FACTA UNIV-SER MATH, V32, P447, DOI 10.22190/FUMI1704447M
Akbarov A, 2012, QUAL RELIAB ENG INT, V28, P663, DOI 10.1002/qre.1399
Alkahtani M, 2019, COMPUT IND ENG, V128, P1027, DOI 10.1016/j.cie.2018.04.033
Alp ZZ, 2018, KNOWL-BASED SYST, V141, P211, DOI 10.1016/j.knosys.2017.11.021
Alparslan-Gök SZ, 2009, MATH METHOD OPER RES, V69, P99, DOI 10.1007/s00186-008-0211-3
Alqahtani Ammar Y., 2018, Journal of Industrial Engineering International, V14, P767, DOI 10.1007/s40092-018-0259-5
Ambilkar P, 2022, INT J PROD RES, V60, P3920, DOI 10.1080/00207543.2021.1933645
Apostolopoulos ID, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13063412
Attardi L, 2005, RELIAB ENG SYST SAFE, V87, P265, DOI 10.1016/j.ress.2004.05.003
Bai SJ, 2018, Arxiv, DOI [arXiv:1803.01271, 10.48550/arXiv.1803.01271, DOI 10.48550/ARXIV.1803.01271]
Banerjee A, 2013, SCIENCE, V341, P363, DOI 10.1126/science.1236498
Bereketli I, 2013, J CLEAN PROD, V54, P188, DOI 10.1016/j.jclepro.2013.03.053
Bhattacharya A, 2005, INT J PROD RES, V43, P3671, DOI 10.1080/00207540500137217
Bokde ND, 2021, APPL ENERG, V281, DOI 10.1016/j.apenergy.2020.116061
Boone T, 2019, INT J FORECASTING, V35, P170, DOI 10.1016/j.ijforecast.2018.09.003
Boratynska A, 2022, INSUR MATH ECON, V105, P194, DOI 10.1016/j.insmatheco.2022.04.001
Borup D, 2023, INT J FORECASTING, V39, P1122, DOI 10.1016/j.ijforecast.2022.05.005
Bouteska A, 2023, RES INT BUS FINANC, V64, DOI 10.1016/j.ribaf.2022.101863
Chang WL, 2012, COMPUT MATH APPL, V63, P144, DOI 10.1016/j.camwa.2011.11.001
Chehade A, 2022, RELIAB ENG SYST SAFE, V218, DOI 10.1016/j.ress.2021.108180
Chen LH, 2010, EUR J OPER RES, V201, P619, DOI 10.1016/j.ejor.2009.03.010
Chen YB, 2008, MANAGE SCI, V54, P477, DOI 10.1287/mnsc.1070.0810
Chowdhury MMH, 2016, INT J PROD ECON, V171, P165, DOI 10.1016/j.ijpe.2015.09.023
Crevecoeur J, 2019, EUR J OPER RES, V277, P930, DOI 10.1016/j.ejor.2019.02.044
Cui RM, 2018, PROD OPER MANAG, V27, P1749, DOI 10.1111/poms.12707
Dai AS, 2019, J SYST SCI SYST ENG, V28, P715, DOI 10.1007/s11518-019-5434-8
Debnath K, 2023, EXPERT SYST APPL, V216, DOI 10.1016/j.eswa.2022.119470
Dey R, 2017, MIDWEST SYMP CIRCUIT, P1597, DOI 10.1109/MWSCAS.2017.8053243
Duong QH, 2022, INT J PROD ECON, V243, DOI 10.1016/j.ijpe.2021.108340
Efat MIA, 2022, ANN OPER RES, DOI 10.1007/s10479-022-04838-6
Fan B, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102131
Fang WL, 2020, ADV ENG INFORM, V44, DOI 10.1016/j.aei.2020.101060
Fantoni G, 2021, COMPUT IND, V124, DOI 10.1016/j.compind.2020.103357
Farhadloo M, 2016, DECIS SUPPORT SYST, V90, P1, DOI 10.1016/j.dss.2016.06.010
Featherman M, 2021, TECHNOL FORECAST SOC, V169, DOI 10.1016/j.techfore.2021.120847
Filz MA, 2021, COMPUT IND, V129, DOI 10.1016/j.compind.2021.103451
Goldberg DM, 2018, DECIS SUPPORT SYST, V105, P52, DOI 10.1016/j.dss.2017.10.012
Grobelny J, 2021, NEURAL COMPUT APPL, V33, P5843, DOI 10.1007/s00521-020-05363-y
Grover P, 2022, ANN OPER RES, V308, P177, DOI 10.1007/s10479-020-03683-9
Guo YJ, 2020, INT J OPER PROD MAN, V40, P575, DOI 10.1108/IJOPM-04-2019-0315
Gupta SK, 2022, RELIAB ENG SYST SAFE, V222, DOI 10.1016/j.ress.2022.108385
Hajek P, 2023, INFORM SYST FRONT, V25, P1985, DOI 10.1007/s10796-022-10346-6
He LN, 2021, INT J PROD RES, V59, P1714, DOI 10.1080/00207543.2020.1724343
He W, 2019, J ENTERP INF MANAG, V32, P152, DOI 10.1108/JEIM-02-2018-0031
Heiman A, 2001, J BUS RES, V54, P71, DOI 10.1016/S0148-2963(00)00181-8
Heydari J, 2017, SERV SCI, V9, P91, DOI 10.1287/serv.2016.0153
Hodkiewicz M, 2021, COMPUT IND, V131, DOI 10.1016/j.compind.2021.103496
Huang ZS, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101897
Huster S, 2022, J CLEAN PROD, V363, DOI 10.1016/j.jclepro.2022.132225
Jabeen F, 2023, TECHNOL FORECAST SOC, V186, DOI 10.1016/j.techfore.2022.122104
Jin J, 2015, ENG APPL ARTIF INTEL, V41, P115, DOI 10.1016/j.engappai.2015.02.006
Jindal A, 2015, BENCHMARKING, V22, P393, DOI 10.1108/BIJ-05-2013-0062
KALBFLEISCH JD, 1991, TECHNOMETRICS, V33, P273, DOI 10.2307/1268780
Kara G, 2019, CENT EUR J OPER RES, V27, P241, DOI 10.1007/s10100-017-0508-5
Karim MR, 2005, INT J QUAL RELIAB MA, V22, P667, DOI 10.1108/02656710510610820
Kharfan M, 2021, ANN OPER RES, V303, P159, DOI 10.1007/s10479-020-03666-w
Kiani F, 2023, MATHEMATICS-BASEL, V11, DOI 10.3390/math11102340
Kleyner A, 2022, QUAL RELIAB ENG INT, V38, P2388, DOI 10.1002/qre.3082
Krivtsov V., 2017, Int. J. Performability Eng., V13, DOI [10.23940/ijpe.17.02.p3.129134, DOI 10.23940/IJPE.17.02.P3.129134]
Krivtsov V.V., 2011, IEEE Cat. No CFP11RAM-CDR
Kropat E, 2019, OPTIM METHOD SOFTW, V34, P586, DOI 10.1080/10556788.2018.1425859
Kumaraswamy N, 2022, EXPERT SYST APPL, V210, DOI 10.1016/j.eswa.2022.118433
Lancioni RA, 2000, IND MARKET MANAG, V29, P45, DOI 10.1016/S0019-8501(99)00111-X
Li H, 2017, SIGNAL PROCESS, V138, P146, DOI 10.1016/j.sigpro.2017.03.019
Li SH, 2006, OMEGA-INT J MANAGE S, V34, P107, DOI 10.1016/j.omega.2004.08.002
Li SP, 2019, COMPUT IND ENG, V135, P348, DOI 10.1016/j.cie.2019.06.008
Li T, 2022, RELIAB ENG SYST SAFE, V218, DOI 10.1016/j.ress.2021.108142
Li XP, 2019, INT J PROD RES, V57, P5414, DOI 10.1080/00207543.2018.1526419
Li XY, 2022, RELIAB ENG SYST SAFE, V217, DOI 10.1016/j.ress.2021.108033
Liagkouras K, 2022, ANN OPER RES, V316, P1493, DOI 10.1007/s10479-020-03554-3
Lin KS, 2021, RELIAB ENG SYST SAFE, V207, DOI 10.1016/j.ress.2020.107327
Lin YH, 2024, J RETAIL CONSUM SERV, V76, DOI 10.1016/j.jretconser.2023.103558
Liu P, 2021, COMPUT IND ENG, V153, DOI 10.1016/j.cie.2020.107040
Liu PH, 2020, KNOWL-BASED SYST, V203, DOI 10.1016/j.knosys.2020.106081
Liu Y, 2021, INT J FORECASTING, V37, P812, DOI 10.1016/j.ijforecast.2020.09.009
Liu ZM, 2022, APPL SOFT COMPUT, V129, DOI 10.1016/j.asoc.2022.109580
Liu ZY, 2023, TECHNOL FORECAST SOC, V187, DOI 10.1016/j.techfore.2022.122253
Luo N, 2023, TECHNOL FORECAST SOC, V192, DOI 10.1016/j.techfore.2023.122547
Mack T., 1993, ASTIN Bull., V23, DOI [DOI 10.2143/AST.23.2.2005092, 10.2143/AST.23.2.2005092]
Maiano L, 2023, EXPERT SYST APPL, V231, DOI 10.1016/j.eswa.2023.120644
Majeske KD, 2007, RELIAB ENG SYST SAFE, V92, P243, DOI 10.1016/j.ress.2005.12.004
Mansour W, 2023, INFORM PROCESS MANAG, V60, DOI 10.1016/j.ipm.2023.103414
Martín-Rojas R, 2023, TECHNOL FORECAST SOC, V190, DOI 10.1016/j.techfore.2023.122421
Michael NE, 2022, APPL ENERG, V324, DOI 10.1016/j.apenergy.2022.119727
Mishra S., 2021, SN Comput. Sci., V2, P1, DOI [10.1007/s42979-021-00455-5, DOI 10.1007/S42979-021-00455-5]
Misic VV, 2020, M&SOM-MANUF SERV OP, V22, P158, DOI 10.1287/msom.2019.0805
Mohammadi HA, 2023, APPL SOFT COMPUT, V135, DOI 10.1016/j.asoc.2023.109990
Mondal A, 2023, APPL ENERG, V343, DOI 10.1016/j.apenergy.2023.121225
Mondal A, 2023, EXPERT SYST APPL, V211, DOI 10.1016/j.eswa.2022.118688
Moorthy S, 1995, MARKET SCI, V14, P442, DOI 10.1287/mksc.14.4.442
Murthy DNP, 2002, INT J PROD ECON, V79, P231, DOI 10.1016/S0925-5273(02)00153-6
Nematzadeh S, 2022, COMPUT BIOL CHEM, V97, DOI 10.1016/j.compbiolchem.2021.107619
Nikseresht A., 2021, Decision Making for Celebrity Branding: An Opinion Mining Approach Based on Polarity and Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)
Nikseresht A, 2023, INT J LOGIST MANAG, DOI 10.1108/IJLM-03-2023-0085
Nikseresht A, 2022, ENVIRON SCI POLLUT R, V29, P42509, DOI 10.1007/s11356-022-19863-y
Ouyang LH, 2022, COMPUT IND, V141, DOI 10.1016/j.compind.2022.103712
PAO YH, 1994, NEUROCOMPUTING, V6, P163, DOI 10.1016/0925-2312(94)90053-1
Paschen J, 2020, J PROD BRAND MANAG, V29, P223, DOI 10.1108/JPBM-12-2018-2179
Qiao BH, 2022, APPL SOFT COMPUT, V129, DOI 10.1016/j.asoc.2022.109586
Rajpathak D, 2020, COMPUT IND, V123, DOI 10.1016/j.compind.2020.103338
Renaud O, 2005, IEEE T SYST MAN CY B, V35, P1241, DOI 10.1109/TSMCB.2005.850182
Saito T, 2022, ANN OPER RES, DOI 10.1007/s10479-022-05136-x
Sarmast Z, 2023, IND MANAGE DATA SYST, V123, P1281, DOI 10.1108/IMDS-07-2022-0427
Shang GZ, 2020, J OPER MANAG, V66, P326, DOI 10.1002/joom.1071
Shang LJ, 2023, RELIAB ENG SYST SAFE, V234, DOI 10.1016/j.ress.2023.109187
Sharifi Z, 2021, RESOUR CONSERV RECY, V167, DOI 10.1016/j.resconrec.2021.105398
Shi QS, 2021, PATTERN RECOGN, V117, DOI 10.1016/j.patcog.2021.107978
Shokouhyar S, 2021, RELIAB ENG SYST SAFE, V216, DOI 10.1016/j.ress.2021.108010
Strijov V, 2010, COMPUT MATH APPL, V60, P981, DOI 10.1016/j.camwa.2010.03.021
Tandon A, 2021, TECHNOL FORECAST SOC, V171, DOI 10.1016/j.techfore.2021.120931
Temoçin BZ, 2014, J COMPUT APPL MATH, V259, P443, DOI 10.1016/j.cam.2013.10.021
Tong P, 2020, P I MECH ENG O-J RIS, V234, P550, DOI 10.1177/1748006X19886162
Van den Oord A, 2016, arXiv, DOI DOI 10.48550/ARXIV.1609.03499FOCUSTOLEARNMORE.ARXIV:1609
Wang H, 2020, COMPUT IND ENG, V140, DOI 10.1016/j.cie.2019.106212
Wang L, 2021, FASH TEXT, V8, DOI 10.1186/s40691-020-00239-0
Wang LY, 2020, QUAL TECHNOL QUANT M, V17, P216, DOI 10.1080/16843703.2019.1584956
Wang XL, 2021, INT J PROD ECON, V233, DOI 10.1016/j.ijpe.2020.107974
Wang XL, 2018, P I MECH ENG O-J RIS, V232, P284, DOI 10.1177/1748006X17742776
Wang X, 2017, RELIAB ENG SYST SAFE, V168, P355, DOI 10.1016/j.ress.2017.04.009
Wang YK, 2019, RELIAB ENG SYST SAFE, V189, P315, DOI 10.1016/j.ress.2019.05.006
Wasserman GS, 1996, IIE TRANS, V28, P967
Wieneke A, 2016, ELECTRON MARK, V26, P245, DOI 10.1007/s12525-016-0226-1
Wu K, 2020, IEEE T FUZZY SYST, V28, P3110, DOI 10.1109/TFUZZ.2019.2956904
Wu SM, 2013, RELIAB ENG SYST SAFE, V114, P1, DOI 10.1016/j.ress.2012.12.021
Wu SM, 2011, EUR J OPER RES, V213, P196, DOI 10.1016/j.ejor.2011.03.009
Xie W, 2017, IISE TRANS, V49, P553, DOI 10.1080/24725854.2016.1263769
Yang SC, 2018, IEEE T FUZZY SYST, V26, P3391, DOI 10.1109/TFUZZ.2018.2831640
Yao XX, 2022, SOFT COMPUT, V26, P189, DOI 10.1007/s00500-021-06455-0
Ye ZS, 2016, RELIAB ENG SYST SAFE, V155, P21, DOI 10.1016/j.ress.2016.05.013
Zafar MR, 2021, MACH LEARN KNOW EXTR, V3, P525, DOI 10.3390/make3030027
Zavala A, 2019, INT J PROD ECON, V208, P544, DOI 10.1016/j.ijpe.2018.12.020
Zhan YZ, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120504
Zhang ZM, 2019, COMPUT IND ENG, V130, P575, DOI 10.1016/j.cie.2019.03.014
Zhao XJ, 2018, RELIAB ENG SYST SAFE, V177, P108, DOI 10.1016/j.ress.2018.05.002
Zheng G., 1999, Journal of Computational Intelligence in Finance, V7, P18
Zheng L, 2023, COMPUT IND ENG, V177, DOI 10.1016/j.cie.2023.109031
Zheng L, 2021, EXPERT SYST APPL, V178, DOI 10.1016/j.eswa.2021.115030
Zheng L, 2020, DECIS SUPPORT SYST, V137, DOI 10.1016/j.dss.2020.113369
Zouein Pierrette, 2010, International Journal of Simulation & Process Modelling, V6, P19, DOI 10.1504/IJSPM.2010.032662
Zouein PP, 2022, J OPER RES SOC, V73, P1517, DOI 10.1080/01605682.2021.1920345
NR 144
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD APR
PY 2024
VL 201
AR 123268
DI 10.1016/j.techfore.2024.123268
EA FEB 2024
PG 26
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA LR5K4
UT WOS:001188539900001
DA 2024-03-27
ER
PT J
AU Cheng, Y
Jiang, H
AF Cheng, Yang
Jiang, Hua
TI How Do AI-driven Chatbots Impact User Experience? Examining
Gratifications, Perceived Privacy Risk, Satisfaction, Loyalty, and
Continued Use
SO JOURNAL OF BROADCASTING & ELECTRONIC MEDIA
LA English
DT Article
AB This study examined how artificial intelligence (AI)-driven chatbots impact user experience. It collected survey data from 1,064 consumers who used any chatbot service from the top 30 brands in the U.S. Results indicated that utilitarian (information), hedonic (entertainment), technology (media appeal), and social (social presence) gratifications obtained from chatbot use positively predicted users' satisfaction with chatbot services of their selected brand. In contrast, perceived privacy risk associated with chatbot use reduced user satisfaction. Data also demonstrated that user satisfaction positively affected both the continued use intention of chatbot services and customer loyalty. Implications of this study are discussed.
C1 [Cheng, Yang] North Carolina State Univ, Dept Commun, Raleigh, NC 27695 USA.
[Jiang, Hua] Syracuse Univ, SI Newhouse Sch Publ Commun, Syracuse, NY USA.
C3 North Carolina State University; Syracuse University
RP Cheng, Y (autor correspondiente), North Carolina State Univ, Dept Commun, Raleigh, NC 27695 USA.
EM ycheng20@ncsu.edu
OI Cheng, Dr. Yang (Alice)/0000-0002-0321-7956
CR Ancu Monica., 2009, J BROADCASTING ELECT, V53
[Anonymous], 2018, J BUS RES
[Anonymous], 2020, BUSINESS INSIDER
[Anonymous], 2017, Forbes
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
BAE MY, 2018, COMPUT HUM BEHAV, V79
Balakrishnan J, 2017, J BEHAV ADDICT, V6, P364, DOI 10.1556/2006.6.2017.058
Bhattacherjee A, 2001, DECIS SUPPORT SYST, V32, P201, DOI 10.1016/S0167-9236(01)00111-7
Chang YP, 2012, COMPUT HUM BEHAV, V28, P995, DOI 10.1016/j.chb.2012.01.001
Cheng Y., 2019, International Journal of Strategic Communication, V13, P76, DOI [10.1080/1553118X.2018.1524382, DOI 10.1080/1553118X.2018.1524382]
CHENG Y, 2020, J CONTINGENCIES CRIS
Cheng Y, 2019, INT J COMMUN-US, V13, P319
Cheng Y, 2015, NEW MEDIA SOC, V17, P1096, DOI 10.1177/1461444814521362
Dehghani M, 2018, BEHAV INFORM TECHNOL, V37, P145, DOI 10.1080/0144929X.2018.1424246
Diddi A., 2006, Journal of Broadcasting Electronic Media, V50
Eeuwen M. V., 2017, Mobile conversational commerce: messenger chatbots as the next interface between businesses and consumers
Farah MF, 2018, INT J BANK MARK, V36, P1386, DOI 10.1108/IJBM-10-2017-0215
FERRIS LA, 2018, J BROADCASTING ELECT, V62
Folstad, 2017, P 4 INT C INT SCI
Fornell C., 2007, The satisfied customer: Winners and losers in the battle for buyer preference
GAN C, 2018, COMPUT HUM BEHAV, V78
GAO L, 2017, INFORM SYST FRONT, V19
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Godlewski LR, 2010, COMMUN Q, V58, P148, DOI 10.1080/01463371003773358
Gogan ICW, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093162
Hagberg J, 2016, INT J RETAIL DISTRIB, V44, P694, DOI 10.1108/IJRDM-09-2015-0140
Han M., 2018, J OPEN INNOVATION TE, V4, DOI 10.3390/joitmc4040046
Heeter C., 1992, Presence: Teleoperators & Virtual Environments, V1, P262, DOI [10.1162/pres.1992.1.2.262, DOI 10.1162/PRES.1992.1.2.262]
Hess T, 2009, J ASSOC INF SYST, V10, P889
Ho S.Y., 2006, ELECTRON MARK, V16, DOI 10.1080/10196780500491162
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Huang M.H., 2018, Journal of Service Research
JAMES ML, 1995, J BROADCASTING ELECT, V39
Jovic D., 2020, Smallbizgenius
Jung JH, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11247063
Kahneman D., 2011, Thinking, Fast and Slow
KEES J, 2017, J ADVERTISING, V46
Kim AJ, 2012, J BUS RES, V65, P1480, DOI 10.1016/j.jbusres.2011.10.014
Kim AJ, 2010, J GLOB FASH MARK, V1, P164, DOI 10.1080/20932685.2010.10593068
LaRose R, 2015, HBK COMMUN MEDIA, P365
LaRose R, 2010, COMMUN THEOR, V20, P194, DOI 10.1111/j.1468-2885.2010.01360.x
Lin CA, 2006, J BROADCAST ELECTRON, V50, P140, DOI 10.1207/s15506878jobem5001_8
Liu ILB, 2016, J ASSOC INF SCI TECH, V67, P56, DOI 10.1002/asi.23371
LU B, 2016, COMPUT HUM BEHAV, V56
Oliver R.L., 1997, SATISFACTION
OLIVER RL, 1980, J MARKETING RES, V17, P460, DOI 10.2307/3150499
PAPACHARISSI Z, 2010, J BROADCASTING ELECT, V44
Papacharissi Z, 2011, COMMUN SOC-SER, P212
PERKS GL, 2019, J BROADCASTING ELECT, V63
Ran W, 2006, NEW MEDIA SOC, V8, P53, DOI 10.1177/1461444806059870
RUBIN AM, 1983, J BROADCASTING, V27, P37, DOI 10.1080/08838158309386471
Russell S., 2010, Artificial intelligence a modern approach, V3rd
RYAN RM, 2006, MOTIV EMOTION, V30
Sangar K, 2012, Gucci-Social media marketing strategies using internet and social networking sites
Shankar V, 2003, INT J RES MARK, V20, P153, DOI 10.1016/S0167-8116(03)00016-8
Shawar B.A., 2007, Ldv Forum, V22, P29
STAFFORD TF, 2004, DECISION SCI, V35
Sundar S. S., 2008, Digital media, youth, and credibility, P73, DOI DOI 10.1162/DMAL.9780262562324.073
Sundar SS, 2010, HUM COMMUN RES, V36, DOI 10.1111/j.1468-2958.2010.01377.x
SUNDAR SS, 2019, P 2019 CHI C HUM FAC, V538
SUNDAR SS, 2013, J BROADCASTING ELECT, V57
Wang EST, 2017, BEHAV INFORM TECHNOL, V36, P2, DOI 10.1080/0144929X.2016.1143033
XU C, 2012, INFORM MANAGEMENT, V49
Yao M., 2017, 100 BEST BOTS BRANDS
Zhao L, 2012, DECIS SUPPORT SYST, V53, P825, DOI 10.1016/j.dss.2012.05.019
NR 65
TC 78
Z9 81
U1 55
U2 270
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0883-8151
EI 1550-6878
J9 J BROADCAST ELECTRON
JI J. Broadcast. Electron. Media
PD OCT 1
PY 2020
VL 64
IS 4
SI SI
BP 592
EP 614
DI 10.1080/08838151.2020.1834296
EA DEC 2020
PG 23
WC Communication; Film, Radio, Television
WE Social Science Citation Index (SSCI)
SC Communication; Film, Radio & Television
GA PZ0LB
UT WOS:000596989800001
DA 2024-03-27
ER
PT J
AU Unal, M
Park, YH
AF Unal, Murat
Park, Young-Hoon
TI Fewer Clicks, More Purchases
SO MANAGEMENT SCIENCE
LA English
DT Article
DE customer experience; one-click buying; retailing; e-commerce; causal
inference; machine learning; generalized random forests
ID ONLINE; MANAGEMENT; ADOPTION; POLICIES; USAGE
AB E-commerce retailers are increasingly faced with challenges of finding ways to provide a seamless shopping experience to customers. The checkout process and its related touch points are especially critical in shaping the customer experience. We study the impact of adopting one-click buying, a feature that reduces the number of steps required to place a purchase order to a single click, on subsequent customer behavior. Using quasi-experimental data over a period of 35 months from an online retailer before and after the launch of one click buying, we find adopting one-click buying is effective in lifting customer purchases and does so by making treated customers purchase more often and more items. The impact of adopting one-click buying on customer purchases after adoption is economically significant, persistent over time, and heterogeneous across customers. Analyzing clickstream data of customer activity online and purchases across product categories, we provide evidence that the increase in purchases is driven by richer engagement through both more visits to the website and more page views on visit and the expansion of purchases across categories. We discuss the implications of our findings for customer experience and targeting.
C1 [Unal, Murat] Amazon Com Serv LLC, Seattle, WA 98109 USA.
[Park, Young-Hoon] Cornell Univ, Samuel Curtis Johnson Grad Sch Management, Ithaca, NY 14853 USA.
C3 Cornell University
RP Unal, M (autor correspondiente), Amazon Com Serv LLC, Seattle, WA 98109 USA.; Park, YH (autor correspondiente), Cornell Univ, Samuel Curtis Johnson Grad Sch Management, Ithaca, NY 14853 USA.
EM mu96@cornell.edu; yp34@cornell.edu
OI Unal, Murat/0000-0002-6638-5500; Park, Young-Hoon/0000-0003-1760-3476
CR Angrist JD, 2009, MOSTLY HARMLESS ECONOMETRICS: AN EMPIRICISTS COMPANION, P1
Ascarza E, 2018, J MARKETING RES, V55, P80, DOI 10.1509/jmr.16.0163
Athey S., 2019, Estimating Treatment Effects with Causal Forests: An Application, V5, P37
Athey S, 2019, ANN STAT, V47, P1148, DOI 10.1214/18-AOS1709
Austin PC, 2009, STAT MED, V28, P3083, DOI 10.1002/sim.3697
Baumgartner H, 2002, J CONSUM RES, V29, P286, DOI 10.1086/341578
Bell DR, 2020, MANAGE SCI, V66, P4096, DOI 10.1287/mnsc.2019.3453
Bertrand M, 2004, Q J ECON, V119, P249, DOI 10.1162/003355304772839588
Bolton RN, 2004, J ACAD MARKET SCI, V32, P271, DOI 10.1177/0092070304263341
Boone C, 2009, MANAGE SCI, V55, P165, DOI 10.1287/mnsc.1080.0899
Bower AB, 2012, J MARKETING, V76, P110, DOI 10.1509/jm.10.0419
Breiman L., 2001, Machine Learning, V45, P5, DOI 10.1023/A:1010933404324
Brynjolfsson E, 2011, MANAGE SCI, V57, P1373, DOI 10.1287/mnsc.1110.1371
Clarke RT, 2007, HYDROL EARTH SYST SC, V11, P408, DOI 10.5194/hess-11-408-2007
Datta H, 2018, MARKET SCI, V37, P5, DOI 10.1287/mksc.2017.1051
De P, 2010, MANAGE SCI, V56, P1930, DOI 10.1287/mnsc.1100.1233
Digiday, 2017, END ER AM 1 CLICK BU
Dutta R., 2003, Proceedings of the 24th International Conference in Information Systems, P15
Fong N, 2019, J MARKETING RES, V56, P310, DOI 10.1177/0022243718817513
Forman C, 2009, MANAGE SCI, V55, P47, DOI 10.1287/mnsc.1080.0932
Gallino S, 2018, M&SOM-MANUF SERV OP, V20, P767, DOI 10.1287/msom.2017.0686
Häubl G, 2000, MARKET SCI, V19, P4, DOI 10.1287/mksc.19.1.4.15178
Hui SK, 2013, J MARKETING, V77, P1, DOI 10.1509/jm.11.0436
Imbens GW, 2015, CAUSAL INFERENCE FOR STATISTICS, SOCIAL, AND BIOMEDICAL SCIENCES: AN INTRODUCTION, P1, DOI 10.1017/CBO9781139025751
Iyengar R, 2022, J MARKETING RES, V59, P1101, DOI 10.1177/00222437221080163
Keele L, 2015, POLIT ANAL, V23, P313, DOI 10.1093/pan/mpv007
Kumar V, 2004, J RETAILING, V80, P317, DOI 10.1016/j.jretai.2004.10.007
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Magento, 2017, NEW INST PURCH CHECK
Manchanda P, 2015, MARKET SCI, V34, P367, DOI 10.1287/mksc.2014.0890
Narang U, 2019, MARKET SCI, V38, P756, DOI 10.1287/mksc.2019.1169
Palmer JW, 2002, INFORM SYST RES, V13, P151, DOI 10.1287/isre.13.2.151.88
Parboteeah DV, 2009, INFORM SYST RES, V20, P60, DOI 10.1287/isre.1070.0157
PIRON F, 1991, ADV CONSUM RES, V18, P509
Prins R, 2007, J MARKETING, V71, P169, DOI 10.1509/jmkg.71.2.169
Rafieian O, 2021, MARKET SCI, V40, P193, DOI 10.1287/mksc.2020.1235
Rejoiner, 2017, VAL IS AM 1 CLICK PA
Ridgway NM, 2008, J CONSUM RES, V35, P622, DOI 10.1086/591108
ROBINS JM, 1994, J AM STAT ASSOC, V89, P846, DOI 10.2307/2290910
ROBINSON PM, 1988, ECONOMETRICA, V56, P931, DOI 10.2307/1912705
Rossi PE, 1996, MARKET SCI, V15, P321, DOI 10.1287/mksc.15.4.321
Shankar V, 2018, J RETAILING, V94, pVI, DOI 10.1016/S0022-4359(18)30076-9
Shannon C. E., 1948, BELL SYST TECH J, V27, P623, DOI [DOI 10.1002/J.1538-7305.1948.TB00917.X, DOI 10.1002/J.1538-7305.1948.TB01338.X]
Shehu E, 2020, J MARKETING RES, V57, P640, DOI 10.1177/0022243720921812
Shi SW, 2014, MARKET SCI, V33, P871, DOI 10.1287/mksc.2014.0872
Simester D, 2020, MANAGE SCI, V66, P3412, DOI 10.1287/mnsc.2019.3379
STERN H, 1962, J MARKETING, V26, P59, DOI 10.2307/1248439
Stuart EA, 2010, STAT SCI, V25, P1, DOI 10.1214/09-STS313
Thaler RH, 2008, MARKET SCI, V27, P15, DOI 10.1287/mksc.1070.0330
Verhoef PC, 2009, J RETAILING, V85, P31, DOI 10.1016/j.jretai.2008.11.001
Wagner RP, 2017, WHY AMAZONS 1 CLICKO
Wang K, 2017, J MARKETING RES, V54, P706, DOI 10.1509/jmr.14.0518
Wang RJH, 2015, J RETAILING, V91, P217, DOI 10.1016/j.jretai.2015.01.002
Witman A, 2019, HEALTH SERV RES, V54, P492, DOI 10.1111/1475-6773.13086
Xu KQ, 2017, MANAGE SCI, V63, P1469, DOI 10.1287/mnsc.2015.2406
NR 55
TC 0
Z9 0
U1 34
U2 79
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD DEC
PY 2023
VL 69
IS 12
BP 7317
EP 7334
DI 10.1287/mnsc.2023.4716
EA FEB 2023
PG 19
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA CU7X1
UT WOS:000936808200001
DA 2024-03-27
ER
PT J
AU Lu, FC
Sinha, J
AF Lu, Fang-Chi
Sinha, Jayati
TI Understanding retail exclusion and promoting an inclusive customer
experience at transforming service encounters
SO JOURNAL OF CONSUMER AFFAIRS
LA English
DT Article
DE AI; customer experience; inclusive marketplace; retail exclusion;
service encounter framework
ID CONSUMER RESPONSES; EMOTIONAL CONTAGION; PROCEDURAL JUSTICE; SOCIAL
EXCLUSION; STATUS DEMOTION; PRICE FAIRNESS; REJECTION; LOYALTY;
EMPLOYEES; SMILE
AB The authors review the marketing practices likely to make customers feel excluded (ignored or rejected) and analyze the potency of retail exclusion in the transforming service encounters due to the infusion of artificial intelligence (AI), robots, and other new technologies. Synthesizing the findings of prior studies, the authors propose an integrative theoretical framework for understanding different perspectives, psychological mechanisms, and outcomes of retail exclusion, and highlight research opportunities for retail exclusion in two contexts of service encounters: interpersonal and technology-powered. The review aims to provide implications on proactive strategies to minimize the adverse effects of retail exclusions and promote inclusive customer experiences.
C1 [Lu, Fang-Chi] Univ Melbourne, Fac Business & Econ, Dept Management & Mkt, Melbourne, Vic, Australia.
[Sinha, Jayati] Florida Int Univ, Coll Business, Miami, FL USA.
[Lu, Fang-Chi] Univ Melbourne, Fac Business & Econ, Dept Management & Mkt, 198 Berkeley St, Melbourne, Vic 3010, Australia.
C3 University of Melbourne; Melbourne Bioinformatics; State University
System of Florida; Florida International University; University of
Melbourne
RP Lu, FC (autor correspondiente), Univ Melbourne, Fac Business & Econ, Dept Management & Mkt, 198 Berkeley St, Melbourne, Vic 3010, Australia.
EM fangchi.lu@unimelb.edu.au
RI Rau, Lea/IXW-9119-2023
CR Alba G, 2020, J RETAIL CONSUM SERV, V54, DOI 10.1016/j.jretconser.2020.102054
Alva M., 2021, AI ADOPTION INCREASE
Anaya GJ, 2016, J SERV MARK, V30, P359, DOI 10.1108/JSM-03-2015-0121
Ascarza E, 2018, J MARKETING RES, V55, P80, DOI 10.1509/jmr.16.0163
BAGOZZI RP, 1975, J MARKETING, V39, P32, DOI 10.2307/1250593
Baker StaceyMenzel., 2005, Journal of Macromarketing, V25, P128, DOI DOI 10.1177/0276146705280622
Baker TL, 2011, PSYCHOL MARKET, V28, P188, DOI 10.1002/mar.20386
Banik S, 2020, SERV IND J, V40, P315, DOI 10.1080/02642069.2018.1477938
Banik S, 2019, J BUS RES, V96, P125, DOI 10.1016/j.jbusres.2018.11.010
Barger PB, 2006, ACAD MANAGE J, V49, P1229, DOI 10.2307/20159829
Barnes DC, 2019, J MARKET THEORY PRAC, V27, P174, DOI 10.1080/10696679.2019.1577686
Barone MJ, 2010, J MARKETING, V74, P121, DOI 10.1509/jmkg.74.2.121
Barone MJ, 2010, J CONSUM PSYCHOL, V20, P78, DOI 10.1016/j.jcps.2009.10.002
Bartneck C, 2007, AI SOC, V21, P217, DOI 10.1007/s00146-006-0052-7
BAUMEISTER RF, 1995, PSYCHOL BULL, V117, P497, DOI 10.1037/0033-2909.117.3.497
Baumeister RF., 1990, J SOC CLIN PSYCHOL, V9, P165, DOI [10.1521/jscp.1990.9.2.165, DOI 10.1521/JSCP.1990.9.2.165]
Beatty SE, 2016, J SERV RES-US, V19, P158, DOI 10.1177/1094670515624978
Belk R., 2020, INT J BUSINESS ANTHR, V10, P49
Berman B., 2001, RETAIL MANAGEMENT, V8th
BITNER MJ, 1990, J MARKETING, V54, P71, DOI 10.2307/1252174
Bonifield C, 2007, MARKET LETT, V18, P85, DOI 10.1007/s11002-006-9006-6
Bove L.L., 2018, FRONT SERV C AUST TE
Brave S, 2005, INT J HUM-COMPUT ST, V62, P161, DOI 10.1016/j.ijhcs.2004.11.002
Broadbent E, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0072589
BUGENTAL DB, 1986, PERS SOC PSYCHOL B, V12, P7, DOI 10.1177/0146167286121001
Burgess G, 2017, QUAL MARK RES, V20, P486, DOI 10.1108/QMR-09-2016-0085
Burgoon J. K., 1993, Journal of Language and Social Psychology, V12, P30, DOI [DOI 10.1177/0261927X93121003, 10.1177/0261927x93121003]
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Chan KW, 2019, J SERV RES-US, V22, P371, DOI 10.1177/1094670519847294
Chang W, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102214
Cho J, 2006, J RETAILING, V82, P25, DOI 10.1016/j.jretai.2005.11.002
Choi S, 2020, SERV SCI, V12, P1, DOI 10.1287/serv.2019.0252
Choi S, 2019, INT J HOSP MANAG, V82, P32, DOI 10.1016/j.ijhm.2019.03.026
CIALDINI RB, 1980, J PERS SOC PSYCHOL, V39, P406, DOI 10.1037/0022-3514.39.3.406
Clarke D., 2020, EXPERIENCE IS EVERYT
Coeckelbergh M, 2012, ETHICS INF TECHNOL, V14, P53, DOI 10.1007/s10676-011-9279-1
Dang JN, 2021, COMPUT HUM BEHAV, V115, DOI 10.1016/j.chb.2020.106612
Darke PR, 2003, J CONSUM PSYCHOL, V13, P328, DOI 10.1207/S15327663JCP1303_13
Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
Edelson S., 2020, FORBES
Edwards D., 2021, RETAIL ROBOTS MARKET
Fahnestock Jeanne., 2011, Rhetorical Style: The Uses of Language in Persuasion
Feather N T, 1999, Pers Soc Psychol Rev, V3, P86, DOI 10.1207/s15327957pspr0302_1
Feinberg FM, 2002, J MARKETING RES, V39, P277, DOI 10.1509/jmkr.39.3.277.19108
Festinger L, 1954, HUM RELAT, V7, P117, DOI 10.1177/001872675400700202
FOLKES VS, 1988, J CONSUM RES, V14, P548, DOI 10.1086/209135
Frey CB, 2017, TECHNOL FORECAST SOC, V114, P254, DOI 10.1016/j.techfore.2016.08.019
Garvey AM, 2023, J MARKETING, V87, P10, DOI 10.1177/00222429211066972
Gong XS, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103005
Grandey AA, 2005, ORGAN BEHAV HUM DEC, V96, P38, DOI 10.1016/j.obhdp.2004.08.002
Gremler DD, 2008, J RETAILING, V84, P308, DOI 10.1016/j.jretai.2008.07.001
Grewal D, 2004, J INTERACT MARK, V18, P87, DOI 10.1002/dir.20024
Grewal D, 2007, J RETAILING, V83, P447, DOI 10.1016/j.jretai.2007.09.003
Grewal D, 2020, J ACAD MARKET SCI, V48, P96, DOI 10.1007/s11747-019-00697-z
Griffiths MA, 2011, J CONSUM AFF, V45, P445, DOI 10.1111/j.1745-6606.2011.01212.x
Guo Y., 2021, CASH MAY NOT KING CH
Gusenbauer M, 2020, RES SYNTH METHODS, V11, P181, DOI 10.1002/jrsm.1378
Gwinner KP, 1998, J ACAD MARKET SCI, V26, P101, DOI 10.1177/0092070398262002
Hadi R, 2020, J CONSUM RES, V47, P256, DOI 10.1093/jcr/ucz039
Haenel CM, 2019, J SERV RES-US, V22, P301, DOI 10.1177/1094670519835312
Haenlein M., 2010, Journal of Relationship Marketing, V9, P200, DOI DOI 10.1080/15332667.2010.522474
Haenlein M, 2006, J MARKETING, V70, P5, DOI 10.1509/jmkg.70.3.5
Haenlein M, 2012, J SERV MARK, V26, P458, DOI 10.1108/08876041211257936
Harris AMG, 2005, J PUBLIC POLICY MARK, V24, P163, DOI 10.1509/jppm.24.1.163.63893
Harris EG, 2020, J MARKET THEORY PRAC, V28, P318, DOI 10.1080/10696679.2020.1763810
Harris K., 2018, HARVARD BUSINES 0313
Hawkes S., 2013, TELEGRAPG
Hawkins A., 2017, The Guardian
Heerdink MW, 2015, J EXP SOC PSYCHOL, V56, P60, DOI 10.1016/j.jesp.2014.09.004
Herhausen D, 2019, J MARKETING, V83, P1, DOI 10.1177/0022242918822300
Hess NJ, 2020, J RETAILING, V96, P344, DOI 10.1016/j.jretai.2019.11.005
Holmqvist J, 2019, J RETAILING, V95, P115, DOI 10.1016/j.jretai.2019.03.004
Holmqvist J, 2012, J SERV RES-US, V15, P430, DOI 10.1177/1094670512441997
Horton WS, 2013, METAPHOR SYMBOL, V28, P148, DOI 10.1080/10926488.2013.797735
Hu M, 2018, J BUS RES, V90, P164, DOI 10.1016/j.jbusres.2018.05.006
Huang MH, 2022, J RETAILING, V98, P209, DOI 10.1016/j.jretai.2021.03.001
Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Hui MK, 2004, J INT BUS STUD, V35, P46, DOI 10.1057/palgrave.jibs.8400067
Hutton M, 2019, CONSUMP MARK CULT, V22, P528, DOI 10.1080/10253866.2018.1561636
Hyken S., 2018, CUSTOMER EXPERIENCE
Jin LY, 2014, J CONSUM RES, V40, P818, DOI 10.1086/673193
KAHNEMAN D, 1986, AM ECON REV, V76, P728
Kearney S, 2019, CONSUMP MARK CULT, V22, P545, DOI 10.1080/10253866.2018.1562701
Kim JJ, 2021, J ACAD MARKET SCI, V49, P71, DOI 10.1007/s11747-020-00719-1
Kim TW, 2020, PSYCHOL SCI, V31, P363, DOI 10.1177/0956797620904985
Koskelainen T, 2023, J CONSUM AFF, V57, P507, DOI 10.1111/joca.12510
KOTLER P, 1971, HARVARD BUS REV, V49, P74
Krumhuber E, 2007, EMOTION, V7, P730, DOI 10.1037/1528-3542.7.4.730
Kumar A, 2020, J BUS RES, V113, P384, DOI 10.1016/j.jbusres.2019.09.030
Lacey R, 2007, J SERV RES-US, V9, P241, DOI 10.1177/1094670506295850
Lamberton C, 2016, J MARKETING, V80, P146, DOI 10.1509/jm.15.0415
Larivière B, 2017, J BUS RES, V79, P238, DOI 10.1016/j.jbusres.2017.03.008
Lee J, 2012, J CONSUM RES, V39, P530, DOI 10.1086/664039
Lee MK, 2018, BIG DATA SOC, V5, DOI 10.1177/2053951718756684
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Lepthien A, 2017, INT J RES MARK, V34, P829, DOI 10.1016/j.ijresmar.2017.02.001
Liu X, 2021, J CONSUM BEHAV, V20, DOI 10.1002/cb.1860
Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
Lu Z, 2021, ANN TOURISM RES, V87, DOI 10.1016/j.annals.2020.103135
Luo XM, 2021, J MARKETING, V85, P14, DOI 10.1177/0022242920956676
MacLellan L., 2019, Quartz
Maslach C, 2001, ANNU REV PSYCHOL, V52, P397, DOI 10.1146/annurev.psych.52.1.397
McIntyre F.S., 1999, J MARKETING THEORY P, V7, P43, DOI [10.1080/10696679.1999.11501828, DOI 10.1080/10696679.1999.11501828]
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Mick DG, 2012, TRANSFORMATIVE CONSUMER RESEARCH FOR PERSONAL AND COLLECTIVE WELL-BEING, P1
Molden DC, 2009, J PERS SOC PSYCHOL, V96, P415, DOI 10.1037/a0012958
MUESER KT, 1984, J PERS SOC PSYCHOL, V46, P469, DOI 10.1037/0022-3514.46.2.469
Newman CL, 2019, J ACAD MARKET SCI, V47, P76, DOI 10.1007/s11747-018-0601-x
Newman DT, 2020, ORGAN BEHAV HUM DEC, V160, P149, DOI 10.1016/j.obhdp.2020.03.008
Ng SC, 2022, J SERV MARK, V36, P991, DOI 10.1108/JSM-10-2021-0368
Nguyen B, 2013, J CONSUM MARK, V30, P17, DOI 10.1108/07363761311290812
Packard G, 2018, J MARKETING RES, V55, P541, DOI 10.1509/jmr.16.0118
Pantano E, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103074
Patagonia, GATH DIV EQ INCL TER
Peterson RA, 2002, J RETAILING, V78, P9, DOI 10.1016/S0022-4359(01)00062-8
Pitardi V, 2022, J SERV MANAGE, V33, P389, DOI 10.1108/JOSM-12-2020-0435
Promberger M, 2006, J BEHAV DECIS MAKING, V19, P455, DOI 10.1002/bdm.542
Pugh SD, 2001, ACAD MANAGE J, V44, P1018, DOI 10.5465/3069445
Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
Puntoni S, 2009, J CONSUM RES, V35, P1013, DOI 10.1086/595022
Radovanovic D, 2020, SOC INCL, V8, P151, DOI 10.17645/si.v8i2.2587
Ramaseshan B, 2017, J SERV MARK, V31, P650, DOI 10.1108/JSM-11-2016-0377
Reardon M., 2007, CNET
Reynolds KL, 2006, J MARKET THEORY PRAC, V14, P95, DOI 10.2753/MTP1069-6679140201
Richman LS, 2009, PSYCHOL REV, V116, P365, DOI 10.1037/a0015250
Riva P., 2016, SOCIAL EXCLUSION PSY
Robinson S, 2020, J BUS RES, V116, P366, DOI 10.1016/j.jbusres.2019.08.038
Rosado-Serrano A, 2018, J BUS RES, V85, P238, DOI 10.1016/j.jbusres.2017.12.049
Rosenbaum MS, 2021, J SERV MARK, V35, P261, DOI 10.1108/JSM-03-2021-0096
Saxe R., 1982, J MARKETING RES, V19, P343, DOI 10.1177/002224378201900307
Schaufeli WB, 2008, APPL PSYCHOL-INT REV, V57, P173, DOI 10.1111/j.1464-0597.2007.00285.x
Shacklett M., 2022, CURR CONTENTS
Shavitt S, 2020, J RETAILING, V96, P40, DOI 10.1016/j.jretai.2019.11.009
Silchenko K, 2020, J CONSUM AFF, V54, P541, DOI 10.1111/joca.12289
Simester D, 2020, MANAGE SCI, V66, P2495, DOI 10.1287/mnsc.2019.3308
SINGELIS TM, 1994, PERS SOC PSYCHOL B, V20, P580, DOI 10.1177/0146167294205014
Singh J, 2000, J MARKETING, V64, P15, DOI 10.1509/jmkg.64.2.15.17998
Sinha J, 2019, J CONSUM RES, V46, P791, DOI 10.1093/jcr/ucz021
Sirdeshmukh D, 2002, J MARKETING, V66, P15, DOI 10.1509/jmkg.66.1.15.18449
Söderlund M, 2021, EUR J MARKETING, V55, P94, DOI 10.1108/EJM-09-2019-0748
Soderlund M, 2021, J RETAIL CONSUM SERV, V59, DOI 10.1016/j.jretconser.2020.102401
Söderlund M, 2017, J RETAIL CONSUM SERV, V37, P168, DOI 10.1016/j.jretconser.2016.10.014
Spangler S., 2017, ISSUETRAK
Srinivasan R, 2021, J MARKETING, V85, P74, DOI 10.1177/0022242921997082
Steinhoff L, 2016, J ACAD MARKET SCI, V44, P88, DOI 10.1007/s11747-014-0405-6
Stewart DW, 2022, J CONSUM AFF, V56, P1423, DOI 10.1111/joca.12495
Sun YX, 2023, J MARKETING RES, V60, P646, DOI 10.1177/00222437221130721
SUTTON RI, 1988, ACAD MANAGE J, V31, P461, DOI 10.5465/256456
Swinhoe D., 2021, RETAILERS EMBRACING
Torraco R. J., 2005, Human Resource Development Review, V4, P356, DOI [10.1177/1534484305278283, DOI 10.1177/1534484305278283, https://doi.org/10.1177/1534484305278283]
Trope Y, 2010, PSYCHOL REV, V117, P440, DOI 10.1037/a0018963
United Nations Department of Economic and Social Affairs Population Division, 2020, STESASERA444 POLL DI
Vaidyanathan R, 2003, J BUS RES, V56, P453, DOI 10.1016/S0148-2963(01)00231-4
van Berlo G, 2014, SERV IND J, V34, P922, DOI 10.1080/02642069.2014.915950
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Van Kleef GA, 2022, ANNU REV PSYCHOL, V73, P629, DOI 10.1146/annurev-psych-020821-010855
van Prooijen JW, 2002, J PERS SOC PSYCHOL, V83, P1353, DOI 10.1037//0022-3514.83.6.1353
Wagner T, 2009, J MARKETING, V73, P69, DOI 10.1509/jmkg.73.3.69
Wang LL, 2017, EUR J MARKETING, V51, P1029, DOI 10.1108/EJM-12-2015-0876
Wang SJ, 2012, J MARKETING, V76, P69, DOI 10.1509/jm.10.0529
Wang Y, 2012, J CONSUM PSYCHOL, V22, P433, DOI 10.1016/j.jcps.2011.11.004
Ward MK, 2014, J CONSUM RES, V41, P590, DOI 10.1086/676980
Wei JH, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.842207
Weiss HM, 1996, RES ORGAN BEHAV, V18, P1
Wilson E., 2009, NEW YORK TIMES
Wu CC, 2012, J BUS RES, V65, P106, DOI 10.1016/j.jbusres.2011.02.005
Xia L, 2017, J PROD BRAND MANAG, V26, P327, DOI 10.1108/JPBM-05-2016-1196
Xia L, 2014, J BUS RES, V67, P2368, DOI 10.1016/j.jbusres.2014.02.002
Xia L, 2010, J RETAILING, V86, P1, DOI 10.1016/j.jretai.2009.10.003
Yalcin G, 2022, J MARKETING RES, V59, P696, DOI 10.1177/00222437211070016
Zeithaml VA, 1996, J MARKETING, V60, P31, DOI 10.2307/1251929
NR 172
TC 1
Z9 1
U1 30
U2 49
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0022-0078
EI 1745-6606
J9 J CONSUM AFF
JI J. Consum. Aff.
PD JUL
PY 2023
VL 57
IS 3
BP 1482
EP 1522
DI 10.1111/joca.12529
EA MAY 2023
PG 41
WC Business; Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA W4MC4
UT WOS:000981370800001
OA hybrid
DA 2024-03-27
ER
PT J
AU Iyengar, R
Park, YH
Yu, Q
AF Iyengar, Raghuram
Park, Young-Hoon
Yu, Qi
TI The Impact of Subscription Programs on Customer Purchases
SO JOURNAL OF MARKETING RESEARCH
LA English
DT Article
DE subscription program; retailing; e-commerce; causal inference; machine
learning; generalized random forest; sunk cost fallacy
ID LOYALTY PROGRAMS; FREQUENCY REWARD; ONLINE; RETENTION; DEMAND;
PSYCHOLOGY; ADOPTION; SERVICE; FEES
AB Subscription programs have become increasingly popular among a wide variety of retailers and marketplace platforms. Subscription programs give members access to a set of exclusive benefits for a fixed fee up front. In this article, the authors examine the causal effect of a subscription program on customer behavior. To account for self-selection and identify the individual-level treatment effects, they combine a difference-in-differences approach with a generalized random forests procedure that matches each member of the subscription program with comparable nonmembers. The authors find that subscription leads to a large increase in customer purchases. The effect of subscription is economically significant, persistent over time, and heterogeneous across customers. Interestingly, only one-third of the effect on customer purchases is due to the economic benefits of the subscription program, and the remaining two-thirds is attributed to the noneconomic effect. Evidence supports that members experience a sunk cost fallacy due to the up-front payment that subscription programs entail. Finally, the authors illustrate how firms can calculate the profitability of a subscription program and discuss the implications for customer retention and subscription programs.
C1 [Iyengar, Raghuram] Univ Penn, Wharton Sch, Dept Mkt, Mkt, Philadelphia, PA 19104 USA.
[Park, Young-Hoon] Cornell Univ, Samuel Curtis Johnson Grad Sch Management, Management, Ithaca, NY 14853 USA.
[Park, Young-Hoon] Cornell Univ, Samuel Curtis Johnson Grad Sch Management, Mkt, Ithaca, NY 14853 USA.
[Yu, Qi] Singapore Management Univ, Lee Kong Chian Sch Business, Mkt, Singapore, Singapore.
C3 University of Pennsylvania; Cornell University; Cornell University;
Singapore Management University
RP Iyengar, R (autor correspondiente), Univ Penn, Wharton Sch, Dept Mkt, Mkt, Philadelphia, PA 19104 USA.
EM riyengar@wharton.upenn.edu; yp34@cornell.edu; qiyu@smu.edu.sg
OI Yu, Qi/0000-0001-7889-0806
CR Abadie A, 2010, J AM STAT ASSOC, V105, P493, DOI 10.1198/jasa.2009.ap08746
Angrist JD, 2009, MOSTLY HARMLESS ECONOMETRICS: AN EMPIRICISTS COMPANION, P1
ARKES HR, 1985, ORGAN BEHAV HUM DEC, V35, P124, DOI 10.1016/0749-5978(85)90049-4
Athey S, 2019, ANN STAT, V47, P1148, DOI 10.1214/18-AOS1709
Baumgartner H, 2002, J CONSUM RES, V29, P286, DOI 10.1086/341578
Bertini M, 2008, MARKET SCI, V27, P236, DOI 10.1287/mksc.1070.0295
Bertrand M, 2004, Q J ECON, V119, P249, DOI 10.1162/003355304772839588
Bijmolt THA, 2005, J MARKETING RES, V42, P141, DOI 10.1509/jmkr.42.2.141.62296
Bolton RN, 2004, J ACAD MARKET SCI, V32, P271, DOI 10.1177/0092070304263341
Bolton RN, 2000, J ACAD MARKET SCI, V28, P95, DOI 10.1177/0092070300281009
Chernozhukov V, 2018, ECONOMET J, V21, pC1, DOI 10.1111/ectj.12097
Datta H, 2018, MARKET SCI, V37, P5, DOI 10.1287/mksc.2017.1051
Don, 2017, FORTUNE
Drèze X, 2009, J CONSUM RES, V35, P890, DOI 10.1086/593946
Dubé JP, 2018, MARKET SCI, V37, P22, DOI 10.1287/mksc.2017.1047
Forman C, 2009, MANAGE SCI, V55, P47, DOI 10.1287/mnsc.1080.0932
Galak J, 2018, ANNU REV PSYCHOL, V69, P1, DOI 10.1146/annurev-psych-122216-011542
Goldfarb A, 2011, MANAGE SCI, V57, P57, DOI 10.1287/mnsc.1100.1246
Goli A, 2022, J MARKETING RES, V59, P11, DOI 10.1177/00222437211016360
Gopalakrishnan A, 2021, MARKET SCI, V40, P508, DOI 10.1287/mksc.2020.1268
Gourville J, 2002, HARVARD BUS REV, V80, P90
Grier SA, 2001, J MARKETING RES, V38, P216, DOI 10.1509/jmkr.38.2.216.18843
Hamilton, 2020, TIME SERIES ANAL PRI
HANUSHEK EA, 1974, AM STAT, V28, P66, DOI 10.2307/2683605
Hartmann WR, 2008, QME-QUANT MARK ECON, V6, P109, DOI 10.1007/s11129-007-9035-3
Hastie T., 2005, ELEMENTS STAT LEARNI, V27, P83, DOI DOI 10.1007/978-0-387-84858-7
Hirano K, 2003, ECONOMETRICA, V71, P1161, DOI 10.1111/1468-0262.00442
Ho TH, 2018, MANAGE SCI, V64, P1761, DOI 10.1287/mnsc.2016.2651
Imbens GW, 2015, CAUSAL INFERENCE FOR STATISTICS, SOCIAL, AND BIOMEDICAL SCIENCES: AN INTRODUCTION, P1, DOI 10.1017/CBO9781139025751
Iyengar R, 2011, MARKET SCI, V30, P820, DOI 10.1287/mksc.1110.0655
Kopalle PK, 2012, MARKET SCI, V31, P216, DOI 10.1287/mksc.1110.0687
Lal R, 2003, QME-QUANT MARK ECON, V1, P179
Leenheer J, 2007, INT J RES MARK, V24, P31, DOI 10.1016/j.ijresmar.2006.10.005
Lewis M, 2006, MARKET SCI, V25, P51, DOI 10.1287/mksc.1050.0150
Lewis M, 2006, J RETAILING, V82, P13, DOI 10.1016/j.jretai.2005.11.005
Lewis M, 2004, J MARKETING RES, V41, P281, DOI 10.1509/jmkr.41.3.281.35986
LIANG KY, 1986, BIOMETRIKA, V73, P13, DOI 10.2307/2336267
Liu YP, 2007, J MARKETING, V71, P19, DOI 10.1509/jmkg.71.4.19
Manchanda P, 2015, MARKET SCI, V34, P367, DOI 10.1287/mksc.2014.0890
McCarthy DM, 2017, J MARKETING, V81, P17, DOI 10.1509/jm.15.0519
Narang U, 2019, MARKET SCI, V38, P756, DOI 10.1287/mksc.2019.1169
Prins R, 2007, J MARKETING, V71, P169, DOI 10.1509/jmkg.71.2.169
Rogers, 2003, DIFFUSION INNOVATION
ROSENBAUM PR, 1983, BIOMETRIKA, V70, P41, DOI 10.1093/biomet/70.1.41
SATO K, 1972, J POLIT ECON, V80, P102, DOI 10.1086/259864
TELLIS GJ, 1988, J MARKETING RES, V25, P331, DOI 10.2307/3172944
THALER R, 1980, J ECON BEHAV ORGAN, V1, P39, DOI 10.1016/0167-2681(80)90051-7
Venkatesan R, 2012, J MARKETING, V76, P76, DOI 10.1509/jm.10.0162
Wang K, 2017, J MARKETING RES, V54, P706, DOI 10.1509/jmr.14.0518
Wood W, 2009, J CONSUM PSYCHOL, V19, P579, DOI 10.1016/j.jcps.2009.08.003
NR 50
TC 11
Z9 14
U1 51
U2 184
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2437
EI 1547-7193
J9 J MARKETING RES
JI J. Mark. Res.
PD DEC
PY 2022
VL 59
IS 6
BP 1101
EP 1119
AR 00222437221080163
DI 10.1177/00222437221080163
EA APR 2022
PG 19
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 5W0OI
UT WOS:000789426100001
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Vermeer, SAM
Araujo, T
Bernritter, SF
van Noort, G
AF Vermeer, Susan A. M.
Araujo, Theo
Bernritter, Stefan F.
van Noort, Guda
TI Seeing the wood for the trees: How machine learning can help firms in
identifying relevant electronic word-of-mouth in social media
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE eWOM; Webcare; Social media; Digital marketing strategies; Automated
content analysis; Sentiment analysis; Machine learning
ID USER-GENERATED CONTENT; TEXT ANALYSIS; ONLINE; WEBCARE; CUSTOMERS;
SENTIMENT; CONSUMERS; PRODUCT; SERVICE; CHATTER
AB The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then-based on a categorization of seven different types of eWOM (e.g., question, complaint)-classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks. (C) 2019 Elsevier B.V. All rights reserved.
C1 [Vermeer, Susan A. M.; Araujo, Theo; van Noort, Guda] Univ Amsterdam, Amsterdam Sch Commun Res ASCoR, POB 15793, NL-1001 NG Amsterdam, Netherlands.
[Bernritter, Stefan F.] Goldsmiths Univ London, Inst Management Studies, London SE14 6NW, England.
C3 University of Amsterdam; University of London; Goldsmiths University
London
RP Vermeer, SAM (autor correspondiente), Univ Amsterdam, Amsterdam Sch Commun Res ASCoR, POB 15793, NL-1001 NG Amsterdam, Netherlands.
EM SA.M.Vermeer@uva.nl; T.B.Araujo@uva.nl; S.Bernritter@gold.ac.uk;
G.vanNoort@uva.nl
RI Bernritter, Stefan/M-4750-2019
OI Bernritter, Stefan/0000-0002-4291-7824; van Noort,
Guda/0000-0002-6314-1455; Araujo, Theo/0000-0002-4633-9339; Vermeer,
Susan/0000-0002-9829-8057
CR [Anonymous], 2017, MARKETING SCI I WORK
Berger J, 2010, MARKET SCI, V29, P815, DOI 10.1287/mksc.1090.0557
Bhandari M, 2018, INT J ADVERT, V37, P125, DOI 10.1080/02650487.2017.1349030
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
Boumans JW, 2016, DIGIT JOURNAL, V4, P8, DOI 10.1080/21670811.2015.1096598
Brown Penelope., 1978, Politeness: Some Universals in Language Usage
Büschken J, 2016, MARKET SCI, V35, P953, DOI 10.1287/mksc.2016.0993
Colicev A, 2018, J MARKETING, V82, P37, DOI 10.1509/jm.16.0055
Colliander J, 2015, INT J ADVERT, V34, P181, DOI 10.1080/02650487.2014.996197
Coombs W.T., 2002, Journal of Public Affairs, V2, P215, DOI DOI 10.1002/PA.115
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
Cui H., 2006, Proceedings of the AAAI conference on artificial intelligence, V6, P1265, DOI DOI 10.5555/1597348.1597389
Demmers J, 2018, INT J ELECTRON COMM, V22, P8, DOI 10.1080/10864415.2018.1396110
Dhillon I. S., 2003, Journal of Machine Learning Research, V3, P1265, DOI 10.1162/153244303322753661
Esuli A, 2010, INT J MARKET RES, V52, P775, DOI 10.2501/S147078531020165X
Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188
Godes D, 2004, MARKET SCI, V23, P545, DOI 10.1287/mksc.1040.0071
Gopinath S, 2014, MARKET SCI, V33, P241, DOI 10.1287/mksc.2013.0820
Grégoire Y, 2015, BUS HORIZONS, V58, P173, DOI 10.1016/j.bushor.2014.11.001
Guo L, 2016, J MASS COMMUN Q, V93, P332, DOI 10.1177/1077699016639231
Hansen N, 2018, INT J RES MARK, V35, P557, DOI 10.1016/j.ijresmar.2018.08.001
Hartmann J., 2018, INT J RES MARK, P1
Homburg C, 2015, J MARKETING RES, V52, P629, DOI 10.1509/jmr.11.0448
Huibers J, 2014, TIJDSCHR COMMUNWET, V42, P165
Humphreys A, 2018, J CONSUM RES, V44, P1274, DOI 10.1093/jcr/ucx104
Kaiser C, 2012, INTERNET RES, V22, P275, DOI 10.1108/10662241211235653
Kent A., 1955, AM DOC, V6, P93, DOI [10.1002/, 10.1002/asi.5090060209, DOI 10.1002/ASI.5090060209]
Kietzmann J, 2013, J PUBLIC AFF, V13, P146, DOI 10.1002/pa.1470
Kübler RV, 2017, INT SER QUAN MARKET, P631, DOI 10.1007/978-3-319-53469-5_19
Kunneman F, 2015, INFORM PROCESS MANAG, V51, P500, DOI 10.1016/j.ipm.2014.07.006
Kupor D, 2018, J CONSUM RES, V45, P490, DOI 10.1093/jcr/ucy021
Li N, 2010, DECIS SUPPORT SYST, V48, P354, DOI 10.1016/j.dss.2009.09.003
Loper E., 2002, ASS COMPUTATIONAL LI, P63, DOI [10.3115/1225403.1225421, DOI 10.3115/1225403.1225421]
Marchand A, 2017, INT J RES MARK, V34, P336, DOI 10.1016/j.ijresmar.2016.09.003
Martínez-Cámara E, 2014, NAT LANG ENG, V20, P1, DOI 10.1017/S1351324912000332
Maslowska E, 2017, INT J ADVERT, V36, P142, DOI 10.1080/02650487.2016.1195622
Moon S, 2017, INT J RES MARK, V34, P265, DOI 10.1016/j.ijresmar.2016.05.007
Munoz-Garcia O., 2012, WORKSH NLP CAN U TAG, P1
Nakhaeizadeh G., 1997, CLASSIFICATION KNOWL, P123
Okazaki S, 2014, INT J MARKET RES, V56, P467, DOI 10.2501/IJMR-2014-008
Ordenes F. V., 2018, J CONSUM RES, P1
Owsley S., 2006, AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), P181
Packard G, 2017, J MARKETING RES, V54, P572, DOI 10.1509/jmr.15.0248
Pai MY, 2013, EXPERT SYST APPL, V40, P1993, DOI 10.1016/j.eswa.2012.10.024
Pauwels K, 2016, INT J RES MARK, V33, P639, DOI 10.1016/j.ijresmar.2016.01.005
Pennebaker JW., 2015, LIWC 2015 OPERATORS
Phillips R.L., 2016, J. Bus. Financ. Librariansh, V21, P1, DOI [10.1080/08963568.2015.1110229, DOI 10.1080/08963568.2015.1110229]
Rosario AB, 2016, J MARKETING RES, V53, P297, DOI 10.1509/jmr.14.0380
Schamari J, 2015, J INTERACT MARK, V30, P20, DOI 10.1016/j.intmar.2014.12.001
Schweidel DA, 2014, J MARKETING RES, V51, P387, DOI 10.1509/jmr.12.0424
Tang TY, 2014, J MARKETING, V78, P41, DOI 10.1509/jm.13.0301
Tirunillai S, 2014, J MARKETING RES, V51, P463, DOI 10.1509/jmr.12.0106
Tirunillai S, 2012, MARKET SCI, V31, P198, DOI 10.1287/mksc.1110.0682
van Laer T, 2010, INT J RES MARK, V27, P164, DOI 10.1016/j.ijresmar.2009.12.010
Van Noort G., 2014, Integrated Communications in the Post-Modern Erea, V1, P77
van Noort G, 2012, J INTERACT MARK, V26, P131, DOI 10.1016/j.intmar.2011.07.001
Weitzl W, 2018, COMPUT HUM BEHAV, V89, P316, DOI 10.1016/j.chb.2018.07.012
Willemsen L., 2013, Advances in Advertising Research, VIV, P55
Willemsen LM, 2011, J COMPUT-MEDIAT COMM, V17, P19, DOI 10.1111/j.1083-6101.2011.01551.x
Wilson AE, 2017, J ACAD MARKET SCI, V45, P534, DOI 10.1007/s11747-017-0515-z
Zhang W, 2011, EXPERT SYST APPL, V38, P2758, DOI 10.1016/j.eswa.2010.08.066
Zhang Z, 2012, ACM TRANS MANAG INF, V3, DOI 10.1145/2151163.2151168
NR 62
TC 72
Z9 77
U1 9
U2 76
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD SEP
PY 2019
VL 36
IS 3
SI SI
BP 492
EP 508
DI 10.1016/j.ijresmar.2019.01.010
PG 17
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA JU4BR
UT WOS:000501622900010
OA hybrid, Green Accepted, Green Published
DA 2024-03-27
ER
PT J
AU Kaczorowska-Spychalska, D
AF Kaczorowska-Spychalska, Dominika
TI How chatbots influence marketing
SO MANAGEMENT-POLAND
LA English
DT Article
DE digital technologies; chatbots; consumer behavior; marketing
AB The role of digital technologies, especially the Internet of Things (IoT) and Artificial Intelligence (AI), increasingly become a key element of diverse interactions between brands and consumers. Homo Cyber Oeconomicus, one of the potential stages of ongoing consumer's evolution, lives between processes of dehumanization of the surrounding world and humanization of digital technologies. While remaining in a constant contact with smart devices, systems and algorithms, they are looking for new values and meanings, which are a metaphor of their desires, fears and behaviors. As a result, the digital ecosystem, as an attempt to combine the humanism idea with technologization processes, poses new challenges to companies/brands, both concerning the quality of interactions with an increasingly digital consumer and tools used in that process. Chatbots can prove to be an interesting solution here, as their spectrum of potential areas of implementation in business systematically increases. The paper attempts to identify the influence of chatbots on marketing taking into account their role in Human-to-Machine interaction process. A part of these considerations is of the character of philosophical discourse on the role of that technology in human life, which is a starting point for the presentation of preliminary assumptions for a model of consumer-chatbot interaction (digital technology) in marketing activity of companies/brands.
C1 [Kaczorowska-Spychalska, Dominika] Univ Lodz, Fac Management, Dept Mkt, Lodz, Poland.
C3 University of Lodz
RP Kaczorowska-Spychalska, D (autor correspondiente), Univ Lodz, Fac Management, Dept Mkt, Lodz, Poland.
OI Kaczorowska-Spychalska, DOMINIKA/0000-0002-2566-0297
CR Aigrain P, 2012, SHARING: CULTURE AND THE ECONOMY IN THE INTERNET AGE, P1
[Anonymous], 2018, MARKETING 4 0 MOVING
[Anonymous], 2015, OUR FINAL INVENTION
[Anonymous], 2014, 2 MACHINE AGE AKILLI
Borek A., 2016, MARKETING SMART MACH
Daugherty P. R., 2018, Human + machine: Reimagining work in the age of AI
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
Gregor B, 2018, MARKETING ERZE TECHN
Harari YN., 2016, HOMO DEUS BRIEF HIST, DOI DOI 10.17104/9783406704024
KELLY K, 2017, NIEUNIKNIONE JAK INT
Kurzweil R., 2015, SINGULARITY IS NEAR
Kurzweil Ray., 2013, How to Create a Mind: The Secret of Human Thought Revealed
Lubian F., 2017, VALUE DIGITAL WORLD
Przegalinska A, 2016, ISTOTY WIRTUALNE JAK, P239
Sathi A., 2016, COGNITIVE INTERNET T
Sinclair Bruce., 2017, IoT Inc.: How Your Company Can Use the Internet of Things to Win in the Outcome Economy: How Your Company Can Use the Internet of Things to Win in the Outcome Economy, V1st
SKINNER C, 2018, CYFROWI LUDZIE
Tegmark M., 2017, Life 3.0
Walsh T., 2017, It's alive! Artificial Intelligence from the logic piano to killer robots
Weinman J., 2015, DIGITAL DISCIPLINES
Wuebben J., 2017, FUTURE MARKETING WIN
[No title captured]
[No title captured]
[No title captured]
NR 24
TC 26
Z9 27
U1 11
U2 101
PU SCIENDO
PI WARSAW
PA DE GRUYTER POLAND SP Z O O, BOGUMILA ZUGA 32A STR, 01-811 WARSAW, POLAND
SN 1429-9321
EI 2299-193X
J9 MANAG-POL
JI Manag.-Pol.
PD JUN
PY 2019
VL 23
IS 1
BP 251
EP 270
DI 10.2478/manment-2019-0015
PG 20
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA IE0GZ
UT WOS:000472066400015
OA gold, Green Submitted
DA 2024-03-27
ER
PT J
AU Li, WW
Cai, Y
Hanafiah, MH
Liao, ZW
AF Li, Wanwan
Cai, Ying
Hanafiah, Mohd Hizam
Liao, Zhenwei
TI An Empirical Study on Personalized Product Recommendation Based on
Cross-Border E-Commerce Customer Data Analysis
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Cross-Border E-Commerce; Recommendation Systems Automatically;
Personalized Recommendation; Deep Learning Technology; Data Sparsity;
Commodity Information
AB Thanks to the rapid growth of cross-border e-commerce platforms, numerous cross-border items are now available to customers. Several serious issues with cross-border e-commerce platforms related to item promotion and consumer product screening have arisen. Particular importance should be placed on studying and implementing personalized recommendation systems based on international e-commerce. In light of the quick expansion of commodities, when making individualized suggestions, traditional recommendation algorithms have had to deal with issues such as scant data, a chilly start to the market, and trouble identifying user preferences. To automatically mine the implicit and latent relationships between users and objects in recommendation systems, this study employs deep learning with nonlinear learning capabilities, which resolves the challenges of user interest mining. The weaknesses of the existing global recommendation research are emphasized, the study of conventional recommendation algorithms mixed with deep learning technology is deep factorization machine (DeepFM) and neural matrix factorization (NeuMF) models. Both models excel in recommending implicit feedback data. The DeepFM model yields the lowest loss function values, while the NeuMF model outperforms the competing models in terms of HR@20 (a commonly used indicator to measure the recall rate) and loss functions. In summary, this research addresses critical issues in cross-border e-commerce by developing personalized recommendation systems and integrating deep learning with traditional recommendation algorithms to enhance global recommendations.
C1 [Li, Wanwan; Hanafiah, Mohd Hizam; Liao, Zhenwei] Univ Kebangsaan Malaysia, Bangi, Malaysia.
[Li, Wanwan; Cai, Ying] Taizhou Vocat & Tech Coll, Taizhou, Peoples R China.
C3 Universiti Kebangsaan Malaysia
RP Hanafiah, MH (autor correspondiente), Univ Kebangsaan Malaysia, Bangi, Malaysia.
FU Scientific Research Fund of Zhejiang Provincial Education Department
[Y202352165]
FX Funding This research was supported by the Scientific
Research Fund of Zhejiang Provincial Education Department (Y202352165) .
CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99
Ankora Carlos, 2022, International Journal of Information Technologies and Systems Approach, P1, DOI 10.4018/IJITSA.304809
Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124
Choi J. W., 2020, Applied Computing and Information Technology, P17
Choi SM, 2012, EXPERT SYST APPL, V39, P8079, DOI 10.1016/j.eswa.2012.01.132
Donahue J, 2014, PR MACH LEARN RES, V32
Fan R. X., 2019, Unpublished master's thesis
Gao O. U., 2019, Transducer and Microsystem Technologies
Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1162/neco.1997.9.8.1735, 10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
Huang GJ, 2020, ALGORITHMS, V13, DOI 10.3390/a13120342
Jianfeng Tang, 2021, 10th International Conference on Computer Engineering and Networks. Advances in Intelligent Systems and Computing (AISC 1274), P233, DOI 10.1007/978-981-15-8462-6_27
Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123
Koren Y, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P447
Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263
Li CY, 2020, NEURAL COMPUT APPL, V32, P11245, DOI 10.1007/s00521-020-05042-y
Li MY, 2022, J ORGAN END USER COM, V34, DOI 10.4018/JOEUC.302893
Li W. X., 2019, Computer Technology and Development, V29, P18
Liu NN, 2013, ACM T INTEL SYST TEC, V4, DOI 10.1145/2414425.2414440
Liu Y., 2020, Computer & Digital Engineering, V366, P44
Liu Y. J., 2014, Network Security Technology & Application, V12, P28
McAuley J, 2015, SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P43, DOI 10.1145/2766462.2767755
Sukte Chudaman Devidasrao, 2022, International Journal of Information Technologies and Systems Approach, P1, DOI 10.4018/IJITSA.304810
Tang Y, 2022, J ORGAN END USER COM, V34, DOI 10.4018/JOEUC.298702
Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122
[王媛媛 Wang Yuanyuan], 2017, [计算机科学, Computer Science], V44, P63
Wang Z, 2020, J PHYS CONF SER, V1575, DOI 10.1088/1742-6596/1575/1/012035
Wenjing Yan, 2020, IOP Conference Series: Materials Science and Engineering, V768, DOI 10.1088/1757-899X/768/7/072010
Wu C. Y., 2017, P INT C LEARNING REP
Xiaofei L., 2020, Computer Science, V47, P56
Yu Bao, 2019, COMPUTER APPL SOFTWA, V36, P307
Yue J, 2015, REMOTE SENS LETT, V6, P468, DOI 10.1080/2150704X.2015.1047045
[岳希 Yue Xi], 2020, [工程科学与技术, Advanced Engineering Sciences], V52, P198
Zhang SA, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3285029
NR 34
TC 0
Z9 0
U1 2
U2 2
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PY 2024
VL 36
IS 1
AR 335498
DI 10.4018/JOEUC.335498
PG 16
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA II1V4
UT WOS:001165615200003
OA gold
DA 2024-03-27
ER
PT J
AU Korbel, JJ
Siddiq, UH
Zarnekow, R
AF Korbel, Jakob J.
Siddiq, Umar H.
Zarnekow, Ruediger
TI Towards Virtual 3D Asset Price Prediction Based on Machine Learning
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE 3D model; virtual asset; virtual product; virtual good; pricing; machine
learning; feature scoring; e-commerce; metaverse
ID MEAN ABSOLUTE ERROR; CORRELATION-COEFFICIENTS; MODERATING ROLE; DESIGN
SCIENCE; INFORMATION; PSYCHOLOGY; INTENTION; SELECTION; PRODUCTS; SPARSE
AB Although 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual 3D asset price prediction tool based on the analysis results. The evaluation revealed that the random forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore, the findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. However, the analysis also showed that the pricing behavior is still substantially influenced by the subjective assessment of virtual 3D asset creators.
C1 [Korbel, Jakob J.; Siddiq, Umar H.; Zarnekow, Ruediger] Tech Univ Berlin, Dept Econ & Management, Informat & Commun Management, D-10623 Berlin, Germany.
C3 Technical University of Berlin
RP Korbel, JJ (autor correspondiente), Tech Univ Berlin, Dept Econ & Management, Informat & Commun Management, D-10623 Berlin, Germany.
EM jakob.j.korbel@tu-berlin.de; uhsiddiq@googlemail.com;
ruediger.zarnekow@tu-berlin.de
OI Korbel, Jakob J./0000-0003-3135-5229
CR 3D Systems, 3D SYST WHAT IS STL
Afrianto M. A., 2020, Journal of Information Systems Engineering and Business Intelligence, V2, DOI [10.20473/jisebi.6.2.123-132, DOI 10.20473/JISEBI.6.2.123-132]
Alasadi S., 2017, J ENG APPL SCI, V12, P4102, DOI [10.3923/jeasci.2017.4102.4107, 10.36478/jeasci.2017.4102.4107, DOI 10.36478/JEASCI.2017.4102.4107]
Algharabat R, 2017, J RETAIL CONSUM SERV, V36, P203, DOI 10.1016/j.jretconser.2017.02.007
Ali J., 2012, INT J COMPUT SCI ISS, V9, P272
Animesh A, 2011, MIS QUART, V35, P789
[Anonymous], 2007, J THEOR APPL ELECT C, DOI DOI 10.3390/JTAER2010002
[Anonymous], TURBOSQUID PRODUCT P
[Anonymous], CGTRADER ANAL
[Anonymous], UNITY MANUAL MAT
[Anonymous], Unity Asset Store
[Anonymous], Makerbot thingiverse
[Anonymous], SKETCHFAB LEADING PL
[Anonymous], AUTODESK FBX
[Anonymous], ADROIT MARKET RES GL
[Anonymous], SKETCHFAB SELLER GUI
[Anonymous], FLASK WEB DEV ONE DR
Arshad M.R., 2019, INT J RECENT TECHNOL, V8, P4138
Bar-Lev SK, 1999, COMMUN STAT-THEOR M, V28, P1813, DOI 10.1080/03610929908832390
Batista GEAPA, 2003, APPL ARTIF INTELL, V17, P519, DOI 10.1080/08839510390219309
Bauer J, 2018, DECIS SUPPORT SYST, V106, P53, DOI 10.1016/j.dss.2017.12.002
Bergstra J, 2012, J MACH LEARN RES, V13, P281
Botchkarev A., 2018, ARXIV, DOI [10.28945/4184, DOI 10.28945/4184]
Brassington G., 2017, P EGU GEN ASS C VIEN
Cao LJ, 2003, NEUROCOMPUTING, V55, P321, DOI 10.1016/S0925-2312(03)00433-8
CGTrader, CGTRADER WHAT PRIC S
CGTrader Marketplace, CGTRADER MARK WORLDS
Chai T, 2014, GEOSCI MODEL DEV, V7, P1247, DOI 10.5194/gmd-7-1247-2014
Chandrashekar G, 2014, COMPUT ELECTR ENG, V40, P16, DOI 10.1016/j.compeleceng.2013.11.024
Chang CW, 2021, J THEOR APPL EL COMM, V16, P584, DOI 10.3390/jtaer16040035
Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
Chen ZS, 2020, J COMPUT APPL MATH, V365, DOI 10.1016/j.cam.2019.112395
Cheung CMK, 2015, ELECTRON COMMER R A, V14, P241, DOI 10.1016/j.elerap.2015.03.001
Chu X, 2016, SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, P2201, DOI 10.1145/2882903.2912574
Chung H.M., 1999, J F MANAGEMENT INFOR, V16, P11
Cleghorn J, 2015, DIGIT EDUC REV, P91
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
de Winter JCF, 2016, PSYCHOL METHODS, V21, P273, DOI 10.1037/met0000079
Dolonius D, 2020, COMPUT GRAPH FORUM, V39, P121, DOI 10.1111/cgf.13917
Drobetz W, 2021, J ASSET MANAG, V22, P507, DOI 10.1057/s41260-021-00237-x
Fairfield JAT, 2005, BOSTON U LAW REV, V85, P1047
Ge ZQ, 2017, IEEE ACCESS, V5, P20590, DOI 10.1109/ACCESS.2017.2756872
Greenstein-Messica A, 2020, ELECTRON COMMER R A, V40, DOI 10.1016/j.elerap.2019.100914
Gu SH, 2020, REV FINANC STUD, V33, P2223, DOI 10.1093/rfs/hhaa009
Hamari J, 2017, COMPUT HUM BEHAV, V71, P59, DOI 10.1016/j.chb.2017.01.042
Harris CR, 2020, NATURE, V585, P357, DOI 10.1038/s41586-020-2649-2
Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
Huang LS, 2008, ANN STAT, V36, P2085, DOI 10.1214/07-AOS531
Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
Islam Md Didarul, 2022, Machine Learning with Applications, V7
Israel JH, 2009, COMPUT GRAPH-UK, V33, P462, DOI 10.1016/j.cag.2009.05.005
Jiang ZH, 2004, J MANAGE INFORM SYST, V21, P111, DOI 10.1080/07421222.2004.11045817
Jovic A, 2015, 2015 8TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), P1200, DOI 10.1109/MIPRO.2015.7160458
Ke D, 2012, DECISION SCI, V43, P1039, DOI 10.1111/j.1540-5915.2012.00384.x
Korbel JJ, 2021, L N INF SYST ORGAN, V46, P492, DOI 10.1007/978-3-030-86790-4_33
Kropp L.A., 2019, P 27 EUROPEAN C INFO
Kuhn M, 2019, Feature engineering and selection: A practical approach for predictive models, DOI DOI 10.1201/9781315108230
Lehdonvirta V, 2009, INFORM COMMUN SOC, V12, P1059, DOI 10.1080/13691180802587813
Lewis R.L., 2000, 2000 ANN M SOC ACAD
Ma Y, 2018, INT J COMPUT COMMUN, V13, P235
Ma Y, 2012, ENSEMBLE MACHINE LEARNING: METHODS AND APPLICATIONS, P1, DOI 10.1007/978-1-4419-9326-7
Mäntymäki M, 2015, INT J INFORM MANAGE, V35, P124, DOI 10.1016/j.ijinfomgt.2014.10.004
Menard S, 2000, AM STAT, V54, P17, DOI 10.2307/2685605
Mohd T., 2019, INT J RECENT TECHNOL, V8, DOI DOI 10.35940/IJRTE.B1084.0982S1119
Mughal MJH, 2018, INT J ADV COMPUT SC, V9, P208
Mystakidis S. M., 2022, ENCYCLOPEDIA, V2022, P486, DOI [10.3390/encyclopedia2010031, DOI 10.3390/ENCYCLOPEDIA2010031]
Natekin A, 2013, FRONT NEUROROBOTICS, V7, DOI 10.3389/fnbot.2013.00021
Nguyen G, 2019, ARTIF INTELL REV, V52, P77, DOI 10.1007/s10462-018-09679-z
Oviedo-Gomez A., 2021, INT J ENERGY EC POLI, V11, P66, DOI [10.32479/ijeep.11346, DOI 10.32479/IJEEP.11346]
Pai HY, 2019, PROCEEDINGS OF THE 2019 IEEE EURASIA CONFERENCE ON IOT, COMMUNICATION AND ENGINEERING (ECICE), P195, DOI 10.1109/ecice47484.2019.8942651
Pan JJ, 2009, COMPUT ANIMAT VIRT W, V20, P121, DOI 10.1002/cav.284
Pandas, about us
Pandey AC, 2020, J AMB INTEL HUM COMP, V11, P719, DOI 10.1007/s12652-019-01330-1
Park H, 2013, COMPUT IND, V64, P854, DOI 10.1016/j.compind.2013.05.006
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Peffers K., 2006, DESIGN SCI RES PROCE
Peffers K, 2007, J MANAGE INFORM SYST, V24, P45, DOI 10.2753/MIS0742-1222240302
Peng Y., 2019, APPL MATH FINANCE, V26, P69, DOI DOI 10.1080/1350486X.2019.1593866
Pfouga A, 2015, ADV TRANSDISCIPL ENG, V2, P575, DOI 10.3233/978-1-61499-544-9-575
Puth MT, 2015, ANIM BEHAV, V102, P77, DOI 10.1016/j.anbehav.2015.01.010
Pyle D., 1999, Data Preparation for Data Mining
Razavi R, 2019, INT J CONTEMP HOSP M, V31, P2149, DOI 10.1108/IJCHM-09-2018-0707
Refaeilzadeh, 2009, ENCY DATABASE SYST, DOI [DOI 10.1007/978-1-4899-7993-3_565-2, 10.1007/978-0-387-39940-9_565]
Riar M, 2023, INTERNET RES, V33, P242, DOI 10.1108/INTR-08-2021-0611
Schmidhuber J, 2015, NEURAL NETWORKS, V61, P85, DOI 10.1016/j.neunet.2014.09.003
Scrapy, SCRAP OP SOURC COLL
Sebastiao H, 2021, FINANC INNOV, V7, DOI 10.1186/s40854-020-00217-x
Shen BQ, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app112311087
Smink AR, 2019, ELECTRON COMMER R A, V35, DOI 10.1016/j.elerap.2019.100854
Spedicato G.A., 2018, Variance, Casualty Actuarial Society, V12, P69
Srivastava A.K., 1995, Econometric Reviews, V14, P229, DOI DOI 10.1080/07474939508800317
Thaseen IS, 2014, 2014 INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND INFORMATICS (IC3I), P879, DOI 10.1109/IC3I.2014.7019692
Tibshirani R, 1996, J ROY STAT SOC B, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x
TurboSquid, TURBOSQUID 3D MOD PR
Wang QY, 2021, INT J DATA SCI ANAL, V12, P175, DOI 10.1007/s41060-021-00252-6
Waskom M., 2021, J OPEN SOURCESOFTW, V6, P3021
Zheng A, 2018, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Zhu DH, 2015, J ELECTRON COMMER RE, V16, P22
Zhu JL, 2018, ANNU REV CONTROL, V46, P107, DOI 10.1016/j.arcontrol.2018.09.003
NR 99
TC 3
Z9 3
U1 9
U2 37
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD SEP
PY 2022
VL 17
IS 3
BP 924
EP 948
DI 10.3390/jtaer17030048
PG 25
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 4R9JV
UT WOS:000857071700001
OA gold
DA 2024-03-27
ER
PT J
AU Fridrich, M
Dostál, P
AF Fridrich, Martin
Dostal, Petr
TI User Churn Model in E-Commerce Retail
SO SCIENTIFIC PAPERS OF THE UNIVERSITY OF PARDUBICE-SERIES D-FACULTY OF
ECONOMICS AND ADMINISTRATION
LA English
DT Article
DE User Model; Churn Prediction; Customer Relationship Management;
Electronic Commerce; Retail; Machine Learning; Feature Importance;
Feature Set Importance
ID PARTIAL DEFECTION; CUSTOMER CHURN; RETENTION; PREDICTION
AB In e-commerce retail, maintaining a healthy customer base through retention management is necessary. Churn prediction efforts support the goal of retention and rely upon dependent and independent characteristics. Unfortunately, there does not appear to be a consensus regarding a user churn model. Thus, our goal is to propose a model based on a traditional and new set of attributes and explore its properties using auxiliary evaluation. Individual variable importance is assessed using the best performing modeling pipelines and a permutation procedure. In addition, we estimate the effects on the performance and quality of a feature set using an original technique based on importance ranking and information retrieval. The performance benchmark reveals satisfying pipelines utilizing LR, SVM-RBF, and GBM learners. The solutions rely profoundly on traditional recency and frequency aspects of user behavior. Interestingly, SVM-RBF and GBM exploit the potential of more subtle elements describing user preferences or date-time behavioural patterns. The collected evidence may also aid business decision-making associated with churn prediction efforts, e.g., retention campaign design.
C1 [Fridrich, Martin; Dostal, Petr] Brno Univ Technol, Inst Informat, Fac Business & Management, Brno, Czech Republic.
C3 Brno University of Technology
RP Fridrich, M (autor correspondiente), BUT, Fac Business & Management, Kolejni 2906-4, Brno 61200, Czech Republic.
EM fridrichmartin@yahoo.com
RI Fridrich, Martin/GWD-1250-2022; Dostál, Petr/H-1584-2014; Dostál,
Petr/JTV-6004-2023
OI Fridrich, Martin/0000-0003-3060-0704; Dostal, Petr/0000-0002-7871-4789
CR Abbasi A, 2015, IEEE INTELL SYST, V30, P35, DOI 10.1109/MIS.2015.19
Aggarwal CC, 2014, CH CRC DATA MIN KNOW, P457
Almuqren L, 2021, FUTURE INTERNET, V13, DOI 10.3390/fi13070175
[Anonymous], 2018, CUSTOMER NEEDS SOLUT
[Anonymous], 2017, Kaggle
Berger P, 2019, IEEE INTELL SYST, V34, P44, DOI 10.1109/MIS.2019.2895788
Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
Brusilovsky P, 1996, USER MODEL USER-ADAP, V6, P87, DOI 10.1007/BF00143964
Buckinx W, 2005, EUR J OPER RES, V164, P252, DOI 10.1016/j.ejor.2003.12.010
Chou YC, 2018, SERV BUS, V12, P685, DOI 10.1007/s11628-018-0371-z
Clemente-Císcar M, 2014, EUR J OPER RES, V239, P276, DOI 10.1016/j.ejor.2014.04.029
Montiel MAD, 2020, PAP REG SCI, V99, P1643, DOI 10.1111/pirs.12552
Fader PS, 2005, MARKET SCI, V24, P275, DOI 10.1287/mksc.1040.0098
Glady N, 2009, EUR J OPER RES, V197, P402, DOI 10.1016/j.ejor.2008.06.027
Gordini N, 2017, IND MARKET MANAG, V62, P100, DOI 10.1016/j.indmarman.2016.08.003
Gronwald K.D., 2017, INTEGRATED BUSINESS
Hand DJ, 2009, MACH LEARN, V77, P103, DOI 10.1007/s10994-009-5119-5
Hughes A., 2012, Strategic Database Marketing 4e: The Masterplan for Starting and Managing a Profitable, Customer-Based Marketing Program
Jahromi AT, 2014, IND MARKET MANAG, V43, P1258, DOI 10.1016/j.indmarman.2014.06.016
Kim K, 2012, EXPERT SYST APPL, V39, P13328, DOI 10.1016/j.eswa.2012.05.069
MacKenzie I., 2018, RETAILERS CAN KEEP C
Mezghani Manel, 2012, P 21 INT C COMP WORL, P969, DOI DOI 10.1145/2187980.2188230
Morgan B., 2018, Forbes
Mozer MC, 2000, IEEE T NEURAL NETWOR, V11, P690, DOI 10.1109/72.846740
Netzer O, 2008, MARKET SCI, V27, P185, DOI 10.1287/mksc.1070.0294
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Perisic A, 2022, IEEE T GAMES, V14, P126, DOI 10.1109/TG.2021.3067114
Rachid AD, 2018, Int J Electr Comput Eng, V8, P2367
Rothmeier K, 2021, IEEE T GAMES, V13, P78, DOI 10.1109/TG.2020.2992282
Schiffman L.G., 2012, CONSUMER BEHAV EUROP, V2nd
Terdiman D., 2018, Fast Company
Williams CKI, 2001, ADV NEUR IN, V13, P682
Xueling Li, 2019, Ingenierie des systemes d'information, V24, P525, DOI 10.18280/isi.240510
Yu XB, 2011, EXPERT SYST APPL, V38, P1425, DOI 10.1016/j.eswa.2010.07.049
NR 34
TC 0
Z9 0
U1 1
U2 6
PU Univ Pardubice, Fac Economics Adm
PI Pardubice
PA Studentska 95, Pardubice, CZECH REPUBLIC
SN 1211-555X
EI 1804-8048
J9 SCI PAP U PARD-SER D
JI Sci. Papers U. Pardubice-Ser. D- Faculty of Econ. Admin.
PY 2022
VL 30
IS 1
AR 1478
DI 10.46585/sp30011478
PG 12
WC Business; Economics; Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA O2ER0
UT WOS:001042007200008
OA gold, Green Published
DA 2024-03-27
ER
PT J
AU Distante, C
Fineo, L
Mainetti, L
Manco, L
Taccardi, B
Vergallo, R
AF Distante, Cosimo
Fineo, Laura
Mainetti, Luca
Manco, Luigi
Taccardi, Benito
Vergallo, Roberto
TI HF-SCA: Hands-Free Strong Customer Authentication Based on a
Memory-Guided Attention Mechanisms
SO JOURNAL OF RISK AND FINANCIAL MANAGEMENT
LA English
DT Article
DE strong customer authentication; transaction risk analysis; risk-based
assessment; PSD2; machine learning; user experience; vocal interaction
ID CLASSIFICATION; AUTOENCODER; NETWORKS; MODEL
AB Strong customer authentication (SCA) is a requirement of the European Union Revised Directive on Payment Services (PSD2) which ensures that electronic payments are performed with multifactor authentication. While increasing the security of electronic payments, the SCA impacted seriously on the shopping carts abandonment: an Italian bank computed that 22% of online purchases in the first semester of 2021 did not complete because of problems with the SCA. Luckily, the PSD2 allows the use of transaction risk analysis tool to exempt the SCA process. In this paper, we propose an unsupervised novel combination of existing machine learning techniques able to determine if a purchase is typical or not for a specific customer, so that in the case of a typical purchase the SCA could be exempted. We modified a well-known architecture (U-net) by replacing convolutional blocks with squeeze-and-excitation blocks. After that, a memory network was added in a latent space and an attention mechanism was introduced in the decoding side of the network. The proposed solution was able to detect nontypical purchases by creating temporal correlations between transactions. The network achieved 97.7% of AUC score over a well-known dataset retrieved online. By using this approach, we found that 98% of purchases could be executed by securely exempting the SCA, while shortening the customer's journey and providing an elevated user experience. As an additional validation, we developed an Alexa skill for Amazon smart glasses which allows a user to shop and pay online by merely using vocal interaction, leaving the hands free to perform other activities, for example driving a car.
C1 [Distante, Cosimo] Natl Res Council Italy, Inst Appl Sci & Intelligent Syst ISASI, I-73100 Lecce, Italy.
[Fineo, Laura] Banca Sella SpA, Dept Mkt, I-13900 Biella, Italy.
[Mainetti, Luca; Vergallo, Roberto] Univ Salento, Dept Innovat Engn, I-73100 Lecce, Italy.
[Manco, Luigi] Vidyasoft srl, I-73047 Monteroni Di Lecce, Italy.
[Taccardi, Benito] Univ Salento, Fac Engn, I-73100 Lecce, Italy.
C3 Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze Applicate
e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR); University of
Salento; University of Salento
RP Vergallo, R (autor correspondiente), Univ Salento, Dept Innovat Engn, I-73100 Lecce, Italy.
EM cosimo.distante@cnr.it; laura.fineo@sella.it;
luca.mainetti@unisalento.it; luigi.manco@vidyasoft.it;
benito.taccardi@studenti.unisalento.it; roberto.vergallo@unisalento.it
RI Mainetti, Luca/N-4360-2015; Distante, Cosimo/M-7996-2013
OI Taccardi, Benito/0000-0001-5353-7248; Distante,
Cosimo/0000-0002-1073-2390; Vergallo, Roberto/0000-0003-3560-806X
CR Ahmed M, 2017, COMPUTER, V50, P76, DOI 10.1109/MC.2017.3571051
Al-Qatf M, 2018, IEEE ACCESS, V6, P52843, DOI 10.1109/ACCESS.2018.2869577
Ali Haseeb., 2019, International Journal of Engineering & Technology, V8, P390, DOI [10.14419/ijet.v8i3.29508, DOI 10.14419/IJET.V8I3.29508]
Alrawashdeh K, 2016, 2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016), P195, DOI [10.1109/ICMLA.2016.167, 10.1109/ICMLA.2016.0040]
Althubiti S, 2018, IEEE SOUTHEASTCON
[Anonymous], 2016, DIR EU 2015 2366 EUR
[Anonymous], 3 KAGGL SOL
[Anonymous], 2 KAGGL SOL
[Anonymous], SELL DAT CHALL
[Anonymous], 1 KAGGL SOL
[Anonymous], 2018, Financial Stability and Payment Systems Report
[Anonymous], CRED CARD FRAUD DET
[Anonymous], 5 KAGGL SOL
[Anonymous], 2017, MANAGING WEB THINGS
[Anonymous], 4 KAGGL SOL
Arun GK, 2020, IIOAB J, V11, P85
Asha R.B., 2021, GLOBAL TRANSITIONS P, V2, P35, DOI [10.1016/j.gltp.2021.01.006, DOI 10.1016/J.GLTP.2021.01.006]
Aygun R. Can, 2017, 2017 IEEE 4th International Conference on Cyber-Security and Cloud Computing (CSCloud), P193, DOI 10.1109/CSCloud.2017.39
Bahdanau Dzmitry., 2016, arXiv
Bank of Italy, BANK IT REG SANDB
Cao V, 2016, LECT NOTES COMPUT SC, V9921, P717, DOI 10.1007/978-3-319-45823-6_67
Chalapathy R., 2019, ARXIV
Chen ZY, 2021, IEEE ACCESS, V9, P83762, DOI 10.1109/ACCESS.2021.3086359
Cheng DW, 2022, IEEE T KNOWL DATA EN, V34, P3800, DOI 10.1109/TKDE.2020.3025588
Cheng DW, 2020, AAAI CONF ARTIF INTE, V34, P362
Cordero Carlos Garcia, 2016, 2016 14th Annual Conference on Privacy, Security and Trust (PST), P317, DOI 10.1109/PST.2016.7906980
Ebong J, 2021, J RISK FINANC MANAG, V14, DOI 10.3390/jrfm14090393
Fu K, 2016, LECT NOTES COMPUT SC, V9949, P483, DOI 10.1007/978-3-319-46675-0_53
Garcia Gonzalez Gaston, 2021, ACM SIGMETRICS Performance Evaluation Review, V48, P49, DOI 10.1145/3466826.3466843
Higa Kyota., 2019, IEEE CONF IMAGING SY
Hu J, 2018, PROC CVPR IEEE, P7132, DOI [10.1109/CVPR.2018.00745, 10.1109/TPAMI.2019.2913372]
Huang G, 2016, LECT NOTES COMPUT SC, V9908, P646, DOI 10.1007/978-3-319-46493-0_39
Imam T, 2022, J RISK FINANC MANAG, V15, DOI 10.3390/jrfm15020077
Intrator Y, 2018, Arxiv, DOI arXiv:1810.05221
Jurgovsky J, 2018, EXPERT SYST APPL, V100, P234, DOI 10.1016/j.eswa.2018.01.037
Kadlubek M, 2022, ENERGIES, V15, DOI 10.3390/en15134668
Latah M, 2018, Arxiv, DOI arXiv:1807.04739
Lin Zilong., 2021, PACIFIC ASIA C KNOWL
Lopez-Martin M, 2017, SENSORS-BASEL, V17, DOI 10.3390/s17091967
Luong M-T., 2015, ARXIV
Malaiya RK, 2018, INT CONF COMPUT NETW, P893, DOI 10.1109/ICCNC.2018.8390278
Matsubara T, 2018, IEEE IJCNN
Mirsky Y, 2018, 25TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2018), DOI 10.14722/ndss.2018.23204
Naseer S, 2018, IEEE ACCESS, V6, P48231, DOI 10.1109/ACCESS.2018.2863036
Oktay O., 2018, ATTENTION U NET LEAR
Park Hyunjong., 2020, IEEECVF C COMPUTER V
Paul PongkuKumar., 2020, THESIS U TURKU TURKU
Porwal U, 2019, IEEE TRUST, P280, DOI 10.1109/TrustCom/BigDataSE.2019.00045
Randhawa K, 2018, IEEE ACCESS, V6, P14277, DOI 10.1109/ACCESS.2018.2806420
Rawat W, 2017, NEURAL COMPUT, V29, P2352, DOI [10.1162/neco_a_00990, 10.1162/NECO_a_00990]
Reshetnikova L, 2021, J RISK FINANC MANAG, V14, DOI 10.3390/jrfm14090401
Rigaki M., 2017, ST 152 WORKSH INT AU
Ring M, 2019, COMPUT SECUR, V82, P156, DOI 10.1016/j.cose.2018.12.012
Rongfang Gao, 2019, Journal of Physics: Conference Series, V1237, DOI 10.1088/1742-6596/1237/5/052023
Ronneberger O, 2015, LECT NOTES COMPUT SC, V9351, P234, DOI 10.1007/978-3-319-24574-4_28
Saarnilehto Ilkka., 2018, PROCIEDINGS SEMINAR, P71
Tang TA, 2016, 2016 INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND MOBILE COMMUNICATIONS (WINCOM), pP258, DOI 10.1109/WINCOM.2016.7777224
Varma D. Suneel, 2019, 2019 International Conference on Communication and Signal Processing (ICCSP), P0001, DOI 10.1109/ICCSP.2019.8697932
Yin CL, 2018, 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD), P228, DOI 10.1109/ICAIBD.2018.8396200
Yu Y, 2017, SECUR COMMUN NETW, DOI 10.1155/2017/4184196
Zolotukhin M, 2016, 2016 23RD INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT), DOI 10.1109/ICT.2016.7500408
Zoppi T, 2020, I C DEPEND SYS NETWO, P81, DOI 10.1109/DSN-S50200.2020.00044
NR 62
TC 3
Z9 3
U1 1
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 1911-8066
EI 1911-8074
J9 J RISK FINANC MANAG
JI J. Risk Financ. Manag.
PD AUG
PY 2022
VL 15
IS 8
AR 342
DI 10.3390/jrfm15080342
PG 24
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 4B4QY
UT WOS:000845765300001
OA Green Published, gold
DA 2024-03-27
ER
PT J
AU Brengman, M
De Gauquier, L
Willems, K
Vanderborght, B
AF Brengman, Malaika
De Gauquier, Laurens
Willems, Kim
Vanderborght, Bram
TI From stopping to shopping: An observational study comparing a humanoid
service robot with a tablet service kiosk to attract and convert
shoppers
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Humanoid Service Robot; Tablet Service Kiosk; Observation study; POS
Conversion Funnel
ID TECHNOLOGIES; BEHAVIOR; IMPACT; SALES; MEDIA
AB This study investigates the effectiveness of a humanoid service robot (HSR) versus a tablet service kiosk (TSK) along the stages of the point-of-sale (POS) conversion funnel. The observational data gathered by means of a field experiment show that the HSR elicited 26 times more interactions (i.e., passersby touching the screen) than the TSK and that these interactions lasted almost +50% as long. Moreover, more people looked at the store and consequently entered it when the HSR was deployed. Furthermore, more unique transactions were registered, and a higher amount was spent during the days when the HSR was present in front of the store. This study proves that implementing an HSR in the store environment is more effective than a TSK in attracting passersby and converting them into buyers.
C1 [Brengman, Malaika; De Gauquier, Laurens; Willems, Kim] Vrije Univ Brussel, Fac Social Sci, Pl Laan 2, B-1050 Brussels, Belgium.
[Brengman, Malaika; De Gauquier, Laurens; Willems, Kim] Vrije Univ Brussel, Solvay Business Sch, Dept Business Mkt & Consumer Behav, Pl Laan 2, B-1050 Brussels, Belgium.
[Vanderborght, Bram] Vrije Univ Brussel, Pl Laan 2, B-1050 Brussels, Belgium.
[Vanderborght, Bram] IMEC, Brubot, Pl Laan 2, B-1050 Brussels, Belgium.
C3 Vrije Universiteit Brussel; Vrije Universiteit Brussel; Vrije
Universiteit Brussel; IMEC
RP Brengman, M (autor correspondiente), Vrije Univ Brussel, Fac Social Sci, Pl Laan 2, B-1050 Brussels, Belgium.; Brengman, M (autor correspondiente), Vrije Univ Brussel, Solvay Business Sch, Dept Business Mkt & Consumer Behav, Pl Laan 2, B-1050 Brussels, Belgium.
EM malaika.brengman@vub.be; Laurens.De.Gauquier@vub.be; kim.willems@vub.be;
Bram.Vanderborght@vub.be
RI Vanderborght, Bram/A-1599-2008
OI Brengman, Malaika/0000-0001-9860-7107; De Gauquier,
Laurens/0000-0002-4343-1412; Willems, Kim/0000-0002-0941-8599
FU VLAIO (Vlaams Agentschap Innoveren & Ondernemen - Flanders Innovation &
Entrepreneurship Agency) under Baekeland Grant [150726]; Digitopia N.V.
(Wijnegem, Belgium); EU [611391]
FX Acknowledgements This work was supported by VLAIO (Vlaams Agentschap
Innoveren & Ondernemen - Flanders Innovation & Entrepreneurship Agency)
under Baekeland Grant Number 150726 ('In search of a sustainable
competitive advantage: Digitally instrumenting bricks-and-mortar
retailing in Flanders) , which is co-funded by Digitopia N.V. (Wijnegem,
Belgium) as well as by the EU FP7 project DREAM grant no. 611391.
CR Aaltonen I, 2017, ACMIEEE INT CONF HUM, P53, DOI 10.1145/3029798.3038362
Abdelkader O.A., 2019, Journal of Theoretical and Applied Information Technology, V97, P1693
Baird N., 2017, ROBOTS WILL BE RETAI
Barry T.E., 1990, INT J ADVERT, V9, P121, DOI [10.1080/02650487.1990.11107138, DOI 10.1080/02650487.1990.11107138]
Bergmann Kirsten, 2012, Intelligent Virtual Agents. Proceedings 12th International Conference, IVA 2012, P126, DOI 10.1007/978-3-642-33197-8_13
Bertacchini F, 2017, COMPUT HUM BEHAV, V77, P382, DOI 10.1016/j.chb.2017.02.064
Bhattacharya P, 2018, HUMANOID ROBOT MARKE
Block L.G., 1997, Journal of Consumer Psychology, V6, P31, DOI DOI 10.1207/S15327663JCP0601_02
Brengman M., 2019, Homo Roboticus, P167
Brignull H., 2003, Proceedings of the IFIP TC13 International Conference on Human-Computer Interaction (Zurich, Switzerland, September 1-5, V3, P17
Burke RR, 2009, J ADVERTISING RES, V49, P180, DOI 10.2501/S0021849909090254
Choi S, 2019, INT J HOSP MANAG, V82, P32, DOI 10.1016/j.ijhm.2019.03.026
Cooper MJ, 2007, IND MARKET MANAG, V36, P173, DOI 10.1016/j.indmarman.2006.03.005
DAFT RL, 1986, MANAGE SCI, V32, P554, DOI 10.1287/mnsc.32.5.554
Dalton NS, 2015, CHI 2015: PROCEEDINGS OF THE 33RD ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, P3889, DOI 10.1145/2702123.2702150
De Gauquier L., 2020, RETAIL FUTURES, P15
De Panafieu O., 2016, ROBOTS RETAIL WHAT D
De Pelsmacker P., 2018, MARKETING COMMUNICAT, VSixth
Evangelista F, 2020, ASIA PAC J MARKET LO, V32, P149, DOI 10.1108/APJML-02-2018-0076
Gartner, 2018, 5 TRENDS EM GARTN HY 5 TRENDS EM GARTN HY
Gefen D., 2003, E-Service Journal, V2, P7, DOI [DOI 10.2979/ESJ.2003.2.2.7, https://doi.org/10.2979/esj.2003.2.2.7]
Geuens M, 2004, TOURISM MANAGE, V25, P615, DOI 10.1016/j.tourman.2003.07.003
Ghirvu A.I., 2013, The USV Annals of Economics and Public Administration, V13, P90
Grewal D, 2020, J ACAD MARKET SCI, V48, P96, DOI 10.1007/s11747-019-00697-z
Gross HM, 2009, 2009 IEEE-RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, P2005, DOI 10.1109/IROS.2009.5354497
Guido G, 2019, J ADVERTISING RES, V59, P219, DOI 10.2501/JAR-2018-030
Hassan S, 2015, PROCD SOC BEHV, V172, P262, DOI 10.1016/j.sbspro.2015.01.363
Heikkilä P, 2019, LECT NOTES ARTIF INT, V11876, P548, DOI 10.1007/978-3-030-35888-4_51
Herse S, 2018, IEEE ROMAN, P7, DOI 10.1109/ROMAN.2018.8525581
Huang EM, 2008, LECT NOTES COMPUT SC, V5013, P228, DOI 10.1007/978-3-540-79576-6_14
Hui SK, 2013, J MARKETING RES, V50, P445, DOI 10.1509/jmr.12.0065
Iwamura Y, 2011, ACMIEEE INT CONF HUM, P449, DOI 10.1145/1957656.1957816
Iwasaki M, 2018, IEEE ROMAN, P300, DOI 10.1109/ROMAN.2018.8525772
Johnson R.D., 1999, Journal of Informatics Education, V4, P15
Kallweit K, 2014, J RETAIL CONSUM SERV, V21, P268, DOI 10.1016/j.jretconser.2014.02.002
Kamei K., 2010, RECOMMENDATION ROBOT, P19
Kanda T, 2010, IEEE T ROBOT, V26, P897, DOI 10.1109/TRO.2010.2062550
Ko BC, 2018, SENSORS-BASEL, V18, DOI 10.3390/s18020401
Lengel R. H., 1988, ACADEMY MANAGEMENT E, V2, P225, DOI DOI 10.5465/AME.1988.4277259
Lewis E., 1908, Financial Advertising
Lin YH, 2013, TOURISM MANAGE, V36, P426, DOI 10.1016/j.tourman.2012.09.017
Luo J. T., 2020, EXPLORING ROLE MEDIA, P1
Memarovic N., 2015, UNDERSTANDING DISPLA, P7
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Meuter ML, 2000, J MARKETING, V64, P50, DOI 10.1509/jmkg.64.3.50.18024
Michelis D, 2011, INT J HUM-COMPUT INT, V27, P562, DOI 10.1080/10447318.2011.555299
Montazeribarforoushi S, 2017, COGENT PSYCHOL, V4, DOI 10.1080/23311908.2017.1363343
Müller J, 2009, LECT NOTES COMPUT SC, V5538, P1, DOI 10.1007/978-3-642-01516-8_1
Muller J., 2012, P SIGCHI C HUM FACT P SIGCHI C HUM FACT, P297
Nakagawa K, 2013, INT J SOC ROBOT, V5, P5, DOI 10.1007/s12369-012-0141-3
Neves A, 2017, VEHITS: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS, P373, DOI 10.5220/0006385903730380
Newman A., 2006, INT J DIGITAL CONTEN, V4, P50
Niemelä M, 2017, ACMIEEE INT CONF HUM, P227, DOI 10.1145/3029798.3038301
Nisbett R.E., 1980, HUMAN INTERFACE STRA
Ogawa Kohei, 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, P516, DOI 10.1109/ROMAN.2009.5326352
Ogino A., 2010, MODELING HUMAN INTER
Parker C., 2018, P ACM INTERACTIVE MO, V2, P1
Ravnik R, 2013, INTERACT COMPUT, V25, P218, DOI 10.1093/iwc/iws023
Ro T, 2007, VIS COGN, V15, P322, DOI 10.1080/13506280600590434
Roggeveen AL, 2016, J RETAILING, V92, P122, DOI 10.1016/j.jretai.2015.08.001
Sabelli AM, 2016, INT J SOC ROBOT, V8, P211, DOI 10.1007/s12369-015-0332-9
Saldien J, 2009, THESIS THESIS
Scherer A, 2015, MIS QUART, V39, P177
Sciutti A, 2018, IEEE TECHNOL SOC MAG, V37, P22, DOI 10.1109/MTS.2018.2795095
Seiler S, 2017, QME-QUANT MARK ECON, V15, P241, DOI 10.1007/s11129-017-9184-y
Seiler S, 2017, MARKET SCI, V36, P565, DOI 10.1287/mksc.2017.1026
Shi C, 2016, ACMIEEE INT CONF HUM, P519, DOI 10.1109/HRI.2016.7451835
Shiomi M, 2013, INT J SOC ROBOT, V5, P251, DOI 10.1007/s12369-013-0180-4
Softbank Robotics, 2019, BETT BUS JUST ADD PE BETT BUS JUST ADD PE
Sorensen H, 2017, J RETAIL CONSUM SERV, V37, P182, DOI 10.1016/j.jretconser.2017.02.003
Su KW, 2016, J AMB INTEL HUM COMP, V7, P817, DOI 10.1007/s12652-016-0343-x
Ullal M.S., 2018, Problems and Prospective in Management, V16, P285, DOI [DOI 10.2139/SSRN.3513259, 10.2139/ssrn.3513259]
Underwood C, 2019, ROBOTS RETAIL EXAMPL
van de Sanden S, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102177
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Weinberg N, 2019, SOFTBANK ROBOTICS BU
WELLS WD, 1966, J MARKETING RES, V3, P227, DOI 10.2307/3149897
Wijaya B.S., 2012, INT RES J BUS STUD, V5, P73, DOI DOI 10.21632/IRJBS.5.1.73-85
Willems K, 2017, INT J RETAIL DISTRIB, V45, P910, DOI 10.1108/IJRDM-10-2016-0177
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Zhang XL, 2014, J MARKETING, V78, P24, DOI 10.1509/jm.12.0106
Zibafar A, 2021, INT J SOC ROBOT, V13, P509, DOI 10.1007/s12369-019-00566-3
NR 82
TC 38
Z9 39
U1 7
U2 52
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD SEP
PY 2021
VL 134
BP 263
EP 274
DI 10.1016/j.jbusres.2021.05.025
EA MAY 2021
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA TX1VR
UT WOS:000682879500022
DA 2024-03-27
ER
PT J
AU De Gauquier, L
Brengman, M
Willems, K
Cao, HL
Vanderborght, B
AF De Gauquier, Laurens
Brengman, Malaika
Willems, Kim
Cao, Hoang-Long
Vanderborght, Bram
TI In or out? A field observational study on the placement of entertaining
robots in retailing
SO INTERNATIONAL JOURNAL OF RETAIL & DISTRIBUTION MANAGEMENT
LA English
DT Article
DE Humanoid service robots; Observation; Field study; POS conversion
funnel; Retail
ID IN-STORE; SERVICE ROBOTS; SALES; ATMOSPHERE; BENEFITS; BEHAVIOR
AB Purpose The purpose of this paper is to empirically investigate the role of the placement (i.e. location) of humanoid service robots (HSRs) for entertainment applications in retailing by inspecting a multitude of performance metrics along the point-of-sale conversion funnel. Design/methodology/approach The study was conducted using unobtrusive observations at a Belgian chocolate store. In total, 42 h of video observation material was collected and analyzed, with an even spread over three conditions: (1) an HSR placed outside, (2) an HSR inside the store and (3) a control condition (no robot stimuli). All passersby and their interactions with the robot and the store were systematically coded and compared. Findings The study found that the better placement of HSRs (inside or outside the store) is contingent on the goals the retailer prioritizes. When the goal is to create awareness and interest toward the store, the HSR should be placed outside, as it has double the stopping power. To induce consumers to enter the store, placement of the HSR inside the store is the better option. Ultimately, however, in terms of the number of transactions and total amount spent, outside placement of the HSR outperforms inside placement. Research limitations/implications This study was not able to verify the internal emotional/cognitive state of the passersby, as the method relied on unobtrusive camera observations. A longitudinal research design would be desirable to exclude potential bias due to the novelty effect. Originality/value While research on robots in retail services is emerging, this study is the first to provide insights on how retailers can decide on the placement of robots inside or outside the store, depending on the particular goals they are aiming to reach at the point of purchase.
C1 [De Gauquier, Laurens] Vrije Univ Brussel, Appl Econ, Brussels, Belgium.
[Brengman, Malaika] Vrije Univ Brussel, Fac Social Sci, Brussels, Belgium.
[Brengman, Malaika] Vrije Univ Brussel, Solvay Business Sch, Brussels, Belgium.
[Willems, Kim] Vrije Univ Brussel, Brussels, Belgium.
[Cao, Hoang-Long] Vrije Univ Brussel, Electromech Engn, Brussels, Belgium.
[Vanderborght, Bram] Vrije Univ Brussel, Robot & Multibody Mech Res Grp, Brussels, Belgium.
[Willems, Kim] Hasselt Univ, Diepenbeek, Belgium.
[Cao, Hoang-Long] Flanders Make, Brussels, Belgium.
[Vanderborght, Bram] IMEC, Brussels, Belgium.
C3 Vrije Universiteit Brussel; Vrije Universiteit Brussel; Vrije
Universiteit Brussel; Vrije Universiteit Brussel; Vrije Universiteit
Brussel; Vrije Universiteit Brussel; Hasselt University; IMEC
RP Brengman, M (autor correspondiente), Vrije Univ Brussel, Fac Social Sci, Brussels, Belgium.
EM laurens.de.gauquier@vub.be; malaika.brengman@vub.be; kim.willems@vub.be;
Hoang.Long.Cao@vub.be; bram.vanderborght@vub.be
RI Vanderborght, Bram/A-1599-2008
OI Willems, Kim/0000-0002-0941-8599; Brengman, Malaika/0000-0001-9860-7107;
Cao, Hoang-Long/0000-0003-2851-5527; De Gauquier,
Laurens/0000-0002-4343-1412
CR Aaltonen I, 2017, ACMIEEE INT CONF HUM, P53, DOI 10.1145/3029798.3038362
[Anonymous], 2017, CREATING CONSUMER BU
Bagdasarov Z, 2020, ORGAN DYN, V49, DOI 10.1016/j.orgdyn.2018.09.002
Ben Mimoun MS, 2012, J RETAIL CONSUM SERV, V19, P605, DOI 10.1016/j.jretconser.2012.07.006
Brengman M., 2019, Homo Roboticus, P167
Brignull H., 2003, Proceedings of the IFIP TC13 International Conference on Human-Computer Interaction (Zurich, Switzerland, September 1-5, V3, P17
Burke RR, 2009, J ADVERTISING RES, V49, P180, DOI 10.2501/S0021849909090254
Choi S, 2019, INT J HOSP MANAG, V82, P32, DOI 10.1016/j.ijhm.2019.03.026
Cooper MJ, 2007, IND MARKET MANAG, V36, P173, DOI 10.1016/j.indmarman.2006.03.005
Cornelius B, 2010, J RETAIL CONSUM SERV, V17, P143, DOI 10.1016/j.jretconser.2009.11.004
Dahl DW, 2005, J CONSUM PSYCHOL, V15, P307, DOI 10.1207/s15327663jcp1504_5
De Gauquier L., 2019, REC ADV RET SERV SCI, P22
De Gauquier L., 2020, RETAIL FUTURES, P15
De Gauquier L, 2018, ACMIEEE INT CONF HUM, P373, DOI 10.1145/3173386.3177535
Doering N, 2015, INT J SOC ROBOT, V7, P203, DOI 10.1007/s12369-014-0257-8
DONOVAN RJ, 1994, J RETAILING, V70, P283, DOI 10.1016/0022-4359(94)90037-X
DONOVAN RJ, 1982, J RETAILING, V58, P34
Esteban P.G., 2017, Paladyn, Journal of Behavioral Robotics, V8, P18, DOI [DOI 10.1515/PJBR-2017-0002, DOI 10.1515/pjbr-2017-0002]
Fam KS, 2011, ASIA PAC J MARKET LO, V23, P165, DOI 10.1108/13555851111120470
Garrido-Morgado A, 2016, FRONT PSYCHOL, V7, DOI 10.3389/fpsyg.2016.00336
Gartner, 2020, GARTN TOP STRAT TECH
Gee FC, 2005, 2005 IEEE INTERNATIONAL WORKSHOP ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN), P151
Glas DF, 2012, J HUM-ROBOT INTERACT, V1, P5, DOI 10.5898/JHRI.1.2.Glas
Grewal D, 2020, J ACAD MARKET SCI, V48, P96, DOI 10.1007/s11747-019-00697-z
Gross HM, 2009, 2009 IEEE-RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, P2005, DOI 10.1109/IROS.2009.5354497
Heikkilä P, 2019, LECT NOTES ARTIF INT, V11876, P548, DOI 10.1007/978-3-030-35888-4_51
Herzog TR, 1997, J ENVIRON PSYCHOL, V17, P165, DOI 10.1006/jevp.1997.0051
Huang Y., 2011, EFFECT IN STORE TRAV
Hui SK, 2013, J MARKETING RES, V50, P445, DOI 10.1509/jmr.12.0065
Inman JJ, 2009, J MARKETING, V73, P19, DOI 10.1509/jmkg.73.5.19
Iwamura Y, 2011, ACMIEEE INT CONF HUM, P449, DOI 10.1145/1957656.1957816
Iwasaki M, 2018, IEEE ROMAN, P300, DOI 10.1109/ROMAN.2018.8525772
Joye Y, 2011, EVOLUTIONARY PSYCHOLOGY IN THE BUSINESS SCIENCES, P289, DOI 10.1007/978-3-540-92784-6_11
Kamei K., 2010, INT C MULT INT WORKS, P19, DOI DOI 10.1145/1891903.1891929
Kanda T, 2010, IEEE T ROBOT, V26, P897, DOI 10.1109/TRO.2010.2062550
Ketron S, 2016, J RETAIL CONSUM SERV, V33, P23, DOI 10.1016/j.jretconser.2016.07.017
Kim S, 2021, INT J HOSP MANAG, V93, DOI 10.1016/j.ijhm.2020.102795
Kim S, 2016, J BUS RES, V69, P5809, DOI 10.1016/j.jbusres.2016.04.178
Kuo I.H., 2008, P 7 C HLTH INF ROT N
Lange F, 2016, J BUS RES, V69, P1014, DOI 10.1016/j.jbusres.2015.08.013
Lee L, 2006, J CONSUM RES, V33, P60, DOI 10.1086/504136
Lewandowski B, 2020, IEEE ROMAN, P363, DOI 10.1109/RO-MAN47096.2020.9223568
Lewis E., 1908, Financial Advertising
Li DJ, 2010, INT J SOC ROBOT, V2, P175, DOI 10.1007/s12369-010-0056-9
Manzocco N., 2018, ROBOT SUSHI RESTAURA
Marin-Hernandez A., 2012, 2012 11th Mexican International Conference on Artificial Intelligence and Applications (MICAI 2012), P26, DOI 10.1109/MICAI.2012.21
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Michelis D, 2011, INT J HUM-COMPUT INT, V27, P562, DOI 10.1080/10447318.2011.555299
Mori M, 1970, ENERGY, V7, P33, DOI DOI 10.1109/MRA.2012.2192811
Nakagawa K, 2013, INT J SOC ROBOT, V5, P5, DOI 10.1007/s12369-012-0141-3
Neves A, 2017, VEHITS: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS, P373, DOI 10.5220/0006385903730380
Niemelä M, 2017, ACMIEEE INT CONF HUM, P227, DOI 10.1145/3029798.3038301
Pandey AK, 2018, IEEE ROBOT AUTOM MAG, V25, P40, DOI 10.1109/MRA.2018.2833157
Ryan T., 2020, CAN ROBOTS ASSIST RE
Sabelli AM, 2016, INT J SOC ROBOT, V8, P211, DOI 10.1007/s12369-015-0332-9
Sameti A, 2017, INTANG CAP, V13, P523, DOI 10.3926/ic.986
Seiler S, 2017, MARKET SCI, V36, P565, DOI 10.1287/mksc.2017.1026
Shankar V, 2011, J RETAILING, V87, pS29, DOI 10.1016/j.jretai.2011.04.007
Shi C, 2016, ACMIEEE INT CONF HUM, P519, DOI 10.1109/HRI.2016.7451835
Shiomi M, 2013, INT J SOC ROBOT, V5, P251, DOI 10.1007/s12369-013-0180-4
Turley LW, 2000, J BUS RES, V49, P193, DOI 10.1016/S0148-2963(99)00010-7
Van de Sanden S., 2020, RETAIL FUTURES
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Lu VN, 2020, J SERV THEOR PRACT, V30, P361, DOI 10.1108/JSTP-04-2019-0088
Willems K., 2021, The Future of Service Post-COVID-19 Pandemic, Volume, V2, P103
Willems K, 2017, TECHNOL FORECAST SOC, V124, P228, DOI 10.1016/j.techfore.2016.10.066
Willems K, 2017, INT J RETAIL DISTRIB, V45, P910, DOI 10.1108/IJRDM-10-2016-0177
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Yoganathan V, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104309
Zhou L., 2003, J INT CONSUM MARK, V16, P37, DOI DOI 10.1300/J046V16N02_03
Zibafar A, 2021, INT J SOC ROBOT, V13, P509, DOI 10.1007/s12369-019-00566-3
NR 71
TC 14
Z9 14
U1 2
U2 19
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0959-0552
EI 1758-6690
J9 INT J RETAIL DISTRIB
JI Int. J. Retail Distrib. Manag.
PD JUL 8
PY 2021
VL 49
IS 7
SI SI
BP 846
EP 874
DI 10.1108/IJRDM-10-2020-0413
EA MAY 2021
PG 29
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA TH6ED
UT WOS:000657046100001
DA 2024-03-27
ER
PT J
AU Zhang, YX
Wang, XY
Zhao, X
AF Zhang, Yuexian
Wang, XueYing
Zhao, Xin
TI Supervising or assisting? The influence of virtual anchor driven by
AI-human collaboration on customer engagement in live streaming
e-commerce
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article; Early Access
DE Virtual anchor driven by AI-human collaboration; Perceived playfulness;
Customer engagement; Humorous response
ID BRAND ENGAGEMENT; CHATBOTS
AB Digital technologies such as artificial intelligence (AI) are driving the growth of live-streaming e-commerce. As a result, a rising number of virtual anchors who are appearing in live-streaming e-commerce, generating customer engagement. However, whether the virtual anchor driven by different types of AI-human collaboration has different impacts on consumer engagement needs to be further investigated. By adopting the use and gratifications theory, this paper investigated the mechanism of the virtual anchor driven by AI-human collaboration on consumer engagement and the moderating effect of the humorous response. The results of two studies demonstrated that the virtual anchor driven by assisted AI-human collaboration contributed to higher levels of perceived playfulness than those driven by supervised AI-human collaboration, leading to increased customer engagement. Meanwhile, it was found that the differences between the supervised and assisted virtual anchor driven by AI-human collaboration on perceived playfulness decrease when the humorous response is present. This paper fills in the gap in virtual anchor research by providing insights into how to enhance the positive effect of customer engagement and giving suggestions for future research on virtual anchors.
C1 [Zhang, Yuexian; Wang, XueYing; Zhao, Xin] Northeastern Univ, Shenyang, Peoples R China.
C3 Northeastern University - China
RP Wang, XY (autor correspondiente), Northeastern Univ, Shenyang, Peoples R China.
EM 15049068802@163.com
OI Zhao, Xin/0000-0002-9622-5196
FU Social Science Foundation of China
FX No Statement Available
CR Alhassan M.D., 2020, Journal of Systems and Information Technology, V22, P351, DOI [10.1108/jsit-01-2020-0010, DOI 10.1108/JSIT-01-2020-0010]
Aw ECX, 2022, TECHNOL FORECAST SOC, V180, DOI 10.1016/j.techfore.2022.121711
Bansal P, 2018, TRANSPORTATION, V45, P641, DOI 10.1007/s11116-016-9745-z
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Bytedance, 2022, Report on Big Data analysis and trend research of China's live streaming e-commerce industry in 2022-2023
Chen T, 2023, COMPUT HUM BEHAV, V144, DOI 10.1016/j.chb.2023.107753
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
de Cicco Roberta, 2021, Journal of Foodservice Business Research, V24, P140, DOI 10.1080/15378020.2020.1826268
Dhar R, 2000, J MARKETING RES, V37, P60, DOI 10.1509/jmkr.37.1.60.18718
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Economist, 2021, OR Commons
Gao W, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103356
Gervasi R, 2020, INT J ADV MANUF TECH, V108, P841, DOI 10.1007/s00170-020-05363-1
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Gu Y, 2023, INFORM MANAGE-AMSTER, V60, DOI 10.1016/j.im.2023.103810
Guo LY, 2021, INTERNET RES, V31, P1718, DOI 10.1108/INTR-02-2020-0078
Guo YY, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102786
Hayes AF, 2015, MULTIVAR BEHAV RES, V50, P1, DOI 10.1080/00273171.2014.962683
He Y, 2022, ELECTRON COMMER R A, V56, DOI 10.1016/j.elerap.2022.101223
Hossain MA, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11041166
Hou FF, 2020, INTERNET RES, V30, P141, DOI 10.1108/INTR-04-2018-0177
Jessup S., 2020, HAW INT C SYST SCI, P1
Jiang QL, 2023, KYBERNETES, V52, P4551, DOI 10.1108/K-12-2021-1346
Kaur P, 2020, TELEMAT INFORM, V53, DOI 10.1016/j.tele.2020.101376
Kim M, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2022.102951
Kim S, 2022, J RES INTERACT MARK, V16, P82, DOI 10.1108/JRIM-01-2021-0020
Koschmann A, 2018, INT J RES MARK, V35, P575, DOI 10.1016/j.ijresmar.2018.07.003
Kujur F, 2020, J THEOR APPL EL COMM, V15, P30, DOI 10.4067/S0718-18762020000100104
Kull AJ, 2021, J BUS RES, V135, P840, DOI 10.1016/j.jbusres.2021.03.005
Li XL, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-30696-8
Liao JY, 2023, J RES INTERACT MARK, V17, P140, DOI 10.1108/JRIM-04-2021-0114
Liu CL, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15075754
Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
Luo HY, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9131576
Luo XM, 2021, J MARKETING, V85, P14, DOI 10.1177/0022242920956676
Lv J, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14148907
Lv XY, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.106993
McLeay F, 2021, J SERV RES-US, V24, P104, DOI 10.1177/1094670520933354
McShane L, 2021, J INTERACT MARK, V53, P96, DOI 10.1016/j.intmar.2020.06.002
Murphy J, 2019, J TRAVEL TOUR MARK, V36, P784, DOI 10.1080/10548408.2019.1571983
Nagel DM, 2021, J BUS RES, V128, P174, DOI 10.1016/j.jbusres.2021.01.026
Niu BZ, 2023, TRANSPORT RES E-LOG, V174, DOI 10.1016/j.tre.2023.103130
Oliveira R, 2021, INT J SOC ROBOT, V13, P1369, DOI 10.1007/s12369-020-00727-9
Pan R, 2022, PROD OPER MANAG, V31, P3387, DOI 10.1111/poms.13784
Payne EHM, 2021, J RES INTERACT MARK, V15, P200, DOI 10.1108/JRIM-12-2020-0252
Peng CM, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102533
Rese A, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102176
Roux T, 2020, ADV HUM-COMPUT INTER, V2020, DOI 10.1155/2020/7636150
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shin H, 2020, EUR J MARKETING, V54, P2013, DOI 10.1108/EJM-08-2018-0522
Siemon D, 2022, GROUP DECIS NEGOT, V31, P871, DOI 10.1007/s10726-022-09792-z
Tan SM, 2020, INT J HUM-COMPUT INT, V36, P1136, DOI 10.1080/10447318.2020.1722399
Tan TM, 2022, TECHNOL FORECAST SOC, V176, DOI 10.1016/j.techfore.2021.121432
Tsai CY, 2022, LEADERSHIP QUART, V33, DOI 10.1016/j.leaqua.2021.101594
Vitezic V, 2021, SERV IND J, V41, P926, DOI 10.1080/02642069.2021.1974406
Vossing M., 2022, Information Systems Frontiers, V24, P1
Wang B, 2022, J RETAIL CONSUM SERV, V69, DOI 10.1016/j.jretconser.2022.103109
Wu Y, 2021, INFORMATION, V12, DOI 10.3390/info12100426
Xiao L, 2021, J SERV RES-US, V24, P9, DOI 10.1177/1094670519878881
Xiao L, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103170
Xu XA, 2022, ANN TOURISM RES, V95, DOI 10.1016/j.annals.2022.103439
Ye CZ, 2022, ASIA PAC J TOUR RES, V27, P506, DOI 10.1080/10941665.2022.2091940
Yun JW, 2023, J RETAIL CONSUM SERV, V74, DOI 10.1016/j.jretconser.2023.103394
Zhang MY, 2021, INT J CONTEMP HOSP M, V33, P3883, DOI 10.1108/IJCHM-11-2020-1256
Zhang M, 2021, J SERV MARK, V35, P312, DOI 10.1108/JSM-01-2020-0009
Zhou Y, 2023, ECON ANAL POLICY, V78, P332, DOI 10.1016/j.eap.2023.02.005
NR 67
TC 0
Z9 0
U1 104
U2 104
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD 2023 NOV 24
PY 2023
DI 10.1007/s10660-023-09783-5
EA NOV 2023
PG 24
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA AM4Z4
UT WOS:001118882500002
DA 2024-03-27
ER
PT J
AU Ma, BJ
Kuo, YH
Jiang, YS
Huang, GQ
AF Ma, Benedict Jun
Kuo, Yong-Hong
Jiang, Yishuo
Huang, George Q.
TI RubikCell: Toward Robotic Cellular Warehousing Systems for E-Commerce
Logistics
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article; Early Access
DE Cellular warehousing (CW); e-commerce logistics (EcL); e-commerce
warehousing; order picking; warehouse automation
ID FULFILLMENT; STRATEGIES; ORDER
AB As e-commerce has become more prevalent, the required logistics operations are challenged by the greater demand for and higher complexity of order picking in warehouses. While goods-to-person (G2P) picking systems, such as robotic mobile fulfillment systems, are becoming popular, there are still challenges in G2P systems, including the unstable performance of human picking due to fatigue and human errors, and the constrained mobility of robots. To tackle these challenges, this article presents a new robotic storage and order picking system, which we call RubikCell. It leverages the strengths of existing warehouse systems and incorporates automatic dispensing, robot-to-goods picking, and pick-while-sort operations. In RubikCell, robots are equipped with trays to store and transport items for an order, instead of moving with heavy pods to workstations as in G2P systems. In addition, the concept of cellular warehousing (CW)-inspired by cellular manufacturing-aims to operate a large warehouse with smaller warehousing cells. This approach reduces the substantial traveling distances of robots, as they move within their dedicated warehousing cells rather than the entire warehouse. A mathematical programming model is developed to address the cell formation problem in CW. Lastly, the implementation of CW principles in RubikCell, forming Robotic CW Systems, renders e-commerce warehousing more flexible, scalable, and reconfigurable. Numerical experiments conducted on this innovative system have confirmed the effectiveness of the cell formation method.
C1 [Ma, Benedict Jun; Kuo, Yong-Hong; Jiang, Yishuo] Univ Hong Kong, Dept Ind & Mfg Syst Engn, Hong Kong, Peoples R China.
[Ma, Benedict Jun] PSL Univ, Ctr Management Sci, Mines Paris, F-75006 Paris, France.
[Kuo, Yong-Hong] Univ Hong Kong, HKU Musketeers Fdn Inst Data Sci, Hong Kong, Peoples R China.
[Huang, George Q.] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China.
C3 University of Hong Kong; Universite PSL; MINES ParisTech; University of
Hong Kong; Hong Kong Polytechnic University
RP Huang, GQ (autor correspondiente), Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China.
EM bjma@connect.hku.hk; yhkuo@hku.hk; jyishuo@connect.hku.hk;
gqhuang@hku.hk
RI Kuo, Yong-Hong/ABF-8035-2021; Ma, Benedict Jun/AAX-9144-2020
OI Kuo, Yong-Hong/0000-0002-6170-324X;
FU Hong Kong RGC TRS project [T32-707/22-N]; 2019 Guangdong Special Support
Talent Program - Innovation and Entrepreneurship Leading Team (China)
[2019BT02S593]
FX This work was supported in part by the Hong Kong RGC TRS project under
Grant T32-707/22-N, and in part by the 2019 Guangdong Special Support
Talent Program - Innovation and Entrepreneurship Leading Team (China)
under Grant 2019BT02S593.
CR Azadeh K, 2019, TRANSPORT SCI, V53, P917, DOI 10.1287/trsc.2018.0873
B. Solutions, 2023, Faster retail order fulfillment and room to grow with advanced warehouse automation and AutoStore
Bansal V, 2021, NAV RES LOG, V68, P810, DOI 10.1002/nav.21978
Beckschäfer M, 2017, LECT NOTES COMPUT SC, V10572, P468, DOI 10.1007/978-3-319-68496-3_31
Bergmann R., 1998, Tech. Rep. WS-98-02
Boysen N, 2019, EUR J OPER RES, V277, P396, DOI 10.1016/j.ejor.2018.08.023
Boywitz D, 2019, IISE TRANS, V51, P368, DOI 10.1080/24725854.2018.1513672
Burbidge JL., 1991, J OPERATIONS MANAGEM, V10, P5, DOI [DOI 10.1016/0272-6963(91)90033-T, 10.1016/0272-6963(91)90033-T]
Cammarano A, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3240213
Chen W, 2022, COMPUT IND ENG, V163, DOI 10.1016/j.cie.2021.107823
Choi TM, 2022, PROD OPER MANAG, V31, P9, DOI 10.1111/poms.13622
Correll N, 2018, IEEE T AUTOM SCI ENG, V15, P172, DOI 10.1109/TASE.2016.2600527
Dauod H, 2022, INT J PROD RES, V60, P5468, DOI 10.1080/00207543.2021.1961037
Feldt J., 2022, Handbook on Digital Business Ecosystems, P714
Gharehgozli A, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102087
Green T.J., 1984, Journal of Operations Management, V4, P85
HERAGU SS, 1994, IEEE T SYST MAN CYB, V24, P203, DOI 10.1109/21.281420
Huang G.Q., 2015, HKIE Trans, V22, P68, DOI DOI 10.1080/1023697X.2015.1043960
Humphries M., 2019, Amazon: Full warehouse automation is 10 years away
Jiang M, 2022, TRANSPORT RES E-LOG, V158, DOI 10.1016/j.tre.2022.102619
Jiao GS, 2023, COMPUT IND ENG, V175, DOI 10.1016/j.cie.2022.108856
Justkowiak JE, 2023, OMEGA-INT J MANAGE S, V120, DOI 10.1016/j.omega.2023.102897
Justkowiak JE, 2023, EUR J OPER RES, V305, P1063, DOI 10.1016/j.ejor.2022.06.051
Keung KL, 2022, ADV ENG INFORM, V52, DOI 10.1016/j.aei.2022.101530
Kong XTR, 2020, COMPUT IND, V118, DOI 10.1016/j.compind.2020.103220
Kong XTR, 2020, COMPUT IND ENG, V139, DOI 10.1016/j.cie.2019.106170
Kong XTR, 2019, PROC CIRP, V83, P339, DOI 10.1016/j.procir.2019.03.095
Kumar S, 2023, TRANSPORT RES E-LOG, V173, DOI 10.1016/j.tre.2023.103088
Lamballais T, 2022, EUR J OPER RES, V300, P937, DOI 10.1016/j.ejor.2021.09.001
Lamballais T, 2017, EUR J OPER RES, V256, P976, DOI 10.1016/j.ejor.2016.06.063
Li N, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3270376
Li XW, 2020, TRANSPORT RES E-LOG, V144, DOI 10.1016/j.tre.2020.102158
Li ZH, 2024, IEEE T ENG MANAGE, V71, P2729, DOI 10.1109/TEM.2022.3189690
Lian YD, 2022, ADV ENG INFORM, V52, DOI 10.1016/j.aei.2022.101572
Liu MY, 2023, COMPUT IND ENG, V181, DOI 10.1016/j.cie.2023.109236
Liu P, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, P2126, DOI 10.1109/ICAL.2008.4636515
Liu S, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3144482
Loffler M., 2023, Transp. Sci., V57, P839
Ma BJ, 2022, COMPUT IND ENG, V173, DOI 10.1016/j.cie.2022.108665
Meller R.D., 2008, P 2008 IND ENG RES C, P770
Mo YJ, 2023, IEEE T IND INFORM, V19, P1823, DOI 10.1109/TII.2022.3189177
Newbury R, 2023, IEEE T ROBOT, V39, P3994, DOI 10.1109/TRO.2023.3280597
Nie QW, 2023, ROBOT CIM-INT MANUF, V82, DOI 10.1016/j.rcim.2023.102543
Pazour JA, 2011, IIE TRANS, V43, P739, DOI 10.1080/0740817X.2010.549099
Reaidy PJ, 2015, INT J PROD ECON, V159, P29, DOI 10.1016/j.ijpe.2014.02.017
Reisman A, 1997, OPER RES, V45, P508, DOI 10.1287/opre.45.4.508
Roy D, 2019, TRANSPORT RES E-LOG, V122, P119, DOI 10.1016/j.tre.2018.11.005
Schiffer M, 2022, MANAGE SCI, V68, P7497, DOI 10.1287/mnsc.2021.4275
Shafer S. M., 1993, Int. J. Purchasing Mater. Manage., V29, P38
Shenoy A, 2022, IEEE INT C INT ROBOT, P2741, DOI 10.1109/IROS47612.2022.9981799
Sheu JB, 2023, PROD OPER MANAG, V32, P794, DOI 10.1111/poms.13899
Vakharia A. J., 1986, J. Oper. Manage., V6, P257
Wang K, 2022, COMPUT IND ENG, V169, DOI 10.1016/j.cie.2022.108229
Wang X., 2018, P 5 INT PHYS INT C, P18
Wang Z, 2022, PROD OPER MANAG, V31, P98, DOI 10.1111/poms.13406
Wemmerlov U., 1986, J OPERATIONS RES, V6, P125, DOI DOI 10.1016/0272-6963(86)90021-5
Yadav S, 2023, IEEE T ENG MANAGE, V70, P1215, DOI 10.1109/TEM.2022.3177188
YounesSinaki R, 2023, INT J PROD RES, V61, P5585, DOI 10.1080/00207543.2022.2105763
Yuan Z, 2017, IEEE T ENG MANAGE, V64, P83, DOI 10.1109/TEM.2016.2634540
Zhang J, 2023, COMPUT IND ENG, V176, DOI 10.1016/j.cie.2022.108918
Zhao Chao, 2022, 2022 International Conference on Robotics and Automation (ICRA), P749, DOI 10.1109/ICRA46639.2022.9811736
Zhuang YL, 2022, TRANSPORT RES E-LOG, V167, DOI 10.1016/j.tre.2022.102920
Zou BP, 2018, TRANSPORT SCI, V52, P788, DOI 10.1287/trsc.2017.0786
Zou BP, 2018, EUR J OPER RES, V267, P733, DOI 10.1016/j.ejor.2017.12.008
NR 64
TC 0
Z9 0
U1 14
U2 14
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PD 2023 NOV 1
PY 2023
DI 10.1109/TEM.2023.3327069
EA NOV 2023
PG 16
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA Y2WY9
UT WOS:001103933900001
DA 2024-03-27
ER
PT J
AU Shi, Y
Wang, T
Alwan, LC
AF Shi, Ye
Wang, Ting
Alwan, Layth C.
TI Analytics for Cross-Border E-Commerce: Inventory Risk Management of an
Online Fashion Retailer
SO DECISION SCIENCES
LA English
DT Article
DE Analytics; Cross-border e-commerce; Inventory; Machine learning
techniques; Maximum entropy; Predictive analytics; Prescriptive
analytics; Tax risk
ID DEMAND UNCERTAINTY; NEWSVENDOR PROBLEM; ENTROPY; MODEL; WAREHOUSES;
AMBIGUITY; DRIVERS; VMI
AB Problem statement: We present a data-driven analytics study of a Chinese fashion retailer. The retailer fulfills cross-border orders using online platforms, but faces inventory problems in its overseas warehouses, owing to operational complexities, such as extensive product offerings, high demand risks, and tax risks in cross-border trade. Traditional approaches (e.g., model-driven approaches) often fail to provide effective solutions. Therefore, this study proposes a new data-driven approach to manage inventory in overseas warehouses. Methodology: A two-stage predictive analytics approach is implemented, as follows: (i) all items are classified into one of two classes, where A-items are profitable to store in overseas warehouses, but B-items are not; (ii) the demand levels of SKUs of A-items are predicted. In the subsequent prescriptive analytics, models are proposed for optimizing inventory decisions related to A-items. These include a deterministic model that uses the predicted demand as the true demand, and a stochastic model that treats the true demand as a random variable. Results: (i) Using a variety of machine learning techniques in the predictive analytics phase, we find the random forest outperforms other methods. (ii) The deterministic model can be solved as a linear program, and the stochastic model with maximum entropy distributions can be solved using Karush-Kuhn-Tucker conditions. (iii) An application of our results shows that the predictive classification reduces costs (an average cost reduction of up to 20%) by avoid shipping unprofitable items to overseas warehouses. Furthermore, the stochastic model provides near-optimal solutions (the smallest performance loss is just 0.00%).
C1 [Shi, Ye; Wang, Ting] Univ Sci & Technol China, Sch Management, Hefei, Peoples R China.
[Alwan, Layth C.] Univ Wisconsin, Sheldon B Lubar Sch Business, Milwaukee, WI 53201 USA.
C3 Chinese Academy of Sciences; University of Science & Technology of
China, CAS; University of Wisconsin System; University of Wisconsin
Milwaukee
RP Wang, T (autor correspondiente), Univ Sci & Technol China, Sch Management, Hefei, Peoples R China.
EM hecules@ustc.edu.cn; sa172040@mail.ustc.edu.cn; alwan@uwm.edu
RI N'Dri, Amoin Bernadine/IWD-7811-2023
OI Wang, Ting/0000-0003-3847-2049
FU National Key R&D Program of China [2018YFB1601401]; National Natural
Science Foundation of China [71801207, 71520107002, 71771201]
FX The authors thank the department editor, associate editor, and two
anonymous reviewers for their comments and suggestions that
significantly improve this paper. Every author has made an equal
contribution to this paper. This work was supported by the National Key
R&D Program of China (No. 2018YFB1601401) and the National Natural
Science Foundation of China (No. 71801207, No. 71520107002, and No.
71771201).
CR Andersson J, 2013, EUR J OPER RES, V228, P190, DOI 10.1016/j.ejor.2013.01.031
BAN GY, 2018, OPERATIONS RES
Ben-Tal A., 2005, Manufacturing & Service Operations Management, V7, P248, DOI 10.1287/msom.1050.0081
Bertsimas D, 2006, OPER RES, V54, P150, DOI 10.1287/opre.1050.0238
Bertsimas D, 2016, PROD OPER MANAG, V25, P2006, DOI 10.1111/poms.2_12637
Bertsimas D, 2011, SIAM REV, V53, P464, DOI 10.1137/080734510
Choudhary D, 2015, INT J PROD RES, V53, P1472, DOI 10.1080/00207543.2014.951093
Disney SM, 2003, OMEGA-INT J MANAGE S, V31, P157, DOI 10.1016/S0305-0483(03)00028-8
Fleischhacker AJ, 2015, DECISION SCI, V46, P1165, DOI 10.1111/deci.12170
Fleischhacker AJ, 2015, EUR J OPER RES, V245, P623, DOI 10.1016/j.ejor.2015.03.014
Gessner GH, 2015, RES TRANSP BUS MANAG, V16, P84, DOI 10.1016/j.rtbm.2015.07.005
Giuffrida M, 2017, INT J PHYS DISTR LOG, V47, P772, DOI 10.1108/IJPDLM-08-2016-0241
Gomez-Herrera E, 2014, INF ECON POLICY, V28, P83, DOI 10.1016/j.infoecopol.2014.05.002
HARTELY VR, 1976, OPERATIONS RES MANAG, P7
Hou S., 2019, NEW SILK ROAD LEADS, P203, DOI [10.1108/978-1-78756-679-820191013/full/html, DOI 10.1108/978-1-78756-679-820191013/FULL/HTML]
James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
JONSSON H, 1987, INT J PROD RES, V25, P191, DOI 10.1080/00207548708919833
Levi R, 2015, OPER RES, V63, P1294, DOI 10.1287/opre.2015.1422
Liu X., 2015, International Journal of Intelligent Information Systems. Special Issue: Content-based Image Retrieval and Machine Learning, V4, P15, DOI DOI 10.11648/J.IJIIS.S.2015040202.14
Maglaras C, 2015, PROD OPER MANAG, V24, P438, DOI 10.1111/poms.12243
Natarajan K, 2018, MANAGE SCI, V64, P3146, DOI 10.1287/mnsc.2017.2773
PAKKALA TPM, 1992, EUR J OPER RES, V57, P71, DOI 10.1016/0377-2217(92)90306-T
RABAH MY, 2002, SWUTC021672271 U TEX
Rockafellar R.T., 2000, J RISK, P21, DOI DOI 10.21314/JOR.2000.038
Saghafian S, 2016, OPER RES, V64, P167, DOI 10.1287/opre.2015.1454
SARMA KVS, 1987, EUR J OPER RES, V29, P70, DOI 10.1016/0377-2217(87)90194-9
SHUYAN C, 2013, ASS INF SYST AIS EL
Valarezo A, 2018, TELECOMMUN POLICY, V42, P464, DOI 10.1016/j.telpol.2018.03.006
van Heel B., 2014, Cross-border e-commerce makes the world flatter
WU G, 2016, 2016 2 INT C SOC SCI
XUE W, 2016, INT J ENTERPRISE INF, V14, P56
Zhan XL, 2018, INT J ENTERP INF SYS, V14, P56, DOI 10.4018/IJEIS.2018040104
Zhou YW, 2003, COMPUT OPER RES, V30, P1753, DOI 10.1016/S0305-0548(02)00104-1
NR 33
TC 28
Z9 28
U1 15
U2 146
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0011-7315
EI 1540-5915
J9 DECISION SCI
JI Decis. Sci.
PD DEC
PY 2020
VL 51
IS 6
BP 1347
EP 1376
DI 10.1111/deci.12429
EA JAN 2020
PG 30
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA PF0QA
UT WOS:000507109200001
DA 2024-03-27
ER
PT J
AU Yoganarasimhan, H
Barzegary, E
Pani, A
AF Yoganarasimhan, Hema
Barzegary, Ebrahim
Pani, Abhishek
TI Design and Evaluation of Optimal Free Trials
SO MANAGEMENT SCIENCE
LA English
DT Article
DE free trials; targeting; personalization; policy evaluation; field
experiment; machine learning; digital marketing; Software as a Service
ID PROMOTIONS; MODELS; IMPACT
AB Free trial promotions are a commonly used customer acquisition strategy in the Software as a Service industry. We use data from a large-scale field experiment to study the effect of trial length on customer-level outcomes. We find that, on average, shorter trial lengths (surprisingly) maximize customer acquisition, retention, and profitability. Next, we examine the mechanism through which trial length affects conversions and rule out the demand cannibalization theory, find support for the consumer learning hypothesis, and show that long stretches of inactivity at the end of the trial are associated with lower conversions. We then develop a personalized targeting policy that allocates the optimal treatment to each user based on individual-level predictions of the outcome of interest (e.g., subscriptions) using a lasso model. We evaluate this policy using the inverse propensity score reward estimator and show that it leads to 6.8% improvement in subscription compared with a uniform 30-days for-all policy. It also performs well on long-term customer retention and revenues in our setting. Further analysis of this policy suggests that skilled and experienced users are more likely to benefit from longer trials, whereas beginners are more responsive to shorter trials. Finally, we show that personalized policies do not always outperform uniform policies, and we should be careful when designing and evaluating personalized policies. In our setting, personalized policies based on other methods (e.g., causal forests, random forests) perform worse than a simple uniform policy that assigns a short trial length to all users.
C1 [Yoganarasimhan, Hema; Barzegary, Ebrahim] Univ Washington, Foster Sch Business, Seattle, WA 98195 USA.
[Pani, Abhishek] Bright Machines, San Francisco, CA 94107 USA.
C3 University of Washington; University of Washington Seattle
RP Yoganarasimhan, H (autor correspondiente), Univ Washington, Foster Sch Business, Seattle, WA 98195 USA.
EM hemay@uw.edu; ebar@uw.edu; abhishek.pani@gmail.com
CR Anderson ET, 2004, MARKET SCI, V23, P4, DOI 10.1287/mksc.1030.0040
[Anonymous], 2019, Forecast: Public cloud services, worldwide, 2016-2022, 4q18 update
Athey S, 2020, Arxiv, DOI arXiv:1702.02896
Bruhn M, 2009, AM ECON J-APPL ECON, V1, P200, DOI 10.1257/app.1.4.200
Cheng HK, 2012, INFORM SYST RES, V23, P488, DOI 10.1287/isre.1110.0348
Dey D, 2013, J MANAGE INFORM SYST, V30, P239, DOI 10.2753/MIS0742-1222300209
Dudik M., 2011, ARXIV11034601
Fader PS, 2009, J INTERACT MARK, V23, P61, DOI 10.1016/j.intmar.2008.11.003
Fong N, 2019, J MARKETING RES, V56, P310, DOI 10.1177/0022243718817513
Foubert B, 2016, MARKET SCI, V35, P810, DOI 10.1287/mksc.2015.0973
Friedman J, 2010, J STAT SOFTW, V33, P1, DOI 10.18637/jss.v033.i01
Guo T, 2021, J MARKETING RES, V58, P115, DOI 10.1177/0022243720972106
Gupta S, 2006, J SERV RES-US, V9, P139, DOI 10.1177/1094670506293810
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Hitsch GJ, 2023, PREPRINT, DOI [10.2139/ssrn.3111957, DOI 10.2139/SSRN.3111957]
HORVITZ DG, 1952, J AM STAT ASSOC, V47, P663, DOI 10.2307/2280784
Imbens GW, 2015, CAUSAL INFERENCE FOR STATISTICS, SOCIAL, AND BIOMEDICAL SCIENCES: AN INTRODUCTION, P1, DOI 10.1017/CBO9781139025751
Kitagawa T, 2018, ECONOMETRICA, V86, P591, DOI 10.3982/ECTA13288
Lewis RA, 2015, Q J ECON, V130, P1941, DOI 10.1093/qje/qjv023
Manski CF, 2004, ECONOMETRICA, V72, P1221, DOI 10.1111/j.1468-0262.2004.00530.x
McCarthy DM, 2017, J MARKETING, V81, P17, DOI 10.1509/jm.15.0519
McKenzie D., 2017, Should we require balance t-tests of baseline observables in randomized experiments?
Mela CF, 1997, J MARKETING RES, V34, P248, DOI 10.2307/3151862
Mutz DC, 2019, AM STAT, V73, P32, DOI 10.1080/00031305.2017.1322143
Pauwels K, 2002, J MARKETING RES, V39, P421, DOI 10.1509/jmkr.39.4.421.19114
PRENTICE RL, 1989, STAT MED, V8, P431, DOI 10.1002/sim.4780080407
Rafieian O., 2019, Optimizing user engagement through adaptive ad sequencing
Rafieian O, 2021, MARKET SCI, V40, P193, DOI 10.1287/mksc.2020.1235
Schwartz EM, 2017, MARKET SCI, V36, P500, DOI 10.1287/mksc.2016.1023
SCOTT CA, 1976, J MARKETING RES, V13, P263, DOI 10.2307/3150736
Simester D, 2020, MANAGE SCI, V66, P3412, DOI 10.1287/mnsc.2019.3379
Simester D, 2020, MANAGE SCI, V66, P2495, DOI 10.1287/mnsc.2019.3308
Sunada T., 2018, Customer learning and revenue-maximizing trial design
Swaminathan A., 2017, Advances in Neural Information Processing Systems, P3632
Swaminathan A, 2015, PR MACH LEARN RES, V37, P814
Tibshirani R, 1996, J ROY STAT SOC B, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x
VanderWeele TJ, 2013, BIOMETRICS, V69, P561, DOI 10.1111/biom.12071
Wang SQ, 2018, OPER RES, V66, P301, DOI 10.1287/opre.2017.1675
Yang J., 2020, arXiv
Yoganarasimhan H, 2020, MANAGE SCI, V66, P1045, DOI 10.1287/mnsc.2018.3255
Zhu M, 2018, J CONSUM RES, V45, P673, DOI 10.1093/jcr/ucy008
NR 41
TC 6
Z9 6
U1 2
U2 2
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD JUN
PY 2023
VL 69
IS 6
BP 3220
EP 3240
PG 21
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA DL8O2
UT WOS:001132292000001
DA 2024-03-27
ER
PT J
AU Yoganarasimhan, H
Barzegary, E
Pani, A
AF Yoganarasimhan, Hema
Barzegary, Ebrahim
Pani, Abhishek
TI Design and Evaluation of Optimal Free Trials
SO MANAGEMENT SCIENCE
LA English
DT Article; Early Access
DE free trials; targeting; personalization; policy evaluation; field
experiment; machine learning; digital marketing; Software as a Service
ID PROMOTIONS; MODELS; IMPACT
AB Free trial promotions are a commonly used customer acquisition strategy in the Software as a Service industry. We use data from a large-scale field experiment to study the effect of trial length on customer-level outcomes. We find that, on average, shorter trial lengths (surprisingly) maximize customer acquisition, retention, and profitability. Next, we examine the mechanism through which trial length affects conversions and rule out the demand cannibalization theory, find support for the consumer learning hypothesis, and show that long stretches of inactivity at the end of the trial are associated with lower conversions. We then develop a personalized targeting policy that allocates the optimal treatment to each user based on individual-level predictions of the outcome of interest (e.g., subscriptions) using a lasso model. We evaluate this policy using the inverse propensity score reward estimator and show that it leads to 6.8% improvement in subscription compared with a uniform 30-days for-all policy. It also performs well on long-term customer retention and revenues in our setting. Further analysis of this policy suggests that skilled and experienced users are more likely to benefit from longer trials, whereas beginners are more responsive to shorter trials. Finally, we show that personalized policies do not always outperform uniform policies, and we should be careful when designing and evaluating personalized policies. In our setting, personalized policies based on other methods (e.g., causal forests, random forests) perform worse than a simple uniform policy that assigns a short trial length to all users.
C1 [Yoganarasimhan, Hema; Barzegary, Ebrahim] Univ Washington, Foster Sch Business, Seattle, WA 98195 USA.
[Pani, Abhishek] Bright Machines, San Francisco, CA 94107 USA.
C3 University of Washington; University of Washington Seattle
RP Yoganarasimhan, H (autor correspondiente), Univ Washington, Foster Sch Business, Seattle, WA 98195 USA.
EM hemay@uw.edu; ebar@uw.edu; abhishek.pani@gmail.com
OI Barzegary, Ebrahim/0000-0002-7268-8778
CR Anderson ET, 2004, MARKET SCI, V23, P4, DOI 10.1287/mksc.1030.0040
Athey S, 2020, Arxiv, DOI arXiv:1702.02896
Bruhn M, 2009, AM ECON J-APPL ECON, V1, P200, DOI 10.1257/app.1.4.200
Cheng HK, 2012, INFORM SYST RES, V23, P488, DOI 10.1287/isre.1110.0348
Dey D, 2013, J MANAGE INFORM SYST, V30, P239, DOI 10.2753/MIS0742-1222300209
Dudik M., 2011, ARXIV11034601
Fader PS, 2009, J INTERACT MARK, V23, P61, DOI 10.1016/j.intmar.2008.11.003
Fong N, 2019, J MARKETING RES, V56, P310, DOI 10.1177/0022243718817513
Foubert B, 2016, MARKET SCI, V35, P810, DOI 10.1287/mksc.2015.0973
Friedman J, 2010, J STAT SOFTW, V33, P1, DOI 10.18637/jss.v033.i01
Gartner, 2019, FOR PUBL CLOUD SERV
Guo T, 2021, J MARKETING RES, V58, P115, DOI 10.1177/0022243720972106
Gupta S, 2006, J SERV RES-US, V9, P139, DOI 10.1177/1094670506293810
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Hitsch GJ, 2023, PREPRINT, DOI [10.2139/ssrn.3111957, DOI 10.2139/SSRN.3111957]
HORVITZ DG, 1952, J AM STAT ASSOC, V47, P663, DOI 10.2307/2280784
Imbens GW, 2015, CAUSAL INFERENCE FOR STATISTICS, SOCIAL, AND BIOMEDICAL SCIENCES: AN INTRODUCTION, P1, DOI 10.1017/CBO9781139025751
Kitagawa T, 2018, ECONOMETRICA, V86, P591, DOI 10.3982/ECTA13288
Lewis RA, 2015, Q J ECON, V130, P1941, DOI 10.1093/qje/qjv023
Manski CF, 2004, ECONOMETRICA, V72, P1221, DOI 10.1111/j.1468-0262.2004.00530.x
McCarthy DM, 2017, J MARKETING, V81, P17, DOI 10.1509/jm.15.0519
McKenzie D., 2017, Should we require balance t-tests of baseline observables in randomized experiments?
Mela CF, 1997, J MARKETING RES, V34, P248, DOI 10.2307/3151862
Mutz DC, 2019, AM STAT, V73, P32, DOI 10.1080/00031305.2017.1322143
Pauwels K, 2002, J MARKETING RES, V39, P421, DOI 10.1509/jmkr.39.4.421.19114
PRENTICE RL, 1989, STAT MED, V8, P431, DOI 10.1002/sim.4780080407
Rafieian O., REV OPTIMAL DYNAMIC
Rafieian O., 2019, Optimizing user engagement through adaptive ad sequencing
Rafieian O, 2021, MARKET SCI, V40, P193, DOI 10.1287/mksc.2020.1235
Schwartz EM, 2017, MARKET SCI, V36, P500, DOI 10.1287/mksc.2016.1023
SCOTT CA, 1976, J MARKETING RES, V13, P263, DOI 10.2307/3150736
Simester D, MANAGEMENT SCI, V66, P3412
Simester D, 2020, MANAGE SCI, V66, P2495, DOI 10.1287/mnsc.2019.3308
Sunada T., 2018, Customer learning and revenue-maximizing trial design
Swaminathan A., 2017, ADV NEUR IN, P3632
Swaminathan A, 2015, PR MACH LEARN RES, V37, P814
VanderWeele TJ, 2013, BIOMETRICS, V69, P561, DOI 10.1111/biom.12071
Wang SQ, 2018, OPER RES, V66, P301, DOI 10.1287/opre.2017.1675
Yang J., 2020, arXiv
Yoganarasimhan H, 2020, MANAGE SCI, V66, P1045, DOI 10.1287/mnsc.2018.3255
Zhu M, 2018, J CONSUM RES, V45, P673, DOI 10.1093/jcr/ucy008
NR 41
TC 8
Z9 8
U1 12
U2 58
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0025-1909
EI 1526-5501
J9 MANAGE SCI
JI Manage. Sci.
PD 2022 AUG 10
PY 2022
BP 1
EP 21
DI 10.1287/mnsc.2022.4507
EA AUG 2022
PG 22
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA 3U2LX
UT WOS:000840807200001
DA 2024-03-27
ER
PT J
AU Lo, PS
Dwivedi, YK
Tan, GWH
Ooi, KB
Aw, ECX
Metri, B
AF Lo, Pei-San
Dwivedi, Yogesh K.
Tan, Garry Wei-Han
Ooi, Keng-Boon
Aw, Eugene Cheng-Xi
Metri, Bhimaraya
TI Why do consumers buy impulsively during live streaming? A deep
learning-based dual-stage SEM-ANN analysis
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Livestreaming commerce; Impulsive buying; Parasocial interaction;
Vicarious experience
ID SOCIAL MEDIA; PARASOCIAL INTERACTION; BEHAVIOR; IMPACT; COMMERCE; MODEL;
SUSCEPTIBILITY; ATTRIBUTES; ENGAGEMENT; CUSTOMERS
AB The power of livestreaming commerce to rake in billions of revenues within hours has thrust this nascent commercial model into the global spotlight; that said, despite the prevalence of impulsive buying in livestreaming commerce, the existing knowledge regarding the phenomenon remains relatively scarce. This research seeks to unravel the critical determinants that influence consumers' impulsive buying in livestreaming. Grounded in the Stimulus-Organism-Response paradigm, a framework is proposed to elucidate the underlying mechanism on how parasocial interaction, social contagion, vicarious experience, scarcity persuasion, and price perception translate into impulsive buying urge and behaviour in livestreaming commerce via the cognitiveaffective processing system. A self-administered online questionnaire survey was conducted with 295 respondents. The data collected was validated empirically through a multi-analytical hybrid structural equation modelling-artificial neural network (SEM-ANN) technique. The results reveal that parasocial interaction, vicarious experience, scarcity persuasion, and price perception can drive cognitive and affective reactions, which in turn, induce impulsive buying urge, subject to the boundary condition of impulsive buying tendency. In sum, the findings have drawn some insightful theoretical and practical implications that can facilitate the advancement of livestreaming commerce in the modern business arena.
C1 [Lo, Pei-San; Tan, Garry Wei-Han; Ooi, Keng-Boon; Aw, Eugene Cheng-Xi] UCSI Univ, UCSI Grad Business Sch, 1 Jalan Menara Gading,UCSI Hts, Cheras 56000, Kuala Lumpur, Malaysia.
[Dwivedi, Yogesh K.] Swansea Univ, Emerging Markets Res Ctr EMaRC, Sch Management, Room 323,Bay Campus, Swansea SA1 8EN, Wales.
[Dwivedi, Yogesh K.] Pune & Symbiosis Int, Symbiosis Inst Business Management, Pune, India.
[Tan, Garry Wei-Han; Ooi, Keng-Boon] Nanchang Inst Technol, Sch Finance & Econ, Nan Chang City, Jiang Xi, Peoples R China.
[Ooi, Keng-Boon] Chang Jung Christian Univ, Coll Management, Tainan, Taiwan.
[Metri, Bhimaraya] Indian Inst Management Nagpur, Nagpur, India.
C3 UCSI University; Swansea University; Symbiosis International University;
Symbiosis Institute of Business Management (SIBM) Pune; Nanchang
Institute Technology; Chang Jung Christian University; Indian Institute
of Management (IIM System); Indian Institute of Management Nagpur
RP Dwivedi, YK (autor correspondiente), Swansea Univ, Emerging Markets Res Ctr EMaRC, Sch Management, Room 323,Bay Campus, Swansea SA1 8EN, Wales.; Dwivedi, YK (autor correspondiente), Pune & Symbiosis Int, Symbiosis Inst Business Management, Pune, India.
EM oeiansan@gmail.com; y.k.dwivedi@swansea.ac.uk; garrytanweihan@gmail.com;
ooikengboon@gmail.com; director@iimnagpur.ac.in
RI Aw, Eugene Cheng-Xi/K-8475-2019; Tan Wei Han, Garry/C-6565-2011; Lo, Pei
San/ADV-5389-2022; OOI, Keng-Boon/I-4143-2019; Dwivedi, Yogesh
Kumar/A-5362-2008
OI Aw, Eugene Cheng-Xi/0000-0001-6712-1171; Tan Wei Han,
Garry/0000-0003-2974-2270; OOI, Keng-Boon/0000-0002-3384-1207; Dwivedi,
Yogesh Kumar/0000-0002-5547-9990; Lo, Pei San/0000-0002-7061-2307
CR Abdelsalam S, 2020, IEEE ACCESS, V8, P89041, DOI 10.1109/ACCESS.2020.2993671
Al-Saedi K., 2019, MOBILE PAYMENT ADOPT, P1
Al-Sharafi MA, 2022, INT J BANK MARK, V40, P1071, DOI 10.1108/IJBM-07-2021-0291
Ang T, 2018, EUR J MARKETING, V52, P2075, DOI 10.1108/EJM-09-2017-0576
Aw ECX, 2021, J BUS RES, V132, P146, DOI 10.1016/j.jbusres.2021.04.025
Aw ECX, 2020, J CONSUM MARK, V37, P895, DOI 10.1108/JCM-10-2019-3474
Baumeister RF, 2002, J CONSUM RES, V28, P670, DOI 10.1086/338209
BEARDEN WO, 1989, J CONSUM RES, V15, P473, DOI 10.1086/209186
Beatty SE, 1998, J RETAILING, V74, P169, DOI 10.1016/S0022-4359(99)80092-X
Block L.G., 1999, J CONSUM PSYCHOL, V8, P343, DOI [10.1207/s15327663jcp0804_01, DOI 10.1207/S15327663JCP0804_01]
Cai J, 2018, TVX 2018: PROCEEDINGS OF THE 2018 ACM INTERNATIONAL CONFERENCE ON INTERACTIVE EXPERIENCES FOR TV AND ONLINE VIDEO, P81, DOI 10.1145/3210825.3210837
Chen CC, 2018, TELEMAT INFORM, V35, P293, DOI 10.1016/j.tele.2017.12.003
Chen YH, 2019, INFORM MANAGE-AMSTER, V56, P236, DOI 10.1016/j.im.2018.09.002
Chopdar PK, 2022, J BUS RES, V140, P657, DOI 10.1016/j.jbusres.2021.11.031
Chung N, 2017, INT J CONTEMP HOSP M, V29, P709, DOI [10.1108/ijchm-10-2015-0608, 10.1108/IJCHM-10-2015-0608]
COHEN J, 1992, PSYCHOL BULL, V112, P155, DOI 10.1037/0033-2909.112.1.155
Dibble JL, 2016, HUM COMMUN RES, V42, P21, DOI 10.1111/hcre.12063
Doherty RW, 1997, J NONVERBAL BEHAV, V21, P131, DOI 10.1023/A:1024956003661
Dolan R, 2019, EUR J MARKETING, V53, P2213, DOI 10.1108/EJM-03-2017-0182
Donthu N, 1996, J ADVERTISING RES, V36, P69
Eisend M, 2008, J ADVERTISING, V37, P33, DOI 10.2753/JOA0091-3367370303
Eroglu SA, 2001, J BUS RES, V54, P177, DOI 10.1016/S0148-2963(99)00087-9
Forrester, 2021, SHOPP IS LAND EUR
Gan CM, 2017, INTERNET RES, V27, P772, DOI 10.1108/IntR-06-2016-0164
Gartner W. C., 1993, Journal of Travel & Tourism Marketing, V2, P191
Goel P, 2022, J BUS RES, V142, P1, DOI 10.1016/j.jbusres.2021.12.041
Gong WQ, 2017, PSYCHOL MARKET, V34, P720, DOI 10.1002/mar.21018
Hair J.F., 2016, A premier on partial least squares structural equation modeling (PLS-SEM), V2nd
Hallanan L., 2020, FORBES
Hancock JT, 2008, DISCOURSE PROCESS, V45, P1, DOI 10.1080/01638530701739181
Hatfield Elaine., 1992, EMOTION SOCIAL BEHAV, DOI 10.1017/CBO9781139174138
Haykin S., 2001, NEURAL NETWORKS COMP
Hennig-Thurau T, 2010, J SERV RES-US, V13, P311, DOI 10.1177/1094670510375460
Hew JJ, 2017, TELEMAT INFORM, V34, P914, DOI 10.1016/j.tele.2017.04.001
Högberg J, 2019, J RETAIL CONSUM SERV, V50, P122, DOI 10.1016/j.jretconser.2019.05.006
HORTON D, 1956, PSYCHIATR, V19, P215, DOI 10.1080/00332747.1956.11023049
Hsiao CC, 2020, ONLINE INFORM REV, V44, P1289, DOI 10.1108/OIR-11-2019-0350
Hsu CL, 2013, INTERNET RES, V23, P69, DOI 10.1108/10662241311295782
Hu MY, 2020, INTERNET RES, V30, P1019, DOI 10.1108/INTR-03-2019-0082
Hu M, 2017, COMPUT HUM BEHAV, V75, P594, DOI 10.1016/j.chb.2017.06.006
International Trade Administration, 2021, MAL COUNTR COMM GUID
Jones MA, 2003, J BUS RES, V56, P505, DOI 10.1016/S0148-2963(01)00250-8
Kang HJ, 2020, J INTERACT MARK, V49, P70, DOI 10.1016/j.intmar.2019.07.002
Kang K, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102251
Kaufman BE, 1999, J ECON BEHAV ORGAN, V38, P135, DOI 10.1016/S0167-2681(99)00002-5
Labrecque LI, 2014, J INTERACT MARK, V28, P134, DOI 10.1016/j.intmar.2013.12.003
Lau A.J., 2021, Machine Learning with Applications, V4, P100034, DOI [https://doi.org/10.1016/j.mlwa.2021.100034, DOI 10.1016/J.MLWA.2021.100034]
Leong LY, 2019, EXPERT SYST APPL, V133, P296, DOI 10.1016/j.eswa.2019.05.024
Leong LY, 2013, EXPERT SYST APPL, V40, P5604, DOI 10.1016/j.eswa.2013.04.018
Lew SS, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101430
Liang HG, 2007, MIS QUART, V31, P59
Lim JS, 2020, COMPUT HUM BEHAV, V108, DOI 10.1016/j.chb.2020.106327
Lim S, 2012, COMPUT HUM BEHAV, V28, P1365, DOI 10.1016/j.chb.2012.02.022
Liu Y, 2013, DECIS SUPPORT SYST, V55, P829, DOI 10.1016/j.dss.2013.04.001
Loewenstein G, 2003, SER AFFECTIVE SCI, P619
Lucas M, 2017, PERS INDIV DIFFER, V105, P330, DOI 10.1016/j.paid.2016.10.004
Luo XM, 2005, J CONSUM PSYCHOL, V15, P288, DOI 10.1207/s15327663jcp1504_3
MACINNIS DJ, 1987, J CONSUM RES, V13, P473, DOI 10.1086/209082
Malaysian Communications and Multimedia Commission, 2020, INT US SURV 2020, P160
Malhotra D, 2010, ORGAN BEHAV HUM DEC, V111, P139, DOI 10.1016/j.obhdp.2009.11.005
Manz C.C., 1981, ACAD MANAGE REV, V6, P105, DOI [DOI 10.2307/257144, 10.5465/amr.1981.4288021, DOI 10.5465/AMR.1981.4288021]
Marshall GW, 2012, J PERS SELL SALES M, V32, P349, DOI 10.2753/PSS0885-3134320305
MCCRACKEN G, 1989, J CONSUM RES, V16, P310, DOI 10.1086/209217
Ming JL, 2021, INT J WEB INF SYST, V17, P300, DOI 10.1108/IJWIS-02-2021-0012
Mosteller J, 2014, J BUS RES, V67, P2486, DOI 10.1016/j.jbusres.2014.03.009
Ooi KB, 2021, IND MANAGE DATA SYST, V121, P312, DOI 10.1108/IMDS-04-2020-0239
Ooi KB, 2016, EXPERT SYST APPL, V59, P33, DOI 10.1016/j.eswa.2016.04.015
Oppotus, 2021, LIV SHOPP FUT E COMM
Oyibo K, 2019, COMPUT HUM BEHAV, V98, P174, DOI 10.1016/j.chb.2019.01.032
Parboteeah DV, 2009, INFORM SYST RES, V20, P60, DOI 10.1287/isre.1070.0157
Park EJ, 2012, J BUS RES, V65, P1583, DOI 10.1016/j.jbusres.2011.02.043
Roberts Debbie, 2010, Nurse Educ Pract, V10, P13, DOI 10.1016/j.nepr.2009.01.017
ROOK DW, 1987, J CONSUM RES, V14, P189, DOI 10.1086/209105
RUBIN AM, 1985, HUM COMMUN RES, V12, P155, DOI 10.1111/j.1468-2958.1985.tb00071.x
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Sokolova K, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.01.011
Sowinska A, 2019, J PRAGMATICS, V152, P76, DOI 10.1016/j.pragma.2018.01.007
STERN H, 1962, J MARKETING, V26, P59, DOI 10.2307/1248439
Sweeneya JC, 2001, PROBLEMS PERSPECTIVE, V5, P252
Tan GWH, 2018, TELEMAT INFORM, V35, P1617, DOI 10.1016/j.tele.2018.04.009
Thaler RH, 2008, MARKET SCI, V27, P15, DOI 10.1287/mksc.1070.0330
Thorson K.S., 2006, Journal of Interactive Advertising, V6, P34, DOI [DOI 10.1080/15252019.2006.10722117, 10.1080/15252019.2006.10722117]
Thürmer JL, 2020, EUR J MARKETING, V54, P2079, DOI 10.1108/EJM-05-2018-0341
Vazquez D, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102135
Verplanken B, 2001, EUR J PERSONALITY, V15, pS71, DOI 10.1002/per.423
[王艳芝 Wang Yanzhi], 2018, [心理科学进展, Advances in Psychological Science], V26, P1915
WEINBERG P, 1982, J BUS RES, V10, P43, DOI 10.1016/0148-2963(82)90016-9
WHEELER L, 1966, PSYCHOL REV, V73, P179, DOI 10.1037/h0023023
Wong CH, 2015, ONLINE INFORM REV, V39, P762, DOI 10.1108/OIR-05-2015-0152
Wong CH, 2014, IND MANAGE DATA SYST, V114, P1050, DOI 10.1108/IMDS-05-2014-0146
Wong LW, 2022, J MARK HIGH EDUC, V32, P113, DOI 10.1080/08841241.2020.1834486
Wong LW, 2020, INT J PROD RES, V58, P2100, DOI 10.1080/00207543.2020.1730463
Wongkitrungrueng A, 2020, J BUS RES, V117, P543, DOI 10.1016/j.jbusres.2018.08.032
Wu Y, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2020.103283
Xiang L, 2016, INT J INFORM MANAGE, V36, P333, DOI 10.1016/j.ijinfomgt.2015.11.002
Xue JL, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2020.103324
Zafar AU, 2021, ASIA PAC J MARKET LO, V33, P945, DOI 10.1108/APJML-08-2019-0495
Zafar AU, 2021, COMPUT HUM BEHAV, V115, DOI 10.1016/j.chb.2019.106178
Zhang YC, 2017, INT J RES MARK, V34, P100, DOI 10.1016/j.ijresmar.2016.07.003
Zheng XB, 2019, INT J INFORM MANAGE, V48, P151, DOI 10.1016/j.ijinfomgt.2019.02.010
Zhong YY, 2022, YOUNG CONSUM, V23, P449, DOI 10.1108/YC-08-2021-1367
Zuo Renshu, 2021, Proceedings of the Fifteenth International Conference on Management Science and Engineering Management. Lecture Notes on Data Engineering and Communications Technologies (78), P610, DOI 10.1007/978-3-030-79203-9_47
NR 102
TC 80
Z9 80
U1 128
U2 366
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD AUG
PY 2022
VL 147
BP 325
EP 337
DI 10.1016/j.jbusres.2022.04.013
EA APR 2022
PG 13
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 1L4GA
UT WOS:000799247400005
OA hybrid
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Arshi, TA
Islam, S
Gunupudi, N
AF Arshi, Tahseen Anwer
Islam, Sardar
Gunupudi, Nirmal
TI Predicting the effect of entrepreneurial stressors and resultant strain
on entrepreneurial behaviour: an SEM-based machine-learning approach
SO INTERNATIONAL JOURNAL OF ENTREPRENEURIAL BEHAVIOR & RESEARCH
LA English
DT Article
DE Hindrance stressors; Entrepreneurial strain; Entrepreneurial behaviour;
Psychological capital; Cross-lagged panel study; Structural equation
modelling; Machine learning
ID PSYCHOLOGICAL STRAIN; JOB CHARACTERISTICS; SELF-EMPLOYMENT; WORK STRESS;
PERSONALITY; RESOURCE; PERFORMANCE; FAMILY; CHALLENGE; CONFLICT
AB Purpose Considerable evidence suggests that although they overlap, entrepreneurial and employee stressors have different causal antecedents and outcomes. However, limited empirical data explain how entrepreneurial traits, work and life drive entrepreneurial stressors and create entrepreneurial strain (commonly called entrepreneurial stress). Drawing on the challenge-hindrance framework (CHF), this paper hypothesises the causal effect of hindrance stressors on entrepreneurial strain. Furthermore, the study posits that entrepreneurial stressors and the resultant strain affect entrepreneurial behaviour. Design/methodology/approach The study adopts an SEM-based machine-learning approach. Cross-lagged path models using SEM are used to analyse the data and train the machine-learning algorithm for cross-validation and generalisation. The sample consists of 415 entrepreneurs from three countries: India, Oman and United Arab Emirates. The entrepreneurs completed two self-report surveys over 12 months. Findings The results show that hindrances to personal and professional goal achievement, demand-capability gap and contradictions between aspiration and reality, primarily due to unique resource constraints, characterise entrepreneurial stressors leading to entrepreneurial strain. The study further asserts that entrepreneurial strain is a significant predictor of entrepreneurial behaviour, significantly affecting innovativeness behaviour. Finally, the finding suggests that psychological capital moderates the adverse impact of stressors on entrepreneurial strain over time. Originality/value This study contributes to the CHF by demonstrating the value of hindrance stressors in studying entrepreneurial strain and providing new insights into entrepreneurial coping. It argues that entrepreneurs cope effectively against hindrance stressors by utilising psychological capital. Furthermore, the study provides more evidence about the causal, reversed and reciprocal relationships between stressors and entrepreneurial strain through a cross-lagged analysis. This study is one of the first to evaluate the impact of entrepreneurial strain on entrepreneurial behaviour. Using a machine-learning approach is a new possibility for using machine learning for SEM and entrepreneurial strain.
C1 [Arshi, Tahseen Anwer] Univ Ras Al Khaimah, Sch Business, Ras Al Khaymah, U Arab Emirates.
[Islam, Sardar] Victoria Univ, Melbourne, Vic, Australia.
[Gunupudi, Nirmal] Majan Univ Coll, Fac Business, Ruwi, Oman.
C3 Victoria University
RP Arshi, TA (autor correspondiente), Univ Ras Al Khaimah, Sch Business, Ras Al Khaymah, U Arab Emirates.
EM tahseen.arshi@aurak.ac.ae; sardar.Islam@vu.edu.au;
nirmalgunupudi@gmail.com
RI ISLAM, MOHAMMAD NOOR/AHE-4032-2022
OI ISLAM, MOHAMMAD NOOR/0000-0001-9975-4889; arshi,
tahseen/0000-0002-5244-7862; Gunupudi, Nirmal Dayanand
Raju/0000-0001-6330-0324; Islam, Sardar M. N./0000-0001-9451-7390
CR Abbas M, 2019, J BUS PSYCHOL, V34, P189, DOI 10.1007/s10869-018-9535-z
Adamoniene R, 2015, PROCD SOC BEHV, V213, P890, DOI 10.1016/j.sbspro.2015.11.501
Adisa TA, 2019, PERS REV, V48, P1634, DOI 10.1108/PR-06-2018-0197
Aldrich H., 1999, Organizations evolving
Amarakoon U, 2019, PERS REV, V48, P1809, DOI 10.1108/PR-03-2018-0087
[Anonymous], 2003, HDB ENTREPRENEURSHIP, DOI DOI 10.1007/0-387-24519-7_9
Antoncic B, 2015, J SMALL BUS MANAGE, V53, P819, DOI 10.1111/jsbm.12089
Aragon-Sanchez A, 2017, INT J ENTREP BEHAV R, V23, P752, DOI 10.1108/IJEBR-06-2016-0172
Arshi T, 2018, J ENTREP, V27, P151, DOI 10.1177/0971355718781245
Avey JB, 2009, HUM RESOUR MANAGE-US, V48, P677, DOI 10.1002/hrm.20294
Ayala JC, 2014, J ECON PSYCHOL, V42, P126, DOI 10.1016/j.joep.2014.02.004
Baker T, 2005, ADMIN SCI QUART, V50, P329, DOI 10.2189/asqu.2005.50.3.329
Bandura A, 2007, J SOC CLIN PSYCHOL, V26, P641, DOI 10.1521/jscp.2007.26.6.641
BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
Baron RA, 2016, J MANAGE, V42, P742, DOI 10.1177/0149206313495411
Delgado-García JB, 2012, J SMALL BUS MANAGE, V50, P408, DOI 10.1111/j.1540-627X.2012.00359.x
Benda BB, 2002, J SOC SERV RES, V29, P55, DOI 10.1300/J079v29n02_03
Berge LIO, 2015, MANAGE SCI, V61, P707, DOI 10.1287/mnsc.2014.1933
Bernerth JB, 2016, PERS PSYCHOL, V69, P229, DOI 10.1111/peps.12103
Bird B, 2012, ENTREP THEORY PRACT, V36, P889, DOI 10.1111/j.1540-6520.2012.00535.x
Bishop C. M, 2007, Pattern Recognition and Machine Learning, DOI DOI 10.1007/978-0-387-45528-0
Boyce CJ, 2013, SOC INDIC RES, V111, P287, DOI 10.1007/s11205-012-0006-z
Bradley S.W., 2015, Wiley Encyclopedia of Management, P1, DOI 10.1002/9781118785317.weom030031
Breakwell G.M., 2014, The psychology of risk, V2nd, DOI DOI 10.1017/CBO9780511819315
Burhanuddin N.A. N., 2019, International Journal of Academic Research in Progressive Education and Development, V8, p133?150, DOI [DOI 10.6007/IJARPED/V8-I3/6302, 10.6007/ijarped/v8-i3/6304]
Cantner U., 2011, P DIME DRUID AC WINT
Cardon MS, 2013, J BUS VENTURING, V28, P373, DOI 10.1016/j.jbusvent.2012.03.003
Carver CS, 2010, ANNU REV PSYCHOL, V61, P679, DOI 10.1146/annurev.psych.093008.100352
Cavanaugh MA, 2000, J APPL PSYCHOL, V85, P65, DOI 10.1037/0021-9010.85.1.65
Cavus M., 2014, INT REV MANAGEMENT M, V4, P42
Charmaine G.C., 2017, MCGILL INT ENTR MIE
Chernyshenko OS, 2007, PSYCHOL ASSESSMENT, V19, P88, DOI 10.1037/1040-3590.19.1.88
Cirillo A, 2021, SMALL BUS ECON, V56, P1571, DOI 10.1007/s11187-019-00279-x
Clough DR, 2019, ACAD MANAG ANN, V13, P240, DOI 10.5465/annals.2016.0132
Coelho F, 2011, J RETAILING, V87, P31, DOI 10.1016/j.jretai.2010.11.004
Cohen S, 2007, INFORM SCIENCES, V177, P3592, DOI 10.1016/j.ins.2007.01.016
Connor-Smith JK, 2007, J PERS SOC PSYCHOL, V93, P1080, DOI 10.1037/0022-3514.93.6.1080
Contreras-Barraza N., 2021, FRONT PSYCHOL, V12, P848
Coulter M., 2003, ENTREPRENEURSHIP ACT, V2nd
Ding G., 2016, Human Resource Development of China, V22, P19, DOI DOI 10.3969/J.ISSN.1004-4124.2016.22.004
Down S, 2008, INT J ENTREP BEHAV R, V14, P4, DOI 10.1108/13552550810852802
Ebbers JJ, 2017, HUM RELAT, V70, P1342, DOI 10.1177/0018726717692852
Fatoki O., 2019, INT J ENTREPRENEURSH, V23, P1
Fernet C, 2013, EUR J WORK ORGAN PSY, V22, P123, DOI 10.1080/1359432X.2011.632161
Fodor OC, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00310
Foo MD, 2009, J APPL PSYCHOL, V94, P1086, DOI 10.1037/a0015599
Forman G., 2010, ACM SIGKDD Explor Newsl, V12, P49, DOI DOI 10.1145/1882471.1882479
Ganster DC, 2013, J MANAGE, V39, P1085, DOI 10.1177/0149206313475815
Gardoni P, 2014, RISK ANAL, V34, P1208, DOI 10.1111/risa.12150
Gareth J, 2013, INTRO STAT LEARNING
Gartner WB, 2010, INT HANDB SER ENTREP, P99, DOI 10.1007/978-1-4419-1191-9_5
Gilboa S, 2008, PERS PSYCHOL, V61, P227, DOI 10.1111/j.1744-6570.2008.00113.x
Gorgievski MJ, 2016, APPL PSYCHOL-INT REV, V65, P437, DOI 10.1111/apps.12073
Grant Sharon, 2012, International Journal of Entrepreneurial Venturing, V4, P351
Grant S, 2007, J OCCUP HEALTH PSYCH, V12, P20, DOI 10.1037/1076-8998.12.1.20
Grawitch MJ, 2010, APPL PSYCHOL-HLTH WE, V2, P127, DOI 10.1111/j.1758-0854.2009.01023.x
Green JA, 2016, ASSESSMENT, V23, P374, DOI 10.1177/1073191115580639
Hachana R, 2018, J INNOV ECON MANAG, P169, DOI 10.3917/jie.027.0169
Hair J.F., 2009, Multivariate data analysis
Halbesleben JRB, 2014, J MANAGE, V40, P1334, DOI 10.1177/0149206314527130
Hamsici OC, 2007, J MACH LEARN RES, V8, P1583
Hart P.M., 2001, Handbook of industrial, work and organizational psychology, V2, P93
Hedia F., 2014, ENTREP RES J, V4, P351
HEDNER T., 2011, Annals of Innovation Entrepreneurship, V2, P7986, DOI DOI 10.3402/AIE.V2I1.6002
Heilbrunn S., 2019, INT J ENTREP BEHAV R, V10, P39
Hessels J, 2017, J BUS VENTURING, V32, P178, DOI 10.1016/j.jhusvent.2016.10.007
Nguyen H, 2016, ENTREP RES J, V6, P207, DOI 10.1515/erj-2014-0041
Hobfoll S.E., 2011, CONSERVATION RESOURC, DOI DOI 10.1093/OXFORDHB/9780195375343.013.0007
Hobfoll SE, 2018, ANNU REV ORGAN PSYCH, V5, P103, DOI 10.1146/annurev-orgpsych-032117-104640
Horan KA, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.560346
Hsu DK, 2016, J BUS VENTURING, V31, P613, DOI 10.1016/j.jbusvent.2016.08.001
Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
Inglehart R., 2018, CITIZEN POLITICS POS
Jensen S.M., 2012, J INQUIRIES PERSPECT, V4, P79
Jex S.M., 2002, Organizational psychology: A scientist-practitioner approach, P179
Jex S.M., 1998, STRESS JOB PERFORMAN
Joensuu-Salo S, 2020, EDUC TRAIN, V62, P325, DOI 10.1108/ET-03-2019-0053
Kariv D., 2008, Journal of Small Business and Entrepreneurship, V21, P449, DOI DOI 10.1080/08276331.2008.10593435
Kellermanns F, 2016, J SMALL BUS MANAGE, V54, P26, DOI 10.1111/jsbm.12126
Kenny DA, 2015, SOCIOL METHOD RES, V44, P486, DOI 10.1177/0049124114543236
Kerr SP, 2018, FOUND TRENDS ENTREP, V14, P279, DOI 10.1561/0300000080
Khairuddin S.M.H.S., 2017, ASIAN J SCI RES, V9, P171
Koeske G.F., 1993, J SOC SERV RES, V17, P107, DOI DOI 10.1300/J079V17N03_06
Kotha R, 2012, J BUS VENTURING, V27, P525, DOI 10.1016/j.jbusvent.2012.02.001
Krasikova DV, 2015, J LEADERSH ORG STUD, V22, P280, DOI 10.1177/1548051815585853
Lanivich SE, 2015, ENTREP THEORY PRACT, V39, P863, DOI 10.1111/etap.12082
Lau TLM, 2012, INT J ENTREP BEHAV R, V18, P673, DOI 10.1108/13552551211268120
Lazarus R. S., 1984, Stress, appraisal, and coping
Lazarus R. S., 1966, PSYCHOL STRESS COPIN
Lechat Thomas, 2017, International Journal of Entrepreneurship and Small Business, V32, P537
Lepine JA, 2005, ACAD MANAGE J, V48, P764, DOI 10.2307/20159696
Lerman M.P., 2020, RES OCCUPATIONAL STR
Lerman MP, 2021, STRATEG ENTREP J, V15, P377, DOI 10.1002/sej.1370
Leyden DP, 2014, RES POLICY, V43, P1157, DOI 10.1016/j.respol.2014.04.010
Liang C., 2011, ACAD ENTREPRENEURSHI, V17, P97
Lin WP, 2015, J ORGAN BEHAV, V36, P94, DOI 10.1002/job.1949
Lobo JM, 2008, GLOBAL ECOL BIOGEOGR, V17, P145, DOI 10.1111/j.1466-8238.2007.00358.x
Lopes M, 2009, ENCY POSITIVE PSYCHO
Loscalzo Y, 2019, SAGE OPEN, V9, DOI 10.1177/2158244019861495
Luca M., 2017, Transilvania University of Brasov.Series VII: Social Sciences and Law, V10, P19
Luca MR, 2011, PROCD SOC BEHV, V30, DOI 10.1016/j.sbspro.2011.10.242
Lupien SJ, 2009, NAT REV NEUROSCI, V10, P434, DOI 10.1038/nrn2639
Luthans F., 2014, PSYCHOL CAPITAL QUES
Luthans F, 2007, PERS PSYCHOL, V60, P541, DOI 10.1111/j.1744-6570.2007.00083.x
Maritz A., 2015, ACERE C AD 2 5 FEBR
MASLACH C, 1981, J OCCUP BEHAV, V2, P99, DOI 10.1002/job.4030020205
Maslach C, 2008, J APPL PSYCHOL, V93, P498, DOI 10.1037/0021-9010.93.3.498
Mazzola JJ, 2019, J ORGAN BEHAV, V40, P949, DOI 10.1002/job.2412
Michel JS, 2009, PERS INDIV DIFFER, V47, P163, DOI 10.1016/j.paid.2009.02.015
O'Brien KE, 2019, J ORGAN BEHAV, V40, P962, DOI 10.1002/job.2405
Omrane A., 2018, FIIB BUS REV, V7, P28
Patel PC, 2019, J BUS VENTURING, V34, P731, DOI 10.1016/j.jbusvent.2018.05.004
Pearsall MJ, 2009, ORGAN BEHAV HUM DEC, V109, P18, DOI 10.1016/j.obhdp.2009.02.002
Penley JA, 2002, PERS INDIV DIFFER, V32, P1215, DOI 10.1016/S0191-8869(01)00087-3
Peterson SJ, 2011, PERS PSYCHOL, V64, P427, DOI 10.1111/j.1744-6570.2011.01215.x
Podsakoff NP, 2007, J APPL PSYCHOL, V92, P438, DOI 10.1037/0021-9010.92.2.438
Poon JML, 2006, INT SMALL BUS J, V24, P61, DOI 10.1177/0266242606059779
Preacher KJ, 2006, J EDUC BEHAV STAT, V31, P437, DOI 10.3102/10769986031004437
Rabenu E, 2017, CURR PSYCHOL, V36, P875, DOI 10.1007/s12144-016-9477-4
Rauch A, 2018, ACAD MANAGE PERSPECT, V32, P340, DOI 10.5465/amp.2016.0184
Riddle BL, 2000, SMALL GR RES, V31, P554, DOI 10.1177/104649640003100503
Robert B.W., 2009, Handbook of individual differences in social behaviour, P369, DOI DOI 10.1037/0033-2909.126.1
Rosseel Y, 2012, J STAT SOFTW, V48, P1, DOI 10.18637/jss.v048.i02
Ryan RM, 2017, SELF-DETERMINATION THEORY: BASIC PSYCHOLOGICAL NEEDS IN MOTIVATION, DEVELOPMENT, AND WELLNESS, P1, DOI 10.1521/978.14625/28806
Sandi C, 2015, NAT REV NEUROSCI, V16, P290, DOI 10.1038/nrn3918
Schenkel MT, 2014, J DEV ENTREP, V19, DOI 10.1142/S1084946714500058
Schjoedt L, 2009, ENTREP THEORY PRACT, V33, P619, DOI 10.1111/j.1540-6520.2009.00319.x
Schonfeld IS, 2015, J OCCUP HEALTH PSYCH, V20, P501, DOI 10.1037/a0038804
Scott P., 2003, ERGONOMICS SA, V15, P67
Searle BJ, 2015, ANXIETY STRESS COPIN, V28, P121, DOI 10.1080/10615806.2014.931378
Selig J.P., 2012, HDB DEVELOPMENTAL RE, P265
Semerci A.B., 2016, Academy of Entrepreneurship Journal, V22, P41
Shepherd D.A., 2014, PSYCHOL APPROACH ENT
Shir N, 2019, J BUS VENTURING, V34, DOI 10.1016/j.jbusvent.2018.05.002
Stam W, 2014, J BUS VENTURING, V29, P152, DOI 10.1016/j.jbusvent.2013.01.002
Staniewski MW, 2016, INT ENTREP MANAG J, V12, P861, DOI 10.1007/s11365-016-0385-8
Starr J.A., 1992, ENTREP THEORY PRACT, P67, DOI DOI 10.1177/104225879201700108
Stephan U, 2020, INT J ENTREP BEHAV R, V26, P1133, DOI 10.1108/IJEBR-06-2019-0362
Stephan U, 2018, ACAD MANAGE PERSPECT, V32, P290, DOI 10.5465/amp.2017.0001
Stewart WH, 2007, J SMALL BUS MANAGE, V45, P401, DOI 10.1111/j.1540-627X.2007.00220.x
Tabachnick B. G., 2007, Using Multivariate Statistics, V5, P481, DOI DOI 10.1037/022267
ten Brummelhuis LL, 2012, AM PSYCHOL, V67, P545, DOI 10.1037/a0027974
Thatcher Miller, 2003, STRESSORS STRESS STR
Thompson N.A., 2020, Research handbook on entrepreneurial behavior, practice and process, P30
Thompson NA, 2020, ENTREP REGION DEV, V32, P247, DOI 10.1080/08985626.2019.1641978
Tsai FS, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.582133
Uy MA, 2013, J BUS VENTURING, V28, P583, DOI 10.1016/j.jbusvent.2012.04.003
van Dam K, 2010, TEACH TEACH EDUC, V26, P965, DOI 10.1016/j.tate.2009.10.038
Wang Y, 2019, FRONT PSYCHOL, V10, DOI 10.3389/fpsyg.2019.01071
Webster JR, 2011, J VOCAT BEHAV, V79, P505, DOI 10.1016/j.jvb.2011.02.001
Wei YX, 2020, J AFFECT DISORDERS, V266, P753, DOI 10.1016/j.jad.2020.01.105
Welter F, 2018, STRATEG ENTREP J, V12, P23, DOI 10.1002/sej.1274
Werbel JD, 2010, J SMALL BUS MANAGE, V48, P421, DOI 10.1111/j.1540-627X.2010.00301.x
Wersebe H, 2018, INT J CLIN HLTH PSYC, V18, P60, DOI 10.1016/j.ijchp.2017.09.002
White JV., 2020, Entrepreneurial and small business stressors, experienced stress, and well-being: Research in occupational stress and well-being, V18, P65, DOI [10.1108/s1479-355520200000018004, DOI 10.1108/S1479-355520200000018004]
Wiklund J, 2019, J BUS VENTURING, V34, P579, DOI 10.1016/j.jbusvent.2019.01.002
Williams EN, 2019, CONTRIB MANAG SCI, P117, DOI 10.1007/978-3-030-19685-1_6
Wilmot MP, 2019, P NATL ACAD SCI USA, V116, P23004, DOI 10.1073/pnas.1908430116
Wilmot P., 2019, MACHINE LEARNING APP
Wincent J, 2009, J BUS PSYCHOL, V24, P225, DOI 10.1007/s10869-009-9102-8
Winkel DE, 2010, J VOCAT BEHAV, V76, P336, DOI 10.1016/j.jvb.2009.10.011
Zedeikaite L., 2009, SOC EDUC, V8, P35
Zhang J, 2014, SOC INDIC RES, V115, P337, DOI 10.1007/s11205-012-0222-6
NR 163
TC 6
Z9 6
U1 13
U2 51
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1355-2554
EI 1758-6534
J9 INT J ENTREP BEHAV R
JI Int. J. Entrep. Behav. Res.
PD OCT 11
PY 2021
VL 27
IS 7
BP 1819
EP 1848
DI 10.1108/IJEBR-08-2020-0529
EA AUG 2021
PG 30
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA WF5CE
UT WOS:000683531600001
DA 2024-03-27
ER
PT J
AU Jiang, M
Huang, GQ
AF Jiang, Min
Huang, George Q.
TI Intralogistics synchronization in robotic forward-reserve warehouses for
e-commerce last-mile delivery
SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
LA English
DT Article
DE Forward-reserve warehouses; Synchronization; Intralogistics operations;
Robotic warehouse systems
ID VARIABLE NEIGHBORHOOD SEARCH; ORDER PICKING; MULTIPLE PICKERS;
ALLOCATING SPACE; STRATEGIES; DESIGN; ASSIGNMENT; SYSTEMS; STORAGE;
MODEL
AB This paper treats intralogistics processing in a typical robotic forward-reserve e-commerce warehouse involving a reserve area with manual order-picking operations and a forward area with robotic parts-to-picker order sorting operations. It is critical to synchronize intralogistics operations between the two areas considering delivery requirements to optimize the performance in terms of makespan and costs. This challenge is formulated as a delivery-driven intralogistics synchronization (DDIS) problem. This paper develops a tailored variable neighborhood search solution method. A series of comprehensive numerical experiments under various scenarios show the superiority and stability of the DDIS as benchmarked with sequential approaches. A substantial reduction can be achieved in both makespan and forward area size, indicating significantly improved intralogistics operational efficiency and space utilization. Managerial insights are also discussed for specific action plans regarding the market size, the labor force, and the trolley configuration.
C1 [Jiang, Min; Huang, George Q.] Univ Hong Kong, Dept Ind & Mfg Syst Engn, HKU Lab Phys Internet, Pokfulam, Hong Kong, Peoples R China.
C3 University of Hong Kong
RP Huang, GQ (autor correspondiente), Univ Hong Kong, Dept Ind & Mfg Syst Engn, HKU Lab Phys Internet, Pokfulam, Hong Kong, Peoples R China.
EM mjiang@connect.hku.hk; gqhuang@hku.hk
CR Albareda-Sambola M, 2009, ASIA PAC J OPER RES, V26, P655, DOI 10.1142/S0217595909002390
Ardjmand E, 2018, INT J PROD ECON, V206, P169, DOI 10.1016/j.ijpe.2018.10.001
Azadeh K, 2019, TRANSPORT SCI, V53, P917, DOI 10.1287/trsc.2018.0873
Bartholdi J. J., 2017, WAREHOUSE DISTRIBUTI
Bartholdi JJ, 2008, IIE TRANS, V40, P1046, DOI [10.1080/07408170802167662, 10.1080/0740817080216766]
Boysen N, 2019, EUR J OPER RES, V277, P396, DOI 10.1016/j.ejor.2018.08.023
Boysen N, 2018, EUR J OPER RES, V270, P386, DOI 10.1016/j.ejor.2018.03.026
Boysen N, 2017, EUR J OPER RES, V262, P550, DOI 10.1016/j.ejor.2017.03.053
Boywitz D, 2019, IISE TRANS, V51, P368, DOI 10.1080/24725854.2018.1513672
Chen BC, 2013, INT J PROD ECON, V141, P360, DOI 10.1016/j.ijpe.2012.08.019
Chen ZL, 2010, OPER RES, V58, P130, DOI 10.1287/opre.1080.0688
de Koster R, 2007, EUR J OPER RES, V182, P481, DOI 10.1016/j.ejor.2006.07.009
de Koster RBM, 2012, INT J PROD RES, V50, P757, DOI 10.1080/00207543.2010.543941
Enright John., 2011, AUTOMATED ACTION PLA, P33
Frazelle E.H., 1994, FORWARD RESERVE PROB
Gu J, 2010, J OPER RES SOC, V61, P1013, DOI 10.1057/jors.2009.39
Gu JX, 2007, EUR J OPER RES, V177, P1, DOI 10.1016/j.ejor.2006.02.025
Gzara F., 2020, INFORMS J OPTIMIZATI, V2, P273, DOI DOI 10.1287/IJOO.2019.0039
HACKMAN ST, 1990, IIE TRANS, V22, P7, DOI 10.1080/07408179008964152
Han B, 2015, EUR J OPER RES, V244, P704, DOI 10.1016/j.ejor.2015.02.008
Hansen P, 2001, EUR J OPER RES, V130, P449, DOI 10.1016/S0377-2217(00)00100-4
He RH, 2020, INT J GEN SYST, V49, P497, DOI 10.1080/03081079.2020.1778681
Henn S, 2015, FLEX SERV MANUF J, V27, P86, DOI 10.1007/s10696-012-9164-1
Heragu SS, 2005, INT J PROD RES, V43, P327, DOI 10.1080/00207540412331285841
Jaghbeer Y, 2020, INT J PROD RES, V58, P4489, DOI 10.1080/00207543.2020.1788734
Jharkharia S, 2019, MANAG RES REV, V43, P223, DOI 10.1108/MRR-01-2019-0013
Jiang M, 2020, TRANSPORT RES E-LOG, V144, DOI 10.1016/j.tre.2020.102138
Jiang XW, 2018, PROCEDIA COMPUT SCI, V126, P1985, DOI 10.1016/j.procs.2018.07.254
Le-Duc T, 2007, EUR J OPER RES, V176, P374, DOI 10.1016/j.ejor.2005.03.052
Lee HY, 2019, INT J PROD RES, V57, P5821, DOI 10.1080/00207543.2018.1552031
Li K, 2016, EXPERT SYST APPL, V57, P12, DOI 10.1016/j.eswa.2016.02.033
Marchet G, 2011, J MANUF TECHNOL MANA, V22, P261, DOI 10.1108/17410381111102252
Menéndez B, 2017, EUR J OPER RES, V263, P82, DOI 10.1016/j.ejor.2017.05.001
Merschformann M., 2017, ARXIV PREPRINT ARXIV
Mladenovic N, 1997, COMPUT OPER RES, V24, P1097, DOI 10.1016/S0305-0548(97)00031-2
Mohammadi S, 2020, INT J PROD ECON, V219, P347, DOI 10.1016/j.ijpe.2019.05.017
Monahan S., 2016, US ECOMMERCE TRENDS
Parikh PJ, 2008, TRANSPORT RES E-LOG, V44, P696, DOI 10.1016/j.tre.2007.03.002
Roy D, 2019, TRANSPORT RES E-LOG, V122, P119, DOI 10.1016/j.tre.2018.11.005
Scholz A, 2017, EUR J OPER RES, V263, P461, DOI 10.1016/j.ejor.2017.04.038
Schubert D, 2021, NAV RES LOG, V68, P721, DOI 10.1002/nav.21954
Silva A, 2020, TRANSPORT RES E-LOG, V140, DOI 10.1016/j.tre.2020.102003
Tang CS, 2019, TRANSPORT RES E-LOG, V129, P1, DOI 10.1016/j.tre.2019.06.004
Valle CA, 2020, EUR J OPER RES, V284, P460, DOI 10.1016/j.ejor.2020.01.022
van den Berg JP, 1998, EUR J OPER RES, V111, P98, DOI 10.1016/S0377-2217(98)80013-1
van Gils T, 2019, TRANSPORT RES E-LOG, V125, P47, DOI 10.1016/j.tre.2019.02.010
van Gils T, 2018, EUR J OPER RES, V267, P1, DOI 10.1016/j.ejor.2017.09.002
Walter R, 2013, EUR J OPER RES, V229, P585, DOI 10.1016/j.ejor.2013.02.047
Weidinger F, 2018, TRANSPORT SCI, V52, P1479, DOI 10.1287/trsc.2018.0826
Wulfraat M., 2021, KIVA SYSTEMS IN DEPT
Wurman PR, 2008, AI MAG, V29, P9
Xie L, 2021, EUR J OPER RES, V288, P80, DOI 10.1016/j.ejor.2020.05.032
Yuan Z, 2017, IEEE T ENG MANAGE, V64, P83, DOI 10.1109/TEM.2016.2634540
Zhang J, 2019, TRANSPORT RES E-LOG, V123, P180, DOI 10.1016/j.tre.2019.01.013
Zhang J, 2018, OMEGA-INT J MANAGE S, V79, P104, DOI 10.1016/j.omega.2017.08.004
Zhuang YL, 2022, EUR J OPER RES, V300, P527, DOI 10.1016/j.ejor.2021.08.003
Zou BP, 2018, EUR J OPER RES, V267, P733, DOI 10.1016/j.ejor.2017.12.008
NR 57
TC 7
Z9 7
U1 9
U2 32
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1366-5545
EI 1878-5794
J9 TRANSPORT RES E-LOG
JI Transp. Res. Pt. e-Logist. Transp. Rev.
PD FEB
PY 2022
VL 158
AR 102619
DI 10.1016/j.tre.2022.102619
EA FEB 2022
PG 19
WC Economics; Engineering, Civil; Operations Research & Management Science;
Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
Science; Transportation
GA 0V2DZ
UT WOS:000788155500004
DA 2024-03-27
ER
PT J
AU Gomes, MA
Wönkhaus, M
Meisen, P
Meisen, T
AF Alves Gomes, Miguel
Wonkhaus, Mark
Meisen, Philipp
Meisen, Tobias
TI TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for
Representing Customer Behavior
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE e-commerce; purchase prediction; real-time purchase prediction;
embeddings; time embeddings; customer representation; machine learning;
C45; C53; C55; L81; L86
ID INTENTION
AB Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer's past purchases, his or her search queries, the time spent on a product page, the customer's age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations. Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used.
C1 [Alves Gomes, Miguel; Wonkhaus, Mark; Meisen, Tobias] Univ Wuppertal, Inst Technol & Management Digital Transformat, D-42119 Wuppertal, Germany.
[Meisen, Philipp] Breinify Inc, San Francisco, CA 94105 USA.
C3 University of Wuppertal
RP Gomes, MA (autor correspondiente), Univ Wuppertal, Inst Technol & Management Digital Transformat, D-42119 Wuppertal, Germany.
EM alvesgomes@uni-wuppertal.de; woenkhaus@uni-wuppertal.de;
philipp.meisen@breinify.com; meisen@uni-wuppertal.de
OI Meisen, Philipp/0000-0002-8024-3074; Alves Gomes,
Miguel/0000-0003-3664-0360; Meisen, Tobias/0000-0002-1969-559X
FU University of Wuppertal
FX The APC was funded by the Open Access Publication Fund of the University
of Wuppertal.
CR Akiba T, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2623, DOI 10.1145/3292500.3330701
Armstrong G., 2014, Principles of marketing
Baumann A, 2018, EXPERT SYST APPL, V94, P137, DOI 10.1016/j.eswa.2017.10.046
Behera RK, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.03.026
Buitinck L., 2013, ECML PKDD WORKSHOP L, P108, DOI [10.48550/arXiv.1309.0238, DOI 10.48550/ARXIV.1309.0238]
Card S. K., 1991, Human Factors in Computing Systems. Reaching Through Technology. CHI '91. Conference Proceedings, P181, DOI 10.1145/108844.108874
Chaudhuri N, 2021, DECIS SUPPORT SYST, V149, DOI 10.1016/j.dss.2021.113622
Chen C, 2022, PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, P2732, DOI 10.1145/3534678.3539053
Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, DOI 10.48550/ARXIV.1810.04805]
Esmeli R, 2022, J BUS RES, V147, P420, DOI 10.1016/j.jbusres.2022.04.012
Esmeli R, 2020, IEEE IJCNN, DOI 10.1109/ijcnn48605.2020.9206871
Esmeli R, 2021, ELECTRON MARK, V31, P697, DOI 10.1007/s12525-020-00448-x
Gomes MA, 2022, PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022, P2873, DOI 10.1145/3511808.3557127
Gomes Miguel Alves, 2021, 2021 IEEE 23 INT C H, DOI DOI 10.1109/HPCC-DSS-SMARTCITY-DEPENDSYS53884.2021.00195
Gomez M, 2023, INT J SPEECH-LANG PA, DOI 10.1080/17549507.2023.2211750
Harris CR, 2020, NATURE, V585, P357, DOI 10.1038/s41586-020-2649-2
Hong T, 2012, EXPERT SYST APPL, V39, P2127, DOI 10.1016/j.eswa.2011.07.114
Huang GJ, 2020, ALGORITHMS, V13, DOI 10.3390/a13120342
Karingula SR, 2022, LECT NOTES COMPUT SC, V13164, P1, DOI 10.1007/978-3-030-95470-3_1
Kazemi S.M., 2019, arXiv, DOI 10.48550/arXiv.1907.05321
Kim KJ, 2004, LECT NOTES COMPUT SC, V3397, P409
Kumar V, 2008, J MARKETING, V72, P50, DOI 10.1509/jmkg.72.5.50
Leeflang PSH, 2014, EUR MANAG J, V32, P1, DOI 10.1016/j.emj.2013.12.001
Li Q, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), P1048, DOI 10.1109/ICDMW.2015.172
Li X, 2020, CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, P785, DOI 10.1145/3340531.3411952
Lin WQ, 2019, 2019 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2019), P9, DOI 10.1145/3350546.3352495
Liu X, 2019, J MARKETING RES, V56, P918, DOI 10.1177/0022243719866690
Martínez A, 2020, EUR J OPER RES, V281, P588, DOI 10.1016/j.ejor.2018.04.034
McKinney W., 2010, P 9 PYTHON SCI C, V445, P51, DOI [10.25080/Majora-92bf1922-00a, DOI 10.25080/MAJORA-92BF1922-00A]
Mikolov T, 2013, Advances in neural information processing systems, DOI DOI 10.48550/ARXIV.1310.4546
Miller R.B., 1968, P DEC 9 11 1968 FA 1, P267, DOI [DOI 10.1145/1476589.1476628, 10.1145/1476589.1476628]
Mokryn O, 2019, ELECTRON COMMER R A, V34, DOI 10.1016/j.elerap.2019.100836
Mulhern FJ, 1999, J INTERACT MARK, V13, P25, DOI 10.1002/(SICI)1520-6653(199924)13:1<25::AID-DIR3>3.0.CO;2-L
Nalmpantis C, 2019, COMM COM INF SC, V1000, P80, DOI 10.1007/978-3-030-20257-6_7
Park D., 2015, INT ACM RECOMMENDER, P1, DOI [10.1145/2813448.2813517, DOI 10.1145/2813448.2813517]
Paszke A, 2019, ADV NEUR IN, V32
Pedregosa F, 2011, J MACH LEARN RES, V12, P2825
Rahman MS, 2020, J GLOB INF MANAG, V28, P39, DOI 10.4018/JGIM.2020010103
Romov P., 2015, P 2015 INT ACM REC S, P1, DOI DOI 10.1145/2813448.2813510
Sheil H, 2018, Arxiv, DOI arXiv:1807.08207
Sismeiro C, 2004, J MARKETING RES, V41, P306, DOI 10.1509/jmkr.41.3.306.35985
Srilakshmi Madiraju, 2022, INTELL SYST APPL, V14, DOI 10.1016/j.iswa. 2022.200070
Statista Retail, 2022, E COMM SAL WORLDW 20
Sutskever I., 2014, ADV NEURAL INFORM PR, V3104, P3112, DOI DOI 10.5555/2969033.2969173
Tercan H, 2021, PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS 2021), VOL 1, P610, DOI 10.5220/0010400706100617
Uribarri G, 2022, CHAOS SOLITON FRACT, V154, DOI 10.1016/j.chaos.2021.111612
VanRossum G, 1995, PYTHON REFERENCE MAN
Vasile F, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P225, DOI 10.1145/2959100.2959160
Vaswani A, 2017, ADV NEUR IN, V30
Wu Z., 2015, IET International Conference on Resilience of Transmission and Distribution Networks (RTDN) 2015
Yao SW, 2021, PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), P2890, DOI 10.1145/3442381.3450129
Zeithaml VA, 2001, CALIF MANAGE REV, V43, P118, DOI 10.2307/41166104
Zeng M, 2019, ELECTRON MARK, V29, P263, DOI 10.1007/s12525-018-0311-8
Zhou GR, 2019, AAAI CONF ARTIF INTE, P5941
NR 54
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD SEP
PY 2023
VL 18
IS 3
BP 1404
EP 1418
DI 10.3390/jtaer18030070
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA S8YO3
UT WOS:001073970900001
OA gold
DA 2024-03-27
ER
PT J
AU Chang, JYS
Cheah, JH
Lim, XJ
Morrison, AM
AF Chang, Jennifer Yee-Shan
Cheah, Jun-Hwa
Lim, Xin-Jean
Morrison, Alastair M.
TI One pie, many recipes: The role of artificial intelligence chatbots in
influencing Malaysian solo traveler purchase intentions
SO TOURISM MANAGEMENT PERSPECTIVES
LA English
DT Article
DE AI chatbot; Tourism; Solo travelers; Purchase intentions; PLS-SEM; fsQCA
ID COMMUNICATION QUALITY; USER EXPERIENCE; BRAND-EQUITY; PLS-SEM;
SATISFACTION; INNOVATION; ADOPTION; ACCEPTANCE; MODEL; TRUST
AB Artificial intelligence (AI) chatbots are pervasive in the travel industry and have significantly alleviated solo travelers' concerns in trip planning and booking. However, many existing AI chatbots have yet to meet the expectations of solo travelers, especially when they demand more personalized information to assist in travel decision-making. Based on complexity theory, this research examines the factors that stimulate solo travelers' purchase intentions when using AI chatbots, particularly covering the three main aspects of marketing efforts, communication quality, and affective characteristics. Drawing from a sample of 281 solo travelers, partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are used to examine the proposed relationships. The PLS-SEM results illustrate that interaction, entertainment, trendiness, communication competence, and satisfaction have significant direct effects on purchase intentions. The fsQCA results further revealed four solutions exhibiting high purchase intentions among solo travelers. Different core, peripheral, and necessary causal conditions in each configuration path were identified. The findings enrich the AI chatbot literature by examining the underlying reasons why solo travelers react differently to this emerging technology and produce practical recommendations for designing AI chatbot systems.
C1 [Chang, Jennifer Yee-Shan] Univ Essex, Edge Hotel Sch, Wivenhoe Pk, Colchester CO4 3SQ, England.
[Cheah, Jun-Hwa] Univ East Anglia, Norwich Business Sch, Norwich NR4 7TJ, Norfolk, England.
[Lim, Xin-Jean] Univ Kebangsaan Malaysia, Fac Econ & Management, Ctr Value Creat & Human Well Being, Bangi, Selangor, Malaysia.
[Morrison, Alastair M.] Univ Greenwich, Old Royal Naval Coll, Fac Business, Sch Mkt & Management, Pk Row, London SE10 9SL, England.
C3 University of Essex; University of East Anglia; Universiti Kebangsaan
Malaysia; University of Greenwich
RP Cheah, JH (autor correspondiente), Univ East Anglia, Norwich Business Sch, Norwich NR4 7TJ, Norfolk, England.
EM y.chang@essex.ac.uk; J.Cheah@uea.ac.uk; lim.xinjean@yahoo.com;
a.morrison@greenwich.ac.uk
RI Morrison, Alastair M./B-5706-2016; LIM, XIN JEAN/ABB-9802-2020; Cheah,
Jun-Hwa/C-6463-2019
OI LIM, XIN JEAN/0000-0002-6794-3607; Cheah, Jun-Hwa/0000-0001-8440-9564
CR Adamopoulou E., 2020, MACHINE LEARNING APP, V2, DOI DOI 10.1016/J.MLWA.2020.100006
Aguilera-Caracuel J, 2014, J BUS RES, V67, P2385, DOI 10.1016/j.jbusres.2014.02.005
Alonso-Vazquez M., 2023, Current Issues in Tourism, P1
Azali M, 2023, J HOSP TOUR RES, V47, P177, DOI 10.1177/1096348020987634
Babin BJ, 2016, J BUS RES, V69, P3133, DOI 10.1016/j.jbusres.2015.12.001
Becker JM, 2023, INT J CONTEMP HOSP M, V35, P321, DOI 10.1108/IJCHM-04-2022-0474
Becker JM, 2013, MIS QUART, V37, P665, DOI 10.25300/MISQ/2013/37.3.01
Bernard, 2017, RES METHODS ANTHR QU
Bernard S, 2022, TOUR MANAG PERSPECT, V41, DOI 10.1016/j.tmp.2021.100932
Bertalanffy L., 1968, General System Theory: Foundations, Development, Applications
Bianchi C, 2022, TOUR REV, V77, P780, DOI 10.1108/TR-12-2020-0611
Bianchi C, 2016, INT J TOUR RES, V18, P197, DOI 10.1002/jtr.2049
Boavida-Portugal I., 2018, Frontiers, V1, P32
Booking.com, 2018, Booking.com research exposes surprising gap between travel ambitions and reality
Booking.com, 2021, GUID BOOK COM SHAR 8
Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
Calvaresi D., 2021, INFORM COMMUNICATION, V2021, P3
Chang JYS, 2024, J MARK ANAL, V12, P71, DOI 10.1057/s41270-023-00240-8
Chang YS, 2022, INT J HOSP MANAG, V104, DOI 10.1016/j.ijhm.2022.103249
Chen CC, 2018, TELEMAT INFORM, V35, P1512, DOI 10.1016/j.tele.2018.03.019
Chen X., 2020, DESIGN USER EXPERIEN, P581
Chen YH, 2007, IND MANAGE DATA SYST, V107, P21, DOI 10.1108/02635570710719034
Cheng Y, 2022, J PROD BRAND MANAG, V31, P252, DOI 10.1108/JPBM-05-2020-2907
Chin W, 2020, IND MANAGE DATA SYST, V120, P2161, DOI 10.1108/IMDS-10-2019-0529
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Coelho PS, 2012, EUR J MARKETING, V46, P331, DOI 10.1108/03090561211202503
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Dabholkar P., 1996, J ACAD MARKET SCI, V24, P3, DOI [10.1177/009207039602400101, DOI 10.1007/BF02893933]
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dolnicar S, 2008, TOURISM MANAGEMENT: ANALYSIS, BEHAVIOUR AND STRATEGY, P129, DOI 10.1079/9781845933234.0129
Edwards C, 2014, COMPUT HUM BEHAV, V33, P372, DOI 10.1016/j.chb.2013.08.013
Emmers-Sommer TM, 2004, J SOC PERS RELAT, V21, P399, DOI 10.1177/0265407504042839
Faul F, 2009, BEHAV RES METHODS, V41, P1149, DOI 10.3758/BRM.41.4.1149
Filieri R, 2016, ANN TOURISM RES, V58, P46, DOI 10.1016/j.annals.2015.12.019
Fink A., 2009, How to conduct surveys: A step-by-step guide
Fionda AM, 2009, J BRAND MANAG, V16, P347, DOI 10.1057/bm.2008.45
Fiss PC, 2007, ACAD MANAGE REV, V32, P1180, DOI 10.5465/AMR.2007.26586092
French L., 2020, Travel Weekly
Frenken K, 2006, ECON INNOV NEW TECH, V15, P137, DOI 10.1080/10438590500141453
Godes D, 2005, MARKET LETT, V16, P415, DOI 10.1007/s11002-005-5902-4
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Haas A, 2014, J RETAILING, V90, P428, DOI 10.1016/j.jretai.2014.05.003
Hair J. F., 2022, A primer on partial least squares structural equation modeling (PLS-SEM), V3rd
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Holzwarth M, 2006, J MARKETING, V70, P19, DOI 10.1509/jmkg.70.4.19
Hosseini S, 2022, INT J TOUR RES, V24, P256, DOI 10.1002/jtr.2498
Hoyer WD, 2020, J INTERACT MARK, V51, P57, DOI 10.1016/j.intmar.2020.04.001
Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
Hwang H., 2020, BEHAVIORMETRIKA, V47, P219, DOI [DOI 10.1007/S41237-019-00085-5, https://doi.org/10.1007/s41237-019-00085-5]
Iskender A, 2024, TOUR HOSP RES, V24, P95, DOI 10.1177/14673584221133667
Ivanov SH, 2022, TOURISM ECON, V28, P3, DOI 10.1177/1354816620946541
Izard C. E., 1977, HUMAN EMOTIONS, DOI [10.1007/978-1-4899-2209-0, DOI 10.1007/978-1-4899-2209-0, DOI 10.1007/S10853-018-3152-0]
Jayawardhena C, 2007, INT J RETAIL DISTRIB, V35, P515, DOI 10.1108/09590550710750377
Jiménez-Barreto J, 2021, INT J CONTEMP HOSP M, V33, P3860, DOI 10.1108/IJCHM-10-2020-1244
Kaczorowska-Spychalska D, 2018, MANAG-POL, V22, P187, DOI 10.2478/manment-2018-0031
Karagöz D, 2021, CURR ISSUES TOUR, V24, P1595, DOI 10.1080/13683500.2020.1816929
Kayak, 2022, Travel Trends for 2023.
Kim AJ, 2012, J BUS RES, V65, P1480, DOI 10.1016/j.jbusres.2011.10.014
Kim S, 2016, J BUS RES, V69, P5809, DOI 10.1016/j.jbusres.2016.04.178
Klook, 2019, Unpacking Solo Travel: Klook's global survey uncovers our love-hate relationship with solo travel
Kock N, 2018, INFORM SYST J, V28, P227, DOI 10.1111/isj.12131
Kock N, 2012, J ASSOC INF SYST, V13, P546, DOI 10.17705/1jais.00302
Koponen JP, 2020, EUR J MARKETING, V54, P1205, DOI 10.1108/EJM-01-2019-0061
Kunze L, 2016, On chatbots
Larson R., 2014, FLOW FDN POSITIVE PS, DOI [10.1007/978-94-017-9088-8, 10.1007/978-94-017-9088-8_2, DOI 10.1007/978-94-017-9088-8_2]
Lee SY, 2017, INT J HUM-COMPUT ST, V103, P95, DOI 10.1016/j.ijhcs.2017.02.005
Letheren Kate, 2017, Embracing the bots: How direct to consumer advertising is about to change forever
Lim XJ, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120763
Lisella M, 2019, The unstoppable trend: Solo Travel growing and going just about anywhere
Liu KF, 2022, COMPUT HUM BEHAV, V127, DOI 10.1016/j.chb.2021.107026
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
McLean G, 2017, COMPUT HUM BEHAV, V76, P494, DOI 10.1016/j.chb.2017.08.005
MEHRABIAN A, 1968, PSYCHOL BULL, V70, P365, DOI 10.1037/h0026483
Mehran J, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.101954
Min S, 2019, J TRAVEL TOUR MARK, V36, P770, DOI 10.1080/10548408.2018.1507866
Moore S., 2018, Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020
Morgan SL, 2010, SOC FORCES, V88, P1934
Muangkammuen P., 2018, 2018 22 INT COMPUTER, P1
Muntinga DG, 2011, INT J ADVERT, V30, P13, DOI 10.2501/IJA-30-1-013-046
MyTravelResearch.com, 2017, Chatbots and the travel industry.
Nath K., 2023, Travel Daily Media
Nikjoo A, 2021, TOUR MANAG PERSPECT, V38, DOI 10.1016/j.tmp.2021.100817
Olya HGT, 2019, J TRAVEL RES, V58, P313, DOI 10.1177/0047287517751676
Ordanini A, 2014, J SERV RES-US, V17, P134, DOI 10.1177/1094670513513337
Ozturk UA, 2016, TOURISM GEOGR, V18, P194, DOI 10.1080/14616688.2015.1124446
Pant P, 2022, TOUR MANAG PERSPECT, V43, DOI 10.1016/j.tmp.2022.100978
Pappas IO, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102128
Pappas IO, 2018, EUR J MARKETING, V52, P1679, DOI 10.1108/EJM-10-2017-0707
Pappas IO, 2016, J BUS RES, V69, P794, DOI 10.1016/j.jbusres.2015.07.010
Pappas N, 2021, INT J HOSP MANAG, V93, DOI 10.1016/j.ijhm.2020.102767
Park N, 2009, CYBERPSYCHOL BEHAV, V12, P729, DOI 10.1089/cpb.2009.0003
Perna A, 2018, J GLOB FASH MARK, V9, P73, DOI 10.1080/20932685.2017.1399081
Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
Pizzi G, 2021, J BUS RES, V129, P878, DOI 10.1016/j.jbusres.2020.11.006
Ponte EB, 2015, TOURISM MANAGE, V47, P286, DOI 10.1016/j.tourman.2014.10.009
Poston RS, 2005, MIS QUART, V29, P221
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Pung JM, 2022, TOUR RECREAT RES, DOI 10.1080/02508281.2022.2117353
Pursel B, 2016, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB (WWW'16 COMPANION), P929, DOI 10.1145/2872518.2891077
Radojevic T, 2015, TOUR MANAG PERSPECT, V16, P247, DOI 10.1016/j.tmp.2015.08.004
Rasoolimanesh SM, 2021, INT J CONTEMP HOSP M, V33, P1571, DOI 10.1108/IJCHM-10-2020-1164
Redman T, 2002, SERV IND J, V22, P51, DOI 10.1080/714005085
Rihoux B., 2008, Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques, DOI DOI 10.4135/9781452226569
Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
Santini FD, 2018, J GLOB FASH MARK, V9, P305
Sarstedt M, 2019, J MARK ANAL, V7, P196, DOI 10.1057/s41270-019-00058-3
Shah S., 2021, Times of India
Shin H, 2022, TOURISM MANAGE, V88, DOI 10.1016/j.tourman.2021.104428
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Solo Traveler, 2021, Solo travel statistics and data: 2021-2022.
Straits Research, 2022, news-release
Um XJ, 2022, TOUR REV, V77, P1166, DOI 10.1108/TR-11-2021-0497
Vendemia MA, 2017, COMPUT HUM BEHAV, V71, P99, DOI 10.1016/j.chb.2017.01.046
Viator, 2020, Understanding the rise of solo travelers: How they're different & how to reach them.
Wang WT, 2012, INTERNET RES, V22, P142, DOI 10.1108/10662241211214548
Wang XH, 2023, TOUR MANAG PERSPECT, V46, DOI 10.1016/j.tmp.2023.101074
Wathen CN, 2002, J AM SOC INF SCI TEC, V53, P134, DOI 10.1002/asi.10016
Waugh J, 2021, Travel technology made simple: Convenience and security
Woodside A.G., 2018, Improving the marriage of modeling and theory for accurate forecasts of outcomes, P1
Woodside AG, 2014, J BUS RES, V67, P2495, DOI 10.1016/j.jbusres.2014.07.006
Woodside AG, 2013, J BUS RES, V66, P463, DOI 10.1016/j.jbusres.2012.12.021
Xu J, 2022, INT J HOSP MANAG, V107, DOI 10.1016/j.ijhm.2022.103340
Yang ECL, 2022, TOUR MANAG PERSPECT, V41, DOI 10.1016/j.tmp.2021.100913
Yang ECL, 2022, INT J TOUR RES, V24, P347, DOI 10.1002/jtr.2506
Yang ECL, 2021, CURR ISSUES TOUR, V24, P2458, DOI 10.1080/13683500.2020.1839025
Yang ECL, 2019, TOUR REV, V74, P1047, DOI 10.1108/TR-10-2018-0150
[杨敬 Yang Jing], 2022, [焊接学报, Transactions of the China Welding Institution], V43, P1
Yang Y, 2021, ANN TOURISM RES, V91, DOI 10.1016/j.annals.2021.103313
Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
Yu CE, 2020, J HOSP MARKET MANAG, V29, P22, DOI 10.1080/19368623.2019.1592733
Zhang M, 2018, J GLOB SCHOLARS MARK, V28, P305, DOI 10.1080/21639159.2018.1513338
Zhao JY, 2020, COMPUT HUM BEHAV, V108, DOI 10.1016/j.chb.2020.106334
Zolkepli IA, 2015, COMPUT HUM BEHAV, V43, P189, DOI 10.1016/j.chb.2014.10.050
NR 134
TC 0
Z9 0
U1 36
U2 36
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2211-9736
EI 2211-9744
J9 TOUR MANAG PERSPECT
JI Tour. Manag. Perspect.
PD NOV
PY 2023
VL 49
AR 101191
DI 10.1016/j.tmp.2023.101191
EA OCT 2023
PG 14
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA X9RU1
UT WOS:001101747200001
DA 2024-03-27
ER
PT J
AU Singh, AK
Kumar, VRP
Shoaib, M
Adebayo, TS
Irfan, M
AF Singh, Atul Kumar
Kumar, V. R. Prasath
Shoaib, Muhammad
Adebayo, Towiwa Sunday
Irfan, Muhammad
TI A strategic roadmap to overcome blockchain technology barriers for
sustainable construction: A deep learning-based dual-stage SEM-ANN
approach
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Blockchain technology; Sustainable construction; Barriers; Partial least
squares structural equation; modeling; Artificial neural networks; India
ID BUILT ENVIRONMENT; INDUSTRY; IMPACT; CHAIN
AB The adoption of blockchain technology in the sustainable construction industry in India is slow. Existing literature in this area has primarily focused on the potential applications of blockchain technology in construction but little attention is given to the barriers, impeding its adoption. This study fills this research gap by identifying the most significant barriers to adopting blockchain technology for India's sustainable construction industry. The collected data were analyzed via a two-stage PLS-SEM-artificial-neural-network (ANN) predictive analytical approach. In total, 722 construction stakeholder surveys were conducted, and the final model of barriers to adopting blockchain technology for sustainable construction was statistically validated. This study's findings suggest that significant organizational, technological, cultural, legal, security and government barriers limit the adoption of blockchain technology for sustainable construction in India. These findings also imply that effective legislative processes and economic incentives are crucial for ensuring blockchain technology integration into sustainable construction and for efficient implementation of practices. As a further step, a roadmap is developed to support decision-makers in overcoming these barriers in the short, medium, and long terms. Finally, multitiered strategies that construction mapping, sustainability, and integration should be adopted to ensure the successful integration of BT into sustainable construction practices in India.
C1 [Singh, Atul Kumar; Kumar, V. R. Prasath] SRM Inst Sci & Technol, Fac Engn & Technol, Dept Civil Engn, Kattankulathur 603203, Tamil Nadu, India.
[Shoaib, Muhammad] Tomas Bata Univ Zlin, Fac Management & Econ, Dept Business Adm, Mostni 5139, Zlin 76001, Czech Republic.
[Adebayo, Towiwa Sunday] Cyprus Int Univ, Fac Econ & Adm Sci, Dept Business Adm, Mersin 10, TR-99040 Haspolat, Turkiye.
[Irfan, Muhammad] Beijing Technol & Business Univ, Sch Econ, Beijing 100048, Peoples R China.
[Irfan, Muhammad] ILMA Univ, Fac Management Sci, Dept Business Adm, Karachi 75190, Pakistan.
[Adebayo, Towiwa Sunday] New Uzbekistan Univ, Dept Econ & Data Sci, 54 Mustaqillik Ave, Tashkent 100007, Uzbekistan.
C3 SRM Institute of Science & Technology Chennai; Tomas Bata University
Zlin; Cyprus International University; Beijing Technology & Business
University
RP Kumar, VRP (autor correspondiente), SRM Inst Sci & Technol, Fac Engn & Technol, Dept Civil Engn, Kattankulathur 603203, Tamil Nadu, India.; Irfan, M (autor correspondiente), Beijing Technol & Business Univ, Sch Econ, Beijing 100048, Peoples R China.
EM prasathv@srmist.edu.in; irfansahar@bit.edu.cn
RI PRASATH KUMAR, V R/ABE-7690-2021; Singh, Atul Kumar/GYU-6499-2022;
Adebayo, Tomiwa Sunday/HCH-3612-2022; Irfan, Muhammad/AAL-9371-2020
OI PRASATH KUMAR, V R/0000-0001-7292-0963; Singh, Atul
Kumar/0000-0003-3092-2041; Irfan, Muhammad/0000-0003-1446-583X; Shoaib,
Muhammad/0000-0003-0970-2343
CR Adu-Gyamfi G, 2022, RENEW SUST ENERG REV, V156, DOI 10.1016/j.rser.2021.111979
Akram SV, 2021, SECUR COMMUN NETW, V2021, DOI 10.1155/2021/6952121
Al Azmi N, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14116427
Al-Otaibi A., 2023, APPL SCI SWITZERLAND, V13, P1, DOI [10.3390/app13042630, DOI 10.3390/APP13042630]
Arpaci I, 2022, TECHNOL SOC, V71, DOI 10.1016/j.techsoc.2022.102120
Ashaari MA, 2021, TECHNOL FORECAST SOC, V173, DOI 10.1016/j.techfore.2021.121119
Badi S, 2021, CONSTR MANAG ECON, V39, P36, DOI 10.1080/01446193.2020.1819549
Bai CG, 2022, J CLEAN PROD, V358, DOI 10.1016/j.jclepro.2022.131896
Balci G, 2021, TRANSPORT RES E-LOG, V156, DOI 10.1016/j.tre.2021.102539
Bardach L, 2020, INT J RES METHOD EDU, V43, P348, DOI 10.1080/1743727X.2020.1791071
Belfitt R.J., 2011, TSBE ENGD C
Benzidia S, 2021, TECHNOL FORECAST SOC, V169, DOI 10.1016/j.techfore.2021.120819
Bibri Simon Elias, 2020, Energy Informatics, V3, DOI 10.1186/s42162-020-00107-7
Biswas B, 2019, COMPUT IND ENG, V136, P225, DOI 10.1016/j.cie.2019.07.005
Bjorklund V., 2019, BLOCKCHAIN SMART CON
Bohmecke-Schwafert M, 2023, TECHNOL FORECAST SOC, V191, DOI 10.1016/j.techfore.2023.122446
Bowen P, 2014, J CONSTR ENG M, V140, DOI 10.1061/(ASCE)CO.1943-7862.0000877
Caldarelli G, 2021, J ORGAN CHANGE MANAG, V34, P507, DOI 10.1108/JOCM-09-2020-0299
Chen KY, 2021, J CLEAN PROD, V287, DOI 10.1016/j.jclepro.2020.125071
Chen YF, 2016, J CONSTR ENG M, V142, DOI 10.1061/(ASCE)CO.1943-7862.0001147
Chung KHY, 2023, TECHNOL FORECAST SOC, V187, DOI 10.1016/j.techfore.2022.122258
Ciotta V, 2021, AUTOMAT CONSTR, V132, DOI 10.1016/j.autcon.2021.103925
Dal Mas F, 2023, TECHNOL FORECAST SOC, V187, DOI 10.1016/j.techfore.2022.122155
Das CP, 2021, TRANSPORT RES D-TR E, V100, DOI 10.1016/j.trd.2021.103055
Dehdasht G, 2023, ENG CONSTR ARCHIT MA, V30, P4294, DOI 10.1108/ECAM-02-2022-0121
Durdyev S, 2022, J BUILD ENG, V46, DOI 10.1016/j.jobe.2021.103736
Durdyev S, 2018, J CLEAN PROD, V204, P564, DOI 10.1016/j.jclepro.2018.08.304
Durdyev S, 2018, J CONSTR ENG M, V144, DOI 10.1061/(ASCE)CO.1943-7862.0001452
Elavarasan RM, 2022, RENEW SUST ENERG REV, V159, DOI 10.1016/j.rser.2022.112204
Elseufy SM, 2022, STRUCTURES, V46, P713, DOI 10.1016/j.istruc.2022.10.100
Ershadi M, 2021, CLEAN ENG TECHNOL, V3, DOI 10.1016/j.clet.2021.100125
Espinoza Perez A.T., 2022, AUTOM
Farooque M, 2020, COMPUT IND ENG, V147, DOI 10.1016/j.cie.2020.106684
Figueiredo K, 2022, J CLEAN PROD, V343, DOI 10.1016/j.jclepro.2022.131047
Fosso Wamba Samuel., 2020, LOGISTIQUEAND MANAGE, V28, P88, DOI 10.1080/12507970.2019.1679046
Friedman N, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121403
Garg RK, 2021, J CLEAN PROD, V311, DOI 10.1016/j.jclepro.2021.127597
Ghanim MS, 2022, SUSTAIN CITIES SOC, V81, DOI 10.1016/j.scs.2022.103830
Gopalakrishnan PK, 2021, WASTE MANAGE, V120, P594, DOI 10.1016/j.wasman.2020.10.027
Götz CS, 2022, SMART SUSTAIN BUILT, V11, P532, DOI 10.1108/SASBE-08-2020-0115
Government A., 2020, NATL BLOCKCHAIN ROAD
Govindan K, 2022, BUS STRATEG ENVIRON, V31, P3769, DOI 10.1002/bse.3031
Guan W, 2023, TECHNOL FORECAST SOC, V192, DOI 10.1016/j.techfore.2023.122552
Hair J., 2017, International Journal of Multivariate Data Analysis, V1, P107, DOI [DOI 10.1504/IJMDA.2017.087624, https://doi.org/10.1504/IJMDA.2017.087624, 10.1504/ijmda.2017.10008574]
Hamledari H, 2021, ADV ENG INFORM, V50, DOI 10.1016/j.aei.2021.101444
Hao Y, 2022, BUS STRATEG ENVIRON, V31, P3100, DOI 10.1002/bse.3066
Harvest, 2018, BLOCKCHAIN EVOLUTION
Hultgren M., 2018, BLOCKCHAIN TECHNOLOG, P1
Irfan M, 2020, ENERGY STRATEG REV, V32, DOI 10.1016/j.esr.2020.100540
Isik C, 2022, ENVIRON SCI POLLUT R, V29, P16472, DOI 10.1007/s11356-021-16720-2
Kamble SS, 2023, ANN OPER RES, V327, P575, DOI 10.1007/s10479-021-04129-6
Karamchandani A, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.10.004
Khalil M., 2022, Quality & Quantity, V56, P2435, DOI [10.1007/s11135-021-01229-0, DOI 10.1007/S11135-021-01229-0]
Khan RU, 2022, TECHNOL FORECAST SOC, V184, DOI 10.1016/j.techfore.2022.122047
Khan SA, 2022, BUS STRATEG ENVIRON, V31, P3742, DOI 10.1002/bse.3029
Kim K, 2020, KSCE J CIV ENG, V24, P2561, DOI 10.1007/s12205-020-0188-x
Kineber AF, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15032458
Kopyto M, 2020, TECHNOL FORECAST SOC, V161, DOI 10.1016/j.techfore.2020.120330
Kouhizadeh M, 2021, INT J PROD ECON, V231, DOI 10.1016/j.ijpe.2020.107831
Kroesen M, 2022, J TRANSP GEOGR, V102, DOI 10.1016/j.jtrangeo.2022.103379
Kulkarni M, 2020, Int. J. Sci. Technol. Res., V9, P1109
Kumar A, 2020, DECISION SCI, V51, P8, DOI 10.1111/deci.12396
Lee D, 2021, AUTOMAT CONSTR, V127, DOI 10.1016/j.autcon.2021.103688
Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
Li J, 2019, AUTOMAT CONSTR, V102, P288, DOI 10.1016/j.autcon.2019.02.005
Lo PS, 2022, J BUS RES, V147, P325, DOI 10.1016/j.jbusres.2022.04.013
Loh XM, 2022, EXPERT SYST APPL, V205, DOI 10.1016/j.eswa.2022.117659
Loosemore M, 2021, INT J PROJ MANAG, V39, P750, DOI 10.1016/j.ijproman.2021.07.002
Ma ZW, 2017, RENEW SUST ENERG REV, V67, P1300, DOI 10.1016/j.rser.2016.06.085
Mansi M, 2015, J PURCH SUPPLY MANAG, V21, P125, DOI 10.1016/j.pursup.2014.12.002
Munianday P, 2022, J MANAGE ENG, V38, DOI 10.1061/(ASCE)ME.1943-5479.0001038
Nawari NO, 2019, J BUILD ENG, V25, DOI 10.1016/j.jobe.2019.100832
Nawari NO, 2019, J INF TECHNOL CONSTR, V24, P209
Nicolas C, 2020, SUSTAIN CITIES SOC, V53, DOI 10.1016/j.scs.2019.101916
Oke AE, 2023, INT J BUILD PATHOL, V41, P988, DOI 10.1108/IJBPA-07-2021-0098
Olanrewaju OI, 2022, SUSTAIN CITIES SOC, V81, DOI 10.1016/j.scs.2022.103865
OReilly A., 2019, CITA BIM GATHER, DOI [10.21427/ksah-3-95, DOI 10.21427/KSAH-3-95]
Parn EA, 2019, ENG CONSTR ARCHIT MA, V26, P245, DOI 10.1108/ECAM-03-2018-0101
Pérez ATE, 2022, INFORM FUSION, V79, P44, DOI 10.1016/j.inffus.2021.09.021
Rane SB, 2019, MANAG ENVIRON QUAL, V31, P741, DOI 10.1108/MEQ-06-2019-0136
Rauf A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132212361
Ren S., 2022, J. Econ. Anal, V1, P50, DOI [10.58567/jea01010004, DOI 10.58567/JEA01010004]
Risso LA, 2023, COMPUT IND ENG, V179, DOI 10.1016/j.cie.2023.109195
Ronaghi MH, 2022, ENVIRON DEV SUSTAIN, V24, P6834, DOI 10.1007/s10668-021-01729-x
Saberi S, 2019, INT J PROD RES, V57, P2117, DOI 10.1080/00207543.2018.1533261
Sadeghi M, 2022, ENVIRON SCI POLLUT R, V29, P10495, DOI 10.1007/s11356-021-16376-y
Sahebi IG, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101427
Sciarelli M, 2022, J STRATEGY MANAG, V15, P495, DOI 10.1108/JSMA-02-2021-0054
Sharma MG, 2020, IIM KOZHIKODE SOC MA, V9, P177, DOI 10.1177/2277975220932343
Shojaei A., 2019, 10 INT STRUCTURAL EN, DOI [10.14455/ISEC.res.2019.78, DOI 10.14455/ISEC.RES.2019.78]
Shojaei A, 2021, J CLEAN PROD, V294, DOI 10.1016/j.jclepro.2021.126352
Singh AK, 2023, J CLEAN PROD, V403, DOI 10.1016/j.jclepro.2023.136840
Singh AK, 2022, J ENG RES-KUWAIT, V10, DOI 10.36909/jer.ACMM.16307
Su D, 2023, TECHNOL FORECAST SOC, V188, DOI 10.1016/j.techfore.2022.122275
Sun YP, 2022, RENEW ENERG, V193, P334, DOI 10.1016/j.renene.2022.04.159
Suprapto M, 2016, INT J PROJ MANAG, V34, P1071, DOI 10.1016/j.ijproman.2015.08.003
Tangi L, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102356
Tawalare A, 2020, J CONSTR ENG M, V146, DOI 10.1061/(ASCE)CO.1943-7862.0001723
Teng JY, 2019, SUSTAIN CITIES SOC, V44, P215, DOI 10.1016/j.scs.2018.09.038
Tezel A, 2021, J MANAGE ENG, V37, DOI 10.1061/(ASCE)ME.1943-5479.0000939
Toufaily E, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103444
Treiblmaier H, 2018, SUPPLY CHAIN MANAG, V23, P545, DOI 10.1108/SCM-01-2018-0029
Tripoli M., 2018, EMERGING OPPORTUNITI, P21
Trivedi A, 2021, J CLEAN PROD, V295, DOI 10.1016/j.jclepro.2021.126301
Vafadarnikjoo A, 2023, ANN OPER RES, V327, P129, DOI 10.1007/s10479-021-04048-6
Wang GQ, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121345
Wang R, 2022, AUTOMAT CONSTR, V134, DOI 10.1016/j.autcon.2021.104080
Wang Y, 2021, WIREL COMMUN MOB COM, V2021, DOI 10.1155/2021/1674412
Wolf D.B., 2020, WORLD EC FORUM INSIG, V2020, P1
Wong LW, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.08.005
Wu HT, 2021, ENERG POLICY, V153, DOI 10.1016/j.enpol.2021.112247
Wu XY, 2023, INT J PROD RES, V61, P3717, DOI 10.1080/00207543.2021.1894497
Xiang H, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.837042
Xu YS, 2023, ENG CONSTR ARCHIT MA, V30, P125, DOI 10.1108/ECAM-04-2021-0335
Yadav VS, 2020, RESOUR CONSERV RECY, V161, DOI 10.1016/j.resconrec.2020.104877
Yang C., 2019, IS THERE ROLE BLOCKC
Yang R, 2020, AUTOMAT CONSTR, V118, DOI 10.1016/j.autcon.2020.103276
Yaqoob I, 2022, NEURAL COMPUT APPL, V34, P11475, DOI 10.1007/s00521-020-05519-w
Yevu SK, 2023, INT J CONSTR MANAG, V23, P964, DOI 10.1080/15623599.2021.1946900
Zhang J, 2022, TECHNOL FORECAST SOC, V182, DOI 10.1016/j.techfore.2022.121881
Zhang MY, 2019, J CONSTR ENG M, V145, DOI 10.1061/(ASCE)CO.1943-7862.0001582
Zhou JL, 2021, SUSTAIN CITIES SOC, V70, DOI 10.1016/j.scs.2021.102910
Zhou YS, 2020, MAR POLICY, V122, DOI 10.1016/j.marpol.2020.104265
Zima K, 2020, BUILDINGS-BASEL, V10, DOI 10.3390/buildings10010016
NR 124
TC 7
Z9 7
U1 25
U2 40
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD SEP
PY 2023
VL 194
AR 122716
DI 10.1016/j.techfore.2023.122716
EA JUN 2023
PG 21
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA M6DY8
UT WOS:001031115400001
DA 2024-03-27
ER
PT J
AU Misra, K
Schwartz, EM
Abernethy, J
AF Misra, Kanishka
Schwartz, Eric M.
Abernethy, Jacob
TI Dynamic Online Pricing with Incomplete Information Using Multiarmed
Bandit Experiments
SO MARKETING SCIENCE
LA English
DT Article
DE dynamic pricing; e-commerce; online experiments; machine learning;
multiarmed bandits; partial identification; minimax regret;
nonparametric econometrics; A/B testing; field experiments
ID BRAND CHOICE; ROBUST; MARKET; MODEL; UNCERTAINTY; ALLOCATION; DECISIONS
AB Pricing managers at online retailers face a unique challenge. They must decide on real-time prices for a large number of products with incomplete demand information. The manager runs price experiments to learn about each product's demand curve and the profitmaximizing price. In practice, balanced field price experiments can create high opportunity costs, because a large number of customers are presented with suboptimal prices. In this paper, we propose an alternative dynamic price experimentation policy. The proposed approach extends multiarmed bandit (MAB) algorithms from statistical machine learning to include microeconomic choice theory. Our automated pricing policy solves this MAB problem using a scalable distribution-free algorithm. We prove analytically that our method is asymptotically optimal for any weakly downward sloping demand curve. In a series of Monte Carlo simulations, we show that the proposed approach performs favorably compared with balanced field experiments and standard methods in dynamic pricing from computer science. In a calibrated simulation based on an existing pricing field experiment, we find that our algorithm can increase profits by 43% during the month of testing and 4% annually.
C1 [Misra, Kanishka] Univ Calif San Diego, Rady Sch Management, La Jolla, CA 92093 USA.
[Schwartz, Eric M.] Univ Michigan, Ross Sch Business, Ann Arbor, MI 48109 USA.
[Abernethy, Jacob] Georgia Inst Technol, Sch Comp Sci, Coll Comp, Atlanta, GA 30332 USA.
C3 University of California System; University of California San Diego;
University of Michigan System; University of Michigan; University System
of Georgia; Georgia Institute of Technology
RP Misra, K (autor correspondiente), Univ Calif San Diego, Rady Sch Management, La Jolla, CA 92093 USA.
EM kamisra@ucsd.edu; ericmsch@umich.edu; prof@gatech.edu
OI Abernethy, Jacob/0000-0002-3115-6804
CR Acquisti A, 2005, MARKET SCI, V24, P367, DOI 10.1287/mksc.1040.0103
AGHION P, 1991, REV ECON STUD, V58, P621, DOI 10.2307/2297825
AGRAWAL R, 1995, ADV APPL PROBAB, V27, P1054, DOI 10.2307/1427934
Akçay Y, 2010, MANAGE SCI, V56, P1345, DOI 10.1287/mnsc.1100.1178
Anderson E, 2015, REV ECON STAT, V97, P813, DOI 10.1162/REST_a_00507
[Anonymous], 1989, Wiley-Interscience Series in Systems and Optimization
Audibert JY, 2009, THEOR COMPUT SCI, V410, P1876, DOI 10.1016/j.tcs.2009.01.016
Auer P, 2002, MACH LEARN, V47, P235, DOI 10.1023/A:1013689704352
Auer P, 2003, J MACHINE LEARNING R, V3, P397, DOI DOI 10.4271/610369
Aviv Y, 2002, WORKING PAPER
Baker W., 2014, USING BIG DATA MAKE
BAYUS BL, 1992, MARKET SCI, V11, P251, DOI 10.1287/mksc.11.3.251
Bergemann D, 2000, REV ECON STUD, V67, P213, DOI 10.1111/1467-937X.00128
Bergemann D, 2008, J EUR ECON ASSOC, V6, P560, DOI 10.1162/JEEA.2008.6.2-3.560
Bergemann D, 2011, J ECON THEORY, V146, P2527, DOI 10.1016/j.jet.2011.10.018
Berger J.O., 1985, STAT DECISION THEORY, DOI [10.1007/978-1-4757-4286-2, DOI 10.1007/978-1-4757-4286-2]
Besbes O, 2009, OPER RES, V57, P1407, DOI 10.1287/opre.1080.0640
Biyalogorsky E, 2004, MARKET SCI, V23, P146, DOI 10.1287/mksc.1030.0030
Biyalogorsky E, 2014, PROD OPER MANAG, V23, P1539, DOI 10.1111/poms.12167
Bonatti A, 2011, AM ECON J-MICROECON, V3, P124, DOI 10.1257/mic.3.3.124
BRADEN DJ, 1994, MARKET SCI, V13, P310, DOI 10.1287/mksc.13.3.310
Brezzi M, 2002, J ECON DYN CONTROL, V27, P87, DOI 10.1016/S0165-1889(01)00028-8
Cesa-Bianchi N., 2006, PREDICTION LEARNING
Chintagunta P, 2016, MARKET SCI, V35, P341, DOI 10.1287/mksc.2016.0996
DENBOER AV, 2015, SURVEYS OPER RES MAN, V20, P1, DOI DOI 10.1016/J.SORMS.2015.03.001
Desai PS, 2010, J MARKETING RES, V47, P90, DOI 10.1509/jmkr.47.1.90
Dube Jean-pierreSanjog Misra., 2017, Scalable Price Targeting
Elmaghraby W, 2003, MANAGE SCI, V49, P1287, DOI 10.1287/mnsc.49.10.1287.17315
Erdem T, 1996, MARKET SCI, V15, P1, DOI 10.1287/mksc.15.1.1
Furman J, 2015, WHITE HOUSE BLO 0206
Gittins J, 2011, Multi-Armed Bandit Allocation Indices
Hall P, 2002, ANN STAT, V30, P1460
Handel BR, 2015, MARKET SCI, V34, P864, DOI 10.1287/mksc.2015.0914
Handel BR, 2013, J ECONOMETRICS, V174, P165, DOI 10.1016/j.jeconom.2013.02.007
Hauser JR, 2009, MARKET SCI, V28, P202, DOI 10.1287/mksc.1080.0459
Hendel I, 2006, ECONOMETRICA, V74, P1637, DOI 10.1111/j.1468-0262.2006.00721.x
Hitsch GJ, 2006, MARKET SCI, V25, P25, DOI 10.1287/mksc.1050.0140
Hviid M, 1999, J ECON MANAGE STRAT, V8, P489, DOI 10.1162/105864099567730
Jiang YC, 2011, MARKET SCI, V30, P737, DOI 10.1287/mksc.1100.0631
Kalyanam K, 1996, MARKET SCI, V15, P207, DOI 10.1287/mksc.15.3.207
KALYANARAM G, 1995, MARKET SCI, V14, pG161, DOI 10.1287/mksc.14.3.G161
Karunamuni R., 2005, Statistical Methodology, V2, P191, DOI DOI 10.1016/J.STAMET.2005.04.001
Kleinberg R, 2014, WORKING PAPER
Kuleshov VolodymyrDoina Precup., 2014, ALGORITHMS MULTIARME
LAI TL, 1987, ANN STAT, V15, P1091, DOI 10.1214/aos/1176350495
LAI TL, 1985, ADV APPL MATH, V6, P4, DOI 10.1016/0196-8858(85)90002-8
Lei Y, 2014, 1252 U MICH ROSS SCH
LODISH LM, 1980, J MARKETING RES, V17, P203, DOI 10.2307/3150930
MANSKI C., 2005, SOCIAL CHOICE PARTIA
Mas-Colell A., 1995, MICROECONOMIC THEORY
MILNOR J, 1954, DECISION PROCESSES, P49
Nair H, 2007, QME-QUANT MARK ECON, V5, P239, DOI 10.1007/s11129-007-9026-4
Nair H, 2004, QME-QUANT MARK ECON, V2, P23, DOI 10.1023/B:QMEC.0000017034.98302.44
Oren SS., 1982, Marketing Science, V1, P287
RAJAN A, 1992, MANAGE SCI, V38, P240, DOI 10.1287/mnsc.38.2.240
RAO RC, 1985, J MARKETING RES, V22, P283, DOI 10.2307/3151425
ROTHSCHILD M, 1974, J ECON THEORY, V9, P185, DOI 10.1016/0022-0531(74)90066-0
Schwartz EM, 2017, MARKET SCI, V36, P500, DOI 10.1287/mksc.2016.1023
Smith S.A., 1986, MARKET SCI, V5, P70, DOI DOI 10.1287/MKSC.5.1.70
Stoye J, 2011, J ECON THEORY, V146, P2226, DOI 10.1016/j.jet.2011.10.004
Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
Thompson WR, 1933, BIOMETRIKA, V25, P285, DOI 10.1093/biomet/25.3-4.285
Urban GL, 2014, MARKET SCI, V33, P27, DOI 10.1287/mksc.2013.0803
WALD ABRAHAM, 1950
Wang ZZ, 2014, PROD OPER MANAG, V23, P1919, DOI 10.1111/poms.12202
WERNERFELT B, 1986, MANAGE SCI, V32, P1562, DOI 10.1287/mnsc.32.12.1562
WHITTLE P, 1980, J ROY STAT SOC B MET, V42, P143
WINER RS, 1986, J CONSUM RES, V13, P250, DOI 10.1086/209064
NR 68
TC 46
Z9 59
U1 5
U2 23
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 0732-2399
EI 1526-548X
J9 MARKET SCI
JI Mark. Sci.
PD MAR-APR
PY 2019
VL 38
IS 2
BP 226
EP 252
DI 10.1287/mksc.2018.1129
PG 27
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA HT9XB
UT WOS:000464924900002
DA 2024-03-27
ER
PT J
AU Wang, SR
Yan, Q
Wang, LL
AF Wang, Siran
Yan, Qiang
Wang, Lingli
TI Task-oriented vs. social-oriented: chatbot communication styles in
electronic commerce service recovery
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article; Early Access
DE Chatbots; Communication styles; Service recovery; Relationship
orientation; Service satisfaction
ID WORD-OF-MOUTH; RECOMMENDATION AGENTS; TRUST; SATISFACTION
AB Chatbots are being increasingly utilized for service recovery in e-commerce. However, chatbot communication styles in service recovery and their impacts on consumer satisfaction remain understudied. In this study, we conducted a scenario-based experiment to explore the appropriate communication styles for chatbots and to identify the underlying mechanisms that influence consumer satisfaction in service recovery. Our findings reveal that a social-oriented chatbot is more effective in delivering service recovery responses compared to a task-oriented chatbot. Interacting with social-oriented chatbots enhances consumers' service recovery satisfaction by increasing their cognition-based trust and affect-based trust. Importantly, we also find that social-oriented chatbots outperform task-oriented chatbots in service tasks that vary in terms of complexity and for consumers with different relationship orientations. Our study contributes to chatbot designs by providing theoretical and practical guidance for online retailers to design appropriate communication styles for chatbots in service recovery.
C1 [Wang, Siran] Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing, Peoples R China.
[Yan, Qiang; Wang, Lingli] Beijing Univ Posts & Telecommun, Sch Modern Post, Beijing, Peoples R China.
C3 Beijing University of Posts & Telecommunications; Beijing University of
Posts & Telecommunications
RP Wang, LL (autor correspondiente), Beijing Univ Posts & Telecommun, Sch Modern Post, Beijing, Peoples R China.
EM wangsiran@bupt.edu.cn; yan@bupt.edu.cn; wang.ll@bupt.edu.cn
RI Paleja, Heer/IQT-1538-2023
OI Wang, Siran/0009-0001-1577-6261
FU Ministry of Education in China [2021090003]; Beijing University of Posts
and Telecommunications [2022RC21, CX2022154]; National Natural Science
Foundation of China [72201038]
FX This work was funded by Ministry of Education in China (Grant No.
2021090003), Beijing University of Posts and Telecommunications (Grant
No. 2022RC21; Grant No. CX2022154), National Natural Science Foundation
of China (Grant No. 72201038).
CR Aggarwal P, 2004, J CONSUM RES, V31, P87, DOI 10.1086/383426
Aguirre-Urreta MI, 2019, DATA BASE ADV INF SY, V50, P45
Ahmad R., 2020, P 26 AM C INF SYST, P1
Arikan E, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103175
Bao Z., 2022, P PACIS, V2022, P1
Benner D., 2021, P ICIS, V2021, P1
Bickmore T., 2001, CHI 2001 Conference Proceedings. Conference on Human Factors in Computing Systems, P396, DOI 10.1145/365024.365304
Bleier A, 2019, J MARKETING, V83, P98, DOI 10.1177/0022242918809930
Bolton LE, 2015, J RETAILING, V91, P140, DOI 10.1016/j.jretai.2014.10.001
Brill TM, 2019, J MARKET MANAG-UK, V35, P1401, DOI 10.1080/0267257X.2019.1687571
Chang W, 2022, J BUS RES, V141, P462, DOI 10.1016/j.jbusres.2021.11.044
Chattaraman V, 2019, COMPUT HUM BEHAV, V90, P315, DOI 10.1016/j.chb.2018.08.048
Chen A., 2022, ARE YOU WILLING FORG
Chen JV, 2021, INTERNET RES, V31, P1376, DOI 10.1108/INTR-11-2019-0447
Chen Y., 2022, Phys Med Rehabil Kurortmedizin, V2022, P1
Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
Cheng XS, 2022, INTERNET RES, V32, P496, DOI 10.1108/INTR-08-2020-0460
Choi S, 2021, J SERV RES-US, V24, P354, DOI 10.1177/1094670520978798
CLARK MS, 1987, J PERS SOC PSYCHOL, V53, P94, DOI 10.1037/0022-3514.53.1.94
CLARK MS, 1993, PERS SOC PSYCHOL B, V19, P684, DOI 10.1177/0146167293196003
Consulting A. R. a., 2022, CHATB MARK SIZ SHAR
Eren BA, 2021, INT J BANK MARK, V39, P294, DOI 10.1108/IJBM-02-2020-0056
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Fota A., 2022, P 55 HAW INT C SYST, P4273
Goles T, 2009, J COMPUT INFORM SYST, V49, P1
Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Han ELZB, 2023, INFORM SYST RES, V34, P1296, DOI 10.1287/isre.2022.1179
Harrison-Walker LJ, 2012, J SERV MARK, V26, P115, DOI 10.1108/08876041211215275
Hayes AF., 2018, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, DOI DOI 10.1111/JEDM.12050
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Hsu CL, 2023, J RETAIL CONSUM SERV, V71, DOI 10.1016/j.jretconser.2022.103211
Huang B, 2021, SERV IND J, V41, P877, DOI 10.1080/02642069.2020.1748014
Huang R, 2020, J BUS RES, V121, P616, DOI 10.1016/j.jbusres.2020.04.034
Jain M, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3174042
Jeong M, 2017, J HOSP TOUR TECHNOL, V8, P5, DOI 10.1108/JHTT-09-2016-0049
Johnson D, 2005, J BUS RES, V58, P500, DOI 10.1016/S0148-2963(03)00140-1
Jones CLE, 2022, J BUS RES, V144, P703, DOI 10.1016/j.jbusres.2022.01.012
Jung NY, 2017, J RETAIL CONSUM SERV, V37, P23, DOI 10.1016/j.jretconser.2017.01.012
Keeling K, 2010, J BUS RES, V63, P793, DOI 10.1016/j.jbusres.2008.12.015
Klein K, 2023, ELECTRON COMMER RES, V23, P2789, DOI 10.1007/s10660-022-09562-8
Komiak S. X., 2004, Information Technology & Management, V5, P181, DOI 10.1023/B:ITEM.0000008081.55563.d4
Lei SI, 2021, INT J CONTEMP HOSP M, V33, P3977, DOI 10.1108/IJCHM-12-2020-1399
LEWIS JD, 1985, SOC FORCES, V63, P967, DOI 10.2307/2578601
Li XS, 2019, J CONSUM RES, V45, P973, DOI 10.1093/jcr/ucy016
Lidén SB, 2003, INT J SERV IND MANAG, V14, P36, DOI 10.1108/09564230310465985
Lv XY, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.106993
Lv XY, 2021, ANN TOURISM RES, V87, DOI 10.1016/j.annals.2020.103114
MacKenzie SB, 2011, MIS QUART, V35, P293
Maxham JG, 2001, J BUS RES, V54, P11, DOI 10.1016/S0148-2963(00)00114-4
MCALLISTER DJ, 1995, ACAD MANAGE J, V38, P24, DOI 10.5465/256727
Miller JL, 2000, J OPER MANAG, V18, P387, DOI 10.1016/S0272-6963(00)00032-2
Poser M., 2021, Proceedings of the 54th hawaii international conference on system sciences p, P1181
Roy R, 2021, J BUS RES, V126, P23, DOI 10.1016/j.jbusres.2020.12.051
Sands S, 2022, PSYCHOL MARKET, V39, P2039, DOI 10.1002/mar.21723
Schaubroeck J, 2011, J APPL PSYCHOL, V96, P863, DOI 10.1037/a0022625
Scherer K. R., 2001, Appraisal processes inemotion: Theory, methods, research, DOI DOI 10.1016/S0166-4115(08)62387-0
Sheth J., 1976, BUYER SELLER INTERAC
Song MM, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103323
Song MM, 2022, ELECTRON COMMER R A, V55, DOI 10.1016/j.elerap.2022.101199
Sun Y, 2022, ELECTRON MARK, V32, P17, DOI 10.1007/s12525-021-00483-2
Verhagen T, 2014, J COMPUT-MEDIAT COMM, V19, P529, DOI 10.1111/jcc4.12066
Wang SK, 2022, PAIN RES MANAG, V2022, DOI 10.1155/2022/9052246
Wang WQ, 2007, J MANAGE INFORM SYST, V23, P217, DOI 10.2753/MIS0742-1222230410
Wang WQ, 2018, MANAGE SCI, V64, P5198, DOI 10.1287/mnsc.2017.2906
Webster C, 2009, J SERV MARK, V23, P104, DOI 10.1108/08876040910946369
Wei C, 2020, J BUS RES, V118, P321, DOI 10.1016/j.jbusres.2020.06.061
WILLIAMS KC, 1985, J MARKETING RES, V22, P434, DOI 10.2307/3151588
Xing XY, 2022, TECHNOL SOC, V70, DOI 10.1016/j.techsoc.2022.102049
Xu XA, 2022, ANN TOURISM RES, V95, DOI 10.1016/j.annals.2022.103439
Xu X, 2019, J TRAVEL RES, V58, P1034, DOI 10.1177/0047287518789285
Xu Y, 2023, NANKAI BUS REV NT, V14, P249, DOI 10.1108/NBRI-06-2021-0041
Xu YZ, 2020, AUSTRALAS MARK J, V28, P189, DOI 10.1016/j.ausmj.2020.03.005
Yao Q, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103082
Zhou YY, 2022, J BUS ETHICS, V178, P849, DOI 10.1007/s10551-022-05045-w
NR 75
TC 2
Z9 2
U1 60
U2 79
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD 2023 AUG 1
PY 2023
DI 10.1007/s10660-023-09741-1
EA AUG 2023
PG 33
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA O0NB7
UT WOS:001040863900001
DA 2024-03-27
ER
PT J
AU Bhilat, EE
El Jaouhari, A
Hamidi, LS
AF Bhilat, El Mehdi El
El Jaouhari, Asmae
Hamidi, L. Saadia
TI Assessing the influence of artificial intelligence on agri-food supply
chain performance: the mediating effect of distribution network
efficiency
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE AI technology; Agri-food supply chain; Distribution logistics;
Efficiency; SEM
ID BIG DATA; INDUSTRY 4.0; MANAGEMENT; LOGISTICS; CAPABILITIES; TECHNOLOGY;
CHALLENGES; FRAMEWORK; BARRIERS; DESIGN
AB The urge for greater agri-food supply chain efficiency (AFSCE) has been gaining in prominence increasingly, spurred on by escalating logistics costs and advances in industry 4.0 technologies (e.g artificial intelligence AI). The latter significant contributions in logistics have led academics to point their attention more to AI utility in operations management. Drawing on dynamic capability view (DCV) and organizational information-processing theories, this study aims to examine the effect of AI based technologies on reducing wastes and minimizing afrifood supply chain costs, hence increasing organization profitability. It also analyzes how outbound logistics and distribution network efficiency (DNE) (warehousing, transportation, packaging, etc.) mediates the relationship between AI and AFSCE. This research investigates the moderating impact of AI adoption impediments (AIAI) on the mediating correlation between AI, DNE and AFSCE as well. Using Partial Least Square-Structural Equation Modeling (PLS-SEM) approach, conceptual model and hypothesis were analyzed and tested with 348 responses collected from executives and managers in the Moroccan agri-food industry. As a novel result, distribution network efficiency and by way of mediation AFSCE and organization performance are found to be directly and positively affected by AI integration. The researchers find also that with more AIAI, the relationship between the aforementioned variables weakens.
C1 [Bhilat, El Mehdi El; Hamidi, L. Saadia] Mohammed V Univ Rabat Souissi, Rabat, Morocco.
[El Jaouhari, Asmae] Sidi Mohamed Ben Abdellah Univ, Higher Sch Technol, Fes, Morocco.
[Bhilat, El Mehdi El] 5 Ave 10 allotment Dayaa Tghat Fez, Rabat, Morocco.
C3 Sidi Mohamed Ben Abdellah University of Fez
RP Bhilat, EE (autor correspondiente), Mohammed V Univ Rabat Souissi, Rabat, Morocco.; Bhilat, EE (autor correspondiente), 5 Ave 10 allotment Dayaa Tghat Fez, Rabat, Morocco.
EM elmehdi.elbhilat@um5r.ac.ma; asmae.eljaouhari@usmba.ac.ma;
s.hamidi@um5r.ac.ma
CR Abbasi R, 2022, SMART AGR TECHNOL, V2, DOI 10.1016/j.atech.2022.100042
Ada N, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126812
Allam Z, 2019, CITIES, V89, P80, DOI 10.1016/j.cities.2019.01.032
Andrejic M, 2016, TRANSPORT POLICY, V45, P99, DOI 10.1016/j.tranpol.2015.09.013
Arora C, 2022, BRIT FOOD J, V124, P2061, DOI 10.1108/BFJ-08-2021-0934
Bag S, 2023, J BUS RES, V154, DOI 10.1016/j.jbusres.2022.113315
Bag S, 2021, INT J PROD ECON, V231, DOI 10.1016/j.ijpe.2020.107844
Behnke K, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.05.025
Ben-Othman S, 2020, MOLECULES, V25, DOI 10.3390/molecules25030510
Bhat SA, 2022, AGRICULTURE-BASEL, V12, DOI 10.3390/agriculture12010040
Boice EN, 2022, J IMAGING, V8, DOI 10.3390/jimaging8090249
Canli Funda., 2017, Morocco-Agri-food value-chains strengthening program for results
Chatur A.A., 2006, Driving costs out of the Supply Chain: Inbound Logistics (Infosys View Point Report)
Chen LJ, 2022, J BUS IND MARK, V37, P1025, DOI 10.1108/JBIM-09-2020-0448
Compagnucci L, 2022, J ENG TECHNOL MANAGE, V65, DOI 10.1016/j.jengtecman.2022.101700
Costa C, 2013, FOOD BIOPROCESS TECH, V6, P353, DOI 10.1007/s11947-012-0958-7
Cravero A, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10050552
de Janvry A, 2010, J AFR ECON, V19, pii7, DOI 10.1093/jae/ejp028
Di Vaio A, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12124851
Dubois A, 2019, INT J LOGIST MANAG, V30, P620, DOI 10.1108/IJLM-12-2017-0325
Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
Esteso A, 2021, APPL MATH MODEL, V96, P20, DOI 10.1016/j.apm.2021.02.027
Falk R.F., 1992, A primer for soft modeling
Feng L, 2016, PROCEEDINGS OF 2016 IEEE ADVANCED INFORMATION MANAGEMENT, COMMUNICATES, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IMCEC 2016), P228, DOI 10.1109/IMCEC.2016.7867206
Feng Tian, 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM), P1, DOI 10.1109/ICSSSM.2016.7538424
Frank AG, 2019, INT J PROD ECON, V210, P15, DOI 10.1016/j.ijpe.2019.01.004
Fugate BS, 2010, J BUS LOGIST, V31, P43, DOI 10.1002/j.2158-1592.2010.tb00127.x
GALBRAITH JR, 1974, INTERFACES, V4, P28, DOI 10.1287/inte.4.3.28
Gilman CP, 1917, ANN AM ACAD POLIT SS, V74, P123, DOI 10.1177/000271621707400118
Hadidi A., 2021, Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications, Studies in Computational Intelligence, P45, DOI DOI 10.1007/978-3-030-51920
Hair J.F., 2009, Multivariate data analysis
Hair J. F., 2021, Partial least squares structural equation modeling (PLS-SEM) using R, P1, DOI 10.1007/978-3-030-80519-7_1
Hallikas J, 2021, SUPPLY CHAIN MANAG, V26, P629, DOI 10.1108/SCM-05-2020-0201
Hancock JT, 2020, J COMPUT-MEDIAT COMM, V25, P89, DOI 10.1093/jcmc/zmz022
Hangl J, 2022, LOGISTICS-BASEL, V6, DOI 10.3390/logistics6030063
Hayes AF, 2018, COMMUN MONOGR, V85, P4, DOI 10.1080/03637751.2017.1352100
Heard BR, 2018, RESOUR CONSERV RECY, V128, P22, DOI 10.1016/j.resconrec.2017.09.021
Helfat CE, 2003, STRATEGIC MANAGE J, V24, P997, DOI 10.1002/smj.332
Helo P, 2022, PROD PLAN CONTROL, V33, P1573, DOI 10.1080/09537287.2021.1882690
Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
Horváth D, 2019, TECHNOL FORECAST SOC, V146, P119, DOI 10.1016/j.techfore.2019.05.021
Ivanov D, 2019, GLOBAL SUPPLY CHAIN, P481, DOI DOI 10.1007/978-3-319-94313-8_16
Javaid M., 2023, Adv Agrochem, V2, P15, DOI [10.1016/j.aac.2022.10.001, DOI 10.1016/J.AAC.2022.10.001]
Karantininis K, 2010, FOOD POLICY, V35, P112, DOI 10.1016/j.foodpol.2009.10.003
Keding C., 2020, Manag. Rev. Q, P1
Khan SAR, 2021, BUS STRATEG ENVIRON, V30, P4001, DOI 10.1002/bse.2853
Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
Kollia I, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10111223
Kosior K., 2018, Roczniki (Annals), V2018, DOI DOI 10.5604/01.3001.0011.8122
Krstic M, 2022, SUSTAIN FUTURES, V4, DOI 10.1016/j.sftr.2022.100097
Kumari S, 2023, OPER MANAGE RES, V16, P489, DOI 10.1007/s12063-022-00295-3
Lacirignola C., 2016, Afkar Idees, P58
Lakshmi Vijaya, 2020, Asia Pac. J. Energy Environ., DOI [DOI 10.18034/APJEE.V6I2.542, 10.18034/apjee.v6i2.542]
Lans T., 2004, International Journal of Training and Development, V8, P73, DOI [10.1111/j.1360-3736.2004.00197.x, DOI 10.1111/J.1360-3736.2004.00197.X]
Lau KH, 2013, INT J PROD ECON, V146, P598, DOI 10.1016/j.ijpe.2013.08.008
Lezoche M, 2020, COMPUT IND, V117, DOI 10.1016/j.compind.2020.103187
Liu HF, 2013, DECIS SUPPORT SYST, V54, P1452, DOI 10.1016/j.dss.2012.12.016
Liu WH, 2022, TRANSPORT RES E-LOG, V163, DOI 10.1016/j.tre.2022.102735
Liu WH, 2022, INT J LOGIST-RES APP, V25, P965, DOI 10.1080/13675567.2020.1837760
Liu Y, 2019, COMPUT IND ENG, V127, P196, DOI 10.1016/j.cie.2018.12.022
Liu Y, 2021, IEEE T IND INFORM, V17, P4322, DOI 10.1109/TII.2020.3003910
Lotfi R, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-26449-8
Lui TKL, 2020, GASTROINTEST ENDOSC, V92, P821, DOI 10.1016/j.gie.2020.06.034
Lummus RR, 2001, IND MANAGE DATA SYST, V101, P426, DOI 10.1108/02635570110406730
Manning L, 2022, TRENDS FOOD SCI TECH, V125, P33, DOI 10.1016/j.tifs.2022.04.025
Maqueira JM, 2021, SUPPLY CHAIN MANAG, V26, P256, DOI 10.1108/SCM-02-2020-0083
Meindl B, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120784
Monteiro J, 2021, PROCEDIA COMPUT SCI, V192, P3020, DOI 10.1016/j.procs.2021.09.074
Morella P, 2021, AGRONOMY-BASEL, V11, DOI 10.3390/agronomy11122526
Ng KKH, 2021, ADV ENG INFORM, V47, DOI 10.1016/j.aei.2021.101246
O'Grady MJ, 2019, ARTIF INTELL AGR, V3, P42, DOI 10.1016/j.aiia.2019.12.001
Olivier Treguer D., 2019, Morocco-Digital and Climate Smart Agriculture Program Project, V1, P5
Patrício DI, 2018, COMPUT ELECTRON AGR, V153, P69, DOI 10.1016/j.compag.2018.08.001
Pishvaee MS, 2010, INT J ADV MANUF TECH, V47, P269, DOI 10.1007/s00170-009-2194-5
Podsakoff PM, 2012, ANNU REV PSYCHOL, V63, P539, DOI 10.1146/annurev-psych-120710-100452
Potts J., 2019, Blockchain in Agriculture, DOI [10.2139/ssrn.3397786, DOI 10.2139/SSRN.3397786]
Pramanik PKD, 2018, LECT NOTE DATA ENG, V14, P1, DOI 10.1007/978-3-319-70688-7_1
Premkumar G, 2005, J MANAGE INFORM SYST, V22, P257, DOI 10.1080/07421222.2003.11045841
Priyadarshini P, 2023, J CLEAN PROD, V385, DOI 10.1016/j.jclepro.2022.135660
Qian JP, 2022, CRIT REV FOOD SCI, V62, P679, DOI 10.1080/10408398.2020.1825925
Racine Eric, 2019, Healthc Manage Forum, V32, P272, DOI 10.1177/0840470419843831
Reay T, 2011, FAM BUS REV, V24, P105, DOI 10.1177/0894486511406427
Routroy S, 2017, J AGRIBUS DEV EMERG, V7, P275, DOI 10.1108/JADEE-06-2016-0039
Ruiz-Real JL, 2020, AGRONOMY-BASEL, V10, DOI 10.3390/agronomy10111839
Sakthivel A.R., 2021, Managing Supply Chain Risk and Disruptions: Post COVID-19, P29, DOI DOI 10.1007/978-3-030-72575-4_3
Sharma M, 2021, J CLEAN PROD, V281, DOI 10.1016/j.jclepro.2020.125013
Sharma S, 2021, LOGISTICS-BASEL, V5, DOI 10.3390/logistics5040066
Smith MJ, 2020, ANIM PROD SCI, V60, P46, DOI 10.1071/AN18522
Stenius O, 2016, OPER RES, V64, P906, DOI 10.1287/opre.2016.1510
Stephens EC, 2020, AGR SYST, V183, DOI 10.1016/j.agsy.2020.102873
Sun TQ, 2019, GOV INFORM Q, V36, P368, DOI 10.1016/j.giq.2018.09.008
Talaviya T., 2020, Artif Intell Agric, V4, P58, DOI DOI 10.1016/J.AIIA.2020.04.002
Teece DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/smj.640
Teece DJ, 2014, ACAD MANAGE PERSPECT, V28, P328, DOI 10.5465/amp.2013.0116
Tomasiello S, 2021, SOFT COMPUT, V25, P7479, DOI 10.1007/s00500-021-05707-3
Tsolakis NK, 2014, BIOSYST ENG, V120, P47, DOI 10.1016/j.biosystemseng.2013.10.014
Turk J, 2016, J ANIM SCI, V94, P53, DOI 10.2527/jas.2016-0547
Turk T., 2019, CUMHUR SCI J, DOI [10.17776/csj.352607, DOI 10.17776/csj.352607]
Tzachor Asaf, 2021, International Journal of Innovation in the Digital Economy, V12, P30, DOI 10.4018/IJIDE.2021070103
Tzachor A, 2020, Report
Vern P., 2022, Agri-Food 4.0, VVolume 27, P17, DOI 10.1108/S1877-636120220000027002
Vilas-Boas JL, 2023, J IND INF INTEGR, V31, DOI 10.1016/j.jii.2022.100393
Vinzi E.V., 2010, Handbook of partial least squares, DOI [DOI 10.1007/978-3-540-32827-8, 10.1007/978-3-540-32827-8_31]
Wang JX, 2020, TRANSPORT RES E-LOG, V135, DOI 10.1016/j.tre.2020.101886
Weerabahu SK, 2022, J AGRIBUS DEV EMERG, V12, P161, DOI 10.1108/JADEE-01-2021-0004
Winfield AFT, 2018, PHILOS T R SOC A, V376, DOI 10.1098/rsta.2018.0085
Wirtz BW, 2019, INT J PUBLIC ADMIN, V42, P596, DOI 10.1080/01900692.2018.1498103
Wolfert S, 2017, AGR SYST, V153, P69, DOI 10.1016/j.agsy.2017.01.023
Wright RT, 2012, COMMUN ASSOC INF SYS, V30
Yang J, 2019, IND MANAGE DATA SYST, V119, P1988, DOI 10.1108/IMDS-01-2019-0029
Yeo SF, 2022, TECHNOL FORECAST SOC, V177, DOI 10.1016/j.techfore.2022.121551
Zerfass A, 2020, J COMMUN MANAG, V24, P377, DOI 10.1108/JCOM-10-2019-0137
Zhang ZQ, 2019, ARTIF INTELL AGR, V1, P14, DOI 10.1016/j.aiia.2019.02.002
Zhao GQ, 2019, COMPUT IND, V109, P83, DOI 10.1016/j.compind.2019.04.002
NR 114
TC 1
Z9 1
U1 12
U2 12
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD MAR
PY 2024
VL 200
AR 123149
DI 10.1016/j.techfore.2023.123149
EA DEC 2023
PG 14
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA FE6K0
UT WOS:001144123300001
DA 2024-03-27
ER
PT J
AU Chung, M
Ko, E
Joung, H
Kim, SJ
AF Chung, Minjee
Ko, Eunju
Joung, Heerim
Kim, Sang Jin
TI Chatbot e-service and customer satisfaction regarding luxury brands
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Chatbot; Communication; Digital marketing; Luxury brand; Service agents
ID SOCIAL MEDIA; COMMUNICATION QUALITY; MOBILE SERVICES; FASHION BRANDS;
EQUITY; LOYALTY; IMPACT; UTILITARIAN; INTENTIONS; EXPERIENCE
AB This study was undertaken to analyze whether luxury fashion retail brands can adhere to their core essence of providing personalized care through e-services rather than through traditional face-to-face interactions, particularly through Chatbot, an emerging digital tool offering convenient, personal, and unique customer assistance. The authors use customer data to test a five-dimension model measuring Chatbot for customer perceptions of interaction, entertainment, trendiness, customization, and problem-solving. The study reveals that Chatbot e-service provides interactive and engaging brand/customer service encounters. Marketers and managers in the luxury context can adopt the instrument to measure whether e-service agents provide desired outcomes and to determine whether they should adopt Chatbot virtual assistance.
C1 [Chung, Minjee; Ko, Eunju; Joung, Heerim] Yonsei Univ, Dept Clothing & Text, Seoul, South Korea.
[Kim, Sang Jin] Changwon Natl Univ, Dept Business Adm, Changwon Si, Gyeongsangnam D, South Korea.
C3 Yonsei University; Changwon National University
RP Ko, E (autor correspondiente), Yonsei Univ, Dept Clothing & Text, Seoul, South Korea.
EM ejko@yonsei.ac.kr
FU National Research Foundation of Korea Grant - Korean Government (MOE)
[NRF-2017S1A2A2041810]
FX This work was supported by the National Research Foundation of Korea
Grant funded by the Korean Government (MOE) (NRF-2017S1A2A2041810).
CR Adjei MT, 2010, J ACAD MARKET SCI, V38, P634, DOI 10.1007/s11747-009-0178-5
Aggarwal P, 2005, J MARKET THEORY PRAC, V13, P16, DOI 10.1080/10696679.2005.11658547
[Anonymous], 2017, Retail Dive
[Anonymous], 2017, Forbes
[Anonymous], 2017, GLOBAL SOCIAL MEDIA
Atwal G, 2009, J BRAND MANAG, V16, P338, DOI 10.1057/bm.2008.48
Bailey J.J., 2000, Journal of Professional Services Marketing, V20, P51, DOI [10.1300/J090v20n02_05, DOI 10.1300/J090V20N02_05]
Balmer JMT, 2006, EUR J MARKETING, V40, P730, DOI 10.1108/03090560610669964
Barry B, 2000, ORGAN SCI, V11, P648, DOI 10.1287/orsc.11.6.648.12537
Ben Mimoun MS, 2017, INFORM MANAGE-AMSTER, V54, P545, DOI 10.1016/j.im.2016.11.008
Bolton RN, 2013, J SERV MANAGE, V24, P245, DOI 10.1108/09564231311326987
Calantone RJ, 2018, J GLOB SCHOLARS MARK, V28, P33, DOI 10.1080/21639159.2017.1410771
Chakrabarty S, 2014, J PERS SELL SALES M, V34, P112, DOI 10.1080/08853134.2014.890899
Chen YB, 2008, MANAGE SCI, V54, P477, DOI 10.1287/mnsc.1070.0810
Chiou JS, 2006, J ACAD MARKET SCI, V34, P613, DOI 10.1177/0092070306286934
Choi E, 2016, J BUS RES, V69, P5827, DOI 10.1016/j.jbusres.2016.04.180
Clokie TL, 2016, BUS PROF COMMUN Q, V79, P442, DOI 10.1177/2329490616657635
Coelho PS, 2012, EUR J MARKETING, V46, P331, DOI 10.1108/03090561211202503
Correa T, 2010, COMPUT HUM BEHAV, V26, P247, DOI 10.1016/j.chb.2009.09.003
Crosby LA, 2002, MARK MANAG, V11, P10
Dabholkar P., 1996, J ACAD MARKET SCI, V24, P3, DOI [10.1177/009207039602400101, DOI 10.1007/BF02893933]
Darke PR, 2016, J RETAILING, V92, P287, DOI 10.1016/j.jretai.2016.02.001
Dhaoui C, 2014, J GLOB FASH MARK, V5, P209, DOI 10.1080/20932685.2014.907605
Edwards C, 2014, COMPUT HUM BEHAV, V33, P372, DOI 10.1016/j.chb.2013.08.013
Emmers-Sommer TM, 2004, J SOC PERS RELAT, V21, P399, DOI 10.1177/0265407504042839
Fionda AM, 2009, J BRAND MANAG, V16, P347, DOI 10.1057/bm.2008.45
Gautam V., 2017, J PROMOTION MANAGEME, P1
Godes D, 2005, MARKET LETT, V16, P415, DOI 10.1007/s11002-005-5902-4
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Haas A, 2014, J RETAILING, V90, P428, DOI 10.1016/j.jretai.2014.05.003
Hagberg J, 2016, INT J RETAIL DISTRIB, V44, P694, DOI 10.1108/IJRDM-09-2015-0140
Holzwarth M, 2006, J MARKETING, V70, P19, DOI 10.1509/jmkg.70.4.19
Hutter K, 2013, J PROD BRAND MANAG, V22, P342, DOI 10.1108/JPBM-05-2013-0299
Insider-Trends, 2017, RET TRENDS FACTF 201
Izard C. E., 1977, HUMAN EMOTIONS
Jian GW, 2014, MANAGE COMMUN Q, V28, P375, DOI 10.1177/0893318914533201
Jin SAA, 2012, MARK INTELL PLAN, V30, P687, DOI 10.1108/02634501211273805
Joosten H, 2016, J SERV MANAGE, V27, P218, DOI 10.1108/JOSM-12-2014-0325
Kang G.D., 2006, MANAG SERV QUAL, V16, P37, DOI [DOI 10.1108/09604520610639955, 10.1108/09604520610639955]
Kim AJ, 2012, J BUS RES, V65, P1480, DOI 10.1016/j.jbusres.2011.10.014
Kim AJ, 2010, J GLOB FASH MARK, V1, P164, DOI 10.1080/20932685.2010.10593068
Kim J, 2018, J GLOB SCHOLARS MARK, V28, P96, DOI 10.1080/21639159.2017.1420423
Kim S, 2016, J BUS RES, V69, P5809, DOI 10.1016/j.jbusres.2016.04.178
Ko E, 2016, J BUS RES, V69, P5749, DOI 10.1016/j.jbusres.2016.04.170
Ladhari R, 2017, J RETAIL CONSUM SERV, V34, P10, DOI 10.1016/j.jretconser.2016.09.005
Lee D, 2017, INT CONF BIG DATA, P437, DOI 10.1109/BIGCOMP.2017.7881752
Lee SY, 2017, INT J HUM-COMPUT ST, V103, P95, DOI 10.1016/j.ijhcs.2017.02.005
LinkedIn, 2012, GUCCI SOCIAL MEDIA M
Liu F, 2012, EUR J MARKETING, V46, P922, DOI 10.1108/03090561211230098
Locker K.O., 1995, Business and administrative communication, V3rd
Lowry PB, 2009, J MANAGE INFORM SYST, V26, P155, DOI 10.2753/MIS0742-1222260107
Maltz E, 2000, J PROD INNOVAT MANAG, V17, P110, DOI 10.1111/1540-5885.1720110
McAlexander JH, 2002, J MARKETING, V66, P38, DOI 10.1509/jmkg.66.1.38.18451
McCroskey JC, 1999, COMMUN MONOGR, V66, P90, DOI 10.1080/03637759909376464
Mohr JJ, 1995, J RETAILING, V71, P393, DOI 10.1016/0022-4359(95)90020-9
Morra MC, 2018, J GLOB FASH MARK, V9, P24
Muntinga DG, 2011, INT J ADVERT, V30, P13, DOI 10.2501/IJA-30-1-013-046
Nysveen H, 2005, J ACAD MARKET SCI, V33, P330, DOI 10.1177/0092070305276149
O'Keefe D. J., 2002, Persuasion: Theory and research, V2nd ed.
Perna A, 2018, J GLOB FASH MARK, V9, P73, DOI 10.1080/20932685.2017.1399081
Perrey J., 2011, RETAIL MARKETING B 2
Redman T, 2002, SERV IND J, V22, P51, DOI 10.1080/714005085
Reynolds KE, 1999, J RETAILING, V75, P11, DOI 10.1016/S0022-4359(99)80002-5
Rios Andrea., 2016, Central European Business Review, V5, P17
Santini F. D. O., 2018, J GLOBAL FASHION MAR, V9, P1
Setia P, 2013, MIS QUART, V37, P565, DOI 10.25300/MISQ/2013/37.2.11
Snavely W.B., 2008, J LEADERSHIP ORG STU, V14, P219, DOI DOI 10.1177/1071791907308464
Spitzberg B.H., 2006, Journal of Computer-Mediated Communication, V11, P629, DOI DOI 10.1111/J.1083-6101.2006.00030.X
TAYLOR SA, 1994, J RETAILING, V70, P163, DOI 10.1016/0022-4359(94)90013-2
The Korea Bizwire, 2017, KOREA BIZWIRE
Vos M, 2009, J COMMUN MANAG, V13, P362, DOI 10.1108/13632540911004623
Wang WT, 2012, INTERNET RES, V22, P142, DOI 10.1108/10662241211214548
Webster C, 2009, J SERV MARK, V23, P104, DOI 10.1108/08876040910946369
Wiedmann KP, 2009, PSYCHOL MARKET, V26, P625, DOI 10.1002/mar.20292
Woodside AG, 2013, ADV CULT TOUR HOSP R, V7, P1, DOI 10.1108/S1871-3173(2013)0000007004
Yonhap, 2018, THE KOREA HERALD
Yuan CL, 2016, J BUS RES, V69, P3795, DOI 10.1016/j.jbusres.2015.12.071
Zhang M, 2018, J GLOB SCHOLARS MARK, V28, P305, DOI 10.1080/21639159.2018.1513338
Zhang Weinan, 2017, ARXIV170102073
Zhao DJ, 2009, GROUP 2009 PROCEEDINGS, P243
Zolkepli IA, 2015, COMPUT HUM BEHAV, V43, P189, DOI 10.1016/j.chb.2014.10.050
NR 81
TC 310
Z9 323
U1 135
U2 654
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD SEP
PY 2020
VL 117
BP 587
EP 595
DI 10.1016/j.jbusres.2018.10.004
PG 9
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA MW2RD
UT WOS:000556889900054
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Phang, DCW
Wang, KL
Wang, QH
Kauffman, RJ
Naldi, M
AF Phang, David C. W.
Wang, Kanliang
Wang, Qiuhong
Kauffman, Robert J.
Naldi, Maurizio
TI How to derive causal insights for digital commerce in China? A research
commentary on computational social science methods
SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
LA English
DT Article
DE Big data; Business insights; Causal inference; Causal methods;
Computational social science (CSS); Consumer behavior; China; Data
analytics; Digital economy; E-commerce; Emerging markets; Empirical
research; Information systems (IS) research; Machine learning (ML);
M-commerce; Policy analytics; Research design; Secondary data; Sensor
data; Streaming data; Social insights; Theory testing
ID BIG DATA ANALYTICS; CONSUMER PURCHASE DECISION; SUPPLY CHAIN;
INFORMATION; ADOPTION; SEARCH; TRUST; TRANSFORMATION; INFORMEDNESS;
SATISFACTION
AB The transformation of empirical research due to the arrival of big data analytics and data science, as well as the new availability of methods that emphasize causal inference, are moving forward at full speed. In this Research Commentary, we examine the extent to which this has the potential to influence how e-commerce research is conducted. China offers the ultimate in data-at-scale settings, and the construction of real-world natural experiments. Chinese e-commerce includes some of the largest firms involved in e-commerce, mobile commerce, social media and social networks. This article was written to encourage young faculty and doctoral students to engage in research that can be carried out in near real-time, with truly experimental or quasi-experimental research designs, and with the clear intention of establishing causal inferences that relate the precursors and drivers of observable outcomes through various kinds of processes. We discuss: the relevant data sources and research contexts; the methods perspectives that are appropriate which blend Computer Science, Statistics and Econometrics, how the research can be made relevant for China; and what kinds of findings and research directions are available. This article is not a tutorial on big data analytics methods in general though, nor does it cover just those published works that demonstrate big data methods and empirical causality in other disciplines. Instead, the empirical research covered is mostly taken from Electronic Commerce Research and Applications, which has published many articles on Chinese e-commerce. This Research Commentary invites researchers in China and the Asia Pacific region to expand their coverage to bring into their empirical work the new methods and philosophy of causal data science.
C1 [Phang, David C. W.] Univ Nottingham, Ningbo, Zhejiang, Peoples R China.
[Wang, Kanliang] Renmin Univ, Renmin Business Sch, Beijing, Peoples R China.
[Wang, Qiuhong; Kauffman, Robert J.] Singapore Management Univ, Sch Informat Syst, Singapore, Singapore.
[Naldi, Maurizio] Univ Roma Tor Vergata, Dept Civil Engn & Comp Sci, Rome, Italy.
C3 University of Nottingham Ningbo China; Renmin University of China;
Singapore Management University; University of Rome Tor Vergata
RP Kauffman, RJ (autor correspondiente), Singapore Management Univ, Sch Informat Syst, Singapore, Singapore.
EM cheewei.phang@nottingham.edu.cn; wangkanliang@rmbs.ruc.edu.cn;
qiuhongwang@smu.edu.sg; rkauffman@smu.edu.sg; maurizio.naldi@uniroma2.it
RI WANG, Qiuhong/L-9577-2016; Naldi, Maurizio/GXW-3240-2022; KAUFFMAN,
Robert J/G-2767-2012; NALDI, MAURIZIO/M-5612-2015
OI Kauffman, Robert J/0000-0002-3757-0010; NALDI,
MAURIZIO/0000-0002-0903-398X; WANG, QIUHONG/0000-0003-3472-5037
CR Aggarwal R., 2012, INFORM SYSTEMS RES 2, V23, P976
Hoang AP, 2018, J MANAGE INFORM SYST, V35, P575, DOI 10.1080/07421222.2018.1451958
Aigner W, 2008, IEEE T VIS COMPUT GR, V14, P47, DOI 10.1109/TVCG.2007.70415
Akter S, 2016, ELECTRON MARK, V26, P173, DOI 10.1007/s12525-016-0219-0
Alliance of Industrial Internet (AII), 2018, WHIT PAP CHIN IND BI
Andrews M, 2016, MARKET SCI, V35, P218, DOI 10.1287/mksc.2015.0905
[Anonymous], 1996, HDB STAT
[Anonymous], P 14 INT C EL COMM A
[Anonymous], 2017, VENT CAP INV FINT RE
Balasubramanian S, 2002, J ACAD MARKET SCI, V30, P348, DOI 10.1177/009207002236910
Banker R. D., 1990, Journal of Management Information Systems, V7, P29
Bauza R, 2013, J NETW COMPUT APPL, V36, P1295, DOI 10.1016/j.jnca.2012.02.007
Benaroch M, 2001, IEEE T ENG MANAGE, V48, P428, DOI 10.1109/17.969422
Biuk-Aghai RP, 2016, IEEE REGION 10 SYMP, P173, DOI 10.1109/TENCONSpring.2016.7519399
Chan SWK, 2017, DECIS SUPPORT SYST, V94, P53, DOI 10.1016/j.dss.2016.10.006
Chang HH, 2017, ELECTRON COMMER R A, V24, P50, DOI 10.1016/j.elerap.2017.06.002
Chang M. R., 2013, P 2013 CHIN SUMM WOR
Chang RM, 2014, DECIS SUPPORT SYST, V63, P67, DOI 10.1016/j.dss.2013.08.008
Chang YP, 2011, COMPUT HUM BEHAV, V27, P1840, DOI 10.1016/j.chb.2011.04.006
Chen G., 2000, WORKING PAPER
Chen G. Q., 2018, CHIN J MANAGE SCI
Chen HC, 2012, MIS QUART, V36, P1165
Chen K, 2018, ELECTRON COMMER R A, V28, P159, DOI 10.1016/j.elerap.2018.01.015
Chen K, 2017, ELECTRON COMMER R A, V26, P81, DOI 10.1016/j.elerap.2017.10.003
China Academy of Information and Communications Technology (CAICT), 2018, WHIT PAP BIG DAT
China Electronics Standardization Institute (CESI), 2017, WHIT PAP IND BIG DAT
Chunmei Gan, 2015, Journal of Systems and Information Technology, V17, P351, DOI 10.1108/JSIT-06-2015-0052
Clemons EK, 2006, J MANAGE INFORM SYST, V23, P149, DOI [10.2753/MIS0742-1222230207, 10.2753/M1S0742-1222230207]
Clemons EK, 2017, J MANAGE INFORM SYST, V34, P425, DOI 10.1080/07421222.2017.1334474
Cohen D., 2014, ADWEEK
Cook TD, 2002, EXPT QUASIEXPERIMENT
Curty RG, 2013, ELECTRON COMMER R A, V12, P260, DOI 10.1016/j.elerap.2013.04.001
De Marchi G, 2016, ANN OPER RES, V236, P15, DOI 10.1007/s10479-014-1578-6
Ding Y., 2015, P 2015 INT C INF SYS
Edin F, 2004, J COMPUT NEUROSCI, V17, P47, DOI 10.1023/B:JCNS.0000023868.18446.a2
Efron B., 2016, COMPUTER AGE STAT IN
Erevelles S, 2016, J BUS RES, V69, P897, DOI 10.1016/j.jbusres.2015.07.001
Evidence-Based Policy-Making Collaborative, 2016, CISC VIS NETW IND GL
Fan SG, 2011, REV DEV FINANC, V1, P47, DOI 10.1016/j.rdf.2010.10.001
Fang Z, 2015, INFORM SYST RES, V26, P552, DOI 10.1287/isre.2015.0586
Feng Z. Y., 2013, CHIN J MANAGE SCI
Fiorini PD, 2018, INT J INFORM MANAGE, V43, P112, DOI 10.1016/j.ijinfomgt.2018.07.005
Fong NM, 2015, J MARKETING RES, V52, P726, DOI 10.1509/jmr.14.0229
Gao BJ, 2016, ELECTRON COMMER R A, V17, P161, DOI 10.1016/j.elerap.2016.04.005
Ghasemaghaei M, 2018, J STRATEGIC INF SYST, V27, P101, DOI 10.1016/j.jsis.2017.10.001
Gibreel O, 2018, ELECTRON COMMER R A, V27, P152, DOI 10.1016/j.elerap.2017.12.008
GOEL AL, 1979, IEEE T RELIAB, V28, P206, DOI 10.1109/TR.1979.5220566
Goel V, 2014, NY TIMES
Goes PB, 2014, MIS QUART, V38, pIII
Gomber P, 2018, J MANAGE INFORM SYST, V35, P220, DOI 10.1080/07421222.2018.1440766
Grover V, 2018, J MANAGE INFORM SYST, V35, P388, DOI 10.1080/07421222.2018.1451951
Gu B, 2007, INFORM SYST RES, V18, P68, DOI 10.1287/isre.1070.0114
Gunasekaran A, 2017, J BUS RES, V70, P308, DOI 10.1016/j.jbusres.2016.08.004
Guo XT, 2016, ELECTRON COMMER R A, V16, P55, DOI 10.1016/j.elerap.2015.11.001
Han H. Q., 2018, FINTECH CHINA INTRO
Han H, 2018, ELECTRON COMMER R A, V30, P38, DOI 10.1016/j.elerap.2018.05.005
Han SQ, 2018, ELECTRON COMMER R A, V28, P244, DOI 10.1016/j.elerap.2018.02.006
Hoang A. P., 2018, 2018 WORKSH STAT CHA
Hoang A. P., 2018, WORKING PAPER
Hofstede G., 1984, ASIA PAC J MANAG, V2, P22, DOI [DOI 10.1177/106939718301800403, 10.1177/106939718301800403]
Hofstede G, 2006, J INT BUS STUD, V37, P882, DOI 10.1057/palgrave.jibs.8400233
Hsiao KL, 2016, ELECTRON COMMER R A, V16, P18, DOI 10.1016/j.elerap.2016.01.001
Hsu CL, 2015, ELECTRON COMMER R A, V14, P46, DOI 10.1016/j.elerap.2014.11.003
Huang JH, 2009, INFORM MANAGE-AMSTER, V46, P100, DOI 10.1016/j.im.2008.12.003
Huang Z, 2017, ELECTRON COMMER R A, V25, P40, DOI 10.1016/j.elerap.2017.08.003
Huang Z, 2013, ELECTRON COMMER R A, V12, P246, DOI 10.1016/j.elerap.2012.12.003
Ittmann HW, 2015, J TRANSP SUPPLY CHAI, V9, DOI 10.4102/jtscm.v9i1.165
Jeske M., 2013, BIG DATA LOGISTICS, P12
Kauffman R. J., 2016, P 2016 HAW INT C SYS, P5048
Kauffman RJ, 2017, ELECTRON COMMER R A, V25, P115, DOI 10.1016/j.elerap.2017.04.004
Kauffman RJ, 2010, J ASSOC INF SYST, V11, P303
Kauffman RJ, 2009, DECIS SUPPORT SYST, V48, P3, DOI 10.1016/j.dss.2009.05.009
Kharas H., 2017, The unprecedented expansion of the global middle class: An update
Kim Y., 2018, WORKING PAPER
Kitchin R, 2014, BIG DATA SOC, V1, DOI 10.1177/2053951714528481
Ko HC, 2018, ELECTRON COMMER R A, V28, P1, DOI 10.1016/j.elerap.2017.12.011
Kohavi R, 2014, PROCEEDINGS OF THE 20TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'14), P1857, DOI 10.1145/2623330.2623341
Koppel M, 2006, COMPUT INTELL-US, V22, P100, DOI 10.1111/j.1467-8640.2006.00276.x
Larmer B, 2017, NY TIMES
Lazer D, 2009, SCIENCE, V323, P721, DOI 10.1126/science.1167742
LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
Lee JY, 2018, J RETAILING, V94, P247, DOI 10.1016/j.jretai.2018.06.002
Li T, 2014, INFORM SYST RES, V25, P345, DOI 10.1287/isre.2014.0521
Liang XJ, 2018, ELECTRON COMMER R A, V27, P83, DOI 10.1016/j.elerap.2017.12.007
Lien CH, 2014, COMPUT HUM BEHAV, V41, P104, DOI 10.1016/j.chb.2014.08.013
Lien CH, 2017, COMPUT HUM BEHAV, V68, P403, DOI 10.1016/j.chb.2016.11.061
Lin JB, 2011, ELECTRON COMMER R A, V10, P615, DOI 10.1016/j.elerap.2011.07.008
Liu DR, 2011, ELECTRON COMMER R A, V10, P94, DOI 10.1016/j.elerap.2010.08.004
Liu J., 2018, CHINA MECH ENG, V2018, P1, DOI DOI 10.1155/2018/3491960
Long D., 2017, CHINAS E COMMERCE MA
Lu BZ, 2016, ELECTRON COMMER R A, V20, P116, DOI 10.1016/j.elerap.2016.10.004
Lu M, 2015, IEEE CONF VIS ANAL, P203, DOI 10.1109/VAST.2015.7347679
Luo XM, 2014, MANAGE SCI, V60, P1738, DOI 10.1287/mnsc.2013.1836
Lv ZP, 2018, ELECTRON COMMER R A, V28, P102, DOI 10.1016/j.elerap.2018.01.003
Malhotra R., 2017, P 2017 INT C BIG DAT
Martens R, 2006, ELECTRON COMMER R A, V5, P140, DOI [10.1016/j.elerap.2005.10.008, 10.1128/EC.5.1.140-147.2006]
Morgan Stanley Research, 2015, CHIN E COMM REV
Müller O, 2016, EUR J INFORM SYST, V25, P289, DOI 10.1057/ejis.2016.2
Natali F., 2017, P 2017 IEEE ACM INT
National Academies of Sciences Engineering and Medicine (NASEM), 2016, COMM MOT VEH DRIV FA
Office of the United States Trade Representative, 2017, 2017 OUT CYCL REV NO
Pearl J., 2009, Causality: Models, Reasoning, and Inference, V2nd ed., DOI DOI 10.1017/CBO9780511803161
Prabu K., 2014, BAIDU LEVERAGES BIG
Qin R, 2017, ELECTRON COMMER R A, V24, P68, DOI 10.1016/j.elerap.2017.07.001
Quah E., 2007, Cost-benefit analysis
Rana R, 2014, COMM COM INF SC, V466, P270
Raphaeli O, 2017, ELECTRON COMMER R A, V26, P1, DOI 10.1016/j.elerap.2017.09.003
Razeghian M, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.06.001
ROSENBAUM PR, 1983, BIOMETRIKA, V70, P41, DOI 10.1093/biomet/70.1.41
Savicky P., 2003, P 2003 CRYPT HARDW E
Schlager T, 2018, J MANAGE INFORM SYST, V35, P319, DOI 10.1080/07421222.2018.1440763
SIMS CA, 1980, ECONOMETRICA, V48, P1, DOI 10.2307/1912017
Song PJ, 2018, INFORM MANAGE-AMSTER, V55, P633, DOI 10.1016/j.im.2018.01.004
Sumita U, 2010, ELECTRON COMMER R A, V9, P217, DOI 10.1016/j.elerap.2009.11.006
Sun Y, 2016, ELECTRON COMMER R A, V18, P37, DOI 10.1016/j.elerap.2016.06.002
Tan CH, 2014, INFORM SYST RES, V25, P307, DOI 10.1287/isre.2014.0519
Tian F, 2016, ELECTRON COMMER R A, V16, P66, DOI 10.1016/j.elerap.2015.10.003
Tian J, 2018, ELECTRON COMMER R A, V28, P194, DOI 10.1016/j.elerap.2018.02.001
Tong F., 2017, ONLINE RETAIL SALES
Tosun A., 2010, P 22 INN APPL ART IN
Tse E., 2016, CHINESE COMPANIES LE
Tsoukias A, 2013, EURO J DECIS PROCESS, V1, P115, DOI 10.1007/s40070-013-0008-3
Van Den Broeke M., 2018, EUR J OPER RES
VentureScanner, 2017, START UP MARK REP DA
Vuylsteke A, 2010, J INTERACT MARK, V24, P309, DOI 10.1016/j.intmar.2010.02.010
Walters J., 2011, CISC VIS NETW IND GL
Wang JN, 2018, ELECTRON COMMER R A, V28, P16, DOI 10.1016/j.elerap.2018.01.002
Wang NX, 2018, ELECTRON COMMER R A, V27, P106, DOI 10.1016/j.elerap.2017.12.004
Wang W., 2018, WHATS HAPPENING CHIN
Weber TA, 2017, J MANAGE INFORM SYST, V34, P341, DOI 10.1080/07421222.2017.1334466
Weber TA, 2014, J MANAGE INFORM SYST, V31, P35, DOI 10.1080/07421222.2014.995520
Wells P., 2014, NEW LABOUR EVIDENCE
Wen XH, 2016, INT J ADV MANUF TECH, V87, P1491, DOI 10.1007/s00170-014-6348-8
Witten IH, 2011, MOR KAUF D, P1
Wu J, 2017, ELECTRON COMMER R A, V22, P24, DOI 10.1016/j.elerap.2017.02.002
Xiao BM, 2015, ELECTRON COMMER R A, V14, P641, DOI 10.1016/j.elerap.2015.10.001
Xinhua, 2018, CHINADAILY 0112
Xu Zongben, 2014, MANAGEMENT WORLD, V11, P158
Yan J., 2017, FOR AFFAIRS 1109
Yang MC, 2018, INFORM SYST RES, V29, P4, DOI 10.1287/isre.2017.0727
Yao XL, 2017, INFORM MANAGE-AMSTER, V54, P599, DOI 10.1016/j.im.2016.12.001
Yu C., 2016, ADV MECH ENG, V8, P7
Zhang C. B., 2016, COMPUT HUM BEHAV, V69
Zhang LX, 2018, BMJ-BRIT MED J, V360, DOI 10.1136/bmj.j5910
Zhang WS, 2013, ACM T INTEL SYST TEC, V4, DOI 10.1145/2414425.2414434
Zhong RY, 2016, COMPUT IND ENG, V101, P572, DOI 10.1016/j.cie.2016.07.013
Zhou MG, 2016, LANCET, V387, P251, DOI 10.1016/S0140-6736(15)00551-6
NR 147
TC 22
Z9 22
U1 12
U2 110
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1567-4223
EI 1873-7846
J9 ELECTRON COMMER R A
JI Electron. Commer. Res. Appl.
PD MAY-JUN
PY 2019
VL 35
AR 100837
DI 10.1016/j.elerap.2019.100837
PG 16
WC Business; Computer Science, Information Systems; Computer Science,
Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science
GA IM0HP
UT WOS:000477668400001
OA Green Accepted
DA 2024-03-27
ER
PT J
AU De Gauquier, L
Willems, K
Cao, HL
Vanderborght, B
Brengman, M
AF De Gauquier, Laurens
Willems, Kim
Cao, Hoang-Long
Vanderborght, Bram
Brengman, Malaika
TI Together or alone: Should service robots and frontline employees
collaborate in retail-customer interactions at the POS?
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Service robot; Frontline employee; Automated social presence; Human
social presence; Retail; Field study; POS conversion Funnel
ID AUTOMATED SOCIAL PRESENCE; TECHNOLOGY; TOUCH
AB This study investigates shopper behavior when interacting with an employee-robot team (vs. both actors in isolation), along the metrics of the POS conversion funnel. An unobtrusive field study was conducted using video observations, evenly spread over four conditions: (1) a control condition (i.e., no stimulus), (2) a frontline employee, (3) a humanoid service robot, and (4) an employee-robot team. The results indicate that the service robot was the better option to generate attention and stop passers-by, but in this condition the least amount of passers-by were lured into the store. While the frontline employee initiated the lowest amount of interactions, he could convert the highest number of passersby into actual buyers. The robot-employee team managed to encourage the highest number of passers-by to look at the store, but did not convert more of them into actual buyers than the robot on its own.
C1 [De Gauquier, Laurens; Willems, Kim; Brengman, Malaika] Vrije Univ Brussel, Dept Business Mkt & Consumer Behav, Pl Laan 2, B-1050 Brussels, Belgium.
[Cao, Hoang-Long; Vanderborght, Bram] Vrije Univ Brussel, Pl Laan 2, B-1050 Brussels, Belgium.
[Cao, Hoang-Long; Vanderborght, Bram] Imec, Brubot, Pl Laan 2, B-1050 Brussels, Belgium.
C3 Vrije Universiteit Brussel; Vrije Universiteit Brussel; IMEC
RP Brengman, M (autor correspondiente), Vrije Univ Brussel, Dept Business Mkt & Consumer Behav, Pl Laan 2, B-1050 Brussels, Belgium.
EM Malaika.Brengman@vub.be
RI Vanderborght, Bram/A-1599-2008
OI Cao, Hoang-Long/0000-0003-2851-5527; De Gauquier,
Laurens/0000-0002-4343-1412; Willems, Kim/0000-0002-0941-8599; Brengman,
Malaika/0000-0001-9860-7107
CR [Anonymous], 2013, PSYCHOL TODAY
Belanche D, 2020, J SERV MANAGE, V31, P267, DOI 10.1108/JOSM-05-2019-0156
Bhimasta RA, 2019, UBICOMP/ISWC'19 ADJUNCT: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2019 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS, P1107, DOI 10.1145/3341162.3350843
Biocca F, 2003, PRESENCE-VIRTUAL AUG, V12, P456, DOI 10.1162/105474603322761270
Bowen DE, 2016, HUM RESOUR MANAGE R, V26, P4, DOI 10.1016/j.hrmr.2015.09.002
Brengman M., 2019, Homo Roboticus, P167
Brengman M, 2021, J BUS RES, V134, P263, DOI 10.1016/j.jbusres.2021.05.025
Choi S, 2019, INT J HOSP MANAG, V82, P32, DOI 10.1016/j.ijhm.2019.03.026
Dahl DW, 2005, J CONSUM PSYCHOL, V15, P307, DOI 10.1207/s15327663jcp1504_5
De Gauquier L., 2020, RETAIL FUTURES, P15
De Gauquier L, 2021, INT J RETAIL DISTRIB, V49, P846, DOI 10.1108/IJRDM-10-2020-0413
De Keyser A, 2019, J SERV MANAGE, V30, P156, DOI 10.1108/JOSM-03-2018-0082
Gartner, 2020, GARTN TOP STRAT TREN
Giebelhausen M, 2014, J MARKETING, V78, P113, DOI 10.1509/jm.13.0056
Guo WX, 2017, EUR J MARKETING, V51, P1577, DOI 10.1108/EJM-05-2016-0278
Hagberg J, 2016, INT J RETAIL DISTRIB, V44, P694, DOI 10.1108/IJRDM-09-2015-0140
Heerink M, 2010, VIRTUAL REAL-LONDON, V14, P77, DOI 10.1007/s10055-009-0142-1
Heikkilä P, 2019, LECT NOTES ARTIF INT, V11876, P548, DOI 10.1007/978-3-030-35888-4_51
Hertzfeld E., 2019, HOTEL MANAGEMENT
Hollebeek L.D., 2021, RISE MACHINES CUSTOM
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Oh H, 2012, J RETAIL CONSUM SERV, V19, P27, DOI 10.1016/j.jretconser.2011.08.003
Jean D., 2018, HENN HOTEL TOKYO GIN
Kim S, 2021, INT J HOSP MANAG, V93, DOI 10.1016/j.ijhm.2020.102795
Knapton S., 2018, TELEGRAPH
Larivière B, 2017, J BUS RES, V79, P238, DOI 10.1016/j.jbusres.2017.03.008
Lee JY, 2018, J ADV NURS, V74, P2094, DOI 10.1111/jan.13711
Lee SM, 2020, SERV BUS, V14, P1, DOI 10.1007/s11628-019-00408-2
Lin IY, 2021, INT J HOSP MANAG, V94, DOI 10.1016/j.ijhm.2021.102876
Marinova D, 2017, J SERV RES-US, V20, P29, DOI 10.1177/1094670516679273
Marketwatch, 2020, RET ROB MARK SIZ FOR
Mayet R, 2019, IEEE ROMAN, DOI 10.1109/ro-man46459.2019.8956418
McLeay F, 2021, J SERV RES-US, V24, P104, DOI 10.1177/1094670520933354
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Michelis D, 2011, INT J HUM-COMPUT INT, V27, P562, DOI 10.1080/10447318.2011.555299
Miley J., 2018, ROBOT GETS FIRED SUP
Nijssen EJ, 2016, J SERV MANAGE, V27, P276, DOI 10.1108/JOSM-08-2015-0233
Paluch S., 2020, Journal of Service Management Research, V4, P3, DOI DOI 10.15358/2511-8676-2020-1-3
Seiler S, 2017, MARKET SCI, V36, P565, DOI 10.1287/mksc.2017.1026
Shin HH, 2020, INT J CONTEMP HOSP M, V32, P2613, DOI 10.1108/IJCHM-09-2019-0798
Sissi C., 2020, WALMART PREFERS HUMA
Smith JB, 1997, J MARKETING, V61, P3, DOI 10.2307/1252186
Solnet D, 2019, J SERV MANAGE, V30, P392, DOI 10.1108/JOSM-12-2018-0380
Song SR, 2023, ENVIRON TECHNOL, V44, P82, DOI 10.1080/09593330.2021.1964001
Tonkin M, 2017, IEEE ROMAN, P42, DOI 10.1109/ROMAN.2017.8172278
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Lu VN, 2020, J SERV THEOR PRACT, V30, P361, DOI 10.1108/JSTP-04-2019-0088
Weinberg N., 2019, ROBOT BUS REV
Weitz BA, 1999, J ACAD MARKET SCI, V27, P241, DOI 10.1177/0092070399272008
Wirtz J, 2007, INT J SERV IND MANAG, V18, P6, DOI 10.1108/09564230710732876
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Yoganathan V, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104309
NR 53
TC 7
Z9 7
U1 10
U2 27
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD JAN
PY 2023
VL 70
AR 103176
DI 10.1016/j.jretconser.2022.103176
EA NOV 2022
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 9A6EB
UT WOS:000934148200006
DA 2024-03-27
ER
PT J
AU Croux, C
Jagtiani, J
Korivi, T
Vulanovic, M
AF Croux, Christophe
Jagtiani, Julapa
Korivi, Tarunsai
Vulanovic, Milos
TI Important factors determining Fintech loan default: Evidence from a
lendingclub consumer platform
SO JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION
LA English
DT Article
DE Big data; Crowdfunding; Financial innovation; Household finance; Lasso
selection methods; Machine learning; Peer to peer lending;
P2P/marketplace lending
ID POST-SELECTION; INFERENCE; MODELS; CREDIT; LASSO; RISK
AB This study examines the default determinants of Fintech loans, utilizing a sample of more than a million of personal loans that were originated through the LendingClub consumer platform during the period 2007-2018. We identify a robust set of contractual loan characteristics, borrower characteristics, and macroeconomic variables that are important in determining the likelihood of default, such as loan maturity, homeownership, loan purposes, occupation, etc. We also find an important role of alternative data in determining the default, even after controlling for the obvious risk characteristics of the borrowers, loan characteristics, and the local economic factors. The results are robust to different empirical approaches. Results imply that it would be important for regulators to provide greater transparency in terms of guidance and regulatory clarity on which alternative data can be used legally without violating fair lending rules. Lenders need to pay closer attention to how they make decisions and understand their own decisions that may be driven by complex algorithms inside the "black boxes."(C) 2020 Elsevier B.V. All rights reserved.
C1 [Croux, Christophe; Vulanovic, Milos] EDHEC Business Sch, Dept Data Sci Econ & Finance, 24 Ave Gustave Delory,CS 50411, F-59057 Roubaix, France.
[Jagtiani, Julapa] Fed Reserve Bank Philadelphia, Supervis Regulat & Credit Dept, Philadelphia, PA 19106 USA.
[Korivi, Tarunsai] Amazoncom, Data Engn, 33 Rives de Clausen 31, L-2165 Luxembourg, Luxembourg.
C3 Universite Catholique de Lille; EDHEC Business School; Federal Reserve
System - USA; Federal Reserve Bank - Philadelphia
RP Vulanovic, M (autor correspondiente), EDHEC Business Sch, Dept Data Sci Econ & Finance, 24 Ave Gustave Delory,CS 50411, F-59057 Roubaix, France.
EM christophe.croux@edhec.edu; Julapa.Jagtiani@phil.frb.org;
ttark@amazon.com; milos.vulanovic@edhec.edu
RI Vulanovic, Milos/A-8815-2012; Croux, Christophe/E-4386-2016
OI Vulanovic, Milos/0000-0001-6628-9300; Croux,
Christophe/0000-0003-2912-3437
CR Adams R.M., 2018, 20181022 BOARD GOV F, DOI [10.17016/2380-7172.2268., DOI 10.17016/2380-7172.2268]
Athey S., 2018, IMPACT MACHINE LEARN
Baker SR, 2016, Q J ECON, V131, P1593, DOI 10.1093/qje/qjw024
Balyuk T, 2017, 2802220 ROTM SCH MAN, DOI [10.2139/ssrn.2802220, DOI 10.2139/SSRN.2802220]
Balyuk T, 2019, 3189236 SSRN
Bastos JA, 2010, J BANK FINANC, V34, P2510, DOI 10.1016/j.jbankfin.2010.04.011
Belloni A, 2012, ECONOMETRICA, V80, P2369, DOI 10.3982/ECTA9626
Belloni A, 2016, J BUS ECON STAT, V34, P606, DOI 10.1080/07350015.2016.1166116
Belloni A, 2014, J ECON PERSPECT, V28, P29, DOI 10.1257/jep.28.2.29
Belloni A, 2013, BERNOULLI, V19, P521, DOI 10.3150/11-BEJ410
Bhanot SP, 2017, J ECON BEHAV ORGAN, V140, P246, DOI 10.1016/j.jebo.2017.04.007
Bjorkegren D., 2018, WORLD BANK EC REV
Braggion Fabio, 2020, CAN TECHNOLOGY UNDER, DOI [10.2139/ssrn.2957411, DOI 10.2139/SSRN.2957411]
Buchak G, 2018, J FINANC ECON, V130, P453, DOI 10.1016/j.jfineco.2018.03.011
Carmichael D., 2014, MODELING DEFAULT PEE, DOI [10.2139/ssrn.2529240, DOI 10.2139/SSRN.2529240]
Chernozhukov V, 2015, ANNU REV ECON, V7, P649, DOI 10.1146/annurev-economics-012315-015826
Chinco A, 2019, J FINANC, V74, P449, DOI 10.1111/jofi.12733
Danisewicz P., 2018, REAL EFFECTS FINANCI, DOI [10.2139/ssrn.3208908, DOI 10.2139/SSRN.3208908]
De Luca G, 2011, STATA J, V11, P213, DOI 10.1177/1536867X1101100204
de Roure C., 2019, P2P LENDERS VERSUS B, DOI [10.2139/ssrn.3174632, DOI 10.2139/SSRN.3174632]
Duarte J, 2012, REV FINANC STUD, V25, P2455, DOI 10.1093/rfs/hhs071
Durovic A, 2017, J CENT BANK THEOR PR, V6, P149, DOI 10.1515/jcbtp-2017-0017
Emekter R, 2015, APPL ECON, V47, P54, DOI 10.1080/00036846.2014.962222
Freyberger J., 2017, W23227 NAT BUR EC RE, DOI [10.3386/w23227., DOI 10.3386/W23227]
Funk, 2011, J INTERNET BANK COMM, V16, P1, DOI DOI 10.1016/J.JBUSVENT.2013.07.003
Goodman LS, 2018, J ECON PERSPECT, V32, P31, DOI 10.1257/jep.32.1.31
Havrylchyk O., 2019, SSRN ELECT J, DOI [10.2139/ssrn.2841316, DOI 10.2139/SSRN.2841316]
HECKMAN JJ, 1979, ECONOMETRICA, V47, P153, DOI 10.2307/1912352
Hertzberg A, 2018, REV FINANC STUD, V31, P3532, DOI 10.1093/rfs/hhy024
Hughes J.P, 2019, CONSUMER LENDING EFF
Jagtiani J, 2018, CLEARING HOUSE BANKI
Jagtiani J., 2019, J ECON BUS
Jagtiani J. L., 2019, FINTECH LENDING MORT
Jagtiani J, 2019, FINANC MANAGE, V48, P1009, DOI 10.1111/fima.12295
Klafft M., 2008, PEER PEER LENDING AU
Koudijs P, 2016, AM ECON REV, V106, P3367, DOI 10.1257/aer.20140259
Kozak S, 2020, J FINANC ECON, V135, P271, DOI 10.1016/j.jfineco.2019.06.008
Kraussl R., 2018, PERFORMANCE MARKETPL, DOI [10.2139/ssrn.3240020., DOI 10.2139/SSRN.3240020]
Meier L, 2008, J R STAT SOC B, V70, P53, DOI 10.1111/j.1467-9868.2007.00627.x
Meinshausen N, 2009, ANN STAT, V37, P246, DOI 10.1214/07-AOS582
Mollick E, 2014, J BUS VENTURING, V29, P1, DOI 10.1016/j.jbusvent.2013.06.005
Mullainathan S, 2017, J ECON PERSPECT, V31, P87, DOI 10.1257/jep.31.2.87
Philippon T., 2016, The fintech opportunity (No. w22476), DOI [10.3386/w22476, DOI 10.3386/W22476]
Rosenberg R., 2013, Microfinance 3.0, P69, DOI DOI 10.1007/978-3-642-41704-7_4
Ryan S.G., 2018, FINTECH ISNT SO DIFF, DOI [10.2139/ssrn.3212902, DOI 10.2139/SSRN.3212902]
Serrano-Cinca C, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0139427
Tang H, 2019, REV FINANC STUD, V32, P1900, DOI 10.1093/rfs/hhy137
Tian SN, 2015, J BANK FINANC, V52, P89, DOI 10.1016/j.jbankfin.2014.12.003
Tibshirani R, 1996, J ROY STAT SOC B, V58, P267, DOI 10.1111/j.2517-6161.1996.tb02080.x
Vallée B, 2019, REV FINANC STUD, V32, P1939, DOI 10.1093/rfs/hhy100
VANDEVEN WPMM, 1981, J ECONOMETRICS, V17, P229, DOI 10.1016/0304-4076(81)90028-2
Varian HR, 2014, J ECON PERSPECT, V28, P3, DOI 10.1257/jep.28.2.3
Wang H., 2018, DOES ONLINE LENDING, DOI [10.2139/ssrn.2958916, DOI 10.2139/SSRN.2958916]
Wei ZY, 2017, MANAGE SCI, V63, P4236, DOI 10.1287/mnsc.2016.2531
NR 54
TC 24
Z9 26
U1 5
U2 69
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-2681
EI 1879-1751
J9 J ECON BEHAV ORGAN
JI J. Econ. Behav. Organ.
PD MAY
PY 2020
VL 173
BP 270
EP 296
DI 10.1016/j.jebo.2020.03.016
PG 27
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LP1FG
UT WOS:000534065100015
OA hybrid
DA 2024-03-27
ER
PT J
AU Matin, A
Khoshtaria, T
Todua, N
Bareja-Wawryszuk, O
Pajewski, T
Todua, N
AF Matin, Arian
Khoshtaria, Tornike
Todua, Nugzar
Bareja-Wawryszuk, Ola
Pajewski, Tomasz
Todua, Nia
TI Determinants of Green Smartphone Application Adoption for Sustainable
Food Consumption Among University Students
SO INTERNATIONAL JOURNAL OF MARKETING COMMUNICATION AND NEW MEDIA
LA English
DT Article
DE Technology adoption; Green smartphone applications; Sustainability; Food
consumption behaviour; UTAUT; SEM; Machine Learning
ID TECHNOLOGY ACCEPTANCE MODEL; MOBILE LIBRARY APPLICATIONS; USER
ACCEPTANCE; ENVIRONMENTAL ATTITUDES; SOCIAL MEDIA;
INFORMATION-TECHNOLOGY; CUSTOMER EXPERIENCE; CONSUMER-BEHAVIOR;
EXTENSION; INTENTION
AB This study aims to investigate the determinants of green smartphone application adoption among users. The study employs content richness model and modified Unified Theory of Acceptance and Use of Technology (UTAUT) as well as extrinsic constructs such as customisation and environmental concerns. A quantitative approach using a survey is utilised by collecting 700 responses. The data is analysed using Structural Equation Modelling (SEM) and three machine learning techniques including Artificial Neural Networks (ANN), Classification Regression Tree (CRT) and Chi-Squared Automatic Interaction Detection (CHAID). The results indicate that UTAUT, customisation and environmental concerns positively impact the adoption of green applications. Further analysis revealed fitness of analytical methods and the importance of variables for the overall sample and the subsamples derived. The study provides theoretical and practical contributions to academics, marketers and software developers in understanding consumer behaviour in the field. The result assist developers and marketers to decipher consumer behaviour towards green applications for sustainable consumption. The research contributes to theory and practice by employing an integrative model to investigate the role of technology in sustainable consumption. Moreover, the findings revealed the fitness of three machine learning methods to analyse the data collected for green consumption and the importance of variables in the model. The data is collected by employing convenience sampling. Hence, the results cannot be generalised accurately. Furthermore, data collection is conducted using a cross-sectional approach. Future researchers can add to the findings using a probability sampling and/or longitudinal data collection to generalise the results and reveal the changes in consumer behaviour.
C1 [Matin, Arian] Int Black Sea Univ, Tbilisi, Georgia.
[Khoshtaria, Tornike] Teaching Univ Geomedi, Tbilisi, Georgia.
[Todua, Nugzar; Todua, Nia] Ivane Javakashvili Tbilisi State Univ, Tbilisi, Georgia.
[Bareja-Wawryszuk, Ola] Siedlce Univ Nat Sci & Humanities, Siedlce, Poland.
[Pajewski, Tomasz] Helena Chodkowska Univ Technol & Econ, Warsaw, Poland.
C3 International Black Sea University
RP Matin, A (autor correspondiente), Int Black Sea Univ, Tbilisi, Georgia.
EM arianmatin@outlook.com; tornike.khoshtaria@geomedi.edu.ge;
nugzar.todua@tsu.ge; ola.bareja-wawryszuk@uph.edu.pl;
tomasz.pajewski@uth.edu.pl; niatodua24@gmail.com
CR Abbad MMM, 2021, EDUC INF TECHNOL, V26, P7205, DOI 10.1007/s10639-021-10573-5
Aburayya A., 2022, INFORM MED UNLOCKED, V28, P100859, DOI DOI 10.1016/J.IMU.2022.100859
Alantari HJ, 2022, INT J RES MARK, V39, P1, DOI 10.1016/j.ijresmar.2021.10.011
Almousa M, 2020, INT J MARKET COMMUN, P78
Alnawas I, 2016, J RETAIL CONSUM SERV, V31, P313, DOI 10.1016/j.jretconser.2016.04.004
Aryadoust V, 2016, EDUC ASSESS, V21, P135, DOI 10.1080/10627197.2016.1166343
Bamberg S, 2003, J ENVIRON PSYCHOL, V23, P21, DOI 10.1016/S0272-4944(02)00078-6
Bekaroo G, 2018, SUSTAIN ENERGY TECHN, V30, P279, DOI 10.1016/j.seta.2018.10.016
Bengtsson F, 2011, J STRATEGIC INF SYST, V20, P96, DOI 10.1016/j.jsis.2010.09.007
Bilgihan A, 2016, INT J QUAL SERV SCI, V8, P102, DOI 10.1108/IJQSS-07-2015-0054
Blewitt ME, 2008, NAT GENET, V40, P663, DOI 10.1038/ng.142
Borges AP, 2021, INT J MARKET COMMUN, V9, P25
Bruggemann P., 2022, Advances in National Brand and Private Label Marketing. Springer Proceedings in Business and Economics, DOI [10.1007/978-3-031-06581-1_5, DOI 10.1007/978-3-031-06581-1_5]
Bryne B.M., 2010, Structural Equation Modelling with AMOS: Basic Concepts, Applications and Programming, V2nd, DOI [10.4324/9781315757421, DOI 10.4324/9781315757421]
Cabero Almenara Julio, 2016, Rev. Lasallista Investig., V13, P18
Chang SC, 2008, BRIT J EDUC TECHNOL, V39, P71, DOI 10.1111/j.1467-8535.2007.00742.x
Chen JL, 2011, COMPUT EDUC, V57, P1501, DOI 10.1016/j.compedu.2011.02.009
Chen YF, 2014, INT J TECHNOL HUM IN, V10, P19, DOI 10.4018/ijthi.2014010102
CHIN WW, 1995, MIS QUART, V19, P237, DOI 10.2307/249690
Chiu CY, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12147002
Choi AS, 2013, ECOL ECON, V89, P24, DOI 10.1016/j.ecolecon.2013.01.027
DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
De Wulf K, 2006, INFORM MANAGE-AMSTER, V43, P434, DOI 10.1016/j.im.2005.10.005
Dehghani M, 2018, BEHAV INFORM TECHNOL, V37, P145, DOI 10.1080/0144929X.2018.1424246
Doligalski T, 2021, ROUT STUD INNOV ORG, P1, DOI 10.4324/9781003207481
DOLL WJ, 1988, MIS QUART, V12, P259, DOI 10.2307/248851
Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
Ebrahimi P, 2022, BIG DATA COGN COMPUT, V6, DOI 10.3390/bdcc6020035
Eiriksdottir E, 2011, HUM FACTORS, V53, P749, DOI 10.1177/0018720811419154
Enginkaya Erkent E, 2021, INT J CONTEMP ECON A, V11, P354, DOI 10.5281/zenodo.5831643
Fraj-Andrés E, 2007, J INT CONSUM MARK, V19, P73, DOI 10.1300/J046v19n03_05
Franzen A, 2010, EUR SOCIOL REV, V26, P219, DOI 10.1093/esr/jcp018
Ghose A, 2019, MANAGE SCI, V65, P1363, DOI 10.1287/mnsc.2017.2991
Givens JE, 2013, SOC SCI RES, V42, P418, DOI 10.1016/j.ssresearch.2012.10.005
Greene G., 2012, J NUTR EDUC BEHAV, V44, pS19, DOI DOI 10.1016/J.JNEB.2012.03.028
Gupta A, 2021, BEHAV INFORM TECHNOL, V40, P1341, DOI 10.1080/0144929X.2020.1748715
Hadler M, 2013, INT SOCIOL, V28, P484, DOI 10.1177/0268580913494661
Haraty RA, 2019, HELIYON, V5, DOI 10.1016/j.heliyon.2019.e01141
Hilpert H., 2014, EUR C INF SYST ECIS
Hsiao CH, 2016, TELEMAT INFORM, V33, P342, DOI 10.1016/j.tele.2015.08.014
Huang WHD, 2013, INTERNET HIGH EDUC, V16, P57, DOI 10.1016/j.iheduc.2012.02.001
Humbani M, 2019, INT J BANK MARK, V37, P646, DOI 10.1108/IJBM-03-2018-0072
Jain K., 2019, International Journal of Technology Marketing, V13, P83, DOI [10.1504/IJTMKT.2018.10020929, DOI 10.1504/IJTMKT.2018.10020929]
Kannan PK, 2017, INT J RES MARK, V34, P22, DOI 10.1016/j.ijresmar.2016.11.006
Kemp A, 2019, BRIT J EDUC TECHNOL, V50, P2394, DOI 10.1111/bjet.12833
Khoshtaria Tornike, 2021, International Journal of Electronic Marketing and Retailing, V12, P394, DOI 10.1504/IJEMR.2021.118305
Kim H.-Y., 2017, International Journal of Fashion Design, Technology and Education, V10, P26, DOI [10.1080/17543266.2016.1177737, DOI 10.1080/17543266.2016.1177737]
Kim J, 2022, ASIA PAC J EDUC, V42, P699, DOI 10.1080/02188791.2020.1776213
Kim J, 2016, INT J CONTEMP HOSP M, V28, P1535, DOI 10.1108/IJCHM-06-2015-0289
Kim Y, 2005, ADV CONSUM RES, V32, P592
Lazzarini GA, 2018, J CLEAN PROD, V198, P564, DOI 10.1016/j.jclepro.2018.07.033
Lea E, 2005, BRIT FOOD J, V107, P855, DOI 10.1108/00070700510629797
Lee CH, 2011, TOURISM MANAGE, V32, P987, DOI 10.1016/j.tourman.2010.08.011
Lee DY, 2013, COMPUT EDUC, V61, P193, DOI 10.1016/j.compedu.2012.10.001
Lee MC, 2010, INT J HUM-COMPUT INT, V26, P601, DOI 10.1080/10447311003781318
Lee YC, 2006, ONLINE INFORM REV, V30, P517, DOI 10.1108/14684520610706406
Lee YH, 2011, EDUC TECHNOL SOC, V14, P124
Barboza MNL, 2019, J INT CONSUM MARK, V31, P66, DOI 10.1080/08961530.2018.1490052
Leong LY, 2019, EXPERT SYST APPL, V133, P296, DOI 10.1016/j.eswa.2019.05.024
Liébana-Cabanillas F, 2018, TECHNOL FORECAST SOC, V129, P117, DOI 10.1016/j.techfore.2017.12.015
Lin CA, 2016, COMPUT HUM BEHAV, V64, P710, DOI 10.1016/j.chb.2016.07.027
Lubowiecki-Vikuk A, 2021, SUSTAIN PROD CONSUMP, V25, P91, DOI 10.1016/j.spc.2020.08.007
Magrath V, 2013, J FASH MARK MANAG, V17, P115, DOI 10.1108/13612021311305173
Matin A., 2023, International Journal of Technology Marketing, V17, P378, DOI [10.1504/IJTMKT.2023.133971, DOI 10.1504/IJTMKT.2023.133971]
Matin A, 2022, INT REV PUB NON MARK, V19, P709, DOI 10.1007/s12208-021-00325-z
Matthes J, 2014, J ADVERTISING, V43, P115, DOI 10.1080/00913367.2013.834804
McLean G, 2016, COMPUT HUM BEHAV, V60, P602, DOI 10.1016/j.chb.2016.02.084
Meyer R, 2010, POPUL ENVIRON, V32, P42, DOI 10.1007/s11111-010-0116-y
Morales-Solana D, 2022, ELECTRON COMMER RES, V22, P1195, DOI 10.1007/s10660-021-09460-5
Mortenson MJ, 2016, INT J INFORM MANAGE, V36, P1248, DOI 10.1016/j.ijinfomgt.2016.07.007
Mousa Ayad Hameed, 2020, IOP Conference Series: Materials Science and Engineering, V671, DOI 10.1088/1757-899X/671/1/012031
Mróz B, 2021, ROUT STUD INNOV ORG, P113, DOI 10.4324/9781003207481-7
Nasidi QY, 2022, INT J MARKET COMMUN, V10, P206, DOI 10.54663/2182-9306.2022.v10.n18.206-228
O'Rourke D, 2016, J IND ECOL, V20, P882, DOI 10.1111/jiec.12310
Osman M, 2021, BASIC APPL SOC PSYCH, V43, P169, DOI 10.1080/01973533.2021.1889553
Osman M, 2019, APPETITE, V138, P60, DOI 10.1016/j.appet.2019.03.015
Parasuraman A, 2015, J SERV RES-US, V18, P59, DOI 10.1177/1094670514539730
Park N, 2009, INT J INFORM MANAGE, V29, P196, DOI 10.1016/j.ijinfomgt.2008.07.001
Park Y, 2012, AUTOMAT CONSTR, V22, P377, DOI 10.1016/j.autcon.2011.09.016
Perera C, 2018, J BUS ETHICS, V152, P843, DOI 10.1007/s10551-016-3376-3
Pindeh N, 2015, PROC ECON FINANC, V37, P372, DOI 10.1016/S2212-5671(16)30139-3
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Rafique H, 2020, COMPUT EDUC, V145, DOI 10.1016/j.compedu.2019.103732
Rafique H, 2018, LIBRI, V68, P99, DOI 10.1515/libri-2017-0041
Remane G., 2016, 20 PAC AS C INF SYST
Remondes J, 2021, INT J MARKET COMMUN, V9, P1
Romano R, 2014, FOOD QUAL PREFER, V33, P27, DOI 10.1016/j.foodqual.2013.11.006
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
Saari UA, 2021, ECOL ECON, V189, DOI 10.1016/j.ecolecon.2021.107155
Sapci O, 2014, J BEHAV EXP ECON, V52, P29, DOI 10.1016/j.socec.2014.06.001
Shaikh AA, 2022, INT J SYST ASSUR ENG, V13, P681, DOI 10.1007/s13198-021-01590-1
Sharma SK, 2019, INT J INFORM MANAGE, V44, P65, DOI 10.1016/j.ijinfomgt.2018.09.013
Shukla A, 2018, VISION-INDIA, V22, P185, DOI 10.1177/0972262918766136
Singh L, 2021, INT J MARKET COMMUN, P82
Song L, 2019, J CLEAN PROD, V218, P685, DOI 10.1016/j.jclepro.2019.01.283
Song SY, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103688
Sousa B, 2021, INT J MARKET COMMUN, P1
Surendran P., 2012, International Journal of Business and Social Research (IJBSR), V2, P175, DOI DOI 10.18533/IJBSR.V2I4.161
Tan BC, 2022, BRIT FOOD J, V124, P1187, DOI 10.1108/BFJ-01-2021-0005
Tarka P., 2017, Archives of Data Science. Series A, V2
Temple NJ, 2020, APPETITE, V144, DOI 10.1016/j.appet.2019.104485
Torlak NG, 2020, INT J PRODUCT PERFOR, V69, P1903, DOI 10.1108/IJPPM-01-2019-0001
Vainio A, 2014, ECOL ECON, V108, P18, DOI 10.1016/j.ecolecon.2014.09.026
Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
vom Brocke J, 2013, COMMUN ASSOC INF SYS, V33, P509
Wallace LG, 2014, INFORM MANAGE-AMSTER, V51, P249, DOI 10.1016/j.im.2013.12.003
Wang RJH, 2015, J RETAILING, V91, P217, DOI 10.1016/j.jretai.2015.01.002
Wang Y, 2017, ENVIRON BEHAV, V49, P1128, DOI 10.1177/0013916516680264
Wang YS, 2012, SERV IND J, V32, P1505, DOI 10.1080/02642069.2010.531271
Wang Y, 2020, TRANSPORTATION, V47, P397, DOI 10.1007/s11116-018-9893-4
Weiss M, 2012, PERS UBIQUIT COMPUT, V16, P655, DOI 10.1007/s00779-011-0432-y
Weller KE, 2014, J NUTR EDUC BEHAV, V46, P324, DOI 10.1016/j.jneb.2014.01.002
Wen D, 2017, COMPUT METH PROG BIO, V140, P131, DOI 10.1016/j.cmpb.2016.12.009
Wen Ling-Yu Melody, 2013, International Journal of Organizational Innovation, V5, P124
Yau HK, 2018, J MARK ANAL, V6, P6, DOI 10.1057/s41270-017-0026-2
Yoon HY, 2016, J ACAD LIBR, V42, P687, DOI 10.1016/j.acalib.2016.08.003
Zhang YC, 2018, MARKET SCI, V37, P425, DOI 10.1287/mksc.2017.1076
NR 120
TC 0
Z9 0
U1 0
U2 0
PU INST SUPERIOR ENTRE DOURO & VOUGA
PI SANTA MARIA DA FEIRA
PA RUA ANT CASTRO CORTE REAL, SANTA MARIA DA FEIRA, AVEIRO 4520-909,
PORTUGAL
SN 2182-9306
J9 INT J MARKET COMMUN
JI Int. J. Market. Commun. New Media
PD DEC
PY 2023
VL 11
IS 21
BP 179
EP 212
DI 10.54663/2182-9306.2023.v11.n21.179-212
PG 34
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA II1E7
UT WOS:001165598200001
OA hybrid
DA 2024-03-27
ER
PT J
AU Pillai, R
Sivathanu, B
AF Pillai, Rajasshrie
Sivathanu, Brijesh
TI Adoption of AI-based chatbots for hospitality and tourism
SO INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
LA English
DT Article
DE AI-based chatbots; Anthropomorphism; Mixed method; Perceived trust;
Perceived intelligence; PLS-SEM; TAM
ID WORD-OF-MOUTH; TECHNOLOGY ACCEPTANCE; PERCEIVED SAFETY; USER ACCEPTANCE;
SOCIAL MEDIA; BIG DATA; SERVICE; ROBOT; TRUST; DETERMINANTS
AB Purpose This study aims to investigate the customers' behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables. Design/methodology/approach To understand the customers' behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis. Findings As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager's commitment to providing travel planning services using AI-based chatbots. Practical implications This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers. Originality/value This study contributes theoretically by extending the TAM to provide better explanatory power with human-robot interaction context-specific constructs - PTR, PNT, ANM and TXN - to examine the customers' chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots' adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.
C1 [Pillai, Rajasshrie] Pune Inst Business Management, Dept Management, Pune, Maharashtra, India.
[Sivathanu, Brijesh] Sri Balaji Univ, Dept Management, Pune, Maharashtra, India.
RP Pillai, R (autor correspondiente), Pune Inst Business Management, Dept Management, Pune, Maharashtra, India.
EM rajasshrie1@gmail.com; brij.jesh2002@gmail.com
RI Pillai, Rajasshrie/GRO-0859-2022; S, BRIJESH/AAQ-4753-2021
OI Sivathanu, Dr. Brijesh/0000-0003-2505-9140
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Ali F, 2019, INT J CONTEMP HOSP M, V31, P2641, DOI 10.1108/IJCHM-10-2018-0832
ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
Ba SL, 2002, MIS QUART, V26, P243, DOI 10.2307/4132332
Bartneck C, 2007, 2007 RO-MAN: 16TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, VOLS 1-3, P367
Bartneck C, 2009, INT J SOC ROBOT, V1, P71, DOI 10.1007/s12369-008-0001-3
Belanche D, 2020, SERV IND J, V40, P203, DOI 10.1080/02642069.2019.1672666
Beldona S, 2018, INT J CONTEMP HOSP M, V30, P2327, DOI 10.1108/IJCHM-03-2017-0148
Bowen J, 2018, WORLDW HOSP TOUR THE, V10, P726, DOI [10.1108/whatt-07-2018-0045, 10.1108/WHATT-07-2018-0045]
Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
Burton-Jones A, 2013, INFORM SYST RES, V24, P632, DOI 10.1287/isre.1120.0444
Candello H, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P3476, DOI 10.1145/3025453.3025919
Cassell J, 2003, USER MODEL USER-ADAP, V13, P89, DOI 10.1023/A:1024026532471
Chi OH, 2020, J HOSP MARKET MANAG, V29, P757, DOI 10.1080/19368623.2020.1721394
Creswell JW, 2007, COUNS PSYCHOL, V35, P236, DOI 10.1177/0011000006287390
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Demers E., 2001, Review of Accounting Studies, V6, P331
Denzin NK, 2012, J MIX METHOD RES, V6, P80, DOI 10.1177/1558689812437186
Deshmukh A, 2018, HAI'18: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON HUMAN-AGENT INTERACTION, P293, DOI 10.1145/3284432.3284445
Dieck MCT, 2017, INT J CONTEMP HOSP M, V29, P530, DOI 10.1108/IJCHM-10-2015-0552
Dubey R, 2018, INT J LOGIST MANAG, V29, P485, DOI 10.1108/IJLM-02-2017-0039
Duffy BR, 2003, ROBOT AUTON SYST, V42, P177, DOI 10.1016/S0921-8890(02)00374-3
El-Manstrly D, 2020, INT J HOSP MANAG, V88, DOI 10.1016/j.ijhm.2020.102535
Epley N, 2007, PSYCHOL REV, V114, P864, DOI 10.1037/0033-295X.114.4.864
Evanschitzky H, 2015, J PROD INNOVAT MANAG, V32, P459, DOI 10.1111/jpim.12241
Fetscherin M, 2008, J ELECTRON COMMER RE, V9, P231
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Galloway DA, 2019, FRONT IMMUNOL, V10, DOI 10.3389/fimmu.2019.00790
Ghanem MM, 2017, TOURISM, V65, P234
Ghosh J., 2018, EXPEDITION 3 0 TRAVE
Gillespie A., 1999, Online Behavior: Stickiness
Glaser B., 1967, SOCIOLOGY, V12, P27, DOI DOI 10.4324/9780203793206
Go H, 2020, TOUR REV, V75, P625, DOI 10.1108/TR-02-2019-0062
Golodov V, 2017, 2017 SECOND RUSSIA AND PACIFIC CONFERENCE ON COMPUTER TECHNOLOGY AND APPLICATIONS (RPC 2017), P72, DOI 10.1109/RPC.2017.8168071
Gray K, 2012, COGNITION, V125, P125, DOI 10.1016/j.cognition.2012.06.007
GUADAGNOLI E, 1988, PSYCHOL BULL, V103, P265, DOI 10.1037/0033-2909.103.2.265
Guide VDR, 2015, J OPER MANAG, V37, pV, DOI 10.1016/S0272-6963(15)00056-X
Gupta Alisha, 2017, Int J Trichology, V9, P50, DOI 10.4103/ijt.ijt_38_17
Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
Guzman I., 2016, CHATBOTS CUSTOMER SE, DOI [10.1007/BF02490459, DOI 10.1007/BF02490459]
Hair JF, 2017, LONG RANGE PLANNING
Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
Ho CC, 2010, COMPUT HUM BEHAV, V26, P1508, DOI 10.1016/j.chb.2010.05.015
Hoque R, 2017, INT J MED INFORM, V101, P75, DOI 10.1016/j.ijmedinf.2017.02.002
Hsu CL, 2014, INFORM MANAGE-AMSTER, V51, P833, DOI 10.1016/j.im.2014.08.005
Hsu CL, 2016, TECHNOL FORECAST SOC, V108, P42, DOI 10.1016/j.techfore.2016.04.012
Hua LY, 2017, INFORM SYST MANAGE, V34, P265, DOI 10.1080/10580530.2017.1330004
Hughes P., 2015, WORLD ROB INT C ROB, P145
IGBARIA M, 1989, J MANAGE, V15, P373, DOI 10.1177/014920638901500302
Io H., 2019, ADV INFORM COMMUNICA, P632
Ivanov S., 2017, Revista Turismo & Desenvolvimento, P1501
Ivanov S, 2019, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2019, P237, DOI 10.1007/978-3-030-05940-8_19
Ivanov S, 2019, J HOSP TOUR TECHNOL, V10, P489, DOI 10.1108/JHTT-08-2018-0087
Ivanov S, 2019, INFORMATION AND COMMUNICATION TECHNOLOGIES IN TOURISM 2019, P249, DOI 10.1007/978-3-030-05940-8_20
Kallmuenzer A, 2019, TOURISM MANAGE, V74, P319, DOI 10.1016/j.tourman.2019.04.002
Kaushik AK, 2015, TOUR MANAG PERSPECT, V16, P278, DOI 10.1016/j.tmp.2015.09.002
Kim HB, 2009, TOURISM MANAGE, V30, P266, DOI 10.1016/j.tourman.2008.07.001
Kim S, 2016, J RES INTERACT MARK, V10, P177, DOI 10.1108/JRIM-06-2015-0046
Klopping I. M., 2004, Information Technology, Learning, and Performance Journal, V22, P35
Komiak S.X., 2003, INFORM TECHNOL MANAG, V5, P181
Kuo CM, 2017, INT J CONTEMP HOSP M, V29, P1305, DOI [10.1108/ijchm-08-2015-0414, 10.1108/IJCHM-08-2015-0414]
Lai YY, 2018, INT J LOGIST MANAG, V29, P676, DOI 10.1108/IJLM-06-2017-0153
Lam SY, 2008, J INTERACT MARK, V22, P19, DOI 10.1002/dir.20119
Law R, 2019, INT J CONTEMP HOSP M, V32, P511, DOI 10.1108/IJCHM-07-2018-0586
Lee L.Y., 2013, J HOSP TOUR MANAG, V40, P1
Lee WH, 2018, INT J MOB COMMUN, V16, P361, DOI 10.1504/IJMC.2018.092666
Leue M, 2014, e-Review of Tourism Res., V5, P1
Li DJ, 2010, INT J SOC ROBOT, V2, P175, DOI 10.1007/s12369-010-0056-9
Lin JCC, 2007, BEHAV INFORM TECHNOL, V26, P507, DOI 10.1080/01449290600740843
Lin TC, 2008, INFORM MANAGE-AMSTER, V45, P410, DOI 10.1016/j.im.2008.06.004
Litvin SW, 2018, INT J CONTEMP HOSP M, V30, P313, DOI 10.1108/IJCHM-08-2016-0461
Lu L, 2019, INT J HOSP MANAG, V80, P36, DOI 10.1016/j.ijhm.2019.01.005
Luo M.M., 2008, INFORM TECHNOLOGY TO, V9, P67
Luque-Martínez T, 2007, SERV IND J, V27, P881, DOI 10.1080/02642060701570586
Ma SF, 2019, ASLIB J INFORM MANAG, V71, P618, DOI 10.1108/AJIM-09-2018-0213
Maciag G.A., 2000, National Underwrite, V104, P19
Mangno F., 2016, INT J CONT HOSPITALI, V29, P1
Mani Z, 2018, J PROD INNOVAT MANAG, V35, P780, DOI 10.1111/jpim.12463
Mariani M, 2020, INT J CONTEMP HOSP M, V32, P1643, DOI 10.1108/IJCHM-04-2019-0378
Martins C, 2014, INT J INFORM MANAGE, V34, P1, DOI 10.1016/j.ijinfomgt.2013.06.002
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
McGinn C, 2019, ACMIEEE INT CONF HUM, P602, DOI [10.1109/HRI.2019.8673161, 10.1109/hri.2019.8673161]
McLean G, 2020, INT J CONTEMP HOSP M, V32, P1795, DOI 10.1108/IJCHM-07-2019-0605
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Meuter ML, 2005, J MARKETING, V69, P61, DOI 10.1509/jmkg.69.2.61.60759
Meuter ML, 2003, J BUS RES, V56, P899, DOI 10.1016/S0148-2963(01)00276-4
Mirnig N, 2017, FRONT ROBOT AI, V4, DOI 10.3389/frobt.2017.00021
Mori M, 1970, ENERGY, V7, P33, DOI DOI 10.1109/MRA.2012.2192811
Morosan C, 2019, INT J CONTEMP HOSP M, V31, P1183, DOI 10.1108/IJCHM-04-2018-0314
Moussawi S, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P115
Moussawi S, 2021, ELECTRON MARK, V31, P343, DOI 10.1007/s12525-020-00411-w
Murphy J, 2019, J TRAVEL TOUR MARK, V36, P784, DOI 10.1080/10548408.2019.1571983
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Oh H, 2013, J BUS RES, V66, P692, DOI 10.1016/j.jbusres.2011.09.005
Pan L., 2017, DESTECH T ENV ENERGY, P271
Parasuraman W, 2000, Journal of Service Research, V2, P307, DOI [10.1177/109467050024001, DOI 10.1177/109467050024001]
Park Y.A., 2007, INFORM COMMUNICATION, P371
Petisca S, 2015, LECT NOTES ARTIF INT, V9388, P522, DOI 10.1007/978-3-319-25554-5_52
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Ponte EB, 2015, TOURISM MANAGE, V47, P286, DOI 10.1016/j.tourman.2014.10.009
Racherla P., 2012, SSRN Electronic Journal, DOI [DOI 10.2139/SSRN.2187056, 10.2139/ssrn.2187056]
Rajan K, 2017, ARTIF INTELL, V247, P1, DOI 10.1016/j.artint.2017.03.003
Ramachandran A., 2018, USER ADOPTION CHATBO, P1
Rietz T., 2019, P 14 INT C WIRTSCH S, V57, P1642
Ringle C.M., 2005, Smart PLS 2.0
Robinson P, 2007, AUST NZ J PUBL HEAL, V31, P388, DOI 10.1111/j.1753-6405.2007.00096.x
Rosenthal-von der Puetten AM, 2017, ACMIEEE INT CONF HUM, P483, DOI 10.1145/2909824.3020251
Roy MC, 2001, INTERNET RES, V11, P388, DOI 10.1108/10662240110410165
Ruijten PAM, 2019, INT J SOC ROBOT, V11, P477, DOI 10.1007/s12369-019-00516-z
Ryan C, 2008, INT J TOUR RES, V10, P329, DOI 10.1002/jtr.660
SAP, 2018, BUILD YOUR OWN CHATB
Saunders MNK, 2016, BRIT J MANAGE, V27, P836, DOI 10.1111/1467-8551.12182
Seilov GA, 2015, INT J CONTEMP HOSP M, V27, P71, DOI 10.1108/IJCHM-12-2013-0547
Sheehan B.T., 2018, Customer service chatbots: Anthropomorphism, adoption and word of mouth (Doctoral dissertation, Queensland University of Technology)
Si S, 2020, ENTREP REGION DEV, V32, P1, DOI 10.1080/08985626.2019.1640485
Sivathanu B, 2019, J SCI TECHNOL POLICY, V10, P143, DOI 10.1108/JSTPM-07-2017-0033
Stadler S, 2014, IEEE ROMAN, P1030, DOI 10.1109/ROMAN.2014.6926388
Stark J, 2018, ACMIEEE INT CONF HUM, P245, DOI 10.1145/3173386.3176998
STEWART I, 1995, SCI AM, V272, P96, DOI 10.1038/scientificamerican0195-96
Sweezey M, 2018, FORBECOM
Tomhawk, 2017, CHATB FUT TRAV
Tsou HT, 2015, INT J INFORM MANAGE, V35, P1, DOI 10.1016/j.ijinfomgt.2014.09.001
Tung VWS, 2017, INT J CONTEMP HOSP M, V29, P2498, DOI 10.1108/IJCHM-09-2016-0520
Tussyadiah I.P., 2018, Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference, P308, DOI DOI 10.1007/978-3-319-72923-7_24
Tussyadiah I.P., 2016, TRAVEL TOURISM RES A, P44
Tussyadiah I, 2020, ANN TOURISM RES, V81, DOI 10.1016/j.annals.2020.102883
Tussyadiah IP, 2009, ANN TOURISM RES, V36, P24, DOI 10.1016/j.annals.2008.10.001
Ukpabi DC, 2018, EUROMED ACAD BUS CON, P1339
van Doorn J, 2017, J SERV RES-US, V20, P43, DOI 10.1177/1094670516679272
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
Wang YY, 2018, INT J INFORM MANAGE, V38, P7, DOI 10.1016/j.ijinfomgt.2017.07.003
WARNER RM, 1986, J PERS SOC PSYCHOL, V50, P792, DOI 10.1037/0022-3514.50.4.792
Weiss A, 2015, 2015 24TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN), P381, DOI 10.1109/ROMAN.2015.7333568
Werner K., 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), P455, DOI 10.1109/CISIS.2012.36
Willemse C, 2019, PHILOS T R SOC B, V374, DOI 10.1098/rstb.2018.0036
xcubelabs, 2017, 5 WAYS CHATB AR CHAN
Yagoda RE, 2012, INT J SOC ROBOT, V4, P235, DOI 10.1007/s12369-012-0144-0
Yeoman I, 2012, FUTURES, V44, P365, DOI 10.1016/j.futures.2011.11.004
You S, 2018, AUTOMAT CONSTR, V96, P161, DOI 10.1016/j.autcon.2018.09.008
Yu CE, 2020, J HOSP MARKET MANAG, V29, P22, DOI 10.1080/19368623.2019.1592733
Zarouali B, 2018, CYBERPSYCH BEH SOC N, V21, P491, DOI 10.1089/cyber.2017.0518
Zhang TR, 2019, TRANSPORT RES C-EMER, V98, P207, DOI 10.1016/j.trc.2018.11.018
Zhou T, 2010, COMPUT HUM BEHAV, V26, P760, DOI 10.1016/j.chb.2010.01.013
NR 144
TC 213
Z9 217
U1 217
U2 754
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 0959-6119
EI 1757-1049
J9 INT J CONTEMP HOSP M
JI Int. J. Contemp. Hosp. Manag.
PD OCT 14
PY 2020
VL 32
IS 10
BP 3199
EP 3226
DI 10.1108/IJCHM-04-2020-0259
EA SEP 2020
PG 28
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA OG3EJ
UT WOS:000570435200001
HC Y
HP N
DA 2024-03-27
ER
PT J
AU Zhuang, YL
Zhou, Y
Hassini, E
Yuan, YF
Hu, XP
AF Zhuang, Yanling
Zhou, Yun
Hassini, Elkafi
Yuan, Yufei
Hu, Xiangpei
TI Rack retrieval and repositioning optimization problem in robotic mobile
fulfillment systems
SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
LA English
DT Article
DE e-commerce warehousing; Robotic mobile fulfillment systems; Rack storage
assignment; Robot-rack assignment
ID WAREHOUSE ORDER PICKING; DESIGN
AB Robotic mobile fulfillment systems provide a new solution for e-commerce retailers to fulfill customers' orders, wherein racks are moved by mobile robots to workstations so pickers can retrieve the purchased products. While such automated parts-to-picker systems can save on labor costs, they raise new operational challenges. In this paper, we investigate the rack storage and robot assignment to racks problem during order processing. We formulate this problem with the goal of minimizing the makespan of the system. Based on a rolling horizon framework and the simulated annealing method, we develop a matheuristic decomposition approach, which involves the solution of a special axial 3-index assignment problem in each stage to solve the problem. We test the performance of the proposed method for both large-scale cases based on a real-world dataset and small-scale instances generated synthetically. Computational results demonstrate the good performance of the proposed approach.
C1 [Zhuang, Yanling; Hu, Xiangpei] Dalian Univ Technol, Sch Econ & Management, 2 Linggong Rd, Dalian 116024, Liaoning, Peoples R China.
[Zhou, Yun; Hassini, Elkafi; Yuan, Yufei] McMaster Univ, DeGroote Sch Business, 1280 Main St West, Hamilton, ON L8S 3L8, Canada.
C3 Dalian University of Technology; McMaster University
RP Hu, XP (autor correspondiente), Dalian Univ Technol, Sch Econ & Management, 2 Linggong Rd, Dalian 116024, Liaoning, Peoples R China.
EM fuding_yanling@mail.dlut.edu.cn; zhouy185@mcmaster.ca;
hassini@mcmaster.ca; yuanyuf@mcmaster.ca; drhxp@dlut.edu.cn
RI Yuan, Yu/KBQ-0606-2024; yuan, yu/HZI-6841-2023; Yuan, Yu/HTM-9814-2023;
Zhuang, Yanling/GWV-0107-2022; Hassini, Elkafi/JGE-1957-2023
OI Zhuang, Yanling/0000-0003-4313-6189; Hassini, Elkafi/0000-0002-5337-5389
FU key projects of the National Natural Science Foundation of China
[71931009, 72010107002]
FX This research was supported by the key projects of the National Natural
Science Foundation of China (71931009, 72010107002) .
CR Boschetti MA, 2009, LECT NOTES COMPUT SC, V5818, P171, DOI 10.1007/978-3-642-04918-7_13
Boysen N, 2019, EUR J OPER RES, V277, P396, DOI 10.1016/j.ejor.2018.08.023
Boysen N, 2017, EUR J OPER RES, V262, P550, DOI 10.1016/j.ejor.2017.03.053
BURKARD RE, 1985, DISCRETE APPL MATH, V12, P21, DOI 10.1016/0166-218X(85)90037-X
Cai JM, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13105644
Ceven E, 2017, TRANSPORT SCI, V51, P52, DOI 10.1287/trsc.2015.0642
Cho HS, 2005, COMPUT IND ENG, V48, P753, DOI 10.1016/j.cie.2004.12.005
de Koster R, 2007, EUR J OPER RES, V182, P481, DOI 10.1016/j.ejor.2006.07.009
Erera AL, 2009, OPER RES, V57, P468, DOI 10.1287/opre.1080.0650
Forma IA, 2015, TRANSPORT RES B-METH, V71, P230, DOI 10.1016/j.trb.2014.10.003
Gharehgozli A, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102087
Giannikas V, 2017, INT J PROD ECON, V189, P63, DOI 10.1016/j.ijpe.2017.04.002
HAN MH, 1987, IIE TRANS, V19, P56, DOI 10.1080/07408178708975370
Jiang M, 2020, TRANSPORT RES E-LOG, V144, DOI 10.1016/j.tre.2020.102138
JORESKOG KG, 1982, J MARKETING RES, V19, P404, DOI 10.2307/3151714
Karp R. M., 1972, INCOM PLEXITY COMPUT, P85
Kim HJ, 2020, IEEE T AUTOM SCI ENG, V17, P1854, DOI 10.1109/TASE.2020.2979897
KIRKPATRICK S, 1983, SCIENCE, V220, P671, DOI 10.1126/science.220.4598.671
Kuzmicz KA, 2019, OMEGA-INT J MANAGE S, V85, P194, DOI 10.1016/j.omega.2018.06.004
Lamballais T, 2017, EUR J OPER RES, V256, P976, DOI 10.1016/j.ejor.2016.06.063
Lee CKM, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100998
Li XW, 2020, TRANSPORT RES E-LOG, V144, DOI 10.1016/j.tre.2020.102158
Marufuzzaman M, 2017, TRANSPORT SCI, V51, P494, DOI 10.1287/trsc.2015.0632
Merschformann M, 2019, OPER RES PERSPECT, V6, DOI 10.1016/j.orp.2019.100128
Mirzaei M, 2021, TRANSPORT RES E-LOG, V146, DOI 10.1016/j.tre.2020.102207
Mueller R. O., 1999, Basic principles of structural equation modeling: An introduction to LISREL and EQS
Roy D, 2019, TRANSPORT RES E-LOG, V122, P119, DOI 10.1016/j.tre.2018.11.005
Song DP, 2008, EUR J OPER RES, V185, P760, DOI 10.1016/j.ejor.2006.12.034
Spieksma FCR, 2000, COMB OPT (SER), V7, P1
Tessensohn TL, 2020, IISE TRANS, V52, P1, DOI 10.1080/24725854.2018.1560517
Van Nieuwenhuyse I, 2009, INT J PROD ECON, V121, P654, DOI 10.1016/j.ijpe.2009.01.013
Vis IFA, 2006, EUR J OPER RES, V170, P677, DOI 10.1016/j.ejor.2004.09.020
Weidinger F, 2018, TRANSPORT SCI, V52, P1479, DOI 10.1287/trsc.2018.0826
Weidinger F, 2018, TRANSPORT SCI, V52, P1412, DOI 10.1287/trsc.2017.0779
Williams HP, 2013, MODEL BUILDING MATH
Wulfraat M., 2012, SUPPLY CHAIN CONSULT
Wurman PR, 2008, AI MAG, V29, P9
Xie L, 2021, EUR J OPER RES, V288, P80, DOI 10.1016/j.ejor.2020.05.032
Yuan R, 2019, PROD OPER MANAG, V28, P354, DOI 10.1111/poms.12925
Yuan Z, 2017, IEEE T ENG MANAGE, V64, P83, DOI 10.1109/TEM.2016.2634540
Zhuang YL, 2022, EUR J OPER RES, V300, P527, DOI 10.1016/j.ejor.2021.08.003
Zou BP, 2017, INT J PROD RES, V55, P6175, DOI 10.1080/00207543.2017.1331050
NR 42
TC 7
Z9 7
U1 14
U2 48
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1366-5545
EI 1878-5794
J9 TRANSPORT RES E-LOG
JI Transp. Res. Pt. e-Logist. Transp. Rev.
PD NOV
PY 2022
VL 167
AR 102920
DI 10.1016/j.tre.2022.102920
EA OCT 2022
PG 28
WC Economics; Engineering, Civil; Operations Research & Management Science;
Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
Science; Transportation
GA 6A1TM
UT WOS:000880443600001
DA 2024-03-27
ER
PT J
AU Gharehgozli, A
Zaerpour, N
AF Gharehgozli, Amir
Zaerpour, Nima
TI Robot scheduling for pod retrieval in a robotic mobile fulfillment
system
SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
LA English
DT Article
DE Distribution; e-commerce fulfillment; Robotic mobile fulfillment
systems; Travel time; Asymmetric traveling salesman problem
ID LARGE NEIGHBORHOOD SEARCH; SEQUENTIAL ORDERING PROBLEM; AUTOMATED
STORAGE; LOCAL SEARCH; GENETIC ALGORITHM; BERTH ALLOCATION; WAREHOUSE;
PICKING; PERFORMANCE; STRATEGIES
AB In order to increase the order picking efficiency, e-commerce retailers have started to implement order picking systems where mobile robots carry inventory pods to pick stations. In pick stations, pickers pick the products from inventory pods and put them in customer bins. In such a robotic mobile fulfillment center, pickers are constantly busy with picking customer orders and avoid non-value adding activities such as walking to reach storage locations. To fulfill customer orders, each robot needs to complete a sequence of missions and each mission includes a set of retrieval requests. We study the operational problem of scheduling a mobile robot fulfilling a set of customer orders from a pick station. The mobile robot needs to bring each pod from a retrieval location to the pick station and return the pod to a storage location. The objective is to minimize the total travel time of the robot which can be considered as a proxy for other objectives such a shorter lead time, higher throughput and less capital investment. We formulate the basic problem as an asymmetric traveling salesman problem. We then extend the model by adding general precedence constraints to give different priorities to customer orders based on their urgency (e.g. same-day, one-day, two-day, and standard orders). We also study a variation of the problem where the pod can be stored in multiple alternative locations. In this case, we model the problem as a generalized asymmetric traveling salesman problem. An adaptive large neighborhood search heuristic is developed to efficiently solve real size instances. The method outperforms the heuristics commonly used in practice.
C1 [Gharehgozli, Amir] Calif State Univ Northridge, Coll Business & Econ, Northridge, CA 91330 USA.
[Zaerpour, Nima] Calif State Univ Northridge, Coll Business Adm, Northridge, CA 91330 USA.
C3 California State University System; California State University
Northridge; California State University System; California State
University Northridge
RP Gharehgozli, A (autor correspondiente), 18111 Nordhoff St, Northridge, CA 91330 USA.
EM amir.gharehgozli@csun.edu; nzaerpour@csusm.edu
RI Gharehgozli, Amir/P-8928-2015; Zaerpour, Nima/AAS-2254-2021
CR Anghinolfi D, 2011, COMPUT OPER RES, V38, P1076, DOI 10.1016/j.cor.2010.10.014
Ascheuer N, 1993, SIAM J OPTIMIZ, V3, P25, DOI 10.1137/0803002
Azadeh K, 2019, TRANSPORT SCI, V53, P917, DOI 10.1287/trsc.2018.0873
BALAS E, 1995, MATH PROGRAM, V68, P241, DOI 10.1007/BF01585767
Beckschäfer M, 2017, LECT NOTES COMPUT SC, V10572, P468, DOI 10.1007/978-3-319-68496-3_31
Bent R, 2004, TRANSPORT SCI, V38, P515, DOI 10.1287/trsc.1030.0049
Bontoux B, 2010, COMPUT OPER RES, V37, P1844, DOI 10.1016/j.cor.2009.05.004
Boysen N, 2019, EUR J OPER RES, V277, P396, DOI 10.1016/j.ejor.2018.08.023
Boysen N, 2017, EUR J OPER RES, V262, P550, DOI 10.1016/j.ejor.2017.03.053
Boysen N, 2016, EUR J OPER RES, V254, P691, DOI 10.1016/j.ejor.2016.04.008
Boysen N, 2016, EUR J OPER RES, V249, P618, DOI 10.1016/j.ejor.2015.09.033
Bozer YA, 2018, INT J PROD RES, V56, P3838, DOI 10.1080/00207543.2018.1424364
Bruglieri M, 2019, DISCRETE APPL MATH, V253, P185, DOI 10.1016/j.dam.2018.03.067
Bruns F, 2016, EUR J OPER RES, V249, P1074, DOI 10.1016/j.ejor.2015.09.036
CARPANETO G, 1980, MANAGE SCI, V26, P736, DOI 10.1287/mnsc.26.7.736
Cohen L., 2015, 8 ANN S COMBINATORIA
De Koster R, 1998, IIE TRANS, V30, P469, DOI 10.1080/07408179808966487
de Koster R, 2007, EUR J OPER RES, V182, P481, DOI 10.1016/j.ejor.2006.07.009
Del Ray J., 2019, ROBOTS ARE TRANSFORM
Enright John., 2011, AUTOMATED ACTION PLA, P33
Escudero L. F., 1994, Annals of Operations Research, V50, P219, DOI 10.1007/BF02085641
Escudero L.F., 1997, TOP, V5, P159
ESCUDERO LF, 1988, EUR J OPER RES, V37, P236, DOI 10.1016/0377-2217(88)90333-5
Eshtehadi R, 2020, COMPUT OPER RES, V115, DOI 10.1016/j.cor.2019.104859
Fareeha A., 2019, US ECOMMERCE SALES G
Fischetti M, 1997, OPER RES, V45, P378, DOI 10.1287/opre.45.3.378
FISCHETTI M, 1995, NETWORKS, V26, P113, DOI 10.1002/net.3230260206
Gagliardi JP, 2012, INT J PROD RES, V50, P7110, DOI 10.1080/00207543.2011.633234
Gambardella LM, 2000, INFORMS J COMPUT, V12, P237, DOI 10.1287/ijoc.12.3.237.12636
Garey M. R., 1976, Mathematics of Operations Research, V1, P117, DOI 10.1287/moor.1.2.117
Gharehgozli A.H, 2017, TRANSP SCI
Gharehgozli A, 2021, EUR J OPER RES, V289, P495, DOI 10.1016/j.ejor.2020.07.038
Gharehgozli A, 2019, TRANSPORT RES E-LOG, V125, P261, DOI 10.1016/j.tre.2019.03.008
Gharehgozli A, 2018, EUR J OPER RES, V267, P977, DOI 10.1016/j.ejor.2017.12.040
Gharehgozli AH, 2016, MARIT ECON LOGIST, V18, P103, DOI 10.1057/mel.2015.3
Gharehgozli AH, 2015, TRANSPORT SCI, V49, P686, DOI 10.1287/trsc.2014.0533
Gharehgozli AH, 2014, INT J PROD RES, V52, P2592, DOI 10.1080/00207543.2013.861618
Gharehgozli AH, 2014, EUR J OPER RES, V235, P431, DOI 10.1016/j.ejor.2013.09.038
Gharout-Sait A, 2019, MICROB DRUG RESIST, V25, P1057, DOI [10.1089/mdr.2018.0471, 10.1057/s41278-019-00131-9]
Giannikas V, 2017, INT J PROD ECON, V189, P63, DOI 10.1016/j.ijpe.2017.04.002
Gonzalez A., 2017, AMAZONS ROBOT ARMY G
Gu JX, 2007, EUR J OPER RES, V177, P1, DOI 10.1016/j.ejor.2006.02.025
Gu JX, 2010, EUR J OPER RES, V203, P539, DOI 10.1016/j.ejor.2009.07.031
Gue KR, 2015, COMPUT OPER RES, V63, P46, DOI 10.1016/j.cor.2015.04.019
Guizzo E., 2008, IEEE spectrum, V45, P26
HALL RW, 1993, IIE TRANS, V25, P76, DOI 10.1080/07408179308964306
Hang Ma, 2017, AI Matters, V3, P15, DOI 10.1145/3137574.3137579
Hanson R, 2018, IFAC PAPERSONLINE, V51, P1493, DOI 10.1016/j.ifacol.2018.08.290
Helsgaun K, 2000, EUR J OPER RES, V126, P106, DOI 10.1016/S0377-2217(99)00284-2
Hornstra RP, 2020, COMPUT OPER RES, V115, DOI 10.1016/j.cor.2019.104858
Iris C, 2017, TRANSPORT RES E-LOG, V105, P123, DOI 10.1016/j.tre.2017.06.013
Karapetyan D, 2012, EUR J OPER RES, V219, P234, DOI 10.1016/j.ejor.2012.01.011
KIRKPATRICK S, 1983, SCIENCE, V220, P671, DOI 10.1126/science.220.4598.671
Kuhn HW., 1955, NAV RES LOG, V2, P83, DOI [DOI 10.1002/NAV.3800020109, DOI 10.1002/NAV.20053, 10.1002/nav.3800020109]
Lamballais T, 2017, EUR J OPER RES, V256, P976, DOI 10.1016/j.ejor.2016.06.063
LAPORTE G, 1987, DISCRETE APPL MATH, V18, P185, DOI 10.1016/0166-218X(87)90020-5
Lee CKM, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100998
Lee HF, 1997, COMPUT IND ENG, V32, P351, DOI 10.1016/S0360-8352(96)00298-7
Ma H, 2017, IEEE INTELL SYST, V32, P6, DOI 10.1109/MIS.2017.4531217
Mahulea C., 2020, Path planning of cooperative mobile robots using discrete event models
Mauri GR, 2016, COMPUT OPER RES, V70, P140, DOI 10.1016/j.cor.2016.01.002
Merschformann M, 2019, OPER RES PERSPECT, V6, DOI 10.1016/j.orp.2019.100128
Merschformann M., 2018, Logistics Res., V11, P1
MILLER DL, 1991, SCIENCE, V251, P754, DOI 10.1126/science.251.4995.754
Montemanni R, 2008, COMPUT OPER RES, V35, P3931, DOI 10.1016/j.cor.2007.05.003
Mountz M. C., 2005, Material handling system and method using mobile autonomous inventory trays and peer-to-peer communica-tions, US/ 6950722, Patent No. 6950722
NOON CE, 1991, OPER RES, V39, P623, DOI 10.1287/opre.39.4.623
RATLIFF HD, 1983, OPER RES, V31, P507, DOI 10.1287/opre.31.3.507
Roodbergen KJ, 2009, EUR J OPER RES, V194, P343, DOI 10.1016/j.ejor.2008.01.038
Roodbergen KJ, 2001, EUR J OPER RES, V133, P32, DOI 10.1016/S0377-2217(00)00177-6
Roodbergen KJ, 2001, INT J PROD RES, V39, P1865, DOI 10.1080/00207540110028128
Ropke S, 2006, EUR J OPER RES, V171, P750, DOI 10.1016/j.ejor.2004.09.004
Ropke S, 2006, TRANSPORT SCI, V40, P455, DOI 10.1287/trsc.1050.0135
Roy D, 2019, TRANSPORT RES E-LOG, V122, P119, DOI 10.1016/j.tre.2018.11.005
Sarasola B, 2020, NETWORKS, V75, P64, DOI 10.1002/net.21905
Seo DI, 2003, LECT NOTES COMPUT SC, V2723, P669
Shao SJ, 2020, TRANSPORT RES B-METH, V133, P1, DOI 10.1016/j.trb.2019.12.004
Shaw P., 1997, NEW LOCAL SEARCH ALG
Snyder LV, 2006, EUR J OPER RES, V174, P38, DOI 10.1016/j.ejor.2004.09.057
Spieckermann S, 2004, INT J PROD RES, V42, P1865, DOI 10.1080/00207540310001646821
Sun P, 2020, TRANSPORT RES E-LOG, V138, DOI 10.1016/j.tre.2020.101942
Tessensohn TL, 2020, IISE TRANS, V52, P1, DOI 10.1080/24725854.2018.1560517
Tompkins J.A., 2003, FACILITIES PLANNING, VThird
van den Berg JP, 1999, IIE TRANS, V31, P407, DOI 10.1080/07408179908969844
van Gils T, 2018, INT J PROD ECON, V197, P243, DOI 10.1016/j.ijpe.2017.11.021
van Gils T, 2018, EUR J OPER RES, V267, P1, DOI 10.1016/j.ejor.2017.09.002
Vaughan TS, 1999, INT J PROD RES, V37, P881, DOI 10.1080/002075499191580
Vis IFA, 2009, OPER RES, V57, P456, DOI 10.1287/opre.1080.0621
Wen M, 2016, COMPUT OPER RES, V76, P73, DOI 10.1016/j.cor.2016.06.013
Wurman PR, 2008, AI MAG, V29, P9
Yu J, 2015, INT J METALCAST, V9, P33
Yuan R, 2018, INT J PROD RES, V56, P333, DOI 10.1080/00207543.2017.1398428
Zaerpour N, 2019, TRANSPORT SCI, V53, P1235, DOI 10.1287/trsc.2018.0884
Zaerpour N, 2019, INFOR, V57, P100, DOI 10.1080/03155986.2018.1532765
Zaerpour N, 2017, IISE TRANS, V49, P653, DOI 10.1080/24725854.2016.1273564
Zaerpour N, 2017, IISE TRANS, V49, P461, DOI 10.1080/24725854.2016.1273563
Zaerpour N, 2017, TRANSPORT SCI, V51, P34, DOI 10.1287/trsc.2015.0586
Zaerpour N, 2015, PROD OPER MANAG, V24, P1266, DOI 10.1111/poms.12321
Zaerpour N, 2013, INT J PROD RES, V51, P6891, DOI 10.1080/00207543.2013.774502
Zhang RY, 2020, COMPUT IND ENG, V139, DOI 10.1016/j.cie.2019.106143
Zou B., 2018, TRANSP SCI
Zou BP, 2018, EUR J OPER RES, V267, P733, DOI 10.1016/j.ejor.2017.12.008
Zou BP, 2017, INT J PROD RES, V55, P6175, DOI 10.1080/00207543.2017.1331050
NR 103
TC 35
Z9 37
U1 8
U2 93
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1366-5545
EI 1878-5794
J9 TRANSPORT RES E-LOG
JI Transp. Res. Pt. e-Logist. Transp. Rev.
PD OCT
PY 2020
VL 142
AR 102087
DI 10.1016/j.tre.2020.102087
PG 19
WC Economics; Engineering, Civil; Operations Research & Management Science;
Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
Science; Transportation
GA OC3EE
UT WOS:000579040400008
OA hybrid
DA 2024-03-27
ER
PT J
AU Klein, K
Martinez, LF
AF Klein, Katharina
Martinez, Luis F.
TI The impact of anthropomorphism on customer satisfaction in chatbot
commerce: an experimental study in the food sector
SO ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE Consumer behavior; Conversational commerce; e-commerce; Chatbot;
Anthropomorphism; Customer experience; Customer satisfaction
ID SOCIAL PRESENCE; EXPERIENCE; INTERACTIVITY; PERSPECTIVE; ACCEPTANCE;
LOYALTY; AGENCY; TRUST; CUES
AB Food retailers are lagging behind other industries in implementing innovative mobile solutions offering their services and purchasing processes on their online platforms. Chatbots can be leveraged as an application to provide customer-centric services while retailers benefit from collecting consumer data. Previous literature on chatbot technology provides evidence that human characteristics enhance the customer experience. This is the first experimental study to investigate consumer attitudes and satisfaction with anthropomorphic chatbots in food e-commerce. A sample of 401 participants was tested to verify the proposed hypotheses. The test group interacted with a standard chatbot without human-like characteristics, while the control group communicated with the anthropomorphically designed agent. The results confirm the vast potential of anthropomorphic cues in chatbot applications and show that they are positively associated with customer satisfaction and mediated by the variables enjoyment, attitude, and trust. The findings suggest that to remain competitive, food retailers should immediately adopt innovative technologies for their omnichannel strategy and incorporate anthropomorphic design cues.
C1 [Klein, Katharina; Martinez, Luis F.] Univ Nova Lisboa, Nova Sch Business & Econ, Campus Carcavelos,Rua Holanda 1, P-2775405 Carcavelos, Portugal.
C3 Universidade Nova de Lisboa
RP Klein, K (autor correspondiente), Univ Nova Lisboa, Nova Sch Business & Econ, Campus Carcavelos,Rua Holanda 1, P-2775405 Carcavelos, Portugal.
EM contact@klein-katharina.de; luis.martinez@novasbe.pt
RI Martinez, Luis F./V-6776-2019
OI Klein, Katharina/0000-0002-8405-6807; Martinez, Luis/0000-0002-9554-5374
FU Fundacao para a Ciencia e a Tecnologia [UID ECO/00124/2019,
UIDB/00124/2020, PINFRA/22209/2016]; POR Lisboa; POR Norte (Social
Sciences DataLab) [PINFRA/22209/2016]; Fundação para a Ciência e a
Tecnologia [UIDB/00124/2020, UID/ECO/00124/2019] Funding Source: FCT
FX This work was funded by Fundacao para a Ciencia e a Tecnologia (UID
ECO/00124/2019, UIDB/00124/2020 and Social Sciences DataLab,
PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab,
PINFRA/22209/2016).
CR Aggarwal P, 2007, J CONSUM RES, V34, P468, DOI 10.1086/518544
Agichtein E, 2020, PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20), P897, DOI 10.1145/3336191.3371882
Alger K., 2018, Forbes
[Anonymous], 2018, ASPECT
Anshu K, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102798
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
Begley S., 2020, Digital disruption at the grocery store
Bilgihan A, 2016, INT J QUAL SERV SCI, V8, P102, DOI 10.1108/IJQSS-07-2015-0054
Bleier A, 2019, J MARKETING, V83, P98, DOI 10.1177/0022242918809930
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Brown SA, 2016, J ASSOC INF SYST, V17, P614, DOI 10.17705/1jais.00436
Burgoon JK, 2000, COMPUT HUM BEHAV, V16, P553, DOI 10.1016/S0747-5632(00)00029-7
Chaudhuri A., 2006, EMOTION REASON CONSU, DOI [10.4324/9780080461762, DOI 10.4324/9780080461762]
Chen JS, 2021, INT J RETAIL DISTRIB, V49, P1512, DOI 10.1108/IJRDM-08-2020-0312
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
Cyr D, 2007, INTERACT COMPUT, V19, P43, DOI 10.1016/j.intcom.2006.07.010
Darke PR, 2016, J RETAILING, V92, P287, DOI 10.1016/j.jretai.2016.02.001
de Melo CM, 2014, J PERS SOC PSYCHOL, V106, P73, DOI 10.1037/a0034251
de Visser EJ, 2016, J EXP PSYCHOL-APPL, V22, P331, DOI 10.1037/xap0000092
Eeuwen M. van, 2017, THESIS U TWENTE
Elder RS, 2017, J CONSUM RES, V44, P877, DOI 10.1093/jcr/ucx070
Epley N, 2008, SOC COGNITION, V26, P143, DOI 10.1521/soco.2008.26.2.143
Epley N, 2008, PSYCHOL SCI, V19, P114, DOI 10.1111/j.1467-9280.2008.02056.x
Epley N, 2007, PSYCHOL REV, V114, P864, DOI 10.1037/0033-295X.114.4.864
Etlinger S., 2017, The Conversational Business: How Chatbots Will Reshape Digital Experiences
Feine J, 2019, INT J HUM-COMPUT ST, V132, P138, DOI 10.1016/j.ijhcs.2019.07.009
Field A., 2009, DISCOVERING STAT USI, V3rd
Francis T., 2018, TRUE GENGENERATION I, V12
Gefen D., 2003, e-Service Journal, V2, P7, DOI DOI 10.2979/ESJ.2003.2.2.7
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Goetz J., 2003, P 12 IEEE INT WORKSH, P5560
Hair J.F., 2009, Multivariate data analysis
Han MC, 2021, J INTERNET COMMER, V20, P46, DOI 10.1080/15332861.2020.1863022
Hassanein K, 2005, INT J ELECTRON COMM, V10, P31, DOI 10.2753/JEC1086-4415100202
Hayes AF, 2018, COMMUN MONOGR, V85, P4, DOI 10.1080/03637751.2017.1352100
Hussain Shafquat, 2019, Web, Artificial Intelligence and Network Applications. Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019). Advances in Intelligent Systems and Computing (AISC 927), P946, DOI 10.1007/978-3-030-15035-8_93
Irfan W, 2019, INT J RETAIL DISTRIB, V47, P350, DOI 10.1108/IJRDM-03-2018-0045
Isbister K, 2000, INT J HUM-COMPUT ST, V53, P251, DOI 10.1006/ijhc.2000.0368
Joshi N., 2020, Forbes
Kim H, 2013, INFORM MANAGE-AMSTER, V50, P169, DOI 10.1016/j.im.2013.02.003
Knijnenburg BP, 2016, ACM T INTERACT INTEL, V6, DOI 10.1145/2963106
Kraus D., 2019, Internationale Tagung Wirtschaftsinformatik, Siegen, V14, P23
Lazar J., 2017, Research Methods in Human-Computer Interaction
Lee J., 2003, J ASSOC INF SYST, V4, P2, DOI [10.17705/1jais.00029, DOI 10.17705/1JAIS.00029]
LEHMANN EL, 1993, J AM STAT ASSOC, V88, P1242
Lim W.M., 2012, MODERN APPL SCI, V6, P48, DOI DOI 10.5539/MAS.V6N5P48
Lindecrantz E., 2020, PERSONALIZING CUSTOM
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
MacKinnon DP, 2007, ANNU REV PSYCHOL, V58, P593, DOI 10.1146/annurev.psych.58.110405.085542
Mathwick C, 2001, J RETAILING, V77, P39, DOI 10.1016/S0022-4359(00)00045-2
Mikalef Patrick, 2013, J. theor. appl. electron. commer. res., V8, P17, DOI 10.4067/S0718-18762013000100003
Moe WW, 2003, J CONSUM PSYCHOL, V13, P29, DOI 10.1207/153276603768344762
Molla A., 2001, J ELECTRON COMMER RE, V2, P131
Nass C, 1996, INT J HUM-COMPUT ST, V45, P669, DOI 10.1006/ijhc.1996.0073
Nowak K. L., 2005, J. Computer-Mediated Commun, V11, P153, DOI [DOI 10.1111/J.1083-6101.2006.TB00308.X, 10.1111/j.1083-6101.2006.tb00308.x, 10.1111/j.10836101.2006.tb00308.x, DOI 10.1111/J.10836101.2006.TB00308.X, 10.1111/j.1083-6101.2006.tb00308]
Oliver R.L., 1997, SATISFACTION
Opreana A., 2014, Expert Journal of Marketing, V2, P1
Osman Z, 2013, EUR J TOUR RES, V6, P192
Qiu LY, 2009, J MANAGE INFORM SYST, V25, P145, DOI 10.2753/MIS0742-1222250405
Repko M., 2020, CNBC
Rhim J, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.107034
Roy R, 2021, J BUS RES, V126, P23, DOI 10.1016/j.jbusres.2020.12.051
Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
Saunders M., 2012, RES METHODS BUSINESS, DOI DOI 10.1007/S13398-014-0173-7.2
Schlosser AE, 2003, J CONSUM RES, V30, P184, DOI 10.1086/376807
Schmitt B.H., 1999, J MARKET MANAG, V15, P53, DOI [10.1362/026725799784870496, DOI 10.1362/026725799784870496]
Schunemann Rogerio, 2014, ISRN Microbiol, V2014, P135675, DOI 10.1155/2014/135675
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Spears N., 2004, Journal of Current Issues Research in Advertising, V26, P53, DOI [DOI 10.1080/10641734.2004.10505164, 10.1080/10641734.2004.10505164]
Specia, 2019, NEW YORK TIMES
Stefanoff S., 2021, UNLOCKING POTENTIAL
Suki N., 2007, International Journal of Business and Society, V8, P81
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Verhoef PC, 2009, J RETAILING, V85, P31, DOI 10.1016/j.jretai.2008.11.001
Wang WQ, 2014, J ASSOC INF SYST, V15, P454
Waytz A, 2010, TRENDS COGN SCI, V14, P383, DOI 10.1016/j.tics.2010.05.006
Weathers D, 2007, J RETAILING, V83, P393, DOI 10.1016/j.jretai.2007.03.009
Yiru Wang, 2020, IEEE Engineering Management Review, V48, P202, DOI 10.1109/EMR.2020.3011054
Yoon D, 2008, PSYCHOL MARKET, V25, P602, DOI 10.1002/mar.20227
Zarantonello L., 2016, The Handbook of Brand Management Scales, V1st
NR 82
TC 18
Z9 18
U1 44
U2 158
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1389-5753
EI 1572-9362
J9 ELECTRON COMMER RES
JI Electron. Commer. Res.
PD DEC
PY 2023
VL 23
IS 4
BP 2789
EP 2825
DI 10.1007/s10660-022-09562-8
EA MAY 2022
PG 37
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA AE1U6
UT WOS:000796310000001
OA Bronze
DA 2024-03-27
ER
PT J
AU Cheng, XS
Bao, Y
Zarifis, A
Gong, WK
Mou, J
AF Cheng, Xusen
Bao, Ying
Zarifis, Alex
Gong, Wankun
Mou, Jian
TI Exploring consumers' response to text-based chatbots in e-commerce: the
moderating role of task complexity and chatbot disclosure
SO INTERNET RESEARCH
LA English
DT Article
DE Text-based chatbot; Trust; Consumers' response; Task complexity;
Identity disclosure
ID SHARING ECONOMY; SERVICE ROBOTS; TRUST MATTER; MODEL; ONLINE; QUALITY;
DESIGN; RESPONSIVENESS; METAANALYSIS; ANTECEDENTS
AB Purpose Artificial intelligence (AI)-based chatbots have brought unprecedented business potential. This study aims to explore consumers' trust and response to a text-based chatbot in e-commerce, involving the moderating effects of task complexity and chatbot identity disclosure. Design/methodology/approach A survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses. Findings First, the consumers' perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers' trust. Third, disclosure of the text-based chatbot negatively moderates the relationship between empathy and consumers' trust, while it positively moderates the relationship between friendliness and consumers' trust. Fourth, consumers' trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions. Research limitations/implications Adopting the stimulus-organism-response (SOR) framework, this study provides important insights on consumers' perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers' positive responses to text-based chatbots. Originality/value Extant studies have investigated the effects of automated bots' attributes on consumers' perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers' responses to a chatbot.
C1 [Cheng, Xusen] Renmin Univ China, Beijing, Peoples R China.
[Bao, Ying] Univ Int Business & Econ, Beijing, Peoples R China.
[Zarifis, Alex] Univ Loughborough, Loughborough, Leics, England.
[Gong, Wankun] Beijing Univ Chem Technol, Beijing, Peoples R China.
[Mou, Jian] Pusan Natl Univ, Management Informat Syst, Kumjeong Ku, South Korea.
C3 Renmin University of China; University of International Business &
Economics; Loughborough University; Beijing University of Chemical
Technology; Pusan National University
RP Cheng, XS (autor correspondiente), Renmin Univ China, Beijing, Peoples R China.
EM xusen.cheng@ruc.edu.cn; bycoco1@outlook.com; zarifis.a@unic.ac.cy;
wk_gong@163.com; jian.mou@pusan.ac.kr
OI Mou, Jian/0000-0001-6497-9105; Gong, Wankun/0000-0002-0271-5849; Bao,
Ying/0000-0001-5411-8723; Zarifis, Alex/0000-0003-3103-4601; Cheng,
Xusen/0000-0002-5218-4628
FU Fundamental Research Funds for the Central Universities; Research Funds
of Renmin University of China [21XNO002]
FX This work is supported by the Fundamental Research Funds for the Central
Universities, and the Research Funds of Renmin University of China
(Grant No.21XNO002).
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Birnbaum GE, 2016, COMPUT HUM BEHAV, V63, P416, DOI 10.1016/j.chb.2016.05.064
BITNER MJ, 1992, J MARKETING, V56, P57, DOI 10.2307/1252042
Bjorn P, 2009, INFORM SYST J, V19, P227, DOI 10.1111/j.1365-2575.2007.00281.x
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Bolton RN, 1998, MARKET SCI, V17, P45, DOI 10.1287/mksc.17.1.45
Breuer C, 2016, J APPL PSYCHOL, V101, P1151, DOI 10.1037/apl0000113
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Chang HJ, 2011, INT REV RETAIL DISTR, V21, P233, DOI 10.1080/09593969.2011.578798
Chen CC, 2012, RES DEV DISABIL, V33, P1832, DOI 10.1016/j.ridd.2012.05.006
Cheng XS, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2020.102281
Cheng XS, 2018, INT J INFORM MANAGE, V41, P57, DOI 10.1016/j.ijinfomgt.2018.03.005
Ciechanowski L, 2019, FUTURE GENER COMP SY, V92, P539, DOI 10.1016/j.future.2018.01.055
Cram WA, 2019, MIS QUART, V43, P525, DOI 10.25300/MISQ/2019/15117
Cyr D, 2009, MIS QUART, V33, P539
Czaplewski AJ, 2002, MARK MANAG, V11, P14
Daugherty PR., 2019, MIT Sloan Manag Rev, V60, P1
Davenport TH, 2019, MIT SLOAN MANAGE REV, V60, P18
Dayan M, 2010, IND MARKET MANAG, V39, P691, DOI 10.1016/j.indmarman.2010.01.001
de Kervenoael R, 2020, TOURISM MANAGE, V78, DOI 10.1016/j.tourman.2019.104042
Desideri L, 2019, COMPUT HUM BEHAV, V90, P331, DOI 10.1016/j.chb.2018.08.013
DONOVAN RJ, 1982, J RETAILING, V58, P34
Duan YQ, 2019, INT J INFORM MANAGE, V48, P63, DOI 10.1016/j.ijinfomgt.2019.01.021
Edwards JC, 2003, J ORGAN BEHAV, V24, P1, DOI 10.1002/job.177
Eroglu SA, 2003, PSYCHOL MARKET, V20, P139, DOI 10.1002/mar.10064
Eroglu SA, 2001, J BUS RES, V54, P177, DOI 10.1016/S0148-2963(99)00087-9
Ert E, 2016, TOURISM MANAGE, V55, P62, DOI 10.1016/j.tourman.2016.01.013
Everard A, 2005, J MANAGE INFORM SYST, V22, P55, DOI 10.2753/MIS0742-1222220303
Fiore AM, 2007, INT J RETAIL DISTRIB, V35, P421, DOI 10.1108/09590550710750313
Fridin M, 2014, COMPUT HUM BEHAV, V33, P23, DOI 10.1016/j.chb.2013.12.016
Froehlich A, 2018, Pros and cons of chatbots in the IT helpdesk
Gilbert RL, 2015, INT J HUM-COMPUT ST, V73, P30, DOI 10.1016/j.ijhcs.2014.08.001
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
Griffith DA, 2007, J MARKETING, V71, P129, DOI 10.1509/jmkg.71.1.129
Heerink M, 2010, INT J SOC ROBOT, V2, P361, DOI 10.1007/s12369-010-0068-5
Hoff KA, 2015, HUM FACTORS, V57, P407, DOI 10.1177/0018720814547570
Hoy W.K., 1999, J SCH LEADERSHIP, V9, P184, DOI [DOI 10.1177/105268469900900301, https://doi.org/10.1177/105268469900900301]
Hua Y, 2020, DECISION SCI, V51, P755, DOI 10.1111/deci.12372
Ischen C., 2019, INT WORKSH CHATB RES, P34, DOI DOI 10.1007/978-3-030-39540-7_3
Jacoby J, 2002, J CONSUM PSYCHOL, V12, P51, DOI 10.1207/S15327663JCP1201_05
Jarvenpaa S. L., 1998, Journal of Management Information Systems, V14, P29
Jensen ML, 2017, MIS QUART, V41, P629, DOI 10.25300/MISQ/2017/41.2.13
Kamboj S, 2018, INT J INFORM MANAGE, V39, P169, DOI 10.1016/j.ijinfomgt.2017.12.001
Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
Kelley ScottW., 1994, J ACAD MARKET SCI, V22, P52, DOI DOI 10.1177/0092070394221005
Kim HW, 2009, MIS QUART, V33, P567
Kim MJ, 2020, J TRAVEL RES, V59, P69, DOI 10.1177/0047287518818915
Krishnan R, 2006, ACAD MANAGE J, V49, P894, DOI [10.2307/20159808, 10.5465/20159808]
Lapointe L, 2005, MIS QUART, V29, P461
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Liang HG, 2019, MIS QUART, V43, P373, DOI 10.25300/MISQ/2019/14360
Lin JB, 2019, INFORM MANAGE-AMSTER, V56, P213, DOI 10.1016/j.im.2018.05.009
Lindell MK, 2000, J APPL PSYCHOL, V85, P331, DOI 10.1037/0021-9010.85.3.331
Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
Mehrabian A., 1974, An approach to environmental psychology
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Mitchell V, 2018, Gartner: why humans will still be at the core of great CX
Mollen A, 2010, J BUS RES, V63, P919, DOI 10.1016/j.jbusres.2009.05.014
Morgeson FP, 2006, J APPL PSYCHOL, V91, P1321, DOI 10.1037/0021-9010.91.6.1321
Murray J, 2019, INT J RETAIL DISTRIB, V47, P1364, DOI 10.1108/IJRDM-01-2019-0016
NASS C, 1994, HUMAN FACTORS IN COMPUTING SYSTEMS, CHI '94 CONFERENCE PROCEEDINGS - CELEBRATING INTERDEPENDENCE, P72, DOI 10.1145/191666.191703
Önkal D, 2009, J BEHAV DECIS MAKING, V22, P390, DOI 10.1002/bdm.637
Piçarra N, 2018, COMPUT HUM BEHAV, V86, P129, DOI 10.1016/j.chb.2018.04.026
Pise R., 2018, CHATBOT MARKET SIZE
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Powell PA, 2017, COMPUT HUM BEHAV, V68, P137, DOI 10.1016/j.chb.2016.11.024
Pugh SD, 2001, ACAD MANAGE J, V44, P1018, DOI 10.5465/3069445
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
Saldanha TJV, 2017, MIS QUART, V41, P267
Seeber I, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103174
Shawar B.A., 2005, International Journal of Corpus Linguistics, V10, P489, DOI DOI 10.1075/IJCL.10.4.06SHA
Shiau WL, 2019, INTERNET RES, V29, P398, DOI 10.1108/IntR-10-2018-0447
Starzyk KB, 2006, J PERS SOC PSYCHOL, V90, P833, DOI 10.1037/0022-3514.90.5.833
Thatcher JB, 2018, MIS QUART, V42, P831, DOI 10.25300/MISQ/2018/11881
Thompson, 2018, MAY AI HELP YOU
Tsai WC, 2002, J APPL PSYCHOL, V87, P1001, DOI 10.1037//0021-9010.87.5.1001
Turing A.M., 1950, Computing machinery and intelligence, P29
van Doorn J, 2010, J SERV RES-US, V13, P253, DOI 10.1177/1094670510375599
Wirtz J, 2018, J SERV MANAGE, V29, P907, DOI 10.1108/JOSM-04-2018-0119
Wood SL, 2001, J MARKETING RES, V38, P157, DOI 10.1509/jmkr.38.2.157.18847
Wu YL, 2018, INTERNET RES, V28, P74, DOI 10.1108/IntR-08-2016-0250
Yuan LY, 2019, J MANAGE INFORM SYST, V36, P450, DOI 10.1080/07421222.2019.1598691
Zhang M, 2016, J BUS ETHICS, V134, P1, DOI 10.1007/s10551-014-2074-2
Zlotowski J, 2017, INT J HUM-COMPUT ST, V100, P48, DOI 10.1016/j.ijhcs.2016.12.008
NR 88
TC 60
Z9 63
U1 92
U2 404
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1066-2243
J9 INTERNET RES
JI Internet Res.
PD MAR 15
PY 2022
VL 32
IS 2
SI SI
BP 496
EP 517
DI 10.1108/INTR-08-2020-0460
EA JUL 2021
PG 22
WC Business; Computer Science, Information Systems; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Computer Science; Telecommunications
GA ZY6TT
UT WOS:000672746600001
DA 2024-03-27
ER
PT J
AU Li, MC
Wang, R
AF Li, Meichan
Wang, Rui
TI Chatbots in e-commerce: The effect of chatbot language style on
customers' continuance usage intention and attitude toward brand
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Chatbots; Human-chatbot interaction; Continuance usage intention;
Parasocial interaction; Brand affiliation
ID PARASOCIAL INTERACTION; COMMUNICATION; IMPACT; INFORMATION; ENGAGEMENT;
RESPONSES; VOICE; CUES
AB The utilization of chatbots has grown in popularity in recent years, leading to an increasing interest among academics and practitioners. This study investigates the effect of chatbot language style on customers' contin-uance usage intention and attitude toward brand. Two scenario-based experiments were conducted to examine the underlying mechanism. The results show that when chatbots adopt an informal (vs. formal) language style, customers' continuance usage intention and brand attitude increase through the mediating role of parasocial interaction. Further, this study identifies brand affiliation as a pertinent moderator, such that the effect of chatbot language style is attenuated for people who have no prior relationship with the brand. The findings contribute to the existing chatbot literature and offer practical implications for brand managers to develop optimal language strategies when deploying chatbots in e-commerce.
C1 [Li, Meichan; Wang, Rui] Jinan Univ, Sch Management, Guangzhou, Peoples R China.
[Li, Meichan] Jinan Univ, 601 Huangpu West Ave, Guangzhou 510632, Peoples R China.
C3 Jinan University; Jinan University
RP Li, MC (autor correspondiente), Jinan Univ, 601 Huangpu West Ave, Guangzhou 510632, Peoples R China.
EM elmca@outlook.com
RI wang, rui/JAC-6240-2023
FU National Natural Science Foundation of China [71302151]; Ministry of
Education of the People's Republic of China Humanities and Social
Sciences Youth Foundation [12YJC630208]; Jinan University Key Discipline
Construction Foundation [GY14011]
FX This research was funded by the National Natural Science Foundation of
China (71302151) , Ministry of Education of the People's Republic of
China Humanities and Social Sciences Youth Foundation (12YJC630208) ,
and Jinan University Key Discipline Construction Foundation (GY14011) .
CR Aggarwal P, 2004, J CONSUM RES, V31, P87, DOI 10.1086/383426
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Atwell E., 2007, Ldv forum, V22, P29
Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
Burgoon J. K., 1993, Journal of Language and Social Psychology, V12, P30, DOI [DOI 10.1177/0261927X93121003, 10.1177/0261927x93121003]
BURGOON JK, 1993, HUM COMMUN RES, V20, P67, DOI 10.1111/j.1468-2958.1993.tb00316.x
Chattaraman V, 2019, COMPUT HUM BEHAV, V90, P315, DOI 10.1016/j.chb.2018.08.048
Chaves AP, 2021, INT J HUM-COMPUT INT, V37, P729, DOI 10.1080/10447318.2020.1841438
Chen JS, 2021, INT J RETAIL DISTRIB, V49, P1512, DOI 10.1108/IJRDM-08-2020-0312
Chen JV, 2021, INTERNET RES, V31, P1376, DOI 10.1108/INTR-11-2019-0447
Cheng XS, 2022, INTERNET RES, V32, P496, DOI 10.1108/INTR-08-2020-0460
Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
De Cicco R, 2020, INT J RETAIL DISTRIB, V48, P1213, DOI 10.1108/IJRDM-12-2019-0406
Deloitte, 2020, CONV AI IS RESH HUM
Drift, 2021, 2021 STAT CONV MARK
Dube S., 2020, E-Commerce Chatbots-Using Chatbots Customer Support to Improve eCommerce Conversion Rate
Elsholz E, 2019, PROCEEDINGS OF THE 2019 CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL (CHIIR'19), P301, DOI 10.1145/3295750.3298956
EPAM, 2017, CHATB ADD CUST TOUCH
Feine J, 2019, INT J HUM-COMPUT ST, V132, P138, DOI 10.1016/j.ijhcs.2019.07.009
Gartner, 2020, DEL CUST SERV COV 19
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Gretry A, 2017, J BUS RES, V74, P77, DOI 10.1016/j.jbusres.2017.01.012
Hair J.F., 2009, Multivariate data analysis
Han MC, 2021, J INTERNET COMMER, V20, P46, DOI 10.1080/15332861.2020.1863022
Hayes AF., 2018, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, DOI DOI 10.1111/JEDM.12050
Holmqvist J, 2017, J BUS RES, V72, P114, DOI 10.1016/j.jbusres.2016.10.005
HORTON D, 1956, PSYCHIATR, V19, P215, DOI 10.1080/00332747.1956.11023049
Huang YS, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103044
iiMedia, 2021, CHIN CHATB SAT REP 2
Jakic A, 2017, J SERV MANAGE, V28, P418, DOI 10.1108/JOSM-12-2016-0325
Jiang K, 2022, J CONSUM BEHAV, V21, P756, DOI 10.1002/cb.2034
Jin SAA, 2010, PRESENCE-TELEOP VIRT, V19, P331, DOI 10.1162/PRES_a_00001
Juniper Research, 2018, CHATB DEL 11BN ANN C
Kasilingam DL, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101280
Kelleher T, 2009, J COMMUN, V59, P172, DOI 10.1111/j.1460-2466.2008.01410.x
Klein K, 2023, ELECTRON COMMER RES, V23, P2789, DOI 10.1007/s10660-022-09562-8
Kronrod A, 2012, J CONSUM RES, V39, P51, DOI 10.1086/661933
Kull AJ, 2021, J BUS RES, V135, P840, DOI 10.1016/j.jbusres.2021.03.005
Kumar V, 2021, J BUS RES, V125, P864, DOI 10.1016/j.jbusres.2020.01.007
Labrecque LI, 2014, J INTERACT MARK, V28, P134, DOI 10.1016/j.intmar.2013.12.003
Leah, 2021, WHAT DO YOUR CUSTOME
Lee S, 2020, INT J HUM-COMPUT INT, V36, P930, DOI 10.1080/10447318.2019.1699748
Lee Y, 2011, ELECTRON COMMER R A, V10, P342, DOI 10.1016/j.elerap.2010.11.005
Liebrecht Christine, 2021, Chatbot Research and Design. 4th International Workshop, CONVERSATIONS 2020. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12604), P16, DOI 10.1007/978-3-030-68288-0_2
Liebrecht Christine, 2020, Chatbot Research and Design. Third International Workshop, CONVERSATIONS 2019. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 11970), P51, DOI 10.1007/978-3-030-39540-7_4
Liebrecht C, 2021, J BUS RES, V132, P124, DOI 10.1016/j.jbusres.2021.03.050
Lou C, 2022, INT J ADVERT, V41, P655, DOI 10.1080/02650487.2021.1951510
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
McArthur T., 2018, Oxford companion to the English language
Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
Papas P., 2018, DIGITAL CUSTOMER CAR
Paulssen M, 2014, EUR J MARKETING, V48, P964, DOI 10.1108/EJM-11-2011-0657
Rapp A, 2021, INT J HUM-COMPUT ST, V151, DOI 10.1016/j.ijhcs.2021.102630
Reports and Data, 2022, GLOB CHATB MARK REAC
Rese A, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102176
Robinson S, 2020, J BUS RES, V116, P366, DOI 10.1016/j.jbusres.2019.08.038
Roy R, 2021, J BUS RES, V126, P23, DOI 10.1016/j.jbusres.2020.12.051
Ruan YY, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103059
Rubin A.M., 2002, Media effects: advances in theory and research, P525, DOI DOI 10.4324/9780429491146
Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
Schuetzler RM, 2020, J MANAGE INFORM SYST, V37, P875, DOI 10.1080/07421222.2020.1790204
Sela A, 2012, J CONSUM RES, V39, P644, DOI 10.1086/664972
Sengupta J, 2002, J CONSUM RES, V29, P39, DOI 10.1086/339920
ServiceBell, 2022, 57 CHATB IND STAT US
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Sheth J., 1976, ADV CONSUM RES, P382
Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
Thomas P, 2018, CHIIR'18: PROCEEDINGS OF THE 2018 CONFERENCE ON HUMAN INFORMATION INTERACTION & RETRIEVAL, P42, DOI 10.1145/3176349.3176388
Tsai WHS, 2021, J RES INTERACT MARK, V15, P460, DOI 10.1108/JRIM-12-2019-0200
Van Noort G., 2015, Integrated communications in the postmodern era, P77, DOI [10.1057/9781137388551_4, DOI 10.1057/9781137388551_4]
Verhagen T, 2014, J COMPUT-MEDIAT COMM, V19, P529, DOI 10.1111/jcc4.12066
Verma A., 2019, ENTREPRENEUR
Wu J., 2022, Nankai Bus. Rev.
Wu LR, 2017, TOURISM MANAGE, V59, P590, DOI 10.1016/j.tourman.2016.09.006
Xu Y, 2023, NANKAI BUS REV NT, V14, P249, DOI 10.1108/NBRI-06-2021-0041
Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
Youn S, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106721
Zarouali B, 2018, CYBERPSYCH BEH SOC N, V21, P491, DOI 10.1089/cyber.2017.0518
Zheng XB, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102043
NR 81
TC 27
Z9 27
U1 85
U2 232
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD MAR
PY 2023
VL 71
AR 103209
DI 10.1016/j.jretconser.2022.103209
EA NOV 2022
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 7A5FE
UT WOS:000898481000011
DA 2024-03-27
ER
PT J
AU Kumar, S
Sheu, JB
Kundu, T
AF Kumar, Suryakant
Sheu, Jiuh-Biing
Kundu, Tanmoy
TI Planning a parts-to-picker order picking system with consideration of
the impact of perceived workload
SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
LA English
DT Article
DE Smart warehouses; Order picking system; Parts-to-picker; Robot-human
coordination; Queueing theory; E-commerce
ID OPEN QUEUING-NETWORKS; STORAGE ASSIGNMENT; PERFORMANCE; FRAMEWORK;
DESIGN
AB In the digital age, coordinating robots and humans is critical in e-commerce warehousing. Motivated by observed industrial practices, this study presents a queueing theory based analytical model to investigate the planning issue of robot-human coordination in a parts-to-picker warehousing system. The critical planning decisions involve finding the optimal number of robots in the warehouse, the expected number of robots at essential locations of the warehouse, and performance analysis of the order picking system. A distinctive feature of this study is the conceptualization of a human factor called perceived workload (which depends on the number of robots) in the order picking planning model for efficient order fulfillment. Our analyses interestingly suggest that deploying more robots in warehouses with a parts-to-picker system does not necessarily increase the warehousing system's performance; instead, a trade-off exists. Additionally, the workload-dependent service rate significantly influences the robots queueing in front of the order picking station (internal queue) and the synchronization station (external queue) in the warehouse. More importantly, this work contributes to the design of a human-centric work environment for parts-to-picker order fulfillment system.
C1 [Kumar, Suryakant; Sheu, Jiuh-Biing] Natl Taiwan Univ, Dept Business Adm, Taipei, Taiwan.
[Kundu, Tanmoy] Indian Inst Technol Jodhpur, Sch Management & Entrepreneurship, Jodhpur, India.
C3 National Taiwan University; Indian Institute of Technology System (IIT
System); Indian Institute of Technology (IIT) - Jodhpur
RP Sheu, JB (autor correspondiente), Natl Taiwan Univ, Dept Business Adm, Taipei, Taiwan.
EM d08741010@ntu.edu.tw; jbsheu@ntu.edu.tw; tanmoykundu@iitj.ac.in
OI Kumar, Suryakant/0000-0002-6871-4013
FU Ministry of Science and Technology of Taiwan, R.O.C [MOST
109-2410-H-002-076-MY3]
FX Acknowledgements This research is supported by grant MOST
109-2410-H-002-076-MY3 from Ministry of Science and Technology of
Taiwan, R.O.C. The authors also wish to thank the editors and referees
for their helpful comments and suggestions. Any errors or omissions
remain the sole responsibility of the authors.
CR Amazon.com Inc, 2021, FAST SAM DAY DEL MAR
Amazon.com Inc, 2022, LOOK BACK 10 YEARS A
Amazon.com Inc, 2021, NEW TECHN IMPR AM EM
Amazon.com Inc, 2019, STOR AM NEXT GEN ROB
[Anonymous], 1998, Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
Azadeh K., 2020, EUR J OPER RES, DOI [10.2139/ssrn.3585396, DOI 10.2139/SSRN.3585396]
Azadeh K, 2019, TRANSPORT SCI, V53, P1213, DOI 10.1287/trsc.2018.0883
Azadeh K, 2019, TRANSPORT SCI, V53, P917, DOI 10.1287/trsc.2018.0873
Battini D, 2016, COMPUT IND ENG, V94, P147, DOI 10.1016/j.cie.2016.01.020
Battini D, 2017, INT J PROD RES, V55, P2764, DOI 10.1080/00207543.2016.1190879
Boucherie R. J., 1993, Queueing Systems Theory and Applications, V13, P251, DOI 10.1007/BF01158934
Bowles R., 2020, WAREHOUSE ROBOTICS E
Boysen N, 2019, EURO J TRANSP LOGIST, V8, P169, DOI 10.1007/s13676-018-0116-0
Brown A.S., 2022, AMAZON ROBOTS NAVIGA
Buitenhek R, 2000, ANN OPER RES, V93, P15, DOI 10.1023/A:1018967622069
Calzavara M, 2019, INT J PROD RES, V57, P2392, DOI 10.1080/00207543.2018.1518609
CHANDY KM, 1975, IBM J RES DEV, V19, P36, DOI 10.1147/rd.191.0036
Chevalier S., 2022, Global retail e-commerce market size 2014-2025
Choi TM, 2022, PROD OPER MANAG, V31, P9, DOI 10.1111/poms.13622
Cimini C, 2021, HUM FACTOR ERGON MAN, V31, P98, DOI 10.1002/hfm.20872
DALLERY Y, 1990, PERFORM EVALUATION, V11, P209, DOI 10.1016/0166-5316(90)90013-9
Day M., 2021, INSIDE AMAZON FLAGSH
de Koster R, 2007, EUR J OPER RES, V182, P481, DOI 10.1016/j.ejor.2006.07.009
De Lombaert T, 2023, INT J PROD RES, V61, P3300, DOI 10.1080/00207543.2022.2079437
Deborah P., 2019, 50000 WAREHOUSES USE
Do HT, 2018, PROD OPER MANAG, V27, P1553, DOI 10.1111/poms.12883
Futch M., 2018, DAWN AUTOMATED WAREH
Gao D, 2017, IEEE T ENG MANAGE, V64, P134, DOI 10.1109/TEM.2017.2666265
Gharehgozli A, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102087
Glock CH, 2019, EUR J OPER RES, V273, P516, DOI 10.1016/j.ejor.2018.08.015
Greene J., 2021, AMAZON MONITORS ITS
Grosse EH, 2017, INT J PROD RES, V55, P1260, DOI 10.1080/00207543.2016.1186296
Grosse EH, 2015, INT J PROD ECON, V170, P882, DOI 10.1016/j.ijpe.2014.12.018
Grosse EH, 2015, INT J PROD RES, V53, P695, DOI 10.1080/00207543.2014.919424
Hanson R, 2018, INT J IND ERGONOM, V64, P199, DOI 10.1016/j.ergon.2017.07.001
JD.com, 2021, JD COM OP AUT WAR EU
Jones C., 2021, AUTONOMOUS MOBILE RO
Kc DS, 2009, MANAGE SCI, V55, P1486, DOI 10.1287/mnsc.1090.1037
Lamballais T, 2022, EUR J OPER RES, V300, P937, DOI 10.1016/j.ejor.2021.09.001
Lamballais T, 2017, EUR J OPER RES, V256, P976, DOI 10.1016/j.ejor.2016.06.063
Larco JA, 2017, INT J PROD RES, V55, P6407, DOI 10.1080/00207543.2016.1165880
Lecher Colin, 2019, How Amazon automatically tracks and fires warehouse workers for ' productivity'
Li XW, 2020, TRANSPORT RES E-LOG, V144, DOI 10.1016/j.tre.2020.102158
Li YN, 2016, IEEE T ROBOT, V32, P1408, DOI 10.1109/TRO.2016.2597322
Li YN, 2014, IEEE-ASME T MECH, V19, P1007, DOI 10.1109/TMECH.2013.2264533
Merschformann M, 2019, OPER RES PERSPECT, V6, DOI 10.1016/j.orp.2019.100128
Michel R., 2016, WAREHOUSE DC OPERATI
Mirzaei M, 2021, TRANSPORT RES E-LOG, V146, DOI 10.1016/j.tre.2020.102207
Qin HL, 2022, INFORMS J APPL ANAL, V52, P42, DOI 10.1287/inte.2021.1100
Ramanathan R, 2010, TRANSPORT RES E-LOG, V46, P950, DOI 10.1016/j.tre.2010.02.002
Sharma M, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3143466
Sheu JB, 2023, PROD OPER MANAG, V32, P794, DOI 10.1111/poms.13899
Shortle J.F., 2017, FUNDAMENTALS QUEUEIN, DOI [10.1002/9781119453765, DOI 10.1002/9781119453765]
Tompkins J.A., 2010, FACILITIES PLANNING
Virmani N, 2023, IEEE T ENG MANAGE, V70, P3976, DOI 10.1109/TEM.2021.3091398
Wang W, 2022, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3142282
Wang Y., 2020, TOP INT MEDIA VISIT
Wang Z, 2022, PROD OPER MANAG, V31, P98, DOI 10.1111/poms.13406
Wurman PR, 2008, AI MAG, V29, P9
Young J, 2021, AMAZON PRIME DAY 202
Yuan Z, 2017, IEEE T ENG MANAGE, V64, P83, DOI 10.1109/TEM.2016.2634540
NR 61
TC 2
Z9 2
U1 23
U2 27
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1366-5545
EI 1878-5794
J9 TRANSPORT RES E-LOG
JI Transp. Res. Pt. e-Logist. Transp. Rev.
PD MAY
PY 2023
VL 173
AR 103088
DI 10.1016/j.tre.2023.103088
EA MAR 2023
PG 18
WC Economics; Engineering, Civil; Operations Research & Management Science;
Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
Science; Transportation
GA M9KZ7
UT WOS:001033342400001
DA 2024-03-27
ER
PT J
AU Wang, CC
Li, YY
Fu, WZ
Jin, J
AF Wang, Cuicui
Li, Yiyang
Fu, Weizhong
Jin, Jia
TI Whether to trust chatbots: Applying the event-related approach to
understand consumers? emotional experiences in interactions with
chatbots in e-commerce
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Chatbots; Emotional experience; Cognitive appraisal theory; Trust;
Event-related potential
ID COGNITIVE APPRAISAL; ERP; POTENTIALS; ATTENTION; BEHAVIOR; PRODUCT;
ALGORITHMS; IMPACT
AB Chatbots can be used in marketing services to substantially improve the consumer experience. Based on cognitive appraisal theory, this study applied an event-related potential (ERP) approach to investigate consumers' emotional experiences and consumer trust in passive interaction with chatbots versus humans, taking into ac-count objective or subjective tasks in e-commerce. The results showed that chatbot (vs. human) service in-teractions automatically drew more consumer attention at the subconscious stage (i.e., a larger P2); consumers purposefully allocated more resources to regulate the negative emotions elicited by chatbots at the conscious stage (i.e., a larger LPP); and there was a lower trust in chatbots than in humans. Moreover, under subjective tasks, the differences between chatbots and human agents in emotional experience (as reflected by LPP) and trust were amplified. The findings will encourage e-retailers to improve the emotional service experience of their chatbots and prioritize the application of chatbots for objective tasks in customer service.
C1 [Wang, Cuicui; Li, Yiyang; Fu, Weizhong] Hefei Univ Technol, Sch Management, 193 Tunxi Rd, Hefei 230009, Peoples R China.
[Wang, Cuicui; Fu, Weizhong] Minist Educ, Key Lab Proc Optimizat & Intelligent Decis Making, Hefei, Peoples R China.
[Jin, Jia] Shanghai Int Studies Univ, Sch Business & Management, Lab Appl Brain & Cognit Sci, 1550 Wenxiang Rd, Shanghai 200083, Peoples R China.
C3 Hefei University of Technology; Shanghai International Studies
University
RP Fu, WZ (autor correspondiente), Hefei Univ Technol, Sch Management, 193 Tunxi Rd, Hefei 230009, Peoples R China.; Jin, J (autor correspondiente), Shanghai Int Studies Univ, Sch Business & Management, Lab Appl Brain & Cognit Sci, 1550 Wenxiang Rd, Shanghai 200083, Peoples R China.
EM weizhongfu@sina.com; jinjia.163@163.com
OI Jin, Jia/0000-0003-1124-2999
FU Humanities and Social Sciences Foundation of the Ministry of Education
of China [20YJAZH098]; National Nature Science Foundation of China
[72271166]; Open project of Shanghai key lab of brain-machine
intelligence [2022KFKT003]; Fundamental Research Funds for the Central
Universities [JS2020HGXJ0032]
FX Acknowledgments This work was supported by the Humanities and Social
Sciences Foundation of the Ministry of Education of China (No.
20YJAZH098) , the National Nature Science Foundation of China (No.
72271166) , Open project of Shanghai key lab of brain-machine
intelligence for informa-tion behavior (No. 2022KFKT003) , and the
Fundamental Research Funds for the Central Universities (No.
JS2020HGXJ0032) . The funders had no role in the study design,
collection, data analysis, or interpre-tation of the data, in the
report?s writing, or in the decision to submit the article for
publication.
CR Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
[Anonymous], 1994, J Clin Neurophysiol, V11, P111
Bai B., 2020, IMPACTS ALGORITHMIC, DOI DOI 10.2139/SSRN.3550887
Becker L, 2020, J ACAD MARKET SCI, V48, P630, DOI 10.1007/s11747-019-00718-x
Cachero-Martínez S, 2021, J RETAIL CONSUM SERV, V60, DOI 10.1016/j.jretconser.2021.102481
Cadario R, 2021, NAT HUM BEHAV, V5, P1636, DOI 10.1038/s41562-021-01146-0
Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
Castelo Noah, 2019, ACR N AM ADV
Chen T, 2021, GOV INFORM Q, V38, DOI 10.1016/j.giq.2020.101520
Cheng XS, 2022, INTERNET RES, V32, P496, DOI 10.1108/INTR-08-2020-0460
Chi OH, 2020, J HOSP MARKET MANAG, V29, P757, DOI 10.1080/19368623.2020.1721394
Choi H, 2019, J TRAVEL RES, V58, P732, DOI 10.1177/0047287518776805
Chong T, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102735
Chung K, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20175016
Darwin C. R., 1859, ORIGIN SPECIES MEANS
Dasu S, 2010, MIT SLOAN MANAGE REV, V52, P33
de Visser EJ, 2018, FRONT HUM NEUROSCI, V12, DOI 10.3389/fnhum.2018.00309
Diab DL, 2011, INT J SELECT ASSESS, V19, P209, DOI 10.1111/j.1468-2389.2011.00548.x
Dietrich DE, 2001, NEUROPSYCHOBIOLOGY, V43, P96, DOI 10.1159/000054874
Dietvorst BJ, 2018, MANAGE SCI, V64, P1155, DOI 10.1287/mnsc.2016.2643
Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
Doney PM, 1997, J MARKETING, V61, P35, DOI 10.2307/1251829
Folstein JR, 2008, PSYCHOPHYSIOLOGY, V45, P152, DOI 10.1111/j.1469-8986.2007.00602.x
Gardener EKT, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0073475
Garvey AM, 2023, J MARKETING, V87, P10, DOI 10.1177/00222429211066972
Gaudiello I, 2016, COMPUT HUM BEHAV, V61, P633, DOI 10.1016/j.chb.2016.03.057
Gkinko L., 2022, COCREATING CONTEXT T, P105, DOI [10.1007/978-3-031-17968-6_7, DOI 10.1007/978-3-031-17968-6_7]
Granulo A, 2021, J CONSUM PSYCHOL, V31, P72, DOI 10.1002/jcpy.1181
Hajcak G, 2006, COGN AFFECT BEHAV NE, V6, P291, DOI 10.3758/CABN.6.4.291
Hajcak G, 2010, DEV NEUROPSYCHOL, V35, P129, DOI 10.1080/87565640903526504
Herbert C, 2006, PSYCHOPHYSIOLOGY, V43, P197, DOI 10.1111/j.1469-8986.2006.00385.x
Heutink J, 2011, NEUROCASE, V17, P297, DOI 10.1080/13554794.2010.504730
Holmes A, 2003, COGNITIVE BRAIN RES, V16, P174, DOI 10.1016/S0926-6410(02)00268-9
Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
Inbar Y, 2010, J PERS SOC PSYCHOL, V99, P232, DOI 10.1037/a0020215
Jin J, 2017, NEUROSCI RES, V125, P21, DOI 10.1016/j.neures.2017.07.006
Kim S, 2016, J BUS RES, V69, P5809, DOI 10.1016/j.jbusres.2016.04.178
Kunz WH, 2019, J SERV MARK, V33, P479, DOI 10.1108/JSM-01-2019-0039
Laros FJM, 2005, J BUS RES, V58, P1437, DOI 10.1016/j.jbusres.2003.09.013
Lazarus R. S., 1991, Emotion and adaptation
Lee KJ, 2021, J TRAVEL TOUR MARK, V38, P399, DOI 10.1080/10548408.2021.1921094
Li B., 2022, HAWAII INT C SYSTEM, DOI [10.24251/HICSS.2022.604, DOI 10.24251/HICSS.2022.604]
Li SS, 2021, J HOSP TOUR MANAG, V47, P72, DOI 10.1016/j.jhtm.2021.03.004
Lin HY, 2015, FRONT PSYCHOL, V6, DOI 10.3389/fpsyg.2015.01927
Logg JM, 2019, ORGAN BEHAV HUM DEC, V151, P90, DOI 10.1016/j.obhdp.2018.12.005
Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
Lu JY, 2016, JUDGM DECIS MAK, V11, P332
Lu YZ, 2017, BIOL PSYCHOL, V125, P105, DOI 10.1016/j.biopsycho.2017.02.010
Luan J, 2016, COMPUT HUM BEHAV, V65, P420, DOI 10.1016/j.chb.2016.08.037
Luck SJ, 2014, INTRODUCTION TO THE EVENT-RELATED POTENTIAL TECHNIQUE, 2ND EDITION, P1
Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
Lv XY, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.106993
Lv XY, 2021, ANN TOURISM RES, V87, DOI 10.1016/j.annals.2020.103114
MacLeod J, 2017, COGN AFFECT BEHAV NE, V17, P592, DOI 10.3758/s13415-017-0499-7
Madsen M., 2000, Measuring human-computer trust, V53, P6
Mauss IB, 2005, EMOTION, V5, P175, DOI 10.1037/1528-3542.5.2.175
Moore S, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102755
Mostafa RB, 2022, EUR J MARKETING, V56, P1748, DOI 10.1108/EJM-02-2020-0084
Munanura IE, 2023, J SUSTAIN TOUR, V31, P581, DOI 10.1080/09669582.2021.1901104
Norris CJ, 2021, EMOTION, V21, P499, DOI 10.1037/emo0000716
Olofsson JK, 2008, BIOL PSYCHOL, V77, P247, DOI 10.1016/j.biopsycho.2007.11.006
Ostrom AL., 2018, Handbook of service science, Vii, P77, DOI DOI 10.1007/978-3-319-98512-1_5
Paulmann S, 2013, FRONT PSYCHOL, V4, DOI 10.3389/fpsyg.2013.00345
Pozharliev R, 2015, J MARKETING RES, V52, P546, DOI 10.1509/jmr.13.0560
Rodgers W, 2021, J BUS RES, V126, P401, DOI 10.1016/j.jbusres.2020.12.039
Roseman IJ., 2001, Appraisal processes in emotion: Theory, methods, research, P3, DOI [10.1093/oso/9780195130072.003.0001, DOI 10.1080/02699939108411034]
Schmitt B, 2019, J CONSUM RES, V46, P825, DOI 10.1093/jcr/ucz038
Schuetzler RM, 2020, J MANAGE INFORM SYST, V37, P875, DOI 10.1080/07421222.2020.1790204
Schupp HT, 2007, J NEUROSCI, V27, P1082, DOI 10.1523/JNEUROSCI.3223-06.2007
Schupp HT, 2000, PSYCHOPHYSIOLOGY, V37, P257, DOI 10.1111/1469-8986.3720257
Schupp HT, 2003, PSYCHOL SCI, V14, P7, DOI 10.1111/1467-9280.01411
SEMLITSCH HV, 1986, PSYCHOPHYSIOLOGY, V23, P695, DOI 10.1111/j.1469-8986.1986.tb00696.x
SMITH CA, 1985, J PERS SOC PSYCHOL, V48, P813, DOI 10.1037/0022-3514.48.4.813
Song CS, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102727
Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
Tiberio L, 2013, ROBOTICS, V2, P92, DOI 10.3390/robotics2020092
Trope Y, 2003, PSYCHOL REV, V110, P403, DOI 10.1037/0033-295X.110.3.403
Wang CC, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.01440
Wang QZ, 2016, ELECTRON COMMER RES, V16, P1, DOI 10.1007/s10660-015-9209-0
Watson L, 2007, EUR J MARKETING, V41, P487, DOI 10.1108/03090560710737570
Yang JM, 2012, NEUROSCI LETT, V514, P198, DOI 10.1016/j.neulet.2012.02.094
Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
Yeomans M, 2019, J BEHAV DECIS MAKING, V32, P403, DOI 10.1002/bdm.2118
Ying TY, 2021, TOURISM MANAGE, V86, DOI 10.1016/j.tourman.2021.104355
Yun JH, 2021, PSYCHOL MARKET, V38, P610, DOI 10.1002/mar.21445
Zhao MN, 2021, J RETAIL CONSUM SERV, V62, DOI 10.1016/j.jretconser.2021.102617
NR 87
TC 12
Z9 12
U1 60
U2 154
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD JUL
PY 2023
VL 73
AR 103325
DI 10.1016/j.jretconser.2023.103325
EA MAR 2023
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 0A0PE
UT WOS:000951531400001
DA 2024-03-27
ER
PT J
AU Kuzyk, O
Kabanova, O
Chyrva, H
Vlasenko, D
Komarnytska, H
AF Kuzyk, Oleg
Kabanova, Olena
Chyrva, Hanna
Vlasenko, Dmytro
Komarnytska, Hanna
TI TRENDS AND PERSPECTIVES ON THE IMPACT OF DIGITAL TECHNOLOGIES ON THE
EFFICACY OF MARKETING COMMUNICATION
SO FINANCIAL AND CREDIT ACTIVITY-PROBLEMS OF THEORY AND PRACTICE
LA English
DT Article
DE MarTech; FinTech; AI & ML; social commerce; influencer marketing; Big
Data & Analytics; web analytics; VR/AR/MR; extended reality; voice
marketing; interac-tive content marketing; reduction of financial and
credit risks
AB The research conducted aimed to determine the most attractive digital marketing tech-nologies for future investment opportunities. This was achieved through a cross-priority ranking analysis, which identified trends in the development of digital technologies in marketing communications and global marketing. Data from 25 expert organisations was used to form a set of 32 digital solutions. Using a developed methodology, these solutions were ranked, revealing the top 6 with the highest potential for transforming marketing communications. The rankings showed a discrepancy between the mentioned frequency ranking and the priority ranking, indicating the influence of marketing organizations' experience. Currently, social media platforms, particularly influencer marketing, are closely integrated with e-commerce, explaining their high mention frequency. However, a cross-ranking analysis revealed a trend towards AI technologies in marketing communications pro-cesses. It has been established that obtaining credit funds for the deployment of modernized marketing campaigns and strategies impacts the financial security of such organizations. Moreover, the utilization of optimal conversion-oriented digital marketing tools allows for a significant reduction in financial risks and an increase in the profitability of com-panies. The study further identified the future direction of global marketing development, which involves immersive strategies that deeply integrate and extensively use AI technologies. These strategies aim to enhance consumer interest and engagement. The practicality of this research lies in its ability to assist stakeholders in making informed investment decisions and reducing the risks of misdirected investments. Further research is recommended to explore practical mechanisms for implementing the identified digital solutions in global marketing processes. This would involve developing a detailed strategy aligned with these technologies.
C1 [Kuzyk, Oleg] Ivan Franko Natl Univ Lviv, Dept Mkt, Lvov, Ukraine.
[Kabanova, Olena] Branch Class Private Univ, Dept Logist Management, Kremenchuk, Ukraine.
[Chyrva, Hanna] Pavlo Tychyna Uman State Pedag Univ, Inst Econ & Business Educ, Dept Econ & Social & Behav Sci, Uman, Ukraine.
[Vlasenko, Dmytro] Natl Univ Bioresources & Nat Management Ukraine, Separate Div Nizhinsky Agrotech Inst, Dept Management & Agr Econ, Kiev, Ukraine.
[Komarnytska, Hanna] Ivan Franko Natl Univ Lviv, Dept Publ Adm & business management, Lvov, Ukraine.
C3 Ministry of Education & Science of Ukraine; Ivan Franko National
University Lviv; Ministry of Education & Science of Ukraine; Pavlo
Tychyna Uman State Pedagogical University; Ministry of Education &
Science of Ukraine; Ivan Franko National University Lviv
RP Kabanova, O (autor correspondiente), Branch Class Private Univ, Dept Logist Management, Kremenchuk, Ukraine.
EM desigura15@gmail.com
RI Komarnic'ka, Ganna/H-8623-2018
OI Komarnic'ka, Ganna/0000-0002-5533-6439
CR Al-Dmour Hani, 2020, Interdisciplinary Journal of Information, Knowledge, and Management, V15, P203, DOI 10.28945/4619
[Anonymous], 2023, Top 10 Digital Marketing Trends for 2023: Stay Ahead of the Curve
[Anonymous], 2023, Top 8 Digital Marketing Trends for Businesses in 2023
[Anonymous], 2023, Intellipaat BlogDec, 23
[Anonymous], 2023, Essential digital marketing vs traditional marketing statistics in 2023-ZipDo
[Anonymous], 2023, Digital marketing vs traditional marketing statistics and trends in 2023-Gitnux
[Anonymous], 2023, Top digital marketing vs traditional marketing statistics in 2023-Amra and Elma LLC
[Anonymous], 2023, Top 10 Upcoming Digital Marketing trends in 2024
[Anonymous], 2023, Top 9 Marketing Technology Trends in 2023 | StartUs Insights
[Anonymous], 2022, Top Technologies that Boosts the Digital Marketing
[Anonymous], 2023, Five Martech Trends for 2023
[Anonymous], 2023, The 7 top digital marketing trends of 2023
Bannikova K., 2019, The potential of organizational culture in the management of human resources of international companies
Barnhart B., 2022, 21 Must-have digita l marketing tools to help you grow
Bennett C., 2023, Business News DailyApril 28
Best Digital Marketing Tools, 2023, Mageplaza
Chornyy A., 2023, PlerdyAugust 11
Dong XB, 2023, INTERNET RES, DOI 10.1108/INTR-02-2022-0121
Duggal N., 2023, Simplilearn SolutionsAugust 9
Dykan V, 2021, J E EUR CENT ASIAN R, V8, P51, DOI 10.15549/jeecar.v8i1.646
Gustavsen A., 2023, From SEO to Social Media: What Are the 8 Types of Digital Marketing?
Hu P, 2023, INT J RES MARK, V40, P109, DOI 10.1016/j.ijresmar.2022.04.006
Jadhav GG, 2023, J INDIAN BUS RES, V15, P76, DOI 10.1108/JIBR-05-2022-0129
Jarboe G., 2023, Search Engine JournalApril 18
Jung SU, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15075711
Karapetov A., 2021, Futurity Economics&Law, V1, P20, DOI [10.57125/FEL.2021.06.25.4, DOI 10.57125/FEL.2021.06.25.4]
Karr D., 2022, Martech ZoneJanuary 22
Kinga E., 2022, DetermSeptember 06
Kovalchuk S. V., 2019, Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, V2, P163, DOI [10.29202/nvngu/2019-2/17, DOI 10.29202/NVNGU/2019-2/17]
Lou C, 2023, J CURR ISS RES AD, V44, P60, DOI 10.1080/10641734.2022.2124471
Malthouse E, 2023, INT J ADVERT, V42, P128, DOI 10.1080/02650487.2022.2122249
Manohar Bhanushali Mahesh, 2023, 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), P1026, DOI 10.1109/ICACITE57410.2023.10182372
Martynenko M., 2023, Futurity Economics&Law, V3, P63, DOI [10.57125/FEL.2023.03.25.07, DOI 10.57125/FEL.2023.03.25.07]
Matthews J., 2023, Veza DigitalMarch 16
Munir A., 2023, International Journal of Data and Network Science, V7, P65, DOI [110.5267/j.ijdns.2022.12.005, DOI 10.5267/J.IJDNS.2022.12.005]
Muzyka B., 2022, Top 12 MarTech Trends in 2023-TechMagic
Nalbant K. G., 2023, Journal of Metaverse, V3, P9, DOI [10.57019/jmv.1148015, DOI 10.57019/JMV.1148015]
Pascucci F, 2023, Italian J Market, P27
Pec T., 2023, ForbesMarc 21
Petrullo L., 2023, DigitalMarketerJune 15
Poperechnaya A., 2023, Sloboda StudioMay 19
Prokopenko O., 2021, Futurity Economics&Law, V1, P23, DOI [10.57125/FEL.2021.03.25.4, DOI 10.57125/FEL.2021.03.25.4]
Sale W., 2023, FiftyFive and FiveAugust 2
Sanakuiev M., 2023, Futurity Economics&Law, V3, P16, DOI [10.57125/FEL.2023.03.25.02, DOI 10.57125/FEL.2023.03.25.02]
Sangarathas D., 2023, Asian Journal of Marketing Management, V2, DOI [10.31357/ajmm.v2i02.6466, DOI 10.31357/AJMM.V2I02.6466]
Shakharova A., 2022, Futurity Education, V2, P19, DOI [10.57125/FED/2022.10.11.21, DOI 10.57125/FED/2022.10.11.21]
Spiryn J., 2023, Brand24January 5
Top 20 Internet Marketing Technologies You Shouldn't Miss, 2022, Serpstat-Growth Hacking Tool for SEO, PPC and Content Marketing
Utomo H., 2022, P 1 LEKANTARA ANN C, DOI [10.4108/eai.3-8-2021.2315084, DOI 10.4108/EAI.3-8-2021.2315084]
Vasylyuk-Zaitseva S., 2023, Futurity Education, V3, P78, DOI [10.57125/FED.2023.09.25.05, DOI 10.57125/FED.2023.09.25.05]
Verbivska Liudmyla, 2022, WSEAS Transactions on Business and Economics, P1824, DOI 10.37394/23207.2022.19.164
Zeng JY, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15097278
NR 52
TC 0
Z9 0
U1 2
U2 2
PU FINTECHALIANCE
PI Kyiv
PA Highway Kharkivska, bldg 180/21, Kyiv, UKRAINE
SN 2306-4994
EI 2310-8770
J9 FINANC CREDIT ACT
JI Financ. Credit Act.
PY 2023
VL 6
IS 53
BP 471
EP 486
DI 10.55643/fcaptp.6.53.2023.4259
PG 16
WC Business, Finance
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA HS2S3
UT WOS:001161435400001
OA hybrid
DA 2024-03-27
ER
PT J
AU Nayal, K
Kumar, S
Raut, RD
Queiroz, MM
Priyadarshinee, P
Narkhede, BE
AF Nayal, Kirti
Kumar, Shashank
Raut, Rakesh D.
Queiroz, Maciel M.
Priyadarshinee, Pragati
Narkhede, Balkrishna E.
TI Supply chain firm performance in circular economy and digital era to
achieve sustainable development goals
SO BUSINESS STRATEGY AND THE ENVIRONMENT
LA English
DT Article
DE AI-IoT; circular economy (CE); resource orchestration theory; SEM;
supply chain flexibility; Sustainable Development Goals (SDG)
ID MANUFACTURING FLEXIBILITY; SCALE DEVELOPMENT; INDUSTRY 4.0; BIG DATA;
MANAGEMENT; AGILITY; MODEL; CAPABILITIES; UNCERTAINTY; COMPETENCE
AB Digitalization of the supply chain (SC) has received plenty of attention from practitioners and researchers in recent years to address the challenges of the business environment and assist firms in achieving a circular SC. Implementing circularity in business operations is a practice of achieving sustainability which is of utmost significance for achieving sustainable development goals established by the United Nations. This study empirically examines the relation among flexibility, AI-IoT adoption, and SC firm performance under the circular economy (CE) environment based on resource orchestration theory (ROT), an integrated version of the extended-resource-based view, and dynamic capability theory (DCT). Data were collected from Indian manufacturing firms through a questionnaire-based survey and examined using "structural equation modeling (SEM)." The study results show that information flexibility and organizational flexibility have the highest impact on AI-IoT adoption. Organizational flexibility shows full mediation with AI-IoT, which would influence the SC firm performance directly. The analysis also indicates that CE will affect the relation between organizational flexibility and AI-IoT adoption. These findings pave the way for cross-country analysis, formation of practical strategies, and policies related to AI-IoT and CE implementations.
C1 [Nayal, Kirti; Kumar, Shashank; Raut, Rakesh D.; Narkhede, Balkrishna E.] Natl Inst Ind Engn NITIE, Dept Operat & Supply Chain Management, Mumbai, Maharashtra, India.
[Queiroz, Maciel M.] Paulista Univ UNIP, Postgrad Program Business Adm, Sao Paulo, India.
[Priyadarshinee, Pragati] Chaitanya Bharathi Inst Technol CBIT, Hyderabad, Telangana, India.
C3 National Institute of Industrial Engineering (NITIE); Chaitanya Bharathi
Institute of Technology
RP Raut, RD (autor correspondiente), Natl Inst Ind Engn NITIE, NITIE, Dept Operat & Supply Chain Management, Mumbai 400087, Maharashtra, India.
EM rraut@nitie.ac.in
RI Priyadarshinee, Pragati/ABF-7111-2020; Queiroz, Maciel M./F-1274-2014;
Queiroz, Maciel M./U-8499-2019; Narkhede, Balkrishna
Eknath/AAL-2918-2020; Kumar, Shashank/AAM-8087-2021; Nayal,
Kirti/JDM-2498-2023; Nayal, Kirti/AAB-4674-2022
OI Priyadarshinee, Pragati/0000-0003-1408-0577; Queiroz, Maciel
M./0000-0002-6025-9191; Narkhede, Balkrishna Eknath/0000-0002-9277-3005;
Kumar, Shashank/0000-0003-0841-987X; nayal, kirti/0000-0003-1477-0500;
Raut, Rakesh/0000-0002-0469-1326
CR Abdallah AB, 2021, INT J PHYS DISTR LOG, V51, P785, DOI 10.1108/IJPDLM-01-2020-0011
Akhtar P, 2018, TECHNOL FORECAST SOC, V136, P307, DOI 10.1016/j.techfore.2017.04.023
[Anonymous], 2019, GLOBAL MAT RESOURCES, DOI DOI 10.1787/9789264307452-EN
Asamoah D, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2020.102195
Asiaei K, 2022, BUS STRATEG ENVIRON, V31, P76, DOI 10.1002/bse.2875
Awan U, 2021, BUS STRATEG ENVIRON, V30, P2038, DOI 10.1002/bse.2731
Bag S, 2021, IND MARKET MANAG, V92, P178, DOI 10.1016/j.indmarman.2020.12.001
Bag S, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120420
Bag S, 2021, PROD PLAN CONTROL, V32, P1368, DOI 10.1080/09537287.2020.1817602
Bai CG, 2020, INT J PROD RES, V58, P5893, DOI 10.1080/00207543.2019.1661532
BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
Baryannis G, 2019, INT J PROD RES, V57, P2179, DOI 10.1080/00207543.2018.1530476
Blum Christopher, 2017, PRIMER PARTIAL LEAST, V5, P94, DOI [10.1016/j.scp.2017.01.001, DOI 10.1016/J.LRP.2013.01.002, 10.1016/j.scitotenv.2014.10.071]
Borges AFS, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102225
Brous P, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.05.008
Cai YJ, 2020, TRANSPORT RES E-LOG, V141, DOI 10.1016/j.tre.2020.102010
Chan HL, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102063
Chang SC, 2003, INT J PROD ECON, V83, P13, DOI 10.1016/S0925-5273(02)00263-3
Chen YS, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102232
Deb SK, 2018, PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE CONFLUENCE 2018 ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING, P758, DOI 10.1109/CONFLUENCE.2018.8442900
Delic M, 2020, INT J PROD ECON, V228, DOI 10.1016/j.ijpe.2020.107689
Diaz-Chao A., 2021, Journal of Cleaner Production, V281, P1, DOI DOI 10.1016/J.JCLEPRO.2020.125264
Dubey R, 2021, INT J PROD RES, V59, P110, DOI 10.1080/00207543.2019.1582820
Dubey R, 2019, MANAGE DECIS, V57, P2092, DOI 10.1108/MD-01-2018-0119
Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067
Esmaeilikia M, 2016, ANN OPER RES, V244, P407, DOI 10.1007/s10479-014-1544-3
Fantazy KA, 2016, J ENTERP INF MANAG, V29, P525, DOI 10.1108/JEIM-10-2014-0102
Farooque M, 2019, J CLEAN PROD, V228, P882, DOI 10.1016/j.jclepro.2019.04.303
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Forslund H, 2021, INT J PHYS DISTR LOG, V51, P4, DOI 10.1108/IJPDLM-08-2019-0265
Frank AG, 2019, INT J PROD ECON, V210, P15, DOI 10.1016/j.ijpe.2019.01.004
GALBRAITH JR, 1974, INTERFACES, V4, P28, DOI 10.1287/inte.4.3.28
Burin ARG, 2020, J PURCH SUPPLY MANAG, V26, DOI 10.1016/j.pursup.2020.100610
Geissdoerfer M, 2017, J CLEAN PROD, V143, P757, DOI 10.1016/j.jclepro.2016.12.048
GERBING DW, 1988, J MARKETING RES, V25, P186, DOI 10.2307/3172650
Gligor D, 2018, J OPER MANAG, V58-59, P73, DOI 10.1016/j.jom.2018.05.002
Gosling J, 2010, INT J PROD ECON, V128, P11, DOI 10.1016/j.ijpe.2009.08.029
Govindan K, 2021, TRANSPORT RES E-LOG, V149, DOI 10.1016/j.tre.2021.102279
Gupta A, 2022, ANN OPER RES, V315, P1617, DOI 10.1007/s10479-020-03913-0
Han JH, 2017, INT J PROD ECON, V187, P196, DOI 10.1016/j.ijpe.2017.02.018
Handfield R.B., 2006, J SUPPLY CHAIN MANAG, V34, P2, DOI [10.1111/j.1745-493X.1998.tb00042.x, DOI 10.1111/J.1745-493X.1998.TB00042.X]
Hitt MA, 2016, J OPER MANAG, V41, P107, DOI 10.1016/j.jom.2015.11.004
Hogevold NM, 2021, INT J PHYS DISTR LOG, V51, P281, DOI 10.1108/IJPDLM-11-2019-0336
Hou DY, 2014, J CLEAN PROD, V84, P439, DOI 10.1016/j.jclepro.2014.01.054
Ivanov D, 2021, INT J PHYS DISTR LOG, V51, P97, DOI 10.1108/IJPDLM-03-2021-409
Jazairy A, 2021, INT J PHYS DISTR LOG, V51, P25, DOI 10.1108/IJPDLM-08-2019-0237
Jin Y, 2014, INT J PROD ECON, V153, P24, DOI 10.1016/j.ijpe.2014.03.016
Kettunen J, 2015, EUR J OPER RES, V244, P892, DOI 10.1016/j.ejor.2015.02.016
Khanday SA, 2021, B ENG GEOL ENVIRON, V80, P5539, DOI 10.1007/s10064-021-02248-9
Kline R. B., 2015, PRINCIPLES PRACTICE
Kristoffersen E, 2020, J BUS RES, V120, P241, DOI 10.1016/j.jbusres.2020.07.044
Kumar RL, 2014, EUR J INFORM SYST, V23, P151, DOI 10.1057/ejis.2012.53
Kumar S, 2021, INT J PROD RES, V59, P3470, DOI 10.1080/00207543.2020.1867923
Kumar S, 2021, TECHNOL SOC, V67, DOI 10.1016/j.techsoc.2021.101737
Kumar S, 2022, INT J PRODUCT PERFOR, V71, P696, DOI 10.1108/IJPPM-10-2020-0511
Kumar S, 2021, J CLEAN PROD, V293, DOI 10.1016/j.jclepro.2021.126023
Lee H, 2014, INT J INFORM MANAGE, V34, P285, DOI 10.1016/j.ijinfomgt.2013.10.003
Li Y, 2020, INT J PROD ECON, V229, DOI 10.1016/j.ijpe.2020.107777
Lieder M, 2016, J CLEAN PROD, V115, P36, DOI 10.1016/j.jclepro.2015.12.042
Lin QJ, 2019, IEEE ACCESS, V7, P20698, DOI 10.1109/ACCESS.2019.2897792
Liu JJ, 2018, INT J PHYS DISTR LOG, V48, P794, DOI 10.1108/IJPDLM-01-2017-0049
Lloréns FJ, 2005, INT J PROD ECON, V98, P273, DOI 10.1016/j.ijpe.2004.05.011
Jabbour ABLD, 2018, ANN OPER RES, V270, P273, DOI 10.1007/s10479-018-2772-8
Luthra S, 2020, INT J PROD RES, V58, P1505, DOI 10.1080/00207543.2019.1660828
Ma SY, 2019, J CLEAN PROD, V226, P892, DOI 10.1016/j.jclepro.2019.04.134
Malhotra MK, 1998, J OPER MANAG, V16, P407, DOI 10.1016/S0272-6963(98)00021-7
Malik M, 2021, BUS STRATEG ENVIRON, V30, P3866, DOI 10.1002/bse.2845
Mehmood A, 2021, BUS STRATEGY DEV, V4, P465, DOI 10.1002/bsd2.171
Mikalef P, 2019, J BUS RES, V98, P261, DOI 10.1016/j.jbusres.2019.01.044
Mishra AA, 2009, J OPER MANAG, V27, P324, DOI 10.1016/j.jom.2008.10.001
Mishra D, 2019, MANAGE DECIS, V57, P1734, DOI 10.1108/MD-03-2018-0324
Nayal K, 2023, INT J LOGIST MANAG, V34, P304, DOI 10.1108/IJLM-01-2021-0002
Novais L, 2020, INT J LOGIST MANAG, V31, P629, DOI 10.1108/IJLM-02-2019-0052
Nunnally JC, 1978, PSYCHOMETRIC THEORY, DOI DOI 10.1037/018882
Pagell M, 2004, J OPER MANAG, V21, P629, DOI 10.1016/j.jom.2003.11.002
Paul PS, 2008, ERGONOMICS, V51, P737, DOI 10.1080/00140130701747483
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Prajogo D, 2012, INT J PROD ECON, V136, P123, DOI 10.1016/j.ijpe.2011.09.022
Rahman M, 2022, PROD PLAN CONTROL, V33, P228, DOI 10.1080/09537287.2020.1810758
Rajput S, 2020, J CLEAN PROD, V277, DOI 10.1016/j.jclepro.2020.123853
Rajput S, 2019, INT J INFORM MANAGE, V49, P98, DOI 10.1016/j.ijinfomgt.2019.03.002
Rosa P, 2020, INT J PROD RES, V58, P1662, DOI 10.1080/00207543.2019.1680896
Rovanto IK, 2021, BUS STRATEG ENVIRON, V30, P1153, DOI 10.1002/bse.2677
Sawe FB, 2021, BUS STRATEG ENVIRON, V30, P2951, DOI 10.1002/bse.2781
Sawhney R, 2006, J OPER MANAG, V24, P476, DOI 10.1016/j.jom.2005.11.008
Sethi A. K., 1990, International Journal of Flexible Manufacturing Systems, V2, P289, DOI 10.1007/BF00186471
Shaban F., 2020, UNCERTAIN SUPPLY CHA, V8, P537, DOI [10.5267/j.uscm.2020.3.002, DOI 10.5267/J.USCM.2020.3.002]
Shalender K., 2015, Global Journal of Flexible Systems Management, Volume, V16, P251, DOI DOI 10.1007/S40171-015-0097-X
Sharma M, 2021, J CLEAN PROD, V281, DOI 10.1016/j.jclepro.2020.125013
Siems E, 2021, BUS STRATEG ENVIRON, V30, P3105, DOI 10.1002/bse.2792
Sirmon DG, 2011, J MANAGE, V37, P1390, DOI 10.1177/0149206310385695
Sreedevi R, 2017, INT J PROD ECON, V193, P332, DOI 10.1016/j.ijpe.2017.07.024
Swafford PM, 2006, J OPER MANAG, V24, P170, DOI 10.1016/j.jom.2005.05.002
Tang CS, 2019, TRANSPORT RES E-LOG, V129, P1, DOI 10.1016/j.tre.2019.06.004
Thomas EF, 2014, EUR J INNOV MANAG, V17, P107, DOI 10.1108/EJIM-06-2013-0055
Thomé AMT, 2014, INT J PROD ECON, V158, P91, DOI 10.1016/j.ijpe.2014.07.024
Huynh TLD, 2020, TECHNOL FORECAST SOC, V159, DOI 10.1016/j.techfore.2020.120188
Tsang YP, 2022, J MANUF SYST, V62, P777, DOI 10.1016/j.jmsy.2021.02.003
United Nations, 2016, SUST DEV GOALS REP 2
Wang JR, 2020, BUS STRATEG ENVIRON, V29, P361, DOI 10.1002/bse.2369
Xu J, 2021, INT J PHYS DISTR LOG, V51, P656, DOI 10.1108/IJPDLM-04-2020-0129
Yadav S, 2021, J ENTERP INF MANAG, V34, P292, DOI 10.1108/JEIM-11-2019-0369
Yang J, 2014, INT J PROD ECON, V150, P104, DOI 10.1016/j.ijpe.2013.12.018
Zhang QY, 2003, J OPER MANAG, V21, P173, DOI 10.1016/S0272-6963(02)00067-0
Zhang QY, 2002, INT J PROD RES, V40, P561, DOI 10.1080/00207540110091695
Zhang Z, 2000, INT J OPER PROD MAN, V20, P496, DOI 10.1108/01443570010314818
Zouari D, 2021, INT J PHYS DISTR LOG, V51, P149, DOI 10.1108/IJPDLM-01-2020-0038
NR 107
TC 52
Z9 52
U1 42
U2 194
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0964-4733
EI 1099-0836
J9 BUS STRATEG ENVIRON
JI Bus. Strateg. Environ.
PD MAR
PY 2022
VL 31
IS 3
BP 1058
EP 1073
DI 10.1002/bse.2935
EA NOV 2021
PG 16
WC Business; Environmental Studies; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Environmental Sciences & Ecology
GA ZL5MM
UT WOS:000719263200001
DA 2024-03-27
ER
PT J
AU Jenneboer, L
Herrando, C
Constantinides, E
AF Jenneboer, Liss
Herrando, Carolina
Constantinides, Efthymios
TI The Impact of Chatbots on Customer Loyalty: A Systematic Literature
Review
SO JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
LA English
DT Article
DE chatbots; trust; satisfaction; commitment; customer experience; loyalty;
service quality; digital marketing; information quality; privacy
ID SATISFACTION; EXPERIENCE; TRUST
AB More and more companies have implemented chatbots on their websites to provide support to their visitors on a 24/7 basis. The new customer wants to spend less and less time and therefore expects to reach a company anytime and anywhere, regardless of time, location, and channel. This study provides insight into the influence of chatbots on customer loyalty. System quality, service quality, and information quality are crucial dimensions that a chatbot must meet to give a good customer experience. To make a chatbot more personal, companies can alter the language style. Human-like chatbots lead to greater satisfaction and trust among customers, leading to greater adoption of the chatbot. The results of this study showed that a connection between chatbots and customer loyalty is very likely. Besides, some customers suffer from the privacy paradox because of personalization. Implications of this study are discussed.
C1 [Jenneboer, Liss] Univ Twente, Dept Commun Sci, NL-7522 NH Enschede, Netherlands.
[Herrando, Carolina] Univ Zaragoza, Dept Mkt, Zaragoza 50009, Spain.
[Constantinides, Efthymios] Univ Twente, Dept High Tech Business & Entrepreneurship, NL-7522 NH Enschede, Netherlands.
C3 University of Twente; University of Zaragoza; University of Twente
RP Jenneboer, L (autor correspondiente), Univ Twente, Dept Commun Sci, NL-7522 NH Enschede, Netherlands.
EM l.jenneboer@student.utwente.nl; cherrando@unizar.es;
e.constantinides@utwente.nl
RI ; Herrando, Carolina/H-4286-2015; Constantinides, Efthymios/C-9806-2013
OI Jenneboer, Liss/0000-0002-5102-3451; Herrando,
Carolina/0000-0002-2653-2473; Constantinides,
Efthymios/0000-0002-9396-7071
CR Akhtar M, 2019, CONF BUS INFORM, P397, DOI 10.1109/CBI.2019.00052
Ameen N, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106761
[Anonymous], 2018, GARTNER SAYS 25 PERC
[Anonymous], 1994, J MARK THEORY PRACT, DOI DOI 10.1080/10696679.1994.11501650
[Anonymous], MSI Research Priorities 2020-2022
Carvajal SA, 2011, AFR J BUS MANAGE, V5, P13187, DOI 10.5897/AJBM11.1162
Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
Armstrong G, 2010, PRINCIPLES MARKETING
Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
Ba SL, 2008, PROD OPER MANAG, V17, P107, DOI 10.3401/poms.1070.0006
Balakrishnan J, 2021, PSYCHOL MARKET, V38, P643, DOI 10.1002/mar.21462
Borau S, 2021, PSYCHOL MARKET, V38, P1052, DOI 10.1002/mar.21480
Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
Bryant A., 2002, J ELECTRON COMMER RE, V3, P32
Chattaraman V, 2012, COMPUT HUM BEHAV, V28, P2055, DOI 10.1016/j.chb.2012.06.009
Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60
Elsholz E, 2019, PROCEEDINGS OF THE 2019 CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL (CHIIR'19), P301, DOI 10.1145/3295750.3298956
Folstad A, 2019, PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON CONVERSATIONAL USER INTERFACES (CUI 2019), DOI 10.1145/3342775.3342784
Folstad A, 2018, LECT NOTES COMPUT SC, V11193, P194, DOI 10.1007/978-3-030-01437-7_16
FORNELL C, 1992, J MARKETING, V56, P6, DOI 10.2307/1252129
Gnewuch Ulrich, 2018, ECIS
Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
Goot Margot J., 2021, Chatbot Research and Design. 4th International Workshop, CONVERSATIONS 2020. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12604), P190, DOI 10.1007/978-3-030-68288-0_13
Gutierrez A, 2019, COMPUT HUM BEHAV, V95, P295, DOI 10.1016/j.chb.2018.09.015
Hagberg J, 2017, J RETAIL CONSUM SERV, V39, P264, DOI 10.1016/j.jretconser.2017.08.005
Hallam C, 2017, COMPUT HUM BEHAV, V68, P217, DOI 10.1016/j.chb.2016.11.033
Hallowell R, 1996, INT J SERV IND MANAG, V7, P27, DOI 10.1108/09564239610129931
Herrmann A, 2007, J PROD BRAND MANAG, V16, P49, DOI 10.1108/10610420710731151
Hokanson S., 1995, MARKETING NEWS, V29, P16
Hoy Matthew B., 2018, Medical Reference Services Quarterly, V37, P81, DOI 10.1080/02763869.2018.1404391
Hoyer WD, 2020, J INTERACT MARK, V51, P57, DOI 10.1016/j.intmar.2020.04.001
Hwang Shinhee, 2019, LNCS, P222, DOI DOI 10.1007/978-3-030-23570-317
Johari Nurul Muizzah, 2019, Advances in Visual Informatics. 6th International Visual Informatics Conference, IVIC 2019. Proceedings. Lecture Notes in Computer Science (LNCS 11870), P135, DOI 10.1007/978-3-030-34032-2_13
Kokolakis S, 2017, COMPUT SECUR, V64, P122, DOI 10.1016/j.cose.2015.07.002
Kormpho P, 2018, 2018 SEVENTH ICT INTERNATIONAL STUDENT PROJECT CONFERENCE (ICT-ISPC), P176
Kvale Knut, 2020, Chatbot Research and Design. Third International Workshop, CONVERSATIONS 2019. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 11970), P187, DOI 10.1007/978-3-030-39540-7_13
LAKE DG, 1995, HUM RESOURCE MANAGE, V34, P463, DOI 10.1002/hrm.3930340308
Leck D., 1992, EMPLOY RESPONSIB RIG, V5, P219, DOI [DOI 10.1007/BF01385049, 10.1007/BF01385049]
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Liebrecht C., 2021, Chatbot research and design, V12604, P16, DOI [10.1007/978-3-030-68288-0_2, DOI 10.1007/978-3-030-68288-0_2]
Ling EC, 2021, PSYCHOL MARKET, V38, P1031, DOI 10.1002/mar.21491
Lyngdoh T, 2021, J BUS RES, V122, P518, DOI 10.1016/j.jbusres.2020.09.031
Maroengsit W, 2019, PROCEEDINGS OF 2019 7TH INTERNATIONAL CONFERENCE ON INFORMATION AND EDUCATION TECHNOLOGY (ICIET 2019), P111, DOI 10.1145/3323771.3323824
Martin KD, 2020, J RETAILING, V96, P474, DOI 10.1016/j.jretai.2020.08.003
McLean G, 2016, COMPUT HUM BEHAV, V60, P602, DOI 10.1016/j.chb.2016.02.084
McMullan R, 2008, EUR J MARKETING, V42, P1084, DOI 10.1108/03090560810891154
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Moriuchi E, 2021, PSYCHOL MARKET, V38, P21, DOI 10.1002/mar.21407
Nordheim CB, 2019, INTERACT COMPUT, V31, P317, DOI 10.1093/iwc/iwz022
Pratminingsih R., 2013, International Journal of Trade, Economics and Finance, V4, P104, DOI DOI 10.7763/IJTEF.2013.V4.268
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Rajaobelina L, 2021, PSYCHOL MARKET, V38, P2339, DOI 10.1002/mar.21548
Rese A, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102176
Rossmann Alexander, 2020, Advances in the Human Side of Service Engineering. Proceedings of the AHFE 2020 Virtual Conference on The Human Side of Service Engineering. Advances in Intelligent Systems and Computing (AISC 1208), P237, DOI 10.1007/978-3-030-51057-2_33
RUST RT, 1993, J RETAILING, V69, P193, DOI 10.1016/0022-4359(93)90003-2
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Shoemaker S., 1999, International Journal of Hospitality Management, V18, P345, DOI 10.1016/S0278-4319(99)00042-0
Sidaoui K, 2020, J SERV MANAGE, V31, P745, DOI 10.1108/JOSM-11-2019-0341
Siswi A.A., 2020, Manag. Anal. J, V9, P17, DOI [10.15294/maj.v9i1.34427, DOI 10.15294/MAJ.V9I1.34427]
Suwono L.V., 2016, Jurnal Dinamika Manajemen, V7, P45
Sweezy M., 2019, SALESFORCE 0804
Toader DC, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12010256
Trivedi J, 2019, J INTERNET COMMER, V18, P91, DOI 10.1080/15332861.2019.1567188
Nguyen TM, 2022, J CONSUM BEHAV, V21, P481, DOI 10.1002/cb.1974
van der Goot M. J., 2020, LECT NOTES COMP SCI, V11970, P173, DOI [10.1007/978-3-030-39540-7_12, DOI 10.1007/978-3-030-39540-7_12]
van Vuuren T, 2012, SOUTH AFR BUS REV, V16, P81
Xu AB, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P3506, DOI 10.1145/3025453.3025496
Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
Youn S, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106721
NR 72
TC 31
Z9 31
U1 24
U2 122
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 0718-1876
J9 J THEOR APPL EL COMM
JI J. Theor. Appl. Electron. Commer. Res.
PD MAR
PY 2022
VL 17
IS 1
BP 212
EP 229
DI 10.3390/jtaer17010011
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA 0B7GD
UT WOS:000774798100001
OA Green Published, gold
DA 2024-03-27
ER
PT J
AU Qin, HL
Xiao, J
Ge, DD
Xin, LW
Gao, JJ
He, SM
Hu, HD
Carlsson, JG
AF Qin, Hengle
Xiao, Jun
Ge, Dongdong
Xin, Linwei
Gao, Jianjun
He, Simai
Hu, Haodong
Carlsson, John Gunnar
TI JD.com: Operations Research Algorithms Drive Intelligent Warehouse
Robots to Work
SO INFORMS JOURNAL ON APPLIED ANALYTICS
LA English
DT Article
DE intelligent warehouse; robotic system; automatic guided vehicle (AGV);
integer program; cutting planes; dispatching; e-commerce; order picking;
order fulfillment; Edelman Award
AB JD.com pioneered same-day delivery as a standard service in China's business to-consumer e-commerce sector in 2010. To balance the urgent need to meet growing demands while maintaining high-quality logistics services, the company built intelligent warehouses that use analytics to significantly improve warehouse efficiency. The brain of the intelligent warehouse system is the dispatching algorithm for storage rack-moving robots, which makes real-time dispatching decisions among robots, racks, and workstations after solving large-scale integer programs in seconds. The intelligent warehouse technology has helped the company decrease its fulfillment expense ratio to a world-leading level of 6.5%. The construction of intelligent warehouses has led to estimated annual savings of hundreds of millions of dollars. In 2020, JD.com delivered 90% of its first-party-owned retail orders on the same day or on the day after the order was placed. The agility of such intelligent warehouses has allowed JD.com to handle 10 times the normal volume of orders during peak sales seasons and has also helped the company respond quickly to COVID-19 and ensure the rapid recovery of production capabilities.
C1 [Qin, Hengle; Ge, Dongdong; Gao, Jianjun; He, Simai; Hu, Haodong] Shanghai Univ Finance & Econ, Res Inst Interdisciplinary Sci, Shanghai 200433, Peoples R China.
[Qin, Hengle; Xiao, Jun] JDcom, JD Logist, Dept AI & Big Data, Beijing 100176, Peoples R China.
[Xin, Linwei] Univ Chicago, Booth Sch Business, Chicago, IL 60637 USA.
[Carlsson, John Gunnar] Univ Southern Calif, Dept Ind & Syst Engn, Los Angeles, CA 90089 USA.
C3 Shanghai University of Finance & Economics; University of Chicago;
University of Southern California
RP Qin, HL (autor correspondiente), Shanghai Univ Finance & Econ, Res Inst Interdisciplinary Sci, Shanghai 200433, Peoples R China.; Qin, HL (autor correspondiente), JDcom, JD Logist, Dept AI & Big Data, Beijing 100176, Peoples R China.
EM qinhengle@jd.com; xiaojun@jd.com; ge.dongdong@shufe.edu.cn;
linwei.xin@chicagobooth.edu; gao.jianjun@shufe.edu.cn;
simaihe@mail.shufe.edu.cn; hu.haodong@shufe.edu.cn; jcarlsso@usc.edu
RI Xin, Linwei/IQR-5058-2023; Xin, Linwei/D-9249-2015
OI Xin, Linwei/0000-0002-8160-6877;
FU National Natural Science Foundation of China [71825003]
FX D. Ge is partially supported by the National Natural Science Foundation
of China [Grants 11831002, 11471205, and 72150001]. J. Gao is partially
supported by the National Natural Science Foundation of China [Grants
71971132 and 61573244]. S. He is partially supported by the National
Natural Science Foundation of China [Grant 71825003].
CR [Anonymous], 2017, CCTV 4
[Anonymous], 2017, CHINA DAILY
[Anonymous], 2018, JD
[Anonymous], 2017, JD
Azadeh K, 2019, TRANSPORT SCI, V53, P917, DOI 10.1287/trsc.2018.0873
Banker S, 2016, ROBOTS WAREHOUSE ITS
Bartholdi J., 2016, Warehouse Distribution Science - Release 0.97. The Supply Chain
Bersimas D, 1997, INTRO LINEAR OPTIMIZ
Boysen N, 2017, EUR J OPER RES, V262, P550, DOI 10.1016/j.ejor.2017.03.053
Burkard R. E., 1999, Linear Assignment Problems and Extensions, P75, DOI [10.1007/978-1-4757-3023-4{_}2, DOI 10.1007/978-1-4757-3023-4_2.]
CCTV, 2018, INTELLIGENCE DRIVES
Cezik T, 2021, VELOCITY BASED STOWA, DOI [10.2139/ssrn.3784731, DOI 10.2139/SSRN.3784731]
Eisenstein DD, 2008, NAV RES LOG, V55, P350, DOI 10.1002/nav.20289
Huang E., 2017, In China, a robot has started delivering packages to people
Lin J, 2017, E COMMERCE GIANT BUI
Mountz M.C., 2008, US Patent, Patent No. [7,402,018, 7402018]
Palmer A, 2018, CHINESE E COMMERCE C
Sierksma G., 2015, Linear and integer optimization: theory and practice
Wang Z, 2022, PROD OPER MANAG, V31, P98, DOI 10.1111/poms.13406
Wulfraat M., 2012, SUPPLY CHAIN CONSULT
Wurman PR, 2008, AI MAG, V29, P9
Yuan R, 2019, PROD OPER MANAG, V28, P354, DOI 10.1111/poms.12925
Yuan R, 2018, INT J PROD RES, V56, P333, DOI 10.1080/00207543.2017.1398428
NR 23
TC 13
Z9 15
U1 22
U2 82
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 2644-0865
EI 2644-0873
J9 INFORMS J APPL ANAL
JI INFORMS J. Appl. Anal.
PD JAN-FEB
PY 2022
VL 52
IS 1
BP 42
EP 55
DI 10.1287/inte.2021.1100
PG 15
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA YV8KA
UT WOS:000752972300005
DA 2024-03-27
ER
PT J
AU Song, CL
AF Song, Chunlai
TI Enhancing Multimodal Understanding With LIUS: A Novel Framework for
Visual Question Answering in Digital Marketing
SO JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING
LA English
DT Article
DE Digital Marketing; Feature Extraction and Fusion; Image Features; LLM;
Text Information; Text-Image Matching; VQA
AB VQA (visual question and answer) is the task of enabling a computer to generate accurate textual answers based on given images and related questions. It integrates computer vision and natural language processing and requires a model that is able to understand not only the image content but also the question in order to generate appropriate linguistic answers. However, current limitations in cross-modal understanding often result in models that struggle to accurately capture the complex relationships between images and questions, leading to inaccurate or ambiguous answers. This research aims to address this challenge through a multifaceted approach that combines the strengths of vision and language processing. By introducing the innovative LIUS framework, a specialized vision module was built to process image information and fuse features using multiple scales. The insights gained from this module are integrated with a "reasoning module" (LLM) to generate answers.
C1 [Song, Chunlai] Kyungil Univ, Dept Global Business, Gyongsan, South Korea.
C3 Kyungil University
RP Song, CL (autor correspondiente), Kyungil Univ, Dept Global Business, Gyongsan, South Korea.
RI hu, guangchen/KEI-6324-2024; CHUN LAI, SONG/AGM-6731-2022
CR Aishwarya R, 2022, ANNU IEEE IND CONF, DOI 10.1109/INDICON56171.2022.10040113
Akula AR, 2021, 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), P2148
Alayrac JB., 2022, ADV NEURAL INFORM PR, V35, P23716, DOI DOI 10.48550/ARXIV.2204.14198
Anderson P, 2018, PROC CVPR IEEE, P6077, DOI 10.1109/CVPR.2018.00636
Antol S, 2015, IEEE I CONF COMP VIS, P2425, DOI 10.1109/ICCV.2015.279
Bao H., 2022, Adv. Neural Inf. Process. Syst., V35, P32897
Chen CQ, 2022, PATTERN RECOGN, V132, DOI 10.1016/j.patcog.2022.108980
Firat M., 2023, Journal of Applied Learning and Teaching, V6
Guo JX, 2023, PROC CVPR IEEE, P10867, DOI 10.1109/CVPR52729.2023.01046
Gupta V, 2022, PROC CVPR IEEE, P5068, DOI 10.1109/CVPR52688.2022.00502
Hamza A., 2023, ACM Transactions on Asian and Low-Resource Language Information Processing
Han K, 2023, IEEE T PATTERN ANAL, V45, P87, DOI 10.1109/TPAMI.2022.3152247
Hou R, 2020, SIGNAL PROCESS-IMAGE, V83, DOI 10.1016/j.image.2020.115782
Li J., 2021, Advances in neural information processing systems, V34, P9694, DOI DOI 10.48550/ARXIV.2107.07651
Li J., 2022, INT C MACHINE LEARNI
Li X., 2023, P IEEECVF INT C COMP
Li X., 2020, COMPUTER VISION ECCV
Li Y., 2021, TAM at VQA-Med 2021: A Hybrid Model with Feature Extraction and Fusion for Medical Visual Question Answering
Lu JS, 2019, ADV NEUR IN, V32
Lu SY, 2023, INT J COMPUT INT SYS, V16, DOI 10.1007/s44196-023-00233-6
Marino K, 2019, PROC CVPR IEEE, P3190, DOI 10.1109/CVPR.2019.00331
Schwenk D., 2022, EUROPEAN C COMPUTER
Shen S., 2021, arXiv preprint arXiv:2107.06383
Shwartz V., Science and Technology, V37, P38, DOI [10.1109/CVPR.2019.00331, DOI 10.1109/CVPR.2019.00331]
Vaswani A, 2017, ADV NEUR IN, V30
Whalen J., 2023, Contemporary Issues in Technology & Teacher Education, V23, P1
Yang ZY, 2022, AAAI CONF ARTIF INTE, P3081
Yang ZC, 2016, PROC CVPR IEEE, P21, DOI 10.1109/CVPR.2016.10
Yu JH, 2022, Arxiv, DOI [arXiv:2205.01917, DOI 10.48550/ARXIV.2205.01917]
Zhang LT, 2023, IEEE INT CONF COMP V, P4631, DOI 10.1109/ICCVW60793.2023.00497
Zhang LM, 2022, MULTIMED TOOLS APPL, V81, P9277, DOI 10.1007/s11042-021-11549-w
Zhang SS, 2022, Arxiv, DOI [arXiv:2205.01068, DOI 10.48550/ARXIV.2205.01068, 10.48550/arXiv.2205.01068]
NR 32
TC 0
Z9 0
U1 1
U2 1
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1546-2234
EI 1546-5012
J9 J ORGAN END USER COM
JI J. Organ. End User Comput.
PY 2024
VL 36
IS 1
AR 336276
DI 10.4018/JOEUC.336276
PG 17
WC Computer Science, Information Systems; Information Science & Library
Science; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Business &
Economics
GA II1V4
UT WOS:001165615200012
OA gold
DA 2024-03-27
ER
PT J
AU Lemardelé, C
Estrada, M
Pagès, L
Bachofner, M
AF Lemardele, Clement
Estrada, Miquel
Pages, Laia
Bachofner, Monika
TI Potentialities of drones and ground autonomous delivery devices for
last-mile logistics
SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
LA English
DT Article
DE Last-mile logistics; E-commerce; Continuous approximation; Delivery
drones; Autonomous delivery robot; Operation costs; Externalities;
Parcel lockers; Paris (France); Barcelona (Spain)
ID LED CONSOLIDATION STRATEGIES; SAME-DAY DELIVERY; TRUCK; EFFICIENCY;
VEHICLES; EXPRESS; COURIER; FRANCE
AB The e-commerce boom has increased the complexity of last-mile logistics operations in urban environments. In this context, unmanned aerial vehicles (UAVs), also known as delivery drones, and ground autonomous delivery devices (GADDs) show great potentialities. The objective of this paper is to provide strategic insights to adequately match these autonomous technologies with some given characteristics of cities and help define relevant decision variables. Using continuous approximation equations, the operations costs as well as the externalities induced by a) GADDs in association with an urban consolidation center (UCC) and b) truck-launched UAVs are estimated. Then, the developed mathematical formulations are applied in two different use cases: a part of the Paris suburbs (France) and the historical center of Barcelona (Spain). In less dense and larger service regions such as the Paris suburbs, truck-launched delivery drones seem more suitable to reduce the carriers? operations costs. In denser neighborhoods such as the Barcelona historical center, GADDs are expected to be more economically profitable. In both use cases, GADDs would generate less externalities. Finally, considering the high uncertainty of some input parameters, a sensitivity analysis of the models is done.
C1 [Lemardele, Clement] Univ Politecn Catalunya Barcelona TECH, Civil Engn Sch Barcelona, C Jordi Girona 1-3,Edificio Omega, Barcelona 08034, Spain.
[Estrada, Miquel] Univ Politecn Catalunya Barcelona TECH, Civil Engn Sch Barcelona, C Jordi Girona 1-3,B1-110, Barcelona 08034, Spain.
[Pages, Laia; Bachofner, Monika] CARNET Barcelona, C Jordi Girona 1-3,Edificio Omega, Barcelona 08034, Spain.
C3 Universitat Politecnica de Catalunya; Universitat Politecnica de
Catalunya
RP Lemardelé, C (autor correspondiente), Univ Politecn Catalunya Barcelona TECH, Civil Engn Sch Barcelona, C Jordi Girona 1-3,Edificio Omega, Barcelona 08034, Spain.
EM clement.lemardele@upc.edu; miquel.estrada@upc.edu;
laia.pages@carnetbarcelona.com; monika.bachofner@carnetbarcelona.com
RI Estrada, Miquel/L-3294-2017
OI Estrada, Miquel/0000-0002-5114-7796; Lemardele,
Clement/0000-0003-1673-6509
CR [Anonymous], 2015, NCFRP38
APUR, 2019, POP DENS
Barcelona city council, 2018, CIUT VELL WEB PAG
Barthuly D., 2019, AUTONOMOUS GROUND VE
Boysen N, 2018, EUR J OPER RES, V271, P1085, DOI 10.1016/j.ejor.2018.05.058
Burns S., 2017, Drone meets delivery truck
Campbell J.F., 2017, SUSTAINABILITY SWITZ
Carlsson JG, 2018, MANAGE SCI, V64, P4052, DOI 10.1287/mnsc.2017.2824
Cepolina E.M., 2014, ROUTING PROBLEM INNO
Cloete S., 2017, SEEKING CONSENSUS EX
Dablanc L., 2015, PARKING FREIGHT VEHI
Daganzo Carlos, 2005, LOGISTICS SYSTEMS AN
DAGANZO CF, 1984, TRANSPORT RES B-METH, V18, P135, DOI 10.1016/0191-2615(84)90027-4
Dayarian I, 2020, TRANSPORT SCI, V54, P229, DOI 10.1287/trsc.2019.0944
Dieke A., 2018, ASSESSMENT EU PARCEL
Ducret R, 2014, RES TRANSP BUS MANAG, V11, P15, DOI 10.1016/j.rtbm.2014.06.009
EEA, 2019, EEA Report, DOI DOI 10.2800/293657
Estrada M, 2017, TRANSPORT RES E-LOG, V104, P165, DOI 10.1016/j.tre.2017.06.009
Estrada M, 2018, TRANSPORT-VILNIUS, V33, P930, DOI 10.3846/transport.2018.6058
European Commission, 2019, Handbook on the external costs of transport
Eurostat, 2019, EL PRIC INCL TAX HOU
Eurostat, 2019, EL GEN STAT
FedEx, 2020, BOX PACK SHIPP MOV
Feng W, 2013, TRANSPORT RES C-EMER, V26, P135, DOI 10.1016/j.trc.2012.06.007
Figliozzi MA, 2017, TRANSPORT RES D-TR E, V57, P251, DOI 10.1016/j.trd.2017.09.011
Giordano A, 2018, TRANSPORT RES D-TR E, V64, P216, DOI 10.1016/j.trd.2017.10.003
Goodchild A, 2018, TRANSPORT RES D-TR E, V61, P58, DOI 10.1016/j.trd.2017.02.017
Hoffmann T, 2018, MACHINES, V6, DOI 10.3390/machines6030033
Holguín-Veras J, 2014, PROCD SOC BEHV, V125, P36, DOI 10.1016/j.sbspro.2014.01.1454
Jennings D, 2019, TRANSPORT RES REC, V2673, P317, DOI 10.1177/0361198119849398
Keen K, 2020, BATTERY COSTS PLUMME
Keeney T., 2015, CAN AMAZON CHARGE 1
Kirschstein T, 2020, TRANSPORT RES D-TR E, V78, DOI 10.1016/j.trd.2019.102209
Lee H. N., 2016, SCI REPORTS, P1
Li Y., 2018, Enterprise Inf. Syst., V00, P1
Macharis C., 2012, DESCRIPTION EVALUATI
Marks M., 2019, SSRN ELECT J
Mazareanu E., 2020, SAME DAY DELIVERY MA
Morganti E, 2018, TRANSPORT POLICY, V63, P90, DOI 10.1016/j.tranpol.2017.12.010
Morganti E, 2014, TRANSP RES PROC, V4, P178, DOI 10.1016/j.trpro.2014.11.014
Moshref-Javadi M, 2020, APPL MATH MODEL, V80, P290, DOI 10.1016/j.apm.2019.11.020
Murray CC, 2015, TRANSPORT RES C-EMER, V54, P86, DOI 10.1016/j.trc.2015.03.005
Okholm H., 2013, E-commerce and delivery
Palmer M., 2020, EUROPES 6 WHEELED DE
Pierce D., 2013, VERGE
Ponza A., 2016, THESIS U
Raimbault N., 2014, THESIS
Ranieri L, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10030782
Roca-Riu M, 2019, 19 SWISS TRANSPORT R
Roca-Riu M., 2016, TRANSP RES PROCEDIA, V12
Samadi S, 2017, ENERGIES, V10, DOI 10.3390/en10030356
SeLoger.com, 2020, PRIX LOCATION M2
Stolaroff JK, 2018, NAT COMMUN, V9, DOI 10.1038/s41467-017-02411-5
Sudbury A.W., 2016, J ECON EDUC, V16, P1
The World Bank, 2020, GDP per capita (current US$)
Ulmer MW, 2019, COMPUT OPER RES, V108, P1, DOI 10.1016/j.cor.2019.03.017
Ulmer MW, 2018, NETWORKS, V72, P475, DOI 10.1002/net.21855
van Duin JHR, 2013, EUR TRANSP
van Duin JHR, 2016, TRANSP RES PROC, V12, P14, DOI 10.1016/j.trpro.2016.02.006
van Duin J.H. R., Transp. Res. Procedia, V46, P37, DOI DOI 10.1016/J.TRPRO.2020.03.161
Wang D., 2019, IEEE Internet of Things Journal, V6, p10 483
Zenezini G, 2018, IFAC PAPERSONLINE, V51, P594, DOI 10.1016/j.ifacol.2018.08.383
Zhang J, 2021, TRANSPORT RES D-TR E, V90, DOI 10.1016/j.trd.2020.102668
NR 63
TC 66
Z9 69
U1 24
U2 184
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1366-5545
EI 1878-5794
J9 TRANSPORT RES E-LOG
JI Transp. Res. Pt. e-Logist. Transp. Rev.
PD MAY
PY 2021
VL 149
AR 102325
DI 10.1016/j.tre.2021.102325
EA APR 2021
PG 51
WC Economics; Engineering, Civil; Operations Research & Management Science;
Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
Science; Transportation
GA RY2YZ
UT WOS:000647784100004
OA Green Submitted
DA 2024-03-27
ER
PT J
AU Aghimien, D
Ikuabe, M
Aghimien, LM
Aigbavboa, C
Ngcobo, N
Yankah, J
AF Aghimien, Douglas
Ikuabe, Matthew
Aghimien, Lerato Millicent
Aigbavboa, Clinton
Ngcobo, Ntebo
Yankah, Jonas
TI PLS-SEM assessment of the impediments of robotics and automation
deployment for effective construction health and safety
SO JOURNAL OF FACILITIES MANAGEMENT
LA English
DT Article; Early Access
DE Health; Automation; Construction; Safety; Robotics; South Africa
ID TECHNOLOGY; MECHANIZATION; BARRIERS; RECOVERY; BEHAVIOR; ISSUES
AB Purpose The importance of robotics and automation (R&A) in delivering a safe built environment cannot be overemphasised. This is because R&A systems can execute a hazardous job function that the construction workforce may not execute. Based on this knowledge, this study aims to present the result of an assessment of the impediments to the deployment of R&A for a safe and healthy construction environment. Design/methodology/approach This study adopted a post-positivist philosophical stance, using a quantitative research approach and a questionnaire administered to construction professionals in South Africa. The data gathered were analysed using frequency, percentage, mean item score, Kruskal-Wallis H-test, exploratory factor analysis and partial least square structural equation modelling (SEM). Findings This study revealed that the impediments to the deployment of R&A could be grouped into: industry, technology, human and cost-related factors. However, SEM assessment showed that only the industry, human and cost-related factors would significantly impact attaining specific health and safety-related outcomes. Practical implications The findings offer valuable benefits to construction organisations as the careful understanding of the identified impeding factors can help lead to better deployment of R&A and the attainment of its inherent safety benefits. Originality/value This study attempts to fill the gap in the shortage of literature exploring the deployment of R&A for a safe construction environment, particularly in developing countries like South Africa, where such studies are non-existent. This paper, therefore, offers a theoretical backdrop for future works on R&A deployment, particularly in developing countries where such a study has not been explored.
C1 [Aghimien, Douglas; Ngcobo, Ntebo] Univ Johannesburg, Fac Engn & Built Environm, Dept Civil Engn Technol, Johannesburg, South Africa.
[Ikuabe, Matthew; Aigbavboa, Clinton] Univ Johannesburg, Fac Engn & Built Environm, CIDB Ctr Excellence, Johannesburg, South Africa.
[Aghimien, Lerato Millicent] Univ Johannesburg, Fac Engn & Built Environm, Dept Construct Management & Quant Surveying, Johannesburg, South Africa.
[Yankah, Jonas] Cape Coast Tech Univ, Fac Built & Nat Environm, Cape Coast, Ghana.
C3 University of Johannesburg; University of Johannesburg; University of
Johannesburg
RP Aghimien, D (autor correspondiente), Univ Johannesburg, Fac Engn & Built Environm, Dept Civil Engn Technol, Johannesburg, South Africa.
EM Aghimiendouglas@gmail.com
RI Ikuabe, Matthew/AGO-2623-2022; Aigbavboa, Clinton/AAS-6493-2020;
Aghimien, Douglas Omoregie/H-4534-2018
OI Aigbavboa, Clinton/0000-0003-2866-3706;
CR Abbasi M.S., 2011, THESIS BRUNEL U LOND
Abusafiya HA., 2017, MODERN APPL SCI, V11, P28, DOI DOI 10.5539/MAS.V11N7P28
Adams M A., 2007, Movement, Stability, and Lumbopelvic Pain: Integration and Research, P167
Aghimien D., 2018, JOINT CIB W099 TG59, P337
Aghimien D.O., 2019, P 36 CIB W78 2019 C, P84
Aghimien D, 2021, CONSTR INNOV-ENGL, V21, P456, DOI 10.1108/CI-02-2020-0026
Aghimien DO, 2020, J ENG DES TECHNOL, V18, P1063, DOI 10.1108/JEDT-09-2019-0237
Aigbavboa C., 2014, THESIS U JOHANNESBUR
Akinradewo O., 2018, P INT C IND ENG OPER, V201
Akinradewo O. I., 2021, IOP Conference Series: Materials Science and Engineering, V1107, DOI 10.1088/1757-899X/1107/1/012011
Akshatha D., 2017, INT J ADV RES SCI EN, P2319
Akter S., 2011, P 17 AM C INF SYST A, P151
Bagozzi RP, 2012, J ACAD MARKET SCI, V40, P8, DOI 10.1007/s11747-011-0278-x
Bahrin MAK, 2016, J TEKNOL, V78, P137
Balaguer C., 2008, Robotics and Automation in Construction
Benitez J, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.05.003
Bin Yahya Mohd Yamani, 2019, MATEC Web of Conferences, V266, DOI 10.1051/matecconf/201926605012
Bock T., 2017, CONSTRUCTION ROBOTS
Bock T, 2015, AUTOMAT CONSTR, V59, P113, DOI 10.1016/j.autcon.2015.07.022
Bohari M., 2017, E2017106 CHALM U TEC
Byrne P., 2005, RISK UNCERTAINTY DEC, V2
Cai S., 2018, IS-ARC. Proceedings of the International Symposium on Automation and Robotics in Construction, V35, P1
Chen Q, 2018, AUTOMAT CONSTR, V94, P22, DOI 10.1016/j.autcon.2018.05.028
Chin WW, 1998, MIS QUART, V22, pVII
COHEN J, 1992, PSYCHOL BULL, V112, P155, DOI 10.1037/0033-2909.112.1.155
Cropp C, 2021, AUTOMATION ROBOTICS
Dall'Omo S., 2017, DRIVING AFRICAN DEV, P1
de Looze MP, 2016, ERGONOMICS, V59, P671, DOI 10.1080/00140139.2015.1081988
Delgado JMD, 2019, J BUILD ENG, V26, DOI 10.1016/j.jobe.2019.100868
Dimick, 2014, OTTAWATHE C BOARD CA, P1
Field A., 2013, DISCOVERING STAT USI, DOI DOI 10.1016/B978-012691360-6/50012-4
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Franke G, 2019, INTERNET RES, V29, P430, DOI 10.1108/IntR-12-2017-0515
Galal S., 2020, NUMBER PEOPLE EMPLOY
Golizadeh H, 2019, CONSTR INNOV-ENGL, V19, P149, DOI 10.1108/CI-09-2018-0074
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hampson K. D., 2014, RD INVESTMENT GLOBAL, P1, DOI 10.4324/9781315774916.
Heckathorn DD, 2011, SOCIOL METHODOL, V41, P355, DOI 10.1111/j.1467-9531.2011.01244.x
Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
Henseler J, 2014, ORGAN RES METHODS, V17, P182, DOI 10.1177/1094428114526928
Huang Z, 2022, ENG CONSTR ARCHIT MA, V29, P3664, DOI 10.1108/ECAM-03-2021-0267
Hwang H, 2010, J MARKETING RES, V47, P699, DOI 10.1509/jmkr.47.4.699
Jung K, 2013, AUTOMAT CONSTR, V32, P62, DOI 10.1016/j.autcon.2012.12.011
Kamaruddin SS, 2016, PROCD SOC BEHV, V222, P111, DOI 10.1016/j.sbspro.2016.05.197
Kamaruddin SS, 2013, PROCD SOC BEHV, V105, P106, DOI 10.1016/j.sbspro.2013.11.012
Keil M, 2000, MIS QUART, V24, P299, DOI 10.2307/3250940
Keski-Säntti P, 2010, INT ARCH OCC ENV HEA, V83, P703, DOI 10.1007/s00420-009-0493-7
Kim S., 2020, Likely impact of COVID-19 on the UK media industry
Kumar V. P., 2016, INDIAN J SCI TECHNOL, V9, P1, DOI [DOI 10.17485/ijst/2016/v9i23/95974, 10.17485/ijst/2016/v9i23/95974]
Lavikka R, 2018, CONSTR MANAG ECON, V36, P635, DOI 10.1080/01446193.2018.1476729
Lawrence M., 2017, Managing automation: Employment, inequality and ethics in the digital age: IPPR Commission on Economic Justice
Li Y, 2019, INT J CONSTR MANAG, V19, P401, DOI 10.1080/15623599.2018.1452101
Mahbub R., 2012, J CIVIL ENG ARCHITEC, V6, DOI [10.17265/1934-7359/2012.07.008, DOI 10.17265/1934-7359/2012.07.008]
Manzo J., 2019, IMPACT CONSTRUCTION
Marcoulides G.A., 2006, MIS QUART, V30, P3, DOI [https://doi.org/10.2307/25148727, DOI 10.2307/25148727]
Maryani A, 2015, PROCEDIA MANUF, V4, P392, DOI 10.1016/j.promfg.2015.11.055
Melchiorri C., 2013, ENCY SYSTEMS CONTROL
MISTRI PS, 2015, INT J ADV RES ENG SC, P1
Mone G., 2014, POPULAR SCI
Muro M., 2017, DIGITALIZATION AM WO
Mzekandaba S., 2018, Tech to contribute to job losses in SA
Oke A., 2018, Proceedings of the International Conference on Industrial Engineering and Operations Management Bandung, Indonesia, 20 March 2018, P2472
Oke AE, 2021, INT J BUILD PATHOL, DOI 10.1108/IJBPA-06-2021-0093
Pallant J., 2011, SPSS SURVIVAL MANUAL
Pan M, 2020, J MANAGE ENG, V36, DOI 10.1061/(ASCE)ME.1943-5479.0000846
Parn EA, 2019, ENG CONSTR ARCHIT MA, V26, P245, DOI 10.1108/ECAM-03-2018-0101
Preacher KJ, 2002, BEHAV GENET, V32, P153, DOI 10.1023/A:1015210025234
Rahman A.B.A., 2006, P 6 AS PAC STRUCT EN
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Ruggiero A.N., 2016, ROBOTICS CONSTRUCTIO
Sacks R., 2010, Journal of Professional Issues in Engineering Education and Practice, V136, P30, DOI 10.1061/(ASCE)EI.1943-5541.0000003
Salento A, 2018, AI SOC, V33, P369, DOI 10.1007/s00146-017-0738-z
Samari M., 2012, IMPLEMENTATION INDUS, V1, P19
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Siderska J, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13148020
Singh R., 2009, The Marketing Review, V9, P199, DOI DOI 10.1362/146934709X467767
Smallwood J.J., 2004, INT COST ENG COUNCIL
Son H, 2010, KSCE J CIV ENG, V14, P131, DOI 10.1007/s12205-010-0131-7
Strukova Z., 2013, J INTERDISCIPLINARY, V1, P121
Tabachnick G.B., 2013, Using multivariate statistics, V6th
Tan W.C.K., 2011, PRACTICAL RES METHOD, V4th ed.
Teizer J., 2016, 33 INT S AUTOMATION, P191, DOI 10.22260/isarc201 6/0024.
Tenenhaus M, 2005, COMPUT STAT DATA AN, V48, P159, DOI 10.1016/j.csda.2004.03.005
Tharshanapriya K, 2017, INDO AM J PHARM SCI, V4, P1559, DOI 10.5281/zenodo.817581
Trujillo D.J., 2020, EPiC Series in Built Environment, V1, P257, DOI DOI 10.29007/1SHP
Vähä P, 2013, AUTOMAT CONSTR, V36, P168, DOI 10.1016/j.autcon.2013.08.002
Williams B., 2010, Journal of Emergency Primary Health Care (JEPHC), V8, P1, DOI [10.33151/ajp.8.3.93, DOI 10.33151/AJP.8.3.93]
Windapo AO, 2013, J CONSTR DEV CTRIES, V18, P65
Wong K.K.-K., 2013, MARKETING B, V24, P1, DOI DOI 10.1108/EBR-10-2013-0128
Wu ZZ, 2019, INT J ENV RES PUB HE, V16, DOI 10.3390/ijerph16173140
Yang GZ, 2018, SCI ROBOT, V3, DOI 10.1126/scirobotics.aar7650
Yap JBH, 2023, J ENG DES TECHNOL, V21, P1414, DOI 10.1108/JEDT-07-2021-0379
NR 92
TC 5
Z9 5
U1 1
U2 7
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1472-5967
EI 1741-0983
J9 J FACIL MANAG
JI J. Facil. Manag.
PD 2022 AUG 31
PY 2022
DI 10.1108/JFM-04-2022-0037
EA AUG 2022
PG 21
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA 4D7EQ
UT WOS:000847302000001
OA hybrid
DA 2024-03-27
ER
PT J
AU Srivastava, P
Mishra, N
Srivastava, S
Shivani, S
AF Srivastava, Praveen
Mishra, Niraj
Srivastava, Shelly
Shivani, Shradha
TI Banking with Chatbots: The Role of Demographic and Personality Traits
SO FIIB BUSINESS REVIEW
LA English
DT Article; Early Access
DE Chatbot; banking industry; PLS-SEM; ANN
ID MOBILE-BANKING; PLS-SEM; ACCEPTANCE; METHODOLOGY; INTENTION; BUSINESS;
ADOPTION; MODELS; TRUST; HABIT
AB This research seeks to investigate the influence of performance expectancy, effort expectancy, facilitating conditions, habit and hedonic motivation on behavioural intention in the context of chatbot utilization within the banking industry. Additionally, the study explores the moderation effects of age, gender and personality type on the relationships between behavioural intention and use behaviour. The study employs a quantitative survey of banking customers, and the data have been analysed using partial least squares structural equation modelling and artificial neural network. The findings suggest that the use and acceptance of chatbots in banking are influenced by a range of factors, including performance expectancy, facilitating conditions and hedonic motivation. The study also reveals that only personality types can moderate the relationship between behavioural intentions and use behaviour. The study provides insights for banks and other financial institutions that are considering the implementation of chatbots as part of their customer service strategy.
C1 [Srivastava, Praveen] Birla Inst Technol, Dept HMCT, Ranchi, Jharkhand, India.
[Mishra, Niraj; Srivastava, Shelly; Shivani, Shradha] Birla Inst Technol Mesra, Dept Management, Ranchi, Jharkhand, India.
[Mishra, Niraj] Birla Inst Technol, Dept Management, Ranchi 835215, Jharkhand, India.
C3 Birla Institute of Technology Mesra; Birla Institute of Technology
Mesra; Birla Institute of Technology Mesra
RP Mishra, N (autor correspondiente), Birla Inst Technol, Dept Management, Ranchi 835215, Jharkhand, India.
EM nirajmishra@bitmesra.ac.in
RI Srivastava, Praveen/AAU-6208-2021
OI Srivastava, Praveen/0000-0001-5310-694X; Mishra,
Niraj/0000-0001-9593-0312
CR Aburayya A., 2022, INFORM MED UNLOCKED, V28, P100859, DOI DOI 10.1016/J.IMU.2022.100859
Adamopoulou E., 2020, MACHINE LEARNING APP, V2, DOI DOI 10.1016/J.MLWA.2020.100006
Al-Azawei A, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101325
Al-Emran M, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3237789
Al-Okaily M, 2023, KYBERNETES, V52, P4876, DOI 10.1108/K-08-2021-0710
Alalwan AA, 2015, J FINANC SERV MARK, V20, P145, DOI 10.1057/fsm.2015.5
Alomari ASA, 2023, COGENT BUS MANAG, V10, DOI 10.1080/23311975.2023.2178092
Alsmadi AA, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141710630
Aruldoss A, 2023, J MARKET THEORY PRAC, DOI 10.1080/10696679.2023.2221442
Bouhia M, 2022, INT J BANK MARK, V40, P1159, DOI 10.1108/IJBM-09-2021-0442
Caldarini G, 2022, INFORMATION, V13, DOI 10.3390/info13010041
Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
Chen Q, 2023, INTERNET RES, V33, P2205, DOI 10.1108/INTR-09-2021-0686
Chen WL, 2020, RES PRACT TECH ENHAN, V15, DOI 10.1186/s41039-020-00127-7
Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
Chiou JS, 2012, BEHAV INFORM TECHNOL, V31, P859, DOI 10.1080/0144929X.2010.549509
Cohen J., 1988, Statistical power analysis for the behavioral sciences, V2nd ed., DOI DOI 10.4324/9780203771587
Cubric M, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101257
De Andrade IM, 2022, REGE-REV GEST, V29, P238, DOI 10.1108/REGE-07-2021-0120
de Guinea AO, 2009, MIS QUART, V33, P433
Dehnert M, 2022, ELECTRON MARK, V32, P1503, DOI 10.1007/s12525-022-00524-4
Dinh CM, 2023, ELECTRON COMMER RES, DOI 10.1007/s10660-022-09662-5
Dreiseitl S, 2002, J BIOMED INFORM, V35, P352, DOI 10.1016/S1532-0464(03)00034-0
Nguyen DM, 2022, J CONSUM BEHAV, V21, P1219, DOI 10.1002/cb.2073
Nguyen DM, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13147625
Eren BA, 2021, INT J BANK MARK, V39, P294, DOI 10.1108/IJBM-02-2020-0056
Faqih KMS, 2022, TECHNOL SOC, V69, DOI 10.1016/j.techsoc.2022.101958
Farah MF, 2018, INT J BANK MARK, V36, P1386, DOI 10.1108/IJBM-10-2017-0215
Farooq MS, 2017, INTERACT TECHNOL SMA, V14, P329, DOI 10.1108/ITSE-06-2016-0015
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Foster JK, 2022, J RES INTERACT MARK, V16, P237, DOI 10.1108/JRIM-01-2020-0007
Gatzioufa P., 2022, Applied Computing and Informatics, DOI [10.1108/ACI-01-2022-0021/FULL/PDF, DOI 10.1108/ACI-01-2022-0021/FULL/PDF]
Gunasinghe A, 2020, INT J TECHNOL ENHANC, V12, P458, DOI 10.1504/IJTEL.2020.110056
Gunasinghe A, 2019, INTERACT TECHNOL SMA, V17, P86, DOI 10.1108/ITSE-05-2019-0020
Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
Husain A. R. A. M., 2022, Future of organizations and work after the 4th industrial revolution: The role of artificial intelligence, big data, automation, and robotics, P145
Illescas-Manzano M., 2021, Journal of Open Innovation, V7, P125, DOI [10.3390/joitmc7020125, DOI 10.3390/JOITMC7020125]
Jang M, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106747
Janssen A, 2022, INT J HUM-COMPUT ST, V168, DOI 10.1016/j.ijhcs.2022.102921
Jiang Y, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107485
John O. P., 1999, HDB PERSONALITY THEO, P102, DOI DOI 10.1525/FQ.1998.51.4.04A00260
Kallel A, 2023, J STRATEG MARK, DOI 10.1080/0965254X.2023.2175020
Kasilingam DL, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101280
Kaushal V, 2023, CONCURR COMP-PRACT E, V35, DOI 10.1002/cpe.7450
Kumar J, 2024, COMPET REV, V34, P92, DOI 10.1108/CR-04-2023-0076
Kwangsawad A, 2022, J INNOV KNOWL, V7, DOI 10.1016/j.jik.2022.100211
Lally P, 2013, HEALTH PSYCHOL REV, V7, pS137, DOI 10.1080/17437199.2011.603640
Lappeman J, 2023, J FINANC SERV MARK, V28, P337, DOI 10.1057/s41264-022-00154-z
Li CY, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2023.103264
Ling EC, 2021, PSYCHOL MARKET, V38, P1031, DOI 10.1002/mar.21491
Lui A, 2018, INF COMMUN TECHNOL L, V27, P267, DOI 10.1080/13600834.2018.1488659
Luo B, 2022, WIRES DATA MIN KNOWL, V12, DOI 10.1002/widm.1434
Mehrolia S, 2023, FIIB BUS REV, DOI 10.1177/23197145231169900
Melián-González S, 2021, CURR ISSUES TOUR, V24, P192, DOI 10.1080/13683500.2019.1706457
Mishra N, 2023, INT J QUAL RELIAB MA, DOI 10.1108/IJQRM-03-2023-0093
Mogaji E, 2021, TELEMAT INFORM, V65, DOI 10.1016/j.tele.2021.101711
Nikolopoulou K, 2021, COMPUT EDUC OPEN, V2, DOI 10.1016/j.caeo.2021.100041
Pinto A. S., 2022, Journal of Information Systems Engineering and Management, V7, P14550, DOI [10.55267/IADT.07.12012, DOI 10.55267/IADT.07.12012]
Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
Rahim NIM, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141912726
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Shandilya G., 2023, Parikalpana-KIIT Journal of Management, V19, P142, DOI [10.23862/kiit-parikalpana/2023/v19/i2/223471, DOI 10.23862/KIIT-PARIKALPANA/2023/V19/I2/223471]
Shandilya Gautam, 2023, ATNA - Journal of Tourism Studies, V18, P51, DOI 10.12727/ajts.29.3
Silva SC, 2023, INT J RETAIL DISTRIB, V51, P285, DOI 10.1108/IJRDM-05-2022-0163
Sugumar M., 2021, Journal of International Technology and Information Management, V30, P38
Talukder MS, 2020, TECHNOL FORECAST SOC, V150, DOI 10.1016/j.techfore.2019.119793
Tan E, 2016, YOUNG CONSUM, V17, P18, DOI 10.1108/YC-07-2015-00537
Vassilakopoulou P, 2023, EUR J INFORM SYST, V32, P10, DOI 10.1080/0960085X.2022.2096490
Venkatesh V, 2012, MIS QUART, V36, P157
Wahyuningsih B. A., 2023, International Journal of Multidisciplinary Research and Publications, V5, P159
Wang XQ, 2023, J ASSOC INF SCI TECH, V74, P339, DOI 10.1002/asi.24621
Yang J, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13116355
NR 73
TC 0
Z9 0
U1 2
U2 2
PU SAGE PUBLICATIONS INDIA PVT LTD
PI NEW DELHI
PA B-1-I-1 MOHAN CO-OPERATIVE INDUSTRIAL AREA, MATHURA RD, POST BAG NO 7,
NEW DELHI 110 044, INDIA
SN 2319-7145
EI 2455-2658
J9 FIIB BUS REV
JI FIIB Bus. Rev.
PD 2024 FEB 11
PY 2024
DI 10.1177/23197145241227757
EA FEB 2024
PG 19
WC Business; Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA HP2N2
UT WOS:001160643600001
DA 2024-03-27
ER
PT J
AU Magno, F
Dossena, G
AF Magno, Francesca
Dossena, Giovanna
TI The effects of chatbots' attributes on customer relationships with
brands: PLS-SEM and importance-performance map analysis
SO TQM JOURNAL
LA English
DT Article
DE Chatbots; e-service agents; New technologies; Customer satisfaction;
Consumer-brand interaction
ID MEDIATING ROLE; INFORMATION; TECHNOLOGY; QUALITY; MANAGEMENT; RESPONSES;
ACCEPTANCE; EXPERIENCE; IMPACT
AB Purpose Many firms are investing in digital services to improve customer experiences. Virtual service agents, or "e-service agents" ("e-agents") such as chatbots, are examples of these efforts. Chatbots are types of virtual-assistant software programs that interact with users through speech or text. This paper aims to investigate whether the perceived hedonic and utilitarian attributes of chatbots can influence customer satisfaction and, consequently, their relationships with brands. Design/methodology/approach Data were collected through a questionnaire-based survey among a sample of Italian consumers. A convenience sampling technique was used. Data were then analyzed through Partial Least Squares Structural Equation Modeling to provide a prediction-oriented model assessment. The findings were then complemented with an importance-performance map analysis (IPMA) to gain more detailed insights and actionable guidelines for managers. Findings The findings highlighted that the perceived hedonic and utilitarian attributes of chatbots positively influenced customer satisfaction and improved customer relationships with the brands. However, the IMPA highlighted that the performance levels of two most important attributes - system quality and experience with chatbot - could be improved resulting in additional improvements of customer satisfaction. Practical implications This study suggests the importance of firms' investments in and adoption of e-agents to strengthen consumer-brand relationships and of considering both the hedonic and utilitarian attributes of their e-agents. Originality/value This article attempts to enrich and consolidate the growing body of literature concerning the impacts of new technologies - and, specifically, chatbots - in service marketing.
C1 [Magno, Francesca; Dossena, Giovanna] Univ Bergamo, Bergamo, Italy.
C3 University of Bergamo
RP Magno, F (autor correspondiente), Univ Bergamo, Bergamo, Italy.
EM francesca.magno@unibg.it
OI Magno, Francesca/0000-0002-5607-6309
CR Agarwal N., 2017, QUAL PROG, V50, P76
Algesheimer R, 2005, J MARKETING, V69, P19, DOI 10.1509/jmkg.69.3.19.66363
Azzopardi E, 2013, TOURISM MANAGE, V35, P222, DOI 10.1016/j.tourman.2012.07.007
Batra R., 1990, MARKET LETT, V2, P159, DOI [DOI 10.1007/BF00436035, 10.1007/BF00436035]
Brah S. A., 2006, International Journal of Physical Distribution & Logistics Management, V36, P192, DOI 10.1108/09600030610661796
Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
Chaiprasit S, 2011, COMPET REV, V21, P188, DOI 10.1108/10595421111117461
Chen MM, 2016, J HOSP TOUR MANAG, V29, P69, DOI 10.1016/j.jhtm.2016.05.001
Cheng Y, 2022, J PROD BRAND MANAG, V31, P252, DOI 10.1108/JPBM-05-2020-2907
Chiarini A, 2020, TQM J, V32, P603, DOI 10.1108/TQM-04-2020-0082
Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
Coyle JR, 2012, J RES INTERACT MARK, V6, P27, DOI 10.1108/17505931211241350
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
de Souza FF, 2022, TQM J, V34, P749, DOI 10.1108/TQM-10-2020-0238
DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60
Dhar R, 2000, J MARKETING RES, V37, P60, DOI 10.1509/jmkr.37.1.60.18718
Dijkstra TK, 2015, MIS QUART, V39, P297, DOI 10.25300/MISQ/2015/39.2.02
Fiore AM, 2005, PSYCHOL MARKET, V22, P669, DOI 10.1002/mar.20079
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Gao LL, 2015, COMPUT HUM BEHAV, V53, P249, DOI 10.1016/j.chb.2015.07.014
Godey B, 2016, J BUS RES, V69, P5833, DOI 10.1016/j.jbusres.2016.04.181
Hair J. F., 2022, A primer on partial least squares structural equation modeling (PLS-SEM), V3rd
Hair J. F., 2017, ADV ISSUES PARTIAL L
Hair J.F., 2020, EQUAZIONI STRUTTURAL
Hair J.F., 2009, Multivariate data analysis
Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
Hoyer WD, 2020, J INTERACT MARK, V51, P57, DOI 10.1016/j.intmar.2020.04.001
Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
Kulviwat S, 2007, PSYCHOL MARKET, V24, P1059, DOI 10.1002/mar.20196
Laranjo L, 2018, J AM MED INFORM ASSN, V25, P1248, DOI 10.1093/jamia/ocy072
Lee YW, 2002, INFORM MANAGE-AMSTER, V40, P133, DOI 10.1016/S0378-7206(02)00043-5
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
MARTILLA JA, 1977, J MARKETING, V41, P77, DOI 10.2307/1250495
Martínez-López FJ, 2013, IND MARKET MANAG, V42, P489, DOI 10.1016/j.indmarman.2013.03.001
May R, 2022, INFORM HEALTH SOC CA, V47, P194, DOI 10.1080/17538157.2021.1983578
Murtarelli G, 2023, TQM J, V35, P719, DOI 10.1108/TQM-01-2022-0010
Nasco SA, 2008, PSYCHOL MARKET, V25, P987, DOI 10.1002/mar.20249
Nelson RR, 2005, J MANAGE INFORM SYST, V21, P199, DOI 10.1080/07421222.2005.11045823
Oh H, 2001, TOURISM MANAGE, V22, P617, DOI 10.1016/S0261-5177(01)00036-X
Pavon G., 2020, Journal of Service Management Research, V4, P35, DOI [10.15358/2511-8676-2020-1-35, DOI 10.15358/2511-8676-2020-1-35]
Pérez JQ, 2020, COMPUT APPL ENG EDUC, V28, P1549, DOI 10.1002/cae.22326
Phadermrod B, 2019, INT J INFORM MANAGE, V44, P194, DOI 10.1016/j.ijinfomgt.2016.03.009
Prajogo DI, 2006, EUR J OPER RES, V168, P35, DOI 10.1016/j.ejor.2004.03.033
Ringle C.M., 2015, SmartPLS 3, DOI DOI 10.1037/0003-066X.55.1.68
Ringle CM, 2016, IND MANAGE DATA SYST, V116, P1865, DOI 10.1108/IMDS-10-2015-0449
Rose S, 2012, J RETAILING, V88, P308, DOI 10.1016/j.jretai.2012.03.001
Roy AS, 2020, TQM J, V32, P401, DOI 10.1108/TQM-09-2019-0232
Sarstedt M, 2023, J MARKET THEORY PRAC, V31, P261, DOI 10.1080/10696679.2022.2056488
Sarstedt M, 2022, PSYCHOL MARKET, V39, P1035, DOI 10.1002/mar.21640
Scheidt S, 2019, INT J INFORM MANAGE, V45, P223, DOI 10.1016/j.ijinfomgt.2018.01.002
Schmitt B, 2019, J CONSUM RES, V46, P825, DOI 10.1093/jcr/ucz038
Shmueli G, 2019, EUR J MARKETING, V53, P2322, DOI 10.1108/EJM-02-2019-0189
Swanson EB, 1997, INFORM SOFTWARE TECH, V39, P845, DOI 10.1016/S0950-5849(97)00043-8
Tasleem M, 2019, INT J QUAL RELIAB MA, V36, P1574, DOI 10.1108/IJQRM-01-2018-0017
Trivedi J, 2019, J INTERNET COMMER, V18, P91, DOI 10.1080/15332861.2019.1567188
Wang R. Y., 1996, Journal of Management Information Systems, V12, P5
Wiedmann KP, 2009, PSYCHOL MARKET, V26, P625, DOI 10.1002/mar.20292
Zarouali B, 2018, CYBERPSYCH BEH SOC N, V21, P491, DOI 10.1089/cyber.2017.0518
NR 59
TC 0
Z9 0
U1 14
U2 32
PU EMERALD GROUP PUBLISHING LTD
PI BINGLEY
PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND
SN 1754-2731
EI 1754-274X
J9 TQM J
JI TQM J.
PD JUN 26
PY 2023
VL 35
IS 5
SI SI
BP 1156
EP 1169
DI 10.1108/TQM-02-2022-0080
EA AUG 2022
PG 14
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA L0IZ8
UT WOS:000844616100001
OA hybrid
DA 2024-03-27
ER
PT J
AU de Kervenoael, R
Schwob, A
Hasan, R
Psylla, E
AF de Kervenoael, Ronan
Schwob, Alexandre
Hasan, Rajibul
Psylla, Evangelia
TI SIoT robots and consumer experiences in retail: Unpacking repeat
purchase intention drivers leveraging computers are social actors (CASA)
paradigm
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Social internet of things; Retail service; Supermarkets; CASA; Smart
robots; Repeat purchase; PLS-SEM
ID CUSTOMER EXPERIENCE; SERVICE ROBOT; INFORMATION-TECHNOLOGY; USER
ACCEPTANCE; ANTHROPOMORPHISM; INTELLIGENCE; PERCEPTIONS; MANAGEMENT;
FRAMEWORK; MACHINES
AB This study contributes to knowledge on the so far limited understanding of how to manage collaboration between Social Internet of Things (SIoT) service robots and consumers in the retail context. Embedded in Computers Are Social Actors (CASA) paradigm, we leverage a Partial Least Approach - Structural Equation Modelling (PLS-SEM) (n = 356) to show that word of mouth, consumer promotion experience, relationship quality, and inspiration significantly impact consumers' repeat purchase intention when SIoT robots are used. Noticeably, while relationship quality is significant, it has a negative coefficient indicating that consumers may have high pre-existing anxiety towards SIoT service robots.
C1 [de Kervenoael, Ronan] Rennes Sch Business, Dept Mkt, Rennes, France.
[Schwob, Alexandre] Excelia Business Sch, Dept Mkt, La Rochelle, France.
[Hasan, Rajibul] EM Normandie Business Sch, Metis Lab, Le Havre, France.
[Psylla, Evangelia] Ex Ogilvy, ELF Media, Grtraveller Magazine, Athens, Greece.
C3 Universite de Rennes
RP de Kervenoael, R (autor correspondiente), Rennes Sch Business, Dept Mkt, Rennes, France.
EM ronan.jouan-de-kervenoael@rennes-sb.com; schwoba@excelia-group.com;
rhasan@em-normandie.fr; lilian.psilla@gmail.com
OI de kervenoael, ronan/0000-0002-9970-5101
CR Ailawadi KL, 2009, J RETAILING, V85, P42, DOI 10.1016/j.jretai.2008.11.002
Al-Emran M., 2020, SOCIAL INTERNET THIN, P197, DOI DOI 10.1007/978-3-030-24513-9_12
Ameen N, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.106976
Amelia A, 2022, J SERV MANAGE, V33, P321, DOI 10.1108/JOSM-10-2020-0374
Amenuvor Fortune Edem, 2020, [Journal of Marketing Studies, 마케팅논집(Journal of Marketing Studies)], V28, P15, DOI 10.22736/jms.28.2.02
Appel M, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106749
Arghashi V, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102756
Argo JJ, 2020, J RETAILING, V96, P25, DOI 10.1016/j.jretai.2019.12.005
Arikan E, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103175
Atzori L, 2011, IEEE COMMUN LETT, V15, P1193, DOI 10.1109/LCOMM.2011.090911.111340
Baidya M.K., 2019, J. Bus. Manag, V25
Bartneck C., 2020, HUMAN ROBOT INTERACT
Bartneck C, 2009, INT J SOC ROBOT, V1, P71, DOI 10.1007/s12369-008-0001-3
Becker L, 2020, J ACAD MARKET SCI, V48, P630, DOI 10.1007/s11747-019-00718-x
Belanche D, 2020, SERV IND J, V40, P203, DOI 10.1080/02642069.2019.1672666
Belk R, 2021, MARKETING THEOR, V21, P25, DOI 10.1177/1470593120961461
Belk R, 2020, J MACROMARKETING, V40, P221, DOI 10.1177/0276146719897361
Benitez J, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.05.003
Berg Julia., 2020, Current Robotics Reports, V1, P27, DOI [10.1007/s43154-020-00005-6, DOI 10.1007/S43154-020-00005-6]
Birnbaum GE, 2016, COMPUT HUM BEHAV, V63, P416, DOI 10.1016/j.chb.2016.05.064
Blasco-Arcas L, 2022, J BUS RES, V146, P436, DOI 10.1016/j.jbusres.2022.03.054
Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
Bock DE, 2020, J SERV MARK, V34, P317, DOI 10.1108/JSM-01-2019-0047
Bortnikov Sergey Petrovich, 2020, Digital Transformation of the Economy: Challenges, Trends and New Opportunities. Advances in Intelligent Systems and Computing (AISC 908), P32, DOI 10.1007/978-3-030-11367-4_3
Böttger T, 2017, J MARKETING, V81, P116, DOI 10.1509/jm.15.0007
Brengman M, 2021, J BUS RES, V134, P263, DOI 10.1016/j.jbusres.2021.05.025
Campa R., 2016, J. Evol. Technol, V26
Cao LL, 2021, INT J RETAIL DISTRIB, V49, P958, DOI 10.1108/IJRDM-09-2020-0350
Celik V, 2013, COMPUT EDUC, V60, P148, DOI 10.1016/j.compedu.2012.06.008
Chang RCS, 2018, COMPUT HUM BEHAV, V84, P194, DOI 10.1016/j.chb.2018.02.025
CHIN W. W., STAT STRATEGIES SMAL, P307
Chin WW, 2003, INFORM SYST RES, V14, P189, DOI 10.1287/isre.14.2.189.16018
Chin WW, 1998, QUANT METH SER, P295
Chuah SHW, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102551
Costanzo M, 2021, FRONT ROBOT AI, V8, DOI 10.3389/frobt.2021.672995
Danaher J, 2019, J POSTHUMAN STUD, V3, P5, DOI 10.5325/jpoststud.3.1.0005
Daqau M.A.M.A., 2019, INT REV MANAG MARK, V9, P22
Darling Kate., 2021, The New Breed: What Our History with Animals Reveals about Our Future with Robots
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
Dawes JG, 2021, EUR J MARKETING, V55, P763, DOI 10.1108/EJM-01-2018-0042
De Gauquier L., 2020, RETAIL FUTURES, P15, DOI DOI 10.1108/978-1-83867-663-620201007
De Gauquier L, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103176
De Gauquier L, 2021, INT J RETAIL DISTRIB, V49, P846, DOI 10.1108/IJRDM-10-2020-0413
de Kervenoael R, 2023, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2023.2212088
de Kervenoael R, 2020, TOURISM MANAGE, V78, DOI 10.1016/j.tourman.2019.104042
de Kervenoael R, 2018, CONSUMP MARK CULT, V21, P417, DOI 10.1080/10253866.2018.1462583
De Keyser A, 2020, J SERV RES-US, V23, P433, DOI 10.1177/1094670520928390
Deloitte, 2015, Navigating the New Digital Divide: Capitalising on Digital Influence in Retail.
Durdevic N, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14063256
Edwards C, 2020, IEEE ROMAN, P1063, DOI 10.1109/RO-MAN47096.2020.9223496
Elbasheer M, 2023, IET COLL INTEL MANUF, V5, DOI 10.1049/cim2.12066
Filieri R, 2022, J SERV RES-US, V25, P614, DOI 10.1177/10946705221103937
Filipe S, 2017, J RETAIL CONSUM SERV, V37, P78, DOI 10.1016/j.jretconser.2017.03.002
FORNELL C, 1982, J MARKETING RES, V19, P440, DOI 10.2307/3151718
Fox J, 2021, CYBERPSYCH BEH SOC N, V24, P294, DOI 10.1089/cyber.2020.0181
Gambino A., 2020, HUMAN MACHINE COMMUN, V1, P71, DOI DOI 10.30658/HMC.1.5
Giger JC, 2019, HUM BEHAV EMERG TECH, V1, P111, DOI 10.1002/hbe2.147
Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
Goodrich Michael A., 2007, Foundations and Trends in Human-Computer Interaction, V1, P203, DOI 10.1561/1100000005
Gordon J.S, 2020, Smart Technologies and Fundamental Rights, P17
Goudey A, 2016, RECH APPL MARKET-ENG, V31, P2, DOI 10.1177/2051570716643961
Graef R, 2023, J DECIS SYST, V32, P439, DOI 10.1080/12460125.2022.2029049
Grewal D, 2009, J RETAILING, V85, P1, DOI 10.1016/j.jretai.2009.01.001
Grewal D, 2021, J BUS RES, V134, P198, DOI 10.1016/j.jbusres.2021.05.004
Grewal D, 2020, J ACAD MARKET SCI, V48, P96, DOI 10.1007/s11747-019-00697-z
Gunkel D, 2019, SURVIVE ROBOT INVASI
Gunkel D.J, 2018, A Networked Self and Human Augmentics, Artificial Intelligence, Sentience, P51
Gunkel DJ, 2018, ETHICS INF TECHNOL, V20, P87, DOI 10.1007/s10676-017-9442-4
Guo B, 2013, J NETW COMPUT APPL, V36, P1531, DOI 10.1016/j.jnca.2012.12.028
Hair JF, 2013, LONG RANGE PLANN, V46, P1, DOI 10.1016/j.lrp.2013.01.001
Hall A., 2017, International Journal of Retail & Distribution Management
Hartzog W., 2014, MD L REV, V74, P785
Hartzog W, 2016, J HUM-ROBOT INTERACT, V5, P70, DOI 10.5898/JHRI.5.3.Hartzog
Heikkilä P, 2019, LECT NOTES ARTIF INT, V11876, P548, DOI 10.1007/978-3-030-35888-4_51
Heller J, 2019, J RETAILING, V95, P94, DOI 10.1016/j.jretai.2019.03.005
Hennig-Thurau T., 2000, Journal of Marketing Management, V16, P55, DOI [10.1362/026725700785100497, DOI 10.1362/026725700785100497]
Henseler J, 2014, ORGAN RES METHODS, V17, P182, DOI 10.1177/1094428114526928
Hentout A, 2019, ADV ROBOTICS, V33, P764, DOI 10.1080/01691864.2019.1636714
Herbrechter S, 2021, Before Humanity, P184
Hermann E, 2022, J BUS ETHICS, V179, P43, DOI 10.1007/s10551-021-04843-y
Holmlund M, 2020, J BUS RES, V116, P356, DOI 10.1016/j.jbusres.2020.01.022
Honig S, 2018, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.00861
Hottat E, 2023, J SERV MANAGE, V34, P696, DOI 10.1108/JOSM-04-2022-0125
Hu MY, 2020, INTERNET RES, V30, P1019, DOI 10.1108/INTR-03-2019-0082
Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
Hwang Hyeon-Chul, 2019, [Journal of Korea Academia-Industrial cooperation Society, 한국산학기술학회논문지], V20, P287, DOI 10.5762/KAIS.2019.20.12.287
Iacobucci D., 2009, Marketing research: Methodological foundations (with Qualtrics Card) South
Inkulu AK, 2022, IND ROBOT, V49, P226, DOI 10.1108/IR-04-2021-0077
Iwasaki M, 2018, IEEE ROMAN, P300, DOI 10.1109/ROMAN.2018.8525772
Jain R, 2017, J SERV THEOR PRACT, V27, P642, DOI 10.1108/JSTP-03-2015-0064
Joe W.Y., 2019, Int. J. Mech. Eng. Robot. Res, V8
Juniper Research, 2023, AI spending by retailer.
Kalaignanam K, 2018, J RETAILING, V94, P265, DOI 10.1016/j.jretai.2018.05.001
Kathleen B, 2022, ACTA PSYCHOL, V222, DOI 10.1016/j.actpsy.2021.103476
Kothgassner OD, 2017, COMPUT HUM BEHAV, V70, P261, DOI 10.1016/j.chb.2016.12.059
Lee SK, 2023, BEHAV INFORM TECHNOL, V42, P2754, DOI 10.1080/0144929X.2022.2145998
Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
Li DJ, 2010, INT J SOC ROBOT, V2, P175, DOI 10.1007/s12369-010-0056-9
Lima J, 2020, IEEE INT CONF AUTON, P239, DOI [10.1109/ICARSC49921.2020.9096164, 10.1109/icarsc49921.2020.9096164]
Longoni C, 2021, When Do We Trust AI's Recommendations More than People's
Loscri V, 2019, PROCEEDINGS OF THE 2019 ACM MOBIHOCWORKSHOP ON PERVASIVE SYSTEMS IN THE IOT ERA (PERSIST-IOT '19), P1, DOI 10.1145/3331052.3332470
Lowry PB, 2014, IEEE T PROF COMMUN, V57, P123, DOI 10.1109/TPC.2014.2312452
Lu L, 2021, INT J HOSP MANAG, V94, DOI 10.1016/j.ijhm.2020.102823
Lu L, 2019, INT J HOSP MANAG, V80, P36, DOI 10.1016/j.ijhm.2019.01.005
Mahmoud AB, 2020, Retail Futures, P165, DOI [10.1108/978-1-83867-663-620201019, DOI 10.1108/978-1-83867-663-620201019]
Makarius EE, 2020, J BUS RES, V120, P262, DOI 10.1016/j.jbusres.2020.07.045
Mamak K, 2022, INT J SOC ROBOT, V14, P1057, DOI 10.1007/s12369-021-00852-z
Mamak K, 2021, FRONT ROBOT AI, V8, DOI 10.3389/frobt.2021.712427
Mann JA, 2015, COMPUT HUM BEHAV, V43, P112, DOI 10.1016/j.chb.2014.10.029
Marek L., 2017, Jagiellonian Journal of Management, V3, P163, DOI [10.4467/2450114XJJM.17.011.9562, DOI 10.4467/2450114XJJM.17.011.9562]
Martin K, 2019, J BUS ETHICS, V160, P835, DOI 10.1007/s10551-018-3921-3
Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
Merdin-Uygur E, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103174
Meyer P, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103173
Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
Mukherjee S, 2021, DECISION-INDIA, V48, P445, DOI 10.1007/s40622-021-00300-x
Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
NASS C, 1994, HUMAN FACTORS IN COMPUTING SYSTEMS, CHI '94 CONFERENCE PROCEEDINGS - CELEBRATING INTERDEPENDENCE, P72, DOI 10.1145/191666.191703
Nastjuk I, 2020, TECHNOL FORECAST SOC, V161, DOI 10.1016/j.techfore.2020.120319
Niemela M., 2019, inSocial Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction, P119
Nitti M, 2015, 2015 IEEE 2ND WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P424, DOI 10.1109/WF-IoT.2015.7389092
Nitti M, 2014, 2014 IEEE WORLD FORUM ON INTERNET OF THINGS (WF-IOT), P405, DOI 10.1109/WF-IoT.2014.6803200
Noble SM, 2022, J RETAILING, V98, P199, DOI 10.1016/j.jretai.2022.04.003
Nyholm S, 2019, CAMB Q HEALTHC ETHIC, V28, P592, DOI 10.1017/S0963180119000537
Odekerken-Schröder G, 2022, J SERV MANAGE, V33, P246, DOI 10.1108/JOSM-10-2020-0372
Oosthuizen K, 2021, AUSTRALAS MARK J, V29, P264, DOI 10.1016/j.ausmj.2020.07.007
Pantano E, 2022, J SERV RES-US, V25, P583, DOI 10.1177/10946705221103538
Pantano E, 2019, J RETAIL CONSUM SERV, V49, P297, DOI 10.1016/j.jretconser.2019.01.019
Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
Peltier J.W., 2023, J. Res. Interact. Market
Piçarra N, 2018, COMPUT HUM BEHAV, V86, P129, DOI 10.1016/j.chb.2018.04.026
Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
Ponce P., 2023, Data and AI Driving Smart Cities, P125
Postnikoff B, 2018, ACMIEEE INT CONF HUM, P313, DOI 10.1145/3173386.3176908
Prentice C, 2021, J RETAIL CONSUM SERV, V62, DOI 10.1016/j.jretconser.2021.102661
Pusca Corneliu Andy, 2020, Science and Technologies for Smart Cities. 5th EAI International Summit SmartCity360. Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST 323), P412, DOI 10.1007/978-3-030-51005-3_33
PWC, 2019, Whom do customers really trust, Global consumer insights survey.
Rauschnabel PA, 2019, J RETAIL CONSUM SERV, V49, P43, DOI 10.1016/j.jretconser.2019.03.004
Rehman AU, 2020, ELECTRONICS-SWITZ, V9, DOI 10.3390/electronics9060918
Rogers, 2003, DIFFUSION INNOVATION
Rond J, 2019, IEEE ROMAN, DOI 10.1109/ro-man46459.2019.8956410
Roopa MS, 2019, COMPUT COMMUN, V139, P32, DOI 10.1016/j.comcom.2019.03.009
Sabanovic S., 2006, 9th IEEE International Workshop on Advanced Motion Control, P576
Sandoval EB, 2021, INT J SOC ROBOT, V13, P851, DOI 10.1007/s12369-020-00658-5
Sarwar MA, 2022, J PUBLIC AFF, V22, DOI 10.1002/pa.2357
Schultze U, 2019, INFORM SYST J, V29, P707, DOI 10.1111/isj.12230
Seeber I, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103174
Shahab Saima, 2022, ICT Analysis and Applications. Lecture Notes in Networks and Systems (314), P289, DOI 10.1007/978-981-16-5655-2_28
Shankar V, 2021, J RETAILING, V97, P13, DOI 10.1016/j.jretai.2020.10.006
Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
Siegel D., 2017, J TECHNOLOGY STUDIES, V43, P58, DOI [10.21061/jots.v43i2.a.1, DOI 10.21061/JOTS.V43I2.A.1]
Söderlund M, 2022, J RETAIL CONSUM SERV, V67, DOI 10.1016/j.jretconser.2022.102999
Song CS, 2022, J BUS RES, V146, P489, DOI 10.1016/j.jbusres.2022.03.087
Song CS, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102727
Song SY, 2022, CLOTH TEXT RES J, V40, P3, DOI 10.1177/0887302X20941261
Song X, 2022, INFORM MANAGE-AMSTER, V59, DOI 10.1016/j.im.2022.103595
Spekman MLC, 2018, COMPUT HUM BEHAV, V85, P308, DOI 10.1016/j.chb.2018.03.043
Stelea GA, 2020, IEEE ACCESS, V8, P153578, DOI 10.1109/ACCESS.2020.3018331
Sternbeck M., 2022, Robotics for Intralogistics in Supermarkets and Retail Stores, P1
Strasser A, 2020, CULTURALLY SUSTAINAB, P295, DOI [10.3233/FAIA200926, DOI 10.3233/FAIA200926]
Subero-Navarro A, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102813
Sunnafrank M, 2004, J SOC PERS RELAT, V21, P361, DOI 10.1177/0265407504042837
Tandon U, 2023, GLOB KNOWL MEM COMMU, DOI 10.1108/GKMC-06-2022-0125
Thellman S, 2020, ISCIENCE, V23, DOI 10.1016/j.isci.2020.101625
Tuomi A, 2021, CORNELL HOSP Q, V62, P232, DOI 10.1177/1938965520923961
Turja T, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103220
Tussyadiah I.P., 2018, Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference, P308, DOI DOI 10.1007/978-3-319-72923-7_24
Tyrväinen O, 2020, J RETAIL CONSUM SERV, V57, DOI 10.1016/j.jretconser.2020.102233
van Pinxteren MME, 2019, J SERV MARK, V33, P507, DOI 10.1108/JSM-01-2018-0045
Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
Venkatesh V, 2012, MIS QUART, V36, P157
Wang C, 2018, J ACAD MARKET SCI, V46, P190, DOI 10.1007/s11747-017-0573-2
Wang YW, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103002
Wellendorf K., 2022, MAST: the Journal of Media Art Study and Theory, V3, P1
Wong A., 2006, Int. J. Qual. Reliab. Manag
Yoganathan V, 2021, TOURISM MANAGE, V85, DOI 10.1016/j.tourman.2021.104309
Yu B, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103503
Yu CE, 2019, TOUR REV, V74, P428, DOI 10.1108/TR-07-2018-0097
Zhong VJ, 2022, ACMIEEE INT CONF HUM, P1140, DOI 10.1109/HRI53351.2022.9889602
Zibafar A, 2021, INT J SOC ROBOT, V13, P509, DOI 10.1007/s12369-019-00566-3
곽소나, 2014, [Archives of Design Research, 디자인학연구], V27, P81
NR 182
TC 0
Z9 0
U1 10
U2 10
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD JAN
PY 2024
VL 76
AR 103589
DI 10.1016/j.jretconser.2023.103589
EA OCT 2023
PG 14
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA Y2IL0
UT WOS:001103553700001
DA 2024-03-27
ER
PT J
AU Lin, SP
Chan, YH
Lu, IY
AF Lin, Shu-Ping
Chan, Ya-Hui
Lu, I-Ying
TI A hybrid framework for understanding mobile robotic financial service
adoption, encompassing utility and trust theories to define service
redesign actions
SO INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS
LA English
DT Article
DE mobile banking; mobile robotic financial service; MRFS; mobile
communication; utility theory; trust theory; adoption behaviour;
structural equation modelling; SEM; service design; service diffusion
ID STRUCTURAL EQUATION MODELS; E-COMMERCE; CONSUMERS; BANKING; ACCEPTANCE;
LOYALTY
AB The mobile robotic financial service (MRFS) is an important disruptive innovation in mobile banking development. However, very few studies have been conducted to investigate MRFS adoption behaviour in order to help define MRFS redesign actions for responding to dynamic markets. Thus, this study's main aims are: 1) to propose a service redesign framework for continuous MFRS innovation; 2) to perform an empirical analysis to explain how to apply it to identify the antecedents of user behaviours, extract determinants, and finally define service redesign actions. Data analysis reveals that the hybrid causal model is partially mediated by extending utility theory to include trust theory. Thus the direct and indirect effects that affect adoption behaviours significantly must be calculated. Finally, action matrix analysis is employed, and the results reveal that the service redesign action changes after the causal dynamics of the hybrid model are considered.
C1 [Lin, Shu-Ping; Chan, Ya-Hui] CTBC Business Sch, Dept Banking & Finance, Tainan, Taiwan.
[Lu, I-Ying] Advantech Co Ltd, 1,Alley 20,Lane 26,Rueiguang Rd, Taipei 114519, Taiwan.
RP Chan, YH (autor correspondiente), CTBC Business Sch, Dept Banking & Finance, Tainan, Taiwan.
EM splin@ctbc.edu.tw; yahui0219@gmail.com; cooper.lu@advantech.com.tw
CR Al-Ajam AliSaleh., 2013, European Journal of Business and Management, V5, P207
Al-Sharafi M.A., 2016, J ENG APPL SCI, V11, P545
Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
Alfina Ika, 2014, Journal of Computer Science, V10, P2518, DOI 10.3844/jcssp.2014.2518.2524
Becker JM, 2013, MIS QUART, V37, P665, DOI 10.25300/MISQ/2013/37.3.01
Brengman M, 2012, MANAG RES REV, V35, P791, DOI 10.1108/01409171211256569
Dadzie KQ, 2018, J BUS IND MARK, V33, P134, DOI 10.1108/JBIM-04-2017-0090
DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
Ganesan S., 1994, J MARKETING, V58, P1, DOI DOI 10.2307/1252265
Gao LL, 2017, INFORM SYST FRONT, V19, P525, DOI 10.1007/s10796-015-9611-0
Gefen D, 2003, MIS QUART, V27, P51, DOI 10.2307/30036519
Gianni G, 2003, INT J SERV IND MANAG, V14, P331, DOI 10.1108/09564230310478855
Hair J.F., 2009, Multivariate data analysis
Hanafizadeh P, 2014, TELEMAT INFORM, V31, P62, DOI 10.1016/j.tele.2012.11.001
Harris LC, 2004, J RETAILING, V80, P139, DOI 10.1016/j.jretai.2004.04.002
Hourahine B., 2004, J FINANCIAL SERVICE, V9, P57
Jeong B.K., 2013, BUSINESS MANAGEMENT, V2, P31, DOI [10.5430/bmr.v2n1p31, DOI 10.5430/BMR.V2N1P31]
Johnson D, 2005, J BUS RES, V58, P500, DOI 10.1016/S0148-2963(03)00140-1
JORESKOG KG, 1970, BIOMETRIKA, V57, P239, DOI 10.2307/2334833
Kesharwani A, 2012, INT J BANK MARK, V30, P303, DOI 10.1108/02652321211236923
Koenig-Lewis N, 2010, INT J BANK MARK, V28, P410, DOI 10.1108/02652321011064917
Lewicki R., 1995, CONFLICT COOPERATION, P133, DOI DOI 10.1177/0149206306294405
Lin JB, 2011, ELECTRON COMMER R A, V10, P615, DOI 10.1016/j.elerap.2011.07.008
Matemba ED, 2018, TECHNOL SOC, V53, P55, DOI 10.1016/j.techsoc.2017.12.001
McKnight DH, 2002, INFORM SYST RES, V13, P334, DOI 10.1287/isre.13.3.334.81
Mohammadi H, 2015, COMPUT HUM BEHAV, V44, P35, DOI 10.1016/j.chb.2014.11.015
Moro A, 2013, J BANK FINANC, V37, P927, DOI 10.1016/j.jbankfin.2012.10.023
Morrow J., 2004, J MANAGEMENT ISSUES, V16, P48
Ngo-Ye T.L., 2018, Issues in Information Systems, V19, P128, DOI DOI 10.48009/1_IIS_2018_128-138
Nicholson CY, 2001, J ACAD MARKET SCI, V29, P3, DOI 10.1177/0092070301291001
Phoon K, 2018, J ALTERN INVEST, V20, P79, DOI 10.3905/jai.2018.20.3.079
Pi S-M., 2012, INT J BUSINESS MANAG, V7, P108, DOI DOI 10.5539/IJBM.V7N9P108
REMPEL JK, 1985, J PERS SOC PSYCHOL, V49, P95, DOI 10.1037/0022-3514.49.1.95
Rogers E., 2005, Diffusion of Innovations, V4th ed., DOI DOI 10.1007/978-3-642-79868-9_2
Sullivan YW, 2018, INT J INFORM MANAGE, V39, P199, DOI 10.1016/j.ijinfomgt.2017.12.008
Treanor J., 2014, The Guardian
Trivedi N, 2018, INFORM SYST FRONT, V20, P239, DOI 10.1007/s10796-016-9719-x
Yang SQ, 2012, COMPUT HUM BEHAV, V28, P129, DOI 10.1016/j.chb.2011.08.019
Yi MY, 2006, INFORM MANAGE-AMSTER, V43, P350, DOI 10.1016/j.im.2005.08.006
Zhang KZK, 2014, INT J INFORM MANAGE, V34, P89, DOI 10.1016/j.ijinfomgt.2013.12.001
Zhou T, 2012, INFORM TECHNOL MANAG, V13, P27, DOI 10.1007/s10799-011-0111-8
NR 42
TC 1
Z9 1
U1 3
U2 18
PU INDERSCIENCE ENTERPRISES LTD
PI GENEVA
PA WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215
GENEVA, SWITZERLAND
SN 1470-949X
EI 1741-5217
J9 INT J MOB COMMUN
JI Int. J. Mob. Commun.
PY 2022
VL 20
IS 2
BP 196
EP 219
DI 10.1504/IJMC.2022.121435
PG 24
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA ZU1GM
UT WOS:000769593700004
DA 2024-03-27
ER
EF