Predicting m-commerce adoption determinants: A neural network approach

Expert Systems with Applications

Volumn 40, Issue 2, 2013, Pages 523-530

  Chong, Alain Yee Loong ^a  

^a University of Nottingham Ningbo China   (China)

Author keywords

Consumer behaviour; M commerce; Neural network; Technology adoption; UTAUT

Indexed keywords

ADOPTION DECISION; CONSUMER INFORMATION; DEMOGRAPHIC PROFILE; EXTENDED MODEL; FACILITATING CONDITIONS; M-COMMERCE; NEURAL NETWORK MODEL; NON-COMPENSATORY MODEL; NON-LINEAR RELATIONSHIPS; ONLINE SURVEYS; PERCEIVED ENJOYMENT; PERCEIVED VALUE; PERSONAL INNOVATIVENESS; REGRESSION MODEL; SOCIAL INFLUENCE; TECHNOLOGY ADOPTION; UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY; UTAUT;

CONSUMER BEHAVIOR; FORECASTING; NEURAL NETWORKS; REGRESSION ANALYSIS;

ELECTRONIC COMMERCE;

EID: 84867690239     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.07.068     Document Type: Article

References (43)

1. Agarwal, N. K.; Wang, Z.; Xu, Y. & Poo, D. C. C. (2007). Factors affecting 3G adoption: An empirical study. In 11th Pacific-Asia conference on information systems, Session 1E (pp. 256-270).
2. R. Agarwal, and J. Prasad Are individual differences germane to the acceptance of new information technologies? Decision Sciences 30 2 1999 361 391
3. N.M.A. Bakar, and I.M. Tahir Applying multiple linear regression and neural network to predict bank performance International Business Research 2 4 2009 176 183
4. H. Barki Quo vadis TAM? Journal of the AIS 8 1 2007 211 218
5. Carlsson, C.; Carlsson, J.; Hyvonen, K.; Puhakainen, J. & Walden, P. (2006). Adoption of mobile devices/services - searching for answers with the UTAUT. In System sciences, 2006. HICSS'06. Proceedings of the 39th annual Hawaii international conference on IEEE (p. 132a).
6. W.K. Chiang, D. Zhang, and L. Zhou Predicting and explaining patronage behavior toward web and traditional stores using neural networks: A comparative analysis with logistic regression Decision Support Systems 41 2 2006 514 531
7. ChinaInternetWatch (2012). It Took Tencent Weixin 433 Days to Reach 100 Million Users. http://www.chinainternetwatch.com/1422/tencent-weibo-100mm-users/ .
8. A.Y.L. Chong, F.T.S. Chan, and K.B. Ooi Predicting Consumer decisions to adopt Mobile Commerce. Cross country empirical examination between China and Malaysia Decision Support Systems 53 1 2011 34 43
9. A.Y.L. Chong, N. Darmawan, K.B. Ooi, and B. Lin Adoption of 3G services among Malaysian consumers: an empirical analysis International Journal of Mobile Communications 8 2 2010 129 149
10. A.Y.L. Chong, K.B. Ooi, B. Lin, and H.J. Bao An empirical analysis of the determinants of 3G adoption in China Computers in Human Behavior 28 2 2012 360 369
11. F.D. Davis Perceived usefulness, perceived ease of use, and user acceptance of information technology MIS Quarterly 13 1989 319 340
12. G.D. Garson Neural networks: An introductory guide for social scientists 1998 Sage Thousand Oaks, CA
13. I. Ha, Y. Yoon, and M. Choi Determinants of adoption of mobile games under mobile broadband wireless access environment Information & Management 44 3 2007 276 286
14. F.J. Hair, W. Black, B. Babin, and R. Anderson Multivariate data analysis: A global perspective 2010 Pearson Education Inc New Jersey
15. Haykin, S. (2001). Neural networks: A comprehensive foundation. Prentice Hall.
16. C.I. Hsu, M.L. Shih, B.W. Huang, B.Y. Lin, and C.N. Lin Predicting tourism loyalty using an integrated Bayesian network mechanism Expert Systems with Applications 36 9 2009 11760 11763
17. M.Y. Hu, M. Shanker, and M.S. Hung Estimation of posterior probabilities of consumer situational choices with neural network classifiers International Journal of Research in Marketing 16 4 1999 307 317
18. L.A. Jackson, K.S. Ervin, P.D. Gardner, and N. Schmitt Gender and the Internet: Women communicating and men searching Sex Roles 44 5 2001 363 379
19. A. Jeyaraj, J.W. Rottman, and M.C. Lacity A review of the predictors, linkages, and biases in IT innovation adoption research Journal of Information Technology 21 1 2006 1 23
20. I. Kaastra, and M. Boyd Designing a neural network for forecasting financial and economic time series Neurocomputing 10 3 1996 215 236
21. H.W. Kim, H.C. Chan, and S. Gupta Value-based adoption of mobile internet: an empirical investigation Decision Support Systems 43 1 2007 111 126
22. L.Y. Leong, K.B. Ooi, A.Y.L. Chong, and B. Lin Influence of individual characteristics, perceived usefulness and perceived ease of use on mobile entertainment adoption in Malaysia - an SEM approach International Journal of Mobile Communications 9 44 2011 359 382
23. J. Lu, J.E. Yao, and C.S. Yu Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology The Journal of Strategic Information Systems 14 3 2005 245 268
24. Q. Min, S. Ji, and G. Qu Mobile commerce user acceptance study in China: a revised UTAUT model Tsinghua Science & Technology 13 3 2008 257 264
25. Mobithinking.com (2012). Global mobile statistics 2012: all quality mobile marketing research, mobile Web stats, subscribers, ad revenue, usage, trends. http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats.
26. S.A. Morris, T.H. Greer, C. Hughes, and W.J. Clark Prediction of CASE adoption: A neural network approach Industrial Management & Data Systems 104 2 2004 129 135
27. J. Ong, Y.S. Poong, and T.H. Ng 3G services adoption among university students: Diffusion of innovation theory Communications of the IBIMA 3 16 2008 114 121
28. Rogers, E. M. (1995) Diffusion of Innovation, New York: Free Press.
29. J.E. Scott, and S. Walczak Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance Information & Management 46 4 2009 221 232
30. R.S. Sexton, R.A. Johnson, and M.A. Hignite Predicting internet/e-commerce use Internet Research 12 5 2002 402 410
31. G. Shmueli, and O.R. Koppius Predictive analytics in information systems research MIS Quarterly 35 3 2010 553 572
32. T.S.H. Teo Demographic and motivation variables associated with Internet usage activities Internet Research 11 2 2001 125 137
33. J.Y.L. Thong, S.J. Hong, and K.Y. Tam The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance International Journal of Human-Computer Studies 64 9 2006 799 810
34. F.M.E. Uzoka Organisational influences on e-commerce adoption in a developing country context using UTAUT International Journal of Business Information Systems 3 3 2008 300 316
35. V. Venkatesh, and H. Bala Technology acceptance model 3 and a research agenda on interventions Decision Sciences 39 2 2008 273 315
36. V. Venkatesh, and F.D. Davis A theoretical extension of the technology acceptance model: Four longitudinal field studies Management Science 46 2 2000 186 204
37. V. Venkatesh, and S. Goyal Expectation disconfirmation and technology adoption: Polynomial modeling and response surface analysis MIS Quarterly 34 2 2010 281 303
38. V. Venkatesh, M.G. Morris, G.B. Davis, and F.D. Davis User acceptance of information technology: Toward a unified view MIS Quarterly 27 3 2003 425 478
39. Y.M. Wang, and T. Elhag A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks Expert Systems with Applications 32 2 2007 336 348
40. T.T. Wei, G. Marthandan, A.Y.L. Chong, K.B. Ooi, and S. Arumugam What drives Malaysian m-commerce adoption? An empirical analysis Industrial Management & Data Systems 109 3 2009 370 388
41. Xin, X. (2004). A model of 3G adoption. In AMCIS 2004 Proceedings Paper 329, New York, USA (pp. 2755-2762).
42. T. Zhou, Y. Lu, and B. Wang Integrating TTF and UTAUT to explain mobile banking user adoption Computers in Human Behavior 26 4 2010 760 767
43. Z. Zigang, and P. Fan Cross-cultural challenges when doing business in China Singapore Management Review 38 3 2004 61 73