A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption

The advancement in wireless and mobile technologies has presented tremendous business opportunity for mobile-commerce (m-commerce). This research aims to examine the factors that influence consumers' m-commerce adoption intention. Variables such as perceived usefulness, perceived ease of use, perceived enjoyment, trust, cost, network influence, and variety of services were used to examine the adoption intentions of consumers. Data was collected from 376 m-commerce users. A multi-analytic approach was proposed whereby the research model was tested using structural equation modeling (SEM), and the results from SEM were used as inputs for a neural network model to predict m-commerce adoption. The result showed that perceived usefulness, perceived enjoyment, trust, cost, network influence, and trust have significant influence on consumers' m-commerce adoption intentions. However, the neural network model developed in this research showed that the best predictors of m-commerce adoption are network influence, trust, perceived usefulness, variety of service, and perceived enjoyment. This research proposed an innovative new approach to understand m-commerce adoption, and the result for this study will be useful for telecommunication and m-commerce companies in formulating strategies to attract more consumers.

[1]  Xu Xin,et al.  A Model of 3G Adoption , 2004, AMCIS.

[2]  August E. Grant,et al.  Individual and network influences on the adoption and perceived outcomes of electronic messaging , 1990 .

[3]  George M. Giaglis,et al.  Mobile Advertising Effectiveness: an Exploratory Study , 2006, 2006 International Conference on Mobile Business.

[4]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[5]  Poong Yew-Siang,et al.  3G Services Adoption among University Students: Diffusion of Innovation Theory , 2008 .

[6]  Michael Y. Hu,et al.  Estimation of posterior probabilities of consumer situational choices with neural network classifiers , 1999 .

[7]  Galit Shmueli,et al.  Predictive Analytics in Information Systems Research , 2010, MIS Q..

[8]  Alain Yee-Loong Chong,et al.  Influence of individual characteristics, perceived usefulness and ease of use on mobile entertainment adoption , 2011, Int. J. Mob. Commun..

[9]  Upkar Varshney,et al.  Mobile Commerce: Framework, Applications and Networking Support , 2002, Mob. Networks Appl..

[10]  G. David Garson,et al.  Neural Networks: An Introductory Guide for Social Scientists , 1999 .

[11]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[12]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[13]  Ying-Ming Wang,et al.  A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks , 2007, Expert Syst. Appl..

[14]  Angappa Gunasekaran,et al.  A review for mobile commerce research and applications , 2007, Decis. Support Syst..

[15]  Imsook Ha,et al.  Determinants of adoption of mobile games under mobile broadband wireless access environment , 2007, Inf. Manag..

[16]  Fred D. Davis,et al.  Dead Or Alive? The Development, Trajectory And Future Of Technology Adoption Research , 2007, J. Assoc. Inf. Syst..

[17]  Alain Yee-Loong Chong,et al.  What drives Malaysian m-commerce adoption? An empirical analysis , 2009, Ind. Manag. Data Syst..

[18]  Anand Jeyaraj,et al.  A review of the predictors, linkages, and biases in IT innovation adoption research , 2006, J. Inf. Technol..

[19]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[20]  Judy E. Scott,et al.  Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance , 2009, Inf. Manag..

[21]  Albert L. Lederer,et al.  The technology acceptance model and the World Wide Web , 2000, Decis. Support Syst..

[22]  R. Tiwari,et al.  The Mobile Commerce Prospects. A Strategic Analysis of Opportunities in the Banking Sector , 2007 .

[23]  Kar Yan Tam,et al.  The Effects of Post-Adoption Beliefs on the Expectation-Confirmation Model for Information Technology Continuance , 2006, Int. J. Hum. Comput. Stud..

[24]  Thompson S. H. Teo.  Demographic and motivation variables associated with Internet usage activities , 2001, Internet Res..

[25]  Elena Maceviciute,et al.  Review of: Einspruch, Eric L. An introductory guide to SPSS® for Windows®. 2nd ed. Thousand Oaks, CA: Sage Publications, 2005 , 2006, Inf. Res..

[26]  Chechen Liao,et al.  An empirical investigation of the factors affecting the adoption of Instant Messaging in organizations , 2008, Comput. Stand. Interfaces.

[27]  Chun-Nan Lin,et al.  Predicting tourism loyalty using an integrated Bayesian network mechanism , 2009, Expert Syst. Appl..

[28]  Steven A. Morris,et al.  Prediction of CASE adoption: a neural network approach , 2004, Ind. Manag. Data Syst..

[29]  Dongsong Zhang,et al.  Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression , 2006, Decis. Support Syst..

[30]  Upkar Varshney,et al.  A Framework for Transaction-Level Quality of Service for M-Commerce Applications , 2007, IEEE Transactions on Mobile Computing.

[31]  Wann-Yih Wu,et al.  Moderating effects of technology acceptance perspectives on e-service quality formation: Evidence from airline websites in Taiwan , 2011, Expert Syst. Appl..

[32]  J. Hair.  Multivariate data analysis : a global perspective , 2010 .

[33]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[34]  Anthony D. Miyazaki,et al.  Reducing online privacy risk to facilitate e‐service adoption: the influence of perceived ease of use and corporate credibility , 2010 .

[35]  Izak Benbasat,et al.  Quo vadis TAM? , 2007, J. Assoc. Inf. Syst..

[36]  Keng Siau,et al.  Mobile Commerce: Promises, Challenges and Research Agenda , 2001, J. Database Manag..

[37]  Sumeet Gupta,et al.  Value-based Adoption of Mobile Internet: An empirical investigation , 2007, Decis. Support Syst..

[38]  Alain Yee-Loong Chong,et al.  Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia , 2012, Decis. Support Syst..

[39]  Pin Luarn,et al.  AIS Electronic Library (AISeL) , 2017 .

[40]  Alain Yee-Loong Chong,et al.  An empirical analysis of the adoption of m-learning in Malaysia , 2011, Int. J. Mob. Commun..

[41]  Alain Yee-Loong Chong,et al.  Adoption of 3G services among Malaysian consumers: an empirical analysis , 2010, Int. J. Mob. Commun..

[42]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[43]  Alain Yee-Loong Chong,et al.  An empirical analysis of the determinants of 3G adoption in China , 2012, Comput. Hum. Behav..

[44]  Zhen Wang,et al.  1th Pacific-asia Conference on Information Systems 19. Factors Affecting 3g Adoption: an Empirical Study , 2022 .

[45]  Wolffried Stucky,et al.  Exploring the Critical Success Factors for Mobile Commerce , 2006, 2006 International Conference on Mobile Business.

[46]  David C. Yen,et al.  User acceptance of wireless technology in organizations: A comparison of alternative models , 2011, Comput. Stand. Interfaces.

[47]  Torsten J. Gerpott,et al.  Attribute perceptions as factors explaining Mobile Internet acceptance of cellular customers in Germany - An empirical study comparing actual and potential adopters with distinct categories of access appliances , 2011, Expert Syst. Appl..

[48]  Shing-Chung Ngan,et al.  Analyzing supply chain operation models with the PC-algorithm and the neural network , 2011, Expert Syst. Appl..