Factors Influencing the Continuance Usage of Online Mobile Payment Apps: A Case Study of WECHAT Users in China

Authors: Isaac Kofi Mensah, Jianing Mi, Feng Cheng

Abstract:

This research paper seeks to investigate the factors determining the continuance usage of online mobile payment applications among WECHAT users in China. Technology Acceptance Model (TAM) and the Diffusion of Innovation (DOI) theory would both be applied as the theoretical foundation for this study. A developed instrument would be administered to the targeted sample of 1000 WECHAT Users in the City of Harbin, China, through an online questionnaire administration platform. Factors such as perceived usefulness, perceived ease of use, perceived service quality, social influence, trust in the internet, internet self-efficacy, relative advantage, compatibility, and complexity would be explored to determine its significant impact on the continuance intention to use mobile payment apps. This study is at the development and implementation stage. The successful completion of this research article would not only provide an insightful understanding of the factors influencing the decision of WECHAT users in China to use mobile payment applications but also enrich the e-commerce adoption literature.

Keywords: Diffusion of innovation (DOI), e-commerce, mobile payment, technology acceptance model (TAM), WECHAT.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315430

References:

[1] TechTarget. Definition e-commerce (electronic commerce or EC) 2016 (cited 2017 26th September, 2017); Available from: http://searchcio.techtarget.com/definition/e-commerce.
[2] Investopedia. Electronic Commerce - ecommerce. 2016 (cited 2017 26th September); Available from: http://www.investopedia.com/terms/e/ecommerce.asp.
[3] Albarq, A. N., Intention to shop online among university students in Jordan. 2006, Graduate School, Universiti Utara Malaysia.
[4] Wu, S.-I., The relationship between consumer characteristics and attitude toward online shopping. Marketing Intelligence & Planning, 2003. 21(1): p. 37-44.
[5] Cheung, C. M. and M. K. Lee, An integrative model of consumer trust in internet shopping. ECIS 2003 Proceedings, 2003: p. 48.
[6] Goldsmith, R. E. and L. R. Flynn, Psychological and behavioral drivers of online clothing purchase. Journal of Fashion Marketing and Management: An International Journal, 2004. 8(1): p. 84-95.
[7] Agarwal, R. and E. Karahanna, Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 2000: p. 665-694.
[8] Lee, J., J. Kim, and J. Y. Moon. What makes Internet users visit cyber stores again? Key design factors for customer loyalty. in Proceedings of the SIGCHI conference on Human Factors in Computing Systems. 2000. ACM.
[9] Torkzadeh, G. and G. Dhillon, Measuring factors that influence the success of Internet commerce. Information Systems Research, 2002. 13(2): p. 187-204.
[10] Wang, N., D. Liu, and J. Cheng. Study on the influencing factors of online shopping. in Proceedings of the 11th Joint Conference on Information Sciences, Published by Atlantis Press. 2008.
[11] Cheung, C.M., G.W. Chan, and M. Limayem, A critical review of online consumer behavior: Empirical research. Journal of electronic commerce in organizations, 2005. 3(4): p. 1.
[12] Cotte, J., et al., Pleasure or utility? Time planning style and web usage behaviors. Journal of interactive marketing, 2006. 20(1): p. 45-57.
[13] Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 1989: p. 319-340.
[14] Lee, M. K., C. M. Cheung, and Z. Chen, Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & management, 2005. 42(8): p. 1095-1104.
[15] Liu, S.-H., H.-L. Liao, and C.-J. Peng, Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behavior. E-learning, 2005. 4(H6): p. H8.
[16] Saadé, R., Web-based educational information system for enhanced learning, EISEL: Student assessment. Journal of Information Technology Education: Research, 2003. 2(1): p. 267-277.
[17] . Mensah, I. K., M. Jianing, and D. K. Durrani, Factors Influencing Citizens' Intention to Use E-Government Services: A Case Study of South Korean Students in China. International Journal of Electronic Government Research (IJEGR), 2017. 13(1): p. 14-32.
[18] Pituch, K. A. and Y.-k. Lee, The influence of system characteristics on e-learning use. Computers & Education, 2006. 47(2): p. 222-244.
[19] Rogers, E., Diffusion of Innovations4 Free Press New York Google Scholar. 1995.
[20] Eastin, M. S., Diffusion of e-commerce: an analysis of the adoption of four e-commerce activities. Telematics and informatics, 2002. 19(3): p. 251-267.
[21] Fayad, R. and D. Paper, The Technology Acceptance Model E-Commerce Extension: A Conceptual Framework. Procedia Economics and Finance, 2015. 26: p. 1000-1006.
[22] Qiu, L. and D. Li, Applying TAM in B2C E-commerce research: An extended model. Tsinghua Science & Technology, 2008. 13(3): p. 265-272.
[23] Johar, M. G. M. and J. A. A. Awalluddin, The role of technology acceptance model in explaining effect on e-commerce application system. International Journal of Managing Information Technology, 2011. 3(3): p. 1-14.
[24] V, T. K., A Diffusion Theory Perspective on theAdoption of Online Shopping Among Youth in Central Kerala. International Journal of Innovative Research in Science, Engineering and Technology, 2014. 3 (10): p. 16688-16694.
[25] Poorangi, M. M., et al., E-commerce adoption in Malaysian Small and Medium Enterprises Practitioner Firms: A revisit on Rogers' model. Anais da Academia Brasileira de Ciências, 2013. 85(4): p. 1593-1604.
[26] Chen, L. and R. Wang, Trust development and transfer from electronic commerce to social commerce: an empirical investigation. American Journal of Industrial and Business Management, 2016. 6(05): p. 568.
[27] Alaimo, K., Olson, C. M., & Frongillo, E. A. (1999). Importance of cognitive testing for survey items: an example from food security questionnaires. Journal of nutrition education, 31(5), 269-275.
[28] Belson, W. A. (1981). The design and understanding of survey questions: Gower Aldershot.
[29] Hunt, S. D., Sparkman Jr, R. D., & Wilcox, J. B. (1982). The pretest in survey research: Issues and preliminary findings. Journal of marketing research, 269-273.
[30] Hilton, C. E. (2017). The importance of pretesting questionnaires: a field research example of cognitive pretesting the Exercise referral Quality of Life Scale (ER-QLS). International Journal of Social Research Methodology, 20(1), 21-34.
[31] De Leeuw, E. D. (2001). Reducing missing data in surveys: An overview of methods. Quality & Quantity, 35(2), 147-160.
[32] Drennan, J. (2003). Cognitive interviewing: verbal data in the design and pretesting of questionnaires. Journal of advanced nursing, 42(1), 57-63.
[33] Van Teijlingen, E. R., & Hundley, V. (2001b). The importance of pilot studies. Social research update, 35(4), 1-4.