ADOPTION OF MOBILE BANKING SERVICES: AN EMPIRICAL EXAMINATION BETWEEN GENERATION Y AND GENERATION Z IN THAILAND
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Keywords

Mobile Banking
Factors
Adoption
Gen Y
Gen Z
Thailand

Abstract

The purpose of this study is to identify the significant factors that affect the adoption of mobile banking services, by conducting an empirical investigation on generation comparison, between Gen Y and Gen Z in Thailand. To test the framework, descriptive analysis, correlation analysis, collinearity analysis, and multiple linear regression analysis were applied to the primary data, which consists of 400 surveys collected from mobile banking users in Gen Y and Gen Z in Thailand. The results show that compatibility, perceived usefulness, and self-efficacy are significantly and positively affect customer intention to adopt the services in both generations. Interestingly, social influence has significantly affected adoption of mobile banking only in Gen Z.

https://doi.org/10.21512/ijobex.v1i1.7156
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References

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Khul & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11-39). Berlin: Springer-Verlag.

Akturan, U., & Tazcan, N. (2010). The effects of innovation characteristics on mobile banking adoption. In Akturan, U., & Tazcan, N. (Eds.), Proceedings of the 10th Global journal on Business and Economics Conference. Rome, Italy.

Amin, H., Baba, R., & Muhammad, M. (2015). An analysis of mobile banking acceptance by Malaysian customers. Sunway Academic Journal, 4, 1-12.

Bank of Thailand. (2015). The Use of Mobile banking and Internet Banking (statistic). Retrieved from http://www2.bot.or.th/ statistics/ BOTWEB STAT. aspx? reportID=688&language=ENG.

Compeau, D.R., & Higgins, C.A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189-211.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Hair, Jr., J. F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (2006). Multivariate Data Analysis (6th ed.). New Jersey: Prentice Hall.

Hodgkinson, D. (2015). Mobile banking 2015: Global trends and their impact on banks, UBS Evidence Lab. Retrieved from https://www.kpmg.com/FR/fr/IssuesA ndInsights/ArticlesPublications/Docu ments/Mobile-Banking-092015.pdf.

Jeong, B. & Yoon, T. (2013). An empirical investigation on consumer acceptance of mobile banking services. Journal of Business and Management Research, 2(1), 31-40.

Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing Compatibility Beliefs in Technology Acceptance Research. MIS Quarterly, 30(4), 781-804.

Langford, P. (2008, December 6). “Gen Y or Boomer, They Think the Same,” The Advertiser, 1, p. 36.

Lin, Y. (2005). Understanding students’ technology appropriation and learning perceptions in online learning environments. Unpublished doctoral dissertation. University of Missouri, Columbia.

O’Brien, R. (2007). A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality & Quantity, 41(5), 673-690.

Pavlou, P. A. (2001). Integrating trust in electronic commerce with the technology acceptance model: model development and validation. In J. DeGross (ed.), Proceedings of the Seventh Americas Conference in Information Systems. New York: ACM, 2001, pp. 816-822.

Rogers, E. M. (1983). Diffusion of Innovations (3rd ed.). New York: Free Press.

Ruangkanjanases, A., & Sahaphong, P. (2015). Predicting Consumer Intention to Purchase Virtual Goods in Online Games: Empirical Examination between Generation X and Generation Y in Thailand. Advanced Science Letters, 6, 18301836.

Singh, P. V., Tan, Y., & Mookerjee, V. (2011). Network effects: The influence of structural social capital on open source software project success. MIS Quarterly, 35(4), 813829.

Teo, T. S. H. (2001). Demographic and motivation variables associated with internet usage activities. Internet Research, 11(2), 125-137.

Tornatzky, L.G. & Klein, K.J. (1982). Innovation characteristics and innovation adoption implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28-45.

Taylor, S., & Todd, P. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6(2), 144-176.

Williams, K. C., & Page, R.A. (2010). Marketing to the Generations. Journal of Behavioral Studies in Business, 3, 1-7.

Yamane, T. (1967). Statistics: An Introductory Analysis (2nd ed.). New York: Harper and Row.

Yu, C.S., Li, C.K. & Chantatub, W. (2015). Analysis of Consumer E-Lifestyles and Their Effects on Consumer Resistance to Using Mobile Banking: Empirical Surveys in Thailand and Taiwan. International Journal of Business and Information, 10(2), 17-41.

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