Factors Affecting Indonesian Consumers to Switch, Using Mobile Banking and Internet Banking Service


The development of technology provides many alternatives and options for bank consumers to obtain services with several conveniences, one of them being through applications and Internet-based services. The Indonesian Financial Services Authority (OJK) revealed that data of e-banking users are quite convincing, where the number of e-banking users (SMS banking, telephone banking, mobile banking, and Internet banking) increased 270%, from 13.6 million customers in 2012 to 50.4 million customers in 2016, while the frequency of e-banking user transactions increased 169%, from 150.8 million transactions in 2012 to 405.4 million transactions in 2016. This study aims to analyze the factors affecting bank consumers in Indonesia to switch using mobile banking and Internet banking services. This research uses quantitative approach through survey method and multiple regression analysis to measure the influence of independent variable to dependent variable. Technology Acceptance Model (TAM) is used to construct hypothetical models, the author also set perceived value as independent variable to complete the measurement. A total of 216 responses were received from 250 questionnaires distributed, which gave a response rate of 86%. Respondents were taken from three different classifications: Government Bank Consumer, Private Bank Consumer and Regional Bank Consumer. Surveys were conducted in Malang, Indonesia. The result of the analysis stated that perceived usefulness, perceived ease of use and perceived value have positive and significant effect on the variable of consumer intention to switch with high significance level.



Keywords: TAM, perceived value, switching intention, consumer behavior

[1] https://www.kominfo.go.id. jumlah pengguna smartphone dan media internet di Indonesia. 2014 [cited 2014 2017]; Available from: https://www.kominfo.go.id.

[2] Internetworldstats.com. The number of internet users and penetration in indonesia. 2016; Available from: http://www.internetworldstats.com/asia.htm.

[3] Keaveney, M.S., Customer Switching Behavior In Service Industries: An Exploratory Study. Journal of marketing, 1995. 59(2).

[4] Keaveney, S.M.d.P., M., Customer Switching Behavior in Online Services: An Exploratory Study of the Role of Selected Attitudinal, Behavioral, and Demographic Factors. Journal of the Academy of Marketing Science, 2001. 29(4): p. 374-390.

[5] Philip Gerrard, J.B.C., Consumer switching behavior in the Asian banking market. Journal of Services Marketing, 2004. 18(3): p. 215-223.

[6] Vishal Vyas, S.R., Drivers of customers’ switching behaviour in Indian banking industry. International Journal of Bank Marketing, 2014. 32(4): p. 321-342.

[7] Michael D. Clemes, C.G., Dongmei Zhang, Customer switching behaviour in the Chinese retail banking industry. International Journal of Bank Marketing, 2010. 28(7): p. 519-546.

[8] Osama Sam Al-Kwifi, Z.U.A., An intellectual journey into the historical evolution of marketing research in brand switching behavior – past, present and future. Journal of Management History, 2015. 21(2): p. 172-193.

[9] Yongqiang Sun, D.L., Sijing Chen, Xingrong Wu, Xiao-Liang Shen, and X.Z. c, Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pullmooring framework. Computers in Human Behavior, 2017. 75: p. 727-738.

[10] Zeeshan Ahmed, M.G., Usman Rafiq, Factors Affecting Consumer Switching Behavior: Mobile Phone Market in Manchester- United Kingdom. International Journal of Scientific and Research Publications, 2015. 5: p. 1-7.

[11] Jishim Jung, H.H., Mihae Oh, Travelers’ switching behavior in the airline industry from the perspective of the push-pull-mooring framework. Tourism Management, 2017. 59: p. 139-153.

[12] Chun-Nan Lin, H.-Y.W., Understanding users’ switching intentions and switching behavior on social networking sites. Aslib Journal of Information Management, 2017. 69(2): p. 201-214.

[13] Sanjukta Pookulangara, J.H., Ge Xiao, Explaining consumers’channel-switching behavior using the theory of planned behavior. Journal o f Retailing and Consumer Services, 2011. 18: p. 311-321.

[14] Farah, M.F., Application of the theory of planned behavior to customer switching intentions in the context of bank consolidations. International Journal of Bank Marketing, 2017. 35(1): p. 147-172.

[15] Davis, F.D., Bagozzi, P R,Warshaw P, User acceptance of computer technology: A comparison of two theoretical models. Management Science, 1989. 35(8): p. 982- 1003.

[16] Cristelle Msaed, S.O.A.-K., Zafar U. Ahmed, Building a comprehensive model to investigate factors behind switching intention of high-technology products. Journal of Product & Brand Management, 2017. 26(2): p. 102-119.

[17] Hong, S.H., Tam, K. and Kim, J., Mobile data service fuels the desire for uniqueness. Communications of the ACM, 2006. 49(9): p. 89-95.

[18] Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D., User acceptance of information technology: toward a unified view. MIS Quarterly, 2003. 27(3): p. 425- 478.

[19] Davis, F.D., Bagozzi, P R,Warshaw P, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 1989. 13(3): p. 319- 340.

[20] Chin W Wynne, T.P., On The use Usefullness,ease of use of structural equation Modeling in MIS Research: A note of Caution. Management Information System Quarterly, 1995. 21(3).

[21] Fang, X., Chan, S., Brzeinski, J. and Xu, S., Moderating effects of task type on wireless technology acceptance. Journal of Management Information Systems, 2005. 22(3): p. 123-157.

[22] Hsin Hsin Chang, K.H.W., Shi Yu Li, Applying push-pull-mooring to investigate channel switching behaviors: M-shopping self-efficacy and switching costs as moderators. Electronic Commerce Research and Applications, 2017. 24: p. 50-67.

[23] Joseph F. Hair Jr. William C. Black, B.J.B.R.E.A., Multivariate Data Analysis. Seventh ed. 2014, United State of America (USA): PEARSON Education Inc. 733.

[24] Zikmund, W.G., et al., Business Research Methods. 8th ed. 2009, USA: SouthWestern College Pub. 674.

[25] Childers, T.L., Carr, C.L., Peck, J. and Carson, S., Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 2001. 77(4): p. 511-536.

[26] Park, Y.a.C., J.V., Acceptance and adoption of the innovative use of Smartphone. Industrial Management & Data Systems, 2007. 107(9): p. 151-175.

[27] Cocosila, M., Trabelsi, H.„ An integrated value-risk investigation of contactless mobile payments adoption. Electron. Commer., 2016. 20: p. 159–170.

[28] Hsu, C.L., Lin, J.C.C., What drives purchase intention for paid mobile apps?– An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 2015. 14(1): p. 46-57