Understanding the FinTech continuance intention of Indonesian users: the moderating effect of gender
This research attempt to analyze risk and benefit factors as well as their influence on sustainability intention of FinTech. We elucidate the Planned Behavior Theory by including the perceived benefits and perceived risk variables to investigate its effect on intention to continue using FinTech. We also examined whether or not men and women are affected differently by the benefits and risk they perceive when using FinTech. Data were collected through online surveys, then being analyzed using GSCA. The results reveal perceived benefits are affected significantly by the convenience aspect and deliver a significant effect on FinTech continuance intention. The risk perceived by FinTech users is affected the most by legal risk. This study proves that gender is able to moderate the perceived risk influence on the intention to continue using FinTech, especially in the female user group.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Agustia, D., & Anridho, N. (2020). Financial inclusion: Does Fintech help in Indonesia? In Financial Technology and Disruptive Innovation in ASEAN (pp. 149–165). IGI Global. https://doi.org/10.4018/978-1-5225-9183-2.ch008
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11–39). Springer. https://doi.org/10.1007/978-3-642-69746-3_2
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior (1st ed.). Pearson.
Arner, D. W., Barberis, J., & Buckey, R. P. (2016). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business, 37, 371.
Arner, D. W., Barberis, J., & Buckley, R. P. (2015). The evolution of Fintech: A new post-crisis paradigm. University of New South Wales Law Research Serie. Researh paper no: 2015/047. https://doi.org/10.2139/ssrn.2676553
Bandura, A. (1986). Social foundations of thought and action (1st ed., Vol. 1986). Prentice-Hall.
Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1), 43.
Bauer, R. A. (1960). Consumer behavior as risk taking. Dynamic marketing for a changing world. In Proceedings of the 43rd Conference of the American Marketing Association (pp. 389–398). Marketing Classics Press.
Bem, S. L. (1981). Gender schema theory: A cognitive account of sex typing. Psychological Review, 88(4), 354–364. https://doi.org/10.1037/0033-295X.88.4.354
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Chan, R. (2015). Asian regulators seek fintech balance. 25 Finance Asia. https://www.financeasia.com/article/asian-regulatorsseek-fintech-balance/401588
Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: the role of brand loyalty. Journal of Marketing, 65(2), 81–93. https://doi.org/10.1509/jmkg.18.104.22.16855
Cheng, T. E., Lam, D. Y., & Yeung, A. C. (2006). Adoption of internet banking: an empirical study in Hong Kong. Decision Support Systems, 42(3), 1558–1572. https://doi.org/10.1016/j.dss.2006.01.002
Chiang, H.-S. (2013). Continuous usage of social networking sites: The effect of innovation and gratification attributes. Online Information Review, 37(6). https://doi.org/10.1108/OIR-08-2012-0133
Cochran, W. G. (1977). Sampling techniques. John Wiley & Sons.
Creswell, J. (2009). Research design-qualitative, quantitative, and mixed methods approaches. Sage Publications Inc.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Deci, E., & Ryan, R. (1985). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media. https://doi.org/10.1007/978-1-4899-2271-7
Dowling, G. R. (1986). Perceived risk: the concept and its measurement. Psychology & Marketing, 3(3), 193–210. https://doi.org/10.1002/mar.4220030307
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Financial Standard Board (FSB). (2019). FSB report assesses FinTech developments and potential financial stability implications. FSB. https://www.fsb.org/wp-content/uploads/R140219.pdf
Francis, B., Hasan, I., Park, J. C., & Wu, Q. (2015). Gender differences in financial reporting decision making: Evidence from accounting conservatism. Contemporary Accounting Research, 32(3), 1285–1318. https://doi.org/10.1111/1911-3846.12098
Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7. https://doi.org/10.17705/1CAIS.00407
Gomber, P., Koch, J.-A., & Siering, M. (2017). Digital finance and FinTech: current research and future research directions. Journal of Business Economics, 87(5), 537–580. https://doi.org/10.1007/s11573-017-0852-x
Ha, C.-S., & Jung, D.-H. (2016). The impact of FinTech user and product characteristics on intention for continuous use. Informatization Policy, 23(4), 59–75.
Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340. https://doi.org/10.3390/sym11030340
Hwang, H., & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69(1), 81–99. https://doi.org/10.1007/BF02295841
Jung, L. (2017). A study on the relationship with attitude and satisfaction of the continuance intention in Fintech. Information, 20(8(B)), 5817–5824.
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213. https://doi.org/10.2307/249751
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001
Kim, Y., Choi, J., Park, Y.-J., & Yeon, J. (2016). The adoption of mobile payment services for “Fintech”. International Journal of Applied Engineering Research, 11(2), 1058–1061.
Lee, S. (2017). Evaluation of mobile application in user’s perspective: case of P2P lending apps in fintech industry. TIIS, 11(2), 1105–1117. https://doi.org/10.3837/tiis.2017.02.027
Liang, T.-P., & Yeh, Y.-H. (2011). Effect of use contexts on the continuous use of mobile services: the case of mobile games. Personal and Ubiquitous Computing, 15(2), 187–196. https://doi.org/10.1007/s00779-010-0300-1
Lyytinen, K., & Hirschheim, R. (1988). Information systems failures – a survey and classification of the empirical literature. Oxford Surveys in Information Technology, 4, 257–309.
Otoritas Jasa Keuangan (OJK). (2020). Laporan Triwulan I-2020. OJK. https://www.ojk.go.id/id/data-dan-statistik/laporan-triwulanan/Documents/OJK%20-%20Laporan%20Triwulan%20I-2020.pdf
Okazaki, S., & Mendez, F. (2013). Exploring convenience in mobile commerce: Moderating effects of gender. Computers in Human Behavior, 29(3), 1234–1242. https://doi.org/10.1016/j.chb.2012.10.019
Ramos, F. (2017). Accessing the determinants of behavioral intention to adopt fintech services among the millennial generation. https://www.semanticscholar.org/paper/Accessing-the-determinants-of-behavioral-intention-Ramos/bbc22583930087a8d8d237243ce2b06420374511
Rogers, E. M. (1983). Diffusion of Innovations (3rd ed.). Free Press.
Rouibah, K., Lowry, P. B., & Hwang, Y. (2016). The effects of perceived enjoyment and perceived risks on trust formation and intentions to use online payment systems: New perspectives from an Arab country. Electronic Commerce Research and Applications, 19(3), 33–43. https://doi.org/10.1016/j.elerap.2016.07.001
Ryu, H.-S. (2018). What makes users willing or hesitant to use Fintech?: the moderating effect of user type. Industrial Management & Data Systems, 118(3). https://doi.org/10.1108/IMDS-07-2017-0325
Sekaran, U., & Bougie, R. (2013). Research methods for business: A skill building approach. John Wiley & Sons.
Shim, Y., & Shin, D.-H. (2016). Analyzing China’s FinTech industry from the perspective of actor–network theory. Telecommunications Policy, 40(2–3), 168–181. https://doi.org/10.1016/j.telpol.2015.11.005
Sun, Q., Wang, C., & Cao, H. (2010). An extended TAM for analyzing adoption behavior of mobile commerce [Conference presentation]. 2009 Eighth International Conference on Mobile Business. Dalian, China. https://doi.org/10.1109/ICMB.2009.16
Taherdoost, H. (2017). Determining sample size; how to calculate survey sample size. International Journal of Economics Management Systems, 2.
Tran, T., Han, K., & Yun, S. (2018). Factors influencing the intention to use mobile payment service using fintech systems: Focused on Vietnam. Asia Life Sciences, (3), 1731–1747.
Triandis, H. C. (1979). Values, attitudes, and interpersonal behavior [Conference presentation]. Nebraska symposium on motivation. Nebraska.
Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034