Share:


Understanding the FinTech continuance intention of Indonesian users: the moderating effect of gender

Abstract

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.

Keyword : FinTech continuance intention, perceived benefit, perceived risk, gender, theory of planned behavior

How to Cite
Nurlaily, F., Aini, E. K., & Asmoro, P. S. (2021). Understanding the FinTech continuance intention of Indonesian users: the moderating effect of gender. Business: Theory and Practice, 22(2), 290-298. https://doi.org/10.3846/btp.2021.13880
Published in Issue
Sep 13, 2021
Abstract Views
949
PDF Downloads
1041
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

AFTECH. (2018). Fintech Indonesia. Fintech. https://fintech.id/dokumen/aftech-annual-member-survey-2018

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.65.2.81.18255

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