Exploring Millennials’ Investment Behavior Shifts in Response to Digital Financial Platforms

Authors

  • Nekky Rahmiyati Universitas 17 Agustus 1945 Surabaya
  • Rizki Sarwo Eddy Wibowo Universitas Gadjah Mada
  • Tyahya Whisnu Hendratni Universitas Pancasila

DOI:

https://doi.org/10.55927/ajabm.v5i1.20

Keywords:

Millennial Investors, Digital Financial Platforms, Investment Behavior, Mixed Methods, Behavioral Finance

Abstract

This study investigates shifts in millennials’ investment behavior in response to the use of digital financial platforms by employing a mixed-methods research design. The quantitative phase involved a survey of 237 millennial investors who actively use mobile trading and online investment applications. Survey data were analyzed using the Statistical Package for the Social Sciences (SPSS) through descriptive statistics, multiple regression analysis, and mediation testing to examine the effects of digital platform usage intensity, financial literacy, and behavioral biases on changes in trading frequency, portfolio diversification, and risk tolerance. The quantitative findings indicate that higher engagement with digital financial platforms significantly increases investment activity and risk-taking tendencies. Behavioral biases, particularly overconfidence and herding behavior, partially mediate the relationship between platform usage and investment decision patterns, while financial literacy helps reduce excessive speculative behavior and supports more structured portfolio allocation. To enrich the statistical findings, the qualitative phase included in-depth interviews with 14 selected participants, providing deeper insights into user motivations, perceived convenience, social influence, and trust in digital investment technologies. The qualitative results reveal that platform accessibility, gamified features, and peer-driven information flows play a crucial role in encouraging more active—yet sometimes less disciplined—investment behavior

References

Baker, T., & Dellaert, B. (2019). Regulating robo-advice across the financial services industry. Iowa Law Review, 103(2), 713–750.

Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797–817. https://doi.org/10.2307/2118364

Barber, B. M., & Odean, T. (2013). The behavior of individual investors. In G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the Economics of Finance (Vol. 2, pp. 1533–1570). Elsevier. https://doi.org/10.1016/B978-0-44-459406-8.00022-6

Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. In G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the Economics of Finance (Vol. 1, pp. 1053–1128). Elsevier. https://doi.org/10.1016/S1574-0102(03)01027-6

Bikhchandani, S., & Sharma, S. (2001). Herd behavior in financial markets. IMF Staff Papers, 47(3), 279–310.

Charness, G., & Gneezy, U. (2012). Strong evidence for gender differences in risk taking. Journal of Economic Behavior & Organization, 83(1), 50–58. https://doi.org/10.1016/j.jebo.2011.06.007

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.

D’Acunto, F., Prabhala, N., & Rossi, A. G. (2019). The promises and pitfalls of robo-advising. Review of Financial Studies, 32(5), 1983–2020. https://doi.org/10.1093/rfs/hhy114

Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining “gamification.” In Proceedings of the 15th International Academic MindTrek Conference (pp. 9–15). ACM. https://doi.org/10.1145/2181037.2181040

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11

Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs. Annals of Family Medicine, 11(2), 115–122. https://doi.org/10.1370/afm.1421

Fisch, J. E., Laboure, M., & Turner, J. A. (2020). The emergence of the robo-advisor. In The Disruptive Impact of FinTech on Retirement Systems (pp. 13–37). Oxford University Press.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

Gomber, P., Koch, J.-A., & Siering, M. (2018). Digital finance and FinTech. Journal of Business Economics, 87(5), 537–580. https://doi.org/10.1007/s11573-017-0852-x

Grinblatt, M., & Keloharju, M. (2009). Sensation seeking, overconfidence, and trading activity. Journal of Finance, 64(2), 549–578. https://doi.org/10.1111/j.1540-6261.2009.01443.x

Haddad, C., & Hornuf, L. (2019). The emergence of the global fintech market. Small Business Economics, 53(1), 81–105. https://doi.org/10.1007/s11187-018-9991-x

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.

Hastings, J. S., Madrian, B. C., & Skimmyhorn, W. L. (2013). Financial literacy, financial education, and economic outcomes. Annual Review of Economics, 5, 347–373. https://doi.org/10.1146/annurev-economics-082312-125807

Huang, R., Huang, X., & Wang, T. (2019). The influence of gamification on consumer financial behavior. Journal of Behavioral Finance, 20(3), 1–12.

Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse investors. Electronic Markets, 28(3), 367–380. https://doi.org/10.1007/s12525-017-0276-6

Kahneman, D., & Tversky, A. (1979). Prospect theory. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Kim, K., & Lee, S. (2020). Millennials’ adoption of financial technology services. Sustainability, 12(15), 1–15. https://doi.org/10.3390/su12156069

Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5

OECD. (2020). OECD/INFE 2020 international survey of adult financial literacy. OECD Publishing.

Odean, T. (1998). Volume, volatility, price, and profit when all traders are above average. Journal of Finance, 53(6), 1887–1934. https://doi.org/10.1111/0022-1082.00078

Puschmann, T. (2017). Fintech. Business & Information Systems Engineering, 59(1), 69–76. https://doi.org/10.1007/s12599-017-0464-6

Statman, M. (2019). Behavioral finance. The Journal of Portfolio Management, 45(7), 7–14. https://doi.org/10.3905/jpm.2019.45.7.007

Statman, M., Thorley, S., & Vorkink, K. (2006). Investor overconfidence and trading volume. Review of Financial Studies, 19(4), 1531–1565. https://doi.org/10.1093/rfs/hhj032

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology. MIS Quarterly, 36(1), 157–178.

World Bank. (2022). Financial consumer protection and digital financial services. World Bank Publications.

Xiao, J. J. (2016). Consumer financial capability and wellbeing. Springer International Publishing. https://doi.org/10.1007/978-3-319-28887-1

Xiao, J. J., & Porto, N. (2017). Financial education and financial satisfaction. International Journal of Bank Marketing, 35(5), 805–817. https://doi.org/10.1108/IJBM-01-2016-0004

Zhang, Y. (2021). Social trading and investment behavior in the digital era. Journal of Behavioral and Experimental Finance, 30, 100510. https://doi.org/10.1016/j.jbef.2021.100510.

Published

2026-02-28