FINANCIAL BEHAVIORAL BIASES (PRESENT BIAS AND OVERCONFIDENCE) ON CONSUMER DEBT DECISION MAKING AMONG MANUFACTURING WORKERS IN BATAM
Published:
2026-04-19Downloads
Abstract
This study investigates the influence of present bias and overconfidence on consumer debt decision-making among manufacturing workers in Batam, Indonesia, incorporating financial literacy as a moderating variable. Batam, a strategic free trade zone and major industrial hub, faces a growing phenomenon of consumer over-indebtedness fueled by the rapid expansion of digital lending platforms and online loans among its workforce. A quantitative cross-sectional survey design was employed, with a sample of 215 manufacturing workers drawn from Batam’s major industrial estates through purposive sampling. Data were collected via validated Likert-scale questionnaires and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS 4.0. The findings reveal that: (1) present bias exerts the strongest positive and significant effect on consumer debt decisions (β = 0.387; p < 0.001); (2) overconfidence positively and significantly affects consumer debt decisions (β = 0.294; p < 0.001); (3) financial literacy significantly weakens the relationship between present bias and consumer debt decisions (β = –0.168; p < 0.01); however, (4) financial literacy does not significantly moderate the relationship between overconfidence and consumer debt decisions (β = –0.074; p > 0.05). These findings reveal an asymmetric moderation effect, indicating that conventional financial education is effective in mitigating present bias but insufficient in reducing overconfidence, which requires fundamentally different intervention approaches targeting metacognition rather than knowledge acquisition. The study contributes to behavioral finance literature by extending the application of hyperbolic discounting and overconfidence theories to consumer debt contexts in developing-country industrial zones and offers practical implications for financial regulators and corporate financial wellness programs.
Keywords:
present bias overconfidence consumer debt financial literacy manufacturing workersReferences
Adil, M., Singh, Y., & Ansari, M. S. (2022). How financial literacy moderate the association between behaviour biases and investment decision? Asian Journal of Accounting Research, 7(1), 17–30. https://doi.org/10.1108/AJAR-09-2020-0086
Akin, A., & Akin, I. (2024). The influence of behavioral biases on financial decision-making: A comprehensive review. Journal of Behavioral Finance, 25(3), 315–332. https://doi.org/10.1080/15427560.2023.2234567
Almansour, B. Y., Elkrghli, S., & Almansour, A. Y. (2023). Behavioral finance factors and investment decision: A mediating role of risk perception. Cogent Economics & Finance, 11(2), 2239032. https://doi.org/10.1080/23322039.2023.2239032
Bouteska, A., Mefteh-Wali, S., & Dang, T. (2023). Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic. Technological Forecasting and Social Change, 187, 122216. https://doi.org/10.1016/j.techfore.2022.122216
Coordinating Ministry for Economic Affairs. (2026). Indonesia’s economy grows strongly in 2025, government targets 5.4–5.6% in 2026. Press Release, February 13, 2026.
Cwynar, A., Cwynar, W., Patena, W., & Sibanda, W. (2020). Young adults’ financial literacy and overconfidence bias in debt markets. International Journal of Business Performance Management, 21(1/2), 95–113. https://doi.org/10.1504/IJBPM.2020.106117
Erta, K., Hunt, S., Iscenko, Z., & Brambley, W. (2023). Applying behavioural economics at the Financial Conduct Authority. Occasional Paper No. 1. London: Financial Conduct Authority.
Gathergood, J. (2012). Self-control, financial literacy and consumer over-indebtedness. Journal of Economic Psychology, 33(3), 590–602. https://doi.org/10.1016/j.joep.2011.11.006
Gödker, K., Odean, T., & Smeets, P. (2025). Disposed to be overconfident. Working Paper, University of Chicago Becker Friedman Institute.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage Publications.
Hamid, F. S. (2025). Behavioral biases and over-indebtedness in consumer credit: Evidence from Malaysia. Cogent Economics & Finance, 13(1), 2449191. https://doi.org/10.1080/23322039.2024.2449191
Hastings, J. S., Madrian, B. C., & Skimmyhorn, W. L. (2013). Financial literacy, financial education, and economic outcomes. Annual Review of Economics, 5(1), 347–373. https://doi.org/10.1146/annurev-economics-082312-125807
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. https://doi.org/10.2307/1914185
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kumar, P., Keller, S., & Beam, E. (2024). The impact of buy now pay later on spending: Evidence from millions of transactions. Journal of Financial Economics, 158, 103876. https://doi.org/10.1016/j.jfineco.2024.103876
Laibson, D. (1997). Golden eggs and hyperbolic discounting. Quarterly Journal of Economics, 112(2), 443–478. https://doi.org/10.1162/003355397555253
Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5
Lusardi, A., & Tufano, P. (2015). Debt literacy, financial experiences, and overindebtedness. Journal of Pension Economics & Finance, 14(4), 332–368. https://doi.org/10.1017/S1474747215000232
Media Center Batam. (2025). Batam investment surges to IDR 33.66 trillion, absorbs 51,000 new workers. Retrieved from https://mediacenter.batam.go.id
Meier, S., & Sprenger, C. (2010). Present-biased preferences and credit card borrowing. American Economic Journal: Applied Economics, 2(1), 193–210. https://doi.org/10.1257/app.2.1.193
Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review, 115(2), 502–517. https://doi.org/10.1037/0033-295X.115.2.502
O’Donoghue, T., & Rabin, M. (1999). Doing it now or later. American Economic Review, 89(1), 103–124. https://doi.org/10.1257/aer.89.1.103
O’Donoghue, T., & Rabin, M. (2015). Present bias: Lessons learned and to be learned. American Economic Review, 105(5), 273–79. https://doi.org/10.1257/aer.p20151085
OJK. (2024). Fintech lending statistics December 2024. Jakarta: Financial Services Authority of Indonesia.
OJK Kepri. (2026). Sharia financial education at Kabil Industrial Estate, OJK warns of illegal online loan dangers. Press Release, March 14, 2026.
Rahayu, D. P., & Musdholifah, M. (2023). The effect of present bias and financial literacy on impulsive borrowing behavior among digital loan users. Jurnal Ilmu Manajemen, 11(2), 412–425.
Salas, L. M. (2024). Financial confidence and debt behavior: The mediating role of overconfidence. Journal of Consumer Affairs, 58(1), 156–178. https://doi.org/10.1111/joca.12567
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. E. Vomberg (Eds.), Handbook of market research (pp. 587–632). Cham: Springer.
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Thaler, R. H. (2015). Misbehaving: The making of behavioral economics. New York: W. W. Norton & Company.
Wibowo, S. A., Hidayat, R., & Santosa, A. (2023). The influence of herding behavior, overconfidence, and risk tolerance on Generation Z investment decisions in Malang. Jurnal Ekonomi dan Bisnis, 10(3), 234–248.
World Bank. (2024). Digital credit: Borrower experiences, provider practices, regulatory approaches. Washington, DC: World Bank Group.
License
Copyright (c) 2026 Etty Sri Wahyuni, Henry Aspan, Faris Ramadhan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
