Analyzing factors affecting users’ intention to use PayLater in Indonesia
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Dewi Khornida Marheni(1)
Department of Management, Faculty of Business and Management, Universitas Internasional Batam
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Richard William(2*)
Department of Management, Faculty of Business and Management, Universitas Internasional Batam
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Hesniati Hesniati(3)
Department of Management, Faculty of Business and Management, Universitas Internasional Batam
(*) Corresponding Author
Keywords:
PayLater, Fintech, Technology Acceptance Model, Perceived Security, Perceived User Risk
Abstract
This study examines the determinants of users’ intention to adopt PayLater services in Indonesia using the Technology Acceptance Model (TAM). Four constructs were tested: Perceived Usefulness, Perceived Ease of Use, Perceived Security, and Perceived User Risk. Data were obtained from 230 Generation Z respondents through an online survey using convenience sampling and analyzed with Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that only Perceived Security has a positive and significant effect on intention to use PayLater, while the other variables show no significant influence. These findings emphasize that users prioritize transaction safety and data protection over convenience or usefulness in adopting digital financial services. The study highlights the growing importance of security perception in fintech adoption and provides practical insights for providers to strengthen trust and promote responsible PayLater usage among young consumers.
Keyword: PayLater; Fintech; Technology Acceptance Model; Perceived Security; Perceived User Risk
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References
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WorldPay. (2025). The Global Payment Report.
Yamin, M. A. Y., & Abdalatif, O. A. A. (2024). Examining consumer behavior towards adoption of quick response code mobile payment systems: transforming mobile payment in the fintech industry. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03189-w
Yu, A. P., Xu, C., & Cho, S. E. (2024). Factors Affecting Customer Use Intention of MyData Services in the Fintech Industry. Journal of Theoretical and Applied Electronic Commerce Research , 19(4), 3412–3428. https://doi.org/10.3390/jtaer19040165
Zhang, W., Siyal, S., Riaz, S., Ahmad, R., Hilmi, M. F., & Li, Z. (2023). Data Security, Customer Trust and Intention for Adoption of Fintech Services: An Empirical Analysis From Commercial Bank Users in Pakistan. SAGE Open, 13(3), 1–17. https://doi.org/10.1177/21582440231181388
Zhang, Y. (2024). Impact of perceived privacy and security in the TAM model: The perceived trust as the mediated factors. International Journal of Information Management Data Insights, 4(2), 100270.
https://doi.org/10.1016/j.jjimei.2024.100270
Alnsour, I. R. (2022). Impact of fintech over consumer experience and loyalty intentions: an empirical study on Jordanian Islamic Banks. Cogent Business and Management, 9(1). https://doi.org/10.1080/23311975.2022.2141098
Appiah, T., & Agblewornu, V. V. (2025). The interplay of perceived benefit, perceived risk, and trust in Fintech adoption: Insights from Sub-Saharan Africa. Heliyon, 11(2), e41992. https://doi.org/10.1016/j.heliyon.2025.e41992
Aprilia, C., & Amalia, R. (2023). Perceived Security and Technology Continuance Theory: An Analysis Of Mobile Wallet Users’ Continuance Intention. Global Business Review, July. https://doi.org/10.1177/09721509221145831
Chawla, U., Mohnot, R., Singh, H. V., & Banerjee, A. (2023). The Mediating Effect of Perceived Trust in the Adoption of Cutting-Edge Financial Technology among Digital Natives in the Post-COVID-19 Era. Economies, 11(12). https://doi.org/10.3390/economies11120286
Chen, A., Wang, S., Mehta, A. M., Asif, M., Xu, S., & Shahzad, M. F. (2025). FinTech adoption for ESG integration through robo advisors, personalization, and perceived trust. Scientific Reports, 15(1), 1–13. https://doi.org/10.1038/s41598-025-17046-6
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
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.
Guttman-kenney, B., Firth, C., & Gathergood, J. (2023). Journal of Behavioral and Experimental Finance Buy now , pay later ( BNPL ) ... on your credit card. Journal of Behavioral and Experimental Finance, 37, 100788.
https://doi.org/10.1016/j.jbef.2023.100788
Hair, J. F., Hult, G. T., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) - Joseph F. Hair, Jr., G. Tomas M. Hult, Christian Ringle, Marko Sarstedt. In Sage.
Hassan, M. S., Mai, N. H., Wahab, N. S. A., Amin, M. Bin, Hassan, M. M., & Oláh, J. (2025). Decentralized fintech platforms adoption intention in cyber risk environment among GenZ: A dual-method approach using PLS-SEM and necessary condition analysis. Computers in Human Behavior Reports, 18(February). https://doi.org/10.1016/j.chbr.2025.100687
Hesniati, H., & Limgestu, R. (2023). Determinants of Intention To Use Islamic Fintech During Covid-19 Pandemic. EKUITAS (Jurnal Ekonomi Dan Keuangan), 7(4), 587–604. https://doi.org/10.24034/j25485024.y2023.v7.i4.5860
Irianto, A. B. P., & Chanvarasuth, P. (2025). Drivers and Barriers of Mobile Payment Adoption Among MSMEs: Insights from Indonesia. Journal of Risk and Financial Management, 18(5). https://doi.org/10.3390/jrfm18050251
Juita, V., Pujani, V., Rahim, R., & Rahayu, R. (2024). Dataset on online impulsive buying behavior of buy now pay later users and non-buy now pay later users in Indonesia using the stimulus-organism-response model. Data in Brief, 54, 110500. https://doi.org/10.1016/j.dib.2024.110500
Katadata. (2024). 2024 Indonesian PayLater User Behavior Report.
Kumar, J., & Rani, V. (2024). Financial innovation and gender dynamics: a comparative study of male and female FinTech adoption in emerging economies. International Journal of Accounting and Information Management.
https://doi.org/10.1108/IJAIM-03-2024-0098
Nurfadilah, D., & Samidi, S. (2021). How the Covid-19 Crisis Is Affecting Customers’ Intention To Use Islamic Fintech Services: Evidence From Indonesia. Journal of Islamic Monetary Economics and Finance, 7(1), 83–114.
https://doi.org/10.21098/jimf.v7i0.1318
OJK. (2025). Indeks Literasi dan Inklusi Keuangan Masyarakat. In Otoritas Jasa Keuangan dan Badan Pusat Statistik.
Projo, N. W. K., Usman, H., Chairy, C., & Haque, M. G. (2022). The exploration role of Sharia compliance in technology acceptance model for e-banking (case: Islamic bank in Indonesia). Journal of Islamic Marketing, 13(5), 1089–1110. https://doi.org/10.1108/JIMA-08-2020-0230
Purwati, A. S. M. (2026). Transformation of Digital Payment: Determinants of Customer E-Money Adoption and Implications for Digital Intention Behavior.
Roh, T., Yang, Y. S., Xiao, S., & Park, B. Il. (2024). What makes consumers trust and adopt fintech? An empirical investigation in China. In Electronic Commerce Research (Vol. 24, Issue 1). Springer US. https://doi.org/10.1007/s10660-021-09527-3
Saadah, K., & Setiawan, D. (2024). Determinants of fintech adoption: evidence from SMEs in Indonesia. LBS Journal of Management & Research, 22(1), 55–65. https://doi.org/10.1108/lbsjmr-11-2022-0076
Sekaran, U., & Bougie, R. (2016). Research Methods For Business: A Skill Building Approach, 7th Edition eBook: Uma Sekaran, Roger Bougie: Kindle Store (7th ed.). John Wiley & Sons.
Sharma, V., Rupeika-Apoga, R., Singh, T., & Gupta, M. (2025). Sustainable Investments in the Blue Economy: Leveraging Fintech and Adoption Theories. Journal of Risk and Financial Management, 18(7), 1–28. https://doi.org/10.3390/jrfm18070368
Truc, L. T. (2024). Empowering tomorrow: Unleashing the power of e-wallets with adoption readiness, personal innovativeness, and perceived risk to client’s intention. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100322. https://doi.org/10.1016/j.joitmc.2024.100322
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model. Management Science Febru, May 2014, 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wei, N., Liang, Y., Wang, H., & Liu, M. (2025). Analysis of mobile fintech adoption based on perceived value and risk theory: findings from PLS-SEM and fsQCA. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-05142-x
WorldPay. (2025). The Global Payment Report.
Yamin, M. A. Y., & Abdalatif, O. A. A. (2024). Examining consumer behavior towards adoption of quick response code mobile payment systems: transforming mobile payment in the fintech industry. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03189-w
Yu, A. P., Xu, C., & Cho, S. E. (2024). Factors Affecting Customer Use Intention of MyData Services in the Fintech Industry. Journal of Theoretical and Applied Electronic Commerce Research , 19(4), 3412–3428. https://doi.org/10.3390/jtaer19040165
Zhang, W., Siyal, S., Riaz, S., Ahmad, R., Hilmi, M. F., & Li, Z. (2023). Data Security, Customer Trust and Intention for Adoption of Fintech Services: An Empirical Analysis From Commercial Bank Users in Pakistan. SAGE Open, 13(3), 1–17. https://doi.org/10.1177/21582440231181388
Zhang, Y. (2024). Impact of perceived privacy and security in the TAM model: The perceived trust as the mediated factors. International Journal of Information Management Data Insights, 4(2), 100270.
https://doi.org/10.1016/j.jjimei.2024.100270
Published
2026-11-04
How to Cite
Marheni, D., William, R., & Hesniati, H. (2026). Analyzing factors affecting users’ intention to use PayLater in Indonesia. Journal of Management : Small and Medium Enterprises (SMEs), 19(3), 2345-2357. https://doi.org/10.35508/jom.v19i3.26413
Section
Articles
