Understanding FinTech Adoption and Continuance: Evidence from Emerging Digital Finance Users in Makassar

Authors

  • Muhammad Ridwan Basalamah Universitas Islam Malang, Malang, Indonesia
  • Rois Arifin Universitas Islam Malang, Malang, Indonesia

DOI:

https://doi.org/10.33096/jmb.v13i1.1633

Keywords:

FinTech adoption, Continuance intention, User satisfaction, UTAUT, Expectation–Confirmation Model (ECM)

Abstract

The expansion of financial technology (FinTech) has increased access to digital financial services, particularly among emerging digital finance users in developing urban contexts. However, while prior research has focused on initial adoption, limited attention has been given to continuance behavior. This study addresses this gap by examining the determinants of FinTech adoption and continuance among users in Makassar, Indonesia, by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation–Confirmation Model (ECM). Data from 200 FinTech users were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that Effort Expectancy and Social Influence significantly influence User Satisfaction, while Facilitating Conditions significantly affect Continuance Intention. Within the post-adoption stage, Confirmation strongly influences both Perceived Usefulness and User Satisfaction, and User Satisfaction emerges as the strongest predictor of Continuance Intention. In contrast, Performance Expectancy does not significantly affect User Satisfaction, indicating that users prioritize usability, accessibility, and social validation over performance benefits. The model explains 70.3% of the variance in Continuance Intention, demonstrating strong explanatory power. These findings emphasize the importance of user-centered design, social influence, and infrastructure support in sustaining FinTech usage, while contributing to the integration of adoption and post-adoption frameworks in emerging digital finance contexts.

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References

Al-Shafi, S., & Weerakkody, V. (2010). Factors affecting e-government adoption in the state of Qatar. European and Mediterranean Conference on Information Systems 2010.

Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020). Technology acceptance model in e-learning context: A systematic review. Computers & Education, 125, 389–400. DOI: 10.1016/j.compedu.2018.06.010

Al-Gahtani, S., Hubona, G. S., & Wang, J. (2021). Information technology adoption and the role of cultural differences. Interactive Learning Environments, 29(2), 147–167. DOI: 10.1080/10494820.2018.1546744

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351–370.

Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. DOI: 10.3389/fpsyg.2019.01652

Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655–690). Springer.

Dahlberg, T., Guo, J., & Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265–284.

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

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). USER ACCEPTANCE OF COMPUTER TECHNOLOGY : A COMPARISON OF TWO THEORETICAL MODELS *. 35(8), 982–1003.

Dong, H. (2018). The influence of culture on mobile payment adoption: A study of Chinese customers in Bangkok. Bangkok University Thesis Repository. Available at: https://dspace.bu.ac.th

Edeh, E., Lo, W.-J., & Khojasteh, J. (2023). Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. In Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, Issue 1). https://doi.org/10.1080/10705511.2022.2108813

Ferdinand, A. (2002). Structural equation modeling in management research. Fakultas Ekonomi UNDIP: Semarang.

Ghozali, I. (2011). Aplikasi Analisis Multivariate Dengan Program SPSS. Universitas Diponegoro.

Ghozali, I., & Latan, H. (2015). Partial least squares konsep, teknik dan aplikasi menggunakan program smartpls 3.0 untuk penelitian empiris. Semarang: Badan Penerbit UNDIP.

Hair, J., Black, W., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis. Prentice Hall.

Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education+ Training.

Marshall, A., Dencik, J., & Singh, R. R. (2021). Open innovation: Digital technology creates new opportunities. Strategy & Leadership. https://doi.org/10.1108/SL-04-2021-0036

Mogaji, E., & Nguyen, N. P. (2022). The dark side of mobile money: Perspectives from an emerging economy. Technological Forecasting and Social Change, 185, 122045.

Nguyen, T. T., Dang, X. T., & Le, P. T. (2023). Determining factors of e-wallet use behavioral intention: Application and extension of the UTAUT model. Journal of Innovation and Knowledge, 8(1), 43–55. DOI: 10.1016/j.jik.2022.07.002

Panetta, I. C., Leo, S., & Delle Foglie, A. (2023). The development of digital payments–Past, present, and future–From the literature. Research in International Business and Finance, 64, 101855.

Prahalad, C. K., & Prahalad, C. K. (2005). The Fortune at the Bottom of the Pyramid. Wharton School Pub.

Rahman, M. S., Mannan, M., & Amir, R. (2018). The rise of mobile Internet: the adoption process at the bottom of the pyramid. Digital Policy, Regulation and Governance, 20(6), 582–599.

Realini, C., & Mehta, K. (2015). Financial Inclusion at the Bottom of the Pyramid. FriesenPress.

Sridharan, S., Barrington, D. J., & Saunders, S. G. (2017). Markets and marketing research on poverty and its alleviation: Summarizing an evolving logic toward human capabilities, well-being goals and transformation. Marketing Theory, 17(3), 323–340.

Srivastava, R. (2022). Marketing at the bottom of the pyramid: A systematic literature review to set the research agenda. Academy of Marketing Studies Journal, 26(4).

Tajuddin, I., Mahmud, A., & Syahnur, M. H. (2023). Determinants of Strategic Factors for Digital Transformation in Micro and Small Enterprises in Makassar City. Signifikan: Jurnal Ilmu Ekonomi, 12(1), 131–144.

Tan, G. W.-H., Ooi, K.-B., Dwivedi, Y. K., & Wei, J. (2022). Guest editorial: Advancing mobile payment research in the age of digital acceleration. Internet Research, 32(6), 1753–1756.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.

Vishnoi, P., Bhardwaj, N., & Vohra, A. (2022). Marketing at the bottom of the pyramid: Literature review and future research agenda. International Journal of Consumer Studies, 46(5), 1517–1536.

Wan, L., Xie, S., & Shu, A. (2020). Toward an understanding of university students’ continued intention to use MOOCs: When UTAUT model meets TTF model. Sage Open, 10(3), 2158244020941858.

Yurdakul, D., Atik, D., & Dholakia, N. (2017). Redefining the bottom of the pyramid from a marketing perspective. Marketing Theory, 17(3), 289–303.

Zikmund, W. G., Carr, J. C., & Griffin, M. (2013). Business Research Methods. Cengage Learning.

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Published

2026-03-30

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Articles

How to Cite

Understanding FinTech Adoption and Continuance: Evidence from Emerging Digital Finance Users in Makassar. (2026). Jurnal Manajemen Bisnis, 13(1), 659-674. https://doi.org/10.33096/jmb.v13i1.1633