Psychological Antecedents of E-Wallet Usage Behavior: Mediating Role of Behavioral Intention in Jakarta
Keywords:
Attitude toward using, Subjective norm, Perceived behavioral control, Usage behavior; Behavioral intention, E-wallet adoptionAbstract
Digital payment systems have transformed financial transactions globally, with e-wallets emerging as predominant platforms in developing economies. Despite substantial adoption growth, prior research yields inconsistent findings regarding psychological antecedents of e-wallet usage behavior, particularly concerning the conditional role of behavioral intention as a transmitting mechanism. This study examines the effects of attitude toward using, subjective norm, and perceived behavioral control on e-wallet usage behavior mediated by behavioral intention through the Theory of Planned Behavior framework. Employing structural equation modeling with data from 125 e-wallet users in Jakarta, Indonesia, the analysis reveals that attitude toward using and perceived behavioral control significantly influence behavioral intention, while subjective norm demonstrates no significant effect. Behavioral intention successfully mediates the perceived behavioral control-usage behavior relationship but fails to transmit normative influences. These findings extend TPB application by identifying boundary conditions wherein normative components exhibit limited relevance for private technology adoption decisions in Jakarta's urban context, while providing preliminary evidence-based guidance for e-wallet providers prioritizing user experience optimization and capability development over social influence strategies, though interpretations should consider the study's measurement and model fit limitations.
References
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. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall. https://openlibrary.org/works/OL1890013W
Ariffin, S. K., Abd Rahman, M. N., Muhammad, A. M., & Zhang, Q. (2021). Understanding the consumer’s intention to use the e-wallet services. F1000Research, 10, 115. https://doi.org/10.12688/f1000research.28198.1
Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471–499. https://doi.org/10.1348/014466601164939
Bank Indonesia. (n.d.). Statistik Sistem Pembayaran dan Infrastruktur Pasar Keuangan (SPIP). https://www.bi.go.id/id/statistik/ekonomi-keuangan/spip/default.aspx
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Berlianawati, A., Nurabiah, N., & Rini, S. (2024). Exploring the mind of Gen Z: Deciphering e-wallet adoption through the lens of TPB theory. Jurnal Ilmiah Akuntansi dan Bisnis, 19(1). https://doi.org/10.24843/JIAB.2024.v19.i01.p09
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge. https://doi.org/10.4324/9781315757421
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555
Daragmeh, A., Lentner, C., & Sági, J. (2021). FinTech payments in the era of COVID-19: Factors influencing behavioral intentions of “Generation X” in Hungary to use mobile payment. Journal of Behavioral and Experimental Finance, 32, 100574. https://doi.org/10.1016/j.jbef.2021.100574
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Ghozali, I. (2023). Model persamaan struktural: Konsep dan aplikasi dengan program AMOS 26.0 (8th ed.). Badan Penerbit Universitas Diponegoro.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. https://books.google.com/books?id=0R9ZswEACAAJ
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2022). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
IBM. (n.d.). IBM SPSS Amos 30 User’s Guide (PDF). IBM Documentation. https://www.ibm.com/docs/en/SSLVMB_30.0.0/pdf/IBM_SPSS_Amos_User_Guide.pdf
Johan, N. F., Mohd Noor, N. A., & Bidin, R. (2022). Factors affecting e-wallet continuance usage intention among millennials in Malaysia. Asian Journal of Business Research, 12(2), 58–76. https://doi.org/10.14707/ajbr.220127
Kementerian Komunikasi dan Digital Republik Indonesia. (n.d.). Transaksi QRIS melonjak 226,54%: Revolusi pembayaran digital di Indonesia. https://www.komdigi.go.id/berita/ekonomi-digital/detail/transaksi-qris-melonjak-22654-revolusi-pembayaran-digital-di-indonesia
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lee, K.-C., Kang, I., & McKnight, D. H. (2006). Transfer from offline trust to key online perceptions: An empirical study. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06). IEEE. https://doi.org/10.1109/HICSS.2006.58
Nevitt, J., & Hancock, G. R. (2001). Performance of bootstrapping approaches to model test statistics and parameter standard error estimation in structural equation modeling. Structural Equation Modeling, 8(3), 353–377. https://doi.org/10.1207/S15328007SEM0803_2
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
Rasa, T., & Laherto, A. (2022). The emergence of transformative agency in a school-based makerspace: Tracing the adoption of a digital fabrication technology. Futures, 140, 102888. https://doi.org/10.1016/j.futures.2022.102888
Schindler, P. S. (2019). Business research methods (13th ed.). McGraw-Hill Education. https://books.google.com/books?id=kGxPzQEACAAJ
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445. https://doi.org/10.1037/1082-989X.7.4.422
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociological methodology 1982 (pp. 290–312). Jossey-Bass. https://doi.org/10.2307/270723
Sugiyono. (2021). Metode penelitian kuantitatif, kualitatif, dan R&D (3rd ed.). Alfabeta.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Visa. (2023, April 10). 2 dari 3 orang Indonesia bersiap meninggalkan uang tunai (Press release). https://www.visa.co.id/about-visa/newsroom/press-releases/nr-id-230410.html
Visa. (2024, March 19). Budaya cashless marak di Indonesia seiring meningkatnya adopsi pembayaran digital (Press release). https://www.visa.co.id/about-visa/newsroom/press-releases/nr-id-240319.html
West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56–75). Sage.
Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913–934. https://doi.org/10.1177/0013164413495237
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Usmar Usmar, Nur Rohman, Yudhistira Adwimurti, Watriningsih
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
