ISSN: 2222-6990
Open access
Mobile banking is now a standard channel for payments and account management, yet usage remains uneven across customer groups. Evidence on what drives actual use is dispersed across technology acceptance research, fintech trust and risk studies, and work on disruption-driven behavior. This systematic review brings these strands together and examines how disruptions such as the coronavirus disease 2019 (COVID-19) pandemic can reshape the determinants of mobile banking and closely related digital payment usage. Database searches (Scopus, Web of Science, and Google Scholar) covering 2000 to January 2026 identified peer-reviewed empirical studies, which were screened using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures. The evidence is organized into six determinant families: technology beliefs and channel features; trust, security, privacy, and perceived risk; social and institutional influence; enabling resources and digital capability; disruption-related threat and coping appraisals; and post-adoption dynamics (satisfaction and habit). Across contexts, perceived usefulness and perceived ease of use show the most consistent associations with usage, while facilitating conditions and perceived risk often become more salient during disruptions. An integrative framework is proposed to connect disruption appraisals with technology beliefs and usage and to guide more comparable measurement and stronger causal designs in future research.
Abdennebi, K., Khouja, M., & Abbes, I. (2023). M-banking adoption from the developing countries perspective: A mediated model. Digital Business, 3(2), 100065. https://doi.org/10.1016/j.digbus.2023.100065
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. https://books.google.com/books?id=guN9AAAAMAAJ
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Booth, A., Sutton, A., Clowes, M., & Martyn-St James, M. (2022). Systematic approaches to a successful literature review (3rd ed.). SAGE. https://books.google.com/books?id=6xxoEAAAQBAJ
Chauhan, V. (2024). Understanding users’ protective behavior and its suppressor effect on the perceived risk in M-wallet/banking use: An Indian urban-rural comparison. Technological Forecasting and Social Change, 201, 123255. https://doi.org/10.1016/j.techfore.2024.123255
Cull, R. J., Foster, V., Jolliffe, D. M., Lederman, D., Mare, D. S., & Veerappan, M. (2023). Digital payments and the COVID-19 shock: The role of preexisting conditions in banking, infrastructure, human capabilities, and digital regulation. Policy Research Working Paper Series 10603. World Bank. https://doi.org/10.1596/1813-9450-10603
Daragmeh, A., Sági, J., & Zéman, Z. (2021). Continuous intention to use e-wallet in the context of the COVID-19 pandemic: Integrating the Health Belief Model (HBM) and Technology Continuous Theory (TCT). Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 132. https://doi.org/10.3390/joitmc7020132
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
de Luna, I. R., Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931–944. https://doi.org/10.1016/j.techfore.2018.09.018
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
Fu, J., & Mishra, M. (2022). The global impact of COVID-19 on fintech adoption. Journal of Financial Intermediation, 50, 100945. https://doi.org/10.1016/j.jfi.2021.100945
Ha, H., Nam, K., & Kim, S. (2024). Mobile payment adoption and use: A systematic review and future agenda. Telecommunications Policy, 48, 102795. https://doi.org/10.1016/j.telpol.2024.102795
Hamzah, M. I. (2024). Fear of COVID-19 disease and QR-based mobile payment adoption: A protection motivation perspective. Journal of Financial Services Marketing, 29(3), 946–963. https://doi.org/10.1057/s41264-023-00246-4
Hedayati, S., Damghanian, H., Farhadinejad, M., & Rastgar, A. A. (2023). Meta-analysis on application of Protection Motivation Theory in preventive behaviors against COVID-19. International Journal of Disaster Risk Reduction, 94, 103758. https://doi.org/10.1016/j.ijdrr.2023.103758
Jafri, J. A., Amin, S. I. M., Rahman, A. A., & Nor, S. M. (2023). A systematic literature review of the role of trust and security in FinTech adoption in banking. Heliyon, 9(11), e22980. https://doi.org/10.1016/j.heliyon.2023.e22980
Kajol, K., Singh, R., & Paul, J. (2022). Adoption of digital financial transactions: A review of literature and future research agenda. Technological Forecasting and Social Change, 184, 121991. https://doi.org/10.1016/j.techfore.2022.121991
Klapper, L., Singer, D., Starita, L., & Norris, M. (2025). The Global Findex Database 2025: Connectivity and financial inclusion in the digital economy. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-2204-9
Leong, L.-Y., Hew, J.-J., Wong, L.-W., & Lin, B. (2022). The past and beyond of mobile payment research: A development of the mobile payment framework. Internet Research, 32(6), 1757–1782. https://doi.org/10.1108/INTR-06-2021-0348
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817
Lythreatis, S., El-Kassar, A.-N., & Singh, S. K. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, 121359. https://doi.org/10.1016/j.techfore.2021.121359
Marikyan, D., Papagiannidis, S., & Stewart, G. (2023). Technology acceptance research: A meta-analysis. Journal of Information Science. Advance online publication. https://doi.org/10.1177/01655515231191177
Neves, C., Oliveira, T., Santini, F. de O., & Gutman, L. F. (2023). Adoption and use of digital financial services: A meta-analysis of barriers and facilitators. International Journal of Information Management Data Insights, 3(2), 100201. https://doi.org/10.1016/j.jjimei.2023.100201
Nguyen, G.-D., & Dao, T.-H. T. (2024). Factors influencing continuance intention to use mobile banking: An extended expectation-confirmation model with moderating role of trust. Humanities and Social Sciences Communications, 11, Article 276. https://doi.org/10.1057/s41599-024-02778-z
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5, 210. https://doi.org/10.1186/s13643-016-0384-4
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Sheeran, P., & Webb, T. L. (2016). The intention–behavior gap. Social and Personality Psychology Compass, 10(9), 503–518. https://doi.org/10.1111/spc3.12265
Souiden, N., Ladhari, R., & Chiadmi, N. E. (2021). Mobile banking services adoption: A systematic review. International Journal of Bank Marketing, 39(2), 214–247. https://doi.org/10.1108/IJBM-04-2020-0182
Spinelli, R., Gastaldi, L., Van Hove, L., & Van Droogenbroeck, E. (2024). Cluster analysis to enrich the Innovation Resistance Theory: New barriers to mobile payment usage in Italy. Technology in Society, 79, 102729. https://doi.org/10.1016/j.techsoc.2024.102729
Sreelakshmi, C. C., & Prathap, S. K. (2024). Effect of COVID-19 health threat on consumer’s perceived value towards mobile payments in India: a means-end model. Journal of Financial Services Marketing, 29, 763–787. https://doi.org/10.1057/s41264-023-00233-9
Tamilmani, K., Rana, N. P., Dwivedi, Y. K., & Sahu, G. P. (2021). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10007-6
van Deursen, A. J. A. M., & van Dijk, J. A. G. M. (2011). Internet skills and the digital divide. New Media & Society, 13(6), 893–911. https://doi.org/10.1177/1461444810386774
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
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Veronika, S. R., Santhanam, D., Shankar, V. K., & Sreejith, S. (2025). A systematic review and future agenda on continuance intentions in mobile apps. International Journal of Information Management Data Insights, 5(2), 100352. https://doi.org/10.1016/j.jjimei.2025.100352
Vik, P. M., Kamer?de, D., & Dayson, K. T. (2024). The link between digital skills and financial inclusion—Evidence from consumers survey data from low-income areas. Journal of Consumer Policy, 47(3), 373–393. https://doi.org/10.1007/s10603-024-09567-w
Walrave, M., Waeterloos, C., & Ponnet, K. (2020). Adoption of a contact tracing app for containing COVID-19: A health belief model approach. JMIR Public Health and Surveillance, 6(3), e20572. https://doi.org/10.2196/20572
Wisniewski, T. P., Polasik, M., Kotkowski, R., & Moro, A. (2024). Switching from cash to cashless payments during the COVID-19 pandemic and beyond. International Journal of Central Banking, 20(3), 303–371. https://www.ijcb.org/journal/ijcb24q3a7.pdf
World Bank. (2021). The Global Findex Database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. https://globalfindex.worldbank.org
Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2010). Explaining Internet banking behavior: Theory of reasoned action, theory of planned behavior, or technology acceptance model? Journal of Applied Social Psychology, 40(5), 1172–1202. https://doi.org/10.1111/j.1559-1816.2010.00615.x
Zewdie, A., Mose, A., Sahle, T., Bedewi, J., Gashu, M., Kebede, N., & Yimer, A. (2022). The health belief model’s ability to predict COVID-19 preventive behavior: A systematic review. SAGE Open Medicine, 10, 20503121221113668. https://doi.org/10.1177/20503121221113668
Zhou, T. (2012). Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Computers in Human Behavior, 28(4), 1518–1525. https://doi.org/10.1016/j.chb.2012.03.021
Ramezanie, E., Mahomed, A. S. B. B., Hashim, H. B., & Basha, N. B. K. (2026). Health, Social, and Technology Drivers of Mobile Banking and Digital Payment Use: A Systematic Review and Research Agenda. International Journal of Academic Research in Business and Social Sciences, 16(5), 487–508.
Copyright: © 2026 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode