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Copyright (c) 2026 Firdaus Basbeth (Author)

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Entrepreneurial Self-Efficacy and Transaction Frequency: An Extension of the Expectation-Confirmation Model in MSMEs
Corresponding Author(s) : Firdaus Basbeth
Jurnal Minds: Manajemen Ide dan Inspirasi,
Vol. 13 No. 1 (2026): June
Abstract
This study examines how entrepreneurial self-efficacy (ESE) shapes MSMEs’ continuance intention to use QRIS within the Expectation Confirmation Model under increasing digital payment adoption. It advances ECM by demonstrating a boundary condition where transaction frequency fails as a moderator, repositioning satisfaction as the dominant post-adoption mechanism over behavioral intensity. Using survey data from 150 MSMEs in Indonesia and SmartPLS4 analysis, the results show that ESE enhances perceived usefulness, which significantly drives continuance intention through satisfaction, while transaction frequency is insignificant. These findings support habit formation and technology embeddedness perspectives, indicating that repeated use loses explanatory power once usage stabilizes. Continuance decisions are therefore primarily affect-driven rather than behavior-driven. The study implies that policymakers and fintech providers should prioritize system reliability and user satisfaction rather than merely increasing transaction frequency to sustain QRIS adoption.
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- Abdul Rahim, N. F., Abbasi, G. A., Iranmanesh, M., Christopher, N., & Amran, A. (2023). Determinants of continuous intention to use e-government services: an extension of technology continuance theory. Journal of Systems and Information Technology, 25(3), 245-267.
- Al Khalifah, E., Hammady, R., Abdelrahman, M., Al-Shamaileh, O., Marghany, M., El-Jarn, H., Darwish, A., & Kurt, Y. (2025). Technology anxiety in virtual reality adoption: examining the impact of age, past experience, and cybersickness. Ieee Access.
- Ampadu, S., Jiang, Y., Gyamfi, S. A., Debrah, E., & Amankwa, E. (2023). Perceived value of recommended product and consumer e-loyalty: an expectation confirmation perspective. Young Consumers, 24(6), 742-766.
- Bandura, A. (1997). Self-efficacy: The exercise of control. Macmillan.
- Bank Indonesia. (2020). Peraturan Bank Indonesia No. 23/2020 tentang Sistem Pembayaran [Bank Indonesia Regulation No. 23/2020 on Payment System].
- Bank Indonesia. (2023). Blueprint sistem pembayaran Indonesia 2025 [Indonesia Payment System Blueprint 2025]. Bank Indonesia.
- Baron, R. A. (1998). Cognitive mechanisms in entrepreneurship: Why and when enterpreneurs think differently than other people. Journal of business venturing, 13(4), 275-294.
- Basbeth, F., Rahayoe, I., & Susanti, Y. F. (2024). Predicting the adoption of online insurance payment technology: The role of second-order trust. Global Business & Management Research, 16.
- Bhatia, S., Singh, N., & Liebana-Cabanillas, F. (2023). Intermittent continued adoption of digital payment services during the COVID-19 induced pandemic. International Journal of Human–Computer Interaction, 39(14), 2905-2919.
- Bhatnagr, P., Rajesh, A., & Misra, R. (2024). Continuous intention usage of artificial intelligence enabled digital banks: a review of expectation confirmation model. Journal of Enterprise Information Management, 37(6), 1763-1787.
- Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
- Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Research, 28(2), 456-476.
- Chang, P.-C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413-427.
- Chen, C. C., Greene, P. G., & Crick, A. (1998). Does entrepreneurial self-efficacy distinguish entrepreneurs from managers? Journal of business venturing, 13(4), 295-316.
- Cheng, Y.-M. (2021a). Drivers of physicians’ satisfaction and continuance intention toward the cloud-based hospital information system. Kybernetes, 50(2), 413-442.
- Cheng, Y.-M. (2021b). Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation–confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202.
- Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares (Vol. 201). Springer.
- Chiu, W., Cho, H., & Chi, C. G. (2021). Consumers' continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Information Technology & People, 34(3), 978-998.
- Chung, K. C., & Lin, C.-H. (2023). Drivers of financial robot continuance usage intentions: An application of self-efficacy theory. Journal of Internet Technology, 24(2), 401-410.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
- Devi, P., & Shunmugasundaram, V. (2025). Understanding adoption intention and perceived satisfaction of startup entrepreneurs towards FinTech services. Journal of Modelling in Management.
- Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294-311.
- Ghaisani, N. P., Kannan, R., Basbeth, F., & Selatan, J. (2022). Consumers' intention to continue using cryptocurrency mobile wallets in Malaysia. International Journal of Management, Finance and Accounting, 3(2), 1-19.
- Hadji, B., & Degoulet, P. (2016). Information system end-user satisfaction and continuance intention: A unified modeling approach. Journal of biomedical informatics, 61, 185-193.
- Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
- Hair, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European business review, 26(2), 106-121.
- Hair Joseph, S., Marko, Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. saGe publications.
- Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
- Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European business review, 26(2), 106-121.
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References
Abdul Rahim, N. F., Abbasi, G. A., Iranmanesh, M., Christopher, N., & Amran, A. (2023). Determinants of continuous intention to use e-government services: an extension of technology continuance theory. Journal of Systems and Information Technology, 25(3), 245-267.
Al Khalifah, E., Hammady, R., Abdelrahman, M., Al-Shamaileh, O., Marghany, M., El-Jarn, H., Darwish, A., & Kurt, Y. (2025). Technology anxiety in virtual reality adoption: examining the impact of age, past experience, and cybersickness. Ieee Access.
Ampadu, S., Jiang, Y., Gyamfi, S. A., Debrah, E., & Amankwa, E. (2023). Perceived value of recommended product and consumer e-loyalty: an expectation confirmation perspective. Young Consumers, 24(6), 742-766.
Bandura, A. (1997). Self-efficacy: The exercise of control. Macmillan.
Bank Indonesia. (2020). Peraturan Bank Indonesia No. 23/2020 tentang Sistem Pembayaran [Bank Indonesia Regulation No. 23/2020 on Payment System].
Bank Indonesia. (2023). Blueprint sistem pembayaran Indonesia 2025 [Indonesia Payment System Blueprint 2025]. Bank Indonesia.
Baron, R. A. (1998). Cognitive mechanisms in entrepreneurship: Why and when enterpreneurs think differently than other people. Journal of business venturing, 13(4), 275-294.
Basbeth, F., Rahayoe, I., & Susanti, Y. F. (2024). Predicting the adoption of online insurance payment technology: The role of second-order trust. Global Business & Management Research, 16.
Bhatia, S., Singh, N., & Liebana-Cabanillas, F. (2023). Intermittent continued adoption of digital payment services during the COVID-19 induced pandemic. International Journal of Human–Computer Interaction, 39(14), 2905-2919.
Bhatnagr, P., Rajesh, A., & Misra, R. (2024). Continuous intention usage of artificial intelligence enabled digital banks: a review of expectation confirmation model. Journal of Enterprise Information Management, 37(6), 1763-1787.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Research, 28(2), 456-476.
Chang, P.-C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413-427.
Chen, C. C., Greene, P. G., & Crick, A. (1998). Does entrepreneurial self-efficacy distinguish entrepreneurs from managers? Journal of business venturing, 13(4), 295-316.
Cheng, Y.-M. (2021a). Drivers of physicians’ satisfaction and continuance intention toward the cloud-based hospital information system. Kybernetes, 50(2), 413-442.
Cheng, Y.-M. (2021b). Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation–confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202.
Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares (Vol. 201). Springer.
Chiu, W., Cho, H., & Chi, C. G. (2021). Consumers' continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Information Technology & People, 34(3), 978-998.
Chung, K. C., & Lin, C.-H. (2023). Drivers of financial robot continuance usage intentions: An application of self-efficacy theory. Journal of Internet Technology, 24(2), 401-410.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Devi, P., & Shunmugasundaram, V. (2025). Understanding adoption intention and perceived satisfaction of startup entrepreneurs towards FinTech services. Journal of Modelling in Management.
Eren, B. A. (2021). Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey. International Journal of Bank Marketing, 39(2), 294-311.
Ghaisani, N. P., Kannan, R., Basbeth, F., & Selatan, J. (2022). Consumers' intention to continue using cryptocurrency mobile wallets in Malaysia. International Journal of Management, Finance and Accounting, 3(2), 1-19.
Hadji, B., & Degoulet, P. (2016). Information system end-user satisfaction and continuance intention: A unified modeling approach. Journal of biomedical informatics, 61, 185-193.
Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hair, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European business review, 26(2), 106-121.
Hair Joseph, S., Marko, Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. saGe publications.
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European business review, 26(2), 106-121.
Hamzah Muchtar, E., Trianto, B., Maulana, I., Alim, M. N., Marasabessy, R. H., Hidayat, W., Junaedi, E., & Masrizal. (2024). Quick response code Indonesia standard (QRIS) E-payment adoption: customers perspective. Cogent Business & Management, 11(1), 2316044.
Hoque, M. E., Susanto, P., Shah, N. U., Khatimah, H., & Mamun, A. A. (2024). Does perceived behavioral control mediate customers' innovativeness and continuance intention of e-money? The moderating role of perceived risk and e-security. International Journal of Emerging Markets, 19(12), 4481-4502.
Hutahaean, A. R., Lubis, F. S., Saputra, D., & Shafie, D. I. (2025). Determinants of E-Wallet and IoT Adoption for MSME Operational Efficiency in Indonesia. 2025 4th International Conference on Creative Communication and Innovative Technology (ICCIT),
Jaiswal, D., Kaushal, V., Mohan, A., & Thaichon, P. (2022). Mobile wallets adoption: pre-and post-adoption dynamics of mobile wallets usage. Marketing Intelligence & Planning, 40(5), 573-588.
Jisham, M., Selvaraj, V., & John, A. (2024). Catalysts of successful WealthTech continuous usage: the role of fit factors, usage experience and digital nudging. Journal of Science and Technology Policy Management.
Jones, M. R., & Karsten, H. (2008). Giddens's structuration theory and information systems research. MIS quarterly, 127-157.
Juita, R., Inan, D. I., & Santoso, B. (2026). Digital market adoption by underserved MSMEs in developing countries: Mediation and moderation by self-efficacy and trust. International Journal of Information Management Data Insights, 6(1), 100384.
Kamboj, S., Matharu, M., & Shukla, Y. (2025). Examining the effect of perceived risk, self-efficacy and individual differences on consumer intention to use contactless mobile payment services. Journal of Science and Technology Policy Management, 16(4), 650-681.
Kar, A. K. (2021). What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “digital service usage satisfaction model”. Information Systems Frontiers, 23(5), 1341-1361.
Khahar, H. H., Vafaei-Zadeh, A., Hanifah, H., & Ramayah, T. (2025). Exploring factors influencing the continued use of personal cloud computing services: an extended expectation-confirmation model. Journal of Science and Technology Policy Management.
Khan, M. A., Raza, A., Ahsan, K., & Irshad, S. (2024). Secure digital wallet by using QR code: mapping of mobile payment in partially connected environment. Mehran University Research Journal Of Engineering & Technology, 43(2), 8-19.
Kock, N., & Dow, K. E. (2025). Statistical significance and effect size tests in SEM: common method bias and strong theorizing.
Leong, M. K., Sidhu, S. K., & Khoo, K. L. (2026). AI chatbot service recovery quality and customer experience: a moderated mediation model of continuance usage intention. Journal of Consumer Marketing, 1-14.
Liébana-Cabanillas, F. (2025). Smart mobile payments: challenges and opportunities of generative AI for the future of digital finance. Management Dynamics, 25(1), 3.
Liebana-Cabanillas, F., Carvajal-Trujillo, E., Villarejo-Ramos, Á. F., & Higueras-Castillo, E. (2020). Antecedents of the intention to use NFC mobile applications in tourism. Journal of Hospitality and Tourism Technology, 11(2), 369-383.
Ly, B., Ly, R., & Ma, S. (2025). User-centric drivers of QR payment adoption in emerging economies: trust, quality, and social influence. Future Business Journal, 11(1), 258.
Mehra, A., Paul, J., Rajput, S., Soni, G., & Ata, S. (2026). Determinants of chatbot continuance intention in airlines: theory and evidence. Journal of Research in Interactive Marketing, 1-23.
Mensah, A., Ofori, K. S., Ampong, G. O. A., Addae, J. A., Kouakou, A. N., & Tumaku, J. (2018). Exploring users’ continuance intention towards mobile SNS: A mobile value perspective. International Conference on Emerging Technologies for Developing Countries,
Nandru, P., SA, S. K., & Chendragiri, M. (2024). Adoption intention of mobile QR code payment system among marginalized street vendors: an empirical investigation from an emerging economy. Journal of Science and Technology Policy Management, 15(6), 1709-1733.
Nandy, M., Kumar, P., & Chauhan, S. (2025). Unlocking the success of mobile banking: a comprehensive meta-analysis. Journal of Systems and Information Technology.
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(1), 1-14.
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.
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization science, 11(4), 404-428.
Pal, A., Herath, T., De’, R., & Rao, H. R. (2020). Contextual facilitators and barriers influencing the continued use of mobile payment services in a developing country: insights from adopters in India. Information Technology for Development, 26(2), 394-420.
Pandita, S., Mishra, H. G., & Bhat, A. A. (2025). Consumer adoption behaviour in ride-sharing economy: an integrated TAM-ECM framework. International Journal of Emerging Markets, 20(6), 2491-2523.
Qatawneh, N., Al-Okaily, A., Al-Okaily, M., & Ur Rehman, S. (2025). Exploring the antecedent factors of continuous intention to use mobile money: insights from emerging markets. Digital Policy, Regulation and Governance, 27(2), 175-200.
Rahi, S., Alghizzawi, M., & Ngah, A. H. (2023). Factors influence user’s intention to continue use of e-banking during COVID-19 pandemic: the nexus between self-determination and expectation confirmation model. EuroMed Journal of Business, 18(3), 380-396.
Salameh, A., & Hayat, N. (2025). Exposing the youth continuous use intention for e-wallet use in Saudi Arabia: the interaction and expectation-confirmation theory. Journal of Science and Technology Policy Management.
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