SLEEP ON MONEY? A TAM-BASED STUDY ON THE ADOPTION INTENTION OF OPEN BANKING IN TAIWAN
DOI:
https://doi.org/10.20319/icssh.2025.423456Keywords:
Open Banking, PLS-SEM, Technology Acceptance Model (TAM), Perceived Risk, Adoption IntentionAbstract
Although Taiwan has promoted Open Banking policies and technologies, the penetration of these services remains relatively low. Understanding the factors that influence adoption intention is crucial to developing more user-oriented services. While prior research on Open Banking has primarily focused on technical feasibility and regulatory frameworks, studies from the consumer perspective are still limited. This study applies the Technology Acceptance Model (TAM) and incorporates perceived risk as an external factor to examine what drives or inhibits Open Banking adoption among Taiwanese consumers. A total of 290 valid responses were collected via an online survey. Path analysis was conducted using partial least squares structural equation modeling (PLS-SEM). The results indicate that most of the core TAM constructs exhibit significantly positive correlations, supporting the model’s explanatory power. Among six dimensions of perceived risk, only Psychological Risk exhibits significant negative influence, suggesting that lack of confidence or not align with user habit may reduce adoption intention. The findings provide practical implications for both supervisors and financial institutions. Enhancing consumer education and improving user experience (UX) design will help alleviate concerns and build trust, thereby advancing wider adoption of Open Banking services.
References
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277.
https://doi.org/10.1037/h0076477
AlBenJasim, S., Dargahi, T., Takruri, H., & Al-Zaidi, R. (2023). FinTech cybersecurity challenges and regulations: Bahrain case study. Journal of Computer Information Systems, 64(6), 835–851.
https://doi.org/10.1080/08874417.2023.2251455
Bauer, R. A. (1960). Consumer behavior as risk taking. In: Hancock, R.S., Ed., Dynamic Marketing for a Changing World, Proceedings of the 43rd. Conference of the American Marketing Association, 389-398.
BCBS. (2019). Report on open banking and application programming interfaces (APIs). Bank for International Settlements.
https://www.bis.org/bcbs/publ/d486.htm
BIS. (2021). Enabling open finance through APIs: Report on payment initiation. BIS Representative Office for the Americas.
https://www.bis.org/publ/othp41.htm
Borgogno, O., & Colangelo, G. (2020). Consumer inertia and competition-sensitive data governance: The case of open banking. Journal of European Consumer and Market Law.
http://dx.doi.org/10.2139/ssrn.3513514
Brooker, G. (1984). An assessment of an expanded measure of perceived risk. Advances in Consumer Research Volume 11, Provo.
Chan, R., Troshani, I., Rao Hill, S., & Hoffmann, A. (2022). Towards an understanding of consumers’ fintech adoption: The case of open banking. International Journal of Bank Marketing, 40(4), 886-917.
https://doi.org/10.1108/IJBM-08-2021-0397
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587
Cox, D. F. (1967). Risk taking and information handling in consume behavior. 6, No. 1. Boston: Harvard University Press.
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
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003. http://www.jstor.org/stable/2632151
Deloitte. (2023). 2024 Banking and capital markets outlook. Deloitte Center for Financial Services.
Deloitte. (2017). How to flourish in an uncertain future: Open banking. Deloitte LLP. https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Financial-Services/gx-fsi-open-banking-florish-uncertain-future.pdf
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134. https://doi.org/10.1086/209386
Fatmawati, E. (2015). Technology acceptance model (TAM) untuk menganalisis penerimaan terhadap sistem informasi di perpustakaan. Journal Iqra, 9(1), 1-13. https://www.neliti.com/publications/196942/technology-acceptance-model-tam-untuk-menganalisis-penerimaan-terhadap-sistem-in
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59, 451-474. https://doi.org/10.1016/S1071-5819(03)00111-3
FISC. (2021). Data empowerment works: Financial supervisory commission's open banking creates a win-win-win situation. Quarterly Journal of Financial Information, 101, 4-9. https://www.fisc.com.tw/TC/Knowledge?Caid=ffea3360-1dae-49f0-ae9a-ef64571057a2&CaStyleId=12
Folkes, V. S. (1988). The availability heuristic and perceived risk. Journal of Consumer Research, 15(1), 13–23.
http://www.jstor.org/stable/2489168
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and Statistics. Journal of Marketing Research, 18, 382-388.
http://dx.doi.org/10.2307/3150980
Frei, C. (2023). Open banking: Opportunities and risks. In: Walker, T., Nikbakht, E., Kooli, M. (eds) The Fintech Disruption. Palgrave Studies in Financial Services Technology. Palgrave Macmillan, Cham.
https://doi.org/10.1007/978-3-031-23069-1_7
FSC. (2024). FSC has approved the transaction-related information standards for phase III of open banking, as submitted by the bankers association and the FISC. Financial Supervisory Commission Taiwan. https://www.fsc.gov.tw/ch/home.jsp?id=96&parentpath=0,2&mcustomize=news_view.jsp&dataserno=202401160003&dtable=News
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61, 101-107.
https://doi.org/10.1093/biomet/61.1.101
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
Hidayat, M. A., & Rudito, P. (2022). The analysis of the “Buy Now, Pay Later” use intention in Indonesia. International Journal of Current Science Research and Review, 5(7), 2384-2388.
https://doi.org/10.47191/ijcsrr/V5-i7-19
Hirnissa, M. T., Zariyawati, M.A., & Ying-ying, T. (2019). Perceived risk towards continual usage intention of online banking. Advanced Journal of Accounting and Finance, 1(1), 10-18.
https://myjms.mohe.gov.my/index.php/ajaf/article/view/7051
Hosmer, L. T. (1995). Trust: The connecting link between organizational theory and philosophical ethics. The Academy of Management Review, 20(2), 379–403. https://doi.org/10.2307/258851
Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453.
Hung, S. Y., Liang, T. P., & Chang, C. M. (2005). A meta-analysis of empirical research using TAM. Journal of Information Management, 12(4), 211-234. http://dx.doi.org/10.6382/JIM.200510.0211
Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. Proceedings of the Annual Conference of the Association for Consumer Research, 10, 382-393.
Jagadhita, P. A. A., & Tjhin, V. U. (2023). The analysis of factors influencing intention to use pay later using technology acceptance model (TAM). Jurnal Cahaya Mandalika, 4(1), 467-479.
https://doi.org/10.36312/jcm.v4i1.1356
Johnson, V., Kiser, A. I. T., Washington, R., & Torres, R. (2017). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111-122.
https://doi.org/10.1016/j.chb.2017.10.035
Kao, C. Y. (2019, October). Big business opportunities in open banking. BusinessNext, 305, 34-69.
Kelly, A. E., & Palaniappan, S. (2023). Using a technology acceptance model to determine factors influencing continued usage of mobile money service transactions in Ghana. Journal of Innovation and Entrepreneurship, 12, Article number: 34.
https://doi.org/10.1186/s13731-023-00301-3
Lee, Y. C. (2022). Overview of Taiwan’s promotion of open banking and personal information risk management issues. Science & Technology Law Review, 34(1), 23-30. https://www.airitilibrary.com/Article/Detail?DocID=a0000571-202201-202209200010-202209200010-23-30
Lee, M. C. (2013). Dilemma and opportunity of Taiwan’s financial development. Asian Financial Quarterly, 2013(10), 36-43.
https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/93133
Lin, Y. T. (2022). Guidelines on data sharing among financial institutions and self-regulatory rules for open banking. Contemporary Law Journal, 2022(6), 134-138. https://www.airitilibrary.com/Article/Detail?DocID=P20240521001-N202410310019-00015
Lopez, R. D. G., & Shih, W. (2021). Analysis of the purchase intention of Bitcoin by applying the technology acceptance model. Review of Integrative Business and Economics Research, 12(1), 21-39.
https://buscompress.com/uploads/3/4/9/8/34980536/riber_12-1_02_m21-401_21-39.pdf
Mansfield-Devine, S. (2016). Open banking: Opportunity and danger. Computer Fraud & Security, 2016(10), 8-13.
https://doi.org/10.1016/S1361-3723(16)30080-X
Marafon, D. L., Basso, K., Espartel, L. B., Barcellos, M.D., & Rech, E. (2018). Perceived risk and intention to use internet banking. International Journal of Bank Marketing, 36, 277-289.
https://doi.org/10.1108/IJBM-11-2016-0166
Nagy, S., Molnár, L., & Papp, A. (2024). Customer adoption of neobank services from a technology acceptance perspective: Evidence from Hungary. Decision Making: Applications in Management and Engineering, 7(1), 187-208. https://doi.org/10.31181/dmame712024883
Nguyen, V. A., & Nguyen, T. P. T. (2020). An integrated model of CSR perception and TAM on intention to adopt mobile banking. The Journal of Asian Finance, Economics and Business, 7(12), 1073-1087.
https://doi.org/10.13106/JAFEB.2020.VOL7.NO12.1073
Nuryyev, G., Spyridou A., Yeh, S., & Achyldurdyyeva, J. (2018). Factors influencing the intention to use cryptocurrency payments: An examination of blockchain economy. Tourman 2018 Conference Proceedings, Rhodes: Greece, 303-310.
https://mpra.ub.uni-muenchen.de/99159/
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.
https://doi.org/10.1080/10864415.2003.11044275
Putri, G. A., Widagdo, A. K., & Setiawan, D. (2023). Analysis of financial technology acceptance of peer-to-peer lending (P2P lending) using extended technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 9(1).
https://doi.org/10.1016/j.joitmc.2023.100027
Rivero, D., & Vives, X. (2023). Open banking: Promise and trade-offs. European Economy: Banks, Regulation, and the Real Sector, 5(1), 45-56.
https://european-economy.eu/book/open-banking/
Sabir, A. A., Ahmad, I., Ahmad, H., Rafiq, M., Khan, M. A., & Noreen, N. (2023). Consumer acceptance and adoption of AI robo-advisors in fintech industry. Mathematics, 11(6), 1311.
https://doi.org/10.3390/math11061311
Satriaji, A., & Prabowo, D. A. (2023). Analisis faktor penggunaan e-money menggunakan metode technology acceptance model (Studi Kasus Mahasiswa Institut Teknologi Telkom Purwokerto). Journal Informatic and Information Technology, 2(2), 85-93. https://journal.ittelkom-pwt.ac.id/index.php/ledger/article/view/1103c
Savas-Hall, S., Koku, P. S., & Mangleburg, T. (2021). Really new services: Perceived risk and adoption intentions. Services Marketing Quarterly, 43(4), 485-503. https://doi.org/10.1080/15332969.2021.1994193
Schiffman, L. G., & Kanuk, L. L. (1994). Consumer behavior. Prentice-Hall, Englewood Cliffs, N. J., 12-21.
Sha, A. M. (2024). Leveraging open banking APIs for enhanced customer experience and personalization. International Journal of Computer Science and Information Technology Research, 5(2), 12-19. https://ijcsitr.com/index.php/home/article/view/IJCSITR_2024_05_02_002
Silva, L. (2007). Post-positivist review of technology acceptance model. Journal of the Association for Information Systems, 8(4), 255-266.
https://aisel.aisnet.org/jais/vol8/iss4/11
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information System Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
Taylor, J. W. (1974). The role of risk in consumer behavior. Journal of Marketing, 38(2), 54-60. https://doi.org/10.2307/1250198
Toukabri, M. T., & Ettis, S. A. (2021). The acceptance and behavior towards e-insurance. International Journal of E-Business Research, 17(2), 1-16. https://doi.org/10.4018/IJEBR.2021040102
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 39(2), 273-315.
https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102. http://www.jstor.org/stable/23015766
Xia, H., Lu, D., Lin, B., Nord, J. H., & Zhang, J. Z. (2023). Trust in fintech: Risk, governance, and continuance intention. Journal of Computer Information Systems, 63(3), 648-662. https://doi.org/10.1080/08874417.2022.2093295
Yadav, P., & Rani, P. (2024). Unpacking neo-banking adoption using TAM: The interplay of trust and digital literacy.
http://dx.doi.org/10.2139/ssrn.5072070
Yang, Q., & Shi, F. (2024). A technology acceptance model (TAM) towards use intention of e-wallet among youth in Malaysia. Electronic Commerce Research and Applications, 21(80), 18-47.
https://journals.ucjc.edu/ubr/article/view/4633
Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253-269. https://doi.org/10.1108/IMDS-08-2014-0243
Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341-352.
http://www.jstor.org/stable/254378
Zetzsche, D. A., Arner, D. W., Buckley, R. P., & Weber, R. H. (2019). The future of data-driven finance and regtech: Lessons from EU Big Bang II. University of New South Wales Law Research Series, No. 19-22.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
