This empirical study explores the factors influencing the intention of working-age individuals to adopt Federated Learning (FL) for information sharing within the general industry, specifically examining the banking sector. By integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), Task-Technology Fit (TTF), and Social Exchange Theory (SET) for the first time, this research investigates both the motivational and practical aspects influencing FL adoption. Analysis conducted using SmartPLS4 on data collected via a 7-point Likert scale survey reveals that “Trust” is the pivotal factor affecting user intentions, followed by social influence, effort expectancy, task-technology fit, and performance expectancy. Notably, perceived benefits substantially impact trust more than perceived costs, and technology characteristics exert a greater influence on task-technology fit than task characteristics, illustrating a privacy paradox in practice. This study fills a gap in understanding the multi-theory integration explaining FL adoption and proposes a foundational framework for implementing FL based on user-centric motivations and task-technology alignment.

Afshan, S., & Sharif, A. (2016). Acceptance of mobile banking framework in Pakistan. Telematics and Informatics, 33(2), 370-387. https://doi.org/10.1016/j.tele.2015.09.005
Ahmad, M. (2018). Review of The Technology Acceptance Model (TAM) in Internet banking and Mobile banking.
Ahmad, R., Nawaz, M. R., Ishaq, M. I., Khan, M. M., & Ashraf, H. A. (2022). Social exchange theory: Systematic review and future directions. Front Psychol, 13, 1015921. https://doi.org/10.3389/fpsyg.2022.1015921
Asad, M., Shaukat, S., Hu, D., Wang, Z., Javanmardi, E., Nakazato, J., & Tsukada, M. (2023). Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey. Sensors (Basel), 23(17). https://doi.org/10.3390/s23177358
Bagozzi, R. P., & Youjae, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/bf02723327
Baltruschat, L. M., Jaiman, V., & Urovi, V. (2023). User acceptability of blockchain technology for enabling electronic health record exchange. Journal of Systems and Information Technology, 25(3), 268-295. https://doi.org/10.1108/jsit-09-2022-0225
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022). Federated learning review: Fundamentals, enabling technologies, and future applications. Information Processing & Management, 59(6). https://doi.org/10.1016/j.ipm.2022.103061
Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review. Telematics and Informatics, 34(7), 1038-1058. https://doi.org/10.1016/j.tele.2017.04.013
Blau, P. M. (1964). Exchange and Power in Social Life.
Borgogno, O., & Colangelo, G. (2019). Data sharing and interoperability: Fostering innovation and competition through APIs. Computer Law & Security Review, 35(5). https://doi.org/10.1016/j.clsr.2019.03.008
Brinberg, D., & Wood, R. (1983). A Resource Exchange Theory Analysis of Consumer Behavior. Journal of Consumer Research, 10(3), 330-338. http://www.jstor.org/stable/2488805
Broby, D. (2021). Financial technology and the future of banking. Financial Innovation, 7(1). https://doi.org/10.1186/s40854-021-00264-y
C. Nguyen, D., Pham, V., Pathirana, P., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, w.-J. (2021). Federated Learning for Smart Healthcare: A Survey. ACM Computing Surveys. https://doi.org/10.1145/3501296
Cheng, Y., Liu, Y., Chen, T., & Yang, Q. (2020). Federated learning for privacy-preserving AI. Communications of the ACM, 63(12), 33-36. https://doi.org/10.1145/3387107
Crabbe, M., Standing, C., Standing, S., & Karjaluoto, H. (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications, 7(5). https://doi.org/10.1504/ijmc.2009.024391
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297-334. https://doi.org/10.1007/BF02310555
Cropanzano, R., Anthony, E. L., Daniels, S. R., & Hall, A. V. (2017). Social Exchange Theory: A Critical Review with Theoretical Remedies. Academy of Management Annals, 11(1), 479-516. https://doi.org/10.5465/annals.2015.0099
Cropanzano, R., & Mitchell, M. (2005). Social Exchange Theory: An Interdisciplinary Review. Journal of Management, 31, 874-900. https://doi.org/10.1177/0149206305279602
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3). https://doi.org/10.2307/249008
Deloitte, W. E. F. (2019). The Next Generation of Data-Sharing in Financial Services: Using Privacy Enhancing Techniques to Unlock New Value. W. E. Forum. https://www.weforum.org/publications/the-next-generation-of-data-sharing-in-financial-services-using-privacy-enhancing-techniques-to-unlock-new-value/
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719-734. https://doi.org/10.1007/s10796-017-9774-y
Emerson, R. M. (1976). Social Exchange Theory. Annual Review of Sociology, 2, 335-362. http://www.jstor.org/stable/2946096
European Commission, D.-G. f. C. N., Content and Technology; Everis Benelux. (2018). Study on data sharing between companies in Europe. P. O. o. t. E. Union. https://op.europa.eu/en/publication-detail/-/publication/8b8776ff-4834-11e8-be1d-01aa75ed71a1/language-en/format-PDF/source-320167618
Faqih, K. M. S., & Jaradat, M.-I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67. https://doi.org/10.1016/j.techsoc.2021.101787
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/10.2307/3151312
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 213-236.
Gouldner, A. W. (1960). The Norm of Reciprocity: A Preliminary Statement. American Sociological Review, 25(2), 161-178. https://doi.org/10.2307/2092623
Graf-Vlachy, L., Buhtz, K., & König, A. (2018). Social influence in technology adoption: taking stock and moving forward. Management Review Quarterly, 68(1), 37-76. https://doi.org/10.1007/s11301-017-0133-3
Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd edition.
Hair, J., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool for Business Research. European Business Review, 26, 106-121. https://doi.org/10.1108/EBR-10-2013-0128
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/ebr-11-2018-0203
Homans, G. C. (1958). Social Behavior as Exchange. American Journal of Sociology, 63(6), 597-606. http://www.jstor.org/stable/2772990
Hu, S., Ngo, D., Zheng, S., Smith, V., & Wu, Z. (2023). Federated Learning as a Network Effects Game. https://doi.org/10.48550/arXiv.2302.08533
Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption. Information & Management, 48(1), 1-8. https://doi.org/10.1016/j.im.2010.09.001
Jackson, J. D., Yi, M. Y., & Park, J. S. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information & Management, 50(4), 154-161. https://doi.org/10.1016/j.im.2013.02.006
Jahan, N., & Kim, S. W. (2020). Understanding online community participation behavior and perceived benefits: a social exchange theory perspective. PSU Research Review, 5(2), 85-100. https://doi.org/10.1108/prr-12-2019-0036
Kairouz, P., McMahan, H., Avent, B., Bellet, A., Bennis, M., Bhagoji, A., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D'Oliveira, R., El Rouayheb, S., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P., Gruteser, M., & Zhao, S. (2019). Advances and Open Problems in Federated Learning.
Kanamori, S., Abe, T., Ito, T., Emura, K., Wang, L., Yamamoto, S., Phong, L. T., Abe, K., Kim, S., Nojima, R., Ozawa, S., & Moriai, S. (2022). Privacy-Preserving Federated Learning for Detecting Fraudulent Financial Transactions in Japanese Banks. Journal of Information Processing, 30(0), 789-795. https://doi.org/10.2197/ipsjjip.30.789
Kapur, P. K., Singh, O., Khatri, S. K., & Verma, A. (2020). Strategic System Assurance and Business Analytics. https://doi.org/10.1007/978-981-15-3647-2
Kawa, D., Punyani, S., Nayak, P., Karkera, A., & Jyotinagar, V. (2019). Credit risk assessment from combined bank records using federated learning. International Research Journal of Engineering and Technology (IRJET), 6(4), 1355-1358.
Kronemann, B., Kizgin, H., Rana, N., & K. Dwivedi, Y. (2023). How AI encourages consumers to share their secrets? The role of anthropomorphism, personalisation, and privacy concerns and avenues for future research. Spanish Journal of Marketing - ESIC, 27(1), 3-19. https://doi.org/10.1108/sjme-10-2022-0213
Lee, C. M., Delgado Fernandez, J., Potenciano Menci, S., Rieger, A., & Fridgen, G. (2023). Federated Learning for Credit Risk Assessment.
Li, Q., Diao, Y., Chen, Q., & He, B. (2021). Federated Learning on Non-IID Data Silos: An Experimental Study.
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2021). A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Transactions on Knowledge and Data Engineering, PP, 1-1. https://doi.org/10.1109/TKDE.2021.3124599
Li, X., Hess, T. J., & Valacich, J. S. (2008). Why do we trust new technology? A study of initial trust formation with organizational information systems. The Journal of Strategic Information Systems, 17(1), 39-71. https://doi.org/https://doi.org/10.1016/j.jsis.2008.01.001
Li, Y., & Wen, G. (2023). Research and Practice of Financial Credit Risk Management Based on Federated Learning. Engineering Letters, 31(1).
Lippert, S. K., & Davis, M. (2006). A conceptual model integrating trust into planned change activities to enhance technology adoption behavior. Journal of Information Science, 32(5), 434-448. https://doi.org/10.1177/0165551506066042
Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222-234. https://doi.org/https://doi.org/10.1016/j.dss.2010.02.008
Marcelin, I., Sun, W., Teclezion, M., & Junarsin, E. (2022). Financial inclusion and bank risk-taking: the effect of information sharing. Finance Research Letters, 50. https://doi.org/10.1016/j.frl.2022.103182
Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703. https://doi.org/10.1016/j.ijinfomgt.2014.06.004
Parkes, A. (2013). The effect of task–individual–technology fit on user attitude and performance: An experimental investigation. Decision Support Systems, 54(2), 997-1009. https://doi.org/10.1016/j.dss.2012.10.025
Pinheiro, P., Sousa, C., & Toscano, C. (2022). Industrial Information Sharing 4.0. Procedia Computer Science, 204, 610-617. https://doi.org/https://doi.org/10.1016/j.procs.2022.08.074
Quach, S., Thaichon, P., Martin, K. D., Weaven, S., & Palmatier, R. W. (2022). Digital technologies: tensions in privacy and data. J Acad Mark Sci, 50(6), 1299-1323. https://doi.org/10.1007/s11747-022-00845-y
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digit Med, 3, 119. https://doi.org/10.1038/s41746-020-00323-1
Robey, D. (1979). User Attitudes and Management Information System Use. The Academy of Management Journal, 22(3), 527-538. https://doi.org/10.2307/255742
Schilling, M. (2016). Strategic Management of Technological Innovation.
Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8(4), 350-353. https://doi.org/10.1037/1040-3590.8.4.350
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129-142. https://doi.org/10.1016/j.tele.2014.05.003
Sharma, R., Singh, G., & Sharma, S. (2020). Modelling internet banking adoption in Fiji: A developing country perspective. International Journal of Information Management, 53. https://doi.org/10.1016/j.ijinfomgt.2020.102116
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., & Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep, 10(1), 12598. https://doi.org/10.1038/s41598-020-69250-1
Shen, Y.-C., Huang, C.-Y., Chu, C.-H., & Hsu, C.-T. (2010). A benefit–cost perspective of the consumer adoption of the mobile banking system. Behaviour & Information Technology, 29(5), 497-511. https://doi.org/10.1080/01449290903490658
Shingi, G. (2020). A federated learning based approach for loan defaults prediction 2020 International Conference on Data Mining Workshops (ICDMW),
Sinaci, A. A., Gencturk, M., Alvarez-Romero, C., Laleci Erturkmen, G. B., Martinez-Garcia, A., Escalona-Cuaresma, M. J., & Parra-Calderon, C. L. (2024). Privacy-preserving federated machine learning on FAIR health data: A real-world application. Comput Struct Biotechnol J, 24, 136-145. https://doi.org/10.1016/j.csbj.2024.02.014
Sok Foon, Y., & Chan Yin Fah, B. (2011). Internet Banking Adoption in Kuala Lumpur: An Application of UTAUT Model. International Journal of Business and Management, 6(4). https://doi.org/10.5539/ijbm.v6n4p161
Souiden, N., Ladhari, R., & Chaouali, W. (2020). Mobile banking adoption: a systematic review. International Journal of Bank Marketing, ahead-of-print. https://doi.org/10.1108/IJBM-04-2020-0182
Suzumura, T., Zhou, Y., Kawahara, R., Baracaldo, N., & Ludwig, H. (2022). Federated Learning for Collaborative Financial Crimes Detection. In H. Ludwig & N. Baracaldo (Eds.), Federated Learning: A Comprehensive Overview of Methods and Applications (pp. 455-466). Springer International Publishing. https://doi.org/10.1007/978-3-030-96896-0_20
Sydow, J., & Windeler, A. (1998). Organizing and Evaluating Interfirm Networks: a Structurationist Perspective on Network Processes and Effectiveness. Organization Science, 9, 265-284.
Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to understand the customers’ acceptance and use of internet banking in Lebanon. Information Technology & People, 29(4), 830-849. https://doi.org/10.1108/itp-02-2014-0034
Teo, Z. L., Jin, L., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., Heng, J., Liu, Y., Goh, R. S. M., & Ting, D. S. W. (2024). Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture. Cell Rep Med, 5(2), 101419. https://doi.org/10.1016/j.xcrm.2024.101419
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly, 15(1), 125-143. https://doi.org/10.2307/249443
Thuan, N. H., Nguyễn, H., Pham, H., & Halibas, A. (2023). Business Innovation for the Post-pandemic Era in Vietnam. https://doi.org/10.1007/978-981-99-1545-3
Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., De Freitas, J. K., Wanyan, T., Johnson, K. W., Bicak, M., Klang, E., Kwon, Y. J., Costa, A., Zhao, S., Miotto, R., Charney, A. W., Bottinger, E., Fayad, Z. A., Nadkarni, G. N., Wang, F., & Glicksberg, B. S. (2021). Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach. JMIR Med Inform, 9(1), e24207. https://doi.org/10.2196/24207
Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3). 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
Venkatesh, V., & Zhang, X. (2010). Unified Theory of Acceptance and Use of Technology: U.S. Vs. China. Journal of Global Information Technology Management, 13, 5-27. https://doi.org/10.1080/1097198X.2010.10856507
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2023). A survey on federated learning: challenges and applications. Int J Mach Learn Cybern, 14(2), 513-535. https://doi.org/10.1007/s13042-022-01647-y
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28(3), 443-488. https://doi.org/10.1108/jeim-09-2014-0088
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated Machine Learning: Concept and Applications.
Yang, W., Zhang, Y., Ye, K., Li, L., & Xu, C.-Z. (2019). Ffd: A federated learning based method for credit card fraud detection. Big Data–BigData 2019: 8th International Congress, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings 8,
Yu, H., Gao, Y., & Lu, Y. (2023). Company data sharing, product innovation and competitive strategies. Expert Systems with Applications, 234. https://doi.org/10.1016/j.eswa.2023.121083
Yu, H., & He, X. (2021). Corporate Data Sharing, Leakage, and Supervision Mechanism Research. Sustainability, 13(2). https://doi.org/10.3390/su13020931
Zheng, W., Yan, L., Gou, C., & Wang, F.-Y. (2021). Federated meta-learning for fraudulent credit card detection Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Yokohama, Japan.
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013