Exploring the factors determining fintech adoption among Indian users integrating ‎Theory of Planned Behaviour ( TPB) and Social Learning Theory (SLT)‎

  • Authors

    • S DURGA VIGNAN FOUNDATION FOR SCIENCE TECHNOLOGY AND RESEARCH
    • Dr Venkateswara Rao Podile Koneru Lakshmaih Education Foundation
    • Dr Visalakshi Narapareddi Assistant Professor, Aditya University‎

    Received date: March 12, 2025

    Accepted date: April 8, 2025

    Published date: April 13, 2025

    https://doi.org/10.14419/gzyjat18
  • Fintech Adoption; Theory of Planned Behaviour; Social Learning Theory; Behavioural ‎Intention; Observational Learning

  • Abstract

    Even though the number of Financial Technology (Fintech) services is on the rise, the ‎behavioural and psychological factors of how the user’s intent remains to a key focus point ‎of research. (In 2020, Rahardjo et al.) This research proposes a comprehensive model for ‎understanding Fintech adoption in India, integrating the Theory of Planned Behaviour (TPB) ‎and Social Learning Theory (SLT). The three TPB constructs, attitude, Subjective norms, and ‎perceived behavioural control (PBC), serve as key determinants of behavioural intention ‎‎(Gao, Y., & Tang, Y., 2023). Conversely, SLT enhances this paradigm by incorporating ‎elements of social influence, observational learning, and reinforcement mechanisms, thus ‎bringing to light how consumers come to establish confidence and trust in ‎Fintech services. By incorporating various theoretical perspectives, this study aims to provide a comprehensive understanding of the influence of social and individual ‎ factors on consumer intentions towards Fintech adoption, as these factors interact in determining behavior. The study is quantitative using survey data from 462 sample responses ‎analysed using Structural Equation Modelling (SEM) through AMOS software. The findings suggest that although social influence and observational learning are key factors influencing user perceptions, both attitude and perceived behavioural control are significantly influential on behavioral intention. Also, intention mediated the relationship between TPB constructs ‎and adoption of Fintech services, while privacy risk moderated the relationship between ‎intention and adoption in digital financial services. These findings emphasize how social ‎learning dynamics, techniques of trust-building are promoting Fintech acceptance among ‎Indian users. This research enhances existing knowledge by incorporating behavioural and ‎psychological viewpoints and is beneficial for financial institutions, legislators, and Fintech ‎firms who seek to promote the usage of digital financial services‎.

  • References

    1. Al-Smadi, A. W., & Al-Smadi, R. W. (2024). Empirical and Theoretical Relationship between FinTech and Accounting Practices: Evidence from Mena Countries. https://doi.org/10.21203/rs.3.rs-3840644/v1.
    2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behaviour and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T.
    3. Analysing Factors Influencing Intention to Use and Actual Use of Mobile Fintech Applications Free Interbank Money Transfer Flip Using UTAUT 2 Model with Trust and Perceived Security. (2022). 2022 10th International Conference on Cyber and IT Service Management (CITSM). https://doi.org/10.1109/CITSM56380.2022.9935838.
    4. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
    5. Beldad, A., De Jong, M., & Steehouder, M. (2011). "How shall I trust the faceless and the intangible?" Computers in Human Behavior, 27(5), 2233-2243. https://doi.org/10.1016/j.chb.2011.07.002.
    6. Bestas, M. (2023). Decentralized Finance (DeFi). arXiv preprint arXiv:2304.01918. https://arxiv.org/abs/2304.01918
    7. Cambridge Centre for Alternative Finance. (2024). The Cambridge Fintech Market Observatory. https://www.jbs.cam.ac.uk/faculty-research/centres/alternative-finance/the-cambridge-fintech-market-observatory/
    8. Chen, S., & Guo, Q. (2024). Fintech and MSEs Innovation: an Empirical Analysis. arXiv preprint arXiv:2407.17293. https://arxiv.org/abs/2407.17293
    9. 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.
    10. 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.
    11. Devi, S. (2023). Progress and Challenges in Digitalisation of Financial Inclusion: The Indian Context. https://doi.org/10.56411/anusandhan.2023.v5i2.60-71.
    12. Gao, Y., & Tang, Y. (2023). A Study on the Mechanism of Digital Technology’s Impact on the Green Transformation of Enterprises: Based on the Theory of Planned Behavior Approach. Sustainability, 15(15), 11854. https://doi.org/10.3390/su151511854.
    13. Gravitating towards Fintech: A study on Undergraduates using UTAUT model. (2023). Journal of Education and Health Promotion, 12, 543–555. https://pmc.ncbi.nlm.nih.gov/articles/PMC10582390/.
    14. Gupta, A., & Arora, N. (2020). Consumer adoption of e-finance in India: Impact of trust and risk. Financial Innovation, 6(1), 1-24.
    15. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 19(1), 37-59.
    16. McKinsey & Company. (2023). Fintechs: A new paradigm of growth. https://www.mckinsey.com/industries/financial-services/our-insights/fintechs-a-new-paradigm-of-growth.
    17. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359. https://doi.org/10.1287/isre.13.3.334.81.
    18. Meganathan, N. (2024). To what extent has UPI contributed to increasing the Penetration of digital financial services in rural India, and what challenges remain? International Journal of Scientific and Research Publications, 14(9), 122–129. https://doi.org/10.29322/IJSRP.14.09.2024.p15323.
    19. Nur, T., & Dewanto, P. A. (2022). The Influence of Attitude toward Behavior, Subjective Norms, Perceived Behavioural Control on the Behavioural Intention of using PayLater Apps moderated by Financial Literacy and Hedonic Value. 1–6. https://doi.org/10.1109/CITSM56380.2022.9936004.
    20. Plaid. (2023). The Fintech Effect 2023: Consumer insights reveal growth. https://plaid.com/blog/consumer-insights-reshaping-finance/.
    21. 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.
    22. Peong, K. K., Peong, K., & Tan, K. Y. (2021). Behavioural Intention of Commercial Banks’ Customers towards Financial Technology Services. 5(4), 10–27. https://doi.org/10.35609/jfbr.2021.5.4(2).
    23. Rahardjo, B., Burhanudin Akbar, B. M., & Novitaningtyas, I. (2020). The Analysis of Intention and Use of Financial Technology. 3(1), 88–102. https://doi.org/10.33005/jasf.v3i1.70.
    24. Ravi, R., & Pandey, N. N. (2024). Intention to use Fintech services: An investigation into the moderation effects of quality of internet access and digital skills. Humanities and Social Sciences Letters, 12(3), 543–555. https://doi.org/10.18488/73.v12i3.3803.
    25. Singh, S., Sahni, M. M., & Kovid, R. K. (2020). What drives Fintech adoption? A multi-method evaluation using an adapted technology acceptance model. Management Decision, 58(8), 1675–1697. https://doi.org/10.1108/MD-09-2019-1318.
    26. Tanda, A., & Schena, C.-M. (2019). The Regulatory Framework and Initiatives (pp. 83–100). Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-22426-4_5.
    27. 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.
    28. Xie, J., Ye, L., Huang, W., & Ye, M. (2021). Understanding Fintech Platform Adoption: Impacts of Perceived Value and Perceived Risk. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1893–1911. https://doi.org/10.3390/jtaer16050106.
    29. Zhou, T. (2011). Understanding mobile internet continuance usage from the perspectives of UTAUT and flow experience. Computers in Human Behavior, 27(3), 877-888. https://doi.org/10.1177/0266666911414596.
    30. Zhou Ayoungman, F., Chowdhury, N. H., Hussain, N., & Tanchangya, P. (2021). User Attitude and Intentions Towards Fintech in Bangladesh. International Journal of Asian Business and Information Management, 12(3), 1–19. https://doi.org/10.4018/IJABIM.20210701.oa30.

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  • How to Cite

    DURGA, S., Podile, D. V. R., & Narapareddi , D. V. (2025). Exploring the factors determining fintech adoption among Indian users integrating ‎Theory of Planned Behaviour ( TPB) and Social Learning Theory (SLT)‎. International Journal of Accounting and Economics Studies, 12(1), 10-18. https://doi.org/10.14419/gzyjat18

    Received date: March 12, 2025

    Accepted date: April 8, 2025

    Published date: April 13, 2025