Journal Press India^®

Author Details ( * ) denotes Corresponding author

This study investigates the influence of financial literacy on the acceptance of digital payment systems among small-scale merchants, aiming to uncover the factors that affect this adoption process. The research used Smart PLS4 software to analyse data and evaluate the impact of different levels of financial literacy on merchants’ propensity to embrace digital payment methods. The findings indicate that merchants with a higher degree of financial literacy are more inclined to use digital payment systems, seeing the benefits of enhanced financial management and more convenience for consumers. On the other hand, those with less financial literacy display more caution due to concerns about the security and complexity of digital systems. This study offers valuable insights into the barriers and incentives for accepting digital payments and suggests the need for targeted financial education programs to support small businesses in embracing new technology.

Keywords

Financial literacy; Digital payment systems; Small-scale merchants; Smart PLS4; Technology adoption

1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision processes, 50(2), 179-211.
2. Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683.
3. Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113-1127.
4. Alkhowaiter, W. A. (2020). Digital payment and banking adoption research in Gulf countries: A systematic literature review. International Journal of Information Management, 53, 102102. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2020.102 102
5. Alkhwaldi, A. F., Alobidyeen, B., Abdulmuhsin, A. A. & Al-Okaily, M. (2023). Investigating the antecedents of HRIS adoption in public sector organizations: integration of UTAUT and TTF. International Journal of Organizational Analysis, 31(7), 3251-3274.
6. Bentler, P. M. & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.
7. Chen, B. & Ren, J. (2022). Does the adoption of digital payment improve the financial availability of farmer households? Evidence from China. Agriculture, 12(9), 1468. Retrieved from https://doi.org/10.3390/agriculture12091468
8. 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.
9. Demirguc-Kunt, A., Klapper, L., Singer, D. & Ansar, S. (2018). The global findex database 2017: Measuring financial inclusion and the fintech revolution. World Bank Publications.
10. Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
11. Gayawan, E. & Ipinyomi, R. A. (2009). A comparison of Akaike, Schwarz and R square criteria for model selection using some fertility models. Australian Journal of Basic and Applied Sciences, 3(4), 3524-3530.
12. Grohmann, A., Klühs, T. & Menkhoff, L. (2018). Does financial literacy improve financial inclusion? Cross country evidence. World Development, 111, 84-96.
13. Guo, X. & Zhong, S. (2023). Unintended consequences: How does digital inclusive finance affect migrants’ urban settlement intentions? International Review of Financial Analysis, 89, 102766. Retrieved from https://doi.org/10.1016/j.irfa.2023.102766
14. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M. & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45, 616-632.
15. Hair, J. F., Hult, G. T. M., Ringle, C. & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
16. Hair, J. & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027.
17. Hair, J. F., Howard, M. C. & Nitzl, C., 2020. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of business research, 109, 101-110.
18. Hasan, I., Iqbal, H. & Khan, M. A. (2021). Financial literacy and adoption of digital payment systems: The moderating role of financial knowledge. Journal of Financial Services Research, 60(3), 349-369.
19. Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135.
20. Hossain, M. A., Islam, S., Rahman, M. M. & Arif, N. U. M. (2024). Impact of online payment systems on customer trust and loyalty in e-commerce analyzing security and convenience. Academic Journal on Science, Technology, Engineering & Mathematics Education, 4(3), 1-15.
21. Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
22. 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.
23. Klapper, L., Lusardi, A. & Panos, G. A. (2013). Financial literacy and its consequences: Evidence from Russia during the financial crisis. Journal of Banking & Finance, 37(10), 3904-3923.
24. Klein, A. (2023). Structural conflicts in central banking: Regulator or operator of a payment system? Retrieved from https://wifpr.wharton.upenn.edu/wp-content/uploads/ 2023/09/Structural-Conflicts-in-Central-Banking.pdf
25. Ligon, E., Malick, B., Sheth, K. & Trachtman, C. (2019). What explains low adoption of digital payment technologies? Evidence from small-scale merchants in Jaipur, India. PloS one, 14(7), e0219450.
26. Lin, X., Suanpong, K., Ruangkanjanases, A., Lim, Y. T. & Chen, S. C. (2022). Improving the sustainable usage intention of mobile payments: extended unified theory of acceptance and use of technology model combined with the information system success model and initial trust model. Frontiers in Psychology, 12, 634911.
27. Lusardi, A. (2019). Financial literacy and the need for financial education: Evidence and implications. Swiss Journal of Economics and Statistics, 155(1), 1-8.
28. Lusardi, A. & Mitchell, O. S. (2011). Financial literacy and planning: Implications for retirement wellbeing. NBER Working Paper No. 17078.
29. Lusardi, A. & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5-44.
30. Lusardi, A. & Mitchell, O. S. (2023). The importance of financial literacy: Opening a new field. Journal of Economic Perspectives, 37(4), 137-154.
31. Morgan, P. J. & Trinh, L. Q. (2020). Fintech and financial literacy in Viet Nam (No. 1154). ADBI Working Paper Series.
32. Najib, M. & Fahma, F. (2020). Investigating the adoption of digital payment system through an extended technology acceptance model: An insight from the Indonesian small and medium enterprises. International Journal on Advanced Science, Engineering and Information Technology, 10(4), 1702-1708.
33. Prasad, S., Sharma, Y. & Suri, A. (2023). Dynamics of technology adoption during street vendors acceptance of digital payment apps: An extension of UTAUT2 using PLS-SEM. Vision, 09722629231187260. Retrieved from DOI:10.1177/09722629231187260
34. Sahi, A. M., Khalid, H., Abbas, A. F. & Khatib, S. F. (2021). The evolving research of customer adoption of digital payment: Learning from content and statistical analysis of the literature. Journal of Open Innovation: Technology, Market, and Complexity, 7(4), 230.
35. Sajid, S., Rashid, R. M. & Haider, W. (2022). Changing trends of consumers’ online buying behavior during COVID-19 pandemic with moderating role of payment mode and gender. Frontiers in Psychology, 13, 919334. Retrieved from DOI:10.1177/0972262 9231187260
36. Sarstedt, M., Radomir, L., Moisescu, O. I. & Ringle, C. M. (2022). Latent class analysis in PLS-SEM: A review and recommendations for future applications. Journal of Business Research, 138, 398-407.
37. Savari, M. & Khaleghi, B. (2023). Application of the extended theory of planned behavior in predicting the behavioral intentions of Iranian local communities toward forest conservation. Frontiers in Psychology, 14, 1121396.
38. Setiawan, B., Purwanto, P., Ikasari, W. S. D. & Suryadi, S. (2024). An inclusive extension of the theory of planned behavior for explaining household food leftover reduction intention among Gen Z. Journal of Social Marketing, 14(3/4), 328-346.
39. 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.
40. Venkatesh, V., Thong, J. Y. & 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.
41. Voss, K. E. (2023). Composite-based and covariance-based structural equations modeling: Moving forward by changing the dialogue. European Journal of Marketing, 57(6), 1780-1792.
42. Wright, S. (1934). The method of path coefficients. The Annals of Mathematical Statistics, 5(3), 161-215.
43. Zhu, Z. & Guo, X. (2023). Financial literacy and the adoption of digital payment systems: An empirical study. Journal of Business Research, 157, 1-10.