MODELLING INTENTION AND TRUST IN AI-BASED DIGITAL PAYMENTS FOR SUSTAINABLE RURAL ENTREPRENEURSHIP AND FINANCIAL INCLUSION: A SEM STUDY IN RURAL DELHI NCR (ASIA)
DOI:
https://doi.org/10.53555/jaes.v22i1.136Keywords:
Sustainable Entrepreneurship, Financial Inclusion, AI-Based Digital Payments, Rural Innovation, Trust and Security, Emerging Economies (Asia)Abstract
Adoption of AI-based digital payment systems plays a crucial role in advancing sustainable rural entrepreneurship, financial inclusion, and inclusive economic participation in emerging Asian economies. This study investigates the factors influencing the adoption of AI-enabled digital payments in rural Delhi NCR by extending the Technology Acceptance Model to include trust and perceived security as key socio-psychological determinants. AI-based digital payments are examined as an essential component of entrepreneurial infrastructure supporting rural microenterprises, informal entrepreneurs, and resilient economic activity. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the study analyses survey data collected from 453 rural respondents who were users or had awareness of digital payment platforms. Findings indicate that trust particularly trust in AI-driven payment security has a strong and significant influence on attitudes and intentions to adopt digital payment systems, highlighting its importance for rural entrepreneurial transactions and microenterprise sustainability. Perceived security emerges as the most influential driver of adoption, while knowledge and awareness of AI technology do not directly affect intention to use, suggesting that practical usability and experiential confidence outweigh awareness alone in rural contexts. Results further demonstrate the role of secure and user-friendly digital payment systems in strengthening financial inclusion, enterprise resilience, and sustainable digital economic development across rural Asia. Policy and managerial implications are drawn for policymakers, financial institutions, and fintech providers seeking to design entrepreneur-friendly AI-based digital payment ecosystems that promote sustainable rural entrepreneurship and inclusive growth in emerging economies.
References
1. Ab Hamid, M. R., Sami, W., & Sidek, M. H. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890(1), 12163.
2. Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., Ayeh, S. A., & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6, 100163.
3. Ahlstrom, D., Bruton, G. D., & Yeh, K. S. (2007). Venture capital in China: Past, present, and future. Asia Pacific Journal of Management, 24(3), 247-268.
4. Ajibade, P. (2018). Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Library Philosophy and Practice, 9.
5. Ashraf, A. R., Thongpapanl, N., & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68–93.
6. Au, Y. A., & Kauffman, R. J. (2008). The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application. Electronic Commerce Research and Applications, 7(2), 141–164.
7. Audretsch, D. B., & Belitski, M. (2017). Entrepreneurial ecosystems in cities: establishing the framework conditions. The Journal of Technology Transfer, 42(5), 1030-1051.
8. Banerji, R., & Singh, A. (2022). An empirical study on consumer attitude and behavioural intention to adopt mobile wallet in India. International Journal of Electronic Banking, 3(2), 83–99.
9. Banerji, R., & Singh, A. (2024). Do social media marketing activities promote customer loyalty? A study on the e-commerce industry. LBS Journal of Management & Research, 22(1), 93–109.
10. Beck, T., Demirgüç-Kunt, A., & Martinez Peria, M. S. (2008). Bank financing for SMEs around the world: Drivers, obstacles, business models, and lending practices. World Bank Policy Research Working Paper, (4785).
11. Beck, T., Pamuk, H., Ramrattan, R., & Uras, B. R. (2018). Payment instruments, finance and development. Journal of Development Economics, 133, 162-186.
12. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.
13. Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783.
14. Chinnaraju, A. (2025). Partial Least Squares Structural Equation Modeling (PLS-SEM) In The AI Era: Innovative Methodological Guide and Framework for Business Research.
15. Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human--Computer Interaction, 39(9), 1727–1739.
16. Davis, F. D. (1987). User acceptance of information systems: the technology acceptance model (TAM).
17. Davis, F. D., & others. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behavior and Technology Adoption, 205(219), 5.
18. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.
19. Dzarma, H. N. (2022). Adoption of Mobile Payment Systems by Small Businesses in Yola. American University of Nigeria.
20. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 51–90.
21. Georgescu, C., & Wren, J. D. (2018). Algorithmic identification of discrepancies between published ratios and their reported confidence intervals and P-values. Bioinformatics, 34(10), 1758–1766.
22. Gomber, P., Koch, J. A., & Siering, M. (2017). Digital Finance and FinTech: current research and future research directions. Journal of business economics, 87(5), 537-580.
23. Grapentine, T. (2000). Path analysis vs. structural equation modeling. Marketing Research, 12(3).
24. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., Ray, S., Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & others. (2021). Evaluation of reflective measurement models. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook, 75–90.
25. 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.
26. Hornbæk, K., & Hertzum, M. (2017). Technology acceptance and user experience: A review of the experiential component in HCI. ACM Transactions on Computer-Human Interaction (TOCHI), 24(5), 1–30.
27. Hussain, S., Gupta, S., & Bhardwaj, S. (2024). Determinants inhibiting digital payment system adoption: an Indian perspective. Qualitative Research in Financial Markets.
28. Jain, A. (2024). Enhancing forensic analysis of digital evidence using machine learning: Techniques, applications, and challenges. Int. J. Innov. Res. Multidiscip. Perspect. Stud.(IJIRMPS), 18, 1–8.
29. Jamil, N. A. (2012). The integration of theory of planned behaviour (TPB) and technology acceptance model (TAM) in determining online purchasing behaviour in Malaysia. Universiti Utara, Malaysia.
30. Janadari, M. P. N., Sri Ramalu, S., & Wei, C. (2016). Evaluation of measurment and structural model of the reflective model constructs in PLS--SEM.
31. Joshi, M. (2017). Digital payment system: A feat forward of india. Research Dimension (ISSN: 2249-3867).
32. Kandoth, S., & Shekhar, S. K. (2022). Social influence and intention to use AI: the role of personal innovativeness and perceived trust using the parallel mediation model. Forum Scientiae Oeconomia, 10(3), 131–150.
33. Karmaker, S., Oishi, M. E. F., Qasem, A., Sami, S. B. S., & Noor, J. (2025). Exploring influential factors of consumer purchase behavior on the adoption of digital payment apps in Bangladesh. Computers in Human Behavior Reports, 17, 100587.
34. Kumar, A., Haldar, P., & Chaturvedi, S. (2025). Factors influencing intention to continue use of e-wallet: Mediating role of perceived usefulness. Vilakshan-XIMB Journal of Management, 22(1), 45–61.
35. Lala, G., & others. (2014). The emergence and development of the technology acceptance model (TAM). Marketing from Information to Decision, 7, 149–160.
36. LINH, T. T. (2025). Adoption of Digital Payment Methods in Vietnam: Key Determinants and Distribution Analysis. Journal of Distribution Science, 23(2), 39–49.
37. Macamo, M. A. (2020). The Effects of Culture, Values, Diffusion of Innovation and Technology Acceptance on Attitudes Towards Digital Banking Adoption. University of South Africa (South Africa).
38. Manrai, R., Goel, U., & Yadav, P. D. (2021). Factors affecting adoption of digital payments by semi-rural Indian women: extension of UTAUT-2 with self-determination theory and perceived credibility. Aslib Journal of Information Management, 73(6), 814–838.
39. Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.
40. Muda, S., Musman, M., Hussin, A. H., & Mohamed Sadique, R. B. (2023). Partial least square structural equation modelling (PLS-SEM) technique. Buletin FPN S3.
41. Naeem, M., Hameed, M., & Taha, M. S. (2020). A study of electronic payment system. IOP Conference Series: Materials Science and Engineering, 767(1), 12008.
42. Nepal, R., Ahdikari, D., Shrestha, B., & Shrestha, S. (2025). Challenges and Opportunities of Digital Payment Adoption in Rural Nepal and Its Impact on Financial Inclusion. International Journal of Advanced Culture Technology, 13(2), 78–84.
43. Njoku, D. O., Iwuchukwu, V. C., Jibiri, J. E., Ikwuazom, C. T., Ofoegbu, C. I., & Nwokoma, F. O. (2024). Machine learning approach for fraud detection system in financial institution: A web base application. Machine Learning, 20(4), 1–12.
44. Oyeyemi, O. P., Kess-Momoh, A. J., Omotoye, G. B., Bello, B. G., Tula, S. T., & Daraojimba, A. I. (2024). Entrepreneurship in the digital age: A comprehensive review of start-up success factors and technological impact. International Journal of Science and Research Archive, 11(1), 182–191.
45. Pai, P.-Y., Lin, S.-W., & Lu, W.-M. (2025). Integration of association rule mining and RFM analysis with machine learning for e-commerce customer value segmentation: a sustainable retail perspective. Quality & Quantity, 1–39.
46. Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194.
47. Prairie, Y. T. (1996). Evaluating the predictive power of regression models. Canadian Journal of Fisheries and Aquatic Sciences, 53(3), 490–492.
48. Putrevu, J., & Mertzanis, C. (2024). The adoption of digital payments in emerging economies: challenges and policy responses. Digital Policy, Regulation and Governance, 26(5), 476–500.
49. Rana, R., & Goel, S. (2025). Accessibility of Digital Payments Methods and Challenges Faced by Users in Rural Areas of NCR: A Thematic Map Analysis. Abhigyan, 43(2), 161–178.
50. Roth, L. J. (2020). Digital transformation: An executive guide to survive and thrive in the new economy. Balboa Press.
51. Sabbir Rahman, M. (2012). Dynamics of consumers’ perception, demographic characteristics and consumers’ behavior towards selection of a restaurant: an exploratory study on Dhaka city consumers. Business Strategy Series, 13(2), 75–88.
52. Samartha, V. (2020). Measuring the effect size of coefficient of determination and predictive relevance of exogenous latent variables on endogenous latent variables through PLS-SEM. International Journal of Pure and Applied Mathematics.
53. Senaviratna, N., Cooray, T., & others. (2019). Diagnosing multicollinearity of logistic regression model. Asian Journal of Probability and Statistics, 5(2), 1–9.
54. Sevinc, I., & Akyildiz, B. (2021). Reflection of digital transformation on cities: smart cities. Public Administration and Public Finance Research, 116.
55. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347.
56. Silva, P. (2015). Davis’ technology acceptance model (TAM)(1989). Information Seeking Behavior and Technology Adoption: Theories and Trends, 205–219.
57. Sindakis, S., & Showkat, G. (2024). The digital revolution in India: bridging the gap in rural technology adoption. Journal of Innovation and Entrepreneurship, 13(1), 29.
58. Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. T. (2006). The search for" optimal" cutoff properties: Fit index criteria in structural equation modeling. The Journal of Experimental Education, 74(3), 267–288.
59. Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1–11.
60. Sujith, T. S., & Julie, C. D. (2017). Opportunities and Challenges of E-payment System in India. International Journal of Scientific Research and Management (IJSRM), 5(09), 6935–6943.
61. Taherdoost, H. (2023). Fintech: Emerging trends and the future of finance. Financial technologies and DeFi: a revisit to the digital finance revolution, 29-39.
62. TAREKEGN, K. (2019). The effect of service quality on customer satisfaction (the case of e-payment system in selected commercial banks). st. mary’s University.
63. Tibbe, T. D., & Montoya, A. K. (2022). Correcting the bias correction for the bootstrap confidence interval in mediation analysis. Frontiers in Psychology, 13, 810258.
64. Trivedi, H., & Sanchiher, S. (2023). Challenges in digital payment adoption in India. International Journal of Education, Modern Management, Applied Science & Social Science, 5(2), 32–38.
65. Vaske, J. J., Beaman, J., & Sponarski, C. C. (2017). Rethinking internal consistency in Cronbach’s alpha. Leisure Sciences, 39(2), 163–173.
66. 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, 157–178.
67. Yang, J., Shen, Q., Tong, X., & Mukhopadhaya, P. (2025). The impact of digital literacy in enhancing individuals’ health in China. BMC Public Health, 25(1), 364.
68. Yecsilyurt, E., & Vezne, R. (2023). Digital literacy, technological literacy, and internet literacy as predictors of attitude toward applying computer-supported education. Education and Information Technologies, 28(8), 9885–9911.
69. Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2010). Explaining internet banking behavior: theory of reasoned action, theory of planned behavior, or technology acceptance model? Journal of Applied Social Psychology, 40(5), 1172–1202.
70. Yousafzai, S., & Yani-de-Soriano, M. (2012). Understanding customer-specific factors underpinning internet banking adoption. International Journal of Bank Marketing, 30(1), 60–81.
