Adoption of UPI (Unified Payments Interface) by Rural People: An Empirical Investigation

Authors

  • Anchal Gulia Research Scholar, Department of Management Studies, IGNOU, Delhi Author
  • Leena Singh Associate Professor, School of Management Studies, IGNOU, Delhi Author

Keywords:

Meta-UTAUT, UTAUT, Rural India, UPI, Behavioural Intention, Technology Adoption

Abstract

The present study is conducted to understand  the adoption of UPI (Unified Payments  Interface) among rural people in India. Meta UTAUT (Unified Theory of Acceptance and  Use of Technology) model is used to evaluate the  critical factors. A well-structured questionnaire  was distributed among 195 users of UPI in  villages of Haryana and the data collected  was analysed using PLS-SEM. A total of 13  hypotheses were proposed and 9 of them were  accepted. Findings show that Social Influence  (SI), Performance Expectancy (PE), Effort  Expectancy (EE), and Facilitating Conditions  (FC) have direct positive influence on Attitude  whereas only PE & FC have direct effect on BI.  Attitude fully mediates the effects of EE & SI on  BI as they do not have direct significant impact  on BI. Whereas, it partially mediates the impact  of FC and PE on BI as they have direct as well as  indirect effect on BI. On the other hand, direct  influence of PE & FC on attitude is strong as  compared to their direct influence on BI. The study explores the critical factors which  influence the adoption of UPI among rural  people and also discusses how this knowledge  can be used to improve UPI adoption among  rural people. UPI has provided newer digital  payment avenue to both urban and rural  consumers in India. However, adoption of UPI  in rural India remains unexplored area. It is one  of the pioneer studies in India context to explore  the adoption of UPI among rural India.

References

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002

Almarashdeh, I., & Alsmadi, M. K. (2017). How to make them use it? Citizens acceptance of M-government. Applied Computing and Informatics, 13(2), 194–199. https://doi.org/10.1016/j. aci.2017.04.001

Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024

Behl, A., & Pal, A. (2016). Analysing the Barriers towards Sustainable Financial Inclusion using Mobile Banking in Rural India. Indian Journal of Science and Technology, 9(15). https://doi.org/10.17485/ ijst/2016/v9i15/92100

Bhuvana, M., & Vasantha, S. (2021). The Impact of COVID-19 on Rural Citizens for Accessing E-Governance Services: A Conceptual Model Using the Dimensions of Trust and Technology Acceptance Model. In Recent Advances in Technology Acceptance Models and Theories (pp. 471–484). https:// doi.org/10.1007/978-3-030-64987-6_27

Chauhan, S. (2015). Acceptance of mobile money by poor citizens of India: Integrating trust into the technology acceptance model. Info, 17(3), 58–68. https://doi.org/10.1108/info-02-2015-0018 7. Chawla, D., & Joshi, H. (2020). The moderating role of gender and age in the adoption of mobile wallet. Foresight, 22(4), 483–504. https://doi.org/10.1108/FS-11-2019-0094

Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In Modern methods for business research. (pp. 295–336). Lawrence Erlbaum Associates Publishers. 9. Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi. org/10.1006/imms.1993.1022

de Luna, I. R., Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931–944. https://doi.org/10.1016/j. techfore.2018.09.018

Deb, M., & Agrawal, A. (2017). Factors impacting the adoption of m-banking: understanding brand India’s potential for financial inclusion. Journal of Asia Business Studies, 11(1), 22–40. https://doi. org/10.1108/JABS-11-2015-0191

Diamantopoulos, A., & Siguaw, J. A. (2006). Formative Versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration. British Journal of Management, 17(4), 263–282. https://doi.org/10.1111/j.1467-8551.2006.00500.x

Doshi, S. (2020, July 8). How COVID-19 is accelerating digitalisation. https://home.kpmg/in/en/ blogs/home/posts/2020/07/how-covid-19-accelerating-digitalisation-banking-payments-industry. html#page-conten

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). 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

Ferrata, L. (2019). Digital financial inclusion - an engine for “leaving no one behind.” Public Sector Economics, 43(4), 445–458. https://doi.org/10.3326/pse.43.4.6

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. In Source: Journal of Marketing Research (Vol. 18, Issue 3). 18. Gupta, K., & Arora, N. (2020). Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model: An Indian perspective. South Asian Journal of Business Studies, 9(1), 88–114. https://doi.org/10.1108/SAJBS-03-2019-0037

Gupta, K. P., Manrai, R., & Goel, U. (2019). Factors influencing adoption of payments banks by Indian customers: extending UTAUT with perceived credibility. Journal of Asia Business Studies, 13(2), 173–195. https://doi.org/10.1108/JABS-07-2017-0111

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https:// doi.org/10.1007/978-3-030-80519-7

Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use “PLS-SEM or CB-SEM: updated guidelines on which method to use.” In Organizational Research Methods, MIS Quarterly, and International Journal (Vol. 1, Issue 2).

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(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hubert, M., Blut, M., Brock, C., Zhang, R. W., Koch, V., & Riedl, R. (2019). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073– 1098. https://doi.org/10.1108/EJM-12-2016-0794

Hussain, M., Mollik, A. T., Johns, R., & Rahman, M. S. (2019). M-payment adoption for bottom of pyramid segment: an empirical investigation. International Journal of Bank Marketing, 37(1), 362–381. https://doi.org/10.1108/IJBM-01-2018-0013

I

Kaur, S., & Arora, S. (2021). Role of perceived risk in online banking and its impact on behavioral intention: trust as a moderator. Journal of Asia Business Studies, 15(1), 1–30. https://doi.org/10.1108/ JABS-08-2019-0252

Krishna Kishore, S. v., & Sequeira, A. H. (2016). An empirical investigation on mobile banking service adoption in rural Karnataka. SAGE Open, 6(1), 1–21. https://doi.org/10.1177/2158244016633731 27. Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260. https://doi.org/10.1016/j.ijinfomgt.2010.07.006

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. https://doi.org/10.1108/ AJIM-12-2020-0396

National Payments Corporation of India. (n.d.). UPI product statistics. Retrieved March 10, 2022, from https://www.npci.org.in/what-we-do/upi/product-statistics

Okello Candiya Bongomin, G., Ntayi, J. M., Munene, J. C., & Malinga, C. A. (2018). Mobile Money and Financial Inclusion in Sub-Saharan Africa: the Moderating Role of Social Networks. Journal of African Business, 19(3), 361–384. https://doi.org/10.1080/15228916.2017.1416214

Patil, P. P., Dwivedi, Y. K., & Rana, N. P. (2017). Digital Payments Adoption: An Analysis of Literature. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10595 LNCS (pp. 61–70). Springer Verlag. https://doi. org/10.1007/978-3-319-68557-1_7

Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54. https://doi. org/10.1016/j.ijinfomgt.2020.102144

Pazarbasioglu, C., Mora, A. G., Uttamchandani, M., Natarajan, H., Feyen, E., & Saal, M. (2020). DIGITAL FINANCIAL SERVICES. http://pubdocs.worldbank.org/en/230281588169110691/ Digital-Financial-Services.pdf

Rana, N. P., Dwivedi, Y. K., Lal, B., Williams, M. D., & Clement, M. (2017). Citizens’ adoption of an electronic government system: towards a unified view. Information Systems Frontiers, 19(3), 549–568. https://doi.org/10.1007/s10796-015-9613-y

Ray, A., Bala, P. K., Dasgupta, S. A., & Sivasankaran, N. (2020). Factors influencing adoption of e-services in rural India – perspectives of consumers and service providers. Journal of Indian Business Research, 12(2), 215–230. https://doi.org/10.1108/JIBR-11-2018-0295

Reserve Bank of India. (2020). CREDIT DELIVERY AND FINANCIAL INCLUSION. https://rbidocs.rbi.org. in/rdocs/AnnualReport/PDFs/2IVCREDITDELIVERYA1FF80FBDB87490C9B01520D262DD431. PDF

Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209–216. https://doi.org/10.1016/j.elerap.2009.07.005

Singh, S. (2019, June 21). The Secret To Higher Fintech Adoption In India May Lie In Kenya’s M-Pesa Success Story. Inc42. https://inc42.com/datalab/india-needs-to-look-at-m-pesas-success-to-boost fintech-adoption/

Sinha, M., Majra, H., Hutchins, J., & Saxena, R. (2019). Mobile payments in India: the privacy factor. International Journal of Bank Marketing, 37(1), 192–209. https://doi.org/10.1108/IJBM-05-2017- 0099

Sivathanu, B. (2019). Adoption of digital payment systems in the era of demonetization in India: An empirical study. Journal of Science and Technology Policy Management, 10(1), 143–171. https://doi. org/10.1108/JSTPM-07-2017-0033

Sobti, N. (2019). Impact of demonetization on diffusion of mobile payment service in India: Antecedents of behavioral intention and adoption using extended UTAUT model. Journal of Advances in Management Research, 16(4), 472–497. https://doi.org/10.1108/JAMR-09-2018-0086

Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967. https://doi.org/10.1016/j.promfg.2018.03.137

Ur Rehman, Z., & Ali Shaikh, F. (2020). Critical Factors Influencing the Behavioral Intention of Consumers towards Mobile Banking in Malaysia. In Technology & Applied Science Research (Vol. 10, Issue 1). www.etasr.com

Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi. org/10.2307/41410412

Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement. In Handbook of Partial Least Squares (pp. 47–82). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_3

Wani, T., & Ali, S. (2015). Innovation Diffusion Theory Review & Scope in the Study of Adoption of Smartphones in India. Journal of General Management Research, 2(2), 98–115. https://www. scmsnoida.ac.in/assets/pdf/journal/vol2Issue2/Article%208-%20Tahir%20Ahmad%20Wani%20 and%20Syed%20Wajid%20Ali.pdf

Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257

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Published

2024-01-31

How to Cite

Adoption of UPI (Unified Payments Interface) by Rural People: An Empirical Investigation . (2024). IITM JOURNAL OF BUSINESS STUDIES (JBS), 11(1), 23–41. Retrieved from https://acspublisher.com/journals/index.php/jbs/article/view/16834