Factors Influencing Payment Bank Service Adoption and Consumer Behavior: A Study of Kolhan Division, Jharkhand
DOI:
https://doi.org/10.66635/6txmqc16Keywords:
Payment Banks, Digital Financial Services, Technology Acceptance Model (TAM), Perceived Usefulness, Financial Inclusion, Consumer AdoptionAbstract
The digital financial services have achieved a fantastic boost in India, and payment banks are one of the factors cum facilitator of enhancing inclusion. However, the payment bank has still not penetrated every part of the India and semi-urban and rural areas like Kolhan Division of Jharkhand. The objective of this work is to study the influence of Factors noticed Usefulness, noticed Ease of Use and Trust on the Adoption consumers intention towards Payment Bank Services. This study uses a quantitative and descriptive approach using Technology Acceptance Model (TAM) The present analysis is an empirical work depends on primary data collected from 200 respondents through structured questionnaire and analysed using SPSS 17.0 & SmartPLS 4 employing PLS-SEM techniques. Results provide robust evidence of the important role played by those three variables in intention to adopt, specifically Perceived Usefulness proving to be the strongest predictor. The research conclusions also showed that ensuring usefulness security trust and ease of use are the key determinants of payment bank acceptance for financial inclusion in this context.
References
1.Ali, M., Raza, S. A., Puah, C. H., & Amin, H. (2020). Mobile banking adoption in emerging markets: Evidence from structural equation modeling. Journal of Financial Services Marketing, 25(3), 1–15.
2.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
3.Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India. International Journal of Bank Marketing, 37(7), 1590–1618. https://doi.org/10.1108/IJBM-09-2018-0256
4.Chong, A. Y. L., Ooi, K. B., Lin, B., & Tan, B. I. (2010). Online banking adoption: An empirical analysis. Industrial Management & Data Systems, 110(4), 592–610. https://doi.org/10.1108/02635571011039055
5.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
6.Dwivedi, Y. K., Rana, N. P., Chen, H., & Williams, M. D. (2019). A meta-analysis of mobile banking adoption. Electronic Commerce Research and Applications, 39, 100-110.
7.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/10.1177/002224378101800104
8.Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
9.Gupta, K., & Arora, N. (2020). Investigating consumer adoption of mobile banking in rural India. International Journal of Information Management, 50, 58–67.
10.Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
11.Kaur, P., Dhir, A., Singh, N., Sahu, G., & Almotairi, M. (2020). An innovation resistance theory perspective on mobile payment adoption. Computers in Human Behavior, 107, 105911. https://doi.org/10.1016/j.chb.2020.105911
12.Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001
13.Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2017). Antecedents of the adoption of mobile payment systems. Computers in Human Behavior, 71, 521–534. https://doi.org/10.1016/j.chb.2017.02.050
14.Lin, H. F. (2011). An empirical investigation of mobile banking adoption. International Journal of Information Management, 31(6), 1–8.
15.Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment adoption: A cross-country analysis. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.026
16.Patel, K., & Patel, S. (2018). Internet banking adoption in India: A structural equation modeling approach. Journal of Financial Services Marketing, 23(3), 136–147. https://doi.org/10.1057/s41264-018-0053-3
17.Rahi, S., Ghani, M. A., & Ngah, A. H. (2018). Factors influencing customer satisfaction in internet banking. Journal of Financial Services Marketing, 23(1), 1–12.
18.Reserve Bank of India. (2014). Guidelines for licensing of payments banks. https://www.rbi.org.in
19.Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and perceived risk in mobile banking adoption. International Journal of Bank Marketing, 37(2), 543–569. https://doi.org/10.1108/IJBM-07-2018-0177
20.Sharma, S. K., Govindaluri, S. M., & Singh, G. (2021). Mobile wallet adoption in India: A behavioral perspective. Technological Forecasting and Social Change, 163, 120–130.
21.Singh, N., & Srivastava, S. (2018). Consumer adoption of mobile payment services in India. International Journal of Bank Marketing, 36(7), 1238–1256.
22.Slade, E. L., Williams, M. D., Dwivedi, Y. K., & Piercy, N. C. (2015). Exploring consumer adoption of mobile payments. Journal of Retailing and Consumer Services, 22, 102–110. https://doi.org/10.1016/j.jretconser.2014.10.001
23.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
24.Zhou, T. (2011). Examining mobile banking user adoption. Computers in Human Behavior, 27(3), 123–131. https://doi.org/10.1016/j.chb.2010.10.004



