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An empirical study on facilitators and inhibitors of adoption of mobile banking in India

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Abstract

Mobile banking liberates people from spatial and temporal constraints and delivers significant convenience but people are still hesitant to use mobile banking. The purpose of this study is to examine the most important factors influencing and impeding consumer adoption of mobile banking. This study uses Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Readiness (TR) as a theoretical basis and integrates it with cognitive resistance to propose a conceptual model for m-banking adoption in India. Data was collected from 536 mobile banking customers from Delhi/NCR, using convenience sampling and was analysed using structural equation modelling. The findings revealed that the impact of facilitating factors namely, performance expectancy, effort expectance, social influence, optimism and innovativeness on behavioural intention to use m-banking is much more than the impact of inhibiting factors namely, discomfort, insecurity and cognitive resistance and also that behavioral intention has a significant impact on adoption of m-banking. In examining the relationship between facilitating factors and behavioral intention, there is no substantial moderation impact of age groups. However, gender moderated the relationship between effort expectancy and behavioral intention, as well as the relationship between optimism and behavioral intention. This research would help policymakers to understand and overcome the reluctance and barriers of adoption and would result in ensuring more adoption of advanced technological channels.

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References

  1. Afshan, S., & Sharif, A. (2016). Acceptance of mobile banking framework in Pakistan. Telematics and Informatics, 33(2), 370–387.

    Google Scholar 

  2. Ajzen, I., Fishbein, M. (1980). Understanding attitudes and predicting social behavior Prentice-Hall Inc. Englewood Cliffs, NJ.

  3. Amoroso, D. L., & Magnier-Watanabe, R. (2012). Building a research model for mobile wallet consumer adoption: The case of mobile Suica in Japan. Journal of theoretical and applied electronic commerce research, 7(1), 94–110.

    Google Scholar 

  4. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.

    Google Scholar 

  5. Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Patil, P., & Dwivedi, Y. K. (2019). An integrated model for m-banking adoption in Saudi Arabia. International Journal of Bank Marketing., 37(2), 452–478.

    Google Scholar 

  6. Bartunek, J. M., & Moch, M. K. (1987). First-order, second-order, and third-order change and organization development interventions: A cognitive approach. The Journal of Applied Behavioral Science, 23(4), 483–500.

    Google Scholar 

  7. Bartunek, J. M., Lacey, C. A., & Wood, D. R. (1992). Social cognition in organizational change: An insider-outsider approach. The Journal of applied behavioral science, 28(2), 204–223.

    Google Scholar 

  8. Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67–102.

    Google Scholar 

  9. Busselle, R., Reagan, J., Pinkleton, B., & Jackson, K. (1999). Factors affecting Internet use in a saturated-access population. Telematics and Informatics, 16(1–2), 45–58.

    Google Scholar 

  10. Chawla, D., & Joshi, H. (2017). Consumer perspectives about mobile banking adoption in India–a cluster analysis. International Journal of Bank Marketing, 35(4), 616–636.

    Google Scholar 

  11. Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India–an empirical study. International Journal of Bank Marketing, 37(7), 1590–1618.

    Google Scholar 

  12. Choi, H., Park, J., Kim, J., & Jung, Y. (2020). Consumer preferences of attributes of mobile payment services in South Korea. Telematics and Informatics, 51, 101397.

    Google Scholar 

  13. Cook, D. A., & Beckman, T. J. (2009). Does scale length matter? A comparison of nine-versus five-point rating scales for the mini-CEX. Advances in Health Sciences Education, 14(5), 655.

    Google Scholar 

  14. Daniel, E. (1999). Provision of electronic banking in the UK and the Republic of Ireland. International Journal of bank marketing, 17(2), 72–83.

    Google Scholar 

  15. 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.

    Google Scholar 

  16. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111–1132.

    Google Scholar 

  17. 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.

    Google Scholar 

  18. Farah, M. F., Hasni, M. J. S., & Abbas, A. K. (2018). Mobile-banking adoption: Empirical evidence from the banking sector in Pakistan. International Journal of Bank Marketing, 36(7), 1386–1413.

    Google Scholar 

  19. Financial, S. (2004). Assessing m-commerce opportunities. Information systems management, 21(2), 53–61.

    Google Scholar 

  20. Finn, R. H. (1972). Effects of some variations in rating scale characteristics on the means and reliabilities of ratings. Educational and Psychological Measurement, 32(2), 255–265.

    Google Scholar 

  21. Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: An application of UTAUT model. International Journal of Business and Management, 6(4), 161.

    Google Scholar 

  22. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 18(3), 382–388.

    Google Scholar 

  23. Georgi, F., & Pinkl, J. (2005). mobile banking in deutschland-der zweite Anlauf. Die Bank, 3, 57–61.

    Google Scholar 

  24. Gerrard, P., Cunningham, J. B., & Devlin, J. F. (2006). Why consumers are not using internet banking: A qualitative study. Journal of services Marketing, 20(3), 160–168.

    Google Scholar 

  25. Gravetter, F. J., Wallnau, L. B., Forzano, L. A. B., & Witnauer, J. E. (2020). Essentials of statistics for the behavioral sciences. Cengage Learning. 10 ed.

  26. Gupta, K., & Arora, N. (2019). 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.

    Google Scholar 

  27. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46(1–2), 1–12.

    Google Scholar 

  28. Hoffman, D. L., Novak, T. P., & Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80–85.

    Google Scholar 

  29. Hong, S. J., Thong, J. Y., Moon, J. Y., & Tam, K. Y. (2008). Understanding the behavior of mobile data services consumers. Information Systems Frontiers, 10(4), 431–445.

    Google Scholar 

  30. 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.

    Google Scholar 

  31. Israel, G. D. (1992). Determining sample size.

  32. Jaruwachirathanakul, B., & Fink, D. (2005). Internet banking adoption strategies for a developing country: The case of Thailand. Internet research, 15(3), 295–311.

    Google Scholar 

  33. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20(1), 141–151.

    Google Scholar 

  34. Kim, G., Shin, B., & Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283–311.

    Google Scholar 

  35. Koenig-Lewis, N., Palmer, A., & Moll, A. (2010). Predicting young consumers’ take up of mobile banking services. International journal of bank marketing, 28(5), 410–432.

    Google Scholar 

  36. Kumar, A., Dhingra, S., Batra, V., & Purohit, H. (2020). A Framework of Mobile Banking Adoption in India. Journal of Open Innovation: Technology, Market, and Complexity, 6(2), 40.

    Google Scholar 

  37. Kuo, K. M., Liu, C. F., & Ma, C. C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC medical informatics and decision making, 13(1), 1–14.

    Google Scholar 

  38. Kwateng, K. O., Atiemo, K. A. O., & Appiah, C. (2019). Acceptance and use of mobile banking: An application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118–151.

    Google Scholar 

  39. Laforet, S., & Li, X. (2005). Consumers’ attitudes towards online and mobile banking in China. International journal of bank marketing, 23(5), 362–380.

    Google Scholar 

  40. Lau, C. M., & Woodman, R. W. (1995). Understanding organizational change: A schematic perspective. Academy of management journal, 38(2), 537–554.

    Google Scholar 

  41. Laukkanen, T. (2007). Internet vs mobile banking: Comparing customer value perceptions. Business process management journal, 13(6), 788–797.

    Google Scholar 

  42. Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research, 69(7), 2432–2439.

    Google Scholar 

  43. Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of consumer marketing, 24(7), 419–427.

    Google Scholar 

  44. Li, Z., Bai, X. (2010). Influences of perceived risk and system usability on the adoption of mobile banking service. In International symposium on computer science and computational technology (ISCSCT) (Vol. 3, pp. 051–054).

  45. Liao, Z., & Wong, W. K. (2008). The determinants of customer interactions with internet-enabled e-banking services. Journal of the Operational Research Society, 59(9), 1201–1210.

    Google Scholar 

  46. Lin, C. A. (1998). Exploring personal computer adoption dynamics. Journal of Broadcasting & Electronic Media, 42(1), 95–112.

    Google Scholar 

  47. Lin, C. A. (2004). Webcasting adoption: Technology fluidity,-user’innovativeness, and media substitution. Journal of Broadcasting & Electronic Media, 48(3), 157–178.

    Google Scholar 

  48. Lin, C. A., & Jeffres, L. W. (1998). Factors influencing the adoption of multimedia cable technology. Journalism & Mass Communication Quarterly, 75(2), 341–352.

    Google Scholar 

  49. López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & management, 45(6), 359–364.

    Google Scholar 

  50. Lu, M. T., Tzeng, G. H., Cheng, H., & Hsu, C. C. (2015). Exploring mobile banking services for user behavior in intention adoption: Using new hybrid MADM model. Service business, 9(3), 541–565.

    Google Scholar 

  51. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in human behavior, 21(6), 873–891.

    Google Scholar 

  52. Luo, X., Lee, C. P., Mattila, M., & Liu, L. (2012). An exploratory study of mobile banking services resistance. International Journal of Mobile Communications, 10(4), 366–385.

    Google Scholar 

  53. Luo, Z. Q., Ma, W. K., So, A. M. C., Ye, Y., & Zhang, S. (2010). Semidefinite relaxation of quadratic optimization problems. IEEE Signal Processing Magazine, 27(3), 20–34.

    Google Scholar 

  54. Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International journal of information management, 34(1), 1–13.

    Google Scholar 

  55. Mattila, M., Karjaluoto, H., & Pento, T. (2003). Internet banking adoption among mature customers: Early majority or laggards? Journal of services marketing, 17(5), 514–528.

    Google Scholar 

  56. Mbrokoh, A. S. (2016). Exploring the factors that influence the adoption of internet banking in Ghana. The Journal of Internet Banking and Commerce, 21(2), 1–20.

    Google Scholar 

  57. Medberg, G., & Heinonen, K. (2014). Invisible value formation: A netnography in retail banking. International Journal of Bank Marketing, 32(6), 590–607.

    Google Scholar 

  58. Moghavvemi, S., Mei, T. X., Phoong, S. W., & Phoong, S. Y. (2021). Drivers and barriers of mobile payment adoption: Malaysian merchants’ perspective. Journal of Retailing and Consumer Services, 59, 102364.

    Google Scholar 

  59. Molesworth, M., & Suortti, J. P. (2002). Buying cars online: The adoption of the web for high-involvement, high-cost purchases. Journal of Consumer Behaviour: An International Research Review, 2(2), 155–168.

    Google Scholar 

  60. Moser, F. (2015). Mobile Banking: A fashionable concept or an institutionalized channel in future retail banking? Analyzing patterns in the practical and academic mobile banking literature. International Journal of Bank Marketing, 33(2), 162–177.

    Google Scholar 

  61. Narteh, B., Mahmoud, M. A., & Amoh, S. (2017). Customer behavioural intentions towards mobile money services adoption in Ghana. The Service Industries Journal, 37(7–8), 426–447.

    Google Scholar 

  62. Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders, 97–146.

  63. Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International journal of information management, 34(5), 689–703.

    Google Scholar 

  64. Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in human behavior, 61, 404–414.

    Google Scholar 

  65. Oreg, S. (2006). Personality, context, and resistance to organizational change. European journal of work and organizational psychology, 15(1), 73–101.

    Google Scholar 

  66. Oreg, S. (2003). Resistance to change: Developing an individual differences measure. Journal of applied psychology, 88(4), 680.

    Google Scholar 

  67. Parasuraman, A. (2000). Technology readiness index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of service research, 2(4), 307–320.

    Google Scholar 

  68. Parasuraman, A., & Colby, C. L. (2001). Techno-ready marketing: How and why your customers adopt technology. Free Press.

    Google Scholar 

  69. Park, J., Yang, S., & Lehto, X. (2007). Adoption of mobile technologies for Chinese consumers. Journal of electronic commerce research, 8(3), 196.

    Google Scholar 

  70. Patel, V. (2016). Use of mobile wallet service by the youth: A study based in Ahmedabad. ASBM Journal of Management, 9(2).

  71. Pattansheti, M., Kamble, S. S., Dhume, S. M., & Raut, R. D. (2016). Development, measurement and validation of an integrated technology readiness acceptance and planned behaviour model for Indian mobile banking industry. International Journal of Business Information Systems, 22(3), 316–342.

    Google Scholar 

  72. Piderit, S. K. (2000). Rethinking resistance and recognizing ambivalence: A multidimensional view of attitudes toward an organizational change. Academy of management review, 25(4), 783–794.

    Google Scholar 

  73. Rahman, M. M. (2013). Barriers to m-commerce adoption in developing countries–a qualitative study among the stakeholders of Bangladesh. The International Technology Management Review, 3(2), 80–91.

    Google Scholar 

  74. Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of consumer marketing, 6(2), 5–14.

    Google Scholar 

  75. Rao, S., & Troshani, I. (2007). A conceptual framework and propositions for the acceptance of mobile services. Journal of theoretical and applied electronic commerce research, 2(2), 61–73.

    Google Scholar 

  76. Raza, S. A., Shah, N., & Ali, M. (2019). Acceptance of mobile banking in Islamic banks: Evidence from modified UTAUT model. Journal of Islamic Marketing., 10(1), 357–376.

    Google Scholar 

  77. RBI. (2019). Payment & Settlement Systems in India: Vision-2019–2021. RBI. Retrieved July 28,2019,from:https://rbidocs.rbi.org.in/rdocs/PublicationReport/PDFs/PAYMENT1C3B80387C0F4B30A56665DD08783324.PDF.

  78. Riffai, M. M. M. A., Grant, K., & Edgar, D. (2012). Big TAM in Oman: Exploring the promise of on-line banking, its adoption by customers and the challenges of banking in Oman. International journal of information management, 32(3), 239–250.

    Google Scholar 

  79. Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328–341.

    Google Scholar 

  80. Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. In Die diffusion von innovationen in der telekommunikation. (pp. 25–38). Springer, Berlin, Heidelberg.

  81. Saxena, N., Gera, N., & Singh, R. P. (2020). Exploring the effect of perceived risk on adoption of mobile banking in India. International Journal of Public Sector Performance Management, 6(5), 722–736.

    Google Scholar 

  82. Saxena, N., Gera, N., Nagdev, K., & Fatta, D. D. (2021). A conjoint analysis of customers’ preferences for e-banking channels. International Journal of Electronic Marketing and Retailing, 12(1), 52–68.

    Google Scholar 

  83. Shankar, A., & Rishi, B. (2020). Convenience matter in mobile banking adoption intention? Australasian Marketing Journal (AMJ), 28(4), 273–285.

    Google Scholar 

  84. Shankar, A., Jebarajakirthy, C., Ashaduzzaman, M. (2020). How do electronic word of mouth practices contribute to mobile banking adoption?. Journal of Retailing and Consumer Services, 52, 101920.

  85. Sharma, S. K., & Govindaluri, S. M. (2014). Internet banking adoption in India. Journal of Indian Business Research, 6(2), 155–169.

    Google Scholar 

  86. Simintiras, A. C., Dwivedi, Y. K., & Rana, N. P. (2014). Can marketing strategies enhance the adoption of electronic government initiatives? International Journal of Electronic Government Research (IJEGR), 10(2), 1–7.

    Google Scholar 

  87. Singh, A. B. (2012). Mobile banking based money order for India Post: Feasible model and assessing demand potential. Procedia-Social and Behavioral Sciences, 37, 466–481.

    Google Scholar 

  88. Singh, S., & Srivastava, R. K. (2020). Understanding the intention to use mobile banking by existing online banking customers: An empirical study. Journal of Financial Services Marketing, 25(3), 86–96.

    Google Scholar 

  89. Singh, S., Srivastava, V., & Srivastava, R. K. (2010). Customer acceptance of mobile banking: A conceptual framework. Sies journal of management, 7(1), 55.

    Google Scholar 

  90. Slade, E., Williams, M., Dwivedi, Y., & Piercy, N. (2015). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing, 23(3), 209–223.

    Google Scholar 

  91. Srijumpa, R., Chiarakul, T., & Speece, M. (2007). Satisfaction and dissatisfaction in service encounters. International Journal of Bank Marketing, 25(3), 173–194.

    Google Scholar 

  92. Srivatsa, H. S., & Srinivasan, R. (2007). Banking channel perceptions: An Indian youth perspective. Conference Paper, International Marketing Conference on Marketing & Society; 8–10 April 2007, IIMK 513–527.

  93. Srivastava, S., & Vishnani, S. (2021). Determinants of mobile bank usage among the bank users in North India. Journal of Financial Services Marketing, 26(1), 34–51.

    Google Scholar 

  94. Statista.com. (2021). Status of online banking in India in 2020 [online]. https://www.statista.com/statistics/1249581/india-status-of-online-banking-adoptio/. Accessed on 01 Dec 2021.

  95. Suoranta, M., Mattila, M., & Munnukka, J. (2005). Technology-based services: A study on the drivers and inhibitors of mobile banking. International Journal of Management and Decision Making, 6(1), 33–46.

    Google Scholar 

  96. Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233–244.

    Google Scholar 

  97. Tan, E., & Lau, J. L. (2016). Behavioural intention to adopt mobile banking among the millennial generation. Young Consumers, 17(1), 18–31.

    Google Scholar 

  98. Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to understand the customers’ acceptance and use of internet banking in Lebanon: A structural equation modeling approach. Information Technology & People, 29(4), 830.

    Google Scholar 

  99. Tarhini, A., Hone, K. S., & Liu, X. (2013). Factors affecting students’ acceptance of e-learning environments in developing countries: A structural equation modeling approach, 3(1), 54–59.

    Google Scholar 

  100. Tarhini, A., Hone, K., Liu, X. (2013). Extending the TAM model to empirically investigate the students' behavioural intention to use e-learning in developing countries. In 2013 Science and information conference (pp. 732–737). IEEE.

  101. Tarhini, A., Hone, K., & Liu, X. (2013). User acceptance towards web-based learning systems: Investigating the role of social, organizational and individual factors in European higher education. Procedia Computer Science, 17, 189–197.

    Google Scholar 

  102. Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International journal of research in marketing, 12(2), 137–155.

    Google Scholar 

  103. Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392.

    Google Scholar 

  104. Tsikriktsis, N. (2004). A technology readiness-based taxonomy of customers: A replication and extension. Journal of Service Research, 7(1), 42–52.

    Google Scholar 

  105. Upadhyay, P., & Chattopadhyay, M. (2015). Examining mobile based payment services adoption issues: A new approach using hierarchical clustering and self-organizing maps. Journal of Enterprise Information Management., 28(4), 490–507.

    Google Scholar 

  106. Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs China. Journal of global information technology management, 13(1), 5–27.

    Google Scholar 

  107. 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.

    Google Scholar 

  108. 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.

    Google Scholar 

  109. Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215.

    Google Scholar 

  110. Walker, R. H., Craig-Lees, M., Hecker, R., & Francis, H. (2002). Technology-enabled service delivery: An investigation of reasons affecting customer adoption and rejection. International Journal of service Industry management, 13(1), 91–106.

    Google Scholar 

  111. Wallis Inquiry (1997), Financial System Inquiry Final Report. Commonwealth of Australia, Canberra.

  112. Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information systems journal, 16(2), 157–179.

    Google Scholar 

  113. Wessels, L., & Drennan, J. (2010). An investigation of consumer acceptance of M-banking. International Journal of bank marketing, 28(7), 547–568.

    Google Scholar 

  114. Wiedemann, D. G., Haunstetter, T., Pousttchi, K. (2008). Analyzing the basic elements of mobile viral marketing-an empirical study. In 2008 7th international conference on mobile businesss, (pp. 75–85). IEEE.

  115. Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & management, 42(5), 719–729.

    Google Scholar 

  116. Yaseen, S. G., & El Qirem, I. A. (2018). Intention to use e-banking services in the Jordanian commercial banks. International Journal of Bank Marketing., 36(3), 557–571.

    Google Scholar 

  117. Yen, H. R. (2005). An attribute-based model of quality satisfaction for internet self-service technology. The Service Industries Journal, 25(5), 641–659.

    Google Scholar 

  118. Yousafzai, S. Y. (2012). A literature review of theoretical models of Internet banking adoption at the individual level. Journal of Financial Services Marketing, 17(3), 215–226.

    Google Scholar 

  119. Yu, C. S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of electronic commerce research, 13(2), 104.

    Google Scholar 

  120. Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the academy of marketing science, 30(4), 362–375.

    Google Scholar 

  121. Zhou, T. (2011). Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207–218.

    Google Scholar 

  122. Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision support systems, 54(2), 1085–1091.

    Google Scholar 

  123. Zhou, T. (2012). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2), 135.

    Google Scholar 

  124. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26(4), 760–767.

    Google Scholar 

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This is the part of the thesis submitted to Bharati Vidyapeeth (Deemed to be University), Pune.

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Saxena, N., Gera, N. & Taneja, M. An empirical study on facilitators and inhibitors of adoption of mobile banking in India. Electron Commer Res 23, 2573–2604 (2023). https://doi.org/10.1007/s10660-022-09556-6

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