Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Exploring the Factors Affecting Consumer’s Adoption of Digital Payment System


Affiliations
1 Research Scholar, University School of Applied Management, Punjabi University, Patiala 147002, Punjab, India
2 Assistant Professor, University School of Applied Management, Punjabi University, Patiala 147002, Punjab, India
     

   Subscribe/Renew Journal


With growth in digital commerce and internet access, digital payments services have huge potential in the country; however, consumer adoption of digital payments system is still low in India. Therefore, the present study tries to validate the unified theory of acceptance and use of technology (UTAUT) model to predict the behavioural intention to use digital payment system. A sample of 112 Undergraduate students of the University of Patiala is used to examine the research hypotheses. The findings indicate that performance expectancy and social influence are important determinants for digital payment system adoption and use, but effort expectancy has a significant negative influence on behavioural intention and facilitating conditions have no influence on behavioural intention to use digital payment system. The study offers several practical implications for digital payments service providers and banks regarding the marketing of new payment systems to increase users’ behavioural intention to use this payment system.

Keywords

No Keywords.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Anderson, J.C. and D.W. Gerbing (1988), Structural Equation Modeling in Practice: A Review and Recommended Two-step Approach, Psychological Bulletin, 103(3): 411-423.
  • Browne, M.W. and R. Cudeck (1992), Alternative Ways of Assessing Model Fit, in K.A. Bollen and J.S. Long (Eds.), Testing Structural Equation Models, Newbury Park, CA: Sage Focus Editions, Vol. 154, pp. 136-162.
  • Chau, P.Y. (1996), An Empirical Assessment of a Modified Technology Acceptance Model, Journal of Management Information Systems, 13(2): 185-204.
  • Chen, L.D. (2008), A Model of Consumer Acceptance of Mobile Payment, International Journal of Mobile Communications, 6(1): 32-52.
  • Chin, W.W. and P.A. Todd (1995), On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution, MIS Quarterly, 19(2): 237-246.
  • Cooper, R.B. and R.W. Zmud (1990), Information Technology Implementation Research: A Technological Diffusion Approach, Management Science, 36(2): 123-139.
  • Cronbach, L.J. (1971), Test Validation, in R. Thorndike (Ed.), Educational Measurement (2nd Ed.), Washington DC: American Council on Education, p. 443.
  • Dahlberg, T., N. Mallat and A. Öörni (2003), Consumer Acceptance of Mobile Payment Solutions, in Proceeding of the International Conference on Mobile Business (pp. 211-218), Vienna, Austria: University of Vienna Publishing.
  • Dahlberg, T., N. Mallat, J. Ondrus and A. Zmijewska (2008), Past, Present and Future of Mobile Payments Research: A Literature Review, Electronic Commerce Research and Applications, 7(2): 165-181.
  • Davis, F.D. (1989), Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 13(3): 319-340.
  • de Sena Abrahão, R., S.N. Moriguchi and D.F. Andrade (2016), Intention of Adoption of Mobile Payment: An Analysis in the Light of the Unified Theory of Acceptance and Use of Technology (UTAUT), RAI Revista de Administração e Inovação, 13(3): 221-230.
  • Dillon, A. and M.G. Morris (1996), User Acceptance of Information Technology: Theories and Models, Annual Review of Information Science and Technology (ARIST), 31: 3-32.
  • Fishbein, M. and I. Ajzen (1975), Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research.
  • Fornell, C. and D.F. Larcker (1981), Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics, Journal of Marketing Research, 18(3): 382–388, doi: 10.1177/002224378101800313.
  • Gefen, D. (2000), E-commerce: The Role of Familiarity and Trust, Omega, 28(6): 725-737.
  • Hamza, A. and A. Shah (2014), Gender and Mobile Payment System Adoption among Students of Tertiary Institutions in Nigeria, International Journal of Computer and Information Technology, 3(1): 13-20.
  • Hoyle, R.H. (1995), Structural Equation Modeling: Concepts, Issues, and Applications, Sage Publications.
  • Jiang, G. and W. Deng (2011), An Empirical Analysis of Factors Influencing the Adoption of Mobile Instant Messaging in China, International Journal of Mobile Communications, 9(6): 563-583.
  • José Liébana-Cabanillas, F., J. Sánchez-Fernández and F. Muñoz-Leiva (2014), Role of Gender on Acceptance of Mobile Payment, Industrial Management and Data Systems, 114(2): 220-240.
  • Karahanna, E., D.W. Straub and N.L. Chervany (1999), Information Technology Adoption across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs, MIS Quarterly, 23(2): 183-213.
  • Kim, C., M. Mirusmonov and I. Lee (2010), An Empirical Examination of Factors Influencing the Intention to Use Mobile Payment, Computers in Human Behaviour, 26(3): 310-322.
  • Legris, P., J. Ingham and P. Collerette (2003), Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model, Information and Management, 40(3): 191-204.
  • Liébana-Cabanillas, F., J. Sánchez-Fernández and F. Muñoz-Leiva (2014), Antecedents of the Adoption of the New Mobile Payment Systems: The Moderating Effect of Age, Computers in Human Behaviour, 35(June): 464-478.
  • Madan, K. and R. Yadav (2016), Behavioural Intention to Adopt Mobile Wallet: A Developing Country Perspective, Journal of Indian Business Research, 8(3): 227-244.
  • Malhotra, Y. and D.F. Galletta (1999), Extending the Technology Acceptance Model to Account for Social Influence: Theoretical Bases and Empirical Validation, in Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, 1999, HICSS-32, Abstracts and CD-ROM of Full Papers, January, pp. 14-pp), IEEE.
  • Mallat, N. (2007), Exploring Consumer Adoption of Mobile Payments – A Qualitative Study, Journal of Strategic Information Systems, 16(4): 413–432.
  • Mathieson, K., E. Peacock and W.W. Chin (2001), Extending the Technology Acceptance Model: The Influence of Perceived User Resources, ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 32(3): 86-112.
  • Mohd Ariffin, N.H., F. Ahmad and U. Mohd Haneef (2020), Acceptance of Mobile Payments by Retailers using UTAUT Model, Indonesian Journal of Electrical Engineering and Computer Science, 19(1): 149.
  • Moore, G.C. and I. Benbasat (1991), Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation, Information Systems Research, 2(3): 192-222.
  • Muflih, M., E.S. Astuti, Z. Arifin, and M. Iqbal (2020), Exploring the Antecedents of Indonesian ECommerce Users’ Usage Intention, International Journal of Entrepreneurship, 24(2): 1-11.
  • Oliveira, T., M. Thomas, G. Baptista and F. Campos (2016), Mobile Payment: Understanding the Determinants of Customer Adoption and Intention to Recommend the Technology, Computers in Human Behaviour, 61(August): 404-414.
  • Padashetty, S. and K.S. Kishore (2013), An Empirical Study on Consumer Adoption of Mobile Payments in Bangalore City-A Case Study, Researchers World, 4(1): 83.
  • Schierz, P., O. Schilke and B. Wirtz (2009), Understanding Consumer Acceptance of Mobile Payment Services: An Empirical Analysis, Electronic Commerce Research and Applications, 9(3): 209–216.
  • Shiau, W.L. and Chau, P.Y. (2012), Understanding Blog Continuance: A Model Comparison Approach, Industrial Management and Data Systems, 112(4): 663-682.
  • Slade, E., M. Williams, Y. Dwivedi and N. Piercy (2015), Exploring Consumer Adoption of Proximity Mobile Payments, Journal of Strategic Marketing, 23(3): 209-223.
  • Steiger, J.H. (1998), A Note on Multiple Sample Extensions of the RMSEA Fit Index, Structural Equation Modeling: A Multidisciplinary Journal, 5(4): 411-419.
  • Tan, G.W.H., K.B. Ooi, S.C. Chong and T.S. Hew (2014), NFC Mobile Credit Card: The Next Frontier of Mobile Payment?, Telematics and Informatics, 31(2): 292-307.
  • Taylor, S. and P. Todd (1995), Assessing IT Usage: The Role of Prior Experience, MIS Quarterly, 19(4): 561-570.
  • Thakur, R. (2013): Customer Adoption of Mobile Payment Services by Professionals across Two Cities in India: An empirical Study using Modified Technology Acceptance Model, Business Perspectives and Research, 1(2): 17-30.
  • Thakur, R. and M. Srivastava (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.
  • Van der Heijden, H. (2003), Factors Influencing the Usage of Websites: The Case of a Generic Portal in The Netherlands, Information and Management, 40(6): 541-549.
  • Venkatesh, V., et. al. (2003), User Acceptance of Information Technology: Toward a Unified View, MIS Quarterly, 27(3): 425–478.
  • Venkatesh, V. and F.D. Davis (2000), A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science, 46(2): 186–204.
  • Venkatesh, V., J.Y. Thong and X. Xu (2012), Consumer Acceptance and Use of Information Technology: Extending The Unified Theory of Acceptance and Use of Technology, MIS Quarterly, 36(1): 157–178.
  • Wang, L. and Y. Yi (2012), The Impact of Use Context on Mobile Payment Acceptance: An Empirical Study in China, in Advances in Computer Science and Education, pp. 293-299. Springer, Berlin, Heidelberg.
  • Wu, J.H. and S.C. Wang (2005), What Drives Mobile Commerce?: An Empirical Evaluation of the Revised Technology Acceptance Model, Information and Management, 42(5): 719-729.
  • Yang, H.C. and L. Zhou (2011), Extending TPB and TAM to Mobile Viral Marketing: An Exploratory Study on American Young Consumers’ Mobile Viral Marketing Attitude, Intent and Behaviour, Journal of Targeting, Measurement and Analysis for Marketing, 19(2): 85-98.
  • Yang, S., Y. Lu, S. Gupta, Y. Cao and R. Zhang (2012), Mobile Payment Services Adoption across Time: An Empirical Study of the Effects of Behavioural Beliefs, Social Influences, and Personal Traits, Computers in Human Behaviour, 28(1): 129-142.
  • Yiu, C.S., K. Grant and D. Edgar (2007), Factors Affecting the Adoption of Internet Banking in Hong Kong—Implications for the Banking Sector, International Journal of Information Management, 27(5): 336-351.
  • Zhou, T., Y. Lu and B. Wang (2010), Integrating TTF and UTAUT to Explain Mobile Banking User Adoption, Computers in Human Behaviour, 26(4): 760-767.
  • Zmijewska, A., E. Lawrence and R. Steele (2004), Towards Understanding of Factors Influencing User Acceptance of Mobile Payment Systems, in ICWI, October, pp. 270-277.
  • Websites
  • https://community.nasscom.in/communities/digital-transformation/fintech/india-digital-payments2020-launching-the-first-adoption-index-time-is-now.html
  • https://www.google.com/search?q=trai+2019+report&oq=TRAI+201&aqs=chrome.2.69i57j0l2j0i22i30l7.7711j1j15&sourceid=chrome&ie=UTF-8

Abstract Views: 49

PDF Views: 1




  • Exploring the Factors Affecting Consumer’s Adoption of Digital Payment System

Abstract Views: 49  |  PDF Views: 1

Authors

Sandeep Kaur
Research Scholar, University School of Applied Management, Punjabi University, Patiala 147002, Punjab, India
Nidhi Walia
Assistant Professor, University School of Applied Management, Punjabi University, Patiala 147002, Punjab, India

Abstract


With growth in digital commerce and internet access, digital payments services have huge potential in the country; however, consumer adoption of digital payments system is still low in India. Therefore, the present study tries to validate the unified theory of acceptance and use of technology (UTAUT) model to predict the behavioural intention to use digital payment system. A sample of 112 Undergraduate students of the University of Patiala is used to examine the research hypotheses. The findings indicate that performance expectancy and social influence are important determinants for digital payment system adoption and use, but effort expectancy has a significant negative influence on behavioural intention and facilitating conditions have no influence on behavioural intention to use digital payment system. The study offers several practical implications for digital payments service providers and banks regarding the marketing of new payment systems to increase users’ behavioural intention to use this payment system.

Keywords


No Keywords.

References





DOI: https://doi.org/10.21648/arthavij%2F2021%2Fv63%2Fi3%2F210630