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Exploring Tourists’ Behavioural Intentions Towards Use of Select Mobile Wallets for Digital Payments

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  • Arif Hasan
  • S. K. Gupta

Abstract

The present study goal is to identify assorted factors and also to investigate their influence on tourists’ behavioural intentions about their usage patterns with respect to M-wallet payment. A conceptual model has been anticipated and authenticated and 600 questionnaires were distributed and 482 were usable. Structural equation modelling was employed to assess research hypotheses. The result shows that the behavioural intention is significantly influenced by perceived value, trust, compatibility and social influence while tourists’ is less optimistic when it comes to use M-wallet with regard to perceived enjoyment. The study also showed that trust followed by compatibility has a more powerful influence on the behavioural intention of tourists in the context of M-payment. The present study is limited to only six M-wallet and that too limited to the certain age category, namely 18–25 in Gwalior city of India. Understanding the different dimensions of behavioural intentions can help M-wallet players to win the trust of tourists and enhance the frequency to use M-wallet for M-payment. The results indicate that M-wallet service providers should consider and manage all affecting factors proactively as mechanisms for intention to use M-wallet. It will establish a behavioural intention model of M-wallet users that can help organizations to manage the formation of behavioural intentions of their users.

Suggested Citation

  • Arif Hasan & S. K. Gupta, 2020. "Exploring Tourists’ Behavioural Intentions Towards Use of Select Mobile Wallets for Digital Payments," Paradigm, , vol. 24(2), pages 177-194, December.
    • Handle: RePEc:sae:padigm:v:24:y:2020:i:2:p:177-194
    DOI: 10.1177/0971890720959519
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    1. Gefen, David, 2000. "E-commerce: the role of familiarity and trust," Omega, Elsevier, vol. 28(6), pages 725-737, December.
    2. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    3. Ziqiang Xin & Guofang Liu, 2013. "Homo Economicus Belief Inhibits Trust," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-5, October.
    4. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    5. Bearden, William O & Netemeyer, Richard G & Teel, Jesse E, 1989. "Measurement of Consumer Susceptibility to Interpersonal Influence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(4), pages 473-481, March.
    6. Hong Yan & Kailing Pan, 2015. "Examining mobile payment user adoption from the perspective of trust transfer," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 15(2/3), pages 136-151.
    7. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    8. Marios Koufaris, 2002. "Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior," Information Systems Research, INFORMS, vol. 13(2), pages 205-223, June.
    9. Cox, Anthony D. & Cox, Dena & Anderson, Ronald D., 2005. "Reassessing the pleasures of store shopping," Journal of Business Research, Elsevier, vol. 58(3), pages 250-259, March.
    10. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    11. Wen-Jang (Kenny) Jih, 2007. "Effects of Consumer-Perceived Convenience on Shopping Intention in Mobile Commerce: An Empirical study," International Journal of E-Business Research (IJEBR), IGI Global, vol. 3(4), pages 33-48, October.
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    2. Jaiswal, Deepak & Mohan, Ashutosh & Deshmukh, Arun Kumar, 2023. "Cash rich to cashless market: Segmentation and profiling of Fintech-led-Mobile payment users," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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