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Predicting Consumer Intention to Use Mobile Payment Services: Empirical Evidence from Vietnam

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  • The Ninh Nguyen
  • Tuan Khanh Cao
  • Phuong Linh Dang
  • Hien Anh Nguyen

Abstract

Mobile payment has relative advantages compared to other payment methods, thus providing benefits for both consumers and the society. This study attempts to examine factors influencing consumer intention to use mobile payment services. Survey data are used to investigate the impact of consumers¡¯ perceptions of mobile payment services and social influence on use intention. Empirical evidence from 489 Vietnamese consumers confirms a significant relationship between the factors and behavioral intention, and reveals that perceived trust is the strongest predictor of intention to use mobile payment services followed by perceived ease of use, perceived enjoyment, perceived behavioral control, perceived usefulness and subjective norm, respectively. The results contribute to the evolving literature, and suggest that mobile payment service providers should particularly focus on building up consumer trust, and making their services clear, understandable and easy to use. Future research directions for extending this study are also discussed.

Suggested Citation

  • The Ninh Nguyen & Tuan Khanh Cao & Phuong Linh Dang & Hien Anh Nguyen, 2016. "Predicting Consumer Intention to Use Mobile Payment Services: Empirical Evidence from Vietnam," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 8(1), pages 117-124, February.
  • Handle: RePEc:ibn:ijmsjn:v:8:y:2016:i:1:p:117-124
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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. Rogers, Everett M, 1976. "New Product Adoption and Diffusion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(4), pages 290-301, March.
    4. 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.
    5. Linck, K. & Pousttchi, Key & Wiedemann, Dietmar Georg, 2006. "Security Issues in Mobile Payment from the Customer Viewpoint," MPRA Paper 2923, University Library of Munich, Germany.
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    Cited by:

    1. Yan, Li-Ya & Tan, Garry Wei-Han & Loh, Xiu-Ming & Hew, Jun-Jie & Ooi, Keng-Boon, 2021. "QR code and mobile payment: The disruptive forces in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    2. Chanchai Phonthanukitithaworn & Carmine Sellitto & Michelle W. L. Fong, 2016. "A Comparative Study of Current and Potential Users of Mobile Payment Services," SAGE Open, , vol. 6(4), pages 21582440166, November.
    3. M Deepa, 2021. "A Study on Consumer Awareness and Satisfaction towards Online Digital Payment - With Special Reference to Pollachi Taluk," ComFin Research, Shanlax Journals, vol. 9(3), pages 25-29, July.
    4. Mohammad Ali Yousef Yamin & Omima Abdalla Abass Abdalatif, 2024. "Examining consumer behavior towards adoption of quick response code mobile payment systems: transforming mobile payment in the fintech industry," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    5. Balakrishnan, Vimala & Shuib, Nor Liyana Mohd, 2021. "Drivers and inhibitors for digital payment adoption using the Cashless Society Readiness-Adoption model in Malaysia," Technology in Society, Elsevier, vol. 65(C).
    6. Sonia Singh & Subhra Mondal & Lata Bajpai Singh & Kalyan Kumar Sahoo & Subhankar Das, 2020. "An Empirical Evidence Study of Consumer Perception and Socioeconomic Profiles for Digital Stores in Vietnam," Sustainability, MDPI, vol. 12(5), pages 1-30, February.
    7. Kalinic, Zoran & Marinkovic, Veljko & Molinillo, Sebastián & Liébana-Cabanillas, Francisco, 2019. "A multi-analytical approach to peer-to-peer mobile payment acceptance prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 143-153.
    8. Bunhov Chov & Phichhang Ou, 2022. "Determinants of the consumer’s adoption of the next-generation mobile payments and banking: a case study of the Bakong system," SN Business & Economics, Springer, vol. 2(10), pages 1-38, October.
    9. Daragmeh, Ahmad & Lentner, Csaba & Sági, Judit, 2021. "FinTech payments in the era of COVID-19: Factors influencing behavioral intentions of “Generation X” in Hungary to use mobile payment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).

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    More about this item

    Keywords

    mobile payment; consumer intention; mobile service providers; Vietnam;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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