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Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail

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  • Jia Gao

    (Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian, Liaoning 116025, China)

  • Ying Rong

    (Antai College of Economics and Management, Data-Driven Management Decision-Making Lab, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Xin Tian

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Yuliang Yao

    (College of Business, Lehigh University, Bethlehem, Pennsylvania 18015)

Abstract

Although facial recognition (FR) payment technology can be more convenient for customers, it is still not consistently used by many customers in retail. Using transaction data from three retail chains, we develop econometric models and an estimation strategy for examining the social presence and herding effects that affect FR payment technology use. Our key findings are as follows: (1) Customers are less likely to use FR payment technology when more customers are in line behind them, waiting and potentially watching—the social presence effect. (2) Customers are more likely to use FR payment technology when more preceding customers use FR payment technology—the herding effect. (3) Customers with more experience using FR payment technology are subject to a weaker social presence effect. The marginal social presence effect can result in a 4.75% reduction in the probability of the focal customer using FR payment technology, and the potential social presence effect can be as high as 48.42%. When the focal customer has one additional experience in using FR payment technology, the social presence effect is reduced by 7.79%. The herding effect can result in a 20.90% increase in the probability of the focal customer using FR payment technology. Theoretical and managerial implications are discussed.

Suggested Citation

  • Jia Gao & Ying Rong & Xin Tian & Yuliang Yao, 2024. "Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail," Information Systems Research, INFORMS, vol. 35(1), pages 16-27, March.
  • Handle: RePEc:inm:orisre:v:35:y:2024:i:1:p:16-27
    DOI: 10.1287/isre.2023.1205
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    References listed on IDEAS

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    1. Jennifer J. Argo & Darren W. Dahl & Rajesh V. Manchanda, 2005. "The Influence of a Mere Social Presence in a Retail Context," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 32(2), pages 207-212, September.
    2. Tom Fangyun Tan & Serguei Netessine, 2014. "When Does the Devil Make Work? An Empirical Study of the Impact of Workload on Worker Productivity," Management Science, INFORMS, vol. 60(6), pages 1574-1593, June.
    3. 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.
    4. Uri Simonsohn & Dan Ariely, 2008. "When Rational Sellers Face Nonrational Buyers: Evidence from Herding on eBay," Management Science, INFORMS, vol. 54(9), pages 1624-1637, September.
    5. Sezer Ülkü & Chris Hydock & Shiliang Cui, 2020. "Making the Wait Worthwhile: Experiments on the Effect of Queueing on Consumption," Management Science, INFORMS, vol. 66(3), pages 1149-1171, March.
    6. Amy Wenxuan Ding & Shibo Li, 2019. "Herding in the consumption and purchase of digital goods and moderators of the herding bias," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 460-478, May.
    7. Robert E. Crossler & France Bélanger, 2019. "Why Would I Use Location-Protective Settings on My Smartphone? Motivating Protective Behaviors and the Existence of the Privacy Knowledge–Belief Gap," Information Systems Research, INFORMS, vol. 30(3), pages 995-1006, September.
    8. Chun-Yu Ho & Nayoung Kim & Ying Rong & Xin Tian, 2022. "Promoting Mobile Payment with Price Incentives," Management Science, INFORMS, vol. 68(10), pages 7614-7630, October.
    9. Dahl, Darren W & Manchanda, Rajesh V & Argo, Jennifer J, 2001. "Embarrassment in Consumer Purchase: The Roles of Social Presence and Purchase Familiarity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(3), pages 473-481, December.
    10. 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.
    11. Belk, Russell W, 1975. "Situational Variables and Consumer Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(3), pages 157-164, December.
    12. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    13. Yuliang Yao & Martin Dresner & Kevin Xiaoguo Zhu, 2019. "“Monday Effect” on Performance Variations in Supply Chain Fulfillment: How Information Technology–Enabled Procurement May Help," Information Systems Research, INFORMS, vol. 30(4), pages 1402-1423, December.
    14. Naresh K. Malhotra & Sung S. Kim & James Agarwal, 2004. "Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model," Information Systems Research, INFORMS, vol. 15(4), pages 336-355, December.
    15. Ben C. F. Choi & Zhenhui (Jack) Jiang & Bo Xiao & Sung S. Kim, 2015. "Embarrassing Exposures in Online Social Networks: An Integrated Perspective of Privacy Invasion and Relationship Bonding," Information Systems Research, INFORMS, vol. 26(4), pages 675-694, December.
    16. Jingqi Wang & Yong-Pin Zhou, 2018. "Impact of Queue Configuration on Service Time: Evidence from a Supermarket," Management Science, INFORMS, vol. 64(7), pages 3055-3075, July.
    17. Masoud Kamalahmadi & Qiuping Yu & Yong-Pin Zhou, 2021. "Call to Duty: Just-in-Time Scheduling in a Restaurant Chain," Management Science, INFORMS, vol. 67(11), pages 6751-6781, November.
    18. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    19. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, December.
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