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Resistance to facial recognition payment in China: The influence of privacy-related factors

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  • Liu, Yu-li
  • Yan, Wenjia
  • Hu, Bo

Abstract

As mobile payment technology is at a nascent stage, the use of facial recognition payment (FRP) services is gradually penetrating the lives of Chinese people. Although the FRP system may have advantages over other payment technologies, a civil lawsuit over refusing to submit facial information and a series of illegal activities related to selling facial information have raised the public's privacy concerns, which might further engender Chinese users' resistance towards FRP. Based on privacy calculus theory and innovation resistance theory, this study builds a research model of FRP and examines it by using a cross-sectional study with 1200 Chinese users. The findings demonstrate that the perceived effectiveness of privacy policy has significant relationships with privacy control, perceived privacy risk, perceived benefits, and resistance. Both privacy control and perceived privacy risk are significantly related to privacy concerns. There is also a significant relationship between the perceived privacy risk and resistance to FRP. Meanwhile, privacy concerns positively affect user resistance, while perceived benefits negatively affect user resistance. In contrast to previous research, the perceived privacy risk has a positive impact on the perceived benefits. This study offers cutting-edge contributions to both academia and industry.

Suggested Citation

  • Liu, Yu-li & Yan, Wenjia & Hu, Bo, 2021. "Resistance to facial recognition payment in China: The influence of privacy-related factors," Telecommunications Policy, Elsevier, vol. 45(5).
    • Handle: RePEc:eee:telpol:v:45:y:2021:i:5:s0308596121000598
    DOI: 10.1016/j.telpol.2021.102155
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    3. Francisco Liébana-Cabanillas & Francisco Muñoz-Leiva & Sebastián Molinillo & Elena Higueras-Castillo, 2022. "Do biometric payment systems work during the COVID-19 pandemic? Insights from the Spanish users' viewpoint," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    4. Prashant Sharma & Saurabh Sharma, 2023. "Mapping the Intellectual Structure of Mobile Payment Research: A Bibliometric Analysis," SAGE Open, , vol. 13(3), pages 21582440231, September.
    5. Irina Dijmãrescu & Mariana Iatagan & Iulian Hurloiu & Marinela Geamãnu & Ciprian Rusescu & Adrian Dijmãrescu, 2022. "Neuromanagement decision making in facial recognition biometric authentication as a mobile payment technology in retail, restaurant, and hotel business models," Oeconomia Copernicana, Institute of Economic Research, vol. 13(1), pages 225-250, March.
    6. Zhong, Junying & Chen, Tiao, 2023. "Antecedents of mobile payment loyalty: An extended perspective of perceived value and information system success model," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    7. Hu, Bo & Liu, Yu-li & Yan, Wenjia, 2021. "Should I scan my face? The influence of perceived value and trust on Chinese users' intention to use facial recognition payment," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238028, International Telecommunications Society (ITS).
    8. Chen, Wenhao & Wang, Min, 2023. "Regulating the use of facial recognition technology across borders: A comparative case analysis of the European Union, the United States, and China," Telecommunications Policy, Elsevier, vol. 47(2).
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