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Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model

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  • Zhong, Yongping
  • Oh, Segu
  • Moon, Hee Cheol

Abstract

Technological development has drastically changed customers' daily lives by offering them new ways to shop. It also creates more opportunities for business to achieve sustainable success; however, both scholars and managers are still having relative difficulty in fully grasping customer behavior in terms of technology acceptance during the Industry 4.0. This study aims to investigate the possible factors that drive Chinese customers' willingness to utilize facial recognition payment. The findings showed that factors such as perceived enjoyment, facilitating conditions, personal innovativeness, coupon availability, perceived ease of use (PEOU), perceived usefulness (PU), and users' attitude are main drivers of customers' decisions to use facial recognition payment. Also, we found that gender differences exist in the adoption of facial recognition payment. Facilitating conditions have stronger effects on men's attitude towards usage, while coupon availability shapes female users' perception of usefulness more powerfully. By testing the extended technology acceptance model (TAM), this study seeks to gain more insight into technological change within society. Overall, investigation of the drivers of customer intention to use facial recognition payment, and exploration of their internal relationships will fulfil theoretical requirements and lead to a better understanding of customers' technology acceptance behavior, which in turn will provide greater theoretical and practical guidance for scholars and managers.

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  • Zhong, Yongping & Oh, Segu & Moon, Hee Cheol, 2021. "Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model," Technology in Society, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:teinso:v:64:y:2021:i:c:s0160791x2031318x
    DOI: 10.1016/j.techsoc.2020.101515
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    2. Liu, Aiping & Urquía-Grande, Elena & López-Sánchez, Pilar & Rodríguez-López, Ángel, 2022. "How technology paradoxes and self-efficacy affect the resistance of facial recognition technology in online microfinance platforms: Evidence from China," Technology in Society, Elsevier, vol. 70(C).
    3. 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.
    4. Dhiman, Neeraj & Jamwal, Mohit & Kumar, Ajay, 2023. "Enhancing value in customer journey by considering the (ad)option of artificial intelligence tools," Journal of Business Research, Elsevier, vol. 167(C).
    5. Schiavo, Gianluca & Businaro, Stefano & Zancanaro, Massimo, 2024. "Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence," Technology in Society, Elsevier, vol. 77(C).
    6. Imdadullah Hidayat-ur-Rehman & Arshad Ahmad & Fahim Akhter & Mohd Ziaur Rehman, 2022. "Examining Consumers’ Adoption of Smart Wearable Payments," SAGE Open, , vol. 12(3), pages 21582440221, August.
    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. Idrees Waris & Rashid Ali & Anand Nayyar & Mohammed Baz & Ran Liu & Irfan Hameed, 2022. "An Empirical Evaluation of Customers’ Adoption of Drone Food Delivery Services: An Extended Technology Acceptance Model," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    9. Soyoung An & Thomas Eck & Huirang Yim, 2023. "Understanding Consumers’ Acceptance Intention to Use Mobile Food Delivery Applications through an Extended Technology Acceptance Model," Sustainability, MDPI, vol. 15(1), pages 1-14, January.
    10. Zhang, Xiaoxue & Zhang, Zizhong, 2024. "Leaking my face via payment: Unveiling the influence of technology anxiety, vulnerabilities, and privacy concerns on user resistance to facial recognition payment," Telecommunications Policy, Elsevier, vol. 48(3).
    11. Wang, Zhen & Chu, Erming, 2024. "The path toward urban carbon neutrality: How does the low-carbon city pilot policy stimulate low-carbon technology?," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 954-975.
    12. 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).
    13. Abderahman Rejeb & Andrea Appolloni, 2022. "The Nexus of Industry 4.0 and Circular Procurement: A Systematic Literature Review and Research Agenda," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
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