Exclusive: Cross-domain artificial intelligence technology

Adoption under perceived risks: Analysis of face recognition payment technology acceptance

  • LI Wenwen ,
  • HAN Wei ,
  • CHEN An
Expand
  • 1. University of Chinese Academy of Sciences, Beijing 100049, China;
    2. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China;
    3. Development Planning Research Institute, China Electronies Technology Group Corporation (CETC), Beijing 100041, China

Received date: 2024-05-13

  Revised date: 2024-11-11

  Online published: 2025-01-06

Abstract

The application of artificial intelligence, represented by face recognition payment, has improved efficiency and optimized user experience, but it has also introduced various risks. In order to regulate its development, it is essential to investigate the factors that influence the adoption of face recognition payment. Current research on the impact of perceived risk facets on face recognition payment remains limited. This study, based on the UTAUT model, analyses the key factors influencing the intention to use face recognition payment (performance expectancy, effort expectancy, social influence, and facilitating conditions) and further examines the influence of five perceived risk facets (time risk, privacy risk, legal risk, financial risk, and health risk) on performance expectancy and effort expectancy. A structural equation analysis of 412 valid survey responses shows that the four factors in the UTAUT model have a significant positive impact on the behavioral intention to use face recognition payment. Privacy risk and financial risk are the facets of users' greatest concern, and both have a significant negative impact on performance expectancy and effort expectancy. This study identifies the specific mechanisms through which different risks affect the adoption of face recognition payment, providing reference and empirical evidence for its risk management and governance.

Cite this article

LI Wenwen , HAN Wei , CHEN An . Adoption under perceived risks: Analysis of face recognition payment technology acceptance[J]. Science & Technology Review, 2024 , 42(23) : 85 -97 . DOI: 10.3981/j.issn.1000-7857.2024.05.00509

References

[1] 国务院. 新一代人工智能发展规划[EB/OL]. [2017-07- 08]. https://www.gov.cn/gongbao/content/2017/content_521- 6427.htm.
[2] Biometric Update. Facial recognition payment systems rolled out in Denmark, growing rapidly in China[EB/OL]. (2019-12-10)[2023-10-20]. https://www.biometricupdate. com/201912/facial-recognition-payment-systems-rolledout-in-denmark-growing-rapidly-in-china.
[3] Fintech Magazine. Romania introduces PayByFace: Facial-recognition payments[EB/OL]. [2020-02-04]. https:// fintechmagazine.com/digital-payments/romania-introduces -paybyface-facial-recognition-payments.
[4] 王继超, 张丽娟, 尤田, 等. 人脸识别的智能防疫系统设计[J]. 河北水利电力学院学报, 2021, 31(4): 55-59.
[5] O'Flaherty K, Clearview A I. The company whose database has amassed 3 billion photos, hacked[EB/OL]. [2020-02-04]. https://www.forbes.com/sites/kateoflahertyuk /2020/02/26/clearview-ai-the-company-whose-databasehas-amassed-3-billion-photos-hacked/? ss=consumertech #6ee0cf1c7606.
[6] European Union. General data protection regulation (GDPR) [R]. Brussels: European Union, 2016.
[7] United Nations. Interim report: Governing AI for humanity [R]. New York: The United Nations Secretary-General's High-level Advisory Body, 2023.
[8] 人工智能安全公司被曝数据泄露,敲响人脸识别安全警钟[EB/OL]. [2024-03-04]. http://www.xinhuanet.com/tech/ 2019-02/26/c_1124161867.htm.
[9] 公众对面部识别应用的调查报告发布[EB/OL]. [2024- 03-04]. http://www.xinhuanet.com/tech/202010/19/c_1126- 627071.htm.
[10] 国家互联网信息办公室. 人脸识别技术应用安全管理规 定(试行)(征求 意见 稿)[EB/OL]. (2023-08-08) [2023-10-20]. https://www. cac. gov. cn/2023-08/08/c_ 1693064670537413.htm.
[11] Venkatesh V, Morris M G, Davis G B, et al. User acceptance of information technology: Toward a unified view [J]. MIS Quarterly, 2003, 27(3): 425-478.
[12] 黄齐娴, 李晓宛, 袁睿洁, 等. 数字鸿沟与数字反哺:基于UTAUT模型下互联网医疗代际使用差异的研究[J]. 中国集体经济, 2022(10): 82-85.
[13] 相彤, 黄亚芳, 吴浩. 老年人对网络直播运动指导使用意愿的调查研究:基于整合技术接受模型与感知风险理论[J/OL]. (2024-08-26). https://kns.cnki.net/kcms/detail/13.1222.R.20240823.1330.010.html.
[14] Zhong Y P, Oh S, Moon H C. Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model[J]. Technology in Society, 2021, 64: 101515.
[15] Riffai M M M A, Grant K, Edgar D. Big TAM in Oman: Exploring the promise of on-line banking, its adoption by customers and the challenges of banking in Oman[J]. International Journal of Information Management, 2012, 32(3): 239-250.
[16] Luo X, Li H, Zhang J, et al. Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services[J]. Decision Support Systems, 2010, 49(2): 222-234.
[17] Kim E, Tadisina S. A model of customers' trust in ebusinesses: Micro-level inter-party trust formation[J]. Journal of Computer Information Systems, 2007, 48(1): 88-104.
[18] Martins C, Oliveira T, Popovič A. Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application[J]. International Journal of Information Management, 2014, 34(1): 1-13.
[19] Anderson E W, Sullivan M W. The antecedents and consequences of customer satisfaction for firms[J]. Marketing Science, 1993, 12(2): 125-143.
[20] Churchill Jr G A, Surprenant C. An investigation into the determinants of customer satisfaction[J]. Journal of Marketing Research, 1982, 19(4): 491-504.
[21] 高海霞. 消费者的感知风险及减少风险行为研究:基于手机市场的研究[D]. 杭州: 浙江大学, 2003.
[22] 李华强, 范春梅, 贾建民, 等. 突发性灾害中的公众风险感知与应急管理——以5·12汶川地震为例[J]. 管理世界, 2009, 25(6): 52-60.
[23] Nicosia F M. Perceived risk, information processing, and consumer behavior: A review article[J]. The Journal of Business, 1969, 42(2): 162-166.
[24] 胡象明, 王锋. 一个新的社会稳定风险评估分析框架: 风险感知的视角[J]. 中国行政管理, 2014(4): 102-108.
[25] 杨瑞仙, 张梦君. 微信用户风险感知因素研究[J]. 现代情报, 2016, 36(5): 94-97.
[26] Forsythe S M, Shi B. Consumer patronage and risk perceptions in Internet shopping[J]. Journal of Business Research, 2003, 56(11): 867-875.
[27] 范为. 大数据时代个人信息保护的路径重构[J]. 环球法律评论, 2016, 38(5): 92-115.
[28] Shin H, Kang J. Reducing perceived health risk to attract hotel customers in the COVID-19 pandemic era: Focused on technology innovation for social distancing and cleanliness[J]. International Journal of Hospitality Management, 2020, 91: 102664.
[29] Tai Y M, Ku Y C. Will stock investors use mobile stock trading? A benefit-risk assessment based on a modified UTAUT model[J]. Journal of Electronic Commerce Research, 2013, 14(1): 67-84.
[30] Han H J, Lee S. Analyzing the relationship between risk perception of fine particular matter and behavioral intention of screen sports through UTAUT model[J]. Korean Journal of Leisure, Recreation & Park, 2019, 43(2): 23- 34.
[31] Zhou T. Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk[J]. Journal of Electronic Commerce Research, 2012, 13(2): 135.
[32] Yang J Y, An J H, Park C U. The effect of perceived risk on the intention to adopt mobile banking services [J]. Journal of Technology Innovation, 2006, 14(3): 183- 208.
[33] Baek H J, Kim J Y, Yoo Y M, et al. A study on the effect of perceived risk on the acceptance intention of MyData service[J]. Journal of The Korea Society of Information Technology Policy & Management, 2019, 11(4): 1287-1291.
[34] Crespo Á H, del Bosque I R, García de los Salmones Sánchez M M. The influence of perceived risk on Internet shopping behavior: A multidimensional perspective [J]. Journal of Risk Research, 2009, 12(2): 259-277.
[35] Nguyen T D, Nguyen T C H. The role of perceived risk on intention to use online banking in Vietnam[C]//2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Piscataway, NJ: IEEE, 2017: 1903-1908.
[36] Flanagin A J, Metzger M J, Pure R, et al. Mitigating risk in ecommerce transactions: Perceptions of information credibility and the role of user-generated ratings in product quality and purchase intention[J]. Electronic Commerce Research, 2014, 14(1): 1-23.
[37] 耿先锋. 顾客参与测量维度、驱动因素及其对顾客满意的影响机理研究:以杭州医疗服务业为例[D]. 杭州: 浙江大学, 2008.
[38] Lee M C. Factors influencing the adoption of Internet banking: An integration of TAM and TPB with perceived risk and perceived benefit[J]. Electronic Commerce Research and Applications, 2009, 8(3): 130-141.
[39] 周涛, 鲁耀斌. 隐私关注对移动商务用户采纳行为影响的实证分析[J]. 管理学报, 2010, 7(7): 1046-1051.
[40] 许晖, 许守任, 王睿智. 消费者旅游感知风险维度识别及差异分析[J]. 旅游学刊, 2013, 28(12): 71-80.
[41] Lim N. Consumers' perceived risk: Sources versus consequences[J]. Electronic Commerce Research and Applications, 2003, 2(3): 216-228.
[42] Hassan A M, Kunz M B, Pearson A W, et al. Conceptualization and measurement of perceived risk in online shopping[J]. Marketing Management Journal, 2006, 16(1): 138-147.
[43] Tavakol M, Dennick R. Making sense of Cronbach’s Alpha[J]. International Journal of Medical Education, 2011, 2: 53-55.
[44] Ammerlaan J W, van Os-Medendorp H, Sont J K, et al. Validation of the Dutch version of the health education impact questionnaire (HEIQ) and comparison of the Dutch translation with the English, German and French HEIQ[J]. Health and Quality of Life Outcomes, 2017, 15(1): 1-11.
[45] Cheung G W, Wang C. Current approaches for assessing convergent and discriminant validity with SEM: Issues and solutions[J]. Academy of Management Proceedings, 2017, 2017(1): 12706.
[46] Hair J F, Black W C, Babin B J, et al. Multivariate data analysis: International version[M]. New Jersey: Pearson, 2007.
[47] Schreiber J B, Nora A, Stage F K, et al. Reporting structural equation modeling and confirmatory factor analysis results: A review[J]. The Journal of Educational Research, 2006, 99(6): 323-338.
[48] Von Eye A, Fuller B E. A comparison of the SEM software packages Amos, EQS, and LISREL[M]//Structural Equation Modeling. Cambridge: Cambridge University Press, 2003: 355-391.
[49] DeVellis R F. Scale development: Theory and applications[M]. London: SAGE Publication, 1991.
[50] Hair J F, Hult G T M, Ringle C M, et al. A primer on partial least squares structural equation modeling(PLSSEM), 2nd edition[M]. London: SAGE Publications, 2016.
[51] Turel O, Serenko A. Satisfaction with mobile services in Canada: An empirical investigation[J]. Telecommunications Policy, 2006, 30(5/6): 314-331.
[52] Karjaluoto H, Shaikh A A, Saarijärvi H, et al. How perceived value drives the use of mobile financial services apps[J]. International Journal of Information Management, 2019, 47: 252-261.
[53] Ahn S J, Lee S H. The effect of consumers’perceived value on acceptance of an Internet-only bank service[J]. Sustainability, 2019, 11(17): 4599.
[54] Hu B, Liu Y, Yan W. Should I scan my face? The influence of perceived value and trust on Chinese users’intention to use facial recognition payment[J]. SSRN Electronic Journal, 2022: 1-30.
[55] Nysveen H, Pedersen P E, Thorbjørnsen H, et al. Mobilizing the brand: The effects of mobile services on brand relationships and main channel use[J]. Journal of Service Research, 2005, 7(3): 257-276.
[56] 曾繁旭, 黄广生. 网络意见领袖社区的构成、联动及其政策影响:以微博为例[J]. 开放时代, 2012(4): 115-131.
[57] Sivathanu B. Adoption of digital payment systems in the era of demonetization in India: An empirical study[J]. Journal of Science and Technology Policy Management, 2019, 10(1): 143-171.
[58] Shin D H. The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption[J]. Interacting with Computers, 2010, 22(5): 428-438.
[59] Arham H K, Sanjaya U H. Potential legal risks arising in cash on delivery (COD) payment mechanism in ecommerce applications[J]. Indonesian Journal of Law and Policy Studies, 2022, 3(1): 61-74.
[60] Leong L Y, Hew J J, Wong L W, et al. The past and beyond of mobile payment research: A development of the mobile payment framework[J]. Internet Research, 2022, 32(6): 1757-1782.
[61] Qiu J Y, Shen B, Zhao M, et al. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations[J]. General Psychiatry, 2020, 33(2): 1-3.
Outlines

/