“Paying with my face” – Understanding users’ adoption and privacy concerns of facial recognition payment

Abstract

Facial recognition payment (FRP) is a widely adopted technology in countries outside of the United States (US), particularly in east Asian countries. However, FRP is still not widely accepted and implemented within the US, partly due to users' privacy concerns of FRP. In our survey study of 164 US participants, we adopted the extended Technology Acceptance Model (TAM) to investigate the relationship between users' privacy concerns, power usage, attitudes, and their intentions of adopting FRP. Our findings showed significant relationships among these assessed variables and indicated that privacy concerns was indeed an important factor in predicting individuals' intentions of adopting FRP, but power users might become early adopters that could spearhead widespread acceptance of FRP within the US due to their lessened privacy concerns.

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References

Adams

D. A.

Nelson

R. R.

Todd

P.A.

(1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly 16, 2 (1992), 227-247.

Bilinski

Dantcheva

Bremond

(2016). Can a Smile Reveal Your Gender? In 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), 16.7736914

Browne

M. W.

Cudeck

(1993). Alternative ways of assessing model fit. In Bollen

K. A.

Long

J. S.

(Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.

Castro

Michael

(2019). Survey: Few Americans want government to limit use of facial recognition technology, particularly for public safety or airport screening.

Davis

F. D.

(1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13, 3 (1989), 319-340.

Dibeklioglu

Alnajar

Salah

A. A.

Gevers

(2015). Combining Facial Dynamics With Appearance for Age Estimation. IEEE Transactions on Image Processing 24, 6 (June 2015), 1928-1943.

Dinev

Hart

(2006). An Extended Privacy Calculus Model for E-Commerce Transactions. Information Systems Research 17, 1 (March 2006), 61-80.

Karahanna

Straub

D. W.

(1999). "The psychological origins of perceived usefulness and ease-of-use." Information & management 35.4 (1999): 237-250.

Faul

Erdfelder

Lang

A. G.

Buchner

(2013). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods 39, 2, 175-191.

10.

Jen

W. Y.

Hung

M. C.

(2010). An Empirical Study of Adopting Mobile Healthcare Service: The Family's Perspective on the Healthcare Needs of Their Elderly Members. Telemedicine and e-Health 16, 1 (February 2010), 41-48.

11.

Liu

2020 Making Cutting-Edge Technology Approachable: A Case Study of Facial-Recognition Payment in China, Nielsen Norman Group.

12.

Liu

Y. L.

Yan

(2021). Resistance to facial recognition payment in China: The influence of privacy-related factors. Telecommunications Policy. 45. 102155.

13.

Moriuchi

(2021). An empirical study of consumers' intention to use biometric facial recognition as a payment method. Psychology & Marketing, 38(10), 1741-1765.

14.

Pikkarainen

Karjaluoto

Pahnila

(2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research 14, 3 (January 2004), 224-235.

15.

Raji

I. D.

, et al. (2020). Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

16.

Roca

J. C.

Chiu

C. M.

Martinez

F. J.

(2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies 64, 8 (August 2006), 683-696.

17.

Rogers

E. M.

(2004). A Prospective and Retrospective Look at the Diffusion Model. Journal of Health Communication 9, sup1 (January 2004), 13-19.

18.

Schermelleh-Engel

Moosbrugger

Muller

(2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 8, 23-74.

19.

Smith

(2019). More than half of U.S. adults trust law enforcement to use facial recognition responsibly. Technical report, Pew Research Center.

20.

Smith

H. J.

Milberg

S. J.

Burke

S. J.

(1996). Information Privacy: Measuring Individuals' Concerns about Organizational Practices. MIS Quarterly 20, 2 (1996), 167196.

21.

Steinacker

Meckel

Kostka

Borth

(2020).

22.

Facial recognition: A cross-national survey on public acceptance, privacy, and discrimination. In International Conference on Machine Learning - Law and ML Workshop.

23.

Sundar

S. S.

Marathe

S. S.

(2010). Personalization versus Customization: the Importance of Agency, Privacy, and Power Usage. Human Communication Research 36, 3 (July 2010), 298-322.

24.

Ward

Bridges

Chitty

(2005). Do Incentives Matter? An Examination of On-line Privacy Concerns and Willingness to Provide Personal and Financial Information. Journal of Marketing Communications 11, 1 (March 2005), 21-40.

25.

Widjaja

A. E.

Chen

J. V.

Sukoco

B. M.

Q. A.

(2019). Understanding users’ willingness to put their personal information on the personal cloud-based storage applications: An empirical study. Computers in Human Behavior 91, (February 2019), 167-185.

26.

(2007). The Effects of Self-Construal and Perceived Control on Privacy Concerns. ICIS 2007 Proceedings (December 2007). Retrieved from https://aisel.aisnet.org/icis2007/125

27.

Dinev

Smith

H. J.

Hart

(2008). Examining the Formation of Individual’s Privacy Concerns: Toward an Integrative View.. ICIS 2008 Proceedings - Twenty Ninth International Conference on Information Systems. 6.

28.

Zhang

Sundar

S. S.

(2019). Proactive vs. reactive personalization: Can customization of privacy enhance user experience? International Journal of Human-Computer Studies 128, (August 2019), 86-99.

29.

Zhang

Feng

Sadeh

(2021). Facial Recognition: Understanding Privacy Concerns and Attitudes Across Increasingly Diverse Deployment Scenarios. In Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021), Aug 9-10 2021.

30.

Zhang

W. K.

Kang

M. J.

(2019). “Factors Affecting the Use of Facial-Recognition Payment: An Example of Chinese Consumers,” in IEEE Access, vol. 7, pp. 154360-154374.

31.

Zhong

Moon

H. C.

(2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology in Society, 64, 101515.