Understanding Continuance Intention toward Facial Recognition Payment: An Integration of ECM and APCO Frameworks

Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509-514. https://doi.org/10.1126/science.aaa1465
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Al-Sharafi, M. A., Al-Qaysi, N., Iahad, N. A., & Al-Emran, M. (2022). Evaluating the sustainable use of mobile payment contactless technologies within and beyond the COVID-19 pandemic using a hybrid SEM-ANN approach. International Journal of Bank Marketing, 40(5), 1071-1095. https://doi.org/10.1108/IJBM-07-2021-0291
Ambalov, I. A. (2018). A meta-analysis of IT continuance: An evaluation of the expectation-confirmation model. Telematics and Informatics, 35(6), 1561-1571. https://doi.org/10.1016/j.tele.2018.03.016
Bahirat, P., Sun, Q., & Knijnenburg, B. P. (2018). Scenario context V/s framing and defaults in managing privacy in household IoT. Companion Proceedings of the 23rd International Conference on Intelligent User Interfaces,
Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201-214. https://doi.org/10.1016/S0167-9236(01)00111-7
Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370. https://doi.org/10.2307/3250921
Bhattacherjee, A., & Lin, C.-P. (2015). A unified model of IT continuance: Three complementary perspectives and crossover effects. European Journal of Information Systems, 24(4), 364-373. https://doi.org/10.1057/ejis.2013.36
Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26. https://doi.org/10.1080/08874417.2008.11645302
Bromberg, D. E., Charbonneau, É., & Smith, A. (2020). Public support for facial recognition via police body-worn cameras: Findings from a list experiment. Government Information Quarterly, 37(1), 101415. https://doi.org/10.1016/j.giq.2019.101415
Chen, W., & Wang, M. (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, 47(2), 102482. https://doi.org/10.1016/j.telpol.2022.102482
Cheng, E., & Shao, G. (2019). Growing backlash in China against A.I. and facial recognition. Retrieved Oct from https://www.cnbc.com/2019/09/06/ai-worries-about-the-dangers-of-facial-recognition-growing-in-china.html
Cheng, X., Qiao, L., Yang, B., & Zhang, X. (2024). Investigation on users’ resistance intention to facial recognition payment: A perspective of privacy. Electronic Commerce Research, 24(1), 275-301. https://doi.org/10.1007/s10660-022-09588-y
Chiu, W., Cho, H., & Chi, C. G. (2020). Consumers' continuance intention to use fitness and health apps: An integration of the expectation–confirmation model and investment model. Information Technology & People, 34(3), 978-998. https://doi.org/10.1108/ITP-09-2019-0463
Collingridge, D. (1982). The social control of technology. New York: St. Martin's Press, 200. http://hdl.handle.net/10822/792071
Culnan, M. J. (1995). Consumer awareness of name removal procedures: Implications for direct marketing. Journal of Direct Marketing, 9(2), 10-19. https://doi.org/10.1002/dir.4000090204
Dang, V. T., Nguyen, N., Nguyen, H. V., Nguyen, H., Van Huy, L., Tran, V. T., & Nguyen, T. H. (2022). Consumer attitudes toward facial recognition payment: An examination of antecedents and outcomes. International Journal of Bank Marketing, 40(3), 511-535. https://doi.org/10.1108/IJBM-04-2021-0135
Dargan, S., & Kumar, M. (2020). A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143, 113114. https://doi.org/10.1016/j.eswa.2019.113114
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Dinev, T., & Hart, P. (2005). Internet privacy concerns and social awareness as determinants of intention to transact. International Journal of Electronic Commerce, 10(2), 7-29. https://doi.org/10.1080/01449290410001715723
Dinev, T., McConnell, A. R., & Smith, H. J. (2015). Research commentary—informing privacy research through information systems, psychology, and behavioral economics: Thinking outside the “APCO” box. Information Systems Research, 26(4), 639-655. https://doi.org/10.1287/isre.2015.0600
Ding, J. (2019). ChinAI #77: A Strong Argument Against Facial Recognition in the Beijing Subway. Retrieved Oct from https://chinai.substack.com/p/chinai-77-a-strong-argument-against
Esteban, V.-F., & Daniel, G.-J. (2016). Face recognition for authentication on mobile devices. Image and Vision Computing, 55, 31-33. https://doi.org/10.1016/j.imavis.2016.03.018
Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430-447. https://doi.org/10.1108/IntR-12-2017-0515
Gao, W., Guo, Q., & Jiang, N. (2023). Information vulnerability in facial recognition payment: An explanation of technology threat avoidance and information boundary theory. Electronic Commerce Research and Applications, 61, 101298. https://doi.org/10.1016/j.elerap.2023.101298
Goes, P. B. (2013). Editor's comments: Information systems research and behavioral economics. MIS Quarterly, 37(3), 3-8.
Gu, J., Xu, Y. C., Xu, H., Zhang, C., & Ling, H. (2017). Privacy concerns for mobile app download: An elaboration likelihood model perspective. Decision Support Systems, 94, 19-28. https://doi.org/10.1016/j.dss.2016.10.002
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Harborth, D., & Pape, S. (2021). Investigating privacy concerns related to mobile augmented reality Apps: A vignette based online experiment. Computers in Human Behavior, 122, 106833. https://doi.org/10.1016/j.chb.2021.106833
Hizam, S. M., Ahmed, W., Fahad, M., Akter, H., Sentosa, I., & Ali, J. (2021). User behavior assessment towards biometric facial recognition system: A SEM-neural network approach. Advances in Information and Communication: Proceedings of the 2021 Future of Information and Communication Conference (FICC),
Hong, W., & Thong, J. Y. (2013). Internet privacy concerns: An integrated conceptualization and four empirical studies. MIS Quarterly, 275-298. http://www.jstor.org/stable/43825946
Hu, B., Liu, Y.-l., & Yan, W. (2023). Should I scan my face? The influence of perceived value and trust on Chinese users’ intention to use facial recognition payment. Telematics and Informatics, 78, 101951. https://doi.org/10.1016/j.tele.2023.101951
Hwang, J., Kim, J. S., Kim, H. M., & Kim, J. J. (2024). Effects of motivated consumer innovativeness on facial recognition payment adoption in the restaurant industry: A cross-cultural study. International Journal of Hospitality Management, 117, 103646. https://doi.org/10.1016/j.ijhm.2023.103646
Ioannou, A., Tussyadiah, I., & Lu, Y. (2020). Privacy concerns and disclosure of biometric and behavioral data for travel. International Journal of Information Management, 54, 102122. https://doi.org/10.1016/j.ijinfomgt.2020.102122
Johnson, E. J., Bellman, S., & Lohse, G. L. (2002). Defaults, framing and privacy: Why opting in-opting out. Marketing Letters, 13, 5-15. https://doi.org/10.1023/A:1015044207315
Jones, A. (2020). Biometrics: The Future of Payments? Global Payments Integrated. Retrieved Septemper from https://www.globalpaymentsintegrated.com/en-us/blog/2020/06/30/biometrics-the-future-of-payments
Khalifa, M., & Liu, V. (2004). The state of research on information system satisfaction. Journal of Information Technology Theory and Application (JITTA), 5(4), 4. https://aisel.aisnet.org/jitta/vol5/iss4/4
Kim, J. J., Kim, I., & Hwang, J. (2021). A change of perceived innovativeness for contactless food delivery services using drones after the outbreak of COVID-19. International Journal of Hospitality Management, 93, 102758. https://doi.org/10.1016/j.ijhm.2020.102758
Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & Security, 64, 122-134. https://doi.org/10.1016/j.cose.2015.07.002
Kostka, G., Steinacker, L., & Meckel, M. (2021). Between security and convenience: Facial recognition technology in the eyes of citizens in China, Germany, the United Kingdom, and the United States. Public Understanding of Science, 30(6), 671-690.
Kudina, O., & Verbeek, P.-P. (2019). Ethics from within: Google Glass, the Collingridge dilemma, and the mediated value of privacy. Science, Technology, & Human Values, 44(2), 291-314.
Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22-42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x
Lee, C. T., & Pan, L.-Y. (2023). Smile to pay: predicting continuous usage intention toward contactless payment services in the post-COVID-19 era. International Journal of Bank Marketing, 41(2), 312-332. https://doi.org/10.1108/IJBM-03-2022-0130
Lee, Y., & Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services. Electronic Commerce Research and Applications, 10(3), 342-357. https://doi.org/10.1016/j.elerap.2010.11.005
Li, C., & Li, H. (2023). Disentangling facial recognition payment service usage behavior: A trust perspective. Telematics and Informatics, 77, 101939. https://doi.org/10.1016/j.tele.2023.101939
Li, C., Li, H., & Wang, P. (2020). Facial payment use in China: An integrated view of privacy concerns and perceived benefits. Pacific Asia Conference on Information Systems,
Liao, M., Agnihotri, D., & Zhong, X. (2022). “Paying with my face”: Understanding users’ adoption and privacy concerns of facial recognition payment. Proceedings of the Human Factors and Ergonomics Society Annual Meeting,
Limedia. (2019). 2019 Facial Payment Research Report in China. Retrieved September from https://www.theceomagazine.com/business/innovation-technology/facial
Lin, T.-C., Huang, S.-L., & Hsu, C.-J. (2015). A dual-factor model of loyalty to IT product: The case of smartphones. International Journal of Information Management, 35(2), 215-228. https://doi.org/10.1016/j.ijinfomgt.2015.01.001
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied psychology, 86(1), 114.
Liu, A., Urquía-Grande, E., López-Sánchez, P., & Rodríguez-López, A. (2022). How technology paradoxes and self-efficacy affect the resistance of facial recognition technology in online microfinance platforms: Evidence from China. Technology in Society, 70, 102041. https://doi.org/10.1016/j.techsoc.2022.102041
Liu, T., Li, W., & Jia, X. (2023). Consumer data vulnerability, peer privacy concerns and continued usage intention of sharing accommodation platforms: The moderating roles of perceived benefits. Information Technology & People, 36(6), 2234-2258. https://doi.org/10.1108/ITP-05-2021-0365
Liu, T., Yang, B., Geng, Y., & Du, S. (2021). Research on face recognition and privacy in China: Based on social cognition and cultural psychology. Frontiers in Psychology, 12, 809736.
Lyu, T., Guo, Y., & Chen, H. (2024). Understanding people's intention to use facial recognition services: The roles of network externality and privacy cynicism. Information Technology & People, 37(3), 1025-1051. https://doi.org/10.1108/ITP-10-2021-0817
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336-355. https://doi.org/10.1287/isre.1040.0032
Moriuchi, E. (2021). An empirical study of consumers' intention to use biometric facial recognition as a payment method. Psychology & Marketing, 38(10), 1741-1765. https://doi.org/10.1002/mar.21495
Nan, D., Kim, Y., Huang, J., Jung, H. S., & Kim, J. H. (2022). Factors affecting intention of consumers in using face recognition payment in offline markets: An acceptance model for future payment service. Frontiers in Psychology, 13, 830152. https://doi.org/10.3389/fpsyg.2022.830152
Nemati, H., Wall, J. D., & Chow, A. (2014). Privacy coping and information-sharing behaviors in social media: A comparison of Chinese and US users. Journal of Global Information Technology Management, 17(4), 228-249. https://doi.org/10.1080/1097198X.2014.978622
Ogbanufe, O., & Gerhart, N. (2018). Watch it! Factors driving continued feature use of the smartwatch. International Journal of Human–Computer Interaction, 34(11), 999-1014. https://doi.org/10.1080/10447318.2017.1404779
Owusu, A., Taana, I. H., Bakare, A. S., Abdurrahaman, D. T., & Broni Jr, F. E. (2020). Investigating the nexus between mobile apps adoption and privacy concerns among users: An empirical analysis from Ghana. Technium Soc. Sci. J., 10, 167.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879
Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0: An updated guide and practical guide to statistical analysis. Pearson.
Rasul, T., Wijeratne, A., Soleimani, S., & Lim, W. M. (2023). Where there’s sugar, there are sugar-related mobile apps. What factors motivate consumers’ continued use of m-Health? Journal of Strategic Marketing, 31(4), 856-876. https://doi.org/10.1080/0965254X.2021.1999307
Ricardo, d. S. A., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221-230. https://doi.org/10.1016/j.rai.2016.06.003
Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12(4), 762-800.
Shahzad, M. F., Xu, S., Lim, W. M., Hasnain, M. F., & Nusrat, S. (2024). Cryptocurrency awareness, acceptance, and adoption: the role of trust as a cornerstone. Humanities and Social Sciences Communications, 11(1), 1-14. https://doi.org/10.1057/s41599-023-02528-7
Shang, D., & Wu, W. (2017). Understanding mobile shopping consumers’ continuance intention. Industrial Management & Data Systems, 117(1), 213-227. https://doi.org/10.1108/IMDS-02-2016-0052
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 989-1015. https://doi.org/10.2307/41409970
Susanto, A., Chang, Y., & Ha, Y. (2016). Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model. Industrial Management & Data Systems, 116(3), 508-525. https://doi.org/10.1108/IMDS-05-2015-0195
Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22, 243-257. https://doi.org/10.1007/s10796-018-9864-5
Varian, H. R. (2009). Economic aspects of personal privacy. Internet Policy and Economics: Challenges and Perspectives, 101-109. https://doi.org/10.1007/b104899_7
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. https://doi.org/10.2307/30036540
Wang, L. (2024). China Payment and Clearing Association: More than 80% of users use mobile payment every day. WWW.XHBY.NET. Retrieved Jun 25 from https://www.xhby.net/content/s66225343e4b01424a8875497.html
Westbrook, R. A., & Oliver, R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction. Journal of Consumer Research, 18(1), 84-91. https://doi.org/10.1086/209243
Wu, X., Zhou, Z., & Chen, S. (2024). A mixed-methods investigation of the factors affecting the use of facial recognition as a threatening AI application. Internet Research. https://doi.org/10.1108/INTR-11-2022-0894
Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems, 12(12), 1. https://doi.org/10.17705/1jais.00281
Yan, Z., Yan, H., & Chen, Y. (2020). Who’s selling my face?. Retrieved Oct from https://news.cctv.com/2020/07/14/ARTIIcp7s37vUIBn3Dx93WOO200714.shtml
Yeung, D., Balebako, R., Gaviria, C. I. G., & Chaykowsky, M. (2020). Face recognition technologies: Designing systems that protect privacy and prevent bias. Rand Corporation.
Yuan, S., Liu, Y., Yao, R., & Liu, J. (2016). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20-34.
Zhang, W. K., & Kang, M. J. (2019). Factors affecting the use of facial-recognition payment: an example of Chinese consumers. IEEE Access, 7, 154360-154374. https://doi.org/10.1109/ACCESS.2019.2927705
Zhong, Y., & Moon, H.-C. (2022). Investigating customer behavior of using contactless payment in China: A comparative study of facial recognition payment and mobile QR-code payment. Sustainability, 14(12), 7150. https://doi.org/10.3390/su14127150

Fei, Z., & Kassim, N. M. (2026). Understanding Continuance Intention toward Facial Recognition Payment: An Integration of ECM and APCO Frameworks. International Journal of Academic Research in Business and Social Sciences, 16(4), 256–281.

Copyright: © 2026 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode