Investigating Factors Influencing Switching from Third-Party Payment Applications to E-CNY in Shanghai
Abstract
Background and Aim: As digital payments become more prevalent, users are shifting from third-party payment platforms (WeChat Pay, Alipay) to E-CNY, China’s central bank digital currency. However, despite its government backing, E-CNY adoption remains limited. This study aims to examine user switching behavior by applying the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to identify the key factors influencing adoption.
Materials and Methods: A total of 308 valid respondents with experience using both E-CNY and third-party payment platforms participated in an online survey. The research was conducted in three stages: (1) a pilot study to test measurement reliability, (2) a descriptive analysis of user demographics, and (3) Structural Equation Modeling (SEM) to test the relationships between variables affecting switching behavior.
Results: The findings indicate that government influence and social influence have the strongest positive impact on switching behavior. Network externalities, perceived ease of use, and perceived usefulness also encourage adoption, while privacy concerns negatively affect switching behavior. Interestingly, trust does not significantly influence user decisions, suggesting that convenience and usability matter more than trust in digital payments.
Conclusion: To increase E-CNY adoption, policymakers should enhance usability, address privacy concerns, provide financial incentives, and expand merchant acceptance. Additionally, integrating E-CNY with existing payment platforms and leveraging peer influence will further encourage user switching.
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References
Andavara, V., Sundaram, B., Bacha, D., Dadi, T., & Karthika, P. (2021). The impact of perceived ease of use on intention to use mobile payment services for data security applications. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 1875-1880.
Bansal, H. S., Taylor, S. F., & St. James, Y. (2005). “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96-116.
Bartlett, M. S. (1951). The effect of standardization on a χ² approximation in factor analysis. Biometrika, 38(4), 337-344.
Bonett, D. G., & Wright, T. A. (2015). Cronbach’s alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1), 3-15.
Cao, X., Han, B., Huang, Y., & Xie, X. (2022). Digital transformation and risk differentiation in the banking industry: evidence from Chinese commercial banks. Asian Economic Papers, 21(3), 1-21.
Chen, X., & Li, S. (2017). Understanding continuance intention of mobile payment services: an empirical study. Journal of Computer Information Systems, 57(4), 287-298.
CNNIC. (2022). China Internet Network Information Center. Retrieved from Statistical report on Internet development in China: https://www.cnnic.net.cn/
Das, M. M., Griffoli, M. M., Nakamura, F., Otten, M. J., Soderberg, G., Sole, M. J., & Tan, B. (2023). Implications of central bank digital currencies for monetary policy transmission (Vol. 9). International Monetary Fund. https://www.imf.org/en/Publications/fintech-notes/Issues/2023/09/15/Implications-of-Central-Bank-Digital-Currencies-for-Monetary-Policy-Transmission-538517
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/https://doi.org/10.1177/002224378101800104
Hair, J. K., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/https://doi.org/10.1007/s11747-014-0403-8
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/https://doi.org/10.1080/10705519909540118
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
Karina, Y. (2024). Factors that influence the intention of the use of eCNY among university students in China. Theoretical Economics Letters, 14(3), 1000-1034.
Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424-440.
Kharpal, A. (2021). According to the former central bank governor, China’s digital yuan is not aimed at displacing the dollar. Retrieved from CNBC: https://www.cnbc.com/2021/04/19/china-says-its-digital-yuan-is-not-aimed-at-displacing-dollar.html
Kharpal, A. (2023). China releases rules for generative AI like ChatGPT after Alibaba, and Baidu launch services. CNBC, 11 April. Retrieved from: https://www.cnbc.com/2023/04/11/china-releases-rules-for-generative-ai-like-chatgpt-afteralibaba-launch.html
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Li, Y., Wareewanich, T., & Chankoson, T. (2024). A Study on Influencing Factors of Willingness to Use E-CNY Based on Logistic Model. International Journal of Interactive Mobile Technologies, 18(4).
Model, A. I. (2003). Trust and tam in online shopping: An integrated model1. MIS Quarterly, 27(1), 51-90.
Naderifar, M., Goli, H., & Ghaljaie, F. (2017). Snowball sampling: A purposeful method of sampling in qualitative research. Strides in development of medical education, 14(3).
Padashetty, S., & Kishore, K. S. (2013). An empirical study on consumer adoption of mobile payments in Bangalore city case study. Researchers World, 4(1), 83.
Qasim, H., & Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers, 18, 1021-1034.
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
Sinha, M., Majra, H., Hutchins, J., & Saxena, R. (2019). Mobile payments in India: the privacy factor. International Journal of Bank Marketing, 37(1), 192-209.
Slotta, D. (2023). Annual transaction value of mobile payments in China from 2013 to 2023. Statista. Retrieved from https://www.statista.com/statistics/1060702/china-mobile-payment-transaction-value/#:~:text=In%202023%2C%20the%20total%20transaction,up%20from%20the%20previous%20year.
Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals' concerns about organizational practices. MIS Quarterly, 20(2), 167–196. https://doi.org/10.2307/249477
Song, X., & Wang, R. (2022). Research on Influencing Factors of Intention to Use E-CNY. 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022), 241-254.
Tronnier, F., & Qiu, W. (2024). How do privacy concerns impact the actual adoption of central bank digital currency? An investigation using the e-CNY in China. Quantitative Finance and Economics, 8(1), 126-152.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/https://doi.org/10.2307/30036540
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of black psychology, 44(3), 219-246.
Wu, G., Yang, J., & Hu, Q. (2022). Research on factors affecting people’s intention to use digital currency: Empirical evidence from China. Frontiers in Psychology, 13, 928735.
Zhou, J., Zhang, L., & Yin, L. (2025). Factors Influencing the Intention to Use ECNY and Its Driving Mechanisms: Based on the Theory of Planned Behavior and Technology Acceptance Model. International Theory and Practice in Humanities and Social Sciences, 2(1), 81-93.