The Dual Role of Self-Efficacy and Perceived Privacy Risk in Shaping Consumer Responses to Recommendation Algorithms_Vol. 3 No. 1 (JMSD 2026)_Journal of Management and Social Development (ISSN: 3005-5741)

Home > Journal of Management and Social Development (ISSN: 3005-5741) > Vol. 3 No. 1 (JMSD 2026) >

The Dual Role of Self-Efficacy and Perceived Privacy Risk in Shaping Consumer Responses to Recommendation Algorithms

Download PDF

DOI: https://doi.org/10.62517/jmsd.202612130

Author(s)

Hui Shi1,2, Li Wang1,2

Affiliation(s)

1Guangzhou Huashang College, Guangzhou, Guangdong, China 2Graduate University of Mongolia, Ulaanbaatar, Mongolia

Abstract

Recommendation algorithms have become a routine part of digital platforms, especially in e-commerce and online information environments. Much of the existing literature explains their adoption in terms of technical quality and system performance. Yet users do not respond to these systems on technical grounds alone. Their reactions are also shaped by whether they feel able to use algorithmic features effectively and whether the underlying personalization process feels acceptable from a privacy standpoint. This study examines how self-efficacy and perceived privacy risk jointly shape consumer responses to recommendation algorithms. Drawing on the Technology Acceptance Model, Task-Technology Fit theory, Social Cognitive Theory, and prior privacy research, the paper develops a dual capability-risk framework and tests it using survey data from 515 valid respondents. The results indicate that self-efficacy is positively associated with perceived ease of use, perceived usefulness, satisfaction, and consumer response, whereas perceived privacy risk is significantly related to satisfaction and consumer response. The findings suggest that recommendation systems are more likely to be accepted when users both feel capable of using them and feel reasonably comfortable with the way personal data are involved.

Keywords

Recommendation Algorithms; Self-efficacy; Perceived Privacy Risk; Consumer Response; Perceived Usefulness; Perceived Ease of Use; Satisfaction

References

[1] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. https://doi.org/10.1109/TKDE.2005.99 [2] Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly, 30(1), 13-28. https://doi.org/10.2307/25148715 [3] Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. https://doi.org/10.1037/0033-295X.84.2.191 [4] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008 [5] Dinev, T., & Hart, P. (2006). Privacy concerns and levels of information exchange: An empirical investigation of intended e-services use. e-Service Journal, 4(3), 25-59. https://doi.org/10.1353/esj.2007.0003 [6] Li, Y., Deng, X., Hu, X., & Liu, J. (2024). The effects of e-commerce recommendation system transparency on consumer trust: Exploring parallel multiple mediators and a moderator. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 2630-2649. https://doi.org/10.3390/jtaer19040126 [7] Wang, Y., Zhu, J., Liu, R., & Jiang, Y. (2024). Enhancing recommendation acceptance: Resolving the personalization-privacy paradox in recommender systems: A privacy calculus perspective. International Journal of Information Management, 76, 102755. https://doi.org/10.1016/j.ijinfomgt.2024.102755 [8] Park, K., & Yoon, H. Y. (2024). Beyond the code: The impact of AI algorithm transparency signaling on user trust and relational satisfaction. Public Relations Review, 50(5), 102507. https://doi.org/10.1016/j.pubrev.2024.102507 [9] Ojokoh, B. A., Isinkaye, F. O., Zhang, M., Tom, J. J., Gabriel, A. J., Afolabi, O., & Afolabi, B. (2025). Privacy and security in recommenders: An analytical review. Artificial Intelligence Review, 58, 351. https://doi.org/10.1007/s10462-025-11333-4 [10] Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19(1), 4-21. https://doi.org/10.1287/mksc.19.1.4.15178 [11]Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. https://doi.org/10.2307/249689 [12]Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. https://doi.org/10.1080/10864415.2003.11044275.