AI-Driven Marketing Personalization and the Consumer Privacy Paradox A Systematic Literature Review of Trust, Engagement, and Purchase Behavior
Article Sidebar
Main Article Content
Abstract
This study aims to synthesize the development of research on AI-driven marketing personalization and the consumer privacy paradox, with particular attention to trust, customer engagement, and purchase behavior. A qualitative systematic literature review was conducted by examining peer-reviewed studies published in major academic databases, including Scopus, Web of Science, ScienceDirect, Emerald Insight, SpringerLink, and Google Scholar. The selected literature was analyzed through thematic synthesis to identify recurring concepts, theoretical perspectives, empirical relationships, and research gaps. The findings indicate that AI-driven personalization improves perceived relevance, usefulness, convenience, and customer experience, but its effectiveness is conditional on consumers’ evaluations of privacy risk, transparency, fairness, and control over personal data. Trust emerged as the central mechanism that reconciles the benefits of personalization with concerns regarding surveillance, data misuse, and algorithmic opacity. The review also shows that customer engagement functions as an important link between trust and purchase-related outcomes, including purchase intention, continuance, loyalty, and advocacy. The principal finding is that personalization does not automatically generate favorable consumer behavior. Its impact depends on ethical data governance, explainable recommendations, privacy assurance, and consumers’ perceptions of organizational responsibility. The study proposes an integrated framework connecting personalization, privacy concern, trust, engagement, and purchase behavior and recommends longitudinal, cross-cultural, experimental, and behavioral research.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Abrokwah-Larbi, K., & Awuku-Larbi, Y. (2023). The impact of artificial intelligence in marketing on the performance of business organizations: Evidence from SMEs in an emerging economy. Journal of Entrepreneurship in Emerging Economies, 16(4), 1090–1117. https://doi.org/10.1108/JEEE-07-2022-0207
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49. https://doi.org/10.1016/j.jretai.2014.09.005
Aguirre, E., Roggeveen, A. L., Grewal, D., & Wetzels, M. (2016). The personalization–privacy paradox: Implications for new media. Journal of Consumer Marketing, 33(2), 98–110. https://doi.org/10.1108/JCM-06-2015-1458
Ameen, N., Hosany, S., & Paul, J. (2022). The personalisation–privacy paradox: Consumer interaction with smart technologies and shopping mall loyalty. Computers in Human Behavior, 126, 106976. https://doi.org/10.1016/j.chb.2021.106976
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
Baek, T. H., & Morimoto, M. (2012). Stay away from me: Examining the determinants of consumer avoidance of personalized advertising. Journal of Advertising, 41(1), 59–76. https://doi.org/10.2753/JOA0091-3367410105
Barth, S., & de Jong, M. D. T. (2017). The privacy paradox: Investigating discrepancies between expressed privacy concerns and actual online behavior. Telematics and Informatics, 34(7), 1038–1058. https://doi.org/10.1016/j.tele.2017.04.013
Bleier, A., & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when, and where. Marketing Science, 34(5), 669–688. https://doi.org/10.1287/mksc.2015.0930
Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252–271. https://doi.org/10.1177/1094670511411703
Canhoto, A. I., Keegan, B. J., & Ryzhikh, M. (2024). Snakes and ladders: Unpacking the personalisation–privacy paradox in the context of AI-enabled personalisation in the physical retail environment. Information Systems Frontiers, 26, 1005–1024. https://doi.org/10.1007/s10796-023-10369-7
Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104–115. https://doi.org/10.1287/orsc.10.1.104
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28(2), 149–165. https://doi.org/10.1016/j.intmar.2013.12.002
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Huang, M. H., & Rust, R. T. (2021a). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Huang, M. H., & Rust, R. T. (2021b). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266
Kshetri, N. (2024). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 75, 102716. https://doi.org/10.1016/j.ijinfomgt.2023.102716
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Lim, W. M., Rasul, T., Kumar, S., & Ala, M. (2022). Past, present, and future of customer engagement. Journal of Business Research, 140, 439–458. https://doi.org/10.1016/j.jbusres.2021.11.014
Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776. https://doi.org/10.1002/mar.21619
Martin, K. D., & Murphy, P. E. (2017a). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. https://doi.org/10.1007/s11747-016-0495-4
Martin, K. D., & Murphy, P. E. (2017b). The role of data privacy in marketing ethics. Journal of Business Ethics, 143(1), 43–55. https://doi.org/10.1007/s10551-015-2782-3
Massara, F., Raggiotto, F., & Voss, W. G. (2021). Unpacking the privacy paradox of consumers: A psychological perspective. Psychology & Marketing, 38(10), 1814–1827. https://doi.org/10.1002/mar.21524
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.1177/002224299405800302
Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs, 41(1), 100–126. https://doi.org/10.1111/j.1745-6606.2006.00070.x
Okazaki, S., Li, H., & Hirose, M. (2009). Consumer privacy concerns and preference for degree of regulatory control. Journal of Advertising, 38(4), 63–77. https://doi.org/10.2753/JOA0091-3367380405
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2022). Assessing behavioral data science privacy issues in government artificial intelligence deployment. Government Information Quarterly, 39(4), 101679. https://doi.org/10.1016/j.giq.2022.101679
Sun, H., Zhang, J., & Liu, Y. (2025). The inverted U-shaped effect of personalization on consumer responses: The roles of privacy concern and avoidance motivation. Journal of Advertising, 54, 1–20. https://doi.org/10.1080/00218499.2025.2462405