Privacy and Trust in AI Personalization Strategy: A Systematic Literature Review
DOI:
https://doi.org/10.63208/210113-538Keywords:
AI Personalization, Algorithmic Trust, Federated Learning, Marketing EthicsAbstract
The rapid adoption of artificial intelligence (AI) has enabled increasingly sophisticated personalization strategies while raising concerns about consumer privacy and trust. This study examines managerial strategies for balancing AI personalization effectiveness with privacy protection and consumer trust. A Systematic Literature Review (SLR) was conducted using the PRISMA 2020 framework to analyze 57 peer-reviewed journal articles published between 2014 and 2024 and retrieved from Scopus and IEEE Xplore. The findings reveal that managing AI personalization requires an integrated socio-technical approach involving three interconnected dimensions: Governance and Regulatory Strategies, Transparency and Control Strategies, and Technical Strategies. Governance and regulatory mechanisms establish institutional requirements for privacy, ethics, and accountability, while transparency and control strengthen consumer understanding and agency. Technical strategies, including privacy-preserving mechanisms, support the practical implementation of responsible personalization. The review further identifies an Ideas Evidence Gap, reflecting limited empirical validation of proposed strategies, and a Diversity Gap caused by limited representation of non-Western contexts. These findings highlight the importance of integrating privacy, transparency, user agency, and responsible governance into sustainable AI personalization strategies.
References
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
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/access.2018.2870052
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
Ali, W., Kumar, R., Deng, Z., Wang, Y., & Shao, J. (2021). A federated learning approach for privacy protection in context-aware recommender systems. The Computer Journal, 64(7), 1016-1027. https://doi.org/10.1093/comjnl/bxab025
Alibeigi, A. (2025). Bridging the gap: Assessing india's digital personal data protection act in light of the EU GDPR. SN Computer Science, 6(7). https://doi.org/10.1007/s42979-025-04269-7
Aljazzaf, Z., Rashed, A., Qureshi, K., & Manuel, P. (2025). Understanding barriers to customer engagement center adoption in digital service systems: Evidence from Kuwait. In Journal of Logistics, Informatics and Service Science. Hong Kong Success Culture Press. https://doi.org/10.33168/jliss.2025.0515
Anshari, M., Hamdan, M., Ahmad, N., Ali, E., & Haidi, H. (2022). COVID-19, artificial intelligence, ethical challenges and policy implications. AI & SOCIETY, 38(2), 707-720. https://doi.org/10.1007/s00146-022-01471-6
Athaide, G. A., Jeon, J., Raj, S. P., Sivakumar, K., & Xiong, G. (2024). Marketing innovations and digital technologies: A systematic review, proposed framework, and future research agenda. Journal of Product Innovation Management, 42(1), 144-165. https://doi.org/10.1111/jpim.12741
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
Bandara, R., Fernando, M., & Akter, S. (2019). Privacy concerns in e-commerce: A taxonomy and a future research agenda. Electronic Markets, 30(3), 629-647. https://doi.org/10.1007/s12525-019-00375-6
Bandara, R., Fernando, M., & Akter, S. (2020). Managing consumer privacy concerns and defensive behaviours in the digital marketplace. European Journal of Marketing, 55(1), 219-246. https://doi.org/10.1108/ejm-06-2019-0515
Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671-732. https://doi.org/10.15779/Z38BG31
Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review. Telematics and Informatics, 34(7), 1038-1058. https://doi.org/10.1016/j.tele.2017.04.013
Benlian, A. (2015). Web personalization cues and their differential effects on user assessments of website value. Journal of Management Information Systems, 32(1), 225-260. https://doi.org/10.1080/07421222.2015.1029394
Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390-409. https://doi.org/10.1016/j.jretai.2015.04.001
Bol, N., Dienlin, T., Kruikemeier, S., Sax, M., Boerman, S. C., Strycharz, J., ... & De Vreese, C. H. (2018). Understanding the effects of personalization as a privacy calculus: Analyzing self-disclosure across health, news, and commerce contexts. Journal of Computer-Mediated Communication, 23(6), 370-388. https://doi.org/10.1093/jcmc/zmy020
Chan‐Olmsted, S. M., Chen, H., & Kim, J. (2024). In smartness we trust: consumer experience, smart device personalization and privacy balance. Journal of Consumer Marketing, 41(6), 597. https://doi.org/10.1108/jcm-12-2021-5072
Chatterjee, P., Das, D., & Rawat, D. B. (2024). Federated learning empowered recommendation model for financial consumer services. IEEE Transactions on Consumer Electronics, 70(1), 2508-2516. https://doi.org/10.1109/tce.2023.3339702
Chen, C., Tian, A. D., & Jiang, R. (2023). When post hoc explanation knocks: consumer responses to explainable AI recommendations. Journal of Interactive Marketing, 59(3), 234-250. https://doi.org/10.1177/10949968231200221
Chen, X., Sun, J., & Liu, H. (2022). Balancing web personalization and consumer privacy concerns: Mechanisms of consumer trust and reactance. Journal of Consumer Behaviour, 21(3), 572-582. https://doi.org/10.1002/cb.1947
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.
Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., & Zanzonelli, D. (2023). Fairness in recommender systems: Research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1), 59-108. https://doi.org/10.1007/s11257-023-09364-z
Dikmen, M., & Burns, C. (2022). The effects of domain knowledge on trust in explainable AI and task performance: A case of peer-to-peer lending. International Journal of Human-Computer Studies, 162, 102792. https://doi.org/10.1016/j.ijhcs.2022.102792
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
Du, Y., Zhou, D., Xie, Y., Shi, J., & Gong, M. (2021). Federated matrix factorization for privacy-preserving recommender systems. Applied Soft Computing, 111, 107700. https://doi.org/10.1016/j.asoc.2021.107700
Eke, C. I., Norman, A. A., Shuib, L., & Nweke, H. F. (2019). A survey of user profiling: State-of-the-art, challenges, and solutions. IEEE Access, 7, 144907. https://doi.org/10.1109/access.2019.2944243
Fan, M., Wang, C., Chen, C., Liu, Y., & Huang, J. (2025). On the trustworthiness landscape of state-of-the-art generative models: A survey and outlook. International Journal of Computer Vision, 133(7), 4317-4348. https://doi.org/10.1007/s11263-025-02375-w
Finck, M., & Pallas, F. (2020). They who must not be identified—distinguishing personal from non-personal data under the GDPR. International Data Privacy Law, 10(1), 11-36. https://doi.org/10.1093/idpl/ipz026
Galandarli, A. (2025). Mitigating AI risks: A comparative analysis of Data Protection Impact Assessments under GDPR and KVKK. Journal of Data Protection & Privacy., 7(3), 252. https://doi.org/10.69554/attt2755
Gironda, J. T., & Korgaonkar, P. K. (2018). iSpy? Tailored versus invasive ads and consumers’ perceptions of personalized advertising. Electronic Commerce Research and Applications, 29, 64-77. https://doi.org/10.1016/j.elerap.2018.03.007
Haque, A. B., Islam, A. K. M. N., & Mikalef, P. (2024). To explain or not to explain: An empirical investigation of AI-based recommendations on social media platforms. Electronic Markets, 35(1). https://doi.org/10.1007/s12525-024-00741-z
Jiang, J., Kahai, S., & Yang, M. (2022). Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty. International Journal of Human-Computer Studies, 165, 102839. https://doi.org/10.1016/j.ijhcs.2022.102839
Kaaniche, N., Laurent, M., & Belguith, S. (2020). Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey. Journal of Network and Computer Applications, 171, 102807. https://doi.org/10.1016/j.jnca.2020.102807
Martin, K. D., & Murphy, P. E. (2016). 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
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734. https://doi.org/10.2307/258792
Monzer, C., Moeller, J., Helberger, N., & Eskens, S. (2020). User perspectives on the news personalisation process: Agency, trust and utility as building blocks. Digital Journalism, 8(9), 1142. https://doi.org/10.1080/21670811.2020.1773291
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2021). Explainable recommendation: when design meets trust calibration. World Wide Web, 24(5), 1857-1884. https://doi.org/10.1007/s11280-021-00916-0
Nikiforov, L. (2024). Groups of persons in the proposed AI Act amendments. European Journal of Risk Regulation, 15(3), 757–771. https://doi.org/10.1017/err.2024.13
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T., Mulrow, C. D., Shamseer, L., Tetzlaff, J., Akl, E. A., Brennan, S., Chou, R., Glanville, J., Grimshaw, J., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E., Mayo‐Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372. https://doi.org/10.1136/bmj.n71
Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. New York: Penguin Press.
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393-404. https://doi.org/10.2307/259285
Schaefer, K. E., Chen, J. Y., Szalma, J. L., & Hancock, P. A. (2016). A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems. Human Factors, 58(3), 377-400. https://doi.org/10.1177/0018720816634228
Seymour, W., & Such, J. M. (2022). Ignorance is Bliss? The Effect of Explanations on Perceptions of Voice Assistants. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2211.12900
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Silalahi, A. D. K. (2025). Can generative artificial intelligence drive sustainable behavior? A consumer-adoption model for AI-driven sustainability recommendations. Technology in Society, 83, 102995. https://doi.org/10.1016/j.techsoc.2025.102995
Swani, K., & Milne, G. R. (2022). Impact of unmet privacy expectations across information, time, and space: evidence from four countries. Journal of Interactive Marketing, 57(2), 278-295.
Tabaghdehi, S. A. H., & Arda, Ö. A. (2025). AI ethics in action: a circular model for transparency, accountability and inclusivity. Journal of Managerial Psychology. https://doi.org/10.1108/jmp-03-2024-0177
Vassøy, B., & Langseth, H. (2024). Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation: B. Vassøy, H. Langseth. Artificial Intelligence Review, 57(4), 101. https://doi.org/10.1007/s10462-023-10663-5
Wahab, O. A., Rjoub, G., Bentahar, J., & Cohen, R. (2022). Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems. Information Sciences, 601, 189. https://doi.org/10.1016/j.ins.2022.04.027
Wang, C., Zheng, Y., Jiang, J., & Ren, K. (2018). Toward privacy-preserving personalized recommendation services. Engineering, 4(1), 21-28. https://doi.org/10.1016/j.eng.2018.02.005
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
Wu, W., Huang, Y., & Qian, L. (2024). Social trust and algorithmic equity: The societal perspectives of users' intention to interact with algorithm recommendation systems. Decision Support Systems. https://doi.org/10.1016/j.dss.2023.114115
Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42-52. https://doi.org/10.1016/j.dss.2010.11.017
Zeng, F., Ye, Q., Li, J., & Yang, Z. (2021). Does self-disclosure matter? A dynamic two-stage perspective for the personalization-privacy paradox. Journal of Business Research, 124, 667-675. https://doi.org/10.1016/j.jbusres.2020.02.006
Zhang, Y., & Chen, X. (2020). Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1), 1-101. https://doi.org/10.1561/1500000066
Zhu, H., Ou, C. X., van den Heuvel, W. J. A., & Liu, H. (2017). Privacy calculus and its utility for personalization services in e-commerce: An analysis of consumer decision-making. Information & Management, 54(4), 427-437. https://doi.org/10.1016/j.im.2016.10.001
Zhu, Y. Q., & Chang, J. H. (2016). The key role of relevance in personalized advertisement: Examining its impact on perceptions of privacy invasion, self-awareness, and continuous use intentions. Computers in Human Behavior, 65, 442-447. https://doi.org/10.1016/j.chb.2016.08.048
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