Privacy and Trust in AI Personalization Strategy: A Systematic Literature Review

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Submitted: Apr 16, 2025
Final Revised: May 28, 2025
Accepted: Jun 06, 2025
Published: Jun 30, 2025
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Volumes: Vol. 3 No. 1 (2025) Pages 1-21
DOI: 10.63208/210113-172

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Abstract

This Systematic Literature Review (SLR) investigates managerial strategies to address the paradox between AI personalization, consumer privacy, and algorithmic trust. This study directly follows up on the research agenda identified by (Athaide et al., 2024) focusing exclusively on the synthesis of solutions. Using a rigorous PRISMA protocol, 57 core journal articles were selected from Scopus and IEEE Xplore, with 84.2% published between 2024-2026. Analysis found that the solution landscape is fragmented, but the most coherent strategies focus on "Governance & Regulation" and "Transparency & Control." The main contribution of this SLR is the Strategic Prioritization Framework, supported by the empirical finding that user control/agency can be a stronger driver of adoption than trust itself. We conclude that ethical personalization strategies must prioritize user control and XAI as foundations for building trust.

Keywords: AI Personalization Algorithmic Trust Federated Learning Marketing Ethics

References

Abuhamdeh, M., Qtaish, O., Kanaker, H., Alshanty, A., Yousef, N., & Alali, A. M. F. (2023). Leveraging Big Data and AI in Mobile Shopping: A Study in the Context of Jordan. International Journal of Advanced Computer Science and Applications, 14(7). https://doi.org/10.14569/ijacsa.2023.0140725.

Ali, W., Kumar, R., Zhou, X., & Shao, J. (2023). Responsible Recommendation Services with Blockchain Empowered Asynchronous Federated Learning. ACM Transactions on Intelligent Systems and Technology, 15(4), 1. https://doi.org/10.1145/3633520.

Ali, W., Zhou, X., & Shao, J. (2024). Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain [Review of Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain]. ACM Computing Surveys, 57(5), 1. Association for Computing Machinery. https://doi.org/10.1145/3708982.

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. Journal of Logistics, Informatics and Service Science, 12(5), 271-288. 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. 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. 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.

Badsha, S., Yi, X., Khalil, I., Liu, D., Nepal‬, Surya, & Lam, K. (2018). Privacy Preserving User Based Web Service Recommendations. IEEE Access, 6, 56647. https://doi.org/10.1109/access.2018.2871447.

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.

Belavady, B., & Donavalli, H. (2025). Privacy-Aware ML Framework for Dynamic Query Formation in Multi-Dimensional Data. International Journal of Advanced Computer Science and Applications, 16(9). https://doi.org/10.14569/ijacsa.2025.0160947.

Bhatnagr, P. (2025). Virtual influencers on Instagram: a text mining study of consumer sentiments in China. Journal of Modelling in Management. https://doi.org/10.1108/jm2-01-2025-0024.

Butt, M. A., Qayyum, A., Ali, H., Al‐Fuqaha, A., & Qadir, J. (2022). Towards secure private and trustworthy human-centric embedded machine learning: An emotion-aware facial recognition case study. Computers & Security, 125, 103058. https://doi.org/10.1016/j.cose.2022.103058.

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. (2023). Federated Learning Empowered Recommendation Model for Financial Consumer Services. IEEE Transactions on Consumer Electronics, 70(1), 2508. https://doi.org/10.1109/tce.2023.3339702.

Chen, Z., Piao, J., Lan, X., Cao, H., Gao, C., Lu, Z., & Li, Y. (2022). Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design. Proceedings of the ACM on Human-Computer Interaction, 6, 1. https://doi.org/10.1145/3555646.

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.

Dienlin, T., & Trepte, S. (2015). Is the privacy paradox a relic of the past? An in-depth analysis of privacy attitudes and privacy behaviors. European Journal of Social Psychology, 45(3), 285-297. https://doi.org/10.1002/ejsp.2049.

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.

Eid, M. A. H., Hashesh, M. A., Sharabati, A. A., Khraiwish, A., Al-Haddad, S., & Abusaimeh, H. (2024). Conceptualizing ethical AI-enabled marketing: Current state and agenda for future research. International Journal of Data and Network Science, 8(4), 2291. https://doi.org/10.5267/j.ijdns.2024.6.002.

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., Chen, C., Wang, C., & Huang, J. (2023). On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2307.16680.

Fiaz, F., Sajjad, S. M., Iqbal, Z., Yousaf, M. N., & Muhammad, Z. (2024). MetaSSI: A Framework for Personal Data Protection, Enhanced Cybersecurity and Privacy in Metaverse Virtual Reality Platforms. Future Internet, 16(5), 176. https://doi.org/10.3390/fi16050176.

Gaidhani, Y., Ramesh, J. V. N., Singh, S., Dagar, R., Rao, T. S. M., Godla, S. R., & El–Ebiary, Y. A. B. (2025). AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making. International Journal of Advanced Computer Science and Applications, 16(4). https://doi.org/10.14569/ijacsa.2025.0160456.

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.

Gromova, E. A., Ferreira, D. B., & Begishev, I. R. (2023). ChatGPT and Other Intelligent Chatbots: Legal, Ethical and Dispute Resolution Concerns. https://doi.org/10.52028/rbadr.v5i10.ART07.RU.

Guha, N., Lawrence, C. M., Gailmard, L. A., Rodolfa, K. T., Surani, F., Bommasani, R., Raji, I. D., Cuéllar, M.-F., Honigsberg, C., Liang, P., & Ho, D. E. (n.d.). AI Regulation Has Its Own Alignment Problem: The Technical and Institutional Feasibility of Disclosure, Registration, Licensing, and Auditing.

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.

Ito‐Jaeger, S., Lane, G., Dowthwaite, L., Webb, H., Patel, M., Rawsthorne, M., Portillo, V., Jirotka, M., & Vallejos, E. P. (2023). TrustScapes: A Visualisation Tool to Capture Stakeholders’ Concerns and Recommendations About Data Protection, Algorithmic Bias, and Online Safety. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231186965.

Jency, I. S. M., & Kumar, R. (2025). FinQuaXBot: enhancing trust and security in personalized investment and tax forecasting using homomorphic encryption and meta-reinforcement learning with explainability. Expert Systems with Applications, 287, 128136. https://doi.org/10.1016/j.eswa.2025.128136.

Kabir, S., Hossain, M. S., & Andersson, K. (2025). A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities [Review of A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities]. Algorithms, 18(9), 556. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/a18090556.

Kehat, R., Hirschprung, R. S., & Alkoby, S. (2024). Enhancing User Acceptance of an AI Agent’s Recommendation in Information-Sharing Environments. Applied Sciences, 14(17), 7874. https://doi.org/10.3390/app14177874.

Kim, J. S., Kim, J. W., & Chung, Y. D. (2021). Successive Point-of-Interest Recommendation With Local Differential Privacy. IEEE Access, 9, 66371. https://doi.org/10.1109/access.2021.3076809.

Lee, H., & Kobsa, A. (2019). Confident Privacy Decision-Making in IoT Environments. ACM Transactions on Computer-Human Interaction, 27(1), 1. https://doi.org/10.1145/3364223

Li, Y., Tan, Z., & Liu, Y. (2023). Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Services. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2305.06212.

Lim, S., & Kim, M. (2025). AI-powered personalized recommendations and pricing: Moderating effects of ethical AI and consumer empowerment. International Journal of Hospitality Management, 130, 104259. https://doi.org/10.1016/j.ijhm.2025.104259

Liu, A., Wang, W., Li, Z., Liu, G., Li, Q., Zhou, X., & Zhang, X. (2017). A Privacy-Preserving Framework for Trust-Oriented Point-of-Interest Recommendation. IEEE Access, 6, 393. https://doi.org/10.1109/access.2017.2765317.

Lu, W., Zhang, J., Li, H., Li, C., & Su, Y.-J. (2025). The Influence of AI-Driven Personalization in Social Media Marketing on Consumer Purchase Decisions and Behavior. International Journal of Accounting and Economics Studies, 12(5), 438. https://doi.org/10.14419/dcggbj32.

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.

Moshed, A., & Al-Jabaly, S. M. (2024). Enhancing marketing success in Jordanian telecom: Strategic IoT integration and brand relationship management for maximized consumer loyalty. Journal of Infrastructure Policy and Development, 8(6), 3858. https://doi.org/10.24294/jipd.v8i6.3858.

Nikiforov, L. (2024). Groups of Persons in the Proposed AI Act Amendments. European Journal of Risk Regulation, 15(3), 757. 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. Penguin Press. ISBN: 978-1594203008.

Reddi, V. J. (2025). Generative AI at the Edge. https://doi.org/10.1145/3733702.

Renda, A., Ducange, P., Marcelloni, F., Sabella, D., Filippou, M. C., Nardini, G., Stea, G., Virdis, A., Micheli, D., Rapone, D., & Baltar, L. G. (2022). Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking. Information, 13(8), 395. https://doi.org/10.3390/info13080395.

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.

Reusens, M., & Baesensa, B. (2025). LLM Anthropomorphization: Balancing Ethics and Business Value.

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.

Sheth, A., Gaur, M., Roy, K., & Faldu, K. (2021). Knowledge-Intensive Language Understanding for Explainable AI. IEEE Internet Computing, 25(5), 19. https://doi.org/10.1109/mic.2021.3101919.

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.

Sun, Y., Freeman, J., Shoenberger, H., & Shen, F. (2025). To Tell or Not to Tell: Investigating the Persuasive Appeal of Information Transparency for AR-Powered E-Commerce Sites. International Journal of Human-Computer Interaction, 1. https://doi.org/10.1080/10447318.2025.2495847.

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.

Tran, M. T. (2025). Advancing retail and service strategies: AI-driven consumer behavior prediction, gamification, and ethical marketing. Journal of Retailing and Consumer Services, 88, 104558. https://doi.org/10.1016/j.jretconser.2025.104558.

Virvou, M. (2023). Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art. Intelligent Decision Technologies, 17(1), 73. https://doi.org/10.3233/idt-230092.

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, J., Qiao, K., & Zhang, Z. (2018). Trust evaluation based on evidence theory in online social networks. International Journal of Distributed Sensor Networks, 14(10). https://doi.org/10.1177/1550147718794629.

Wang, J., Tang, H., Man, S. S., Chen, Y., Zhou, S., & Chan, A. H. S. (2025). Critical Factors in Young People’s Use and Non-Use of AI Technology for Emotion Regulation: A Pilot Study. Applied Sciences, 15(13), 7476. https://doi.org/10.3390/app15137476.

Wang, S., Chen, Z., Xiao, Y., & Lin, C.-Y. (2021). Consumer Privacy Protection With the Growth of AI-Empowered Online Shopping Based on the Evolutionary Game Model. Frontiers in Public Health, 9, 705777. https://doi.org/10.3389/fpubh.2021.705777.

Wang, Y. (2023). TRUSTWORTHY AI AND DATA LINEAGE Balancing Trustworthiness and Efficiency in Artificial Intelligence Systems: An Analysis of Tradeoffs and Strategies. https://doi.org/10.1109/MIC.2023.3303031.

WANG, Y., XU, Y., & ZHONG, Y. (2025). Does Artificial Intelligence Invariably Enhance ESG Performance?

Wu, H., Li, M., & Zhang, H. (2019). Enabling Smart Anonymity Scheme for Security Collaborative Enhancement in Location-Based Services. IEEE Access, 7, 50031. https://doi.org/10.1109/access.2019.2911107.

Wu, W., Huang, Y., & Qian, L. (2023). Social trust and algorithmic equity: The societal perspectives of users’ intention to interact with algorithm recommendation systems. Decision Support Systems, 178, 114115. https://doi.org/10.1016/j.dss.2023.114115.

Xia, J., Yang, Y., Wang, S., Yin, H., Cao, J., & Yu, P. S. (2023). Bayes-Enhanced Multi-View Attention Networks for Robust POI Recommendation. IEEE Transactions on Knowledge and Data Engineering, 36(7), 2895. https://doi.org/10.1109/tkde.2023.3329673.

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.

Yan, W., Li, C., Huang, Y., & Yang, L. (2019). Low-Complexity Decentralized Recommendation System With Similarity Constraints. IEEE Access, 7, 146922. https://doi.org/10.1109/access.2019.2946485.

Zhang, H., Wu, B. Y., Yuan, X., Pan, S., Tong, H., & Pei, J. (2024). Trustworthy Graph Neural Networks: Aspects, Methods, and Trends. Proceedings of the IEEE, 112(2), 97. https://doi.org/10.1109/jproc.2024.3369017.

Ziller, C., Loepp, B., Kindermann, B., Köchling, G., & Fadeeva, Y. (2025). Willingness to share personal data online: The role of social influence and sustainability. Technology in Society, 83, 102974. https://doi.org/10.1016/j.techsoc.2025.102974.

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