SYSTEMATIC REVIEW OF CONSUMER RESISTANCE TO AI DRIVEN PERSONALIZATION

Muhammad Ali

doi:10.62207/r2vy5689

Authors

Muhammad Ali STIE Bhakti Pembangunan, Jakarta, Indonesia Author

DOI:

https://doi.org/10.62207/r2vy5689

Keywords:

AI Personalization, Consumer Resistance, Data Privacy, Autonomy, Algorithmic Anxiety

Abstract

The integration of artificial intelligence (AI) into marketing personalization strategies has created a paradox between service efficiency and consumer resistance. This study aims to synthesize the dominant psychological factors that trigger consumer resistance to AI-based personalization through a conceptual approach.narrative reviewBy analyzing literature from Scopus, Web of Science, and PsycINFO databases (2015–2025), this study identified three main clusters of resistance: cognitive antecedents (intrusiveness and privacy), affective antecedents (algorithmic anxiety and fear of manipulation), and threats to individual autonomy and agency. The synthesis of results shows that perceived vulnerability (79%) far outweighs perceived convenience benefits (62%), which is exacerbated by the “black box” nature of AI. The study concludes that mitigating resistance requires a transition from simply algorithmic accuracy to transparent and humanistic AI design. The theoretical contribution lies in the integrationCommunication Privacy Management Theory, Psychological Reactance Theory, and Social Cognitive Theory serves as a unified framework for understanding digital resistance. Practically, this study offers marketers guidance on building trust through empowering user control.

References

Agila, G. (2025). IMPACT OF AI-DRIVEN PERSONALIZED MARKETING ON CONSUMER BUYING BEHAVIOUR IN THE E-COMMERCE SECTOR. International Journal of Apllied Mathematics, 38(11s), 2240-2255. https://doi.org/10.12732/ijam.v38i11s.1323

al., S. (2023). Ethical Considerations in AI-Based Marketing: Balancing Profit and Consumer Trust.. tjjpt, 44(3), 1301-1309. https://doi.org/10.52783/tjjpt.v44.i3.474

AlJalaud, E. and Hosny, M. (2024). Counterfactual Explanation of AI Models Using an Adaptive Genetic Algorithm With Embedded Feature Weights. Ieee Access, 12, 74993-75009. https://doi.org/10.1109/access.2024.3404043

Antón, A., Earp, J., & Young, J. (2010). How internet users' privacy concerns have evolved since 2002. Ieee Security & Privacy, 8(1), 21-27. https://doi.org/10.1109/msp.2010.38

Babar, M. (2025). Understanding Consumer Trust in AI-Driven Marketing: A Qualitative Study on Psychological Perception and Brand Interaction. Research Journal for Social Affairs, 3(7), 41-47. https://doi.org/10.71317/rjsa.003.07.0579

Bauer, K., Zahn, M., & Hinz, O. (2021). Expl(Ai)Ned: The Impact of Explainable Artificial Intelligence on Cognitive Processes. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3872711

Beets, B., Newman, T., Howell, E., Bao, L., & Yang, S. (2023). Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review. Journal of Medical Internet Research, 25, e40337. https://doi.org/10.2196/40337

Bodorin, B. (2025). AI, Security, and Trust in the Digital Wallet: Evidence from Current Romanian FinTech Users. International Journal of Financial Studies, 14(1), 1. https://doi.org/10.3390/ijfs14010001

Boudi, A., Boudi, M., Chan, C., & Boudi, F. (2024). Ethical Challenges of Artificial Intelligence in Medicine. Cureus. https://doi.org/10.7759/cureus.74495

Buvaneswari, P. and Swetha, M. (2024). Ad Avoidance in Digital Advertising: The Impact of Privacy Disruption and Perceived Intrusiveness., 57-76. https://doi.org/10.2991/978-94-6463-433-4_6

Chang, S., Liu, A., & Lin, S. (2015). Exploring privacy and trust for employee monitoring. Industrial Management & Data Systems, 115(1), 88-106. https://doi.org/10.1108/imds-07-2014-0197

Chatterjee, S. and PRABHAKAR, R. (2025). The Two Sides Of AI: Exploring Consumer Perceptions of AI's Benefits and Drawbacks. International Journal for Multidisciplinary Research, 7(3). https://doi.org/10.36948/ijfmr.2025.v07i03.44166

Chen, L., & Wang, X. (2023). Navigating the privacy paradox: Consumer behavior in the age of algorithmic marketing. Journal of Digital Consumer Psychology, 15(2), 112–128.

Cooney-Waterhouse, T., Ou, W., Mukherji, S., Frytak, J., Saha, P., & Waterhouse, D. (2025). Bridging the Trust Gap in Artificial Intelligence for Health care: Lessons from Clinical Oncology. Ai in Precision Oncology, 2(3), 85-91. https://doi.org/10.1089/aipo.2025.0001

Cui, W. and Mohib, M. (2025). Exploring the Impact of AI on Consumer Behavior and Digital Marketing Through the Lens of Social Media. Concurrency and Computation Practice and Experience, 37(23-24). https://doi.org/10.1002/cpe.70284

Danish, F. (2024). Trust Lost, Ethics Found? Rethinking LinkedIn’s Algorithmic Marketing Practices. Journal of Ecohumanism, 4(1). https://doi.org/10.62754/joe.v4i1.6815

Fussey, W. Applying and Developing the Intrusion into Seclusion Tort in New Zealand.. https://doi.org/10.26686/wgtn.17013260.v1

Giroux, M., Kim, J., Lee, J., & Park, J. (2022). Artificial Intelligence and Declined Guilt: Retailing Morality Comparison Between Human and AI. Journal of Business Ethics, 178(4), 1027-1041. https://doi.org/10.1007/s10551-022-05056-7

Global Consumer AI Report. (2024). The state of AI and consumer trust: Annual industry insight. TechTrends Publishing.

Guru, D. (2025). The Role of Artificial Intelligence and Machine Learning in Personalizing Financial Services: An Empirical Study. International Scientific Journal of Engineering and Management, 04(06), 1-9. https://doi.org/10.55041/isjem04336

Han, J. and Ko, D. (2025). Consumer Autonomy in Generative AI Services: The Role of Task Difficulty and AI Design Elements in Enhancing Trust, Satisfaction, and Usage Intention. Behavioral Sciences, 15(4), 534. https://doi.org/10.3390/bs15040534

Hassan, N., Abdelraouf, M., & El-Shihy, D. (2025). The moderating role of personalized recommendations in the trust–satisfaction–loyalty relationship: an empirical study of AI-driven e-commerce. Future Business Journal, 11(1). https://doi.org/10.1186/s43093-025-00476-z

Hayrapetyan, D. and Darbinyan, S. (2025). The role of personalization in influencing digital trust and impulse buying in e-commerce. Theoretical and Experimental Psychology, 18(2), 120-137. https://doi.org/10.11621/tep-25-15

Hidayat, M., Muhammad, W., & Isdayanti, P. (2025). Digital Marketing Ethics in the Age of AI: A Comparative Analysis of Transparency and Consumer Trust in E-Commerce Platforms. JMI, 4(1), 723-740. https://doi.org/10.51903/jmi.v4i1.178

Ikram, M., Asghar, H., Kâafar, M., & Mahanti, A. (2014). On the Intrusiveness of JavaScript on the Web., 31-33. https://doi.org/10.1145/2680821.2680837

Jakhodia, Y., Gupta, T., & Singh, T. (2025). Impact of Artificial Intelligence on Consumer Online Purchase Intention., 297-316. https://doi.org/10.4018/979-8-3373-1681-9.ch014

Kim, H. and Han, S. (2025). Triggering the Personalization Backfire Effect: The Moderating Role of Situational Privacy Concern. Behavioral Sciences, 15(10), 1323. https://doi.org/10.3390/bs15101323

Kim, J., Giroux, M., & Lee, J. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology and Marketing, 38(7), 1140-1155. https://doi.org/10.1002/mar.21498

Kronemann, B., Kizgin, H., Rana, N., & Dwivedi, Y. (2023). How AI encourages consumers to share their secrets? The role of anthropomorphism, personalisation, and privacy concerns and avenues for future research. Spanish Journal of Marketing - Esic, 27(1), 3-19. https://doi.org/10.1108/sjme-10-2022-0213

Li, F. (2024). Research on the Legal Protection of User Data Privacy in the Era of Artificial Intelligence. Science of Law Journal, 3(1). https://doi.org/10.23977/law.2024.030107

Löecherbach, F. Diversity of News Consumption in a Digital Information Environment.. https://doi.org/10.5463/thesis.165

Longoni, C., Cian, L., & Kyung, E. (2022). Algorithmic Transference: People Overgeneralize Failures of AI in the Government. Journal of Marketing Research, 60(1), 170-188. https://doi.org/10.1177/00222437221110139

McLaren, R. and Steuber, K. (2012). Emotions, communicative responses, and relational consequences of boundary turbulence. Journal of Social and Personal Relationships, 30(5), 606-626. https://doi.org/10.1177/0265407512463997

Menard, P. and Bott, G. (2024). Artificial intelligence misuse and concern for information privacy: New construct validation and future directions. Information Systems Journal, 35(1), 322-367. https://doi.org/10.1111/isj.12544

Meshram, S. (2022). Privacy intrusion. International Journal of Health Sciences, 6475-6485. https://doi.org/10.53730/ijhs.v6ns6.11106

Mumtaz, F., Qaisar, M., Zahid, A., & Ahmed, N. (2025). The Impact of Artificial Intelligence on Consumer Decision-Making in Digital Marketing. J. Asian Dev. Studies, 14(2), 372-387. https://doi.org/10.62345/jads.2025.14.2.30

Muralidhar, D. (2024). Thesis Summary: Operationalizing User-Inclusive Transparency in Artificial Intelligence Systems. Proceedings of the Aaai Conference on Artificial Intelligence, 38(21), 23401-23402. https://doi.org/10.1609/aaai.v38i21.30401

Neyazi, T., Ng, S., Hobbs, M., & Yue, A. (2023). Understanding user interactions and perceptions of AI risk in Singapore. Big Data & Society, 10(2). https://doi.org/10.1177/20539517231213823

Niarossa, F. and Haryanto, B. (2025). The Influence of Consumer Persuasion Knowledge in the Context of Online Targeted Advertising (OTA) on Ad Intrusiveness and Privacy Concerns: The Emergence of Ad Avoidance Behavior. International Journal of Current Science Research and Review, 08(03). https://doi.org/10.47191/ijcsrr/v8-i3-20

Okolo, C., Olorunsogo, T., & Babawarun, O. (2024). A comprehensive review of AI applications in personalized medicine. International Journal of Science and Research Archive, 11(1), 2544-2549. https://doi.org/10.30574/ijsra.2024.11.1.0338

Pal, D., Babakerkhell, M., & Roy, P. (2022). How Perceptions of Trust and Intrusiveness Affect the Adoption of Voice Activated Personal Assistants. Ieee Access, 10, 123094-123113. https://doi.org/10.1109/access.2022.3224236

Petronio, S. and Child, J. (2020). Conceptualization and operationalization: utility of communication privacy management theory. Current Opinion in Psychology, 31, 76-82. https://doi.org/10.1016/j.copsyc.2019.08.009

Querci, I., Barbarossa, C., Romani, S., & Ricotta, F. (2022). Explaining how algorithms work reduces consumers' concerns regarding the collection of personal data and promotes AI technology adoption. Psychology and Marketing, 39(10), 1888-1901. https://doi.org/10.1002/mar.21705

Raji, M., Olodo, H., Oke, T., Addy, W., Ofodile, O., & Oyewole, A. (2024). E-commerce and consumer behavior: A review of AI-powered personalization and market trends. GSC Advanced Research and Reviews, 18(3), 066-077. https://doi.org/10.30574/gscarr.2024.18.3.0090

Rustamova, N. (2025). Algorithmic Audiences: Navigating Identity, Influence, and Power in the Age of Platformized Media. Int. J. Mass Commun., 3, 135-144. https://doi.org/10.6000/2818-3401.2025.03.09

Sahli, A. and Zhai, Y. (2024). Cultivating consumer resilience: understanding and navigating adolescents’ responses to intrusive advertising. International Journal of Organizational Analysis, 33(5), 1260-1276. https://doi.org/10.1108/ijoa-01-2024-4181

Sahu, J. and Sankhla, C. (2025). Personalized Marketing in the Digital Age: The Role of AI in Consumer Behavior Analytics. EEL, 15(3), 292-303. https://doi.org/10.52783/eel.v15i3.3415

Saurwein, F. and Spencer-Smith, C. (2021). Automated Trouble: The Role of Algorithmic Selection in Harms on Social Media Platforms. Media and Communication, 9(4), 222-233. https://doi.org/10.17645/mac.v9i4.4062

Smith, J., & Miller, R. (2024). Algorithm anxiety: The psychological impact of predictive personalization. Academic Press.

Srinivasan, R. and Sarial‐Abi, G. (2021). When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors. Journal of Marketing, 85(5), 74-91. https://doi.org/10.1177/0022242921997082

Sutanto, J., Palme, E., Tan, C., & Phang, C. (2013). Addressing the Personalization–Privacy Paradox: An Empirical Assessment from a Field Experiment on Smartphone Users1. Mis Quarterly, 37(4), 1141-1164. https://doi.org/10.25300/misq/2013/37.4.07

Swart, J. (2021). Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media. Social Media + Society, 7(2). https://doi.org/10.1177/20563051211008828

Tehreem, S. (2025). Explainable AI in Public Policy: Quantifying Trust and Distrust in Algorithmic Decision-Making across Marginalized Communities. amla, 01(01), 01-17. https://doi.org/10.64220/amla.v1i1.004

Theophilou , E. , Lomonaco , F. , Donabauer , G. , Ognibene , D. , Sanchez-Queen , J. , & Hernandez‐Leo , D. (2023). AI and Narrative Scripts to Educate Adolescents About Social Media Algorithms: Insights About AI Overdependence, Trust and Awareness., 415-429. https://doi.org/10.1007/978-3-031-42682-7_28

Waykar, Y. (2023). Human-AI Collaboration in Explainable Recommender Systems: An Exploration of User-Centric Explanations and Evaluation Frameworks. Interantional Journal of Scientific Research in Engineering and Management, 07(07). https://doi.org/10.55041/ijsrem24775

Yazdani, A. and Darbani, S. (2023). The Impact of AI on Trends, Design, and Consumer Behavior. aitechbesosci, 1(4), 4-10. https://doi.org/10.61838/kman.aitech.1.4.2

Zhu, Q., Sun, R., Lv, D., Shen, Y., & Qin, S. (2025). Research on the “Privacy Paradox” behaviors of online users from the perspective of attitude ambivalence: ERPs evidence. Plos One, 20(8), e0330438. https://doi.org/10.1371/journal.pone.0330438

Ziakis, C. and Vlachopoulou, M. (2023). Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information, 14(12), 664. https://doi.org/10.3390/info14120664