CONSUMER RESISTANCE TO AI ADVERTISING: INSIGHTS FROM BIBLIOMETRIC MAPPING AND SYSTEMATIC REVIEW
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no3E.475Keywords:
Artificial intelligence (AI) advertising, consumer resistance, bibliometric mapping, systematic review.Abstract
The rapid diffusion of artificial intelligence (AI) in advertising has attracted increasing scholarly attention, largely centered on consumer acceptance, effectiveness, and personalization. However, emerging research suggests that AI-driven advertising can also provoke consumer resistance due to concerns over manipulation, surveillance, transparency, and ethical governance. This study offers a systematic and integrative overview of this literature by combining bibliometric mapping and systematic review methods applied to 1085 peer-reviewed journal articles indexed in Scopus. Bibliometric analysis identifies publication trends, influential journals and authors, core intellectual foundations, and major thematic clusters in AI advertising research. The systematic review complements these findings by synthesizing empirical evidence with a specific focus on resistance-oriented mechanisms. Results indicate that the field is dominated by acceptance-focused, performance-driven frameworks, while resistance-related constructs remain marginal and weakly theorized: within the advertising-relevant segment of the corpus, no resistance construct exceeds 4.4% of records, against 31.7% for optimisation-related terms, and psychological reactance appears in no author or index keyword field across the entire corpus.
References
[1] C. Ziakis and M. Vlachopoulou, “Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review,” Information, vol. 14, no. 12, Art. no. 664, 2023, doi: https://doi.org/10.3390/info14120664
[2] R. Bano, F. Azim, Z. Mahmood, A. Sanaullah, and O. Ali, “The Role of Artificial Intelligence in Personalized Marketing: Enhancing Customer Experience, Predictive Targeting, and Brand Engagement,” The Critical Review of Social Sciences Studies, vol. 3, no. 2, pp. 50–65, 2025, doi: https://doi.org/10.59075/d94kvf44
[3] Research and Markets, AI in Social Media Market Report, Research and Markets, 2024.
[4] J. Kietzmann, J. Paschen, and E. Treen, “Artificial Intelligence in Advertising: How Marketers Can Leverage AI Along the Consumer Journey,” Journal of Advertising Research, vol. 58, no. 3, pp. 263–267, 2018, doi: https://doi.org/10.2501/JAR-2018-035
[5] T. H. Davenport, A. Guha, D. Grewal, and T. Bressgott, “How Artificial Intelligence Will Change the Future of Marketing,” Journal of the Academy of Marketing Science, vol. 48, no. 1, pp. 24–42, 2020, doi: https://doi.org/10.1007/s11747-019-00696-0
[6] K. Plangger, D. Grewal, K. de Ruyter, and C. Tucker, “The future of digital technologies in marketing: A conceptual framework and an overview,” Journal of the Academy of Marketing Science, vol. 50, no. 6, pp. 1125–1134, Nov. 2022, doi: https://doi.org/10.1007/s11747-022-00906-2
[7] A. Kapoor and M. Kumar, “Frontiers: Generative AI and Personalized Video Advertisements,” Marketing Science, vol. 44, no. 4, pp. 733–747, 2025, doi: https://doi.org/10.1287/mksc.2023.0494
[8] A. Y. Onifade, J. C. Ogeawuchi, A. A. Abayomi, and O. Aderemi, “Advances in CRM-Driven Marketing Intelligence,” International Journal of Advanced Multidisciplinary Research and Studies, vol. 4, no. 6, pp. 2313–2325, 2024, doi: https://doi.org/10.62225/2583049X.2024.4.6.4326
[9] B. Gao, Y. Wang, H. Xie, Y. Hu, and Y. Hu, “Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations,” SAGE Open, vol. 13, no. 4, 2023, doi: https://doi.org/10.1177/21582440231210759
[10] N. Singh and D. Adhikari, “AI-Driven Personalization in eCommerce Advertising,” International Journal for Research in Applied Science and Engineering Technology, vol. 11, no. 12, pp. 1692–1698, 2023, doi: https://doi.org/10.22214/ijraset.2023.57695
[11] C. Longoni, A. Bonezzi, and C. K. Morewedge, “Resistance to Medical Artificial Intelligence,” Journal of Consumer Research, vol. 46, no. 4, pp. 629–650, 2019, doi: https://doi.org/10.1093/jcr/ucz013
[12] D. A. Frank and T. Otterbring, “Autonomy, Power and the Special Case of Scarcity: Consumer Adoption of Highly Autonomous Artificial Intelligence,” British Journal of Management, vol. 35, no. 4, pp. 1700–1723, 2024, doi: https://doi.org/10.1111/1467-8551.12780
[13] U. A. Qadri, A. M. A. Moustafa, and M. Waqas, “When and how AI personalization drives sustainable purchases: The roles of relevance, privacy, and transparency in eco-friendly advertising,” Journal of Retailing and Consumer Services, vol. 89, Art. no. 104592, 2026, doi: https://doi.org/10.1016/j.jretconser.2025.104592
[14] A. Aljarah, B. Ibrahim, and M. López, “In AI, We Do Not Trust! The Nexus Between Awareness of Falsity in AI-Generated CSR Ads and Online Brand Engagement,” Internet Research, vol. 35, no. 3, pp. 1406–1426, 2025, doi: https://doi.org/10.1108/INTR-12-2023-1156
[15] H. Liu, M. F. S. De Costa, M. A. Yasin, and Q. Ruan, “A study on how social media influences on impulsive buying,” Expert Systems, vol. 42, no. 1, Art. no. e13448, 2025, doi: https://doi.org/10.1111/exsy.13448
[16] U. Usman, T. Kim, A. Garvey, and A. Duhachek, “The Persuasive Power of AI Ingratiation: A Persuasion Knowledge Theory Perspective” Journal of the Association for Consumer Research, vol. 9, no. 3, pp. 319–331, 2024, doi: https://doi.org/10.1086/730280
[17] M. H. Huang and R. T. Rust, “A Strategic Framework for Artificial Intelligence in Marketing,” Journal of the Academy of Marketing Science, vol. 49, no. 1, pp. 30–50, 2021, doi: https://doi.org/10.1007/s11747-020-00749-9
[18] T. Chen, F. Liu, X. Shen, J. Wu, and Y. Liu, “Conceptualization of privacy concerns and their influence on consumers' resistance to AI-based recommender systems in e-commerce,” Industrial Management & Data Systems, vol. 125, no. 5, pp. 1844–1868, 2025, doi: https://doi.org/10.1108/IMDS-03-2024-0251
[19] Y. Mou and X. Meng, “Alexa, it is creeping over me – Exploring the impact of privacy concerns on consumer resistance to intelligent voice assistants,” Asia Pacific Journal of Marketing and Logistics, vol. 36, no. 2, pp. 261–292, 2024, doi: https://doi.org/10.1108/APJML-10-2022-0869
[20] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010.
[21] S. A. Susanu, G. Bleoju, A. T. Petropoulos, and A. Matric, “Artificial Intelligence Applications for Marketing,” Economics and Applied Informatics, pp. 109–119, 2025, doi: https://doi.org/10.35219/eai15840409517
[22] X. Qin and Z. Jiang, “The Impact of AI on the Advertising Process: The Chinese Experience,” Journal of Advertising, vol. 48, no. 4, pp. 338–346, 2019, doi: https://doi.org/10.1080/00913367.2019.1652122
[23] G. Chen, P. Xie, J. Dong, and T. Wang, “Understanding Programmatic Creative: The Role of AI,” Journal of Advertising, vol. 48, no. 4, pp. 347–355, 2019, doi: https://doi.org/10.1080/00913367.2019.1654421
[24] A. Chinnaraju, “AI-Powered Consumer Segmentation and Targeting: A Theoretical Framework for Precision Marketing by Autonomous (Agentic) AI,” International Journal of Scientific Research Archive, vol. 14, no. 2, pp. 401–424, 2025, doi: https://doi.org/10.30574/ijsra.2025.14.2.0370
[25] Z. Xia and R. Shannon, “Navigating the Digital Frontier: Exploring the Dynamics of Customer–Brand Relationships Through AI Chatbots,” Sustainability, vol. 17, no. 5, Art. no. 2173, Mar. 2025, doi: https://doi.org/10.3390/su17052173
[26] J. Looi and L. A. Kahlor, “Artificial Intelligence in Influencer Marketing: A Mixed-Method Comparison of Human and Virtual Influencers on Instagram,” Journal of Interactive Advertising, vol. 24, no. 2, pp. 107–126, 2024, doi: https://doi.org/10.1080/15252019.2024.2313721
[27] C. Lou, S. T. J. Kiew, T. Chen, T. Y. M. Lee, J. E. C. Ong, and Z. Phua, “Authentically Fake? How Consumers Respond to the Influence of Virtual Influencers,” Journal of Advertising, vol. 52, no. 4, pp. 540–557, 2023, doi: https://doi.org/10.1080/00913367.2022.2149641
[28] T. H. Baek, J. Kim, and J. H. Kim, “Effect of Disclosing AI-Generated Content on Prosocial Advertising Evaluation,” International Journal of Advertising, vol. 45, no. 1 pp. 171–192, 2026, doi: https://doi.org/10.1080/02650487.2024.2401319
[29] J. Yang, “Exploring the Productivity of Generative AI-Powered Ad Campaigns: A Consumer Response Perspective,” Journal of Interactive Advertising, vol. 25, no. 3, pp. 222–239, 2025. doi: https://doi.org/10.1080/15252019.2025.2551598
[30] H. G. Musa, I. Fatmawati, N. Nuryakin, and M. Suyanto, “Marketing research trends using technology acceptance model (TAM): a comprehensive review of researches (2002–2022),” Cogent Business & Management, vol. 11, no. 1, Art. no. 2329375, 2024, doi: https://doi.org/10.1080/23311975.2024.2329375
[31] C. Whang and H. Im, “"I Like Your Suggestion!" the role of humanlikeness and parasocial relationship on the website versus voice shopper's perception of recommendations,” Psychology & Marketing, vol. 38, no. 4, pp. 581–595, 2021, doi: https://doi.org/10.1002/mar.21437
[32] L. Zhang and C. Hur, “The Impact of Generative AI Images on Consumer Attitudes in Advertising,” Administrative Sciences, vol. 15, no. 10, Art. no. 395, 2025, doi: https://doi.org/10.3390/admsci15100395
[33] M. Ienca, “On Artificial Intelligence and Manipulation,” Topoi, vol. 42, no. 3, pp. 833–842, 2023, doi: https://doi.org/10.1007/s11245-023-09940-3
[34] U. A. Qadri, A. M. A. Moustafa, and M. Abd Ghani, “They misused me! Digital literacy’s dual role in AI marketing manipulation and unethical young consumer behavior,” Young Consumers, vol. 26, no. 5, pp. 744–764, 2025, doi: https://doi.org/10.1108/YC-08-2024-2207
[35] J. W. Brehm, A Theory of Psychological Reactance. Oxford: Academic Press, 1966.
[36] D. Roux, “Consumer Resistance: Proposal for an Integrative Framework,” Recherche et Applications en Marketing, vol. 22, no. 4, pp. 59–79, 2007, doi: https://doi.org/10.1177/205157070702200403
[37] M. L. Fransen, E. G. Smit, and P. W. J. Verlegh, “Strategies and Motives for Resistance to Persuasion: An Integrative Framework,” Frontiers in Psychology, vol. 6, Art. no. 1201, 2015, doi: https://doi.org/10.3389/fpsyg.2015.01201
[38] R. H. Tsiotsou, L. Hatzithomas, and M. Wetzels, “Display Advertising from a Resistance Perspective,” Journal of Research in Interactive Marketing, vol. 18, no. 2, pp. 198–219, 2024, doi: https://doi.org/10.1108/JRIM-09-2022-0302
[39] K. H. Wong, H. H. Chang, Y. H. Lin, and S. Y. Lin, “How do mobile app users react to embedded advertising? A perspective from psychological reactance theory,” Behaviour & Information Technology, vol. 44, no. 7, pp. 1457–1474, 2025, doi: https://doi.org/10.1080/0144929X.2024.2359644
[40] Z. Mani and I. Chouk, “Consumer Resistance to Innovation in Services,” Journal of Product Innovation Management, vol. 35, no. 5, pp. 780–807, 2018, doi: https://doi.org/10.1111/jpim.12463
[41] S. Laumer and A. Eckhardt, “Why Do People Reject Technologies: A Review of User Resistance Theories,” in Information Systems Theory, New York: Springer, 2011, pp. 63–86, doi: https://doi.org/10.1007/978-1-4419-6108-2_4
[42] S. C. Boerman and E. A. van Reijmersdal, “Disclosing influencer marketing on YouTube to children: The moderating role of para-social relationship,” Frontiers in Psychology, vol. 10, Art. no. 3042, 2019, doi: https://doi.org/10.3389/fpsyg.2019.03042
[43] H. T. Bui, “Examining the Effect of AI Advertising Involvement Disclosure on Advertising Value and Purchase Intentions,” Journal of Research in Interactive Marketing, pp. 1–20, 2025, doi: https://doi.org/10.1108/JRIM-02-2025-0066
[44] P. Phua, N. Hartnett, V. Beal, G. Trinh, and R. Kennedy, “When Brands Go Dark: A Replication and Extension: Examining Market Share of Brands That Stop Advertising for a Year or Longer,” Journal of Advertising Research, vol. 63, no. 2, pp. 172–184, Jun. 2023, doi: https://doi.org/10.2501/JAR-2023-009
[45] I. Zupic and T. Čater, “Bibliometric Methods in Management and Organization,” Organizational Research Methods, vol. 18, no. 3, pp. 429–472, 2015, doi: https://doi.org/10.1177/1094428114562629
[46] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to Conduct a Bibliometric Analysis: An Overview and Guidelines,” Journal of Business Research, vol. 133, pp. 285–296, 2021, doi: https://doi.org/10.1016/j.jbusres.2021.04.070
[47] M. Aria and C. Cuccurullo, “Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis,” Journal of Informetrics, vol. 11, no. 4, pp. 959–975, 2017, doi: https://doi.org/10.1016/j.joi.2017.08.007
[48] D. Tranfield, D. Denyer, and P. Smart, “Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review,” British Journal of Management, vol. 14, no. 3, pp. 207–222, 2003, doi: https://doi.org/10.1111/1467-8551.00375
[49] D. Denyer and D. Tranfield, “Producing a Systematic Review,” in The Sage Handbook of Organizational Research Methods. London, U.K.: Sage Publications Ltd., 2009, pp. 671–689.
[50] J. Paul and A. R. Criado, “The art of writing literature review: What do we know and what do we need to know?,” International Business Review, vol. 29, no. 4, Art. no. 101717, 2020, doi: https://doi.org/10.1016/j.ibusrev.2020.101717
[51] H. Small, “Co-citation in the scientific literature: A new measure of the relationship between two documents,” Journal of the American Society for Information Science, vol. 24, no. 4, pp. 265–269, 1973, doi: https://doi.org/10.1002/asi.4630240406
[52] N. Van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010, doi: https://doi.org/10.1007/s11192-009-0146-3
[53] M. Callon, J. P. Courtial, W. A. Turner, and S. Bauin, “From Translations to Problematic Networks: An Introduction to Co-Word Analysis,” Social Science Information, vol. 22, no. 2, pp. 191–235, 1983, doi: https://doi.org/10.1177/053901883022002003
[54] L. Arango, S. P. Singaraju, and O. Niininen, “Consumer Responses to AI-Generated Charitable Giving Ads,” Journal of Advertising, vol. 52, no. 4, pp. 486–503, 2023, doi: https://doi.org/10.1080/00913367.2023.2183285
[55] I. Ajzen, “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes, vol. 50, no. 2, pp. 179–211, 1991, doi: https://doi.org/10.1016/0749-5978(91)90020-T
[56] J. Arsenyan and A. Mirowska, “Almost Human? A Comparative Case Study on the Social Media Presence of Virtual Influencers,” International Journal of Human–Computer Studies, vol. 155, Art. no. 102694, 2021, doi: https://doi.org/10.1016/j.ijhcs.2021.102694
[57] M. Friestad and P. Wright, “The Persuasion Knowledge Model: How People Cope with Persuasion Attempts,” Journal of Consumer Research, vol. 21, no. 1, pp. 1–31, 1994, doi: https://doi.org/10.1086/209380
[58] M. Ali, P. Sapiezynski, M. Bogen, A. Korolova, A. Mislove, and A. Rieke, “Discrimination Through Optimization: How Facebook’s Ad Delivery Can Lead to Biased Outcomes,” Proceedings of the ACM on Human-Computer Interaction, vol. 3, no. CSCW, pp. 1–30, 2019, doi: https://doi.org/10.1145/3359301
[59] F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989, doi: https://doi.org/10.2307/249008
[60] M. C. Campbell and A. Kirmani, “Consumers’ Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent,” Journal of Consumer Research, vol. 27, no. 1, pp. 69–83, 2000, doi: https://doi.org/10.1086/314309
