CONSUMER RESISTANCE TO AI ADVERTISING: INSIGHTS FROM BIBLIOMETRIC MAPPING AND 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.