When Personalization Feels Like Surveillance: A Marketing Framework for Artificial Intelligence-Personalized Advertising

Abstract

Artificial intelligence (AI)-personalized advertising can increase message relevance, consumer engagement, and persuasive effectiveness, yet the same targeting practices can expose the extent of consumer tracking and trigger concerns about surveillance, manipulation, and loss of autonomy. Existing research documents both the benefits and the risks of personalization, but it does not fully explain how personalization intensity, data-use transparency, and meaningful consumer control jointly shape advertising evaluations and brand-level outcomes. Through an integrative synthesis of personalized advertising, privacy-calculus theory, psychological reactance, procedural fairness, and brand-trust research, this conceptual article develops a dual-pathway framework. The value-creation pathway predicts that high-quality personalization strengthens perceived relevance and advertising value, which subsequently enhance purchase intention, brand trust, and continued engagement. The surveillance-threat pathway predicts that unexpected data use, sensitive inferences, excessive specificity, and opaque targeting practices increase perceived surveillance and intrusiveness, thereby stimulating reactance, advertising avoidance, and trust erosion. The framework distinguishes transparency from control and argues that these mechanisms become most effective when combined: transparency clarifies how and why personal data are used, whereas control allows consumers to modify, limit, or refuse personalization. Their joint presence creates procedural legitimacy and can preserve the benefits of personalization while reducing perceived vulnerability. Twelve propositions specify the principal mediating mechanisms, boundary conditions, and an inverted-U relationship between personalization intensity and net marketing response. The article also proposes a three-study empirical validation agenda and practical design principles for developing personalized advertising systems that improve relevance and effectiveness without undermining consumer autonomy, privacy expectations, or long-term brand trust.