Personalization vs Privacy: Examining the Paradox in AI-Driven Marketing and Its Impact on Consumer Trust

AI Marketing, Personalization-Privacy Paradox, Consumer Trust, Privacy Calculus Theory, Meta-Analytic Review, Data Transparency, Information Boundary Theory, E-Commerce Ethics

As artificial intelligence fundamentally transforms digital marketing, consumers are caught in a deepening tension — simultaneously seeking the convenience of personalized experiences while fearing the data exposure that enables them. This paper conceptualizes and examines the personalization-privacy paradox as it operates within AI-driven marketing ecosystems, with particular focus on its effects on consumer trust. Grounded in Privacy Calculus Theory (PCT), the Technology Acceptance Model (TAM), and Information Boundary Theory, the study develops a theoretically rigorous framework that maps the interplay among Perceived Personalization, Privacy Concerns, and Consumer Trust. To substantiate this framework, a systematic literature-based meta-analytic synthesis is conducted, drawing on quantitative effect sizes and directional findings reported across forty-two peer-reviewed empirical studies published between 1994 and 2023. The synthesized evidence consistently supports the hypothesized relationships: personalization exerts a positive effect on trust (mean weighted r ≈ 0.38), while privacy concerns exert a significant negative effect (mean weighted r ≈ −0.41). Critically, the analysis identifies a robust negative moderation effect whereby elevated privacy concern substantially attenuates — and in some conditions reverses — the trust-building function of personalization. Three boundary conditions (data sensitivity, platform transparency, and individual privacy orientation) are shown to shape this moderation. The paper advances theoretical understanding of the paradox, offers evidence-based managerial guidelines, and charts a detailed agenda for primary empirical research.