AI-Based Personalization and Its Impact on Online Consumer Trust

Abstract

The rapid growth of artificial intelligence (AI) has transformed digital commerce by enabling highly personalized consumer experiences. AI-driven personalization systems analyze real-time behavioral data, preferences, and contextual signals to deliver customized product recommendations, advertisements, content, and pricing strategies. While personalization improves relevance and engagement, it also raises critical issues related to privacy, data security, algorithmic transparency, and perceived manipulation. These factors directly influence online consumer trust, which is a foundational determinant of long-term platform success. This study systematically examines how AI-based personalization affects consumer trust in online environments. Using an integrated conceptual framework combining technology acceptance theory, trust transfer theory, and personalization–privacy paradox, the paper explores the psychological and behavioral mechanisms through which AI-driven personalization builds or erodes trust. The paper further proposes a validated conceptual model highlighting key antecedents such as perceived usefulness, transparency, data security, control, perceived fairness, and algorithmic explainability. The study contributes to theory by bridging AI personalization with trust formation literature and offers practical insights for marketers, platform designers, and policymakers..