AI-Driven Marketing Personalization and the Consumer Privacy Paradox: A Systematic Literature Review of Trust, Engagement, and Purchase Behavior - Lili Suryati; Edison Parulian (Golden Ratio of Data in Summary, 2026)
This study aims to synthesize the development of research on AI-driven marketing personalization and the consumer privacy paradox, with particular attention to trust, customer engagement, and purchase behavior. A qualitative systematic literature review was conducted by examining peer-reviewed studies published in major academic databases, including Scopus, Web of Science, ScienceDirect, Emerald Insight, SpringerLink, and Google Scholar. The selected literature was analyzed through thematic synthesis to identify recurring concepts, theoretical perspectives, empirical relationships, and research gaps. The findings indicate that AI-driven personalization improves perceived relevance, usefulness, convenience, and customer experience, but its effectiveness is conditional on consumers’ evaluations of privacy risk, transparency, fairness, and control over personal data. Trust emerged as the central mechanism that reconciles the benefits of personalization with concerns regarding surveillance, data misuse, and algorithmic opacity. The review also shows that customer engagement functions as an important link between trust and purchase-related outcomes, including purchase intention, continuance, loyalty, and advocacy. The principal finding is that personalization does not automatically generate favorable consumer behavior. Its impact depends on ethical data governance, explainable recommendations, privacy assurance, and consumers’ perceptions of organizational responsibility. The study proposes an integrated framework connecting personalization, privacy concern, trust, engagement, and purchase behavior and recommends longitudinal, cross-cultural, experimental, and behavioral research.