<![CDATA[Purpose: This study aims to systematically review the role of Explainable Artificial Intelligence (XAI) in personalized marketing by examining AI algorithms, interpretability techniques, and their implications for consumer trust. Research Method: A systematic literature review was conducted by analyzing peer-reviewed journal articles and conference papers related to AI, XAI, and personalized marketing. The study synthesizes findings across technical and behavioral dimensions to provide an integrated understanding of the research domain. Results and Discussion: The results indicate that machine learning, deep learning, and recommender systems are the primary algorithms used in personalized marketing. However, increasing model complexity reduces interpretability, creating a need for XAI techniques such as LIME, SHAP, and attention mechanisms. The findings further reveal that XAI enhances consumer trust by improving transparency, understandability, and fairness, although contextual factors, including privacy concerns and user characteristics, influence this relationship. Implications: This study contributes theoretically by integrating technical and behavioral perspectives into a unified framework. In practice, it provides managers with guidance on designing transparent and trustworthy AI systems and highlights the need for ethical, user-centered AI implementation. Originality: This study is original in integrating technical perspectives on XAI with behavioral perspectives on consumer trust in personalized marketing. It offers a unified framework explaining how explainability supports transparency, fairness, and trust in AI-driven marketing.]]>
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