<![CDATA[The rapid development of artificial intelligence in the e-commerce industry has driven the adoption of increasingly complex algorithm based personalization systems, yet the dominance of black box models presents challenges related to transparency, fairness, and consumer trust. Objective: This study aims to develop and evaluate an adaptive personalization framework based on Explainable Artificial Intelligence (XAI), integrated with secure data governance and consumer trust metrics. Research Method: The study employs a Design Science Research approach combined with controlled experiments involving 240 active e-commerce users divided into two groups: an XAI system and a conventional black box system. Data were analyzed using Structural Equation Modeling to examine the relationships among transparency perception, personalization relevance, trust, and behavioral intention. Results: The findings indicate that transparency significantly influences trust, and trust strongly affects users’ behavioral intentions. The XAI based system significantly enhances perceptions of transparency, fairness, accountability, and privacy assurance compared to the black box system, without reducing personalization relevance. These results demonstrate that integrating explainability mechanisms and secure data governance can strengthen trust and promote long-term engagement within digital e-commerce ecosystems. ]]>
Copyrights © 2023
