The Impact of AI-Driven Recommender Systems on Consumer Behavior in Thailand The Roles of Trust and Satisfaction
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Abstract
AI-driven recommender systems (RS) play a central role in shaping consumer decision-making in digital commerce. While prior research emphasizes personalization accuracy, less attention has been paid to how trust-related and satisfaction-related perceptions jointly influence consumer behavior, particularly in emerging digital markets. This study examines the impact of recommender system trust and satisfaction on consumer behavior in Thailand’s e-commerce context. Using a cross-sectional survey of 403 Thai online consumers, constructs related to trust (privacy, transparency, perceived fairness) and satisfaction (personalization, autonomy, diversity) were measured using validated Likert-scale instruments. Data were analyzed using reliability testing, Pearson correlation analysis, and multiple linear regression. Results indicate that privacy, transparency, and perceived fairness are positively associated with trust, while personalization, autonomy, and diversity are positively associated with satisfaction. Both trust and satisfaction significantly predict consumer behavior, explaining 68.7% of its variance, with satisfaction emerging as the strongest predictor. The findings highlight the importance of transparent, fair, and autonomy-preserving recommender system design in enhancing consumer engagement and purchase intentions. The study contributes to recommender system research by integrating ethical perception variables with behavioral outcomes in a Southeast Asian digital commerce setting.
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