Artificial Intelligence-Driven Recommendations and Functional Food Purchases: Understanding Consumer Decision-Making

Amid rapid advancements in artificial intelligence (AI), personalized recommendation systems have become a key factor shaping consumer decision-making in functional food purchases. However, the influence of AI recommendation characteristics on purchase intention, particularly the underlying mediating mechanisms, remains underexplored. This study aims to investigate how AI recommendation features (personalization and transparency), along with functional food attributes (perceived health benefits and perceived naturalness), influence purchase intention through the mediating roles of perceived packaging and perceived value. Grounded in the Stimulus–Organism–Response framework, data were collected via a structured questionnaire survey, and structural equation modeling was employed for hypothesis testing and model validation. The results indicate that AI recommendation personalization significantly enhances purchase intention both directly and indirectly, while transparency influences purchase intention only through perceived value, emphasizing its role in fostering trust rather than directly driving purchasing behavior. Additionally, perceived health benefits positively influence purchase intention both directly and through mediation, whereas perceived naturalness affects purchase intention only indirectly via perceived value. These findings contribute to consumer behavior research by elucidating psychological mechanisms underlying AI-driven purchase decisions while also providing insights for functional food marketers on how to effectively integrate AI recommendation systems to enhance consumer engagement.