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

Area of Science:

• Consumer Behavior
• Artificial Intelligence
• Food Science

Background:

• Personalized recommendation systems powered by artificial intelligence (AI) significantly influence consumer decisions in the functional food market.
• The specific impact of AI recommendation characteristics and their mediating psychological mechanisms on purchase intention requires further investigation.

Purpose of the Study:

• To examine how AI recommendation features (personalization, transparency) and functional food attributes (perceived health benefits, perceived naturalness) affect purchase intention.
• To elucidate the mediating roles of perceived packaging and perceived value in the relationship between AI recommendations, food attributes, and purchase intention.

Main Methods:

• Utilized the Stimulus-Organism-Response framework for study design.
• Collected data through a structured questionnaire survey.
• Employed structural equation modeling for hypothesis testing and model validation.

Main Results:

• AI recommendation personalization directly and indirectly enhances purchase intention.
• AI transparency influences purchase intention solely through perceived value, indicating a trust-building role.
• Perceived health benefits directly and indirectly impact purchase intention, while perceived naturalness influences it only indirectly via perceived value.

Conclusions:

• AI personalization is a key driver of functional food purchases, while transparency builds trust.
• Functional food attributes like health benefits and naturalness, mediated by perceived value, also shape purchasing decisions.
• Findings offer insights for marketers on leveraging AI to improve consumer engagement with functional foods.