The Impact of Ai-Powered Recommendation System Attributes on Consumer Purchase Intention: The Mediation Effect of Perceived Value

Abstract

In the context of the rapid growth of artificial intelligence, AI-based recommender systems have become increasingly important in e-commerce and content platforms and, over time, have become a key factor shaping consumers’ choices. In accordance with the Stimulus-Organism-Response (S-O-R) model, this paper examines how AI recommender system attributes influence purchase intention through consumer perception. In particular, AI recommender systems are conceptualized as having three core features: predictive capability, accuracy, and dynamism and adaptability. Perceived value is introduced as a mediator variable and is further subdivided into functional value, emotional value, and cognitive value. Data was gathered through a questionnaire survey and processed empirically using SPSS and PROCESS. It was found that all three characteristics of AI recommender systems have strong positive impacts on consumers’ purchase intention, while perceived value plays a partial mediating role. Of these, functional value has the highest explanatory power in consumer decision-making. The research contributes to the existing literature on AI recommender systems by considering the psychological processes behind their implementation and offers useful insights into the effective recommendation strategies for digital platforms.