Influence of AI recommendation method and product type on consumers' acceptance: an event-related potential study

Currently, artificial intelligence (AI) recommendations are widely used to alleviate the phenomenon of information overload, and how to enhance the effectiveness of AI recommendations is a very important issue. Based on theory of uses and gratifications, this paper applied neuroscience technology of event-related potential (ERP) to investigate how different AI recommendation methods (explicit and implicit) and product types (similar and related) affect consumers' decision-making process and neuropsychology mechanisms. Behavioral results showed that consumers were more likely to accept the implicit recommendations when recommending similar products. However, when recommending related products, consumers were more willing to accept explicit recommendations. At the neural level, ERP results provided underlying cognitive evidence for exploring consumers' decision-making on AI recommendations. There was a two-stage cognitive process of consumers on different AI recommendation methods and product types. In the early cognitive stage, a greater P2 amplitude was elicited by recommendation of similar products than that of related products, reflecting an automatic and primary attention allocation process. In the later cognitive stage, the recommendation method of implicit than that of explicit evoked a larger P3 amplitude when recommending similar products, while the recommendation method of explicit than that of implicit induced a greater P3 amplitude when recommending related products, reflecting an advanced categorization evaluation process. These findings have important theoretical and practical implications for gaining a deeper understanding of consumers' decision making on AI recommendations and promoting the development of AI recommendations.