"Guess You Like It" - How personalized recommendation timing and product type influence consumers' acceptance: An ERP study.

Neurosci Lett

Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China. Electronic address:

Published: June 2023

Personalized recommendation has been increasingly used in online shopping environment, and improving the effectiveness of personalized recommendation is an important issue. On the basis of two-stage decision theory and preference inconsistency theory, our study adopted the neuroscientific methodology of event-related potential to investigate the decision-making process and psychological mechanism of consumers for personalized recommendation under different recommendation timings (browsing and decision stages) and recommended product types (similar and related). Behavioral results showed that consumers' acceptance of similar product recommendations was higher than that of related product recommendations during the browsing stage, whereas no difference was observed in consumers' acceptance of the two product types during the decision stage. More importantly, neurophysiology results provided underlying psychological mechanism for exploring consumers' decision-making process for personalized recommendations. Consumers' psychological mechanism of the personalized recommendations might be divided into two processes, the early automatic cognitive process indicated by the N2 component, and the late advanced cognitive process indicated by the P3 component. We suggested that N2 reflects the perceptual mismatch between the recommended products and the target products, and P3 reflects the attention capture during categorization evaluation of the recommended product and the target product. These findings have important theoretical and practical significance for the deeper understanding of consumers' decision-making process and psychological mechanism in personalized recommendation, as well as improving the effectiveness of personalized recommendation.

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Source
http://dx.doi.org/10.1016/j.neulet.2023.137261DOI Listing

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