The impact of AI recommendation’s price and product accuracy on customer satisfaction: SEM-SOR theoretical approach

Abstract

This study investigated the impact of AI price and product recommendation accuracy on customer satisfaction based on the stimulus-organism response (SOR) theory. Analysing online users’ surveys through PLS-SEM, this study found that brand sensitivity significantly moderates the relationship between AI recommendation's product accuracy and personalized service satisfaction but not the link between price accuracy and personalized service satisfaction. Investment strategy exhibited an insignificant moderating effect on the relationship between personalized service satisfaction, product and price accuracy. The results revealed that accurate price and product recommendation directly impact personalized service satisfaction. This is the first study to examine AI recommendation’s effects on personalized service satisfaction with the multi-moderation of investment strategy and brand sensitivity. The findings extend the SOR framework in literature to the AI-powered online shopping. They provide practical information for e-commerce providers to enhance customer satisfaction by adopting AI technologies that accurate recommend price and product to their potential customers. Policy makers may implement related policies to enhance AI price and product recommendation transparency by encouraging the usage of explainable artificial intelligence and providing more education to customers about AI recommendations in online shopping platforms.