Personalized or Pressurized? The Influence of AI-Powered Personalized Recommendation Triggered Information Stressor on Consumers’ Discontinuance Purchase Behavior

This study centers on AI-powered personalized recommendation triggered information stressors, which are categorized into four types based on information quality metrics: information irrelevance, information redundancy, information narrowing and information overload. According to the attentional resources theory and the impact of information stressors on users' core tasks, these are further classified into focal stressors and background stressors. Based on the Stressor-Strain-Outcome (SSO) model, this study analyzes how information stressors generated by AI-powered personalized recommendation affects consumers' discontinuance purchase behavior. PLS-SEM was used to analyze 368 valid questionnaire data. The findings indicate that information narrowing and information redundancy exert significant positive influences on information overload. Furthermore, information irrelevance and information overload have substantial positive impacts on algorithm aversion and emotional exhaustion. Additionally, algorithm aversion demonstrates a significant positive effect on emotional exhaustion, while both algorithm aversion and emotional exhaustion significantly and positively influence consumers' discontinuance purchase behavior.