Toward Circular Consumption Patterns: AI-Enabled Decision Support in End-of-Life Scenarios for Defective Products

The shift toward a circular economy is crucial for sustainable resource use. Emerging technologies such as artificial intelligence (AI) chatbots can help consumers make end-of-life (EOL) decisions for obsolete or defective products, thereby promoting product lifespan extension and resource reuse. However, information systems (IS) research has largely focused on AI-enabled sustainable product purchases. In this study, we investigate the configurations of factors that influence consumers' behavioral intention to use AI chatbots for EOL decision support during the product-use stage. Drawing on the theory of consumption values and insights from chatbot and consumer psychology literature, we apply fuzzy set qualitative comparative analysis (fsQCA) to survey data. Based on seven causal conditions—convenience value, social value, emotional value, epistemic value, consumers' anticipated guilt, product attachment, and busy mindset—we identify eight configurations that predict high behavioral intentions to use a chatbot for EOL decision support. In most configurations, high epistemic and convenience values co-occur, while social value plays a minor role. Anticipated guilt, combined with a busy mindset, can drive high behavioral intentions even when other service values are low. Our findings provide valuable insights for the design and promotion of “circular chatbots” and point to promising directions for future IS research.