Beyond Isolated Factors: Investigating the Configurations That Shape User Responses to AI Advice

As artificial intelligence (AI) increasingly supports decision-making across various sectors, understanding how users perceive and respond to AI advice is critical. While previous studies have identified individual factors impacting this interaction between user and AI advice, these factors often interact in complex ways. This study explores how combinations of factors, including performance expectancy, effort expectancy, personal data use, and prediction explainability, influence user decisions to accept or reject AI advice. Using General Systems Theory as a framework and conducting experiments in a medical context with a Symptom Checker application, we aim to investigate these interdependencies through fuzzy-set qualitative comparative analysis. Our findings will contribute to the growing body of research on AI-advised decision-making by identifying key configurations that drive user acceptance or rejection of AI advice, offering insights both for academia and practical applications.