Why do users resist chatbots? Exploring intrapersonal and systemic barriers in AI-based services

Abstract

As conversational artificial intelligence becomes increasingly embedded in digital service environments, understanding user resistance to chatbot-based information systems has become critical. While prior research has focused primarily on adoption drivers, less attention has been given to the mechanisms underlying resistance. Drawing on user resistance theory and human–computer interaction research, this study examines how intrapersonal factors (technological anxiety, perceived incompetence, and privacy concerns) and system-related factors (repetitiveness, impersonality, and irrelevance) jointly influence resistance and subsequent usage intention. Survey data from 400 chatbot users were analyzed using partial least squares structural equation modeling. The findings reveal that technological anxiety, perceived incompetence, impersonality, and irrelevance significantly increase resistance, which in turn reduces intention to continue use. By contrast, privacy concerns and repetitiveness show limited effects. By jointly modeling intrapersonal and system-related barriers within a single framework, the study clarifies their relative contribution to resistance and offers chatbot developers concrete guidance for reducing both psychological and interactional sources of user disengagement. The study advances understanding of resistance in artificial intelligence–based information systems and provides implications for improving interaction quality and sustaining user engagement.