Unpacking AI Chatbot Dependency: A Dual-Path Model of Cognitive and Affective Mechanisms

Abstract

With AI chatbots becoming increasingly embedded in everyday life, growing concerns have emerged regarding users’ psychological dependency on these systems. While previous studies have mainly addressed utilitarian drivers, less attention has been paid to the cognitive and affective mechanisms driving chatbot dependency. Drawing upon Uses and Gratifications Theory, Compensatory Internet Use Theory, and Attachment Theory, this study proposes a dual-path model that investigates how instrumental motivations (e.g., information-seeking, entertainment, efficiency) and affective motivations (e.g., companionship, loneliness, anxiety) influence chatbot dependency through two mediating mechanisms: cognitive reliance and emotional attachment. Using survey data collected from 354 participants, the model was tested through structural equation modeling (SEM). The results indicate that information-seeking and efficiency significantly predict cognitive reliance, which subsequently enhances chatbot dependency. In contrast, entertainment does not exhibit a significant influence. Furthermore, affective motivations such as companionship, loneliness, and anxiety are indirectly linked to dependency through emotional attachment, with loneliness demonstrating the strongest indirect effect. These findings underscore the dual influence of functional cognition and emotional vulnerability in fostering chatbot dependency, emphasizing the importance of emotionally sensitive and ethically responsible AI design.