Examining generative AI user addiction from a C-A-C perspective

Abstract

The rapid development of generative AI represented by ChatGPT has attracted a large number of users, but also brings problems such as user addiction, which may undermine its sustainable development. Drawing on a cognition-affect-conation (C-A-C) perspective, this research examined generative AI user addiction. We used a mixed method of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to conduct data analysis. The results show that perceived anthropomorphism, perceived interactivity, perceived intelligence, and perceived personalization influence flow experience and attachment, both of which further affect user addiction. The fsQCA revealed three configurations triggering user addiction, among which flow experience and attachment are the common core conditions. The results imply that generative AI companies need to prevent user addiction and ensure a sustainable development.