The emergence of artificial intelligence-generated content (AIGC) is changing the constraints that limit user innovation. To explore the influence mechanism of AIGC on user innovation behavior, this study obtained public archival data from multiple sources (including official media reports such as technology news reports, knowledge-sharing posts, and AI-focused articles from authoritative platforms, totaling 1,502 articles) and semi-structured interview textual materials (more than 120,000 words). Using the methodology of grounded theory, we systematically develop a TCEU (technology-content-environment-user) theoretical model that investigates the influence of AIGC on user innovation behavior. Results show that the AIGC mainly affects user innovation behavior through technical factors (instrumental rationality, value rationality), content factors (generation quality, characteristics of information sources, content presentation features, characteristics of content production), and user factors (individual characteristics, emotional state, innovation characteristics, risk cognition, self-efficacy). Among them, technical factors and content factors are external driving factors, and user factors are internal driving factors. At the same time, environmental factors (technology popularity, platform convenience) play moderating roles on the influences of technical factors, content factors, and user factors. In addition, users using AIGC to complete innovative behaviors may also experience some alienation outcomes, mainly manifested as cognitive fixation, degradation risk, and problem risk. The TCEU theoretical framework developed in this study provides a theoretical foundation for systematically analyzing the impact mechanisms of AIGC on user innovation behavior. This study offers practical guidance to facilitate AIGC’s effective support for user innovation behavior and to prevent the alienation outcomes of user innovation behavior.
Chen et al. (Fri,) studied this question.