Sustainable Human-AI Collaboration in Creative Contexts: An Integrated Approach of CASA, UTAUT, Psychological Ownership, and Self-Determination Theory

This study examines the psychological mechanisms that enable sustainable human–AI collaboration in creative tasks. Drawing on the CASA paradigm, we frame generative AI as a social collaborator and integrate UTAUT, psychological ownership theory, and self-determination theory to explain users’ continued engagement with AI tools. We test how AI performance expectancy influences psychological ownership, collaboration satisfaction, and continuance intention, and whether these mechanisms vary by creative context (pure vs. work-related) or collaboration type (human-led vs. AI-led). Results show that performance expectancy enhances ownership, satisfaction, and continuance intention, with ownership and satisfaction further reinforcing continued use. However, in AI-led collaboration, its positive effect on satisfaction is weakened, while creative context shows no significant differences, suggesting that core psychological processes generalize across creative purposes. This study extends UTAUT by incorporating psychological mechanisms into human–AI collaboration and provides a theoretical basis for sustainable use of generative AI.