Beyond Adoption: The AITAD Framework for Sustaining AIGC’s Creative Revolution

The rapid proliferation of Artificial Intelligence Generated Content (AIGC) tools has shifted research focus from initial adoption to sustained engagement. Existing technology acceptance models primarily address initial adoption and inadequately capture AIGC’s generative capabilities and continuance dynamics. Integrating the Unified Theory of Acceptance and Use of Technology with Diffusion of Innovations theory, this study proposes and tests the AIGC Technology Acceptance and Diffusion (AITAD) framework. Through a three stage process—Reassessment, Confirmation, and Resolution—the framework models how performance expectancy, effort expectancy, and innovation attributes (relative advantage, compatibility, social influence) drive cognitive and emotional attitudes, shaping sustained use and mitigating discontinuance. Data from 486 AIGC users in China were analyzed using structural equation modeling. Results confirm that performance expectancy and compatibility significantly influence attitudes, with cognitive and emotional pathways mediating continuance intention. The AITAD framework advances theory by addressing the acceptance discontinuance anomaly and offers practical insights for developers, enterprises, and policymakers.