ArticlePloS one2026
The autonomy paradox in AI-generated content adoption: Creative-specific alternative to TAM model in China's micro-short drama industry.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In China's booming micro-short drama industry, Artificial Intelligence Generated Content (AIGC) presents creators with an 'autonomy paradox': improving efficiency while sparking fears of lost control, amplified by collectivist culture that heightens tensions between AI-driven productivity and loss of autonomy. Based on a mixed-methods study of 607 micro-short drama creators, this research proposes and tests the Creative Industries Technology Acceptance Model (CITAM), which builds upon TAM foundations while adapting constructs for creative contexts to reveal key dynamics in adoption intentions. Building upon TAM theoretical foundations while introducing innovation compatibility (IC) and creative autonomy retention (CAR), CITAM is grounded in Diffusion of Innovations and Self-Determination Theory to address both rational and psychological adoption factors in creative contexts. Using a mixed methods approach with SEM in 607 surveys and 10 in-depth interviews, the results reveal that CAR positively influences AIGC adoption through IC as a mediator, while CAR negatively moderates the positive influence of IC on adoption intentions, highlighting a modest but significant psychological tension. Qualitative insights on 'Creativity Amplification' complement this, showing that creators perceive AIGC as an idea enhancer, not a replacement for the essential 'human spark.' CITAM provides a customized extension of TAM for creative industries, offering practical guidance. The findings can help developers design tools that preserve the agency of the creator and inform policy makers about balancing AIGC innovation with creator rights. These discoveries offer an initial framework for the adoption of ethical AI in the global creative economy, calling for cross-cultural validation to improve generalizability in AI-driven creative ecosystems.
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