Abstract
With the continuous advancement of artificial intelligence (AI) technology-particularly the widespread adoption of generative AI-the application of AI tools in the design field has become increasingly prevalent, demonstrating considerable potential for innovation. However, existing research has primarily focused on the acceptance and use of AI technologies by general users, while relatively limited attention has been paid to the behavioral mechanisms of professional creative users, such as digital media designers, in subscribing to advanced AI-assisted drawing tools. Prior studies have often overlooked the nuanced decision-making processes and unique cognitive factors that shape subscription behavior within creative work contexts. To address this gap, the present study draws on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and their extended frameworks, integrating grounded theory's three-stage coding approach with covariance-based structural equation modeling (CB-SEM) to construct and validate an innovative research framework that bridges qualitative exploration with quantitative validation. Seven key variables-perceived trust, perceived usefulness, perceived ease of use, social influence, output quality, personal innovativeness, and price value-were identified through semi-structured interviews and empirically examined based on data from 394 participants. The results indicate that perceived ease of use indirectly influences subscription intention through perceived usefulness, while output quality affects it through the mediating role of perceived trust. Social influence, price value, and perceived trust all exhibit significant positive effects on subscription intention, whereas personal innovativeness does not demonstrate a statistically significant impact. Theoretically, this study extends the applicability of TAM and UTAUT to the relatively under-researched context of the creative industry, achieves methodological innovation through the integration of qualitative and quantitative approaches, and provides practical guidance and a decision-making foundation for AI tool developers and the intelligent transformation of the creative sector.