The cognitive acceptance of generative AI image tools based on TPB-TAM model and multi-theory integration

The rapid development and application of generative AI image tools are profoundly reshaping the landscape of image generation. As a primary user group, designers' acceptance of these tools directly impacts their application effectiveness and industry trends. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), extracts key variables from the Information Systems Success Model (ISSM) and the theory of perceived risk, and introduces the concepts of user experience and technological anxiety to construct a comprehensive model of designers' behavioral intentions to use generative AI image tools across different design types. Using AMOS software and the CB-SEM structural equation model to analyze valid data, this study reveals the significant impact of subjective norms, cognitive attitudes, and perceived behavioral control on usage intention in different design contexts. It also highlights the differentiated influence of external variables, such as system information quality, on intermediary variables like subjective norms. Through these analyses, the study clarifies the specific impact mechanisms of various external variables on behavioral intention. This study offers a new perspective on understanding designers' cognitive acceptance of generative AI image tools and proposes differentiated promotion and training strategies, providing valuable guidance for industry practice.