# Exploring Designer Trust in Artificial Intelligence-Generated Content: TAM/TPB Model Study
## 1 Introduction
This research not only enhances the generality and compatibility of AIGC across various industries but also supports the intelligent transformation of the creative design industry, facilitating the upgrade from AI-generated content to Artificial Intelligence-Generated Design (AIGD). In this study, the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) were employed to explain changes in designers' behavior. Previous behavioral models often explored the impact of trust or perceived risk on behavior change in isolation, merely confirming that trust in technology significantly positively influences its extended use, without considering the influence of perceived risk on usage behavior. Based on these classic theoretical foundations, this paper adopts an integrated model of TAMs and the TPB as the primary research framework. It primarily examines the hierarchical relationship between the antecedent factors in the TAM/TPB and perceived risk, as subdivided into specific risk dimensions, and their relationship with trust. Furthermore, it establishes a behavioral model that affects users' willingness to use AIGC. This provides a theoretical basis for the widespread use and rapid development of AIGC in the future and offers theoretical support and practical guidance for AIGC technology providers to understand designers' risk perceptions and implement risk mitigation measures. The research results showed a significant gap from the expected outcomes, with only 4 out of the 11 proposed hypotheses being supported. This indicates that, in the field of art and design, designers hold a generally negative attitude towards AIGC. This finding highlights areas for improvement in the future development of artificial intelligence technology. To explore the key factors influencing designers' behavior, our research focuses not merely on the binary judgment of users accepting or rejecting new technology but delves deeper into the underlying logic behind their acceptance or rejection of new technology. Our aim is to uncover the risk factors or trust elements that users consider when making decisions, as well as the reasons and motivations behind their attitudes towards new technology. By doing so, we can adjust the work methods and content of future designers to better meet users' needs and expectations. Users' attitudes towards new technology are influenced not only by personal preferences but also by a combination of factors. These factors may include users' perceptions of the technology, its usability and ease of use, its potential impact on their daily lives, and the interaction of personal and social factors. By deeply researching these factors, we can better understand the process of attitude formation towards new technology. Analyzing the logic behind users' acceptance or rejection of new technology can provide valuable insights for future designers. These insights can guide designers in considering users' expectations and needs when developing new technologies, thereby better meeting users' expectations. Additionally, by adjusting designers' work methods and content, we can improve the acceptability and adoption rate of new technologies, further promoting technological advancement and societal progress. In conclusion, this study delves into the underlying logic behind users' acceptance or rejection of new technology and uses these findings to improve future designers' work methods and content. This has a positive impact on enhancing collaborative innovation between designers and AIGC, not only increasing designers' work efficiency but also optimizing their user experience when using new technology [5]. Its contributions are as follows: on one hand, from the perspective of technology providers, it offers users more trustworthy, user-friendly, and efficient products; on the other hand, from the perspective of user acceptance and understanding, it seeks the best collaborative innovation model with AIGC.