# Exploring Designer Trust in Artificial Intelligence-Generated Content: TAM/TPB Model Study
## 2 Literature Review
### Theoretical Framework
#### 2.2.3 The Theoretical Model of This Study: Integration of TAMs and the TPB  
Although the integration of the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) is widely applied in research on user behavior change and technology adoption, studies on its application in the field of art and design within the context of artificial intelligence are relatively scarce. This scarcity can be attributed to two main reasons: On one hand, the rapid adoption of artificial intelligence technology across various domains has yet to yield a systematic and authoritative academic discourse. On the other hand, the art and design field, characterized by both rational and emotional aspects, demands innovation and uniqueness, which contrasts sharply with the standardized solutions offered by AIGC. This discrepancy highlights the challenges artificial intelligence will face in expanding its applications. Therefore, it is essential to propose new research models to explore the potential applications of artificial intelligence in the field of art and design. The integration of the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) is commonly used to understand consumers' attitudes and behavioral changes towards new technologies, including AI-based intelligent products [33]. This integrated model has established precedents across various fields. For instance, in traditional agriculture, scholar [PERSON] used TAMs and the TPB to study German farmers' acceptance of AI and the factors influencing their behavior [34]. In e-commerce, it has measured elderly people's continued willingness to engage in online shopping [35]. In higher education, it has explored students' acceptance of MOOCs and behavioral changes [36]. In healthcare, it has assessed the impact of COVID-19 on users' shopping behavior [37]. Scholar [PERSON] integrates the Theory of Planned Behavior (TPB) with Partial Least Squares Path Modeling (PLS-PM) in structural equation modeling (SEM), identifying personal attitude (AC) and perceived behavioral control (CCP) as key influencing factors in users' behavioral changes [28]. These studies collectively affirm the broad applicability of TAMs and the TPB, showing how these frameworks can help understand the factors affecting designers' attitudes and behavioral changes towards AI technology. A TAM focuses on two subjective variables--perceived usefulness and perceived ease of use--and their effects on user intentions and behaviors, while the TPB addresses two feedback variables influenced by external factors: subjective norms and perceived behavioral control. Integrating these models can further elucidate users' attitudes towards new technologies and predict behavioral changes. Many scholars have applied this combined model in various fields to enhance its explanatory power. By merging TAMs with the TPB, the limitations of each individual model are addressed, providing a comprehensive evaluation framework that includes perceived usefulness, perceived ease of use, subjective norms, and perceived behavioral control [38]. Consequently, this research, based on the integration of TAMs and the TPB, incorporates direct variables of functional and emotional risks, effectively explaining the key factors influencing designers' attitudes [39]. This study integrates the TAM/TPB research measurement model to explore the impact of three influencing factors--perceived characteristics, perceived risk, and trust--on designers' behavior, as well as the logical relationships among the antecedent factors. The structural equation model is employed to test the validity of the experimental hypotheses. Based on the original TAM/TPB framework, designers' perceived characteristics include perceived usability, perceived ease of use, and subjective norms. Considering designers' intrinsic traits, perceived risk is divided into functional risk and emotional risk. Trust refers to designers' willingness to use AIGC, which directly determines their actual usage behavior.