Exploring User Behavioral Intentions and Their Relationship With AI Design Tools: A Future Outlook on Intelligent Design

In the context of the swift advancement and integration of generative artificial intelligence (AI) technologies into the design realm, comprehending user acceptance of these advanced tools has emerged as a pivotal issue. The primary purpose of this research is to examine and understand the factors influencing user acceptance and behavioral intentions towards generative artificial intelligence (AI) technologies in the design field. To achieve this, this research delineates a multifaceted theoretical model, underpinned by an analysis of user behavioral intentions and their driving factors. The model encapsulates eight principal constructs: intention to continue use, self-efficacy, perceived usefulness, satisfaction, facilitating conditions, expectation confirmation, trust in technology, and social influence. An empirical examination of 13 related hypotheses was conducted. Utilizing data from 339 valid questionnaires, the outcomes of the structural equation modeling lent support to all posited hypotheses. The research delineates that users' sustained intention to utilize generative AI technology is directly contingent upon factors such as perceived usefulness, satisfaction, self-efficacy, and trust in technology. Notably, perceived usefulness and self-efficacy are identified as pivotal determinants of satisfaction. Furthermore, social influence and expectation confirmation are found to augment perceived usefulness, while facilitating conditions to enhance both self-efficacy and expectation confirmation. These findings yield novel insights into the theory of user behavior, charting a course for the refinement of generative AI design tools. Such enhancements are aimed at fostering a more extensive application and acceptance of these tools in the relevant fields.