Exploring designers’ continuance usage intention of AI-assisted design tools: integrating TTF, design processes characteristics, TAM, creative self-efficacy, and social influence
Peiyao Cheng, Wanting Sun, Quanzhen Huang, Shumeng Hou · Journal of Engineering Design · 2026
Generative Artificial intelligence (GAI) technology drives numerous applications to boost work productivity. The AI-assisted design tools, such as Midjourney and Stable Diffusion, have been frequently used by designers. Despite the popularity of these AI-assisted design tools, we still lack a comprehensive understanding of how these tools assist the design tasks and design processes, influence designers’ perceptions, including objective and subjective perspectives, and continuance intentions of using them. This study addresses this gap by proposing a conceptual framework that integrates task-technology fit theory to capture uniqueness of design tasks, characteristics of design process to reflect the influences on design processes, technology acceptance model to learn designers’ objective perceptions of these tools, creative self-efficacy to reflect designers’ subjective perceptions of design processes, and social influences. A survey was conducted (N = 269) and the data was analysed through PLS-SEM and ANN. Results show that designers’ continuance intention of using AI-assisted design tools is predicted by perceived usefulness, perceived ease of use, creative self-efficacy, and social influence. These factors are further explained by their perceived fit between design tasks and AI-assisted design tools as well as characteristics of design processes (i.e. flexibility, originality, elaborateness). Implications for theory and practice are discussed.