Unveiling Generative Adversarial Network Adoption in Design Processes

With rapid advancements in artificial intelligence, Generative Adversarial Network (GAN) technology is increasingly embedded in designers' workflows, supporting a range of tasks from basic to complex. Despite its potential, the adoption of GAN technology in design processes faces challenges such as user acceptance and the understanding of its capabilities. In order to address this gap and challenge, this research analyzed responses from 376 valid participants (from an initial 439), using Partial Least Squares Structural Equation Modeling (PLS-SEM). Eight hypotheses were tested, which four were accepted and the rest rejected. Results indicated that perceived usefulness, ease of use, task-technology fit, and computer anxiety do not significantly impact behavioral intention toward GAN adoption. In contrast, social influence, perceived self-efficacy, and perceived enjoyment showed a positive impact on adoption intentions. Additionally, this study has found that Generation Z respondents are particularly influenced by social trends and enjoyment, which are factors that often outweigh traditional motivators like ease of use. These findings underscore the importance of community influence and enjoyment in driving technology adoption among younger designers. In the end, this study highlights important factors for integrating GAN technology into creative processes to provide insights, and foster innovation for technology developers, educators, and designers.