Revealing the Role of Technostress in the Continuous Intention of Using AIGC Among Designers: An Investigation Based on UTAUT and TTF Models

As artificial intelligence continues to evolve, its application in the realm of design through Artificial Intelligence Generated Content (AIGC) has seen a marked increase. However, limited research exists on the effects of technostress and task-technology fit on designers’ sustained usage intentions toward AIGC. This study aims to identify key factors influencing designers’ continuous intention to use AIGC. It integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Task-Technology Fit (TTF) model and introduces technostress as a moderating variable. A theoretical framework was constructed to explain the continuous intention of using AIGC among designers. The research utilized a stratified purposive sampling method to enlist participants, gathering data from 443 design students and professional designers in China. Data were analyzed using structural equation modeling. The results reveal that: (a) designers’ performance expectancy from AIGC has a strong positive effect on continuous intention; (b) both technology and task characteristics substantially enhance task-technology fit, which in turn indirectly augments continued intention via user satisfaction; (c) an interaction between UTAUT and TTF models enhances performance expectancy through task-technology fit; (d) technostress negatively moderates the impact of performance and effort expectancy, along with social influence and user satisfaction, on continuous intention. These results provide actionable insights for the design industry to facilitate the effective and durable adoption of AIGC. Additionally, the study offers strategic recommendations for AIGC developers and managers to optimize user experience and sustain user engagement.