Explaining designers’ adoption of AIGC image generation tools: A configurational analysis integrating PLS-SEM and fsQCA

Generative artificial intelligence is reshaping how concept generation, visual exploration, and solution iteration are organized in design tasks, yet designers’ behavioral intention toward AIGC image-generation tools is difficult to explain through efficiency or ease of use alone. This study places UTAUT2 and the task–technology fit model within a unified analytical framework and incorporates personal innovativeness and design ethics to examine the associations among design task characteristics, AIGC technology characteristics, adoption evaluations, designers’ characteristics, and behavioral intention. Based on questionnaire data from 338 respondents with self-reported prior use of AIGC image-generation tools, this study employs PLS-SEM to examine average associations among variables and combines it with fsQCA to identify configurations associated with high behavioral intention and non-high behavioral intention. The PLS-SEM results show that task–technology fit is significantly and positively associated with behavioral intention, performance expectancy, effort expectancy, and price value, while performance expectancy, effort expectancy, hedonic motivation, price value, and habit are also significantly and positively associated with behavioral intention. The direct paths from social influence, facilitating conditions, personal innovativeness, and design ethics do not reach statistical significance; in this study, design ethics mainly concerns awareness of copyright, intellectual property, creative attribution, and transparent use. The baseline fsQCA identifies three configurations associated with high behavioral intention. A2 and A3 combine hedonic motivation with task–technology fit, facilitating conditions, habit, and other conditions and remain under the more stringent case-frequency threshold. A1 contains six cases, has relatively low raw coverage, and disappears when the case-frequency threshold is increased, supporting an exploratory interpretation of this threshold-sensitive configuration. Configurations associated with non-high behavioral intention are not simple reversals of those associated with high behavioral intention. The findings suggest that designers’ evaluations of AIGC are grounded in the fit between technological capabilities and design tasks, while hedonic motivation, task–technology fit, facilitating conditions, price value, and habit occupy different positions across configurations. These findings may provide reference points for AIGC tool development, design organization management, and designer capability development.