Leveraging generative AI for role allocation: effects on person–role fit and research team performance

Purpose This exploratory study examines whether the explanatory power of established technology-perception mechanisms extends beyond GenAI acceptance to individual researchers' perceptions of research team performance. It integrates perceived ease of use and perceived usefulness with team composition quality and person–role fit to explore how technological and human factors jointly explain perceptions of performance in GenAI-supported research teams. Design/methodology/approach The study used an exploratory quantitative design. Survey data were collected from 140 members of research teams who use GenAI for role allocation. Partial least squares structural equation modelling (PLS-SEM) was used to test direct and indirect relationships among technology perceptions, team composition, person–role fit, and perceived research team performance. Findings Higher perceived research team performance is primarily associated with technology-related perceptions rather than structural factors. Perceived usefulness has a significant positive relationship with perceived research team performance and mediates the effect of perceived ease of use on perceived performance. Team composition quality contributes to person–role fit, although person–role fit does not exert a strong direct effect on perceived performance. Originality/value Rather than proposing a new technology acceptance mechanism, the study offers an exploratory theoretical extension of the technology acceptance model (TAM) by shifting attention from adoption intentions to perceived collective performance after GenAI has entered team workflows. One contribution lies in integrating TAM mechanisms with team composition and person–role fit within a socio-technical framework. The study also contributes by identifying perceived usefulness as the principal mechanism connecting perceived ease of use with perceived research team performance. The non-significant relationships of person–role fit reveal a boundary condition of purely human-centred explanations in GenAI-supported teamwork.