Unleashing innovation: how fit between organizational generative AI adoption and employee AI literacy drives creative performance

Abstract

As organizations increasingly pursue top-down adoption of generative artificial intelligence (GenAI) to drive innovation and efficiency, a key challenge is whether rising GenAI-related job demands are aligned with employees' AI abilities, such as AI literacy, in ways that support their creative performance. Prior research has largely treated organizational GenAI adoption and AI literacy as separate drivers of innovation, paying limited attention to whether GenAI-related job demands match employees' AI literacy. Drawing on person-job (P-J) fit theory, we conceptualize organizational GenAI adoption as a source of new job demands and employees' AI literacy as a key individual ability, and examine how their alignment affects creative performance, as well as the role of work motivation in this process. We collected multi-wave data from 324 leader-employee dyads and tested the hypotheses using polynomial regression and response surface analysis. We find that the fit between organizational GenAI adoption and employees' AI literacy is positively associated with employee creative performance and that high-high fit yields higher employee creative performance than low-low fit. Among misfit configurations, overqualification is more conducive to creative performance than underqualification. Furthermore, work motivation (including autonomous and controlled motivation) mediates the relationship between fit and creative performance. From a P-J fit perspective, this study enhances our understanding of how the fit between organizational GenAI adoption and employee AI literacy shapes employee outcomes, thereby contributing to the literature on GenAI-enabled innovation.