Generative AI adoption and employee outcomes: a conservation of resources perspective on job crafting, career commitment, and the moderating role of liking of AI

Abstract While the integration of generative AI into employees’ workflows is increasingly prevalent in organizations, little is known about its implications for employees’ organizational behavior. This study applies the Conservation of Resources theory to examine how generative AI adoption affects employee outcomes—specifically voice quality, cyberloafing, and cheating behaviors—through the sequential mediating roles of job crafting and career commitment, while also considering the moderating effect of liking of AI. Data collected from 291 pairs of participants across two waves in Chinese enterprises reveal that generative AI adoption positively influences job crafting, expressed through three dimensions: seeking resources, seeking challenges, and optimizing demands. These dimensions individually mediate the positive relationship between generative AI adoption and career commitment, which in turn shapes employee outcomes. Notably, liking of AI amplifies the positive effects of seeking resources and optimizing demands on career commitment, with this effect being more pronounced among employees with higher liking of AI. However, this moderation does not hold for seeking challenges. The study concludes by discussing its theoretical and practical contributions.