Generative AI usage and employee innovation performance: the chain mediation mechanism of resource acquisition, integration, and activation

Background: Generative artificial intelligence is profoundly reshaping knowledge work, yet the cognitive mechanisms through which AI use relates to employee innovation performance remain underexplored. Objective: Integrating Conservation of Resources theory and Cognitive Appraisal Theory, this study proposes that generative AI use is associated with innovation performance through a three-stage partially serial mediation pathway: resource acquisition (cognitive divergence), resource integration (cognitive elaboration), and resource activation (challenge appraisal), with work stress moderating each stage. Methods: Survey data from 612 knowledge workers were analyzed using structural equation modeling and Monte Carlo bootstrap methods. Results: (1) Generative AI use is positively associated with employee innovation performance; (2) the three-stage serial mediation model received support, with indirect effects accounting for 72.9% of the total effect; and (3) work stress moderation exhibits a stage-dependent pattern: non-significant at the resource acquisition stage but significantly strengthening mediation efficiency at the integration and activation stages. Conclusion: This study advances a chain mediation framework that offers a more coherent theoretical account of AI-enabled cognitive transformation and provides organizations with specific intervention points for shifting from tool adoption to cognitive empowerment.