AI Insight
This study examined how generative AI use relates to employee innovation performance among 612 knowledge workers. The research found that AI use connects to innovation through a three-stage cognitive process: acquiring diverse perspectives (cognitive divergence), integrating information (cognitive elaboration), and perceiving challenges positively (challenge appraisal), with these indirect pathways accounting for 72.9% of the total effect. Work stress was found to strengthen the relationship at the integration and activation stages but not during initial resource acquisition.
Why it matters
The findings provide organizations with specific intervention points for maximizing AI's impact on employee innovation, suggesting that simply adopting AI tools is insufficient without supporting the underlying cognitive processes. Understanding how work stress differentially affects each stage can help managers design better implementation strategies for generative AI in knowledge work environments.
Understand the Science
BackgroundGenerative artificial intelligence is profoundly reshaping knowledge work, yet the cognitive mechanisms through which AI use relates to employee innovation performance remain underexplored.ObjectiveIntegrating 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.MethodsSurvey 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.ConclusionThis 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.