Fueling innovation and mitigating burnout: the dual impact of organizational learning in AI-enabled change
Yue Yang, Guoqian Xi, Liming Zhang, Qicheng Lu, Bolin WangPurpose
This study investigates how organizational learning influences employee innovative behavior in the context of rapid artificial intelligence (AI) development and AI-enabled organizational change. It explores the dual mechanisms of knowledge acquisition and psychological strain, specifically focusing on the roles of absorptive capacity and job burnout.
Design/methodology/approach
Drawing on conservation of resources (COR) theory and social exchange theory (SET), this study develops and tests a dual-pathway moderated mediation model. Data were collected through a two-wave, time-lagged survey of 373 employees in Chinese enterprises experiencing AI-related workplace changes. The data were analyzed using confirmatory factor analysis, regression-based mediation and moderation analyses, and bootstrapped conditional indirect-effect analysis.
Findings
The results indicate that organizational learning supports employee innovation through two mediating pathways: by enhancing absorptive capacity and by reducing job burnout. Knowledge sharing and employee–AI collaboration further shape these pathways. Specifically, both contextual factors strengthen the learning-related stages of the cognitive and emotional pathways, while employee–AI collaboration also buffers the negative relationship between job burnout and employee innovative behavior in AI-enabled work settings.
Originality/value
These findings advance the understanding of cognitive and emotional resource mechanisms in AI-enabled workplaces. The study highlights the dual role of organizational learning in facilitating both knowledge resource gain and psychological resource protection during technological transformation and organizational change.