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Knowledge transfer is a crucial ingredient of employee innovation, yet affective work events may disrupt knowledge flow among employees. This study aims to investigate a previously overlooked, yet frequently occurring affective work experience, namely, that of being envied, and examine how perceptions of being envied may drive contrastive knowledge behaviors of sharing and hiding, which subsequently impact employee innovation. The study further examines how the zero-sum game beliefs of the envied individual may moderate these mechanisms.

This study builds on territorial and belongingness theories to delineate the contrastive motivations for knowledge hiding and knowledge sharing. This study tests a moderated mediation model through a multisource survey design involving 225 employees.

The results support the notion that perceptions of being envied are linked to both knowledge hiding and knowledge sharing; however, the indirect effect of being envied on innovation is observed only through knowledge sharing. The indirect positive link between perceptions of being envied and innovation via knowledge sharing is weakened when the envied employee holds high zero-sum game beliefs.

This study advances knowledge scholarship by identifying and testing the organizationally relevant but largely overlooked antecedent of being envied at work. The results provide useful insights to practitioners on how sharing or hiding knowledge serves as a strategic asset in response to being envied at work and how this may in turn impact employee innovation.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.