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This study aims to understand the impact of student entrepreneurship and university support on faculty intrapreneurship. The authors also analyze the role of the university’s dynamic and ordinary capabilities and the environmental dynamism in which the university is embedded.

With a large survey data set involving 680 professors and 2,230 students from 70 Brazilian universities, the authors use a multimethod approach with partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA).

The PLS-SEM results demonstrate that student entrepreneurship indirectly influences faculty intrapreneurship through the interaction of students with faculty and entrepreneurs, in addition to proving the intense influence of university support on faculty intrapreneurship, especially in a slow-growth environment. Additionally, the authors confirmed the moderating effect of universities’ dynamic and ordinary capabilities on student interaction and university support, respectively, and some exciting differences considering the ecosystem dynamism. The fsQCA results deepened the differences between environments, presenting different configurations between the antecedents that lead to high levels of faculty intrapreneurship in fast and slow-growth environments.

The study makes a unique and significant contribution to the literature on faculty intrapreneurship by examining the cross-interactions between individual, organizational and environmental levels about the promotion of faculty intrapreneurship. From a practical point of view, it is possible to identify more effective, innovative and systematic ways to encourage faculty intrapreneurship in a developing country. The findings help open up the black box of faculty intrapreneurship.

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.