The mining sector faces a complex dilemma as an economic development agent through social upliftment in places where mining corporations operate. Resource extraction is destructive and non-renewable, making it dirty and unsustainable. To ensure corporate sustainability, this paper examines the effects of knowledge management (KM), organizational learning (OL), and innovation capability (IC) on Indonesian coal mining’s organizational performance (OP). We used factor and path analysis to examine the relationships between the above constructs. After forming a conceptual model, principal component analysis validated the factor structure of a collection of observed variables. Path analysis examined the theories. The hypothesized framework was confirmed, indicating a positive association between constructs. However, due to mining industry peculiarities, IC does not affect organizational performance (OP). This study supports the importance of utilizing people and their relevant skills to improve operational performance. The findings have implications for managers of coal mining enterprises, as they suggest that KM and OL are critical drivers of OP. Managers should focus on creating an environment that facilitates knowledge sharing and learning, as this will help improve their organizations’ performance.

Universities play a key role in university-industry-government interactions and are important in innovation ecosystem studies. Universities are also expected to engage with industries and governments and contribute to economic development. In the age of artificial intelligence (AI), governments have introduced relevant policies regarding the AI-enabled innovation ecosystem in universities. Previous studies have not focused on the provision of a dynamic capabilities perspective on such an ecosystem based on policy analysis. This research work takes China as a case and provides a framework of AI-enabled dynamic capabilities to guide how universities should manage this based on China’s AI policy analysis. Drawing on two main concepts, which are the innovation ecosystem and dynamic capabilities, we analyzed the importance of the AI-enabled innovation ecosystem in universities with governance regulations, shedding light on the theoretical framework that is simultaneously analytical and normative, practical, and policy-relevant. We conducted a text analysis of policy instruments to illustrate the specificities of the AI innovation ecosystem in China’s universities. This allowed us to address the complexity of emerging environments of innovation and draw meaningful conclusions. The results show the broad adoption of AI in a favorable context, where talents and governance are boosting the advance of such an ecosystem in China’s universities.