The Relationship between Ambidextrous Knowledge Sharing and Innovation within Industrial Clusters: Evidence from China
This study examines the influence of ambidextrous knowledge sharing in industrial clusters on innovation performance from the perspective of knowledge-based dynamic capabilities.
The key factor to improving innovation performance in an enterprise is to share knowledge with other enterprises in the same cluster and use dynamic capabilities to absorb, integrate, and create knowledge. However, the relationships among these concepts remain unclear. Based on the dynamic capability theory, this study empirically reveals how enterprises drive innovation performance through knowledge sharing.
Survey data from 238 cluster enterprises were used in this study. The sample was collected from industrial clusters in China’s Fujian province that belong to the automobile, optoelectronic, and microwave communications industries. Through structural equation modeling, this study assessed the relationships among ambidextrous knowledge sharing, dynamic capabilities, and innovation performance.
This study contributes to the burgeoning literature on knowledge management in China, an important emerging economy. It also enriches the exploration of innovation performance in the cluster context and expands research on the dynamic mechanism from a knowledge perspective.
Significant relationships are found between ambidextrous knowledge sharing and innovation performance. First, ambidextrous knowledge sharing positively influences the innovation performance of cluster enterprises. Further, knowledge absorption and knowledge generation capabilities play a mediating role in this relationship, which confirms that dynamic capabilities are a partial mediator in the relationship between ambidextrous knowledge sharing and innovation performance.
The results highlight the crucial role of knowledge management in contributing to cluster innovation and management practices. They indicate that cluster enterprises should consider the importance of knowledge sharing and dynamic capabilities for improving innovation performance and establish a multi-agent knowledge sharing platform.
Researchers could further explore the role of other mediating variables (e.g., organizational agility, industry growth) as well as moderating variables (e.g., environmental uncertainty, learning orientation).
This study provides a reference for enterprises in industrial clusters to use knowledge-based capabilities to enhance their competitive advantage.
Future research could collect data from various countries and regions to test the research model and conduct a comparative analysis of industrial clusters.