Innovation communities (ICs), comprising groups of innovators engaged in frequent collaboration and knowledge exchange, play a pivotal role as a driving force behind knowledge-based innovation. It is not well understood how the knowledge structure of ICs impacts their innovation performance. This study applies network analysis methods to identify 18,654 ICs from 20 years of patent collaboration data worldwide. Based on the data, we measure both knowledge redundancy and knowledge complementarity to understand ICs’ knowledge structure. The empirical analyses indicate an inverted U-shaped relationship between knowledge redundancy and innovation performance in ICs, and this relationship becomes weaker with higher levels of knowledge complementarity. These findings provide new insights into the configuration of knowledge bases at the IC level. A deeper understanding of the ICs’ knowledge structure will help all innovation organisations overcome innovation-related challenges in uncertain and turbulent environments.
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