The Cognitive Structure of Clusters: Conceptualizing the Knowledge Based Cluster Theory

The knowledge-based cluster theory (KBCT) has been used to explain how clusters enable knowledge flows and innovation based on the physical and cognitive proximities of the agents. Physical proximity allows learning to occur in more flexible ways, while cognitive proximity suggests the similarity of the knowledge base, which is fundamental for mutual understanding among clustered agents. Those two factors are the basis of an 'industrial atmosphere' of contextual knowledge dispersed in the air, which is important to foster innovation. Although the theory has been applied in many fields of research, as yet, there is no framework that consolidates its main elements and can be used to guide its application in empirical studies. The research paper proposes a conceptual framework to investigate the cognitive structure of clusters and its effect on knowledge flows and innovation. The framework is designed to capture the influence of intentional and serendipitous knowledge flows on innovation, based on vertical (cooperative) and horizontal (competitive) cognitive ties. The originality of this paper lies in its attempt to capture the effect of intentional and unintentional knowledge flows on innovation. The paper contributes to multiple research fields by proposing a conceptual framework to operationalize the KBCT. Specifically, the paper should be of use to researchers, managers, and decision-makers who wish to investigate environments with high potential to foster innovations and innovative behavior in clustered firms.