Social Capital, Cognitive Load and Innovation in a Competitiveness Cluster: The Combined Use of Symmetric (PLS-SEM) and Asymmetric (FsQCA) Approaches

This study aims to examine the effects of social capital and cognitive load on innovation through the mediating role of knowledge based dynamic capabilities (KBDC) in a competitiveness cluster. Data were collected online from enterprises in the Technopark of Casablanca, Morocco. The authors used Partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to enhance the findings. The results of PLS-SEM showed that network density and trust significantly, directly or indirectly, influence knowledge combination capabilities and innovation. However, the findings from fsQCA underscored manifold combinations among the respondents by emphasizing the positive effects of network centrality, information load and knowledge generation capabilities on innovation among enterprises in the competitiveness cluster. This study is the first to apply cognitive load theory as a novel theoretical lens, alongside with social capital theory, to provide a comprehensive understanding of the antecedents of innovation by focusing on inter-organizational level. It also assesses the mediating effect of KBDC between dimensions of social capital and cognitive load on innovation. Using both symmetric (PLS-SEM) and asymmetric approaches (FsQCA), this study identifies the predictors as well as sufficient combinations of dimensions to predict innovation in the context of competitiveness clusters. This study posed several theoretical and practical implications that will benefit future researchers and practitioners in the context of competitiveness clusters. This implies that public policies should concentrate on establishing a conducive environment to foster social capital, promoting knowledge circulation and encourage innovation.