Our article aims to answer the call for studies on new perspectives of complex projects and their governance. We adopt the social network approach to investigate the implications of network relations for the governance of project networks. We analyze quantitative and qualitative data following two theoretical models: flow and coordination. Our results show how the supply, contractual, and information networks influence the governance of project networks. We contribute to the literature explaining the dependence of the project network governance to network relations. It is necessary to use different theoretical models to analyze the coordination and control of complex project networks.

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PurposeThe purpose of this article is to compare design choices and assess the structural complexity of six manufacturing supply chains (SCs) of the Brazilian wind turbine industry.Design/methodology/approachThe research method is quantitative modeling. This study adopts the social network perspective to provide a broad set of network metrics for comparative analysis and characterization of the structural configuration and complexity of SCs. Transaction costs and the risk of disruption supported the metrics employed in the study. Network size, network density, core-size and centralization metrics stem from transaction costs, whereas constraint and betweenness centrality stem from risk of disruption.FindingsThe main conclusion is that, in the Brazilian wind manufacturing industry, increasing the SC structural complexity by adding redundant ties to minimize disruption risks, even implying higher transaction costs, increases the capacity to win orders.Research limitations/implicationsOnly the Brazilian wind turbine industry was studied. Therefore, findings are not general, but specific, to the case.Practical implicationsManagers and practitioners of the Brazilian wind turbine industry should focus on increasing the complexity of their SCs, even if it increases transaction costs, to ensure due dates compliance in orders.Originality/valueTo the best of the available knowledge, there is no commonly accepted or shared measurement for SC complexity, and this study proposed an alternative approach to bridge this research gap, the structural perspective of social networks. Traditional measures were complemented by new metrics, and the power of the application of social network analysis to SC investigations was empirically demonstrated in different levels of analysis.

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