Relating supply network structure to productive efficiency: A multi-stage empirical investigation

Abstract

The potential of Social Network Analysis (SNA) to characterize supply network structure is of growing interest in supply chain management, although the related literature provides few empirical investigations. This study identifies those SNA measures most closely associated with supply chain efficiency, using archival inter-firm relationship data collected from U.S. public companies in multiple industries. In a three-stage procedure, a DEA model is applied to measure firm- and chain-level efficiencies, followed by a correlation analysis to group SNA variables into clusters of high correlation. These clusters are used in a step-wise regression algorithm to identify those variables most relevant to productive efficiency while accounting for multicollinearity. The supply network structural characteristics that emerge as significant are consistent with many hypothesized relationships in the literature, although not without exceptions, such as an interesting tradeoff between the benefit of connectedness and a penalty for closeness.