Structural anatomy and evolution of supply chain alliance networks: A multi-method approach

摘要

We investigate the evolution of supply chain alliance networks with a focus on the influence of structural, firm-, and industry-level mechanisms. While several structural supply chain characteristics have been found to be significant drivers of firm innovation and performance, a dearth of studies exists examining how these characteristics change over time by influencing one another. We develop and empirically test hypotheses on the impact of prior structural configurations and the moderating roles of absorptive capacity and industry growth on the temporal trajectory of supply chain alliance network structures. Adopting a multi-method approach, we jointly use econometric analyses and simulation experiments to examine our hypotheses from complementary angles. Specifically, we characterize the dynamic relationship between the structural mechanisms on a longitudinal dataset of 2221 unique firms and 13,668 firm-year observations spanning 25 years. We find empirical support for negative crossover effects between two key structural properties of supply chain alliance networks, a positive moderation of a firm's absorptive capacity, and a negative moderation of industry growth on the structural reinforcement. We conduct corresponding simulation experiments based on a separable temporal exponential random graph model (STERGM) to track the temporal changes in the simulated networks' key measures. The simulation results concur with most of our empirical findings and provide additional insights complementary to our econometric analysis results. By focusing on the mechanism of temporal changes in network structural properties, our study contributes to supply chain management research with a supply network perspective and interfirm alliance network research by broadening its scope into structural dynamism. Our multi-method approach demonstrates how multiple complementary methodologies can foster a more nuanced understanding of managing supply chain alliance network management.