Surviving the Network

Abstract

This thesis examines the role of network structure and centrality in influencing firm behavior and market resilience. Using the Text-Based Network Industry Classification (TNIC) dataset developed by Hoberg and Phillips and social network analysis (SNA), the study investigates competitive dynamics among U.S. publicly traded firms from 2013 to 2019. Key metrics such as clustering coefficients and centrality measures (degree, betweenness, and eigenvector) are analyzed to assess their impact on firm survival.

The findings reveal that the network exhibits characteristics of a scale-free structure, with dominant hubs playing a crucial role in maintaining structural cohesion and facilitating resource flow. In addition, central network positions are shown to enhance resilience by improving access to critical information and resources. However, excessive clustering poses risks, as it can lead to over-embeddedness, reduce adaptability, and create uncertainty regarding firm survival.

This research demonstrates that the benefits of centrality, including degree, betweenness, and eigenvector measures, depend on broader market dynamics and a firm’s ability to leverage its network position effectively. By contributing to the theoretical understanding of network-driven advantages, this study offers valuable insights for firms and policymakers seeking to improve market stability and manage systemic risks by improving their network positioning. It underscores the importance of strategic adaptability in interconnected markets, highlighting the delicate balance between collaboration and competition in shaping firm and network outcomes.