Sustainable supply network as complex adaptive system : temporal exponential random graph modelling

Abstract

The aerospace and defense (A&D) industry is under growing pressure to decarbonize, requiring a deeper understanding of how supply chains adapt to environmental sustainability initiatives. This thesis investigates the dynamics of sustainable supply networks in the A&D industry, conceptualizing them as complex adaptive systems. By utilizing Temporal Exponential Random Graph Models (TERGM) and data from the Bloomberg and Refinitiv Eikon databases, this research examines how environmental sustainability pressures influence the formation of ties within the A&D supply network from 2018 to 2022. The analysis reveals that reciprocity, geographic proximity, shared industry, and alignment on emission policies are significant factors in tie formation. Interestingly, the presence of an emission policy emerges as a key driver of collaboration formation, while the significance of market capitalization and firm age in tie formation appears to change. These findings offer valuable insights into how supply networks adapt to sustainability pressures, setting a foundation for fostering greener practices in the A&D industry.