Examining communication network behaviors, structure and dynamics in an organizational hierarchy: A social network analysis approach

Effectively understanding and enhancing communication flows among employees within an organizational hierarchy is crucial for optimizing operational and decision-making efficiency. To fill this significant gap in research, we propose a systematic and comprehensive social network analysis approach, coupled with a newly formulated communication vector and matrix, to examine communication behaviors and dynamics in an organizational hierarchy. We use the Enron email dataset, consisting of 619,499 emails, as an illustrative example to bridge the micro-macro divide of organizational communication research. A series of centrality measures are employed to evaluate the influential ability of individual employees, revealing descending influential ability and changing behaviors according to hierarchy. We also uncover that employees tend to communicate within the same functional teams through the identification of community structure and the proposed communication matrix. Furthermore, the emergent dynamics of organizational communication during a crisis are examined through a time-segmented dataset, showcasing the progressive absence of the legal team, the responsibility of top management, and the presence of hierarchy. By considering both individual and organizational perspectives, our work provides a systematic and data-driven approach to understanding how the organizational communication network emerges dynamically from individual communication behaviors within the hierarchy, which has the potential to enhance operational and decision-making efficiency within organizations. • Develop a communication vector and matrix to describe communication patterns. • Uncover latent links between organizational hierarchy and communication network. • Reveal varying communication behavior and importance of employees based on their roles. • Demonstrate the impact of crises on organizational communication dynamics.