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Exploring communication networks to understand organizational crisis using exponential random graph models

Author

Listed:
  • Shahadat Uddin

    (The University of Sydney)

  • Jafar Hamra

    (The University of Sydney)

  • Liaquat Hossain

    (The University of Sydney)

Abstract

In recent social network studies, exponential random graph (ERG) models have been used comprehensively to model global social network structure as a function of their local features. In this study, we describe the ERG models and demonstrate its use in modelling the changing communication network structure at Enron Corporation during the period of its disintegration. We illustrate the modelling on communication networks, and provide a new way of classifying networks and their performance based on the occurrence of their local features. Among several micro-level structures of ERG models, we find significant variation in the appearance of A2P (Alternating k-two-paths) network structure in the communication network during crisis period and non-crisis period. We also notice that the attribute of hierarchical positions of actors (i.e., high rank versus low rank staff) have impact on the evolution process of networks during crisis. These findings could be used in analyzing communication networks of dynamic project groups and their adaptation process during crisis which could lead to an improved understanding how communications network evolve and adapt during crisis.

Suggested Citation

  • Shahadat Uddin & Jafar Hamra & Liaquat Hossain, 2013. "Exploring communication networks to understand organizational crisis using exponential random graph models," Computational and Mathematical Organization Theory, Springer, vol. 19(1), pages 25-41, March.
    • Handle: RePEc:spr:comaot:v:19:y:2013:i:1:d:10.1007_s10588-011-9104-8
    DOI: 10.1007/s10588-011-9104-8
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    References listed on IDEAS

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    1. Jana Diesner & Terrill L. Frantz & Kathleen M. Carley, 2005. "Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 201-228, October.
    2. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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