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The underlying geometry of organizational dynamics: similarity-based social space and labor flow network communities

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  • Hernan Mondani

    (Stockholm University)

Abstract

In this article, we use Swedish longitudinal register data to study the effect that similarity in organizational properties has on the interaction between organizations. We map out the social space of large organizations in the Stockholm Region and the interplay between social distance and the network communities of employee movements between organizations. We firstly use homogeneity analysis to describe the dynamics of organizations in terms of the time evolution of their similarity. Our results show that most categorical variables are quite stable over time. Organizations linked through employee movement edges have a lower average distance in social space than non-linked organizations. Secondly, we look at network community dynamics in social space. Employee flows between organizations in different communities exhibit a so-called gravity law from spatial statistics, decaying more slowly than observed geographical networks, meaning that employees reach out regions of social space further than of physical space. Finally, the rate of change of distance in homogeneity space exhibits a statistical distribution similar to the ones found in various other growth processes in natural and man-made systems.

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  • Hernan Mondani, 2018. "The underlying geometry of organizational dynamics: similarity-based social space and labor flow network communities," Computational and Mathematical Organization Theory, Springer, vol. 24(3), pages 378-400, September.
  • Handle: RePEc:spr:comaot:v:24:y:2018:i:3:d:10.1007_s10588-017-9260-6
    DOI: 10.1007/s10588-017-9260-6
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    7. Hernan Mondani & Petter Holme & Fredrik Liljeros, 2014. "Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.
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    Cited by:

    1. Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.

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