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Multilevel social spaces: The network dynamics of organizational fields

Published online by Cambridge University Press:  06 June 2017

JAMES HOLLWAY,
ALESSANDRO LOMI,
FRANCESCA PALLOTTI and
CHRISTOPH STADTFELD
JAMES HOLLWAY
Affiliation:
Department of International Relations/Political Science, Graduate Institute of International and Development Studies, Chemin Eugène-Rigot 2A, 1211, Genève, Switzerland (e-mail: james.hollway@iheid.ch)
ALESSANDRO LOMI
Affiliation:
University of Italian Switzerland, Via Buffi 13, Lugano 6900, Switzerland (e-mail: alessandro.lomi@usi.ch)
FRANCESCA PALLOTTI
Affiliation:
Department of International Business and Economics, University of Greenwich, Old Royal Naval College, Park Row, London, SE10 9LS, United Kingdom (e-mail: f.pallotti@greenwich.ac.uk)
CHRISTOPH STADTFELD
Affiliation:
Chair of Social Networks, ETH Zürich, Clausiusstrasse 50, 8092, Zürich, Switzerland (e-mail: christoph.stadtfeld@ethz.ch)
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Abstract

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In this paper, we seek to advance an updated concept of social space that integrates the multilayer and dynamic statistical network methods currently at the disposal of social network researchers. We demonstrate the analytic value of the new concept of social space that we propose with the help of an illustrative analysis of an organizational field involving organizations' external and internal decisions that congeal into a multilevel system of action that shapes the space of possibilities for other participants in the field. Through these internal and external decisions, organizations seek certain positions in their social space while simultaneously modifying that social space over time. We conclude by arguing that network researchers' choices of goodness-of-fit statistics should reflect a consideration about the dimensions of social space of most interest to the nodes involved.

Keywords

affiliation networkcollaborationcompetitiongoodness-of-fitinterorganizational networkmultilevel networkmultilevel triad censusorganizational changeorganizational fieldssocial space

Information

Type
Research Article
Information
Network Science , Volume 5 , Special Issue 2: Modeling, Analysis, and Mining of Multilayer Networks , June 2017, pp. 187 - 212
Copyright
Copyright © Cambridge University Press 2017 

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