Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings
dc.contributor.author | White, Douglas R. | en_US |
dc.contributor.author | Owen-Smith, Jason | en_US |
dc.contributor.author | Moody, James | en_US |
dc.contributor.author | Powell, Walter W. | en_US |
dc.date.accessioned | 2006-09-11T15:10:55Z | |
dc.date.available | 2006-09-11T15:10:55Z | |
dc.date.issued | 2004-05 | en_US |
dc.identifier.citation | White, Douglas R.; Owen-Smith, Jason; Moody, James; Powell, Walter W.; (2004). "Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings." Computational & Mathematical Organization Theory 10(1): 95-117. <http://hdl.handle.net/2027.42/44715> | en_US |
dc.identifier.issn | 1381-298X | en_US |
dc.identifier.issn | 1572-9346 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/44715 | |
dc.description.abstract | Social action is situated in fields that are simultaneously composed of interpersonal ties and relations among organizations, which are both usefully characterized as social networks. We introduce a novel approach to distinguishing different network macro-structures in terms of cohesive subsets and their overlaps. We develop a vocabulary that relates different forms of network cohesion to field properties as opposed to organizational constraints on ties and structures. We illustrate differences in probabilistic attachment processes in network evolution that link on the one hand to organizational constraints versus field properties and to cohesive network topologies on the other. This allows us to identify a set of important new micro-macro linkages between local behavior in networks and global network properties. The analytic strategy thus puts in place a methodology for Predictive Social Cohesion theory to be developed and tested in the context of informal and formal organizations and organizational fields. We also show how organizations and fields combine at different scales of cohesive depth and cohesive breadth. Operational measures and results are illustrated for three organizational examples, and analysis of these cases suggests that different structures of cohesive subsets and overlaps may be predictive in organizational contexts and similarly for the larger fields in which they are embedded. Useful predictions may also be based on feedback from level of cohesion in the larger field back to organizations, conditioned on the level of multiconnectivity to the field. | en_US |
dc.format.extent | 316786 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Economics / Management Science | en_US |
dc.subject.other | Artificial Intelligence (Incl. Robotics) | en_US |
dc.subject.other | Management | en_US |
dc.subject.other | Operation Research/Decision Theory | en_US |
dc.subject.other | Methodology of the Social Sciences | en_US |
dc.subject.other | Sociology | en_US |
dc.subject.other | Social Cohesion | en_US |
dc.subject.other | Complex Networks | en_US |
dc.subject.other | Organizational Fields | en_US |
dc.subject.other | Scaling and Attachment | en_US |
dc.subject.other | Macro-micro Linkages | en_US |
dc.title | Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Industrial and Operations Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Sociology and Organization Studies, University of Michigan, Ann Arbor, MI, 48104-2590, USA. | en_US |
dc.contributor.affiliationother | Research Focus Group in Social Dynamics and Evolution, Institute for Mathematical Behavioral Sciences, University of California at Irvine, Irvine, CA, 92697, USA. | en_US |
dc.contributor.affiliationother | Sociology, Ohio State, University, Columbus, OH, 43215, USA. | en_US |
dc.contributor.affiliationother | Education, Sociology, and Graduate School of Business, Stanford University, Stanford, CA, 94305-3084, USA. | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/44715/1/10588_2005_Article_5273175.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/B:CMOT.0000032581.34436.7b | en_US |
dc.identifier.source | Computational & Mathematical Organization Theory | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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