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Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings

dc.contributor.authorWhite, Douglas R.en_US
dc.contributor.authorOwen-Smith, Jasonen_US
dc.contributor.authorMoody, Jamesen_US
dc.contributor.authorPowell, Walter W.en_US
dc.date.accessioned2006-09-11T15:10:55Z
dc.date.available2006-09-11T15:10:55Z
dc.date.issued2004-05en_US
dc.identifier.citationWhite, 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.issn1381-298Xen_US
dc.identifier.issn1572-9346en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/44715
dc.description.abstractSocial 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.extent316786 bytes
dc.format.extent3115 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherKluwer Academic Publishers; Springer Science+Business Mediaen_US
dc.subject.otherEconomics / Management Scienceen_US
dc.subject.otherArtificial Intelligence (Incl. Robotics)en_US
dc.subject.otherManagementen_US
dc.subject.otherOperation Research/Decision Theoryen_US
dc.subject.otherMethodology of the Social Sciencesen_US
dc.subject.otherSociologyen_US
dc.subject.otherSocial Cohesionen_US
dc.subject.otherComplex Networksen_US
dc.subject.otherOrganizational Fieldsen_US
dc.subject.otherScaling and Attachmenten_US
dc.subject.otherMacro-micro Linkagesen_US
dc.titleNetworks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddingsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelIndustrial and Operations Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumSociology and Organization Studies, University of Michigan, Ann Arbor, MI, 48104-2590, USA.en_US
dc.contributor.affiliationotherResearch 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.affiliationotherSociology, Ohio State, University, Columbus, OH, 43215, USA.en_US
dc.contributor.affiliationotherEducation, Sociology, and Graduate School of Business, Stanford University, Stanford, CA, 94305-3084, USA.en_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/44715/1/10588_2005_Article_5273175.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1023/B:CMOT.0000032581.34436.7ben_US
dc.identifier.sourceComputational & Mathematical Organization Theoryen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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