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      How to Make the Team: Social Networks vs. Demography as Criteria for Designing Effective Teams

      1 , 2 , 3
      Administrative Science Quarterly
      JSTOR

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          Abstract

          We compare two alternative approaches for evaluating the potential of a work group or team: one that focuses on team members' demographic characteristics and one that focuses on the members' social networks. Given that people's network contacts often share their demographic attributes (i.e., the network is homophilous), the two approaches seem equivalent and the first seems preferable because it is easier to implement. In this paper, we demonstrate several important limits to this rationale. First, we argue and show, in an analysis of 1,518 project teams in a contract research and development firm, that even when internal organizational networks are significantly homophilous with respect to demographic variables, the very logic of the causal structure that underlies theories of demographic diversity carries ambiguous performance implications. This ambiguity is due to the fact that demographic diversity has opposing effects on two social network variables—internal density and external range—each of which has a positive effect on a team's performance. We also demonstrate that a focus on demographic criteria is problematic because the demographic makeup of an organization can place inherent limits on a manager's ability to shape the demographic composition of a team. The ambiguous performance implications and the inherent limits placed on a manager's ability to manage a team's demography reduce the likelihood that a manager's interventions will be successful. The performance implications of managing a team's social capital, however, are clear.

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          The Strength of Weak Ties

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            Social Capital in the Creation of Human Capital

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              Exploration and Exploitation in Organizational Learning

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                Author and article information

                Journal
                Administrative Science Quarterly
                Administrative Science Quarterly
                JSTOR
                0001-8392
                1930-3815
                March 2004
                June 23 2016
                March 2004
                : 49
                : 1
                : 101-133
                Affiliations
                [1 ] Columbia University
                [2 ] Massachusetts Institute of Technology
                [3 ] Carnegie Mellon University
                Article
                10.2307/4131457
                2b6a99e2-9803-4216-bc63-9f5fc9550aa5
                © 2004

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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