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The Structural Wings of Matthew Effects: The Contribution of Three-Level Network Data to the Analysis of Cumulative Advantage

Abstract : The article presents a three-level network approach to the Matthew effect as a multilevel complement to Burt’s (2005) conception of the relationship between networks and performance. We first introduce a three-level dataset and the specificity of this data structure for explorations of cumulative advantage. Second, we present a population of scientists and provide a heuristic visualization—as “paragliders”—of their position in this multilevel structure. Third, we cluster these actors into groups of performance to better understand who is in a multilevel position to make progress with performance measurements over time. We use Ronald Burt’s representation of the combined effect of brokerage beyond group and closure within group to show that several of these performance groups do not follow his own principles very closely. Fourth, we provide evidence, for one of these groups, of the usefulness of combining constraint in the personal network of its members with constraint in their “extended” network—that is, for looking at the relationship between performance and borrowing from “dual alters” in the extended network. This shows that, when it is present, network lift from threelevel structural wings is provided by different levels for different actors. Hypotheses induced by the paraglider metaphor can thus be tested progressively to provide new understandings of the structural conditions under which the Matthew effect of cumulative advantage operates. Finally, we list limitations of this three-level approach to the Matthew effect as examined here and suggest further developments for this approach.
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Submitted on : Thursday, March 24, 2016 - 3:37:08 PM
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Emmanuel Lazega, Marie-Thérèse Jourda. The Structural Wings of Matthew Effects: The Contribution of Three-Level Network Data to the Analysis of Cumulative Advantage. Methodological Innovations, SAGE, 2016, 9, pp.2059799115622764. ⟨10.1177/2059799115622764⟩. ⟨hal-01293374⟩



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