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The Network‐Performance Relationship in Knowledge‐Intensive Contexts—A Meta‐Analysis and Cross‐Level Comparison
Authors:Julia Brennecke  Natalie Stoemmer
Affiliation:1. Centre for Transformative Innovation, Faculty of Business and Law, Swinburne University of Technology, PO Box 218, Mail H25, Hawthorn VIC 3122, Australia;2. Department of Business Administration, Albert‐Ludwigs‐Universit?t Freiburg, Platz der Alten Synagoge 1, 79085 Freiburg, Germany
Abstract:This study examines the generalizability of the network‐performance relationship across individual and group levels, focusing on knowledge‐intensive contexts. Drawing on a meta‐analytical approach, we synthesize the results of 102 empirical studies to test whether network characteristics such as centrality, brokerage, and tie strength similarly influence the job performance of individuals and groups. Results show that while there are no differences in the direction of the network‐performance relationship across levels, there are substantial differences in magnitude. Individual performance profits more strongly from a high number of direct connections, whereas groups reap higher benefits from brokerage positions. Additional analyses reveal that the network measurement method, tie content, and performance criteria function as moderators of the network performance relationship, but their influence is consistent neither across network characteristics nor across levels. By meta‐analytically comparing and contrasting the network‐performance relationship for individuals and groups, we contribute to multilevel research on networks and organizations. Particularly, we move toward the development of a multilevel homology theory of networks. Implications for theory, practice, and future research are discussed. © 2017 Wiley Periodicals, Inc.
Keywords:group network  individual network  job performance  meta‐analysis  multilevel
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