The Network‐Performance Relationship in Knowledge‐Intensive Contexts—A Meta‐Analysis and Cross‐Level Comparison

The Network‐Performance Relationship in Knowledge‐Intensive Contexts—A Meta‐Analysis and... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Human Resource Management Wiley

The Network‐Performance Relationship in Knowledge‐Intensive Contexts—A Meta‐Analysis and Cross‐Level Comparison

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018 Wiley Periodicals, Inc.
ISSN
0090-4848
eISSN
1099-050X
D.O.I.
10.1002/hrm.21823
Publisher site
See Article on Publisher Site

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.

Journal

Human Resource ManagementWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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