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Cited by 339

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Structure and Learning in Self-Managed Teams: Why “Bureaucratic” Teams Can Be Better Learners

Bunderson, J. Stuart, Boumgarden, Peter·2010·Organization Science

Abstract

This paper considers the effect of team structure on a team's engagement in learning and continuous improvement. We begin by noting the uncertain conceptual status of the structure concept in the small groups literature and propose a conceptualization of team structure that is grounded in the long tradition of work on formal structure in the sociology and organization theory literatures. We then consider the thesis that, at least in self-managed teams dealing with stable tasks, greater team structure—i.e., higher levels of specialization, formalization, and hierarchy—can promote learning by encouraging information sharing, reducing conflict frequency, and fostering a climate of psychological safety; that is, we examine a mediated model in which the effect of structure on learning and improvement in teams is mediated by psychological safety, information sharing, and conflict frequency. This model was largely supported in a study of self-managed production teams in a Fortune 100 high-technology firm, although the observed pattern of mediation was more complex than anticipated. Higher structure was also associated with actual productivity improvements in a subsample of these teams. The theory and results of this study advance our understanding of team learning and underscore the importance of team structure in research on team processes and performance.

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Metadata

Authors
Bunderson, J. S., & Boumgarden, P.
Venue
Organization Science
Published
2010-06
Volume
21
Issue
3
Pages
609-624
ISSN
  • 1047-7039 (ISSN-L)
  • 1526-5455
OpenAlex
W2167896490
Cited by
339
References
14
FWCI
10.53
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.