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Upper Echelons Research Revisited: Antecedents, Elements, and Consequences of Top Management Team Composition

Carpenter, Mason A., Geletkanycz, Marta A., Sanders, Wm. Gerard·2004·Journal of Management

Abstract

This study reviews recent research building on Hambrick and Mason’s [Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. Academy of Management Review, 9: 193–206] upper echelons (UE) perspective with the aim of identifying challenges and opportunities for future UE-based organizations research. Our review highlights a number of central facets of the UE perspective: It is at once a theoretical framework predicting that organizations will be a reflection of their top management teams and a methodology that relies on executive demography as a measurement proxy for underlying individual and group cognitions and behaviors. In proposing new research directions, we challenge organizations researchers to (1) reconsider the universality of the top management team (TMT) construct, (2) carefully explore the practical and theoretical meaning of TMT demographic characteristics vis-à-vis the deeper constructs they are presumed to proxy, (3) integrate other determinants of managerial cognition and behavior into UE theorizing, and (4) revisit the roles of causality and intertemporal dynamics among the antecedents, consequences, and composition of top management teams.

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Metadata

Authors
Carpenter, M. A., Geletkanycz, M. A., & Sanders, W. G.
Venue
Journal of Management
Published
2004-12
Volume
30
Issue
6
Pages
749-778
ISSN
  • 0149-2063 (ISSN-L)
  • 1557-1211
OpenAlex
W2148049298
Cited by
2,248
References
4
FWCI
25.71
Type
journal article
Language
en
Open access
closed

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Carpenter, M. A., Geletkanycz, M. A., & Sanders, W. G. (2004). Upper Echelons Research Revisited: Antecedents, Elements, and Consequences of Top Management Team Composition. Journal of Management. https://doi.org/10.1016/j.jm.2004.06.001

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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.