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Cited by 176

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

A multilevel, complexity theory approach to understanding gender bias in leadership

Open access
Hogue, Mary, Lord, Robert G.·2007·The Leadership Quarterly

Abstract

Abstract We use principles from multilevel theory, complexity theory, and connectionist theory to integrate existing gender bias explanations into a comprehensive model of gender bias in leadership, one that can be used to examine and understand how throughout the leadership process gender bias occurs and can affect women negatively. The synthesis of connectionism and complexity theories provides an opportunity to suggest novel solutions to this important leadership problem, but it also shows why multiple solutions applied at individual, group, and organizational levels all may be required to change the way agents and systems of agents respond to potential female leaders.

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Crossref · 10.1016/j.leaqua.2007.04.006

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Metadata

Authors
Hogue, M., & Lord, R. G.
Venue
The Leadership Quarterly
Publisher
Elsevier BV
Published
2007-08
Volume
18
Issue
4
Pages
370-390
ISSN
1048-9843 (ISSN-L)
OpenAlex
W2026246444
Cited by
176
References
13
FWCI
15.19
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A multilevel, complexity theory approach to understanding gender bias in leadership. https://app.cochat.ai/papers/10.1016/j.leaqua.2007.04.006
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Hogue, M., & Lord, R. G. (2007). A multilevel, complexity theory approach to understanding gender bias in leadership. The Leadership Quarterly. https://doi.org/10.1016/j.leaqua.2007.04.006

Data sources

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.