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Cited by 338

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Stakeholder management as a predictor of CEO compensation: main effects and interactions with financial performance

Coombs, Joseph E., Gilley, K. Matthew·2005·Strategic Management Journal

Abstract

We test the effects of stakeholder management on CEOs' salaries, bonuses, stock options, and total compensation. We also examine the extent to which the interaction of stakeholder management and financial performance determines compensation. Using a longitudinal database of 406 Fortune 1000 firms, our results suggest that stakeholder management is relevant to boards of directors when setting CEO compensation. Specifically, we found a significant, negative main effect of stakeholder management on CEO salaries. Further, we found that stakeholder management typically reduces the rewards CEOs may get for increasing levels of financial performance. In tandem, these results indicate that CEOs may jeopardize their personal wealth by pursuing stakeholder‐related initiatives. Copyright © 2005 John Wiley & Sons, Ltd.

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Metadata

Authors
Coombs, J. E., & Gilley, K. M.
Venue
Strategic Management Journal
Publisher
Wiley
Published
2005-07-25
Volume
26
Issue
9
Pages
827-840
ISSN
  • 0143-2095 (ISSN-L)
  • 1097-0266
OpenAlex
W2044661706
Cited by
338
References
1
FWCI
39.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Stakeholder management as a predictor of CEO compensation: main effects and interactions with financial performance. https://app.cochat.ai/papers/10.1002/smj.476
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Coombs, J. E., & Gilley, K. M. (2005). Stakeholder management as a predictor of CEO compensation: main effects and interactions with financial performance. Strategic Management Journal. https://doi.org/10.1002/smj.476

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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.