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Cited by 239

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Looking good by doing good: The antecedents and consequences of stakeholder attention to corporate disaster relief

Madsen, Peter M., Rodgers, Zachariah J.·2015·Strategic Management Journal

Abstract

Stakeholder theory suggests a relationship between corporate social responsibility ( CSR ) and corporate financial performance ( CFP ) because certain stakeholders reward certain types of CSR . This argument assumes that stakeholders attend to firms' CSR activities—an assumption that has yet to be examined. We fill this gap by extending stakeholder theory to the context of stakeholder attention to firm CSR and exploring the antecedents and consequences of stakeholder attention to corporate disaster relief CSR . We test the resulting hypotheses on a sample of public companies that engaged in natural disaster relief efforts, finding that stakeholder attention partially mediated the relationship between disaster relief and CFP and that stakeholder attention to corporate disaster relief was driven by the legitimacy, urgency, and enactment of disaster relief CSR initiatives. Copyright © 2014 John Wiley & Sons, Ltd.

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Metadata

Authors
Madsen, P. M., & Rodgers, Z. J.
Venue
Strategic Management Journal
Publisher
Wiley
Published
2014-04-18
Volume
36
Issue
5
Pages
776-794
ISSN
  • 0143-2095 (ISSN-L)
  • 1097-0266
OpenAlex
W1913555304
Cited by
239
References
3
FWCI
31.95
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Looking good by doing good: The antecedents and consequences of stakeholder attention to corporate disaster relief. https://app.cochat.ai/papers/10.1002/smj.2246
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Madsen, P. M., & Rodgers, Z. J. (2015). Looking good by doing good: The antecedents and consequences of stakeholder attention to corporate disaster relief. Strategic Management Journal. https://doi.org/10.1002/smj.2246

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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.