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Record W4406117559 · doi:10.3390/jrfm18010017

Analyzing Corporate Social Responsibility, CEO Gender, and Compensation Structure: Evidence from U.S. Firms

2025· article· en· W4406117559 on OpenAlexvenueno aff
Dmitriy Chulkov, Joungyeon Kim

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsExecutive compensationCompensation (psychology)BusinessCorporate social responsibilityAccountingPsychologyPublic relationsSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This article examines how CEO compensation structure and CEO gender were associated with corporate social responsibility (CSR) performance in U.S. firms in the period between 2003 and 2013. Building on prior research in economics, finance, accounting, and management, which suggests gender differences in commitment to CSR, this study provides empirical evidence that female CEOs were positively associated with higher CSR performance. The analysis further shows that a higher proportion of equity in CEO compensation was positively associated with CSR, whereas higher proportions of cash bonuses and long-term incentive plans were negatively associated with CSR. Notably, a higher proportion of a cash bonus in CEO compensation further reduced CSR in firms led by female CEOs. These findings offer valuable insights for firms seeking to design executive compensation packages that align CEO behavior with the firms’ CSR objectives. This study contributes to the growing body of literature on CSR by providing empirical evidence on the role of CEO gender and compensation structure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.267
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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