An examination of the dimensions of CEO power and corporate social responsibility
Bibliographic record
Abstract
Purpose This study empirically aims to examine the relation between CEO power and firm engagement in corporate social responsibility (CSR). It undertakes an in-depth analysis of how the structural, ownership and expert dimensions of CEO power affect individual dimensions of CSR. Design/methodology/approach This study uses ordinary least squares and industry fixed-effects regressions. It also uses instrumental variable-generalized method of moment regressions to test the robustness of empirical results. Findings Results indicate that CEO power is negatively related to CSR. However, the relation between CEO power and CSR is influenced by CSR strengths, as power is negatively related to CSR strengths and is not related to CSR concerns. Results also indicate that the structural and ownership dimensions of CEO power are negatively related to CSR, and the expert dimension has no significant effect on CSR. Moreover, results show that CEO power is not related to the product dimension of CSR performance. Research limitations/implications CEO power is measured using the structural, ownership and expert dimensions of power. However, CEOs also acquire power through social networks and connections outside the corporation which is not covered in this study. Originality/value This study uses comprehensive measures of CEO power and CSR. It is the first study that examines the effect of dimensions of CEO power on individual dimensions of CSR performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".