Do ESG Risk Scores and Board Attributes Impact Corporate Performance? Evidence from Saudi-Listed Companies
Bibliographic record
Abstract
This research examines the link between environmental, social, and governance (ESG) risk ratings and board characteristics on corporate performance. Using 2023 data from 117 companies on the Saudi Stock Exchange, the study employs Ordinary Least Squares (OLS) regression and Python for data analysis. Our findings reveal a negative effect of ESG risk scores on financial performance measures, indicating that higher ESG risks hinder firm performance measured by ROE and ROIC. Furthermore, both the size and independence of the board decrease corporate performance in Saudi firms. Family-controlled ownership structures often limit the effectiveness of independent directors in enhancing performance. In Saudi firms, women’s board participation shows an insignificant impact on corporate performance, suggesting that the Tokenism Theory may apply. It is recommended that firms empower women in leadership roles and develop robust ESG risk management frameworks to mitigate risks and enhance financial performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".