The Impact of CEO and Firm Attributes on ESG Performance: Evidence from an Emerging Market
Bibliographic record
Abstract
The research aims to unveil the impact of CEO traits and firm attributes on corporate environmental, social, and governance (ESG) performance within the Egyptian context as an emerging market. Using the quantitative research approach, we analyzed a panel of data from 43 listed firms in the S&P/EGX ESG index from 2014 to 2022 through three statistical models to examine how CEO power, confidence, and tenure influence corporate sustainability practices. Our findings reveal that CEO power and confidence influence ESG performance and shape the firm’s strategy. However, there is no significant influence related to CEO tenure. Moreover, we found mixed evidence regarding the impact of firm financial attributes, such as the positive impact of firm size and operating cash flow on ESG performance and the negative impact of firm listing tenure. Our findings contribute to the literature by adding new empirical evidence in this arguable area from an emerging market and provide new insights into the significant influence of the firm’s first man (CEO) in shaping its sustainability practices, especially ESG. In addition, it gives professional authorities and policymakers insights into the nexus between the CEO and the firm’s ESG strategies, disclosure, and performance. Moreover, it can motivate future research to re-examine the role of CEO traits in shaping ESG performance in other countries to create a comprehensive understanding of this knowledge field.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".