Factors Affecting Corporate Environmental, Social and Governance (ESG) Reporting: A Literature Review
Bibliographic record
Abstract
This study aims to further explore the factors that influence corporate environmental, social, and governance (ESG) reporting in public listed companies (PLCs) around the world. The information was gathered from prior studies, which were conducted globally. The results are in line with the legitimacy theory, which holds that companies should disclose more ESG information in order to justify their continued existence. Discussion and findings in this study are significant to businesses and stakeholders, as well as policymakers. While businesses may think of ways to improve ESG reporting in order to compete on the global stage, stakeholders may put pressure on businesses to reveal more information about ESG, and also on policymakers to create an egalitarian framework on ESG that is suitable for businesses in their respective regions. The findings suggest that several factors have played a crucial role in influencing PLCs to disclose ESG reporting in their annual reports. The factors include company size, profitability, board of directors’ attributes, economic sustainability performance (ESP), financial leverage, audit committee external members, and the existence of a female director (or directors) on the corporate board. Most prior studies have found that these determinants have positive relationships with the tendency of PLCs to include ESG aspects in their annual report.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".