The Impact of ESG Disclosure on Financial Reporting Quality: Evidence from KSA
Bibliographic record
Abstract
This study investigates the impact of environmental, social, and governance (ESG) disclosures on the quality of financial reporting (FRQ) among nonfinancial firms listed on the Saudi Stock Exchange. Using panel data analysis, the study examines the relationship between ESG disclosures and FRQ across three models, incorporating variables such as Environmental Disclosure, Social Disclosure, Governance Disclosure, firm Size, Industry classification, and the presence of Big Four audit firms. The analysis is based on a sample of 25 nonfinancial firms from various sectors, representing 125 observations spanning the period from 2019 to 2023. The findings reveal significant positive associations between comprehensive ESG disclosures and enhanced FRQ, underscoring the role of transparency and accountability in corporate reporting practices. The results suggest that firms adopting robust ESG reporting frameworks can improve financial transparency and stakeholder trust in Saudi Arabia's business environment.
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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 | 0.047 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".