Evaluating Board Characteristics’ Influence on the Readability of Annual Reports: Insights from the Egyptian Banking Sector
Bibliographic record
Abstract
This study aims to examine the impact of board characteristics (BCs) on banks’ annual reports readability (BARR). Further, it examines whether bank size (BS) moderates the association between BC and BARR. The study employs a sample of 208 bank-year observations from both listed and non-listed banks in the Egyptian stock exchange (EGX), utilizing data spanning from 2016 to 2023. The study employs a random-effect regression model to test the hypotheses and discuss the results. The results suggest that BARR has a significant association with board meetings, gender and cultural diversity. Furthermore, BS played a moderating role in determining the association between BCs and BARR, supporting the second hypothesis. The findings show that the BCs and disclosure quality differ for banks of varying sizes. The findings have practical implications for the Egyptian banking sector, highlighting that board structure is critical to transparency and maintaining public trust. Additionally, the results focus policymakers’ attention on standardizing the contents and structure of banks’ annual reports, with the aim of reducing managers’ manipulation of disclosures and reducing the level of information asymmetry between stockholders, as suggested by the agency theory.
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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 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".