Does Governance Affect Compliance with IFRS 7?
Bibliographic record
Abstract
Although there has been considerable research on the impact of corporate governance on corporate voluntary disclosure, empirical evidence on how governance affects compliance with mandatory disclosure requirements is limited. We contribute to governance and disclosure literature by examining the impact of corporate governance on compliance with IFRS 7 for the banking sector in Gulf Cooperation Council (GCC). We use a self-constructed disclosure index to measure compliance with IFRS 7. We use regression analyses to examine the impact of board characteristics, audit committee characteristics and ownership structure on compliance with IFRS 7. Using a sample of 335 bank-year observations for GCC listed banks over the period 2011–2017, we report evidence that corporate governance variables affect compliance with IFRS 7. However, the significance of these variables depends on the type of the regression model used. Our findings suggest that governance matters for mandatory disclosure requirements. So to improve the level of compliance, regulators, official authorities, and policymakers should intensify their efforts toward improving corporate governance codes, following up their implementation and enhancing the enforcement mechanisms.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".