Audit committee quality and audit report lag: the role of mandatory adoption of IFRS in Saudi companies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper aims to examine the effect of audit committee characteristics on audit report lag, and also explores whether this effect will vary between before and after mandatory adoption of IFRS in Saudi listed companies. Based on a Saudi sample of 388 firm-year observations from 2015 to 2018, the Poisson regression analysis shows that among audit committee characteristics, only audit committee financial experience significantly influences the timing of financial reporting. The result indicates a weak influence of audit committees on timeliness of financial reporting, which is consistent with the results of most of previous studies. On the other hand, the results show a strong impact of the adoption of IFRS on the context of that relationship, where the results show the impact of IFRS on audit report lag, audit committee quality and the association between them.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it