The Impact of the Audit Committees' Properties on the Quality of the Information in the Banking Financial Reports: A Survey on Saudi Commercial Banks
Bibliographic record
Abstract
This study aims to identify the impact of the audit committees' properties on the quality of the information of the banking financial reports in the Saudi commercial banks by identifying the effect of identifying tasks and duties, independence, accounting and banking experience and efficiency of the audit committee on achieving the quality of the Saudi banking and financial reports. 110 questionnaires were distributed on the research sample and 105 questionnaires were received and analyzed through ANOVA. Results indicate that the availability of the audit committees' properties affect increasing the quality of the financial reports in the Saudi banking at the level of properties as a whole where the (P) probable value was (0.000 ), which is less than 0.05. It represents the functions and duties of the audit committee, the committee's independence in banks, the availability of the accounting and banking experience for the members of the audit committee and the efficiency of the audit committees at banks. The study recommends more emphasis on the diversity of the experiences of the members of the audit team and thus; the committee can performs its functions in a more efficient and effective way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.037 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".