The moderating effect of the effectiveness of the internal audit function on the relationship between CAATS and corporate governance and the quality of financial reports in commercial banks: Evidence from the Kingdom of Saudi Arabia
Bibliographic record
Abstract
This study sought to determine how CAATS, corporate governance (CG), and EIAF positively and directly influence the quality of financial reports (QFR). In addition to identifying the moderating effect of EIAF on the links between CAATS, CG and QFR in Saudi commercial banks. To this end, data was collected from 293 participants, including administrative managers, board members, internal auditors, certified public accountants, and staff members from the audit and internal audit departments as well as the accounting departments. Using structural equation modelling (SEM) via SmartPls, data was analyzed. The study showed that QFR is positively and significantly affected by CAATS, CG, and EIAF. Moreover, EIAF does not moderate the effect of CAATS and CG on QFR. Hence, this study enriches accounting literature and has implications for both practitioners and policymakers.
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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 | 0.007 | 0.025 |
| 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 | 0.000 | 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".