Smart Framework for National Audit Quality Management: An Empirical Comparative Evidence from Egypt
Bibliographic record
Abstract
This paper develops and empirically validates a smart national framework for audit quality management in Egypt, aligned with ISQM-1, ISQM-2, and ISA 220 (Revised). A mixed-methods design combines a nationwide auditor survey with SEM/CFA/ANOVA and comparative case insights from six countries. Results indicate that ISQM adoption and digital maturity (AI-enabled analytics, dashboards, blockchain-based evidence) significantly enhance audit quality, with complementary strengths between public oversight (ASA) and private firms. The framework integrates leadership & governance, risk assessment, digital infrastructure, engagement performance, and monitoring & remediation, enabling proactive, risk-based quality management beyond retrospective control. Benchmarking against the UK, USA, Singapore, Canada, Australia, and UAE underscores enforcement, regulator-led digital platforms, and leadership accountability. The study unifies agency, institutional, and digital-governance perspectives into an actionable model for emerging economies. Practically, it recommends regulator dashboards, phased ISQM adoption, targeted digital training, and SME support. The findings provide a roadmap for Egypt to strengthen transparency, investor trust, and international convergence in audit quality.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".