Internal Auditors’ Characteristics and Audit Fees: Evidence from Egyptian Firms
Bibliographic record
Abstract
Reliance of external auditors on the work of internal auditors is very important but yet, complex decision tasks that require professional judgment as it is influenced by a number of factors, characteristics of internal auditors are of the most important factors to be considered. The paper obtains various criteria relating to the evaluation of internal audit organizational status, work performed, competence, and professional due care as stipulated in Professional Auditing Standards including the Egyptian Auditing Standard (EAS)No.610 “Using the work of internal auditors”. This study revealed that internal auditors’ characteristics assist in increasing external auditors’ reliance on their works and so minimize external auditors’ efforts and so fees. Data on internal audit characteristics are obtained from survey respondents of Egyptian companies and audit fee data are obtained from their annual reports. Results indicate that lower external audit fees are associated with top management support for internal auditors, not imposing constraints on internal auditors works, is ready to act upon internal audit staff findings and recommendations, adequacy of education of internal auditors, good practices for hiring and training internal auditors. Also, lower external audit fees are associated with adequacy of working paper documentation supporting internal auditors’ conclusions, sufficiency of internal auditors’ evidences.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".