Evaluation of Internal Audit Standards as a Foundation for Carrying out and Promoting a Wide Variety of Value-Added Tasks-Evidence from Emerging Market
Bibliographic record
Abstract
This research paper aims to evaluate the effectiveness of internal audit standards as a foundation for carrying out and promoting a wide variety of value-added tasks in emerging markets. Three Jordanian telecommunications firms were the subject of the study. In each firm, the non-executive directors, who serve on the Audit Committee, also received a questionnaire that was designed for this objective. In total 85 questionnaires were accepted and analyzed using traditional statistical methods such as descriptive statistics, arithmetic means, standard deviations, and percentages, and resolution data were examined using the statistical application SPSS. According to the annual report for the year 2021, telecommunication businesses generally followed IIA International Internal Audit Standards. Application Standards were employed to a high degree in second place, after Attribute Standards, which were used primarily in the first place. In those firms, performance standards were not used. The study also found that this form of application is moderately constrained by a few challenges and barriers. The study recommended that these organizations broaden the scope and scale of internal auditing standards, particularly performance requirements. Finally, the generalization of research findings is limited because the study is limited to three Jordanian telecommunication companies.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".