Determinants influencing the effectiveness of internal auditing and the responsibility of auditors in fraud detection in an emerging country
Bibliographic record
Abstract
Internal audit has become an essential part of firms in the age of Industry 4.0 due to its ability to identify errors or violations leading to fraud in firms (Alzeban & Gwilliam, 2014; Cohen & Sayag, 2010). This study is conducted to investigate the relationship between variables such as the quality of internal audit, the capability of the internal audit team, the independence of internal audit, and the support of leadership on the effectiveness of internal audit (EIA). Specifically, the study also examines the relationship between the EIA and the responsibility of auditors in detecting fraud. Data were gathered through a survey of 325 questionnaires from joint stock firms in the context of Vietnam, using SPSS 22 software and SmartPLS 3.0 software to analyze the regression of influencing determinants. The results reveal that: 1) the quality of the internal audit, the capability of the internal audit team, the independence of the internal audit, and the support of leadership have an impact on the internal audit effectiveness; 2) the EIA, the responsibility of auditors, and auditor training have a positive and significant impact on fraud detection. Therefore, the importance of internal audit in identifying accounting fraud and the need for firms to design internal audit processes and training to improve the effectiveness of their operations are highlighted.
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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 | 0.003 | 0.012 |
| 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) | 0.001 | 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".