Pentagon fraud model and financial statement fraud: The moderating role of Islamic corporate governance
Bibliographic record
Abstract
The study aims to investigate the impact of Pentagon Fraud Model in five key factors, namely Pressure, Opportunity, Rationalization, Capability, and Action, on the risk of Financial Statement Fraud (FSF). Additionally, it explores the moderating role of Islamic Corporate Governance (ICG) in the relationship between these factors and FSF. The research employed a quantitative approach, utilizing survey data from a sample of 270 respondents. The findings support the hypotheses that all five factors significantly influence FSF, with Pressure exerting the highest impact. Furthermore, ICG is found to moderate the relationships between these factors and FSF, underscoring its role in reducing financial fraud risk. Practically, this study offers essential guidance for organizations in managing FSF risks more effectively. Integrating ethical and governance principles, as moderated by ICG, can help strengthen internal controls, ethics training, and a culture of integrity. Policymakers should consider these findings for enhancing regulatory frameworks, and educational institutions can integrate these results into their curriculum. The study's contribution lies in shedding light on the significance of ICG as a risk-mitigating factor in the context of FSF. The findings provide valuable insights for academics, practitioners, and policymakers. They enhance the understanding of FSF and its mitigating measures and offer a foundation for further research in the field.
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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 | 0.005 | 0.024 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".