Perception of Corporate Governance Factors in Mitigating Financial Statement Fraud in Emerging Markets: Jordan Experience
Bibliographic record
Abstract
This study investigates the influence of corporate governance on reducing financial statement fraud (FSF) in Jordanian service and industrial companies listed on the Amman Stock Exchange from 2018 to 2022. To achieve this, the study employed the Beneish M-score model to assess the likelihood of FSF and logistic regression to examine the influence of corporate governance structure on fraud mitigation. The study identified 13 independent variables, including board size, board director’s independence, board director’s compensation, non-duality of CEO and chairman positions, board diversity, audit committee size, audit committee accounting background, number of annual audit committee meetings, external audit fees, board family business, the presence of women on the board of directors, firm size, and market listing on FSF. The study included 74 companies from both sectors—33 from the industrial sector and 41 from the service sector. Primary data was collected from financial statements and other information published in annual reports between 2018 and 2022. The results of the study revealed a total of 295 cases of fraud during the examined period. Out of the 59 companies analyzed, 21.4% demonstrated a low probability of fraud, while the remaining 78.6% (232 observations) showed a high probability of fraud. The results indicate that the following corporate governance factors significantly impact the mitigation of financial statement fraud (FSF): independent board directors, board diversity, audit committee accounting backgrounds, the number of audit committee meetings, family business involvement on the board, and firm characteristics. The study provides several recommendations, highlighting the importance for companies to diversify their boards of directors by incorporating different perspectives and experiences.
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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 | 0.005 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".