The Moderating Role of Board Ownership on The Relationship Between Gender Diversity and Accounting Fraud: Evidence From KSA
Bibliographic record
Abstract
This study examines the impact of gender diversity on corporate boards and its effect on accounting fraud, with a specific focus on the moderating role of ownership. The relevance of this research lies in its potential to enhance corporate governance and fraud prevention strategies. The purpose is to investigate whether increased gender diversity is associated with reduced instances of accounting fraud and to explore how ownership influences this relationship. Using data from 30 non-financial companies listed on the Saudi Stock Exchange from 2019 to 2023, totaling 150 observations, the study employs three distinct models—Altman, Springate, and Zmijewski—for comprehensive statistical analysis. Results consistently indicate a negative relationship between gender diversity on boards and accounting fraud across all models. Specifically, the Altman model shows a strong negative relationship (t-test: -14.027, p-value: 0.000), the Springate model indicates a significant negative relationship (t-test: -2.707, p-value: 0.025), and the Zmijewski model reveals a highly significant negative relationship (t-test: -25.547, p-value: 0.000). Furthermore, ownership significantly moderates this relationship in all models, with varying effects: positive moderation in the Altman model (t-test: 4.567, p-value: 0.000) and negative moderation in the Springate (t-test: -5.455, p-value: 0.001) and Zmijewski models (t-test: -9.342, p-value: 0.000). In conclusion, increasing gender diversity on boards is associated with reduced accounting fraud. Ownership's moderating effect varies across models but underscores the importance of board composition and ownership structure in corporate governance and fraud prevention efforts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".