Whistleblowing Disclosure as a Shield Against Earnings Management: Evidence from the Insurance Sector
Bibliographic record
Abstract
One of the fundamental components of internal controls, a whistleblowing system (WBS) is crucial for preventing fraud, addressing irregularities, and enhancing good governance. The purpose of this study is to investigate the impact of whistleblower disclosures on earnings management in Saudi Arabia’s Takaful Insurance (TKI) sector between 2017 and 2023. To this end, a whistleblowing index was constructed as a tool to evaluate the whistleblowing framework’s effectiveness. Using the Dynamic Generalized Method of Moments (GMM) to account for endogeneity, it was found that most Saudi insurance companies increased their efforts to disclose information about whistleblowers, which significantly reduced earnings management practices. Specifically, the study concludes that the size of the audit committee (ACS) significantly and negatively affects how insurance businesses manage their earnings when a whistleblower system is in place. Additionally, there is a notable and adverse effect on earnings management from board size (BSZ), the percentage of non-executive independent members (PNIM), and Shariah board size (SBS). However, it was found that earnings management is unaffected by the frequency of board meetings (BMFR). This study adds to the body of knowledge by demonstrating how corporate governance enhances the effectiveness of the whistleblowing system.
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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 | 0.015 |
| 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 | 0.001 | 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".