Corporate Governance Dynamics in Saudi Arabia: Audit Committee Composition, Family Ownership, and Financial Performance
Bibliographic record
Abstract
The purpose of this research is to examine how the audit committee composition, focusing on independence and the presence of external financial experts, impacts a firm's performance metrics (Tobin’s-Q and Return on Equity-ROE) in the Kingdom of Saudi Arabia (KSA). Additionally, the study aims to understand how family ownership influences this relationship. The research undertook ordinary least squares (OLS) regression analysis on a dataset comprising 485 firm-year observations. The results show a positive association between having independent directors on the audit committee (AudC) and both Tobin’s-Q and ROE. When the audit committee includes external financial experts, it is linked to a higher ROE but a lower Tobin’s-Q. However, the presence of family ownership acts as a negative moderator in these associations, counteracting the positive effects of the audit committee's composition on firm performance. This indicates that, in certain institutional settings, investors view audit committee independence as detrimental, impacting their assessment of the firm's value. This study sheds light on the nuanced understanding of how the composition and utilization of audit committees play out in a specific institutional context, especially within public family-owned firms. It emphasizes the need for a careful consideration of audit committee composition, tailored to the unique business environment of the firm. Regulatory requirements aimed at safeguarding non-family investors may not align well with the dynamics of family businesses. This research significantly contributes to the understanding of corporate governance and its application in family-owned enterprises.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".