Audit committee chairman characteristics and corporate performance: Empirical evidence from Saudi Arabia
Bibliographic record
Abstract
The purpose of this paper is to investigate the impact of corporate governance (CG) characteristics, specifically audit committee chairman (ACC) characteristics. (tenure, expertise, and directorship) on corporate performance (CP). The study was executed on 44 firms, which were registered under the finance sector at Bursa Saudi Arabia. In terms of its scope, the study stretched over quite a long period of time and observed a considerable number of firms; more specifically, it lasted from 2015 to 2019, and observed 195 firms. The relationship between the characteristics of audit committee (AC) directors and CP has been studied extensively in the past. Nevertheless, few studies have investigated the ACC's characteristics. To the best of the researcher's knowledge, no study has yet studied the effect of CG's characteristics, specifically, the ACC characteristics on CP. The study’s conclusions indicate that corporate governance (CG) characteristics, specifically audit committee chairman (ACC) characteristics (tenure and expertise) are positively related to the performance of finance companies. However, the audit committee chairman’s multiple directorships, on the other hand, has no relationship with corporate performance. Review of literature on the audit committee chairman characteristics used in this study is offered, the practical implications and the recommendations for future research works is also emphasized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".