Corporate Governance Mechanisms and Firm Performance in Saudi Arabia
Bibliographic record
Abstract
This paper examines the impact of corporate governance mechanisms including board size, independence, and meeting frequency, audit committee size and meeting frequency, CEO duality and ownership concentration on the operational, financial and market performance of Saudi listed firms using a contingent theoretical-based framework drawing on agency theory, stewardship theory and resource dependence theory. This study examines 210 listed Saudi Stock Exchange firms over the timeframe 2017 to 2019. The paper applies both a manual content and regression analysis approach. The results show that firm performance deteriorates with board size and independence, audit committee and meeting frequency, and the presence of CEO role duality, while performance improves with board meeting frequency and ownership concentration. Thus, Saudi firms should respond by maintaining smaller boards and more frequent meetings, keeping the Chair and CEO roles separate, and maintaining smaller audit committees with more focused meetings. Further, the appointment of independent directors only makes a meaningful contribution to firm performance where they are truly independent. Finally, more concentrated ownership tends to encourage better firm performance due to the regime of monitoring and discipline concomitant with more powerful shareholders. The implications of this paper are threefold. First, the implementation by Saudi Arabia of the latest corporate governance regulations and IFRS adoption almost certainly impact firm performance markedly. Second, corporate governance regulations should recognize the role of more frequent board meetings and more concentrated ownership in enhancing corporate performance. Third, stakeholders should apply pressure on investee firms to maintain smaller boards, engage genuinely independent directors, separate the role of Chairman and CEO, and maintain smaller audit committees with fewer and more effective meetings. The results should help corporate boards when deciding on the best corporate governance mechanisms to enhance firm performance. Further, the study should provide policy makers with a better understanding of the corporate governance structures required to promote better performance by drawing on existing theories and the empirical modelling, in an emerging economy setting such as Saudi Arabia, a new and broader data set, thereby informing better future policy and protecting shareholders’ interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".