Corporate governance in the Middle East and North Africa: A systematic review of current trends and opportunities for future research
Bibliographic record
Abstract
Abstract Research Question/Issue We systematically review the corporate governance (CG) literature on the Middle East and North Africa (MENA), organize it into six main themes and their subthemes, and propose several opportunities for future research. Research Findings/Insights We highlight CG's unique characteristics in the MENA region as well as differences and similarities across MENA countries. We shed light on how organizations are governed in this region especially that their ownership structures are centered on families and the state, and that Islam plays a major role in their governance. Our review establishes a solid foundation for future research directed at CG practices in the MENA region and encourages policymakers and practitioners to improve CG in the region. Theoretical/Academic Implications To the best of our knowledge, this is the first systematic literature review covering CG in the MENA region. In an effort to encourage the continuing evolution of this research stream and augment its contributions to the broader CG literature, we develop an extensive research agenda focusing on several key topics that deserve further attention such as ownership and countries' political regimes, family business and royal families, Sharia law, and executive compensation, among others. Practitioner/Policy Implications This review invites policymakers and investors to consider implementing better policies aimed at improving CG practices, specifically by fomenting transparency, developing financial markets, providing stronger protections for minority shareholders, and enhancing compliance with existing and new regulations.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".