Factors influencing quality corporate governance in Sub Saharan Africa: an empirical study
Bibliographic record
Abstract
Purpose – This study aims to examine the factors influencing the quality of corporate governance in South Africa (SA). Firm-level variables including performance, firm size, leverage, investment opportunities and audit quality were identified from the corporate governance literature. Design/methodology/approach – The study used ordinary least squares regression on firm-specific and corporate governance variables obtained from panel data of 247-firm years obtained from the annual reports of the 50 largest companies listed on the Johannesburg Stock Exchange (JSE) Securities Exchange of SA. Findings – This study found leverage, firm size and investment opportunities as the main factors influencing the quality of corporate governance in SA. Research limitations/implications – The research findings should be interpreted in the light of the following limitations. First, the study sample consists of the 50 largest firms listed in the JSE of SA. Because these are large companies, the results may not be generalized to other smaller firms operating in SA. Second, this study is constrained to SA. Firms in other developing countries may differ from their SA counterparts. Originality/value – The results of this study are important to the King Committee and other corporate governance regulators in Sub-Saharan Africa, in their effort to improve corporate governance practices and probably minimize corporate failure and protect the well-being of the minority shareholders. Furthermore, the study contributes to our understanding of the variables affecting the quality of corporate governance in developing economies of Africa.
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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 | 0.002 |
| 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".