Factors influencing quality corporate governance in Sub Saharan Africa: an empirical study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it