Bibliographic record
Abstract
Purpose – This study aims to examine the factors influencing the quality of corporate governance in South Africa (SA) and Kenya. 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 panel data of 247-firm years obtained from the annual reports of the 50 largest companies listed on the Johannesburg Securities Exchange (JSE) of SA and 234-firm years obtained from the 49 companies listed on the Nairobi Stock Exchange (NSE). The author then used content analysis to extract the study variables from the annual reports and multiple regression analysis to determine their relationship. Findings – The study found audit quality and firm performance as the main factors influencing the quality of corporate governance in Kenya and SA. There are also differences in the quality of corporate governance between the two countries. Research limitations/implications – First the study sample consists of the 50 largest firms listed in the JSE of SA and another 49 companies listed in the NSE of Kenya. Since these are large companies, the results may not be generalized to other smaller firms operating in both SA and Kenya. Second, this study is constrained to SA and Kenya. Firms in other developing countries may differ from their SA and Kenyan 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, minimize corporate failure and protect the well-being of the minority shareholders. Furthermore, the study contributes to the understanding of the variables affecting the quality of corporate governance in developing economies of Africa.
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 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 | 0.005 |
| 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".