Corporate Governance and Financial Performance: Family Firms vs. Non-Family Firms
Bibliographic record
Abstract
The essence of a family business captures the distinguishing factors differentiating them from non-family businesses. Among these factors, the constructs of family firms’ managing and governance elements are perceived differently by non-family firms. This is especially important in a developing country such as South Africa (SA) with many governance challenges. The objectives of this study were, first, to identify relationships among financial performance, corporate governance, and ownership concentrations of listed family and non-family businesses in SA. Next, a comparison was made between the different ownership structures. Secondary data were collected using purposive sampling from 2015 to 2019. These data were analysed using panel data analysis and descriptive statistics. The results show that family firms place a greater emphasis on ownership concentration, board size, and board gender diversity, which have a significant relationship with financial performance. Only board size was significant to financial performance for non-family firms. The results indicate that family businesses should appoint female family members as directors on their boards, given the significance of gender-diverse boards for financial performance. Non-family businesses should also consider having smaller boards. Theoretically, this study expands on the literature regarding family businesses in SA. However, the findings cannot be generalised due to a single industry being selected. This study should be replicated in different industries to compare the results.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".