The Effects of Independent Non-Executive Directors (INED) on Company Performance – A Comparison of Family and Non- Family-Controlled Business
Bibliographic record
Abstract
This study will examine the influence of and relationship between independent non-executive directors (INEDs) and the performance of family -and non-family -controlled businesses listed on the Hong Kong Stock Exchange (SEHK). It is well known and reported that family-managed businesses dominate different industrial sectors around the world and that one third of the companies listed in the Standard and Poor 500 Index in the US are managed by families, who are also the companies’ major shareholders. Many previous studies argue that INEDs can improve corporate governance and firm performance. It is worthwhile to study whether the increase in the number of INEDs will affect the behaviour of major shareholders and the performance of these family-managed firms or not. The study aims to help policymakers/regulators determine whether further revision of the current INED policy is necessary. The results can be further investigated and applied to other emerging markets/regions worldwide with family-controlled enterprises.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".