The Effects of Independent Non-Executive Directors (INED) on Company Performance — A Comparison of H-Shares and Red Chips Companies
Bibliographic record
Abstract
This study will examine the influence of and relationship between independent non-executive directors (INEDs) and the performance of non-state-owned enterprises (NSOEs) and state-owned enterprises (SOEs) businesses listed on the Hong Kong Stock Exchange (SEHK). It is well known and reported that the number of Chinese companies coming to Hong Kong for listing on the SEHK has increased since the first listing of H-share company Tsingtao Brewery Co Ltd (00168.HK) in 1993. As the market capitalisation and influence in Hong Kong of these companies has increased in the last 20 years, the effects of their performance have received more attention. It is important to examine whether an increase in the number of independent non-executive directors (INEDs) will influence the behavior of major shareholders and the performance of state-owned enterprises (SOEs). This study aims to assist policymakers and regulators in assessing the need for further revisions to the current INED policy. Additionally, the findings may be applicable to other emerging markets and regions around the world that feature SOEs.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".