Corporate Governance Mechanisms and Intellectual Capital Efficiency: Evidence from Malaysia
Bibliographic record
Abstract
The objective of this study is to investigate the relationship between Corporate Governance (CG) mechanisms and Intellectual Capital (IC) efficiency following the revision of the Malaysian Code of Corporate Governance (MCCG) in 2012. A final sample of 150 large companies was chosen from the companies listed on the main board of Bursa Malaysia for 2014. The Value Added Intellectual Coefficient (VAIC™) model was utilized to measure the IC efficiency and tested using multiple regression analysis. The multiple regression analysis revealed that board size and frequency of audit committee meetings have a significant and positive association with IC efficiency, but no evidence existed for an association between board composition and role duality on IC efficiency.The result of this study could be useful for regulators and policy makers, particularly to the Securities Commission Malaysia, to further revise and strengthen its MCCG. This study adds to the shortage of literature by providing evidence on the effects of CG attributes on IC efficiency subsequent to the revised Malaysian Code on Corporate Governance (MCCG) 2012.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".