Benchmarking Malaysian Government-Linked Companies’ Corporate Governance and Sustainable Development Goals Performance with Public Companies of Developed Countries
Bibliographic record
Abstract
Purpose: This study examines the impact of Corporate Governance (CG) and Sustainable Development Goal (SDG) practices on the financial market and company performances of public sector companies in Malaysia, benchmarking against the public listed countries in United States, United Kingdom, Canada and Singapore. The benchmarking is done between a developing country against four developed countries. Design/Methodology/Approach: Panel data regression is adopted for methodology, and the research timeframe is 2017 to 2021. Eight-panel data models, which are stock return, volatility, investor sentiment, profitability, liquidity, solvency, financial efficiency and repayment capacity models are selected. Findings: The result shows that board responsibilities, remuneration, audit committee, risk management and internal control, engagement with stakeholders and conduct of general meetings are the CG variables to affect the financial market and company performance. SDG 4, 5, 8, 10, 11, 13, 16 and 17 are significant to the financial market and company performance. Originality/Value: The result of this study contributes to policymakers, regulators and practitioners in identifying the best CG and SDG practices that can help the Malaysian GLCs to gain better financial performance. The results assist the Malaysian government in understanding the gap between CG and SDG practices compared to developed countries and advocate the Malaysian companies to adopt better practices. Keywords: Corporate governance, sustainability, financial market, performance, GLC.
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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 | 0.009 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".