Risk Management and Value Creation: Empirical Findings from Government Linked Companies in Malaysia
Bibliographic record
Abstract
<p>This study is an attempt to assess the status of current level of value creation among the Government Linked Companies (GLCs) in Malaysia. This study collected primary data based on a set of questionnaire survey among 134 executives and managers of GLCs in Malaysia. The data were collected based on opinions of the ten factors of value creation practices by using the five-point Likert scale. The data were analysed using descriptive statistics. Further, the reliability of the data was tested using Cronbach’s alpha test, the validity of the data was tested by checking the normality test through skewness and kurtosis, and the consistency of the data was tested using factor analysis. On an average, 80.6% of the respondents agreed that they focus on these factors of value creation. Overall, the federal owned GLCs place more emphasis on certain elements of value creation than the state owned GLCs. Among the elements of value creation, the state owned GLCs emphasize the most on quality development and brand value creation, where the federal owned GLCs emphasized the most on reputation. The GLCs engaged in service sector emphasized the most on brand value and the GLCs engaged in manufacturing sector emphasized the most on customer satisfaction and quality development. This study suggest that GLCs in Malaysia improve the overall value creation by emphasizing on responsiveness, average return on investment, sales growth, profit growth and average return on sales.</p>
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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 | 0.000 | 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 | 0.000 |
| 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".