Examining the Relationship Between Enterprise Risk Management and Firm Performance in Malaysia
Bibliographic record
Abstract
This study aims to examine the relationship between enterprise risk management (ERM) implementation and firm performance in Malaysia. Using the sample from 2010 to 2016, this study examines the relationship between ERM and firm performance among Malaysian top 100 public listed firms registered on the Index FTSE Bursa Malaysia 100 (FBM100) KLSE. This study also provides comparisons before and after the introduction of Bursa Malaysia Guidelines 2013. This study shows a positive and significant coefficient between profitability and firm performance towards ERM implementation. However, this study shows insignificant relationship between firm size, financial leverage and audit firm with firm performance. This study also shows that there is an increase in the mean score and standard deviation of these variables after the implementation of Bursa Malaysia Guideline 2013. The findings in this study provides an understanding to the Malaysian public listed firms on the importance of ERM and subsequently, maximise the benefits of ERM especially after the introduction of Bursa Malaysia Guidelines 2013 for the benefits of their stakeholders and regulatory improvement in future.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".