Corporate Social Responsibility Disclosure and Firm Performance of Malaysian Public Listed Firms
Bibliographic record
Abstract
Corporate Social Responsibility (CSR) disclosure has become a rising concern for the public listed firms worldwide due to its ability to enhance firm’s market performance and financial performance. The main objective of this study is to investigate the relationship between CSR disclosure and firm performance of Bursa Malaysia’s listed companies based on their market value added (MVA), return on equity (ROE) and return on assets (ROA). 324 samples of public listed companies’ annual report for the period of 2014 to 2016 were obtained from Bursa Malaysia and examined. The extent of their CSR disclosure were measured and analyzed. After accounting for control variables such as firm size, firm age, firm leverage and firm liquidity, the result shows that there is a positive significant relationship between CSR disclosure and firm performance in terms of ROA and ROE. This reveals that high level of CSR disclosure helps firms to achieve optimum performance through increased competitiveness, improved firm’s image amongst society, and creates new opportunities in the marketplace. The findings also showed mix results among the control variables towards firm performance. For future research, this paper recommends to extend the study by using different CSR disclosure measurement, different firm performance measurement such as return on investments (ROI) and Tobin’s Q and different samples.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| 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".