Effects of Environmental and Social Disclosures Practice on Firm Risks: Evidence From a Developing Nation
Bibliographic record
Abstract
The purpose of this study is to explore the effects of environmental and social disclosures (ESD) on firm risk in Malaysia.The study utilises stakeholder theory because it explains the responsibilities of the firms towards the wide range of stakeholders that will then contribute to the economic performance of the firms and it has been widely used by researchers in corporate social responsibility studies.The data were collected through content analysis.The extent of ESD was obtained from the annual reports and sustainability reports for the year 2017, while the firm risk was calculated based on the share prices obtained from Bursa Station.The finding indicates the level of ESD is still low among the top 100 listed companies in Malaysia and the result from the hypotheses testing found the relationships between ESD with specific firm risks (total risk, systematic risk, unsystematic risk) are insignificant.The discovery infers investors have minimal demands and reliance on ES information of the country's public listed companies in making financial-related decisions.Apart from investors, other company's stakeholder groups also may not value the importance of ES-related activities including ESD.Nevertheless, this study provides an insight for (i) the regulators namely the government and Bursa Malaysia that continuous initiatives should be performed on ESD guidance and (ii) the companies on the need for new strategies towards a successful implementation of ESD.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".