Stakeholders’ Expectations on Human Capital Disclosure vs. Corporate Reporting Practice in Malaysia
Bibliographic record
Abstract
Corporate disclosure of human capital has received growing research attention in different countries and markets. While past studies have explored the antecedent and implications of reporting human capital, studies on how far those disclosure practices actually meet the stakeholders’ expectations are still lacking. Hence, this study attempt to apply the stakeholder theory to frame the human capital reporting practices by the corporations in Malaysia. The methodology of this study is twofold; firstly, to develop human capital reporting measurement items as per the stakeholders’ expectation and their perceived importance of those items through a Delphi technique, and secondly, to determine the extent of human capital disclosure practices through a content analysis of the annual reports. The findings indicate that despite stakeholders’ high perceived importance on human capital disclosures, the corporate reporting practices are still at an inferior stage. This study contributes in such a way to fill the gap in the literature by exploring the current extent of human capital reporting by the listed corporations in Malaysia and how far such disclosure met the stakeholders’ expectations. This study also highlights the significance of the stakeholders’ voice and participation as one of the main driver towards sustainability reporting.
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 | 0.009 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 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".