The Influence of Financial Indicators on Vietnamese Enterprise’s Sustainability Reports Disclosing Process
Bibliographic record
Abstract
Sustainability reporting has become increasingly crucial for businesses worldwide, communicating environmental, social, and governance (ESG) performance to stakeholders. Despite the growing importance of sustainability reporting, there remains a gap in understanding how financial indicators influence the disclosure process, particularly in Vietnamese enterprises. This paper aims to address this gap by investigating the influence of financial indicators on the sustainability reporting practices of Vietnamese companies. Employing a mixed-methods approach, combining a quantitative analysis of financial data with a qualitative assessment of sustainability reports, the research seeks to uncover the nuanced relationship between financial performance metrics and the quality and extent of sustainability disclosures. The research was conducted to identify, evaluate, and measure financial factors affecting the quality of companies’ sustainability reports in Vietnam. The research is based on scoring the sustainable development reports of the top 100 listed joint stock companies on the HOSE—Ho Chi Minh City Stock Exchange. Based on the research model of Dissanayake, in the case of Vietnam, we build a scoring model for the sustainable development report based on GRI standards and add additional criteria appropriate to the situation of each listed company on the Vietnam stock exchange. Based on the research overview, our team tested hypotheses related to the short-term current ratio, total asset turnover ratio (AT), return on equity ratio (ROE), and debt-to-equity ratio (DE). The empirical results show that the AT and ROE significantly positively affect the sustainability reports; the DE hurts the sustainability reports. The findings are expected to provide valuable insights into the factors shaping sustainability reporting practices in Vietnam and contribute to the existing literature on corporate disclosure and sustainability.
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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 | 0.014 | 0.062 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".