Does Sustainability Pay Off? Examining Governance, Performance, and Debt Costs in Southeast Asian Companies (A Survey of Public Companies in Indonesia, Malaysia, Singapore, and Thailand for the 2021–2023 Period)
Bibliographic record
Abstract
Sustainability performance is an important criterion for investors and lenders when making financing decisions. This study aims to analyze whether sustainability governance influences sustainability performance and the extent to which sustainability performance affects a company’s cost of debt. This study analyzed 209 publicly listed companies in Indonesia, Malaysia, Singapore, and Thailand. Sustainability governance was measured using two proxies from the Refinitiv Eikon database: (1) the existence of a sustainability committee and (2) the existence of sustainability assurance. Sustainability performance and the cost of debt were assessed using scores obtained from the same database. Quantitative analysis was performed using descriptive statistics, ANOVA, and structural equation modeling (SEM) with path analysis. The results showed that sustainability governance has a strong positive impact on sustainability performance. However, the results also show that higher sustainability performance leads to a higher cost of debt. This finding suggests that companies that integrate sustainability into their core business strategies face challenges in obtaining funding to support sustainability initiatives. This research implies that a well-developed sustainable ecosystem needs to be established before companies can realize a lower cost of debt.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".