Impact of Environmental, Social, and Governance Activities on the Financial Performance of Indian Health Care Sector Firms: Using Competition as a Moderator
Bibliographic record
Abstract
Environmental, social, and governance (ESG) activities have become essential and viable activities of corporations because of the increase in concern for environmental, social, and governance issues. The motive of this research is to measure the effect of ESG on the financial performance (FP) of healthcare corporations using the market-to-book value (MTB) ratio as a proxy of FP. A sample of 33 pharma companies in India from 2011 to 2020 has been considered. The study relies on the panel data method to assess the association between ESG and FP. The potential moderating role of competition has also been studied to simplify their relationship in this framework. The finding of this study is that there is a significant negative association between ESG and FP, and it is also found that when competition is used as a moderator, it results in a significantly positive impact on the ESG and FP of healthcare companies. This study increases the understanding of the association between ESG and FP and helps corporations to formulate corporate strategies and stakeholders to make investment decisions. The originality of this study is that it addresses the impact of competition on ESG and FP of the healthcare industry and will become foundational literature for future studies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".