How does transparency into global sustainability initiatives influence firm value? Insights from Anglo‐American countries
Bibliographic record
Abstract
Abstract Corporations use global sustainability reporting principles, certifications, guidelines, and indices to promote corporate transparency. However, the effectiveness of adopting these global transparency approaches, either separately or collectively, in increasing firm value is as yet unclear. Thus, we examine whether different global transparency approaches engender different outcomes related to firm value and whether adopting a comprehensive or integrated global transparency approach could better enhance firm value. We use a sample comprising 6978 firm‐year observations of firms listed in the United States (S&P 500), Canada (S&P‐TSX 221), and the United Kingdom (FTSE 350) from 2013 to 2019. A fixed‐effects regression model is then used to examine the primary associations in this study. This technique was complemented by a two‐step dynamic generalised method of moment (GMM) model to overcome the expected endogeneity concerns. Our findings indicate that adopting global sustainability reporting principles, certifications, and an integrated global transparency approach is positively attributable to the market value of firms. In contrast, firms' adoption of international guidelines and environmental, social, and governance (ESG) ratings cannot predict the firm value in the study context. Our evidence implies that firms' adoption of an integrated global transparency approach adds the most value to those firms when compared with adopting a standalone transparency approach across the three sampled countries. Our study provides practical implications for policymakers and corporate managers and suggests avenues for future studies to build upon our findings.
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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 | 0.008 |
| 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 | 0.003 | 0.001 |
| 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".