Determinants of GRI G3 Application Levels: The Case of the Fortune Global 500
Bibliographic record
Abstract
ABSTRACT The objectives of this research are to analyze the determinants for the adoption of the Global Reporting Initiative (GRI) G3 guidelines and GRI G3 application levels (undeclared, C, C+, B, B+, A, A+). Content analysis of the 2009 sustainability reports published by the world's 500 largest companies (Fortune Global 500) was conducted. Based on legitimacy theory and signalling theory, the study yielded two main findings. First, the binary logistic regression reveals that the adoption of GRI G3 guidelines is influenced by company size, profitability, business culture of a country, and industry. These results are consistent with past research on the factors influencing sustainability reporting. Second, the ordinal logistic regression shows that the GRI G3 application level is influenced by the industry in which a firm operates but not by company size, profitability or business culture of a country. In high‐risk industries, the GRI G3 application level is more likely to be considered as a signal to manage the reputational risk of the companies. Copyright © 2012 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".