How Does Corporate Information Environment Influence CSR?
Bibliographic record
Abstract
This study investigates the impact of outsiders’ demand for more information (or transparency) on corporate social responsibility (CSR) initiatives. Drawing on a dataset of U.S. companies from 2010 to 2023, CSR performance is measured using ASSET4 ratings, while CSR disclosure levels are captured through the number of words and sentences in reports. Utilizing within-industry and -firm OLS regressions, our analyses reveal a positive relationship between the demand for more information and future CSR investments, showing that firms with higher demand for information not only enhance their CSR performance but also expand the length of their CSR reports. These results suggest that increased pressures for information encourage organizations to engage more deeply with social responsibility, resulting in more robust CSR activities and more comprehensive reporting practices. This study contributes to the existing literature by highlighting the strong predictive role of outsiders’ demand for more information in promoting CSR investment and disclosure, and by offering important insights for policymakers and practitioners on fostering corporate responsibility through enhanced transparency.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".