Audit quality, media coverage, environmental, social, and governance disclosure and firm investment efficiency
Bibliographic record
Abstract
Purpose The purpose of this study is twofold: first, to introduce two determinants of environmental, social and governance (ESG) disclosure transparency, namely, audit quality and public media exposure; and second, to investigate the impact of ESG transparency on firm-level investment efficiency. Design/methodology/approach Ordinary least square (OLS) regressions are applied to explore the relationship between the two variables of interest (audit quality and public media exposure) and ESG transparency on a sample of publicly listed Canadian firms during the period 2008 to 2017. Then, an econometric model is used to investigate the association between ESG transparency and investment efficiency under two identified scenarios, under-investment and over-investment. Findings Results show that audit quality and public media exposure are two main drivers of ESG transparency, hence, commitment to high-quality audits and exposure to high public media coverage drive firms to disclose more extensive and transparent ESG information. The authors also find a negative association between ESG transparency and firm-level investment inefficiency. Thus, ESG transparency generates influential incremental information that helps mitigate the information asymmetry between firms and stakeholders while fostering better resource allocation through investment efficiency. Originality/value This study contributes to the corporate social responsibility (CSR) and ESG literature by identifying audit quality and public media exposure as two determinants of ESG transparency; and by noting that higher ESG transparency has a significant economic effect on capital investment decisions through higher firm-level investment efficiency.
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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 | 0.002 | 0.014 |
| 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) | 0.003 | 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".