Measuring the impact of corporate governance on non-financial reporting in the top HEIs worldwide
Bibliographic record
Abstract
Purpose This study aims to measure the relationship between corporate governance and non-financial reporting (NFR) in higher education institutions (HEIs). Board effectiveness, student engagement, audit quality, Vice-Chancellor (VC) pay and VC gender are targeted for analysis. Design/methodology/approach This study is based on content analysis. The authors used the EU NFR Directive (2014/95/EU) to measure NFR. This includes environmental, corporate social responsibility, human rights, corporate board effectiveness and corruption and bribery. Cross-sectional data was collected from 89 HEIs worldwide across 15 different countries over three years. Content analysis, the weighted scoring method and panel data analysis are used to obtain the results. Findings Through a neo-institutional theoretical lens, this study provides a broader understanding of NFR content disclosure practices within HEIs. The findings reveal that the audit quality, VC pay and VC gender are significantly and positively associated with NFR content disclosure. However, board effectiveness has a significant negative impact on NFR content disclosure. More interestingly, the findings reveal that student engagement has an insignificant association with NFR content disclosure and there significant difference on the level of NFR content disclosure across universities situated in the different geographical region such as the USA, Australia, the UK and EU, Asia and Canada. The findings have important implications for regulators and policymakers. The evidence appears to be robust when controlling for possible endogeneities. Originality/value The study contributes to the literature on corporate non-financial disclosure as it provides new insights of corporate governance mechanisms and NFR disclosure within HEIs.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".