Measuring the impact of corporate governance on non-financial reporting in the top HEIs worldwide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it