Nexus Between Corporate Social Responsibility, Environmental Disclosure and Financial Reporting Quality Among Listed Firms in Nigeria
Bibliographic record
Abstract
This paper seeks to provide a novel approach and insight into the synergies between corporate social responsibility (CSR), environmental disclosure (ED) and financial reporting quality (FRQ) which is emerging and changing rapidly. The study examined the nexus between corporate social responsibility (CSR), environmental disclosure (ED) and financial reporting quality (FRQ) among corporate entities listed on the Nigeria Stock Exchange (NSE). Data were collected from a sample of 169 listed firms in Nigeria. The research used a panel data set comprising of 624 firm year observations spanning the period 2015 to 2017. The empirical results of the study revealed that there exists a significant relationship between environmental disclosure(ED), firm size (FS), and financial reporting quality (FRQ). However, empirical evidence shows an insignificant relationship between social disclosure (SD), leverage and financial reporting quality (FRQ). We therefore recommend a proposal for the establishment of an inductive corporate social responsibility/environmental disclosure/financial reporting framework that future scientists/scholars can institute to explore the determinants of corporate social responsibility (CSR), environmental disclosure (ED) and financial reporting quality (FRQ) in developing countries.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".