Ethical Behavioural Disclosure and Financial Performance of Listed Industrial Goods Firms in Nigeria
Bibliographic record
Abstract
Purpose: Interests in the nexus between ethical performance and financial performance have generated mixed results. However, despite the number and the variety of studies, the evidences available suggest not been comprehensively examined. Also, there is no sufficient effort to examine this relationship within the industrial good sector in Nigeria. This calls for further study. Design/Methodology: This study uses a functional coefficient regression technique to estimate panel-varying betas and alpha in three financial performance models. The empirical data were collected from the Nigerian Stock Exchange over a period of 2010-2019. Data were analyzed using random effects models after accounting for multicollinearity, heteroskedasticity, normality. Findings: Results show that human resource development disclosure and community service disclosure have significant positive effects on financial performance while environmental and product safety disclosures have significant negative effects on financial performance. Originality/Value: The study therefore concludes that ethical behavioural practices of listed industrial firms have significant effects on financial performance. The study recommends among others that industrial firms should redirect their ethical practices into human resource development and community services since their disclosures enhance financial performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".