Nigerian 3C-Index Rating of Corporate Social Responsibility and the Profitability of Some Companies Listed on the Nigerian Stock Exchange
Bibliographic record
Abstract
This study sought to ascertain the link between Corporate Social Responsibility (CSR) rating and the profitability of companies listed on the Nigerian Stock Exchange (NSE), following the release of the first ever country rating of Corporate Citizenship Index (3C-Index) in 2013. The study further sought to ascertain whether significant differences exist between the performances of companies that received high CSR ratings as compared to those that received low ratings. Secondary data were extracted from the 2013 to 2017 annual reports and accounts of companies that got different CSR ratings classified as high and low. The multiple regression and Mann-Whitney rank test (U-test) were used to test the propositions. The findings from the regression showed a positive but insignificant relationship between CSR rating and firm performance but a significantly positive relationship with the size of firms. The results of the U-tests were mixed, whereas the Return on Assets (ROA) of companies with high CSR ratings did not differ significantly from companies with low CSR ratings, the Return on Equity (ROE) of companies with high CSR ratings was significantly greater than that of companies with low CSR ratings. This finding suggests that CSR may be in its infancy among the study sample but is beginning to take roots as evident by the positive βs statistics and a significant difference in the ROE of the companies as captured by the non-parametric statistics. It is recommended that the period of the study be extended in the intermediate and long-run to determine if the relationship might become significant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".