Firm’s Engagement in Corporate Social Responsibilities in Nigeria
Bibliographic record
Abstract
This study examined firms engagement in corporate social responsibilities in Nigeria using secondary data derived from the audited annual reports of the five (5) selected listed Oil and Gas Companies in Nigeria from 2008 - 2017. The study proxied firm’s involvement by Firm Age (FMA), Employee Turnover (EPT), Customers Satisfaction (CSF) and Reputation (RPT) as the independent variables, while Responsibility of firms in Nigeria was proxied by CSR as the dependent variable. The study applied GRETL software, and used Ordinary Least Square (OLS) for the estimation of the result. The results revealed that the independent variables: EPT, CSF and RPT have positive significant impact on CSR while FMA shows a negative impact. The coefficient of R-squared which is 0.935067 shows that all the independent variables have 94% positive impact on CSR while the coefficient of Adjusted R-squared, 0.931820 suggests that 93% of all independent variables could be explained by the changes in CSR. The study concludes that firm’s age is not a strong and powerful measure of CSR as it does not play a significant role in determining the CSR of oil and gas sector in Nigeria. Thus, it was recommended that the management of the selected oil and gas sector should maintain quality assets that are durable. This is necessitated by the potential of the organizations that have such assets to invest more substantial funds towards Corporate Social Responsibility.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".