Exploring the Impact of Strategic Proactivity on Perceived Corporate Social Responsibility in Nigeria’s Petroleum Industry: A Structural Equation Modeling Approach
Bibliographic record
Abstract
In the past few years, there has been a gradual re-orientation taking place in the relationship between business and society in the Nigerian petroleum industry. The re-emergence of democracy has led to an increased awareness about the role of oil companies in their host-communities. Oil companies are aware of this, and have devoted increased effort towards contributing to their host-communities. Despite the fact that, of recent, these oil companies are contributing more than ever, there is an increase in the conflict between these companies and the stakeholders in their host-communities. This is threatening their sustainability. This problem highlights a gap in theory and practice of CSR. In recent times, there have been calls for shift of scholarly focus towards a performance based CSR theory and practice. This paper empirically tested this performance based perspective by exploring the interactive process that leads to CSR outcomes. This was done through a quantitative research study. 623 members of Eket and Ibeno youth councils took part in a survey from which 591 valid samples were generated. A structural equation modelling (SEM) statistical technique was employed. The results showed a positive relationship between strategic proactivity and perceived CSR, with perceived economic value dimension demonstrating partial mediating impact on the relationship.
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".