The Moderating Effect of External Environment on the Relationship Between Strategic Entrepreneurship and Performance of Selected Oil and Gas Service Firms in Lagos and Rivers States, Nigeria
Bibliographic record
Abstract
Globally, oil and gas service industry is one of the major contributors to the economic development of many nations. However, the industry is faced with problems of poor entrepreneurial orientation, inflexible planning and poor management of external environmental challenges. These problems have negatively affected their overall performance. This study therefore examined the effect of strategic entrepreneurship on overall performance. The study adopted cross-sectional survey research design with a target population of 9,324 owners and managers of oil and gas service companies operating in Lagos and Rivers States, Nigeria. A multi-stage sampling technique was adopted to select the sample size of 733 using the Cochran (1997) formula. The data was analyzed using descriptive statistics and multiple and hierarchical regression methods of analyses. Findings revealed that strategic entrepreneurship components (entrepreneurial orientation and planning flexibility) had significant effect on firm performance (R2 = .216, F-stat = 34.743, p<0.05). Strategic entrepreneurship components significantly affected sales growth (Adj. R2 = .582, F-stat = 98.422, p<0.05); market share (Adj. R2 = .511, F-stat = 58.132, p<0.05); and profitability (Adj. R2 = .410, F-stat = 42.982, p<0.05). External environment significantly moderated the relationship between strategic entrepreneurship and firm performance (ΔR2 = .593, ΔF = 19.256; F-stat = 67.765, p<0.05) all at 5% level of significance. Implications of the findings and recommendations were made.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".