The Moderating Effect of Environmental Turbulence on the Strategic Agility-Performance Relationship: Empirical Evidence from Lagos State, Nigeria
Bibliographic record
Abstract
Scholars in strategic management argued that strategic agility measures do enhance firm performance and mitigate environmental turbulence risks. This study therefore examined the moderating effect of environmental turbulence on the relationship between strategic agility and performance of oil and gas marketing companies in Lagos State, Nigeria. Population of the study was 515 managers of major oil and gas marketing companies in Lagos State. Cross-sectional survey research design was adopted with total enumeration. The research instrument was found reliable and valid with Cronbach’s alpha and KMO greater than 0.7 and 0.5 respectively. The data was analyzed using descriptive statistics, Pearson correlation, and multiple and hierarchical regression methods of analyses. Findings revealed that among oil and gas marketing companies in Lagos State, Nigeria, there was positive and significant relationship between strategic agility and performance; strategic agility had positive and significant effect on performance while environmental turbulence significantly moderated the relationship between strategic agility and performance. The study concluded that strategic agility affected and related with firm performance and also environmental turbulence moderated the relationship between strategic agility and performance of oil and gas marketing companies in Lagos State, Nigeria. Therefore, it is recommended that oil and gas marketing companies in Nigeria should fully and dynamically embrace strategic agility practices and continuously develop their capabilities for proper and timely sensing of and responding to changes in their business environment in order to improve their performance over their competitors. Limitations of the study and areas for future research were highlighted.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".