Corporate Growth Strategies, External Operating Environment and Firm Performance: An Empirical Survey of Large Manufacturing Firms in Nairobi City County, Kenya
Bibliographic record
Abstract
Theoretical literature in strategic management describes performance as outcome of firm’s strategic objectives, which are developed and executed at the corporate level of management. Conceptual propositions also suggest that the external operating environment of a firm influences the relationship between its corporate strategies and performance. This paper examines the direct effect of corporate growth strategies on performance of large manufacturing firms in Nairobi City County, Kenya. The strategies under study are market development, product development and diversification. The paper also examines the moderating effect of external operating environment on the relationship between corporate growth strategies and performance of the large manufacturing firms. The authors adopted indicators of competitive position, consumer behaviour and credit accessibility to measure external operating environment.Multistage probability sampling technique was used to select study sample of 189 firms. One hundred forty eight firms responded where primary data was collected using a semi-structured questionnaire. Data was analysed using descriptive and inferential statistics. The study findings indicate that corporate growth strategies have a positive and significant impact on a firm’s performance. It also found out that external operating environment has a moderating effect on the relationship between corporate growth strategies and firm performance. The study has important implications for managers and policy makers of the manufacturing firms.
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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 | 0.001 |
| 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) | 0.002 | 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".