Competitive Strategies and Performance of Construction Companies in Kisumu County, Kenya
Bibliographic record
Abstract
Competitive strategy is intended to grant an organization the ability to outperform its rivals and gain market leadership. Research on performance implications of competitive strategies is vast strategic management but without much consensus. This study set out to establish the competitive strategies adopted by construction firms in Kisumu County and to determine their influence on the companies’ performance. Through a cross sectional descriptive survey, data were obtained from a randomly drawn sample of eighty four (84) construction companies using a structured questionnaire and analyzed using multivariate regression analysis. The findings of the study indicate that the construction firms adopted cost leadership, product differentiation, growth strategies, and grand strategies. The study reports strong positive correlation between competitive strategies and performance as well as statistically significant influence of competitive strategies on performance. Grand strategies account for a larger variation performance followed by generic and growth strategies respectively. Independently, differentiation strategy accounted for a larger proportion of unit change in performance followed by market penetration, strategic alliances and innovation in that order. Out of the study results, the firms are advised to aggressively adopt a mix of competitive strategies. The study’s findings support the postulations of game theory and the strategic conflicts model as anchoring theories. Arising from the study’s limitations, suggestions for further research have been advanced along different lines.
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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 | 0.001 |
| 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".