Dynamic Capabilities and Competitive Advantage of Companies Listed at Nairobi Securities Exchange
Bibliographic record
Abstract
The purpose of this study was to examine the relationship between dynamic capabilities and competitive advantage of companies listed at Nairobi Securities Exchange. The specific objectives were to establish the influence of dynamic capabilities on competitive advantage of companies listed at Nairobi Securities Exchange. The study applied cross sectional descriptive survey as its research design and all the firms listed at the NSE formed the study population. The study established dynamic capabilities explain 44.8% of variation in competitive advantage. The hypothesis that dynamic capabilities construct has a significant influence on competitive advantage of companies listed at Nairobi Securities Exchange was therefore supported. The study recommends that all listed firms should encourage the development of dynamic capabilities as they are instrumental in combating environmental challenges and consequently ensure the attainment of a competitive advantage. The results contribute to theory development, policy and management practice with regard to the essentiality of dynamic capabilities in the realization of competitive advantage. The limitation of the study is that it used the top management individuals as the target respondents as opposed to including other employees in the organization. Nevertheless, this did not compromise the findings since top managers understand the workings of the firm and are able to discern the various aspects of the operations and strategy. Consequently, the study points out room for more research using a larger population, longitudinal studies and incorporating other companies that are not listed at Nairobi Securities Exchange.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".