Nexus between Cost Leadership Strategy and Performance: Fact or Fallacy in Milk Processing Firms in Kenya
Bibliographic record
Abstract
Milk processing firms as a constituent of the food processing sector play a crucial function both economically and nutritionally. However, performance in the industry continues to be impended by high costs leading to low profitability margins, decline in output and collapse of some firms while others show stunted growth. It is hypothesized that this situation can be remedied by pursuing cost leadership strategy through economies of scale, economies of scope and operational efficiency. Extant literature however is scanty on how this strategy is employed by milk processing firms in Kenya with studies done failing to focus on how the firms manage costs as a driver for better performance. This has made it difficult to determine whether the hypothesized effect is a fact or fallacy. This study thus was an investigation of the effect of cost leadership strategy on performance of milk processing firms in Kenya. It was anchored on the balanced scorecard model complemented by the resource based view and capability based view theories. The study empirically examined the relationship using data from milk processing firms in Kenya obtained from a sample of 168 key respondents. The findings showed that cost leadership strategy had a positive and significant effect on performance of milk processing firms in Kenya. The study recommends that milk processors improve their performance by cutting costs through measures to increase their scale of operations, expand into related business areas and improve operational processes. The government and other the regulatory bodies should implement corresponding supportive policies and reforms.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".