Effect of Turnaround Strategies on Performance of Public Sugar Processing Companies in The Western Region, Kenya.
Bibliographic record
Abstract
Public sugar processing companies in Kenya’s western region have continued to face persistent challenges such as mismanagement, outdated machinery, heavy indebtedness, and declining competitiveness compared to regional producers. These constraints have weakened their performance and sustainability, raising the need for effective turnaround strategies. The purpose of this study was to examine the effect of cost reduction, reorganization, and modernization strategies on the performance of these public sugar firms. The study adopted a quantitative research design and targeted senior management staff from five public sugar companies, namely Mumias, Nzoia, South Nyanza, Chemelil, and Muhoroni. A census approach was applied to the 51 respondents, and structured questionnaires were used to collect data. Descriptive and inferential analyses were conducted with the aid of SPSS, including correlation and multiple linear regression to test relationships between turnaround strategies and organizational performance. The findings revealed a strong positive relationship between the three strategies and performance outcomes, with a correlation coefficient (R) of 0.89 and an R² of 0.79, indicating that 79% of performance variation was explained by the combined effect of the strategies. The adjusted R² value of 0.76 further confirmed the robustness of the model. Regression analysis established that cost reduction strategies had the greatest effect (β = 0.41, p < .001), followed by reorganization strategies (β = 0.34, p = .001) and modernization strategies (β = 0.29, p = .002). All predictors were statistically significant, affirming their importance in enhancing organizational outcomes. The study concluded that cost optimization, structural reorganization, and modernization initiatives are critical drivers of performance, competitiveness, and sustainability in the sugar sector. It recommended that management prioritize selective cost-saving measures, strengthen organizational restructuring, and invest in modernization programs to revitalize public sugar processing companies. These measures are vital for enhancing efficiency, improving profitability, and ensuring the long-term survival of the industry, which remains central to Kenya’s economy and rural livelihoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".