Leadership Practices, Stakeholder Involvement and Performance of National Government Departments in Kenya
Bibliographic record
Abstract
The performance in most of the National Government departments in Kenya has been average over the years leading to disparities in access to resources and quality services. There is scanty literature on leadership practices and the performance of the departments available. Thus, this study assessed the influence of leadership practices on the performance of the departments moderated by stakeholder involvement. The study adopted quantitative and qualitative mixed research design guided by positivism research philosophy. It used a validated semi – structured questionnaire for data collection from a sample of 195 respondents drawn from National Government Heads of Departments in the Counties. The resultant data was analyzed to generate descriptive and inferential statistics which were used to draw inferences. The study established that leadership practices significantly influence the performance of departments in the National Government of Kenya moderated by stakeholder involvement. To improve on the performance, the management should review the stakeholder involvement management and the leadership practices adopted with a view of re – engineering the implementation process to provide for a performance improvement framework. The respondents were drawn from the National Government Departments in the Counties which excluded the views of Heads of Departments based at the headquarters of the National Government Departments. This is the first study on leadership practices, stakeholder involvement and performance of the National Government departments in Kenya to the best of the researchers. It added knowledge on the leadership practices and stakeholder involvement influence on the performance of public sector organizations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".