Improving Public Service Delivery Through Good Corporate Governance: Lessons From the Embu County Government, Kenya
Bibliographic record
Abstract
Poor service delivery in Embu County, marked by governance lapses, corruption, and inefficiencies, indicates a gap in understanding how corporate governance principles, such as stakeholder inclusivity, transparency, public participation, and accountability, influence effective service delivery in the context of devolved governance. This study therefore sought to examine the effect of stakeholders’ transparency, inclusivity, public contribution, and accountability on service delivery. The theoretical basis for this research was anchored on SERVQUAL Model. The study was in addition underpinned by Agency Theory, Stewardship Theory, and Institutional Performance Theory and Resource-Based Theory. A descriptive survey research design was applied, targeting 248 workers from Embu County from which a sample of 153 respondents was selected using a proportionate stratified and simple random sampling technique. The findings revealed that stakeholders' inclusivity, transparency, public participation, and accountability jointly explained 62.9% of the variation in service delivery in Embu County Government (Adjusted R² = 0.615). Regression analysis showed that stakeholders' inclusivity (β = 0.208, p = 0.020), transparency (β = 0.053, p = 0.007), public participation (β = 0.465, p = 0.000), and accountability (β = 0.164, p = 0.042) were all positively and significantly related to service delivery. The study concludes that stakeholders’ inclusivity, transparency, public participation, and accountability significantly affect service delivery, with public participation having the most substantial impact. In view of the findings, the study recommends that Embu County Government should improve corporate governance practices by institutionalizing structured public participation frameworks, improving financial transparency, and reinforcing stakeholder engagement mechanisms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".