The Intervening Influence of Internal Controls on the Relationship Between Board Practices and Performance of Government Owned Entities in Kenya
Bibliographic record
Abstract
There is a growing public debate on the role of boards in government-owned entities due to poor performance, corporate scandals, and increased corruption. Good board practices and internal controls, including enterprise risk management, are crucial for enhancing performance. Research findings on their impact have been contradictory. However, improving transparency, accountability, and adherence to governance frameworks can positively influence performance. Addressing governance issues is crucial to mitigate resource mismanagement and corruption, leading to better overall performance. The objective was to determine relationships among board practices, internal controls, and government-owned entities’ performance. Data was collected from 153 properly completed questionnaires out of the 157 returned, representing 65.38% of the entire population of 234 government-owned entities. The findings established that internal controls positively and significantly intervened in the relationship between board practices and performance. Implementing good board practices and internal controls promotes accountability and transparency, leading to enhanced organizational performance. Government-owned entities should prioritize implementing effective board practices and internal controls to enhance their overall performance.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".