Effect of Revenue Collection Process Innovations on Financial Performance of Selected County Governments in Kenya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most of the county governments in Kenya lack enough finances to fund their recurrent and development budgets which has led to stalling of projects. In order to meet their financial obligations, county government should devise innovative ways to increase revenue collection. County governments in Kenya are faced with a number of tax collection challenges which include; a narrow tax base which reduces potential revenues and makes the county more dependent than it could be on a small section of society. The general objective of the study was to investigate the effect of revenue collection processes innovations on the financial performance of selected county governments in Kenya. The study adopted a descriptive research design. The target population consisted of all the employees in the county revenue collection department. Clustered random sampling technique was used in this study to select the respondents. The total sample in this study was 124 respondents. Data was primarily collected to provide information regarding a specific topic. Primary data was gathered by use of a semi-structured questionnaire and captured through a 5-point type Likert scale. The collected primary data was analyzed using Statistical Package for Social Science (SPSS) version 20. A linear regression analysis was conducted on the data set. The Pearson Product Moment was used to analyze the data in which correlation coefficient (R) and the coefficient of determination (R 2 ) of the variables was established. The findings revealed a strong positive relationship between the independent variables and the dependent variable. The study recommends that all the staff that is in revenue department in all county governments should be trained on revenue collection.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it