STRATEGIC RESPONSES TO INTEREST RATES CAPPING BY CENTRAL BANK OF KENYA AND ITS EFFECT ON FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN KENYA
Bibliographic record
Abstract
The governments used interest caps for economic and political reasons; the most common involves providing support to specific area of the industry or economic area. It can be used when the government identifies market failure in a certain industries, or that the interest rate cap attempts forcing more focus on the financial resources in the same sector than what a market can determine. The study specific objectives of this study were to establish the effect of Re-organization on interest rates capping on financial performance and to establish the effect of downsizing strategies on interest capping on financial performance. The classical theory of interest and loan funds theory were used for the study. Empirical studies were on Re-organization and downsizing. Descriptive survey research design was used with population of 43 Finance Managers and 43 Business Development Managers. Census was employed for the study with 86 respondents. Questionnaire was the main data collection instrument. Pilot study was conducted to test validity of the questionnaires. The gathered data was analyzed through the use of descriptive and inferential statistics through SPSS while tables and figures were used for data presentation. The study found out that interest rates capping has led to Re-organization among commercial banks in Kenya . Interest rates capping was found to lead to reduction of workforce among commercial banks in Kenya. Key Words: Downsizing, Re-organization
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".