Relationship between Strategic Recruitment and Employee Retention in Commercial Banks in Kenya
Bibliographic record
Abstract
The purpose of the study was to examine the relationship between strategic employee recruitment practices and employee retention in commercial banks in Kenya. A survey design was used to gather the information needed to achieve the objectives. Qualitative and quantitative techniques were used. The study was carried out in commercial banks in Kenya which had operating Licenses from the Central bank of Kenya. Questionnaires were used to collect the data. The data was analysed using descriptive statistics mainly percentages and frequency distribution. Correlation and regression analysis were used to test the relationship between the variables. The study established that the organizations practiced strategic recruitment such as the use of associations, psychometric tests, websites, targeting specific professionals, employing head hunting strategies, offer incentives, ranking of potential candidates and utilization of internet and other technologies which influenced the employee recruitment. The study concluded that the strategic employee recruitment influenced the employee retention. The study recommended that the management of all commercial banks should embrace strategic recruitment with the view of retaining their talents and thereby cutting the cost of recruitment and the loss of talents which are valuable to the organizations’ competitiveness.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".