Influence of Financial Incentives on Employee Performance in Ferry Service, Mombasa County
Bibliographic record
Abstract
The main purpose of the study was to assess the influence of financial incentives on employee performance in Ferry Service, Mombasa. The study employed goal theory, Herzberg theory and self-efficacy theory. The study employed a descriptive survey study design. The study was carried out at Ferry Service, Mombasa with a population of 268 employees. Stratified random sampling with the non-proportionate allocation of sample sizes was used to draw samples from the target population. The researcher used questionnaires to collect data from the respondents. The data that was collected was organised, tabulated and analysed using descriptive statistics. Statistical Package for Social Science (SPSS) Version 26.0 was used to assist in data analysis. The analysed data was presented in tables and figures, percentages and frequencies. The study found that financial incentives were administered by the organisation to which salary and fringe benefits highly influence employee performance the most while bonus was found to be the least since not all employees were benefiting from the incentive except a few employees who represent a quarter of the total employees. The respondents highly agreed that the employees are required to be aware of the fringe benefits offered by the organisation to enhance their performance which was represented by a mean of 1.60 and a standard deviation of 0.769; other respondents agreed that every organisation provides total rewards packages to the employees in addition to the basic pay represented by a mean of 1.97 and a standard deviation of 1.09 while others agreed that some benefits are given to the employees to attract and retain them and to avoid absenteeism from work which was represented by a mean of 2.14 and standard deviation of 1.158. The study concludes that financial incentives provide a big role in influencing employee performance in the organisation. The study recommends that financial incentives given to employees should be emphasised and increased to influence employees to work more and provide what is best for them. The organisation should provide financial incentives to its employees in a fair manner without discrimination. Incentives are there to motivate employees to work more and more and not to discomfort them.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".