Effects of Financial Rewards on Turnover Intention in Tanzania Private Organization: The Case of Bagamoyo Sugar Limited
Bibliographic record
Abstract
This study explored the effects financial rewards on Turnover Intention in Tanzania Private Sector. The study was guided by the need to determine the effect of financial rewards to employees on turnover intention at Bagamoyo Sugar Limited. A case study was adopted, employees of a private company in farm Bagamoyo Sugar Limited. 35 respondents were asked using closed ended questionnaire, the results were analyzed on SPSS using cross tabulations. In respect to the effect of financial rewards to employees on turnover intention at Bagamoyo Sugar Limited it was noted that more than three quarter of respondents agrees there is effects and it was recommended that employer should ensure they improve financial rewards in order to retain their employees, The test for independence of variables used Pearson Chi Square results showed, even though statistical the results agrees there is effects of financial rewards of turnover intention, the two variables can be independent of each other, thus the study draws conclusion that private organization should continue to improve financial rewards and decision to turnover can be done by employee even when financial rewards are relatively better.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".