How Do Employee Attitude Surveys Contribute to Staff Turnover Intentions in a University Setting?
Bibliographic record
Abstract
This study aimed to determine the effect of employee attitude surveys on academic staff turnover intentions in chartered universities in Kenya. The specific objectives were; to determine the extent of employee attitude surveys practices among universities in Kenya; assess the level of turnover intentions among academic staff in chartered universities in Kenya, and determine the effect of employee attitude surveys practices on turnover intentions among academic staff in chartered universities in Kenya. The study was anchored on the Universalistic theory and the Unfolding model of voluntary turnover. A positivism research philosophy guided the study, and a descriptive cross-sectional survey design was used. The study obtained primary data from a representative sample of 364 academic staff members drawn from 15 chartered universities in Kenya. The study found that employee attitude surveys have been practized to a low extent and produced correspondingly low staff turnover intentions. Two dimensions of employee attitude surveys significantly negatively affect staff turnover intentions. The study called on future research to apply more robust statistical techniques anchored on mixed methods design for a more comprehensive explanation of the direction of the causal effects of attitude surveys on staff turnover intentions.
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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 | 0.023 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".