"Predicting Voluntary Turnover with Culture, Employee Values and Their Congruence"
Bibliographic record
Abstract
A one year longitudinal study of 477 part time employees from hundreds of companies was conducted to examine the potential of P-O fit to predict voluntary turnover. Employees completed fit measures, perceived fit, job satisfaction, affective commitment, turnover intentions and job search at time 1 and 136 provided data one year later on voluntary turnover. After accounting for the 30% of the turnover due to employee shocks, our results indicated that congruence between personal values and organizational culture at time 1 as well as main effects of company culture and individual values was a strong predictor of voluntary turnover with 69% of the variance in Voluntary Turnover accounted for. Logistic regression provided classification estimates that predicted leavers versus stayers 83% of the time. Results indicate that P-O Fit effects on voluntary turnover were best described directly than through a mediated model involving perceived fit, job satisfaction and job search behaviours. Implications for the use of fit in personnel selection are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".