Behavioural indicators of turnover intention: the case of young professionals in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The relationships between employees' turnover intention and a broad range of potential behavioural indicators of such intention were explored through a survey of 279 young professionals in China. The results suggested that these employees' turnover intention could be predicted from their observable behaviours. In combination, increased task neglect and self-centred voice, and decreased personal industry and loyal boosterism explained 23% of the variance in turnover intention. However, this group's turnover intention was not related to their interpersonal helping or organization-centred voice behaviours. Loyal boosterism was the strongest predictor of turnover intention, followed by personal industry and then by task neglect. Keywords: Chinaemployee retentionhuman resource managementorganizational citizenship behaviourturnover intention Acknowledgements This research was supported by Grant #410-2002-1284 from the Social Sciences and Humanities Research Council of Canada.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it