Human resource management practices and voluntary turnover: a study of internal workforce and external labor market contingencies
Bibliographic record
Abstract
We tested relationships between employee quit rates and two bundles of human resource (HR) practices that reflect the different interests of the two parties involved in the employment relationship. To understand the boundary conditions for these effects, we examined an external contingency proposed to influence the exchange-based effects of HR practices on subsequent quit rates – the local industry-specific unemployment rate – and an internal contingency proposed to shape employees’ conceptualization of their exchange relationship – their employment status (i.e. full-time, part-time and temporary employment). Analyses of lagged data from over 200 Canadian establishments show that inducement HR practices (e.g. extensive benefits) and performance expectation HR practices (e.g. performance-based bonuses) had different effects on quit rates, and the former effect was moderated by unemployment rate. The effects of HR practices on quit rates did not differ between FT and PT employees, but a different pattern of main and interactive effects was found among temporary workers. These findings suggest that employees’ exchange-based decisions to leave may be less affected by the number of hours they expect to work each week, and more by the number of weeks they expect to work.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".