Furloughed Employees’ Voluntary Turnover: The Role of Procedural Justice, Job Insecurity, and Job Embeddedness
Bibliographic record
Abstract
During the COVID-19 lockdown period, several employers used furloughs, that is, temporary layoffs or unpaid leave, to sustain their businesses and retain their employees. While furloughs allow employers to reduce payroll costs, they are challenging for employees and increase voluntary turnover. This study uses a two-wave model (Time 1: n = 639/Time 2: n = 379) and confirms that furloughed employees’ perceived justice in furlough management and job insecurity (measured at Time 1) explain their decision to quit their employer (measured at Time 2). In addition, our results confirm that furloughed employees’ job embeddedness (measured at Time 1) has a positive mediator effect on the relationship between their perceived procedural justice in furlough management (measured at Time 1) and their turnover decision (Time 2). We discuss the contribution of this study to the fields of knowledge and practice related to turnover and furlough management to reduce their financial, human, and social costs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".