Collective turnover: organization design and processes or contagion effects?
Bibliographic record
Abstract
Purpose Using organization-level data, the purpose of this paper is to investigate whether and how turnover spreads at different job levels (i.e. managers, non-managers) and how vacancy rate and manager span of control precipitate continued turnover. Design/methodology/approach Organization-level longitudinal data were collected quarterly from 40 Canadian organizations on various HR metrics from 2009 to 2012, totaling 232 observations. The authors used covariate balance propensity score (CBPS) weighting to make stronger causal inferences. Findings The organization-level data provided limited support for turnover spreading at different job levels. Instead, vacancy rate predicted subsequent non-manager turnover rates, whereas span of control predicted subsequent manager turnover rates. Practical implications The implications of this research are twofold. First, to offset continued turnover among non-managers, it may be wise for organizations to fill vacancies promptly, particularly when unfilled positions affect job demands and resources of those who remain. Second, to minimize ongoing manager turnover, organizations may benefit from redesigning work units to have smaller manager-to-employee ratios. Originality/value This study adds to the collective turnover literature by demonstrating that organizational factors play a substantive role in predicting continued manager and non-manager turnover. Moreover, by using longitudinal data and CBPS weighting, this research allowed for establishing temporal precedence and greater confidence that these factors play a causal role. Lastly, this research highlights how the factors precipitating collective turnover differ between managers and non-managers.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".