Collective turnover: organization design and processes or contagion effects?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it