Voluntary Turnover Rate Fluctuations, Human Resource Practices, and Innovation: A Within‐Organization Investigation
Bibliographic record
Abstract
ABSTRACT Organizations constantly navigate voluntary employee departures to facilitate organizational effectiveness. To date, most studies on the implications of voluntary turnover rate have been conducted at the between‐organization level, comparing organizations with varying levels of voluntary turnover rates. Building upon complex adaptive system (CAS) theory, we develop within‐organization theorizing regarding the implications of voluntary turnover rate fluctuations for organizational innovation. We propose a U‐shape threshold model, where the relationship between voluntary turnover rate fluctuations and innovation takes a negative form when voluntary turnover rate fluctuations are within the normal range for an organization, and a positive form when voluntary turnover rate fluctuations surpass a critical threshold reflecting far‐from‐equilibrium conditions. Furthermore, we investigate how organizations may utilize human resource (HR) practices to shift the critical threshold. Specifically, we argue that increased reliance on interaction‐facilitating HR practices (employee participation and group‐based pay) lowers the threshold, while increased reliance on interaction‐inhibiting HR practices (individual‐based pay) raises the threshold. With firm‐fixed effects modeling, we found general support for our hypotheses using a large‐scale, multi‐level, longitudinal dataset from Statistics Canada (7110 workplace‐year observations from 1980 workplaces). We provide a novel theoretical lens to understand the nature and management of collective turnover.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".