Lean management strategy and innovation: moderation effects of collective voluntary turnover and layoffs
Why this work is in the frame
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Bibliographic record
Abstract
We investigated the impact of lean management strategy on organisational innovation. Integrating lean management and human resource management, we consider how and why different types of reductions in the workforce can influence the relationship between lean management strategy and organisational innovation in different ways. Specifically, we examined the moderating effects of collective voluntary turnover and layoffs in the relationship between lean management strategy and innovation. We tested our hypotheses using data from a large longitudinal dataset. We found that lean management strategy was significantly associated with organisational innovation. We also found that employee layoffs positively moderated the relationship between lean management strategy and innovation, whereas employee turnover negatively moderated the relationship between lean management strategy and innovation. Overall, we found that the effects of layoffs and collective voluntary turnover on the relationship between lean management strategy and organisational innovation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it