Effect of lean implementation on team psychological safety and learning
Bibliographic record
Abstract
Purpose Frontline teams are at the centre of lean transformations, but the teams also transform as they implement lean. This study examines these changes and seeks to understand how lean relates to team psychological safety and learning. Design/methodology/approach This research setting is the Romanian division of a leading European energy company. The authors collected team-level audit and survey data, which the authors used to test the effect of lean implementation on team psychological safety and learning. The authors’ team-level data are complemented with qualitative interviews conducted with team members and headquarters leaders. Findings The results of the regression analyses show that leanness is positively associated with team psychological safety, which is in turn positively associated with learning. Thus, this research provides evidence that leanness – mediated by team psychological safety – increases team learning. Practical implications Lean changes team dynamics and learning positively by ensuring and promoting an emotionally sound work environment with clear team structures, an appropriate level of autonomy, and strong leadership. Originality/value This paper contributes evidence of important psychological mechanisms that characterise team-level lean implementation. Particularly, the authors highlight how team psychological safety mediates the relationship between leanness and team learning.
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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 | 0.028 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".