Investigating the Implementation of Toyota’s Human Resources Management Practices in the Aerospace Industry
Bibliographic record
Abstract
Many companies try to follow Toyota’s production model to achieve better performance. In their attempts, however, they primarily focus on Lean Production tools, often overlooking the role of employees and HRM practices. In this work, we aim to investigate the implementation of Toyota’s HRM practices in the aerospace sector. For this purpose, we used a qualitative methodology, whereby data were collected through semi-structured interviews with thirty office and production employees from a Canadian aerospace company. Our results show that the company under study adopted several of Toyota’s HRM practices, including training, communication, respect, supervisor/manager support, fairness, and occupational health and safety. These findings underscore the importance of Toyota’s HRM practices in the aerospace sector. Notably, however, not all of Toyota’s HRM practices were adopted, and among those adopted, we found considerable differences in implementation. Overall, our findings provide novel insights into the implementation of HRM practices in the aerospace sector and highlight the flexibility in their implementation to adapt to the context of the target company.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".