The effects of HRM approach on quality management techniques and performance
Bibliographic record
Abstract
Using data from 250 companies from Brazil and Denmark, this study aims to investigate the effects of commitment- and control-oriented human resource management (HRM) on the relationship between four QM technique groups, namely goal setting (GS), continuous improvement (CI), measurement (MS) and failure prevention and control (FPC) techniques, and performance. Both HRM approaches affect the QM techniques and performance positively. However, the association with control-oriented HRM has a stronger performance effect for three QM techniques groups (CI, MS and FPC) than the association with commitment-oriented HRM. Only for the GS techniques, the effects of control- and commitment-oriented HRM on performance are not statistically significantly different. These results show that HRM practices may contribute to enable QM techniques to have a positive effect on performance. Additionally, the results demonstrate that control-oriented HRM supports the QM techniques better in improving performance than commitment-oriented HRM for most groups of QM techniques studied. These findings suggest an important duality: while previous studies suggest that QM practices thrive in a commitment-oriented HRM environment, this research shows that QM techniques are best supported through control-oriented HRM. Further research, going beyond the two country samples, is needed to explore the implications of this duality.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".