Performance Management Design and Effectiveness in Quality‐Driven Organizations
Bibliographic record
Abstract
Abstract Performance management practices, such as performance appraisals, are often considered incompatible with the principles of quality management. If designed appropriately, however, performance management systems could support rather than hinder quality. This study investigates the alignments of performance management system components to a strategic emphasis on quality. Findings from a sample of 312 organizations generally support the idea that organizations are adapting their performance management systems in ways that are compatible with a quality emphasis. In addition, the study shows that a greater alignment of quality and performance management is associated with greater perceived effectiveness of the performance management system. Résumé Les pratiques de gestion du rendement, comme les évaluations du rendement, sont souvent considérées incompatibles avec les principes de la gestion de la qualité. Cependant, si elles sont développées correctement, les systèmes de gestion du rendement peuvent soutenir plutôt que nuire à la qualité. La présente étude s'intéresse aux alignements entre les composantes des systèmes de gestion du rendement et l'emphase stratégique sur la qualité. Les analyses réalisées avec un échantillon de 312 organisations indiquent une certaine cohérence entre les systèmes de gestion du rendement et l'emphase stratégique sur la qualité. Les organisations semblent donc adapter leurs systèmes de gestion du rendement aux exigences de la qualité. De plus, une plus grande cohérence entre la stratégie qualité et la gestion du rendement est associée avec des perceptions positives à l'égard de l'efficacité du système de gestion du rendement.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".