Gestion des ressources humaines et performance de la firme à capital intellectuel élevé: une application des perspectives de contingence et de configuration
Bibliographic record
Abstract
Résumé L'objectif de cette étude est de vérifier dans quelle mesure les pratiques de GRH prescrites par deux modèles théoriques prédisent la performance organisation-nelle perçue de 175 firmes à capital intellectuel élevé. Les résultats indiquent que l'index de configuration des pratiques de GRH (complémentarité) apporte généralement une augmentation de la prédiction de la performance organisationnelle en supplément de celle prédite par l'index de contingence (apprentissage organisation-nel) et que ce dernier apporte à son tour partiellement une augmentation de la prédiction de la performance organisationnelle en supplément de celle prédite par le précédent. Abstract The aim of the present study is to examine to what extent the Human Resource Management (HRM) practices, put forward in two theoretical models, predict the perceived organizational performance of 175 firms characterized by a high intellectual capital. Results indicate that the index of configuration of HRM practices (complementarity) generally causes an increase of the prediction of organizational performance beyond the one predicted by the index of contingency (organizational learning). The latter, in turn, partially brings about an increase of the prediction of organizational performance beyond the one predicted by the former.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".