Impact of Culture on Human Resource Management Practices: A 10‐Country Comparison
Bibliographic record
Abstract
Le Mode`le de Culture Fit explique la manie`re dont l’environnement socio‐culturel influence la culture interne au travail et les pratiques de la direction des ressources humaines. Ce mode`le a e´te´ teste´ sur 2003 salarie´s d’entreprises prive´es dans 10 pays. Les participants ont rempli un questionnaire de 57 items, destine´ a` mesurer les perceptions de la direction sur 4 dimensions socio‐culturelles, 6 dimensions de culture interne au travail, et les pratiques HRM (Management des Ressources Humaines) dans 3 zones territoiriales. Une analyse ponde´re´e par re´gressions multiples, au niveau individuel, a montre´ que les directeurs qui caracte´risaient leurs environnement socio‐culturel de fac¸on fataliste, supposaient aussi que les employe´s n’e´taient pas malle´ables par nature. Ces directeurs ne pratiquaient pas l’enrichissement des postes et donnaient tout pouvoir au contrôle et a` la re´mune´ration en fonction des performances. Les directeurs qui appre´ciaient une grande loyaute´ des employe´s supposaient qu’ils remplissent entre eux des obligations re´ciproques et s’engagaient dans la voie donnant pouvoir aux pratiques HRM. Les directeurs qui percevaient le paternalisme et une forte distance de l’autorite´ dans leur environnement socio‐culturel, supposaient une re´activite´ des employe´s, et en outre ne pourvoyaient pas a` l’enrichissement des postes et a` la de´le´gation. Des mode`les spe´cifiques a` la culture qui mettent en relation ces 3 groupes de variables ainsi que les applications de ces recherches pour la psychologie industrielles trans‐culturellesont e´te´ de´battus.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".