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Enregistrement W1842621406

Organizational Performance and Complementarity in Human Resources Management Practices

2003· article· en· W1842621406 sur OpenAlexaffabout
Jacques Barrette, Jules Carrière

Notice bibliographique

RevueSSRN Electronic Journal · 2003
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueAccounting and Organizational Management
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésComplementarity (molecular biology)RemunerationOrganizational performanceBusinessHuman resource managementCLARITYStaffingHuman resourcesOrganizational effectivenessKnowledge managementMarketingEconomicsManagementComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

For more than ten years, much published material has argued that human resource management (HRM) can play a major role in improving organizational performance. Several researchers claim that to exert a significant impact on organizational performance, HR practices need to be integrated or complementary with each other. However, the concept of complementarity suffers from a lack of operational clarity and has been essentially approached from a statistical standpoint that has limited our understanding of the architecture of the overall system of HR practices. On the other hand, several authors assert that the complementarity of HR practices cannot be studied outside its organizational context, especially in the industrial sector. They argue that differences in the nature of activity between service organizations and manufacturing companies are likely to have implications on which practices are adopted and how these practices impact the human and corporate performance of the organizations in question.In its first phase, this study proposes an operational definition of the concept of complementarity that can be used to select which practices to include in an organization’s HRM system. This complementarity has been defined as “the set of practices originating from various areas of HRM activity whose combined application can be rationally justified and empirically demonstrated to have a synergistic effect on organizational performance in a given sector.” Thus, on the basis of this definition, the authors developed a number of “complex items,” incorporating HRM practices from four major operational areas : staffing, remuneration, training and performance assessment. Each of these combinations embodies a link of complementarity between practices, and the additional impact of each combination is our way of measuring its complementarity. This study has the dual purpose of first verifying the hypothesis that “the more practices from different areas of HRM are complementary, the more they will improve organizational performance” (H. 1) and, second, that “it is likely that the impact of complementary practices will vary depending on whether the organizations concerned belong to the manufacturing or service sector” (H. 2).The items to measure organizational performance come from a previous study. These data were derived from questionnaires completed by 177 Canadian firms and the internal reliability varies from .77 to .90. To measure the degree of complementarity between HRM practices, 22 items were developed with an internal reliability of .84. The data were obtained from 238 manufacturing companies and 325 service organizations. The results corroborated Hypothesis 1, indicating that the complementarity of HR practices was responsible for a significant increase in productivity/efficiency, competitive positioning and client acquisition/growth. The results also corroborated Hypothesis 2, showing that, when the two different economic sectors are compared in terms of dependant variables, a higher degree of complementarity is particularly associated in service companies with increased productivity and efficiency, better competitive positioning and a greater number of clients and increased market share. In the case of manufacturing companies, the results indicate that the higher degree of complementarity has particular impact on the first two factors. The results are discussed in the light of current research and the limitations of the research are presented.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,042
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,061

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,042
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,007
Études des sciences et des technologies0,0020,011
Communication savante0,0060,005
Science ouverte0,0010,011
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,224
Écart entre enseignants0,214 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2003
Routes d'admission2
Résumé présentoui

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