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Enregistrement W2989590245 · doi:10.1108/jamr-06-2019-0101

An evaluation of alternative business excellence models using AHP

2019· article· en· W2989590245 sur OpenAlexaboutno aff
Nitin Gupta, Prem Vrat

Notice bibliographique

RevueJournal of Advances in Management Research · 2019
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueQuality and Supply Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAnalytic hierarchy processExcellenceQuality (philosophy)Rank (graph theory)Computer scienceProcess (computing)Operations researchManagement scienceMathematicsEngineering

Résumé

récupéré en direct d'OpenAlex

Purpose The purpose of this paper is to compare some major National Quality Award/Business Excellence Models (NQA/BEM) in terms of the criteria employed and their relative weights. It shows that these models vary both in terms of criteria and their weights. Whereas some of them are changing weights frequently, others are almost static. It employs the analytic hierarchy process (AHP) to allocate scores to 12 criteria identified in the model by Agrawal et al. (1998) to propose a modified quality award model similar to that. The six quality award models used in the USA, Canada, Europe, Australia, Japan and India are compared with the proposed model using AHP and their relative rankings are obtained. Design/methodology/approach First, a literature review is done to identify various quality award models globally, with their features being compared. Furthermore, paired comparison technique is used to rationalize the relative weights of proposed 12 criteria, and then AHP is again used to rank this proposed model with six major award models. Findings This paper shows that the six NQA models vary substantially on parameter weights. They do not include some relevant criteria to evaluate the organizational performance holistically. It also reveals how some models have been revising criteria weights very frequently, whereas others are static. In some models, the results get much higher weightage than enablers, and hence the performance may not be sustainable. The modified Agrawal et al. (1998) model is taken as a base model, with weights rationalized in it using the AHP. The rankings obtained using AHP reveal that proposed model scores over the other six prominent quality award models. The result also reveals that for organizational excellence, the quality of people plays a major role in the successful implementation of quality processes. Hence, it is very important to focus on improving the quality of people before expecting improvement in the quality of products and services. Research limitations/implications The paired comparison results are based on the researchers’ own perception and do not consider interdependence among the criteria, which is a limitation of AHP. Analytic network process can be further explored to overcome the limitation. The proposed model has not been tested in a variety of real-world situations, which can constitute a scope for further work in the direction. Practical implications The proposed model framework and weightages evolved using AHP can provide a universally acceptable quality award model framework. The companies can adopt it with or without modifications to address their contextual adaptation. It can possibly become a standard model framework globally. This model does not capture the measurement of the softer aspects that impact the people quality. As people play an important role in the success of the implementation of any practice, hence measurement of people quality is another important aspect that can be further studied and researched. Originality/value This comparative study & analysis of National Quality Award/Business Excellence Models using AHP is presented for the first time. The authors have not come across any such studies in their literature review. This paper is an original conceptualization of the application of the AHP on the various Quality Award model parameters, and it has been submitted exclusively to JAMR for publishing.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,135
Score d'incertitude au seuil0,561

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0130,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,007
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,168
Tête enseignante GPT0,441
Écart entre enseignants0,273 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
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

Citations12
Publié2019
Routes d'admission1
Résumé présentoui

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