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Enregistrement W2051856813 · doi:10.1108/so-11-2013-0022

On the attractiveness of the UK for outsourcing services

2014· article· en· W2051856813 sur OpenAlexaboutno aff
Ilan Oshri, M. N. Ravishankar

Notice bibliographique

RevueStrategic Outsourcing An International Journal · 2014
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueOutsourcing and Supply Chain Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOutsourcingBusinessCompetition (biology)AttractivenessCompetitive advantageKnowledge process outsourcingMarketingExcellenceIndustrial organizationOffshoring

Résumé

récupéré en direct d'OpenAlex

Purpose – Outsourcing is in a new era: an era of value-adding services, innovation and transformation. An era that shifts competition to skills and expertise where the main focus of key players in the industry is on the strategic impact of outsourcing services. As the outsourcing landscape is changing, so competition between countries for outsourcing work is reconstructing. It is no longer competition for low costs, but a search for superior skills, both technical and managerial, that provides the strategic guidance and operational excellence needed in the twenty-first century. While the professional and academic literature has extensively studied the comparative advantage of low-cost locations such as India, we know very little about the attractiveness of Western countries, such as the UK, for outsourcing services. To contribute to this end, the purpose of this paper is to examine the UK attractiveness in light of three key trends in the outsourcing industry: the maturity of the outsourcing industry drives more client firms to seek impact on business and strategic performance from their vendors; client firms and vendors deploy complex sourcing models that increase the importance of sourcing managerial capabilities, such as relationship management, vis-à-vis technical and delivery capabilities; locations with promising entry points to lucrative markets are becoming attractive for outsourcing investments as part of the firm's growth strategy. Design/methodology/approach – The empirical base of this study is based on a comparative analysis of eight European destinations (UK, Germany, France, The Netherlands, Spain, Ireland, Czech Republic and Poland) to conclude that the UK, as a talent-base, value-adding country that also offers advanced sourcing capabilities, has positioned its economy to attract investments from both outsourcing vendors and client firms. While the authors acknowledge the relative high-cost base of the UK economy, they assert that the high service standards, access to skills, entry point to mainland Europe and the USA, government support and supportive infrastructure are superior value propositions offered by the UK in the context of outsourcing services. Findings – The findings of this study highlight the contribution of Western economies to outsourcing and their fairly strong comparative position to specific line of services such as contact centers, research and development and specific business process outsourcing services. Research limitations/implications – The main limitation of this study is the use of a country attractiveness framework which has been mainly used for low-cost countries. The authors therefore acknowledge the need to develop a country attractiveness framework which is suitable for Western countries. Practical implications – This study offers decision makers an extensive tool to assess their outsourcing investments by considering both low-cost and Western countries based on the value expected from each investment. Originality/value – This is the first study on the attractiveness of a Western country, such as the UK, which the authors defined as a talent-based, value-adding and advanced sourcing (TAVAAS) country. Through the examination of its comparative attractiveness the authors highlight the potential of the UK and many other Western countries such as USA, Germany or Canada to attract outsourcing investments.

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)0,002
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: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,331
Score d'incertitude au seuil0,920

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
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 Opus0,026
Tête enseignante GPT0,253
Écart entre enseignants0,227 · 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'étudeThéorique ou conceptuel
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

Citations7
Publié2014
Routes d'admission1
Résumé présentoui

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