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Enregistrement W7073608193

An Analysis of Ownership Forms in Offshore Higher Education Markets: A Resource Based Perspective

2010· article· en· W7073608193 sur OpenAlexaboutno aff

Notice bibliographique

RevueResearchArchive–Te Puna Rangahau (Victoria University of Wellington) · 2010
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueGeological and Geochemical Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHigher educationReputationEquity (law)InternationalizationGlobalizationService (business)International educationService providerOffshoringForeign direct investmentLegitimacy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This PhD research seeks to consider overseas investment in a new and important context: education. Estimated at approximately US$ 65 billion and representing roughly 3% of global services exports (Alderman, 2001), trade in education services is fast becoming a global business (Czinkota, 2006). In Australia, New Zealand and the United States, for example, educational service is estimated to be, respectively, the third, fourth and fifth largest service sector export (Vincent-Lancrin, 2004). The globalisation and internationalisation of higher education manifest themselves in various forms, of which transnational education or 'offshore' programmes - those taught outside of a host academic institution's country of origin - have been experiencing rapid increases over the past decade. Most of this growth, to date, has taken place through contractual arrangements such as licensing (e.g. twinning and articulation arrangements). However, there are also a substantial number of academic institutions that are currently delivering transnational education through equity modes of entry (e.g. branch campus operations). In this context, this PhD research, using universities as the unit of analysis, seeks to understand the dynamics of transnational education - how it is happening and why it is happening - grounded in the strategy and international business literatures. In particular, the research question being addressed in this study is: What resources are associated with entry mode choice for education providers entering overseas markets? Using a multi-method research design consisting of both qualitative and quantitative analysis, seven different types of resources are specifically examined in this study: Geographical experience, Industry experience, Transfer experience, Organisational culture, Financial resources, Reputation and Learning intent. Using the resource-based view (RBV) as its theoretical underpinning, this study hypothesises that the more access to these resources an education service provider might have, the more they will favour a higher level of ownership in offshore education developments. This overall hypothesis builds on the basic assumption of the RBV that organisations in possession of resources which are potential sources of competitive advantage in a target market, would favour a \nmode of entry that facilitates control over and protection of the resources. This fundamental assumption of the RBV differs to that of the transaction cost approach, which typically views shared-control modes as the default mode of entry. The conceptual model developed in this study further postulates that the resource-entry mode relationship is moderated by institutional distance. The education sector in most countries is a regulated sector, where authorities monitor the quality of education. Therefore, when investing offshore, education service providers are likely to operate around some form of regulated institutional environments that are likely to affect their mode of entry decisions. From the collected 308 instances of foreign market entry of universities in the United Kingdom (UK), United States (US), Canada (CA), Australia (AU), New Zealand (NZ) and Ireland (IR), analysis is conducted at both an aggregate and geographical grouping level (i.e. UK/IR, AU/NZ and US/CA). To assess the sensitivity of the obtained results, three estimation techniques are also analysed: Ordinary Least Squares (OLS), Tobit and Negative Binomial regressions. To further assess the sensitivity and robustness of the observed findings for the moderating role of institutional distance, three measures of distance are analysed: World Competitiveness Yearbook, Economic Freedom Index and Hofstede (1980) cultural indices.\nFrom the different groupings and estimation techniques, the empirical findings show that support is obtained for Transfer experience, but only when using OLS estimation. Mixed support is obtained for Geographical experience, Industry experience and Financial resources. The hypotheses with respect to the other types of resources are not supported. These findings suggest that, contrary to the basic premise of the RBV, a higher level of ownership might not always be the preferred entry mode in the offshore education context. The observed findings also do not support the moderating hypothesis of institutional distance on the resource-entry mode relationship. This lack of support is consistent across all three measures of distance analysed. Several possible explanations for these observed findings are conjectured in Chapter 7. These explanations are not purely theoretical conjectures but are also enriched on the basis of the interviews conducted as part of the exploratory stage of this study. The greatest takeaway from this study is that the observed findings, which do not fully conform to mainstream international business and strategic management theories, can be attributed to context/industry specific conditions. Traditional international business and strategy research has largely focused on "for-profit" firms. Given that universities are "not-for-profit" organisations, it needs to be recognised that their international operations are different from those of regular multinational firms. These findings provide initial steps in improving our understanding of the internationalisation of the education services sector.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
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 enseignants0,044
Score d'incertitude au seuil0,991

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,000

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 Opus0,012
Tête enseignante GPT0,228
Écart entre enseignants0,217 · 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.

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

Citations0
Publié2010
Routes d'admission1
Résumé présentoui

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