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Record W7073608193

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

2010· article· en· W7073608193 on OpenAlexaboutno aff

Bibliographic record

VenueResearchArchive–Te Puna Rangahau (Victoria University of Wellington) · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationReputationEquity (law)InternationalizationGlobalizationService (business)International educationService providerOffshoringForeign direct investmentLegitimacy
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.012
GPT teacher head0.228
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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