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Record W1974487029 · doi:10.1108/jbim-07-2013-0153

Innovations in marketing of higher education: Foreign market entry mode of not-for-profit universities

2014· article· en· W1974487029 on OpenAlexaff
Vik Naidoo, Terry Wu

Bibliographic record

VenueJournal of Business and Industrial Marketing · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsInternationalizationMarketingExtant taxonProfit maximizationProfit (economics)OriginalityBusinessInternational businessValue propositionGrounded theoryIndustrial organizationEconomicsQualitative researchSociologyMicroeconomicsManagement

Abstract

fetched live from OpenAlex

Purpose – The purpose of this study is to examine the innovations in the international activities of not-for-profit (NFP) universities. While the entry mode literature is well addressed, particularly by international marketing and business scholars, an academically interesting and managerially relevant question relates to the applicability of extant research to the emerging phenomenon of internationalization in the NFP sector. Design/methodology/approach – Using an inductive constructivist qualitative methodology grounded in 12 case studies of internationalization in the NFP education sector, this study applies Dunning’s eclectic framework as its theoretical anchor. Findings – This study identified that entry mode choice in the NFP context may not always be reconciled with extant literature derived mostly from a for-profit context. In particular, the broader definition of offshore equity investment is in sharp contrast to previous entry mode research which is largely, if not exclusively, grounded in a for-profit context. Originality/value – Extant frameworks developed to explain the entry mode phenomena tend to assume a profit maximization philosophy. The propositions advocated in this study are a step further to develop our understanding of internationalization of NFP universities.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.313
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2014
Admission routes1
Has abstractyes

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