MétaCan
Menu
Back to cohort
Record W2145503717 · doi:10.1002/smj.2051

Do regions matter? An integrated institutional and semiglobalization perspective on the internationalization of <scp>MNEs</scp>

2013· article· en· W2145503717 on OpenAlexaff
Jean-Luc Arrègle, Toyah L. Miller, Michael A. Hitt, Paul W. Beamish

Bibliographic record

VenueStrategic Management Journal · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsWestern University
Fundersnot available
KeywordsInternationalizationContext (archaeology)Perspective (graphical)Explanatory powerEconomic geographyBusinessPoliticsCapital (architecture)Economic systemInternational tradeEconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

Traditional research suggests a relationship between country‐level institutions and the location choices of MNEs . However, more recent theory suggests MNEs also focus on regions (semiglobalization). Therefore, this study examines institutional effects in the context of semiglobalization by considering the influences of three formal institutions (i.e., regulatory control, political democracy, capital investments) of countries and geographic regions on MNEs ' location choices of internationalization. We use a sample of Japanese MNEs operating in 45 countries within eight regions. The results show that their degree of internationalization into a country is influenced by both country and regional institutional environments. Additionally, a semiglobalization perspective provides better explanatory power than does the country‐level perspective. These results present a new perspective on how MNEs consider institutional environments in their international strategy . Copyright © 2013 John Wiley &amp; Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.247
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations214
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueStrategic Management JournalSame topicInternational Business and FDIFrench-language works237,207