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Record W3123819923 · doi:10.1002/gsj.1023

Acquisitions as entrepreneurship: asymmetries, opportunities, and the internationalization of multinationals from emerging economies

2012· article· en· W3123819923 on OpenAlexaff
Anoop Madhok, Mohammad Keyhani

Bibliographic record

VenueGlobal Strategy Journal · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsYork University
Fundersnot available
KeywordsInternationalizationEmerging marketsMultinational corporationBusinessEntrepreneurshipArgument (complex analysis)Industrial organizationNew VenturesAsset (computer security)Competitive advantageInternational tradeMarket economyEconomicsMarketing

Abstract

fetched live from OpenAlex

Abstract We investigate the rapid internationalization of many multinationals from emerging economies through acquisition in advanced economies. We conceptualize these acquisitions as an act and form of entrepreneurship, aimed to overcome the ‘liability of emergingness’ incurred by these firms and to serve as a mechanism for competitive catch‐up through opportunity seeking and capability transformation. Our explanation emphasizes (1) the unique asymmetries (and not necessarily advantages) distinguishing emerging multinationals from advanced economy multinationals due to their historical and institutional differences, as well as (2) a search for advantage creation when firms possess mainly ordinary resources. The argument shifts the central focus from advantage to asymmetries as the starting point for internationalization and, additionally, highlights the role of learning agility rather than ability as a potential ‘asset of emergingness.’

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)0.004
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0000.003
Research integrity0.0000.001
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 head0.034
GPT teacher head0.262
Teacher spread0.228 · 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

Citations608
Published2012
Admission routes1
Has abstractyes

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