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Record W2045635179 · doi:10.1108/17554250810909428

The international competitiveness of Asian firms

2008· article· en· W2045635179 on OpenAlex
Alan M. Rugman, Chang Hoon Oh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of strategy and management · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsBrock University
Fundersnot available
KeywordsCompetitor analysisBusinessCompetition (biology)ExploitGlobalizationOriginalityCompetitive advantageIndustrial organizationInternational marketInternational tradeMarketingEconomicsMarket economy

Abstract

fetched live from OpenAlex

Purpose Conventional studies of international competitiveness use country‐level data, but the aim of this paper is to extend this work by using firm level data of large Asian firms. Design/methodology/approach The authors gathered the regional sales and assets data for large Asian firms listed in latest Fortune Global 500 from their annual reports. They then applied the data to the firm specific advantage/country specific advantage matrix and the regional matrix frameworks developed by Rugman. Findings It is found that most Asian firms do not operate globally, but focus on their home region. Thus, Asian firms exploit and develop their FSAs regionally. Only a few large Japanese and Korean firms have significant sales outside of Asia. Large Asian firms vie with their regional competitors in their home region market. Originality/value International competitiveness does not necessarily mean globalization or global competition. International strategic management should consider the reality of regional competition.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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