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Record W4283786230 · doi:10.55482/jcim.2022.32899

Toward a 2-Stage Theory of Emerging Market Multinationals

2022· article· en· W4283786230 on OpenAlexvenueno aff
Robert Grosse

Bibliographic record

VenueJournal of Comparative International Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsEmerging marketsFlexibility (engineering)Industrial organizationGovernment (linguistics)BusinessProduction (economics)Value (mathematics)Market shareMarket economyMarketingEconomicsMicroeconomicsFinanceManagement

Abstract

fetched live from OpenAlex

I present a 2-stage theory in which emerging market firms first compete domestically based on traditional competitive advantages. Once they achieve a threshold stage, then they go overseas and are able to succeed based on company and industry characteristics, as well as emerging market characteristics. I expect to see that factors enabling firms to get to the threshold stage will include: brand value, low-cost production, experience/age, company size, possibly membership in a business group, and international sales (but not technology). Then I would expect that factors enabling firms to succeed internationally should depend on the industrial sector and the target market, as well as demonstrating emerging market idiosyncrasies such as ability to deal with high uncertainty in government policies and economic conditions as well as flexibility in dealing with business conditions. Most analyses of emerging market MNEs focus only on the last set of factors that are common to emerging markets.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.007
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.048
GPT teacher head0.299
Teacher spread0.251 · 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 designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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