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Record W2118139016 · doi:10.5539/ibr.v6n8p55

Internationalization of R&D by Brazilian Multinational Companies

2013· article· en· W2118139016 on OpenAlexvenueno aff
Simone Vasconcelos Ribeiro Galina, Paulo Guilherme Moura

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationMultinational corporationSubsidiaryBusinessProduct (mathematics)International marketBusiness administrationInternational tradeIndustrial organizationFinance

Abstract

fetched live from OpenAlex

Traditionally, most of previous studies on international R&D are not only conducted in advanced countries but also carried on with companies headquartered in developed economies. This paper presents an analysis of the R&D internationalization of Brazilian Multinational Companies (BMNCs) in terms of the major driving forces to globalize R&D and coordination of international R&D activities. This research was based on a multiple case study with seven BMNCs, which was leaded by a framework with specific issues on international R&D (driving forces, roles of subsidiaries, R&D management). We found out that BMNCs have internationalized its product development encouraged by both market-driven and technology-driven factors. Our conclusion is that, when compared to companies from developed countries (literature), BMNCs perform internationalization of R&D activities with very similar characteristics. These finds are additional evidences to contribute to the ongoing discussion about the internationalization of companies from developing countries.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.351
Teacher spread0.290 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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