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Record W3110963333 · doi:10.1108/bfj-07-2019-0554

The internationalization process of agrifood firms: a proposed conceptual framework

2020· article· en· W3110963333 on OpenAlexaff
Alexandre Borges Santos, Mário Otávio Batalha, Bruno Larue

Bibliographic record

VenueBritish Food Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsInternationalizationForeign direct investmentBusinessOriginalityConceptual frameworkIndustrial organizationMarketingAppealExploratory researchEmpirical researchProcess (computing)Value (mathematics)Qualitative researchEconomicsInternational trade

Abstract

fetched live from OpenAlex

Purpose This research seeks to develop a better understanding of internationalization patterns of agrifood firms and explains why different paths are adopted. Further, a conceptual framework to support public and private decision-making is proposed. Design/methodology/approach An exploratory qualitative research framework was developed featuring case studies about three highly internationalized Brazilian meat processing firms. Top managers were interviewed, and documents were collected to support the intraand crosscase analyses. Findings Results suggest that meat processing firms tend to adopt a dual internationalization pattern. Distribution-oriented foreign direct investment (FDI) is normally established gradually, whilst horizontal FDI – the establishment of foreign production facility – tends to be conducted through a fast-paced expansion mode. Interestingly, it was found that food safety issues play a central role in internationalization decisions. Originality/value An extension to the Uppsala model was provided by considering agrifood characteristics in the analysis. The results have broad appeal to managers and policymakers. Agribusiness managers could use the theoretical and empirical evidence to support their internationalization decisions. Policymakers can also use this research to gain a better understanding of how agrifood firms expand internationally to either attract or foster FDI.

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 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)0.001
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 score0.494
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.018
GPT teacher head0.230
Teacher spread0.213 · 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 teacher head, 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

Citations10
Published2020
Admission routes1
Has abstractyes

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