The internationalization process of agrifood firms: a proposed conceptual framework
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".