The role of knowledge in international expansion
Bibliographic record
Abstract
Purpose This paper aims to critically review and integrate the literature available on Uppsala (incremental) and Born Global (rapid) internationalization models and propose an integrative model that applies to both the initial and subsequent stages in internationalization. Design/methodology/approach This study draws on a systematic review and analysis of the relevant literature, using 87 articles from 28 journals which deal with the Uppsala and/or Born Global conceptualizations. Findings To date, the two views of internationalization have been presented as competing and fundamentally different explanations, as past research focuses mostly on the original 1977 Uppsala model without accounting for its five subsequent extensions (1990-2013) and not considering in sufficient depth the critical role of the knowledge construct in both models. Research limitations/implications The study focuses on English-only publications dealing expressly with the Born Global and Uppsala models; while some studies which address the focal theme tangentially may have been missed, the systematic approach to identifying the key studies of interest and the focus on a carefully delineated research domain provides confidence that the main studies relevant to the theme have been captured. Originality/value The study highlights the important role of knowledge in the internationalization of firms, and it addresses the current divide between the “incremental” and “rapid” conceptualizations which have impeded the development of theory, by positing six research propositions and an integrative model that accounts for both the incremental and rapid approaches.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.006 |
| 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".