Family business and international business: Breaking silos and establishing a rigorous way forward
Bibliographic record
Abstract
Over the past decade, a consensus has crystallized recognizing the significance of family firm internationalization in international business (IB) research. This recognition comes with substantial opportunities, yet it also presents challenges, such as the pressing need for a more cohesive integration of the family business and IB domains. In this article, we (re)emphasize the relevance of family firm internationalization for IB research considering three IB grand challenges and two important aspects of internationalization where family firms can particularly contribute. We also propose several theoretical and methodological avenues for future studies to help further increase the understanding of family firm internationalization and of IB theories. Finally, we provide an overview of the core insights from the articles included in the related special issue and develop integrative conclusions about the research.
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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 | 0.084 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.013 | 0.052 |
| Scholarly communication | 0.028 | 0.055 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".