Family firm internationalization: Heritage assets and the impact of bifurcation bias
Bibliographic record
Abstract
Research Summary: We develop a new conceptual framework to uncover governance‐related determinants of family firms’ internationalization, building upon internalization theory. We assess how family firm governance features determine internationalization patterns on two key dimensions: location choice and operating mode. We focus on family governance characteristics that might drive suboptimal internationalization patterns and on removing such suboptimality. We conclude that bifurcation bias , defined as the de facto differential treatment of family or heritage assets versus nonfamily assets, represents a critical family firm‐specific barrier to achieving efficiency in international operations. In the short run, the key difference in international governance is between bifurcation‐biased family MNEs and all other types of MNEs. In the longer run, inefficient, bifurcation‐biased decision making will make place for comparatively more efficient governance. Managerial Summary: Family firms are susceptible to bifurcation bias —a default preferential treatment of family members and resource bundles that hold positive emotional meaning to the family, that is, heritage assets . Such preferential treatment contrasts with that afforded to professional, nonfamily managers and other resources, with which the founding family does not entertain a positive emotional connection. If left unremedied, bifurcation bias will lead to poor decisions in family‐owned multinationals that undertake international expansion, in terms of the choices of which markets to enter and how to enter these. These types of dysfunctional decisions will lead to a decline in competitiveness as compared to nonfamily multinationals. Family firms should, therefore, identify and actively prevent bifurcation bias by implementing the specific safeguarding strategies suggested in this study.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".