A values-based analysis of bifurcation bias and its impact on family firm internationalization
Bibliographic record
Abstract
Recent analysis of family firm internationalization has shown that international performance can suffer when family owners or managers engage in an unwarranted, strict separation between two ‘generic categories’ of resources in their strategic decision-making. On the one hand, there are resources perceived as ‘being part of the family in the long run’, and therefore unique and worthy of nurturing. On the other hand, there are resources perceived as ‘with the family only for the short run’, and therefore having commodity-type status and being fungible. The concept of bifurcation bias describes this frequently observed, affect-based phenomenon of separating all resources utilized by the firm into two categories. Bifurcation bias has been argued to be particularly damaging in the context of internationalization, whereby accurate assessment and complex recombination of resources is critical. In this paper, we extend the analysis of bifurcation bias in family firms. We examine how the personal values of family firm owners and non-family members, as well as the dominant cultural values in the relevant, surrounding societies (both home and host), can influence the magnitude and dysfunctional effects of this phenomenon. Infusing values-based analysis into assessing how bifurcation bias plays out in family firms, can improve our understanding of family firm heterogeneity, has implications for empirical research, and may help invalidate some overgeneralized narratives in research on family-firm international behaviour.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".