Bibliographic record
Abstract
Abstract We infuse new internalization theory (NIT) with insights from family firm research, specifically, micro-foundational drivers of family managers’ decision-making such as socio-emotional wealth (SEW) preferences and bifurcation bias. We argue that the international governance of family firms is best explained through a model that combines efficiency-based logic (rooted in NIT) and affect-based logic (rooted in SEW). Such explanation expands the boundaries of NIT by addressing both international competitive success of family firms vis-à-vis their non-family counterparts and their divergence from efficient international governance and consequent failure in host markets. Disciplined pursuit of functional (as opposed to dysfunctional) aspects of SEW can facilitate the firm’s ability to economize on bounded rationality and reliability and create an environment conducive to value generation in host markets. However, unconstrained pursuit of SEW will lead to governance inefficiencies. Family firms can enhance the functional impact of SEW by implementing anticipative, large-scale strategies to economize on bifurcation bias. Ultimately, the main prediction of NIT holds—only efficient governance will be sustained in the long run.
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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 | 0.001 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".