Success and failure in family firm internationalization: The case of Rothschild
Bibliographic record
Abstract
Abstract In this study, we examine over 100 years of Rothschild’s international activity: we analyze this multinational enterprise’s (MNE’s) cross-border transfer of family-derived firm-specific advantages (FSAs) and the challenges of recombination thereof with novel resources. Family-derived FSAs hinge on the resources and features of the owning and controlling family. During the period of Rothschild’s international success in 19th-century Europe, the family itself served as a recombination mechanism for linking family-derived FSAs with complementary resources across borders. However, deploying family-derived FSAs in the American market and recombining these with nonfamily resources was hindered by bifurcation bias (a dysfunctional, affect-based heuristic characterizing family firms). We trace the manifestations of bifurcation bias over time and investigate its multifaceted impact on Rothschild’s internationalization. We suggest that effective resource recombination in host markets can be constrained by a two-stage authentication process. Here, the bifurcation bias in a first stage can influence which complementary resources in foreign markets will be accessed and how these will be integrated inside the firm. In a second stage, bifurcation bias can affect the functioning of the very actors supposed to be linking the complementary resources to be utilized and the extant, family-derived FSAs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".