Reducing uncertainty in follow‐up foreign direct investment: Imitation by family firms
Bibliographic record
Abstract
Research Summary This paper examines to what extent family firms rely on imitation when deciding on foreign investment growth, highlighting the role of mimetic isomorphism and social categories. We propose that family firms pursue trait‐based imitation to reduce uncertainty in follow‐up foreign direct investment (FDI), and test our predictions on a large sample of German family firms. We find that family firms imitate successful peers that are also owned by a family. A family firm's tendency to imitate is strengthened during the initial years in a foreign market or when the firm is publicly listed. Performance below or above social aspiration strengthens or weakens imitation, respectively. We discuss the implications for research at the nexus of foreign investment growth, social categories, and the pursuit of imitation by family firms. Managerial Summary Experience in a foreign market helps firms to reduce uncertainty in follow‐up FDI. Besides experience gathered from own investments, firms might also learn from investment decision of peers in this market. By examining the foreign investment strategies of family firms over time, this study identifies mimetic behavior in follow‐up FDI. The inclination to imitate visible, family‐owned peer firms is stronger during the initial years in a foreign market, when underperforming relative to competitors, and when there are outside shareholders. Family firm managers rely less on imitation when outperforming their competitors.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".