The pursuit of international opportunities in family firms: Generational differences and the role of knowledge‐based resources
Bibliographic record
Abstract
Research Summary: We argue that willingness (attitude toward risk, return, and socioemotional wealth), ability (extent of control), and resource availability influence the internationalization of family firms. We hypothesize that the internationalization of family firms led by founding and later generation family members differs from the internationalization of nonfamily firms and from each other and that knowledge‐based resources moderate the relationship. Longitudinal analysis of 4,925 firm‐year observations of S&P 1500 manufacturing firms from 2002 to 2008 shows that compared to nonfamily firms, family firms run by founding (later generation) family members internationalize less (more). Knowledge resources increase (decrease) the internationalization of founder‐led (later generation) family firms. Overall, how family ownership influences firm behavior is likely to vary as much by its type as its amount. Managerial Summary: We explore the internationalization of family firms based on a sample of S&P 1500 manufacturing firms from 2002 to 2008. Compared to nonfamily firms, family firms run by founding family members internationalize less, and family firms run by later generation members internationalize more. However, as knowledge resources increase, the internationalization of founder‐led family firms increases, whereas the internationalization of firms led by later generation family members decreases. Therefore, our findings suggest that knowledge resources can facilitate or hamper international expansion in family firms, depending on the generation of family control. These findings underscore the role of goals, governance, and resources as important drivers of differences in internationalization between family and nonfamily firms, as well as of variations in internationalization among family firms.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".