Family Control, Regulatory Environment, and the Growth of Entrepreneurial Firms: International Evidence
Bibliographic record
Abstract
Abstract Manuscript Type Empirical Research Issue We investigate the joint effects of family control and the regulatory environment on entrepreneurial growth through the lens of socio‐emotional wealth (SEW) theory. Research Findings Taking into consideration both economic and non‐economic goals of entrepreneurial firms, measured by sales growth and employment growth respectively, we find that, compared to their non‐family‐controlled counterparts, family‐controlled firms tend to have lower sales growth rates, but higher employment growth rates. Furthermore, less favorable regulatory environments reduce both sales and workforce growth rates to a greater extent for family‐controlled firms than for non‐family‐controlled firms. Theoretical/Academic Implications We add to the corporate governance and family business management literature by documenting that the regulatory environment moderates the corporate governance effect of family control on the economic and non‐economic goals of family‐controlled firms. The findings also contribute to the family business management literature by enriching and providing strong evidence in favor of the SEW theory through our exploration of the moderating role that macro‐governance plays in the family control‐SEW relation. This research also makes contributions to the entrepreneurship literature, laying a foundation for future empirical studies on entrepreneurial growth by separating its economic from its non‐economic dimensions. Practitioner/Policy Implications Our findings provide practical implications for both policy makers and entrepreneurs. They not only help entrepreneurs better understand growth strategies in various macro‐governance settings, but also provide governments and policymakers with potential policy implications to encourage entrepreneurial and economic growth. Policies that improve the macro‐governance environment can help family firms to prosper by contributing to their economic and non‐economic growth, both of which are important for economic development.
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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 | 0.003 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".