International entrepreneurial culture of born global and non-born global family firms: a configurational approach
Bibliographic record
Abstract
Purpose Delving into family business heterogeneity, this study applies fuzzy-set qualitative comparative analyses (fsQCA) to explain overlooked differences in the international performance of born global family firms (BGFFs) and non-born global family firms (n-BGFFs); through the lens of assemblage theory of family business internationalization, the study develops distinctive configurations of international entrepreneurial culture (IEC) for BGFFs and n-BGFFs. Design/methodology/approach This study compares the theoretical tenets of IEC among 167 BGFFs versus 192 n-BGFFs in Malaysia using fsQCA – a configurational method. The study further deploys necessity analysis of fsQCA (NCA) to determine the necessity conditions within the identified configurations. Findings BGFFs manifest elevated levels of international entrepreneurial orientation, international motivation and international non-competitor network orientation. In contrast, n-BGFFs rely on international markets, learning and competitor network orientations to secure international performance. Furthermore, necessary condition analysis (NCA) reveals that international entrepreneurial orientation and international motivation are the necessity conditions for BGFFs. In contrast, international market, learning and competitor network orientation are all required for n-BGFFs’ international performance. Originality/value This study is timely and contributes to advancing the international business theory of family firm internationalization. It also offers better theorizing for family firms’ heterogeneity, locating the source of that heterogeneity not just in the speed of internationalization but also in the composition of their different IECs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".