Family governance and firm performance: exploring the intermediate effects of family functioning and competitive advantage
Bibliographic record
Abstract
Purpose Scholars and practitioners agree that governance practices are at the core of what differentiates family firms from other forms of business. Yet, there is a lack of consensus in the extant literature about how and the extent to which family governance affects firm performance. This study aims to address the matter by taking a more comprehensive unified systems perspective to explore the pathways through which variations in family governance mechanisms simultaneously affect both the business and the family system. Design/methodology/approach This study utilises a global dataset sourced from a survey and structural equation modelling to empirically measure several intermediate and final outcomes of family governance. Findings This study finds that the use of family protocols, as well as formal and informal meetings, have positive effects on the functioning of the family, whereas family involvement in the top management team diminishes the firm's competitive advantage. In turn, this study demonstrates that both family functioning and competitive advantage are positively related to firm performance. Originality/value By taking into consideration the complexity of the family and business systems, and measuring their interlinkages, this study advances knowledge by providing a more complete picture of the family governance/firm performance relationship.
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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 | 0.016 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".