Do Family Firms Have Better Reputations Than Non‐Family Firms? An Integration of Socioemotional Wealth and Social Identity Theories
Bibliographic record
Abstract
Abstract We draw from socioemotional wealth and social identity research to develop a theory on reputational differences among family and non‐family firms. We propose that family members identify more strongly with their family firm than non‐family members do with either a family or non‐family firm. Heightened identification motivates family members to pursue a favourable reputation because it allows them to feel good about themselves, thus contributing to their socioemotional wealth. We hypothesize that when the family's name is part of the firm's name, the firm's reputation is higher because family members are particularly motivated for their firm to have a better reputation. Family members also need organizational power to pursue a favourable reputation; thus, we hypothesize that the level of family ownership and family board presence should be associated with more favourable reputations. We find support for our theory in a sample of large firms from eight countries with disparate governance systems and cultures.
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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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".