TMT Family Members’ Education and Firm Innovation: Evidence from Chinese Family Firms
Bibliographic record
Abstract
This study investigates the effect of the educational level of top management team (TMT) family members on firm innovation among publicly listed family firms in China. Using a panel of 14,338 firm-year observations from 2015 to 2023, this study employs fixed effects regressions to show that the educational background of family members positively influences firm innovation, measured by the proportion of R&D personnel and capitalized R&D expenditures. Moreover, this positive effect is more pronounced under greater industry competition, higher transparency, and smaller firms. The mediation analysis identifies potential channels of asset tangibility, ownership concentration, and management fees through which family education influences firm innovation. Sectoral heterogeneity reveals a more pronounced effect within the manufacturing and service sectors, while no statistically significant relationship emerges in the agriculture sector. Concerns over endogeneity are mitigated using lagged family education, two-stage least squares regressions, and panel vector autoregressions. The baseline result remains robust when firm innovation is alternatively measured by the number of patents. These findings contribute to the literature on innovation in family firms and offer implications for investors, corporate decision-makers, and policymakers in emerging markets.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".