Human and Relational Capital behind the Structural Power of Female CEOs in China
Bibliographic record
Abstract
How do human and relational attributes explain chief executive officers’ (CEOs’) structural power in Chinese listed firms? Do gender differences in human and relational capital attributes help CEOs gain structural power? Integrating human and relational capital theory, this study contributes by revealing the influence of individual-level factors on CEO structural power, an influence that is gender-dependent. We show that CEOs with elite education, longer work experience, political ties, and more outside directorships are more likely to gain structural power. The positive relationship between outside directorships and CEO structural power is stronger for females, whereas the relationship between political ties and CEO structural power is stronger for males. Our results extend the literature on the connections between relational capital and CEO structural power in China, and advance the knowledge on female CEOs and leadership. We explore deeper to investigate gender inequality in terms of structural power in a fast emerging economy. We show that although female CEOs are growing in number in Chinese firms, it remains difficult for them to gain the same structural power as their male counterparts and that they need to leverage different human and relational capital attributes compared to males.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".