How does female representation affect long-term firm performance? Capturing causal complexity with a configurational approach
Bibliographic record
Abstract
Purpose Via dialectical perspective and configurational approach, this paper aims to explore the relationship between female representation and long-term firm performance when combined with environmental conditions. Design/methodology/approach For necessary condition analysis and time-series qualitative comparative analysis, a sample of 614 listed Chinese manufacturing firms between 2017 and 2020 was obtained. Findings The inclusion of female executives can aid firms in their long-term performance and resilience. Seven configurations, categorized as chimpanzee type, African elephant type and queen bee type, can prompt long-term firm performance. Chimpanzee-type configuration is the most prevalent path for firms to achieve long-term performance. Practical implications Firms could reconsider the role of female executives in achieving long-term success, assist in breaking the invisible “glass ceiling” and “glass cliff,” and refrain from viewing them as mere “tokens.” Policymakers can improve female representation by institutionally guaranteeing women’s opportunities for empowerment, education and promotion. Originality/value This study presents evidence for the legitimacy of female representation by demonstrating the intricate causality between female representation and firm performance beyond the controversy between business ethics and coercive policy. This paper also builds upon and extends the literature on female representation and provides alternative ways to improve female representation by combining female executives’ percentages, professionalism and positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".