Bibliographic record
Abstract
Purpose The purpose of this paper is to examine, within a succession framework, the impact of the gender composition of boards of directors on the gender of the CEOs they appoint, and to assess the impact of newly appointed CEOs' gender on risk taking by the firm. Design/methodology/approach The authors estimate a two‐stage least squares regression using data on 679 CEO successions in North American firms. Findings The results show that successor CEOs are more likely to be female the greater the percentage of females on the board, regardless of other succession characteristics such as whether the new CEO is from inside or outside the firm. Furthermore, a change in CEO from male to female is associated with a decrease in several measures of firm risk taking. Research limitations/implications The sample is restricted to relatively large, exchange‐traded North American firms and may not generalize to other groups. Practical implications The findings suggest that women aspiring to CEO positions and firms wishing to promote women should monitor board composition to ensure female representation. Other steps that the firm may take to promote women to this position (such as looking outside the firm) have an insignificant impact when board composition is taken into account. Originality/value The findings are novel and inform CEO succession research by demonstrating which succession process characteristics work to increase females' chances and which have no effect. Female CEOs are likely to provide leadership that reduces the risk profile of the firm.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".