<scp>CEO</scp> gender and responses to shareholder activism
Bibliographic record
Abstract
Abstract Recent literature finds that firms led by female CEOs are more likely to be targeted by activist shareholders and that female CEOs are more likely to cooperate with activist shareholders' requests. Our study complements this literature by using two controlled experiments and a series of semi‐structured interviews with CEOs and CFOs to investigate how a CEO's response to shareholder activism influences investors' reactions and whether these reactions differ depending on the gender of the CEO or on how their response is explained. In the first experiment, we find that investors evaluate a firm as less attractive when a female CEO uses an uncooperative response rather than a cooperative response to shareholder activism, absent any explanation for the CEO's response. Conversely, investors evaluate a firm as less attractive when a male CEO uses a cooperative response rather than an uncooperative response. In the second experiment, where there is an added explanation for the CEO response, we find that investors react more positively to a female CEO's uncooperative response when the explanation is more communal (vs. agentic). Our interviews with CEOs and CFOs provide insights into how the gender of firms' leadership may play a role when activist shareholders target firms. Our results collectively suggest that investors rely on gender stereotypes when evaluating the responses of male and female executives to shareholder activism and that these evaluations affect their investment judgments. Our results also suggest a potential alternative explanation for the finding that female CEOs are more likely to cooperate with activist shareholders than are male CEOs. Rather than inherent differences in the management styles of male and female CEOs, responses to activist shareholders may be driven, at least in part, by managers anticipating that they will be penalized by investors for deviating from gender‐stereotypical behavior.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".