<scp>CEO</scp> (in)activism and investor decisions
Bibliographic record
Abstract
Abstract Many CEOs engage in activism by publicly expressing their views on social, environmental, and political issues, while other CEOs refrain from doing so—a behavior we term CEO inactivism. We use two experiments to examine how CEO (in)activism impacts investor decisions. Our results are consistent with our theoretical predictions. When a CEO expresses an activist position that is consistent versus inconsistent with investors' views, investors invest more in the CEO's firm because they perceive the CEO more positively. We also find that CEO inactivism can lead to investment decisions that are as favorable as when the CEO expresses a position consistent with investors' views; our process evidence suggests that this may occur because CEO inactivism increases the likelihood that investors believe the CEO shares their position on a social issue. Finally, we do not find evidence that investor decisions are influenced by whether CEO (in)activism is in response to an external prompt. This study contributes to the emerging literature on CEO activism, a unique form of voluntary disclosure, by providing evidence about how CEO (in)activism influences investors. We also contribute to the literature examining the impact of social media disclosure on investor decisions. Finally, our findings have practical implications for CEOs, who increasingly face external pressures to engage in activism.
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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".