The Acquisitive Nature of Extraverted CEOs
Bibliographic record
Abstract
This study examines how extraversion, a personality trait that signifies more or less positive affect, assertive behavior, decisive thinking, and desires for social engagement, influences chief executive officers’ (CEOs’) decisions and the ensuing strategic behavior of firms. Using a novel linguistic technique to assess personality from unscripted text spoken by 2,381 CEOs of S&P 1500 firms over ten years, we show that CEOs’ extraversion influences the merger and acquisition (M&A) behavior of firms above and beyond other well-established personality traits. We find that extraverted CEOs are more likely to engage in acquisitions, and to conduct larger ones, than other CEOs and that these effects are partially explained by their higher representation on boards of other firms. Moreover, we find that the acquisitive nature of extraverted CEOs reveals itself particularly in so-called “weaker” situations, in which CEOs enjoy considerable discretion to behave in ways akin to their personality traits. Subsequent analyses show that extraverted CEOs are also more likely than other CEOs to succeed in M&As, as reflected by stronger abnormal returns following acquisition announcements.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".