The ‘Matthew Effect’ in Strategic Decision-Making: How CEO Status Affects Investment Decisions
Bibliographic record
Abstract
A failure of a large corporate investment project launched by a CEO may affect not only CEO’s bonus and equity holdings, but also personal status and future career prospects. Yet, Merton’s (1968) “Matthew effect” would suggest that the risks and benefits associated with risky investment projects may be different for CEOs of different status. High-status CEOs are better able to decouple their status from firm’s performance failure and are in a better position to claim credit for firm’s successes. On the other hand, since third parties have greater reluctance to accept quality claims of low-status actors, lower status CEOs might not receive a commensurate credit for the success of a project, but might be exposed to a disproportionate amount of blame in case of project failure. Since actors are usually aware of the presence of the Matthew effect in their field, we can expect that the differential outcomes produced by it will be factored into actors’ expected utility calculations, affecting CEOs’ attitudes to risk and visibility of their investment prospects. The findings from a 19-year sample of U.S.-based cellular telephone operators provide support for the theorized relationship between the Matthew effect and CEOs’ investment choices.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".