Signal Incongruence and Its Consequences: A Study of Media Disapproval and CEO Overcompensation
Bibliographic record
Abstract
We draw on the signaling and infomediary literature to examine how media evaluations of CEO overcompensation (a negative cue associated with selfishness and greed) are affected by the presence of corporate philanthropy (a positive cue associated with altruism and generosity). In line with our theory on signal incongruence, we find that firms engaged in philanthropy receive more media disapproval when they overcompensate their CEO, but they are also more likely to decrease CEO overcompensation as a response. Our study contributes to the signaling literature by theorizing about signal incongruence and to infomediary and corporate governance research by showing that media disapproval can lead to lower executive compensation. We also reconcile two conflicting views on firm prosocial behavior by showing that, in the presence of incongruent cues, philanthropy can simultaneously enhance and damage media evaluations of firms and CEOs. Taken together, these findings shed new light on the media as agents of external corporate governance for firms and open new avenues for research on executive compensation. The online appendices are available at https://doi.org/10.1287/orsc.2018.1209 .
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".