How investor status affects judgments of management credibility: The role of company identification and locus of attribution
Bibliographic record
Abstract
Abstract This study investigates the joint effects of investor status and locus of attribution on investors' judgments of management credibility. We study these effects in the context of an adverse event disclosure. Building on social identity and ultimate attribution error theory, we predict and find that under external attribution, current investors perceive management as more credible than prospective investors do. In contrast, we predict and find that investor status does not affect perceived management credibility under internal attribution. We provide evidence supporting our theory that company identification explains these findings. In addition, we document that the differences in credibility are mainly driven by perceptions of management's trustworthiness, rather than competence. Moreover, our results indicate that these differences in credibility judgments affect earnings expectations, thus inducing disagreement among investors. Our findings have important practical implications, including that company identification can be an asset to companies and that communicating adverse events with an external attribution reduces perceived management credibility for prospective investors.
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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 | 0.005 | 0.055 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".