To read or to listen? Does disclosure delivery mode impact investors' reactions to managers' tone language?
Bibliographic record
Abstract
Abstract We examine how disclosure delivery mode—oral versus written—influences investors' reactions to managers' tone language. We hypothesize that listening to disclosures, relative to reading them, causes managers' qualitative word choices to have a greater impact on investors' judgments. We theorize that this effect occurs because oral delivery mode promotes heuristic processing and qualitative tone language is an easy‐to‐process disclosure element. The results from an experiment in a conference call setting are consistent with our hypothesis and suggest a boundary condition. Specifically, the interaction of mode and tone language is significant in a setting where heuristic processing is likely (good earnings news) but not in a setting where investors are likely to scrutinize the disclosure (bad earnings news). Our results inform investors about the potential consequences of how they consume disclosures. Specifically, we show that investors are more susceptible to managers' tone language when listening to disclosures containing good news than when reading them.
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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 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".