Language, perceived warmth, and investors' reactions to audit committee reports
Bibliographic record
Abstract
Abstract An audit committee (AC) report is the primary channel through which investors learn about the responsibilities and activities of an AC. AC members may use personal language (“we”) or impersonal language (“the audit committee”) in an AC report. Psychology research suggests that personal (vs. impersonal) language signals that the language user has greater warmth and sense of communion (i.e., being part of a larger group). Applying this theory, we predict that an AC's use of personal (vs. impersonal) language leads investors to perceive a warmer and more communal AC, and that an AC's perceived warmth/communion (cued by personal language) positively impacts investor judgments. We further posit that the positive effect of personal language is stronger when AC compensation is largely short term than long term. This is because investors need more assurance of AC oversight effectiveness when AC compensation is short term, which makes investors rely more on heuristic cues such as AC language to make judgments. Consistent with this prediction, we find that when AC compensation is largely short term, nonprofessional investors (proxied by Master of Business Administration students) react more positively to an AC's use of personal language than impersonal language. The effect of AC language is insignificant when AC compensation is largely long term, as the long‐term compensation structure already provides assurance about the AC's oversight effectiveness, and thus, investors rely less on heuristic cues. Furthermore, we find that the perceived warmth of AC members explains the effect of AC language. Finally, our interviews with nonprofessional investors corroborate some of our main findings and validate their practice implications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".