Information Processing Biases in Impairment Decisions: Effect of Reversibility of Impairment Losses and Disclosure Transparency
Bibliographic record
Abstract
ABSTRACT We investigate the effect of regulatory requirements on impairment decisions and managers' search for and evaluation of impairment information. We manipulate reversibility of impairment losses (“can be reversed” versus “cannot be reversed”) and transparency in disclosures of impairment assumptions (more transparent versus less transparent) in a 2 × 2 experiment. We find that managers are more willing to impair when impairment losses can be reversed than when they cannot be reversed, but this effect does not vary with disclosure transparency. We also find that managers display information search bias in all four experimental conditions, however, regulatory requirements do not result in differences in the level of information search bias across the conditions. In contrast, regulatory requirements affect the differences in the level of information evaluation bias across conditions. We find that when impairment losses cannot be reversed, information evaluation bias is higher when disclosures are more transparent than less transparent. JEL Classification: M40; M41.
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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 | 0.011 | 0.116 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".