Does Susceptibility to the Numerosity Heuristic Impact Juror Assessments of Auditors' Liability?*
Bibliographic record
Abstract
ABSTRACT We provide evidence that regulatory guidance aimed at improving audit efficiency and effectiveness—allowing auditor reliance on a multi‐location client's competent and objective internal audit function (IAF)—can unintentionally increase auditors' litigation risk. Our research is important in demonstrating how client characteristics and juror cognitive processing, such as the number of client locations and jurors' susceptibility to the numerosity heuristic, factors beyond auditors' control, can exacerbate their litigation exposure. Consistent with theoretical predictions, we find that susceptibility to the numerosity heuristic contributes to jurors assessing an increased likelihood of misstatement on multi‐location compared to single‐location audits. Furthermore, these assessments of higher misstatement risk on multi‐location audits lead jurors to perceive that auditor reliance on the client's IAF in multi‐location audits is less appropriate (i.e., not normal). Accordingly, jurors judge that auditors are more negligent when they rely on the IAF during multi‐location audits than when they do not, but IAF reliance does not impact auditor negligence on single‐location audits. Our results suggest auditor reluctance to use a qualified IAF, despite client pressure and regulatory allowance, can provide potential benefits to firms in terms of reduced litigation exposure. Thus, we demonstrate the legal regime can undermine the objectives of regulators' guidance to enhance audit efficiency and corporate governance.
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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 | 0.026 | 0.245 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".