Greater Reliance on Major Customers and Auditor Going‐Concern Opinions
Bibliographic record
Abstract
ABSTRACT In this study, we predict and provide evidence that distressed firms that rely more heavily on major customers for sales have a comparatively higher incidence of receiving going‐concern opinions (GCOs). Moreover, we find that the effect of increased reliance on major customers is driven by firms that are more distressed. We also theorize that variations in key characteristics of the relationship between a distressed firm and its largest major customer are incrementally linked to GCOs, and present evidence consistent with this. Specifically, we find that the effect of greater reliance on major customers is driven by firms that are relatively smaller than their largest major customer. Additionally, we find that greater reliance on major customers is positively (negatively) associated with GCOs when firms are in a shorter (longer) relationship with their major customer and when firms have a different auditor to (same auditor as) the largest major customer. Overall, our study indicates that supply chain relationships are relevant business risks associated with auditors' going‐concern assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.010 |
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 teacher head, 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".