The Effects of Offshoring Audit Tasks on Jurors’ Evaluations of Damage Awards Against Auditors
Bibliographic record
Abstract
Abstract Offshoring is the process of using unaffiliated foreign companies or affiliated offshore entities (AOEs) to manufacture goods or perform services. The Big 4 public accounting firms offshore tax services (Houlder, 2007) and, more recently, have started to offshore audit tasks of their U.S.-based clients to AOEs located in India (Daugherty & Dickins, 2009). While the benefits of offshoring might be substantial, there are also costs associated with moving domestic work to foreign locations. One of these costs may be greater damage awards in lawsuits involving an audit failure where audit tasks were performed overseas as opposed to the United States. This study investigates that possibility by experimentally examining the effect of offshoring audit tasks requiring different levels of judgment on the amount of damages awarded by potential jurors as a result of an audit failure. The results show potential jurors awarded greater damages against the auditor when audit tasks were performed offshore than when they were performed in the United States. There was no effect of the level of judgment of the audit task on damages awarded. Since this study examines offshoring to only one location, India, results may not be generalizable to other offshore locations.
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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 | 0.016 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".