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The Effects of Offshoring Audit Tasks on Jurors’ Evaluations of Damage Awards Against Auditors

2013· book-chapter· en· W2505796366 on OpenAlexfundno aff
Brian Daugherty, Denise Dickins, Meindert Fennema

Bibliographic record

VenueAdvances in accounting behavioral research · 2013
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
FundersUniversity of British ColumbiaEast Carolina UniversityLibrary Company of Philadelphia
KeywordsOffshoringDamagesAuditBusinessAccountingTask (project management)Work (physics)Quality auditSubmarine pipelineMarketingEconomicsEngineeringOutsourcingManagementLawPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.372
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2013
Admission routes1
Has abstractyes

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