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Record W2946530898 · doi:10.5430/ijfr.v10n3p77

Auditor’s Ethical Judgments: The Influence of Moral Intensity, Ethical Orientation and Client Importance

2019· article· en· W2946530898 on OpenAlexvenueno aff
Razana Juhaida Johari, Zuraidah Mohd Sanusi, Arumega Zarefar

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsnot available
Fundersnot available
KeywordsIdealismPsychologyRelativismAuditConstruct (python library)Social psychologyOrientation (vector space)EpistemologyAccountingComputer sciencePhilosophyBusiness

Abstract

fetched live from OpenAlex

This study examined auditors’ ethical judgments using two theoretical perspectives; (1) Moral intensity constructs of Jones’ (1991) Model and (2) Forsyth’s (1980) framework of individual ethical orientation. The importance of the moral issues and how they affected the auditors’ ethical judgments together with the influence of individual’s ethical orientation and the client importance is discussed. A research instrument consisted of two scenarios with different level of moral intensity issues and utilized a 12-item of moral intensity measurement and a Forsyth’s (1980) scale to measure ethical orientation along two dimensions, idealism and relativism. The client importance is manipulated in this between-subjects study. The results of 152 auditors’ found that the effects of the moral intensity construct and the client importance on auditors’ ethical judgments is different based on the issues intensity level of the scenarios. Whereas, both dimensions of the individual ethical orientation (idealism and relativism) are found significant in both of the scenarios tested. The limitations of the study and recommendation for future studies are also discussed.

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)0.019
metaresearch head score (Gemma)0.047
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.235
GPT teacher head0.523
Teacher spread0.288 · 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 designObservational
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

Citations11
Published2019
Admission routes1
Has abstractyes

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