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Record W2945171479 · doi:10.5430/ijfr.v10n3p132

Do Perceived Pressure and Perceived Opportunity Influence Employees’ Intention to Commit Fraud?

2019· article· en· W2945171479 on OpenAlexvenueno aff
Tuan Zainun Tuan Mat, Danny Shahmizi Teh Ismawi, Erlane K Ghani

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCommitBusinessGlobePsychologyComputer science

Abstract

fetched live from OpenAlex

Fraud has always remains an issue for organisations throughout the globe due to the increasing number of fraud occurrences over the years which resulted to the increasing amount of losses. Despite the large amount of efforts by organisations through internal control implementation and the amount of money spent to combat fraud, the numbers of fraud occurrences are still increasing. This study examines the employees’ intention to commit fraud. Specifically, this study examines the influence of perceived pressure and perceived opportunity on employees’ intention to commit fraud. In addition, this study examines the effect of capability in moderating the influence of perceived pressure and perceived opportunity on employees’ intention to commit fraud. Using online questionnaire on 158 employees from various sectors, this study shows that the employees have a low intention to commit fraud. This study also shows an existence of linear relationship between perceived pressure and perceived opportunity on employees’ intention to commit fraud. Consistent with previous studies, this study found that capability of the employees does not influence them to commit fraud. The findings in this study assist the employers to adopt appropriate strategies in preventing and detecting fraud possibility among their employees.

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)0.009
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.289
GPT teacher head0.511
Teacher spread0.221 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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