Mapping decisions of reporting asset misappropriation within an accounting department using behavioral, cognitive, and cultural traits

Nora Alaniz-Bouqayes, University of Texas at El Paso

Abstract

The problem of global fraud continues to be pandemic with the cost to organizations exceeding $3.9 trillion of lost revenues every year. Accounting research is slowly embracing the behavioral science research and has expanded limited literature addressing the traits of fraud perpetrators. This study begins to examine behavioral, cognitive, and cultural traits of reporters of fraud in accounting departments. This study narrows the area of interest to asset misappropriation as part of the Occupational Fraud and Abuse Classification System created by the Association of Fraud Examiners. An initial instrument to measure traits is developed and used to map decision paths that yield greater instances of reporting fraudulent behavior. Three hundred six responses from four national origins completed a survey instrument that measure the traits of Skepticism, Experience, and Judgement. After exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) a four factor model was deemed a good fit for the data. Using structural equation modeling analysis to replicate the decision making process the results indicate that all three traits are influential in an increase of reporting fraud. The Judgment trait had the strongest influence on the decision to report fraudulent behavior. Secondary analysis to test for variance of trust paths, as discussed by Rogers (2010), resulted in finding differences based on national culture. On the Rational-choice trust path, Low Secrecy Index participants had a stronger influence on the decision to report fraudulent behavior than High Secrecy Index participants.

Subject Area

Accounting|Behavioral psychology|Ethnic studies

Recommended Citation

Alaniz-Bouqayes, Nora, "Mapping decisions of reporting asset misappropriation within an accounting department using behavioral, cognitive, and cultural traits" (2016). ETD Collection for University of Texas, El Paso. AAI10250912.
https://scholarworks.utep.edu/dissertations/AAI10250912

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