Analysis of predictors of organizational losses due to occupational corruption. Issue 4 (August 2015)

• Record Type: Journal Article
• Authors: Timofeyev, Yuriy
• Is Part Of: International business review. Volume 24:Issue 4(2015:Aug.)
• Page Start: 630
• Page End: 641
• Publication Date: 2015-08
• Subjects: Fraud -- Multilevel model -- Occupational corruption; International business enterprises -- Periodicals; 338.8805
• DOI: 10.1016/j.ibusrev.2014.11.007
• Languages: English
• ISSNs: 0969-5931

Abstract

Highlights: Case-specific predictors dominate in the variance of organizational losses. Higher perpetrator's income and higher victim's revenue predict higher losses. Strain, tenure, prior fraud-related punishments do not predict losses. Perpetrators' positions with information advantages predict higher losses. Abstract: This article focuses on the empirical analysis of individual level predictors of organizational losses due to occupational corruption – intentional actions, in which employees misuse their influence in business transactions in a way that violates their duty to the employer or in order to gain a direct or indirect benefit at the cost of the employer. Although organizations suffer enormously from occupational corruption, so far no empirical studies link micro and macro data on occupational corruption together in order to explain what predicts these losses. In this study, I examine intra-class correlations for assessing the impact of micro and country level predictors of organizational losses due to occupational corruption, and propose a linear model for estimating micro level predictors since they account for the largest percentage in the variance of organizational losses. For the purpose of analysis, I have used the original global micro level data based on victimization survey on 1694 occupational corruption cases reported by certified fraud examiners of 37 countries between January 2002 and December 2011. The results determine that in order to prevent losses due to occupational corruption, organizations should care more about individuals they employ rather than the country or industry they operate in or organization type they have, although minor differences in issue-specific predictors inside and outside the US exist.