THE EFFECT OF EXPERIENCE AND KNOWLEDGE ON FRAUD DETECTION WITH INTUITION AS INTERVENING VARIABLE

Suhardi Suhardi

MPRA Paper from University Library of Munich, Germany

Abstract: This study aims to examine the effect of the knowledge of government internal supervisors through the intuition of government internal supervisors to detect irregularities. The sample in this study was the auditor who served at the Regency and municipal Inspectorates in Bangka Belitung totaling 122 respondents. This study uses path analysis to examine the relationship between hypothesized variables. The results of the study concluded that the experience and knowledge of government internal supervisors had a significant effect on deviation detection. In addition to the knowledge of government internal supervisors through government intuition, internal supervisors influence the detection of irregularities. This research needs to be further developed, to get stronger empirical results, such as by adding other variables from deviation detection, further research can also expand the object of research, and use the experimental method.

Keywords: Experience; Knowledge; Intuition; Fraud detection. (search for similar items in EconPapers)
JEL-codes: M40 (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-knm and nep-law
References: View complete reference list from CitEc
Citations:

Published in JEM Jurnal Ekonomi dan Manajemen 2.4(2018): pp. 100-116

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/92160/1/MPRA_paper_92160.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:92160

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().