Understanding fraud detection with the heptagon fraud model and income tax moderation: evidence from Indonesia / Rachel Jodyan, Timothy Marvel Winowod and Levana Dhia Prawati
According to the ACFE Occupational Fraud (2022), manufacturing businesses had 194 potential cases of fraud and Indonesia had more than 239 (ACFE, 2019). This prompted studies that aimed to gather empirical data to investigate the possibility if tax incentives have an impact on Fraudulent Financial R...
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Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
UiTM Press
2024
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Subjects: | |
Online Access: | https://ir.uitm.edu.my/id/eprint/108906/1/108906.pdf https://ir.uitm.edu.my/id/eprint/108906/ |
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Summary: | According to the ACFE Occupational Fraud (2022), manufacturing businesses had 194 potential cases of fraud and Indonesia had more than 239 (ACFE, 2019). This prompted studies that aimed to gather empirical data to investigate the possibility if tax incentives have an impact on Fraudulent Financial Reporting (FFR) in Indonesian public manufacturing firms. Only 291 of the 53 firms' data from the six-year period (2017–2022) were ultimately used. This research used the Heptagon Fraud Model namely; Incentive/Pressure, Opportunity, Attitude/Rationalization, Capability, Arrogance, Ignorance and Greed as the independent variable and indicators related to tax using Income Tax Rate to measure the role of the moderating variable. SPSS research results revealed that Incentive/Pressure and Arrogance had positive influence on detecting FFR whereas Ignorance, Opportunity, Attitude/Rationalization, Capability, and Greed had a negative impact on detecting Fraudulent Financial Reporting. In addition, Income Tax Rate had a strong impact on Incentive, Capability and Greed in influencing FFR. This research explained the phenomenon of FFR and how it could benefit regulators, management, and various stakeholders in FFR detection. Accordingly, this study contributes to previous studies in the income tax rate context and adds to its puzzle by providing wider indicators on the fraud model. |
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