Understanding Fraud Detection with the Heptagon Fraud Model and Income Tax Moderation: Evidence from Indonesia

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.

Publication Details

Journal
Management and Accounting Review
Published
2024-12-01
DOI
https://doi.org/10.24191/mar.v23i03-03
Primary Topic
Corporate Governance and Financial Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding Fraud Detection with the Heptagon Fraud Model and Income Tax Moderation: Evidence from Indonesia

Management and Accounting Review
Corporate Governance and Financial Management
article

Understanding Fraud Detection with the Heptagon Fraud Model and Income Tax Moderation: Evidence from Indonesia

article en

Abstract

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.

Management and Accounting ReviewVol. 23(3)
Peace, Justice and strong institutions
Openalex Percentile: Top 54%
Corporate Governance and Financial Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.