Big data analytics could be a panacea for the IRS by enabling creation of taxpayer profiles to better capture noncompliance using artificial intelligence and machine learning, requiring fewer costly manpower hours. Privacy, fair information practices, and embedded biases are critiques of such practices, and it is unknown how taxpayers will respond. Deterrence theory suggests improved audit effectiveness will increase compliance but excludes elements of tax morale, including perceived fairness. We find evidence supporting a moderated mediation model where procedural fairness mediates the relationship between audit procedures and tax compliance, moderated by participatory monitoring, which captures how effects vary when taxpayers willingly increase traceability of their income by advertising online. When taxpayers advertise business online, use of advanced technologies in audit selection significantly increases compliance with no significant effect on perceived fairness; when they do not, use of advanced technologies has no effect on compliance, but significantly decreases perceived fairness.
Big Data Analytics in IRS Audit Procedures and Its Effects on Tax Compliance: A Moderated Mediation Analysis
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Erica Neuman, Robert Sheu; Big Data Analytics in IRS Audit Procedures and Its Effects on Tax Compliance: A Moderated Mediation Analysis. Journal of the American Taxation Association 2021; doi: https://doi.org/10.2308/JATA-2020-038
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