Individuals are increasingly using expert system tax programs as a substitute for paid professionals when preparing their income tax returns. This study examines ways that expert systems encourage the same aggressive results documented when paid professionals are used. Examining the use of expert systems and the related behavior of taxpayers reveals aggressive reporting related to the commonly used warning alerts in tax programs. Using an experimental economics setting in which participants report liabilities with the possibility of penalties for noncompliant reporting, participants filled out a Claim Form mimicking a Schedule C in one of four conditions: manual preparation, no alerts, alerts triggered at a high threshold of reporting aggression, and alerts triggered at a low level of reporting aggression. Comparing the amounts deducted in each condition revealed that warning alerts with low thresholds of activation decreased aggressive reporting while warning alerts with high thresholds of activation increased aggressive reporting. Survey instruments measuring user satisfaction indicated significantly lower satisfaction when (high or low level) warning alerts were used versus no warning alerts. Contrary to expectations, respondents using the expert system tax program with high threshold warning alerts compared to no warning alerts reported a significantly higher perception of accuracy. This study demonstrates the extreme to which taxpayers are swayed by perceived aspects of the tax software that are irrelevant to the facts of their tax situations. Exactly what taxpayers need to be given by way of guidance and direction to comport their behavior to the tax laws is a critical question of public policy.
| Identifer | oai:union.ndltd.org:TEXASAandM/oai:repository.tamu.edu:1969.1/114 |
| Date | 30 September 2004 |
| Creators | Olshewsky, Steven J. |
| Contributors | Smith, L. Murphy, Kratchman, Stanley H., Bravenec, Lorence L., Samuelson, Charles D. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Electronic Dissertation, text |
| Format | 193607 bytes, 141834 bytes, electronic, application/pdf, text/plain, born digital |
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