Abstract

This dissertation comprises three studies, one qualitative and two experimental, that center on auditor's use of data analytics. Data analytics hold the potential for auditors to reallocate time spent on labor intensive tasks to judgment intensive tasks (Brown-Liburd et al. 2015), ultimately improving audit quality (Raphael 2017). Yet the availability of these tools does not guarantee that auditors will incorporate the data analytics into their judgments (Davis et al. 1989; Venkatesh et al. 2003). The first study investigates implications of using data analytics to structure the audit process for nonprofessionalized auditors. As the public accounting profession continues down a path of de-professionalization (Dirsmith et al. 2015), data analytics may increasingly be used as a control mechanism for guiding nonprofessionalized auditors' work tasks. Results of this study highlight negative ramifications of using nonprofessionalized auditors in a critical audit setting. The second study examines how different types of data analytics impact auditors' judgments. This study demonstrates the joint impact that the type of data analytical model and type of data analyzed have on auditors' judgments. This study contributes to the literature and practice by demonstrating that data analytics do not uniformly impact auditors' judgments. The third study examines how auditors' reliance on data analytics is impacted by the presentation source and level of risk identified. This study provide insights into the effectiveness of public accounting firms' development of data scientist groups to incorporate the data analytic skillset into audit teams. Collectively, these studies contribute to the literature by providing evidence on auditors' use of data analytics. Currently, the literature is limited to demonstrating that auditors are not effective at identifying patterns in data analytics visualizations when viewed before traditional audit evidence (Rose et al. 2017). The three studies in this dissertation highlight that not all data analytics influence judgments equally.

Notes

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Graduation Date

2018

Semester

Summer

Advisor

Sutton, Steven

Degree

Doctor of Philosophy (Ph.D.)

College

College of Business Administration

Degree Program

Business Administration; Accounting

Format

application/pdf

Identifier

CFE0007151

URL

http://purl.fcla.edu/fcla/etd/CFE0007210

Language

English

Release Date

August 2018

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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Accounting Commons

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