Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Business Administration

Major Professor

Uday Murthy, Ph.D.

Co-Major Professor

Lisa Gaynor, Ph.D.

Committee Member

Kristina Demek, Ph.D.

Committee Member

Sandra Schneider-Wright, Ph.D.


experiment, graphs, mindsets, user interactivity


Although data analytic technologies provide auditors with powerful tools for identifying high-risk areas during an audit (Austin, Carpenter, Christ, and Nielson 2019), they are not a substitute for necessary interpretation and judgment (Brown-Liburd, Issa, and Lombardi 2015). A major barrier for making better use of data analytic techniques and tools is the skillset needed to make necessary interpretations and judgments based on data visualizations within the tool (Appelbaum, Kogan, and Vasarhelvi 2017; Earley 2015; PwC 2015). Default visualizations provided to auditors could be suboptimal in relation to the underlying data, which could limit auditors’ ability to identify anomalies without some intervention to reconfigure the visualization. Failure to identify significant anomalies has implications for inferences related to audit risk assessment. This study examines the effectiveness of a documentation focus intervention aimed to invoke a mindset conducive to anomaly identification and risk assessment judgment and decision-making. My findings indicate that without the use of supporting focus documentation, auditors will likely anchor on initially provided data visualizations even when they are in less-than-optimal form. The initial suboptimal data visualization is detrimental to anomaly identification performance and in turn, leads to lower assessed risk. However, supporting focus documentation encourages auditors to search for and document a greater number of high-risk evidence items and spend more time interacting with the data visualizations which alleviates the tendency to anchor. Overall, this study should be of interest to public accounting firms and standard setters who wish to improve audit quality and efficiency, particularly when using data analytic visualizations during risk assessment.

Included in

Accounting Commons