Author: Megan Seymore

Publisher:

Published: 2019

Total Pages: 175

ISBN-13:

The purpose of this dissertation was to examine how managers' judgments from an internal auditor's recommendation are influenced by some aspects of newer data sources and the related visualizations. This study specifically examined how managers' judgments from an internal auditor's recommendation are influenced by the (1) supportiveness of non-financial data with the internal auditor's recommendation and (2) evaluability of visual representations for non-financial data used to communicate the recommendation. This was investigated in a setting where financial data does not support the internal auditor's recommendation. To test my hypotheses, I conducted an experiment that uses an inventory write-down task to examine the likelihood that a manager agrees with an internal auditor's inventory write-down recommendation. This task was selected as it requires making a prediction and both financial and newer non-financial data sources are relevant to inform this judgment. The study was conducted with MBA students who proxy for managers in organizations. Evaluability of visual representations was operationalized as the (1) proximity of financial and non-financial graphs, and (2) type of non-financial graph as requiring a length judgment or not. This dissertation contributes to accounting literature and the internal auditing profession. First, I contribute to recent experimental literature on data analytics by providing evidence that newer non-financial data sources will be integrated into managers' judgments even when financial data is inconsistent. However, I also identified that the effectiveness of appropriate agreement with an internal auditor's recommendation depends on the evaluability of the visualizations for non-financial data. Second, I expand on the literature that examines managers' agreement with recommendations from internal auditors by examining an unexplored yet relevant context of using newer non-financial data sources and communicating these results. Specifically, I identified how the evaluability of visual representations for non-financial data interacts with the supportiveness of non-financial data with the internal auditor's recommendation to create differences in managers' agreement with the recommendation. I also identified confidence in the internal auditor's recommendation as an explanatory variable in some situations. My findings also have practical value for the internal auditing profession to understand the importance of appropriate visualizations in audit reporting.