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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program


Year Degree Awarded


Month Degree Awarded


First Advisor

Jeremiah Bentley

Subject Categories



An important aspect of data-generated metrics is the degree to which they can be manipulated by users. Agents providing reports often (a) have incentive conflicts with principals and (b) have access to and an ability to manipulate data used in those reports. This study investigates how data manipulation affects advisors’ tendency to provide biased recommendations when incentives between advisors and advisees are misaligned and aligned. Drawing on theories in deception and persuasion, I posit that, when incentives are misaligned, advisors will provide a more biased recommendation when the evidence used to support their recommendation is more manipulable. I also predict and find that, when incentives are aligned, advisors will make the most mutually beneficial recommendation for both parties when evidence is less manipulable and the evidence is shown to the advisee. Overall, I find results consistent with my theory. The results of this study inform both theory and practice by showing how data manipulability can affect agents’ ability to act on their self-interests under different incentive structures.


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Accounting Commons