How do non-professional investors, jurors, and AICPA peer reviewers evaluate data and analytics-based substantive auditing procedures?

To minimize the associated risks (e.g., litigation, regulatory scrutiny, etc.) of implementing increasingly advanced data and analytics-based substantive auditing techniques, audit firms should ensure that key audit stakeholder groups sufficiently understand such procedures and believe that they maintain or elevate audit quality. However, little is known about how various stakeholder groups view data and analytics-based substantive procedures. Ballou, Grenier, and Reffett (2021) address this question by examining how three key audit stakeholder groups (investors, jurors, and AICPA peer reviewers) view two commonly employed data and analytics- based auditing techniques (population testing and predictive modeling). Our paper summarizes Ballou et al.’s (2021) study by summarizing its research questions, experimental method, and results. We then conclude with a discussion of the study’s implications for audit practice and, in particular, the steps that audit firms should take to ensure stakeholder comfort.