The Effects of the Internal Control Opinion and Use of Audit Data Analytics on Perceptions of Audit Quality, Assurance, and Auditor Negligence
41 Pages Posted: 21 Aug 2017
Date Written: August 17, 2017
Using advanced audit data analytics tools, public accounting firms propose that they can analyze the entire population of accessible client transactions. While firms emphasize the potential benefits to audit efficiency and effectiveness, they caution that this approach provides no greater than the current level of “reasonable” assurance than does traditional auditing techniques. In the context of a subsequent audit failure where an investor has initiated a law suit, we examine whether the audit methodology (audit data analytics versus traditional auditing techniques) and the type of internal control opinion (unqualified versus adverse) affect perceptions of audit quality and assurance and perceptions of auditor negligence. In the wake of auditors issuing more adverse ICFR opinions, there may be differences in how users perceive assurance levels and attribute blame in circumstances of an audit failure. We develop our expectations using the theory of blame attribution. Using a 2x2 full factorial experimental design, we predict and find that when auditors issue an unqualified ICFR opinion, jurors assess auditors as more negligent when auditors employ traditional auditing techniques (compared to audit data analytic techniques). In subsequent analyses, we find that use of audit data analytic tools increases perceptions of audit quality, such that jurors assess more blame to the plaintiff for their loss, assigning less negligence to the auditor. We view this finding as indicative of the jurors believing that when the auditor uses audit data analytics the auditor has exceeded the minimally expected audit testing techniques. Overall, our study informs regulators, practitioners, and academics about the perceived assurance effects of using advanced technological tools on the audit as well as the corresponding litigation effects.
Keywords: auditor liability, audit data analytics, audit quality, statistical sampling
JEL Classification: M41, M42
Suggested Citation: Suggested Citation