Auditing with Data and Analytics: External Reviewers' Judgments of Audit Quality and Effort

Abstract

Audit firms hesitate to take full advantage of data and analytics (D&A) audit approaches because they lack certainty about how external reviewers evaluate those approaches. We propose that external reviewers use an effort heuristic when evaluating audit quality, judging less effortful audit procedures as lower quality, which could shape how external reviewers evaluate D&A audit procedures. We conduct two experiments in which experienced external reviewers evaluate one set of audit procedures (D&A or traditional) within an engagement review, while holding constant the procedures’ level of assurance. Our first experiment provides evidence that external reviewers rely on an effort heuristic when evaluating D&A audit procedures—they perceive D&A audit procedures as lower in quality than traditional audit procedures because they perceive them to be less effortful. Our second experiment confirms these results and evaluates a theory-based intervention that reduces reviewers’ reliance on the effort heuristic, causing them to judge quality similarly across D&A and traditional audit procedures.

Keywords: data and analytics, external reviewer, audit quality; effort heuristic

JEL Classification: M41; M42