Stakeholder Perceptions of Data and Analytics Based Auditing Techniques

Abstract

Public accounting firms have invested significant resources to develop reliable substantive tests using large data sets and sophisticated algorithms ("data and analytics based procedures"). Developing reliable procedures, however, is just one hurdle firms must clear before leveraging such data sets and algorithms. In particular, firms will also need to convince audit stakeholders that relying on data and analytics based procedures will not only enhance audit efficiency, but also improve, or at least maintain, audit effectiveness. This study provides exploratory, experimental evidence to indicate how three key audit stakeholder groups-non-professional investors, peer reviewers, and jurors-perceive two prominent data and analytics based audit procedures (population testing and predictive modeling) relative to a more traditional substantive procedure (sample testing). Results suggest that key audit stakeholders are generally open to, and, in some cases, favorably disposed to the use of data and analytics based audit procedures. Nevertheless, participants also expressed some concerns about the appropriateness of relying on data and analytics based procedures, particularly predictive modeling, as primary sources of substantive evidence. This paper reports these and other key findings to develop an agenda for future research to help firms better understand and ultimately address stakeholder concerns.

The effects of person‐specific, task, and environmental factors on digital transformation and innovation in auditing: A review of the literature

“…As previously noted, Emett et al (2021) find that external reviewers perceive audit procedures using data analytic techniques as lower quality than traditional audit procedures. This result contrasts with Ballou et al's (2021) findings that peer reviewers' perceptions of audit quality are not affected by auditors' data analytics use. Emett et al (2021) conduct a second experiment to evaluate an intervention to mitigate the effort heuristic.…”

“…How stakeholders perceive auditors' use of advanced technology potentially impacts their perceptions of audit and financial reporting quality and, in turn, their perception of the level of assurance provided by data analytic techniques. Ballou et al (2021) examine the perceptions of key stakeholders when auditors perform full population testing and predictive modeling data analytics-based procedures relative to traditional sample-based substantive testing. They find that investors' willingness to invest is unaffected by the use of data analytic techniques.…”

“…While the above studies highlight the beneficial uses of predictive analytics in the audit process, Ballou et al (2021) find stakeholders views of predictive analytics as enhancing audit quality are mixed. Investors' view predictive modeling more favorably than full population testing and traditional sample‐based substantive testing when the risk of material misstatement is high.…”

The Impact of Audit Technology on Audit Task Outcomes: Evidence for Technology‐Based Audit Techniques*

“…Extant work suggests a few reasons why technology should improve audit task effectiveness. Technology can allow more direct testing of more transactions, which can improve audit effectiveness (Ballou et al 2020; Barr‐Pulliam et al 2021; Cardinaels et al 2021; Emett et al 2021). In addition, technology allows for gathering and displaying data in unique ways to better interpret audit evidence, and thus enhance audit effectiveness, than audit procedures performed without technology (Dilla et al 2010; Jans et al 2014; Rose et al 2017; Anderson et al 2020; Pickard et al 2020; Loraas and Holt 2021; Jans and Eulerich 2022).…”

Auditing with data and analytics: External reviewers' judgments of audit quality and effort

“…More recent research provides qualitative field evidence on how D&A audit approaches are perceived by auditors, peer reviewers, and standard setters (Austin et al, 2021; Christ et al, 2021; Walker & Brown‐Liburd, 2019). In addition, experimental research focuses on how D&A tools influence auditor judgments (Anderson et al, 2021; Barr‐Pulliam et al, 2020; Bibler et al, 2023; Commerford et al, 2022; Koreff & Perreault, 2023; Peters, 2023) and on how auditors' use of D&A tools influences investor, manager, juror, and peer reviewer judgments (Ballou et al, 2021; Barr‐Pulliam et al, 2022; Kipp et al, 2020).…”