One hundred and fifty auditors participated in a study that examines whether auditors’ probabilistic judgments are closer to a Bayesian benchmark when auditors make judgments using a frequency response mode versus a probability response mode. We test a series of hypotheses that examine the effect of using a frequency response mode by professional auditors both within and outside their knowledge domain (fraud or medical case context) on assessing the likelihood of rare events. The results show that the auditors’ responses across the two case contexts (fraud and medical case) using a frequency response mode are closer to the Bayesian benchmark. In addition, we find that (1) the deviations in the auditors’ responses from the Bayesian benchmark across both response modes are significantly smaller for the fraud case in the low base rate condition only and (2) the deviations in the auditors’ responses from Bayesian benchmark for the fraud case using a frequency response mode relative to the probability response mode are smaller in the low base rate condition than the other two base rate conditions. These findings contribute to research on auditor judgment and decision-making, and demonstrate how the use of a frequency response mode can improve auditors’ assessment of fraud.

In recent years, standard setters worldwide have considered how to enhance financial statement users’ understanding of the estimation uncertainty contained in many financial statement items. Our study examines two disclosures expected to help investors evaluate the reliability of subjective fair value estimates: a quantitative sensitivity analysis (QSA) and the auditor’s quantitative materiality threshold. Using an experiment, we predict and find that investors judge the reliability of a reported estimate to be higher and are more willing to invest when a QSA disclosure is indicative of low sensitivity (i.e., greater precision) compared to high sensitivity (i.e., greater imprecision), but only if the auditor’s materiality threshold is also disclosed. When materiality is not disclosed, investors fail to recognize differences in reliability between the two levels of sensitivity, even though the amount of imprecision in the low sensitivity condition represents a fraction of materiality, while in the high sensitivity condition, this amount exceeds materiality multiple times over. Furthermore, when both disclosures are absent and only a qualitative description of sensitivity is provided—as required by current standards—investors perceive the disclosure to be relatively uninformative and respond to the ambiguous disclosure by decreasing their willingness to invest. The results of our study should be informative to accounting and auditing standard setters as they continue to consider the types of disclosures that may help investors understand the most complex and subjective aspects of financial reporting.

Recent reviews of the academic literature indicate that little is known regarding how users evaluate the materiality levels auditors use or respond to quantitative materiality disclosure. Regulators around the world have taken different stances on whether materiality should, or should not, be disclosed in the auditor’s report. In response to the dearth of research on these policy decisions, we examine the effect of audit materiality disclosures, or lack thereof, on professional investors’ decision making across different investment contexts (debt vs. equity, public vs. private). Our study is designed to test global audit public policy and as such our hypotheses are motivated by assertions made by regulators, auditing standards, and audit theory. Among a sample of 246 professional investors in our main experiment and 91 professional investors in two supplemental experiments, we find no consistent evidence that investors incorporate materiality disclosures into their investment decisions. Most importantly, we find evidence that investors’ understanding of materiality is not in line with regulator assertions. For example, investors fail to make consistent connections between the amount of disclosed audit materiality and the level of auditor effort. Our results hold across debt and equity investment settings for both public and private companies. In sum, our findings suggest that disclosures of audit materiality are not well understood by professional investors and are not viewed as decision relevant. This research informs practitioners, regulators, and academics regarding the effect of materiality disclosure on investor decision making as well as stakeholders’ views and expectations of overall materiality.

In this study, we examine auditors' reliance on artificial intelligence (AI) systems that are designed to provide evidence around complex estimates. In an experiment with highly experienced auditors, we find that auditors are more hesitant to rely on evidence from AI‐based systems compared to human specialists, consistent with algorithm aversion. Importantly, we also find that a small amount of control (i.e., providing input to specialists) can mitigate this aversion, though this effect depends on auditors' personal locus of control (LOC). Providing input increases reliance on evidence from AI systems for auditors who believe they have little control over their outcomes (i.e., an external LOC). In contrast, auditors with an internal LOC are particularly hesitant to rely on AI‐based evidence, and providing input has little impact on their reliance. Interviews with experienced auditors corroborate our findings and suggest auditors feel a greater sense of control working with human specialists relative to AI‐based systems. Overall, our results suggest perceived control plays an important role in auditors' aversion to AI and that auditors' individual traits can affect this aversion.