Complex accounting estimates are becoming increasingly important to financial statements. Yet, such estimates create ample opportunities for bias. Although both management and independent auditors are tasked with ensuring these estimates are free from error and bias, evaluating the appropriateness of these measures can be quite difficult. Drawing on unconscious thought theory, we predict and find across three experiments that engaging in unconscious thought can improve accounting practitioners’ evaluations of accounting estimates. In Experiment 1, we use practicing auditors as participants and find that unconscious thought improves less‐experienced auditors’ ability to recognize income‐decreasing patterns of bias. In contrast, more‐experienced auditors respond similarly to both incoming‐increasing and income‐decreasing bias, regardless of whether they use conscious or unconscious processing. In Experiments 2 and 3, we provide evidence that prompting managers to engage in unconscious thought also improves their recognition of patterns of bias within estimates. Overall, our findings demonstrate how unconscious processing can be intentionally prompted to improve accounting professionals’ ability to recognize subtle patterns of bias that they might otherwise overlook.

Companies, including public accounting firms, are integrating systems with advanced algorithms into decision-making processes to assist with developing and evaluating complex estimates. However, individuals may hesitate to rely on algorithmic output, particularly under conditions of uncertainty. We conduct two experiments examining whether and how a system’s ability to adapt—an emerging feature of machine learning—interacts with uncertainty to influence accounting professionals’ reliance on algorithmic advice. In Experiment 1, we find that auditors are more willing to rely on advice from learning algorithms than static algorithms when estimation uncertainty is relatively high. Experiment 2 replicates this result in a general accounting context where preparers develop their own estimates. Our findings demonstrate that accounting professionals’ reliance on algorithms is contextually dependent, and highlights algorithm adaptability as an important technological feature that can promote advice utilization, particularly when adaptability is likely important to the judgment context (e.g., when estimation uncertainty is high).

Differentiating real earnings management (REM) from normal business decisions poses a unique challenge for auditors, researchers, and investors. The ambiguity associated with REM, and the fact that REM does not violate GAAP, may explain why its use is on the rise. While some assert that auditors are not, and should not be, concerned with REM, recent research suggests that REM may influence some auditor judgments. Using Correspondent Inference Theory (CIT) as our theoretical framework, we extend REM research by investigating the ways in which auditors respond to REM and how auditors deal with the intrinsic ambiguity associated with REM. We administer a 3×2 between‐subjects experiment to 113 highly‐experienced auditors, manipulating the level of ambiguity surrounding the observed REM (Explicit REM, Potential REM, or No REM) and the earnings context in which the client engages in REM (the client beat or missed the consensus earnings forecast). We find that auditors respond to REM by lowering assessments of management tone (i.e., management's commitment to a culture of high ethical standards), being more likely to discuss the issue with the audit committee, and being less likely to retain the client. Auditors respond to Explicit REM regardless of the earnings context, but respond to Potential (i.e., ambiguous) REM only when the client beats the forecast. Finally, we find that management tone mediates the relation between REM and auditor responses, even after controlling for various audit‐related risks. Thus, for auditors, REM appears to be primarily a “people” issue, as REM provides a negative signal about management.

Recent research reveals that accruals-based earnings management (AEM) is decreasing while real earnings management (REM) is increasing, suggesting the correlation is due to regulatory scrutiny. However, based on Correspondent Inference Theory, we predict and find that when management uses REM, auditors are more restrictive of management's subjective estimates, making it more difficult for management to use income-increasing AEM. Our experiment manipulates the presence versus absence of REM, and whether the audit difference potentially impacts the client's ability to meet an earnings target. Using a serial mediation model, we find that when auditors observe REM, they perceive these operating decisions as aggressive, leading them to perceive management as aggressive, ultimately causing greater proposed adjustments on an unrelated audit difference. We contribute to the literature by demonstrating that when auditors observe REM, their altered perceptions about management can cascade, affecting how they respond to management estimates in unrelated financial statement accounts.

In a quasi-experiment with external (EA), in-house internal (IIA), and outsourced internal auditors (OIA) as participants in their natural roles, we compare auditors’ internal control evaluations in the presence of differing management reporting motives. Grounded in Organizational Identity (OID) Theory, we find EAs’ evaluations are more (less) lenient when management’s motive is less (more) self-serving. We provide evidence that management’s motives affect EAs’ evaluations because management is a conduit for EAs’ OID and because EAs adopt an affiliative protective orientation. In contrast, we find no evidence management’s motives affect IIAs’ evaluations or that management is an OID conduit for IIAs. Finally, we find that although OIAs and IIAs exhibit similar OID levels, on average, OIAs’ evaluations are more lenient than IIAs’. Our results clarify how auditors form OID, how it manifests into protective behaviors, and suggest EAs’ preference to rely on OIAs’ work over IIAs’ may inadvertently diminish audit quality.

Informed by Perceptual Deterrence Theory, we conduct multiple experiments to investigate when and how auditor actions can help deter manager opportunism. In Study 1A, we find that managers are less likely to use real earnings management (REM) when they expect auditors to both increase scrutiny and communicate their observations to the board. However, this effect occurs only when managers' operational decisions are inconsistent (versus consistent) with peer behavior. Study 1B findings suggest that increased auditor scrutiny alone (without auditor-board communication) is not likely to deter REM. In Study 2, we find that increased auditor scrutiny with communication to the board effectively deters both accruals-based earnings management (AEM) and REM, reducing the total level of manager opportunism. However, without communication, increased auditor scrutiny deters AEM, but also induces more REM. Our findings highlight the importance of auditor-board communication and demonstrate how auditor actions can contribute to the deterrence of manager opportunism. Data Availability: Contact the authors.

Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion”—the tendency to discount computer‐based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm's AI system (instead of a human specialist) propose smaller adjustments to management's complex estimates, particularly when management develops their estimates using relatively objective (vs. subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.

In this study, we examine how information foraging by auditors affects audit evidence collection in two distinct contexts, and show how a small change to audit methodology mitigates the potentially harmful effects of foraging. Information Foraging Theory explains how, while navigating an information environment, individuals learn to acquire information through personally experiencing the costs incurred and the values obtained from information. Consistent with the theory, we find that auditors react to the immediately felt costs of information collection (e.g., time and effort) at the expense of a more global consideration of information value (i.e., auditors collect lower-quality audit evidence). However, foraging behavior is moderated by removing the personal cost to the individual auditor (identifying audit evidence for another member of the audit team to collect), further demonstrating that these personally felt costs influence auditor choices in a way that reduces the quality of information collected. We contribute to the literature by demonstrating how information foraging can influence evidence quality and, thus, audit quality, and how a slight alteration of audit methodology can mitigate this behavior.

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.