Publications

This study examines the long-term evolution of audit pricing dynamics in a market characterized by continuous audit fee disclosure. Using data from the Dutch audit market over the period 2008-2019 and exploiting the 2014 EU Audit Reform as an exogenous fee shock, we analyze how audit fees adjust to prior-year mispricing. We find that audit fees exhibit mean-reverting behavior, with both overpricing and underpricing associated with subsequent fee adjustments. Further, results show that an exogenous fee-increasing shock temporarily increases fee dispersion. Under continuous fee disclosure, the availability of post-shock fee information facilitates pricing adjustments, with downward fee adjustments in the subsequent year. While bargaining power between auditors and clients is symmetric on average, clients exhibit stronger bargaining power in the one to two years following the fee-increasing shock. Taken together, our findings highlight the role of fee transparency in facilitating price adjustments and restoring pricing efficiency over time.

The findings of this project provide insights into how social learning from errors affects audit quality and how individual, relational, and organizational characteristics shape error learning. Together, these findings point to the following conclusions.

It is not how often an auditor seeks help that improves audit quality; it is how valuable that help is.
The findings show that seeking help frequently is, in itself, linked to lower audit quality. What matters is the learning value auditors derive from the help-seeking interactions they do have. Auditors who have access to a broader network of colleagues and gain more valuable learning from these interactions tend to achieve higher-quality outcomes.

It is not the personality of the auditor that drives learning; it is who the auditor can turn to and the error management climate of the firm.
Personality mainly shapes how many colleagues an auditor turns to, not how often they seek help or how valuable it is. What matters more is who they can turn to. The more accessible and psychologically safe a colleague is, the more auditors turn to them for help. Hierarchy especially drives the value of that help. Auditors learn most from hierarchically superior colleagues. While modest, the climate of the audit firm plays a role too. Help-seeking frequency remains largely the same regardless of whether the climate is open or blame-oriented, but an open error management climate especially improves how much auditors learn from the help they seek.

This masterclass explored how financial auditors integrate Environmental, Social, and Governance (ESG) information into their client risk assessments. Drawing on the FAR study “Expanding the Risk Lens in Financial Audits: Auditor Perceptions of ESG Integration,” presenters Andreea-Ioana Stanescu and Yasemin Zengin-Karaibrahimoglu shared insights from interviews with auditors and ESG specialists in the Netherlands. The session addressed the practical challenges of evaluating qualitative and forward-looking ESG data and examined the dynamics of collaboration between audit teams and sustainability experts.

In the current audit landscape, environmental, social, and governance factors are increasingly critical to financial risk assessment. However, integrating this information poses unique challenges. How do financial auditors determine which ESG issues are most relevant? Why do certain topics receive more focus than others? And how do audit teams navigate complex, hard-to-measure data?

During this interactive masterclass, recorded on 9 September 2026, researchers from the University of Groningen discussed the initial findings of their FAR-funded study, conducted alongside colleagues from Alliance Manchester Business School and the Stockholm School of Economics. Based on extensive interviews with professionals from Big Four and medium-sized audit firms across the Netherlands, the presentation fostered an engaged discussion on translating qualitative ESG information into concrete financial risks. Viewers will gain valuable insights into the current state of ESG integration and explore the evolving methodologies required to prepare the audit profession for the future.

Voor studenten en jonge accountants is de keuze tussen een Big 4- en een non-Big 4-kantoor belangrijk voor hun verdere loopbaan. Inzicht in de verschillen tussen Big 4- en non-Big 4-kantoren kan deze kantoren helpen bij het aantrekken, ontwikkelen en behouden van talent. Op basis van een vragenlijst onder 161 werkzame jonge accountants in de controlepraktijk bij Nederlandse Big 4- en non-Big 4-kantoren is onderzocht of jonge accountants verschillen ervaren in hun werk-privébalans en ontwikkelingsmogelijkheden. Ook is onderzocht of bij een Big 4- of non-Big 4-kantoor werkzame jonge accountants verschillende persoonlijkheidskenmerken hebben. De onderzoeksresultaten wijzen op een duidelijke afweging tussen werkdruk enerzijds en ontwikkelinfrastructuur anderzijds. Kantoren kunnen deze afweging gebruiken om zich te positioneren ten aanzien van potentiële medewerkers.

While prior studies have examined specific aspects of attracting and retaining qualified auditors, an integrative framework for understanding the different stages of an auditor career and how these stages interact over time to shape auditor human capital and professional identity is lacking. We address this gap by synthesizing the literature along the Auditor Career Path framework, which we conceptualize as comprising six stages: Attraction, Recruitment, Onboarding, Development, Retention, and Separation. By organizing and integrating existing research within this framework, we identify gaps in the current understanding of auditors’ career paths, including how frictions and interventions at one stage propagate to others, and derive insights to help audit firms more effectively attract, develop, and retain their human capital, while also outlining a research agenda that connects these stages for future work.

The extent to which audit quality meets the expectations of stakeholders can lead to heated debates. One reason is that the level of attained audit quality is comprised of objective and subjective elements. In this paper we propose a set of measures defined by the output the user of financial statements expects: error detection and correction and opinions/advice based on sufficient audit evidence. We argue that detection/correction and opinions are determined by input factors like adequate effort and team composition and choice of the adequate procedures. These audit-firm choices, in turn depend on an adequate assessment of the auditee characteristics by the audit firm which assessment, in turn, is determined by the structure or the audit firm itself.

Four insights from the literature on the effects of AI on audit practice

1. AI is improving audit quality and efficiency

The literature consistently shows that, when properly implemented, AI strengthens audit effectiveness. AI helps auditors to identify fraud risks, detect misstatements, analyze entire populations of transactions rather than samples, and reduce human error. What is more, it automates repetitive and time-consuming tasks, allowing audits to be completed more efficiently.

Key message: Overall, AI is enhancing both the effectiveness and efficiency of audit engagements, supporting the profession’s ability to provide timely and reliable assurance.

2. AI changes auditors’ work experience in both positive and negative ways

AI introduces new demands on individual auditors by requiring additional skills and creating uncertainty about future professional roles. These developments may increase mental load and psychological strain. At the same time, AI reduces auditors’ workload and involvement in repetitive tasks, enabling them to focus on more meaningful and value-adding work. The result is a dual effect, where AI simultaneously increases stress while also improving job experience and perceived meaningfulness of auditors’ work.

Key message: Understanding how AI affects auditors’ well-being, engagement, motivation, and job satisfaction is highly relevant.

3. Professional judgment becomes the central challenge

An important concern emerging from the literature is the effect of AI on auditors’ professional judgment and skills. As AI increasingly handles data collection, processing, and basic analytical work, auditors have fewer opportunities to engage in experiential learning, which is essential for building professional judgment and expertise. Reduced exposure to traditional audit tasks may hinder the development of professional expertise and skepticism, particularly among junior auditors. This risk is amplified by the fact that AI can sometimes produce inaccurate outputs that require careful critical evaluation. This creates a challenge: AI imperfections increase the need for critical judgment while potentially weakening some of the mechanisms through which that judgment is developed.

Key message: Future success with AI in auditing will depend not only on technological capabilities but also on maintaining and strengthening auditors’ professional skepticism, independent judgment, and ability to challenge AI-generated outputs.

4. AI should be viewed as an interconnected system of benefits and risks

The literature review demonstrates that AI simultaneously creates resources and demands for audit practice. Improvements in accuracy and efficiency may coexist with deskilling, increased cognitive demands, and challenges to professional judgment. These effects do not operate independently. They influence and potentially offset one another.

Key message: The ultimate impact of AI on auditing will depend not on any single effect, but on how its benefits and risks combine and evolve over time.

Auditors are selected and paid for by the organizations they audit. Policymakers are concerned that this structure influences auditor independence which, in turn, impairs quality. Accordingly, policymakers consider whether audit quality is enhanced if the auditor is appointed by an external (independent) party.

The authors study the working of such a model in a natural setting for local subsidiary audits conducted by the big-4 as part of group audits, where the local auditor is either assigned to the subsidiary through parent firm management or self-selected by the subsidiary.

The authors find that audit partners assigned to subsidiaries receive less information from the auditee, issue fewer going concern opinions, identify fewer control deficiencies, identify and correct fewer misstatements, and are less likely to constrain earnings management compared to self-selected auditors.

Some further preliminary evidence the authors collect suggests that assigned auditors produce lower audit quality through effort reduction.

Audit firms across jurisdictions face a persistent and increasingly acute challenge in attracting and retaining early-career audit talent. A commonly cited explanation for this trend is that today’s students and junior professionals are less willing to accept the demanding working conditions traditionally associated with auditing. While workload and work-life balance undoubtedly play an important role, this explanation implicitly assumes that students possess an accurate understanding of what early-career audit work actually entails.

We argue that this assumption is questionable. Drawing on evidence from our recent Accounting Horizons study (Dierynck, Marangoni, Peters, and Weijers 2025), we suggest that an important, but underappreciated, driver of the audit talent shortage is an expectations-reality gap: a systematic mismatch between what students believe the junior auditor role involves and what junior auditors actually experience in practice. Understanding this gap is critical for audit practice. If students base their career decisions on inaccurate or overly pessimistic beliefs about audit work, firms may lose potential entrants before recruitment efforts can meaningfully engage them. Moreover, misaligned expectations at entry may contribute to early dissatisfaction and turnover, further weakening the talent pipeline.

Audit firms are rapidly integrating Generative AI (GenAI) into their workflows. While these tools can enhance efficiency and support complex judgments, the key challenge is not whether AI provides useful input, but whether auditors use it appropriately. The literature shows that auditors’ reliance on AI is shaped more by behavioral responses, system design, and organizational context than by the underlying technology. Three insights emerge.

First, auditors face a calibration problem. They may under-rely on AI due to algorithm aversion, discounting AI-based evidence, relative to human experts, even when it is equally reliable. At the same time, they may over-rely on AI when outputs appear authoritative, fluent, or easy to use. Both problems impair audit quality: under-reliance biases judgments toward management, while over-reliance reduces professional skepticism.

Second, reliance depends critically on how AI is designed and embedded in the audit process. Features such as perceived control (e.g., the ability to provide input), adaptability of algorithms, and task–technology-fit influence whether auditors trust and use AI outputs. AI is more effective when it aligns with task uncertainty and complexity, and when auditors can meaningfully engage with the system. Poorly designed or poorly communicated tools risk being ignored or misused.

Third, AI affects not only decisions but also how auditors think about decisions. GenAI can improve understanding of complex evidence and help auditors better identify when to raise issues, particularly in remote settings. However, AI can also inflate confidence while reducing self-monitoring, making auditors less aware of when they may be wrong. This creates a risk of overconfidence and inappropriate reliance.

Overall, the literature highlights that successful AI adoption is also a behavioral and organizational challenge, not just a technological one. To realize the benefits of AI, audit firms should consider three key levers. First, governance: providing clear guidance on when and how AI should be used and evaluated. Second, design and communication: ensuring that tools align with task demands and enable auditors to meaningfully engage with the system. Third, training and oversight: developing auditors’ ability to critically assess AI outputs and appropriately calibrate their reliance.