Projects

The number of private equity (PE) investments in accounting firms has grown significantly in recent years, both in the Netherlands and globally. This has inevitably attracted attention from the popular press and regulators, often voicing divergent views. Proponents of PE investments see these investments as a chance for audit firms to pay-off legacy obligations and invest more
significantly in audit technology, quality management, and new service lines such as sustainability reporting and assurance. These investments may be challenging to pursue using traditional
financing methods for small and mid-sized audit firms. Critics fear that PE-investments will lead to an overemphasis on short-term financial goals that may threaten auditor independence and
professional integrity.

Audit firms are “choice architects” of audit tools, as they choose elements that guide auditors’ actions, which can act as “nudges” (Thaler and Sunstein 2021). Prior research shows that
defaults in workpaper systems⎯a type of nudge that is created by prepopulation of current year workpapers with prior year work⎯impair audit staff’s accuracy at an objective risk assessment task (Bonner, Majors, and Ritter 2018). Building on this research, we propose to examine effects of providing an AI decision aid on more experienced auditors’ accuracy in a risk assessment task that requires greater professional judgment, in weighting and combining conflicting cues. Importantly, we will compare judgments of auditors viewing the AI-suggested ratings and evidence (i.e., the AI decision aid content) prepopulated within the current year workpaper, creating an AI default, and possibly an inference that their audit firm implicitly endorses use of the AI-suggested ratings, versus auditors accessing the AI content in a separate file. We will conduct a 2 x 2 + 1 between-participants experiment, in which we manipulate 1) whether auditors receive the AI decision aid and 2) whether workpapers are prepopulated or blank.

Recent developments in technology, such as automated decision-support systems and generative AI, promise to have a significant impact on auditors’ judgment and decision-making processes
(e.g., EY, 2023; IAASB, 2021; Iacone, 2023). Emerging technologies have the potential to complete routine tasks more efficiently than staff, generating outputs to be assessed by auditors so that they will complete fewer of these tasks themselves (e.g., Susskind & Susskind, 2016; Saunders, Keune & Hawkins, 2023; Sutton, Arnold & Holt, 2023). In the short-term, this exposes auditors to two established behavioral risks: under-reliance on technology, termed algorithm aversion (e.g., Commerford, Dennis, Joe & Ulla, 2022; Commerford et al., 2024), and over-reliance on technology, known as algorithm appreciation and automation bias (e.g., Parasuraman & Manzey, 2010; Peters, 2025). Building on the Theory of Technological Dominance (Sutton, Arnold & Holt, 2018; 2023), the first aim of our project is to clarify under which conditions auditors are predicted to under- or over-rely on automated tools in the judgment and decision-making process. We try to fulfil this aim using an interdisciplinary systematic literature view, as well as interviews with auditors of all ranks and relevant members of L&D departments, national offices, and firm taskforces on technology use.

As AI-assisted tools become more prevalent in audit engagements, understanding their impact on fraud risk assessments is crucial. While AI and machine learning can help to detect red flags, the timing of its introduction in the fraud process is crucial. This topic is important as auditors often lack a critical mindset in fraud risk assessments and engage in motivated reasoning, underestimating or ignoring crucial fraud indicators. We propose a between-subjects experiment where auditors either do not receive AI-generated reports or receive such reports based on machine learning (ML) before or after assessing fraud risk factors.

As the corporate sustainability landscape rapidly evolves, the emphasis of stakeholders on Environmental, Social, and Governance (ESG) performance over mere profitability has ushered in an era where organizations are progressively enhancing their ESG disclosures. This transition, while reflective of a broader commitment to sustainability, raises critical questions about the authenticity of these disclosures and the potential for greenwashing. The voluntary nature of ESG assurance, juxtaposed with mandatory financial audits, casts doubt on its effectiveness and reliability, exacerbated by unclear regulatory standards and the diverse expertise of assurance providers. This backdrop sets the stage for our research, which seeks to explore the intersection of regulatory and technological innovations, specifically Artificial Intelligence (AI), in refining ESG assurance practices to address these challenges.
The imperative for this research is accentuated by the inherent complexities within the prevailing assurance framework, marked by variable assurance scopes and a disparate array of service providers. Such a landscape not only jeopardizes the uniformity and dependability of assurance reports but also underscores the risk of regulatory interventions potentially leading to unintended cost increases and negatively affecting market dynamics. In this context, the role of strategic regulatory actions becomes crucial, particularly in identifying and curbing greenwashing practices, thereby directly enhancing the quality of assurance. Simultaneously, the advent of AI as a tool to improve the operational efficiency, effectiveness, and overall quality of assurance processes emerges as a pivotal development.

This research program sets out to contribute to the discussion on how to enhance audit quality through a systematic examination of the relational interplay between auditors and their clients and how their governance structure is organized. In doing so, we are able to identify unexplored mechanisms between auditors and key governance actors that fundamentally shape audit quality. 

The institutional context in and conditions under which auditors perform their work are important determinants of audit quality. However, the relative effect sizes of the various institutional conditions and (potential) interventions on audit quality are widely unknown.

This PhD project aims to focus on (1)  quality-oriented audit firm culture and (2) the extend selection and hiring mechanism of auditors (audit firms) influence audit quality. Furthermore, this project investigates (3) which Audit Quality Indicators (AQI’s) currently under-utilized (despite its potential availability) are informative of audit quality, especially compared to the currently suggested AQIs considered to influence audit quality.

Over the past decade, the FAR has developed into a unique platform where rigorous academic research
and professional practice meet in a constructive and valuable way. What makes FAR distinctive is not only its access to rich, real-world audit data, but above all the continuous dialogue between researchers and practitioners. This booklet demonstrates how FAR brings that collaboration to life. It features an interview  ith FAR’s departing academic director Jan Bouwens and his successor Anna Gold. Their reflections provide a personal and institutional perspective on FAR’s development over the past ten years and the direction it is taking into the future. The interview illustrates how FAR’s mission, bringing academia and practice closer together, has evolved from an ambition into an everyday reality, while also highlighting where further steps can and must be taken. To illustrate the common ground between academia and practice, the booklet then presents four project descriptions, each briefly showcasing a FAR related study, followed by a researcher’s perspective and a practitioner’s reflection. These contributions show that FAR research is not conducted about practice, but with practice.

Research synthesis, within six months.