ESG assurance is becoming a core component of corporate reporting as the CSRD and ESRS place double materiality (i.e., covering both financial and impact materiality) at the heart of sustainability disclosures. This shift creates a fundamentally new assurance domain characterized by heightened professional judgment, evolving audit methodologies, and expanding auditor–client interaction.

This project aims to open the “black box” of ESG assurance by examining how auditors and their clients interpret, operationalize, and provide assurance over double materiality assessments in both voluntary and mandatory reporting settings. We conceptualize double materiality as the regulatory and conceptual lens through which key audit challenges—judgment, assurance scope, innovation, and consistency—can be observed in practice.

Specifically, we investigate how auditors apply emerging CSRD and ESRS guidance when assessing financial and impact materiality; how these judgments shape engagement acceptance, planning, evidence gathering, and the development of new assurance methods; and how audit firms strive for consistency and credibility across engagements and offices. We further examine how double materiality assessments integrate sustainability and financial considerations and how they influence perceptions of firm-wide risk exposure and future prospects. A central focus is auditor–client and stakeholder interaction. We study how auditors engage clients around double materiality judgments, how these interactions affect reporting processes and governance structures, how differences of opinion are resolved, how ESG and financial assurance engagements influence one another, and how emerging practices are institutionalized within audit firms.

We will conduct semi-structured interviews with audit partners and managers involved in CSRD-related engagements, as well as selected audit firm leaders, to provide in-depth insight into the challenges, innovations, and emerging best practices shaping double materiality assurance.

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.

Despite extensive conceptual debates predominantly climate-change-related risks (e.g. IFRS 2019; IAASB, 2020)4, the benefits and challenges of integrating ESG risks into audit planning and how this integration may affect the ability of auditors to deliver high-quality audits remain unexplored. Therefore, this study primarily aims to contribute to the auditing, more specifically business risk audit literature by examining the opportunities and challenges that auditors face when processing and integrating ESG risk with financial information, as well as their inherent impact on audit quality (AQ). Furthermore, we aim to examine the relevance and complexity of the ESG information in the business risk audit by considering the reasons driven not only by the nature and extent of the ESG information but also by the nature and extent of the client’s operations, more specifically, in a multinational group audit.In terms of methodology, we employ a multi-methodological approach that builds on interviews, surveys, and archival data. We believe that considering multiple data sources would offer complementary insights that can lead to a more comprehensive understanding of our exploratory research. Overall, this methodological approach would help us to test the manner in which auditors perceive (1) relevance (usefulness) and (2) complexity (cost of collecting, processing, and interpreting) of integrating ESG risks in their client firms’ risk assessments has a real impact on AQ.

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 objective of this project is to support audit firms in managing auditors’ process of personal and professional adaptation to AI in ways that ensure the realization of the potential associated with AI. Our approach is rooted in self-regulation theory. In line with this, we propose that the introduction of AI in audit practice requires processes of self-regulation by auditors, i.e., attempts to (1) alter the AI use to re-establish a fit with their personal and professional standards, (2) alter these standards to re-establish the fit with their AI use, or (3) a combination thereof. The outcomes of self-regulation can be both behaviors that contribute and/or impede the realization of the potential of AI in auditing.We argue that the audit firm can mobilize management control practices to assist auditors in their adjustment (as it affects both auditors’ AI use and their personal and professional standards). Specifically, we ask which characteristics these management control practices need to have and in which combinations they will meaningfully contribute to auditors’ processes of adjustment to AI’s use. Moreover, we ask how these processes relate, ultimately, to audit quality and the efficiency of the audit process.In recognition of the relative novelty of AI use in audit practice, we propose to answer the research questions with a multimethod approach in which the findings of the initial interview study inform and recalibrate the subsequent survey and experimental study. Answering our research questions will inform audit practice about (1) how auditors self-regulate to AI; (2) how the audit firm’s existing management control practices impede or support these processes of self-regulation; and (3) how the audit firm’s management control practices can be optimized to support auditors’ self-regulation in ways that ensure the realization of the potential of AI.

This project will especially focus on quantifying the relationship between social learning from errors and audit quality, considering the role that a firm’s error management climate (EMC) plays in fostering learning behaviors in individual auditors, addressing three key gaps in extant research. First, despite suggestions from research as well as regulators that learning from errors may be a critical mechanism for long-term improvement of audit quality, no study to date has investigated the relationship between the auditor’s social learning (i.e., help-seeking) behavior and audit quality (i.e., detecting, analyzing and reporting potential material misstatements) (Smeets et al., 2022).Second, this project will also investigate to what extent individual auditor characteristics (auditor’s personality traits) and firm size might drive this social learning from errors. To date, very little is known about how individual auditor characteristics and more specifically auditors’ personality traits might drive error learning. Literature in psychology and organizational behavior provides however ample evidence that personality is an important driver in explaining individuals’ professional behavior and performance (i.e., Naveh et al., 2015; Kerckhofs et al., 2023a, 2023b).Finally, research on EMC and learning behavior in audit firms has mainly considered larger (Big 4) firms. There are, however, reasons to believe that the conditions for EMC and the learning environment are different in smaller audit firms compared to larger firms: “errors in the Mid-Market segment may be less consequential and/or visible, possibly leaving more opportunities for the emergence of a ‘learning organization’” (FAR Call 2023).