2020B03 – Learning and performing in audit firms: The role of the organizational context
Project Number – 2020B03
Research output

2020B03 – Learning and performing in audit firms: The role of the organizational context

What?

What?

The first study investigates how an organizational context in which auditor-artificial intelligence interactions are prevalent affects auditors’ cognitive processing and subsequently the degree of professional skepticism exercised by auditors. If auditors over rely on artificial intelligence, both learning and performance are likely to be inhibited. Experienced audit practitioners expect that during the next five years, artificial intelligence is the area in which there will be the most innovation in the audit profession (Austin, Carpenter, Christ, and Nielson 2019). However, although artificial intelligence may lead to performance gains in the audit, artificial intelligence does not easily replicate auditor expertise. Therefore, audit firms emphasize that artificial intelligence will not replace auditors but enhance them (KPMG 2016, PwC 2017). Thus, with the adoption of artificial intelligence, auditors increasingly have to apply professional skepticism to artificial intelligence (Olsen and Gold 2018).The second study investigates the organizational context of seeking and giving performance feedback in the audit environment. Performance feedback is a cornerstone of the modern audit environment as it aids learning (Hattie and Timperley 2007), improves performance (London 2003), and improves judgment and decision-making (Bonner 2008). Yet, regulators, audit partners, and audit staff have significant concerns about the sufficiency and effectiveness of performance feedback (e.g., PCAOB 2010, Lambert and Agoglia 2011, Westermann et al. 2015). For instance, Westermann et al. (2015) show that audit partners worry that supervisors struggle to provide negative or critical evaluations to their staff, as they fear this leads to employee dissatisfaction and turnover.The third study investigates how the context of audit teams affects learning and performance. More specifically, we investigate the role of audit team familiarity (i.e., the shared experience with team members) in on-the-job learning and team performance. Auditors tend to work on multiple engagements with multiple supervisors and peers, implying that auditors frequently change supervisors and peers. Yet, we know little about whether or how the resulting lack of team familiarity affects auditor learning and performance.

Why?

In the past decade, the Dutch auditing profession has been under widespread public and political pressure to improve audit quality and change its culture from one focused on compliance and efficiency to one focused on adaptation and learning (e.g., WTA 2014; MCA 2016; 2020; CTA 2020). Inspection reports of the Dutch Financial Markets Authority (AFM 2010, 2014) had long shown too many deficiencies in critical aspects of the audit, leading to external pressures to reform.Investigating how the organizational context can contribute to a culture in which auditors focus on adaptation and on-the-job learning is important for at least three reasons. First, against the backdrop of changing expectations from society and stakeholders, rapid technological developments, and the increasing complexity of information systems, it is of utmost importance for auditors to learn new professional skills, competencies, and qualities on-the-job to keep pace with these developments (ICAS & FRC 2016). Second, auditors indicate that they want to develop themselves by learning on-the-job, exercising professional judgments, and solving problems (Wallage, Bouwens, and Bik 2018). However, technocratic aspects of the job such as standardization, regulatory burdens, and a focus on compliance hamper auditors from doing so, leading to lower audit quality and increased turnover intentions (Pierce and Sweeney 2004, Martinow, Moroney, and Harding 2020). Third, Westermann, Bedard, and Earley (2015) find that on-the-job learning functions as the primary means to transmit technical knowledge and social norms to new auditors.

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Audit firms increasingly use automated tools and techniques to conduct structured audit tasks. As a result, auditors’ role shifts from preparing workpapers to reviewing and validating the work conducted by the automation. Amid these developments, regulators have expressed concerns that auditors may rely too heavily on automated outputs, which could potentially lead to behavioral biases and reductions in audit effectiveness. Using an experiment with 119 professional auditors, I predict and find that auditors are subject to  automation bias – the tendency to use automated cues as a heuristic replacement for vigilant information seeking and processing. Specifically, I find that auditors reduce effort and are less effective in reviewing work conducted by automated tools compared to reviewing identical work conducted by a human colleague. To alleviate the negative effects of automation on reviewer effectiveness, I employ a theory-driven counterarguing mindset intervention. Consistent with my predictions, I find that this intervention alleviates the reductions in effort and effectiveness induced by automation. Additionally, this study also investigates whether the reduction in effectiveness caused by automation spills over to unrelated subsequent tasks, in which no automation is used. The results do not display any spillover effects.
The quality of statutory audits in the Netherlands has been the subject of heated debate for several years. The government, audit firms, the Dutch Professional Body of Accountants (NBA), and the Dutch Authority for the Financial Markets (AFM) have all announced far-reaching improvement measures to enhance audit quality. This raises the question of the current state of audit quality in the Netherlands and how it has developed over recent years. Audit quality is a complex and multifaceted concept that consists of input, process, and outcome levels. This study focuses on the outcome level of statutory audits: the financial reporting quality of the audited financial statements. It does so using a widely applied measure from academic literature: discretionary accruals. Results show that the outcome quality of statutory audits of listed companies significantly improved during the period 2000–2018.
Drawing on literature in auditing and workplace learning, this paper develops the Auditor Learning Framework. The Auditor Learning Framework distinguishes auditor learning processes along two dimensions: the location of learning (on-the-engagement or off-the-engagement) and the role of the others in the learning process (active or passive). We review the auditing literature and classify papers that directly or indirectly enhance our knowledge of auditor workplace learning into our framework to identify gaps in our understanding of the auditor learning processes. Our study provides a comprehensive view of auditor learning processes and provides suggestions for future research.
Drawing on literature in auditing and workplace learning, this paper develops the Auditor Learning Framework. The Auditor Learning Framework distinguishes auditor learning processes along two dimensions: the location of learning (on-the-engagement or off-the-engagement) and the role of the others in the learning process (active or passive). We review the auditing literature and classify papers that directly or indirectly enhance our knowledge of auditor workplace learning into our framework to identify gaps in our understanding of the auditor learning processes. Our study provides a comprehensive view of auditor learning processes and provides suggestions for future research. This is the final draft. The article is published in Accounting, Organizations and Society. The article is written by Bart Dierynck, Kathryn Kadous, and Christian Peters. Please find the article here: https://www.sciencedirect.com/science/article/abs/pii/S0361368223001058  
It is essential that auditors continuously learn. The need for continuous learning is fostered by the changing expectations from society and stakeholders, rapid technological developments, and the increasing complexity of information systems. Auditing regulators and oversight bodies are concerned that certain aspects of the auditing profession may form barriers to effective learning. To gain a better insight in how auditors can effectively learn, it is important to identify and distinguish the different learning processes in the auditing profession. Based on our recent literature review we differentiate between seven learning processes. In this FAR Practice Note, we highlight the most important insights from the academic literature for each learning process. Based on this, stakeholders can develop tools to facilitate auditor learning processes in practice.
Het is essentieel dat accountants voortdurend leren. De noodzaak om continu te blijven leren neemt verder toe door de veranderende verwachtingen van maatschappij en stakeholders, snelle technologische evoluties en de toenemende complexiteit van informatiesystemen. Externe toezichthouders spreken echter de zorg uit dat bepaalde factoren het leren van accountants in de weg staan. Om een beter inzicht te krijgen in hoe accountants leren is het belangrijk om de verschillende leerprocessen in het accountantsberoep te onderscheiden. Op basis van recent literatuuronderzoek onderscheiden wij zeven leerprocessen. In deze Practice Note lichten wij per leerproces de belangrijkste bevindingen uit de wetenschappelijke literatuur toe. Op basis hiervan kunnen praktische handvatten worden ontwikkeld om deze leerprocessen te verbeteren.
Large audit firms typically ‘‘offshore’’ simple and repetitive audit tasks such as reconciliations to shared service centers. Offshoring however comes at the expense of coordination costs, delays in the process, and challenges regarding the liability risk to the auditor. This paper presents an open-source algorithm to extract data from (draft) annual reports (PDF files) using Python to automate, rather than outsource, the data extraction for reconciliations. The algorithm resulted in a significant time saving for the audit of a large Dutch asset management firm. Researchers apply the algorithm to minimize hand-collection of financial statement data. Data Availability: The algorithm this paper presents is open-source and publicly available. It was a joint project with Jeroen Bellinga, Seyit Hocuk, Wim Janssen and Alaa Khzam. Wim Janssen and Alaa Khzam developed the programming code.   Article AAA
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Project info

Project Lead

Christian Peters

Research team

Christian Peters
Bart Dierynck
Kathryn Kadous

Involved University

Project Number – 2020B03

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