Kris Hardies

Professor

Kris Hardies obtained his Ph.D. from the Vrije Universiteit Brussel in 2011, worked at the University of Florida in 2012 and is currently an associate professor at the Universiteit Antwerpen where he teaches accounting and auditing at the BSc and MSc level. His research interests include professional skepticism, personality and individual differences, capital markets and firm behavior, and gender inequality. His recent research focuses on the effects of personality and individual differences among auditors on audit quality. His work has been published in journals such as Auditing: A Journal of Practice & Theory, Accounting Horizon, European Accounting Review, International Journal of Auditing, and Economics Letters.

When Do Auditors Rely on (Bad) AI Advice?
Generative AI systems (“GenAI”) can provide auditors with natural-language recommendations that resemble professional advice. Such tools have the potential to support audit judgments. However, a lack of transparency in their processes and reasoning also raises practical questions: when, and to what extent, should auditors rely on AI-generated advice? Because GenAI recommendations are not directly explainable, auditors must rely on indirect cues to assess credibility. In practice, a key indirect cue is AI performance. Firms and software providers commonly disclose stated accuracy levels, either framed in terms of accuracy (“the AI system is 95% accurate”) or in terms of error (“the AI system has a 5% error rate”). Framing AI performance in terms of either “accuracy” or “error” may affect auditors’ reliance in unanticipated ways. The key issue for audit practice is whether these performance cues support appropriate calibration. That is, auditors should use sound advice but remain skeptical of weak output. In this practitioner report, we summarize evidence from an experimental study with practicing auditors examining the effects of stated accuracy and performance framing on reliance on high-quality and low-quality GenAI advice. Our findings show that performance communication influences reliance decisions, with implications for the design and implementation of GenAI in judgment-intensive audit tasks.
Read PDF
More Info
A Systematic Literature Review on Companies’ Auditor Selection Processes
We provide a systematic review of the academic literature on companies’ auditor selection process, that is, the process through which companies select and hire their auditor. Some process elements over which companies exercise discretion include the decision-makers and the extent of their influence (e.g., the audit committee, the CFO); timing (initiation, duration); the procedures and decision-making approach (e.g., formal tendering, assessment criteria, documentation, evaluation); and the eventual appointment of the auditor (e.g., shareholder voting on auditor ratification). By synthesizing the research, we identify key activities, decision points, and participants’ expectations in the selection process. We also consider academic research findings in light of practitioner guidance on best practices for auditor selection. Finally, even though most of the relevant studies use archival data to infer aspects of the selection process from associations between publicly observable auditor/company characteristics and auditor appointment outcomes, we highlight the limitations of this evidence for understanding companies’ processes, and we offer suggestions for future research. Our review provides valuable insights for audit academics, audit regulators, and practitioners interested in companies’ actual practices for auditor selection and appointment.  
Read PDF
More Info
Understanding Auditors’ Reliance on Emerging Audit Technologies
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.
Read PDF
More Info
Auditors’ Professional Skepticism: Traits, Behavioral Intentions, and Actions
This study examines what drives auditors’ professional skepticism, a critical factor for audit quality. Using data from 663 auditors across six Dutch firms, the research explores how individual differences (such as gender, experience, and knowledge) and personality traits (Big Five and Dark Triad) influence skepticism.
It also applies the Theory of Planned Behavior to link skepticism traits with attitudes, social norms, and perceived control, showing how these factors shape intentions and actual skeptical actions during audits.
Key findings reveal that social pressure (subjective norms) is the strongest predictor of skeptical behavior, and that traits like conscientiousness, openness, and narcissism are positively associated with skepticism, while psychopathy and high agreeableness reduce it.
The study offers practical insights for audit firms and regulators on fostering a culture that promotes skepticism and improving interventions to enhance audit quality.
Read PDF
More Info
Professional Skepticism Traits and Fraud Brainstorming Quality
Auditing standards emphasize that fraud detection is an important objective of an audit and require the exercise of professional skepticism (PS) and a discussion among the engagement team to prevent and detect fraud. The purpose of this study is to examine whether professional skepticism is a driver of fraud brainstorming quality. We investigate the relationship between fraud brainstorming quality, using the measure of Brazel et al. (2010), and auditor’s professional skepticism traits. Using proprietary data from Dutch audit firms on 125 engagements, we find that neutral trait skepticism and professional moral courage of the partner have a significant effect on fraud brainstorming quality. We observe a higher attendance rate and contribution of specialists, more extensive discussion, longer preparation, and longer sessions for engagements led by partners with high neutral trait skepticism and high moral courage. We find no significant results for partners with a high presumptive doubt trait. Additional cross-sectional analyses show that the effect of professional skepticism on FBQ depends on situational, organizational conditions.  
Read PDF
More Info
Using Field-Based Evidence to Understand the Antecedents to Auditors’ Skeptical Actions
KEY TAKE-AWAYS We provide field-based evidence on antecedents to auditors’ skeptical actions, using over 600 auditors across all ranks from six audit firms as participants. We evaluate the relative importance of situational, client, and individual auditor characteristics, along with measures of auditors’ cognitive processing in relation to their self-reports of skeptical actions on one of their own audits. We find that the most important antecedents are each audit firm’s overall professional orientation, auditors’ individual feelings of accountability, their trait skepticism, their motivation to perform well on the engagement, and their intentions to behave skeptically. Auditors’ intentions are most influenced by social norms and less influenced by attitudes towards and self-efficacy about behaving skeptically. Other important antecedents include each audit firm’s quality control systems, certain individual auditors’ personality traits, their client-related industry expertise, and their audit knowledge. Our findings support various aspects of prior conceptual models and suggest ways in which audit firms can promote skeptical actions.  
Read PDF
More Info
Professional skepticism traits and fraud brainstorming quality
Auditing standards emphasize that fraud detection is an important objective of an audit and require the exercise of professional skepticism (PS) and a discussion among the engagement team to prevent and detect fraud. The purpose of this study is to examine whether professional skepticism is a driver of fraud brainstorming quality. We investigate the relationship between fraud brainstorming quality, using the measure of Brazel et al. (2010), and auditor’s professional skepticism traits. Using proprietary data from Dutch audit firms on 125 engagements, we find that neutral trait skepticism and professional moral courage of the partner have a significant effect on fraud brainstorming quality. We observe a higher attendance rate and contribution of specialists, more extensive discussion, longer preparation, and longer sessions for engagements led by partners with high neutral trait skepticism and high moral courage. We find no significant results for partners with a high presumptive doubt trait. Additional cross-sectional analyses show that the effect of professional skepticism on FBQ depends on situational, organizational conditions.
Read PDF
More Info
No related news.
No related events.

Filter projects: 

Project Lead
University Filter
1 - 10 of 52 projects