Novice and expert judgment in the presence of going concern uncertainty The influence of heuristic biases and other relevant factors

Asokan Anandarajan, Gary Kleinman, Dan Palmon

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Purpose - Prior literature provides clear evidence that the judgments of experts differ from those of non-experts. For example, Smith and Kida concluded that the extent of common biases that they investigated often are reduced when experts perform job related tasks as compared to students. The aim in this theoretical study is to examine whether "heuristic biases significantly moderate the understanding of experts versus novices in the going concern judgment?" Design/methodology/approach - The authors address the posited question by marshalling extant literature on expert and novice judgments and link these to concepts drawn from the cognitive sciences through the Brunswick Lens Model. Findings - The authors identify a number of heuristics that may bias the going concern decision, based on the work of Kahneman and Tversky among others. They conclude that experience mitigates the unintentional consequences played by heuristic biases. Practical implications - The conclusions have implications for the education and training of auditors, and for the expectation gap. They suggest that both awareness of factors that affect understanding of auditing reports and greater attention to training are important in reducing the expectation gap. Originality/value - This paper develops additional theoretical understanding of factors that may impact the expectation gap. While there has been limited prior discussion of the impact of cognitive factors on differences between experts and novices, the paper significantly expands the range of factors discussed. As such, it should provide a stimulus to new research in this important area.

Original languageEnglish (US)
Pages (from-to)345-366
Number of pages22
JournalManagerial Auditing Journal
Issue number4
StatePublished - 2008

All Science Journal Classification (ASJC) codes

  • General Economics, Econometrics and Finance
  • General Business, Management and Accounting
  • Organizational Behavior and Human Resource Management


  • Cognition
  • Experiential learning
  • Training
  • Uncertainty management


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