SUMMARY Both researchers (e.g., Nelson 2009) and regulators (e.g., the PCAOB) have emphasized the importance of exercising the appropriate level of professional skepticism when conducting an audit. However, professional skepticism remains a hard concept to define and measure. In addition, it is often difficult to determine if a lack of skepticism is the primary cause of audit deficiencies and if so, what factors led to the lack of skepticism. The purpose of this paper is threefold: (1) extend the work of Nelson (2009) by synthesizing research related to auditors' professional skepticism to identify antecedents to both skeptical judgment and skeptical action, (2) identify areas where research is lacking on a particular dimension and suggest avenues for future research, and (3) discuss the implications of research findings for regulators and auditing professionals. We adopt two foundational aspects of the framework introduced in the seminal paper by Nelson (2009), which proposes that lack of skepticism can either be the result of a failure in problem recognition (lack of skeptical judgment) or a failure to act on a problem recognized (lack of skeptical action). We organize research studies into four categories of antecedents: studies relating to auditor characteristics, evidence characteristics, client characteristics, and environmental characteristics. We find that while research studies provide insights into both the antecedents to skeptical judgments and actions, the majority of research efforts to date have focused on the antecedents to skeptical judgments and on auditor characteristics in particular. Research findings have implications for practice, but in order to understand how skeptical judgment translates into skeptical action, additional research on skeptical action will need to be conducted.
SYNOPSIS This paper addresses information processing weaknesses and limitations that can impede the effective use and analysis of Big Data in an audit environment. Drawing on the literature from psychology and auditing, we present the behavioral implications Big Data has on audit judgment by addressing the issues of information overload, information relevance, pattern recognition, and ambiguity. We also discuss the challenges that auditors encounter when incorporating Big Data in audit analyses and the various analytical tools that are currently used by companies in the analysis of Big Data. The manuscript concludes by raising questions that future research might address related to utilizing Big Data in auditing.
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