Big data analytics and auditor judgment: an experimental study

Purpose - Auditors’ skills and knowledge of data analytics and big data can influence their judgment at the audit planning stage. At this stage, the auditor will determine the level of audit risk and estimate how long the audit will take. This study aims to test whether big data and data analytics affect auditors’ judgment by adopting the cognitive fit theory. Design/methodology/approach - This was an experimental study involving 109 accounting students as participants. The 2 × 2 factorial design between subjects in a laboratory setting was applied to test the hypothesis. Findings - First, this study supports the proposed hypothesis that participants who are provided with visual analytics information will rate audit risk lower than text analytics. Second, participants who receive information on unstructured data types will assess audit risk (audit hours) higher (longer) than those receiving structured data types. In addition, those who receive information from visual analytics results have a higher level of reliance than those receiving text analytics. Practical implications - This research has implications for external and internal auditors to improve their skills and knowledge of data analytics and big data to make better judgments, especially when the auditor is planning the audit. Originality/value - Previous studies have examined the effect of data analytics (predictive vs anomaly) and big data (financial vs non-financial) on auditor judgment, whereas this study examined data analytics (visual vs text analytics) and big data (structured and unstructured), which were not tested in previous studies.