Do perfectionism types matter? Auditors' ability to detect fraud and the moderating role of time budget pressure: evidence from Egypt

Abstract

Purpose This study aims to examine how auditors' perfectionism types and time budget pressure (TBP) influence fraud detection in Egypt. Design/methodology/approach We utilize a mixed-methods approach, combining questionnaires with an experimental case study in a within-subjects quasi-experimental design. Based on Almost Perfect Scale-Revised (APS-R), perfectionism traits were categorized using cluster analysis into adaptive, maladaptive, and non-perfectionism. Auditors from Egyptian firms performed fraud-related tasks with TBP manipulated. Findings Auditors' perfectionism types significantly influence fraud detection capabilities. Adaptive perfectionists demonstrated higher relevance in identifying fraud factors and excelled in accurately assessing fraud risks and audit procedures planning. Conversely, maladaptive perfectionists identified more but less relevant factors. TBP notably impacted maladaptive and non-perfectionist auditors' planning quality, unlike adaptive perfectionists, who showed resilience. Practical implications Findings provide insights to audit firms to bolster audit quality through team formations and task assignments, harnessing the strengths of adaptive and maladaptive perfectionists. Regulatory entities are positioned to integrate safeguards that recognize auditor capabilities and vulnerabilities, particularly under TBP. Considering psychological assessments in auditor selection and development assures alignment of traits with audit tasks, enhancing audit quality. Originality/value This study breaks new ground in the effects of auditor perfectionism on fraud detection, considering situational factors like TBP in emerging markets. Through a mixed-methods approach and cluster analysis, it reveals how different perfectionism traits influence audit effectiveness, offering insights not previously considered in auditing literature and suggesting practical applications for enhancing fraud detection in similar contexts.