The impacts of the use of data analytics and the performance of consulting activities on perceived internal audit quality

Purpose This research paper aims to investigate the effects of internal audit’s (IA) use of data analytics and the performance of consulting activities on perceived IA quality. Design/methodology/approach The authors conduct a 2 × 2 between-subjects experiment among upper and middle managers where the use of data analytics and the performance of consulting activities by internal auditors are manipulated. Findings Results highlight the importance of internal auditor use of data analytics and performance of consulting activities to improve perceived IA quality. First, managers perceive internal auditors as more competent when the auditors use data analytics. Second, managers perceive internal auditors’ recommendations as more relevant when the auditors perform consulting activities. Finally, managers perceive an improvement in the quality of relationships with internal auditors when auditors perform consulting activities, which is strengthened when internal auditors combine the use of data analytics and the performance of consulting activities. Research limitations/implications From a theoretical perspective, this research builds on the IA quality framework by considering digitalization as a contextual factor. This research focused on the perceptions of one major stakeholder of the IA function: senior management. Future research should investigate the perceptions of other stakeholders and other contextual factors. Practical implications This research suggests that internal auditors should prioritize the development of the consulting role in their function and develop their digital expertise, especially expertise in data analytics, to improve perceived IA quality. Originality/value This research tests the impacts of the use of data analytics and the performance of consulting activities on perceived IA quality holistically, by testing Trotman and Duncan’s (2018) framework using an experiment.