Explaining the (non-) adoption of advanced data analytics in auditing: A process theory

Felix Krieger, Paul Drews and Patrick Velte

International Journal of Accounting Information Systems, 2021, vol. 41, issue C

Abstract: Audit firms are increasingly engaging with advanced data analytics to improve the efficiency and effectiveness of external audits through the automation of audit work and obtaining a better understanding of the client’s business risk and thus their own audit risk. This paper examines the process by which audit firms adopt advanced data analytics, which has been left unaddressed by previous research. We derive a process theory from expert interviews which describes the activities within the process and the organizational units involved. It further describes how the adoption process is affected by technological, organizational and environmental contextual factors. Our work contributes to the extent body of research on technology adoption in auditing by using a previously unused theoretical perspective, and contextualizing known factors of technology adoption. The findings presented in this paper emphasize the importance of technological capabilities of audit firms for the adoption of advanced data analytics; technological capabilities within audit teams can be leveraged to support both the ideation of possible use cases for advanced data analytics, as well as the diffusion of solutions into practice.

Keywords: Audit digitization; Audit data analytics; Big data; Machine learning; Advanced data analytics in auditing; Audit innovation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:41:y:2021:i:c:s1467089521000130

DOI: 10.1016/j.accinf.2021.100511

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