Explaining the (Non-) Adoption of Advanced Data Analytics in Auditing: A Process Theory
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Explaining the (Non-) Adoption of Advanced Data Analytics in Auditing : A Process Theory. / Krieger, Felix; Drews, Paul; Velte, Patrick.
In: International Journal of Accounting Information Systems, Vol. 41, 100511, 01.06.2021.Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Explaining the (Non-) Adoption of Advanced Data Analytics in Auditing
T2 - A Process Theory
AU - Krieger, Felix
AU - Drews, Paul
AU - Velte, Patrick
N1 - Publisher Copyright: © 2021 The Authors
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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.
AB - 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.
KW - Management studies
KW - audit digitization
KW - audit data analytics
KW - audit innovation
KW - Informatics
KW - big data
KW - machine learning
KW - advanced data analytics in auditing
UR - http://www.scopus.com/inward/record.url?scp=85105100536&partnerID=8YFLogxK
U2 - 10.1016/j.accinf.2021.100511
DO - 10.1016/j.accinf.2021.100511
M3 - Journal articles
VL - 41
JO - International Journal of Accounting Information Systems
JF - International Journal of Accounting Information Systems
SN - 1467-0895
M1 - 100511
ER -