Explaining the (Non-) Adoption of Advanced Data Analytics in Auditing: A Process Theory

Felix Krieger; Paul Drews; Patrick Velte

doi:10.1016/j.accinf.2021.100511

Explaining the (Non-) Adoption of Advanced Data Analytics in Auditing: A Process Theory

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

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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, Jahrgang 41, 100511, 01.06.2021.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Bibtex

@article{1108cd858c87478d90a75bf3f09ad956,

title = "Explaining the (Non-) Adoption of Advanced Data Analytics in Auditing: A Process Theory",

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{\textquoteright}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 = "Management studies, audit digitization, audit data analytics, audit innovation, Informatics, big data, machine learning, advanced data analytics in auditing",

author = "Felix Krieger and Paul Drews and Patrick Velte",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors",

year = "2021",

month = jun,

day = "1",

doi = "10.1016/j.accinf.2021.100511",

language = "English",

volume = "41",

journal = "International Journal of Accounting Information Systems",

issn = "1467-0895",

publisher = "Elsevier B.V.",

}

RIS

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

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 -