Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
Management accounting and the concepts of exploratory data analysis and unsupervised machine learning : a literature study and future directions. / Nielsen, Steen.
In: Journal of Accounting and Organizational Change, Vol. 8, No. 5, 10.2022, p. 811-853.Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
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TY - JOUR
T1 - Management accounting and the concepts of exploratory data analysis and unsupervised machine learning
T2 - a literature study and future directions
AU - Nielsen, Steen
N1 - Publisher Copyright: © 2022, Emerald Publishing Limited.
PY - 2022/10
Y1 - 2022/10
N2 - Purpose: This paper contributes to the literature by discussing the impact of machine learning (ML) on management accounting (MA) and the management accountant based on three sources: academic articles, papers and reports from accounting bodies and consulting companies. The purpose of this paper is to identify, discuss and provide suggestions for how ML could be included in research and education in the future for the management accountant. Design/methodology/approach: This paper identifies three types of studies on the influence of ML on MA issued between 2015 and 2021 in mainstream accounting journals, by professional accounting bodies and by large consulting companies. Findings: First, only very few academic articles actually show examples of using ML or using different algorithms related to MA issues. This is in contrast to other research fields such as finance and logistics. Second, the literature review also indicates that if the management accountants want to keep up with the demand of their qualifications, they must take action now and begin to discuss how big data and other concepts from artificial intelligence and ML can benefit MA and the management accountant in specific ways. Originality/value: Even though the paper may be classified as inspirational in nature, the paper documents and discusses the revised environment that surrounds the accountant today. The paper concludes by highlighting specifically the necessity of including exploratory data analysis and unsupervised ML in the field of MA to close the existing gaps in both education and research and thus making the MA profession future-proof.
AB - Purpose: This paper contributes to the literature by discussing the impact of machine learning (ML) on management accounting (MA) and the management accountant based on three sources: academic articles, papers and reports from accounting bodies and consulting companies. The purpose of this paper is to identify, discuss and provide suggestions for how ML could be included in research and education in the future for the management accountant. Design/methodology/approach: This paper identifies three types of studies on the influence of ML on MA issued between 2015 and 2021 in mainstream accounting journals, by professional accounting bodies and by large consulting companies. Findings: First, only very few academic articles actually show examples of using ML or using different algorithms related to MA issues. This is in contrast to other research fields such as finance and logistics. Second, the literature review also indicates that if the management accountants want to keep up with the demand of their qualifications, they must take action now and begin to discuss how big data and other concepts from artificial intelligence and ML can benefit MA and the management accountant in specific ways. Originality/value: Even though the paper may be classified as inspirational in nature, the paper documents and discusses the revised environment that surrounds the accountant today. The paper concludes by highlighting specifically the necessity of including exploratory data analysis and unsupervised ML in the field of MA to close the existing gaps in both education and research and thus making the MA profession future-proof.
KW - Accountant
KW - Analytics
KW - Business translator
KW - Exploratory data analysis
KW - Machine learning
KW - Management accounting
KW - Statistics
KW - Unsupervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85126023567&partnerID=8YFLogxK
U2 - 10.1108/JAOC-08-2020-0107
DO - 10.1108/JAOC-08-2020-0107
M3 - Journal article
AN - SCOPUS:85126023567
VL - 8
SP - 811
EP - 853
JO - Journal of Accounting and Organizational Change
JF - Journal of Accounting and Organizational Change
SN - 1832-5912
IS - 5
ER -