Management accounting and the concepts of exploratory data analysis and unsupervised machine learning: a literature study and future directions

Abstract

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.

A literature review on the impact of digitalisation on management control

“…The task of describing the impact of digitalisation on different fields of MC (e.g. design of the MC function, MC roles, organisation of MC) received increasing attention in MC research (Nielsen, 2022). Over the years, digitalisation in MC has been studied from many different angles using different theories such as functionalist, behavioural relations, institutional theories, actor network theories, interpretive and critical perspectives (Nielsen, 2022).…”

“…design of the MC function, MC roles, organisation of MC) received increasing attention in MC research (Nielsen, 2022). Over the years, digitalisation in MC has been studied from many different angles using different theories such as functionalist, behavioural relations, institutional theories, actor network theories, interpretive and critical perspectives (Nielsen, 2022). This has been done using a variety of different methodologies such as field studies, archival studies, experimental studies and theoretical discussions (Nielsen, 2022).…”

“…Over the years, digitalisation in MC has been studied from many different angles using different theories such as functionalist, behavioural relations, institutional theories, actor network theories, interpretive and critical perspectives (Nielsen, 2022). This has been done using a variety of different methodologies such as field studies, archival studies, experimental studies and theoretical discussions (Nielsen, 2022). However, only few research results have ever been used in the practical world despite the fact that MC can be characterised as a practical field that constantly faces new challenges from the business world (Merchant, 2012).…”

Using this DEMATEL Corporate social responsibility CSR

“…Changed "although this effect was statistically significant" to "although this effect was not statistically significant" to correct the meaning of the sentence. Changed "Nonsignificant" to "Nonsunlight" assuming that it was a typo [33,34]. Changed "chondrile" to "radiative" since "chondrile" is not a known term in climate science and "radiative" is a more appropriate term to describe the warming effect of solar radiation [35].…”

De digitale transformatie van finance & control: een analyse van de gevolgen voor controllers

“…Dataanalyse kan voor verschillende doeleinden worden aangewend. Zo kunnen exploratieve analyses van data bases bestaande uit zowel gestructureerde als ongestruc tureerde data helpen om preciezere cost drivers en pools te identificeren in activitybased costing (Nielsen 2022) of nieuwe KPI's te formuleren (Kiron and Schrage 2019). Bovendien kunnen externe data, zoals weergerelateerde of macroeconomische gegevens, worden gebruikt om het budget dynamischer te maken en tot een effectievere plan ning en accuratere begroting te komen (Warren et al 2015).…”