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.

Citation

Nielsen, S. (2022), "Management accounting and the concepts of exploratory data analysis and unsupervised machine learning: a literature study and future directions", Journal of Accounting & Organizational Change, Vol. 18 No. 5, pp. 811-853. https://doi.org/10.1108/JAOC-08-2020-0107