Identifying research trends of machine learning in business: a topic modeling approach

Abstract

Purpose

This paper aims to discuss the suitability of topic modeling as a review method, identifies and compares the machine learning (ML) research trends in five primary business organization verticals.

Design/methodology/approach

This study presents a review framework of published research about adopting ML techniques in a business organization context. It identifies research trends and issues using topic modeling through the Latent Dirichlet allocation technique in conjunction with other text analysis techniques in five primary business verticals – human resources (HR), marketing, operations, strategy and finance.

Findings

The results identify that the ML adoption is maximum in the marketing domain and minimum in the HR domain. The operations domain witnesses the application of ML to the maximum number of distinct research areas. The results also help to identify the potential areas of ML applications in future.

Originality/value

This paper contributes to the existing literature by finding trends of ML applications in the business domain through the review of published research. Although there is a growth of research publications in ML in the business domain, literature review papers are scarce. Therefore, the endeavor of this study is to do a thorough review of the current status of ML applications in business by analyzing research articles published in the past ten years in various journals.

Citation

Pramanik, P. and Jana, R.K. (2023), "Identifying research trends of machine learning in business: a topic modeling approach", Measuring Business Excellence, Vol. 27 No. 4, pp. 602-633. https://doi.org/10.1108/MBE-07-2021-0094