Soft computing in business: exploring current research and outlining future research directions

Abstract

Purpose

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

Design/methodology/approach

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

Findings

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

Practical implications

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

Originality/value

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

Citation

Singh, S., Singh, S., Koohang, A., Sharma, A. and Dhir, S. (2023), "Soft computing in business: exploring current research and outlining future research directions", Industrial Management & Data Systems, Vol. 123 No. 8, pp. 2079-2127. https://doi.org/10.1108/IMDS-02-2023-0126