Big data research in the business, management and accounting field: Revealing the thematic structure based on co-word analysis

Abstract

Objective. This study aims to describe the thematic structure of big data research in business, management, and accounting. Design/Methodology/Approach. A co-word analysis was performed on 12,345 documents retrieved from Scopus from 2014-2023 in the category “business, management, and accounting”. Modularity algorithms were used to identify themes and subthemes, and the clustering of terms was qualitatively analyzed. Results/Discussion. Five main themes were identified: (1) Business and economic data analysis, (2) big data analytics in marketing, consumer behavior, and employee dynamics, (3) scalable machine learning and big data optimization, smart cities and urban development, (4) IoT-Driven innovations in industry 4.0 for optimized supply chain management, and (5) Social media and sentiment analysis in modern tourism and hospitality. The grouping of terms demonstrated the broad application of big data in healthcare, education, tourism, industry, organizational development, finance, social media, marketing, and hospitality. Conclusion. Big data is a field of application. This is evident in each cluster, where there are sub-themes that are nothing more than applying big data principles in sectors such as manufacturing, tourism services, education, health, and urbanization. Generally, the findings here are similar to other studies that have analyzed broader or more selective literature.