Uncovering the Clusters of Economic Growth and Globalisation Research Using Topic Modelling: A Machine Learning Perspective

Economic growth and globalisation are intricately linked, constituting a dynamic relationship that shapes the trajectory of nations and their development. This research employs a combination of bibliometric analysis and a machine learning technique (structural topic modelling) on a dataset of 1255 research articles from Elsevier’s Scopus database spanning 2002-2022 in the field of economic growth and globalisation. The outcomes of structural topic modelling unveil five distinct thematic clusters: i) Economic growth and human development, ii) Economic growth and technology, iii) Economic growth and environment, iv) Economic growth and foreign trade, and v) Economic growth and policy decisions. The largest cluster is Topic 4 which explores the relationship between economic growth and foreign trade, representing 33.7% of the corpus. The prominent journal, "Environmental Science and Pollution Research," contributes 90 articles (7.2% of the total articles). The study’s forthcoming research goals emphasise the necessity for the efficient utilisation of digital technologies, automation, machine learning, and interconnectedness to promote growth in the manufacturing and services sectors. This will help in fetching the best results in the integration of economic growth and globalisation. These results enhance our understanding of key areas in this field. These findings hold significant importance for a wide audience, encompassing regulators, policymakers, scholars, financial institutions, and industry professionals.