Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Adrian Domenteanu, Bianca Cibu and Camelia Delcea ()
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Adrian Domenteanu: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Bianca Cibu: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Camelia Delcea: Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania

Sustainability, 2024, vol. 16, issue 7, 1-34

Abstract: Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

Keywords: Industry 5.0; machine learning; big data analytics; bibliometric analysis; Bibliometrix (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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