Skip to main content
Dryad logo

International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases

Citation

Mizukami, Yuji; Nakano, Junji (2022), International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases, Dryad, Dataset, https://doi.org/10.5061/dryad.x3ffbg7ns

Abstract

In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers' areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to “improve the efficiency of all social systems,” “create new industries,” and “increase intellectual productivity.”  For the analysis, we used 19-year bibliographic data (2000–2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea.

Methods

We used the bibliographic data of Web of Science (WoS) core collection, one of the biggest bibliographic databases from 2000 to 2018. The analysis of the visualization organizational diversity used data from 2018; studies on AI, big data, and IoT have been continuously increasing, reaching 3,133, 5,155, and 4,662 related papers in 2018, respectively. The 23 Essential Science Indicators Subject Areas in the Web of Science Core Collection were used for the article specialties. This data was generated from the "Web of Science Categories” using a conversion table (Thomson Reuters Community, 2012).

Usage Notes

The dataset consists of 60 CSV files ( Top 20 countries in number of papers on AI, Big Data, and IoT technologies) which can be used with a variety of software.

The data format consists of three parts: source node, target note, and number of edge connections. For example, it can be imported into Cytoscape (https://cytoscape.org/) to display a network diagram.

Funding

Japan Society for the Promotion of Science, Award: Grant-in-Aid 21K02668

Japan Society for the Promotion of Science, Award: Grant-in-Aid 20K11715

Institute of Statistical Mathematics, Award: 2019-ISMCRP-1026

Institute of Statistical Mathematics, Award: 2021-ISMCRP-2036