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An analysis of international coauthorship networks in the supply chain analytics research area

Abstract

This work characterized the research community of supply chain analytics (SCA) with respect to coauthorship, a special kind of collaboration. A characterization of coauthorship in terms of researchers’ countries, institutions and individuals was elaborated, so three different one-mode networks were studied. Besides, the SCA research community is characterized in terms of Supply Chain Management (SCM) research streams. Coauthorship among researchers working on different streams is also analyzed. Metrics that depict the importance of the network nodes were studied such as degree, betweenness and closeness. This study found out an intense collaboration between USA and countries such as China, India, United Kingdom and Canada. Researchers from Canada and Ireland are better situated (central) in the network, although they have not published a considerable amount of papers. The presence of cliques and the small-world effect were also observed in these networks. In terms of research streams, more research on SCA located at the Strategic Management, Technology-focused and Logistics streams was found. The most common links between research streams are on the one side, Technology-focused with both Strategic Management and Logistics and on the other side Strategic Management with both Logistics and Organizational behavior. SCA researchers are rarely working with a focus on Marketing. This study contributes to the SCA literature by identifying the most central actors in this area and by characterizing the area in terms of SCM research streams. This study may contribute to the development of more focused research incentive programs and collaborations.

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Correspondence to Marcelo Werneck Barbosa.

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Barbosa, M.W., Ladeira, M.B. & de la Calle Vicente, A. An analysis of international coauthorship networks in the supply chain analytics research area. Scientometrics 111, 1703–1731 (2017). https://doi.org/10.1007/s11192-017-2370-6

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  • DOI: https://doi.org/10.1007/s11192-017-2370-6

Keywords

  • Social network analysis
  • Coauthorship networks
  • Big data analytics
  • Business analytics
  • Supply chain analytics