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.
Similar content being viewed by others
References
Abbasi, A., Hossain, L., & Leydesdorff, L. (2012). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6(3), 403–412. doi:10.1016/j.joi.2012.01.002.
Behara, R. S., Sunil, B., & Smart, P. A. (2014). Leadership in OM research: A social network analysis of European researchers. International Journal of Operations & Production Management, 34(12), 1537–1563.
Bonnes, K. (2014). Predictive analytics for supply chains: A systematic literature review. In 21st twente student conference on IT. Netherlands.
Borgatti, S. P. (2002). Netdraw: Graph visualization software. Harvard: Analytic Technologies.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis. Harvard: Analytic Technologies.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2014). UCINET. In R. Alhajj & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 2261–2267). New York, NY: Springer.
Bose, R. (2009). Advanced analytics: Opportunities and challenges. Industrial Management & Data Systems, 109(2), 155–172. doi:10.1108/02635570910930073.
Cainelli, G., Maggioni, M. A., Uberti, T. E., & De Felice, A. (2015). The strength of strong ties: How coauthorship affect productivity of academic economists? Scientometrics, 102(1), 673–699. doi:10.1007/s11192-014-1421-5.
Carter, C. R., Ellram, L. M., & Tate, W. L. (2007a). The use of social network analysis in logistics research. Journal of Business Logistics, 28(1), 137–168.
Carter, C. R., Leuschner, R., & Rogers, D. S. (2007b). A social network analysis of the Journal of Supply Chain Management: Knowledge generation, knowledge diffusion and thought leadership. Journal of Supply Chain Management, 43(2), 15–28. doi:10.1111/j.1745-493X.2007.00028.x.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
Colicchia, C., & Strozzi, F. (2012). Supply chain risk management: A new methodology for a systematic literature review. Supply Chain Management: An International Journal, 17(4), 403–418.
Côrte-Real, N., Ruivo, P., & Oliveira, T. (2014). The diffusion stages of business intelligence & analytics (BI&A): A systematic mapping study. Procedia Technology, 16, 172–179. doi:10.1016/j.protcy.2014.10.080.
Croom, S., Romano, P., & Giannakis, M. (2000). Supply chain management: An analytical framework for critical literature review. European Journal of Purchasing & Supply Management, 6(1), 67–83. doi:10.1016/S0969-7012(99)00030-1.
Davenport, T. H. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership, 42(4), 45–50. doi:10.1108/SL-05-2014-0034.
Davenport, T. H., & Jeanne, G. H. (2007). Competing on analytics: The new science of winning. Cambridge: Harvard Business Press.
Davenport, T. H., Morison, R., & Harris, J. G. (2010). Analytics at work: Smarter decisions, better results. Boston: Harvard Business Press.
De Stefano, D., Giordano, G., & Vitale, M. P. (2011). Issues in the analysis of coauthorship networks. Quality & Quantity, 45(5), 1091–1107. doi:10.1007/s11135-011-9493-2.
Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203. doi:10.1016/j.joi.2010.10.008.
Fabbe-Costes, N., & Jahre, M. (2008). Supply chain integration and performance: A review of the evidence. The International Journal of Logistics Management, 19(2), 130–154. doi:10.1108/09574090810895933.
Finardi, U., & Buratti, A. (2016). Scientific collaboration framework of BRICS countries: An analysis of international coauthorship. Scientometrics, 109(1), 433–446. doi:10.1007/s11192-016-1927-0.
Fischbach, K., Putzke, J., & Schoder, D. (2011). Coauthorship networks in electronic markets research. Electronic Markets, 21(1), 19–40. doi:10.1007/s12525-011-0051-5.
Giannakis, M. (2012). The intellectual structure of the supply chain management discipline. Journal of Enterprise Information Management, 25(2), 136–169. doi:10.1108/17410391211204392.
Hearnshaw, E. J. S., & Wilson, M. M. J. (2013). A complex network approach to supply chain network theory. International Journal of Operations & Production Management, 33(4), 442–469. doi:10.1108/01443571311307343.
Henneberg, S. C., Swart, J., Naudé, P., Jiang, Z., & Mouzas, S. (2009). Mobilizing ideas in knowledge networks: A social network analysis of the human resource management community 1990–2005. The Learning Organization, 16(6), 443–459. doi:10.1108/09696470910993927.
Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130–141. doi:10.1016/j.dss.2014.05.013.
Hu, C., & Racherla, P. (2010). A social network perspective of tourism research collaborations. Annals of Tourism Research, 37(4), 1012–1034. doi:10.1016/j.annals.2010.03.008.
Kilubi, I. (2016). Investigating current paradigms in supply chain risk management—a bibliometric study. Business Process Management Journal, 22(4), 662–692.
Kohavi, R., & Rothleder, N. J. (2002). Emerging trends in business analytics. Communications of the ACM, 45(8), 45–48.
Kumar, S. (2015). Coauthorship networks: A review of the literature. Aslib Journal of Information Management, 67(1), 55–73. doi:10.1108/AJIM-09-2014-0116.
Kumar, S. (2016). Efficacy of a giant component in coauthorship networks. Aslib Journal of Information Management, 68(1), 19–32. doi:10.1108/AJIM-12-2014-0172.
Kumar, S., & Jan, J. M. (2014). Relationship between authors’ structural position in the collaboration network and research productivity. Program: Electronic Library and Information Systems, 48(4), 355–369. doi:10.1108/PROG-01-2013-0002.
Liu, L. (2010). Supply chain integration through business intelligence. In International conference on Management and Service Science (MASS) (pp. 1–4). IEEE. http://doi.org/10.1109/ICMSS.2010.5576813.
Marion, L. S., Garfield, E., Hargens, L. L., Lievrouw, L. A., White, H. D., & Wilson, C. S. (2003). Social network analysis and citation network analysis: Complementary approaches to the study of scientific communication sponsored by SIG MET. Proceedings of the American Society for Information Science and Technology, 40(1), 486–487. doi:10.1002/meet.1450400186.
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., & Nix, N. W. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1–25. doi:10.1002/j.2158-1592.2001.tb00001.x.
Milojević, S. (2010). Modes of collaboration in modem science: Beyond power laws and preferential attachment. Journal of the American Society for Information Science and Technology, 61(7), 1410–1423. doi:10.1002/asi.21331.
Munoz, D. A., Queupil, J. P., & Fraser, P. (2016). Assessing collaboration networks in educational research. International Journal of Educational Management, 30(3), 416–436. doi:10.1108/IJEM-11-2014-0154.
Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl), 5200–5205. doi:10.1073/pnas.0307545100.
Nooy, W. De. (2003). Fields and networks: Correspondence analysis and social network analysis in the framework of field theory. Poetics, 31(5–6), 305–327. doi:10.1016/S0304-422X(03)00035-4.
Pilkington, A., & Meredith, J. (2009). The evolution of the intellectual structure of operations management—1980–2006: A citation/co-citation analysis. Journal of Operations Management, 27(3), 185–202. doi:10.1016/j.jom.2008.08.001.
Raisinghani, M. S., & Meade, L. L. (2005). Strategic decisions in supply-chain intelligence using knowledge management: An analytic-network-process framework. Supply Chain Management: An International Journal, 10(2), 114–121. doi:10.1108/13598540510589188.
Randall, W. S., & Mello, J. E. (2012). Grounded theory: An inductive method for supply chain research. International Journal of Physical Distribution & Logistics Management, 42(8/9), 863–880. doi:10.1108/09600031211269794.
Sahay, B. S., & Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Information Management & Computer Security, 16(1), 28–48. doi:10.1108/09685220810862733.
Sangari, M. S., & Razmi, J. (2015). Business intelligence competence, agile capabilities, and agile performance in supply chain: An empirical study. The International Journal of Logistics Management, 26(2), 356–380. doi:10.1108/IJLM-01-2013-0012.
Schoenherr, T., & Speier-Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132. doi:10.1111/jbl.12082.
Shi, M., & Yu, W. (2013). Supply chain management and financial performance: Literature review and future directions. International Journal of Operations & Production Management, 33(10), 1283–1317. doi:10.1108/IJOPM-03-2012-0112.
Sloane, A., & O’Reilly, S. (2013). Production planning & control: The management of operations the emergence of supply network ecosystems: A social network analysis perspective. Production Planning and Control: The Management of Operations, 24(7), 621–639. doi:10.1080/09537287.2012.659874.
Souza, G. C. (2014). Supply chain analytics. SOURCE Business Horizons, 57(5), 595.
Stefanovic, N., & Stefanovic, D. (2009). Supply chain business intelligence: Technologies, issues and trends. In M. Bramer (Ed.), Artificial intelligence an international perspective (pp. 217–245). Berlin: Springer. doi:10.1007/978-3-642-03226-4_12.
Stock, J. R., & Boyer, S. L. (2009). Developing a consensus definition of supply chain management: A qualitative study. International Journal of Physical Distribution & Logistics Management, 39(8), 690–711. doi:10.1108/09600030910996323.
Trkman, P., McCormack, K., de Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318–327. doi:10.1016/j.dss.2010.03.007.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.
Ye, Q., Li, T., & Law, R. (2011). A coauthorship network analysis of tourism and hospitality research collaboration. Journal of Hospitality & Tourism Research, 37(1), 51–76. doi:10.1177/1096348011425500.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-017-2370-6