Advertisement

Scientometrics

, Volume 119, Issue 2, pp 789–803 | Cite as

A research framework to explore knowledge evolution and scholarly quantification of collaborative research

  • Shahadat UddinEmail author
  • Nazim Choudhury
  • Md Ekramul Hossain
Article

Abstract

Most of the present research problems require the participation of scientists who can bring complementary skills. For this reason, research collaboration among scientists from different disciplines have become very common in interdisciplinary science. Understanding knowledge evolution and scholarly impact of collaborative research is very important to explore its contribution to the overall progress of science over the time. With the advent of modern technologies, scientific collaboration in different research areas has been expanded not only to various sectors and industries but also across regional and international boundaries. This eventually yields scholarly competence and contributes towards knowledge evolution. Both the economic imperative demands and enrichment of individual’s command of resources and techniques to address increasingly interdisciplinary research issues have triggered an upsurge in intra- and inter-country collaborative research trends. There are many bibliometric methods available in the literature for exploring various aspects of such collaborative efforts. To date, however, there is a lack of a conceptualized framework that can be followed to explore knowledge evolution and scholarly quantification for any research domain. Using metadata from scholarly publications and bibliometric methods, this study first proposed a research framework for exploring knowledge evolution and scholarly quantification of collaborative research. To understand topical knowledge evolution, collaboration dynamics and their impact on research outcomes, the proposed framework was then employed to the project management research.

Keywords

Knowledge evolution Scholarly quantification Knowledge map Collaborative research Project management 

References

  1. Abbasi, A., Hossain, L., Uddin, S., & Rasmussen, K. J. (2011). Evolutionary dynamics of scientific collaboration networks: Multi-levels and cross-time analysis. Scientometrics, 89(2), 687–710.CrossRefGoogle Scholar
  2. Adams, J. (2006). UK–USA academic collaboration. In G. Roberts (Ed.), International partnerships of research excellence. Oxford: Wolfson College.Google Scholar
  3. Adams, J. (2012). Collaborations: The rise of research networks. Nature, 490(7420), 335–336.CrossRefGoogle Scholar
  4. Banerjee, S. (2017). Analysis of a planetary scale scientific collaboration dataset reveals novel patterns. In First complex systems digital campus world E-conference 2015. Springer.Google Scholar
  5. Choudhury, N., & Uddin, S. (2016). Time-aware link prediction to explore network effects on temporal knowledge evolution. Scientometrics, 108(2), 745–776.CrossRefGoogle Scholar
  6. Davenport, S., Davies, J., & Grimes, C. (1998). Collaborative research programmes: Building trust from difference. Technovation, 19(1), 31–40.CrossRefGoogle Scholar
  7. Edler, J., Cunningham, P., & Flanagan, K. (2009). Drivers of international collaboration in research. In Background report 2, country report EU countries. DG research. Technopolis: Luxembourg.Google Scholar
  8. Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: Strengths and weaknesses. The FASEB Journal, 22(2), 338–342.CrossRefGoogle Scholar
  9. Fine, M. A., & Kurdek, L. A. (1993). Reflections on determining authorship credit and authorship order on faculty–student collaborations. American Psychologist, 48(11), 1141.CrossRefGoogle Scholar
  10. Guan, J., Zuo, K., Chen, K., & Yam, R. C. (2016). Does country-level R&D efficiency benefit from the collaboration network structure? Research Policy, 45(4), 770–784.CrossRefGoogle Scholar
  11. He, T. (2009). International scientific collaboration of China with the G7 countries. Scientometrics, 80(3), 571–582.CrossRefGoogle Scholar
  12. Heimerl, F., Han, Q., Koch, S., & Ertl, T. (2016). CiteRivers: Visual analytics of citation patterns. IEEE Transactions on Visualization and Computer Graphics, 22(1), 190–199.CrossRefGoogle Scholar
  13. Hunter, L., & Leahey, E. (2008). Collaborative research in sociology: Trends and contributing factors. The American Sociologist, 39(4), 290–306.CrossRefGoogle Scholar
  14. Ioannidis, J. P. (2014). How to make more published research true. PLOS Medicine, 11(10), e1001747.CrossRefGoogle Scholar
  15. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.CrossRefGoogle Scholar
  16. Kaur, J., et al. (2012). Scholarometer: A social framework for analyzing impact across disciplines. PLoS ONE, 7(9), e43235.CrossRefGoogle Scholar
  17. Khasseh, A. A., Soheili, F., Moghaddam, H. S., & Chelak, A. M. (2017). Intellectual structure of knowledge in iMetrics: A co-word analysis. Information Processing and Management, 53(3), 705–720.CrossRefGoogle Scholar
  18. Kim, K.-W. (2006). Measuring international research collaboration of peripheral countries: Taking the context into consideration. Scientometrics, 66(2), 231–240.CrossRefGoogle Scholar
  19. Larson, E. W., & Gray, C. F. (2015). A guide to the project management body of knowledge: PMBOK ( ® ) guide. Newtown Square: Project Management Institute.Google Scholar
  20. Lau, C.-Y., et al. (2014). International collaborative research partnerships: Blending science with management and diplomacy. Journal of AIDS & Clinical Research, 5(12), 385.Google Scholar
  21. Low, W. Y., Ng, K. H., Kabir, M., Koh, A. P., & Sinnasamy, J. (2014). Trend and impact of international collaboration in clinical medicine papers published in Malaysia. Scientometrics, 98(2), 1521–1533.CrossRefGoogle Scholar
  22. Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27(2 Part 1), 209–220.Google Scholar
  23. Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.MathSciNetCrossRefzbMATHGoogle Scholar
  24. Peng, D. X., Heim, G. R., & Mallick, D. N. (2014). Collaborative product development: The effect of project complexity on the use of information technology tools and new product development practices. Production and Operations Management, 23(8), 1421–1438.CrossRefGoogle Scholar
  25. Ronda-Pupo, G. A., & Guerras-Martin, L. Á. (2012). Dynamics of the evolution of the strategy concept 1962–2008: A co-word analysis. Strategic Management Journal, 33(2), 162–188.CrossRefGoogle Scholar
  26. Rousseau, R. (2014). Library science: Forgotten founder of bibliometrics. Nature, 510(7504), 218.CrossRefGoogle Scholar
  27. Söderlund, J. (2004). Building theories of project management: Past research, questions for the future. International Journal of Project Management, 22(3), 183–191.CrossRefGoogle Scholar
  28. Subramanyam, K. (1983). Bibliometric studies of research collaboration: A review. Journal of Information Science, 6(1), 33–38.CrossRefGoogle Scholar
  29. Uchitpe, M., Uddin, S., & Lynn, C. (2016). Predicting the future of project management research. Procedia-Social and Behavioral Sciences, 226, 27–34.CrossRefGoogle Scholar
  30. Uddin, S., & Khan, A. (2016). The impact of author-selected keywords on citation counts. Journal of Informetrics, 10(4), 1166–1177.CrossRefGoogle Scholar
  31. Uddin, S., Khan, A., & Baur, L. A. (2015). A framework to explore the knowledge structure of multidisciplinary research fields. PLoS ONE, 10(4), e0123537.CrossRefGoogle Scholar
  32. van Eck, N., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.Google Scholar
  33. Vanclay, J. K. (2013). Factors affecting citation rates in environmental science. Journal of Informetrics, 7(2), 265–271.CrossRefGoogle Scholar
  34. Walker, D. H., et al. (2008). Collaborative academic/practitioner research in project management: Examples and applications. International Journal of Managing Projects in Business, 1(2), 168–192.CrossRefGoogle Scholar
  35. Wasserman, S., & Faust, K. (2003). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.zbMATHGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Complex Systems Research GroupThe University of SydneyDarlingtonAustralia
  2. 2.Distributed Systems Group, Department of Computer Science and EngineeringThe University of South FloridaTampaUSA

Personalised recommendations