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Scientometrics

, Volume 98, Issue 3, pp 2145–2159 | Cite as

Exploring the modelling and simulation knowledge base through journal co-citation analysis

  • Navonil MustafeeEmail author
  • Korina Katsaliaki
  • Paul Fishwick
Article

Abstract

Co-citation analysis is a form of content analysis that can be applied in the context of scholarly publications with the purpose of identifying prominent articles, authors and journals being referenced to by the citing authors. It identifies co-cited references that occur in the reference list of two or more citing articles, with the resultant co-citation network providing insights into the constituents of a knowledge domain (e.g., significant authors and papers). The contribution of the paper is twofold; (a) the demonstration of the added value of using co-citation analysis, and for this purpose the underlying dataset that is chosen is the peer-reviewed publication of the Society for Modeling and Simulation International (SCS)—SIMULATION; (b) the year 2012 being the 60th anniversary of the SCS, the authors hope that this paper will lead to further acknowledgement and appreciation of the Society in charting the growth of Modeling and Simulation (M&S) as a discipline.

Keywords

Modelling and Simulation (M&S) Co-citation analysis Simulation research Society for Modeling and Simulation International SIMULATION: Transactions of the Society for Modeling and Simulation International 

References

  1. Brailsford, S. C., Harper, P. R., Patel, B., & Pitt, M. (2009). An analysis of the academic literature on simulation and modelling in healthcare. Journal of Simulation, 3, 130–140.CrossRefGoogle Scholar
  2. Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl. 1), 5303–5310.CrossRefGoogle Scholar
  3. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.CrossRefGoogle Scholar
  4. Chen, C., Ibekwe-Sanjuan F., & Hou J. (2010). The structure and dynamics of co-citation clusters: A multiple-perspective co-citation analysis. Journal of the American Society for Information Science and Technology (forthcoming). http://arxiv.org/ftp/arxiv/papers/1002/1002.1985.pdf Retrieved April 8 2012.
  5. Claver, E., Gonzalez, R., & Llopis, J. (2000). An analysis of research in information systems (1981–1997). Information and Management, 37(4), 181–195.CrossRefGoogle Scholar
  6. De Boer, P.-T. (2006). Analysis of state-independent importance-sampling measures for the two-node tandem queue. ACM Transactions on Modeling and Computer Simulation, 16(3), 225–250.CrossRefGoogle Scholar
  7. Dupuis, P., Sezer, A. D., & Wang, H. (2007). Dynamic importance sampling for queueing networks. Annals of Applied Probability, 17(4), 1306–1346.zbMATHMathSciNetCrossRefGoogle Scholar
  8. Galliers, R. D., Whitley, E. A., & Paul, R. J. (2007). Guest editorial: The European information systems academy. European Journal of Information Systems, 16(1), 3–4.CrossRefGoogle Scholar
  9. Glasserman, P., & Kou, S. G. (1995). Analysis of an importance sampling estimator for tandem queues. ACM Transactions on Modeling and Computer Simulation, 5(1), 22–42.zbMATHMathSciNetCrossRefGoogle Scholar
  10. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., & Young, T. (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research, 203(1), 1–13.CrossRefGoogle Scholar
  11. JCR Science Edition. (2013). Journal citation reports. ISI Web of Knowledge. http://www.webofknowledge.com/JCR Retrieved August 1 2012.
  12. Jun, J. B., Jacobson, S. H., & Swisher, J. R. (1999). Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society, 50(2), 109–123.zbMATHCrossRefGoogle Scholar
  13. Katsaliaki, K., & Mustafee, N. (2011). Applications of simulation research within the healthcare context. Journal of the Operational Research Society, 62(8), 1431–1451.Google Scholar
  14. Katsaliaki, K., Mustafee, N., Dwivedi, Y. K., Williams, T., & Wilson, J. M. (2010). A profile of OR research and practice published in the journal of the operational research society. Journal of the Operational Research Society, 61(1), 82–94.zbMATHCrossRefGoogle Scholar
  15. Kroese, D. P., & Nicola, V. F. (2002). Efficient simulation of a tandem Jackson network. ACM Transactions on Modeling and Computer Simulation, 12(2), 119–141.CrossRefGoogle Scholar
  16. Liu, G. (2013). Visualization of patents and papers in terahertz technology: A comparative study. Scientometrics, 94(3), 1037–1056.CrossRefGoogle Scholar
  17. Mustafee, N. (2011). Evolution of IS research based on literature published in two leading is journals—EJIS and MISQ. In Proceedings of the 19th European Conference on Information Systems (ECIS 2011), Paper 228.Google Scholar
  18. Mustafee, N., Katsaliaki, K., Fishwick, P., & Williams, M. D. (2012). SCS—60 years and counting! A time to reflect on the Society’s scholarly contribution to M&S from the turn of the Millennium. Simulation: Transactions of the Society for Modeling and Simulation International, 88(9), 1047–1071.CrossRefGoogle Scholar
  19. Mustafee, N., Katsaliaki, K., & Taylor, S. J. E. (2010). Profiling literature in healthcare simulation. Simulation: Transactions of the Society of SCS, 86(8-9), 543–558.CrossRefGoogle Scholar
  20. Niazi, M., & Hussain, A. (2011). Agent-based computing from multi-agent systems to agent-based models: A visual survey. Scientometrics, 89(2), 479–499.CrossRefGoogle Scholar
  21. Palvia, P., Pinjani, P., & Sibley, E. H. (2007). A profile of information systems research published in information and management. Information & Management, 44(1), 1–11.CrossRefGoogle Scholar
  22. Parekh, S. (1989). A quick simulation method for excessive backlogs in networks of queues. IEEE Transactions on Automatic Control, 34(1), 54–66.zbMATHMathSciNetCrossRefGoogle Scholar
  23. Raghuram, S., Tuertscher, P., & Garud, R. (2009). Mapping the field of virtual work: A co-citation analysis. Information Systems Research, 21(4), 983–999.CrossRefGoogle Scholar
  24. SAGE. (2013). Simulation: Transactions of The Society for Modelling and Simulation International -Publisher’s Homepage. SAGE publications. http://www.sagepub.com/journals/Journal201571. Retrieved April 8 2013.
  25. SCS. (2013). About SCS. http://www.scs.org/about. Retrieved April 8 2013.
  26. Terzi, S., & Cavalieri, S. (2004). Simulation in the supply chain context: A survey. Computers in Industry, 53(1), 3–16.CrossRefGoogle Scholar
  27. Thomson Scientific Solutions. (2013). ISI Web of Knowledge. http://apps.isiknowledge.com Retrieved August 1 2013.
  28. Yilmaz, L. (2011). Reflections on the 60th year anniversary of SCS. Simulation: Transactions of the Society of SCS, 87(1–2), 3–4.Google Scholar
  29. Zhao, R., & Wang, Ju. (2011). Visualizing the research on pervasive and ubiquitous computing. Scientometrics, 86(3), 593–612.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2013

Authors and Affiliations

  • Navonil Mustafee
    • 1
    Email author
  • Korina Katsaliaki
    • 2
  • Paul Fishwick
    • 3
  1. 1.Centre for Innovation and Service ResearchUniversity of Exeter Business SchoolExeterUK
  2. 2.School of Economics and Business AdministrationInternational Hellenic UniversityThessaloníkiGreece
  3. 3.Arts and Technology Program, AT10The University of Texas at DallasRichardsonUSA

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