Mapping the Science of Soft Operations Research Based on ISI Subject Areas

Document Type: Original Research

Authors

1 Professor in Operations Research, Faculty of Management, University of Tehran, Iran

2 PhD Candidate in Operations Research, Faculty of Management, University of Tehran, Iran

3 PhD Student in Organizational Behavior, Faculty of Management, University of Tehran, Iran

Abstract

The present study aimed at visualizing the Soft Operations Research (SOR) domain. For this purpose, the six-phase mapping process was used. In this process, the first phase deals with extracting the data. Analysis unit is selected in the second phase. In the third and fourth phases, frequency of articles is studied and similarities among the extracted articles are determined, respectively. In the fifth phase, the related areas are identified by using a clustering algorithm. Finally, subject areas are mapped and analyzed by using the clustering steps in the sixth phase. Based on the results of the mapping process in this research, six clusters in the areas of management and social sciences, engineering sciences, behavioral sciences, geology and environmental studies, sociology and natural resources and mathematics were obtained.

Keywords


Börner, K. (2010). Atlas of science. MIT Press.
Börner, K., Boyack, K. W., Milojević, S., & Morris, S. (2012). An introduction to modeling science: Basic model types, key definitions, and a general framework for the comparison of process models. In Models of Science Dynamics (pp. 3-22). Springer Berlin Heidelberg.
Börner, K., Chen, C., & Boyack, K. W. (2005). Visualizing knowledge domains. Annual review of information science and technology, 37(1), 179-255.
Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351.374.
De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek: Cambridge University Press.
E Silva, M. C., & Teixeira, A. A. (2012). Methods of assessing the evolution of science: A review. European Journal of Scientific Research, 68(4), 616-635.
 Heyer, R. (2004). Understanding Soft Operations Research: The methods, their application and its future in the Defence setting: DTIC Document.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.
Klavans, R., & Boyack, K. W. (2007). Is there a convergent structure of science? A comparison of maps using the ISI and Scopus databases. Paper presented at the Proceedings of ISSI.
Klavans, R., & Boyack, K. W. (2008). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60(3), 455-476.
Lucio-Arias, D., & Scharnhorst, A. (2012). Mathematical approaches to modeling science from an algorithmic-historiography perspective. In Models of Science Dynamics (pp. 23-66). Springer Berlin Heidelberg.
Price, D. J. D. S. (1965). Statistical studies of networks of scientific papers. Paper presented at the Statistical Association Methods for Mechanized Documentation: Symposium Proceedings.
Shiffrin, R. M., & Börner, K. (2004). Mapping knowledge domains. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5183-5185.
Small, H. (1999). Visualizing science by citation mapping. Journal of the American society for Information Science, 50(9), 799-813.
Steinbach, M., Karypis, G., & Kumar, V. (2000). A comparison of document clustering techniques. In KDD workshop on text mining (Vol. 400, No. 1, pp. 525-526).
Van Den Besselaar, P., & Heimeriks, G. (2006). Mapping research topics using word-reference co-occurrences: A method and an exploratory case study. Scientometrics, 68(3), 377-393.