Half a Century of Fuzzy Decision Making in Italy: A Bibliometric Analysis

Authors

DOI:

https://doi.org/10.31181/msa21202521

Keywords:

Bibliometrics, Scopus Database, Italy, VOSviewer

Abstract

Italy is a developed country, and researchers in fuzzy decision-making are publishing actively. However, the extent and impact of contributions remain unexplored in scholarly evaluations. Thus, this study aims to provide a bibliometric analysis of fuzzy research in Italy from 1973–2024, utilizing the Scopus database and VOSviewer software. The main findings show a significant growth, with total publications increasing from 45 (1973-1999) to 543 (2000-2024). Early research focused on foundational concepts like fuzzy sets and fuzzy logic, while recent studies emphasize applied domains such as decision-making, artificial intelligence, and machine learning. Leading contributors include Fausto Cavallaro from the University of Molise, with the University of Naples Federico II (41 total papers) as the most productive institution. Collaboration analysis identifies India as the primary partner. The topical and keyword analysis reveals that the focus of the area of research is less aligned with global trends in fuzzy theories. Institutions can encourage strategic publishing to improve global rankings and citation impact.

Downloads

Download data is not yet available.

References

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X

Broadus, R. N. (1987). Toward a definition of ''bibliometrics." Scientometrics, 12(3), 373-379. https://doi.org/10.1007/bf02016680. DOI: https://doi.org/10.1007/BF02016680

Kumar, R. (2025). Bibliometric Analysis: Comprehensive Insights into Tools, Techniques, Applications, and Solutions for Research Excellence. Spectrum of Engineering and Management Sciences, 3(1), 45-62. https://doi.org/10.31181/sems31202535k. DOI: https://doi.org/10.31181/sems31202535k

Elsevier. (2018). Research Metrics Guidebook. https://www.elsevier.com/research-intelligence/resource-library/research-metrics-guidebook.

Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429-431. https://doi.org/10.1038/520429a. DOI: https://doi.org/10.1038/520429a

Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569. https://doi.org/10.1073/pnas.0507655102. DOI: https://doi.org/10.1073/pnas.0507655102

Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3. DOI: https://doi.org/10.1007/s11192-009-0146-3

Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25, 348.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W.M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285. https://doi.org/10.1016/j.jbusres.2021.04.070. DOI: https://doi.org/10.1016/j.jbusres.2021.04.070

Ding, Y., Rousseau, R., & Wolfram, D. (2014). Measuring scholarly impact: Methods and practice. Springer, Switzerland. DOI: https://doi.org/10.1007/978-3-319-10377-8

Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24, 265. https://doi.org/10.1002/asi.4630240406. DOI: https://doi.org/10.1002/asi.4630240406

Kessler, M.M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10-25. https://doi.org/10.1002/asi.5090140103. DOI: https://doi.org/10.1002/asi.5090140103

Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information 22(2), 191-235. https://doi.org/10.1177/053901883022002003. DOI: https://doi.org/10.1177/053901883022002003

Tukey, J.W. (1977). Exploratory data analysis. Addison-Wesley, MA.

Blanco-Mesa, F., Merigó, J. M., & Gil-Lafuente, A. M. (2017). Fuzzy decision making: A bibliometric-based review. Journal of Intelligent & Fuzzy Systems, 32(3), 2033-2050. https://doi.org/10.3233/JIFS-161640. DOI: https://doi.org/10.3233/JIFS-161640

López Herrera, A. G., Cobo Martín, M. J., Herrera Viedma, E., Herrera Triguero, F., Bailón Moreno, R., & Jiménez Contreras, E. (2009). Visualization and evolution of the scientific structure of fuzzy sets research in Spain. Information Research, 14(4), 421.

Yu, D., Xu, Z., & Wang, W. (2018). Bibliometric analysis of fuzzy theory research in China: A 30-year perspective. Knowledge-Based Systems, 141, 188-199. https://doi.org/10.1016/j.knosys.2017.11.018. DOI: https://doi.org/10.1016/j.knosys.2017.11.018

Merino-Arteaga, I., Alfaro-García, V. G., & Merigó, J. M. (2022). Fuzzy systems research in the United States of America and Canada: A bibliometric overview. Information Sciences, 617, 277-292. https://doi.org/10.1016/j.ins.2022.10.116. DOI: https://doi.org/10.1016/j.ins.2022.10.116

Nica, I., Delcea, C., & Chiriță, N. (2024). Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study. Mathematics, 12(5), 782. https://doi.org/10.3390/math12050782. DOI: https://doi.org/10.3390/math12050782

Liu, W., & Liao, H. (2017). A bibliometric analysis of fuzzy decision research during 1970-2015. International Journal of Fuzzy Systems, 19(1), 1-14. https://doi.org/10.1007/s40815-016-0272-z. DOI: https://doi.org/10.1007/s40815-016-0272-z

Alfaro-García, V. G., Merigó, J. M., Pedrycz, W., & Gómez Monge, R. (2020). Citation analysis of fuzzy set theory journals: Bibliometric insights about authors and research areas. International Journal of Fuzzy Systems, 22(5), 2414-2448. https://doi.org/10.1007/s40815-020-00924-8. DOI: https://doi.org/10.1007/s40815-020-00924-8

Saaty, T.L. (2013). Analytic Hierarchy Process. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_31. DOI: https://doi.org/10.1007/978-1-4419-1153-7_31

Atanassov, K. T. (1999). Intuitionistic fuzzy sets. In: Intuitionistic fuzzy sets: theory and applications (pp. 1-137). Heidelberg: Physica-Verlag HD. DOI: https://doi.org/10.1007/978-3-7908-1870-3_1

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009. DOI: https://doi.org/10.1016/j.omega.2014.11.009

Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press. DOI: https://doi.org/10.1201/b11032

Published

2025-08-05

How to Cite

Saqlain, M. (2025). Half a Century of Fuzzy Decision Making in Italy: A Bibliometric Analysis. Management Science Advances, 2(1), 133-143. https://doi.org/10.31181/msa21202521