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Tytuł artykułu

Economic disciplines in the context of the new list of journals - network analysis

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The aim of this article is an attempt to characterize economic disciplines, i.e. economics and finance, as well as management and quality studies, based on their assignments to scientific journals, and to identify the place of these two disciplines in relation to each other and their links with other disciplines. Design/methodology/approach: Quantitative and network analysis methods were used for graphical representation and description of the complex relationships. The analysis was based on the data published on 31 July 2019, constituting a list of scientific journals. Findings: The results confirm a strong link between the two disciplines, but they also show some differences between them. The discipline of economics and finance is particularly strongly linked with the discipline of social and economic geography and spatial management. This is not the case for management and quality studies, which is more closely linked to disciplines outside social sciences. Research limitations/implications: The results are based only on a quantitative approach to the relationships between disciplines, therefore, they should not be used to draw too far-reaching conclusions, e.g. on the differences between these two disciplines in methods, subject matter or facilities under analysis. Further research may take into account, for example, different research trends and approaches within the disciplines themselves. Originality/value: Presented network approach shows the connections between scientific disciplines in a new holistic way. The results could be especially interesting for researchers whose studies are interdisciplinary.
Rocznik
Tom
Strony
7--20
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
  • Silesian University of Technology, Faculty of Organization and Management, Department of Economics and Informatics, Poland
Bibliografia
  • 1. Bastian, M., Heymann, S., and Jacomy, M. (2009, March). Gephi: an open source software for exploring and manipulating networks. In Third international AAAI conference on weblogs and social media.
  • 2. Batorski D., and Zdziarski M. (2009). Analiza sieciowa i jej zastosowania w badaniach organizacji i zarządzania. Problemy Zarządzania, 7(6), pp. 157-184.
  • 3. Bedi, P., and Sharma, C. (2016). Community detection in social networks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(3), pp. 115-135.
  • 4. Gorynia, M., and Kowalski, T. (2013). Nauki ekonomiczne i ich klasyfikacja a wyzwania współczesnej gospodarki, Ekonomista, 4, pp. 457-474.
  • 5. Ivens, A.B., von Beeren, C., Blüthgen, N., and Kronauer, D.J. (2016). Studying the complex communities of ants and their symbionts using ecological network analysis. Annual Review of Entomology, 61, pp. 353-371.
  • 6. Jacomy, M., Venturini, T., Heymann, S., and Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one, 9(6), p. e98679.
  • 7. Janik, A., Ryszko, A., and Szafraniec, M. (2020). Scientific Landscape of Smart and Sustainable Cities Literature: A Bibliometric Analysis. Sustainability, 12(3), p. 779.
  • 8. Jordan, M.I., and Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), pp. 255-260.
  • 9. MNiSW, Nowe rozporządzenie ws. dyscyplin - to rzetelna ocena badań naukowych, 20.09.2018. Retrieved from https://www.gov.pl/web/nauka/nowe-rozporzadzenie-ws-dyscyplin-to-rzetelna-ocena-badan-naukowych, 02.11.2019.
  • 10. Murtagh, F., and Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), pp. 86-97.
  • 11. Newman, M.E. (2004). Analysis of weighted networks. Physical review E, 70(5), p. 056131.
  • 12. OECD, Revised field of science and technology (fos) classification in the frascati manual, 2007. Retrieved from https://www.oecd.org/science/inno/38235147.pdf, 02.11.2019.
  • 13. Otte, E., and Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of information Science, 28(6), pp. 441-453.
  • 14. Sudoł, S. (2016). Zarządzanie jako dyscyplina naukowa. Przegląd Organizacji, 4, pp. 4-11.
  • 15. Tang, L., and Liu, H. (2010). Graph mining applications to social network analysis. In: Managing and Mining Graph Data (pp. 487-513). Boston, MA: Springer.
  • 16. Verma, M., Srivastava, M., Chack, N., Diswar, A.K., and Gupta, N. (2012). A comparative study of various clustering algorithms in data mining. International Journal of Engineering Research and Applications (IJERA), 2(3), pp. 1379-1384.
  • 17. Vincenot, C.E. (2018). How new concepts become universal scientific approaches: insights from citation network analysis of agent-based complex systems science. Proceedings of the Royal Society B: Biological Sciences, 285(1874), p. 20172360.
  • 18. Wang, S., Lu, J., Gu, X., Du, H., and Yang, J. (2016). Semi-supervised linear discriminant analysis for dimension reduction and classification. Pattern Recognition, 57, pp. 179-189.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3380b24c-2574-423d-afe1-1d48a4237400
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