Abstract
Countries allocate some of their GDP as research and development shares for the sustainability of R&D activities. From time to time, it was aimed to measure how effectively these allocated shares are used by different disciplines. In this study, the efficiency and performance of economics researches in 15 OECD member countries is ranked and evaluated by using bibliometric elements for the period of 2010–2017. 7 different criteria, which are thought to affect the efficiency and performance of economics research, have been determined. By taking the opinions of 5 different experts, criterion weights were calculated with Hesitant Fuzzy Analytic Hierarchy Process (Hesitant Fuzzy AHP) method and sequences were obtained by Operational Competitiveness Rating Analysis Method method. The top five countries with the highest performance are England, Germany, Italy, Australia and France and the lowest is Hungary. The results show that if the economics research performance is high in a country, the number of documents indexed in Web of Science, the number of citations and the percentage of documents cited are also high, and the quality of the produced scientific output is also independent of the number of researchers and the allocated research budgets.
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Ayhan, M. B. (2018). An integrated hesitant fuzzy AHP and TOPSIS approach for selecting summer school. Sakarya University Journal of Science,22(2), 269–284.
Boltürk, S., Onar, Ç., Öztayşi, B., Kahraman, C., & Göztepe, K. (2016). Multi-attribute warehouse location selection in humanitarian logistics using hesitant fuzzy AHP. International Journal of the Analytic Hierarchy Process, 8(2), 271–298.
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems,17, 233–247.
Chatzimichael, K., Kalaitzidakis, P., & Tzouvelekas, V. (2017). Measuring the publishing productivity of economics departments in Europe. Scientometrics,113(2), 889–908.
Chen, C. P., Hu, J. L., & Yang, C. H. (2013). Produce patents or journal articles? A cross-country comparison of R&D productivity change. Scientometrics,94, 833–849.
Clermont, M. (2016). Effectiveness and efficiency of research in Germany over time: An analysis of German business schools between 2001 and 2009. Scientometrics,108, 1347–1381.
Conroy, M., & Dusausansky, R. (1995). The productivity of economics departments in the US: Publications in the core journals. Southern Economic Journal,33(4), 1966–1971.
Courtault, J. M., Hayek, N., Rimbaux, E., & Tong, Z. (2010). Research in economics and management in France: A bibliometric study using the h-index. Journal of Socio-Economics,39, 329–337.
Docampo, D., & Lawrence, C. (2017). Academic performance and institutional resources: a cross-country analysis of research universities. Scientometrics,110, 739–764.
Dundar, H., & Lewis, D. R. (1998). Determinants of research productivity in higher education. Research in Higher Education,39(6), 607–631.
EDIRC https://edirc.repec.org/ (Access date: 01.2020)
ESI (EssentialScience Indicators) https://clarivate.com/webofsciencegroup/solutions/essential-science-indicators/. Access date 01 2020
Filev, D., & Yager, R. R. (1998). On the issue of obtaining OWA operatör weights. Fuzzy Sets and Systems,94(2), 157–169.
Işık, A. T., & Adalı, E. A. (2016). A new integrated decision making approach based on SWARA and OCRA methods for the hotel selection problem. International Journal of Advanced Operations Management,8(2), 140–151.
Jakuszewicz, J. (2013). DEA model for assessment of institutional research productivity in Poland. Journal of Engineering Management and Competitiveness,3(2), 74–78.
Johnes, J., & Yu, L. (2008). Measuring the research performance of Chinese higher education institutions using data envelopment analysis. China Economic Review,19, 679–696.
Jurajda, S., Kozubek, S., Münich, D., & Skoda, S. (2017). Scientific publication performance in post-communist countries: still lagging far behind. Scientometrics,112, 315–328.
Kocher, M. G., Luptacik, M., & Sutter, M. (2006). Measuring productivity of research in economics: A cross-country study using DEA. Socio-Economic Planning Sciences,40, 314–332.
Liu, H., & Rodriguez, R. M. (2014). A fuzzy envelope for Hesitant fuzzy linguistic term set and its application to multicriteria decision making. Information Sciences,258, 220–238.
Madic, M., Antucheviciene, J., Radovanovic, M., & Petkovic, D. (2016). Determination of manufacturing process conditions by using MCDM methods: Application in laser cutting. Engineering Economics,27(2), 144–150.
Makkonen, T., & van der Have, R. (2013). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics,94, 247–262.
Mixon, F. G., Jr., & Upadhyaya, K. P. (2016). Ranking economics departments in the US South: an update. Applied Economics Letters,23(17), 1224–1228.
Olson, J. E. (1994). Institutional and technical constraints on faculty gross productivity in American doctoral universities. Research in Higher Education,35(5), 549–567.
Ozdagoglu, A., & Çirkin, E. (2019). Electronic device selection in industrial products and machinery industry: Comparative analysis with Ocra and Maut method. International Journal of Contemporary Economics and Administrative Sciences,9(1), 119–134.
Özbek, A. (2015). Efficiency analysis of foreign-capital banks in Turkey by OCRA and MOORA. Research Journal of Finance and Accounting,6(13), 21–30.
Öztaysi, B., Onar, S. C., Boltürk, E., & Kahraman, C. (2015). Hesitant fuzzy analytic hierarchy process. In IEEE international conference fuzzy systems (FUZZ-IEEE), pp. 1–7.
Parkan, C. (1994). Operational competitiveness ratings of production units. Managerial and Decision Economics,15(3), 201–221.
Parkan, C., & Wu, M. L. (2000). Comparison of three modern multi criteria decision-making tools. International Journal of Systems Science,31(4), 497–517.
Rhaiem, M. (2017). Measurement and determinants of academic research efficiency: A systematic review of the evidence. Scientometrics,110, 581–615.
Rodriguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems,20(1), 109–119.
Stanujkic, D., Zavadskas, E. K., Liu, S., Karabasevic, D., & Popovic, G. (2017). Improved OCRA method based on the use of interval grey numbers. Journal of Grey System,29(4), 49–60.
Şahin, K., & Candan, G. (2018). Scientific productivity and cooperation in Turkic world: A bibliometric analysis. Scientometrics,115(3), 1199–1229.
Thursby, J. G. (2000). What do we say about ourselves and what does it mean? Yet another look at economics department research. Journal of Economic Literature,38(2), 383–404.
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intellıgent Systems,25(6), 529–539.
Wolszczak-Derlacz, J., & Parteka, A. (2011). Efficiency of European public higher education institutions: A two-stage multicountry approach. Scientometrics,89, 887–917.
World Bank Open Data https://data.worldbank.org/. Accessed 01 2020
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Candan, G. Efficiency and performance analysis of economics research using hesitant fuzzy AHP and OCRA methods. Scientometrics 124, 2645–2659 (2020). https://doi.org/10.1007/s11192-020-03584-5
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DOI: https://doi.org/10.1007/s11192-020-03584-5