Abstract
The article introduces a relational input–output model for the productivity analysis of university research. The comparative analyses focus on top university research in hard sciences from 4 East Asian countries (Hong Kong, Singapore, South Korea, Taiwan) and 4 North European countries (Denmark, Finland, Norway, Sweden), universities of which get altogether 95 recognitions in the HEEACT Top 300 rankings in the Natural Sciences (Sci), Technology (Tec) or Clinical Medicine (Med). According to productivity ratings (A0, A, A+, A++), Taiwan receives 10 A++ ratings (Sci 5, Tec 5), Sweden 9 (Sci 4, Med 4, Tec 1) and Hong Kong 9 (Tec 4, Med 2, Sci 1). The smallest numbers of A++ ratings are found in Norway, 1 (Med) and Finland 3 (all in Med). The only university with an A++ rating in the top of all three fields is the National University of Singapore. The Pohang University of Science and Technology (South Korea) and the National Tsing Hua University (Taiwan) are exceptionally productive in Sci and Tec; Karolinska Institutet (Sweden) and the University of Helsinki (Finland) belong to the top in Med. Even though Northern European countries are ranked higher in the ‘knowledge economy indicators’, East Asians fare better by indicators of learning outcomes and by productivity of university research in Natural Sciences and Technology; North European countries are stronger in Clinical Medicine.
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Notes
Iceland was the only Scandinavian country to be left out, because its universities do not appear in the HEEACT Top 300 field ranking in hard sciences which forms our output database.
In our article we follow the classification of the HEEACT Top 300 Field-based Ranking database for the ‘hard sciences’ i.e. Natural Sciences (Chemistry, Geosciences, Mathematics, Physics, Space sciences, Psychology); Clinical Medicine (Clinical Medicine, Psychiatry) and Technology (Computer Science, Engineering, Materials Science). This HEEACT classification, in its turn, relies on subject categories drawn from SCI and SSCI (altogether 132 SCI and SSCI subject categories are included under the three ‘hard sciences’).
For a detailed guide on utilizing bibliometric analyses in research evaluation see Moed (2005).
Input data for the Chonbuk National University (Tec) and the University of Ulsan (Med) from South Korea, Malmö University (Med) from Sweden as well as Chang Gung University (Med) and the National Chung Hsing University (Tec) from Taiwan have been complemented from national and local sources due to their unavailability in the QS.
To calculate disposable man years DMY from a known total amount of resources (TF) and a known total amount of ‘recognized units’ (in this case 161 from which 95 in Sci, Tec or Med). we apply the following group of equations.
$$ \left\{ {\begin{array}{*{20}l} {\begin{array}{*{20}l} {\alpha_{ij} a_{\text{AGR}} F_{\text{AGR}} + \alpha_{ij} a_{\text{MED}} F_{\text{MED}} + \alpha_{ij} a_{\text{TEC}} F_{\text{TEC}} + \alpha_{ij} a_{\text{SCI}} F_{\text{SCI}} + \alpha_{ij} a_{\text{SOC}} F_{\text{SOC}} + \alpha_{ij} a_{\text{LIFE}} F_{\text{LIFE}} = TF_{i} } \\ {a_{\text{AGR}} + a_{\text{MED}} + a_{\text{TEC}} + a_{\text{SCI}} + a_{\text{SOC}} + a_{\text{LIFE}} = 1} \\ \end{array} } \\ {\sum\nolimits_{i = 1}^{52} {\sum\nolimits_{j = 1}^{6} {\alpha_{ij} = {\text{TF}}} } } \\ \end{array} } \right. $$where F k denotes number of faculty in field k, ak denotes field k’s share of the total research man-years (1) and α ij denotes university i’s unit j’s share of the total faculty (TF). DMYs by fields (6) and universities (52) are obtained by solving the group of equations for each α ij .
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Kivinen, O., Hedman, J. & Kaipainen, P. Productivity analysis of research in Natural Sciences, Technology and Clinical Medicine: an input–output model applied in comparison of Top 300 ranked universities of 4 North European and 4 East Asian countries. Scientometrics 94, 683–699 (2013). https://doi.org/10.1007/s11192-012-0808-4
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DOI: https://doi.org/10.1007/s11192-012-0808-4