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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

Author

Listed:
  • Osmo Kivinen

    (University of Turku)

  • Juha Hedman

    (University of Turku)

  • Päivi Kaipainen

    (University of Turku)

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.

Suggested Citation

  • Osmo Kivinen & Juha Hedman & Päivi Kaipainen, 2013. "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," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 683-699, February.
    • Handle: RePEc:spr:scient:v:94:y:2013:i:2:d:10.1007_s11192-012-0808-4
    DOI: 10.1007/s11192-012-0808-4
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    References listed on IDEAS

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    Cited by:

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    2. Osmo Kivinen & Juha Hedman & Kalle Artukka, 2017. "Scientific publishing and global university rankings. How well are top publishing universities recognized?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 679-695, July.
    3. Olcay, Gokcen Arkali & Bulu, Melih, 2017. "Is measuring the knowledge creation of universities possible?: A review of university rankings," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 153-160.
    4. Calabrese, Armando & Capece, Guendalina & Costa, Roberta & Di Pillo, Francesca & Giuffrida, Stefania, 2018. "A ‘power law’ based method to reduce size-related bias in indicators of knowledge performance: An application to university research assessment," Journal of Informetrics, Elsevier, vol. 12(4), pages 1263-1281.
    5. Pin-Hua Lin & Jong-Rong Chen & Chih-Hai Yang, 2014. "Academic research resources and academic quality: a cross-country analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 109-123, October.
    6. Vicente Safón, 2013. "What do global university rankings really measure? The search for the X factor and the X entity," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 223-244, November.

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