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An international comparison of relative contributions to academic productivity

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  • Wen-Chi Hung

    (Science & Technology Policy and Information Center
    National Chiao Tung University)

  • Ling-Chu Lee

    (Science & Technology Policy and Information Center)

  • Min-Hua Tsai

    (Science & Technology Policy and Information Center)

Abstract

This paper presents a methodology for measuring the improvements in efficiency and adjustments in the scale of R&D (Research & Development) activities. For this purpose, this study decomposes academic productivity growth into components attributable to (1) world academic frontier change, (2) R&D efficiency change, (3) human capital accumulation, and (4) capital accumulation. The world academic frontier at each point in time is constructed using data envelopment analysis (DEA). This study calculates each of the above four components of academic productivity for 27 countries over 1990–2003, and finds that the components which contribute to academic productivity growth vary with the different countries’ characteristics and development stages. Human capital has more weight in terms of the quantity of academic research, and capital accumulation plays a more important role in the citation impact of academic research.

Suggested Citation

  • Wen-Chi Hung & Ling-Chu Lee & Min-Hua Tsai, 2009. "An international comparison of relative contributions to academic productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 703-718, December.
    • Handle: RePEc:spr:scient:v:81:y:2009:i:3:d:10.1007_s11192-008-2210-9
    DOI: 10.1007/s11192-008-2210-9
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    References listed on IDEAS

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    Cited by:

    1. Young-Don Cho & Hoo-Gon Choi, 2013. "Principal parameters affecting R&D exploitation of nanotechnology research: a case for Korea," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 881-899, September.
    2. Yamin Du & Wonchul Seo, 2022. "A Comparative Study on the Efficiency of R&D Activities of Universities in China by Region Using DEA–Malmquist," Sustainability, MDPI, vol. 14(16), pages 1-13, August.
    3. Giovanni Abramo & Ciriaco Andrea D’Angelo & Flavia Di Costa, 2011. "A national-scale cross-time analysis of university research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 399-413, May.
    4. Holý, Vladimír, 2024. "Ranking-based second stage in data envelopment analysis: An application to research efficiency in higher education," Operations Research Perspectives, Elsevier, vol. 12(C).
    5. Kairui Zuo & Jiancheng Guan, 2017. "Measuring the R&D efficiency of regions by a parallel DEA game model," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 175-194, July.
    6. Cullmann, Astrid & Zloczysti, Petra, 2013. "Towards an Efficient Use of R&D ? Accounting for Heterogeneity in the OECD," CEPR Discussion Papers 9345, C.E.P.R. Discussion Papers.
    7. Ling-Chu Lee & Pin-Hua Lin & Yun-Wen Chuang & Yi-Yang Lee, 2011. "Research output and economic productivity: a Granger causality test," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 465-478, November.
    8. Jiancheng Guan & Kairui Zuo, 2014. "A cross-country comparison of innovation efficiency," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 541-575, August.
    9. Guan, JianCheng & Zuo, KaiRui & Chen, KaiHua & Yam, Richard C.M., 2016. "Does country-level R&D efficiency benefit from the collaboration network structure?," Research Policy, Elsevier, vol. 45(4), pages 770-784.
    10. Stepan Zemtsov & Maxim Kotsemir, 2019. "An assessment of regional innovation system efficiency in Russia: the application of the DEA approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 375-404, August.
    11. 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.

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