IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v82y2010i1d10.1007_s11192-009-0030-1.html
   My bibliography  Save this article

Modeling macro-R&D production frontier performance: an application to Chinese province-level R&D

Author

Listed:
  • Jiancheng Guan

    (School of Management, Fudan University)

  • Kaihua Chen

    (School of Management, Beijing University of Aero and Astro)

Abstract

This paper proposes a novel methodological framework for effectively measuring the production frontier performance (PFP) of macro-scale (regional or national) R&D activities themselves associated with two improved models: a non-radial data envelopment analysis (DEA) model and a nonradial Malmquist index. In particular, the framework can provide multidimensional information to benchmark various R&D efficiency indexes (i.e., technical efficiency, pure technical efficiency and scale efficiency) as well as the total factor R&D productivity change (determined by three components: “catch-up” of R&D efficiency, “frontier shift” of R&D technology as well as “exploitation” of R&D scale economics effect) at a comparable production frontier. It can be used to not only investigate the potential and sustainable capacity of innovation but also screen and finance R&D projects at the regional or national level. We have applied the framework to a province-level panel dataset on R&D activities of 30 selected Chinese provinces.

Suggested Citation

  • Jiancheng Guan & Kaihua Chen, 2010. "Modeling macro-R&D production frontier performance: an application to Chinese province-level R&D," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 165-173, January.
    • Handle: RePEc:spr:scient:v:82:y:2010:i:1:d:10.1007_s11192-009-0030-1
    DOI: 10.1007/s11192-009-0030-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-009-0030-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-009-0030-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Eric C. & Huang, Weichiao, 2007. "Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach," Research Policy, Elsevier, vol. 36(2), pages 260-273, March.
    2. Seema Sharma & V. J. Thomas, 2008. "Inter-country R&D efficiency analysis: An application of data envelopment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 483-501, September.
    3. Ray, Subhash C & Desli, Evangelia, 1997. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Comment," American Economic Review, American Economic Association, vol. 87(5), pages 1033-1039, December.
    4. Faber, Jan & Hesen, Anneloes Barbara, 2004. "Innovation capabilities of European nations: Cross-national analyses of patents and sales of product innovations," Research Policy, Elsevier, vol. 33(2), pages 193-207, March.
    5. Michael Fritsch & Viktor Slavtchev, 2007. "What determines the efficiency of regional innovation systems?," Jena Economics Research Papers 2007-006, Friedrich-Schiller-University Jena.
    6. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    7. Wang, Eric C., 2007. "R&D efficiency and economic performance: A cross-country analysis using the stochastic frontier approach," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 345-360.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Can Huang & Celeste Amorim Varum & Joaquim Borges Gouveia, 2006. "Scientific productivity paradox: The case of China's S&T system," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 449-473, November.
    10. Daniel K. N. Johnson, 2002. "The OECD Technology Concordance (OTC): Patents by Industry of Manufacture and Sector of Use," OECD Science, Technology and Industry Working Papers 2002/5, OECD Publishing.
    11. Wei Meng & Zhenhua Hu & Wenbin Liu, 2006. "Efficiency evaluation of basic research in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 85-101, October.
    12. P. Byrnes & R. Färe & S. Grosskopf, 1984. "Measuring Productive Efficiency: An Application to Illinois Strip Mines," Management Science, INFORMS, vol. 30(6), pages 671-681, June.
    13. Khem Sharma & Pingsun Leung & Halina Zaleski, 1997. "Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 8(4), pages 447-459, November.
    14. Giovanni Abramo & Ciriaco Andrea D’Angelo & Fabio Pugini, 2008. "The measurement of Italian universities’ research productivity by a non parametric-bibliometric methodology," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(2), pages 225-244, August.
    15. Pastor, J. T. & Ruiz, J. L. & Sirvent, I., 1999. "An enhanced DEA Russell graph efficiency measure," European Journal of Operational Research, Elsevier, vol. 115(3), pages 596-607, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen Kaihua & Kou Mingting, 2014. "Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 627-657, March.
    2. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    3. Liu, John S. & Lu, Wen-Min, 2010. "DEA and ranking with the network-based approach: a case of R&D performance," Omega, Elsevier, vol. 38(6), pages 453-464, December.
    4. Ibrahim Alnafrah, 2021. "Efficiency evaluation of BRICS’s national innovation systems based on bias-corrected network data envelopment analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-28, December.
    5. Jianping Liu & Kai Lu & Shixiong Cheng, 2018. "International R&D Spillovers and Innovation Efficiency," Sustainability, MDPI, vol. 10(11), pages 1-23, October.
    6. Lee, Seonghee & Lee, Hakyeon, 2015. "Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach," Journal of Informetrics, Elsevier, vol. 9(4), pages 942-953.
    7. Marta Foddi & Stefano Usai, 2013. "Regional Knowledge Performance in Europe," Growth and Change, Wiley Blackwell, vol. 44(2), pages 258-286, June.
    8. Khoshnevis, Pegah & Teirlinck, Peter, 2018. "Performance evaluation of R&D active firms," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 16-28.
    9. Aristovnik, Aleksander, 2014. "Efficiency of the R&D Sector in the EU-27 at the Regional Level: An Application of DEA," MPRA Paper 59081, University Library of Munich, Germany.
    10. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    11. Pastor, Jesus T. & Lovell, C.A. Knox & Aparicio, Juan, 2020. "Defining a new graph inefficiency measure for the proportional directional distance function and introducing a new Malmquist productivity index," European Journal of Operational Research, Elsevier, vol. 281(1), pages 222-230.
    12. Ron Boschma & Simona Iammarino & Raffaele Paci & Jordy Suriñach & Corinne Autant-Bernard & Sylvie Chalaye & Elisa Gagliardini & Stefano Usai, 2017. "European Knowledge Neighbourhood: Knowledge Production in EU Neighbouring Countries and Intensity of the Relationship with EU Countries," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 108(1), pages 52-75, February.
    13. M. Foddi & S. Usai, 2012. "Regional innovation performance in Europe," Working Paper CRENoS 201221, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    14. Seema Sharma & V. J. Thomas, 2008. "Inter-country R&D efficiency analysis: An application of data envelopment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 483-501, September.
    15. Zhang, Daqun & Banker, Rajiv D. & Li, Xiaoxuan & Liu, Wenbin, 2011. "Performance impact of research policy at the Chinese Academy of Sciences," Research Policy, Elsevier, vol. 40(6), pages 875-885, July.
    16. Xiangyu Guo & Canhui Deng & Dan Wang & Xu Du & Jiali Li & Bowen Wan, 2021. "International Comparison of the Efficiency of Agricultural Science, Technology, and Innovation: A Case Study of G20 Countries," Sustainability, MDPI, vol. 13(5), pages 1-16, March.
    17. Svetlana V. Ratner & Svetlana A. Balashova & Andrey V. Lychev, 2022. "The Efficiency of National Innovation Systems in Post-Soviet Countries: DEA-Based Approach," Mathematics, MDPI, vol. 10(19), pages 1-23, October.
    18. Subhash C. Ray, 2014. "Data Envelopment Analysis: An Overview," Working papers 2014-33, University of Connecticut, Department of Economics.
    19. André Klevenhusen & Jonas Coelho & Leo Warszawski & Jorge Moreira & Peter Wanke & João J. Ferreira, 2021. "Innovation Efficiency in OECD Countries: a Non-parametric Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(3), pages 1064-1078, September.
    20. Chun, Dongphil & Hong, Sungjun & Chung, Yanghon & Woo, Chungwon & Seo, Hangyeol, 2016. "Influencing factors on hydrogen energy R&D projects: An ex-post performance evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1252-1258.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:82:y:2010:i:1:d:10.1007_s11192-009-0030-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.