IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i7p2316-d156219.html
   My bibliography  Save this article

Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach

Author

Listed:
  • Jung Ho Park

    (Technology Management, Economics, and Policy Graduate Program, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 151-742, Korea)

  • Kwangsoo Shin

    (Department of Biomedical Convergence, College of Medicine, Chungbuk National University, Chungdae-ro 1, Seowon-gu, Cheongju-si, Chungbuk 28644, Korea)

Abstract

Government R&D investments are steadily increasing with the perception that R&D plays an important role in technological innovation and sustainable economic growth. In particular, because biotechnology is recognized as one of the next growth engines, the Korean government has recently increased their investment in biotechnology R&D. However, careful analysis of the efficiency of government-sponsored R&D projects is still lacking. In this paper, we measured the technical efficiency and technology gap ratio to investigate the efficiency of Korean government-sponsored R&D projects of 16 sub-biotechnologies from 2007 to 2013 using a metafrontier Data Envelopment Analysis approach. There was no improvement in overall efficiency between 2007 and 2013. Biochip development technology has been the most efficient sub-biotechnology field and the least efficient fields have been biotechnology product safety and efficacy assessment technology. Medical science and engineering is the closest to optimal production technology among sub-biotechnologies. The efficiency of universities and government-funded research institutes is high, while the efficiency of companies is relatively low. The results suggest that the government should improve the R&D planning process and establish a customized R&D investment strategy that considers the characteristics of technologies and the seven organization types of R&D conductors to increase R&D efficiency.

Suggested Citation

  • Jung Ho Park & Kwangsoo Shin, 2018. "Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2316-:d:156219
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/7/2316/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/7/2316/
    Download Restriction: no
    ---><---

    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. Hong, Jin & Feng, Bing & Wu, Yanrui & Wang, Liangbing, 2016. "Do government grants promote innovation efficiency in China's high-tech industries?," Technovation, Elsevier, vol. 57, pages 4-13.
    3. Diogo Cunha Ferreira & Rui Cunha Marques, 2016. "Malmquist and Hicks–Moorsteen Productivity Indexes for Clusters Performance Evaluation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1015-1053, September.
    4. Sreejith Aravindakshan & Frederick Rossi & T. S. Amjath-Babu & Prakashan Chellattan Veettil & Timothy J. Krupnik, 2018. "Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia," Journal of Productivity Analysis, Springer, vol. 49(2), pages 153-171, June.
    5. Abramo, Giovanni & D’Angelo, Ciriaco Andrea, 2014. "Assessing national strengths and weaknesses in research fields," Journal of Informetrics, Elsevier, vol. 8(3), pages 766-775.
    6. 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.
    7. Lee, Chi-Chuan & Huang, Tai-Hsin, 2017. "Cost efficiency and technological gap in Western European banks: A stochastic metafrontier analysis," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 161-178.
    8. Dong-hyun Oh & Jeong-dong Lee, 2010. "A metafrontier approach for measuring Malmquist productivity index," Empirical Economics, Springer, vol. 38(1), pages 47-64, February.
    9. Guo, Di & Guo, Yan & Jiang, Kun, 2016. "Government-subsidized R&D and firm innovation: Evidence from China," Research Policy, Elsevier, vol. 45(6), pages 1129-1144.
    10. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    11. Hashimoto, Akihiro & Haneda, Shoko, 2008. "Measuring the change in R&D efficiency of the Japanese pharmaceutical industry," Research Policy, Elsevier, vol. 37(10), pages 1829-1836, December.
    12. Chen, Ying-Hsiu & Lai, Po-Lin & Piboonrungroj, Pairach, 2017. "The relationship between airport performance and privatisation policy: A nonparametric metafrontier approach," Journal of Transport Geography, Elsevier, vol. 62(C), pages 229-235.
    13. Hsu, Fang-Ming & Hsueh, Chao-Chih, 2009. "Measuring relative efficiency of government-sponsored R&D projects: A three-stage approach," Evaluation and Program Planning, Elsevier, vol. 32(2), pages 178-186, May.
    14. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    15. Leoncini, R. & Maggioni, M. A. & Montresor, S., 1996. "Intersectoral innovation flows and national technological systems: network analysis for comparing Italy and Germany," Research Policy, Elsevier, vol. 25(3), pages 415-430, May.
    16. Lin, Boqiang & Du, Kerui, 2013. "Technology gap and China's regional energy efficiency: A parametric metafrontier approach," Energy Economics, Elsevier, vol. 40(C), pages 529-536.
    17. Torben Tiedemann & Tammo Francksen & Uwe Latacz-Lohmann, 2011. "Assessing the performance of German Bundesliga football players: a non-parametric metafrontier approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(4), pages 571-587, December.
    18. 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.
    19. D.S. Prasada Rao & Christopher J. O'Donnell & George E. Battese, 2003. "Metafrontier Functions for the Study of Inter-regional Productivity Differences," CEPA Working Papers Series WP012003, School of Economics, University of Queensland, Australia.
    20. Baumann, Julian & Kritikos, Alexander S., 2016. "The link between R&D, innovation and productivity: Are micro firms different?," Research Policy, Elsevier, vol. 45(6), pages 1263-1274.
    21. Hagedoorn, John & Cloodt, Myriam, 2003. "Measuring innovative performance: is there an advantage in using multiple indicators?," Research Policy, Elsevier, vol. 32(8), pages 1365-1379, September.
    22. Chris Freeman & Luc Soete, 1997. "The Economics of Industrial Innovation, 3rd Edition," MIT Press Books, The MIT Press, edition 3, volume 1, number 0262061953, December.
    23. Wang, Qunwei & Zhao, Zengyao & Zhou, Peng & Zhou, Dequn, 2013. "Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach," Economic Modelling, Elsevier, vol. 35(C), pages 283-289.
    24. Bronzini, Raffaello & Piselli, Paolo, 2016. "The impact of R&D subsidies on firm innovation," Research Policy, Elsevier, vol. 45(2), pages 442-457.
    25. 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.
    26. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    27. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    28. Lee, Hakyeon & Park, Yongtae & Choi, Hoogon, 2009. "Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach," European Journal of Operational Research, Elsevier, vol. 196(3), pages 847-855, August.
    29. Holger Görg & Eric Strobl, 2007. "The Effect of R&D Subsidies on Private R&D," Economica, London School of Economics and Political Science, vol. 74(294), pages 215-234, May.
    30. Chen, Yunwei & Yang, Zhiping & Shu, Fang & Hu, Zhengyin & Meyer, Martin & Bhattacharya, Sujit, 2009. "A patent based evaluation of technological innovation capability in eight economic regions in PR China," World Patent Information, Elsevier, vol. 31(2), pages 104-110, June.
    31. 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.
    32. Thomas, V.J. & Sharma, Seema & Jain, Sudhir K., 2011. "Using patents and publications to assess R&D efficiency in the states of the USA," World Patent Information, Elsevier, vol. 33(1), pages 4-10, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lee, Hyoungsuk & Choi, Yongrok & Seo, Hyungjun, 2020. "Comparative analysis of the R&D investment performance of Korean local governments," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    2. Jaeho Shin & Yeongjun Kim & Changhee Kim, 2021. "The Perception of Occupational Safety and Health (OSH) Regulation and Innovation Efficiency in the Construction Industry: Evidence from South Korea," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    3. Hwai-Shuh Shieh & Jin-Li Hu & Yong-Ze Ang, 2020. "Efficiency of Life Insurance Companies: An Empirical Study in Mainland China and Taiwan," SAGE Open, , vol. 10(1), pages 21582440209, February.
    4. Myoungjae Choi & Ohjin Kwon & Dongkyu Won & Wooseok Jang, 2021. "Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    5. Baocheng He & Jiawei Wang & Jiaoyang Wang & Kun Wang, 2018. "The Impact of Government Competition on Regional R&D Efficiency: Does Legal Environment Matter in China’s Innovation System?," Sustainability, MDPI, vol. 10(12), pages 1-18, November.

    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. Junhee Bae & Yanghon Chung & Hyesoo Ko, 2021. "Analysis of efficiency in public research activities in terms of knowledge spillover: focusing on earthquake R&D accomplishments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 2249-2264, September.
    2. Xiangyu Teng & Danting Lu & Yung-ho Chiu, 2019. "Emission Reduction and Energy Performance Improvement with Different Regional Treatment Intensity in China," Energies, MDPI, vol. 12(2), pages 1-18, January.
    3. 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.
    4. Afsharian, Mohsen & Ahn, Heinz & Harms, Sören Guntram, 2019. "Performance comparison of management groups under centralised management," European Journal of Operational Research, Elsevier, vol. 278(3), pages 845-854.
    5. Walheer, Barnabé, 2018. "Aggregation of metafrontier technology gap ratios: the case of European sectors in 1995–2015," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1013-1026.
    6. Chiu, Yung-Ho & Lee, Jen-Hui & Lu, Ching-Cheng & Shyu, Ming-Kuang & Luo, Zhengying, 2012. "The technology gap and efficiency measure in WEC countries: Application of the hybrid meta frontier model," Energy Policy, Elsevier, vol. 51(C), pages 349-357.
    7. Jianping Liu & Kai Lu & Shixiong Cheng, 2018. "International R&D Spillovers and Innovation Efficiency," Sustainability, MDPI, vol. 10(11), pages 1-23, October.
    8. Walheer, Barnabé, 2023. "Meta-frontier and technology switchers: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 305(1), pages 463-474.
    9. Kerstens, Kristiaan & O’Donnell, Christopher & Van de Woestyne, Ignace, 2019. "Metatechnology frontier and convexity: A restatement," European Journal of Operational Research, Elsevier, vol. 275(2), pages 780-792.
    10. Arjomandi, Amir & Dakpo, K. Hervé & Seufert, Juergen Heinz, 2018. "Have Asian airlines caught up with European Airlines? A by-production efficiency analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 389-403.
    11. 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.
    12. Chiu, Yung-ho & Luo, Zhengying & Chen, Yu-Chuan & Wang, Zebin & Tsai, Min-Pei, 2013. "A comparison of operating performance management between Taiwan banks and foreign banks based on the Meta-Hybrid DEA model," Economic Modelling, Elsevier, vol. 33(C), pages 433-439.
    13. Fang-Rong Ren & Ze Tian & Yu-Ting Shen & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Energy, CO 2 , and AQI Efficiency and Improvement of the Yangtze River Economic Belt," Energies, MDPI, vol. 12(4), pages 1-17, February.
    14. Mercedes Beltrán-Esteve & José Gómez-Limón & Andrés Picazo-Tadeo & Ernest Reig-Martínez, 2014. "A metafrontier directional distance function approach to assessing eco-efficiency," Journal of Productivity Analysis, Springer, vol. 41(1), pages 69-83, February.
    15. Hung, Chia-Liang, 2017. "Social networks, technology ties, and gatekeeper functionality: Implications for the performance management of R&D projects," Research Policy, Elsevier, vol. 46(1), pages 305-315.
    16. Kao, Chiang & Liu, Shiang-Tai, 2016. "A parallel production frontiers approach for intertemporal efficiency analysis: The case of Taiwanese commercial banks," European Journal of Operational Research, Elsevier, vol. 255(2), pages 411-421.
    17. Vladimír Holý & Karel Šafr, 2018. "Are economically advanced countries more efficient in basic and applied research?," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 933-950, December.
    18. Liu, Hui-hui & Yang, Guo-liang & Liu, Xiao-xiao & Song, Yao-yao, 2020. "R&D performance assessment of industrial enterprises in China: A two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    19. Lee, Hakyeon & Shin, Juneseuk, 2014. "Measuring journal performance for multidisciplinary research: An efficiency perspective," Journal of Informetrics, Elsevier, vol. 8(1), pages 77-88.
    20. Yu, Ming-Miin & Lin, Chung-I & Chen, Kuan-Chen & Chen, Li-Hsueh, 2021. "Measuring Taiwanese bank performance: A two-system dynamic network data envelopment analysis approach," Omega, Elsevier, vol. 98(C).

    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:gam:jsusta:v:10:y:2018:i:7:p:2316-:d:156219. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.