IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v23y2023i1d10.1007_s10660-022-09599-9.html
   My bibliography  Save this article

RETRACTED ARTICLE: Assessing the efficiency of innovation entities in China: evidence from a nonhomogeneous data envelopment analysis and Tobit

Author

Listed:
  • Yu Zhu

    (Anhui Polytechnic University
    University of Science and Technology of China)

  • Feng Yang

    (University of Science and Technology of China)

  • Bengang Gong

    (Anhui Polytechnic University)

  • Wei Zeng

    (Zhejiang Sci-Tech University)

Abstract

Universities, research institutes, and firms are the main entities in the national innovation system. Owing to the heterogeneity of their outputs, prior studies have focused on their independent efficiency evaluation. This study adopts the nonhomogeneous data envelopment analysis model to assess the efficiency of three innovation entities in 30 provinces in China on a common platform. Results show that firms have the highest efficiency, and research institutes have the lowest efficiency. Innovation entities perform poorly due to the inefficiency of their subunits. Additionally, the 30 provinces are divided into three clusters by using the hierarchical clustering method. Moreover, Tobit regressions are used to estimate the impact of five environmental factors on the innovation efficiency of the three entities. The regression results show that the more open the region, the stronger the positive impact on the innovation efficiency of research institutes and firms. The regional economic environment has different degrees of negative impact on the three innovation entities. The direction and intensity of the impact of education input, government support, and information infrastructure on the three entities exhibit a large dispersion. The results provide important information for improving the efficiency of innovation entities.

Suggested Citation

  • Yu Zhu & Feng Yang & Bengang Gong & Wei Zeng, 2023. "RETRACTED ARTICLE: Assessing the efficiency of innovation entities in China: evidence from a nonhomogeneous data envelopment analysis and Tobit," Electronic Commerce Research, Springer, vol. 23(1), pages 175-205, March.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:1:d:10.1007_s10660-022-09599-9
    DOI: 10.1007/s10660-022-09599-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-022-09599-9
    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/s10660-022-09599-9?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. Amara, Nabil & Rhaiem, Mehdi & Halilem, Norrin, 2020. "Assessing the research efficiency of Canadian scholars in the management field: Evidence from the DEA and fsQCA," Journal of Business Research, Elsevier, vol. 115(C), pages 296-306.
    2. Paunov, Caroline & Rollo, Valentina, 2016. "Has the Internet Fostered Inclusive Innovation in the Developing World?," World Development, Elsevier, vol. 78(C), pages 587-609.
    3. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    4. Tziogkidis, Panagiotis & Philippas, Dionisis & Leontitsis, Alexandros & Sickles, Robin C., 2020. "A data envelopment analysis and local partial least squares approach for identifying the optimal innovation policy direction," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1011-1024.
    5. Yu, Anyu & Shi, Yu & You, Jianxin & Zhu, Joe, 2021. "Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 292(1), pages 199-212.
    6. Jie Wu & Ganggang Zhang & Qingyuan Zhu & Zhixiang Zhou, 2020. "An efficiency analysis of higher education institutions in China from a regional perspective considering the external environmental impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 57-70, January.
    7. Chen, Kaihua & Kou, Mingting & Fu, Xiaolan, 2018. "Evaluation of multi-period regional R&D efficiency: An application of dynamic DEA to China's regional R&D systems," Omega, Elsevier, vol. 74(C), pages 103-114.
    8. Ungkyu Han & Mette Asmild & Martin Kunc, 2016. "Regional R&D Efficiency in Korea from Static and Dynamic Perspectives," Regional Studies, Taylor & Francis Journals, vol. 50(7), pages 1170-1184, July.
    9. Orsa Kekezi & Johan Klaesson, 2020. "Agglomeration and innovation of knowledge intensive business services," Industry and Innovation, Taylor & Francis Journals, vol. 27(5), pages 538-561, May.
    10. Joanna Wolszczak-Derlacz & Aleksandra Parteka, 2011. "Efficiency of European public higher education institutions: a two-stage multicountry approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 887-917, December.
    11. 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.
    12. Cook, Wade D. & Harrison, Julie & Rouse, Paul & Zhu, Joe, 2012. "Relative efficiency measurement: The problem of a missing output in a subset of decision making units," European Journal of Operational Research, Elsevier, vol. 220(1), pages 79-84.
    13. Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2018. "Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain," Technovation, Elsevier, vol. 74, pages 42-53.
    14. Mario Coccia & Greta Falavigna & Alessandro Manello, 2015. "The impact of hybrid public and market-oriented financing mechanisms on the scientific portfolio and performances of public research labs: a scientometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 151-168, January.
    15. Ruixue Jiang & Yi Yang & Yao Chen & Liang Liang, 2021. "Corporate diversification, firm productivity and resource allocation decisions: The data envelopment analysis approach," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(5), pages 1002-1014, May.
    16. Du, Juan & Chen, Yao & Huo, Jiazhen, 2015. "DEA for non-homogenous parallel networks," Omega, Elsevier, vol. 56(C), pages 122-132.
    17. Mona Barat & Ghasem Tohidi & Masoud Sanei & Shabnam Razavyan, 2019. "Data envelopment analysis for decision making unit with nonhomogeneous internal structures: An application to the banking industry," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(5), pages 760-769, May.
    18. 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.
    19. Antonis Adam & Sofia Tsarsitalidou, 2019. "Environmental policy efficiency: measurement and determinants," Economics of Governance, Springer, vol. 20(1), pages 1-22, March.
    20. Min, Sujin & Kim, Juseong & Sawng, Yeong-Wha, 2020. "The effect of innovation network size and public R&D investment on regional innovation efficiency," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    21. Stefan Schweikl & Robert Obermaier, 2020. "Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects," Management Review Quarterly, Springer, vol. 70(4), pages 461-507, November.
    22. Min Yang & Yuqi Wei & Liang Liang & Jingjing Ding & Xianmei Wang, 2021. "Performance evaluation of NBA teams: A non-homogeneous DEA approach," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(6), pages 1403-1414, June.
    23. Wade D. Cook & Julie Harrison & Raha Imanirad & Paul Rouse & Joe Zhu, 2013. "Data Envelopment Analysis with Nonhomogeneous DMUs," Operations Research, INFORMS, vol. 61(3), pages 666-676, June.
    24. Lee, Jiyoung & Kim, Chulyeon & Choi, Gyunghyun, 2019. "Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea," European Journal of Operational Research, Elsevier, vol. 278(2), pages 533-545.
    25. Yongjun Li & Xiyang Lei & Alec Morton, 2019. "Performance evaluation of nonhomogeneous hospitals: the case of Hong Kong hospitals," Health Care Management Science, Springer, vol. 22(2), pages 215-228, June.
    26. Weizhen Yue & Jun Gao & Weilan Suo, 2020. "Efficiency evaluation of S&T resource allocation using an accurate quantification of the time-lag effect and relation effect: a case study of Chinese research institutes," Research Evaluation, Oxford University Press, vol. 29(1), pages 77-86.
    27. Kafouros, Mario & Wang, Chengqi & Piperopoulos, Panagiotis & Zhang, Mingshen, 2015. "Academic collaborations and firm innovation performance in China: The role of region-specific institutions," Research Policy, Elsevier, vol. 44(3), pages 803-817.
    28. Shamohammadi, Mehdi & Oh, Dong-hyun, 2019. "Measuring the efficiency changes of private universities of Korea: A two-stage network data envelopment analysis," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    29. Fukuyama, Hirofumi & Weber, William L. & Xia, Yin, 2016. "Time substitution and network effects with an application to nanobiotechnology policy for US universities," Omega, Elsevier, vol. 60(C), pages 34-44.
    30. Liang, Xinning & Liu, Anita M.M., 2018. "The evolution of government sponsored collaboration network and its impact on innovation: A bibliometric analysis in the Chinese solar PV sector," Research Policy, Elsevier, vol. 47(7), pages 1295-1308.
    31. Potter, Antony & Paulraj, Antony, 2021. "Unravelling supplier-laboratory knowledge spillovers: Evidence from Toyota's central R&D laboratory and subsidiary R&D centers," Research Policy, Elsevier, vol. 50(4).
    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. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "A parallel DEA-based method for evaluating parallel independent subunits with heterogeneous outputs," Journal of Informetrics, Elsevier, vol. 14(3).
    2. Wang, Qian & Ren, Shuming, 2022. "Evaluation of green technology innovation efficiency in a regional context: A dynamic network slacks-based measuring approach," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    3. Shi, Xing & Wu, Yanrui & Fu, Dahai, 2020. "Does University-Industry collaboration improve innovation efficiency? Evidence from Chinese Firms⋄," Economic Modelling, Elsevier, vol. 86(C), pages 39-53.
    4. Yukun Shi & Duchun Wang & Zimeng Zhang, 2022. "Categorical Evaluation of Scientific Research Efficiency in Chinese Universities: Basic and Applied Research," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    5. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    6. Jiawei Yang & Lei Fang, 2022. "Average lexicographic efficiency decomposition in two-stage data envelopment analysis: an application to China’s regional high-tech innovation systems," Annals of Operations Research, Springer, vol. 312(2), pages 1051-1093, May.
    7. Zhong, Meirui & Huang, Gangli & He, Ruifang, 2022. "The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data," Energy, Elsevier, vol. 246(C).
    8. Josef Jablonský, 2019. "Data Envelopment Analysis Models in Non-Homogeneous Environment," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 67(6), pages 1535-1540.
    9. Sun, Yu & Yang, Feng & Wang, Dawei & Ang, Sheng, 2023. "Efficiency evaluation for higher education institutions in China considering unbalanced regional development: A meta-frontier Super-SBM model," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    10. 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.
    11. Bresciani, Stefano & Puertas, Rosa & Ferraris, Alberto & Santoro, Gabriele, 2021. "Innovation, environmental sustainability and economic development: DEA-Bootstrap and multilevel analysis to compare two regions," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    12. Cao, Ting & Cook, Wade D. & Kristal, M. Murat, 2022. "Has the technological investment been worth it? Assessing the aggregate efficiency of non-homogeneous bank holding companies in the digital age," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    13. Avilés-Sacoto, Sonia Valeria & Cook, Wade D. & Güemes-Castorena, David & Zhu, Joe, 2020. "Modelling Efficiency in Regional Innovation Systems: A Two-Stage Data Envelopment Analysis Problem with Shared Outputs within Groups of Decision-Making Units," European Journal of Operational Research, Elsevier, vol. 287(2), pages 572-582.
    14. Lee, Jun Gon & Park, Min Jae, 2020. "Evaluation of technological competence and operations efficiency in the defense industry: The strategic planning of South Korea," Evaluation and Program Planning, Elsevier, vol. 79(C).
    15. Prokop, Viktor & Hajek, Petr & Stejskal, Jan, 2021. "Configuration Paths to Efficient National Innovation Ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    16. Sueyoshi, Toshiyuki & Ryu, Youngbok, 2022. "Performance assessment on technology transition from small businesses to the U.S. Department of Defense," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    17. Chen, Kaihua & Kou, Mingting & Fu, Xiaolan, 2018. "Evaluation of multi-period regional R&D efficiency: An application of dynamic DEA to China's regional R&D systems," Omega, Elsevier, vol. 74(C), pages 103-114.
    18. Xi Xiong & Guo-liang Yang & Kai-di Liu & De-qun Zhou, 2022. "A proposed fixed-sum carryovers reallocation DEA approach for social scientific resources of Chinese public universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4097-4121, July.
    19. Wu, Jie & Li, Mingjun & Zhu, Qingyuan & Zhou, Zhixiang & Liang, Liang, 2019. "Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs," Energy Economics, Elsevier, vol. 78(C), pages 468-480.
    20. Zhang, Liangliang & Huang, Shoujun, 2022. "Social capital and regional innovation efficiency: The moderating effect of governance quality," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 343-359.

    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:elcore:v:23:y:2023:i:1:d:10.1007_s10660-022-09599-9. 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.