IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v74y2021ics0038012120301439.html

Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective

Author

Listed:
  • Lin, Shoufu
  • Lin, Ruoyun
  • Sun, Ji
  • Wang, Fei
  • Wu, Weixiang

Abstract

This study firstly adopts Data Envelopment Analysis (DEA) window analysis with an ideal window width to dynamically investigate the technological innovation efficiency of China's high-tech industry during 2009–2016, simultaneously from provincial, regional and industrial perspective. The ideal window widths in the high-tech industry and its five sub-industries are all 4. The findings indicate that the efficiency of high-tech industry is low and presents a wave-shaped trend, as well as presents large inter-provincial and inter-regional differences. The efficiency in eastern region is always the highest, while the efficiency in northeastern region is the lowest. Moreover, the efficiencies in eastern region and western region both presented wave-shaped decrease trends, while the efficiencies in central region and northeastern region both presented wave-shaped increase trends. There are significant inter-regional and inter-provincial differences in efficiency of each sub-industry. The distributions of efficiencies of various provinces in five sub-industries are different. No province has always been on the innovation frontier for the entire evaluation period. The province with larger number of years on the frontier generally has the higher efficiency score, although there are some exceptions. Among the provinces on the frontier in various industries, the eastern provinces account for a large proportion.

Suggested Citation

  • Lin, Shoufu & Lin, Ruoyun & Sun, Ji & Wang, Fei & Wu, Weixiang, 2021. "Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:soceps:v:74:y:2021:i:c:s0038012120301439
    DOI: 10.1016/j.seps.2020.100939
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012120301439
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2020.100939?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Ferrier, G. D. & Kerstens, K. & Vanden Eeckaut, P., 1994. "Radial and nonradial technical efficiency measures on a DEA reference technology: a comparison using US banking data," LIDAM Reprints CORE 1156, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Yung-ho Chiu & Chin-wei Huang & Yu-Chuan Chen, 2012. "The R&D value-chain efficiency measurement for high-tech industries in China," Asia Pacific Journal of Management, Springer, vol. 29(4), pages 989-1006, December.
    3. Qingxian An & Fanyong Meng & Sheng Ang & Xiaohong Chen, 2018. "A new approach for fair efficiency decomposition in two-stage structure system," Operational Research, Springer, vol. 18(1), pages 257-272, April.
    4. 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.
    5. Wang, Chun-Hsien & Lu, Yung-Hsiang & Huang, Chin-Wei & Lee, Jun-Yen, 2013. "R&D, productivity, and market value: An empirical study from high-technology firms," Omega, Elsevier, vol. 41(1), pages 143-155.
    6. Valdmanis, Vivian, 1992. "Sensitivity analysis for DEA models : An empirical example using public vs. NFP hospitals," Journal of Public Economics, Elsevier, vol. 48(2), pages 185-205, July.
    7. Li, Lan-bing & Liu, Bing-lian & Liu, Wei-lin & Chiu, Yung-Ho, 2017. "Efficiency evaluation of the regional high-tech industry in China: A new framework based on meta-frontier dynamic DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 24-33.
    8. Robert Webb, 2003. "Levels of efficiency in UK retail banks: a DEA window analysis," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 10(3), pages 305-322.
    9. tone, Kaoru, 2010. "Variations on the theme of slacks-based measure of efficiency in DEA," European Journal of Operational Research, Elsevier, vol. 200(3), pages 901-907, February.
    10. Sueyoshi, Toshiyuki & Yuan, Yan, 2015. "Comparison among U.S. industrial sectors by DEA environmental assessment: Equipped with analytical capability to handle zero or negative in production factors," Energy Economics, Elsevier, vol. 52(PA), pages 69-86.
    11. Shoufu Lin & Ji Sun & Shanyong Wang, 2019. "Dynamic evaluation of the technological innovation efficiency of China’s industrial enterprises," Science and Public Policy, Oxford University Press, vol. 46(2), pages 232-243.
    12. 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.
    13. Ariel Pakes & Mark Schankerman, 1984. "The Rate of Obsolescence of Patents, Research Gestation Lags, and the Private Rate of Return to Research Resources," NBER Chapters, in: R&D, Patents, and Productivity, pages 73-88, National Bureau of Economic Research, Inc.
    14. Sueyoshi, Toshiyuki & Aoki, Shingo, 2001. "A use of a nonparametric statistic for DEA frontier shift: the Kruskal and Wallis rank test," Omega, Elsevier, vol. 29(1), pages 1-18, February.
    15. Pekka Korhonen & Sari Stenfors & Mikko Syrjänen, 2003. "Multiple Objective Approach as an Alternative to Radial Projection in DEA," Journal of Productivity Analysis, Springer, vol. 20(3), pages 305-321, November.
    16. E Revilla & J Sarkis & A Modrego, 2003. "Evaluating performance of public–private research collaborations: A DEA analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(2), pages 165-174, February.
    17. Zhang, Rui & Sun, Kai & Delgado, Michael S. & Kumbhakar, Subal C., 2012. "Productivity in China's high technology industry: Regional heterogeneity and R&D," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 127-141.
    18. Cullinane Kevin & Song Dong-Wook & Ji Ping & Wang Teng-Fei, 2004. "An Application of DEA Windows Analysis to Container Port Production Efficiency," Review of Network Economics, De Gruyter, vol. 3(2), pages 1-23, June.
    19. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    20. Necmi Avkiran & Kaoru Tone & Miki Tsutsui, 2008. "Bridging radial and non-radial measures of efficiency in DEA," Annals of Operations Research, Springer, vol. 164(1), pages 127-138, November.
    21. Sueyoshi, Toshiyuki & Goto, Mika & Sugiyama, Manabu, 2013. "DEA window analysis for environmental assessment in a dynamic time shift: Performance assessment of U.S. coal-fired power plants," Energy Economics, Elsevier, vol. 40(C), pages 845-857.
    22. Qingxian An & Fanyong Meng & Beibei Xiong & Zongrun Wang & Xiaohong Chen, 2020. "Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach," Annals of Operations Research, Springer, vol. 290(1), pages 707-729, July.
    23. 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.
    24. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2009. "An occurrence of multiple projections in DEA-based measurement of technical efficiency: Theoretical comparison among DEA models from desirable properties," European Journal of Operational Research, Elsevier, vol. 196(2), pages 764-794, July.
    25. Qingxian An & Fanyong Meng & Beibei Xiong, 2018. "Interval cross efficiency for fully ranking decision making units using DEA/AHP approach," Annals of Operations Research, Springer, vol. 271(2), pages 297-317, December.
    26. Mette Asmild & Joseph Paradi & Vanita Aggarwall & Claire Schaffnit, 2004. "Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry," Journal of Productivity Analysis, Springer, vol. 21(1), pages 67-89, January.
    27. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "DEA environmental assessment of coal fired power plants: Methodological comparison between radial and non-radial models," Energy Economics, Elsevier, vol. 34(6), pages 1854-1863.
    28. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, March.
    29. Hartman, Thomas E. & Storbeck, James E., 1996. "Input congestion in loan operations," International Journal of Production Economics, Elsevier, vol. 46(1), pages 413-421, December.
    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. Li Ji & Shigui Tao & Miaoyi Li & Clifford James Gere & Yung-ho Chiu, 2025. "Evaluation of innovation quality in China’s high-tech industries—technological development, innovation achievement transformation, and persistent innovation," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-18, December.
    2. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    3. Halkos, George Emm. & Tzeremes, Nickolaos G., 2009. "Exploring the existence of Kuznets curve in countries' environmental efficiency using DEA window analysis," Ecological Economics, Elsevier, vol. 68(7), pages 2168-2176, May.
    4. Halkos, George E. & Tzeremes, Nickolaos G., 2009. "Economic efficiency and growth in the EU enlargement," Journal of Policy Modeling, Elsevier, vol. 31(6), pages 847-862, November.
    5. Mehmet APAN & İhsan ALP & Ahmet ÖZTEL, 2019. "Determination of the Efficiencies of Textile Firms Listed in Borsa İstanbul by Using DEA-Window Analysis," Sosyoekonomi Journal, Sosyoekonomi Society, issue 27(42).
    6. Sueyoshi, Toshiyuki & Goto, Mika, 2015. "Japanese fuel mix strategy after disaster of Fukushima Daiichi nuclear power plant: Lessons from international comparison among industrial nations measured by DEA environmental assessment in time horizon," Energy Economics, Elsevier, vol. 52(PA), pages 87-103.
    7. Yang Huang & Meiqiang Wang, 2024. "Efficiency evaluation of China’s high-tech industry with a dynamic network data envelopment analysis game cross-efficiency model," Operational Research, Springer, vol. 24(1), pages 1-36, March.
    8. Yang, Xuehui & Zhang, Huirong & Li, Yan, 2022. "High-speed railway, factor flow and enterprise innovation efficiency: An empirical analysis on micro data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    9. Halkos, George E. & Tzeremes, Nickolaos G., 2011. "Oil consumption and economic efficiency: A comparative analysis of advanced, developing and emerging economies," Ecological Economics, Elsevier, vol. 70(7), pages 1354-1362, May.
    10. 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).
    11. Wan, Qunchao & Chen, Jin & Yao, Zhu & Yuan, Ling, 2022. "Preferential tax policy and R&D personnel flow for technological innovation efficiency of China's high-tech industry in an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    12. Iveta Palecková, 2017. "Application of Window Malmquist Index for Examination of Efficiency Change of Czech Commercial Banks," DANUBE: Law and Economics Review, European Association Comenius - EACO, issue 3, pages 173-190, September.
    13. Bolós, V.J. & Benítez, R. & Coll-Serrano, V., 2024. "Chance constrained directional models in stochastic data envelopment analysis," Operations Research Perspectives, Elsevier, vol. 12(C).
    14. Takashi Hiraide & Shinya Hanaoka & Takuma Matsuda, 2022. "The Efficiency of Document and Border Procedures for International Trade," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    15. Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2020. "Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"," Technovation, Elsevier, vol. 94.
    16. Toshiyuki Sueyoshi & Mika Goto, 2019. "DEA Non-Radial Approach for Resource Allocation and Energy Usage to Enhance Corporate Sustainability in Japanese Manufacturing Industries," Energies, MDPI, vol. 12(9), pages 1-22, May.
    17. Shih-Heng Yu, 2019. "Benchmarking and Performance Evaluation Towards the Sustainable Development of Regions in Taiwan: A Minimum Distance-Based Measure with Undesirable Outputs in Additive DEA," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1323-1348, August.
    18. Arazmuradov, Annageldy, 2011. "Energy consumption and carbon dioxide environmental efficiency for former Soviet Union economies. evidence from DEA window analysis," MPRA Paper 36903, University Library of Munich, Germany, revised 24 Feb 2012.
    19. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Managi, Shunsuke, 2014. "Non-Radial Directional Performance Measurement with Undesirable Outputs," MPRA Paper 57189, University Library of Munich, Germany.
    20. Hirofumi Fukuyama & Hiroya Masaki & Kazuyuki Sekitani & Jianming Shi, 2014. "Distance optimization approach to ratio-form efficiency measures in data envelopment analysis," Journal of Productivity Analysis, Springer, vol. 42(2), pages 175-186, October.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:soceps:v:74:y:2021:i:c:s0038012120301439. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.