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Measurement and Evaluation of China’s Regional Innovation Efficiency: Analysis Based on Network Super SBM-Malmquist Model

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Digital Transformation on Manufacturing, Infrastructure & Service (DTMIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 684))

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Abstract

The verification data of innovation efficiency comes from the status quo of innovation efficiency in thirty-one provinces in China from 2010 to 2020. To observe the development and changes of China’s regional innovation efficiency more clearly, the study takes 11 years as the overall time to study China’s regional innovation efficiency. The analysis method is to use the network super SBM-Malmquist model to measure and analyze the efficiency of innovation. Results show that recent years the innovation efficiency of China is increasing, the speed of technological efficiency is not as fast as the technological progress index. Technological progress has a greater impact on innovation efficiency than technological efficiency. There are regional differences in the growth rate of innovation efficiency in various provinces of China. According to the research conclusions, in terms of increasing the investment in fiscal innovation expenditure and reforming the investment method of fiscal innovation expenditure, countermeasures and suggestions to further improve the efficiency of regional innovation in China put forward.

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References

  1. Fare, R., Grosskopf, S., Norris, M., Zhang, Z.: Productivity growth, technical progress, and efficiency change in industrialized countries. Am. Econ. Rev. 84(1), 66–83 (1994)

    Google Scholar 

  2. Cruz-Cázares, C., Bayona-Sáez, C., García-Marco, T.: You can’t manage right what you can’t measure well: technological innovation efficiency. Res. Policy 42(6–7), 1239–1250 (2013)

    Article  Google Scholar 

  3. Wang, X., Shi, J., Wang, Z.: Accurately cognising the digital economy and facilitating its healthy and sustainable development in China. Sustain. Dev. Eng. Econ. 3(4), 61–74 (2022)

    MathSciNet  Google Scholar 

  4. Nasierowski, W., Arcelus, F.J.: On the efficiency of national innovation systems. Socioecon. Plann. Sci. 37(3), 215–234 (2003)

    Article  Google Scholar 

  5. Chen, K., Guan, J.: Measuring the efficiency of China’s regional innovation systems: application of network data envelopment analysis (DEA). Reg. Stud. 46(3), 355–377 (2012)

    Article  Google Scholar 

  6. Broekel, T.: Collaboration intensity and regional innovation efficiency in Germany-a conditional efficiency approach. Ind. Innov. 19(2), 155–179 (2012)

    Article  Google Scholar 

  7. Li, Z., Yang, S.: Fiscal decentralization, government innovation preference and regional innovation efficiency. Manag. World 34(12), 17 (2018)

    Google Scholar 

  8. Yang, B., Cao, H.: Evaluation of the internationalization efficiency of technological innovation in universities in various regions of my country based on the super-efficiency SBM-Malmquist model. Res. Sci. Technol. Manag. (2018). (in Chinese)

    Google Scholar 

  9. Zhang, C., Yang, Z.: Stock liquidity, agency efficiency and enterprise technology innovation—an empirical study based on poisson regression. East China Econ. Manag. 10(11), 8 (2018)

    Google Scholar 

  10. Yang, Q., Liu, X., Sun, S.: Regional differences in China’s science and technology innovation efficiency and identification of their causes—based on major national regional development strategies. Sci. Res. 40(5), 12 (2022)

    Google Scholar 

  11. Li, H., Zhou, Y.: Research on influencing factors of resource allocation of regional science and technology innovation information elements based on spatial measurement. Inf. Sci. 35(12), 6 (2017)

    MathSciNet  Google Scholar 

  12. Daming, Y., Xizi, H.: Evaluation of the innovation efficiency of interprovincial industrial ecological technology in the Yangtze River economic belt. Econ. Geogr. 9, 1–24 (2016)

    Google Scholar 

  13. Song, L., Su, N.: A stochastic frontier analysis of the impact of open innovation on the efficiency of technological innovation in my country—based on the perspective of external expenditure of R&D funds. Contemp. Econ. Manag. 39(11) (2017)

    Google Scholar 

  14. Yixin, W., Kong, R.: Research on the efficiency and key influencing factors of scientific and technological innovation of industrial enterprises above designated size from the perspective of value chain-based on the DEA-tobit two-stage model. Sci. Technol. Manag. Res. (3), 7 (2019)

    Google Scholar 

  15. Tone, K., Toloo, M., Izadikhah, M.: A modified slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 287(2), 560–571 (2020)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, Polytechnic, 29, St. Petersburg, Russia

    Shuquan Li & Marina Ianenko

Authors
  1. Shuquan Li
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  2. Marina Ianenko
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Correspondence to Shuquan Li .

Editor information

Editors and Affiliations

  1. Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia

    Igor Ilin

  2. St. Cyril and St. Methodius University of Veliko Turnovo, Veliko Tarnovo, Bulgaria

    Mariana Mateeva Petrova

  3. Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia

    Tatiana Kudryavtseva

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Li, S., Ianenko, M. (2023). Measurement and Evaluation of China’s Regional Innovation Efficiency: Analysis Based on Network Super SBM-Malmquist Model. In: Ilin, I., Petrova, M.M., Kudryavtseva, T. (eds) Digital Transformation on Manufacturing, Infrastructure & Service. DTMIS 2022. Lecture Notes in Networks and Systems, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-031-32719-3_38

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