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Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors

Author

Listed:
  • Qian Wang

    (School of Economics, Fujian Normal University, Fuzhou 350117, China
    School of Economics & Management and Law, University of South China, Hengyang 421001, China)

  • Yang Chen

    (School of Economics, Fujian Normal University, Fuzhou 350117, China)

  • Heshan Guan

    (School of Economics & Management and Law, University of South China, Hengyang 421001, China)

  • Oleksii Lyulyov

    (Department of Marketing, Sumy State University, 40007 Sumy, Ukraine)

  • Tetyana Pimonenko

    (Department of Marketing, Sumy State University, 40007 Sumy, Ukraine)

Abstract

Innovation is the engine and accelerator that drives high-quality economic and enterprise development. In recent years, the output of scientific and technological innovation in China has been high, but the phenomenon of low efficiency and low quality of innovation occurs frequently. In this study, first, technological innovation efficiency (TIE) was measured. Then, a dynamic evaluation and analysis of spatial-temporal characteristics of efficiency were performed. Lastly, the driving factors of innovation efficiency were explored. TIE was calculated dynamically in 30 provinces of China from 2011 to 2019 based on the improved super-efficiency SBM-DEA model. Then, the kernel density estimation method was adopted to analyse the spatial-temporal differentiation characteristics and dynamic evolution process of provincial efficiency. The findings confirm that from 2011 to 2019, the top five provinces for TIE in China were Beijing (1.0), Shanghai (0.96), Hainan (0.96), Jilin (0.94) and Tianjin (0.91). The provinces with lowest average efficiency were Qinghai (0.77), Ningxia (0.73) and Inner Mongolia (0.73). The significant differences in the level of technological innovation in different regions were caused by the long-term and in-depth implementation of the government’s strategy of revitalising science and driving innovation in parts of areas. The findings of kernel function confirm that the TIE in most parts of China was gradually polarised. Furthermore, the results show that for every 1 unit of government R&D funding support, the average marginal utility of the expected TIE will reach 0.192, which is more significant in the central and western regions. On this basis, combined with environmental factors of innovation market, infrastructure, financing and enterprise innovation potential, the article also extracts the driving factors that affect the differences in provincial efficiency. The findings provide a reference for guiding provinces to carry out innovation activities independently and improve innovation quality and efficiency.

Suggested Citation

  • Qian Wang & Yang Chen & Heshan Guan & Oleksii Lyulyov & Tetyana Pimonenko, 2022. "Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8321-:d:857701
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    3. Iryna Nyenno & Vyacheslav Truba & Liudmyla Tokarchuk, 2023. "Managerial Future of the Artificial Intelligence," Virtual Economics, The London Academy of Science and Business, vol. 6(2), pages 72-88, June.
    4. Liyan Sun & Zhuoying Wang & Li Yang, 2023. "Research on the Dynamic Coupling and Coordination of Science and Technology Innovation and Sustainable Development in Anhui Province," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    5. Shaojian Qu & Can Feng & Shan Jiang & Jinpeng Wei & Yuting Xu, 2022. "Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-21, August.

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