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Research on the Dynamic Evolution of Scientific and Technological Innovation Efficiency in Universities and Identification of Influencing factors—based on Markov Chain Estimation and GMM Model

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  • Quanzhi Luo

Abstract

A scientific and comprehensive measurement of the efficiency of scientific and technological innovation activities is the basis and prerequisite for evaluating the existing environment and conditions of universities and is conducive for improving the efficiency and promoting the development of provincial economy. The paper selects the DEA (Data Envelopment Analysis) method to measure the efficiency of scientific and technological innovation in universities of China from 2007 to 2019 and studies its spatial differences. Then, the paper uses Markov chain estimation to describe the dynamic evolution process of the efficiency, and finally uses system GMM (generalized method of moments) model to identify the key influencing factors of its efficiency. The conclusions obtained are as follows: (1) The scientific and technological innovation efficiency in universities presents the highest distribution characteristics in the eastern part, the western part of China has the lowest, and the central part is midway of the two regions. There are significant differences between regions. (2) During the entire observation period, the efficiency in each region showed a path-dependent characteristic. After 2016, the mobility of different levels of technological innovation efficiency of universities in eastern part of China has increased, while in the central and western parts of China it tends to be stable. (3) Economic advantages, location advantages, government support, research and development foundation, and the efficiency of scientific and technological innovation in universities have a significant relationship, and the results are different in different regions.

Suggested Citation

  • Quanzhi Luo, 2021. "Research on the Dynamic Evolution of Scientific and Technological Innovation Efficiency in Universities and Identification of Influencing factors—based on Markov Chain Estimation and GMM Model," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, June.
  • Handle: RePEc:hin:jnlmpe:9831124
    DOI: 10.1155/2021/9831124
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    Cited by:

    1. Yaliu Yang & Yuan Wang & Cui Wang & Yingyan Zhang & Cuixia Zhang, 2022. "Temporal and Spatial Evolution of the Science and Technology Innovative Efficiency of Regional Industrial Enterprises: A Data-Driven Perspective," Sustainability, MDPI, vol. 14(17), pages 1-21, August.

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