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.
Highlights
Sanjiang University, Mechanical and Electrical Engineering College, No 310, Longxi Road, Yuhuatai District, Nanjing 210012, Jiangsu Province, China
Model Selection. e traditional Data Envelopment Analysis (DEA) model was first proposed by Chames et al with the purpose of analyzing the input and output efficiency of decision-making units under the premise of variable returns to scale. e model under the input orientation is as follows:
Research and development (R&D) full-time personnel are selected as human capital input variables
Summary
Li and Zhang [27] adopted data envelopment analysis CCR model (DEA-CCR) to dynamically inspect scientific research and innovation of universities’ performance before and after the implementation of highlevel university construction projects. Existing documents have important reference value, but there are still the following shortcomings: (1) some scholars analyze the dynamic evolution process of scientific and technological innovation efficiency in universities and add the lack of existing documents, they lack the provincial perspective. This article intends to use 30 provincial administrative units in China as a sample to measure efficiency, analyze the dynamic evolution process, and examine the changing trend in different regions and find the affecting factors
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