A Comparative Study on the Efficiency of R&D Activities of Universities in China by Region Using DEA–Malmquist

Abstract

This paper analyzes the efficiency of the input and output of R&D activity and the status of its development and management in universities in each region of China and proposes suggestions for improvement. The DEA–Malmquist model was used to analyze the static and dynamic science and technology statistics for universities in each region during 2006–2019 to reveal changes in the input and output of R&D activity. The overall efficiency of the R&D activity of universities in all regions of China was low. Among the 27 regions studied, DEA revealed effective efficiency in 20 regions in 2006, accounting for 74.07%, and in 19 regions in 2019, accounting for 70.37%. The Malmquist index was greater than 1 in 17 regions in 2006–2019, with an average value of 1.023 during 2006–2019. The technological progress of R&D activity in universities in each region plays a major role in the improvement of the overall efficiency. Conclusions: The efficiency of the R&D activity of Chinese universities in all regions is low in general, with large disparities between regions. The R&D activity of Chinese universities lacks scientific management. It is necessary to optimize the allocation of research resources, construct an evaluation system for the efficiency of R&D activity, and offer incentives for research to improve the output and promote the transformation of results in Chinese universities.

Research on the Transformation Efficiency of Scientific and Technological Achievements of Minis-try-affiliated Universities Based on Network DEA

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A Study on the Matching Efficiency of Malaysia's Labor Market during the Covid-19 Pandemic using the DEA-Malmquist Productivity Index

“…Meanwhile, Sheldon (2003) measures the efficiency of public employment services in Switzerland by employing the non-parametric method DEA. Du and Seo (2022) use the same method to analyze the efficiency of R&D activities of universities in China.…”

Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

“…If the assumption is improper, it will cause estimation deviation [13]. In addition, some scholars have applied the DEA method to green development efficiency [14] and to R&D activity efficiency [15].…”