Chinese Provincial Difference in the Efficiency of Universities’ Scientific and Technological Activities Based on DEA with Shared Input

Abstract

The universities’ scientific and technological activities have become the key link of science and technology and economy, and the core of promoting the national innovation capability and competitiveness. To achieve high-quality development of China’s science and technology, it is of great significance to measure the efficiency of universities’ scientific and technological activities accurately and explore their regional differences. Based on the innovation value chain, we construct a two-stage efficiency measure index system of universities’ scientific and technological activity, and then according to the universities’ statistical data in China from 2010 to 2017, by taking provincial universities as the research object, we exploit a two-stage network DEA model with shared input to measure the provincial efficiency of universities’ scientific and technological activities in China; what is more, we explore the provincial differences in efficiency of universities’ scientific and technological activities. Finally, we extract the influencing factors on the efficiency of universities’ scientific and technological activities using the dynamic panel data model. The results show that there are serious problems in the transformation between knowledge output and results in the universities’ scientific and technological activities. The scientific and technological activities of regional universities in China are not balanced. For provincial universities’ efficiency of scientific and technological activities, there exist great differences among different time and space, stages, and resource utilization modes. The endogenous factors such as policy system and technology service are much more important factors.

Understanding the performance of universities to promote technological innovation and transformation: An application to China based on a new network data envelopment analysis cross‐efficiency model

“…As for the choice of production variables, this study refers to Xiong et al (2018) and Zhao et al (2022). Production inputs ( X (1) ) at the first stage contain gross R&D labour and gross R&D expense.…”

“…According to Yang et al (2018), Xiong et al (2018) and Zhao et al (2022), this study selects four outputs for the first stage. The outputs ( Y (1) ) from R&D stage include published academic papers, published S&T works, international project acceptance and technology achievement award.…”

“…Academically, many researchers have considered how to improve the performance of the R&D activities in Chinese universities (e.g. An et al, 2019; Yang et al, 2018; ; Zhang et al, 2020; Zhao et al, 2022). The abovementioned efforts show that how to improve the performance of the R&D activities has become an important target for Chinese universities.…”

“…Currently, there may exist an important problem regarding how to improve R&D activities in Chinese universities. The problem is that Chinese universities generally pay more attention to R&D performance but limited attention to S&T transformation (An et al, 2019; Xiong et al, 2018; Zhao et al, 2022). The limited attention to transformation may be due to the performance evaluation system of universities initiated by the Chinese government.…”

“…These methods suffer from one important deficiency, since they require assumptions of the production functions. The second group of studies utilize Data Envelopment Analysis (DEA) models (An et al, 2019; Yang et al, 2018; Zhang et al, 2020; Zhao et al, 2022). In this regard, DEA models belong to non‐parametric methods and do not need priori assumptions of the production functions.…”