Eliminating congestion by increasing inputs in R&D activities of Chinese universities

Xian-tong Ren, Hirofumi Fukuyama () and Guo-liang Yang

Omega, 2022, vol. 110, issue C

Abstract: In recent years, China's research and development (R&D) investment has increased rapidly, which has led to concerns about overinvestment in China's R&D activities. This paper investigates the congestion in the case of R&D activities in Chinese universities, where congestion is known as a phenomenon whereby an increase in inputs leads to a decrease in maximum possible outputs, and tries to answer the question that how much input should be reduced at least to eliminate congestion in China's R&D activities. To minimise the input reduction in eliminating congestion without reducing outputs, we redefine the concept of congestion and explore the way to eliminate congestion by increasing inputs. In this paper, congestion is divided into two categories, relative congestion and absolute congestion, according to whether it can be eliminated by increasing inputs. Corresponding congestion identification and measurement methods are developed on the framework of data envelopment analysis. Based on our methods, we explore the possibility of eliminating congestion in the case of R&D activities in Chinese universities from an innovative perspective of increasing inputs, which is consistent with China's policy to increase investment in R&D activities. We find that most of the congestion in R&D activities of Chinese universities belongs to relative congestion, which indicates that the R&D activities in Chinese universities can be further invested to increase scientific achievements. Based on our findings, policy suggestions are proposed for promoting the development of R&D activities in Chinese universities.

Keywords: Data envelopment analysis; Congestion; Excessive inputs; R&D activities in Chinese universities (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.omega.2022.102618

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