A delay grey incidence analysis framework for assessing drivers and obstacles of R&D performance at Chinese universities. Issue 1 (5th February 2018)

• Record Type: Journal Article
• Authors: Liu, Yong; Quan, Bing-ting; Li, Hui
• Is Part Of: Grey systems. Volume 8:Issue 1(2018)
• Page Start: 2
• Page End: 13
• Publication Date: 2018-02-05
• Subjects: Knowledge creation -- Technology transfer -- R&D performance -- Delay grey incidence analysis; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.5
• DOI: 10.1108/GS-08-2017-0026
• Languages: English
• ISSNs: 2043-9377

Abstract

Abstract : Purpose: The purpose of this paper is to construct a novel delay grey incidence analysis model to analyze drivers and obstacles of university R&D performance. Design/methodology/approach: With respect to the fact that university R&D activities typically experience two stages of knowledge creation and technology transfer, and different drivers and obstacles come into play to affect the conversion of R&D investment to outcomes at each stage, based on the thought of grey incidence analysis and the specific characteristics of science and technology (sci-tech) development, a novel delay grey incidence analysis model is proposed in this paper, and then according to the yearbook statistical data, Chinese university R&D activities are investigated and the drivers and obstacles of university R&D performance are analyzed. Findings: The results show that the R&D full-time staff and R&D funds of basic research are the key drivers of influencing factors, and the sci-tech innovation talents in universities' R&D institutions and experiment development funds are the restraining factors to improve R&D performance in the stage of knowledge creation; the expenses of R&D achievement application and technology service and the full-time staff of achievement application and technology service are the key drivers and obstacles of influencing the aggregate amount of patent sale respectively. Practical implications: This research helps policy makers to reflect on their university R&D policies and understand how to enhance the technology transfer rate in China. Originality/value: The paper succeeds in identifying key drivers and obstacles of affecting university R&D performance in China by examining the input and output incidence at both the knowledge creation and technology transfer stages.