Multi-stage collaborative efficiency measurement of sci-tech finance
Network-DEA analysis and spatial impact research
Bibliographic Data
| ID | 15068313 |
|---|---|
| Authors | Liying Yu (0000-0002-8570-7181, Shanghai University), Weishu Li (Shanghai University), Zixuan Chen (0000-0001-9109-4484, Shanghai University), Mingkang Shi (Shanghai University), Hongda Liu (0000-0002-2628-7041, Shanghai University, corresponding author) |
| Year | 2022 |
| Volume | 35 |
| Issue | 1 |
| Pages | 300-324 |
| Publication date | 2022-12-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Economic Research-Ekonomska Istraživanja (JOURNAL) |
| Journal identifiers | ISSN: 1331-677X • E-ISSN: 1848-9664 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/1331677x.2021.1891110 |
| OpenAlex | W3134545596 |
| Language | EN |
| Citations received | 2 |
| References cited | 45 |
Sci-tech and finance plays an increasingly important role and have become an important driving force in economic development. In China, the problem of insufficient financial support for sci-tech innovation is important to enterprises. According to the internal relationship between different stages of Sci-tech and the finance system, this paper is aimed at exploring the efficiency measurement method between sci-tech and finance systems. Firstly the multi-stage collaborative structure of sci-tech finance is built, where the system of sci-tech is divided into three stages including the R&D stage, transformation stage of sci-tech achievements and industrialization stage, and the financing channel is the input of the finance system into the sci-tech system at different stages. The measurement method of the multi-stage collaborative efficiency between sci-tech and finance systems is put forward by the framework of network DEA. Then, taking China as an example, we collect the information of 30 provinces and cities from 2009 to 2016 and measure the efficiency of each system and the collaborative efficiency of the both. The efficiency’s spatial correlation is tested by means of Moran index. Finally, the influencing factors of the collaborative efficiency are analyzed based on the spatial econometric regression model, which considers the financing channels and human capital. To sum up, there are significant differences in the sci-tech finance collaborative efficiency among regions in China. Among them, the collaborative efficiency of Beijing, Shanghai and Jiangsu ranks in the top three. Comparing the different stages of the sci-tech system, the commercialization stage is a weak link in many regions of China. Human capital and financing channels of sci-tech finance have different degrees of positive impact on the sci-tech finance collaborative efficiency. Among them, human capital plays a greater role in promoting the sci-tech finance collaborative development
Beijing · Business · China · Economics · Geography · High tech · Industrial organization · Industrialisation · Efficiency Analysis Using DEA · Energy, Environment, Economic Growth · Innovation Policy and RD · Finance
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2022 - 2023 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |