Journal of Geographical Sciences >
The innovation networks shaped by large innovative enterprises in urban China
Ma Haitao (1979-), PhD and Associate Professor, specialized in urban geography and planning. E-mail: maht@igsnrr.ac.cn |
Received date: 2022-03-07
Accepted date: 2022-09-06
Online published: 2023-03-21
Supported by
The Third Xinjiang Scientific Expedition Program(2021xjkk0905)
National Natural Science Foundation of China(41971209)
National Natural Science Foundation of China(41571151)
National Natural Science Foundation of China(41901186)
Studies investigating innovation networks shaped by large innovative enterprises (LI-ENTs), which play a very important role in intercity diffusion of technology and knowledge, are rather thin on the ground. Using location information of LI-ENTs in China, we performed a headquarter-branch analysis to generate intercity innovation linkages and analyzed the patterns and dynamics of the generated network of knowledge diffusion. Although the network covers 353 cities across China, its spatial distribution is extremely uneven, with a few cities and city-dyads dominating the structure of the network. Furthermore, intercity linkages of innovation within and of urban agglomerations, as well as their central cities, stand out. With regard to network dynamics, the economic development level, innovation ability, and administrative level of cities, as well as the geographical, institutional, and technological proximity between cities are all found to have a positive impact on intercity linkages of innovations, whilst the impact of FDI on the national distribution of Chinese innovative enterprises is negative. Most importantly, the status of cities within the urban agglomeration exerts a significant positive effect in relation to the innovative enterprises’ expansions, which reflects that the top-down forces of government and the bottom-up forces of market function together.
MA Haitao , Yehua Dennis WEI , HUANG Xiaodong , ZHANG Weiyang . The innovation networks shaped by large innovative enterprises in urban China[J]. Journal of Geographical Sciences, 2023 , 33(3) : 599 -617 . DOI: 10.1007/s11442-022-2065-7
Figure 1 Research frameworkNote: LI-ENTs means large innovative enterprises. |
Table 1 The evaluation of Chinese innovative enterprises by various authoritative institutions |
Evaluation | Institutions | Number |
---|---|---|
Innovative (Pilot) Enterprises | Ministry of Science and Technology of China, State- owned Assets Supervision and Administration Commission of China, All-China Federation of Trade Unions | 676 (2012-2016) |
National Technological Innovation Demonstration Enterprises | Ministry of Industry and Information Technology of China, The Ministry of Finance of China | 495 (2011-2017) |
The List of 1000 Top Chinese Innovative Enterprises | China’s Renmin University | 1000 (2017) |
China’s 100 most innovative companies | Clarivate Analytic | 200 (2016, 2017) |
Chinese corporate in 2017 Global Innovation 1000 | Strategy& | 113 (2017) |
Table 2 Description of independent variables |
Variable | Description | Source |
---|---|---|
GDPPC | Gross Domestic Production per capita (in log) | China city statistical yearbooks, 2012-2016 |
STUDENT | Number of university/college students (in log) | China city statistical yearbook, 2012-2016 |
EXPEDITURE | Amount of science and technology expenditure (in log) | China city statistical yearbook, 2012-2016 |
FDI | Foreign direct investment (in log) | China city statistical yearbook, 2012-2016 |
ADMIN | Urban administrative level (in log) | Administrative division of China |
POPSHARE | Proportion of the population of an urban agglomeration located in a given city (in log) | China city statistical yearbook, 2012-2016 |
DISTANCE | Euclidean distance between two cities (in log) | Calculated from cities longitude and latitude |
PATENT | Degree of deviation in patent types between cities | Shanghai intellectual property platform |
RELATIONSHIP | Administrative relationship between cities (in log) | Administrative division of China |
Note: We used five-year average data for cities from 2012 to 2016. |
Figure 3 The city locations of the branches of China’s 1778 largest innovative enterprises |
Figure 4 Intercity networks constructed by China’s 1778 largest innovative enterprises |
Table 3 Internal and external connections of the three most important urban agglomerations in China |
Urban agglomeration | Innovative enterprise flows within the region | Innovative enterprise flows out of the region | ||||
---|---|---|---|---|---|---|
Total strength | Average strength | P/R (%) | Total strength | Average strength | P/R (%) | |
Beijing-Tianjin-Hebei | 1556 | 119.69 | 43 | 15063 | 44.30 | 82 |
Yangtze River Delta | 3393 | 130.50 | 23 | 13180 | 40.31 | 36 |
Pearl River Delta | 1016 | 112.89 | 33 | 8686 | 25.25 | 53 |
Note: P/R refers to proportion of the primate city (the top one) in the region. |
Table 4 City node attribution in the network (top 10) |
Ranking | Total-degree | Out-degree | In-degree | Radiating cities | Attractive cities |
---|---|---|---|---|---|
1 | Beijing (13714) | Beijing (9926) | Beijing (3788) | Beijing (297) | Beijing (139) |
2 | Shanghai (6271) | Shenzhen (3423) | Shanghai (3493) | Shenzhen (168) | Shanghai (138) |
3 | Shenzhen (5324) | Shanghai (2778) | Shenzhen (1901) | Shanghai (146) | Chengdu (95) |
4 | Hangzhou (2822) | Hangzhou (1737) | Chengdu (1457) | Nanjing (137) | Shenzhen (93) |
5 | Guangzhou (2493) | Guangzhou (1221) | Guangzhou (1272) | Chengdu (134) | Guangzhou (84) |
6 | Chengdu (2427) | Wuhan (1042) | Tianjin (1303) | Tianjin (129) | Tianjin (83) |
7 | Tianjin (2320) | Tianjin (1017) | Hangzhou (1085) | Hangzhou (129) | Wuhan (82) |
8 | Wuhan (2117) | Suzhou (993) | Nanjing (1084) | Wuhan (127) | Nanjing (75) |
9 | Nanjing (1923) | Chengdu (970) | Wuhan (1075) | Guangzhou (113) | Xi’an (74) |
10 | Suzhou (1793) | Nanjing (839) | Chongqing (934) | Changsha (99) | Chongqing (72) |
Table 5 Regression result of the influence factors in intercity networks based on LI-ENTs |
Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | Model (6) | |
---|---|---|---|---|---|---|
O_LnGDPPC | 1.941*** | 1.165*** | 0.513*** | 0.581*** | 0.625*** | 0.616*** |
(0.029) | (0.027) | (0.033) | (0.033) | (0.034) | (0.034) | |
O_LnSTUDENT | 0.625*** | 0.388*** | 0.276*** | 0.310*** | 0.289*** | |
(0.011) | (0.013) | (0.015) | (0.016) | (0.016) | ||
O_LnEXPEDITURE | 0.446*** | 0.377*** | 0.439*** | 0.443*** | ||
(0.015) | (0.016) | (0.018) | (0.018) | |||
O_LnADMIN | 0.621*** | 0.602*** | 0.558*** | |||
(0.048) | (0.048) | (0.049) | ||||
O_LnFDI | -0.087*** | -0.079*** | ||||
(0.013) | (0.013) | |||||
O_Ln(POPSHARE+1) | 0.884*** (0.155) | |||||
D_LnGDPPC | 1.410*** | 0.689*** | 0.249*** | 0.287*** | 0.296*** | 0.281*** |
(0.027) | (0.026) | (0.032) | (0.032) | (0.032) | (0.032) | |
D_LnSTUDENT | 0.594*** | 0.421*** | 0.280*** | 0.278*** | 0.240*** | |
(0.011) | (0.013) | (0.015) | (0.015) | (0.016) | ||
D_LnEXPEDITURE | 0.328*** | 0.240*** | 0.250*** | 0.260*** | ||
(0.015) | (0.015) | (0.018) | (0.018) | |||
D_LnADMIN | 0.877*** | 0.889*** | 0.811*** | |||
(0.050) | (0.050) | (0.050) | ||||
D_LnFDI | -0.012 | 0.003 | ||||
(0.012) | (0.012) | |||||
D_Ln(POPSHARE+1) | 1.514*** | |||||
(0.145) | ||||||
LnDISTANCE | -0.502*** | -0.298*** | -0.135*** | -0.200*** | -0.208*** | -0.268*** |
(0.025) | (0.021) | (0.020) | (0.020) | (0.020) | (0.021) | |
RELATIONSHIP | 0.884*** | 1.208*** | 1.335*** | 1.343*** | 1.335*** | 1.261*** |
(0.067) | (0.053) | (0.050) | (0.050) | (0.050) | (0.050) | |
LnPATENT | -0.921*** | -5.226*** | -3.162*** | -2.211*** | -1.938*** | -2.112*** |
(0.245) | (0.203) | (0.197) | (0.203) | (0.210) | (0.211) | |
_cons | -34.963*** | -31.039*** | -25.494*** | -22.542*** | -23.321*** | -22.442*** |
(0.538) | (0.443) | (0.441) | (0.457) | (0.481) | (0.483) | |
/lnalpha | 1.131*** | -0.788*** | -1.799*** | -1.818*** | -1.750*** | -1.716*** |
(0.034) | (0.088) | (0.158) | (0.140) | (0.134) | (0.129) | |
Obs. | 82082 | 82082 | 82082 | 82082 | 82082 | 82082 |
Pseudo R2 | 0.166 | 0.273 | 0.299 | 0.308 | 0.309 | 0.312 |
Standard errors are in parenthesis. *** p<0.01, ** p<0.05, * p<0.1 |
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