Research on the Spatial Network Structure and Influencing Factors of the Allocation Efficiency of Agricultural Science and Technology Resources in China
Abstract
:1. Introduction
2. Methodology and Data Source
2.1. SBM Model
2.2. The Modified Gravitational Model
2.3. Social Network Analysis SNA
2.4. Quadratic Assignment Procedure (QAP)
3. Results
3.1. Analysis of Allocation Efficiency
3.2. The Characteristics and Evolution Trend of the Overall Correlation Network Structure
3.3. Spatially Correlated Individual Network Characteristics
3.3.1. Degree Centrality
3.3.2. Close to Centrality
3.3.3. Middle Centrality
3.4. Small World Analysis
3.5. Block Model Analysis
3.6. Core–Periphery Analysis
3.7. Analysis of Influencing Factors Based on QAP Method
3.8. Regression Analysis Based on QAP Method
4. Discussion
5. Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Input Indicators | The Name of the Indicator | Output Indicators | The Name of the Indicator |
---|---|---|---|
Human input | Practitioners (persons) in scientific research institutions | Academic output | Published scientific papers (articles) |
R&D personnel (people) | Publishing scientific and technological works (kinds) | ||
Full-time equivalent of the R&D personnel (human year) | |||
Material input | Actual completion of capital investment (thousands of dollars) | Technical output | Number of patents accepted (pieces) |
Research Infrastructure (Thousands) | Number of patents granted (pieces) | ||
Year-end fixed assets (thousands of yuan) | Number of patents for valid inventions (pieces) | ||
Financial input | Internal expenditure of scientific research institutions this year (thousands of yuan) | Economic output | Technical income from non-government funds (thousands of dollars) |
Internal expenditure on R&D (thousands of dollars) | Production and operation income (thousands of yuan) |
Index | Indicator Meaning |
---|---|
Network density | Indicates the density and complexity of the spatial network relationship of the allocation efficiency of ASTR; the greater the value, the closer the connection between regions. |
Network correlation | Reflects the stability and fragility of the network structure of agricultural science and technology resource allocation efficiency. |
Network level | Characterizes the asymmetrical reachability in the spatial network node of agricultural science and technology resource allocation efficiency. |
Network efficiency | Indicates the number of spatially associated channels for the ASTR allocation efficiency; the lower the network efficiency value, the more associated channels. |
Degree centrality | Measures the status of each member in the overall network; the higher the value, the greater the relationship generated by the member, and the more prominent the central position in the network. |
Close to centrality | Depicts the degree of direct correlation between a single member and other members in the associated network; the higher the value, the more direct relationships the member has. |
Middle centrality | Reflects the degree of control of a member of the network over the relationship between other members, that is, the degree of mediation; the higher the value, the more obvious the mediation. |
Index | Formula | Description | |
---|---|---|---|
The overall network | Network density | The ratio of the actual number of relationships to the total number of theoretical maximum relationships | |
Network correlation | The degree of direct or indirect reachability between any two members | ||
Network level | The degree of asymmetrical reachability between members in the network | ||
Network efficiency | Extent of redundant connections in the network | ||
Individual network | Degree centrality | The ratio of the number of members directly associated with a member to the maximum possible total number of members directly associated | |
Close to centrality | The sum of the shortcuts distance between a member and other members in the network | ||
Middle centrality | The extent to which members of the network play an intermediary role for other members |
The Proportion of the Internal Relationships of the Plates | The Proportion of Relationships Received by the Plate | |
---|---|---|
Close to 0 | Less than 0 | |
≥(gq-1)/(g-1) | Two-way overflow plate | Net benefit plate |
<(gq-1)/(g-1) | Net spillover plate | Middlemen plate |
Type | Meaning |
---|---|
Net spillover plate | Members of this plate sent significantly more spillover relationships than those received by other plates |
Net benefit plate | Members of this plate received significantly more spillover relationships than those sent by other plates |
Two-way overflow plates | There are more spillover relationships between the internal members of the plate and more external spillovers to the plate |
Middlemen plate | The internal members of the plate have relatively few connections and more contacts with external members outside of the plate The member sends and receives the spillover relationships with members external to the plate |
Province | Degree Centrality | Close to Centrality | Middle Centrality | ||||||
---|---|---|---|---|---|---|---|---|---|
Point-Out | Point-In | The Total | Centrality | Rank | Centrality | Rank | Centrality | Rank | |
Beijing | 8 | 24 | 32 | 83.333 | 3 | 85.714 | 3 | 14.110 | 2 |
Tianjin | 8 | 17 | 25 | 60.000 | 5 | 71.429 | 5 | 5.245 | 5 |
Hebei | 5 | 4 | 9 | 20.000 | 29 | 55.556 | 29 | 0.152 | 27 |
Shanxi | 6 | 5 | 11 | 23.333 | 20 | 56.604 | 20 | 0.227 | 21 |
Inner-Mongolia | 5 | 5 | 10 | 23.333 | 20 | 56.604 | 20 | 0.225 | 24 |
Liaoning | 5 | 3 | 8 | 20.000 | 29 | 55.556 | 29 | 0.086 | 30 |
Jilin | 6 | 1 | 7 | 20.000 | 29 | 55.556 | 29 | 0.086 | 30 |
Heilongjiang | 8 | 1 | 9 | 26.667 | 13 | 57.692 | 13 | 0.245 | 19 |
Shanghai | 6 | 26 | 32 | 86.667 | 2 | 88.235 | 2 | 13.522 | 3 |
Jiangsu | 3 | 27 | 30 | 90.000 | 1 | 90.909 | 1 | 15.541 | 1 |
Zhejiang | 4 | 20 | 24 | 70.000 | 4 | 76.923 | 4 | 7.038 | 4 |
Anhui | 3 | 10 | 13 | 33.333 | 10 | 60.000 | 10 | 0.795 | 10 |
Fujian | 7 | 9 | 16 | 43.333 | 7 | 63.830 | 7 | 1.985 | 7 |
Jiangxi | 7 | 6 | 13 | 23.333 | 20 | 56.604 | 20 | 0.226 | 22 |
Shandong | 8 | 10 | 18 | 40.000 | 8 | 62.500 | 8 | 1.091 | 9 |
Henan | 6 | 7 | 13 | 26.667 | 13 | 57.692 | 13 | 0.312 | 16 |
Hubei | 7 | 3 | 10 | 23.333 | 20 | 56.604 | 20 | 0.139 | 28 |
Hunan | 7 | 3 | 10 | 23.333 | 20 | 56.604 | 20 | 0.226 | 22 |
Guangdong | 10 | 11 | 21 | 46.667 | 6 | 65.217 | 6 | 2.370 | 6 |
Guangxi | 6 | 4 | 10 | 26.667 | 13 | 57.692 | 13 | 0.295 | 17 |
Hainan | 7 | 1 | 8 | 23.333 | 20 | 56.604 | 20 | 0.134 | 29 |
Chongqing | 7 | 4 | 11 | 26.667 | 13 | 57.692 | 13 | 0.360 | 15 |
Sichuan | 9 | 3 | 12 | 30.000 | 12 | 58.824 | 12 | 0.550 | 11 |
Guizhou | 7 | 2 | 9 | 23.333 | 20 | 56.604 | 20 | 0.238 | 20 |
Yunnan | 8 | 2 | 10 | 26.667 | 13 | 57.692 | 13 | 0.412 | 13 |
Tibet | 8 | 0 | 8 | 26.667 | 13 | 57.692 | 13 | 0.364 | 14 |
Shaanxi | 10 | 2 | 12 | 33.333 | 10 | 60.000 | 10 | 0.499 | 12 |
Gansu | 10 | 3 | 13 | 40.000 | 8 | 62.500 | 8 | 1.343 | 8 |
Qinghai | 8 | 0 | 8 | 26.667 | 13 | 57.692 | 13 | 0.222 | 25 |
Ningxia | 7 | 0 | 7 | 23.333 | 20 | 56.604 | 20 | 0.220 | 26 |
Xinjiang | 7 | 0 | 7 | 23.333 | 20 | 56.604 | 20 | 0.251 | 18 |
Average | 6.871 | 6.871 | 13.742 | 35.914 | - | 62.130 | - | 2.210 | - |
Plate | Number of Relationships Received | Member Number | Number of External Relationships Receiving Plates | Number of Spillover Plate Relationships | Total Number of Spillover Relationships | Proportion of Internal Relationships Expected (%). | Actual Proportion of Internal Relationships (%). | Plate Role Division | |||
---|---|---|---|---|---|---|---|---|---|---|---|
i | ii | iii | iv | ||||||||
I | 6 | 2 | 13 | 3 | 3 | 45 | 18 | 24 | 6.6667 | 25.0000 | Two-way overflow |
II | 1 | 7 | 3 | 19 | 5 | 86 | 23 | 24 | 13.3333 | 23.3333 | Net benefit |
III | 25 | 27 | 13 | 3 | 10 | 18 | 55 | 80 | 30.0000 | 19.1176 | broker |
IV | 19 | 57 | 2 | 13 | 13 | 25 | 78 | 97 | 40.0000 | 14.2857 | Net spillover |
Plate | Density Matrix | Image Matrix | ||||||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 | |
1 | 1.000 | 0.133 | 0.433 | 0.077 | 1 | 0 | 1 | 0 |
2 | 0.067 | 0.350 | 0.060 | 0.292 | 0 | 1 | 0 | 1 |
3 | 0.833 | 0.540 | 0.144 | 0.023 | 1 | 1 | 0 | 0 |
4 | 0.487 | 0.877 | 0.015 | 0.083 | 1 | 1 | 0 | 0 |
Variable | Actual Correlation Factor | The Level of Significance | The Mean of the Correlation Coefficient | Standard Deviation | Minimum | Maximum | p ≥ 0 | p < 0 |
---|---|---|---|---|---|---|---|---|
Distance | 0.1763 | 0.0002 | 0.0005 | 0.0362 | −0.1234 | 0.1335 | 0.0002 | 1.000 |
Indus | 0.1806 | 0.0098 | −0.0002 | 0.0659 | 0.1643 | 0.2644 | 0.0098 | 0.9904 |
Labor | 0.0428 | 0.2110 | −0.0006 | 0.0576 | −0.1441 | 0.2445 | 0.2110 | 0.7892 |
Urban | 0.3478 | 0.0002 | 0.0003 | 0.0654 | −0.1664 | 0.2968 | 0.0002 | 1.0000 |
Mech | 0.0267 | 0.2985 | −0.0001 | 0.0606 | −0.1481 | 0.2753 | 0.2985 | 0.7017 |
Pgdp | 0.5026 | 0.0002 | 0.0007 | 0.0635 | −0.1461 | 0.2903 | 0.0002 | 1.000 |
Inform | 0.0019 | 0.4601 | 0.0002 | 0.0586 | −0.1656 | 0.2135 | 0.4601 | 0.5401 |
Variable | 2009 | 2012 | 2015 | 2018 |
---|---|---|---|---|
Distance | 0.2122 *** (0.0353) | 0.2598 *** (0.0338) | 0.2531 *** (0.0003) | 0.2694 (0.0350) |
Indus | −0.0175 (0.3580) | 0.0504 * (0.3529) | 0.0314 (0.3729) | 0.0119 (0.2617) |
Labor | 0.0135 (0.0000) | 0.0029 (0.0000) | 0.0058 (0.0000) | 0.0330 (0.0000) |
Urban | −0.2109 ** (0.0024) | −0.1227 ** (0.0022) | −0.0807 * (0.0022) | −0.0697 ** (0.0017) |
Mech | 0.0384 (0.0000) | 0.0278 (0.0000) | 0.0674 (0.0000) | 0.0214 (0.0000) |
Pgdp | 0.6955 *** (0.0000) | 0.5988 *** (0.0000) | 0.5760 *** (0.0000) | 0.5938 *** (0.0000) |
Inform | 0.0490 (0.0003) | 0.0157 (0.0003) | 0.0106 (0.0004) | 0.0042 (0.0004) |
R2 | 0.3043 | 0.3081 | 0.3086 | 0.3258 |
Adj-R2 | 0.2989 | 0.3028 | 0.3034 | 0.3207 |
p-Value | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Observations | 930 | 930 | 930 | 930 |
The number of random displacements | 5000 | 5000 | 5000 | 5000 |
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Wang, Y.; Chen, Z.; Wang, X.; Hou, M.; Wei, F. Research on the Spatial Network Structure and Influencing Factors of the Allocation Efficiency of Agricultural Science and Technology Resources in China. Agriculture 2021, 11, 1170. https://doi.org/10.3390/agriculture11111170
Wang Y, Chen Z, Wang X, Hou M, Wei F. Research on the Spatial Network Structure and Influencing Factors of the Allocation Efficiency of Agricultural Science and Technology Resources in China. Agriculture. 2021; 11(11):1170. https://doi.org/10.3390/agriculture11111170
Chicago/Turabian StyleWang, Yameng, Zhe Chen, Xiumei Wang, Mengyang Hou, and Feng Wei. 2021. "Research on the Spatial Network Structure and Influencing Factors of the Allocation Efficiency of Agricultural Science and Technology Resources in China" Agriculture 11, no. 11: 1170. https://doi.org/10.3390/agriculture11111170