Do External or Internal Technology Spillovers Have a Stronger Influence on Innovation Efficiency in China?
Abstract
:1. Introduction
2. Literature Review and Hypotheses Development
2.1. External and Internal Technology Spillovers in Regional Innovation
2.2. Hypotheses Development
2.2.1. FDI and Innovation Efficiency
2.2.2. UIC and Innovation Efficiency
2.2.3. FDI and UIC
3. Methods
3.1. Conceptual Framework for the Innovation Process
3.2. Network DEA
3.3. Variables in the Panel Data Mode
3.3.1. Dependent Variables
3.3.2. Independent Variables
3.3.3. Control Variables
3.4. Data
4. Results
4.1. Evaluation of Innovation Efficiency
4.2. Econometric Analysis
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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N | Mean | SD | Correlation | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||||
1. CRE_EFCY | 210 | 0.395 | 0.272 | 1 | |||||||||||
2. COM_EFCY | 210 | 0.371 | 0.175 | 0.539 * | 1 | ||||||||||
3. FDI | 210 | 785,464.8 | 807,681.2 | 0.671 * | 0.380 * | 1 | |||||||||
4. C_INS-UNI | 210 | 7837.8 | 15,337.5 | 0.025 | 0.086 | 0.256 * | 1 | ||||||||
5. C_IND-INS | 210 | 56,884.4 | 70,615.9 | 0.486 * | 0.256 * | 0.571* | 0.539 * | 1 | |||||||
6. C-IND-UNI | 210 | 30,586.9 | 33,438.0 | 0.494 * | 0.206 * | 0.559 * | 0.512 * | 0.749 * | 1 | ||||||
7. R&D investment | 210 | 4.227 | 5.982 | 0.133 * | 0.234 * | 0.396 * | 0.837 * | 0.524 * | 0.486 * | 1 | |||||
8. Q_PERSONNEL | 210 | 1.713 | 0.434 | −0.155 * | −0.058 | −0.058 | 0.025 | −0.104 | −0.088 | 0.080 | 1 | ||||
9. per-capita GDP | 210 | 4.267 | 2.119 | −0.043 | −0.042 | 0.066 | 0.119 * | 0.058 | 0.120 * | 0.241 * | 0.175 * | 1 | |||
10. GOVERN_SUP | 210 | 0.245 | 0.137 | −0.690 * | −0.273 * | −0.364 * | 0.370 * | −0.136 * | −0.149 * | 0.228 * | 0.131 | 0.014 | 1 | ||
11. INFRATRUCTURE | 210 | 42.609 | 13.628 | 0.328 * | 0.306 * | 0.448 * | 0.493 * | 0.445 * | 0.283 * | 0.638 * | −0.010 | −0.073 | −0.064 | 1 | |
12. Q_UNIVERSITY | 210 | 1.605 | 1.226 | −0.068 | 0.020 | 0.099 | 0.075 | 0.012 | 0.074 | 0.216 * | 0.392 * | 0.639 * | 0.033 | −0.009 | 1 |
13. AVE_FIRMSIZE | 210 | 32,640.8 | 20,849.0 | −0.062 | 0.022 | −0.095 | −0.035 | −0.058 | −0.123 * | −0.053 | 0.404 * | 0.138 * | 0.084 | −0.014 | −0.060 |
Independent Variables | CRE_EFCY (1) | COM_EFCY (2) | ||
---|---|---|---|---|
Coefficient | S.E | Coefficient | S.E | |
R&D investment | 0.3381 | 0.233 | 0.4360 | 0.281 |
Q_PERSONNEL | −0.0003 | 0.028 | −0.0195 | 0.028 |
per-capita GDP | 0.0138 | 0.029 | −0.0251 | 0.026 |
GOVERN_SUP | −0.3114 *** | 0.020 | −0.0666 *** | 0.024 |
R&D_INFRATRUCTURE | 0.0471 | 0.040 | 0.0602 | 0.045 |
Q_UNIVERSITY | −0.0249 | 0.017 | ||
AVE_FIRMSIZE | 0.0292 | 0.020 | ||
FDI | 0.0577 *** | 0.008 | 0.0204 ** | 0.009 |
CONSTANT | −0.9962 *** | 0.176 | −0.4601 * | 0.266 |
Number of obs | 210 | 210 | ||
Hausman chi2 Test | 103.03, p < 0.01 | 4.93, p > 0.05 | ||
Model effects | fixed effect | random effect |
Independent Variables | CRE_EFCY (3) | COM_EFCY (4) | ||
---|---|---|---|---|
Coefficient | S.E | Coefficient | S.E | |
R&D investment | 0.4339 | 0.304 | 0.4244 | 0.297 |
Q_PERSONNEL | −0.0551 | 0.099 | −0.0110 | 0.029 |
per-capita GDP | 0.0566 | 0.055 | −0.0206 | 0.026 |
GOVERN_SUP | −0.3293 *** | 0.023 | −0.0768 *** | 0.024 |
R&D_INFRATRUCTURE | 0.0788 | 0.055 | 0.0508 | 0.046 |
Q_UNIVERSITY | −0.0034 | 0.044 | ||
AVE_FIRMSIZE | 0.0264 | 0.020 | ||
C_INS-UNI | 0.0314 *** | 0.007 | ||
C_IND-INS | 0.0499 ** | 0.020 | ||
C-IND-UNI | −0.0221 | 0.017 | ||
CONSTANT | −0.6401 *** | 0.234 | −0.4718 * | 0.279 |
Number of obs | 210 | 210 | ||
Hausman chi2 Test | 81.09, p < 0.01 | 4.09, p > 0.05 | ||
Model effects | fixed effect | random effect |
Independent Variables | CRE_EFCY (5) | COM_EFCY (6) | ||
---|---|---|---|---|
Coefficient | S.E | Coefficient | S.E | |
R&D investment | 0.3948 | 0.287 | 0.3731 | 0.300 |
Q_PERSONNEL | 0.0002 | 0.094 | −0.0124 | 0.028 |
per-capita GDP | 0.0420 | 0.052 | −0.0186 | 0.026 |
GOVERN_SUP | −0.3086 *** | 0.022 | −0.0677 * | 0.025 |
R&D_INFRATRUCTURE | 0.0420 | 0.053 | 0.0513 | 0.046 |
Q_UNIVERSITY | −0.0031 | 0.041 | ||
AVE_FIRMSIZE | 0.0271 | 0.020 | ||
C_INS-UNI | 0.0085 | 0.008 | ||
C_IND-INS | 0.0393 * | 0.022 | ||
C-IND-UNI | −0.0227 | 0.017 | ||
FDI | 0.0498 *** | 0.010 | 0.0132 | 0.011 |
CONSTANT | −0.9959 *** | 0.232 | −0.5168 * | 0.281 |
Number of obs | 210 | 210 | ||
Hausman chi2 Test | 282.06, p < 0.01 | 5.10, p > 0.05 | ||
Model effects | fixed effect | random effect |
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Qin, X.; Du, D. Do External or Internal Technology Spillovers Have a Stronger Influence on Innovation Efficiency in China? Sustainability 2017, 9, 1574. https://doi.org/10.3390/su9091574
Qin X, Du D. Do External or Internal Technology Spillovers Have a Stronger Influence on Innovation Efficiency in China? Sustainability. 2017; 9(9):1574. https://doi.org/10.3390/su9091574
Chicago/Turabian StyleQin, Xionghe, and Debin Du. 2017. "Do External or Internal Technology Spillovers Have a Stronger Influence on Innovation Efficiency in China?" Sustainability 9, no. 9: 1574. https://doi.org/10.3390/su9091574