JACIII Vol.22 No.4 pp. 437-447
doi: 10.20965/jaciii.2018.p0437


Research on Innovation Knowledge Spillover Effect of China’s High-Tech Industry R&D-Base on Multidimensional Spatial Weight Matrices

Xiuwu Zhang* and Chengkun Liu**,†

*School of Statistics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen 361021, China

**School of Economics and Finance, Huaqiao University
No.269, Chenghua North Road, Fengze District, Quanzhou 362021, China

Corresponding author

May 22, 2017
December 24, 2017
July 20, 2018
high-tech industry, spillover effect, spatial econometric model

Based on the panel data of R&D activities of the provincial high-tech industry in China from 1998 to 2014, this paper adopts the spatial weight matrix of different dimensions including geographical distance, technical distance, economic distance, proximity distance, and human capital distance, to construct a spatial econometric model to analyze the knowledge spillover effects of R&D activities through both local and transnational routes. The results show that in the case of spatial auto-correlation of the dependent variables, the results of the spatial panel model are more accurate and reliable than those obtained by the conventional panel model. The spatial coefficients of the spatial econometric model based on five different spatial weight matrices are all very significant, and there is a clear spatial correlation between the R&D activities of high-tech industries in different regions. Labor input and exports have a positive impact on innovation output, but the introduction of technology will hinder independent innovation in China’s high-tech industry, and the impact of capital investment to innovation output is uncertain, as it closely relates to the set of models. In addition, the space knowledge spillover effect through the local approach is larger than that produced by the transnational route.

Cite this article as:
X. Zhang and C. Liu, “Research on Innovation Knowledge Spillover Effect of China’s High-Tech Industry R&D-Base on Multidimensional Spatial Weight Matrices,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.4, pp. 437-447, 2018.
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