The interaction between the innovation and the output of China'S High-tech industries based on grey relational analysis

Yuan, Chaoqing; Yang, Yingjie

doi:10.1109/smc.2014.6974159

Cited by 2 publications

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“…The differences between resource allocation may lead to different outputs and give rise to an imbalance in regional development in an interactive way: high-tech industries support and drive regional economic green growth, become the driver of regional industrial structure upgrade, stimulate and strengthen the technological innovation and institutional innovation of the regional economy; in turn, the regional economic foundation provides an important support for the development of high-tech industries. The regional resource endowment and resource allocation capabilities have been the key conditions for the innovation development of high-tech industries [14,15]. With China's rapid development, the regional imbalance within China's high-tech industries has become even more prominent.…”

Section: Introductionmentioning

confidence: 99%

Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

2020

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Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.

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Section: Introductionmentioning

confidence: 99%

Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

2020

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A novel discrete grey multivariable model and its application in forecasting the output value of China’s high-tech industries

Ding

2019

Computers & Industrial Engineering

118

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The interaction between the innovation and the output of China'S High-tech industries based on grey relational analysis

Abstract: Innovation and industrial outputs of China's Hightech industries are interacted, and a loop model is used to describe this relation. Based on this, grey relational analysis is applied to measure this relation. And the results show the bidirectional positive relationship.

Cited by 2 publications

References 1 publication

Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA

A novel discrete grey multivariable model and its application in forecasting the output value of China’s high-tech industries

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