scholarly journals Are Non-R&D Innovation Activities Actually Effective for Innovation Sustainability? Empirical Study from Chinese High-Tech Industry

2018 ◽  
Vol 11 (1) ◽  
pp. 174 ◽  
Author(s):  
Jian Hou ◽  
Jiancheng Chen ◽  
Hongfeng Song ◽  
Gang Wang
Keyword(s):  
Negative Impact ◽  
Critical Mass ◽  
Threshold Level ◽  
Threshold Effect ◽  
Developed Countries ◽  
Innovation Performance ◽  
High Tech ◽  
Regional Innovation ◽  
High Tech Industry ◽  
Innovation Activities

Compared with developed countries, the paper explores whether non-R&D innovation activities in China actually are effective and provides a guidance on how we can choose a sustainable innovation mode for non-R&D, especially considering the “threshold effect” of the heterogeneity of different regional innovation levels. The dynamic threshold panel models of the non-R&D (NRD) effect on the basis of the threshold of regional innovation level is constructed to verify the complex “threshold effect” characteristics between non-R&D and innovation performance. The empirical results are discussed according to the panel data for 30 provinces in China concerning the high-tech industry. Results argue that the mechanism of non-R&D innovation activities on innovation performance have a significantly different “threshold effect.” Interestingly, when the threshold of regional innovation keeps a low level, the negative effect of non-R&D innovation activities is much larger. When the threshold level of regional innovation increases, reaching the critical mass, the negative impact of non-R&D innovation activities on innovation performance becomes smaller. However, once the regional innovation level crosses the critical mass, the negative impact of non-R&D shows a significantly increasing tendency. Specifically, neither much higher nor much lower regional innovation level is conducive to the promoting effect of non-R&D activities. The negative impact of non-R&D on performance will decrease to the minimum only in the regions within the moderate threshold level (critical mass). The dynamic nonlinear mechanism between non-R&D activities and innovation performance is empirically studied to assist high-tech enterprises for innovation sustainability based on heterogeneity of different regional innovation levels.

scholarly journals How Do Biotech Cluster Firms Catch Knowledge Spillovers? The Strong Impact Of The Institutional Mechanisms

2017 ◽  
Vol 33 (6) ◽  
pp. 1187-1204
Author(s):  
Najoua Boufaden
Keyword(s):  
Knowledge Spillovers ◽  
Innovation Performance ◽  
High Tech ◽  
Strong Impact ◽  
Regional Innovation ◽  
Negative Effects ◽  
Face To Face ◽  
Innovation Activities ◽  
The Impact ◽  
Critical Resources

This paper deals with the nature of the mechanisms supporting knowledge spillovers diffusion in high-tech clusters. The literature in the geography of innovation focuses on the existence of local knowledge spillovers, which are enhanced by geographic and technological proximity. However, the mechanisms explaining the diffusion of spillovers are not well understood. If knowledge spillovers exist, how does this knowledge diffuse among the actors? Do spillovers spread in the air, as suggested by Marshall? Or, are there mechanisms that explain their dissemination? Based on a firm survey data base and an original methodology, the paper explores the determinants of knowledge spillovers. The paper has twofold purposes; the first one is to determine the main mechanisms within a region enabling the diffusion of spillovers. The second objective is measuring the impact of these main mechanisms on firm’s innovation performance, indicating which of these mechanisms are more effective in transporting knowledge spillovers between agents. The results show new empirical evidences on the role played by institutions[1] in the dissemination of externalities. However, informal mechanisms, such as face-to-face contacts commonly stressed in the literature, have no significant and negative effects in this case. [1] Institutions are defined here as a kind of structures that matter in structuring social interactions (Hogdson, 2006). Institutions can enable or constraint choices and actions. So it can enhance agent behaviors and actions that otherwise would not exist. According to this definition, formal institutions supporting R&D and innovation activities of SMEs in the biotech industry can enable or constraint actions of these firms regarding accessibility to critical resources available in a given region such as knowledge, information, finance, etc. Finally, we can assume that Institutions structures can explain variation in regional innovation performance.

Threshold Effect of Knowledge Accumulation between Innovation Path and Innovation Performance: New Evidence from China’s High-tech Industry

2018 ◽  
Vol 23 (1) ◽  
pp. 163-184 ◽  
Author(s):  
Heng Chen ◽  
Jian Hou ◽  
Wei Chen
Keyword(s):  
Innovation Management ◽  
Threshold Model ◽  
Innovation System ◽  
Threshold Effect ◽  
Innovation Performance ◽  
High Tech ◽  
Dynamic Threshold ◽  
High Tech Industry ◽  
Knowledge Accumulation ◽  
The Relationship

In the process of transitioning from closed to open innovation, regions in developing countries need to understand how to choose the most effective path within the complex innovation system while considering their own innovation factors. Based on provincial panel data from China’s high-tech industry and the improved dynamic threshold model, we introduce the threshold of knowledge accumulation (KLA) into the non-linear mechanism between innovation paths and innovation performance to compare the dynamic threshold effect and its heterogeneity. This research provides interesting insights into innovation paths, showing that the relationship between the innovation path and innovation performance is significantly influenced by the threshold effect of KLA. As the level of KLA strengthens, its effects on each innovation path change. Overall, this article shows how KLA affects the relationship between the innovation path and innovation performance. The article concludes with a discussion of the implications of these insights for innovation management and policy.

scholarly journals Resource Misallocation Threshold for High-Tech Industrial Specialization on Technological Innovation Performance for Pharmaceutical Manufacturing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lei Liang ◽  
Zhiyong Fan
Keyword(s):  
Technological Innovation ◽  
Threshold Model ◽  
Threshold Effect ◽  
Innovation Performance ◽  
Pharmaceutical Manufacturing ◽  
High Tech ◽  
High Tech Industry ◽  
Resource Misallocation ◽  
Industrial Specialization ◽  
Double Threshold

This paper examines the threshold effect of resource misallocation between specialization and technological innovation performance in the high-tech industry in China. Contrary to the existing literature, this paper applies the static panel threshold model to evaluate the threshold effect of industrial specialization on the innovation performance, including the single threshold effect and double threshold effect. The empirical results shed new light on the topic and signified that high-tech industry specialization has a nonlinear effect on innovation performance and the resource misallocation threshold. This paper unveils a novel, double threshold effect of resource misallocation between industrial specialization and innovation performance. Moreover, the twenty-eight regions in China are divided into three types according to a resource misallocation, which unfold the current situation of the resource misallocation in pharmaceutical manufacturing—providing directional guidance for structural reforms of the supply side of pharmaceutical manufacturing in various regions.

scholarly journals Research on the Evaluation of Innovation Performance of Regional High-tech industry Based on Grey Relation Analysis

2020 ◽  
Vol 214 ◽  
pp. 02049
Author(s):  
Peizhe Li
Keyword(s):  
Performance Evaluation ◽  
Performance Level ◽  
Innovation Performance ◽  
Good Effect ◽  
High Tech ◽  
Entropy Weight ◽  
High Tech Industry ◽  
Innovation Activities ◽  
Correlation Analysis Method ◽  
Entropy Weight Method

Scientific evaluation of the innovation performance of regional high-tech industry has an important impact on promoting the healthy development of high-tech industry and promoting regional economic growth. On the basis of constructing the index system of regional high-tech industry innovation performance evaluation, the index weight is determined based on the entropy weight method, and the gray correlation analysis method is used to evaluate and rank the regional high-tech industry innovation performance. The results show that there is a large gap in the innovation performance level of regional high- tech industries in China, and the innovation performance level of coastal areas is higher, such as Guangdong, Jiangsu, Zhejiang and other provinces; the innovation performance level of western areas is lower, such as inner Ningxia, Xinjiang, Qinghai and other provinces. The evaluation result of this method is scientific and objective, and it has a good effect in high-tech industry innovation performance evaluation. The conclusion of this paper can provide corresponding enlightenment for the innovation activities of regional high-tech industries in China.

scholarly journals Relationships among Government Funding, R&D Model and Innovation Performance: A Study on the Chinese Textile Industry

2020 ◽  
Vol 12 (2) ◽  
pp. 644
Author(s):  
Zhiying Zhang ◽  
Hua Cheng ◽  
Yabin Yu
Keyword(s):  
Textile Industry ◽  
Industrial Development ◽  
Innovation Policy ◽  
Threshold Effect ◽  
Government Funding ◽  
Innovation Performance ◽  
High Tech ◽  
Promoting Effect ◽  
Pillar Industry ◽  
The Relationship

The textile industry is a traditional pillar industry of the national economy in China. The strategic goal of Chinese innovation is to upgrade and transform traditional industries and make them develop in coordination with high-tech industries, so as to realize sustainable industrial development. At the core of industrial sustainable development, the innovation of the textile industry in China has become an important issue worthy of attention. Based on resource-based theory and signal transfer theory, the relationship between government funding, R&D models and the innovation performance of the Chinese textile industry is studied. The results show that government funding has a significant, direct promoting effect on the internal R&D and science-based cooperation of enterprises. Government funding indirectly promotes market-based cooperation through internal R&D. The promoting effect of internal R&D on innovation performance is greater than that of cooperative R&D. Internal R&D and cooperative R&D have more promoting effects on R&D reserve performance than those on market performance. Government funding indirectly promotes innovation performance through the mediation of internal R&D and science-based cooperation. The threshold effect of cooperative R&D indicates that only when the cooperative R&D intensity exceeds the threshold can government funding foster innovation performance more effectively. The conclusions can provide theoretical guidance for the formulation of innovation policy.

scholarly journals The Impact of OFDI on the Technical Complexity of High-tech Industry Export in Home Country

2020 ◽  
Vol 214 ◽  
pp. 02012
Author(s):  
Chunxiang Liu ◽  
Yalan Gao
Keyword(s):  
Developing Countries ◽  
Data Model ◽  
Home Country ◽  
Developed Countries ◽  
Dynamic Panel Data ◽  
High Tech ◽  
High Tech Industry ◽  
The Developed Countries ◽  
The Impact ◽  
D Investment

This paper calculates the technical complexity of high-tech industry export in 38 countries from 1997 to 2017, discusses the mechanism of OFDI on the technical complexity of high-tech industry export in the home country, and empirically tests the impact of OFDI on the technical complexity of high-tech industry export in the home country by using the System GMM method of dynamic panel data model. The results show that OFDI can improve the technical complexity of high-tech industry export in the home country. After further analysis, it is found that OFDI can only significantly improve the technical complexity of high-tech industry exports from developing countries, but to a certain extent inhibit the developed countries. In addition, FDI, R & D investment, human capital, openness to the outside world and self owned technology can promote the export technology complexity of a country’s high-tech industry, while the impact of capital endowment on the export technology complexity of developed and developing countries’ high-tech industry is different.

scholarly journals Has the High-Tech Industry along the Belt and Road in China Achieved Green Growth with Technological Innovation Efficiency and Environmental Sustainability?

2019 ◽  
Vol 16 (17) ◽  
pp. 3117 ◽  
Author(s):  
Chang Li ◽  
Mingyang Li ◽  
Lu Zhang ◽  
Tingyi Li ◽  
Hanzhen Ouyang ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Technological Innovation ◽  
Environmental Sustainability ◽  
Negative Impact ◽  
High Tech ◽  
Green Growth ◽  
Tobit Regression ◽  
High Tech Industry ◽  
Belt And Road ◽  
The Belt And Road

From the perspective of green growth, which seeks to coordinate and make sustainable the development of resources, the environment, and the economy, this study’s aim was to find out whether the high-tech industry along the Belt and Road (B&R) is sustainable and effective in using resources, reducing environmental pollution, and increasing performance. This study used panel data covering 16 provinces (municipalities) along the B&R in China between 2009 and 2016. This study used the directional distance function (DDF) and the global Malmquist–Luenberger (GML) index model to analyze the technological innovation efficiency (TIE) of the high-tech industry (HTI) while considering the undesirable output (environmental pollution). Further, supplemented by ArcGIS geographical analysis, this study carried out a comparative analysis of the TIE and its decomposition in the HTI along the B&R from geographical and time-series dimensions. Moreover, the panel Tobit regression model was used to analyze the influencing factors of TIE. The results show that the direct financial support of the government has no impact on the improvement of TIE in the HTI, the government’s regulation of environmental pollution can significantly affect the improvement of the TIE, the intensity of R&D has a significantly negative impact on the TIE, a higher level of R&D personnel in the HTI can be helpful in improving TIE, and increasing the import and export trade volumes of the HTI can promote TIE.

Research on Regional Innovation Capability Based on BP Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 3191-3194
Author(s):  
Ya Qun Gao
Keyword(s):  
Life Cycle ◽  
Innovation System ◽  
High Tech ◽  
Innovation Capability ◽  
Regional Innovation ◽  
High Tech Industry ◽  
Life Cycle Theory ◽  
Economy Condition ◽  
Network Platform ◽  
System Life

The region innovation system has provided the network platform for the knowledge dissemination and the flowing, simultaneously in the region the high-tech industry colony's knowledge overflow promoted the knowledge in region sharing, becomes the region innovation the driving force. This article has constructed the region innovation system life cycle model based on the baud life cycle theory, has studied under the knowledge economy condition the high-tech industry colony knowledge overflow to the region innovation system different life cycle influence mechanism.

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