High-tech Industry Innovation Research Articles

Regional Breakthrough Innovation Change Strategies, Ecological Location Suitability of High-Tech Industry Innovation Ecosystems, and Green Energy

Regional Breakthrough Innovation Change Strategies, Ecological Location Suitability of High-Tech Industry Innovation Ecosystems, and Green Energy

Innovation ecosystems beyond construction projects – a case study of Swedish cross laminated timber building networks

ABSTRACT While innovation ecosystems have been proven effective for driving innovation in high-tech industries, their application in construction with cross laminated timber (CLT) products have received limited attention, with most studies adopting a broad perspective on ecosystems, encompassing the entire supply network around individual building projects. This study explores niche innovation ecosystems within the context of construction with CLT products. Data from 15 CLT-based building projects in mid Sweden were collected through observations and interviews and analysed using a method combining existing theory on innovation ecosystem structure with social network analysis. Innovation ecosystem structure was identified within five of the projects, encompassing two unique ecosystems. These ecosystems feature interorganisational links and actor-level expected value capture in two dimensions: value creation within the standard contractual framework of the projects and the innovation ecosystem superimposed on these projects. The novel method bridges the gap between innovation ecosystem research and CLT practices. The findings reveal that niche innovation ecosystems interact with CLT-based building projects through collaborative efforts across multiple projects and partners. These ecosystems feature alignment links that indicate both intra – and inter-project interdependencies, potentially driving innovation in construction. Role extensions within these ecosystems highlight the complexity and adaptability required in the construction industry.

Evaluating the impact of the innovation efficiency of high-tech industry on carbon emissions: a case study of the manufacturing industry in China.

Amid China's economic shift to high-quality development, addressing environmental challenges like greenhouse gas emissions and manufacturing pollution, there is a crucial demand for sustainable and eco-friendly development strategies. This study aims to investigate the impact of innovation efficiency in the high-tech industry on carbon emissions. It seeks to explore regional differences, mechanisms, and the influence of energy consumption structures in achieving sustainable development goals. Utilizing data from 30 provinces spanning 2009 to 2020, the study employs the DEA-Malmquist index model, spatial and temporal classification evaluation, and a panel measurement model to assess the efficiency of innovation and development in high-tech industries and their relationship with carbon emissions. The results indicate several key findings: (1) The overall operational efficiency of high-tech industry innovation and development in China is steadily increasing. However, there are distinct characteristics observed among provinces and cities, reflecting diverse input and output types. (2) High-tech industry innovation efficiency significantly contributes to carbon emission reduction, and there is regional heterogeneity in this impact. The central and western regions exhibit greater effects compared to other provinces and cities. (3) The optimization of the energy structure is identified as a mechanism through which high-tech industry innovation efficiency reduces carbon emissions. Moreover, different intervals of high-tech industry innovation efficiency yield varying effects on carbon emissions. This research underscores the importance of fostering high-tech industry innovation efficiency as a means to reduce carbon emissions. It also identifies key areas for future policy development and resource allocation, emphasizing the support needed for low-carbon technology research and development.

Low-carbon innovation efficiency of high-tech industries and its influencing factors

Abstract China has a vast territory, with an abundance of natural resources. Industrial investment of provinces promulgating and implementing state policies and measures on the basis of the various regions and industries are slowly widening the gap, which leads to significant differences in the level of industrial development. The imbalance between development at both provincial and municipal levels has become more prominent. The state has formulated a policy of coordinated development of the regional economy. There are differences between regions and industries in the demand for technological innovation of high-tech industry. This article is from the regional perspective, industry perspective and the perspective of the two. This paper, through the research of technology innovation and output data of China’s 31 provinces, municipalities and autonomous regions, finds the delivery rate is an independent variable of the average number of employees, R&D funds, R&D internal expenditure, external spending, new product sales income, the absorption of funding expenditure, technical transformation of funds for fixed assets, and gross profit as a technological low-carbon innovation efficiency measure. Using factor analysis, multiple regression methods and SPSS software, from the perspective of two regions and industries, enables empirical analysis of the influencing factors of this variable.

The Relationship between High-Tech Industrial Agglomeration and Regional Innovation: A Meta-Analysis Investigation in China

High-tech industrial agglomeration enhances the technological level and value of regional industries. It is considered to be a new and effective way to drive China’s regional innovation and development at present. Numerous studies indicate that high-tech industrial agglomeration contributes positively to regional innovation, but the current academic discussion on this issue has not yet reached a unified opinion. In various research contexts, it has also been pointed out that there may be a negative correlation or non-linear relationship between the two. This contradictory relationship makes it difficult to generalize the current research findings to realistic application scenarios. Therefore, to clarify the relationship between the two scientifically, this study employs meta-analysis, reanalyzing 833 effect values derived from 69 independent research samples based on Chinese data. The findings reveal a moderately positive correlation (r = 0.204) between the agglomeration of high-tech industries and regional innovation in China. In particular, high-tech industrial agglomeration significantly contributes to regional innovation under the paths of diversified agglomeration and competitive agglomeration. We further found that sampling region, measurement approach, measurement perspective, research methodology, and year of publication all exhibit significant moderating effects on the relationship between the two variables. Based on meta-analysis, this study not only scientifically responds to the controversy of the relationship between high-tech industrial agglomeration and regional innovation but also further reveals the inner conduction mechanism between the two. It is of great significance in exploring future studies in related fields.

How Does Economic Resilience Enhance the Innovation Capability of the High-Tech Industry? Evidence from China

There is still a significant gap between most high-tech fields in China and developed countries, and the relationship between the economic resilience of innovation systems and the innovation capability of the high-tech industry has received attention. Using data from 30 provinces in China, this study adopts the fuzzy-set qualitative comparative analysis approach to analyze the relationship between economic resilience and high-tech industry innovation capability, and the findings are as follows: (1) Technological talents and economic development are necessary conditions for explaining the high innovation capability of high-tech industries. (2) The combination of economic resilience and different factors constitutes the equivalent configuration of two high innovation capabilities and four low innovation capabilities. (3) Under high-intensity technological competition between governments, the increased agglomeration of technological talents, and high-quality economic development, the strengthening of economic resilience is conducive to enhancing the innovation capability of high-tech industries. (4) Under low-intensity technological competition between governments, a well-developed technology market, and increased agglomeration of technological talents, the strengthening of economic resilience is adverse to enhancing the innovation capability of high-tech industries. The main contribution of this study is to clarify the configuration mechanism of economic resilience that acts on the innovation capability of the high-tech industry and reveal the complex interaction between economic resilience and multiple factors.

How to Improve the Synergetic Development Capabilities of the Innovation Ecosystems of High-Tech Industries in China: An fsQCA Analysis Based on the TOE Framework

This study explores the synergetic development capability of China’s high-tech industry innovation ecosystem and the diversification path necessary to improve it. Based on the four-spiral perspective, the system synergetic development index was constructed, and the composite system collaborative degree model was used to measure the synergetic development capability of the high-tech industry innovation ecosystem across 30 provinces and cities in China from 2012 to 2020. The influencing factor model of improving the system’s synergetic development capability was constructed under the technology–organization–environment (TOE) framework, and a fuzzy set qualitative comparative analysis (fsQCA) method was used to explore the multiple paths available to improve the synergetic development ability of the system. The results show that the four-helix system’s synergetic development capability is poor and needs to be improved. The “technology–organization–environment” conditions cannot be used alone as the necessary conditions for the improvement in the system’s synergetic development capability but need to exert a joint, matching effect through the combination of different factors. In addition, market openness is crucial on the path toward the highly synergetic development of the system. The research results not only provide a theoretical basis for comprehensively improving the system’s synergetic development capability but also provide a practical reference for the differentiation path being revealed. In short, this study has important theoretical and practical significance to promoting the synergetic development and benign evolution of the high-tech industry innovation ecosystem.

The impact of technology transfer on the green innovation efficiency of Chinese high-tech industry.

Promoting technology transfer is an important strategic measure for China to promote industrial innovation. However, there is little research exploring the influence of technology transfer on the green innovation efficiency (GIE) of China's high-tech industry (HTI). From the perspective of process, green innovation in HTI is a continuous three-stage system including research and development (R&D), commercialization, and diffusion. Therefore, we measure the GIE of China's HTI by using a three-stage network data envelopment analysis (NDEA) model considering environmental pollution and establish a series of regression models to investigate the role of the two main ways of technology transfer, domestic technology acquisition (DTA) and foreign technology introduction (FTI), in improving the GIE of HTI. The results show that the average GIE of China's HTI is 0.7727 from 2011 to 2020. Except for Jiangsu, Guangdong, Qinghai, and Xinjiang, green innovation in HTI in other provinces in China is inefficient. DTA has significantly promoted GIE in HTI. FTI has a positive impact on the GIE of HTI but is not statistically significant. The robustness test confirmed these results. This study is helpful to understand the differences between the effects of DTA and FTI on the GIE of China's HTI, to provide a basis for adjusting technology transfer policies.

Measuring the Development Capability of the Innovation Ecosystem from a Quadruple Helix Perspective—An Empirical Analysis Based on Panel Data for Chinese High-Tech Industries

With the rapid development of high-tech industries, the research perspective of industrial innovation has gradually evolved from the innovation system to innovation ecosystem. Whether the innovation ecosystem of China’s high-tech industry can achieve the benign evolution and development of the system under the new global pattern has become a real issue. Based on the perspective of the four-helix innovation model, in this study we construct an index system of the industrial innovation ecosystem development and designs a framework system of the industrial innovation ecosystem development capability, including the coordinated development capability, evolutionary development capability, and sustainable development capability at three levels. We construct measurement models of different capability dimensions and multi-dimensionally analyses of the regional development differences and change trends of each capability dimension of the system. The results show that the coordinated development capability of China’s high-tech industrial innovation ecosystem is poor, which inhibits the comprehensive capability of the system’s development to a certain extent. Although the evolutionary and sustainable development capabilities fluctuate greatly and have significant regional differences, they are measured at a better level and thereby contribute to the development of the system.

The impact of government support and market competition on China's high-tech industry innovation efficiency as an emerging market

With the building of an innovative country in an emerging market, the role of government support (GS) and market competition (MC) in the innovation efficiency of China's high-tech industry (HTI) has become increasingly prominent. Considering innovation value chain (IVC) theory, based on 2004–2016 panel data covering 30 regional HTIs in mainland China, the spatial autoregressive (SAR) Tobit model, stepwise regression method with spatial factors, and threshold panel model are applied to examine the main effects, moderating effects, mediating effects, and nonlinear effects of GS and MC on the innovation efficiency of China's HTI. GS significantly inhibits the industry's technological development efficiency but promotes its innovation achievement transformation efficiency. However, the impact of MC on the industry's technological innovation efficiency has stage homogeneity, significantly promoting both stages of innovation efficiency. Economic policy uncertainty (EPU) exerts a significant moderating effect on GS and technological development efficiency. The transmission mechanism test reveals that technology market development (TMD) has a partial mediating effect between GS and technological development efficiency, while a masking effect exists between MC and technological development efficiency. A complete mediating effect exists between GS and innovation achievement transformation efficiency, while a partial mediating effect exists between MC and innovation achievement transformation efficiency. Regional heterogeneity is apparent. Of note, the impact of MC on the industry's technological development and innovation achievement transformation efficiency has a nonlinear effect based on GS.

Green Innovation Capability of High-Tech Industry in Yangtze River Economic Belt and its Spillover Effect

As an important pillar of comprehensive national power, the innovation capacity of high-tech industries has always been a hot spot for research. This paper considers environmental factors and selects panel data of 11 provinces(cities) in Yangtze River Economic Belt (YREB) from 2011-2020 to study the green innovation capacity of their high-tech industries and its spillover effects. Finally, the following conclusions are drawn: (1) the green innovation capacity of provinces(cities) in the YREB shows a large gap between them, showing the geographical characteristics of "high in the east and low in the west"; (2) The direct and spillover effects of government support on green innovation in high-tech industries in each region are the largest; increasing government funding support for R&D can effectively enhance the green innovation capacity within each region and between regions.

Does Industrial Agglomeration Promote Regional Innovation Convergence in China? Evidence From High-Tech Industries

The literature examining the importance of industrial agglomeration in innovation performance has expanded greatly in recent years, but the impact of industrial agglomeration, especially high-tech industrial agglomeration on regional innovation convergence, has been neglected. Using quantitative data from 30 provinces in China pertaining to the period 2005–2016, in this article, we analyze regional innovation convergence by focusing on three different types of convergence: σ-convergence, absolute σ-convergence, and conditional β-convergence. On the basis of the spatial effects, this article empirically investigates the impact of high-tech industrial agglomeration on regional innovation convergence in China by using spatial econometric models. The empirical results show that there is significant spatial autocorrelation in regional innovation. Thus, in the assessment of regional innovation convergence, the spatial effects should not be neglected. Moreover, our results demonstrate that regional innovation exists three different types of convergence (i.e., σ-convergence, absolute β-convergence, and conditional β-convergence). In addition, high-tech industrial agglomeration can promote regional innovation convergence. Finally, the grouping of high-tech industries into five subindustries shows that industrial agglomeration has different effects on regional innovation convergence. Overall, our findings provide convincing empirical evidence of the relationship between the high-tech industrial agglomeration and regional innovation convergence and valuable insights into how to realize regional innovation convergence.

Research on The Evaluation Model of Symbiosis Degree in High-tech Industrial innovation Ecosystem-An Empirical Study Based on the Panel Data of China from 2012 to 2018

On the basis of previous symbiotic analysis and research, the scientific and technological activities between symbiotic subjects are included into the evaluation index system of symbiotic network, and the high-tech industry innovation ecosystem evaluation model is established. The symbiosis degree was empirically measured using the entropy method with the data of high-tech industries in 32 provinces, autonomous regions and municipalities in China from 2012 to 2018. Results showed that though a significant development of overall symbiosis degree has been seen, there are still significant differences in the development speed and stage among regional innovation ecosystems.

Impact of Public Support for Innovation on Company Performance: Review and Meta-Analysis

In recent years, there has been a significant shift in public policies directed to firms, with several governments launching public measures to support innovation. To promote firms’ innovation, governments can implement incentives such as subsidizing companies, inducing them to undertake those investments. Hence, the aim of this study is to review the results of a set of studies published on the evaluation of public support to firms’ innovation. To achieve this goal, we conduct a literature review that combines bibliometric analysis, network, qualitative analysis (con-tent review), and quantitative analysis through meta-regression. The analysis was performed using the VOSviewer and Stata software and the results of the meta-regression (regression with Logit model) suggest that there are no characteristics of studies on this topic that affect the results obtained. It was verified that, in the set of articles analyzed, there is strong evidence that public support for innovation leads to favorable results for the company’s performance. The main trends in the topics addressed were also identified and analyzed and, in addition to financial support for innovation, support for small and medium-sized companies, support for the development of the high-tech industry, and open and collaborative innovation stand out.

Influence Mechanism of High-Tech Industrial Agglomeration on Green Innovation Performance: Evidence from China

This paper uses panel data from 30 provinces and cities in China between 2008 and 2017. It calculates the green innovation performance of each province and city based on the super-efficiency SBM model of unexpected output and measures the high-tech industrial agglomeration degree of each province and city by using the location entropy method. The influence of high-tech industrial agglomeration on green innovation is also empirically tested. It is found that the agglomeration level of high-tech industry and green innovation performance in Eastern China are much higher than those in Central and Western regions. There is a significant positive relationship between high-tech industrial agglomeration and green innovation performance. Human capital has a mediating effect in the positive impact of high-tech industry agglomeration on green innovation performance. The moderating effect of environmental regulation is markedly established. The higher the level of environmental regulation, the stronger the mediating effect of human capital. Based on the above conclusions, this paper puts forward relevant policy suggestions for promoting the organic combination of green and innovation-driven development and for promoting green transformation in China.

Creating and Managing EU Funded Research Networks: An Exploratory Case

The collaborative European funded research and development landscape drives competitiveness among innovative organisations. Recently it has seen the rise of public private partnerships significantly impacting the dynamics of these networks. Thus, the complexity of managing research networks has intensified with the increased diversity of research network members. Additionally, the emergence of the academic entrepreneur has augmented the focus of educational institutions to include innovation and building start-up organisations. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness; the nature of relationships, links and nodes within a research network, specifically their structure, configuration and quality. The contribution of this paper extends our understanding for establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. The concept of embeddedness is what differentiates network theory from economic theory. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. One challenge is competition between network members over ownership and sharing of data. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, incubation and operations. The network capability is enhanced by the recognition of network theory, open innovation and social exchange with the understanding that the network structure has an impact on innovation and social exchange in research networks and subsequently on research output. The research concludes that the success of collaborative research is reliant upon establishing a common language and understanding between network members to realise their research objectives.

The Impact of Government Support and Market Competition on China's High-Tech Industry Innovation Efficiency as an Emerging Market

The Impact of Government Support and Market Competition on China's High-Tech Industry Innovation Efficiency as an Emerging Market

CNS: Application of Dynamic Resource Allocation Based on 5G Communication in Technology Innovation of Complex High-Tech Industries

In order to improve the technological innovation effect of compound high-tech industry, this paper studies its dynamic resource allocation technology with the support of 5G communication technology, and analyzes the application of 5G communication technology in technological innovation of compound high-tech industry. In the uplink and downlink of wireless energy-carrying communication system, this paper studies the joint optimization of subcarrier allocation and power allocation of uplink and downlink to maximize the uplink and downlink weighted sum rate of the system. Moreover, this paper adopts the joint optimization algorithm of power and sub-carrier based on Lagrangian dual method and ellipsoid method to obtain the maximum information transmission rate of uplink and downlink. In addition, this paper constructs an intelligent innovation management system. The research results show that the dynamic resource allocation method based on 5G communication proposed in this paper has a very good effect in the application of technological innovation in complex high-tech industries.

Coupling Coordination between Technology Transfer in Universities and High-Tech Industries Development in China

Only through successful transformation and application in industries, technology achievements in universities can promote economic and social development. Meanwhile, technological progress in high-tech industries depends in part on universities’ technology achievements. Coupling coordination between technology transfer in universities (TTU) and high-tech industries development (HTID) is of great significance to the sustainable development of the regional sci-tech innovation system. In this paper, the coupling mechanism of TTU and HTID is analyzed and comprehensive evaluation index systems are established by using the evaluation indicator to screen and assign weights based on information contribution rate. The coupling coordination index (CCI) and productivity index (PI) are introduced to derive the spatiotemporal characteristics of the coupling and coordination of TTU and HTID from 2010 to 2019 in China and analyze its influence factors by System-Generalized Method of Moments (SYS-GMM). The results show the following: (1) the overall national TTU-HTID CCI shows a gradual upward trend and large regional differences; (2) CCI of most provinces is increasing, but the differences are widening; (3) the national TTU PI is in a state of volatility and decline, but the ability of TTU is still much higher than the level of HTID; (4) technological innovation in high-tech industries, high-tech industry technology absorption capacity, high-tech industry development level, industry-university cooperation, and technological finance development have significant roles in promoting the coupling coordination of TTU-HTID. Meanwhile, the sci-tech innovation level in universities, technology transferability in universities, government support, development of sci-tech intermediary, and development of other research institutions have no significant impact.

Образовательные стандарты и запросы современности: противоречия или возможности

This is an empirical study of professional competencies. The article addresses two main tasks: the demands of the labor market presented to specialists in the field of trade and marketing; and the identification of the professional competencies formed by the standard for the preparation of FSES HE 3+ and whether it meets the modern requirements of employers. Two hypotheses are investigated: (i) can the competencies inherent in the educational standard develop the necessary skills required by this profession in the modern labor market; and (ii) do the professional competencies reflect the necessary personal qualities demanded by employers. The analysis shows that the educational standard of the FSES HE 3+ is rather poorly adapted to the modern requirements of the labor market. As a rule the professional competencies in it develop a specialist who is ready to engage in his own entrepreneurial activity, while the training of such personnel is carried out at the expense of the state budget. This implies the need to work in large organizations and to possess a wider set of skills, including the ability for expert and analytical work, systemic thinking, etc. The increasing innovation in high-tech industries as Russia moves towards a digital economy will increase the demand for labor marketers who have design, engineering, visualization and skills for working with BigData. It is these skills that should be integrated into the new FSES of HE3 ++ to train a specialist o meet the requirements of the 21st century.