Technological innovation activities in Turkey: the case of manufacturing industry, 1995–1997

Technovation ◽  
2001 ◽  
Vol 21 (3) ◽  
pp. 189-196 ◽  
Author(s):  
Ali Uzun
Keyword(s):  
Technological Innovation ◽  
Manufacturing Industry ◽  
Innovation Activities

scholarly journals Analysis on the Efficiency of Technology Innovation of Listed Companies in China's Machinery Manufacturing Industry

2018 ◽  
Vol 2 (3) ◽  
pp. 64
Author(s):  
Ye Ren
Keyword(s):  
Panel Data ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
Technology Innovation ◽  
Listed Companies ◽  
Innovation Activities ◽  
Dea Method ◽  
Definition Of ◽  
Machinery Manufacturing

on the basis of the definition of technological innovation and efficiency, based on the DEA method, this paper takes the panel data of 20 listed companies in China's machinery manufacturing industry from 2011 to 2015 as samples, evaluating the technological innovation efficiency of listed companies in China's machinery manufacturing industry. Finally, it sums up and puts forward effective countermeasures to improve the technological innovation of listed companies in China's machinery manufacturing industry. It is hoped that it will play a guiding role in the technological innovation activities of listed companies in China's machinery manufacturing industry.

Artificial Intelligence as a Technological Innovation to Accelerate Economic Development

2019 ◽  
Vol 12 (3) ◽  
pp. 125-133
Author(s):  
S. V. Shchurina ◽  
A. S. Danilov
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Retail Trade ◽  
High Tech ◽  
Industrial Complex ◽  
Technological Gap ◽  
The Government

The subject of the research is the introduction of artificial intelligence as a technological innovation into the Russian economic development. The relevance of the problem is due to the fact that the Russian market of artificial intelligence is still in the infancy and the necessity to bridge the current technological gap between Russia and the leading economies of the world is coming to the forefront. The financial sector, the manufacturing industry and the retail trade are the drivers of the artificial intelligence development. However, company managers in Russia are not prepared for the practical application of expensive artificial intelligence technologies. Under these circumstances, the challenge is to develop measures to support high-tech projects of small and medium-sized businesses, given that the technological innovation considered can accelerate the development of the Russian economy in the energy sector fully or partially controlled by the government as well as in the military-industrial complex and the judicial system.The purposes of the research were to examine the current state of technological innovations in the field of artificial intelligence in the leading countries and Russia and develop proposals for improving the AI application in the Russian practices.The paper concludes that the artificial intelligence is a breakthrough technology with a great application potential. Active promotion of the artificial intelligence in companies significantly increases their efficiency, competitiveness, develops industry markets, stimulates introduction of new technologies, improves product quality and scales up manufacturing. In general, the artificial intelligence gives a new impetus to the development of Russia and facilitates its entry into the five largest world’s economies.

The Effect of Technological Innovation and Market Size on Manufacturing Structure: Evidence from the World’s Largest Tobacco Producer and Seller

2021 ◽  
Vol 7 (5) ◽  
pp. 2422-2444
Author(s):  
Song Teng ◽  
Liu Yuxin
Keyword(s):  
Technological Innovation ◽  
Manufacturing Industry ◽  
Tobacco Industry ◽  
Industrial Structure ◽  
Rapid Development ◽  
Market Size ◽  
Chinese Government ◽  
Explanatory Variables ◽  
Industrial Sectors ◽  
The Impact

Objectives: As the world’s largest tobacco producer and seller, China’s rapid development of the tobacco industry is inextricably linked to the promotion and support of the manufacturing industry. The optimization and adjustment of the manufacturing structure (MS) is critical in determining the competitiveness of the manufacturing industry. This study examines the impact of technological innovation and market size on MS optimization in China using provincial data from 2001 to 2016. We obtain the following main results. First, market size and technological innovation are important drivers in optimizing MS. Technological innovation increases productivity and results in the redistribution of production factors across industrial sectors, altering the industrial structure. The market size facilitates labor division, which boosts productivity. Second, institutional innovation is critical for optimizing MS. It strengthens the impact of technological innovation and market size on MS rationalization. Furthermore, the study’s findings are robust to a variety of estimation techniques, several alternative proxies for core explanatory variables, and a long list of control variables. An important implication of the study’s findings is that the Chinese government should implement effective institutional reforms to accelerate China’s manufacturing industry’s development. China’s tobacco industry, in particular, will achieve higher quality development based on the transformation and upgrading of the overall manufacturing industry.

scholarly journals Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

2019 ◽  
Vol 11 (8) ◽  
pp. 2342 ◽  
Author(s):  
Kao ◽  
Nawata ◽  
Huang
Keyword(s):  
Decision Making ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
Innovation System ◽  
Government Procurement ◽  
Smart Manufacturing ◽  
Technological Innovations ◽  
Industrial Competitiveness ◽  
Industrial Sustainability ◽  
Study Results

Technological innovations are regarded as the tools that can stimulate economic growth and the sustainable development of technology. In recent years, as technologies based on the internet of things (IoT) have rapidly developed, a number of applications based on IoT innovations have emerged and have been widely adopted by various public and private sectors. Applications of IoT in the manufacturing industry, such as manufacturing intelligence, not only play a significant role in the enhancement of industrial competitiveness and sustainability, but also influence the diffusion of innovative applications that are based on IoT innovations. It is crucial for policy makers to understand these potential reasons for stimulating IoT industrial sustainability, as they can facilitate industrial competitiveness and technological innovations using supportive means, such as government procurement and financial incentives. Therefore, there is a need to ascertain different factors that may affect IoT industrial sustainability and further explore the relationship between these factors. However, finding a set of factors that affects IoT industrial sustainability is not easy. Recently, the robustness of a theoretical framework, termed the technological innovation system (TIS), has been verified and has been used to explore and analyze technological and industrial development. Thus, it is suitable for this research to use this theoretical model. In order to find out appropriate factors and accurately analyze the causality among factors that influence IoT industrial sustainability, this research presents a Bayesian rough Multiple Criteria Decision Making (MCDM) model based on TIS functions by integrating random forest (RF), decision making trial and evaluation (DEMATEL), Bayesian theory, and rough interval numbers. The proposed analytical framework is validated by an empirical case of defining the causality between TIS functions to enable the industrial sustainability of IoT in the Taiwanese smart manufacturing industry. Based on the empirical study results, the cause group consists of entrepreneurial activities, knowledge development, market formation, and resource mobilization. The effect group is composed of knowledge diffusion through networks’ guidance of the search, and creation of legitimacy. Moreover, the analytical results also provide several policy suggestions promoting IoT industrial sustainability that can serve as the basis for defining innovation policy tools for Taiwan and late coming economies.

scholarly journals Analysis of the Effect of R&D Planning Support for SMEs Using Latent Growth Modeling

2020 ◽  
Vol 12 (3) ◽  
pp. 1018
Author(s):  
Ju Hwan Seo ◽  
Daemyeong Cho
Keyword(s):  
Technological Innovation ◽  
Growth Modeling ◽  
Latent Growth Modeling ◽  
Sustainable Growth ◽  
Policy Support ◽  
Technology Commercialization ◽  
Latent Growth ◽  
Planning Stage ◽  
Planning Support ◽  
Innovation Activities

Small and medium enterprises (SMEs) in Korea play a pivotal role in the national economy. However, due to scale limitations, the resources available to SMEs for innovation activities are limited. Therefore, as a means to compensate for the market failure of SMEs, which can be caused by resource limitations, and to ensure sustainable growth, the government supports the SMEs for innovation activities. Korea has fulfilled SME R&D planning support projects since 2002 to efficiently support SMEs’ technology commercialization activities through systematic support in the early stages of the technology commercialization cycle. In order to analyze the effectiveness of SME R&D planning support program, this study conducted latent growth modeling analysis using financial data of three years of SMEs participating in the program. According to the analysis results, SMEs receiving R&D planning support funds have an increase in sales compared to those that do not. In particular, it was analyzed that the sales increase effect of companies supported by R&D funds continuously appeared more clearly. The result of this analysis is that policy support for SMEs can help technological innovation activities necessary for R&D planning, which is an early stage of SME technological innovation. This study is meaningful in that it quantitatively analyzes using latent growth model and the contribution of government support at the R&D planning stage to SMEs’ innovation capabilities and their contribution to performance. However, it has a limitation that it does not address the specific impact path of the planning stage support on innovation capacity and innovation performance.

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