scholarly journals Evaluating the Performance of Systemic Innovation Problems of the IoT in Manufacturing Industries by Novel MCDM Methods

2019 ◽  
Vol 11 (18) ◽  
pp. 4970 ◽  
Author(s):  
Kao ◽  
Nawata ◽  
Huang
Keyword(s):  
Empirical Study ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
Innovation Policy ◽  
Interdisciplinary Collaboration ◽  
Evaluation Model ◽  
Policy Instruments ◽  
Smart Manufacturing ◽  
Systemic Innovation ◽  
Entrepreneurial Activities

The Internet of Things (IoT) is an important technological innovation that can enhance industrial competitiveness and sustainability. Thus, governments need to carefully construct an innovation portfolio that promotes sustainable IoT development. To help define an accurate innovation policy and promote development of the IoT industries, potential problems in terms of systemic perspectives should be examined. Such problems, so-called “systemic innovation problems”, influence and block sustainable development of IoT technology as well as the IoT industry. However, past studies that explored systemic innovation problems in IoT-related industries are limited. Thus, this research aims to explore systemic innovation problems related to configuring an IoT innovation policy portfolio. A hybrid Bayesian rough based evaluation model was used to derive the most feasible policy instruments. The modified Delphi, Bayesian Rough Decision-Making Trial and Evaluation Laboratory Based Network Procedures (BR-DNP), and the modified Bayesian rough Vlse Kriterijumska Optimizacija I Kompromisno Resenje (MBR-VIKOR) were introduced. Gaps in performance corresponding to each systemic innovation problem can thus be assessed based on the features of technological innovation systems. The applicability of the proposed model for promoting industrial sustainability of IoT in the Taiwanese smart manufacturing industry (based on the opinions provided by Taiwanese experts) was verified by an empirical study. Eleven systemic innovation problems that influence the development of the IoT for the smart manufacturing industry were compared and ranked. Based on the results of the empirical study, the performance-gap ratio of “low level of interdisciplinary collaboration” problem is the lowest, as compared to other systemic innovation problems. In addition, the systemic functions of entrepreneurial activities and knowledge development are relatively more important than other systemic functions. The empirical results can serve as a basis for planning an IoT innovation policy portfolio definition and roadmap. Moreover, suggestions for enhancing current systemic innovation problems are provided for policy makers and industrial researchers, according to the results of the evaluation.

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 How Technological Innovation Affect China’s Pharmaceutical Smart Manufacturing Industrial Upgrading

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Su Wang ◽  
Yuwen Chen
Keyword(s):  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Digital Transformation ◽  
Pharmaceutical Manufacturing ◽  
Smart Manufacturing ◽  
Manufacturing Companies ◽  
Industrial Upgrading ◽  
Depth Analysis ◽  
Innovative Resources

In recent years, a new generation of information technology has provided sufficient technical support for the smart manufacturing industry. In order to promote the upgrading of China’s pharmaceutical smart manufacturing industry, the direction of industrial upgrading and transformation will be discussed from the perspective of technological innovation. According to the input and output data of technological innovation in China’s pharmaceutical manufacturing industry from 2007 to 2019, the DEA method is used to analyze the allocation of innovative resources in China’s pharmaceutical manufacturing industry in recent years. The study found that the efficiency of technological innovation in China’s pharmaceutical manufacturing industry fluctuated greatly from 2007 to 2019, with a low overall level and varying degrees of wasted resources. On this basis, an in-depth analysis of the system architecture of the pharmaceutical smart manufacturing industry under the Industry 4.0 environment was performed. Finally, four paths for the digital transformation of China’s pharmaceutical manufacturing industry are proposed. Chinese pharmaceutical manufacturing companies need to use new technologies to carry out comprehensive intelligent upgrading and digital transformation to improve innovation efficiency.

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.

scholarly journals Going Abroad, Friends on Board: Cross-Border Venture Capital and Syndication Strategy in China

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110074
Author(s):  
Zhiyi Qiu ◽  
Rong Chen ◽  
Ye Yang
Keyword(s):  
Empirical Study ◽  
Venture Capital ◽  
Emerging Markets ◽  
Cultural Distance ◽  
Cross Border ◽  
Entry Strategy ◽  
China Mainland ◽  
Entrepreneurial Activities

Cross-border venture capitals (CBVCs) are increasingly prevailing in recent decades, inter alia in emerging markets like China. The venture capital (VC) firms investing outside their home countries are faced with foreignness which is broadly regarded as liability. The primary aim of this article is to contribute to our understanding how foreignness affects VC’s strategy when entering emerging markets, particularly with respect to the foreignness originated from cultural distance. The data consist of over 5,000 CBVC deals taking place in China mainland from 1988 to 2016. Our empirical study shows that, with foreignness growing, it turns from liability into advantage in the context of CBVCs. We find an inverse U-shape relationship between foreignness and syndication, with VC firm’s reputation as the moderator. Besides, foreign VC firms establish local subsidiary when faced with foreignness, which serves as alternative to syndication. The key contribution of this article is that foreignness turns from liability into advantage in emerging markets, which exerts a curvilinear impact on the entry strategy of VC firms. This study advances the knowledge of foreignness and VC strategy, and sheds new light on entrepreneurial activities in emerging markets.

Impact of capital enrichment on resource allocation efficiency in China’s manufacturing industry

2021 ◽  
pp. 1-17
Author(s):  
Lina Ma ◽  
Fengju Xu ◽  
Lihua Wang ◽  
Akther Taslima
Keyword(s):  
Resource Allocation ◽  
Manufacturing Industry ◽  
Evaluation Model ◽  
Development Trend ◽  
Inhibitory Effect ◽  
Capital Allocation ◽  
Curvilinear Relationship ◽  
Allocation Efficiency ◽  
Capital Allocation Efficiency ◽  
Resource Allocation Efficiency

Capital enrichment (CE) results from capital flows, which reflect the capital distribution among different regions and industries. This paper constructs the evaluation model of resource allocation efficiency from the perspective of capital and innovation resources. It expounds on CE’s theoretical mechanism by using the panel data from 2011 to 2018 for system GMM estimation. It finds that the manufacturing capital allocation efficiency (CAE) and innovation resource allocation efficiency (IRAE) show a volatile development trend. Both static and dynamic panel models show that there is a significant U-shaped curvilinear relationship between CE and CAE, CE and IRAE. CE’s inhibitory effect on CAE and IRAE decreases with the improvement of CE until it exceeds the critical value of 8.27 and 8.93. After that, its impact on CAE and IRAE changes from negative to positive.

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