scholarly journals Internal Relationship and Impact Path Between Innovation and Entrepreneurship: Based on China's High-tech Industry

2020 ◽  
pp. 15-32
Author(s):  
Kai Zhao ◽  
Lixiang Wang
Keyword(s):  
Technological Innovation ◽  
Malmquist Index ◽  
High Tech ◽  
Policy Recommendations ◽  
High Tech Industry ◽  
Index Decomposition ◽  
The Government ◽  
Reform And Opening Up ◽  
Internal Relationship ◽  
Innovation And Entrepreneurship

Innovation is the source of entrepreneurship, entrepreneurship is the value embodiment of innovation, and the two are inseparable. At a time when dividends such as population, reform and opening up, and resources and environment are gradually disappearing, China urgently needs to accelerate scientific and technological innovation to support economic development, incubate scientific and technological enterprises, and ease labor market pressure with technological progress and efficiency improvement. This paper focuses on China’s high-tech industry, which is dominated by scientific and technological innovation. Starting from the overall, local, and regional perspectives, it organically integrates the traditional DEA, similar SFA, Malmquist index decomposition, chain multiple intermediary effect, and other multilevel research through cross-level analysis. Based on the research foundation of innovation efficiency after eliminating environmental and random factors, it deeply discusses the action path and impact mechanism of “double innovation” and provides targeted policy recommendations for the government and relevant local departments. The research confirms that the total effect of innovation on entrepreneurship is always positive, i.e., promoting “people-to-people innovation” is conducive to promoting “mass entrepreneurship” whether it is analyzed from the whole or from the part.

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 Unleashing The Growth Potential Of Indian MSME Sector

2017 ◽  
Vol 20 (2) ◽  
pp. 35-52 ◽  
Author(s):  
Sumanjeet Singh ◽  
Minakshi Paliwal
Keyword(s):  
Economic Growth ◽  
Economic Structure ◽  
Total Export ◽  
Growth Potential ◽  
High Tech ◽  
Policy Measures ◽  
The Government ◽  
Future Growth ◽  
Innovation And Entrepreneurship

The MSME sector occupies a position of strategic significance in the Indian economic structure. This sector contributes nearly eight per cent to country’s GDP, employing over 80 million people in nearly 36 million widely-dispersed enterprises across the country; accounting for 45 per cent of manufactured output, 40 per cent of the country’s total export, and producing more than 8000 valueadded products ranging from traditional to high-tech. Furthermore, these enterprises are the nurseries for innovation and entrepreneurship, which will be key to the future growth of India. It is also an acknowledged fact that this sector can help realise the target of the proposed National Manufacturing Policy to enhance the share of manufacturing in GDP to 25 per cent and to create 100 million jobs by the end of 2022, as well as to foster growth and take India from its present two trillion dollar economy to a 20 trillion dollar economy. Despite the sector’s high enthusiasm and inherent capabilities to grow, its growth story still faces a number of challenges. In this light, the present paper examines the role of Indian MSMEs in India’s economic growth and explores various problems faced by the sector. The paper also attempts to discuss various policy measures undertaken by the Government to strengthen Indian MSMEs. Finally, the paper proposes strategies aimed at strengthening the sector to enable it to unleash its growth potential and help make India a 20 trillion dollar economy.

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.

The Academic Reserve

2019 ◽  
Vol 34 (2) ◽  
pp. 75-91 ◽  
Author(s):  
Gil Baram ◽  
Isaac Ben-Israel
Keyword(s):  
High School ◽  
Technological Innovation ◽  
Military Service ◽  
High School Graduates ◽  
High Tech ◽  
Special Program ◽  
High Tech Industry ◽  
Start Up ◽  
Israel Defense Forces

Why is Israel world-renowned as the ‘start-up nation’ and a leading source of technological innovation? While existing scholarship focuses on the importance of skill development during Israel Defense Forces (IDF) service, we argue that the key role of the Academic Reserve has been overlooked. Established in the 1950s as part of David Ben-Gurion’s vision for a scientifically and technologically advanced defense force, the Academic Reserve is a special program in which the IDF sends selected high school graduates to earn academic degrees before they complete an extended term of military service. After finishing their service, most participants go on to contribute to Israel’s successful high-tech industry. By focusing on the role of the Academic Reserve, we provide a broader understanding of Israel’s ongoing technological success.

scholarly journals An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zheng-Xin Wang ◽  
Ling-Ling Pei
Keyword(s):  
Dynamic Model ◽  
Optimal Algorithm ◽  
Good Alternative ◽  
Parameters Optimization ◽  
High Tech ◽  
Grey System ◽  
Unknown Parameters ◽  
High Tech Industry ◽  
Economic Output ◽  
The Government

The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data andnseries related, abbreviated as GDMC(1,n), performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n),ninterpolation coefficients (taken as unknown parameters) are introduced into the background values of thenvariables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1,n)model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n)model. The modelling results can assist the government in developing future policies regarding high-tech industry management.

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