Инновационно-технологическое взаимодействие оборонной индустрии и гражданского сектора в США: исторический опыт и актуальные тенденции
Military industrial complex played a leading role in technological development during several post-World War II decades in all major economies. Many technological innovations became possible primarily because of a generous financial and organization support from the government. Large defense contracts facilitated an establishment and a rapid growth of high-tech industries (information and communication industry, in particular), and an accelerated 50 transfer of advanced technologies from defense to civil sector of the economy. However, the last two decades have seen a clear reverse trend: military industrial complex in the leading developed countries has become increasingly dependent on different innovation products and solutions generated by non-defense private companies. This paper examines the main determinants and factors which influenced this process with the example of the United States. Special attention is paid to demonstrating a transformation of technological innovation cooperation between the key players in the defense and civil sectors of the U.S. economy, including defense acquisition reforms and emergence of new mechanisms of public-private partnerships between defense and civil companies in the implementation of long-term collaborative projects.
The article derives from the results of ethnographic research conducted by the author in 2003- 2010 and draws on fi eldwork data and focused biographical interviews (2007-2010) with technical specialists working in Moscow, St. Petersburg, Minsk, and Rostov-on-Don. The goal of the article is to take the area known as data recovery for a case study and illustrate the active part that user communities play in maintaining computerized technologies, developing innovations, and shaping technological service markets.
Business Studies practice listening tasks which are based on authentic sources, specially designed for the English state exam of the 4th year Public Administration students.
Proceedings of TISLID'10
Chapter 6 presents an analysis of Russian innovation system accompanied by an overview of state science, technology and innovation (STI) policy practice.
The authors cover the most urgent institutional cleavages, including the split-offs of science and industry, issues of institutional model of the R&D sector, sectoral discrepancies and regional polarization.
An outline of STI policy framework evolution is presented, including the most recent Strategy for Socio-Economic Development of Russia till 2020 topics. A special regard is paid to linkage-stimulating policy instruments, including grants for joint research for Universities, R&D organisations and companies, technology platforms, regional innovation clusters program and elaboration of innovation development plans for state-owned companies.
Over the last two decades national policy makers drew special attention to the implementation of policy tools which foster international cooperation in the fields of science, technology, and innovation. In this paper, we look at cases of Russian-German collaboration to examine the initiatives of the Russian government aimed at stimulating the innovation activity of domestic corporations and small and medium enterprises. The data derived from the interviews with companies’ leaders show positive effects of bilateral innovative projects on the overall business performance alongside with major barriers hindering international cooperation. To overcome these barriers we provide specific suggestions relevant to the recently developed Russian Innovation Strategy 2020.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.