ISSN 1995-459X print E-ISSN 2312-9972 online ISSN 2500-2597 online English
Editor-in-chief Leonid Gokhberg
|
2016. vol. 10. No. 2
|
Strategies
|
7–27
|
Robotics technology and the increasing sophistication of artificial intelligence are breakthrough innovations with significant growth prospects. They have the potential to disrupt existing socio-economic facets of everyday life. Yet few studies have analysed the development of robotics innovation. This paper closes this gap by analysing current developments in innovation in robotics; how it is diffused, and what role is played by intellectual property (IP). The paper argues that robotics clusters are mainly located in the US and Europe, despite a growing presence in South Korea and China. The robotics innovation ecosystem builds on cooperative networks of actors, including individuals, research institutions, and firms. Governments play a significant role in supporting robotics innovation through funding, military demand, and national robotics strategies. Robotics competitions and prizes provide for an important incentive to innovation. Patents are used to exclude third parties to secure freedom of operation, license technologies, and avoid litigation. The countries with the highest number of patent claims are Japan, China, South Korea, and the US. The growing stock of patents owned by universities and PROs, particularly in China, is noteworthy too. Automotive and electronics companies are still the largest patent filers, but medical technologies and the Internet are emerging as new actors in the field. Secrecy is often used as a tool to appropriate innovation. Copyright protection is relevant to robotics also, mainly for its role in protecting software. Finally, open-source robotics platforms are increasingly used in the early stages of the innovation process as they allow new actors in the robotics field to optimize their initial spending on innovation. |
Innovation
|
29–42
|
Spending on innovation increased annually in the 2000s in Russia’s regions, but innovation productivity varies greatly between regions. In the current climate of sanctions between Russia and Western countries and limitations on international technology transfer, there is a growing need to analyse the factors influencing regional innovation. Previous empirical studies using a knowledge production function approach have found that the main factor of the growth of regional innovation is increasing spending on research and development (R&D). Our econometric analyses show that the quality of human capital, a product of the number of economically active urban citizens with a higher education (the so-called creative class) has the greatest influence on the number of potentially commercializable patents. Other significant factors were buying equipment, which indicates a high rate of wear and tear of Russian machinery, and spending on basic research. The ‘centre-periphery’ structure of Russia’s innovation system favours the migration of highly qualified researchers to leading regions, which weakens the potential of the ‘donor regions’. However, at the same time, we see significantly fewer limitations on knowledge spillovers in the form of patents and — in this case — proximity to the ‘centres’ is a positive factor. |
Science
|
44–56
|
The continuous growth of investment in R&D in Russia and the world increases the demand for optimal allocation of public funds to support the most productive scientific performers. These are, however, hard to conceptualize and measure. First, we need to consider the nature of research activity itself and, second, we need to evaluate a number of factors that influence such activities at the national, institutional and individual levels. One of the key issues is motivation of academic personnel, who are considered to be the main producers of new knowledge. Therefore, it is necessary to analyse the employment characteristics of researchers, and develop adequate mechanisms to facilitate their scientific productivity.This paper aims to examine determinants of publication activity among doctorate holders employed in an academic sector in Russia. Data for the analysis was derived from a survey on the labour market for highly qualified R&D personnel conducted in 2010 by the HSE, within the framework of the OECD / UNESCO Institute for Statistics / Eurostat international project on Careers of Doctorate Holders (CDH). With the use of regression analysis, we assess the effects of scientific capital, international cooperation, employment, and socio-demographic characteristics of researchers on their productivity, which is measured through their total publication output as well as through the number of papers in peer-reviewed academic journals.The differences between factors were assessed for two generations of researchers – below 40 years old, and above. It was shown that the quality of scientific capital, measured through diversity of research experience, has a stronger impact on research productivity, rather than the age or other socio-demographic characteristics of doctorate holders. It was also demonstrated that direct economic stimuli and actual research productivity of researchers are weakly correlated. Consequently, we identified that a potentially winning strategy for universities and research institutions that want to improve their performance indicators would be to provide younger scholars with wider opportunities for professional growth, including intense global cooperation in the professional community. |
Master Class
|
58–80
|
This article reviews various approaches to measuring business innovation with the aim of drawing lessons for measuring social innovations, and offers several methodological and policy conclusions. First, Innovation Union Scoreboard (IUS) indicators, in principle, could be useful in settings where the dominant mode of innovation is based on R&D activities. In practice, however, both R&D and non-R&D-based modes of innovation are important. IUS, therefore, only provides a partial picture. Social innovations can rely on R&D-based technological innovations; their essence, however, tends to be organizational, managerial, and behavioural changes. The IUS indicators do not capture these types of changes. Second, an assessment of the 81 indicators used to compile the Global Innovation Index reveals that it would not be fruitful to rely on such indicators to capture social innovations. Third, given the diversity among innovation systems, a poor performance signalled by a composite indicator does not automatically identify the area(s) necessitating the most urgent policy actions; only tailored, thorough comparative analyses can do so. Finally, analysts and policy makers need to be aware of the differences between measuring (i) social innovation activities (or efforts); (ii) the framework for social innovations (pre-requisites, available inputs, skills, norms, values, behavioural patterns, etc.); and (iii) the economic, societal, and environmental impacts of social innovations. |
|
81–91
|
The end of the 20th century was marked by several studies that revealed the collective mechanisms of the development of knowledge as a joint activity in working teams. Thus, the idea that acquiring knowledge was an unproblematic transfer of what is already available and can be unilaterally transferred and assimilated was rejected [Lave, Wenger, 1991]. The aim of this paper is to study the possibilities of electronic network platforms to use the collective nature of knowledge in the interests of further developing knowledge and innovation through online communication of professionals.Based on a literature review on the development of knowledge, the paper compares the basic principles of knowledge application in formulating new decisions during real joint activity and during online communication within specialized platforms for ‘knowledge exchange’. The author argues that electronic networking platforms contribute to the fragmentation of knowledge representation of participants, eluding a common sense and purpose. Thus, such platforms blur the boundary between knowledge and information. The article indicates that the desire to increase the effectiveness of collective creativity via online communication risks not developing competencies, discretion, and exploration of others’ experiences. Instead, this desire leads to strengthening external control and separation of functions into primary routine operations when an individual participant is valued not for his/ her knowledge and previous experience, but for his/ her communicative capabilities. The produced effect is akin to the industrial revolution of the machine era; when this effect is widespread, there are risks that knowledge workers will be turned into easily replaceable, piecemeal workers. To avoid this, electronic platforms should either learn to recreate the conditions of offline micro-environments of innovation, or not claim to fulfil the role of knowledge production. |
|
|