Повышение конкурентоспособности регионов Приволжского федерального округа через развитие инновационной инфраструктуры
This paper discusses the role of higher education institutions within the framework of the knowledge triangle between academic education, scientific research and innovation, as it has gained importance in recent years as a framework for innovation policies especially in the OECD and Europe. First, complementary concepts of universities’ outreach activities and extended role model such as ‘third mission’, ‘triple helix’, ‘entrepreneurial or civic university’ models and ‘smart specialization’ are reflected against their fit with the concept of the knowledge triangle, also with respect to new requirements for university governance. Second, a new understanding of spillovers between public sectors research and the business sector according to knowledge triangle is presented.
Coffee shops are rich in experience, even if their explicit functions seems to be the provision of consumables. It has been common to ascribe business model innovation to the international coffee shop chains that have helped spread coffee culture to East Asian countries. This chapter examines coffee shops in two of these countries (Thailand and Taiwan). It finds much innovative effort underway, centred not only on the food and beverages supplied, but also on other elements shaping consumer experience – such as decor, location of activities and arrangement of devices, and the roles assigned to staff. High levels of innovation, of many forms, appear in both large chains and smaller shops; even the most traditional venues are balancing their maintenance of heritage with the introduction of novelty. Since many experience industries are also likely to display substantial innovation, and researchers should look beyond the narrow categories that have been the focus of most innovation studies.
This paper examines how export and export destination stimulates innovation by Russian manufacturing firms. The discussion is guided by the theoretical models for heterogeneous firms engaged in international trade which predict that, because more productive firms generate higher profit gains, they are able to afford high entry costs, and trade liberalization encourages the use of more progressive technologies and brings higher returns from R&D investments. We will test the theory using a panel of Russian manufacturing firms surveyed in 2004 and 2009, and use export entry and export destinations to identify the causal effects on various direct measures of technologies, skill and management innovations. We find evidence on exporters’ higher R&D financing, better management and technological upgrades. Exporters, most noticeably long-time and continuous exporters, are more active in monitoring their competitors, both domestically and internationally, and more frequently employ highly qualified managers. Exporters are more active in IT implementation. When it comes to export destination, we find that non-CIS exporters are more prone to learning. However, we cannot identify that government or foreign ownership shows any impact on learning-by-exporting effects.
The article says about business opportunities of Microwave Technology arising in development of Smart City concept. Microwave Technology experiences mostly incremental advances what means business opportunities should be analyzed in niches of integrated solutions where Smart City belongs to. Despite varying interpretations of this concept Internet of Things regularly gets into the list of underlying technologies that assist implementation of Smart City. Internet of Things provide major context for business opportunities of Microwave Technology.
Three approaches are developed for assessment of different types of organizational ambidexterity proposed in the relevant literature. The new model for measurement of organizational ambidexterity using data envelopment analysis (DEA) is introduced. The DEA score based on innovation activity inputs and two different performance outputs acts as a proxy for organizational ambidexterity. Sustainability goals and product ambidexterity are also analyzed as the key characteristics of ambidextrous behavior. The introduced three approaches are tested for their aptness to complement each other as well as to support a strategic decision-making. Empirical examples from energy and pharma sectors associate organizational ambidexterity with firms’ performance. We measured the organizational ambidexterity of energy and pharma companies by (1) pursuing long-term versus short-term organizational performance measured as a DEA two-output efficiency score; (2) the share of disruptive products in a company’s activities assessed through the proportion of R&D expenditure or sales; and (3) sustainability versus financial performance of the company, where the Green ranking and participation in innovative financing programs were used as proxies for sustainable development. Positive relation between performance and organizational ambidexterity for energy sector are discovered. At the same time, orientation towards sustainability disrupts performance of pharmaceutical companies. Results of the OA impact on performance are highly industry-sensitive and depend on the methods used in empirical assessment. Our findings suggest that the scarcity of data sources make all three approaches complementary and mainly functional for strategic decision-making.
The paper provides a number of proposed draft operational guidelines for technology measurement and includes a number of tentative technology definitions to be used for statistical purposes, principles for identification and classification of potentially growing technology areas, suggestions on the survey strategies and indicators. These are the key components of an internationally harmonized framework for collecting and interpreting technology data that would need to be further developed through a broader consultation process. A summary of definitions of technology already available in OECD manuals and the stocktaking results are provided in the Annex section.
Researchers focus on understanding the nature of ecosystems and societies as well as explaining how paradigms change. These efforts are presented and disseminated through scholarly work in scientific literature. The pool of knowledge generated through databases allows one to track how our understanding changes and how paradigms shift through time. The present study is concerned with the domain of innovation policy, which is affected directly by societal and technological change and is a good archetype for demonstrating the scientific change perspective. In recent years, scientometrics has been frequently used to measure and analyze progress in science, technology and innovation. This study makes use of a combination of scientometric analysis and evolutionary framework analysis to demonstrate the evolution of innovation policy domain. Kuhn’s seminal approach is applied for classifying and interpreting the phases across the evolution of the domain within a 30-year timeframe. The analysis demonstrates that the innovation policy domain is at the “crisis stage” as a result of ongoing with transformations in the society, technology, economy and policy. These transformations affect both supply and demand sides of innovation and call for an evolution in the innovation policy domain. Although this by no means represents that the innovation policy domain is in a “deadlock”, the present study asserts that there is a new quest in innovation policy by adapting, re-framing or re-constructing the scope of the domain. The anticipated paradigm shift is expected to lead to a more de-centralized and distributed understanding of the world for innovation policy making.
National Research University – Higher School of Economics (Moscow) and author has been researching the leasing market of Russia for 19 years. The article presents based on the author's survey of the leasing market dynamics on the value of new contracts and leasing portfolios, structure of the Russian leasing market on major industries such as aircraft leasing, leasing of mechanical equipment, railway rolling stock, car leasing, leasing of real estate, etc., as well as regional structure of leasing. The article contains the author's calculations of the three options of leverage in leasing on the basis of the methodology developed by the author. The article presents the regression model that identifies four factors influence the cost of real estate leasing contracts for 118 deals, identified by the author.