ISSN 1995-459X print E-ISSN 2312-9972 online ISSN 2500-2597 online English
Editor-in-chief Leonid Gokhberg
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2018. vol. 12. No. 1
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Strategies
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6–24
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Foresight studies provide essential information used by the government, industry and academia for technology planning and knowledge expansion. They are complicated, resource-intensive, and quite expensive. The approach, methods, and techniques must be carefully identified and selected. Despite the global importance of foresight activities, there are no frameworks to help one develop and plan a proper foresight study. This paper begins to close this gap by analyzing and comparing different schools of thought and updating the literature with the most current tools and methods. Data mining techniques are used to identify articles through an extensive literature review. Social Network Analysis (SNA) techniques are used to identify and analyze leading journals, articles, and researchers. A framework is developed here to provide a guide to help in the selection of methods and tools for different approaches. |
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25–45
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Less than a decade since its official introduction, smart specialization, which guides the selection of priorities for innovative development, has proven to be a far-reaching academic idea and political instrument. In the European Union, smart specialization is mentioned among ex ante conditions for receiving subsidies from European structural and investment funds. Its core principles are considered in innovation strategies in Australia, South Korea, and some countries of Latin America. In Russia, smart specialization is also being introduced in the agenda of policymakers. The paper seeks to reveal which levels of governance should be involved in the design of a smart specialization strategy and which factors should be the focus of attention when using this approach. The research is based upon an analysis of the innovation strategies of seven Russian regions, conducted with the adapted RIS3 Self-Assessment Wheel. The results of the study empirically confirm that most principles of smart specialization are considered, at least formally, in the traditional innovation strategies of Russian regions. At the same time, without common rules for the selection, verification, and synchronization of innovative priorities as well as a single analytical database, organizational support, and expertise, even regions considered strong innovators fail to find their smart specialization. |
Innovation
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47–55
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The presence of additive manufacturing (AM), in particular 3D printing, is relatively young, but dynamic field that is changing the face of many sectors. Additive production technologies provide wide opportunities for the creation of complex and personalized products and the reduction of time, labor, and other expenses. This paper will focus on AM in healthcare and identify the main areas for its application and the most popular materials. The period under analysis is from January 2005 to April 2015. The analysis involved an iterative search to establish the best queries for retrieving data and a patent analysis. The obtained results were assessed by experts in the field. Through this research, three main applications were identified with dental prosthetics being the most prolific. A wide range of materials were identified, where plastics predominate. Polyethylene was most frequently patented for vascular grafts and tendon replacements, while ceramics were found to be the most useful material for dental applications. Only a few patents disclosed the use of metals, titanium being the most prevalent. This research provides valuable insights for the advancement of additive manufacturing in healthcare applications. |
Science
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57–66
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A research landscape is a high-level description of the current state of a certain scientific field and its dynamics. High-quality research landscapes are important tools that allow for more effective research management. This paper presents a novel framework for the mapping of research. It relies on full-text mining and topic modeling to pool data from many sources without relying on any specific taxonomy of scientific fields and areas. The framework is especially useful for scientific fields that are poorly represented in scientometric databases, i.e., Scopus or Web of Science. The high-level algorithm consists of (1) full-text collection from reliable sources; (2) the automatic extraction of research fields using topic modeling; (3) semi-automatic linking to scientometric databases; and (4) a statistical analysis of metrics for the extracted scientific areas. Full-text mining is crucial due to (a) the poor representation of many Russian research areas in systems like Scopus or Web of Science; (b) the poor quality of Russian Science Index data; and (c) the differences between taxonomies used in different data sources. Major advantages of the proposed framework include its data-driven approach, its independence from scientific subjects’ taxonomies, and its ability to integrate data from multiple heterogeneous data sources. Furthermore, this framework complements traditional approaches to research mapping using scientometric software like Scopus or Web of Science rather than replacing them. We experimentally evaluated the framework using agricultural science as an example, but the framework is not limited to any particular domain. As a result, we created the first research landscape covering young researchers in agricultural science. Topic modeling yielded six major scientific areas within the field of agriculture. We found that statistically significant differences between these areas exist. This means that a differentiated approach to research management is critical. Further research on this subject includes the application of the framework to other scientific fields and the integration of other collections of research and technical documentation (especially patents). |
Master Class
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68–75
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The paper investigates the social role of education and the relevance of university programs to the real needs of society, which has gained especial political importance in recent years. Attention to this topic, in turn, has fueled interest in the concept of the «knowledge triangle», which implies a synergistic effect from the interplay of education, research, and innovation. Existing studies on the interaction of higher education institutions (HEIs) with society and policy in this field are primarily focused on the links between science and innovation and on the contributions of HEIs to economic development and growth. Many researchers focus on the interaction between universities and the industrial sector but ignore HEIs’ involvement in creating innovations in the public services sector. This is rather peculiar, considering that innovation in the public sector has received increased policy attention over the recent period, and is seen as essential for improving the efficiency and quality of public services and for addressing some of the major societal challenges, linked, for example, to an ageing population and maintaining the welfare state. This paper looks at the healthcare sector, where HEIs interact with private industry as well as public healthcare services. It builds upon a study from Norway carried out in 2015 in the framework of an OECD project, which mapped and analyzed knowledge triangle policies and practices at the national and institutional level. This study showed that interplay between education, research, and innovation is a key concern in the national policy for the development of the health sector, and that knowledge triangle interactions with both the private and public sector is a central aspect of the current practices at the medical departments at Norwegian HEIs. The linkages between the medical faculties and public healthcare services are especially interesting, as they provide patterns of interaction beyond those identified in the existing literature and because education plays a central role. |
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76–85
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This paper offers a broad view on foreseeing innovation, which is not limited solely to early detection at the micro level. The author defines innovations as ongoing processes of changes in the various fields of social and economic life, which result from human creative activity. Noting that innovation is an uncertain, relatively chaotic, and disordered process characterized by inherent risks, the author aims to define the most general and universal barriers impeding one’s ability to recognize the signs of future innovation and to anticipate their consequences. Considering examples of «disruptive innovation» in the technological, social, political, and economic spheres of life, the author sees these innovations as arising from certain condition and events, not as simple random occurences. Most of them are effects of particular causes. However, these causes are often hidden within events that are difficult to observe and phenomena encapsulated in weak signals. The inability to detect and recognize such pre-emerging warnings of upcoming innovations may be attributed to the massive amount of information signals and noise flooding today’s world. This problem is excaerbated by the lack of knowledge, techniques, and experience for dealing with huge amounts of information, the lack of the required skills, and, finally, by human cognitive biases. Faced with this deluge of misinformation, any person can eventually be misled and make mistakes. This paper posits that, in order to mitigate such risks, an individual must avoid the three cognitive biases: the symmetry of delusions, aggressive neglect, and the curse of knowledge. These cognitive biases are the barriers to foreseeing innovation. |
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