This paper introduces a systematic technology trend monitoring (TTM) methodology based on an analysis of bibliometric data. Among the key premises for developing a methodology are: (1) the increasing number of data sources addressing different phases of the STI development, and thus requiring a more holistic and integrated analysis; (2) the need for more customized clustering approaches particularly for the purpose of identifying trends; and (3) augmenting the policy impact of trends through gathering future-oriented intelligence on emerging developments and potential disruptive changes. Thus, the TTM methodology developed combines and jointly analyzes different datasets to gain intelligence to cover different phases of the technological evolution starting from the ‘emergence’ of a technology towards ‘supporting’ and ‘solution’ applications and more ‘practical’ business and market-oriented uses. Furthermore, the study presents a new algorithm for data clustering in order to overcome the weaknesses of readily available clusterization tools for the purpose of identifying technology trends. The present study places the TTM activities into a wider policy context to make use of the outcomes for the purpose of Science, Technology and Innovation policy formulation, and R&D strategy making processes. The methodology developed is demonstrated in the domain of “semantic technologies”.
The main aim of this study is to compare Russian regions according to their ability to create new technologies efficiently and to identify factors that determine these differences over a long period of time. We apply data envelopment analysis (DEA) to assess the relationship between the results of patenting and resources of a regional innovation system (RIS). Unlike previous studies, we apply the DEA method over a long period, comparing regions to one another and over time. In general, RIS efficiency in Russia increased during the period, especially in the least developed territories. There was significant regional differentiation. The most efficient RIS were formed in the largest agglomerations with leading universities and research centers: the cities Moscow and Saint Petersburg and the Novosibirsk, Voronezh, and Tomsk regions. Econometric calculations show that RIS efficiency was higher in technologically more developed regions with the oldest universities and larger patent stock. Time is a crucial factor for knowledge accumulation and creating links between innovative agents within RIS. Entrepreneurial activity was also a significant factor because it helps to convert ideas and research into inventions and new technologies and it enhances the interaction between innovative agents. It is advantageous to be located near major innovation centres because of more intensive interregional knowledge spillovers. Public support of more efficient regions can lead to a more productive regional innovation policy.
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.
A Triple Helix (TH) network of bi- and trilateral relations among universities, industries, and governments can be considered as an ecosystem in which uncertainty can be reduced when functions become synergetic. The functions are based on correlations among distributions of relations, and therefore latent. The correlations span a vector space in which two vectors (P and Q) can be used to represent forward “sending” and reflexive “receiving,” respectively. These two vectors can also be understood in terms of the generation versus reduction of uncertainty in the communication field that results from interactions among the three bi-lateral channels of communication. We specify a system of Lotka–Volterra equations between the vectors that can be solved. Redundancy generation can then be simulated and the results can be decomposed in terms of the TH components. Furthermore, we show that the strength and frequency of the relations are independent parameters in the model. Redundancy generation in TH arrangements can be decomposed using Fourier analysis of the time-series of empirical studies. As an example, the case of co-authorship relations in Japan is re-analyzed. The model allows us to interpret the sinusoidal functions of the Fourier analysis as representing redundancies.
For several decades the Soviet academic psychology community was isolated from the West, yet after the collapse of the Soviet Union each of the 15 countries went their own way in economic, social, and scientific development. The paper analyses publications from post-Soviet countries in psychological journals in 1992–2017, i.e. 26 years after the collapse of the Soviet Union. Over the period in question, 15 post-Soviet countries had published 4986 papers in psychology, accounting for less than one percent of the world output in psychological journals. However, the growth of post-Soviet countries’ output in psychological journals, especially that of Russia and Estonia, is observed during this period. Over time, post-Soviet authors began to write more papers in international teams, constantly increasing the proportion of papers in which they are leaders and main contributors. Their papers are still underrepresented in the best journals as well as among the most cited papers in the field and are also cited lower than the world average. However, the impact of psychological papers from post-Soviet countries increases with time. There is a huge diversity between 15 post-Soviet countries in terms of contribution, autonomy, and impact. Regarding the number of papers in psychological journals, the leading nations are Russia, Estonia, Lithuania, Ukraine, and Georgia. Estonia is the leader in autonomy in publishing papers in psychological journals among post-Soviet countries. Papers from Estonia and Georgia are cited higher than the world average, whereas papers from Russia and Ukraine are cited below the world average. Estonia and Georgia also boast a high number of Highly cited papers.
We consider the “Matthew effect” in the citation process which leads to reallocation (or misallocation) of the citations received by scientific papers within the same journals. The case when such reallocation correlates with a country where an author works is investigated. Russian papers in chemistry and physics published abroad were examined. We found that in both disciplines in about 60% of journals Russian papers are cited less than average ones. However, if we consider each discipline as a whole, citedness of a Russian paper in physics will be on the average level, while chemistry publications receive about 16% citations less than one may expect from the citedness of the journals where they appear. Moreover, Russian chemistry papers mostly become undercited in the leading journals of the field. Characteristics of a “Matthew index” indicator and its significance for scientometric studies are also discussed.
Impact factors for 20 journals ranked first by Journal Citation Reports (JCR) were compared with the same indicator calculated on the basis of citation data obtained from Scopus database. A significant discrepancy was observed as Scopus, though results differed from title to title, found in general more citations than listed in JCR. This also affected ranking of the journals. More thorough examination of two selected titles proved that the divergence resulted mainly from difference in coverage of two products, although other important factors also play their part.
Technologies may have significant effects on productivity in the agricultural sector as documented in the related literature. However, those impacts vary from country to country. These differences could partially reflect the distinct scientific landscapes, science technology and innovation (STI) policies and approaches to R&D. In order to explain the cross-country volatility of agricultural productivity, we aim to study issues of STI development in the agricultural sector in each country. Among other characteristics of STI in general and the scientific landscape, in particular, we looked at the diversification of research publication between subfields of agricultural science. We estimated the research diversification parameter and studied its relation to economic performance of an agricultural sector. Our main finding shows that R&D funding, if carefully balanced with the diversification of agricultural science, could improve research performance and eventually productivity in an agricultural sector.
In this study, the evolution of the connected health concept is analysed and visualized to investigate the ever-tightening relationship between health and technology as well as emerging possibilities regarding delivery of healthcare services. A scientometric analysis was undertaken to investigate the trends and evolutionary relations between health and information systems through the queries in the Web of Science database using terms related to health and information systems. To understand the evolutionary relation between different concepts, scientometric analyses were conducted within five-year intervals using the VantagePoint, SciMAT, and CiteSpace II software. Consequently, the main stream of publications related to the connected health concept matching telemedicine cluster was determined. All other developments in health and technologies were discussed around this main stream across years. The trends obtained through the analysis provide insights about the future of healthcare and technology relationship particularly with rising importance of privacy, personalized care along with mobile networks and mobile infrastructure.
This paper investigates the social space of physics research institutions. Scientific capital is a well-known concept for measuring and assessing the accumulated recognition and the specific scientific power developed by Pierre Bourdieu. The scientific capital of a physics research institution manifests itself as a reputation, a high-profile name in the field of physics, symbols of academic recognition, and scientific status. Using citation statistics from the Web of Science Core Collection and sociological data of dedicated survey “The Monitoring of the Labor Market for Highly Qualified R&D Personnel” we construct the social space of Russian physics institutions. The analysis reveals generalized grounds of social space of Russian physics institutions: principles of visibility and scientific capital. The study highlights internal differentiation of physics institutions on three groups (“major”, “high energy”, and “secondary” institutions). The social space of physics research institutions provides a map of field of physics in Russia. This research may be a useful starting point for developing a more comprehensive study of the field of physics.
Since early 1960s, there has been a growing interest in the emergence and development of new technologies accompanied by a strong wish from decision makers to govern related processes at the corporate and national levels. One of the key categories that appeared to set up analytical and regulatory frameworks was the ‘advanced technology’ category. Primarily associated with computer electronics and microelectronics, it soon had new meanings derived from a variety of professional discussions primarily in the social sciences. Later in a new term, ‘emerging technologies’, appeared to highlight the speed of change in a wide range of promising research areas. This paper focuses on the evolution of academic discussions concerning advanced and emerging technologies in social sciences literature for the period from 1955 until 2015. In order to identify whether studies in these areas constitute separate research fields, the paper studies the evolution of co-citation networks and the centrality characteristics of transitionary references. It was shown that social studies in emerging technologies demonstrate better consistency in background in literature. However, an analysis of transitionary references and their centrality characteristics can hardly confirm the existence of separate research fields in both cases. The suggested method for the identification and tracking of papers mediating ongoing discussions in a selected knowledge network may be helpful in understanding the evolution of weakly conceptualized and growing research areas.
When scientists change jobs, they bring to their new workplace the experience, tacit knowledge and social ties they acquired at their previous workplace. Not only is the level of mobility important when discussing knowledge transfer between academic organizations or between regions, but the topology of a mobility network is also of crucial importance. This study presents a comparison of the structure of internal migration networks for Russian and American physicists, more specifically for scholars working in the field of applied physics. The comparison resulted in the set of hypotheses of how the features of the network are connected to the overall scientific productivity of the system.
In this study we compare internationalization of academic journals in six fields of science. Internationalization was investigated through journals’ concentration on publishing papers from particular countries, relationship between the geographical distributions of editors and authors, and relationship between language of publication and the geographical distribution of papers. Having analyzed more than 1,000 journals we can state that social sciences literature in the fields considered is still nationally and linguistically fragmented more than natural sciences literature, but in some cases the gap is not so big. One of the consequences concerning research output assessment is that usefulness of international databases having national disparity in coverage is still limited in social sciences.
This paper is devoted to the challenges of measuring, analyzing and visualizing research capacity of university. We identify the related methodological issues, propose solutions and apply these solutions to a complex analysis of the research potential of three departments of a Russian university. First, we briefly review the current literature on different aspects of an analysis of research capacity of university. The next step is a discussion on the key challenges faced when analyzing the publication activity of a university. Further, we discuss the opportunities offered by and limitations of using the Web of Science and Scopus databases to determine the research capabilities of universities. In the empirical section of the paper, we analyse the research capacity of university departments and individual employees using simple yet illustrative tools of bibliometric analysis. We also make recommendations for university administrative personnel, which can be derived from our analysis.
The article discusses the process of textually mediated communication in science and proposes an approach that complements citation analysis. Namely, it addresses the question of how the author’s text is read by the reader and whether the reader interprets the text in the same manner as the author. Fifty-seven scholarly contributions (articles, book chapters and book reviews), written by three social scientists, were content analyzed with the help of the QDA Miner and WordStat computer programs. The outcomes of the qualitative coding were compared with the outcomes of the analysis of word co-occurrences and the outcomes of the analysis on the basis of a dictionary based on substitution. Our findings suggest that texts have plural interpretations. Depending on the reading context, either the author’s or the reader’s perspective prevails. Also, both the author and the reader may read the text in a either deep or perfunctory manner. Deep reading necessitates spending significant time and cognitive resources.
Built upon a 30-year dataset collected from the web of science database, the present research aims to offer a comprehensive overview of papers, authors, streams of research, and the most influential journals that discuss product and process innovation in the manufacturing environment. The dataset is composed of 418 papers from more than 150 journals from the period between 1985 and 2015. Homogeneity analysis by means of alternating least squares (HOMALS) and social network analysis (SNA) are used to accomplish the objectives listed above through the keywords given by authors. Initially, the paper highlights and discusses the similarity between the topics debated by the main journals in this field. Subsequently, a wide-range map of topics is presented highlighting five main areas of interests; namely, performance, patent, small firm, product development, and organization. A SNA is also performed in order to validate the results that emerged from HOMALS. Finally, several insights about future research avenues in the manufacturing field are provided.
his article provides the comprehensive analysis of research landscape in BRICS countries in different aspects: level of their publication activity and contribution to the global process of knowledge generation; thematic structure of publications of BRICS countries, their scientific specialization; quality of articles measured by citation indicators; similarity of thematic structures of publications; international research collaboration profiles; and finally closeness and relative influence of each country in intra-BRICS collaborating pairs.
Special sections of the article are devoted to review of the literature, which discusses the main articles on various aspects of BRICS countries' publication activity and their international research collaboration and to description the database and set of various bibliometric indicators, used in our analysis. We use Scopus database and the timespan of our research covers 2001 – 2015 years that allows us to identify key points in development of research landscapes of BRICS countries. The empirical part of the article is structured as follows. First, we provide the overview of publication activity and thematic structure of BRICS countries. Second, we measure the closeness of thematic structure of publications vs. each other and vs. general research agenda in the world using different indices of structural difference. Third part is the analysis of research collaboration with clear visualization of its thematic structure, identification of potential areas of collaboration and detection the influential countries in intra-BRICS collaborating pairs. We use wide range of bibliometric indicators: citation indicators; indices of structural difference; indicators of scientific collaboration. We apply different approaches to visualise data in form of different illustrative graphs including colored tables to do our research easy-to-read-and understand.
The results of the study may be of interest to decision makers in determining the conscientious research story of the BRICS countries and priorities setting for multilateral scientific and technological cooperation, as well as for researchers dealing with relevant problems.
In early 2016 a new database was launched on the Web of Science platform—Russian Science Citation Index. The database is free to all Web of Science subscribers except those from the post-Soviet states. This database includes papers from 652 selected Russian journals and is based on the data from national citation index—Russian Index of Science Citation (RISC). RISC was launched in 2005 but it is scarcely known to the English-language audience. The paper describes the history, current structure and user possibilities of RISC. We focus on the novel features of RISC which are crucial to bibliometrics and are unavailable in the international citation indexes.
In this paper, the results of a study on the development of social network analysis (SNA) and its evolution over time, using the analysis of bibliographic networks are presented. The dataset consists of articles from the Web of Science Clarivate Analytics database obtained by searching for the keyword “social network*” and those published in the main journals in the field (in total 70,000+ publications). From the data, we constructed several networks. In this paper, the focus is on the analysis of the citation network. Analyzing the obtained network, we evaluated the SNA field’s growth and identified the most cited works. Using the normalized Search path count weights, we extracted the main path, key-route paths, and link islands in the citation network. Based on the probabilistic flow node values, we also identified the most important articles. Our results show that the number of published papers almost doubles each 3 years. We confirmed the finding that the authors from the social sciences, who were most active through the whole history of the field development, experienced the “invasion” of physicists from the 2000s. However, starting from the 2010s, a new very active group of animal social network analysts took the leading position.