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”.
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.
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.
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.
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.