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A methodology for technology trend monitoring: the case of semantic technologies

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Abstract

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”.

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Notes

  1. http://carrot2.org.

  2. The priority areas include Information and Communication Technologies, Living Systems and Biotechnologies, Nanotechnologies, Transportation and Aerospace Technologies, Technologies for the Rational Use of Natural Resources, and Energy Efficiency and Energy Saving Technologies.

  3. http://issek.hse.ru/trendletter/. Last visited on October 27, 2015.

  4. http://www.hse.ru/data/2014/03/03/1330240475/Foresight%202030.pdf. Last visited on October 27, 2015.

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Acknowledgments

The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy by the Russian Academic Excellence Project ‘5-100’. The authors are grateful for the immense help of Sergey Kuznetsov’s team (Higher School of Economics) in the development of clustering algorithms and Mr. Evgeny Klochickin (Ph.D. candidate at the Manchester Institute of Innovation Research Manchester Business School) in the process of extracting and analysing the data.

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  1. National Research University Higher School of Economics, Moscow, Russian Federation

    Oleg Ena, Nadezhda Mikova, Ozcan Saritas & Anna Sokolova

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  1. Oleg Ena
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  2. Nadezhda Mikova
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  3. Ozcan Saritas
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  4. Anna Sokolova
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Correspondence to Anna Sokolova.

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Oleg Ena, Nadezhda Mikova, Ozcan Saritas and Anna Sokolovaequal have contributed equally to this paper.

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Ena, O., Mikova, N., Saritas, O. et al. A methodology for technology trend monitoring: the case of semantic technologies. Scientometrics 108, 1013–1041 (2016). https://doi.org/10.1007/s11192-016-2024-0

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