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Detecting and Validating Global Technology Trends Using Quantitative and Expert-Based Foresight Techniques

Author

Listed:
  • Ilya Kuzminov

    (National Research University Higher School of Economics)

  • Pavel Bakhtin

    (National Research University Higher School of Economics)

  • Elena Khabirova

    (National Research University Higher School of Economics)

  • Irina V. Loginova

    (National Research University Higher School of Economics)

Abstract

This paper contributes to the conceptualisation and operationalisation of the “technology trend” discussion in the scope of the Technology Foresight paradigm. It proposes a consistent logical approach to analysing technology trends and increase predictive potential of futures studies. The approach integrates Big Data analysis into the Foresight studies’ toolset by means of applying text mining, namely computerised analysis of large volumes of unstructured text-based industry-relevant analytics. It comprises methodological results such as analytical decomposition of the trend concept, including trend attributes (inherent characteristics) and various trend types and empirical results of detection and classification of global technology trends in the agricultural sector. The study makes a significant contribution to the development of a conceptual apparatus for trend analysis as a sub-area of Foresight methodology. The agricultural field is used to demonstrate the application the methodology. The empirical results can be applied by federal and regional authorities responsible for promoting development of the sectors to design relevant strategies and programmes, and by companies to set their long-term marketing and investment priorities.

Suggested Citation

  • Ilya Kuzminov & Pavel Bakhtin & Elena Khabirova & Irina V. Loginova, 2018. "Detecting and Validating Global Technology Trends Using Quantitative and Expert-Based Foresight Techniques," HSE Working papers WP BRP 82/STI/2018, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:82sti2018
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    References listed on IDEAS

    as
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    5. Pavel Bakhtin & Ozcan Saritas & Alexander Chulok & Ilya Kuzminov & Anton Timofeev, 2017. "Trend monitoring for linking science and strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2059-2075, June.
    6. Nadezhda Mikova & Anna Sokolova, 2014. "Global technology trends monitoring: theoretical Frameworks and best practices," Foresight-Russia Форсайт, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 8(4 (eng)), pages 64-83.
    7. Ilya Kuzminov & Pavel Bakhtin & Elena Khabirova & Maxim Kotsemir & Alina Lavrynenko, 2018. "Mapping the Radical Innovations in Food Industry: A Text Mining Study," HSE Working papers WP BRP 80/STI/2018, National Research University Higher School of Economics.
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    More about this item

    Keywords

    technology trends; innovation; science and technology forecasting; science and technology progress; foresight; text mining; survey; bibliometrics; patent analysis;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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