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

NRU Higher School of Economics , 2018. No. 82.

Kuzminov I., Bakhtin P. D., Khabirova E., Loginova I.

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.

Keywords: форсайт, инновации, innovation, foresight, survey, анкетный опрос, опрос, text mining, методология форсайта, технологические тренды, technology trends, библиометрический анализ, Technology mining and patent analysis, technological foresight, technology trends monitoring, patent analysis, Научно-технологическое прогнозирование, мониторинг технологических трендов, патентный анализ, научно-технологический прогресс, технологический Форсайт, текст-майнинг, STI foresight, bibliometrics, science and technology forecasting, science and technology progress

Publication based on the results of: Development of quantitative approaches for global technology trend analysis and identification (2018)