Big-Data-Augmented Approach to Emerging Technologies Identification: Case of Agriculture and Food Sector

24 Pages Posted: 29 Nov 2017

See all articles by Leonid Gokhberg

Leonid Gokhberg

National Research University Higher School of Economics

Ilya Kuzminov

Higher School of Economics

Pavel Bakhtin

National Research University Higher School of Economics

Elena Khabirova

National Research University Higher School of Economics

Alexander Chulok

National Research University Higher School of Economics - Institute for Statistical Studies and Economics of Knowledge

Anton Timofeev

National Research University Higher School of Economics

Alina Lavrynenko

National Research University Higher School of Economics

Date Written: November 28, 2017

Abstract

The paper discloses a new approach to emerging technologies identification, which strongly relies on capacity of big data analysis, namely text mining augmented by syntactic analysis techniques. It discusses the wide context of the task of identifying emerging technologies in a systemic and timely manner, including its place in the methodology of foresight and future-oriented technology analysis, its use in horizon scanning exercises, as well as its relation to the field of technology landscape mapping and tech mining. The concepts of technology, emerging technology, disruptive technology and other related terms are assessed from the semantic point of view. Existing approaches to technology identification and technology landscape mapping (in wide sense, including entity linking and ontology-building for the purposes of effective STI policy) are discussed, and shortcomings of currently available studies on emerging technologies in agriculture and food sector (A&F) are analyzed. The opportunities of the new big-data-augmented methodology are shown in comparison to existing results, both globally and in Russia. As one of the practical results of the study, the integrated ontology of currently emerging technologies in A&F sector is introduced. The directions and possible criteria of further enhancement and refinement of proposed methodology are contemplated, with special attention to use of bigger volumes of data, machine learning and ontology-mining / entity linking techniques for the maximum possible automation of the analytical work in the discussed field. The practical implication of the new approach in terms of its effectiveness and efficiency for evidence-based STI policy and corporate strategic planning are shortly summed up as well.

Keywords: Emerging technologies, foresight, strategic planning, STI policy, Russian Federation, agriculture, food sector, text mining, tech mining, STI landscape mapping, horizon scanning

JEL Classification: C55, O1, O3

Suggested Citation

Gokhberg, Leonid and Kuzminov, Ilya and Bakhtin, Pavel and Khabirova, Elena and Chulok, Alexander and Timofeev, Anton and Lavrynenko, Alina, Big-Data-Augmented Approach to Emerging Technologies Identification: Case of Agriculture and Food Sector (November 28, 2017). Higher School of Economics Research Paper No. WP BRP 76/STI/2017. Available at SSRN: https://ssrn.com/abstract=3078499 or http://dx.doi.org/10.2139/ssrn.3078499

Leonid Gokhberg

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

Ilya Kuzminov

Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

Pavel Bakhtin

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

Elena Khabirova (Contact Author)

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

Alexander Chulok

National Research University Higher School of Economics - Institute for Statistical Studies and Economics of Knowledge ( email )

Myasnitskaya st. 18
Moscow, 101000
Russia

Anton Timofeev

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

Alina Lavrynenko

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

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