EconPapers    
Economics at your fingertips  
 

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

Leonid Gokhberg (), Ilya Kuzminov (), Pavel Bakhtin (), Elena Tochilina (), Alexander Chulok (), Anton Timofeev () and Alina Lavrinenko ()
Additional contact information
Leonid Gokhberg: National Research University Higher School of Economics
Ilya Kuzminov: National Research University Higher School of Economics
Pavel Bakhtin: National Research University Higher School of Economics
Elena Tochilina: National Research University Higher School of Economics
Alexander Chulok: National Research University Higher School of Economics
Anton Timofeev: National Research University Higher School of Economics
Alina Lavrinenko: National Research University Higher School of Economics

No WP BRP 76/STI/2017, HSE Working papers from National Research University Higher School of Economics

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 (search for similar items in EconPapers)
JEL-codes: C55 O1 O3 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2017
New Economics Papers: this item is included in nep-agr, nep-big, nep-cis and nep-tra
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Published in WP BRP Series: Science, Technology and Innovation / STI, November 2017, pages 1-24

Downloads: (external link)
https://wp.hse.ru/data/2017/11/28/1161789532/76STI2017.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hig:wpaper:76sti2017

Access Statistics for this paper

More papers in HSE Working papers from National Research University Higher School of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Shamil Abdulaev ().

 
Page updated 2020-07-02
Handle: RePEc:hig:wpaper:76sti2017