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

Author

Listed:
  • 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)

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

Suggested Citation

  • Leonid Gokhberg & Ilya Kuzminov & Pavel Bakhtin & Elena Tochilina & Alexander Chulok & Anton Timofeev & Alina Lavrinenko, 2017. "Big-Data-Augmented Approach to Emerging Technologies Identification: Case of Agriculture and Food Sector," HSE Working papers WP BRP 76/STI/2017, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:76sti2017
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    References listed on IDEAS

    as
    1. Pavel Bakhtin & Ozcan Saritas, 2016. "Tech Mining for Emerging STI Trends Through Dynamic Term Clustering and Semantic Analysis: The Case of Photonics," Innovation, Technology, and Knowledge Management, in: Tugrul U. Daim & Denise Chiavetta & Alan L. Porter & Ozcan Saritas (ed.), Anticipating Future Innovation Pathways Through Large Data Analysis, chapter 0, pages 341-360, Springer.
    2. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    3. Dominique Guellec & Bruno van Pottelsberghe de la Potterie, 2003. "R&D and Productivity Growth: Panel Data Analysis of 16 OECD Countries," OECD Economic Studies, OECD Publishing, vol. 2001(2), pages 103-126.
    4. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    5. Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
    6. Farshad Madani, 2015. "‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 323-335, October.
    7. U. Srinivasan & William Cheung & Reg Watson & U. Sumaila, 2010. "Food security implications of global marine catch losses due to overfishing," Journal of Bioeconomics, Springer, vol. 12(3), pages 183-200, October.
    8. Fan, Peilei & Watanabe, Chihiro, 2006. "Promoting industrial development through technology policy: Lessons from Japan and China," Technology in Society, Elsevier, vol. 28(3), pages 303-320.
    9. Daniele Rotolo & Ismael Rafols & Michael Hopkins & Loet Leydesdorff, 2014. "Scientometric Mapping as a Strategic Intelligence Tool for the Governance of Emerging Technologies," SPRU Working Paper Series 2014-10, SPRU - Science Policy Research Unit, University of Sussex Business School.
    10. David Pimentel, 2006. "Soil Erosion: A Food and Environmental Threat," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 8(1), pages 119-137, February.
    11. Park, Han Woo & Leydesdorff, Loet, 2010. "Longitudinal trends in networks of university-industry-government relations in South Korea: The role of programmatic incentives," Research Policy, Elsevier, vol. 39(5), pages 640-649, June.
    12. Bildosola, Iñaki & Río-Bélver, Rosa María & Garechana, Gaizka & Cilleruelo, Ernesto, 2017. "TeknoRoadmap, an approach for depicting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 25-37.
    13. Hauke Simon & Jens Leker, 2016. "Using Startup Communication For Opportunity Recognition — An Approach To Identify Future Product Trends," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(08), pages 1-22, December.
    14. Leonid Gokhberg & Konstantin Fursov & Ian Miles & Giulio Perani, 2013. "Developing and using indicators of emerging and enabling technologies," Chapters, in: Fred Gault (ed.), Handbook of Innovation Indicators and Measurement, chapter 15, pages 349-380, Edward Elgar Publishing.
    15. Michael Keenan & Paul Cutler & John Marks & Richard Meylan & Carthage Smith & Emilia Koivisto, 2012. "Orienting international science cooperation to meet global 'grand challenges'," Science and Public Policy, Oxford University Press, vol. 39(2), pages 166-177, March.
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    Cited by:

    1. 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

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