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A methodology for technology trend monitoring: the case of semantic technologies

Author

Listed:
  • Oleg Ena

    (National Research University Higher School of Economics)

  • Nadezhda Mikova

    (National Research University Higher School of Economics)

  • Ozcan Saritas

    (National Research University Higher School of Economics)

  • Anna Sokolova

    (National Research University Higher School of Economics)

Abstract

This paper introduces a systematic technology trend monitoring (TTM) methodology based on an analysis of bibliometric data. Among the key premises for developing a methodology are: (1) the increasing number of data sources addressing different phases of the STI development, and thus requiring a more holistic and integrated analysis; (2) the need for more customized clustering approaches particularly for the purpose of identifying trends; and (3) augmenting the policy impact of trends through gathering future-oriented intelligence on emerging developments and potential disruptive changes. Thus, the TTM methodology developed combines and jointly analyzes different datasets to gain intelligence to cover different phases of the technological evolution starting from the ‘emergence’ of a technology towards ‘supporting’ and ‘solution’ applications and more ‘practical’ business and market-oriented uses. Furthermore, the study presents a new algorithm for data clustering in order to overcome the weaknesses of readily available clusterization tools for the purpose of identifying technology trends. The present study places the TTM activities into a wider policy context to make use of the outcomes for the purpose of Science, Technology and Innovation policy formulation, and R&D strategy making processes. The methodology developed is demonstrated in the domain of “semantic technologies”.

Suggested Citation

  • Oleg Ena & Nadezhda Mikova & Ozcan Saritas & Anna Sokolova, 2016. "A methodology for technology trend monitoring: the case of semantic technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1013-1041, September.
    • Handle: RePEc:spr:scient:v:108:y:2016:i:3:d:10.1007_s11192-016-2024-0
    DOI: 10.1007/s11192-016-2024-0
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Xiaoli & Daim, Tugrul & Huang, Lucheng & Li, Zhiqiang & Shaikh, Ruqia & Kassi, Diby Francois, 2022. "Monitoring the development trend and competition status of high technologies using patent analysis and bibliographic coupling: The case of electronic design automation technology," Technology in Society, Elsevier, vol. 71(C).
    3. Nguyen Thanh Viet & Alla G. Kravets, 2022. "The New Method for Analyzing Technology Trends of Smart Energy Asset Performance Management," Energies, MDPI, vol. 15(18), pages 1-26, September.
    4. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    5. Worasak Klongthong & Veera Muangsin & Chupun Gowanit & Nongnuj Muangsin, 2021. "A Patent Analysis to Identify Emergent Topics and Convergence Fields: A Case Study of Chitosan," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    6. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    7. Christian Mühlroth & Laura Kölbl & Michael Grottke, 2023. "Innovation signals: leveraging machine learning to separate noise from news," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2649-2676, May.
    8. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    9. Ozcan Saritas & Pavel Bakhtin & Ilya Kuzminov & Elena Khabirova, 2021. "Big data augmentated business trend identification: the case of mobile commerce," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1553-1579, February.
    10. 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.
    11. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
    12. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

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