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Characterization of strategic emerging technologies: the case of big data

Author

Listed:
  • Iñaki Bildosola

    (University of the Basque Country (UPV/EHU))

  • Gaizka Garechana

    (University of the Basque Country (UPV/EHU))

  • Enara Zarrabeitia

    (University of the Basque Country (UPV/EHU))

  • Ernesto Cilleruelo

    (University of the Basque Country (UPV/EHU))

Abstract

Current enterprises face organizational and cultural barriers to adopt and harness the potential of strategic emerging technologies. Late adoption of these technologies will affect competitiveness from which it will be hard to recover. Within the frame of technology analysis field, the present work aims at introducing an approach to obtain the characterization of emerging technologies, which facilitates understanding and identifies their potential. This characterization is based on the analysis of scientific activity, to which a set of quantitative methods is applied, namely bibliometrics, text mining, principal component analysis and time series analysis. The outcome is based on obtaining a set of dominant sub-technologies, which are described by means of individual time series, which also allow evolution of the technology as a whole to be forecasted. The approach is applied to the Big Data technology field and the results suggest that sub-technologies such as Mobile Telecommunications and Internet of things will lead this field in the near future.

Suggested Citation

  • Iñaki Bildosola & Gaizka Garechana & Enara Zarrabeitia & Ernesto Cilleruelo, 2020. "Characterization of strategic emerging technologies: the case of big data," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 45-60, March.
    • Handle: RePEc:spr:cejnor:v:28:y:2020:i:1:d:10.1007_s10100-018-0597-9
    DOI: 10.1007/s10100-018-0597-9
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Min Song & Su Yeon Kim, 2013. "Detecting the knowledge structure of bioinformatics by mining full-text collections," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 183-201, July.
    3. Antonio Fernández-Cano & Manuel Torralbo & Mónica Vallejo, 2012. "Time series of scientific growth in Spanish doctoral theses (1848–2009)," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 15-36, April.
    4. 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.
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    Cited by:

    1. Josefa Mula & Marija Bogataj, 2021. "OR in the industrial engineering of Industry 4.0: experiences from the Iberian Peninsula mirrored in CJOR," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1163-1184, December.

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