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.
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References
An XY, Wu QQ (2011) Co-word analysis of the trends in stem cells field based on subject heading weighting. Scientometrics 88(1):133–144
Bengisu M, Nekhili R (2006) Forecasting emerging technologies with the aid of science and technology databases. Technol Forecast Soc Change 73(7):835–844
Bildosola I, Rio-Belver RM, Garechana G, Cilleruelo E (2017) TeknoRoadmap, an approach for depicting emerging technologies. Technol Forecast Soc Change 117:25–37
Bondiombouy C, Valduriez P (2016) Query processing in multistore systems: an overview. Int J Cloud Comput 5(4):309–346
Börner K, Chen C, Boyack KW (2003) Visualizing knowledge domains. Ann Rev Inf Sci Technol 37(1):179–255
Burghard C (2012) Big data and analytics key to accountable care success. IDC Health Insights, Framingham
Chew R, Genicola K, Li B, Philip J, Tichanona Z (2013) ISACA: big data impacts and benefits (whitepaper). Rolling Meadows, ISACA, IL, USA
Daim TU, Rueda G, Martin H, Gerdsri P (2006) Forecasting emerging technologies: use of bibliometrics and patent analysis. Technol Forecast Soc Change 73(8):981–1012
Feldman R, Sanger J (2007) The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, Cambridge
Fernández-Cano A, Torralbo M, Vallejo M (2012) Time series of scientific growth in Spanish doctoral theses (1848–2009). Scientometrics 91(1):15–36
Ferrucci D, Brown E, Chu-Carroll J et al (2010) Building Watson: an overview of the DeepQA project. AI Mag 31(3):59–79
Firat AK, Woon WL, Madnick S (2008) Technological forecasting—a review. Composite Information Systems Laboratory (CISL), Massachusetts Institute of Technology, Cambridge
Gartner IT Glossary (n.d.) Retrieved from http://www.gartner.com/it-glossary/big-data/
Georghiou L (2008) The handbook of technology foresight: concepts and practice. Edward Elgar Publishing, Cheltenham
Harvey AC (1989) Forecasting, structural time series and the kalman filter. Cambridge University Press, Cambridge
Hyndman RJ, Khandakar Y (2007) Automatic time series for forecasting: the forecast package for R (No. 6/07). Monash University, Department of Econometrics and Business Statistics, Melbourne
Jun S, Uhm D (2010) Technology forecasting using frequency time series model: bio-technology patent analysis. J Mod Math Stat 4(3):101–104
Kajikawa Y, Yoshikawa J, Takeda Y, Matsushima K (2008) Tracking emerging technologies in energy research: toward a roadmap for sustainable energy. Technol Forecast Soc Change 75(6):771–782
Kostoff RN (2005) U.S. Patent No. 6,886,010. U.S. Patent and Trademark Office, Washington
Kostoff RN, Geisler E (1999) Strategic management and implementation of textual data mining in government organizations. Technol Anal Strateg Manag 11(4):493–525
Losiewicz P, Oard DW, Kostoff RN (2000) Textual data mining to support science and technology management. J Intell Inf Syst 15(2):99–119
Maddala GS, Lahiri K (1992) Introduction to econometrics, vol 2. Macmillan, New York
Major EJ, Cordey-Haye M (2000) Engaging the business support network to give SMEs the benefit of foresight. Technovation 20(11):589–602
Martino JP (2003) A review of selected recent advances in technological forecasting. Technol Forecast Soc Change 70(8):719–733
Perrey J, Spillecke D, Umblijs A (2013) Smart analytics: how marketing drives short-term and long-term growth. McKinsey Quarterly, New York City
Popper R, Keenan M, Miles I, Butter M, Sainz G (2007) Global foresight outlook. mapping foresight in europe and the rest of the world. European Commission, European Foresight Monitoring Network, Brussels
Porter AL, Cunningham SW (2004) Tech mining: exploiting new technologies for competitive advantage, vol 29. Wiley, Hoboken
Porter AL, Detampel MJ (1995) Technology opportunities analysis. Technol Forecast Soc Change 49(3):237–255
Porter AL, Ashton WB, Clar G, Coates JF, Cuhls K, Cunningham SW et al (2004) Technology futures analysis: toward integration of the field and new methods. Technol Forecast Soc Change 71(3):287–303
Salerno M, Landoni P, Verganti R (2008) Funded research projects as a tool for the analysis of emerging fields: the case of nanotechnology. In: SPRU 40th anniversary conference—the future of science, technology and innovation policy, vol 6
Song M, Kim SY (2013) Detecting the knowledge structure of bioinformatics by mining full-text collections. Scientometrics 96(1):183–201
Zhang Y, Guo Y, Wang X, Zhu D, Porter AL (2013) A hybrid visualization model for technology roadmapping: bibliometrics, qualitative methodology and empirical study. Technol Anal Strateg Manag 25(6):707–724
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Bildosola, I., Garechana, G., Zarrabeitia, E. et al. Characterization of strategic emerging technologies: the case of big data. Cent Eur J Oper Res 28, 45–60 (2020). https://doi.org/10.1007/s10100-018-0597-9
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DOI: https://doi.org/10.1007/s10100-018-0597-9