Skip to main content
SpringerLink
Log in

An approach for modelling and forecasting research activity related to an emerging technology

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The understanding of emerging technologies and the analysis of their development pose a great challenge for decision makers, as being able to assess and forecast technological change enables them to make the most of it. There is a whole field of research focused on this area, called technology forecasting, in which bibliometrics plays an important role. Within that framework, this paper presents a forecasting approach focused on a specific field of technology forecasting: research activity related to an emerging technology. This approach is based on four research fields—bibliometrics, text mining, time series modelling and time series forecasting—and is structured in five interlinked steps that generate a continuous flow of information. The main milestone is the generation of time series that measure the level of research activity and can be used for forecasting. The usefulness of this approach is shown by applying it to an emerging technology: cloud computing. The results enable the technology to be structured into five main sub-technologies which are characterised through five time series. Time series analysis of the trends related to each sub-technology shows that Privacy and Security has been the most active sub-technology to date in this area and is expected to maintain its level of interest in the near future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Anderson, B. D. O., & Moore, J. B. (1979). Optimal filtering. Englewood, NJ: Prentice-Hall.

    MATH  Google Scholar 

  • Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835–844.

    Article  Google Scholar 

  • Bildosola, I., Rio-Bélver, R., & Cilleruelo, E. (2015). Forecasting the big services era: Novel approach combining statistical methods, expertise and technology roadmapping. In P. Cortés, E. Maeso-González, & A. Escudero-Santana (Eds.), Enhancing synergies in a collaborative environment (pp. 371–379). Springer, Berlin.

    Chapter  Google Scholar 

  • Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255.

    Article  Google Scholar 

  • Bowman, K. O., & Shenton, L. R. (1975). Omnibus contours for departures from normality based on √b 1 and b 2. Biometrika, 62, 243–250.

    MathSciNet  MATH  Google Scholar 

  • Brunk, G. (2003). Swarming of innovations, fractal patterns, and the historical time series of US patents. Scientometrics, 56(1), 61–80.

    Article  Google Scholar 

  • Chen, Y. H., Chen, C. Y., & Lee, S. C. (2011). Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies. International Journal of Hydrogen Energy, 36(12), 6957–6969.

    Article  Google Scholar 

  • Cisco Visual Networking Index. (2015). Global mobile data traffic forecast update. 2014–2019 (white paper).

  • Cooley, R., Mobasher, B., & Srivastava, J. (1997, November). Web mining: Information and pattern discovery on the world wide web. In Proceedings of the ninth IEEE international conference on tools with artificial intelligence, 1997 (pp. 558–567). IEEE.

  • Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012.

    Article  Google Scholar 

  • Dilaver, Z., & Hunt, L. C. (2011). Modelling and forecasting Turkish residential electricity demand. Energy Policy, 39(6), 3117–3127.

    Article  Google Scholar 

  • Engle, R. F. (1979). Estimating structural models of seasonality. In A. Zellner (Ed.), Seasonal analysis of economic time series (pp. 281–308). Washington, DC: U.S. Bureau of the Census.

    Google Scholar 

  • Fernández, F. J. (1990). Estimation and testing of a multivariate exponential smoothing model in the frequency domain. Journal of Time Series Analysis, 11, 89–105.

    Article  MathSciNet  MATH  Google Scholar 

  • Fernández, F. J., & Harvey, A. C. (1990). Seemingly unrelated time series equations and a test for homogeneity. Journal of Business & Economic Statistics, 8(1), 71–81.

    Google Scholar 

  • Fernández-Cano, A., Torralbo, M., & Vallejo, M. (2012). Time series of scientific growth in Spanish doctoral theses (1848–2009). Scientometrics, 91(1), 15–36.

    Article  Google Scholar 

  • Ghosh, B., Basu, B., & O’Mahony, M. (2009). Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Transactions on Intelligent Transportation Systems, 10(2), 246–254.

    Article  Google Scholar 

  • Gonzalez, P., & Moral, P. (1995). An analysis of the international tourism demand in Spain. International Journal of Forecasting, 11(2), 233–251.

    Article  Google Scholar 

  • Harvey, A. C. (1989). Forecasting, structural time series and the Kalman filter. Cambridge: Cambridge University Press.

    Google Scholar 

  • Jun, S., & Uhm, D. (2010). Technology forecasting using frequency time series model: Bio-technology patent analysis. Journal of Modern Mathematics and Statistics, 4(3), 101–104.

    Article  Google Scholar 

  • Kajikawa, Y., Yoshikawa, J., Takeda, Y., & Matsushima, K. (2008). Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting and Social Change, 75(6), 771–782.

    Article  Google Scholar 

  • Kitagawa, G., & Gersch, W. (1984). A smoothness priors–state space modeling of time series with trend and seasonality. Journal of the American Statistical Association, 79(386), 378–389.

    Google Scholar 

  • Kongthon, A. (2004). A text mining framework for discovering technological intelligence to support science and technology management. Doctoral dissertation, Georgia Institute of Technology.

  • Koopman, S. J., Harvey, A. C., Doornik, J. A., & Shephard, N. (2009). STAMP 8.2: Structural time series analyser, modeler, and predictor. London: Timberlake Consultants.

    Google Scholar 

  • Kostoff, R. N., & Geisler, E. (1999). Strategic management and implementation of textual data mining in government organizations. Technology Analysis & Strategic Management, 11(4), 493–525.

    Article  Google Scholar 

  • Martin, H., & Daim, T. U. (2012). Technology roadmap development process (TRDP) for the service sector: A conceptual framework. Technology in Society, 34(1), 94–105.

    Article  Google Scholar 

  • Martino, J. P. (2003). A review of selected recent advances in technological forecasting. Technological Forecasting and Social Change, 70(8), 719–733.

    Article  Google Scholar 

  • Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology Special Publication 800-145, U.S. Department of Commerce.

  • Mikut, R., & Reischl, M. (2011). Data mining tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(5), 431–443.

    Google Scholar 

  • Miller, H. G., & Veiga, J. (2009). Cloud computing: Will commodity services benefit users long term? IT Professional Magazine, 11(6), 57.

    Article  Google Scholar 

  • Pagan, A. (1975). A note on the extraction of components from time series. Econometrica, 43, 163–168.

    Article  MATH  Google Scholar 

  • Porter, A. L., Watts, R. J., & Anderson, T. R. (2005). Mining PICMET: 1997–2005 papers help you track management of technology developments. In Portland international conference on management of engineering and technology (PICMET), Portland, OR.

  • Proietti, T. (2002). Forecasting with structural time series models. In P. Clements & D. F. Henry (Eds.), A companion to economic forecasting (pp. 105–132). Malden, MA: Blackwell.

    Google Scholar 

  • Sahandi, R., Alkhalil, A., & Opara-Martins, J. (2013). Cloud computing from SMEs perspective: A survey based investigation. Journal of Information Technology Management, 24(1), 1–12.

    Google Scholar 

  • Schlink, U., Herbarth, O., & Tetzlaff, G. (1997). A component time-series model for SO2 data: Forecasting, interpretation and modification. Atmospheric Environment, 31(9), 1285–1295.

    Article  Google Scholar 

Download references

Acknowledgements

Significant research assistance in the text mining process was provided by Rosamaría Río Bélver (TFM research group). The VantagePoint license used belonged to ‘TFM research group’ (https://sites.google.com/site/tfmresearch/).

Author information

Authors and Affiliations

  1. Department of Industrial Organization and Management Engineering, Foresight, Technology and Management (FTM) Group, University of the Basque Country (UPV/EHU), C/Alameda de Urquijo, s/n, 48013, Bilbao, Spain

    Iñaki Bildosola

  2. Department of Econometrics and Statistics, University of the Basque Country (UPV/EHU), Avda. Lehendakari Agirre 83, 48015, Bilbao, Spain

    Pilar Gonzalez & Paz Moral

  3. Institute for Public Economics, University of the Basque Country (UPV/EHU), Avda. Lehendakari Agirre 83, 48015, Bilbao, Spain

    Pilar Gonzalez

Authors
  1. Iñaki Bildosola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pilar Gonzalez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paz Moral
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iñaki Bildosola.

Appendix

Appendix

Syntax of the query corresponds to WOS advanced search interface

TITLE: (“CLOUD COMPUTING”) NOT TITLE: (OVERVIEW) NOT TITLE: (REVIEW) NOT TITLE: (“BASED ON CLOUD COMPUTING”) NOT TITLE: (“CLOUD COMPUTING APPLICATION”) NOT TITLE: (“APPLYING CLOUD COMPUTING”).

REFINED BY: WEB OF SCIENCE CATEGORIES: (COMPUTER SCIENCE THEORY METHODS OR TELECOMMUNICATIONS OR COMPUTER SCIENCE HARDWARE ARCHITECTURE OR COMPUTER SCIENCE SOFTWARE ENGINEERING) AND DOCUMENT TYPES: (PROCEEDINGS PAPER OR ARTICLE) AND [EXCLUDING] PUBLICATION YEARS: (2009 OR 2008).

INDEXES = SCI-EXPANDED, CPCI-S TIMESPAN = 1900–2015.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bildosola, I., Gonzalez, P. & Moral, P. An approach for modelling and forecasting research activity related to an emerging technology. Scientometrics 112, 557–572 (2017). https://doi.org/10.1007/s11192-017-2381-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-017-2381-3

Keywords

Mathematics Subject Classification

JEL Classification

Search

Navigation