IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v112y2017i1d10.1007_s11192-017-2381-3.html
   My bibliography  Save this article

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

Author

Listed:
  • Iñaki Bildosola

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

  • Pilar Gonzalez

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

  • Paz Moral

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

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.

Suggested Citation

  • Iñaki Bildosola & Pilar Gonzalez & Paz Moral, 2017. "An approach for modelling and forecasting research activity related to an emerging technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 557-572, July.
    • Handle: RePEc:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2381-3
    DOI: 10.1007/s11192-017-2381-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-017-2381-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-017-2381-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    2. Pagan, Adrian R, 1975. "A Note on the Extraction of Components from Time Series," Econometrica, Econometric Society, vol. 43(1), pages 163-168, January.
    3. Arnold Zellner, 1979. "Seasonal Analysis of Economic Time Series," NBER Books, National Bureau of Economic Research, Inc, number zell79-1, December.
    4. Fernandez, F Javier & Harvey, Andrew C, 1990. "Seemingly Unrelated Time Series Equations and a Test for Homogeneity," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 71-81, January.
    5. Gregory G. Brunk, 2003. "Swarming of innovations, fractal patterns, and the historical time series of US patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 61-80, January.
    6. Gonzalez, Pilar & Moral, Paz, 1995. "An analysis of the international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 11(2), pages 233-251, June.
    7. Robert F. Engle, 1979. "Estimating Structural Models of Seasonality," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 281-308, National Bureau of Economic Research, Inc.
    8. Martin, Hilary & Daim, Tugrul U., 2012. "Technology roadmap development process (TRDP) for the service sector: A conceptual framework," Technology in Society, Elsevier, vol. 34(1), pages 94-105.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    2. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    3. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    4. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jerry A. Hausman & Mark W. Watson, 1983. "Seasonal Adjustment with Measurement Error Present," NBER Working Papers 1133, National Bureau of Economic Research, Inc.
    2. Marcel Prokopczuk & Yingying Wu, 2013. "Estimating term structure models with the Kalman filter," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 4, pages 97-113, Edward Elgar Publishing.
    3. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez & Ana Belén Trigo Iglesias, 2014. "Modelización econométrica de la demanda de turistas británicos a España," Working Papers 1404, Universidade de Vigo, Departamento de Economía Aplicada.
    4. Thomas M. Fullerton JR., 2001. "Specification of a Borderplex Econometric Forecasting Model," International Regional Science Review, , vol. 24(2), pages 245-260, April.
    5. Ketenci, Natalya, 2009. "The ARDL Approach to Cointegration Analysis of Tourism Demand in Turkey: with Greece as the substitution destination," MPRA Paper 86602, University Library of Munich, Germany.
    6. Yamaka, Woraphon & Zhang, Xuefeng & Maneejuk, Paravee & Ramos, Vicente, 2023. "Asymmetric effects of third-country exchange rate risk: A Markov switching approach," Annals of Tourism Research, Elsevier, vol. 103(C).
    7. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    8. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. Halim TATLI, 2022. "Long-term price and income elasticity of residential natural gas demand in Turkey," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(630), S), pages 101-122, Spring.
    10. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    11. Beatriz Plaza & Pilar Gonzalez-Casimiro & Paz Moral-Zuazo & Courtney Waldron, 2013. "Culture-led City Brands as Economic Engines: Theory and Empirics," ACEI Working Paper Series AWP-05-2013, Association for Cultural Economics International, revised Oct 2013.
    12. Thanh-Lam Nguyen & Jui-Chan Huang & Chuang-Chi Chiu & Ming-Hung Shu & Wen-Ru Tsai, 2013. "Forecasting Model for the International Tourism Demand in Taiwan," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    13. Dilaver, Zafer & Hunt, Lester C., 2011. "Turkish aggregate electricity demand: An outlook to 2020," Energy, Elsevier, vol. 36(11), pages 6686-6696.
    14. Rentizelas, Athanasios & Georgakellos, Dimitrios, 2014. "Incorporating life cycle external cost in optimization of the electricity generation mix," Energy Policy, Elsevier, vol. 65(C), pages 134-149.
    15. Garcia-Ferrer, Antonio & Queralt, Ricardo A., 1997. "A note on forecasting international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 13(4), pages 539-549, December.
    16. Carlos A. Medel, 2018. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986–2009 with X-12-ARIMA," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 47-87, April.
    17. Ms. Evridiki Tsounta, 2008. "What Attracts Tourists to Paradise?," IMF Working Papers 2008/277, International Monetary Fund.
    18. Lin, Boqiang & Wang, Yao, 2020. "Analyzing the elasticity and subsidy to reform the residential electricity tariffs in China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 189-206.
    19. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    20. Nikos Sakkas & Sofia Yfanti & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2021. "Interpretable Forecasting of Energy Demand in the Residential Sector," Energies, MDPI, vol. 14(20), pages 1-17, October.

    More about this item

    Keywords

    Technology forecasting; Research-activity forecasting; Bibliometrics; Text mining; Trend analysis; Structural time series models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2381-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.