Skip to main content

Technology Forecasting: Recent Trends and New Methods

  • Chapter
  • First Online:

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

Because of the big and increasing importance of technology, the analysis and forecasting of technology trends and futures are more and more important. Although precise predictions are not possible, technology forecasting provides useful insights that are badly needed. The basic principles and methods of technology forecasting basically remain the same. However, the current capacities of obtaining information, communicating and processing data have modified fundamentally the application and possibilities of the methods. Additionally, new methods derived from the existing ones have appeared. The technology forecasting methods have been divided into five blocks: environmental scanning, expert opinion, trend analysis and statistical methods, modelling and simulation, scenarios and roadmapping. For each block, main concepts, recent evolution and new methods are presented.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Agami, N. M. E., Omran, A. M. A., Saleh, M. M., & El-Shishiny, H. E. E. D. (2008). An enhanced approach for trend impact analysis. Technological Forecasting and Social Change, 75, 1439–1450. https://doi.org/10.1016/j.techfore.2008.03.006.

    Article  Google Scholar 

  • Axtell, R. L., & Andrews, C. J. (2002). Agent-based modeling and industrial ecology. Systems Modeling Environment, 5, 5–8.

    Google Scholar 

  • Bildosola, I., Rio-Bélver, R., Cilleruelo, E., & Garechana, G. (2018). Depicting big data: Producing a technological profile. In Closing the gap between practice and research in industrial engineering (pp. 1–8).

    Google Scholar 

  • Björneborn, L., & Ingwersen, P. (2001). Perspective of Webometrics. Scientometrics, 50, 65–82.

    Article  Google Scholar 

  • Börjeson, L., Höjer, M., Dreborg, K. H., et al. (2006). Scenario types and techniques: Towards a user’s guide. Futures, 38, 723–739. https://doi.org/10.1016/j.futures.2005.12.002.

    Article  Google Scholar 

  • Bouwman, H., & Van der Duin, P. (2003). Technological forecasting and scenarios matter: Research into the use of information and communication technology in the home environment in 2010. Foresight, 5, 8–19. https://doi.org/10.1108/14636680310494717.

    Article  Google Scholar 

  • Bryant, B. P., & Lempert, R. J. (2010). Thinking inside the box: A participatory, computer-assisted approach to scenario discovery. Technological Forecasting and Social Change, 77, 34–49. https://doi.org/10.1016/j.techfore.2009.08.002.

    Article  Google Scholar 

  • Carbonell, J., Sánchez-Esguevillas, A., & Carro, B. (2018). Easing the assessment of emerging technologies in technology observatories: Findings about patterns of dissemination of emerging technologies on the internet. Technology Analysis & Strategic Management, 30, 113–129. https://doi.org/10.1080/09537325.2017.1337886.

    Article  Google Scholar 

  • Chanchetti, L. F., Oviedo Diaz, S. M., Milanez, D. H., et al. (2016). Technological forecasting of hydrogen storage materials using patent indicators. International Journal of Hydrogen Energy, 41, 18301–18310. https://doi.org/10.1016/j.ijhydene.2016.08.137.

    Article  Google Scholar 

  • Cho, Y., & Daim, T. (2013). Technology forecasting methods. In T. U. Daim, T. Oliver & J. Kim (Eds.). Research and technology management in the electricity industry: Methods, tools case studies (pp. 67–112). London: Springer.

    Google Scholar 

  • Ciarli, T., Coad, A., & Rafols, I. (2016). Quantitative analysis of technology futures: A review of techniques, uses and characteristics. Science and Public Policy, 43, 630–645. https://doi.org/10.1093/scipol/scv059.

    Article  Google Scholar 

  • de Paulo, A. F., & Porto, G. S. (2017). Solar energy technologies and open innovation: A study based on bibliometric and social network analysis. Energy Policy, 108, 228–238. https://doi.org/10.1016/j.enpol.2017.06.007.

    Article  Google Scholar 

  • Di Zio, S. (2018). Convergence of experts’ opinions on the territory: The Spatial Delphi and the Spatial Shang. In L. Moutinho & M. Sokele (Eds.), Innovative research methodologies in management (Vol. II, pp. 1–29). Springer.

    Google Scholar 

  • Di Zio, S., & Pacinelli, A. (2011). Opinion convergence in location: A spatial version of the Delphi method. Technological Forecasting and Social Change, 78, 1565–1578. https://doi.org/10.1016/j.techfore.2010.09.010.

    Article  Google Scholar 

  • Duwe, D., Herrmann, F., & Spath, D. (2018). Forecasting the diffusion of product and technology innovations: Using Google Trends as an example. In International Conference on Management of Engineering and Technology (pp. 1–7). IEEE.

    Google Scholar 

  • Firat, A. K., Woon, W. L., & Madnick, S. (2008). Technological forecasting—A review. Composite Information Systems Laboratory (CISL), Massachusetts Institute of Technology.

    Google Scholar 

  • Forrester, J. W. (2007). System dynamics—The next fifty years. The Journal of the System Dynamics Society, 23, 359–370.

    Google Scholar 

  • Georghiou, L. (2008). The handbook of technology foresight: Concepts and practice. Cheltenham: Edward Elgar Publishing.

    Google Scholar 

  • Gerdari, N., Daim, T. U., & Rueda, G. (2011). Review of technology forecasting. In T. U. Daim (Ed.), Technology assessment: Forecasting future adoption of emerging technologies (pp. 73–85). Berlin: Erich Schmidt Verlag GmbH & Co KG.

    Google Scholar 

  • González-Fernández, M., & González-Velasco, C. (2018). Can Google econometrics predict unemployment? Evidence from Spain. Economic Letters, 170, 42–45. https://doi.org/10.1016/j.econlet.2018.05.031.

    Article  Google Scholar 

  • Gordon, T. J. (1992). The methods of futures research. The Annals of the American Academy of Political and Social Science, 522, 25–35.

    Google Scholar 

  • Gordon, T. J. (2009a). Cross impact. In Futures Research Methodology (1–21).

    Google Scholar 

  • Gordon, T. J. (2009b). Science and technology roadmapping. In T. J. Gordon & J. C. Glenn (Eds.), Futures Research Methodology 3.0 (pp. 1–24).

    Google Scholar 

  • Gordon, T. J., & Glenn, J. (2018). Interactive scenarios. In L. Moutinho & M. Sokele (Eds.), Innovative research methodologies in management (pp. 31–61). Springer.

    Google Scholar 

  • Gordon, T., & Pease, A. (2006). RT Delphi: An efficient, “round-less” almost real time Delphi method. Technological Forecasting and Social Change, 73, 321–333. https://doi.org/10.1016/j.techfore.2005.09.005.

    Article  Google Scholar 

  • Haleem, A., Mannan, B., Luthra, S., et al. (2019). Technology forecasting (TF) and technology assessment (TA) methodologies: A conceptual review. Benchmarking: An International Journal, 26, 48–72. https://doi.org/10.1108/BIJ-04-2018-0090.

    Article  Google Scholar 

  • Hesselink, L. X. W., & Chappin, E. J. L. (2019). Adoption of energy efficient technologies by households—Barriers, policies and agent-based modelling studies. Renewable and Sustainable Energy Reviews, 99, 29–41. https://doi.org/10.1016/j.rser.2018.09.031.

    Article  Google Scholar 

  • Huang, L., Guo, Y., & Porter, A. L. (2009). A systematic technology forecasting approach for new and emerging science and technology: Case study of nano-enhanced biosensors. In 2009 Atlanta Conference on Science and Innovation Policy (pp. 1–10). IEEE.

    Google Scholar 

  • Jun, S. (2012). Central technology forecasting using social network analysis. In Computer applications for software engineering, disaster recovery, and business continuity (pp. 1–8). Berlin: Springer.

    Google Scholar 

  • Kim, L., & Ju, J. (2019). Can media forecast technological progress? A text-mining approach to the on-line newspaper and blog’s representation of prospective industrial technologies. Information Processing & Management, 56, 1506–1525. https://doi.org/10.1016/j.ipm.2018.10.017.

    Article  Google Scholar 

  • Kim, N., Lučivjanská, K., Molnár, P., & Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208–220. https://doi.org/10.1016/j.frl.2018.05.003.

    Article  Google Scholar 

  • Kopaygorodsky, A. (2018). Technology of intelligent service for energy technology forecasting. In Vth International Workshop “Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Cloud Computing and Cyber Security” (IWCI 2018) (pp. 106–110).

    Google Scholar 

  • Lee, S., Kim, Y., & Moon, K. (2015a). Justifiable trend impact analysis based on adaptive neuro-fuzzy system. Information, 18, 4219–4227.

    Google Scholar 

  • Lee, C., Song, B., & Park, Y. (2015b). An instrument for scenario-based technology roadmapping: How to assess the impacts of future changes on organisational plans. Technological Forecasting and Social Change, 90, 285–301. https://doi.org/10.1016/j.techfore.2013.12.020.

    Article  Google Scholar 

  • Lempert, R. J. (2002). A new decision sciences for complex systems. Proceedings of the National Academy of Sciences USA, 99, 7309–7313. https://doi.org/10.1073/pnas.082081699.

    Article  Google Scholar 

  • Lempert, R. (2019). Robust decision making (RDM). In J. H. Kwakkel & M. Haasnoot (Eds.), Decision making under deep uncertainty: From theory to practice (pp. 23–51). Springer International Publishing.

    Google Scholar 

  • Levary, R. R., & Han, D. (1995). Choosing a technological forecasting method. Industrial Management, 37, 14–18.

    Google Scholar 

  • Li, S., Garces, E., & Daim, T. (2019). Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles. Technological Forecasting and Social Change, 148, 119731. https://doi.org/10.1016/j.techfore.2019.119731.

    Article  Google Scholar 

  • Macal, C. M., & North, M. J. (2008). Agent-based modeling and simulation: ABMS examples. In S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, & J. W. Fowler (Eds.), Proceedings of the 2008 Winter Simulation Conference (pp. 101–112). IEEE.

    Google Scholar 

  • Madnick, S., & Woon, W. L. (2008). Technology forecasting using data mining and semantics. MIT/MIST Collaborative Research.

    Google Scholar 

  • Martino, J. P. (1993). Technology forecasting for decision making (3rd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Martino, J. P. (2003). A review of selected recent advances in technological forecasting. Technological Forecasting and Social Change, 70, 719–733. https://doi.org/10.1016/S0040-1625(02)00375-X.

    Article  Google Scholar 

  • Mietzner, D., & Reger, G. (2005). Advantages and disadvantages of scenario approaches for strategic foresight. International Journal Technology Intelligence and Planning, 1, 220–239.

    Article  Google Scholar 

  • Miles, I., Saritas, O., & Sokolov, A. (2016). Foresight for science, technology and innovation. Foresight for Science, Technology and Innovation. https://doi.org/10.1007/978-3-319-32574-3.

  • National Research Council. (2010). Existing technology forecasting methodologies. In Persistent forecasting of disruptive technologies (pp. 37–39). Washington, DC: National Academies Press.

    Google Scholar 

  • National Research Council, Division on Engineering and Physical Sciences, Committee on Forecasting Future Disruptive Technologies. (2017). Existing Technology Forecasting Methodologies. In Persistent forecasting of disruptive technologies (p. 17). Washington, DC: National Academies Press.

    Google Scholar 

  • Nugroho, Y., & Saritas, O. (2009). Incorporating network perspectives in foresight: A methodological proposal. Foresight, 11, 21–41.

    Article  Google Scholar 

  • Owaishiz, A., Smith, M., Almuzel, M., Beseau, D., Daim, T., & Yalcin, H. (2019). Identifying technology and research communication case of wireless power. In 2019 IEEE Technology & Engineering Management Conference (pp. 1–5). IEEE.

    Google Scholar 

  • Park, J. H., & Kwahk, K. Y. (2013). The effect of patent citation relationship on business performance: A social network analysis perspective. Journal of Intelligence and Information Systems, 19, 127–139.

    Article  Google Scholar 

  • Phillips, J. G., Heidrick, T. R., & Potter, I. J. (2007). Technology futures analysis methodologies for sustainable energy technologies. International Journal of Innovation and Technology Management, 4, 171–190.

    Article  Google Scholar 

  • Picanço-Castro, V., Porto, G. S., & Swiech, K. (2018). Uncovering innovation features and emerging technologies in molecular biology though patent analysis. Recombinant Glycoprotein Production: Methods in Molecular Biology.

    Google Scholar 

  • Popescu, M. (2017). Analysing trends and correlations from internet searches: Case study of Romania. Annales Universitatis Apulensis: Series Oeconomica, 1, 82–86.

    Google Scholar 

  • Puig-Pey, A., Sanfeliu, A., Solé-Parellada, F., Bolea, Y., Casanovas, J., & Grau, A. (2019) Public end-users driven technological innovation (PDTI) in urban scenarios. In Advances in robotics research: From lab to market (pp. 47–68).

    Google Scholar 

  • Quid. (2019). Turn text into context. https://quid.com/. Accessed October 25, 2019.

  • Ramm, T. D., Watson, C. S., & White, C. J. (2018). Strategic adaptation pathway planning to manage sea-level rise and changing coastal flood risk. Environmental Science & Policy, 87, 92–101. https://doi.org/10.1016/j.envsci.2018.06.001.

    Article  Google Scholar 

  • Raux, C. (2003). A systems dynamics model for the urban travel system. In AET European Transport Conference (pp. 1–21).

    Google Scholar 

  • Ray, M., Rai, A., Singh, K. N., et al. (2017). Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India. Technological Forecasting and Social Change, 118, 128–133. https://doi.org/10.1016/j.techfore.2017.02.012.

    Article  Google Scholar 

  • Reddi, K. R., & Moon, Y. B. (2011). System dynamics modeling of engineering change management in a collaborative environment. International Journal of Advanced Manufacturing Technology, 55, 1225–1239. https://doi.org/10.1007/s00170-010-3143-z.

    Article  Google Scholar 

  • Rhinehart, N., Mcallister, R., & Kitani, K. (2019). Precog: Prediction conditioned on goals in visual multi-agent settings. arXiv Prepr. arXiv1905.01296.

    Google Scholar 

  • Roberts, E. B. (1969). Exploratory and normative technological forecasting: A critical appraisal. Technological Forecasting, 1, 113–127.

    Article  Google Scholar 

  • Roper, A. T., Cunningham, S. W., Porter, A. L., et al. (2011). Forecasting and management of technology (3rd ed.). Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15, 353–375. https://doi.org/10.1016/S0169-2070(99)00018-7.

    Article  Google Scholar 

  • Ryu, J., & Byeon, S. C. (2011). Technology level evaluation methodology based on the technology growth curve. Technological Forecasting and Social Change, 78, 1049–1059. https://doi.org/10.1016/j.techfore.2011.01.003.

    Article  Google Scholar 

  • Salze, P., Beck, E., Douvinet, J., Amalric, M., Bonnet, E., Daudé, E., et al. (2014). TOXI-CITY: An agent-based model for exploring the effects of risk awareness and spatial configuration on the survival rate in the case of industrial accidents. Cybergeo: European Journal of Geography.

    Google Scholar 

  • Segev, A., Jung, S., & Choi, S. (2014). Analysis of technology trends based on diverse data sources. IEEE Transactions on Services Computing, 8, 903–915.

    Article  Google Scholar 

  • Sun, S., Wei, Y., Tsui, K. L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10. https://doi.org/10.1016/j.tourman.2018.07.010.

    Article  Google Scholar 

  • Technology Futures Analysis Methods Working Group. (2004). Toward integration of the field and new methods. Technological Forecasting and Social Change, 71, 287–303.

    Article  Google Scholar 

  • Trensition. (2019). Personalized trend insight and foresight analytics. https://www.trensition.eu/. Accessed October 25, 2019.

  • Van der Besselaar, P., & Leydesdorff, L. (1996). Mapping change in scientific specialties: A scientometric reconstruction of the development of artificial intelligence. Journal of the American Society for Information Science, 47, 415–436.

    Article  Google Scholar 

  • Verna, M., Kishore, K., Kumar, M., et al. (2018). Google search trends predicting disease outbreaks: An analysis from India. Healthcare Informatics Research, 24, 300–308.

    Article  Google Scholar 

  • Walters, J. P., & Javernick-Will, A. N. (2015). Long-term functionality of rural water services in developing countries: A system dynamics approach to understanding the dynamic interaction of factors. Environmental Science and Technology, 49, 5035–5043. https://doi.org/10.1021/es505975h.

    Article  Google Scholar 

  • Wu, L., & Brynjolfsson, E. (2015). The future of prediction: How Google searches foreshadow housing prices and sales. In Economic analysis of the digital economy (pp. 89–118). Chicago: University of Chicago Press.

    Google Scholar 

  • Zhang, G., & Tang, C. (2018). How R&D partner diversity influences innovation performance: An empirical study in the nano-biopharmaceutical field. Scientometrics, 116, 1487–1512.

    Article  Google Scholar 

  • Zülch, G., & Börkircher, M. (2012). Technical engine for democratization of modeling, simulations, and predictions. In Proceedings of the 2012 Winter Simulation Conference (WSC). IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jordi Olivella-Nadal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Calleja-Sanz, G., Olivella-Nadal, J., Solé-Parellada, F. (2020). Technology Forecasting: Recent Trends and New Methods. In: Machado, C., Davim, J. (eds) Research Methodology in Management and Industrial Engineering. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-40896-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-40896-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40895-4

  • Online ISBN: 978-3-030-40896-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics