Technology Forecasting: Recent Trends and New Methods

  • Gema Calleja-Sanz
  • Jordi Olivella-NadalEmail author
  • Francesc Solé-Parellada
Part of the Management and Industrial Engineering book series (MINEN)


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.


  1. 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. Scholar
  2. Axtell, R. L., & Andrews, C. J. (2002). Agent-based modeling and industrial ecology. Systems Modeling Environment, 5, 5–8.Google Scholar
  3. 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
  4. Björneborn, L., & Ingwersen, P. (2001). Perspective of Webometrics. Scientometrics, 50, 65–82.CrossRefGoogle Scholar
  5. Börjeson, L., Höjer, M., Dreborg, K. H., et al. (2006). Scenario types and techniques: Towards a user’s guide. Futures, 38, 723–739. Scholar
  6. 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. Scholar
  7. 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. Scholar
  8. 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. Scholar
  9. 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. Scholar
  10. 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
  11. 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. Scholar
  12. 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. Scholar
  13. 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
  14. 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. Scholar
  15. 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
  16. Firat, A. K., Woon, W. L., & Madnick, S. (2008). Technological forecasting—A review. Composite Information Systems Laboratory (CISL), Massachusetts Institute of Technology.Google Scholar
  17. Forrester, J. W. (2007). System dynamics—The next fifty years. The Journal of the System Dynamics Society, 23, 359–370.Google Scholar
  18. Georghiou, L. (2008). The handbook of technology foresight: Concepts and practice. Cheltenham: Edward Elgar Publishing.Google Scholar
  19. 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
  20. González-Fernández, M., & González-Velasco, C. (2018). Can Google econometrics predict unemployment? Evidence from Spain. Economic Letters, 170, 42–45. Scholar
  21. 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
  22. Gordon, T. J. (2009a). Cross impact. In Futures Research Methodology (1–21).Google Scholar
  23. 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
  24. 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
  25. Gordon, T., & Pease, A. (2006). RT Delphi: An efficient, “round-less” almost real time Delphi method. Technological Forecasting and Social Change, 73, 321–333. Scholar
  26. 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. Scholar
  27. 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. Scholar
  28. 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
  29. 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
  30. 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. Scholar
  31. Kim, N., Lučivjanská, K., Molnár, P., & Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208–220. Scholar
  32. 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
  33. Lee, S., Kim, Y., & Moon, K. (2015a). Justifiable trend impact analysis based on adaptive neuro-fuzzy system. Information, 18, 4219–4227.Google Scholar
  34. 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. Scholar
  35. Lempert, R. J. (2002). A new decision sciences for complex systems. Proceedings of the National Academy of Sciences USA, 99, 7309–7313. Scholar
  36. 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
  37. Levary, R. R., & Han, D. (1995). Choosing a technological forecasting method. Industrial Management, 37, 14–18.Google Scholar
  38. 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. Scholar
  39. 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
  40. Madnick, S., & Woon, W. L. (2008). Technology forecasting using data mining and semantics. MIT/MIST Collaborative Research.Google Scholar
  41. Martino, J. P. (1993). Technology forecasting for decision making (3rd ed.). New York: McGraw-Hill.Google Scholar
  42. Martino, J. P. (2003). A review of selected recent advances in technological forecasting. Technological Forecasting and Social Change, 70, 719–733. Scholar
  43. Mietzner, D., & Reger, G. (2005). Advantages and disadvantages of scenario approaches for strategic foresight. International Journal Technology Intelligence and Planning, 1, 220–239.CrossRefGoogle Scholar
  44. Miles, I., Saritas, O., & Sokolov, A. (2016). Foresight for science, technology and innovation. Foresight for Science, Technology and Innovation.
  45. National Research Council. (2010). Existing technology forecasting methodologies. In Persistent forecasting of disruptive technologies (pp. 37–39). Washington, DC: National Academies Press.Google Scholar
  46. 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
  47. Nugroho, Y., & Saritas, O. (2009). Incorporating network perspectives in foresight: A methodological proposal. Foresight, 11, 21–41.CrossRefGoogle Scholar
  48. 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
  49. 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.CrossRefGoogle Scholar
  50. 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.CrossRefGoogle Scholar
  51. 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
  52. Popescu, M. (2017). Analysing trends and correlations from internet searches: Case study of Romania. Annales Universitatis Apulensis: Series Oeconomica, 1, 82–86.Google Scholar
  53. 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
  54. Quid. (2019). Turn text into context. Accessed October 25, 2019.
  55. 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. Scholar
  56. Raux, C. (2003). A systems dynamics model for the urban travel system. In AET European Transport Conference (pp. 1–21).Google Scholar
  57. 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. Scholar
  58. 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. Scholar
  59. Rhinehart, N., Mcallister, R., & Kitani, K. (2019). Precog: Prediction conditioned on goals in visual multi-agent settings. arXiv Prepr. arXiv1905.01296.Google Scholar
  60. Roberts, E. B. (1969). Exploratory and normative technological forecasting: A critical appraisal. Technological Forecasting, 1, 113–127.CrossRefGoogle Scholar
  61. Roper, A. T., Cunningham, S. W., Porter, A. L., et al. (2011). Forecasting and management of technology (3rd ed.). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  62. Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15, 353–375. Scholar
  63. Ryu, J., & Byeon, S. C. (2011). Technology level evaluation methodology based on the technology growth curve. Technological Forecasting and Social Change, 78, 1049–1059. Scholar
  64. 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
  65. Segev, A., Jung, S., & Choi, S. (2014). Analysis of technology trends based on diverse data sources. IEEE Transactions on Services Computing, 8, 903–915.CrossRefGoogle Scholar
  66. 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. Scholar
  67. Technology Futures Analysis Methods Working Group. (2004). Toward integration of the field and new methods. Technological Forecasting and Social Change, 71, 287–303.CrossRefGoogle Scholar
  68. Trensition. (2019). Personalized trend insight and foresight analytics. Accessed October 25, 2019.
  69. 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.CrossRefGoogle Scholar
  70. 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.CrossRefGoogle Scholar
  71. 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. Scholar
  72. 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
  73. 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.CrossRefGoogle Scholar
  74. 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

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gema Calleja-Sanz
    • 1
  • Jordi Olivella-Nadal
    • 2
    Email author
  • Francesc Solé-Parellada
    • 3
  1. 1.Serra Húnter fellow, Institute of Industrial and Control EngineeringUniversitat Politècnica de Catalunya - BarcelonaTech (UPC)BarcelonaSpain
  2. 2.Institute of Industrial and Control EngineeringUniversitat Politècnica de Catalunya - BarcelonaTech (UPC)BarcelonaSpain
  3. 3.Universitat Politècnica de Catalunya - BarcelonaTech (UPC)BarcelonaSpain

Personalised recommendations