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An examination of factors affecting accuracy in technology forecasts

Author

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  • Fye, Shannon R.
  • Charbonneau, Steven M.
  • Hay, Jason W.
  • Mullins, Carie A.

Abstract

Private and public organizations use forecasts to inform a number of decisions, including decisions on product development, competition, and technology investments. We evaluated technological forecasts to determine how forecast methodology and eight other attributes influence accuracy. We also evaluated the degree of interpretation required to extract measurable data from forecasts. We found that, of the nine attributes, only methodology and time horizon had a statistically significant influence on accuracy. Forecasts using quantitative methods were more accurate than forecasts using qualitative methods, and forecasts predicting shorter time horizons were more accurate that those predicting longer time horizons. While quantitative methods produced the most accurate forecasts, expert sourcing methods produced the highest number of forecasts whose events had been realized, indicating that experts are best at predicting if an event will occur, while quantitative methods are best at predicting when. We also observed that forecasts are as likely to overestimate how long it will take for a predicted event to occur as they are to underestimate the time required for a prediction to come to pass. Additionally, forecasts about computers and autonomous or robotic technologies were more accurate than those about other technologies, an observation not explained by the data set. Finally, forecasts obtained from government documents required more interpretation than those derived from other sources, though they had similar success rates.

Suggested Citation

  • Fye, Shannon R. & Charbonneau, Steven M. & Hay, Jason W. & Mullins, Carie A., 2013. "An examination of factors affecting accuracy in technology forecasts," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1222-1231.
    • Handle: RePEc:eee:tefoso:v:80:y:2013:i:6:p:1222-1231
    DOI: 10.1016/j.techfore.2012.10.026
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    Cited by:

    1. Liao, Hua & Cai, Jia-Wei & Yang, Dong-Wei & Wei, Yi-Ming, 2016. "Why did the historical energy forecasting succeed or fail? A case study on IEA's projection," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 90-96.
    2. André Souza Oliveira & Raphael Oliveira dos Santos & Bruno Caetano dos Santos Silva & Lilian Lefol Nani Guarieiro & Matthias Angerhausen & Uwe Reisgen & Renelson Ribeiro Sampaio & Bruna Aparecida Souz, 2021. "A Detailed Forecast of the Technologies Based on Lifecycle Analysis of GMAW and CMT Welding Processes," Sustainability, MDPI, vol. 13(7), pages 1-23, March.
    3. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    4. Harwood, Stephen & Eaves, Sally, 2020. "Conceptualising technology, its development and future: The six genres of technology," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    5. Kajikawa, Yuya & Mejia, Cristian & Wu, Mengjia & Zhang, Yi, 2022. "Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    6. Apreda, Riccardo & Bonaccorsi, Andrea & dell'Orletta, Felice & Fantoni, Gualtiero, 2019. "Expert forecast and realized outcomes in technology foresight," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 277-288.
    7. Kott, Alexander & Perconti, Philip, 2018. "Long-term forecasts of military technologies for a 20–30 year horizon: An empirical assessment of accuracy," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 272-279.
    8. June Young Lee & Sejung Ahn & Dohyun Kim, 2021. "Deep learning-based prediction of future growth potential of technologies," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    9. André Souza Oliveira & Bruno Caetano dos Santos Silva & Cristiano Vasconcellos Ferreira & Renelson Ribeiro Sampaio & Bruna Aparecida Souza Machado & Rodrigo Santiago Coelho, 2021. "Adding Technology Sustainability Evaluation to Product Development: A Proposed Methodology and an Assessment Model," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    10. Wachtmeister, Henrik & Henke, Petter & Höök, Mikael, 2018. "Oil projections in retrospect: Revisions, accuracy and current uncertainty," Applied Energy, Elsevier, vol. 220(C), pages 138-153.

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