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Technology forecasting: A case study of computational technologies

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  • Adamuthe, Amol C.
  • Thampi, Gopakumaran T.

Abstract

This research presents trend projection and technology maturity curve of six computational technologies including three disruptive technologies namely mainframes, minicomputers and cloud computing. This investigation is beneficial to sensitize different stakeholders for making effective strategic policies and decisions. Time series data of patent and paper from U.S. patent office, European patent office, IEEE and ScienceDirect is used for forecasting. Use of two technology indicators from four sources made the forecasting results more reliable for decision makers. Six functions are tested to identify the best-fitted trend line. Results indicate that most of the technologies are better fitted to polynomial trend line of 2nd order. All computational technologies except cloud computing have undergone both upward and downward trends. Cloud computing shows a very high upward trend. Maturity curve is forecasted using the best-fitted growth curve method. Gompertz growth curve is better fitted than the logistic curve for many instances. Majority of the technologies follows introduction, growth, maturity and decline pattern. The life cycle pattern and growth rate of each technology is different. Growth pattern of mainframes and minicomputers is similar to the S-shaped curve. Growth pattern of grid computing and autonomic computing is similar to the “S-shaped” curve for research papers dataset.

Suggested Citation

  • Adamuthe, Amol C. & Thampi, Gopakumaran T., 2019. "Technology forecasting: A case study of computational technologies," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 181-189.
    • Handle: RePEc:eee:tefoso:v:143:y:2019:i:c:p:181-189
    DOI: 10.1016/j.techfore.2019.03.002
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    1. Miriam Steurer & Robert J. Hill & Markus Zahrnhofer & Christian Hartmann, 2012. "Modelling the Emergence of New Technologies using S-Curve Diffusion Models," Graz Economics Papers 2012-05, University of Graz, Department of Economics.
    2. Schilling, Melissa A. & Esmundo, Melissa, 2009. "Technology S-curves in renewable energy alternatives: Analysis and implications for industry and government," Energy Policy, Elsevier, vol. 37(5), pages 1767-1781, May.
    3. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    4. Ho, Jonathan C. & Saw, Ewe-Chai & Lu, Louis Y.Y. & Liu, John S., 2014. "Technological barriers and research trends in fuel cell technologies: A citation network analysis," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 66-79.
    5. Dubaric, Ervin & Giannoccaro, Dimitris & Bengtsson, Rune & Ackermann, Thomas, 2011. "Patent data as indicators of wind power technology development," World Patent Information, Elsevier, vol. 33(2), pages 144-149, June.
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    Cited by:

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