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Forecasting the Diffusion of Innovations: Implications for Time-Series Extrapolation

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Book cover Principles of Forecasting

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 30))

Abstract

The selection of an S-shaped trend model is a common step in attempts to model and forecast the diffusion of innovations. From the innovation-diffusion literature on model selection, forecasting, and the uncertainties associated with forecasts, we derive four principles.

  1. 1.

    No single diffusion model is best for all processes.

  2. 2.

    Unconditional forecasts based on a data-based estimate of a fixed saturation level form a difficult benchmark to beat.

  3. 3.

    Simpler diffusion models tend to forecast better than more complex ones.

  4. 4.

    Short-term forecasts are good indicators of the appropriateness of diffusion models.

We describe the evidence for each principle in the literature and discuss the implications for practitioners and researchers.

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Author information

Authors and Affiliations

  1. The Management School, Imperial College, University of London, UK

    Nigel Meade

  2. Faculty of Management, University of Northern British Columbia, Canada

    Towhidul Islam

Authors
  1. Nigel Meade
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  2. Towhidul Islam
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Editor information

Editors and Affiliations

  1. The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    J. Scott Armstrong

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© 2001 Springer Science+Business Media New York

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Meade, N., Islam, T. (2001). Forecasting the Diffusion of Innovations: Implications for Time-Series Extrapolation. In: Armstrong, J.S. (eds) Principles of Forecasting. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-0-306-47630-3_26

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  • DOI: https://doi.org/10.1007/978-0-306-47630-3_26

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-7401-5

  • Online ISBN: 978-0-306-47630-3

  • eBook Packages: Springer Book Archive

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