An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services

  • Charles V. Trappey
  • Hsin-ying Wu


Many successful technology forecasting models have been developed but little research has explored the relationship between sample set size and forecast prediction accuracy. This research studies the forecast accuracy of large and small data sets using the simple logisticl, Gompertz, and the extended logistic models. The performance of the models were evaluated using the mean absolute deviation and the root mean square error. A time series dataset of four electronic products and services were used to evaluate the model performance. The result shows that the extended logistic model fits large and small datasets better than the simple logistic and Gompertz models. The findings also show that that the extended logistic model is well suited to predict market growth with limited historical data as is typically the case for short lifecycle products and services.


Extended logistic model Technology forecasting 


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  1. [1]
    Bengisu M, Nekhili R. Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change 2006;73:835–844.CrossRefGoogle Scholar
  2. [2]
    Chen C-P. The Test of Technological Forecasting Models: Comparison between Extended Logistic Model, Fisher-Pry Model, and Gompertz Model. Master thesis, National Chiao Tung University, 2005.Google Scholar
  3. [3]
    Kotlor P. Marketing management. 11th ed. 2003, New Jersey: Prentice Hall.Google Scholar
  4. [4]
    Levary RR, Han D. Choosing a technological forecasting method. Industrial Management 1995;37:14.Google Scholar
  5. [5]
    Martino JP. Technological Forecasting for Decision Making. 3rd ed. 1993, New York: McGraw-Hill.Google Scholar
  6. [6]
    Meade N, Islam T. Forecasting with growth curves: An empirical comparison. International Journal of Forecasting 1995;11:199–215.CrossRefGoogle Scholar
  7. [7]
    Meade N, Islam T. Technological forecasting—model selection, model stability, and combining models. Management Science 1998;44:1115.zbMATHCrossRefGoogle Scholar
  8. [8]
    Meyer PS, Ausubel JH. Carrying Capacity A Model with Logistically Varying Limits. Technological Forecasting and Social Change 1999;61:209–214.CrossRefGoogle Scholar
  9. [9]
    Meyer PS, Yung JW, Ausubel JH. A Primer on Logistic Growth and Substitution. Technological Forecasting and Social Change 1999;61: 247–271.CrossRefGoogle Scholar
  10. [10]
    Rai LP, Kumar N. Development and application of mathematical models for technology substitution. PRANJANA 2003;6: 49–60.Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Charles V. Trappey
    • 1
  • Hsin-ying Wu
    • 1
  1. 1.National Chiao Tung UniversityHsinchuTaiwan

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