Predicting the Path of Technological Innovation: SAW vs. Moore, Bass, Gompertz, and Kryder

  • Authors:

  • Ashish Sood;Gareth M. James;Gerard J. Tellis;Ji Zhu

  • Affiliations:

  • Goizueta School of Business, Emory University, Atlanta, Georgia 30322;Marshall School of Business, University of Southern California, Los Angeles, California 90089;Marshall School of Business, University of Southern California, Los Angeles, California 90089;Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109

  • Venue:
  • Marketing Science
  • Year:
  • 2012

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Abstract

Competition is intense among rival technologies, and success depends on predicting their future trajectory of performance. To resolve this challenge, managers often follow popular heuristics, generalizations, or “laws” such as Moore's law. We propose a model, Step And Wait (SAW), for predicting the path of technological innovation, and we compare its performance against eight models for 25 technologies and 804 technologies-years across six markets. The estimates of the model provide four important results. First, Moore's law and Kryder's law do not generalize across markets; neither holds for all technologies even in a single market. Second, SAW produces superior predictions over traditional methods, such as the Bass model or Gompertz law, and can form predictions for a completely new technology by incorporating information from other categories on time-varying covariates. Third, analysis of the model parameters suggests that (i) recent technologies improve at a faster rate than old technologies; (ii) as the number of competitors increases, performance improves in smaller steps and longer waits; (iii) later entrants and technologies that have a number of prior steps tend to have smaller steps and shorter waits; but (iv) technologies with a long average wait time continue to have large steps. Fourth, technologies cluster in their performance by market.