Predicting the Path of Technology Innovation

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. In this talk we propose a new statistical 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. Our analysis of the data shows a number of interesting 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.

Our speaker will be Gareth James, PhD. the E. Morgan Stanley Chair in Business Administration, Vice Dean for Faculty and Academic Affairs, Marshall School of
Business, University of Southern California.

This meeting will be a joint meeting with the Southern California Chapter of the American Statistical Association.