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

Marketing Science, Forthcoming

54 Pages Posted: 22 Jul 2012  

Ashish Sood

University of California Riverside

Gareth James

University of Southern California - Marshall school of Business

Gerard J. Tellis

University of Southern California - Marshall School of Business, Department of Marketing

Ji Zhu

University of Michigan at Ann Arbor

Date Written: July 22, 2012

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” like the Moore’s Law. We propose a model, Step And Wait (SAW), for predicting the path of technological innovation and 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; none 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 long average wait time continue to have large steps. Fourth, technologies cluster in their performance by market.

Keywords: technology evolution, innovation, SAW model, Moore’s Law, Kryder’s Law, Bass Model, technological prediction

Suggested Citation

Sood, Ashish and James, Gareth and Tellis, Gerard J. and Zhu, Ji, Predicting the Path of Technological Innovation: SAW Versus Moore, Bass, Gompertz, and Kryder (July 22, 2012). Marketing Science, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2115237

Ashish Sood (Contact Author)

University of California Riverside ( email )

United States
6782059931 (Phone)

HOME PAGE: http://soba.ucr.edu/directory/faculty.html?netid=asood

Gareth James

University of Southern California - Marshall school of Business ( email )

Marshall School of Business
BRI 401, 3670 Trousdale Parkway
Los Angeles, CA 90089
United States

Gerard J. Tellis

University of Southern California - Marshall School of Business, Department of Marketing ( email )

Hoffman Hall 701
Los Angeles, CA 90089-0443
United States
213-740-5031 (Phone)
213-740-7828 (Fax)

HOME PAGE: http://gtellis.net

Ji Zhu

University of Michigan at Ann Arbor ( email )

500 S. State Street
Ann Arbor, MI 48109
United States

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