Abstract

http://ssrn.com/abstract=2115237
 
 

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


Ashish Sood


Georgia State University

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

July 22, 2012

Marketing Science, Forthcoming

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.

Number of Pages in PDF File: 54

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

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Date posted: July 22, 2012  

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: http://ssrn.com/abstract=2115237

Contact Information

Ashish Sood (Contact Author)
Georgia State University ( email )
United States
HOME PAGE: http://robinson.gsu.edu/profile/ashish-sood/
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 )
701 Tappan St. Rm E2600
Ann Arbor, MI 48109
United States
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