A population-growth model for multiple generations of technology products

Hongmin Li, Hans Armbruster, Karl G. Kempf

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

In this paper, we consider the demand for multiple, successive generations of products and develop a population-growth model that allows demand transitions across multiple product generations and takes into consideration the effect of competition. We propose an iterative-descent method for obtaining the parameter estimates and the covariance matrix, and we show that the method is theoretically sound and overcomes the difficulty that the units-in-use population of each product is not observable. We test the model on both simulated sales data and Intel's high-end desktop processor sales data. We use two alternative specifications for product strength in this market: performance and performance/price ratio. The former demonstrates better fit and forecast accuracy, likely due to the low price sensitivity of this high-end market. In addition, the parameter estimate suggests that, for the innovators in the diffusion of product adoption, brand switchings are more strongly influenced by product strength than within-brand product upgrades in this market. Our results indicate that compared with the Bass model, Norton-Bass model, and Jun-Park choice-based diffusion model, our approach is a better fit for strategic forecasting that occurs many months or years before the actual product launch.

Original languageEnglish (US)
Pages (from-to)343-360
Number of pages18
JournalManufacturing and Service Operations Management
Volume15
Issue number3
DOIs
StatePublished - Jun 1 2013

Keywords

  • Diffusion
  • Forecasting
  • Multiple-generation demand model
  • Product transitions

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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