A Population-Growth Model for Multiple Generations of Technology Products

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.

• 저널Manufacturing & Service Operations Management · 15(3) · 343–360
• 토픽Innovation Diffusion and Forecasting · Management Science and Operations Research
• DOI10.1287/msom.2013.0430
• 저자Hongmin Li, Dieter Armbruster, Karl G. Kempf