A Population-Growth Model for Multiple Generations of Technology Products

• 成果类型: Article
• 署名作者: Li, Hongmin; Armbruster, Dieter; Kempf, Karl G.
• 署名单位: Arizona State University; Arizona State University-Tempe; Arizona State University; Arizona State University-Tempe; Intel Corporation; Intel USA
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2013.0430
• 发表日期: 2013
• 页码: 343-360
• 关键词: product transitions; forecasting; multiple-generation demand model; diffusion

摘要

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 Inters 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.