In the rapidly growing, competitive information and communications technology market, demand forecasting for new technologies is difficult, yet important. Our study describes a forecasting methodology designed for newly introduced technology for which limited data is available that uses algebraic estimation, Bayesian updating, and conjoint analysis. In the estimation procedure of diffusion model, initial information is derived through expert judgment, then updated using Bayes' theorem with available sales data. A conjoint analysis based on separate surveys of multilevel decision makers is used to derive a description of a competitive environment among multiple alternatives. The model is applied to the home networking (HN) market for new construction in South Korea, for which there exists various alternative technologies. The forecast shows that among HN technologies wireless LAN will command the highest market share at any time during the forecasted period. Based on simulation experiments, important factors affecting demand for HN technologies are identified-both consumer preference and the development of technological standards have a significant impact on the diffusion of HN technologies. © 2006 Elsevier Inc. All rights reserved.
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