Forecasting Technology Adoption in Uncertain and Dynamic Markets: A Market Coverage Diffusion Approach

Abstract

How innovations spread in a fast‐changing and uncertain market is vital for accurate forecasting and planning. This study introduces a generalized diffusion framework that incorporates evolving market coverage along with dynamic market and market uncertainty. A random variable is used to represent market uncertainty, while three coverage functions, exponential, delayed, and logistic are used to capture different patterns of market growth. The models are evaluated on two different data sets: quarterly sales of battery electric vehicles (BEVs) and quarterly sales of Apple iPhones. The performance of the proposed models is compared to that of some benchmark diffusion models using various error and information‐based measures. The results indicate that the proposed models outperform the existing ones. Among the proposed models, the model with the logistic coverage provides the best fit for both data sets as it can capture the early growth and later stabilization more effectively. The study also highlights the differences in adoption patterns for infrastructure‐driven technologies, such as BEVs, and fast‐moving consumer products, like smartphones.