An integrated two-stage diffusion of innovation model with market segmented learning

With the aid of the Internet, both firms and customers have access to vast amounts of data. The aim of the proposed model is to provide a method that utilizes data to understand and predict how potential customers will value innovations, and communicate thoughts in a non-fully connected market. Innovation diffusion models have been studied extensively, and are often formulated using either macro-level approaches that aggregate much of the market behavior, or using micro-level approaches that employ microeconomic information pertaining to the potential market and the innovation. We propose a two-stage integrated model that benefits from both the macro- and micro-level approaches, and we add emphasis to modeling when, what, and how customers communicate and process information. The proposed model incorporates heterogeneous potential customers and adopters, segmented Bayesian learning, and the adopter's satisfaction levels to describe biasing and word-of-mouth behavior in a non-fully connected market.