Temporal models of innovation diffusion

Some new models for describing the time pattern of the spread of innovations through a population are presented in this paper. In general, most of the existing temporal models of innovation diffusion are binomial models in the sense that they tacitly assume that the population can be divided, without loss of generality, into adopters and potential adopters. This paper discusses the limitations of the binomial models and presents the development and applications of a model where the potential adopter population is dynamic in nature. In addition, the paper explores the possibility of developing an analytical framework to study innovation diffusion processes where the population had polynomial dimensions — polynomial in the sense that it is comprised of members who are adopters, rejectors, disapproves, and uncomitted. Finally, an attempt has also been made to develop some stochastic models to complement the most commonly used deterministic binomial models. Based on available data, a few case studies are also included to illustrate the workability of the developed models.