Exploratory analysis of the impact of information dynamics on innovation diffusion

Understanding the dynamic nature of innovation diffusion processes, and mechanisms underlying these dynamics is crucial, since such an understanding is potentially very important in designing effective innovation support policies and developing better diffusion forecasts. The role of information diffusion in conditioning the diffusion dynamics of an innovation is the locus of this study. In order to investigate this, a simulation model that distinguishes between the real attributes of the innovation and their perceived levels by the user groups has been developed. The model makes it possible to separately trace the diffusion dynamics of innovation and the information about an innovation. Additionally, the formulation of the model enables the message broadcasted via word-of-mouth to change in nature from positive to negative, or vice versa. This generic model is used in an exploratory way, which is discussed as a novel approach for conducting a simulation-based analysis. Such an exploration covers a wide range of plausible diffusion behaviors, and aims to demonstrate the extent to which information imperfections and dynamics may influence the diffusion process. During experiments it is observed that information imperfections as well as the pace of learning processes may yield significant changes in the diffusion patterns. These changes may be in the form of altering the basic characteristics of the well-known S-shaped diffusion curve, as well as stopping the diffusion at much lower levels than full adoption. The analysis presented in the article shows that exploratory analysis is a promising way to utilize simulation models for developing general insights about dynamics processes.

Research Highlights
► We study the impact of information diffusion processes on innovation diffusion.
► We use a simple diffusion model with dynamic internal influence formulation.
► An exploratory analysis relying on extensive number of experiments is introduced.
► We show how information dynamics can lead to counterintuitive diffusion cases.
► Cases like ‘self-deceiving crowds’ and ‘misguiding front-runners’ are discussed.