Why the Bass Model Fits without Decision Variables

TLDR

The Bass diffusion model accurately captures adoption curves across many products and innovations. The study extends the Bass model to incorporate decision variables such as price and advertising. The authors compare this extended model to alternative diffusion approaches, providing both theoretical justification and empirical validation. The generalized model reduces to the classic Bass model, explains its empirical success without decision variables, and enables practical product‑planning applications.

Abstract

Over a large number of new products and technological innovations, the Bass diffusion model (Bass 1969) describes the empirical adoption curve quite well. In this study, we generalize the Bass model to include decision variables such as price and advertising. The generalized model reduces to the Bass model as a special case and explains why the Bass model works so well without including decision variables. We compare our generalized Bass model to other approaches from the literature for including decision variables into diffusion models, and our results provide both theoretical and empirical support for the generalized Bass model. We also show how our generalized Bass model can be used for product planning purposes.