Timely Forecasts of Diffusion of Innovations: The Bass Model in Emerging Markets

Abstract

This article aims to establish a methodology to estimate the parameters of the Bass model of diffusion of innovations at the take-off stage of the innovation in emerging markets and thus draw timely diffusion forecasts. This article analyses four cases of diffusion of innovations in emerging markets. Besides gradient-based methods for model estimation such as ordinary least squares (OLS) and non-linear least squares (NLS), this article uses global optimization techniques such as genetic algorithms (GA) and simulated annealing (SA) with diffusion data till the take-off stage. This study attempts to respond to the problems of scant data by interpolation until the take-off stage. After that, a comprehensive comparison of different methods is made using the standard error diagnostic measures. The results indicate that a combination of NLS, GA and SA with interpolated data reduces error margins to a commonly acceptable level even with scant and noisy data, thus providing managers a methodology to make timely forecasts of diffusion of innovations in emerging markets.