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.

BASS Model Analysis in “Crossing the Chasm” in E-Cars Innovation Diffusion Scenarios

“…When referring to the topic of technology diffusion, it is worth citing the article of Mitra et al [1], where the authors analyze four cases of diffusion of innovations in emerging markets. Going further, it is also worth mentioning the thematic focus of the publication, related to the attempt to determine the number of electric vehicles (incl.…”

“…Almost 30% of total EU CO 2 emissions come from this sector, 72% of which come from road transport. In road transport, passenger cars are the main source of pollution-they are responsible for almost 61% of emissions [1].…”

“…The article considers forecasting the development of innovation evolution based on the information before the first inflection point (sometimes also called LIP-left inflection point). Forecasting ahead of this point is a very challenging task [1]. It was noted in [13] that in this type of forecast very large errors may be generated.…”