The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies
By Jérôme Massiani, Andreas Marcus Gohs. 2015
157 citations recorded by the source corpus.
Bass diffusion models are one of the competing paradigms to forecast the diffusion of innovative products or technologies. This approach posits that diffusion patterns can be modelled through two mechanisms: Innovators adopt the new product and imitators purchase the new product when getting in contact with existing users. Crucial for the implementation of the method are the values assigned to the two parameters, usually referred to as p and q, which mathematically describe innovation and imitation mechanisms. The present paper investigates how to select adequate values for the Bass model parameters to forecast new automotive technologies diffusion with a focus on Electric Vehicles. It considers parameters provided by the literature as well as ad hoc parameter estimations based on real market data for Germany. Our investigation suggests that researchers may be in trouble in electing adequate parameter values since the different eligible parameter values exhibit dramatic variations. Literature values appear discussible and widely variable while ad hoc estimates appear poorly conclusive. A serious problem is that ad-hoc estimates of the Bass p value are highly sensitive to the assumed market potential M. So for plausible values of M, p varies on a high scale. Unless more consolidation takes place in this area, or more confidence can be placed on ad hoc estimates, these findings issue a warning for the users of such approaches and on the policy recommendations that would derive from their use.
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/69e6f435142a5d77d0a7c3f40ef25d39a4edcc1c
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Citations in the corpus
- Forecasting the trajectory of electric vehicle sales and the consequences for worldwide CO2 emissions (2020, result): “Moreover, their relevance for EV predictions has been demonstrated in prior studies (e.g., Jensen et al., 2017; Massiani and Gohs, 2015).”