The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies

Abstract

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 modeled 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 is based on the findings of a research project about policy measures to promote the diffusion of Electric Vehicles in Germany. It investigates how practitioners could choose 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.

Testing the impact of direct and indirect flood warnings on population behaviour using an agent-based model

“…Whilst this is a manipulation of the Bass model function, it remains consistent with the Bass model theory, stipulating that human adoption of a process or product is more likely to happen based upon internal systematic influence, or imitation, rather than through external influence on the social system, or by innovation. Wherein the available choices may be reduced to "yes", "no", and "maybe", probabilistically represented as roughly one-third each for a given scenario (Dechter and Pearl, 1986;Hart et al, 1968;Hornor, 1998;Mahajan et al, 1990;Massiani and Gohs, 2015;Sultan et al, 1996).…”

“…Neal et al, 2012); however, thus far, ABM applications have not taken full advantage of the latest developments in flood inundation modelling. To date, studies that have driven an ABM with a hydrodynamic model are those of Dawson et al (2011), Lumbroso et al (2011), and Medina et al (2016), with the coupled flood agent-institution modelling framework of Abebe et al (2019) also providing mentionable overlap. The Dawson et al (2011) example used a simple diffusive wave model that solves Manning's equation over a raster grid of cells; it was implemented within an ABM to simulate a coastal flood and showed considerable potential.…”

An agent-based model for flood risk warning

“…Model theory, stipulating that human adoption of a process or product is more likely to happen based upon internal systematic influence, or imitation, rather than through external influence on the social system, or by innovation. Wherein the available choices may be reduced to 'yes', 'no' and 'maybe', probabilistically represented as roughly one-third each for a given scenario (Dechter & Pearl, 1986;Hart et al, 1968;Hornor, 1998;Mahajan et al, 1990;Massiani & Gohs, 2015;Sultan, Farley & Lehmann, 1996).…”

“…In this application, the Bass model gives an indication of this duration based on the relative probabilistic magnitudes of (p) and (q) for a population of 108,000 agents. The overall significance of this application is that it allows conclusions to be made as to how influential external policy protocols are for the population in relation to their internal 'sense' during flood event response (Massiani & Gohs, 2015, Sultan et al, 2003. and potentially macro, scale human responses to flood events (Guyot & Honiden, 2006).…”

An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

“…Consumers are more inclined to adopt a new technology after they actually observe others using it around themselves. We assume EVs as a new technology when compared with fossil fuel powered vehicles [46]. Spatial remoteness or closeness will affect consumers' choice of adoption [16].…”