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Predicting the Potential Market for Electric Vehicles

Lookup NU author(s): Professor Elisabetta Cherchi



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


Forecasting the potential demand for electric vehicles is a challenging task. Because most studies for newtechnologies rely on stated preference (SP) data, market share predictions will reflect shares in the SP dataand not in the real market. Moreover, typical disaggregate demand models are suitable to forecast demand inrelatively stable markets, but show limitations in the case of innovations. When predicting the market for newproducts it is crucial to account for the role played by innovation and how it penetrates the new market overtime through a diffusion process. However, typical diffusion models in marketing research use fairly simpledemand models. In this paper we discuss the problem of predicting market shares for new products and suggesta method that combines advanced choice models with a diffusion model to take into account that new productsoften need time to gain a significant market share. We have the advantage of a relatively unique databank whererespondents were submitted to the same stated choice experiment before and after experiencing an electricvehicle. Results show that typical choice models forecast a demand that is too restrictive in the long period.Accounting for the diffusion effect, instead allows predicting the usual slow penetration of a new product inthe initial years after product launch and a faster market share increase after diffusion takes place.

Publication metadata

Author(s): A Jensen, E Cherchi, S Mabit, Ortúzar J de D

Publication type: Article

Publication status: Published

Journal: Transportation Science

Year: 2017

Volume: 51

Issue: 2

Pages: 427-440

Print publication date: 01/05/2017

Online publication date: 08/07/2016

Acceptance date: 01/08/2015

Date deposited: 18/10/2016

ISSN (print): 0041-1655

ISSN (electronic): 1526-5447

Publisher: Institute for Operations Research and the Management Sciences


DOI: 10.1287/trsc.2015.0659


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