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Lookup NU author(s): Professor Elisabetta Cherchi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2021Among the many approaches towards fuel economy, the adoption of electric vehicles (EV) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations towards EV. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g. only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption.
Author(s): Bas J, Cirillo C, Cherchi E
Publication type: Article
Publication status: Published
Journal: Technological Forecasting and Social Change
Year: 2021
Volume: 168
Online publication date: 15/04/2021
Acceptance date: 15/03/2021
Date deposited: 19/05/2021
ISSN (print): 0040-1625
Publisher: Elsevier Inc.
URL: https://doi.org/10.1016/j.techfore.2021.120759
DOI: 10.1016/j.techfore.2021.120759
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