Developing a Prediction Model for the Adoption of Electric Vehicles
Abstract
Transport activities are major contributors to greenhouse gas emissions. Mass penetration of electric vehicles into the market will have a number of impacts and benefits, including the ability to substantially decrease greenhouse gas emissions from the transportation sector. Therefore, it is expected that in coming years this technology will progressively penetrate the market. This paper presents preliminary work on developing a prediction model for electric vehicle adoption by modeling the conditions under which an individual is more or less likely to adopt an electric vehicle. This model is developed by considering demographic determinants as well as behavioral and attitudinal measures that affect individual adoption of the technology. Analyzing these outcomes generates empirical findings that better inform electric vehicle technology and policy development. This study takes into account preferences of potential customers and therefore provides engineering managers with critical information for developing future electric vehicle technology.
Recommended Citation
O. Egbue and S. Long, "Developing a Prediction Model for the Adoption of Electric Vehicles," Proceedings of the International Annual Conference of the American Society for Engineering Management (2013, Minneapolis, MN), pp. 269 - 275, American Society for Engineering Management (ASEM), Oct 2013.
Meeting Name
International Annual Conference of the American Society for Engineering Management (2013: Oct. 3-5, Minneapolis, MN)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Electric Vehicle Adoption; Prediction Model; Commerce; Gas Emissions; Greenhouse Gases; Mathematical Models; Technology; Empirical Findings; Engineering Managers; Policy Development; Potential Customers; Transport Activity; Transportation Sector; Vehicle Technology
International Standard Book Number (ISBN)
978-1632660541
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2013 American Society for Engineering Management (ASEM), All rights reserved.
Publication Date
01 Oct 2013