The roles of self-identity, symbolic attributes, and contextual barriers in EV adoption
Despite the fact that electric vehicles (EVs) have the potential to dramatically reduce emissions contributing to climate change, market share remains low, necessitating research to identify factors that could encourage more widespread adoption. Environmentalist self-identity has been identified as an important predictor of pro-environmental intention and behavior, including in the EV adoption. Additionally, those who evaluate EVs higher on measures of symbolic attributes—that is, a reflection of status and/or identity – are also more likely to adopt. This suggests that those with strong environmentalist identities may be motivated to adopt EVs specifically to build, reinforce, or display this aspect of their identity. To date, this idea has not been fully tested. At the same time, a variety of contextual barriers, including up-front costs, limited range, and long commutes, may hinder EV adoption, even among those with strong environmentalist self-identities. New mobility ownership models (e.g., subscription-based models), may effectively remove such barriers, yielding a stronger association between identity and adoption. Although contextual factors are generally accepted as central to transportation decisions, their role in moderating the influence on individual-level factors such as self-identity merits further study. The main objectives of this research are to 1) test symbolic attributes as a mediator of the relationship between self-identity and EV adoption intent, and 2) examine the potential moderating role of contextual barriers on the link between self-identity and EV adoption, including barriers may attenuate this link, and new mobility models that may strengthen it.
Methods: Surveys and discrete choice modeling with residents in the Los Angeles, CA, and Atlanta, GA metropolitan areas
Significance: The outcomes of this project will have implications for EV marketing, policy development, and mobility ownership models.
Project Phase: Data collection in progress, expected to be complete in Summer 2019