Do determinants of EV purchase intent vary across the spectrum? Evidence from Bayesian analysis of US survey data

While electric vehicle (EV) adoption has been widely studied, most research focuses on the average effects of predictors on purchase intent, overlooking variation across the distribution of EV purchase intent. This paper makes a threefold contribution by analyzing four unique explanatory variables, leveraging large-scale US survey data from 2021 to 2023, and employing Bayesian ordinal probit and Bayesian ordinal quantile modeling to evaluate the effects of these variables—while controlling for other commonly used covariates—on EV purchase intent, both on average and across its full distribution. By modeling purchase intent as an ordered outcome—from “not at all likely” to “very likely”—we reveal how covariate effects differ across levels of interest. This is the first application of ordinal quantile modeling in the EV adoption literature, uncovering heterogeneity in how potential buyers respond to key factors. For instance, confidence in development of charging infrastructure and belief in environmental benefits are linked not only to higher interest among respondents likely to adopt EVs, but also to reduced resistance among more skeptical respondents. Notably, we identify a gap between the prevalence and influence of key predictors: although few respondents report strong infrastructure confidence or frequent EV information exposure, both factors are strongly associated with increased intent across the spectrum. These findings suggest clear opportunities for targeted communication and outreach, alongside infrastructure investment, to support widespread EV adoption.