From data to sustainability: Using explainable AI to promote electric vehicle development and understand consumer preferences

Motivation The global need to accelerate the development and consumption of electric vehicles (EVs) as a sustainable alternative to traditional transport. Purpose To identify potential consumers willing to switch to EVs and understand the underlying drivers of their behavior by utilizing Explainable Artificial Intelligence (EAI). Approach and methods The study analysed data from 1,964 users. Technical characteristics (Class A) were evaluated using Probit regression and one‐way ANOVA, while sociodemographic characteristics (Class B) were assessed via logistic regression. A user mining model was developed to predict behaviour, with Shapley Additive exPlanations (SHAP) used to determine the relative contribution of each predictor. Additionally, simulated enhancements in product indicators were modeled to identify “persuadable” potential buyers. Findings Purchasing decisions are significantly influenced by a combination of sociodemographic and technical factors. SHAP analysis successfully quantified the impact of these predictors, and potential user modeling confirmed that improvements in specific technical features could effectively convert reluctant consumers into EV adopters. Policy implications To drive EV adoption, developers and policy‐makers should prioritize targeted technical enhancements. Marketing and product development strategies should focus on those specific technical indicators that the model shows have the highest potential to shift consumer behaviour towards adoption.