“Should I stay or should I go?” a dual-theory behavioral analysis of electric vehicle adoption and retention

Achieving sustainable transport systems requires actionable insights into the behavioral dynamics underpinning both the initial adoption and sustained use of electric vehicles (EVs). This research addresses this challenge through two independent empirical studies integrating the Value-Belief-Norm theory and the Expectation Confirmation Theory within a segmentation-based approach. Study 1 (n = 265) examines the psychological determinants of EV adoption, identifying the ascription of responsibility as the most influential factor and revealing five distinct consumer profiles: Engaged Changemakers, Hesitant Traditionalists, Idealistic Altruists, Pragmatic Individualists, and Detached Skeptics. Study 2 (n = 434) explores continuance intention among current EV users, revealing that satisfaction and confirmation of expectations are critical predictors. Six segments emerge: Satisfied Pragmatists, Highly Satisfied Altruists, Highly Satisfied Realists, Disappointed but Responsible, Reluctant Skeptics, and Self-Interested Dropouts. These findings underscore a clear behavioral distinction: while ethical and normative concerns dominate the adoption stage, experiential satisfaction and alignment between expectations and performance are central to retention. By combining two established behavioral theories with data-driven segmentation, the research offers differentiated, evidence-based policy and marketing recommendations, emphasizing value-driven communication to boost adoption and user experience improvements to secure retention. This dual framework advances understanding of EV consumer behavior and provides practical guidance for accelerating the transition to low-emission mobility. • A dual VBN-ECT model captures both EV adoption and continuance behaviors. • Ascription of responsibility is the main driver of EV purchase intention. • Continuance is shaped by satisfaction, environmental concern and altruistic values. • Segmentation identifies profiles needing ethical, financial, or structural support.