Will users practice what they preach? Exploring the influencing factors of the intention behaviour gap in electric vehicles adoption

• We investigated the intention-behaviour gap for EV adoption. • EV purchase behaviour is simulated and measured based on a novel design-space-game. • An extended theory of planned behaviour (TPB) is utilized to test the intention-behaviour gap. • EV purchase intention has a positive effect on behaviour and explains 13% of the variance in behaviour. • EV adoption behaviour is also affected by external factors related to instrumental, utilitarian, and technical aspects. This study explores the intention-behavior gap in electric vehicle (EV) adoption, focusing on the often overlooked discrepancy between users’ stated intentions and their actual purchasing behavior. We began by developing a theoretical framework and formulating research hypotheses based on a comprehensive EV adoption literature review and theory of planned behavior. Subsequently, data from a simulated vehicle purchase game involving 2647 participants in Canada were utilized to test these hypotheses by a structural equation model method. Then, potential EV users were categorized according to different intention-simulated behavior relationships through the K-means clustering method to further explore the characteristics of user groups with and without intention-simulated behavior gap. The findings indicate that while purchase intentions have a positive influence on simulated behavior, their predictive power is limited, explaining only 10.89 % of the variance in simulated behavior. Key factors such as gender, house type, and homeownership significantly moderate this relationship (p < 0.001). Four segments emerged from the analysis, with two—“green image with no action” users and “low intention” EV buyers—emerging as primary contributors to the intention-behavior gap. In the gap group (n = 1329), the relationship between intention and simulated behavior was negative (−0.526 standardized regression weight, p < 0.001). While concerns about emissions motivated EV adoption intentions (p < 0.001), actual purchasing behavior was more strongly influenced by budget (p = 0.033), body style preference (p = 0.001), and the availability of home charging facilities (p < 0.001). The most important contributions of this study are the measurement and evaluation of the EV purchase intention-behavior gap. The findings demonstrate that, while the assumptions in the theory of planned behavior about the interaction between individual psychological factors are sound, more elements must be incorporated to improve the predictive power of intentions on behaviour. Future studies might explore additional aspects across a wider range of regional contexts and consumer segments, as well as longitudinal behavior patterns, to gain a better understanding of the changing dynamics of EV adoption. This study adds to the continuing discussion about effective EV adoption techniques, with practical implications for policymakers and marketers seeking to close the intention-behavior gap.