Impacts of Consumers’ Heterogeneity on Decision-Making in Electric Vehicle Adoption: An Integrated Model

Wen Xu (), Irina Harris, Jin Li, Peter Wells and Gordon Foxall
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Wen Xu: Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK
Irina Harris: Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK
Jin Li: Cardiff School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
Peter Wells: Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK
Gordon Foxall: Cardiff Business School, Cardiff University, Cardiff CF10 3EU, UK

Sustainability, 2025, vol. 17, issue 11, 1-25

Abstract: Understanding consumer heterogeneity is crucial for analysing attitude formation and its role in innovation diffusion. Traditional top-down models struggle to reflect the nuanced characteristics and activities of the consumer population, while bottom-up approaches like agent-based modelling (ABM) offer the ability to simulate individual decision-making in social networks. However, current ABM applications often lack a strong theoretical foundation. This study introduces a novel, theory-driven ABM framework to examine the heterogeneity of consumer attitude formation, focusing on electric vehicle (EV) adoption across consumer segments. The model incorporates non-linear decision-making rules grounded in established consumer theories, incorporating Rogers’s Diffusion of Innovations, Social Influence Theory, and Theory of Planned Behaviour. The consumer agents are characterised using UK empirical data, and are segmented into early adopters, early majority, late majority, and laggards. Social interactions and attitude formation are simulated, micro-validated, and optimised using supervised machine learning (SML) approaches. The results reveal that early adopters and early majority are highly responsive to social influences, environmental beliefs, and external events such as the pandemic and the war conflict in performing pro-EV attitudes. In contrast, late majority and laggards show more stable or delayed responses. These findings provide actionable insights for targeting segments to enhance EV adoption strategies.

Keywords: consumer decision-making; consumer segments; electric vehicle purchasing; social interaction; agent-based modelling; supervised machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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