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Rapid urbanization in densely populated cities has led to a dramatic increase in fossil fuel-powered vehicles, worsening air pollution, and public health concerns. Addressing these challenges requires sustainable mobility solutions, among which the adoption of electric vehicles (EVs) stands out as a viable alternative. However, customers' decision-making toward EV adoption remains complex and underexplored, particularly in developing countries. To bridge this gap, this study investigates the key antecedents shaping attitudes toward EVs and their influence on adoption intention. Adopting a quantitative research design, data were collected from 363 licensed drivers in Bangladesh through purposive sampling. To enhance the rigor and reliability of results, a multistage analytical approach was employed, combining linear modelling (PLS-SEM) and non-linear analysis (ANN). The PLS-SEM findings revealed that eco-friendly benefit, perceived return, operational economic benefit, and neuroticism are the most critical antecedents influencing attitude. Attitude, in turn, was found to strongly predict EV adoption intention. Complementing these results, Artificial Neural Network (ANN) analysis ranked the relative importance of the antecedents, highlighting neuroticism (100 %) as the most influential predictor of attitude, which itself emerged as the strongest driver of EV adoption intention. Additionally, risk aversion was identified as a moderator of the relationship between attitude and EVs adoption intention. Overall, the integration of PLS-SEM and ANN confirmed the pivotal role of attitude in shaping EV adoption intention in densely populated cities. The findings provide valuable insights for both academic researchers and industry stakeholders in understanding customer decision-making dynamics and promoting environmentally sustainable transportation.
Chang et al. (Thu,) studied this question.