An Empirical Analysis of Electric Vehicle in Urban Transportation Market Using Deep-Learning Techniques

Market segmentation becomes a vital strategy for emerging markets to investigate and execute for widespread adoption of emerging mobility technologies like Electric Vehicles (EVs). As a low emission and low operating cost vehicle, EV adoption is anticipated to increase drastically in the near future. As a result, it will stimulate a significant amount of future academic study interest. The primary goal of this study is to investigate and identify several groups of prospective EV buyers according to psychographic, behavioural, and socioeconomic factors by combining the methodologies of “perceived benefits-attitude-intention.” To operationalize and validate data segments, the study used powerful analytical techniques such cluster analysis, multiple discriminant analysis, and the Chi-square test. According to the research, there are three separate youthful consumer and market segmentation groups known as Behavioral Segmentation, Psychographic Segmentation and Demographic segmentation who are thought to be forming relationships with EV customers. The consequences are suggested, which can provide some useful direction for academics and policymakers to promote the adoption of EVs against the backdrop of the developing sustainable transportation sector.