E-Mobility Evolution: Shaping Perceptions and Driving Customer Behavior

The objective of this empirical study is to gain an understanding of customer perceptions and satisfaction regarding electric automobiles. To reduce dependence on oil, mitigate greenhouse gas emissions, and enhance air quality, electric vehicles (E-Vehicles) are receiving significant media coverage. Governments worldwide are implementing rules to promote their use. Electric vehicles rely on electricity rather than fossil fuels such as petrol or diesel to recharge their batteries. Electric car charging is more cost-effective than buying petrol or diesel due to its higher efficiency and reduced power expenses. Families must be cognizant of the primary sources of pollution in urban settings and proactively mitigate their exposure to potentially lethal contaminants. The objective of this study is to gather insights from consumers regarding their knowledge and inclination to purchase electric vehicles, as well as their shopping patterns, to promote environmental sustainability. The latest inquiries (2021) are offered after the 2002 study that laid the foundation for the discipline. The determination is that the null hypothesis cannot be refuted. Based on the findings, it is determined that the null hypothesis cannot be rejected. Based on the null hypothesis, it might be concluded that the responder, customer, or potential customer has a lack of preference for electric vehicles. Put simply, there is a lack of consumer demand or interest in purchasing electric vehicles. Customers, potential customers, and respondents lack familiarity with e-cars due to their limited exposure to fuel-based vehicles. The decision has been made to employ a descriptive research methodology, which is characterized by its distinctiveness and advantageous nature. To collect the data, questionnaires were distributed to respondents, specifically individuals who visited the showrooms and were either current users or potential consumers. ANOVA is a statistical method employed to analyze variation to conduct hypothesis testing efficiently.