Environmental concerns, technological factors, total cost of ownership, and charging infrastructure accelerate intention to electric vehicle adoption: A non-linear value-attitude-intention model

Abstract

The transportation industry is responsible for over 20 % of global carbon dioxide emissions, primarily due to the widespread use of Internal Combustion Engine vehicles. Conventional fuels, such as petrol and Diesel, are the most significant contributors to greenhouse gases, which lead to several health problems, global warming, climate change, and depletion of natural resources. The Electric vehicle is found to have multi-dimensional advantages over traditional fuel vehicles based on the Literature. The primary advantages of electric vehicles (EVs) include reduced operating and maintenance costs, high efficiency (∼3 times), no exhaust emissions, minimal air and noise pollution, and reduced depletion of fossil fuel sources. Besides the advantages and numerous government initiatives, the adoption rate of EVs is still very low. Thus, the study aimed to examine the impact of charging infrastructure, Technological factors, Total ownership cost, and Environmental concerns on the intention to purchase EVs in India, grounded on the theory of planned behavior and value-attitude-behavior hierarchy. A structured questionnaire was designed to collect the sample from the relevant respondents. A confirmatory composite analysis was done to test the measurement model. A total of 553 cleaned data are used to test the hypotheses using Warp-PLS-Structural Equation Modelling. Most of the relationships obtained were of the WARP2 type, i.e., j-type, U-type, or exponential type. All the factors significantly contributed to adoption, but the most prominent value-added factors influencing the adoption of EVs are environmental concerns (β = 0.172, f² = 0.074, p < 0.001), charging infrastructure (β = 0.119, f² = 0.036, p < 0.001), and technological factors (β = 0.075, f² = 0.029, p = 0.002). In contrast, the Total cost of ownership (β = 0.058, f² = 0.019, p = 0.007), although significant, has a weak effect size. The study developed the “Non-Linear Value-Attitude-Intention” model. The study also strengthens the methodology by using confirmatory composite analysis to test the measurement model. The study further validated the outcome using robustness tests. The study will help the government to improve the charging infrastructure by changing the construction codes to require EV parking and charging stations in malls, businesses, workplaces, etc. Steps must be formulated to minimize the total cost of ownership through discounts and other related policies.