The transportation sector is a significant contributor to CO2 emissions in India, and the increasing sales of fossil fuel vehicles due to rising incomes and urbanization exacerbate this issue. Electric vehicles (EVs) are a potential solution, but several barriers hinder their adoption, including high upfront costs, limited driving range, and inadequate charging infrastructure. These challenges are particularly pronounced in emerging economies like India. The Indian government has implemented initiatives like the National Electric Mobility Mission Plan (NEMMP) and the Faster Adoption and Manufacturing of Electric Vehicles (FAME) to address supply-side challenges, such as improving charging infrastructure and providing subsidies. Despite these efforts, EV sales remain low, highlighting the importance of understanding consumer preferences. This study aims to investigate the preferences of Indian car buyers for EVs by addressing four research questions: 1. What product, service, and policy attributes influence the preferences of Indian car buyers for EVs? 2. How do attitudinal factors such as environmental concerns and trust in technology affect EV adoption? 3. What is the willingness to pay (WTP) of Indian consumers for improved EV attributes? 4. Do Indian car buyers exhibit reference dependence when comparing EVs to internal combustion engine vehicles (ICEVs), and how does this affect WTP estimates? This study is unique in its comprehensive analysis of EV preferences in the Indian context, using a large sample size and an advanced methodological approach to estimate WTP, considering attitudinal characteristics and reference dependence.

Existing literature on consumer preferences for EVs reveals a significant body of research, primarily focused on developed countries like Australia, Nordic countries, European countries, Canada, the USA, and South Korea. These studies primarily utilize stated preference (SP) data and focus on estimating WTP for improvements in product and service attributes such as driving range and charging infrastructure. The impact of policy interventions, such as access to specialized lanes and reduced charging fees, has also been explored. Attitudinal factors, including risk aversion, environmental consciousness, trust in technology, and social norms, have been identified as influential factors affecting consumer preferences. In contrast, the literature on EV preference elicitation in India is sparse and limited, with most studies employing methods like structural equation modeling (SEM) or correlation analysis, often with small sample sizes. These studies have identified barriers to EV adoption such as poor charging infrastructure, lack of awareness, and high upfront costs. This study fills the gap in the literature by providing the first comprehensive analysis of WTP for various EV attributes in India, accounting for both attitudinal factors and reference dependence.

This study employs a hybrid choice modeling approach, specifically an Integrated Choice and Latent Variable (ICLV) model, to analyze stated preference (SP) data collected from over 1000 Indian consumers. A discrete choice experiment was designed to elicit preferences for EVs under various scenarios with varying attribute levels. The attributes considered included product attributes (on-road purchase price, running cost, and driving range), service attributes (slow charging time, fast charging time, and availability of fast charging stations), and policy attributes (reserved parking and access to specialized lanes). The ICLV model consists of two components: a discrete choice model and a structural equation model (SEM). The discrete choice model, a binary probit model, estimates the effect of vehicle attributes, attitudinal characteristics, and their interactions on the choice between an ICEV and an EV. The SEM component uses a trivariate linear regression model to estimate the relationships between latent variables (climate doubters, EV-tech believers, early adopters) and demographic characteristics, and then a multivariate ordered probit model to link latent variables to observed attitudinal indicators measured on a five-point Likert scale. Reference dependence was incorporated using a curvature-based utility specification, allowing for a more realistic estimation of WTP by considering the deviation of EV attributes from the reference ICEV attributes. The composite marginal likelihood (CML) approach was used to estimate the ICLV model parameters. WTP was calculated using the ratio of the marginal utility of an attribute to the marginal utility of price, taking into account the non-linear utility specification.

The study found that purchase price, driving range, operating cost, and fast charging time significantly influence consumer preferences for EVs. Slow charging time, the density of fast-charging stations, and privilege-driven policies (reserved parking and specialized lanes) did not have a significant effect. Reference dependence was evident, as the curvature parameter was less than 1 for most attributes, indicating diminishing sensitivity to deviations from the ICEV reference point. Higher knowledge about EVs and positive social network influences positively affected the likelihood of purchasing an EV. Climate doubters showed less inclination towards EVs, while early adopters exhibited a stronger preference. Interactions between latent variables and vehicle attributes revealed significant heterogeneity in WTP across different demographic groups. WTP estimates indicated a willingness to pay an additional US$10–34 to reduce fast charging time by 1 minute, US$7–40 to add a kilometer to driving range (at 200 km), and US$104–692 to save US$1 per 100 km in operating cost. The SEM component showed that females, those with higher incomes and education, and individuals in the private sector had greater trust in EV technology and a higher likelihood of early adoption. Annual discount rates derived from WTP for operating cost savings suggest that Indian car buyers undervalue future fuel cost savings compared to prevailing market interest rates.

The findings address the research questions by identifying key factors influencing EV adoption in India. The significant influence of driving range and fast charging time highlights the need for improvements in battery technology and charging infrastructure. The low impact of privilege-driven policies suggests that focusing on product and service attributes is more crucial in the initial stages of EV adoption. The heterogeneity in WTP across demographic groups underscores the importance of targeted marketing strategies and policy interventions. The undervaluation of future cost savings suggests that policies solely based on fuel tax reductions may be less effective. The study's findings contribute to a better understanding of consumer behavior towards EVs in a developing country context, providing valuable insights for policymakers and automakers.

This study provides the first comprehensive analysis of Indian consumers' willingness to pay for electric vehicle attributes, incorporating attitudinal factors and reference dependence. Key findings highlight the importance of driving range, fast charging time, and operating costs in shaping consumer preferences, while suggesting that privilege-driven policies might be less effective in the early adoption stages. Future research should focus on validating these findings on a larger, more representative sample, including additional vehicle attributes and considering the interaction between consumer demand and supply-side factors within a market equilibrium framework.

The study's sample, while large, may not perfectly represent the entire Indian population of potential EV buyers. The snowball sampling method could lead to biases in the sample. The stated preference data relies on hypothetical scenarios, which may not entirely reflect actual purchase decisions. The choice experiment design, while comprehensive, did not include all possible attributes, such as vehicle body type and performance characteristics.