Marketing

Quantum mechanics

Context (archaeology)

Electric light

Linguistics

Engineering

Philosophy

Aerospace engineering

Biology

Environmental economics

Government (linguistics)

Archaeology

Range (aeronautics)

Economics

Power (physics)

Market economy

Computer science

Electric vehicle

Business

Paleontology

Subsidy

Physics

Geography

Urbanization

Driving range

Automotive engineering

Economic growth

Electric cars

Electrical engineering

Engineering physics

The Review of Socionetwork Strategies

Electric mobility has been around for a long time. In recent years, with advancements in technology, electric vehicles (EVs) have shown a new potential to meet many of the challenges being faced by humanity. These challenges include increasing dependence on fossil fuels, environmental concerns, challenges posed by rapid urbanization, urban mobility, and employment. However, the adoption of electric vehicles has remained challenging despite consumers having a positive attitude toward EVs and big policy pushes by governments in many countries. Marketers from the electric vehicle (EV) industry are finding it difficult to identify genuine buyers for their products. In this context, the present study attempts to develop a machine learning model to predict whether a person would "Buy" or "Won't Buy" an electric vehicle in India. To develop the model, an exploration of EV context was done first by conducting a text analysis of online content relating to electric vehicles. The objective was to find frequently occurring words to gain a meaningful understanding of the consumer's interests and concerns relating to electric vehicles. The machine learning model indicates that age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior are significant predictors of electrical vehicle purchase in India. The level of education, employment, and government subsidy are not significant predictors of EV uptake.

Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning Approach