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

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  • Sushil Kumar Dixit

    (Lal Bahadur Shastri Institute of Management)

  • Ashirwad Kumar Singh

    (Denave India Pvt Ltd)

Abstract

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.

Suggested Citation

  • Sushil Kumar Dixit & Ashirwad Kumar Singh, 2022. "Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning Approach," The Review of Socionetwork Strategies, Springer, vol. 16(2), pages 221-238, October.
    • Handle: RePEc:spr:trosos:v:16:y:2022:i:2:d:10.1007_s12626-022-00109-9
    DOI: 10.1007/s12626-022-00109-9
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    2. Farida Shaban & Pelopidas Siskos & Christos Tjortjis, 2023. "Electromobility Prospects in Greece by 2030: A Regional Perspective on Strategic Policy Analysis," Energies, MDPI, vol. 16(16), pages 1-17, August.

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