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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.

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This is an exploratory research highlighting the concerns and reactions of Indian working-class people towards the COVID-19. It was observed that most of the Indian working-class people were seriously concerned about the pandemic and responded well to the measures suggested by the Governments and other agencies in a big way. Most of the respondents believed the pandemic will be effectively controlled across the globe within one year. Word cloud and other data visualization techniques were used to analyze the reactions of the Indian working class towards the Central and State government’s initiatives to contain COVID-19. In the word cloud of the top 150 popular words for both central and state governments Lockdown, People and Government have taken the central stage. The word streaming analysis suggests the intense relationship among the most frequent words in the dataset. For the central government, it was social distancing and for state government, it was social distancing and relationship between central and state governments. The sentiment analysis for both central and state government was neutral, mostly. The researchers are of the view that the research will provide a deeper insight into human perception and behavior towards the measures initiated by the Central and State Governments in any similar difficult situations. Further the concerns identified may be taken into consideration by the Government while designing the policy measures and other interventions by the Government.

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