Highlights Studies the impact of latent factors on EV adoption behaviour among young educated Indian buyers. Market segmentation to develop predictable and consistent subclasses of customers. Segments and profiles the potential young electric vehicle buyers. Studies the differences in the personality traits between the different segments.

    Abstract Market segmentation is an essential tool that can be used for the effective and extensive adoption of emerging innovations such as electric vehicles. This study uses a multi-item questionnaire to collect data from young educated Indian students, which is used to understand the factors that affect the adoption of electric vehicles among potential vehicle buyers. Confirmatory factor analysis revealed ten latent variables that impact the adoption of electric vehicles, out of which eight variables, viz. social image, social influence, anxiety (or perceived risks), perceived environmental benefits, performance expectancy, effort expectancy, facilitating conditions, and attitude, are used for segmenting and profiling potential vehicle buyers into clusters, and the other two latent factors viz. environmental enthusiasm and technological enthusiasm are used to measure and analyse the differences in personality traits across the different clusters. Segmentation technique, based on k-means clustering, revealed three clusters in the respondents which are found to show positive, neutral (slightly positive), and negative perceptions towards all clustering variables and are subsequently labelled as “innovation adoption leads”, “innovation adoption indifferents”, and “innovation adoption idlers” respectively. The findings from this research provide better knowledge and understanding to decision-makers and can help accelerate the widespread acceptance of electric vehicles.


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    Titel :

    Who will buy electric vehicles? Segmenting the young Indian buyers using cluster analysis


    Beteiligte:
    Bhat, Furqan A. (Autor:in) / Verma, Meghna (Autor:in) / Verma, Ashish (Autor:in)

    Erscheinungsdatum :

    2024-01-06




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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