Different field trials and corresponding acceptance studies with new technologies have been carried out between 2010 and 2013 at the Chair of Energy Economics at the Karlsruhe Institute of Technology (KIT). Those involved Electric Vehicle (EV) users, Liquefied Petroleum Gas (LPG) and Compressed Natural Gas (CNG) vehicle users as well as persons with strong interest in EV and smart home technologies. In order to characterize early adopters the same item-sets con-cerning attitudes regarding climate change, prices and innovations as well as corresponding socio-demographic characteristics, were used throughout all these studies and have been joined now and analyzed together. Additionally, regression methods have been applied in order to characterize early EV adopters based on a subsample of EV company car users in the French-German context. A binary logit model explaining private EV purchase intention has been developed. According to this model, early private EV adopters are likely to have a higher level of income, to have a household equipped with two or more cars and to travel more than 50 kilometers a day, not necessarily by car. This model additionally shows that possi-bilities to experience EV (e.g. by test drives) are important leverages to support adoption of EV by private car buyers. Respondents who already decided to pri-vately purchase an EV show significantly lower general price sensitivities than the LPG and CNG vehicle users.


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    Titel :

    On the road to an electric mobility mass market : how can early adopters be characterized?



    Kongress:

    Future Mobility - Markets and Policy Measures in the Evolution of Electric Mobility ; 2013 ; Oldenburg Future Mobility - Markets and Policy Measures in the Evolution of Electric Mobility (Oldenburg) : 2013.12.05-06


    Erschienen in:

    Erscheinungsdatum :

    2015


    Format / Umfang :

    1 Online-Ressource (circa 34 Seiten)


    Anmerkungen:

    Illustrationen



    Medientyp :

    Report , Konferenzband


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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