Highlights How do rideshare drivers vs. commuters perceive electric vehicle (EV) attributes? Impact of symbolic attributes on EV adoption is stronger for commuters. Impact of some instrumental attributes on EV adoption stronger for rideshare drivers. Rideshare drivers are more willing to adopt EVs than commuters. Symbolic attributes mediate the path between environmental identity and EV adoption.

    Abstract Research examining electric vehicle (EV) adoption intent is growing, but neglects rideshare drivers (e.g., Uber & Lyft), who drive up to 13% of all miles driven in metropolitan areas. This study uses Noppers’ framework on sustainable innovation adoption to compare rideshare drivers to commuters on symbolic and instrumental attribute (e.g., costs, maintenance) perceptions, and their influences on EV adoption intent. It is possible that rideshare drivers have stronger symbolic attribute ratings than commuters because more people see their vehicles for extended periods of time, so what the vehicle signals about drivers may be more salient to drivers; alternatively, because rideshare drivers use their vehicles for work, drivers may perceive vehicles in primarily instrumental terms. We test these competing hypotheses using a survey of 136 rideshare drivers and 378 commuters. Relative to commuters, rideshare drivers rated symbolic attributes higher and EV maintenance costs somewhat more favorably, but significantly fewer had access to a charging spot. A regression model found that both instrumental and symbolic attributes positively predicted EV adoption intent for both groups. Relative to commuters, symbolic attributes were weaker predictors of EV adoption intent among rideshare drivers, whereas instrumental attributes, particularly perceptions that the purchase cost of an EV was lower than that of a gasoline car, were stronger.


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    Titel :

    Predictors of electric vehicle adoption intent in rideshare drivers relative to commuters


    Beteiligte:
    Rye, Jamie (Autor:in) / Sintov, Nicole D. (Autor:in)


    Erscheinungsdatum :

    2023-12-12




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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