The high demand and acute timeliness that characterizes instant delivery entail the challenges of high labor costs and an increase in courier traffic accidents. Autonomous delivery vehicles (ADVs) may serve as a key solution, with their attendant reduced labor input and higher efficiency. Customers play a key role in the successful implementation of ADVs on a large scale. However, understanding the factors that affect customers’ intentions to use ADVs is still limited. Compared to autonomous driving, ADV customers are ultimately not the real users, who only are served by ADVs during the last leg of a trip. On account of this, the Technology Acceptance Model (TAM) may not be well-fitted for explaining the dynamics involved in ADV adoption. Within the context of ADVs, our study identified influencing factors that have not been captured by prior studies. This study incorporates infection risk, use experience, and social awkwardness into the Diffusion of Innovation (DOI) theory to explore customers’ intentions to use ADVs. Data from 691 survey respondents were collected to validate the research design. The results demonstrate that compatibility, social influence, infection risk, green image, social awkwardness, and use experience all have a significantly positive impact on customers’ intentions to adopt ADV services, while complexity and perceived risk both exhibited a negative impact. But no effect could be found for relative advantage, which may be because of the fact that customers only need ADVs to meet their delivery demand. This study contributes to understanding customers’ adoption intentions toward ADVs, informing policymakers in formulating ADV regulations and standards, and promoting the large-scale application of ADVs in instant delivery services.


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    Title :

    Customer’s Adoption Intentions toward Autonomous Delivery Vehicle Services: Extending DOI Theory with Social Awkwardness and Use Experience


    Contributors:
    Miaojia Lu (author) / Chengyuan Huang (author) / Ran Wang (author) / Hao Li (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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