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A total of 296 participants were presented with information on this technology and then asked to complete a questionnaire on their perceptions to gauge their behavioral intention concerning acceptance. Structural equation modeling of the partial least squares flavor (PLS-SEM) was employed to analyze the collected data. The results indicated that the perceived usefulness of the technology played the greatest role in end-user acceptance decisions, followed by the influence of others, and then the enjoyment received by interacting with the technology. Furthermore, the perception of risk associated with using autonomous delivery vehicles for last-mile delivery led to a decrease in acceptance. However, most participants did not perceive the use of this technology to be risky. The paper concludes by summarizing the implications our findings have on the respective stakeholders, and proposing the next steps in this area of research.<\/jats:p>","DOI":"10.1007\/978-3-031-18158-0_37","type":"book-chapter","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T14:26:17Z","timestamp":1666362377000},"page":"519-532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Investigating End-User Acceptance of Last-Mile Delivery by Autonomous Vehicles in the United States"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6745-810X","authenticated-orcid":false,"given":"Antonios","family":"Saravanos","sequence":"first","affiliation":[]},{"given":"Olivia","family":"Verni","sequence":"additional","affiliation":[]},{"given":"Ian","family":"Moore","sequence":"additional","affiliation":[]},{"given":"Aboubacar","family":"Sall","sequence":"additional","affiliation":[]},{"given":"Jen","family":"Arriaza","sequence":"additional","affiliation":[]},{"given":"Sabrina","family":"Jivani","sequence":"additional","affiliation":[]},{"given":"Audrey","family":"Bennett","sequence":"additional","affiliation":[]},{"given":"Siqi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Dongnanzi","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Stavros","family":"Zervoudakis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.jom.2017.06.001","volume":"53\u201356","author":"J Abbey","year":"2017","unstructured":"Abbey, J., Meloy, M.: Attention by design: using attention checks to detect inattentive respondents and improve data quality. 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