Acceptance of self-driving cars: Does their posthuman ability make them more eerie or more desirable?

Andrew Gambino, S. Shyam Sundar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The arrival of self-driving cars and smart technologies is fraught with controversy, as users hesitate to cede control to machines for vital tasks. While advances in engineering have made autonomous technology a reality, design work is needed to motivate their mass adoption. What are the key predictors of acceptance of self-driving cars? Is it the ease of use or coolness aspect? Is it the perceived control for users? We decided to find out with a survey (N = 404) assessing acceptance of self-driving cars and discovered that the strongest predictor is “posthuman ability,” suggesting that individuals are much more accepting of technology that can clearly outclass human abilities.

Original languageEnglish (US)
Title of host publicationCHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450359719
DOIs
StatePublished - May 2 2019
Event2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019 - Glasgow, United Kingdom
Duration: May 4 2019May 9 2019

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019
CountryUnited Kingdom
CityGlasgow
Period5/4/195/9/19

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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    Gambino, A., & Shyam Sundar, S. (2019). Acceptance of self-driving cars: Does their posthuman ability make them more eerie or more desirable? In CHI EA 2019 - Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems [3312870] (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3290607.3312870