abstract

Acceptance of Self-Driving Cars: Does Their Posthuman Ability Make Them More Eerie or More Desirable?

Authors:

Andrew Gambino,

S. Shyam SundarAuthors Info & Claims

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: LBW2513, Pages 1 - 6

https://doi.org/10.1145/3290607.3312870

Published: 02 May 2019 Publication History

Get Access

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 such autonomous technology a reality, considerable design work is needed to motivate their mass adoption. What are the key predictors of people's acceptance of self-driving cars? Is it the ease of use or coolness aspect? Is it the degree of 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.

References

[1]

Fred D. Davis, Richard P. Bagozzi and Paul R. Warshaw. 1989. User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35: 982--1003.

Crossref

Google Scholar

[2]

Haiyan Jia, Mu Wu, Eun H. Jung, Alice Shapiro, and S. Shyam Sundar. 2012. Balancing human agency and object agency: an end-user interview study of the internet of things. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (pp. 1185--1188). ACM.

Digital Library

Google Scholar

[3]

Mishel Johns et al. 2016. Exploring shared control in automated driving. In Human-Robot Interaction (HRI) ACM/IEEE International Conference (pp. 91--98). IEEE.

Digital Library

Google Scholar

[4]

Ki-Joon Kim, Dong-Hee Shin, and Eunil Park. 2015. Can coolness predict technology adoption? Effects of perceived coolness on user acceptance of smartphones with curved screens. Cyberpsychology, Behavior, and Social Networking, 18: 528533.

Crossref

Google Scholar

[5]

Keith Kirkpatrick. 2015. The moral challenges of driverless cars. Communications of the ACM, 58: 19--20

Digital Library

Google Scholar

[6]

Masahiro Mori, Karl F. MacDorman, and Norri Kageki, 2012. The uncanny valley {from the field}. IEEE Robotics & Automation Magazine, 19: 98--100.

Crossref

Google Scholar

[7]

Andreas Riener, 2014. Who cares about trust, grade of traveling & quality of user experience in a world of autonomous cars? In Proceedings of the Automotive User Interfaces and Interactive Vehicular Applications (pp. 1--3). ACM.

Digital Library

Google Scholar

[8]

S. Shyam Sundar, Haiyan Jia, T. Franklin Waddell and Yan Huang. 2015. Toward a theory of interactive media effects (TIME). The handbook of the psychology of communication technology, 47--86.

Google Scholar

[9]

S. Shyam Sundar, Daniel J. Tamul, and Mu Wu. 2014. Capturing "cool": Measures for assessing coolness of technological products. International Journal of Human-Computer Studies, 72: 169--180.

Digital Library

Google Scholar

[10]

Noel Sharkey and Amanda Sharkey. 2010. The crying shame of robot nannies: an ethical appraisal. Interaction Studies, 11: 161--190.

Crossref

Google Scholar

[11]

Carmen Stavrositu and S. Shyam Sundar, 2012. Does blogging empower women? Exploring the role of agency and community. Journal of Computer-Mediated Communication, 17: 369--386.

Digital Library

Google Scholar

[12]

T. Franklin Waddell, S. Shyam Sundar, and Eun H. Jung. 2014. The young and the vulnerable?: Perceived negative effects of robots on youngsters prevent older adults from adopting companion robots. In Proceedings of the extended abstracts of the 32nd annual ACM conference on Human factors in computing systems (pp. 1981--1986). ACM.

Digital Library

Google Scholar

[13]

Artificial Intelligence and Life in 2030: 100 year study on AI. 2016 report.

Google Scholar

[14]

Dolf Zillmann. 1988. Mood management through communication choices. American Behavioral Scientist, 31: 327--340.

Crossref

Google Scholar

Cited By

View all

Derakhshan SNosrat Nezami FWächter MStephan APipa GKönig P(2024)A Situated Inspection of Autonomous Vehicle Acceptance – A Population Study in Virtual RealityInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2358577(1-20)Online publication date: 3-Jun-2024
https://doi.org/10.1080/10447318.2024.2358577
Mosaferchi SCalifano RNaddeo A(2024)Shed Light on the Path of Human-Machine Interaction in Autonomous Vehicles: Where Did We Come from, Where We Are Going? Part I, State of the ArtDesign Tools and Methods in Industrial Engineering III10.1007/978-3-031-58094-9_33(301-309)Online publication date: 7-May-2024
https://doi.org/10.1007/978-3-031-58094-9_33
Silberer JAstfalk SPlaning PMüller P(2023)User needs over time: the market and technology maturity model (MTMM)Journal of Innovation and Entrepreneurship10.1186/s13731-023-00302-212:1Online publication date: 7-Jun-2023
https://doi.org/10.1186/s13731-023-00302-2
Show More Cited By

Index Terms

Acceptance of Self-Driving Cars: Does Their Posthuman Ability Make Them More Eerie or More Desirable?
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

Recommendations

Integrating guanxi into technology acceptance

A research model of continuance intention on mobile social-messaging applications based on guanxi theory is proposed.Partial least squares path modelling was employed to test the proposed hypotheses.Guanxi influences continuance intention, perceived ...
Efficient parking control algorithms for self-driving cars

We explored the problems which will soon arise while parking in car parks. These include structure of parking lot suitable for autonomous cars, finding the closest parking slot available, and navigation to the location. In this paper, we explored the ...
Examining Healthcare Providers' Acceptance of Data From Patient Self-Monitoring Devices Using Structural Equation Modeling With the UTAUT2 Model

As wide-scale adoption by the market and consumers of ubiquitous devices or mobile apps that track fitness, sleep, nutrition, and basic metabolic parameters increases, it is vital to understand the attitudes of healthcare providers toward these devices. ...

Comments

Information & Contributors

Information

Published In

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

3673 pages

ISBN:9781450359719

DOI:10.1145/3290607

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2019

Check for updates

Author Tags

Qualifiers

Abstract

Conference

CHI '19

Sponsor:

SIGCHI

CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
991
Total Downloads

Downloads (Last 12 months)89
Downloads (Last 6 weeks)22

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Derakhshan SNosrat Nezami FWächter MStephan APipa GKönig P(2024)A Situated Inspection of Autonomous Vehicle Acceptance – A Population Study in Virtual RealityInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2358577(1-20)Online publication date: 3-Jun-2024
https://doi.org/10.1080/10447318.2024.2358577
Mosaferchi SCalifano RNaddeo A(2024)Shed Light on the Path of Human-Machine Interaction in Autonomous Vehicles: Where Did We Come from, Where We Are Going? Part I, State of the ArtDesign Tools and Methods in Industrial Engineering III10.1007/978-3-031-58094-9_33(301-309)Online publication date: 7-May-2024
https://doi.org/10.1007/978-3-031-58094-9_33
Silberer JAstfalk SPlaning PMüller P(2023)User needs over time: the market and technology maturity model (MTMM)Journal of Innovation and Entrepreneurship10.1186/s13731-023-00302-212:1Online publication date: 7-Jun-2023
https://doi.org/10.1186/s13731-023-00302-2
Brown BBroth MVinkhuyzen E(2023)The Halting problem: Video analysis of self-driving cars in trafficProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581045(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581045
Liu CZhou SLiu DLi JHuang ZMa X(2023)CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA PlatformsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580932(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580932
Mou YMeng X(2023)Alexa, it is creeping over me – Exploring the impact of privacy concerns on consumer resistance to intelligent voice assistantsAsia Pacific Journal of Marketing and Logistics10.1108/APJML-10-2022-086936:2(261-292)Online publication date: 26-Jul-2023
https://doi.org/10.1108/APJML-10-2022-0869
Park JLee KKim H(2023)Integrated Recognition Assistant Framework Based on Deep Learning for Autonomous Driving: Human-Like Restoring Damaged Road Sign InformationInternational Journal of Human–Computer Interaction10.1080/10447318.2023.220427440:15(3982-4002)Online publication date: 27-Apr-2023
https://doi.org/10.1080/10447318.2023.2204274
Parekh DPoddar NRajpurkar AChahal MKumar NJoshi GCho W(2022)A Review on Autonomous Vehicles: Progress, Methods and ChallengesElectronics10.3390/electronics1114216211:14(2162)Online publication date: 11-Jul-2022
https://doi.org/10.3390/electronics11142162
Mara MMeyer K(2022)Acceptance of Autonomous Vehicles: An Overview of User-Specific, Car-Specific and Contextual DeterminantsUser Experience Design in the Era of Automated Driving10.1007/978-3-030-77726-5_3(51-83)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-77726-5_3
Woźniak PKarolus JLang FEckerth CSchöning JRogers YNiess JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Creepy Technology:What Is It and How Do You Measure It?Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445299(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445299

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Cited By

Index Terms

Recommendations

Integrating guanxi into technology acceptance

Efficient parking control algorithms for self-driving cars

Examining Healthcare Providers' Acceptance of Data From Patient Self-Monitoring Devices Using Structural Equation Modeling With the UTAUT2 Model

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

HTML Format

Share

Share this Publication link

Share on social media

Affiliations