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Rider-to-rider discriminatory attitudes and ridesharing behavior

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sword-2020-08-31T12:06:52.original.xml (130 B)
Original SWORD entry document
Author(s)
Moody, Joanna Charlotte
•
Middleton, Scott (Scott Russell)
•
Zhao, Jinhua
Date Issued
April 2019
Journal
Transportation Research Part F: Traffic Psychology and Behaviour
Publisher
Elsevier BV
Citation
Moody, Joanna et al. "Rider-to-rider discriminatory attitudes and ridesharing behavior." Transportation Research Part F: Traffic Psychology and Behaviour 62 (April 2019): 258-273 © 2019 Elsevier Ltd
Version
Author's final manuscript
Abstract
Using online survey data from N = 2041 Uber and Lyft users in the United States collected in 2016 and 2018, this paper establishes the validity, reliability, and invariance of a measure of rider-to-rider race and social class discrimination. This measure is then incorporated into three structural models that investigate associations between rider-to-rider discriminatory attitudes and four aspects of ridesharing behavior. We find no significant relationship between rider-to-rider discriminatory attitudes and whether a TNC user has ever used a ridesharing service (such as uberPOOL or Lyft Line). However, among those who have used ridesharing services before, rider-to-rider discriminatory attitudes are strongly negatively predictive of an individual's level of satisfaction with the sharing option, and marginally negatively predictive of an individual's percentage of shared TNC trips. Furthermore, among those who have not yet used ridesharing services, rider-to-rider discriminatory attitudes are strongly negatively predictive of willingness to consider using uberPOOL or Lyft Line in the future. Together, these findings suggest that rider-to-rider discriminatory attitudes may discourage sustained and frequent use of ridesharing services among TNC users. Further research is required to identify strategies for addressing discriminatory attitudes in the ridesharing context and overcoming reluctance to sharing.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
http://creativecommons.org/licenses/by-nc-nd/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/127268
DOI of Published Version
https://doi.org/10.1016/j.trf.2019.01.003
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