TY - JOUR
T1 - Toward a geographic understanding of the sharing economy
T2 - Systemic biases in UberX and TaskRabbit
AU - Thebault-Spieker, Jacob
AU - Terveen, Loren
AU - Hecht, Brent
N1 - Funding Information:
This research was supported by NSF Grants IIS-1707296, IIS-1218826, IIS-0808692 and a Microsoft FUSE Sharing Economy Research Award.
Publisher Copyright:
© 2017 ACM.
PY - 2017/4
Y1 - 2017/4
N2 - Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography-distance decay, structured variation in population density, mental maps, and "the Big Sort" (spatial homophily)-manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.
AB - Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography-distance decay, structured variation in population density, mental maps, and "the Big Sort" (spatial homophily)-manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.
KW - Geography residential segregation big sort mental maps distance decay population density location-aware computing
KW - Mobile crowdsourcing
KW - Sharing economy
UR - http://www.scopus.com/inward/record.url?scp=85018780705&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018780705&partnerID=8YFLogxK
U2 - 10.1145/3058499
DO - 10.1145/3058499
M3 - Article
AN - SCOPUS:85018780705
SN - 1073-0516
VL - 24
JO - ACM Transactions on Computer-Human Interaction
JF - ACM Transactions on Computer-Human Interaction
IS - 3
M1 - 21
ER -