Toward a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit

被引:65
作者
Thebault-Spieker, Jacob [1 ,3 ]
Terveen, Loren [1 ,3 ]
Hecht, Brent [2 ,4 ]
机构
[1] Univ Minnesota, Minneapolis, MN 55455 USA
[2] Northwestern Univ, Evanston, IL 60208 USA
[3] 200 Union St SE, Minneapolis, MN 55455 USA
[4] 2240 N Campus Dr, Evanston, IL 60201 USA
基金
美国国家科学基金会;
关键词
Sharing economy; geography; residential segregation; big sort; mental maps; distance decay; population density; location-aware computing; mobile crowdsourcing; INFORMATION; NETWORK;
D O I
10.1145/3058499
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:40
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