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Racialised rider experiences in Uber: Evidence from an audit-based field study

  • Awais Piracha
  • , Karen Connelly
  • , Jehonathan Ben
  • , Kevin Dunn
  • , Craig McGarty
  • , Yin Paradies
  • , Amanuel Elias
  • , Susanna Rouillard
  • Centre for the Study of Contemporary Muslim Societies
  • Western Sydney University
  • Comillas Pontifical University
  • Deakin University

Research output: Contribution to journal › Article › peer-review

Abstract

This paper examines how race and ethnicity shape service outcomes and rider experiences in Uber in Melbourne, Australia. It reports one of the first Australian field studies of rider experiences in a ride sharing platform, using trained field testers from Anglo Australian, East Asian Australian and African Australian backgrounds. Between January and February 2023, testers undertook 447 Uber rides and recorded wait times, cancellations, route directness, driver tone and race related comments. The study combines audit-based field testing with naturalistic observation of real-world ride share interactions. The findings show little evidence of ethnicity-based difference in access outcomes. Wait times were similar across groups, cancellation rates were not higher for African or East Asian riders, and route deviations were infrequent. Differences were more evident in interpersonal experience. African riders reported less favourable driver tone, while African and East Asian riders were more likely than Anglo riders to report race related comments. These findings suggest that racialisation in this setting was less visible in-service refusal than in conversation, tone, assumption and comfort during the ride. The paper argues that platform mediated mobility should be assessed not only through access measures, but also through the quality of interaction once access is provided. The study also highlights important methodological trade-offs in conducting field research within commercial ride sharing platforms. Its real-world design strengthens ecological validity, but limits causal inference because rides were not matched across identical geographies, routes and operating conditions. Future research should incorporate stronger geographic controls, driver side analysis and more granular platform data.

Original languageEnglish
Article number107607
JournalCities
Volume179
DOIs
Publication statusPublished - Dec 2026

Bibliographical note

Publisher Copyright:
Copyright © 2026. Published by Elsevier Ltd.

Keywords

  • Ethnicity
  • Ridesharing
  • Sharing economy
  • Transport equity
  • Uber

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