On-Demand Labor: Tactics of Flexibility and Control

This poster explores the perspectives of workers in on-demand positions man-aged through online platforms. Our interview-based study considers how Uber drivers perceive their employment status, how they view Uber’s management tactics and how they modify their work practices in order to control their schedules and wages. We found that workers tended to view themselves as independent contractors and had strong understandings of the tactics used by Uber to manage the supply of drivers. However, we also found that they do not view their responses to these tactics as a form of resistance. Instead of seeing their employment status as a problem that calls for collective action, drivers were primarily interested in increasing their own wages

What are the Concerns of Using a Ride-Sharing Service?: An Investigation of Uber

This study investigates why people do or do not use a ride-sharing service (RSS). We investigate users’ perceptions on three main concerns (security, safety, and surcharge justification), and their relationship to the actual usage of a RSS. We also propose two internal mechanisms (reference systems and policy changes) influencing the main factors of RSS use. We used Uber as a target RSS and gathered preliminary survey data. Our results reveal that safety is a significant factor for RSS use, and policy changes can reduce the effect of surcharge justification on RSS use. These preliminary findings support our arguments on the concerns of RSS use. This research in progress will be theoretically and empirically extended in the near future

Digital Forensics for Mobility as A Service Platform: Analysis of Uber Application on iPhone and Cloud

Uber is a ride-hailing smartphone application (app) that allows users to order a ride in a highly efficient manner. The Uber app provides Mobility as a Service and allows users to easily order a ride in a private car with just a few clicks. Uber stores large amounts of data on both the mobile device the app is being used on, and in the cloud. Examples of this data include geolocation data, date/time, origin/destination addresses, departure/arrival times, and distance. Uber geolocation data has been previously researched to investigate the privacy of the Uber app; however, there is minimal research relating to the other data the Uber app collects. Because this data could be of significance in a forensic investigation, it is important to determine where the majority of the Uber data are stored, either in the cloud or on the mobile device itself, and if one of these storage locations contains more information than the other. In this study, we analyzed the Uber app by forensically imaging the iPhone running the Uber app in three different acquisition phases. The different acquisitions allowed us to compare the data before and after data population, determine where the majority of the Uber data are stored, and determine if jailbreaking the iPhone provided more data than the previous acquisitions. Obtaining and analyzing the data in this study was done using Magnet AXIOM and Cellebrite forensic software suites

Surge pricing on a service platform under spatial spillovers: evidence from Uber

Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with demand. We develop an optimization model to characterize the relationship between surge price and spillover. We test predicted relationships using a spatial panel model on a dataset from Ubers operation. Results reveal that Ubers pricing accounts for both capacity and price spillover. There is a debate in the management community on the ecacy of labor welfare mechanisms associated with shared capacity. We conduct counterfactual analysis to provide guidance in regards to the debate, for managing congestion, while accounting for consumer and labor welfare through this online platform.First author draf

Representing Precarious Work in the Sharing Economy through (De)motivations of Uber Contractors

The potential contribution of sharing economy to the global economy is increasing. Despite this, there are unanswered questions related to corporate social responsibility in the sharing economy. In this article, we draw from a case study on Uber and its contractors to understand the precarious work entangled to their relationship in two contexts. By unpacking the motivating and demotivating dimensions of this relationship in two context, the United States and Finland, we explore the underlying phenomenon of precarious work in the context of sharing economy

Shifting from Driving to Riding: a study of the impacts of on-demand cab services on public transit ridership and vehicle ownership in Hyderabad, India

This thesis explores the effects of on-demand cab services, Uber and Ola, on public transit ridership and vehicle ownership in Hyderabad, India. India has grown to be Uber’s third largest market in the world but still lacks any comprehensive policies at the federal level to regulate on-demand cab services. These services have risen in popularity and have led to the evolution of new ownership and financial models to help populations afford a car to “drive to work.” This research examines the spatial effects of this rising popularity on public transit ridership and vehicle ownership in Hyderabad. This research found that there has been a shift in the proportion of on-demand cabs and cars to all vehicles from 2010 to 2016. This research also found that annual occupancy ratio along bus routes in the city has decreased from 2014 to 2016. This research found that these relationships are localized in the city. This thesis concludes by recommending further studies be carried out to understand the full extent of these effects to effectively incorporate these technologies and plan for the future mobility of city residents

Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges.

As modern transportation systems become more complex, there is need for mobile applications that allow travelers to navigate efficiently in cities. In taxi transport the recent proliferation of Uber has introduced new norms including a flexible pricing scheme where journey costs can change rapidly depending on passenger demand and driver supply. To make informed choices on the most appropriate provider for their journeys, travelers need access to knowledge about provider pricing in real time. To this end, we developed OpenStreetcab a mobile application that offers advice on taxi transport comparing provider prices. We describe its development and deployment in two cities, London and New York, and analyse thousands of user journey queries to compare the price patterns of Uber against major local taxi providers. We have observed large heterogeneity across the taxi transport markets in the two cities. This motivated us to perform a price validation and measurement experiment on the ground comparing Uber and Black Cabs in London. The experimental results reveal interesting insights: not only they confirm feedback on pricing and service quality received by professional drivers users, but also they reveal the tradeoffs between prices and journey times between taxi providers. With respect to journey times in particular, we show how experienced taxi drivers, in the majority of the cases, are able to navigate faster to a destination compared to drivers who rely on modern navigation systems. We provide evidence that this advantage becomes stronger in the centre of a city where urban density is high