論文アブストラクト： Uber is a ride-sharing platform that is part of the 'gig-economy,' where the platform supports and coordinates a labor market in which there are a large number of ephemeral, piecemeal jobs. Despite numerous efforts to understand the impacts of these platforms and their algorithms on Uber drivers, how to better serve and support drivers with these platforms remains an open challenge. In this paper, we frame Uber through the lens of Stakeholder Theory to highlight drivers' position in the workplace, which helps inform the design of a more ethical and effective platform. To this end, we analyzed Uber drivers' forum discussions about their lived experiences of working with the Uber platform. We identify and discuss the impact of the stakes that drivers have in relation to both the Uber corporation and their passengers, and look at how these stakes impact both the platform and drivers' practices.
論文アブストラクト： Algorithms increasingly mediate how work is evaluated in a wide variety of work settings. Drawing on our interviews with 15 Airbnb hosts, we explore the impact of algorithmic evaluation on users and their work practices in the context of Airbnb. Our analysis reveals that Airbnb hosts engage in a double negotiation on the platform: They must negotiate efforts not just to attract potential guests but also to appeal to only partially transparent evaluative algorithms. We found that a perceived lack of control and uncertainty over how algorithmic evaluation works can create anxiety among some Airbnb hosts. We present a framework for understanding this double negotiation, as well as a case study of coping strategies that hosts employ to deal with their anxiety. We conclude with a discussion of design solutions that can help reduce algorithmic anxiety and increase confidence in algorithmic systems.
論文アブストラクト： With a seemingly endless stream of tasks, on-demand labor markets appear to offer workers flexibility in when and how much they work. This research argues that platforms afford workers far less flexibility than widely believed. A large part of the "inflexibility" comes from tight deadlines imposed on tasks, leaving workers little control over their work schedules. We experimentally examined the impact of offering workers control of their time in on-demand crowdwork. We found that granting higher "in-task flexibility" dramatically affected the temporal dynamics of worker behavior and produced a larger amount of work with similar quality. In a second experiment, we measured the compensating differential and found that workers would give up significant compensation to control their time, indicating workers attach substantial value to in-task flexibility. Our results suggest that designing tasks which give workers direct control of their time within tasks benefits both buyers and sellers of on-demand crowdwork.