Peeking Into the On-Demand Economy: Crowd Behavior and Incentive Design
AbstractAn increasing number of digital and mobile technologies have emerged today to match customers, in almost real time, with a potentially global pool of self-employed labor, leading to the rise of the on-demand economy, which has brought about dramatic changes in our society. It creates new business models and new dynamics of labor allocation. It enables new models of computation, that is, human-in-the-loop computing. And it leads to new forms of knowledge creation---people all over the world are contributing to scientific studies in dozens of fields, either by making scientific observations as amateur scientists or by participating in online experiments as subjects. Despite its already significant impact, the on-demand economy has still been considered as a black-box approach to soliciting labor from a crowd of on-demand workers. Little is known about how the on-demand economy works and how it can work better.
In this dissertation, using one of the leading on-demand crowdsourcing platforms---Amazon Mechanical Turk---as an example, I present my findings in opening up the black box of on-demand economy. I investigate two lines of problems in this dissertation: first, I focus on understanding who the crowd of on-demand workers are and how they behave in on-demand work; second, I explore how effective incentives can be designed for on-demand work. Through a set of experimental studies, I provide a more precise picture of the on-demand workers, showing that they display significant temporal variations, value social interactions, and desire more flexibility and autonomy. Furthermore, based on a combination of experimental, computational and design methods, I also show the effectiveness of extrinsic financial incentives in influencing on-demand workers, the feasibility of algorithmically controlling the provision of monetary rewards in a session of on-demand tasks in a cost-efficient way, as well as the potential of incorporating intrinsic motivator like curiosity in on-demand work through clever designs of task interfaces.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:39987873
- FAS Theses and Dissertations