Recent years have seen the growth of physical crowdsourcing systems (e.g., Uber; TaskRabbit) that motivate large numbers of people to provide new and improved physical tasking and delivery services on-demand. In these systems, opportunistically relying on people to make convenient contributions may lead to incomplete solutions, while directing people to do inconvenient tasks requires high incentives. To increase people’s willingness to participate and reduce the need to incentivize participation, we study on-the-go crowdsourcing as an alternative approach that suggests tasks along people’s existing routes that are conveniently on their way. We explore as a first step in this paper the design of task notification policies that decide when, where, and to whom to suggest tasks. Situating our work in the context of practical problems such as package delivery and lost-and-found searches, we conducted controlled experiments that show how small changes in task notification policy can influence individual participation and actions in significant ways that in turn affect system outcomes. We discuss the implications of our findings on the design of future on-the-go crowdsourcing technologies.
|Original language||English (US)|
|Title of host publication||Proceedings, The Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2016)|
|Editors||Arpit Ghosh, Matthew Lease|
|Number of pages||10|
|State||Published - 2016|
Kim, Y., Harburg, E., Azria, S., Shaw, A. D., Gerber, E. M., Gergle, D. R., & Zhang, H. (2016). Studying the Effects of Task Notification Policies on Participation and Outcomes in On-the-Go Crowdsourcing. In A. Ghosh, & M. Lease (Eds.), Proceedings, The Fourth AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2016) (pp. 99-108). AAAI.