A Required Work Payment Scheme for Crowdsourced Disaster Response: Worker Performance and Motivations

Crowdsourcing is an increasingly popular approach for processing data in response to disasters. While volunteer crowdsourcing may suffice for high-profile disasters, paid crowdsourcing may be necessary to recruit workers for less prominent events. Thus, understanding the impact of payment schemes on worker behavior and motivation may improve outcomes. In this work, we presented workers recruited from Amazon Mechanical Turk with a disaster response task in which they could provide a variable number of image ratings. We paid workers a fixed amount to provide a minimum number of image ratings, allowing them to voluntarily provide more if desired; this allowed us to examine the impact of different amounts of required work. We found that requiring no ratings resulted in workers voluntary completing more work, and being more likely to indicate motivation related to interest on a post survey, than when small numbers of ratings were required. This is consistent with the motivational crowding-out effect, even in paid crowdsourcing. We additionally found that providing feedback on progress positively impacted the amount of work done.