The ubiquity of smartphones has led to the emergence of mobile crowdsourcing markets, where smartphone users participate to perform tasks in the physical world. Mobile crowdsourcing markets are uniquely different from their online counterparts in that they require spatial mobility, and are therefore impacted by geographic factors and constraints that are not present in the online case. Despite the emergence and importance of such mobile marketplaces, little to none is known about the labor dynamics and mobility patterns of agents. This paper provides an in-depth exploration of labor dynamics in mobile task markets based on a year-long dataset from a leading mobile crowdsourcing platform. We find that a small core group of workers (< 10%) account for a disproportionately large proportion of activity (> 80%) generated in the market. We find that these super agents are more efficient than other agents across several dimensions: a) they are willing to move longer distances to perform tasks, yet they amortize travel across more tasks, b) they work and search for tasks more efficiently, c) they have higher data quality in terms of accepted submissions, and d) they improve in almost all of these efficiency measures over time. We find that super agent efficiency stems from two simple optimizations -they are 3× more likely than other agents to chain tasks and they pick fewer lower priced tasks than other agents. We compare mobile and online micro-task markets, and discuss differences in demographics, data quality, and time of use, as well as similarities in super agent behavior. We conclude with a discussion of how a mobile micro-task market might leverage some of our results to improve performance.
Musthag et al. REF analyzed the labor dynamics in the mobile micro task market space for a physical crowdsourcing based platform with respect to factors such as demographics and data quality.
Labor dynamics in a mobile micro-task market
CHI '13
Computer Science