Exploring task properties in crowdsourcing - An empirical study on Mechanical Turk

In the last years, crowdsourcing has emerged as a new approach for outsourcing work to a large number of human workers in the form of an open call. Amazon's Mechanical Turk (MTurk) enables requesters to efficiently distribute micro tasks to an unknown workforce which selects and processes them for small financial rewards. While worker behavior and demographics as well as task design and quality management have been studied in detail, more research is needed on the relationship between workers and task design. In this paper, we conduct a series of explorative studies on task properties on MTurk. First, we identify properties that may be relevant to workers' task selection through qualitative and quantitative preliminary studies. Second, we provide a quantitative survey with 345 participants. As a result, the task properties are ranked and set into relation with the workers' demographics and background. The analysis suggests that there is little influence of education level, age, and gender. Culture may influence the importance of bonuses, however. Based on the explorative data analysis, five hypotheses for future research are derived. This paper contributes to a better understanding of task choice and implies that other factors than demographics influence workers' task selection.