Breaking monotony with meaning: Motivation in crowdsourcing markets

It is found that when a task was framed more meaningfully, workers were more likely to participate and the meaningful treatment increased the quantity of output while the shredded treatment decreased the quality of output.

Abstract

We conduct the first natural field experiment to explore the relationship\nbetween the "meaningfulness" of a task and worker effort. We employed about\n2,500 workers from Amazon's Mechanical Turk (MTurk), an online labor market, to\nlabel medical images. Although given an identical task, we experimentally\nmanipulated how the task was framed. Subjects in the meaningful treatment were\ntold that they were labeling tumor cells in order to assist medical\nresearchers, subjects in the zero-context condition (the control group) were\nnot told the purpose of the task, and, in stark contrast, subjects in the\nshredded treatment were not given context and were additionally told that their\nwork would be discarded. We found that when a task was framed more\nmeaningfully, workers were more likely to participate. We also found that the\nmeaningful treatment increased the quantity of output (with an insignificant\nchange in quality) while the shredded treatment decreased the quality of output\n(with no change in quantity). We believe these results will generalize to other\nshort-term labor markets. Our study also discusses MTurk as an exciting\nplatform for running natural field experiments in economics.\n