Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets

TL;DR

This study experimentally shows that framing tasks with meaningful context increases worker participation and output quantity in crowdsourcing, while negative framing reduces quality, highlighting the importance of task design.

Contribution

First natural field experiment demonstrating how task framing affects effort and quality in crowdsourcing markets like MTurk.

Findings

Meaningful framing increases participation and output quantity.

Shredded framing decreases output quality.

Task framing significantly influences worker effort and performance.

Abstract

We conduct the first natural field experiment to explore the relationship between the "meaningfulness" of a task and worker effort. We employed about 2,500 workers from Amazon's Mechanical Turk (MTurk), an online labor market, to label medical images. Although given an identical task, we experimentally manipulated how the task was framed. Subjects in the meaningful treatment were told that they were labeling tumor cells in order to assist medical researchers, subjects in the zero-context condition (the control group) were not told the purpose of the task, and, in stark contrast, subjects in the shredded treatment were not given context and were additionally told that their work would be discarded. We found that when a task was framed more meaningfully, workers were more likely to participate. We also found that the meaningful treatment increased the quantity of output (with an insignificant change in quality) while the shredded treatment decreased the quality of output (with no change in quantity). We believe these results will generalize to other short-term labor markets. Our study also discusses MTurk as an exciting platform for running natural field experiments in economics.