标题： 打破单调性，赋予意义：众包市场中的动机 显示英文标题

标题： Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets

摘要： 我们进行了首个自然实地实验，以探索任务的“意义感”与工人努力之间的关系。我们从亚马逊的机械土耳其人（MTurk）雇佣了大约2,500名工人来标注医学图像。尽管任务相同，但我们实验性地操纵了任务的表述方式。有意义处理组的受试者被告知他们正在标注肿瘤细胞以协助医学研究人员，零上下文条件组（对照组）的受试者未被告知任务的目的，而截然相反的是，被撕碎处理组的受试者没有上下文信息且被告知他们的工作将会被丢弃。我们发现，当任务表述得更有意义时，工人更有可能参与。我们还发现，有意义的处理增加了产出的数量（质量变化不显著），而被撕碎的处理降低了产出的质量（数量无变化）。我们认为这些结果将在其他短期劳动力市场中得到推广。我们的研究还讨论了MTurk作为经济学中运行自然实地实验的一个令人兴奋的平台。 显示英文摘要

摘要： 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.