دانلود مقاله ISI انگلیسی شماره 5133
عنوان فارسی مقاله

شکستن یکنواختی با معنا : انگیزش در بازارهای منبع یابی جمعیت

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
5133 2013 11 صفحه PDF سفارش دهید 7410 کلمه
خرید مقاله
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عنوان انگلیسی
Breaking monotony with meaning: Motivation in crowdsourcing markets
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Economic Behavior & Organization, Volume 90, June 2013, Pages 123–133

کلمات کلیدی
آزمایش مزرعه طبیعی - انگیزه کارگر - منبع یابی جمعیت - بازارهای کار آنلاین
پیش نمایش مقاله
پیش نمایش مقاله شکستن یکنواختی با معنا : انگیزش در بازارهای منبع یابی جمعیت

چکیده انگلیسی

We conduct the first natural field experiment to explore the relationship between the “meaningfulness” of a task and worker effort. We employed about 2500 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.

مقدمه انگلیسی

Economists, philosophers, and social scientists have long recognized that non-pecuniary factors are powerful motivators that influence choice of occupation. For a multidisciplinary literature review on the role of meaning in the workplace, we recommend Rosso et al. (2010). Previous studies in this area have generally been based on ethnographies, observational studies, or laboratory experiments. For instance, Wrzesniewski et al. (1997) used ethnographies to classify work into jobs, careers, or callings. Using an observation study, Preston (1989) demonstrated that workers may accept lower wages in the non-profit sector in order to produce goods with social externalities. Finally, Ariely et al. (2008) showed that labor had to be both recognizable and purposeful to have meaning. In this paper, we limit our discussion to the role of meaning in economics, particularly through the lens of competing differentials. We perform the first natural field experiment (Harrison and List, 2004) in a real effort task that manipulates levels of meaningfulness. This method overcomes a number of shortcomings of the previous literature, including: interview bias, omitted variable bias, and concerns of external validity beyond the laboratory. We study whether employers can deliberately alter the perceived “meaningfulness” of a task in order to induce people to do more and higher quality work and thereby work for a lower wage. We chose a task that would appear meaningful for many people if given the right context—helping cancer researchers mark tumor cells in medical images. Subjects in the meaningful treatment were told the purpose of their task is to “help researchers identify tumor cells;” subjects in our zero-context group were not given any reason for their work and the cells were instead referred to as mere “objects of interest” and laborers in the shredded group were given zero context but also explicitly told that their labelings would be discarded upon submission. Hence, the pay structure, task requirements, and working conditions were identical, but we added cues to alter the perceived meaningfulness of the task. We recruited workers from the United States and India from Amazon's Mechanical Turk (MTurk), an online labor market where people around the world complete short, “one-off” tasks for pay. The MTurk environment is a spot market for labor characterized by relative anonymity and a lack of strong reputational mechanisms. As a result, it is well-suited for an experiment involving the meaningfulness of a task since the variation we introduce regarding a task's meaningfulness is less affected by desires to exhibit pro-social behavior or an anticipation of future work (career concerns). We ensured that our task appeared like any other task in the marketplace and was comparable in terms of difficulty, duration, and wage. Our study is representative of the kinds of natural field experiments for which MTurk is particularly suited. Section 2.2 explores MTurk's potential as a platform for field experimentation using the framework proposed in [Levitt and List, 2007] and [Levitt and List, 2009]. We contribute to the literature on compensating wage differentials (Rosen, 1986) and the organizational behavioral literature on the role of meaning in the workplace (Rosso et al., 2010). Within economics, Stern (2004) provides quasi-experimental evidence on compensating differentials within the labor market for scientists by comparing wages for academic and private sector job offers among recent Ph.D. graduates. He finds that “scientists pay to be scientists” and require higher wages in order to accept private sector research jobs because of the reduced intellectual freedom and a reduced ability to interact with the scientific community and receive social recognition. Ariely et al. (2008) use a laboratory experiment with undergraduates to vary the meaningfulness of two separate tasks: (1) assembling Legos and (2) finding 10 instances of consecutive letters from a sheet of random letters. Our experiment augments experiment 1 in Ariely et al. (2008) by testing whether their results extend to the field. Additionally, we introduce a richer measure of task effort, namely task quality. Where our experiments are comparable, we find that our results parallel theirs. We find that the main effects of making our task more meaningful is to induce a higher fraction of workers to complete our task, hereafter dubbed as “induced to work.” In the meaningful treatment, 80.6 percent of people labeled at least one image compared with 76.2 percent in the zero-context and 72.3 percent in the shredded treatments. After labeling their first image, workers were given the opportunity to label additional images at a declining piecerate. We also measure whether the treatments increase the quantity of images labeled. We classify participants as “high-output” workers if they label five or more images (an amount corresponding to roughly the top tercile of those who label) and we find that workers are approximately 23 percent more likely to be high-output workers in the meaningful group. We introduce a measure of task quality by telling workers the importance of accurately labeling each cell by clicking as close to the center as possible. We first note that MTurk labor is high quality, with an average of 91 percent of cells found. The meaning treatment had an ambiguous effect, but the shredded condition in both countries lowered the proportion of cells found by about 7 percent. By measuring both quantity and quality we are able to observe how task effort is apportioned between these two “dimensions of effort.” Do workers work “harder” or “longer” or both? We found an interesting result: the meaningful condition seems to increase quantity without a corresponding increase in quality and the shredded treatment decreases quality without a corresponding decrease in quantity. Investigating whether this pattern generalizes to other domains may be a fruitful future research avenue. Finally, we calculate participants’ average hourly waged based on how long they spent on the task. We find that subjects in the meaningful group work for $1.34 per hour, which is 6 cents less per hour than zero context participants and 14 cents less per hour than shredded condition participants. We expect our findings to generalize to other short-term work environments such as temporary employment or piecework. In these environments, employers may not consider that non-pecuniary incentives of meaningfulness matter; we argue that these incentives do matter, and to a significant degree. Section 2 provides background on MTurk and discusses its use as a platform for conducting economic field experiments. Section 3 describes our experimental design. Section 4 presents our results and discussion and Section 5 concludes. Appendix A provides full details on our experimental design and Appendix B is a technical appendix for conducting experiments using the MTurk platform. Both appendices are available online.

نتیجه گیری انگلیسی

Our experiment is the first that uses a natural field experiment in a real labor market to examine how a task's meaningfulness influences labor supply. Overall, we found that the greater the amount of meaning, the more likely a subject is to participate, the more output they produce, the higher quality output they produce, and the less compensation they require for their time. We also observe an interesting effect: high meaning increases quantity of output (with an insignificant increase in quality) and low meaning decreases quality of output (with no change in quantity). It is possible that the level of perceived meaning affects how workers substitute their efforts between task quantity and task quality. The effect sizes were found to be the same in the US and India. Our finding has important implications for those who employ labor in any short-term capacity besides crowdsourcing, such as temp-work or piecework. As the world begins to outsource more of its work to anonymous pools of labor, it is vital to understand the dynamics of this labor market and the degree to which non-pecuniary incentives matter. This study demonstrates that they do matter, and they matter to a significant degree. This study also serves as an example of what MTurk offers economists: an excellent platform for high internal validity natural field experiments while evading the external validity problems that may occur in laboratory environments.

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