Dr. Yuqing Ren
Associate Professor, Information and Decision Sciences
Carlson School of Management Minneapolis, MN
Online marketplaces for work like Amazon Mechanical Turk serve as new platforms to source mundane yet important tasks such as cleaning data or tagging images. While these platforms provide a fast and cost effective way of getting work done, low payment and the lack of face-to-face contact make it difficult for job requesters to motivate and monitor workers. In this study, we explore task significance as a new approach to motivate workers and improve work quality by informing workers of the purpose of the task and who benefits from it. We conducted a laboratory experiment and a field experiment using Amazon Mechanical Turk in which participants proofread either Wikipedia articles to help the public or digitized books to help underprivileged people access e-books. Task significance improved work quality in both experiments, especially when participants recalled the purpose statement information. A majority of participants who received the purpose statement, however, ignored it. Further analysis showed that delivering the purpose statement in rich media formats did not increase the likelihood of recall but worker attributes such as English ability, income levels, and personality traits influenced the likelihood of recall. Compared to task significance, increasing monetary payment by 50% had no impact on work quality. Intrinsic motivation such as task enjoyment had both a direct positive effect on work quality and an interaction with task significance in the sense that workers performed the highest when both types of motivations were high. Overall, our research highlights the promise of task significance as a way to motivate online crowds and also the unexpected challenge of promoting task significance in an online context.
Yuqing Ren is an Associate Professor of Information and Decision Sciences and the Mary and Jim Lawrence Fellow at the Carlson School of Management. She holds a Ph.D. from Carnegie Mellon University. Ren's research focuses on the design and management of information technologies to promote meaningful social connections and effective collaboration. Her research interests are business use of social media, online community design, distributed collaboration, knowledge management, and computational modeling of social and organizational systems. Ren's research on online community design and Wikipedia collaboration has been funded by National Science Foundation. Her work has been published at Human-Computer Interaction, Information Systems Research, Journal of Management Studies, Journal of MIS, Management Science, MIS Quarterly, Organization Science, Organization Studies, The Academy of Management Annals, and the proceedings of AOM, CSCW, HICSS, ICIS, and SIGCHI. Ren has been serving as a Senior Editor and Diffusion Editor for Organization Science since 2016. She has also served as an Associate Editor for Management Science (Information Systems Department) from 2015 to 2017. Ren also served on the editorial board of Organization Science from 2008 to 2016.
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