A comparison of social, learning, and financial strategies on crowd engagement and output quality

Authors:
Lixiu Yu;Paul André;Aniket Kittur;Robert Kraut
Affiliations:
Carnegie Mellon University, pitssburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, USA;Carnegie Mellon University, Pittsburgh, PA, USA
Venue:
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Year:
2014

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Abstract

A significant challenge for crowdsourcing has been increasing worker engagement and output quality. We explore the effects of social, learning, and financial strategies, and their combinations, on increasing worker retention across tasks and change in the quality of worker output. Through three experiments, we show that 1) using these strategies together increased workers' engagement and the quality of their work; 2) a social strategy was most effective for increasing engagement; 3) a learning strategy was most effective in improving quality. The findings of this paper provide strategies for harnessing the crowd to perform complex tasks, as well as insight into crowd workers' motivation.