The Crowd on the Assembly Line: Designing Tasks for a Better Crowdsourcing Experience

Leveraging crowd potentials through low paid crowdsourcing micro-tasks has attracted great attention in the last decade as it proves to be a powerful new paradigm to get large amounts of work done quickly. A main challenge for crowdsourcers has been to design tasks that trigger optimum outputs from the crowd while providing crowdsourcees with an experience that would attract them to the platform in the future. Drawing mainly from expectancy theory and the motivation through design of work model, we develop and test a theoretical framework to explore the impact of extrinsic reward valence and perceived task characteristics on perceived output measures in crowdsourcing contexts. We specifically focus on the impact of three crowdsourcing task dimensions: autonomy, skill use, and meaningfulness. Our findings provide support for our model and suggest ways to improve task design, use extrinsic rewards, and provide an enhanced crowdsourcing experience for participants.