Convergence of Crowdsourcing Ideas: A Cognitive Load perspective

Many organizations use crowdsourcing for problem solving, innovation, and consultation. In open innovation and community crowdsourcing initiatives the volume of generated ideas may prevent a careful evaluation if each individual contribution. To overcome this challenge, crowd workers can perform a convergence activity. Convergence involves reducing a large set of ideas to a focused subset of ideas that are worthy of further consideration. While convergence is a critical process for situations were large volumes of ideas must be processed, little is known what affects convergence quality and satisfaction with the convergence process and outcomes. We propose an experimental study that adopts Cognitive Load Theory as its theoretical lens to investigate the effects of task complexity, idea presentation, and instructional guidance on convergence quality and satisfaction. This study has the potential to further our understanding of convergence processes in crowdsourcing and inform the design and guidance of crowdsourcing initiatives.