Convergence of Crowdsourcing Ideas: A Cognitive Load perspective

Shixuan Fu, Gert-Jan de Vreede, Xusen Cheng, Isabella Seeber, Ronald Maier, Barbara Weber

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationICIS 2017 PROCEEDINGS
Number of pages11
PublisherAssociation for Information Systems
Publication date2017
ISBN (Print)9781510853690
Publication statusPublished - 2017
Event38th International Conference on Information Systems: Transforming Society with Digital Innovation - Seoul, Korea, Republic of
Duration: 10 Dec 201713 Dec 2017
https://icis2017.aisnet.org/

Conference

Conference38th International Conference on Information Systems
CountryKorea, Republic of
CitySeoul
Period10/12/201713/12/2017
Internet address

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