Building Social Legoland through Collaborative Crowdsourcing: Effect of Marginality on Collaboration and Task Outcomes

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Abstract

This study examines crowdsourcing in the context of solving social development issues, through the lens of team collaboration. Specifically, it uncovers how marginality drives functional diversity in collaborative crowdsourcing, and how that affects team collaboration patterns and task outcomes. With data scraped from a global crowdsourcing community, Openideo, this current study employed large-scale behavioral data to test the hypotheses. Exponential random graph modeling (ERGM) results showed that certain dimensions of marginality significantly drove the network of collaborative crowdsourcing, such as research skills, idea skills, and the level of activeness in the community. Structural equation modeling results showed that members who have higher skills were more like to be more central in the team collaboration and also more likely to win challenges. This indicates that marginality could be the source for functional diversity and collaboration at the team level; however it is not necessarily the source of creativity. This study extends prior research on crowdsourcing by focusing on how to apply this open innovation model to solve social challenges. Furthermore, by modeling both relational outcomes and task outcomes, this study suggests a more comprehensive framework of evaluating the dynamics of crowdsourcing (190 words).

Author

• Rong Wang, Northwestern University