Winning by Learning? Effect of Knowledge Sharing in Crowdsourcing Contests

Yuan Jin (), Ho Cheung Brian Lee (), Sulin Ba () and Jan Stallaert ()
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Yuan Jin: Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, Texas 79409
Ho Cheung Brian Lee: Department of Supply Chain and Information Systems, Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802
Sulin Ba: Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269
Jan Stallaert: Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269

Information Systems Research, 2021, vol. 32, issue 3, 836-859

Abstract: A crowdsourcing contest connects solution seekers to online users who compete with each other to solve the seeker’s problem by generating innovative ideas. Knowledge sharing that occurs in such a contest may play an important role in the process of contestants generating high-quality solutions. On the one hand, more knowledge resources may lower the participation cost and help improve crowdsourcing performance. On the other hand, the shared knowledge may also interrupt contestants’ independent solution search processes and distract contestants. This study demonstrates the existence of knowledge sharing’s impact on crowdsourcing contestants’ performance and identifies the influence of different shared knowledge dimensions on crowdsourcing contestants. The results indicate that having a knowledge sharing process on the platform does not necessarily improve crowdsourcing contestants’ performance. We show that the effectiveness of knowledge sharing is influenced by the volume, quality, and generativity of shared knowledge. The shared knowledge is only beneficial when it is of high quality or of high generativity. In addition, we examine the effects of the breadth and depth of knowledge generativity in the knowledge sharing process and find that a high degree of derivation breadth improves contestants’ performance. The findings provide implications for a crowdsourcing contest platform to utilize the knowledge sharing feature effectively. The key to making full use of this feature is to ensure a high quality of the shared knowledge and to encourage more contributions of generative knowledge, especially the generative knowledge of great breadth.

Keywords: crowdsourcing contest; crowdsourcing platform; knowledge sharing; knowledge derivation; contestant performance (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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