Winning by Learning? Effect of Knowledge Sharing in Crowdsourcing Contests

• 成果类型: Article
• 署名作者: Jin, Yuan; Lee, Ho Cheung Brian; Ba, Sulin; Stallaert, Jan
• 署名单位: Texas Tech University System; Texas Tech University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Connecticut
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2020.0982
• 发表日期: 2021
• 页码: 836-859
• 关键词: innovation contests; performance; networks; feedback; DESIGN; IMPACT; PARTICIPATION; COOPETITION; communities; exploration

摘要

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.