Organizations are increasingly engaging the community through crowdsourcing platforms to evolve innovative solutions to challenging business problems. Participants on such platforms often simultaneously cooperate and compete with one another to earn top honors. This paper addresses the imperative to understand the dynamics of knowledge sharing in such a coopetitive environment. Specifically, our study relies on the conceptual foundations of social exchange and social capital theories to investigate how help rendered (e.g., exchanging ideas or sharing knowledge) by participants in an online coopetitive crowdsourcing setting affects their performance. Furthermore, the study examines the moderating effects of the intensity of competition. Results of our econometrics analyses suggest that help given in a highly competitive contest, as opposed to a less competitive one, is more likely to be reciprocated, but less likely to improve the contributor’s contest performance. In addition, our study found that help received by participants positively impacts their contest performance, and partially mediates the relationship between help rendered and contest performance. This research also provides insight into what motivates participants to share knowledge under conditions of coopetition. The findings of our study have strong implications for both theory and practice.
Dissanayake, Indika; Nerur, Sridhar; Wang, Jingguo; Yasar, Mahmut; and Zhang, Jie
"The Impact of Helping Others in Coopetitive Crowdsourcing Communities,"
Journal of the Association for Information Systems: Vol. 22
, Article 7.
Available at: https://aisel.aisnet.org/jais/vol22/iss1/7
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