[Objective] This paper analyzes the contributions of the crowdsourcing community members, aiming to encourage them to share more knowledge. [Methods] We adopted qualitative comparison method based on fuzzy sets to study these members from a competitive knowledge crowdsourcing community. Then, we chose condition variables from community environment, motivation theory, and sunk cost effect. Finally, we used the degree of knowledge contribution as the result variable. [Results] There are two types of high-level knowledge sharing configurations: (I) Communities with administrators could lead members to a high degree of knowledge sharing through currency rewards and sunk costs (time or money); (II) Community members invested money and time could also promote high-level knowledge sharing. There are two types of low-level knowledge sharing: (I) In communities without administrators, it is hard for members to share knowledge;(II)Community members without money or time investments had low level of knowledge sharing. [Limitations] We only studied one website and the cross-section of research data might influence our results. [Conclusions] The proposed method helps the knowledge sharing community improve member management, as well as the quality of crowdsourcing tasks.
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