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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 60-69    DOI: 10.11925/infotech.2096-3467.2019.0339
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Knowledge Sharing of Competitive Crowdsourcing Community Based on fsQCA
Xinyuan Lu,Xuelin Wang(),Qiaofeng Dai
Hubei Electronic Commerce Research Center, Wuhan 430079, China
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[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.

Key wordsFuzzy-Set Qualitative Comparative Analysis      Configuration      Knowledge Sharing     
Received: 29 March 2019      Published: 18 December 2019
ZTFLH:  C93-0  
Corresponding Authors: Xuelin Wang     E-mail:

Cite this article:

Xinyuan Lu,Xuelin Wang,Qiaofeng Dai. Knowledge Sharing of Competitive Crowdsourcing Community Based on fsQCA. Data Analysis and Knowledge Discovery, 2019, 3(11): 60-69.

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结果变量 划分标准 补充
知识贡献程度 高程度知识贡献(1); 低程度知识贡献(0) 参考指标: (1) 发帖总数; (2) 回帖总数
条件变量 划分标准 补充
管理员(1); 非管理员(0) 所在圈子由网站管理员创建记为1, 非管理员记为0
完全隶属成员=85 th 百分位数;
完全不隶属=15 th 百分位数;
将第85百分位数记为1, 第15百分位数记为0, 中位数记为0.5, 剩余值缩放在(0, 1)
金卡会员(1); 银卡会员(0.85); 普通会员(0) 金卡会员记为1, 银卡会员记为0.85, 普通会员记为0
完全隶属成员=85 th百分位数;
完全不隶属=15 th百分位数;
将第85百分位数记为1, 第15百分位数记为0, 中位数记为0.5, 剩余值缩放在(0, 1)
前因变量 充分性一致率(Consistency) 必要性覆盖率(Coverage)
高程度知识贡献 低程度知识贡献 高程度知识贡献 低程度知识贡献
圈主身份(CI) 0.784514 0.424522 0.745645 0.548245
~CI 0.256415 0.687955 0.561474 0.721546
在线时长(OT) 0.756464 0.456454 0.256446 0.853265
~OT 0.461654 0.687916 0.156546 0.925461
圈币(CC) 0.665461 0.516549 0.465461 0.851665
~CC 0.456166 0.687941 0.465478 0.912654
会员身份(MS) 0.718447 0.316546 0.966461 0.894645
~MS 0.665135 0.956561 0.365461 0.894651
条件变量 高程度知识贡献
Ha1 Ha2 Hb
圈主身份(CI) ?
在线时长(OT) ?
Raw Coverage 0.476165 0.541144 0.316544
Unique Coverage 0.032474 0.057971 0.098971
Consistency 0.826546 0.846654 0.847815
Overall Solution Coverage 0.787453
Overall Solution Consistency 0.812454
前因变量 高程度知识贡献
La1 La2 Lb
圈主身份(CI) ? ?
在线时长(OT) ?
会员身份(MS) ?
Raw Coverage 0.365454 0.451354 0.456131
Unique Coverage 0.056461 0.071323 0.061321
Consistency 0.815465 0.826547 0.844578
Overall Solution Coverage 0.711354
Overall Solution Consistency 0.824657
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