Research on Factors Influencing Knowledge Contribution in Online Q&A Communities: Based on an Empirical Study of QCA on Zhihu

An important reason for the slowdown of online Q&A (Question and Answer) communities is that the number of users with knowledge input behavior is relatively small and the intensity of input behavior is decreasing, which is precisely the key to the sustainable development of communities. Therefore, it is of great significance to explore the factors affecting knowledge contribution behavior to maintain the sustainable development of the community. Based on the social exchange theory from the perspective of configuration, the theoretical model of the factors influencing knowledge contribution behavior is constructed by sorting out nine dimensions of variables from users’ self-improvement and social identity, writing a distributed crawler program to capture a total of 5,000 sample data from Zhihu community, using fuzzy set qualitative comparative analysis for empirical research. We found that users’ knowledge contribution behavior is influenced by multiple prefactors, there may be three different types of constitutive paths that influence users’ knowledge contribution behavior: self-improvement, social identity, and both. At the same time, users’ self-improvement and social identity play a linkage role and jointly influence knowledge contribution behaviors. From the perspective of configuration, the platform can find multiple influencing factors in various ways, so as to enhance the enthusiasm of users for knowledge contribution and promote the sustainable development of the online Q & A community. In addition, this paper further deepen the study of the factors influencing knowledge contribution behavior and develop new research perspectives for the study.

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Journal
SAGE Open
Published
2024-10-01
DOI
https://doi.org/10.1177/21582440241305158
Primary Topic
Expert finding and Q&A systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on Factors Influencing Knowledge Contribution in Online Q&A Communities: Based on an Empirical Study of QCA on Zhihu

Juan Du, Xin Feng, Wenzhu Li, Jiangfei Chen
SAGE Open
Expert finding and Q&A systems
article

Research on Factors Influencing Knowledge Contribution in Online Q&A Communities: Based on an Empirical Study of QCA on Zhihu

Juan Du, Xin Feng, Wenzhu Li, Jiangfei Chen
article en

Abstract

An important reason for the slowdown of online Q&A (Question and Answer) communities is that the number of users with knowledge input behavior is relatively small and the intensity of input behavior is decreasing, which is precisely the key to the sustainable development of communities. Therefore, it is of great significance to explore the factors affecting knowledge contribution behavior to maintain the sustainable development of the community. Based on the social exchange theory from the perspective of configuration, the theoretical model of the factors influencing knowledge contribution behavior is constructed by sorting out nine dimensions of variables from users’ self-improvement and social identity, writing a distributed crawler program to capture a total of 5,000 sample data from Zhihu community, using fuzzy set qualitative comparative analysis for empirical research. We found that users’ knowledge contribution behavior is influenced by multiple prefactors, there may be three different types of constitutive paths that influence users’ knowledge contribution behavior: self-improvement, social identity, and both. At the same time, users’ self-improvement and social identity play a linkage role and jointly influence knowledge contribution behaviors. From the perspective of configuration, the platform can find multiple influencing factors in various ways, so as to enhance the enthusiasm of users for knowledge contribution and promote the sustainable development of the online Q & A community. In addition, this paper further deepen the study of the factors influencing knowledge contribution behavior and develop new research perspectives for the study.

SAGE OpenVol. 14(4)
Beijing University of Posts and Telecommunications (CN), Yanshan University (CN), Shijiazhuang Tiedao University (CN), Hebei GEO University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hebei Province
Openalex Percentile: Top 63%
Expert finding and Q&A systems
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