Abstract
In recent years, the public has increasingly relied on online knowledge-sharing platforms as credible and valuable sources to acquire information and knowledge. This work employed the network text analysis approach to explore the determinants of knowledge adoption from a structural perspective. First, we proposed a quantitative method to measure knowledge adoption behavior on a collective level based on the knowledge network. Then, we discussed knowledge concept adoption and knowledge relationship adoption. To build knowledge networks and analyze their structural features, this work collected 74 761 knowledge concepts from the Zhihu platform and 62 368 knowledge concepts in the Stack Overflow platform. The regression analysis results showed that the structural characteristics of knowledge concepts and knowledge relationships substantially affect their adoption. This study advances our understanding of knowledge adoption in an online knowledge-sharing platform and provides a structural analysis approach to large-scale online content data.
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