情报科学 ›› 2023, Vol. 41 ›› Issue (3): 109-118.

• 业务研究 • 上一篇    下一篇

基于集体价值创造的在线健康社区价值共创行为研究 ——以“百度抑郁症吧”为例

  

  • 出版日期:2023-03-01 发布日期:2023-04-10

  • Online:2023-03-01 Published:2023-04-10

摘要: 【目的/意义】通过分析在线健康社区成员的互动模式和网络结构,探讨其价值共创行为特征,为社区的持
续运营和管理提供建议。【方法/过程】以百度抑郁症吧为研究对象,采取质性和定量混合的研究方法,首先采用内
容分析法对所爬取的数据进行编码,再利用社交网络分析对各价值网络的网络结构特征和关键用户进行了探究。
【结果/结论】结果显示,在在线健康社区中成员以独特的方式进行价值共创,其产生的三种类型的价值互相共存,
其中整体网络与社交价值网络相关性最高,知识价值网络和文化价值网络相关性最低;社区成员之间的联系并不
密集,并不存在核心-边缘结构;网络中的大多数关键用户角色存在重叠;并根据本文的研究结果提出了三点针对
性建议。【创新/局限】首次从在线社区的整体网络结构和成员之间的交互关系探究在线健康社区价值共创行为。
但在数据的选择、收集与研究方法的运用上仍有所不足,未来需加以改进。

Abstract: 【Purpose/significance】By analyzing the interaction mode and network structure of online health community members, this
paper discusses their value co-creation behavior, and provides suggestions for the sustainable operation and management of the com?
munity.【Method/process】Taking Baidu's "Depression Bar"as the research object, a mixed qualitative and quantitative research
method is adopted. First, the content analysis method is used to encode the crawled data, and then social network analysis is used to
explore the network structure characteristics and key users of each value network.【Result/conclusion】The results show that in the on? line health community, members create collective value in a unique way, and the three types of value generated coexist with each
other, among which the overall network and social value network have the highest correlation, knowledge value network and cultural
value network The value network has the lowest correlation; the connections between community members are not dense, and there is no core-periphery structure; most of the key user roles in the network overlap; According to the research results of this paper, three tar? geted suggestions are put forward.【Innovation/limitation】For the first time, explore the value co-creation behavior of online health community from the overall network structure of online community and the interaction between members. However, there are still some deficiencies in data selection, collection and application of research methods, which need to be improved in the future.