开放式创新社区领先用户识别——知识基础观视角  

Identifying Lead Users in Open Innovation Community from Knowledge-based Perspectives

作　　者：单晓红[1] 王春稳 刘晓燕[1] 韩晟熙 杨娟[1] Shan Xiaohong;Wang Chunwen;Liu Xiaoyan;Han Shengxi;Yang Juan(School of Economics and Management,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学经济与管理学院,北京100124

出　　处：《数据分析与知识发现》2021年第9期85-96,共12页Data Analysis and Knowledge Discovery

基　　金：国家社会科学后期资助项目(项目编号:20FGLB004);国家自然科学基金面上项目(项目编号:71974009)的研究成果之一。

摘　　要：【目的】探索开放式创新社区中识别不同领域领先用户的方法,为企业解决获取外部知识资源的问题。【方法】首先利用LDA提取用户主题构建用户知识二分网络,其次融合领先用户知识结构特征和传统个体属性特征,提出基于指数随机图模型的链路预测方法识别不同领域的领先用户,并以华为产品定义社区为例进行实证研究。【结果】识别出华为社区内20个领先用户,平均链接概率都大于0.900,并且与传统链接预测方法相比,ERGM方法AUC最大,达到0.9967;ARC最小,下降到0.0132。【局限】未考虑时间因素对用户知识的影响。【结论】本研究丰富了领先用户识别角度和方法,为后续基于知识的领先用户识别研究奠定了基础。[Objective]This paper explores ways to identify lead users in different fields of the open innovation community,aiming to help enterprises obtain external knowledge resources.[Methods]First,we used the LDA to extract user topics and construct a user knowledge bipartite network.Then,we combined the characteristics of the lead users’knowledge structure and traditional individual attributes.Third,we proposed a link prediction method based on the Exponential Random Graph Model to identify lead users in different fields.Finally,we conducted an empirical study using the Joint Definition Community as an example.[Results]We identified 20 lead users and found their average link probability was greater than 0.900.Compared with traditional link prediction methods,our method had the largest AUC of 0.9967,and the smallest ARC of 0.0132.[Limitations]Our model did not include the impacts of time factors on user knowledge.[Conclusions]This research enriches the perspectives and methods of lead user identification and lays a solid foundation for the follow-up studies.

关 键 词：开放式创新社区 领先用户 知识基础观 链路预测 指数随机图模型 

分 类 号：C939[经济管理—管理学]