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

被引：0
|
作者：
Shan X. [1 ]
Wang C. [1 ]
Liu X. [1 ]
Han S. [1 ]
Yang J. [1 ]
机构：
[1] School of Economics and Management, Beijing University of Technology, Beijing
来源：
Data Analysis and Knowledge Discovery | 2021年 / 5卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Exponential Random Graph Model; Knowledge-Based View; Lead Users; Link Prediction; Open Innovation Community;
D O I：
10.11925/infotech.2096-3467.2021.0237
中图分类号：
学科分类号：
摘要：
[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 0.996 7, and the smallest ARC of 0.013 2. [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. © 2023 Chin J Gen Pract. All rights reserved.
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页码：85 / 96
页数：11
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