融合知识特征与协同属性的创新用户群发现研究  

Discovering the Innovation User Groups by Converging Knowledge Characteristics and Collaborative Attributes

作　　者：唐洪婷 李志宏[2] 张沙清[1,3] Tang Hongting;Li Zhihong;Zhang Shaqing(Guangdong University of Technology,Guangzhou 510520;South China University of Technology,Guangzhou 510641;Huizhou Guangdong University of Technology IoT Cooperative Innovation Institute Co.,Ltd.,Huizhou 516025)

机构地区：[1]广东工业大学,广州510520 [2]华南理工大学,广州510641 [3]惠州市广工大物联网协同创新研究院有限公司,惠州516025

出　　处：《情报学报》2021年第5期534-546,共13页Journal of the China Society for Scientific and Technical Information

基　　金：国家自然科学基金面上项目“基于超网络的大众协同创新社区用户知识主体挖掘方法研究”(71571073);广东省软科学研究项目“大众协同创新平台的动力机制及其发展对策研究”(2020A1010020038);广东省自然科学基金面上项目“基于区块链的大众创新社区知识共享治理机制研究”(2019A1515011370);惠州市高校科研专项资金项目“惠州市物联网信息技术实验室”(HSJS201807)。

摘　　要：企业创新社区作为用户参与的典型应用,可以为企业的产品开发与改进提供创新灵感。然而,互联网平台的开放性使得社区中的用户及其生成内容趋于无组织性,进而导致社区的知识组织与创新孕育效能低下。特别地,独立用户的创新活动常常受到其认知的限制。为了促使企业创新社区的用户创意得以有效聚集和碰撞,本文聚焦具体知识情景,对最具知识潜能与合作潜能的协同创新用户群进行识别,以最大限度的激发用户间的协同效用。因此,本文充分利用超网络的复杂系统分析能力,提出用户的知识特征与用户间协同属性的量化方法,并利用遗传算法,实现创新用户群发现的最优化求解。本文利用MIUI社区的真实数据进行实验研究,验证了该方法的可行性与有效性。本文着眼于创新用户群体的识别,完善了知识量化体系,同时,对在线协同创新等社区运营实践具有较好的指导意义。The open innovation community,as a typical application of innovation including user participation,can effectively gather user wisdom and provide new ideas for enterprise in product improvement and innovation.However,the openness of the community brings with it unorganized user-generated content,which in turn leads to a low efficiency of knowledge innovation in the community.At the same time,users,as independent individuals,are often limited by their cognitive constraints.To effectively gather and organize the users’wisdom in an open innovation community,this research proposes to identify innovation user groups with great potential for product innovation under a specific knowledge context.To this end,we utilize a super-network model in analyzing complex systems to quantify the knowledge characteristics of users as well as their collaborative attributes.A genetic algorithm is used to achieve the optimal group solution.By conducting experimental research using real data from the MIUI Community,this research proves the feasibility and effectiveness of user identification.The findings theoretically contribute to the development of knowledge quantification and user identification.In addition,this research provides decision references for community management practices,especially for collaborative innovation.

关 键 词：知识特征 协同创新 用户发现 企业创新社区 超网络 

分 类 号：F273.1[经济管理—企业管理] F49[经济管理—国民经济]