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作 者:戚桂杰[1] 李娜 王凯平[1] 徐琳越 Qi Guijie;Li Na;Wang Kaiping;Xu Linyue(School of Management,Shandong University,Jinan 250100,China)
出 处:《现代情报》2020年第12期45-54,共10页Journal of Modern Information
基 金:国家自然科学基金项目“企业开放式创新平台模式与组织特质动态匹配研究”(项目编号:71572097)。
摘 要:[目的/意义]对于一个创新平台的繁荣与发展来说,用户间进行社交互动并为平台做出贡献是至关重要的。因此,本文研究用户交互中的内容特征(信息冗余)对用户知识贡献的影响。[方法/过程]爬取典型开放式创新平台(LEGO IDEAS)的数据,使用Python NLTK包挖掘创意文本内容,并结合信息论中的冗余度计算方法和零膨胀负二项回归模型进行实证分析。[结果/结论]结果表明,用户交互过程中获取的信息量正向影响用户知识贡献,促进用户发布更多创意,但当获取的创意之间存在冗余信息时,信息冗余会调节获取信息量对用户知识贡献的影响。[Purpose/Significance]For the prosperity and development of an innovative platform,it is very important for users to have social interaction and contribute to the platform.In OIP environment,information interaction between users is frequent and the amount of information is increasing day by day.A large number of repeated and similar information makes information redundancy a problem that cannot be ignored gradually.Therefore,this paper studies the influence of content characteristics(information redundancy)in user interaction on user knowledge contribution.[Method/Process]The paper crawled the data of typical open innovation platform(LEGO IDEAS),used Python NLTK package to mine creative text content,and combined redundancy calculation method in information theory and zero inflated negative binomial regression model to make empirical analysis.[Result/Conclusion]The results showed that the amount of information acquired during user interaction positively affected the user's knowledge contribution and promoted users to publish more ideas.However,when there was redundant information between the acquired ideas,information redundancy would adjust the influence of the acquired amount of information on the user's knowledge contribution.In the process of user innovation,the content characteristics of user interaction are more important than quantity.Enterprises that build open innovation platforms should encourage users to actively participate in the interaction,and at the same time guide users to participate in the exchange and learning of low redundant ideas.
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