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The Factors of Knowledge Help-Seeking Behavior Intention in Virtual Community under Moderate Effect of Different Platform
Yu Xia, Min Zhang, Xiaotong Liu
Wuhan University School of Information Management, China, People's Republic of
[Purpose/significance] The article aims to explore the factors of knowledge help-seeking behaviour in social relational virtual community and Community-Driven Knowledge Services and differences of factors’ effects, which is of positive significance to promote the function of knowledge sharing in virtual community and improve the efficiency of community management. [Method/Process] From the perspectives of internal cognitive and external influence, this paper proposes research hypothesis and theoretical model by integrating self-efficacy theory, expectancy theory and social cognitive theory. By investigating the users of Baidu Zhidao and Wechat Moments, this research obtains respectively 256 and 222 valid questionnaires and applied SmartPLS2.0 to test hypotheses and model. [Result/Conclusion] The results show that, the factors in two kind of virtual communities are different. In social relational virtual community, perceived trust and outcome efficiency affect the knowledge help-seeking intention; in community-driven knowledge services, outcome efficiency and community atmosphere have effect on knowledge help-seeking intention; self-efficacy has no effect on help-seeking intention in two kind of virtual community.
Study on Big Data Knowledge Service Framework based on Knowledge Fusion
Hao Fan, Fei Wang
Wuhan University, China, People's Republic of
This paper analyzes the demand of knowledge fusion for knowledge service, constructs the framework of knowledge service system based on the knowledge fusion process model, and provides methodological support for the realization of big data knowledge service. This paper firstly analyzes the contents of knowledge fusion oriented to knowledge service demands, designs a knowledge service hierarchical architecture based on knowledge fusion； Then, this paper constructs the process model of knowledge fusion and analyzes its implementation patterns in the big data environment; Finally, this paper proposes a framework of big data knowledge service system based on knowledge fusion processes. In the big data environment, knowledge fusion is the necessary prerequisite and effective approach to implementing knowledge service. Combining the processes of knowledge fusion and knowledge service organically together, and constructing the framework of knowledge service system based on knowledge fusion, which is an effective solution to achieving multi-level, personalized and innovative knowledge service.
The Incidence Relationship among Influence Factors, Enterprise Knowledge Searching Behavior and Innovation Performance: taking 55 Listed Pharmaceutical Companies of China as Examples
Ying Li1, Ruixiao Zhang2
1Nankai University，China; 2Tianjin Library，China
Knowledge searching activity is one of the most important parts of the enterprises innovation. A theoretical model of enterprise knowledge searching behavior is developed on the basis of literature review and accordingly 14 research hypotheses are put forward. In order to verify the above theoretical model and assumptions 55 pharmaceutical listed companies of China are selected as samples. Then statistical data of patents, finance and industry of samples are collected and download from PatSnap Database, Wind Financial Terminal and CSMAR. By using software of Excel and SPSS the data is processed and analyzed. In the end this paper finds incidence relationship among several internal and external factors of sample enterprises, knowledge searching behaviors and innovation performance. There are significant correlation for Enterprise R&D Intensity and slack resources with knowledge search depth. And cumulativeness and opportunity of technology have positive effects on knowledge breadth & depth. But the degree of competition in the market have a negative effect on knowledge search breadth & depth. The correlation among knowledge search breadth &......
Predicting the Influence of Microblog Entries on Emergent Infectious Diseases
Lu An1, Siyao Zhou1, Chuanming Yu2, Gang Li1
1Wuhan university, China, People's Republic of; 2Zhongnan University of Economics and Law, People’s Republic of China
Emergent infectious diseases have great harm to the public's life and property. Microblog, as an important social media platform, plays an important role in the production and dissemination of information. Studying the method of predicting impact of microblog entries regarding outbreaks of infectious diseases helps to understand the features and patterns of highly influential microblog entries and assist relevant departments to find in advance potential risks and possible problems when infectious diseases spread and make appropriate preparations. This paper uses Latent Dirichlet Allocation (LDA) model to extract topics from microblog contents, and constructs a decision tree model combined with user and release time attributes, in order to predict impact of microblog entries on infectious diseases. The precision of the proposed method for the prediction of the impact of microblogs on infectious diseases reached 86.5%. The features of user and topic play higher roles than the release time in the impact of microblog entries on emergent infectious disease.
Detecting Knowledge Management Community based on Formal Concept Analysis
Ping Liu, Wenting Su
Wuhan University, China, People's Republic of
Knowledge management sprang up in the 90's of the last century. As the emerging field in knowledge economy era, knowledge management is the hot issue of library and information science, computer science, management and so on. In two decades, the research of knowledge management have been quite hot, a great amount of researchers have studied it deeply and extensively from multiple perspectives. Therefore, it’s essential to detect the academic communities of knowledge management and reveal research focuses and core authors of this field. In this paper, we proposed a new FCA based method to detect academic communities, and this method effectively revealed the core academic communities and research hotspot of knowledge management. Compared with existing methods, the FCA based method can present the hierarchy and intellectual structure of academic communities more objectively.