Search (7 results, page 1 of 1)

  • × author_ss:"Li, X."
  • × language_ss:"e"
  • × year_i:[2010 TO 2020}
  1. Li, X.; Thelwall, M.; Kousha, K.: ¬The role of arXiv, RePEc, SSRN and PMC in formal scholarly communication (2015) 0.04 
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    Abstract
    Purpose The four major Subject Repositories (SRs), arXiv, Research Papers in Economics (RePEc), Social Science Research Network (SSRN) and PubMed Central (PMC), are all important within their disciplines but no previous study has systematically compared how often they are cited in academic publications. In response, the purpose of this paper is to report an analysis of citations to SRs from Scopus publications, 2000-2013. Design/methodology/approach Scopus searches were used to count the number of documents citing the four SRs in each year. A random sample of 384 documents citing the four SRs was then visited to investigate the nature of the citations. Findings Each SR was most cited within its own subject area but attracted substantial citations from other subject areas, suggesting that they are open to interdisciplinary uses. The proportion of documents citing each SR is continuing to increase rapidly, and the SRs all seem to attract substantial numbers of citations from more than one discipline. Research limitations/implications Scopus does not cover all publications, and most citations to documents found in the four SRs presumably cite the published version, when one exists, rather than the repository version. Practical implications SRs are continuing to grow and do not seem to be threatened by institutional repositories and so research managers should encourage their continued use within their core disciplines, including for research that aims at an audience in other disciplines. Originality/value This is the first simultaneous analysis of Scopus citations to the four most popular SRs.
    Date
    20. 1.2015 18:30:22
    Object
    Social Science Research Network
    Source
    Aslib journal of information management. 67(2015) no.6, S.614-635
  2. Li, X.; Schijvenaars, B.J.A.; Rijke, M.de: Investigating queries and search failures in academic search (2017) 0.02 
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    Content
    Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457316304071.
    Source
    Information processing and management. 53(2017) no.3, S.666-683
  3. Li, X.; Cox, A.; Ford, N.; Creaser, C.; Fry, J.; Willett, P.: Knowledge construction by users : a content analysis framework and a knowledge construction process model for virtual product user communities (2017) 0.01 
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    Abstract
    Purpose The purpose of this paper is to develop a content analysis framework and from that derive a process model of knowledge construction in the context of virtual product user communities, organization sponsored online forums where product users collaboratively construct knowledge to solve their technical problems. Design/methodology/approach The study is based on a deductive and qualitative content analysis of discussion threads about solving technical problems selected from a series of virtual product user communities. Data are complemented with thematic analysis of interviews with forum members. Findings The research develops a content analysis framework for knowledge construction. It is based on a combination of existing codes derived from frameworks developed for computer-supported collaborative learning and new categories identified from the data. Analysis using this framework allows the authors to propose a knowledge construction process model showing how these elements are organized around a typical "trial and error" knowledge construction strategy. Practical implications The research makes suggestions about organizations' management of knowledge activities in virtual product user communities, including moderators' roles in facilitation. Originality/value The paper outlines a new framework for analysing knowledge activities where there is a low level of critical thinking and a model of knowledge construction by trial and error. The new framework and model can be applied in other similar contexts.
  4. Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018) 0.01 
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    Source
    Information processing and management. 54(2018) no.6, S.1345-1358
  5. Li, X.; Rijke, M.de: Characterizing and predicting downloads in academic search (2019) 0.01 
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    Source
    Information processing and management. 56(2019) no.3, S.394-407
  6. Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.-L.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy (2016) 0.01 
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    Source
    Information processing and management. 52(2016) no.1, S.61-72
  7. Su, S.; Li, X.; Cheng, X.; Sun, C.: Location-aware targeted influence maximization in social networks (2018) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 69(2018) no.2, S.229-241

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