Search (1 results, page 1 of 1)

  • × author_ss:"Xu, Y.(C.)"
  • × author_ss:"Liu, X."
  1. Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011) 0.01
    0.007500738 = product of:
      0.022502214 = sum of:
        0.022502214 = product of:
          0.06750664 = sum of:
            0.06750664 = weight(_text_:network in 4366) [ClassicSimilarity], result of:
              0.06750664 = score(doc=4366,freq=4.0), product of:
                0.19402927 = queryWeight, product of:
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.043569047 = queryNorm
                0.34791988 = fieldWeight in 4366, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.4533744 = idf(docFreq=1398, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4366)
          0.33333334 = coord(1/3)
      0.33333334 = coord(1/3)
    
    Abstract
    An innovative model to measure the influence among scientific journals is developed in this study. This model is based on the path analysis of a journal citation network, and its output is a journal influence matrix that describes the directed influence among all journals. Based on this model, an index of journals' overall influence, the quality-structure index (QSI), is derived. Journal ranking based on QSI has the advantage of accounting for both intrinsic journal quality and the structural position of a journal in a citation network. The QSI also integrates the characteristics of two prevailing streams of journal-assessment measures: those based on bibliometric statistics to approximate intrinsic journal quality, such as the Journal Impact Factor, and those using a journal's structural position based on the PageRank-type of algorithm, such as the Eigenfactor score. Empirical results support our finding that the new index is significantly closer to scholars' subjective perception of journal influence than are the two aforementioned measures. In addition, the journal influence matrix offers a new way to measure two-way influences between any two academic journals, hence establishing a theoretical basis for future scientometrics studies to investigate the knowledge flow within and across research disciplines.