, Volume 120, Issue 3, pp 1387–1409 | Cite as

On an approach to boosting a journal’s citation potential

  • Guoliang LyuEmail author
  • Ganwei Shi


This study explored an approach to boosting a journal’s citation potential by proposing the Referenced Value of a journal (RFVj) as a new measure for evaluating a journal’s performance. Because RFVj can be defined once a journal has been published, it offers advantages regarding the properties of timeliness and certainty over traditional indicators such as journal impact factor (JIF) and the total number of citations of a journal. Additionally, RFVj has merits with both calculation simplicity and data reproducibility. Taking the underlying assumption that a journal’s long-term citations, which is used as a reliable proxy of a journal’s impact, can be obtained through a longer citation time window (CTW), we investigated the correlation between RFVj and a journal’s cumulative citation counts (a journal’s Times Cited, TCj) with a longer CTW. The results indicate that RFVj has a statistically significant correlation with TCj, based on which we argue that by improving its RFVj a journal can improve its TCj, namely a journal can boost its citation potential by improving its RFVj.


Cited half-life Journal impact factor Linked cited reference Long-term citations Referenced value Citation potential 


  1. Abramo, G. (2018). Revisiting the scientometric conceptualization of impact and its measurement. Journal of Informetrics, 12(3), 590–597.CrossRefGoogle Scholar
  2. Abramo, G., Cicero, T., & D’angelo, C. A. (2011). Assessing the varying level of impact measurement accuracy as a function of the citation window length. Journal of Informetrics, 5(4), 659–667.CrossRefGoogle Scholar
  3. Abramo, G., & D’angelo, C. A. (2011). Evaluating research: From informed peer review to bibliometrics. Scientometrics, 87(3), 499–514.CrossRefGoogle Scholar
  4. Abramo, G., D’angelo, C. A., & Felici, G. (2019). Predicting publication long-term impact through a combination of early citations and journal impact factor. Journal of Informetrics, 13(1), 32–49.CrossRefGoogle Scholar
  5. Adams, J. (2005). Early citation counts correlate with accumulated impact. Scientometrics, 63(3), 567–581.CrossRefGoogle Scholar
  6. Ahlgren, P., Colliander, C., & Sjogarde, P. (2018). Exploring the relation between referencing practices and citation impact: A large-scale study based on Web of Science data. Journal of the Association for Information Science and Technology, 69(5), 728–743.CrossRefGoogle Scholar
  7. Antonoyiannakis, M. (2018). Impact Factors and the Central Limit Theorem: Why citation averages are scale dependent. Journal of Informetrics, 12(4), 1072–1088.CrossRefGoogle Scholar
  8. Baumgartner, S. E., & Leydesdorff, L. (2014). Group-based trajectory modeling (GBTM) of citations in scholarly literature: Dynamic qualities of “transient” and “sticky knowledge claims”. Journal of the Association for Information Science and Technology, 65(4), 797–811.CrossRefGoogle Scholar
  9. Bergstrom, C. T., & West, J. D. (2008). Assessing citations with the Eigenfactor (TM) Metrics. Neurology, 71(23), 1850–1851.CrossRefGoogle Scholar
  10. Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.CrossRefGoogle Scholar
  11. Bornmann, L., & Haunschild, R. (2016). Citation score normalized by cited references (CSNCR): The introduction of a new citation impact indicator. Journal of Informetrics, 10(3), 875–887.CrossRefGoogle Scholar
  12. Campanario, J. M. (2014). Analysis of the distribution of cited journals according to their positions in the h-core of citing journal listed in Journal Citation Reports. Journal of Informetrics, 8(3), 534–545.CrossRefGoogle Scholar
  13. Didegah, F., & Thelwall, M. (2013). Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics, 7(4), 861–873.CrossRefGoogle Scholar
  14. El Aichouchi, A., & Gorry, P. (2018). Delayed recognition of Judah Folkman’s hypothesis on tumor angiogenesis: When a Prince awakens a Sleeping Beauty by self-citation. Scientometrics, 116(1), 385–399.CrossRefGoogle Scholar
  15. Finardi, U. (2013). Correlation between Journal Impact Factor and Citation Performance: An experimental study. Journal of Informetrics, 7(2), 357–370.CrossRefGoogle Scholar
  16. Franceschini, F., Galetto, M., Maisano, D., & Mastrogiacomo, L. (2012). The success-index: An alternative approach to the h-index for evaluating an individual’s research output. Scientometrics, 92(3), 621–641.CrossRefGoogle Scholar
  17. Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471–479.CrossRefGoogle Scholar
  18. Garfield, E. (2005). The agony and the ecstasy—the history and meaning of the journal Impact Factor. In International Congress on Peer Review and Biomedical Publication. Chicago, USA. Accessed 15 Dec 2018.
  19. Glanzel, W. (2013). High-end performance or outlier? Evaluating the tail of scientometric distributions. Scientometrics, 97(1), 13–23.CrossRefGoogle Scholar
  20. Glanzel, W., & Moed, H. F. (2002). Journal impact measures in bibliometric research. Scientometrics, 53(2), 171–193.CrossRefGoogle Scholar
  21. Glanzel, W., Schlemmer, B., & Thijs, B. (2003). Better late than never? On the chance to become highly cited only beyond the standard bibliometric time horizon. Scientometrics, 58(3), 571–586.CrossRefGoogle Scholar
  22. Haddawy, P., Saeed-Ul, H., Asghar, A., & Amin, S. (2016). A comprehensive examination of the relation of three citation-based journal metrics to expert judgment of journal quality. Journal of Informetrics, 10(1), 162–173.CrossRefGoogle Scholar
  23. Huang, H., Andrews, J., & Tang, J. (2012). Citation characterization and impact normalization in bioinformatics journals. Journal of the American Society for Information Science and Technology, 63(3), 490–497.CrossRefGoogle Scholar
  24. Hyland, K. (2004). Disciplinary discourses: Social interactions in academic writing. Ann Arbor: The University of Michigan Press.Google Scholar
  25. Kosmulski, M. (2011). Successful papers: A new idea in evaluation of scientific output. Journal of Informetrics, 5(3), 481–485.CrossRefGoogle Scholar
  26. Kuo, W., & Rupe, J. (2007). R-impact: Reliability-based citation impact factor. IEEE Transactions on Reliability, 56(3), 366–367.CrossRefGoogle Scholar
  27. Lehmann, S., Jackson, A. D., & Lautrup, B. E. (2006). Measures for measures. Nature, 444(7122), 1003–1004.CrossRefGoogle Scholar
  28. Leydesdorff, L. (2008). Caveats for the use of citation indicators in research and journal evaluations. Journal of the American Society for Information Science and Technology, 59(2), 278–287.CrossRefGoogle Scholar
  29. Leydesdorff, L., Bornmann, L., Marx, W., & Milojevic, S. (2014). Referenced publication years spectroscopy applied to iMetrics: Scientometrics, journal of informetrics, and a relevant subset of JASIST. Journal of Informetrics, 8(1), 162–174.CrossRefGoogle Scholar
  30. Leydesdorff, L., & Cozzens, S. (1993). The delineation of specialties in terms of journals using the dynamic journal set of the science citation index. Scientometrics, 26(1), 135–156.CrossRefGoogle Scholar
  31. Liang, C., Sun, Y., & Wang, Y. (2014). Research on references characteristics of highly-cited papers. Science-Technology and Publication, 7, 119–122.Google Scholar
  32. Lü, L., et al. (2016). vital nodes identification in complex networks. Physics Reports, 650, 1–63.MathSciNetCrossRefGoogle Scholar
  33. Marx, W., & Bornmann, L. (2015). On the causes of subject-specific citation rates in Web of Science. Scientometrics, 102(2), 1823–1827.CrossRefGoogle Scholar
  34. Mingers, J. (2008). Exploring the dynamics of journal citations: Modelling with s-curves. Journal of the Operational Research Society, 59(8), 1013–1025.CrossRefzbMATHGoogle Scholar
  35. Moed, H. F., Leeuwen, T. V., & Reedijk, J. (1998). A new classification system to describe the aging of scientific journals and their impact factors. Journal of Documentation, 54(4), 387–419.CrossRefGoogle Scholar
  36. Munteanu, R., & Apetroae, M. (2007). Journal relatedness: An actor-actor and actor-objectives case study. Scientometrics, 73(2), 215–230.CrossRefGoogle Scholar
  37. Nansen, C., & Meikle, W. G. (2014). Journal impact factors and the influence of age and number of citations. Molecular Plant Pathology, 15(3), 223–225.CrossRefGoogle Scholar
  38. Panagopoulos, G., Tsatsaronis, G., & Varlamis, I. (2017). Detecting rising stars in dynamic collaborative networks. Journal of Informetrics, 11(1), 198–222.CrossRefGoogle Scholar
  39. Persson, R. A. X. (2017). Bibliometric author evaluation through linear regression on the coauthor network. Journal of Informetrics, 11, 299–306.CrossRefGoogle Scholar
  40. Radicchi, F., Weissman, A., & Bollen, J. (2017). Quantifying perceived impact of scientific publications. Journal of Informetrics, 11(3), 704–712.CrossRefGoogle Scholar
  41. Rodriguez, J. M. (2017). Disciplinarity and interdisciplinarity in citation and reference dimensions: knowledge importation and exportation taxonomy of journals. Scientometrics, 110(2), 617–642.CrossRefGoogle Scholar
  42. Seglen, P. O. (1997). Citations and journal impact factors: Questionable indicators of research quality. Allergy, 52(11), 1050–1056.CrossRefGoogle Scholar
  43. Shideler, G. S., & Araujo, R. J. (2016). Measures of scholarly journal quality are not universally applicable to determining value of advertised annual subscription price. Scientometrics, 107(3), 963–973.CrossRefGoogle Scholar
  44. Song, Y., Situ, F. L., Zhu, H. J., & Lei, J. Z. (2018). To be the Prince to wake up Sleeping Beauty: The rediscovery of the delayed recognition studies. Scientometrics, 117(1), 9–24.CrossRefGoogle Scholar
  45. Teixeira, A. A. C., Vieira, P. C., & Abreu, A. P. (2017). Sleeping Beauties and their princes in innovation studies. Scientometrics, 110(2), 541–580.CrossRefGoogle Scholar
  46. Thor, A., Bornmann, L., Marx, W., & Mutz, R. (2018). Identifying single influential publications in a research field: New analysis opportunities of the CRExplorer. Scientometrics, 116(1), 591–608.CrossRefGoogle Scholar
  47. Tsatsaronis, G., et al. (2011). How to Become a Group Leader? or Modeling Author Types Based on Graph Mining. In Research and Advanced Technology for Digital Libraries, Tpdl 2011 (Vol. 6966, pp. 15–26). Berlin, Germany: Emerald Grp Publish; Ex Libris; Swets Informat Serv; IOS Press; Ashgate Publish Grp; Coalit Networked Informat.Google Scholar
  48. Van Raan, A. F. J., & Winnink, J. (2018). Do younger Sleeping Beauties prefer a technological prince? Scientometrics, 114(2), 701–717.CrossRefGoogle Scholar
  49. Waltman, L., & Van Eck, N. J. (2013). Source normalized indicators of citation impact: An overview of different approaches and an empirical comparison. Scientometrics, 96(3), 699–716.CrossRefGoogle Scholar
  50. Wang, J. (2013). Citation time window choice for research impact evaluation. Scientometrics, 94(3), 851–872.CrossRefGoogle Scholar
  51. Yanovsky, V. I. (1981). Citation analysis significance of scientific journals. Scientometrics, 3, 223–233.CrossRefGoogle Scholar
  52. Yuen, J. (2018). Comparison of impact factor, eigenfactor metrics, and SCImago Journal Rank Indicator and h-index for neurosurgical and spinal surgical journals. World Neurosurgery, 119, E328–E337.CrossRefGoogle Scholar
  53. Zitt, M., & Small, H. (2008). Modifying the journal impact factor by fractional citation weighting: The audience factor. Journal of the American Society for Information Science and Technology, 59(11), 1856–1860.CrossRefGoogle Scholar
  54. Zong, Z. J., Liu, X. Z., & Fang, H. (2018). Sleeping beauties with no prince based on the co-citation criterion. Scientometrics, 117(3), 1841–1852.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Zhejiang Sci-Tech UniversityHangzhouPeople’s Republic of China

Personalised recommendations