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Qualifying threshold of “take-off” stage for successfully disseminated creative ideas

  • Guoqiang Liang
  • Haiyan Hou
  • Xiaodan Lou
  • Zhigang HuEmail author
Article
  • 36 Downloads

Abstract

The creative process is essentially Darwinian and only a small proportion of creative ideas are selected for further development. However, the threshold that identifies this small fraction of successfully disseminated creative ideas at their early stage has not been thoroughly analyzed through the lens of Rogers’s innovation diffusion theory. Here, we take highly cited (top 1%) research papers as an example of the most successfully disseminated creative ideas and explore the time it takes and citations it receives at their “take-off” stage, which play a crucial role in the dissemination of creativity. Results show the majority of highly cited papers will reach 10% and 25% of their total citations within 2 years and 4 years, respectively. Interestingly, our results also present a minimal number of articles that attract their first citation before publication. As for the discipline, number of references, and Price index, we find a significant difference exists: Clinical, Pre-Clinical & Health and Life Sciences are the first two disciplines to reach the C10% and C25% in a shorter amount of time. Highly cited papers with limited references usually take more time to reach 10% and 25% of their total citations. In addition, highly cited papers will attract citations rapidly when they cite more recent references. These results provide insights into the timespan and citations for a research paper to become highly cited at the “take-off” stage in its diffusion process, as well as the factors that may influence it.

Keywords

Innovation diffusion Citation analysis Creativity Highly cited paper 

Notes

Acknowledgements

This contribution is based upon work supported by The National Social Science Foundation of China under Grant No. 14BTQ030. We acknowledge the support of the Chinese Scholarship Council. We are grateful to Yi Bu for providing the dataset and Weiwei Gu, as well as the anonymous reviewer for helpful comments on earlier versions of this article.

References

  1. Ahlgren, P., Colliander, C., & Sjögårde, 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.  https://doi.org/10.1002/asi.23986.CrossRefGoogle Scholar
  2. Aksnes, D., Rorstad, K., Piro, F., & Sivertsen, G. (2011). Are female researchers less cited? A large-scale study of norwegian scientists. Journal of the American Society for Information Science and Technology, 62(4), 628–636.  https://doi.org/10.1002/asi.2148610.1002/asi.CrossRefGoogle Scholar
  3. Beaudry, C., & Larivière, V. (2016). Which gender gap? Factors affecting researchers’ scientific impact in science and medicine. Research Policy, 45(9), 1790–1817.  https://doi.org/10.1016/j.respol.2016.05.009.CrossRefGoogle Scholar
  4. Bornmann, L. (2013). The problem of citation impact assessments for recent publication years in institutional evaluations. Journal of Informetrics, 7(3), 722–729.  https://doi.org/10.1016/j.joi.2013.05.002.CrossRefGoogle Scholar
  5. Bornmann, L., & Daniel, H. (2006). Selecting scientific excellence through committee peer review: A citation analysis of publications previously published to approval or rejection of post-doctoral research fellowship applicants. Scientometrics, 68(3), 427–440.CrossRefGoogle Scholar
  6. 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.  https://doi.org/10.1108/00220410810844150.CrossRefGoogle Scholar
  7. Bornmann, L., Ye, A. Y., & Ye, F. Y. (2017). Sequence analysis of annually normalized citation counts: an empirical analysis based on the characteristic scores and scales (CSS) method. Scientometrics, 113(3), 1665–1680.  https://doi.org/10.1007/s11192-017-2521-9.CrossRefGoogle Scholar
  8. Bouabid, H. (2011). Revisiting citation aging: A model for citation distribution and life-cycle prediction. Scientometrics, 88(1), 199–211.  https://doi.org/10.1007/s11192-011-0370-5.CrossRefGoogle Scholar
  9. Bourdieu, P. (1991). The peculiar history of scientific reason. Sociological Forum, 6(1), 3–26.  https://doi.org/10.1007/bf01112725.CrossRefGoogle Scholar
  10. Boyack, K. W., & Klavans, R. (2014). Atypical combinations are confounded by disciplinary effects. STI 2014 Leiden, 64–71.Google Scholar
  11. Callaham, M., Wears, R., & Weber, E. (2002). Journal prestige, publication bias, and other characteristics associated with citation of published studies in peer-reviewed journals. Journal of the American Medical Association, 287(21), 2847–2850.CrossRefGoogle Scholar
  12. Costas, R., van Leeuwen, T. N., & Bordons, M. (2012). Referencing patterns of individual researchers: Do top scientists rely on more extensive information sources? Journal of the American Society for Information Science and Technology, 63(12), 2433–2450.  https://doi.org/10.1002/asi.22662.CrossRefGoogle Scholar
  13. Didegah, F., & Thelwall, M. (2013a). Determinants of research citation impact in nanoscience and nanotechnology. Journal of the American Society for Information Science and Technology, 64(5), 1055–1064.  https://doi.org/10.1002/asi.22806.CrossRefGoogle Scholar
  14. Didegah, F., & Thelwall, M. (2013b). Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics, 7(4), 861–873.  https://doi.org/10.1016/j.joi.2013.08.006.CrossRefGoogle Scholar
  15. Egghe, L., Rao, I. K. R., & Rousseau, R. (1995). On the influence of production on utilization functions: Obsolescence or increased use? Scientometrics, 34(2), 285–315.  https://doi.org/10.1007/bf02020425.CrossRefGoogle Scholar
  16. Fortunato, S., Bergstrom, C. T., Borner, K., Evans, J. A., Helbing, D., Milojevic, S., et al. (2018). Science of science. Science.  https://doi.org/10.1126/science.aao0185.Google Scholar
  17. Gosnell, C. F. (1943). The rate of obsolescence in college library book collections/as determined by an analysis of three select lists of books for college libraries (Thesis edition ed.). New York: New York University.Google Scholar
  18. Haslam, N., Ban, L., Kaufmann, L., Loughnan, S., Peters, K., et al. (2008). What makes an article influential? Predicting impact in social and personality psychology. Scientometrics, 76(1), 169–185.  https://doi.org/10.1007/s11192-007-1892-8.CrossRefGoogle Scholar
  19. Hu, Z., & Wu, Y. (2018). A probe into causes of non-citation based on survey data. Social Science Information Sur Les Sciences Sociales, 57(1), 139–151.CrossRefGoogle Scholar
  20. King, J. (1987). A review of bibliometric and other science indicators and their role in research evaluation. Journal of Information Science, 13(5), 261–276.CrossRefGoogle Scholar
  21. Kong, X., Jiang, H., Wang, W., Bekele, T. M., Xu, Z., et al. (2017). Exploring dynamic research interest and academic influence for scientific collaborator recommendation. Scientometrics, 113(1), 369–385.  https://doi.org/10.1007/s11192-017-2485-9.CrossRefGoogle Scholar
  22. Lee, Y.-N., Walsh, J. P., & Wang, J. (2015). Creativity in scientific teams: Unpacking novelty and impact. Research Policy, 44(3), 684–697.  https://doi.org/10.1016/j.respol.2014.10.007.CrossRefGoogle Scholar
  23. Li, J., & Ye, F. Y. (2016). Distinguishing sleeping beauties in science. Scientometrics, 108(2), 821–828.CrossRefGoogle Scholar
  24. Merton, R. K. (1961). Singletons and multiples in scientific discovery: A chapter in the sociology of science. Proceedings of the American Philosophical Society, 105(5), 470–486.Google Scholar
  25. Merton, R. K. (1968). The Matthew effect in science: The reward and communication systems of science are considered. Science, 159(3810), 56–63.  https://doi.org/10.1126/science.159.3810.56.CrossRefGoogle Scholar
  26. Min, C., Ding, Y., Li, J., Bu, Y., Pei, L., et al. (2018). Innovation or immitation: the diffusion of citations. Journal of the Association for Information Science and Technology, 69(10), 1271–1282.  https://doi.org/10.1002/asi.24047.CrossRefGoogle Scholar
  27. Moed, H. F. (1989). Bibliometric measurement of research performance and Price’s theory of differences among the sciences. Scientometrics, 15(5–6), 473–483.  https://doi.org/10.1007/bf02017066.CrossRefGoogle Scholar
  28. Moed, H. F., Burger, W. J. M., Frankfort, J. G., & Raan, A. F. J. V. (1985). The use of bibliometric data for the measurement of university research performance. Research Policy, 14(3), 131–149.  https://doi.org/10.1016/0048-7333(85)90012-5.CrossRefGoogle Scholar
  29. Mukherjee, S., Romero, D. M., Jones, B., & Uzzi, B. (2017). The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: The hotspot. Science Advances, 3(4), e1601315.  https://doi.org/10.1126/sciadv.1601315.CrossRefGoogle Scholar
  30. Onodera, N., & Yoshikane, F. (2015). Factors affecting citation rates of research articles. Journal of the Association for Information Science and Technology, 66(4), 739–764.  https://doi.org/10.1002/asi.23209.CrossRefGoogle Scholar
  31. Pobiedina, N., & Ichise, R. (2016). Citation count prediction as a link prediction problem. Applied Intelligence, 44(2), 252–268.  https://doi.org/10.1007/s10489-015-0657-y.CrossRefGoogle Scholar
  32. Rogers, E. M. (1995). Diffusion of innovations (Fourth Edition ed.). New York: The Free Press.Google Scholar
  33. Roldan-Valadez, E., & Rios, C. (2015). Alternative bibliometrics from impact factor improved the esteem of a journal in a 2-year-ahead annual-citation calculation: Multivariate analysis of gastroenterology and hepatology journals. European Journal of Gastroenterology and Hepatology, 27(2), 115–122.  https://doi.org/10.1097/MEG.0000000000000253.CrossRefGoogle Scholar
  34. Roth, C., Wu, J., & Lozano, S. (2012). Assessing impact and quality from local dynamics of citation networks. Journal of Informetrics, 6(1), 111–120.  https://doi.org/10.1016/j.joi.2011.08.005.CrossRefGoogle Scholar
  35. Schubert, A., & Glanzel, W. (1986). Mean response time: A new indicator of journal citation speed with application to physics journals. Czecholovak Journal of Physics, 36(1), 121–125.CrossRefGoogle Scholar
  36. Simonton, D. K. (1997). Creative productivity: A predictive and explanatory model of career trajectories and landmarks. Psychological Review, 104(1), 66–89.  https://doi.org/10.1037/0033-295x.104.1.66.CrossRefGoogle Scholar
  37. Tahamtan, I., Safipour Afshar, A., & Ahamdzadeh, K. (2016). Factors affecting number of citations: A comprehensive review of the literature. Scientometrics, 107(3), 1195–1225.  https://doi.org/10.1007/s11192-016-1889-2.CrossRefGoogle Scholar
  38. Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468–472.  https://doi.org/10.1126/science.1240474.CrossRefGoogle Scholar
  39. van Raan, A. F. J. (2004). Sleeping beauties in science. Scientometrics, 59(3), 467–472.CrossRefGoogle Scholar
  40. Vanclay, J. K. (2013). Factors affecting citation rates in environmental science. Journal of Informetrics, 7(2), 265–271.  https://doi.org/10.1016/j.joi.2012.11.009.CrossRefGoogle Scholar
  41. Wang, J. (2013). Citation time window choice for research impact evaluation. Scientometrics, 94(3), 851–872.  https://doi.org/10.1007/s11192-012-0775-9.CrossRefGoogle Scholar
  42. Wang, D., Song, C., & Barabasi, A. L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132.  https://doi.org/10.1126/science.1237825.CrossRefGoogle Scholar
  43. Yan, R., Tang, J., Liu, X., Shan, D., & Li, X. (2011a). Citation count prediction: Learning to estimate future citations for literature. In Paper presented at the CIKM’11 proceedings of the 20th ACM international conference on information and knowledge management, Glasgow, Scotland, UK.Google Scholar
  44. Yan, R., Tang, J., Liu, X., Shan, D., & Li, X. (2011b). Citation count prediction: Learning to estimate future citations for literature. In Paper presented at the proceedings of the 20th ACM international conference on information and knowledge management. Google Scholar
  45. Yu, T., Yu, G., Li, P.-Y., & Wang, L. (2014). Citation impact prediction for scientific papers using stepwise regression analysis. Scientometrics, 101(2), 1233–1252.  https://doi.org/10.1007/s11192-014-1279-6.CrossRefGoogle Scholar
  46. Zhai, Y., Ding, Y., & Wang, F. (2018). Measuring the diffusion of an innovation: A citation analysis. Journal of the Association for Information Science and Technology, 69(3), 368–379.CrossRefGoogle Scholar
  47. Zhou, W., Gu, J., & Jia, Y. (2018). h-Index-based link prediction methods in citation network. Scientometrics, 117(1), 381–390.  https://doi.org/10.1007/s11192-018-2867-7.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.WISE LabDalian University of TechnologyDalianChina
  2. 2.School of Informatics, Computing, and EngineeringIndiana UniversityBloomingtonUSA
  3. 3.Institute of Systems ScienceBeijing Normal UniversityBeijingChina

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