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A Survey-Based Structural Equation Model Analysis on Influencing Factors of Non-Citation


Affiliations
1 School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
2 Chinese Academy of Science and Technology for Development, Beijing 100038, China
3 School of Information Management, Nanjing University, Nanjing, Jiangsu 210093, China
 

Although bibliometric approach has been frequently utilized to analyse reasons behind non-citation and show relations between uncitedness and impact factors, the survey-based structural equation model approach is not usually used. Therefore, a Likert scale questionnaire was designed to collect data on non-citation and its various types of determinants. The survey-based structural equation model was used to analyse mutual relations and correlation degrees between non-citation rate and its various determinants. As a result, the categories ‘academic status of journal’ and ‘personal features of papers’ were found to be two extremely significant determinants of non-citation rate. Their path coefficients reached 0.83 and 0.43 respectively. Accordingly, the category ‘contents and topics of papers’ was shown to have extremely significant indirect influence on non-citation rate through ‘academic status of journal’. The three observed variables of ‘academic status of journal’ including ‘public praise of journal’, ‘impact factor of journal’, and ‘member of SCI, EI and Scopus Journals’, showed the highest values of indirect effect on the non-citation rate. Furthermore, there were weaker correlations among ‘academic status of journal’, ‘personal features of papers’, ‘contents and topics of papers’ and ‘publicity and recommendation’ except between ‘contents and topics of papers’ and ‘academic status of journal’. Meanwhile, the six observed variables of ‘publicity and recommendation’ and ‘contents and topics of papers’ show the smaller values at ≤0.12 of indirect effect on non-citation rate. Our empirical results suggest some significant determinants of non-citation rate that might enlighten researchers on how to improve the chance of having their works cited, and assist them in expanding their research impact. These findings can also help journal editors to identify contributions with high-citation potential.

Keywords

Determinants, Impact Factor, Influencing Factors, Non-Citation, Questionnaire, Structural Equation Model, Uncitedness.
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  • Thelwall, M., Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions. J. Infor., 2016, 10(2), 622–633.

  • Garfield, E., Uncitedness III – the importance of not being cited. Curr. Contents, 1973, 8, 5–6.

  • Garfield, E., To be an uncited scientist is no cause for shame. The Scientist, 1991, 5(6), 12.

  • Peters, H. P. F. and Raan, A. F. J. V., On determinants of citation scores: a case study in chemical engineering. J. Am. Soc. Inform. Sci., 1994, 45(1), 39–49.

  • Yue, W., Predicting the citation impact of clinical neurology journals using structural equation modeling with partial least squares. Dissertation, University of New South Wales, Sydney, 2004.

  • Yue, W. and Wilson, C. S., An integrated approach for the analysis of factors affecting journal citation impact in clinical neurology. Proc. Am. Soc. Inform. Sci. Technol., 2004, 41(1), 527–536.

  • Didegah, F. and Thelwall, M., Determinants of research citation impact in nanoscience and nanotechnology. J. Am. Soc. Inform. Sci. Technol., 2013, 64(55), 055–1064.

  • Didegah, F. and Thelwall, M., Which factors help authors produce the highest impact research? Collaboration, journal and document properties. J. Inform., 2013, 7(4), 861–873.

  • Zhao, S. X., Uncitedness of reviews. Curr. Sci., 2015, 109(8), 1377–1378.

  • Hu, Z. W. and Wu, Y. S., A probe into causes of non-citation based on survey data. Soc. Sci. Inform.; http://arxiv.org/pdf/1507.06879

  • Zhou, P. and Leydesdorff, L., A comparative study of the citation impact of Chinese journals with government priority support. Frontiers in Research Metrics and Analytics, 2016; http://journal.frontiersin.org/article/10.3389/frma.2016.00003/full.

  • Van Leeuwen, T. N. and Moed, H. F., Characteristics of journal impact factors: the effects of uncitedness and citation distribution on the understanding of journal impact factors. Scientometrics, 2005, 63(2), 357–371.

  • Egghe, L., The mathematical relation between the impact factor and the uncitedness factor. Scientometrics, 2008, 76(1), 118–123.

  • Egghe, L., The distribution of the uncitedness factor and its functional relation with the impact factor. Scientometrics, 2010, 83(3), 689–695.

  • Hsu, J. W. and Huang, D. W., A scaling between impact factor and uncitedness. Phys. A – Stat. Mech. Appl., 2012, 391(5), 2129–2134.

  • Burrell, Q. L., A stochastic approach to the relation between the impact factor and the uncitedness factor. J. Inform., 2013, 7(3), 676–682.

  • Bornmann, L. and Daniel, H. D., Multiple publication on a single research study: does it pay? The influence of number of research articles on total citation counts in biomedicine. J. Am. Soc. Inform. Sci., 2007, 58(8), 1100–1117.

  • Boyack, K. W. and Klavans, R., Predicting the importance of current papers. In Proceedings of ISSI 2005 (eds Ingwersen, P. and Larsen, B.), Karolinska University Press, Stockholm, pp. 335–342.

  • Kulkarni, A. V., Busse, J. W. and Shams, I., Characteristics associated with citation rate of the medical literature. PLOS ONE, 2007, 2(5), e403.

  • Egghe, L., Guns, R. and Rousseau, R., Thoughts on uncitedness: nobel laureates and Fields medalists as case studies. J. Am. Soc. Inform. Sci. Technol., 2011, 62(8), 1637–1644.

  • Li, J. and Ye, F. Y., A probe into the citation patterns of high-quality and high-impact publications. Malaysian J. Lib. Inform. Sci., 2014, 19(2), 17–33.

  • Liang, L. M., Zhong, Z. and Rousseau, R., Uncited papers, uncited authors and uncited topics: a case study in library and information science. J. Inform., 2015, 9, 50–58.

  • Hu, Z. W. and Wu, Y. S., Regularity in the time-dependent distribution of the percentage of never-cited papers: An empirical pilot study based on the six journals. J. Inform., 2014, 8(1), 136–146.

  • Yue, W., Predicting the citation impact of clinical neurology journals using structural equation modeling with partial least squares. Dissertation, University of New South Wales, Sydney, 2004.

  • Yue, W. and Wilson, C. S., An integrated approach for the analysis of factors affecting journal citation impact in clinical neurology. Proc. Am. Soc. Inform. Sci. Technol., 2004, 41(1), 527–536.

  • Cho, K., Hong, T. and Hyun, C., Effect of project characteristics on project performance in construction projects based on structural equation model. Exp. Syst. Appl., 2009, 36(7), 10461–10470.

  • Keline, R. B. (ed.), Principles and Practice of Structural Equation Modeling, The Guilford Press Inc, New York, 2005.

  • Byrne, B. M. (ed.), Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, Routledge, New York, 2010.

  • Stern, R. E., Uncitedness in the biomedical literature. J. Am. Soc. Inform. Sci., 1990, 41, 193–196.

  • Evans, J. A. and Reimer, J., Open access and global participation in science. Science, 2009, 323(5917), 1025.

  • Davis, P. M. and Walters, W. H., The impact of free access to the scientific literature: a review of recent research. J. Med. Libr. Assoc. Jmla, 2011, 99(3), 208.

  • MacCallum, R. C., Widaman, K. F., Zhang, S. and Hong, S., Sample size in factor analysis. Psychol. Meth., 1999, 4, 84–99.

  • Bryant, F. B. and Yarnold, P. R., Principal components analysis and exploratory and confirmatory factor analysis. In Reading and Understanding Multivariale Statistics (eds Grimm, L. G. and Yarnold, R. R.), American Psychological Association, Washington, DC, 1995, pp. 99–136.

  • Kline, P., Psychometrics and Psychology, Academic Press, London, 1979.

  • Cronbach, L. J., Coefficient alpha and the internal structure of tests. Psychometrika, 1951, 16(3), 297–334.

  • Hair, J. F., Tatham, R. L., Anderson, R. E. and Black, W., Multi-variate Data Analysis, Upper Saddle River: Prentice Hall, 1998.

  • Breslow, N., A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika, 1970, 57(3), 579–594.

  • Nunnally, J. C., Assessment of reliability. In Psychometric Theory, McGraw-Hill, New York, 1978, 2nd edn.

  • Cortina, J. M., What is coefficient alpha? An examination of theory and applications. J. Appl. Psychol., 1993, 78, 98–104.

  • Bollen, K. A. and Long, J. S., Testing structural equation models. Newbury Park, Sage Publications, 1993.

  • Egghe, L., The functional relation between the impact factor and the uncitedness factor revisited. J. Inform., 2013, 7(1), 183–189.

  • Webster, G. D., Jonason, P. K. and Schember, T. O., Hot topics and popular papers in evolutionary psychology: analyses of title words and citation counts in evolution and human behavior, 1979–2008. Evolutionary Psychol., 2009, 7(3), 348–362.


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  • A Survey-Based Structural Equation Model Analysis on Influencing Factors of Non-Citation

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Authors

Zewen Hu
School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
Yishan Wu
Chinese Academy of Science and Technology for Development, Beijing 100038, China
Jianjun Sun
School of Information Management, Nanjing University, Nanjing, Jiangsu 210093, China

Abstract


Although bibliometric approach has been frequently utilized to analyse reasons behind non-citation and show relations between uncitedness and impact factors, the survey-based structural equation model approach is not usually used. Therefore, a Likert scale questionnaire was designed to collect data on non-citation and its various types of determinants. The survey-based structural equation model was used to analyse mutual relations and correlation degrees between non-citation rate and its various determinants. As a result, the categories ‘academic status of journal’ and ‘personal features of papers’ were found to be two extremely significant determinants of non-citation rate. Their path coefficients reached 0.83 and 0.43 respectively. Accordingly, the category ‘contents and topics of papers’ was shown to have extremely significant indirect influence on non-citation rate through ‘academic status of journal’. The three observed variables of ‘academic status of journal’ including ‘public praise of journal’, ‘impact factor of journal’, and ‘member of SCI, EI and Scopus Journals’, showed the highest values of indirect effect on the non-citation rate. Furthermore, there were weaker correlations among ‘academic status of journal’, ‘personal features of papers’, ‘contents and topics of papers’ and ‘publicity and recommendation’ except between ‘contents and topics of papers’ and ‘academic status of journal’. Meanwhile, the six observed variables of ‘publicity and recommendation’ and ‘contents and topics of papers’ show the smaller values at ≤0.12 of indirect effect on non-citation rate. Our empirical results suggest some significant determinants of non-citation rate that might enlighten researchers on how to improve the chance of having their works cited, and assist them in expanding their research impact. These findings can also help journal editors to identify contributions with high-citation potential.

Keywords


Determinants, Impact Factor, Influencing Factors, Non-Citation, Questionnaire, Structural Equation Model, Uncitedness.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi11%2F2302-2312