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

Abstract Views: 444  |  PDF Views: 122

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