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| Impact of Article Similarity on Citation Counts during Researchers' Career Development |
| Zhang Lihua1, Zhang Kangning1, Zhao Yingguang2, Zhang Zhiqiang3,4 |
1.School of Information, Shanxi University of Finance and Economics, Taiyuan 030006
2.Beijing Jiaotong University Library, Beijing 100044
3.Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041
4.Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 |
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Abstract During each researcher's career development, it is fundamental that they pay attention to the serious issue of identifying an appropriate time to change their research direction, if needed. This study aims to explore the impact of article similarity on citation counts during researchers' career development. Based on the data of authors and papers of “Business & Economics” and “Computer Science, Artificial Intelligence,” a negative binomial regression model was constructed with article similarity as the independent variable; citation counts of article as the dependent variable; and the number of authors, length of papers, number of references, journal influence factors, and author's academic age as control variables. The results show that article similarity during a researcher's career development in the two disciplines presents the distribution characteristic of “high in the middle and low on both sides.” Notably, 39.5 percent of researchers in Business & Economics switched their research topics, and article similarity proved to have no effect on citation counts. However, 45.6 percent of researchers in Computer Science, Artificial Intelligence also changed their research topics, and article similarity impacted citation counts.
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Received: 03 September 2021
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