A quantitative analysis of researcher citation personal display considering disciplinary differences and influence factors

Xingchen Li, Qiang Wu () and Yuanyuan Liu
Additional contact information
Xingchen Li: University of Science and Technology of China
Qiang Wu: University of Science and Technology of China
Yuanyuan Liu: University of Science and Technology of China

Scientometrics, 2017, vol. 113, issue 2, No 21, 1093-1112

Abstract: Abstract Personal websites are a good place not only for the scientists to show a wealth of content, but also for researchers to excavate some useful information related to quantitative evaluation. Based on researchers’ personal websites this study aims to investigate the degree of citation personal display (CPD) in three major disciplines (chemistry, mathematics, and physics), as well as disciplinary differences in CPD. This paper also studies the factors which have influences on CPD by using binary logistic regression. The datasets studied consisted of 5771 researchers in 39 U.S. universities. Results show that CPD varies significantly by discipline, with chemistry researchers having the highest CPD (15.3%), followed by physics researchers (12.7%), and mathematics researchers (7.1%). The binary logistic models indicate that total citations, h-index, and citations per publication have significantly positive effects on CPD in chemistry; for mathematics, total citations and h-index do; and for physics, only total citations does (p

Keywords: Personal website; Citation personal display; Bibliometric indicators; Citation analysis; Binary logistic regression; Disciplinary differences; Influence factors (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-017-2501-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:113:y:2017:i:2:d:10.1007_s11192-017-2501-0

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-017-2501-0

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().