Abstract In the solar energy field, scientists publish numerous scientific articles every year. Some are highly-cited, while others may not even be cited. In this paper, we introduce two underlying scientific properties of a paper to explain this paper’s highly-cited or un-cited probability: scientific relatedness and intellectual base. We utilize two main network techniques, knowledge element coupling network (concurrence-based) and paper citation network (citation-based) analyses, to measure scientific relatedness and intellectual base, respectively. What’s more, we conduct descriptive analyses of un-cited and highly-cited papers at the country, organization and journal levels. Then we map knowledge element co-occurrence networks and paper citation networks to compare the network characteristics of un-cited and highly-cited papers. Further, we use article data in the solar energy field between 2004 and 2010 to examine our hypotheses. Findings from Ordered Logit Models indicate that when the scientific relatedness of a paper is high, this paper is more likely to be un-cited, whereas less likely to be highly-cited. The paper with higher intellectual base has a higher possibility to be highly-cited, whereas a low possibility to be un-cited. Overall, this paper provides important insights into the determinant factors of a paper’s citation levels, which is helpful for researchers maximizing the scientific impact of their efforts.
['Jingjing Zhang', 'Jiancheng Guan']
Scientific relatedness and intellectual base: a citation analysis of un-cited and highly-cited papers in the solar energy field