Usage impact factor: The effects of sample characteristics on usage‐based impact metrics

Abstract

There exist ample demonstrations that indicators of scholarly impact analogous to the citation-based ISI Impact Factor can be derived from usage data; however, so far, usage can practically be recorded only at the level of distinct information services. This leads to communityspecific assessments of scholarly impact that are difficult to generalize to the global scholarly community. In contrast, the ISI Impact Factor is based on citation data and thereby represents the global community of scholarly authors. The objective of this study is to examine the effects of community characteristics on assessments of scholarly impact from usage. We define a journal Usage Impact Factor that mimics the definition of the Thomson Scientific ISI Impact Factor. Usage Impact Factor rankings are calculated on the basis of a large-scale usage dataset recorded by the linking servers of the California State University system from 2003 to 2005. The resulting journal rankings are then compared to the Thomson Scientific ISI Impact Factor that is used as a reference indicator of general impact. Our results indicate that the particular scientific and demographic characteristics of a discipline have a strong effect on resulting usagebased assessments of scholarly impact. In particular, we observed that as the number of graduate students and faculty increases in a particular discipline, Usage Impact Factor rankings will converge more strongly with the ISI Impact Factor.

Who reads research articles? An altmetrics analysis of Mendeley user categories

“…Rowlands & Nicholas, 2007;Bollen & Van De Sompel, 2008) using usage datasets that were mainly local (institutional), publisher dependent and not publically-accessible, and therefore not practical for most researchers. Thus, this research gives a practical solution with global usage data for multiple disciplines for the first time.…”

“…Outside academia, practitioners and developers use research publications in their daily activities (Bollen & Van De Sompel, 2008) but the roles, tasks and the contexts in which they use information can affect their information seeking behaviours (Leckie, Pettigrew, & Sylvain, 1996). For example, a survey of non-author physicians in Canada discovered that 73% of the participants read journal articles (McAlister, Graham, Karr, & Laupacis, 1999).…”

A multi-metric approach for research evaluation

“…Bollen, Van de Sompel and their team [12][13][14] apply different usage or download data in the context of a large project called MESUR (Metrics from Scholarly Use of Resources). This group also makes use of clickstream data, virtual trails of users surfing the Internet [15].…”

Going beyond Citations: SERUM — a new Tool Provided by a Network of Libraries

“…Most suggestions are based on the classical citation indicators from the Journal Citation Reports (JCR), using download data (usually full-text article requests) instead of citations. The corresponding usage metrics are usage impact factor (UIF) (Rowlands and Nicholas, 2007;Bollen and Van de Sompel, 2008), usage immediacy index (Rowlands and Nicholas, 2007), download immediacy index (Wan et al, 2008), and usage half-life (Rowlands and Nicholas, 2007).…”

“…Usage metrics can therefore be regarded as complementary to citation metrics; they reflect usage in a much broader scope (Duy and Vaughan, 2006) and have caused the emergence of the field of bibliometric research (Bollen and Van de Sompel, 2008;Schloegl and Gorraiz, 2009). This paper suggests an approach to providing global usage metrics which is supported by libraries.…”