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Scholarly usage data provides unique opportunities to address the known shortcomings of citation analysis. However, the collection, processing and analysis of usage data remains an area of active research. This article provides a review of the state-of-the-art in usage-based informetric, i.e. the use of usage data to study the scholarly process.
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Bibliometric indicators, citation counts and/or download counts are increasingly being used to inform personnel decisions such as hiring or promotions. These statistics are very often misused. Here we provide a guide to the factors which should be considered when using these so-called quantitative measures to evaluate people. Rules of thumb are given for when begin to use bibliometric measures when comparing otherwise similar candidates.
Properties of a percentile-based rating scale needed in bibliometrics are formulated. Based on these properties, P100 was recently introduced as a new citation-rank approach (Bornmann, Leydesdorff, & Wang, in press). In this paper, we conceptualize P100 and propose an improvement which we call P100_. Advantages and disadvantages of citation-rank indicators are noted.
We perform a statistical analysis of scientific-publication data with a goal to provide quantitative analysis of scientific process. Such an investigation belongs to the newly established field of scientometrics: a branch of the general science of science that covers all quantitative methods to analyze science and research process. As a case study we consider download and citation statistics of the journal `Europhysics Letters (EPL), as Europes flagship letters journal of broad interest to the physics community. While citations are usually considered as an indicator of academic impact, downloads reflect rather the level of attractiveness or popularity of a publication. We discuss peculiarities of both processes and correlations between them.
Field-normalization of citations is bibliometric standard. Despite the observed differences in citation counts between fields, the question remains how strong fields influence citation rates beyond the effect of attributes or factors possibly influencing citations (FICs). We considered several FICs such as number of pages and number of co-authors in this study. We wondered whether there is a separate field-effect besides other effects (e.g., from numbers of pages and co-authors). To find an answer on the question in this study, we applied inverse-probability of treatment weighting (IPW). Using Web of Science data (a sample of 308,231 articles), we investigated whether mean differences among subject categories in citation rates still remain, even if the subject categories are made comparable in the field-related attributes (e.g., comparable of co-authors, comparable number of pages) by IPW. In a diagnostic step of our statistical analyses, we considered propensity scores as covariates in regression analyses to examine whether the differences between the fields in FICs vanish. The results revealed that the differences did not completely vanish but were strongly reduced. We received similar results when we calculated mean value differences of the fields after IPW representing the causal or unconfounded field effects on citations. However, field differences in citation rates remain. The results point out that field-normalization seems to be a prerequisite for citation analysis and cannot be replaced by the consideration of any set of FICs in citation analyses.
Trade and investment between developing regions such as China and Latin America (LATAM) are growing prominently. However, insights on crucial factors such as innovation in business and management (iBM) about both regions have not been scrutinized. This study presents the research output, impact, and structure of iBM research published about China and LATAM in a comparative framework using Google Scholar, Dimensions, and Microsoft Academic. Findings showed i) that iBM topics of both regions were framed within research and development management, and technological development topics, ii) significant differences in output and impact between regions, and iii) the same case for platforms.
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