PurposeThe purpose of this paper is to present a narrative review of studies on the citing behavior of scientists, covering mainly research published in the last 15 years. Based on the results of these studies, the paper seeks to answer the question of the extent to which scientists are motivated to cite a publication not only to acknowledge intellectual and cognitive influences of scientific peers, but also for other, possibly non‐scientific, reasons.Design/methodology/approachThe review covers research published from the early 1960s up to mid‐2005 (approximately 30 studies on citing behavior‐reporting results in about 40 publications).FindingsThe general tendency of the results of the empirical studies makes it clear that citing behavior is not motivated solely by the wish to acknowledge intellectual and cognitive influences of colleague scientists, since the individual studies reveal also other, in part non‐scientific, factors that play a part in the decision to cite. However, the results of the studies must also be deemed scarcely reliable: the studies vary widely in design, and their results can hardly be replicated. Many of the studies have methodological weaknesses. Furthermore, there is evidence that the different motivations of citers are “not so different or ‘randomly given’ to such an extent that the phenomenon of citation would lose its role as a reliable measure of impact”.Originality/valueGiven the increasing importance of evaluative bibliometrics in the world of scholarship, the question “What do citation counts measure?” is a particularly relevant and topical issue.

In this study, we examined empirical results on the h index and its most important variants in order to determine whether the variants developed are associated with an incremental contribution for evaluation purposes. The results of a factor analysis using bibliographic data on postdoctoral researchers in biomedicine indicate that regarding the h index and its variants, we are dealing with two types of indices that load on one factor each. One type describes the most productive core of a scientist's output and gives the number of papers in that core. The other type of indices describes the impact of the papers in the core. Because an index for evaluative purposes is a useful yardstick for comparison among scientists if the index corresponds strongly with peer assessments, we calculated a logistic regression analysis with the two factors resulting from the factor analysis as independent variables and peer assessment of the postdoctoral researchers as the dependent variable. The results of the regression analysis show that peer assessments can be predicted better using the factor 'impact of the productive core' than using the factor 'quantity of the productive core.'

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With the ready accessibility of bibliometric data and the availability of ready-to-use tools for generating bibliometric indicators for evaluation purposes, there is the danger of inappropriate use. Here we present standards of good practice for analyzing bibliometric data and presenting and interpreting the results. Comparisons drawn between research groups as to research performance are valid only if (1) the scientific impact of the research groups or their publications are looked at by using box plots, Lorenz curves, and Gini coefficients to represent distribution characteristics of data (in other words, going beyond the usual arithmetic mean value), (2) different reference standards are used to assess the impact of research groups, and the appropriateness of the reference standards undergoes critical examination, and (3) statistical analyses comparing citation counts take into consideration that citations are a function of many influencing factors besides scientific quality.

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How does one measure the quality of science? The question is not rhetorical; it is extremely relevant to promotion committees, funding agencies, national academies and politicians, all of whom need a means by which to recognize and reward good research and good researchers. Identifying high-quality science is necessary for science to progress, but measuring quality becomes even more important in a time when individual scientists and entire research fields increasingly compete for limited amounts of money. The most obvious measure available is the bibliographic record of a scientist or research institute-that is, the number and impact of their publications.Currently, the tool most widely used to determine the quality of scientific publications is the journal impact factor (IF), which is calculated by the scientific division of Thomson Reuters (New York, NY, USA) and is published annually in the Journal Citation Reports ( JCR). The IF itself was developed in the 1960s by Eugene Garfield and Irving H. Sher, who were concerned that simply counting the number of articles a journal published in any given year would miss out small but influential journals in their Science Citation Index (Garfield, 2006). The IF is the average number of times articles from the journal published in the past two years have been cited in the JCR year and is calculated by dividing the number of citations in the JCR year-for example, 2007-by the total number of articles published in the two previous years-2005 and 2006. Lutz Bornmann (left) and Hans-Dieter Daniel are at the ETH Zurich, Switzerland.