A systematic empirical comparison of different approaches for normalizing citation impact indicators

We address the question how citation-based bibliometric indicators can best be normalized to ensure fair comparisons between publications from different scientific fields and different years. In a systematic large-scale empirical analysis, we compare a traditional normalization approach based on a field classification system with three source normalization approaches. We pay special attention to the selection of the publications included in the analysis. Publications in national scientific journals, popular scientific magazines, and trade magazines are not included. Unlike earlier studies, we use algorithmically constructed classification systems to evaluate the different normalization approaches. Our analysis shows that a source normalization approach based on the recently introduced idea of fractional citation counting does not perform well. Two other source normalization approaches generally outperform the classification-system-based normalization approach that we study. Our analysis therefore offers considerable support for the use of source-normalized bibliometric indicators

Benford's Law and articles of scientific journals: comparison of JCR(A (R)) and Scopus data

Benford's Law is a logarithmic probability distribution function used to predict the distribution of the first significant digits in numerical data. This paper presents the results of a study of the distribution of the first significant digits of the number of articles published of journals indexed in the JCR(A (R)) Sciences and Social Sciences Editions from 2007 to 2011. the data of these journals were also analyzed by the country of origin and the journal's category. Results considering the number of articles published informed by Scopus are also presented. Comparing the results we observe that there is a significant difference in the data informed in the two databases.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)INPE, Natl Inst Space Res, BR-12227010 Sao Jose Dos Campos, SP, BrazilUniversidade Federal de São Paulo, UNIFESP, BR-12231280 Sao Jose Dos Campos, SP, BrazilITA, Aeronaut Inst Technol, BR-12228900 Sao Jose Dos Campos, SP, BrazilUniversidade Federal de São Paulo, UNIFESP, BR-12231280 Sao Jose Dos Campos, SP, BrazilWeb of Scienc

A review of the literature on citation impact indicators

Citation impact indicators nowadays play an important role in research evaluation, and consequently these indicators have received a lot of attention in the bibliometric and scientometric literature. This paper provides an in-depth review of the literature on citation impact indicators. First, an overview is given of the literature on bibliographic databases that can be used to calculate citation impact indicators (Web of Science, Scopus, and Google Scholar). Next, selected topics in the literature on citation impact indicators are reviewed in detail. The first topic is the selection of publications and citations to be included in the calculation of citation impact indicators. The second topic is the normalization of citation impact indicators, in particular normalization for field differences. Counting methods for dealing with co-authored publications are the third topic, and citation impact indicators for journals are the last topic. The paper concludes by offering some recommendations for future research

Extending the Foresight of Phillip Ein-Dor: Causal Knowledge Analytics

Phillip Ein-Dor advocated that electronic journals be more than a PDF of the established text model. He envisioned a transformation of scholarship. The need for such a transition has only grown since the first issue of JAIS in 2000 because the continuing growth and fragmentation of knowledge limits the generation of new knowledge. We propose drawing on analytics and AI to accelerate and transform scholarship, providing an appropriate tribute to a visionary IS leader

Extracting Causal Claims from Information Systems Papers with Natural Language Processing for Theory Ontology Learning

The number of scientific papers published each year is growing exponentially. How can computational tools support scientists to better understand and process this data? This paper presents a software-prototype that automatically extracts causes, effects, signs, moderators, mediators, conditions, and interaction signs from propositions and hypotheses of full-text scientific papers. This prototype uses natural language processing methods and a set of linguistic rules for causal information extraction. The prototype is evaluated on a manually annotated corpus of 270 Information Systems papers containing 723 hypotheses and propositions from the AIS basket of eight. F1-results for the detection and extraction of different causal variables range between 0.71 and 0.90. The presented automatic causal theory extraction allows for the analysis of scientific papers based on a theory ontology and therefore contributes to the creation and comparison of inter-nomological networks

Influence of journals indexed from a country on its research output: An empirical investigation

Scientific journals are currently the primary medium used by researchers to report their research findings. The transformation of print journals into e-journals has simplified the process of submissions to journals and also their access has become wider. Journals are usually published by commercial publishers, learned societies as well as Universities. There are different number of journals published from different countries. This paper attempts to explore whether the number of journals published from a country influences its research output. Scopus master journal list is analysed to identify journals published from 50 selected countries with significant volume of research output. The following relationship are analysed: (a) number of journals from a country and its research output, (b) growth rate of journals and research output for different countries, (c) global share of journals and research output for different countries, and (d) subject area-wise number of journals and research output in that subject area for different countries. Factors like journal packing density are also analysed. The results obtained show that for majority of the countries, the number of journals is positively correlated to their research output volume, though some other factors also play a role in growth of research output. The study at the end presents a discussion of the analytical outcomes and provides useful suggestions on policy perspectives for different countries.Comment: 6 figures and 4 table

“Formal and informal networkedness among German Academics”: exploring the role of conferences and co-publications in scientific performance

This paper builds on the established finding that the performance of scholars depends on their interpersonal networks. Until now, these networks have largely been measured by analysing the credits and acknowledgements on their publications, especially their co-authorships. First, it seeks to clarify inconsistencies in existing findings by providing a comprehensive analysis of the effects of co-authorship among the overall population of actively publishing researchers from Germany. Second, it acknowledges that co-publication is only one very formal and explicit form of academic networking and develops a new indicator based on an academic’s inferred co-presence at conferences. Comparing the impact of these two different aspects of networkedness, we find that hierarchy and influence play a stronger role in determining a scientist’s performance in the context of informal networks than they do when considering formal co-publication networks