How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

What is research collaboration

This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature—an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981–2004) and terrorism (1990–2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified. © 2006 Wiley Periodicals, Inc.

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CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature

Evaluating scientific quality is a notoriously difficult problem which has no standard solution. Ideally, published scientific results should be scrutinised by true experts in the field and given scores for quality and quantity according to established rules. In practice, however, what is called peer review is usually performed by committees with general competence rather than with the specialist's insight that is needed to assess primary research data. Committees tend, therefore, to resort to secondary criteria like crude publication counts, journal prestige, the reputation of authors and institutions, and estimated importance and relevance of the research field,1 making peer review as much of a lottery as of a rational process.2 3

On this background, it is hardly surprising that alternative methods for evaluating research are being sought, such as citation rates and journal impact factors, which seem to be quantitative and objective indicators directly related to published science. The citation data are obtained from a database produced by the Institute for Scientific Information (ISI) in Philadelphia, which continuously records scientific citations as represented by the reference lists of articles from a large number of the world's scientific journals. The references are rearranged in the database to show how many times each publication has been cited within a certain period, and by whom, and the results are published as the Science Citation Index (SCI) . On the basis of the Science Citation Index and authors' publication lists, the annual citation rate of papers by a scientific author or research group can thus be calculated. Similarly, the citation rate of a scientific journal—known as the journal impact factor—can be calculated as the mean citation rate of all the articles contained in the journal.4 Journal impact factors, which are published annually in SCI Journal Citation Reports , are widely regarded as …

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Why the impact factor of journals should not be used for evaluating research

Bibliometric methods are used in multiple fields for a variety of purposes, namely for research evaluation. Most bibliometric analyses have in common their data sources: Thomson Reuters' Web of Science (WoS) and Elsevier's Scopus. The objective of this research is to describe the journal coverage of those two databases and to assess whether some field, publishing country and language are over or underrepresented. To do this we compared the coverage of active scholarly journals in WoS (13,605 journals) and Scopus (20,346 journals) with Ulrich's extensive periodical directory (63,013 journals). Results indicate that the use of either WoS or Scopus for research evaluation may introduce biases that favor Natural Sciences and Engineering as well as Biomedical Research to the detriment of Social Sciences and Arts and Humanities. Similarly, English-language journals are overrepresented to the detriment of other languages. While both databases share these biases, their coverage differs substantially. As a consequence, the results of bibliometric analyses may vary depending on the database used. These results imply that in the context of comparative research evaluation, WoS and Scopus should be used with caution, especially when comparing different fields, institutions, countries or languages. The bibliometric community should continue its efforts to develop methods and indicators that include scientific output that are not covered in WoS or Scopus, such as field-specific and national citation indexes.

The journal coverage of Web of Science and Scopus: a comparative analysis

Preface.- Executive Summary.- Part 1 General Introduction and Main Conclusions.- Part 2 Empirical and Theoretical Chapters.- Part 2.1 Assessing Basic Science Research Departments and Scientific Journals.- Part 2.2 The ISI Citation Indexes.- Part 2.3 Assessing Social Sciences and Humanities.- Part 2.4 Accuracy Aspects.- Part 2.5 Theoretical Aspects.- Part 2.6 Citation Analysis and Peer Review.- Part 2.7 Macro Studies.- Part 2.8 New Developments.- References.- Index of Keywords, Cited Works and Cited Authors.

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Citation Analysis in Research Evaluation

Handbook of Quantitative Science and Technology Research

The Impact Factor introduced by Eugene Garfield is a fundamental citation-based measure for significance and performance of scientific journals. It is perhaps the most popular bibliometric product used in bibliometrics itself, as well as outside the scientific community. First, a concise review of the background and history of the ISI impact factor and the basic ideas underlying it are given. A cross-citation matrix is used to visualise the construction of the Impact Factor and several related journal citation measures.Both strengths and flaws of the impact factor are discussed. Several attempts made by different authors to introduce more sophisticated journal citation measures and the reasons why many indicators aiming at a correction of methodological limitations of the Impact Factor were not successful are described. The next section is devoted to the analysis of basic technical and methodological aspects. In this context, the most important sources of possible biases and distortions for calculation and use of journal citation measures are studied. Thereafter, main characteristics of application contexts are summarised. The last section is concerned with questions of statistical reliability of journal citation measures. It is shown that in contrast to a common misbelief statistical methods can be applied to discrete "skewed" distributions, and that the statistical reliability of these statistics can be used as a basis for application of journal impact measures in comparative analyses. Finally, the question of sufficiency or insufficiency of a single, howsoever complex measure for characterising the citation impact of scientific journals is discussed.

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Journal impact measures in bibliometric research

Measuring contextual citation impact of scientific journals

After a review of developments in the quantitative study of science, particularly since the early 1970s, I focus on two current main lines of ‘measuring science’ based on bibliometric analysis. With the developments in the Leiden group as an example of daily practice, the measurement of research performance and, particularly, the importance of indicator standardisation are discussed, including aspects such as interdisciplinary relations, collaboration, ‘knowledge users’. Several important problems are addressed: language bias; timeliness; comparability of different research systems; statistical issues; and the ‘theory–invariance’ of indicators. Next, an introduction to the mapping of scientific fields is presented. Here basic concepts and issues of practical application of these ‘science maps’ are addressed. This contribution is concluded with general observations on current and near-future developments, including network–based approaches, necessary ‘next steps’ are formulated, and an answer is given to the question ‘Can science be measured?’ 1. TOWARD A METRIC OF SCIENCE REVISITED From the early sixties onwards we see a strong increase in quantitative material on the state-of-the art in science and technology. National institutes of statistics, UNESCO, OECD, and the European Commission are main examples of organisations starting to collect systematically data on the 1 The book Toward a metric of Science: The Advent of Science Indicators (Elkana et al., 1978) has always been a one of my major sources of inspiration. This contribution to the Handbook is based on earlier publications by the author (Van Raan 2000a; Van Raan and Noyons 2002).

Henk F. Moed

Papers

Citation Analysis in Research Evaluation

Handbook of Quantitative Science and Technology Research

Journal impact measures in bibliometric research

Measuring contextual citation impact of scientific journals

Handbook of Quantitative Science and Technology Research: The Use of Publication and Patent Statistics in Studies of S&T Systems