Tracking areas of strategic importance using scientometric journal mappings

doi:10.1016/0048-7333(94)90054-X

Research Policy

Volume 23, Issue 2, March 1994, Pages 217-229

https://doi.org/10.1016/0048-7333(94)90054-X Get rights and content

Abstract

In science policy, it is often important to track emerging developments: new fields, fast-changing areas that are the focus of special funding efforts, or areas of growth or decline. This article presents methods to produce literature-based indicators for such areas using journal-to-journal citations. Using case studies of AIDS, superconductivity, and oncogenes, we posit that the inclusion of a new journal can be used as an indicator of structural change if the addition indicates the emergence of a new journal category. Using the cases of robotics and artificial intelligence, we illustrate the development of areas chosen for priority funding. Again using artificial intelligence, we demonstrate the importance of constructing even such simple measures of scientific performance as publication counts using dynamic rather than constant journal sets. Change in performance within a subfield can be systematically distinguished from change in the delineations among subfields over time.

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Detecting potential opportunities for scientific advancement and technological progress is vital for academia and technology-based firms. While patent data have become an extensively employed source for technological forecasting, scientific publications that represent advanced knowledge provide a potential for technological commercialization. Thus, the primary purpose of this study intends to discover potential opportunities for scientific advancement and technological innovation by comparing the information in scientific publications and patents. Accordingly, this study applies text mining and the arbitrarily ORiented projected CLUSter generation (ORCLUS) algorithm to cluster important scientific and technological topics. To identify potential opportunities for scientific advancement or technological commercialization, the cosine similarity of tf-idf vectors is used to detect the semantic similarity between clustered scientific fields and technological fields. Smart health monitoring technology is the case employed in this study. The results not only demonstrate the effectiveness of the text-based clustering approach and semantic similarity for exploring technological opportunities but also identify the potential patenting opportunities in the management of smart health monitoring and the potential scientific advancement in home health care systems.
Conceptual definition of technology emergence: A long journey from philosophy of science to science policy
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Citation Excerpt :
Therefore, in the first phase (Fig. 2), we assume that without describing the emergence basis appropriately, we might frame [65] our future thinking while coming up with new ideas and concepts because of the randomness of outcomes and the bias of findings. As stated by L. Leydesdorff, Cozzens, and Vandenbesselaar [66]; different perspectives and different inputs might create different outcomes. For overcoming these bottlenecks, we assert that the emergence basis should demonstrate two distinctive aspects -- highly creative and highly synergistic.
Technology and innovation policy has been the core of economic development since the 1950s. Moreover, the increasing rate of technological developments allowed policy and strategy makers to think about technology emergence for proactive planning. In this sense, technological emergence became popular with different applications and foresight studies. However, even though it is a popular concept in the literature, an accepted formal definition is lacking. In this study, we propose a definition based on review as; “Technological emergence is a cyclic process in highly creative scientific networks that demonstrates qualitative novelty, qualitative synergy, trend irregularity, high functionality, and continuity aspects in a specified time frame”. We discuss and explain the definition by demonstrating it in a multi-dimensional model to make it understandable and applicable for future studies. Finally, we discuss it with existing literature and propose future implications.
Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation
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Identification of such high-potential research and development areas is a challenging task. Making well-informed decisions requires detailed knowledge of past findings and current trends, and a deep understanding of emerging technology pathways (Leydesdorff et al., 1994; Yoon and Park, 2005). At the same time, it asks for a broad perspective to oversee future needs while identifying the opportunities that arise in neighboring research domains.
Science foresight comprises a range of methods to analyze past, present and expected research trends, and uses this information to predict the future status of different fields of science and technology. With the ability to identify high-potential development directions, science foresight can be a useful tool to support the management and planning of future research activities. Science foresight analysts can choose from a rather large variety of approaches. There is, however, relatively little information about how the various approaches can be applied in an effective way. This paper describes a three-step methodological framework for science foresight on the basis of published research papers, consisting of (i) life-cycle analysis, (ii) text mining and (iii) knowledge gap identification by means of automated clustering. The three steps are connected using the research methodology of the research papers, as identified by text mining. The potential of combining these three steps in one framework is illustrated by analyzing scientific literature on wind catchers; a natural ventilation concept which has received considerable attention from academia, but with quite low application in practice. The knowledge gaps that are identified show that the automated foresight analysis is indeed able to find uncharted research areas. Results from a sensitivity analysis further show the importance of using full-texts for text mining instead of only title, keywords and abstract. The paper concludes with a reflection on the methodological framework, and gives directions for its intended use in future studies.
What is an emerging technology?
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Citation Excerpt :
Abbreviations or acronyms take time to appear and, when they do, signal persistence; they also indicate shared interpretations and thus coherence (Reardon, 2014). Additional signals of coherence may come from the creation of conference sessions, tracks, dedicated conferences and subsequently from journal special issues and new specialist journals (Leydesdorff et al., 1994). New categories in established classification systems may also be created (Cozzens et al., 2010).
There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet, as an area of study, emerging technologies lack key foundational elements, namely a consensus on what classifies a technology as ‘emergent’ and strong research designs that operationalise central theoretical concepts. The present paper aims to fill this gap by developing a definition of ‘emerging technologies’ and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of ‘emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.
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A recent observation reports that review papers and meta-analysis papers have more citation impact than standard articles, which reflects the problem of excessive publications [8]. Although strategic decisions on research and development (R&D) should be made after observing and analyzing global R&D trends by reading research papers or patents [2], there is a tendency to miss emerging and potential research areas because of the flood of publications. It brings the risk that R&D will miss potential emerging research areas.
The detection of promising academic research is vital for firms in a variety of sectors. Bibliometric tools can be used to detect such research hidden in a pile of papers and patents; however, the relationship between academic research and industrial technology development has not been well documented. In this paper, we introduced patent relatedness, which measures the semantic similarity of papers and patents, and conducted a case study on polymer electrolyte fuel cells (PEFC). The results show that in an academic research area with a small number of papers, recent average publication year, low patent relatedness has a high potential to increase in subsequent years. Research areas are identified by clustering the citation network of academic papers, and their patent relatedness and time series variation were measured and analyzed. Our results showed that potential research areas were characterized by small but emerging features. Using these findings, we identified the potential PEFC research areas and the research capability of each country.

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^☆: We gratefully acknowledge the support of the National Science Foundation (grant SRS-8810197) for the research reported here.

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Tracking areas of strategic importance using scientometric journal mappings☆

Abstract

Social Networks

The Chronology of AIDS Research

Nature

On-line Approaches to Measuring National Scientific Output — A Cautionary Tale

Science and Public Policy

Possible High Tc Superconductivity in the Ba-La-Cu-O System

Zeitschrift für Physik B

Assessing Assessments of British Science. Some Facts and Figures to accept or Decline

Scientometrics

The Bibliometric Assessment of UK Scientific Performance — Some Comments on Martin's Reply

Scientometrics

From Translations to Problematic Networks: An Introduction to Co-word Analysis

Social Science Information

What do Citations count? The Rhetoricfirst Model

Scientometrics

Journal Systems as Macro Indicators of Structural Change in the Sciences

Structural Equivalence in a Journal Network

Journal of the American Society of Information Science

How ISI selects Journals for Coverage: Quantitative and Qualitative Considerations

Current Contents

Information Theory. Structural Models for Qualitative Data

The Development of Frames of Reference

Scientometrics

Various Methods for the Mapping of Science

Scientometrics

The Prediction of Science Indicators Using Information Theory

Scientometrics

Irreversibilities in Science and Technology Networks: An Empirical and Analytical Approach

Scientometrics

The Impact of Citation Behaviour on Citation Structure