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Emerging research fronts in science and technology: patterns of new knowledge development

By Samuel Phineas Upham

Part 1

Abstract Research fronts represent the most dynamic areas of science and technology

and the areas that attract the most scientific interest. We construct a methodology to

identify these fronts, and we use quantitative and qualitative methodology to analyze and

describe them. Our methodology is able to identify these fronts as they form—with

potential use by firms, venture capitalists, researchers, and governments looking to identify

emerging high-impact technologies. We also examine how science and technology absorbs

the knowledge developed in these fronts and find that fronts which maximize impact have

very different characteristics than fronts which maximize growth, with consequences for

the way science develops over time.


Keywords Innovation  Clusters  Emerging science  Research fronts


Areas of scientific research that generate intense interest from other scientists tend to be

perceived as the most promising (Braam et al. 1988; Hirschman 1970), are particularly

well funded (Boyack and Borner 2003), and are more likely to result in commercial

discoveries (Narin et al. 1997; Trajtenberg 1990). In this paper we study small clusters of

highly cited research, called ‘‘research fronts.’’ We work to provide quantitative and

qualitative support for continued, focused study of these areas as important for understanding

the development of science and technology more broadly. These areas of intensive

work are interesting to R&D laboratories looking for future innovation breakthroughs,

venture capitalists looking to allocate investment, governments interested in promoting

emerging science, and researchers hoping to work on promising topics.

The long-term goal of this work is to develop a robust and efficient methodology for

identifying and tracking highly cited research areas at the micro-specialty level. This

includes detecting them as they emerge and understanding the role these fronts play in the

development of science and technology. The broad requirement of this methodology is that

it does not presuppose the existence of any specific research area, such as would be

required in a traditional literature-searching approach, nor any prior knowledge about the

scientific area, but instead relies on an objective, comprehensive monitoring of citations. It

should be possible to increase or decrease the sensitivity of the detection by adjusting

parameters and make direct comparisons of different time slices. In addition, the method

should be multidisciplinary and utilize field normalization to obtain a systematic view

across different disciplines. The scope should be scalable from the micro-structure to the

macro-structure of science to see the context of the innovation. Finally, the method should

capture both social aspects and the topical content of scientific areas.

In order to establish this methodology for the identification and exploration of emergent

research fronts, we first describe the distinguishing factors of such fronts. In the second

section, we provide an overview of the existing literature and its contribution to the field.

The third section of this paper delves into the proposed methodology of co-citation clusters.

In the forth section, we describe our quantitative data, the rationale behind the choice

of variables as well as the regression model to analyze the relationship between emergence

and absorption of research fronts. The qualitative analysis of the following section builds

on this methodology to highlight examples from scientific research being conducted in top

U.S. universities. The case studies ground our work with specific examples and explore

interesting aspects of these cases, suggesting future extensions for this work. Finally, we

discuss the overarching findings of this study, and reiterate the contributions of our proposed

methodology to the field of science and technology studies.

Characteristics of research fronts

A research front can be conceptualized as the convergence of scientific findings and social

interests. New scientific findings may initiate the process of front formation by attracting

the interest of more scientists who form social ties and generate more findings. The

relevance and bearing of each new finding is continuously defined and evolved by the

group. The foci of interests can be driven, of course, by the sources of funding as well as

perceived scientific potential. This combined intellectual and social process is seen most

vividly in the publications and citation patterns in science and technology. It is manifest in

the emergence of clusters of highly cited papers representing the key scientific findings that

are cited jointly. The authors of these cited and citing papers form what Derek Price has

called an ‘‘invisible college.’’

For successful research fronts that generate important findings there are two possible

outcomes—they can grow independently as areas of study, or be ‘‘absorbed’’ by others as

the result of their impact. In the first case, the front may initially grow in size and then split

off as a new field of research or even develop into a new discipline (Small and Greenlee

1990). Alternatively, in the second case, a successful front may have great influence on its

field and thus be incorporated by it, effectively being ‘‘absorbed’’ by the appropriation of

the insights of the front within a broader field. This process of absorption, as it pertains to

specific findings and papers in science, was described by Robert Merton as ‘‘obliteration by

incorporation,’’ in which explicit mention of prior knowledge can disappear because of its

very success in generating interest and use (Merton 1968, 1972).

In this study we distinguish between these two outcomes by differentiating between

fronts that ‘‘emerge’’ by growing in size (growth) and fronts that are ‘‘absorbed’’ as a result

of their papers being increasingly cited (impact), resulting in a kind of absorption through

diffusion. Which fronts emerge and which are absorbed, we hypothesize, is significant in

determining the shape and structure of scientific and technical research as it evolves.

One notable aspect of research fronts is their potential to span traditional scientific

disciplines. Potentially, for example, fronts that combine disciplines and challenge existing

paradigms will have more difficulty being absorbed and may, in aggregate, presage paradigm

shifts (Kuhn 1970). The progress of science is a result of the virtual and actual

collaboration of thousands of scientists who, formally and informally, share their findings

and build on one another’s work. The research on explicit collaboration between scientists

has emphasized the value of cross-company alliances, informal networks, and social

capital (Gittelman 2003). Interdisciplinary work, as a process for sharing information and

as an inspiration for analogies, is often seen as one of the drivers of innovation (Amir 1985;

Birnbaum 1981a, b; Ponzi 2002). In the absence of such interdisciplinary work, knowledge

tends to become more compartmentalized and the interference of disruptive paradigms

become more likely as tensions between distinct theories accumulate (Fleming and

Sorenson 2001). We examine the role of interdisciplinary research in research fronts when

knowledge combines from different disciplines in an intellectually cohesive manner (Cuhls


We use the Web of Science database from Thomson Scientific of over 8,500 journals

and over one million articles per year in the sciences and social sciences and analyze it for

emerging fronts representing new micro-evolutions in science. Our data consist of highly

cited articles clustered through co-citation to obtain closely related sets of articles. Earlier

studies have shown that these clusters, or fronts, correspond to small, socially cohesive

groups of scientists working on closely related topics (Small 2003). A few studies have

attempted to find or predict emerging areas of research, particularly over such a comprehensive

database (Small 2005). Using single link clustering, we have a rapid and

efficient means to analyze large data sets in successive time frames to identify new or

continuing clusters. Our methodology builds on the contributions made by earlier research

aimed at delineating the patterns of science’s progression.


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