Emerging Technologies for the Enterprise
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Research Policy 44 (2015) 1827–1843
Contents lists available at ScienceDirect
Research Policy
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / r e s p o l
hat is an emerging technology?
aniele Rotolo a,b,∗, Diana Hicks b, Ben R. Martin a,c
SPRU – Science Policy Research Unit, University of Sussex, Brighton BN1 9SL, United Kingdom School of Public Policy, Georgia Institute of Technology, Atlanta 30332-0345, United States Centre for Science and Policy (CSAP) and Centre for Business Research, Judge Business School, University of Cambridge, Cambridge CB2 1QA, nited Kingdom
r t i c l e i n f o
rticle history: eceived 11 December 2014 eceived in revised form 15 June 2015 ccepted 16 June 2015 vailable online 9 August 2015
eywords: merging technologies onceptualisation efinition ttributes of emergence
a b s t r a c t
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 ele- ments, 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 frame- work 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
perationalisation etection and analysis ramework cientometrics ndicators cience and Technology Studies (STS)
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 indica- tors 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.
© 2015 Elsevier B.V. All rights reserved.
. Introduction
Emerging technologies have been the subject of much debate n academic research and a central topic in policy discussions and nitiatives. Evidence of the increasing attention being paid to the henomenon of emerging technologies can be found in the grow-
ng number of publications dealing with the topic and news articles entioning emerging technologies (in their headlines or lead para-
raphs), as depicted in Fig. 1. Increasing policy interest in emerging echnologies, however, must be set against a literature where no onsensus has emerged as to what qualifies a technology to be mergent. Definitions proposed by a number of studies overlap, ut also point to different characteristics. For example, certain def-
nitions emphasise the potential impact emerging technologies are apable of exerting on the economy and society (e.g. Porter et al., 002), especially when they are of a more ‘generic’ nature (Martin,
∗ Corresponding author at: SPRU – Science Policy Research Unit, University of ussex, Brighton BN1 9SL, United Kingdom. Tel.: +44 1273 872980.
E-mail addresses: d.rotolo@sussex.ac.uk (D. Rotolo), iana.hicks@pubpolicy.gatech.edu (D. Hicks), b.martin@sussex.ac.uk (B.R. Martin).
ttp://dx.doi.org/10.1016/j.respol.2015.06.006 048-7333/© 2015 Elsevier B.V. All rights reserved.
1995), while others give great importance to the uncertainty asso- ciated with the emergence process (e.g. Boon and Moors, 2008) or to the characteristics of novelty and growth (e.g. Small et al., 2014). The understanding of emerging technologies also depends on the analyst’s perspective. An analyst may consider a technology emer- gent because of its novelty and expected socio-economic impact, while others may see the same technology as a natural extension of an existing technology. Also, emerging technologies are often grouped together under ‘general labels’ (e.g. nanotechnology, syn- thetic biology), when they might be better treated separately given their different socio-technical features (e.g. technical difficulties, involved actors, applications, uncertainties).
The lack of consensus over definitions is matched by an ‘eclec- tic’ and ad hoc approach to measurement. A wide variety of methodological approaches have been developed, especially by the scientometric community, for the detection and analysis of emer- gence in science and technology domains (e.g. Porter and Detampel, 1995; Boyack et al., 2014; Glänzel and Thijs, 2012). These methods,
favoured, because they take advantage of growing computational power and large new datasets and allow one to work with more sophisticated indicators and models, lack strong connections to well thought out concepts that one is attempting to measure, a basic
1828 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843
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News articles Publications in all disciplines Publications in social sciences
Fig. 1. Publications (left axis) and news articles (right axis) including the variations of the term “emerging technologies”. Publications were retrieved by querying SCOPUS: “TITLE(“emerg* technol*”) OR TITLE(“emergence of* technolog*”) OR TITLE(“techn* emergence”) OR TITLE(“emerg* scien* technol*”)”. Publications in social sciences were defined as those assigned to the SCOPUS categories “Business, Management and Accounting”, “Decision Sciences”, “Economics, Econometrics and Finance”, “Multidisciplinary”, “Psychology”, and “Social Sciences”. News articles were identified by searching for “emerg* near2 technolog*” in article headlines and lead paragraphs as reported in FACTIVA. F conc 1 ly gro
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rom 1980 to 2013, the average yearly growth rates of the number of publications 2.5% and 23.8%, respectively. The total number of publications in SCOPUS has year
ource: search performed by authors on SCOPUS and FACTIVA.
enet of good research design. Often no definition of the central con- ept of an emerging technology is provided. It is no surprise there- ore that approaches to the detection and analysis of emergence end to differ greatly even with the use of the same or similar meth- ds. The operationalisation of emergence is also in a state of flux. t changes as new categorisations (e.g. new terms in institution- lised vocabularies, new technological classes) are created within atabases. This, in turn, makes less clear the exact nature of the phe- omena that these scientometric methods enable us to examine.
These problems in the effort to understand emerging technolo- ies limit the utility of the research and so may hamper resource llocation and the development of regulations, which, in turn, have
major role in supporting and shaping the directionality of tech- ological emergence.
The present paper addresses both the conceptual and method- logical gaps. We aim to elaborate a framework that links what is onceptualised as ‘emerging technologies’ with its measurement, hus providing guidance to future research (e.g. development of ovel methods for the detection of emergence and analysis of its haracteristics) and to policy-making (e.g. resource allocation, reg- lation). To do so, we first attempt to clarify the conceptualisation f emerging technologies by integrating different conceptual con- ributions on the topic into a more precise and coherent definition f ‘emerging technology’. We begin with the definition of ‘emer- ence’ or ‘emergent’, which is the process of coming into being, or f becoming important and prominent. This is then enriched and ontextualised with a review of major contributions to innovation tudies that have focused on technological emergence, highlight- ng both their common and contradictory features. Conceptual ttempts to grapple with emergence in complex systems theory are
lso discussed where relevant to the idea of emergent technology.
The result is the delineation of five key attributes that qual- fy a technology as emerging. These are: (i) radical novelty, (ii) elatively fast growth, (iii) coherence, (iv) prominent impact, and
erning emerging technologies in all disciplines and in social sciences have been of wn on average by 4.9%.
(v) uncertainty and ambiguity. Specifically, we conceive of an emerging technology as a radically novel and relatively fast growing technology characterised by a certain degree of coherence persisting over time and with the potential to exert a considerable impact on the socio-economic domain(s) which is observed in terms of the compo- sition of actors, institutions and patterns of interactions among those, along with the associated knowledge production processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous.
Second, the framework for operationalising emerging technolo- gies is developed on the basis of the attributes we identified. The scientometric literature forms the core of the methods dis- cussed because, as mentioned, this field has been remarkably active in developing methodologies for the detection and analysis of emergence in science and technology. The reviewed methods are grouped into five main categories: (i) indicators and trend analysis, (ii) citation analysis (including direct citation and co-citation anal- ysis, and bibliographic coupling), (iii) co-word analysis, (iv) overlay mapping, and (v) hybrid approaches that combine two or more of the above. Because scientometric techniques cannot address all the attributes comprehensively, we also discuss approaches developed in other fields.
The paper is organised as follows. The next section introduces the concept of emergence and its various components. In Sec- tion 3, these elements are integrated with key innovation studies proposing definitions of technological emergence, and a definition of emerging technologies is then elaborated. Section 4 reviews methods to both detect and analyse emergence, and then examines the use of those approaches to operationalise the proposed defini- tion and the various attributes of emerging technologies. Section
5 discusses the limits of current methodologies for the detection and analysis of emerging technologies and identifies directions for future research. Section 6 summarises the main conclusions of the study.
D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 1829
Table 1 Dictionary definitions of the concept of emergence.
Dictionary definition of ‘emerge’/‘emergent’ Attributes
“the process of coming into being, or of becoming important and prominent” (New Oxford American Dictionary)
come into being; important; prominent
“to become manifest: become known [. . .]” (Merriam-Webster’s Collegiate Dictionary) become manifest; become known
“to rise up or come forth [. . .] to become evident [. . .] to come into existence” (The American Heritage Desk Dictionary and Thesaurus)
evident; come into existence
“move out of something and become visible [. . .] come into existence or greater prominence [. . .] become known [. . .] in the process of coming into being or prominence” (Concise Oxford English Dictionary)
visible; prominent; become known; come into being
“starting to exist or to become known [. . .] to appear by coming out of something or out from behind become known; to appear
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O c e p d i t L a s s s i a n f
g s e G n p o d p t ( i o (
( s c
to identify those that develop or provide definitions of emerg- ing technologies — we searched for ‘defining’ sentences within the publication full-text by using the keywords listed above. This
1 The terminology of ‘emerging technologies’ has become central to a number of research traditions and especially to the scientometric, bibliometric and tech- mining domains (cf. Avila-Robinson and Miyazaki, 2011), which, as discussed, have
something” (Cambridge Dictionaries Online)
ource: search performed by authors on major English dictionaries.
. The concept of emergence
The word ‘emerge’ or ‘emergent’ means “the process of coming nto being, or of becoming important and prominent” (New Oxford merican Dictionary) or “to rise up or come forth [. . .] to become vident [. . .] to come into existence” (The American Heritage Desk ictionary and Thesaurus). Table 1 presents dictionary definitions f emergent. The primary attribute of emergence is ‘becoming’ — hat is, coming into existence. Emergent is not a static property; t is a label for a process. The endpoint of the process is variously escribed as visible, evident, important or prominent. Thus, among he dictionaries there is some disagreement as to whether acknowl- dged existence is enough for emergence, or beyond that, a certain evel of prominence is needed in order to merit application of the erm emergence.
There is a second definition of emergent given the by the New xford American Dictionary as: a property arising as an effect of omplex causes and not analysable simply as the sum of their ffects. An additional definition is: arising and existing only as a henomenon of independent parts working together, and not pre- ictable on the basis of their properties. This concept of emergence
s used in the study of complex systems. It can be traced back o the 19th Century in the proto-emergentism movement when ewes (1875) referred to ‘emergent effects’ in chemical reactions s those effects that cannot be reduced to the components of the ystem, i.e. the effects for which it is not possible to trace all the teps of the processes that produced them. Its application in the tudy of the dynamics of complex systems in physics, mathemat- cs, and computer science gave rise to other fundamental theories nd schools of thought such as complex adaptive system theory, on-linear dynamical system theory, the synergetics school, and
ar-from-equilibrium thermodynamics (see Goldstein, 1999). A number of studies focusing on the definitional issue of emer-
ence were produced by scholars in complex system theory — ee Table A1 in Appendix for an overview of the definitions of mergence proposed by major studies in complex system theory. oldstein (1999), for example, defined emergence as “the arising of ovel and coherent structures, patterns, and properties during the rocess of self-organization in complex systems” (1999, p. 49). An ntological and epistemological definition of emergence is instead eveloped by de Haan (2006). Ontological emergence is “about the roperties of wholes compared to those of their parts, about sys- ems having properties that their objects in isolation do not have” 2006, p. 294), while epistemological emergence it is about “the nteractions between the objects that cause the coming into being f those properties, in short the mechanisms producing novelty” 2006, p. 294).
Though research on complex systems may have a certain cachet and perhaps for this reason scholars of emerging technologies ometimes attempt to work with the meaning of emergent as onceived by the complex system approach), we maintain that
questions about emerging technologies are not fundamentally about understanding the origins and the causal nature of full system interaction; rather they are about uncertainty, novelty, identifica- tion at an early stage, and visibility and prominence. It is true that some technologies in themselves may be complex systems in the sense of exhibiting adaptation, self-organisation, and emergence, an example being parts of materials science (Ivanova et al., 1998). However, other technologies exhibit ‘complicatedness’ rather than ‘complexity’ as defined in complex system theory — for example, engineering systems. These systems are designed for specific pur- poses, but they do not adapt and self-organise to changes in the environment (Ottino, 2004). It is also true that emerging tech- nologies may arise from complex innovation systems (Katz, 2006), but we would contend that in the phrase ‘emerging technology’, ‘emerging’ is generally understood in the standard sense, not the complex system usage.
3. Defining emerging technologies
To further clarify what is meant by emerging technology, we reviewed literature in innovation studies dealing with definitional issues of emerging technologies. To identify relevant studies, we searched for “emerg* technolog*”, “tech* emergence”, “emergence of* technolog*”, or “emerg* scien* technol*” in publication titles by querying SCOPUS (see the left-hand column of Table 2).1 We restricted the search to the title field to limit results to publications primarily focused on emerging technologies. The search identified a total of 2201 publications from 1971 to mid 2014.2 Within this sample we selected those publications in social science domains, thus reducing the sample to 501 records (see Fig. 1).
We then read the abstracts and accessed the full-text of these studies where necessary both to identify additional documents from the list of cited references and to exclude studies that are not relevant to the scope of this paper. We found that about 50% of the studies in the sample refer to a specific industrial context (e.g. listing and discussing emerging technologies in a given industry) or to the educational sector (e.g. emergence of novel technologies to improve education and learning). These were deemed not rele- vant to our study. The remaining studies were further examined
been remarkably active in developing methods for the operationalisation of emer- gence. In other words, ‘emerging technologies’ have become a category of its own. For this reason, we do not include epistemologically related terms, such as ‘radical’, ‘disruptive’, ‘discontinuous’, ‘nascent’ and ‘breakthrough’.
2 The search was performed on 13th May 2014.
1830 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843
Table 2 Search strategies used to identify the set of relevant publications for the conceptualisation and operationalisation of emerging technologies.
Conceptualisation Operationalisation
Search terms “emerg* technolog*” “emerg* technolog*” “tech* emergence” “tech* emergence” “emergence of* technolog*” “emergence of* technolog*” “emerg* scien* technol*” “emerg* scien* technol*”
“emerg* topic*” “emergence of* topic*”
Field(s) of search Title Title, abstract, keywords
Focus Social sciences Scientometric journals: Journal of the Association for Information Science &Technology (formerly the Journal of the American Society for Information Science &Technology), Journal of Informetrics, Research Evaluation, Research Policy, Scientometrics, Technological Forecasting &Social Change, Technology Analysis &Strategic Management
S
l p m e t i T
T D
S
Number of studies 501 155
ource: authors’ elaboration as based on SCOPUS data.
ed to a core set of 12 studies from science and technology (S&T) olicy studies, evolutionary economics, management, and sciento- etrics that contributed to the conceptualisation of technological
mergence. These are listed with their definitions of emerging
echnologies in Table 3. We analysed the textual content of the def- nitions reported in Table 3 to extract all the component concepts. hese were grouped into the attributes discussed below and used to
able 3 efinitions of emerging technologies (studies are chronologically ordered).
Study Domain Definition (elaborated
Martin (1995) S&T policy “A ‘generic emerging benefits for a wide ran
Day and Schoemaker (2000) Management “[. . .] emerging techn industry or transform innovations [. . .] as w separate research stre
Porter et al. (2002) S&T policy “Emerging technologi in the coming (roughl
Corrocher et al. (2003) Evolutionary economics “The emergence of a n institutional and socia firms or research labo and evolution of know
Hung and Chu (2006) S&T policy “Emerging technologi changing the basis of
Boon and Moors (2008) S&T policy “Emerging technologi aspects, such as the ch actor network and the
Srinivasan (2008) Management “I conceptualize emer characteristics [. . .] an emerging technologie application’ — four ch technologies, converg technologies — shiftin within the firm to out
Cozzens et al. (2010) S&T policy “Emerging technology settled down into any emerging technologie in the process of trans yet; (4) increasingly s
Stahl (2011) S&T policy “[. . .] emerging techn relevance within the n development process [. . .] Despite this, thes capabilities, constrain
Alexander et al. (2012) S&T policy “Technical emergence members of an expert extension of) human u
Halaweh (2013) Management Characteristics of (IT) ethical concerns, cost 108)
Small et al. (2014) Scientometrics “[. . .] there is nearly u newness) and growth
ource: search performed by authors on SCOPUS and extended to cited references.
construct our definition of emerging technologies. Extracted con- cepts excluded from our list of attributes will also be discussed.
The first defining attribute of emerging technology, explicitly included in two of the 12 core articles, is radicalnovelty: “novelty
(or newness)” (Small et al., 2014) may take the form of “dis- continuous innovations derived from radical innovations” (Day and Schoemaker, 2000) and may appear either in the method or
or adopted)
technology’ is defined [. . .] as a technology the exploitation of which will yield ge of sectors of the economy and/or society” (p. 165)
ologies as science-based innovation that have the potential to create a new an existing ones. They include discontinuous innovations derived from radical ell as more evolutionary technologies formed by the convergence of previously ams” (p. 30) es are defined [. . .] as those that could exert much enhanced economic influence y) 15-year horizon.” (p. 189) ew technology is conceptualised [. . .] as an evolutionary process of technical, l change, which occurs simultaneously at three levels: the level of individual ratories, the level of social and institutional context, and the level of the nature ledge and the related technological regime.” (p. 4)
es are the core technologies, which have not yet demonstrated potential for competition” (p. 104) es are technologies in an early phase of development. This implies that several aracteristics of the technology and its context of use or the configuration of the ir related roles are still uncertain and non-specific” (p. 1915) ging technologies in terms of three broad subheads: their sources [. . .], their d their effects [. . .] Specifically, I consider two aspects of the sources of s — the ‘relay race evolution’ of emerging technologies, and ‘revolution by aracteristics of emerging technologies — the clockspeed nature of emerging ence, dominant designs, and network effects — and three effects of emerging g value chains, digitization of goods, and the shifting locus of innovation (from side the firm).” (pp. 633–634)
— a technology that shows high potential but hasn’t demonstrated its value or kind of consensus.” (p. 364). “The concepts reflected in the definitions of s, however, can be summarised four-fold as follows: (1) fast recent growth; (2) ition and/or change; (3) market or economic potential that is not exploited fully cience-based.” (pp. 365–366) ologies are defined as those technologies that have the potential to gain social ext 10 to 15 years. This means that they are currently at an early stage of their
. At the same time, they have already moved beyond the purely conceptual stage. e emerging technologies are not yet clearly defined. Their exact forms, ts, and uses are still in flux” (pp. 3–4)
is the phase during which a concept or construct is adopted and iterated by [. . .] community of practice, resulting in a fundamental change in (or significant nderstanding or capability.” (p. 1289)
emerging technologies “are uncertainty, network effect, unseen social and , limitation to particular countries, and a lack of investigation and research.” (p.
niversal agreement on two properties associated with emergence — novelty (or .” (p. 2)
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he function of the technology. To achieve a new or a changed urpose/function, emerging technologies build on different basic rinciples (Arthur, 2007) (e.g. cars with an internal combustion ngine vs. an electric engine, cytology-based techniques vs. molec- lar biology technologies). Novelty is not only a characteristic of echnologies deriving from technical revolutions, i.e. technologies ith relatively limited prior developments (e.g. DNA sequencing
echnologies, molecular biology, nano-materials), but it may also e generated by putting an existing technology to a new use. The volutionary theory of technological change views this as the spe- iation process of technology, that is the process of applying an xisting technology from one domain to another domain or ‘niche’ Adner and Levinthal, 2002). The niche is characterised by a selec- ion process that is different from the one where the technology as initially applied. The niche specifically may differ in terms of
daptation (the needs of the niche) and abundance of resources. The echnology applied in the niche may adapt and then emerge as well s potentially invading other domains including the initial domain giving rise to a ‘revolution’ or a process of ‘creative destruction’). his implies that ‘evolutionary’ technology (those not characterised y revolutionary technical developments) can also be radically ovel in domains of application different from those where the echnology was initially developed. Adner and Levinthal (2002) rovided a compelling example of the speciation process by repor- ing on the evolution of wireless communication technology. This echnology was created for laboratory purposes, and specifically for he measurement of electromagnetic waves. Yet, it found numerous ubsequent applications. Wireless communication technology first nabled communication with locations (e.g. lighthouses) otherwise ot reachable with wired telegraphy. Then, applications expanded o the transmission of voice (radiotelephony and broadcasting), nd, more recently, to data transmission (Wi-Fi). With each shift, ireless communication technology appeared radically novel in
ts new domain of application, although the technology itself had xisted since the early laboratory and telegraphy applications. The volutionary theory of technological change teaches us that radical ovelty may characterise innovations based on both revolutionary nd evolutionary inventions resulting from the speciation process. owever, the term ‘evolutionary’ is also used to refer to incremen-
al technological advances. To avoid ambiguity, we opted to use he term ‘radical novelty’ rather than ‘revolutionary/evolutionary’ nd to contextualise it in relation to the domain(s) in which the echnology is arising.3
The second defining attribute of emerging technologies, iden- ified by three of the 12 core articles is “clockspeed nature” Srinivasan, 2008) or “fast growth” (Cozzens et al., 2010), or at least growth” (Small et al., 2014). Growth may be observed across a umber of dimensions such as the number of actors involved (e.g. cientists, universities, firms, users), public and private funding, nowledge outputs produced (e.g. publications, patents), proto- ypes, products and services, etc. As with the radical novelty ttribute, the fast growth of a technology needs to be contextu- lised. A technology may grow rapidly in comparison with other echnologies in the same domain(s), therefore relativelyfastgrowth
ay be a better term. The third attribute of emerging technologies, identified by four
f the 12 core articles is coherence that persists over time. The core rticles variously describe this attribute as “convergence of previ- usly separated research streams” (Day and Schoemaker, 2000),
3 The word ‘novelty’ alone may also create ambiguity with regard to the types of echnologies we aim to include in our conceptualisation of emerging technologies. echnologies of a more incremental nature, as derived from the improvement of xisting technologies, are somewhat novel. For the sake of conceptual clarity, we herefore prefer to add the attribute ‘radical’ to the word ‘novelty’.
y 44 (2015) 1827–1843 1831
“convergence in technologies” (Srinivasan, 2008), and technolo- gies that “have already moved beyond the purely conceptual stage” (Stahl, 2011). Alexander et al. (2012) point instead to the role of “an expert community of practice”, which adopts and iterates the concepts or constructs underlying the particular emerging tech- nology. The concept of a community of practice suggests that both a number of people and a professional connection between those people are necessary. Coming together, intertwining and staying together are all entailed in coherence. Coherence refers to internal characteristics of a group such as ‘sticking together’, ‘being united’, ‘logical interconnection’ and ‘congruity’. The status of external rela- tions is also important. The emerging technology must detach itself from its technological ‘parents’ to some degree to merit a separate identity. Furthermore, it must stay detached for some period of time to be seen as self-sustaining (Glänzel and Thijs, 2012). As we stated above, emergence is a process and coherence, detachment and identity do not characterise a final state, but are always in the process of realisation, presenting challenging issues of boundary delineation and classification. Perspective matters since an analyst may see an exciting emerging technology about to make a major economic impact in something a scientist sees as long past the exciting emerging phase.
The fourth defining attribute of emerging technologies, identi- fied by nine of the 12 core articles is to yield “benefits for a wide range of sectors” (Martin, 1995), “create new industry or trans- form existing ones” (Day and Schoemaker, 2000), “exert much enhanced economic influence” (Porter et al., 2002), or change “the basis of competition” (Hung and Chu, 2006). Corrocher et al. (2003) also point to the pervasiveness of the impact that the emerg- ing technology may exert by crosscutting multiple levels of the socio-economic system, i.e. organisations and institutions, as well as knowledge production processes and technological regimes. Accordingly, we identify prominentimpact as another key attribute of emerging technologies. Most of the core articles conceived the prominent impact of emerging technologies as exerted on the entire socio-economic system. In this usage the concept of emerg- ing technologies becomes very close to that of ‘general purpose technologies’ and so excludes technologies prominent within a specific domain. We wish to include relatively smaller scale promi- nence in our definition. For example, a diagnostic technology may emerge and significantly reshape the clinical practices associated with a given disease, profoundly affecting one disease domain but not others. In other words, our definition allows for prominent impact with narrow scope (emergence in one or a few domains), as well as wide-ranging impact across domains and potentially the entire socio-economic system (e.g. ICT and molecular biology). Such a perspective suggests, as with the attributes of radical novelty and relatively fast growth, the importance of contextualising the promi- nent impact of the observed technology within the domain(s) from which the technology emerges.
The final defining attribute of emerging technologies, identified in seven of the 12 core articles is that the prominent impact of emerging technologies lies somewhere in the future — the tech- nology is not finished. Thus, uncertainty features in the emergence process. The non-linear and multi-factor nature of emergence pro- vides emergence with a certain degree of autonomy, which in turn makes predicting a difficult task (de Haan, 2006; Mitchel, 2007). As a consequence, knowledge of the probabilities associated with each possible outcome (e.g. potential applications of the technol- ogy, financial support for its development, standards, production costs) may be particularly problematic (Stirling, 2007). Core articles expressed this attribute in terms of the ‘potential’ that emerging
technologies have for changing the existing ‘ways of doing things’ (e.g. Boon and Moors, 2008; Hung and Chu, 2006; Stahl, 2011).
However, these definitions seem not to disentangle explic- itly another important aspect of emergence from the concept of
1 h Policy 44 (2015) 1827–1843
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ncertainty. This is ambiguity. Ambiguity arises because proposed pplications are still malleable, fluid and in some cases contradic- ory, i.e. even the knowledge of possible outcomes of emergence s incomplete. A variety of possible outcomes may occur because ocial groups encountered during emergence hold diverging val- es and ascribe different meanings to the technology (Mitchel, 007). It is worth noting that uncertainty and ambiguity are, how- ver, not mutually exclusive (Stirling, 2007). These are not discrete onditions. A continuum exists as defined by the extent to which nowledge of possible outcomes and likelihood for each outcome s incomplete. For example, it may be problematic evaluating the robabilities associated with known possible outcomes, but at the ame time there may also be a lack of knowledge of other possible utcomes such as unintended/undesirable consequences deriving rom the (potentially uncontrolled) use of the technology. Uncer- ainty and ambiguity are key starting concepts for a wide variety f science and technology studies (STS) focusing on the role of the xpectations in technological emergence (e.g. van Lente and Rip, 998).
The studies reviewed here introduced various additional oncepts such as the science-based-ness, network effects, and arly-stage development of emerging technologies. While the last f these seems to be implicit in the definition of emergence and the ey role of networks (of users adopting the technology) is certainly ot a unique feature of emerging technologies, the association ith science-based-ness is less clear. The importance of science
especially public science) for the development of industrial tech- ologies is widely accepted on the basis of substantial evidence e.g. Narin et al., 1997). However, even today not all technological evolutions may depend on breakthrough advances in science. In ertain domains, a technology can be developed without the need or deep scientific understanding of how the phenomenon under- ying it works — “it is possible to know how to produce an effect
ithout knowing how an effect is produced” (Nightingale, 2014, p. ). For example, Vincenti (1984) provided evidence of this in the ase of the construction of airplanes in the 1930s. The different arts of an airplane were initially joined using rivets with dome- haped heads. These types of rivets, however, caused resistance o the air, thus reducing the aerodynamic efficiency of the plane. s other dimensions of airplane performance were improving (e.g. peed), the aerodynamic efficiency became increasingly relevant. he dome-shaped rivets were therefore replaced with rivets flush ith the surface of the airplane. This was a major improvement for
he aerodynamics of airplanes in 1930s, but it required no major cientific breakthrough.4 A more recent example is the develop- ent of smartphones which did not require major advancements
n science since most of the technologies used already existed — he integration of these technologies, and advances in design for he creation of novel user interfaces instead provided the founda- ion of the innovation.5 For these reasons, ‘science-based-ness’ does ot feature in our definition of emerging technologies.
In summary, as reported in Table 4, our review of innovation tudies identified five main defining characteristics or attributes
f emerging technologies: (i) radical novelty, (ii) relatively fast rowth, (iii) coherence, (iv) prominent impact, and (v) uncertainty nd ambiguity. Combining these attributes, we define an emerging
4 Other classical examples include prehistoric cave dwellers using fire for cooking ithout any scientific understanding of it, the development of steam engines that redated the development of thermodynamics, or the Wright brothers testing flying evices before the field of aerodynamics was established. 5 The innovation was architectural rather than modular according to the distinc-
ion proposed by Henderson and Clark (1990). Also, smartphone technology can e considered as an example of emerging technology of an evolutionary nature. s discussed above, the radical novelty of this technology is the result of existing
echnologies converging in new domains of applications. T a
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Relatively fast growth Coherence Prominent impact
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Fig. 2. Pre-emergence, emergence, and post-emergence: attributes and ‘stylised’ trends.
gence. This process led to a final set of 55 publications,6 which were then classified in terms of the methodological approach adopted to detect or analyse emergence (e.g. indicators, citation
6 We excluded 76 studies that did not operationalise emergence (e.g. use of emerging technologies as empirical context for various analyses, examination of ethical issues associated with emerging technologies), three studies focused on the review of scientometric methods for the analysis of emerging technologies, and two studies elaborating document search strategies based on a modular lexical approach. 33 studies that were concerned with Future-oriented Technology Analysis (FTA) techniques (e.g. foresight, forecasting, roadmapping, Constructive Technol- ogy Assessment (CTA)) were also not included in the review. While about 67% of these do not rely on scientometrics, the remaining FTA studies in the sample pro- pose frameworks for selecting, rather than identifying, emerging technologies, or adopt conventional scientometric/bibliometric approaches, which will be instead discussed with the review of the selected scientometric studies. FTA methods, how- ever, remain crucial for more prospective analyses of emerging technologies and decision-making on possible future scenarios (e.g. Porter et al., 2004; Irvine and Martin, 1984; Ciarli et al., 2013). 14 STS studies included in the sample will be instead referenced in our review and discussion when the operationalisation of the attributes of emergence with the use of scientometric approaches is limited by a lack of data or by the nature of the considered attribute. It is worth noting that our search did not capture ‘technometric’ studies (e.g. Grupp, 1994; Saviotti and
D. Rotolo et al. / Researc
echnology as a radically novel and relatively fast growing technol- gy characterised by a certain degree of coherence persisting over time nd with the potential to exert a considerable impact on the socio- conomic domain(s) which is observed in terms of the composition f actors, institutions and patterns of interactions among those, along ith the associated knowledge production processes. Its most promi- ent impact, however, lies in the future and so in the emergence phase
s still somewhat uncertain and ambiguous. It is reasonable to assume that the attributes of emergence range
rom ‘low’ to ‘high’ levels. Nonetheless, to try and pin them down o some absolute level is rather meaningless. As discussed, the ttributes of emergence (especially radical novelty and relatively ast growth) provide an indication of emergence when they are onsidered in the domain in which the given technology is arising nd therefore in relation to other technologies that may exist in hat domain. Most importantly, these attributes are likely to co- volve and assume very different levels over different periods of mergence. In the early stage of emergence (‘pre-emergence’), a echnology is likely to be characterised by high levels of radical ovelty as compared to other technologies in the domain in which
t is arising. However, the impact the technology can exert on that omain is still relatively low. The technology has not yet gone eyond the purely conceptual stage, multiple communities are
nvolved in its development, and the delineation of the boundary of he technology is particularly problematic (i.e. low levels of coher- nce). As a consequence, its growth is relatively slow or not yet egun, and high levels of uncertainty and ambiguity are associated ith the future developments of the technology — the technology ay not even emerge. The technology may then acquire a cer-
ain momentum. Some trajectories of development may have been elected out and certain dimensions of performance prioritised and mproved. A community of practice may have also emerged. The echnology thus becomes more coherent. Its impact is also rela- ively less uncertain and ambiguous, and the technology starts to ake off in terms of publications, patents, researchers, firms, pro- otypes/products, etc. However, at the same time, it is likely that he radical novelty of the technology will diminish — other tech- ologies that exploit different basic principles may be emerging as ell in the domain in which the considered technology is emerg-
ng. We conceived ‘emergence’ as this phase where the attributes f emergence are subject to dramatic change. Finally, impact and rowth may enter a stable or declining phase, the technology loses ts radical novelty, knowledge of the possible outcomes of the echnology becomes more complete (probabilities can be perhaps ssigned to outcomes), and the community of practice may become ell-established (e.g. regular conferences, dedicated journals). The
echnology enters in a ‘post-emergence’ period. In line with the -shaped patterns highlighted in early studies on the growth of cience (e.g. De Solla Price, 1963) and in technological adoption iterature (e.g. Mansfield, 1961; Rogers, 1962), we ‘stylised’ the hange in the levels of the attributes of emergence as following n S curve (or more strictly, a reversed S curve in two of the five ases). This is qualitatively depicted in Fig. 2.
Defining ‘emerging technology’ is, however, only half the battle. f the definition is to be useful, we must show how the attributes an be measured and thus how technologies can be classified as merging or not. In the next section, we link our definition to he operationalisation of our definition of emerging technologies.
e rely mainly on scientometric techniques, bringing in other pproaches to fill certain gaps.
. A framework for the operationalisation of emergence
Scientometric research has developed methods to detect emer- ence in science and technology and is therefore central to
Source: authors’ elaboration.
operationalising our definition. From the vast literature that touches on emerging technologies, we drew upon studies that offer ideas on operationalising our five attributes. We identified relevant scientometric studies by including the term ‘topic’ in the search string we used to select research works dealing with definitional issues of emerging technologies — ‘topic’ is often used in sciento- metrics to refer to the emergence of a new set of research activities in science and technology (e.g. Small et al., 2014; Glänzel and Thijs, 2012). The search was also extended to publication titles, abstracts and keywords, but narrowed to journals mainly or to a significant extent oriented toward the publication of novel scientometric tech- niques (see the right-hand column of Table 2). The search in SCOPUS returned 155 publications.
The examination of cited references of these publications enabled us to retrieve additional studies that were not cap- tured with the search string, but are potentially relevant to for our analysis. This increased the initial sample to 183 studies. We then analysed these publications to identify studies that were relevant to the operationalisation of the attributes of emer-
Metcalfe, 1984; Sahal, 1985). This research stream has been particularly important for the measurement of technology and technological change. Nonetheless, techno- metric models tend to rely on a variety of assumptions and often require data, the collection of which can be particularly labour-intensive (e.g. extraction and coding of data on the features of the considered technologies) (e.g. Coccia, 2005).
1834 D. Rotolo et al. / Research Policy 44 (2015) 1827–1843
Table 5 Methods for the detection and analysis of emergence in science and technology (studies are ordered by technique and publication year).
Method/study Data Operationalisation of emergence
Indicators and trends Porter and Detampel (1995) Publications/patents Count of keywords in publication abstracts and trend analysis based on Fisher–Pry curves Kleinberg (2002) Publications/e-mails ‘Burst of activity’ detected as state transitions of an infinite-state automaton Bengisu (2003) Publications Positive slope of the line derived by regressing the number of publications on time and no
decrease of more than 10% or stability (no increase) in the last period or continuous decline in the last three periods of observation
Watts and Porter (2003) Publications Indicators of emergence: cohesion (based on cosine similarity between documents), entropy, and F-measure
Bettencourt et al. (2008) Publications Epidemic model to describe the increasing number of authors involved in an emerging field Bettencourt et al. (2009) Publications Increasing densification (average number of edges per node), stable/decreasing diameter
(average path length between nodes), and increasing fractional count of edges in the largest component of the co-authorship network
Moed (2010) Publications Journals characterised by high values of Source Normalised Impact per Paper (SNIP) indicator Schiebel et al. (2010) and
Roche et al. (2010) Publications Publication keywords initially labelled as “unusual terms”, by using tf-idf and Gini coefficient,
that subsequently become “cross section terms”, i.e. they diffuse in several research domains Guo et al. (2011) Publications Indicators of emergence: frequency of keywords (ISI WoS keywords, authors’ keywords, and
MeSH terms), growing number of authors, and interdisciplinarity (based year-average Rao-Stirling diversity index) of cited references
Järvenpää et al. (2011) Mixed Absolute and cumulative count of the number of basic and applied research publications, patents, and news
Abercrombie et al. (2012) Mixed Normalised number of publications and citations, patents, and web news fitted to a polynomial function
Jun (2012) and Jun et al. (2014) News Normalised searching traffic (Google trends) Avila-Robinson and Miyazaki
(2013b,a) Publications/patents Overview of indicators to analyse emergence
de Rassenfosse et al. (2013) Patents Count of the priority patent applications filed by a country’s inventor, regardless of the patent office in which the application is filed
Ho et al. (2014) Publications Cumulative number of publications fitted to a logistic curve
Citations analysis Direct citation Seminal paper: Garfield et al.
(1964) Publications –
Kajikawa and Takeda (2008), Kajikawa et al. (2008) and Takeda and Kajikawa (2008)
Publications Clusters of publications with the highest average publication year
Scharnhorst and Garfield (2010)
Publications Historiographic approach combined with ‘field mobility’ of publications
Shibata et al. (2011) Publications Clusters of publications with the highest values of betweenness centrality Iwami et al. (2014) Publications Publications (‘leading papers’) with high values of in-degree (‘height’), large variation of
in-degree between one year and the next year (‘slope’), or large cumulative in-degree (‘area’) as defined on the basis of the yearly direct citation network
Co-citation Seminal paper: Small (1973) Publications – Small (2006) Publications Clusters with no continuing publications from the prior period Cho and Shih (2011) Patents Technological patent classes (IPC) that span structural holes in the co-citation network Érdi et al. (2012) Patents Clusters of patents present in a given time period and not in the previous period Boyack et al. (2014) Publications Yearly clustered publications of which references overlap less than 30% with references cited
by previous clusters Bibliographic coupling Seminal paper: Kessler (1963) Publications – Morris et al. (2003) Publications Clusters of publications that cite more recent clusters of publications, namely emerging
research fronts Kuusi and Meyer (2007) Patents Clusters of patents as source to identify guiding images (‘leitbild’) of technological
development
Co-word analysis Seminal paper: Callon et al.
(1983) Publications –
Lee (2008) Publications Clusters in the co-word network that show low values of degree, high betweenness, and low closeness, i.e. those clusters that are more likely to turn into hub in the future.
Ohniwa et al. (2010) Publications MeSH terms (clustered with co-word analysis) that are included in the top-5% by incremental rate in a given year — the increment rate for a MeSH term is defined as the number of time the terms occurred at the time t, t + 1, and t + 2 out the number of times the term occurred at t − 1, t, t + 1, and t + 2
Yoon et al. (2011) Patents Small and dense sub-networks in the ‘invention property-function’ network Furukawa et al. (2015) Publications Sessions of conferences in which previous sessions converge according to the average cosine
similarity (based on tf-idf-identified keywords) between the papers included in the sessions Zhang et al. (2014) Publications Combination of cluster analysis with term clumping and principal component analysis
Overlay mapping Rafols et al. (2010) Publications Overlays of publications projected on a basemap of ISI WoS subject categories linked by cosine
similarity of co-citations patterns between journals Bornmann and Leydesdorff
(2011) Publications Overlays of publications on Google maps to identify cities publishing more than expected
D. Rotolo et al. / Research Policy 44 (2015) 1827–1843 1835
Table 5 (Continued)
Method/study Data Operationalisation of emergence
Leydesdorff and Rafols (2011) Publications Overlays of publications and co-authorship networks on Google maps to trace collaboration activity
Leydesdorff et al. (2012) Publications Overlays of publications projected on a basemap of MeSH terms linked by cosine similarity (based on the co-occurrence of MeSH terms at the publication level)
Leydesdorff and Bornmann (2012)
Patents Overlays of patents on Google maps to identify cities patenting more than expected
Leydesdorff et al. (2013) Publications Overlays of publications projected on the basemap of journals linked by cosine similarity of co-citations patterns between journals
Kay et al. (2014) Patents Overlays of patents projected on the basemap of 466 IPC classes linked by cosine similarity of citing-to-cited relationships between classes — the basemap is built by using patents included in 2011 PATSTAT
Leydesdorff et al. (2014) Patents Overlays of patents projected on the basemap of 124 3-digit or 630 4-digit IPC classes linked by cosine similarity based on co-citations between classes — the basemap is built by using patents granted at the United States Patent and Trademark Office (USPTO) from 1976 to 2011
Hybrid Chen (2006): co-citation
analysis and burst detection Publications Trends in the bipartite network of research-front terms (burst detection) and intellectual base
articles — the network includes three types of links: co-occurring research front terms, co-cited intellectual base articles, and a research-front term citing an intellectual base article
Leydesdorff et al. (1994): co-citation analysis and bibliographic coupling
Publications New journals that build on multiple existing areas, i.e. they load on multiple factors obtained by the factor analysis of the matrix of the cited references, and have unique ‘being cited’ patterns, i.e. they are ‘central tendency journals’ reporting highest load on a given factor as obtained by the factor-analysis of the matrix of received citations
Glänzel and Thijs (2012): co-word, direct citation analyses and bibliographic coupling
Publications Existing clusters with exceptional growth, completely new clusters with roots in other clusters, and existing clusters with a topic shift
Gustafsson et al. (2015): co-occurrence of IPC classes
Patents Technological co-classification to identify clusters of patents and detect guiding images or ‘leitbild’ from patent full-text
Small et al. (2014): direct and co-citation analyses
Publications Clusters of publications that show high growth and are new both to the direct citation and co-citation models
Yan (2014): co-word analysis and topic modelling
Publications Topics that are not a close variation of other topics, i.e. a topic i in the year t is emerging if no predecessors are found and no other topics are transformed into topic i at t + 1
Chang and Breitzman (2009), Breitzman and Thomas (2015): direct citation and co-citation analyses
Patents Clusters of patents (co-citation clustering) that form around ‘hot’ patents — defined as those patents that are highly cited (top 5–10%) by patents issued in the last two years and the citations of which mostly come from patents issued in the last two years
S refere
p d p s g b f w a w w a a e e g
u W t e h r o b T r e e
ource: search performed by authors on SCOPUS and extended to publication cited
atterns between documents, co-occurrence of words in text), ata sources used (e.g. publications, patents, news articles), and roposed operationalisation of emergence. This information is ummarised in Table 5 where studies are grouped into five roups: (i) indicators and trend analysis studies that are mainly ased on document counts; (ii) citation analysis studies which ocus on examining citation patterns between documents; (iii) co- ord analysis studies that build on the co-occurrence of words
cross document text; (iv) overlay mapping technique studies, hich use projections to position a given set of documents ithin a wider or more global structure (e.g. a map of science);
nd (v) hybrid studies that combine two or more of the above pproaches. Table 5 shows how definitions of emergence varied, ven within the same group of techniques, thus providing further vidence of the low level of consensus on what constitutes emer- ence.
Given the definitional weaknesses in the original studies, our se of a particular study often varies from that of its authors. e will briefly introduce the major techniques and our interpre-
ation of the contribution they make to measuring attributes of merging technologies. For each attribute, we will first describe ow it can be operationalised for contemporary and then for ret- ospective cases of emerging technologies. When data scarcity r the nature of the attribute of emergence limit the applica- ility of scientometrics, we will discuss qualitative approaches. he role of experts remains crucial for the validation of the
esults obtained with the use of the techniques discussed below, specially for qualitative approaches to the operationalisation of mergence.
nces.
4.1. Radical novelty
Emerging technologies are radically novel, i.e. they fulfill a given function by using a different basic principle as compared to what was used before to achieve a similar purpose. Publications and patents are of limited use in assessing radical novelty in contem- porary technology. In contrast, news articles, editorials, review and perspective articles in professional as well as academic journals represent valuable sources, providing participant perspectives on if and why a technology is viewed as radically novel. These docu- ments may also provide an understanding of the basic principles underpinning the examined technology.
In contrast, in retrospective analyses citation and co-word anal- yses can be particularly effective for identifying radical novelty. Relatively large amounts of data can be exploited to map the cog- nitive networks of a knowledge domain over time. Citation analysis builds on citation patterns among documents to generate a network in which nodes are documents and links between nodes repre- sent (i) a direct citation between two documents (direct citation analysis) (Garfield et al., 1964), (ii) the extent to which two doc- uments are cited by the same documents (co-citation analysis) (Small, 1973), or (iii) to what extent two documents cite the same set of documents (bibliographic coupling) (Kessler, 1963). Co-word analysis instead exploits the text of documents to create a network of keywords (or key phrases) that are linked according to the text to which they co-occur across the set of selected documents (Callon
et al., 1983).
On the premise that clusters of documents or words in these networks represent different knowledge areas of a domain or
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836 D. Rotolo et al. / Researc
ifferent literatures on which the domain builds, several studies ave considered the appearance of clusters not previously present
n the network as a signal of novelty (e.g. Érdi et al., 2012; Kajikawa nd Takeda, 2008). Others dispute this interpretation. Given the ontinuous evolution of science and technology, one is unlikely o find a cluster again in subsequent annual networks so the per- entage of clusters that would qualify as newly appearing tends o be relatively high. For this reason, additional criteria have been uggested such as the appearance of new clusters that also link therwise weakly connected (e.g. betweenness centrality) clusters e.g. Shibata et al., 2011; Furukawa et al., 2015), that form around ocuments that are highly cited by recent documents and the cita- ions of which also are mostly from recent documents (Breitzman nd Thomas, 2015), or that cite more recent clusters as identi- ed by the (Salton) similarity of their references (Morris et al., 003).
Small et al. (2014) have recently proposed a hybrid approach ased on a combination of direct citation and co-citation models as pplied to publication data. This approach is particularly focused on he detection of novelty, which is defined in terms of clusters that re new to the co-citation model — that is, clusters with limited verlap with the cited documents included in clusters in previ- us years (Boyack et al., 2014) — as well as to a parallel direct itation model. By combining bibliographic coupling, co-word anal- sis, and direct citation analysis, Glänzel and Thijs (2012) instead efined novelty (namely emerging topics) as three cases of clus- ers: those that show exceptional growth, those that are completely ew but with their roots in other clusters, or already existing ones hat exhibit a topic shift. Yan (2014) combined co-word analysis ith Natural Language Process (NLP) approaches (topic modelling).
mergence, as reflected in novelty, is then associated with the ppearance of topics that are not a close variation of other topics alculated on the basis of the Jenson–Shannon Divergence.7 Specif- cally, a topic i appearing at time t is considered to be emerging f it has no predecessors and none of the identified topics trans- orms into topic i at t + 1. A different perspective is provided by charnhorst and Garfield (2010) who extended the analysis of his- oriographs (based on direct citations) to trace the extent to which ublications move across fields as they receive citations from new elds (namely ‘field mobility’). Assuming that these publications re associated with a basic principle used for technological appli- ations, this approach enables one to identify which fields may be sing a different knowledge base and thus in which fields radi- ally novel technologies are potentially emerging. However, this equires a priori knowledge of the basic principle and the set of ocuments associated with it.
Research in scientometrics has also focused on the develop- ent of techniques to expand the ‘local’ (domain) perspective that
itation or text-based approaches may provide. This effort has gen- rated a number of overlay mapping techniques (for an overview ee Rotolo et al., 2014), which in turn may be particularly well uited to detecting radical novelty. The basic idea is to project a iven set of documents (e.g. publications associated with a research omain) on a basemap through the use of an overlay. The basemap an represent the ‘global’ science structure at the level of the sci- ntific discipline (ISI Web of Science (WoS) subject categories) (e.g.
afols et al., 2010), journal (e.g. Leydesdorff et al., 2013), Medical ubject Headings (MeSH) (Leydesdorff et al., 2012), or the techno- ogical structure at the level of patent classes (e.g. Kay et al., 2014;
7 The Jenson–Shannon Divergence is a measure of similarity between empiri- ally determined distributions (e.g. co-occurrence of words in documents) based on hannon entropy measures (for more details see Lin, 1991).
y 44 (2015) 1827–1843
Leydesdorff et al., 2014).8 Once the set of documents (publications or patents) associated with a given domain has been identified, the projection of these documents over different time slices on the global map of science or technology may reveal the increas- ing involvement of new scientific or technological areas. This may suggest that new knowledge areas are being accessed to conduct research, and thus that potentially different basic principles are drawn upon to achieve a given purpose.
Among the studies within the ‘indicators and trends’ group of techniques, Moed (2010) proposed the source normalised impact per paper (SNIP) indicator for the evaluation of journals’ impact and claims it is relevant for identifying emerging technologies. This indicator is defined as the ratio between the journal’s raw impact per paper (number of citations in the year of analysis to the jour- nal’s papers published in the three previous years, divided by the number of the journal’s papers in these three years) and the rela- tive database citation potential in the subject field covered by the journal (mean number of 1–3-year-old references per paper citing the journal and published in journals included in the considered database divided by that for the median journal in the database). Moed (2010) argued that the SNIP indicator, and specifically high values of this indicator, also provides information on the extent to which a considered journal covers emerging topics. Given the focus on recent citations and database coverage, the SNIP indicator is clearly associated with the radical novelty attribute of emergence. This indicator is, however, evaluated at the aggregate level of the journal and journal-by-journal. It is therefore less clear whether signals of radical novelty (i.e. relatively high values of SNIP) are associated with one or multiple emerging topics the considered journal may cover. In addition, the SNIP may not capture signals of radical novelty in those instances of journals that cover few emerging topics and therefore characterised by low values of SNIP.
All these techniques have various advantages and limitations. The qualitative analysis of news articles, editorials, review and per- spective articles, for example, may be effective for contemporary analyses. However, the technical language used in these documents may be an important barrier to a non-expert’s efforts to inde- pendently assess radical novelty. The application of citation and co-word analyses is strongly dependent on time. Data need to be longitudinal in order to permit the tracing of cognitive dynamics and associated changes in the knowledge structure. Co-word anal- ysis and bibliographic coupling are, however, less sensitive to time than direct citation and co-citation analyses and can be applied as documents become available (e.g. Breitzman and Thomas, 2015). Finally, overlay mapping provides a global perspective on emer- gence for the assessment of radical novelty, but interpretation of the resulting maps is mainly based on visual inspection.
4.2. Relatively fast growth
Emerging technologies show relatively fast growth rates com- pared to non-emerging technologies. The assessment of this attribute is particularly problematic for contemporary analyses. Growth is not yet observed in terms of publications and patents, for example, so scientometric indicators cannot be used. Early indica- tions of growth may be revealed from the analysis of funding data,
big data, and altmetrics. This is an important research direction for future studies on the operationalisation of the relatively fast growth attribute, as we will discuss later in the paper.
8 The elements of the basemap are linked according to similarity based on the co-occurrence of citations or, in the case of MeSH, the co-occurrence of terms. The same approach can be used to project a sample of publications and patents onto geo- graphical maps (e.g. Google maps) to reveal the most active cities and collaborative activities (see Table 5).
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D. Rotolo et al. / Researc
In the case of retrospective analyses, ‘relatively fast growth’ is erhaps the most frequently measured attribute of emergence in cientometrics. Most studies assume rapid growth as a sine qua on condition of emergence, and so a number of operationalisa- ion approaches have been proposed. Indicators and trend analyses ased on the yearly or cumulative count of documents — publi- ations, patents or news articles, according to the nature of the xamined technology and the availability of data — over a given bservation period are widely used. Documents are generally iden- ified over time by using expert-defined keywords appearing in the ublication titles and abstracts (e.g. Porter and Detampel, 1995) r by exploiting more institutionalised vocabularies such as the eSH classification in the case of publication counts in the biomed-
cal domain (e.g. Guo et al., 2011). With a focus on patent data, e Rassenfosse et al. (2013) proposed counting the priority patent pplications filed by a country’s inventor, regardless of the patent ffice in which the application is filed, as an indicator to identify ast growth and therefore potential emerging technologies. How- ver, yearly publication or patent counts are always dynamic, so the roblem becomes one of setting a criterion by which to distinguish he signal from the noise, that is differentiating emerging technol- gy from other increasing trends. Some theoretical foundations are eeded to do this.
Rapid growth is also detected by fitting the document count to a unction (e.g. forms of logistic function such as Fisher–Pry curves).9
engisu (2003), for example, regressed the number of publications ver publication year and defined emerging technologies as those echnologies showing a positive slope and a decrease of less than 0% or stability (no increase) in the last period compared to the revious one, or no continuous decline in the last three periods of bservation. Ho et al. (2014) instead fitted the cumulative num- er of publications to a logistic curve, whereas Abercrombie et al. 2012) extended the count of publications to patents, web news, nd commercial applications. Data were then normalised and fit- ed to a polynomial function for comparison — a similar approach s employed by Järvenpää et al. (2011) and Jun et al. (2014).
The number of documents is also used to detect ‘bursts of activ- ty’, i.e. the appearance of a topic in a document stream. This relies n the approach of Kleinberg (2002), who modelled the number of ublications and e-mails containing a given set of keywords as an
nfinite-state automaton, i.e. a self-operating virtual machine that ay assume a non-finite number of states and where the transi-
ion from one state to another is regulated by a ‘transition function’ similarly to Markov models). The frequency of state transitions ith certain features identifies bursts of activity, which are used
s a proxy for fast growth. The burst detection approach is com- ined with co-citation analysis by Chen (2006) to build a bipartite etwork10 of research-fronts linked with intellectual base arti- les. This network is then analysed in order to identify emerging rends.
Schiebel et al. (2010) and Roche et al. (2010) proposed instead n approach to emergence that is based on a diffusion model and diachronic cluster analysis to identify topics) that combines
9 Fisher–Pry curves were developed to model technological substitution between wo competing technologies (Fisher and Pry, 1971). This family of curves is built n the basis of three assumptions: (i) technological advancements are the result f competitive substitutions of one method (technology) used to satisfy a given eed for another; (ii) the new technology completely replaces the old technology; nd (iii) the market share follows Pearl’s Law, i.e. “the fractional rate of fractional ubstitution of new for old is proportional to the remaining amount of the old left o be substituted” (Fisher and Pry, 1971, p. 75). 10 A bipartite network is a network in which nodes can be partitioned into two istinct groups, N1 and N2 , and all the links connect one node from N1 with a node rom N2 , or vice versa (Wassermann and Faust, 1994).
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a modified tf-idf11 with the Gini coefficient to identify three stages: “unusual terms”, “established terms”, and “cross section terms”. Unusual terms are those that are rare in publications since they describe a research discovery at a very early stage. When research intensifies, terms first become more established in the original domain and subsequently they may diffuse into other domains, thus becoming cross section terms. Terms that change their clas- sification (i.e. that show pathways) from unusual to cross section terms from one period to another are characterised by rapid diffu- sion and therefore relatively fast growth. This approach, however, is highly dependent on the thresholds of the tf-idf and Gini coef- ficient selected to classify terms as well as on the duration of the periods used to trace changes in the classification of terms.
Citation and co-word analyses can also be used to identify the relatively rapid growth of a potential emerging technology. Longitudinal analysis of the size of the clusters of documents or words obtained with the application of these techniques can detect knowledge areas that show rapid growth. For example, Ohniwa et al. (2010) used co-word analysis to cluster MeSH terms. For each MeSH term an increment rate was calculated in year t as the number of times the term occurred at time t + 1 and t + 2 out of the number of times the term occurred at t − 1, t, t + 1, and t + 2. Fast growing topics are those in the top 5% of the increment rate in a given year.
Glänzel and Thijs (2012) combined bibliographic coupling, co- word analysis, and a direct citation model. First, documents are clustered in time slices according to their cosine similarity resulting from bibliographic coupling and textual similarity. The core clus- ters identified through this process are next linked across different time slices via direct citations. Emergence is then detected by iden- tifying clusters with exceptional growth — the study also considers emerging clusters to be those that are completely new with roots in other clusters or existing clusters exhibiting a topic shift, but this clearly refers to the radical novelty attribute of emergence.
Overlay mapping techniques can visually reveal knowledge areas characterised by a rapid increase in the number of documents (publications or patents) in the ‘global’ maps of science or technol- ogy and which therefore, in comparison with other areas, may be growing at a faster pace. (Overlay mapping can also reveal diffusion across disciplines and technological areas.)
Other studies instead operationalised relatively fast growth by examining the growing number of authors involved in an emerg- ing field over time (e.g. Guo et al., 2011; Bettencourt et al., 2008). For example, Bettencourt et al. (2008) found that the growth of the population of authors in a given field tends to be relatively well described with epidemic models that consider novel ideas as spreading by ‘infecting’ authors.
4.3. Coherence
Coherence and its persistence over time distinguish technolo- gies that have acquired a certain identity and momentum from those still in a state of flux and therefore not yet emerging. Coher- ence in contemporary technologies may be detected by examining the scientific discourse around a given emerging technology. Ini- tially, a variety of terms may be in use and reduction in the number of terms may signal increasing coherence. Abbreviations or acronyms take time to appear and, when they do, signal persis-
tence; 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
11 The tf-idf (term frequency-inverse document frequency) is an indicator that reflects the importance of a word to a document in relation to a corpus. Specifically, the tf-idf is the result of the product between two indicators: the term frequency and inverse document frequency.
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nd subsequently from journal special issues and new specialist ournals (Leydesdorff et al., 1994). New categories in established lassification systems may also be created (Cozzens et al., 2010).
In retrospective analyses, entropy measures can be used (Watts nd Porter, 2003) as well as clustering and factor analysis of citation nd text networks. The coherence of clusters of documents or terms an be assessed in comparison to the overall network by applying, or example, local network density measures as well as by exam- ning cluster persistence over time. Furukawa et al. (2015) propose sing year-to-year coherence of conference sessions to indicate mergence. They applied co-word analysis to generate ‘chronologi- al’ networks of conference sessions (nodes) linked by their (cosine) imilarity as based on the keywords included in the sessions’ papers
keywords were selected using the tf-idf indicator. Within these etworks, emerging topics are defined as sessions where previous onferences’ sessions converge according to similarity.
In a similar vein, Yoon et al. (2011) developed a NLP algo- ithm capable of identifying properties and functions in the entences of patent abstracts.12 The method generates an ‘inven- ion property–function’ network (IPFN). Nodes in this network epresent properties and functions. A property is what a system is or as and is expressed by using ‘adjectives+nouns’, whereas a func- ion is what a system does and is expressed by using ‘verbs+nouns’. inks between nodes are defined by the co-occurrence of proper- ies and functions in patents. Emerging properties and functions re those clustered in small and highly dense sub-networks — i.e. e facto showing a certain degree of coherence.
The approaches discussed above examine cognitive dynamics. owever, coherence can also be assessed on the basis of changes in
he social structure. In this regard, Bettencourt et al. (2009) exam- ned the evolution of co-authorship networks at the level of the cientist to identify network patterns associated with the emer- ence of new scientific fields. Increasing average number of edges er nodes (densification), stable or decreasing average path length etween two nodes (diameter), and increasing fractional count of dges in the largest component of the considered network were uggested as signals of emergence and specifically of the topical ransition of a field. These indicators clearly refer to increasing onnectedness of the co-authorship network, identifying emerging ommunities as an indicator of emerging technology.
.4. Prominent impact
Emerging technologies exert a prominent impact on specific omains or more broadly on the socio-economic system by chang-
ng the composition of actors, institutions, patterns of interactions mong those, and the associated knowledge production processes. cientometric methods cannot identify contemporary prominent mpact due to a lack of data and the difficulty in delineating the echnology in its very early stages (e.g. keywords may still be used y groups of actors with different meanings and in different con- exts). Mixed qualitative–quantitative approaches used by Science
nd Technology Studies (STS) scholars on the role of expectations n driving technological change are of a particular relevance.13
he main argument of the sociology of expectations is that “novel
12 This enables one to overcome the main limitation of co-word analysis tech- iques, that is the need to define an initial set of keywords before the analysis can e performed. 13 Scientometrics can be considered as the more quantitative end of STS work. For his reason, the distinction we make between the two traditions is not intended o be a particularly strong one. However, it also true that there has been relatively ittle interaction between scientometrics and STS since the late1980s. Each of these radition has its own conferences and journals, and only a handful of researchers perate at the interface — most individuals would identify themselves as either
scientometricians’ or ‘STS’ scholars.
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technologies and fundamental changes in scientific principle do not substantively pre-exist themselves, except and only in terms of the imaginings, expectations and visions that have shaped their poten- tial” (Borup et al., 2006, p. 285). These expectations are “real-time representations of future technological situations and capabilities [. . .] wishful enactments of a desired future” (Borup et al., 2006, p. 285) and they play a generative role by stimulating and steering as well as coordinating actions. Evidence of this has been found in a number of emerging fields such as gene therapy, pharma- cogenomics, and nanotechnology (e.g. Selin, 2007; Hedgecoe and Martin, 2003; Martin, 1999). Expectations of the performance of novel technologies or, more generally, the ability of novel tech- nologies to address societal problems are both important.
News articles, editorials, review and perspective articles in pro- fessional and academic journals, vision reports and technological roadmaps have all been used to identify statements represent- ing multiple and potentially competing expectations surrounding a technology (e.g. Alkemade and Suurs, 2012; van Lente and Bakker, 2010; Bakker et al., 2011). STS work has also illuminated the central role played by hype in technology emergence. Actors who under- stand the constitutive role of expectations have an incentive to raise expectations in order to motivate the funding and activity needed to realise their preferred technological future. Hype, or over-claimed expectation, is often the result. This over-claiming can touch most attributes of emergence and especially prominent impact. For example, press releases prior to the launch of the Seg- way claimed it would ‘change walking’. Similarly, in the case of coherence, for the government to fund nanotechnology research, they must believe nanotechnology is a ‘thing’, as opposed to a name applied by some to a rather miscellaneous selection of materials sci- ence research activities. Therefore, proponents have an incentive to claim coherence where others might disagree.
These studies have been retrospective, but their data sources are contemporaneous with technology emergence so the method could be extended to contemporary analyses. Moreover, mapping of expectations can be combined with scientometrics when suitable data become available. Gustafsson et al. (2015), for example, used technological co-classification to identify clusters of patents, the full-text of which is subsequently analysed qualitatively to detect guiding images or leitbild, which are generalisations shared by sev- eral actors which guide actors towards similar objectives. Guiding images are used to explain the dynamics of expectations.
Retrospective analyses can rely more extensively on sci- entometrics, although this has not been done very often. Scientometricians have mostly focused on the detection and anal- ysis of growth and novelty, whereas impact seems to be taken for granted. Nonetheless, scientometrics can greatly contribute to eval- uating the impact of a potentially emerging technology. A number of techniques can be used to produce intelligence on the emergence process. These include the analysis of highly cited documents, of authorship data to generate intelligence about the actors drawn into knowledge creation processes over time (e.g. private vs. pub- lic organisations and incumbents vs. newcomers), and of changes in the collaboration structure as mapped with co-authorship data (e.g. Hicks et al., 1986; Melin and Persson, 1996). Impact on knowl- edge production processes can instead be assessed by examining the dynamics of cognitive networks obtained from the study of the citations or the co-occurrence of terms across a particular set of documents.
4.5. Uncertainty and ambiguity
Emerging technologies are characterised by uncertainty in their possible outcomes and uses, which may be unintended and unde- sirable, as well as by ambiguity in the meanings different social groups associate with the given technology (Stirling, 2007; Mitchel,
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007). For analyses of contemporary emerging technologies, news rticles, editorials, review and perspective articles on professional nd academic journals can be examined to qualitatively assess the egree of uncertainty and ambiguity associated with an emerg-
ng technology as well as to identify possible multiple visions of he future associated with the technology. As for the evaluation of ow prominent the impact of an emerging technology will be, an TS approach to the mapping of expectations can be used for the ssessment of uncertainty and ambiguity.
For retrospective analyses, the evaluation of uncertainty and mbiguity remains largely unexplored in scientometric studies, owever. The few attempts made along these lines tend to overlap ith those already discussed for the evaluation of the coherence
ttribute, since the main focus has been on the measurement of he reduction of uncertainty in scientific communication rather han on uncertainty and ambiguity associated with the potential mpact or uses of emerging technologies. For example, the creation f a novel category (such as a new subject category in the classi- cation of ISI WoS), in which subsequent journals associated with he emerging technology under examination may fall, is conceived s an indicator of increasing redundancy in the communication rocess — as new journals are established and achieve a critical ass to justify the creation of a new category, the redundancy of
he communication process associated with the considered emerg- ng technology has also increased. Increasing redundancy, in turn,
ay indicate diminishing uncertainty.14 In a similar vein, Lucio- rias and Leydesdorff (2009) considered words in publication titles
which are selected by authors to position knowledge claims at given time), cited references (which enable authors to position nowledge claims in the existing socio-cognitive domain), and time s key dimensions describing the scientific discourse at the research ront of a specialty. The mutual information exchanged between hese dimensions (measured in terms of Shannon entropy) is sug- ested as an indicator of uncertainty reduction. The gap in the ssessment of uncertainty and ambiguity represents, however, an mportant arena for future research, as we will discuss in the next ection.
. Discussion
We characterised emerging technologies on the basis of five ttributes — (i) radical novelty, (ii) relatively fast growth, (iii) coher- nce, (iv) prominent impact, and (v) uncertainty and ambiguity — nd used these to develop a framework for a coherent and system- tic operationalisation of emerging technologies. A wide variety of cientometric methods are available to operationalise the various ttributes of emergence. Nonetheless, these are strongly dependent n time, on the nature of the attribute, and on the data used.
Scientometric techniques are intrinsically more effective for etrospective analyses than contemporary examinations. Time is equired before documents such as publications and patents can e observed and techniques can be applied longitudinally. For xample, measuring growth is particularly problematic for more ontemporary analyses. Techniques using future citations are more ensitive to this issue than methods that rely on data available when ocuments are published (e.g. co-word analysis and bibliographic oupling). Lags in database indexing may also contribute to the time imitations of scientometric approaches.
Scientometrics is also of little use in the operationalisation of ncertainty and ambiguity. The focus of scientometrics has been ainly on the detection of what is emerging, rather than on cha-
acterising the potential of what is detected to be emerging. To the
14 Personal communication with Loet Leydesdorff on 2 October 2014.
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best of our knowledge, this area is largely unexplored. Likewise, the methods reviewed in this paper show no explicit focus on how the societal aspect of prominent impact can be assessed. This is some- what surprising when one considers the extensive scientometric work carried out for research evaluation purposes.
Furthermore, most studies have focused on publications and patents — data that are not only sensitive to time, but also provide limited perspectives on the multifaceted phenomenon of emerg- ing technologies. A few studies have focused on the use of news articles and big data sources (e.g. Google Trends). These are clearly emerging streams in scientometric and data-mining research, but so far little attention has been paid to the use of these novel data sources in the context of emerging technologies.
The risk that detected technological emergence may be merely an artefact of the method used adds to these limitations. The reviewed methodologies rely on different models, data, thresh- olds, clustering algorithms and parameters, the selection of which may bias the detection of emergence towards certain patterns. For example, technological emergence is often detected with compara- tively static analyses rather than with dynamic examinations. Data for a given observation period are divided into time windows and algorithms are then applied to the sample of data included in each time window. Results may vary with the number and length of time windows. Shorter time windows may not identify certain patterns of emergence because they do not capture a critical mass of docu- ments, while longer time windows may miss cases of technologies that exhibit emerging features for a shorter period (e.g. promising technologies that eventually do not emerge). Also, the identified emerging technologies may be biased towards certain topics. Small et al. (2014), for example, found that topics identified as emerging by the combined ‘direct citation-co-citation’ approach are in areas that are more likely to offer practical outcomes than non-emerging topics. This may suggest that such areas attract more resources, which, in turn, may favour the recruitment of researchers (Small et al., 2014). Yet, the identification of these emerging areas may also be the result of the model and data used. The field could move forward more confidently if instead of every study using a differ- ent data set, a standard model dataset was developed to which all techniques could be applied and the results compared (Katz, 1996).
We have argued that qualitative STS approaches can be par- ticularly powerful for overcoming the limits of scientometrics, for instance, in relation to prominent impact and to uncertainty and ambiguity. For example, mapping expectations through content analysis of news, review articles, and policy documents can provide important insights. Because STS focuses on human agency, the importance of expectations and visions in steering emergence as well as the examination of niche-regime dynamics is more appar- ent. Hence, this tradition attempts to address questions of how emergence happens. This may favour meaningful interpretations of scientometric data and possibly a better conceptual understanding. Scientometrics, in turn, can bring a more robust empirical approach to the STS research tradition, including the capability to address measurement error by means of statistical inference as well as to increase the generalisability of results. Few studies have followed a combined scientometrics-STS approach. Kuusi and Meyer (2007), for example, applied a bibliographic coupling approach to identify clusters of patents and then to map ‘guiding images’ used by differ- ent actors to develop a consensus around the goals and directions during different phases of development of an emerging field.15 Yet,
there remains great potential for substantial links and a deeper syn- thesis between the two traditions focusing on the examination of emergence in science and technology.
15 As noted earlier in Section 4.4, a similar mixed approach has been adopted by Gustafsson et al. (2015).
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The conceptualisation and operationalisation of emerging tech- ologies offer a number of opportunities for future research. From
conceptual point of view, more understanding of the origins of merging technologies is required. In the early phase of emergence high levels of radical novelty and of uncertainty and ambiguity, ow levels of growth, coherence, and impact) some technologies cquire a certain momentum to become ‘emerging’ (when the evels of attributes are subject to more dramatic change), other echnologies instead arrive at the verge of becoming emergent, but ventually not emerge at all. Funding and research programmes, he power distribution among actors, communities of practices, nd regulations are likely to exert a significant impact on this pro- ess. However, more systematic research is required on the factors hat enable a technology to eventually become emergent. This also xtends to the empirical investigation of emergence. Studies often end to analyse emerging technologies, without comparing them ith a counter-factual sample of technologies that had the poten-
ial of becoming emergent, but eventually did not emerge. Likewise, e have limited knowledge of the end point of the emergence pro-
ess, i.e. when emergence is over, or perhaps prematurely grinds o a halt or reverses.
The limitations of the use of scientometrics for the operational- sation of some the attributes also represent important avenues for uture research. In this regard, the use of novel data sources such s publication-full-text and funding data seems particular promis- ng. For example, publication full-text data have been mainly used o improve the accuracy of standard scientometric approaches e.g. co-citation and co-word clustering) (e.g. Boyack et al., 2013; lenisson et al., 2005). However, the analysis of the full-text of ublications may also provide information for operationalising he uncertainty and ambiguity attribute of emergence. Instances f multiple and competing envisioned applications of an emerg- ng technology may be identified in publication sections such as he introduction and discussion, which also have the advantage f being structured in a relatively standard manner across pub- ications as compared to other sections. Sentiment and narrative nalysis techniques may be particularly suitable for the extraction f this information.
Funding data may also provide relevant information for the perationalisation of emerging technologies. For example, uncer- ainty and ambiguity may be indicated by more extensive public unding than private investment. Growth in funding may indicate elatively fast growth, thus overcoming the time lag between actual mergence and emergence detected in publications and patents. he amount of funding can also cast light on the expected impact f the technology. Relatively large investments suggest promi- ent impact is expected. Nonetheless, the coverage of funding ata remains limited (Hopkins and Siepel, 2013). A number of atabases (e.g. Researchfish, FundRef, RCUK Gateway to Research, IH RePORTER) have been recently built with aim of providing ccess to these data. Such databases include data on funding from ajor funders (e.g. government departments, research councils,
arge charities and foundations), but inevitably lack information on large variety of relatively small funding organisations that may be mportant, especially in the early phases of development. The use f funding data as reported by authors in the acknowledgements ection of publications provides better coverage of funders but no nformation on the amount of funding.
The use of big data and altmetrics (e.g. download statistics, umber of retweets, Mendeley readers, citations in blogs or news rticles) add to the set of potential data sources. Given that these ata are produced in a ‘real-time’ manner as compared to con-
entional scientometric data, they seem particularly promising in nabling the development of indicators for early detection. For xample, publication download statistics can provide an early indi- ation of relatively fast growth and perhaps of prominent impact
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in the academic domain as compared to conventional citation data. Numbers of tweets and citations in blogs or news articles may instead provide an indication of attention outside the aca- demic domain. Nonetheless, there is first a need to improve our understanding of these data as well as how the data can be com- pared across different cases of emerging technologies. We hope the framework offered here can be used to structure exploration of novel data sources for the detection of emerging technology.
6. Conclusions
Emerging technologies have assumed increasing relevance in the context of policy-making for their perceived ability to change the status quo (e.g. Martin, 1995; Day and Schoemaker, 2000; Alexander et al., 2012; Cozzens et al., 2010). This has spurred ad hoc governmental actions such as the “Future & Emerging Technolo- gies” (FET) initiative funded by the European Commission in 2013 and the “Foresight and Understanding from Scientific Exposition” (FUSE) research program funded by the US Intelligence Advanced Research Projects Activities (IARPA) in 2011. The FUSE program, for example, in pursuit of potential uses of big data, has aimed to develop methods for the reliable early detection of emergence in science and technology by mining the full-text of publications and patents. Policy interest has been matched by the academic com- munity who have developed a variety of methods for the detection and analysis of technological emergence in recent years especially in the scientometric domain (e.g. Small et al., 2014; Glänzel and Thijs, 2012).
Despite this broad interest, a widely accepted definition of emerging technologies and an agreed conceptually grounded framework for their operationalisation are both still missing. We showed that emerging technologies are either loosely defined in the empirical literature or often no definition at all is provided. As a con- sequence, operationalisations of emergence tend to differ greatly even between approaches using the same techniques. In addition, the understanding of what is an emerging technology differs across actors: some individuals may conceive a technology to be emergent because they expect impact on the socio-economic system, while others may see the same technology as old and no longer emergent. This, in turn, has significant implications for policy making and the governance of emerging technologies.
The present paper has attempted to move the field forward by systematically delineating the concept of technological emer- gence linked to measurement options. To do so, we first developed a definition of emerging technologies that is able to capture the multifaceted nature of emerging technologies, and then proposed a framework for their operationalisation drawing on, but not limited to, scientometric analysis. We identified five attributes of emerg- ing technologies: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambigu- ity, and defined emerging technologies as: “a relatively fast growing and radically novel technology characterised by a certain degree of coherence persisting over time and with the potential to exert a con- siderable impact on the socio-economic domain(s) which is observed in terms of the composition of actors, institutions and the patterns of interactions among those, along with the associated knowledge pro- duction processes. Its most prominent impact, however, lies in the future and so in the emergence phase is still somewhat uncertain and ambiguous”.
We then developed a coherent and systematic framework for operationalising these attributes of emergence. Scientometric liter-
ature was the main source of potential measures. Relevant studies were reviewed and linked to the attributes of emergence. Our analysis showed that scientometric analysis is particularly appro- priate for the operationalisation of growth, novelty and coherence.
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elatively fast growth is operationalised in many studies and often valuated by counting documents over time (such as news articles, ublications, and patents) (e.g. Porter and Detampel, 1995). Radical ovelty is identified with the appearance of new clusters of docu- ents or words in citation or co-word analyses (e.g. Kajikawa and
akeda, 2008), while other studies point to the importance of also onsidering the extent to which the new cluster is connected to lusters in the same year of observation or to clusters identified in revious years (e.g. Small et al., 2014). Indicators based on entropy easures or on the appearance of new categories (e.g. journals,
echnological classes, terms in institutionalised vocabularies) were dentified as more suitable for assessing coherence (e.g. Cozzens t al., 2010).
Nonetheless, important limitations exist on the scientometric ontribution to the operationalisation of emerging technologies. he evaluation of uncertainty and ambiguity as well as prominent mpact is, for example, largely unexplored in scientometrics. Also,
ethods often rely on few data sources, mostly publication and atent data, which tend to be not suitable for the analysis of con- emporary cases of emerging technologies — these data require ime to be generated. The risk that detecting apparent emergence
ay be merely an artefact of selected models adds to these limita- ions.
We have argued that the qualitative investigation of emerg- ng technologies conducted by STS researchers seems particularly romising in complementing scientometrics for the purpose of perationalising the attributes of emergence. The mapping of xpectations of emerging technologies by means of qualitative nalysis of documents such as news, review articles, and policy ocuments can, for example, provide important insights on the ncertainty and ambiguity and the prominent impact attributes f emergence, especially in the case of contemporary analyses. TS approaches can also provide meaningful interpretation of the esults of scientometrics, thus potentially reducing the likelihood f detecting false positives or missing patterns.
We envisage a number of opportunities for future research. irst, future research should pay more attention to the origins of merging technologies. We have limited knowledge of factors that nable certain technologies to become emergent while other do ot emerge at all. This also extends to the research design used for he investigation of emerging technologies. Studies often examine merging technologies without delineating a counter-factual sam- le of technologies that did not emerge but which nevertheless had he potential to emerge. Similarly, we have limited knowledge on hen a technology ceases to be emergent and what factors shape
his process. Second, the increasing access to publication full-text, unding data, altmetrics, and, more generally big data, may provide ignificant opportunities for future research in scientometrics to evelop indicators and methods for the evaluation of attributes of mergence for which the current ‘state of the art’ in scientometrics an provide only a limited contribution.
In summary, we have showed that considerable disagreement xists on what is technological emergence and how it should e operationalised. This has important implications for policy- aking in the context of emerging technologies (e.g. resource
llocation, creation of research programmes, drawing up of reg- lations), which, in turn, exerts a direct effect on the emergence rocess itself. The present paper has attempted to contribute to his ongoing and urgent debate in science policy research through onceptual clarification of the phenomenon of emergence. This is a ecessary precondition for a coherent and systematic operationali- ation of emerging technologies, for future research developments,
or a better understanding of the phenomenon, and, therefore or more informed policy-making and governance of emerging echnologies.
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Acknowledgements
We acknowledge the support of the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) (award PIOF-GA-2012-331107 — “NET-GENESIS: Network Micro-Dynamics in Emerging Technolo- gies” www.danielerotolo.com/netgenesis). We are grateful to Loet Leydesdorff, Nils Newman, Alan Porter, Andrew Stirling, Jan Youtie, the two anonymous referees of the SPRU Working Paper Series (SWPS), and the two anonymous referees of Research Policy for their comments, criticisms and suggestions. A previous version of this paper was presented at the SPRU Wednesday Seminar Series at the University of Sussex (27 May 2015), the Technology Pol- icy Assessment Centre (TPAC) Seminar Series at the School of Public Policy of the Georgia Institute of Technology (4 December 2014), and the 2015 Eu-SPRI Conference (9–12 June 2015, Helsinki, Finland).
Appendix
Table A1 The concept of ‘emergence’ in complex systems theory (studies are ordered chronologically).
Study Definition
Bedeau (1997) “[. . .] Emergent phenomena are somehow constituted by, and generated from, underlying processes [. . .] are somehow autonomous from underlying processes” (p. 375) “[. . .] there is a system, call it S, composed out of micro level parts [. . .] S has various macro level states (macrostates) and various micro level states (microstates) [. . .] there is a microdynamic, call it D, which governs the time evolution of S’s microstates [. . .] I define weak emergence as follows: Macrostate P of S with microdynamic D is weakly emergent iff P can be derived from D and S’s external conditions but only by simulation” (pp. 377–378)
Goldstein (1999) “Emergence [. . .] as the arising of novel and coherent structures, patterns, and properties during the process of self-organization in complex systems [. . .] common properties that identify them as emergent: • Radical novelty: emergents have features that are not previously observed in the complex system under observation [. . .] • Coherence or correlation: emergents appear as integrated wholes that tend to maintain some sense of identity over time. This coherence spans and correlates the separate lower-level components into a higher-level unity. • Global or macro level: [. . .] the locus of emergent phenomena occurs at a global or macro level [. . .] • Dynamical: emergent phenomena are not pre-given wholes but arise as a complex system evolves over time [. . .] • Ostensive: emergents are recognized by showing themselves, i.e. they are ostensively recognized [. . .]” (pp. 49–50)
Corning (2002) “Emergent phenomena be defined as a subset of the vast (and still expanding) universe of cooperative interactions that produce synergistic effects of various kinds, both in nature and in human societies [. . .] all emergent phenomena produce synergistic effects, but many synergies do not entail emergence. In other words, emergent effects would be associated specifically with contexts in which constituent parts with different properties are modified, reshaped, or transformed by
their participation in the whole.” (pp. 23–24)
1842 D. Rotolo et al. / Research Polic
Table A1 (Continued)
Study Definition
Chalmers (2006) “A high-level phenomenon is strongly emergent with respect to a low-level domain when the high-level phenomenon arises from the low-level domain, but truths concerning that phenomenon are not deducible even in principle from truths in the low-level domain [. . .] a high-level phenomenon is weakly emergent with respect to a low-level domain when the high-level phenomenon arises from the low-level domain, but truths concerning that phenomenon are unexpected given the principles governing the low-level domain.” (p. 244)
de Haan (2006) “Emergence is about the properties of wholes compared to those of their parts, about systems having properties that their objects in isolation do not have. Emergence is also about the interactions between the objects that cause the coming into being of those properties, in short the mechanisms producing novelty.” (p. 294)
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- What is an emerging technology?
- 1 Introduction
- 2 The concept of emergence
- 3 Defining emerging technologies
- 4 A framework for the operationalisation of emergence
- 4.1 Radical novelty
- 4.2 Relatively fast growth
- 4.3 Coherence
- 4.4 Prominent impact
- 4.5 Uncertainty and ambiguity
- 5 Discussion
- 6 Conclusions
- Acknowledgements
- Appendix
- References