Multi-disciplinarity breeds diversity: the influence of innovation project characteristics on diversity creation in nanotechnology
Multi-disciplinarity breeds diversity: the influence of innovation project characteristics on diversity creation in nanotechnology
Cristina P a´ez-Avile´s 0 1 2 3 4
Frank J. Van Rijnsoever 0 1 2 3 4
Esteve Juanola-Feliu 0 1 2 3 4
Josep Samitier 0 1 2 3 4
0 INGENIO (CSIC-UPV), Universitat Polite`cnica de Vale`ncia , Camino de Vera, 46022 Valencia , Spain
1 Innovation Studies, Copernicus Institute of Sustainable Development, Utrecht University , Heidelberglaan 2, 3584 CS Utrecht , The Netherlands
2 Department of Electronics, Bioelectronics and Nanobioengineering Research Group (SIC-BIO), University of Barcelona , Mart ́ı i Franque`s 1, Planta 2, 08028 Barcelona , Spain
3 CIBER-BBN-Biomedical Research Networking Center in Bioengineering , Biomaterials and Nanomedicine, Mar ́ıa de Luna 11, Edificio CEEI, 50018 Saragossa , Spain
4 IBEC-Institute for Bioengineering of Catalonia, Nanosystems Engineering for Biomedical Applications Research Group , Baldiri Reixac 10-12, 08028 Barcelona , Spain
Nanotechnology is an emerging and promising field of research. Creating sufficient technological diversity among its alternatives is important for the long-term success of nanotechnologies, as well as for other emerging technologies. Diversity prevents early lock-in, facilitates recombinant innovation, increases resilience, and allows market growth. Creation of new technological alternatives usually takes place in innovation projects in which public and private partners often collaborate. Currently, there is little empirical evidence about which characteristics of innovation projects influence diversity. In this paper we study the influence of characteristics of EU-funded nanotechnology projects on the creation of technological diversity. In addition to actor diversity and the network of the project, we also include novel variables that have a plausible influence on diversity creation: the degree of multi-disciplinarity of the project and the size of the joint knowledge base of project partners. We apply topic modelling (Latent Dirichlet allocation) as a novel method to categorize technological alternatives. Using an ordinal logistic regression model, our results show that the largest contribution to diversity comes from the multidisciplinary nature of a project. The joint knowledge base of project partners and the geographical distance between them were positively associated with technological diversity creation. In contrast, the number and diversity of actors and the degree of clustering
showed a negative association with technological diversity creation. These results extend
current micro-level explanations of how the diversity of an emerging technology is created.
The contribution of this study could also be helpful for policy makers to influence the level
of diversity in a technological field, and hence to contribute to survival of emerging
Nanotechnology is an important emerging technology, with many potential applications.
Examples include nanoparticles (Davis 1997), nanosensors (Qu et al. 2013),
nanocomposites, (Paul and Robeson 2008) carbon nanotubes and nanomaterials (Salata 2004), that
can be used for biomedical, environmental, textile, food, or construction purposes.
Healthcare is one field being highly revolutionized at the level of nano-scale manipulation
(Gabellieri and Frima 2011; Pautler and Brenner 2010) since nano-devices and
nanomedicines can harvest accurate diagnosis, cheaper and faster biomedical facilities, less
invasive procedures and more targeted drugs (Bjørn Larsen 2011). All these applications
can lead to great economic and societal benefits (Miyazaki and Islam 2007; Roco 2013).
Hence, nanotechnology is continuously being incorporated as an essential part of industrial
and governmental R&D agendas (Miyazaki and Islam 2007; Roco 2013). Nanotechnology
R&D funding has grown worldwide from US$1.2 billion in 2000 to over US$18 billion in
2010 with an average annual growth rate of 0.27 (Roco et al. 2011; Roco 2013).
Nanotechnology has also been selected as one of the six Key Enabling Technologies (KETs) by
the European Commission, and the current Horizon 2020 (H2020) Framework Programme
prioritizes the industrial implementation and cross-fertilization of nanotechnologies in
industry (Højgaard et al. 2012; Kalisz and Aluchna 2012).
For an emerging technology like nanotechnology, creating sufficient technological
diversity among its alternatives is important for its long-term success (Negro et al. 2008;
Van den Bergh 2008; Van Rijnsoever et al. 2015). Innovation is an evolutionary process of
variation and selection (Edquist 1997; Hekkert et al. 2007). Technological diversity helps
to prevent an early lock-in, facilitates recombinant innovation, increases resilience of the
technology in case of unexpected circumstances, and allows market growth (Dosi 1982;
Faber and Frenken 2009; Van Rijnsoever et al. 2015).
However, technological diversity could also increase production costs; hamper
economies of scale, and impede standardization and the learning of routines. It also leads to
coordination difficulties among actors.
The diversity of a technology changes as new technological alternatives are created
(Murmann and Frenken 2006; Saviotti and Metcalfe 1984; Van Rijnsoever et al. 2015). If a
new technological alternative represents a common technological design, diversity
decreases. Technological alternatives that have a novel or less common design increase
diversity (Abernathy 1979; Frenken et al. 1999).
The creation of new technological alternatives often takes place in innovation projects
in which different actors such as firms, universities, and research institutes collaborate
(Cooke et al. 1997; Edquist and Hommen 1999; Niosi 2011). For emerging technologies,
these innovation projects are often publicly supported, for example, through EU-funding.
Hence, funding instruments can be used as a tool for policy makers to influence the level of
technological diversity (Pandza et al. 2011; Van Rijnsoever et al. 2015), and thus to secure
the long-term viability of the technology.
Simulations (Jonard and Yfldizoglu 1998) and conceptual works (Edquist and Hommen
1999) indicate that the creation and persistence of technological diversity depends on
learning from their neighbourhood and network externalities. Yet, there is little empirical
evidence about the characteristics of innovation projects that influence diversity. Van
Rijnsoever et al. (2015) demonstrated that diversity created by an innovation project is
related to the network position and actor composition of a project. Adding to insights from
innovation systems (Hekkert et al. 2007), Van Rijnsoever et al. (2015) argue that it is also
important to consider the structure of the network to make a technology successful in the
long term. In nanotechnology European founded projects, Pandza et al. (2011) found that
there is a significant degree of collaborative diversity in terms of international and
institutional affiliation in a research network. This should be beneficial to technological
diversity creation, but they did not test this implication empirically. In this paper we extend
these current approaches by studying the influence of characteristics of EU-funded
nanotechnology projects on the creation of technological diversity. In addition to actor
diversity and the network of the project, we also include novel variables that have a
plausible influence on diversity creation. The degree of multi-disciplinarity of the project
and the knowledge base of the actors in the project can increase the chances that unique
novel combinations are made, increasing technological diversity.
Further, to understand technological diversity we need to study the content of the
documents. Scholars use pre-existing categories like patent classes or Web of Science
categories to measure diversity (Leydesdorff et al. 2014; Rafols and Meyer 2010). Another
approach to determine diversity is to look at the network of citations of the documents
(Rafols and Meyer 2010). Yet, these approaches are mainly applicable to patent or
publication data, and not to EU-projects. Hence, to study diversity, we apply topic modelling
(Blei and Lafferty 2009) as a novel approach to categorize technological designs that are
described in 69 EU-projects from 2014 to 2015. This method allows us to calculate
diversity creation in an efficient manner.
We relate the change in technological diversity caused by a project to the independent
variables mentioned above and show that the largest contribution to diversity comes from
the multi-disciplinary nature of a project and the nanotechnology knowledge base of the
actors in a project. Moreover, our results largely confirm the results by Van Rijnsoever
et al. (2015). Policy makers can use these results to use subsidies as a tool to influence the
level of diversity in a technological field.
In this section we describe the concept of creation of technological diversity by innovation
projects as our dependent variable. Next, we formulate hypotheses about how this
technological diversity is related to our independent variables: the multidisciplinary diversity
of projects, the knowledge base of actors in a project, the number and diversity of actors in
a project, the degree of clustering, and the geographical distance among them.
2.1 Technological diversity
Technological diversity refers to evenness in distribution of technological alternatives
(Foray and Gru¨ bler 1990). These alternatives can be designs (Carlsson and Jacobsson
1997; Murmann and Frenken 2006; Van Rijnsoever et al. 2015), technical characteristics
(Murmann and Frenken 2006; Saviotti and Metcalfe 1984; Van Rijnsoever et al. 2015) or
numbers of different technological lineages represented in a technology (Gjesfjeld et al.
Technological diversity is a macro-level concept, as it applies to a set of technologies.
The concept is related to the micro-level concepts of radical and incremental innovation,
which are commonly used to assess specific innovations or the performance of firms.
Radical innovations are new technologies and are often based on the combination of
different technologies (Fleming 2001). As radical innovations are new, they increase
technological diversity by definition. In contrast, incremental innovations can be achieved
without novel information or the integration of different technologies (Wuyts et al. 2004),
and can either decrease or increase technological diversity, depending on how abundant the
incrementally improved technological design is among existing alternatives. Incremental
innovations on rare technological designs increase diversity, while incremental innovations
on common technological designs decrease diversity.
The evolutionary economics literature states that technological diversity contributes to
more rapid technological change (Cohen and Klepper 1992). For this they give three
reasons. First, diversity mitigates the possibility of an undesirable lock-in, reducing the
likelihood that superior alternatives remain undiscovered or underdeveloped (Abernathy
1979; Cowan and Foray 1998; Frenken and Nuvolari 2004). Second, diversity increases the
chances of making recombinant innovations, and hence of further developing the
technology. Third, technological diversity means that there are more alternatives, which
provides flexibility (Hannan and Freeman 1989; Stirling 2007). As a consequence, diversity
increases the resilience of a technology against unexpected environmental changes, which
are particularly common in emerging stages (Negro et al. 2008). These reasons influence
the long-term success of an emerging technology and are important to consider when
analysing the functionality of innovation systems (Van Rijnsoever et al. 2015).
Technological diversity can thus be used to help assess the long-term viability of a technology.
However, having too much diversity also has drawbacks (Bassett-Jones 2005; Lettl
et al. 2009). For instance, from a neoclassical economic perspective, the generation of
diversity in products and production processes hampers the creation of economies of scale
and the development of standards (Cohendet and Llerena 1997; Van den Bergh 2008).
Moreover, too much diversity could have a negative influence on learning processes
(routines) to exploit the technology (Foray 1997). In this context, more diversity requires
time, effort, and co-ordination among actors (Leten et al. 2007) to resolve possible
differences of perspective (Sirmon and Lane 2004). In addition, in a system with more
diversity, more information needs to be codified, which also leads to increasing costs
(Cohendet and Llerena 1997).
These advantages and disadvantages imply that there is an optimal level of diversity
(Van den Bergh 2008). Despite there being no established parameters to obtain this optimal
level, it is possible to analyse factors that influence the creation or otherwise of diversity
(Van Rijnsoever et al. 2015). Innovation subsidies can be such a factor, but this is not
enough to stimulate diversity creation. We argue that it is also necessary to consider how
these subsidies are distributed.
2.2 Networks of innovation projects
In line with innovation system thinking, Van Rijnsoever et al. (2015) argue that the concept
of technological diversity creation needs to be studied at a project level, rather than at the
level of the individual actor because new technological alternatives are the output of
innovation projects and not of the actors themselves. Actors collaborate in projects on new
innovations. These collaborative projects can be seen as planned tasks that actors execute
over a settled period of time to reach a desirable outcome. Actors contribute knowledge,
resources and skills required for successful innovation to these projects, and share the risks
of failure (Atkinson et al. 2006). Each project has specific characteristics that we study
here. We discuss how a project’s degree of multi-disciplinarity, and the composition of the
project in terms of the prior knowledge base of actors, the number of actors, the diversity of
actor types, geographical distance between partners and network position influence
2.2.1 Degree of multi-disciplinarity of a project
The concept of discipline has been subject to much debate. For instance, it has been used
with ‘‘inter’’, ‘‘trans’’ and ‘‘cross’’ prefixes. Schummer (2004) makes a distinction between
multi and interdisciplinary: multi-disciplinary refers to the involvement of many
disciplines, meanwhile interdisciplinary refers to the interaction between disciplines
(Schummer 2004). In the context of this paper, we follow Rafols and Meyer and define
multidisciplinarity as the spanning of a diversity of knowledge areas, which could be disciplines,
technological fields or industrial sectors (Rafols and Meyer 2010).
Many scholars have analysed multi-disciplinary projects from the perspective of
collaboration between team members (Chin et al. 2002; Cummings 2005; Teasley and
Wolinsky 2001; Van Rijnsoever and Hessels 2011), or on the skills required to manage
these types of projects (Dewulf et al. 2007; Ko¨ nig et al. 2013). In this research,
multidisciplinarity of a project relates to the different types of disciplines, technological fields or
industrial sectors that are involved in the project. We make an explicit distinction between
the multi-disciplinarity within projects, which is the diversity of disciplines, technological
fields or industrial sectors in a project, and the diversity among projects.
To the best of our knowledge, no research has focused on how the degree of
multidisciplinarity within projects contributes to the technological diversity creation among
projects. Yet, there are good reasons to suspect such a relation. On the one hand, a
multidisciplinary environment favours a greater diversity of idea generation and promotes
creativity (Alves et al. 2007). Due to the juxtaposition of ideas, tools, and people from
different domains (Cummings 2005), multi-disciplinarity within projects enhances
recombinant innovation (Baber et al. 1995; Ferna´ndez-Ribas and Shapira 2009; Rhoten
2004; Schmickl and Kieser 2008). Hence, the chances that novel technological alternatives
On the other hand one can argue that a high degree of multi-disciplinarity creates
difficulties with the assimilation and integration of different ideas (Nooteboom 1999). Too
much distance between disciplines can further lead to communication problems (Jeong and
Lee 2015). However, it is to be expected that these potential difficulties are mitigated in the
formation process of the consortium, and that partners have sufficient proximity to
collaborate successfully (Boschma, 2005). We thus expect that the degree of
multidisciplinarity of a project has a positive effect on the creation of technological diversity.
This leads to our first hypothesis:
Hypothesis 1 The degree of multi-disciplinarity within a project is positively associated
with the creation of technological diversity among projects.
2.2.2 Knowledge base
Technological diversity is associated with prior technological knowledge of inventors
(Lazear 2004; Lettl et al. 2009). This prior knowledge can be measured as patents and
publications since they are two quantitative proxies of knowledge production (Lee et al.
2013; Zucker et al. 2007). In particular patents are used to measure knowledge diversity
(Carnabuci and Operti 2013).
Innovative outcomes are the result of the combination of existing knowledge and ideas
(Dubiansky 2006). Hence, prior knowledge is likely to be positively associated with the
creation of technological diversity. There are several reasons to support this idea. For
instance, previous studies showed that R&D intensity and patents increase with the degree
of technological diversification of firms (Garcia-Vega 2006).
Prior knowledge also strengthens the absorptive capacity of actors by increasing ‘‘the
prospect that incoming information will relate to what is already known’’ (Cohen and
Levinthal 1990, p. 131). Hence, a large knowledge base enhances the ability of an actor to
make novel combinations. Moreover, a larger prior knowledge base demonstrates that
actors have the experience and routines needed to combine knowledge (Kogut and Zander
1992). This effect is even stronger if the joint knowledge base of all project partners is
larger, as it further increases the chances of making novel combinations.
Hypothesis 2 The size of the joint knowledge base of actors within a project is positively
associated with the creation of technological diversity.
2.2.3 Number of actors
Number of actors refers to ‘‘the size of the project consortium in terms of distinct actors’’
(Van Rijnsoever et al. 2015, p. 1097). There are two positions in the literature regarding the
number of actors in a project. The first and most common position is that when there is a
large number of parties involved, the process of communication, agreement or problem
solving require a complex process of integration of knowledge and synchronization
(Gilsing et al. 2008; Hippel 2005; Jeong and Lee 2015; Lorenzoni and Lipparini 1999).
The co-ordination required in this scenario demands conformity to standards or rules and
thus, less novelty (Tatikonda and Rosenthal 2000) or diversity creation. Similar arguments
can be found in the team size literature from social psychology (Curral et al. 2001;
Kozlowski and Bell 2003).
The second position argues that larger project teams foster a more dynamic
collaboration resulting in faster outcomes, shorter product life cycles and competitive advantages
(Edmondson and Nembhard 2009). Larger project teams also provide a larger chance of
recombining different types of knowledge, expertise and ideas, and thus innovation
(Powell et al. 1996; Ruef 2002).
Yet, few studies explicitly investigate the influence of the number of organisations
involved on the creation of technological diversity. In this context, existing evidence
suggests that there is a negative association between the number of project partners and the
creation of technological diversity (Van Rijnsoever et al. 2015). The argument is that
intense collaborations could result in conformity of norms and conventions producing less
novelty (Tatikonda and Rosenthal 2000). Keeping this in mind we propose the following
Hypothesis 3 The number of actors in a project has a negative association with the
creation of technological diversity.
2.2.4 Diversity of actors
Innovation projects commonly involve different actor types that come from different
institutional spheres (Hsu et al. 2011). In this paper we distinguish the following actor
types: private for-profit entities (PRC), research centres (REC), higher or secondary
education establishments (HES), public bodies (PUB) and others (OTH). According to the
European Commission, PRC are small or medium-sized enterprises, excluding for-profit
educational establishments, HES are legal entities recognised as such by its national
education system, REC are non-profit actors whose main objectives are carrying out
research or technological development, PUB are actors that have a legal entity of public
institutions governed by public laws and OTH, which are any entity not falling into the
previous four categories (European Commission 2015a).
Previous studies on the nanotechnology innovation networks demonstrated that
networks in this field are indeed characterized by a high degree of international and
institutional diversity. Pandza et al. (2011) demonstrated that usually, the inter-institutional
collaboration is taking place between private industry and public research actors (Pandza
et al. 2011). Juanola et al. (2012) also showed that the development of nano-enabled
biomedical devices requires the interaction between multiple actors such as universities,
public research institutions, industries, and hospitals or health care institutions.
Arguments have been made for either a positive or negative relation regarding the
diversity of actors. A positive aspect of involving actors’ partners from different
institutional spheres is that each actor type brings to the project unique knowledge and skills
which can be recombined to form novel concepts and designs (Mo 2016), creating more
technological diversity (Van Rijnsoever et al. 2015). On the other hand, a project with less
diversity in actor types could hamper diversity creation because collaborating with the
same type of partners could lead to redundant information and collaborative inertia
(Pandza et al. 2011; Rothaermel 2005).
However, having too much diversity among actor types requires the capacity to manage
collaborative research and to take advantage of the knowledge from the network to achieve
the goals of the project. Not all the actor types have these managerial capabilities (Pandza
et al. 2011). Additionally, researchers from diverse types of organisations need to
understand different points of view, people from different institutional backgrounds, cultures or
even diverse technical language (Pa´ez-Avile´s et al. 2015). Again, this is something that can
be taken into account when forming the project consortium. Hence, we expect a positive
relation between diversity of actor types and technological diversity creation:
Hypothesis 4 The diversity of actor types in a project has a positive association with the
creation of technological diversity.
2.2.5 Degree of clustering
As actors can participate in multiple projects, a network emerges in which projects are
nodes and actors are ties between the nodes. Clustering is a property of a local network
structure which refers to the likelihood that two actors that are connected to a third actor
are also connected to one another (Eslami et al. 2013; Kaiser 2008). The more they are
connected, the higher the degree of local clustering (Wasserman and Faust 1994).
There is a debate about the effect of clustering on innovation. On the one hand,
clustered networks are argued to be dense local neighbourhoods where actors trust each other,
shared norms emerge, information is verified or diffused (Ahuja 2000; Powell et al. 1996;
Schilling and Phelps 2007) and novel combinations are being made (Uzzi and Spiro 2005).
However, too much clustering can have negative effects on innovation. Many of the ties
are redundant, yet costly to maintain (Burt 2004). Also, sharing the same information
sources also means that knowledge becomes more homogenous. Moreover, the shared
norms can hamper creativity. The opposite of clustering is that there are ‘‘structural holes’’
in a network (Burt 2004). Structural holes occur when two actors that are connected to a
focal partner are not connected to each other (Burt 2001, 2004). This means that the focal
partner has access to two different sources of information, which allows for making novel
combinations (Burt 2004) that add more to technological diversity (Van Rijnsoever et al.
2015). Hence, we hypothesize:
Hypothesis 5 The degree of clustering around a project is negatively associated with the
creation of technological diversity.
2.2.6 Geographical distance
Geographical distance between actors in a project is another network dimension that
influences knowledge diffusion (Marrocu et al. 2013). Based on the theory of regional
innovation systems (Cooke 2001), it has been shown that higher concentration of ‘‘talents’’
in a region helps to connect and exchange knowledge resulting in enhanced innovations
(Boschma 2005; Kakko and Inkinen 2009). Geographical proximity also enables
knowledge spill-overs among neighbouring actors in related industries (Cooke 2008).
However, knowledge is bound to a geographical location, and the content of knowledge
bases varies geographically (Boschma et al. 2014; Frenken and Hoekman 2014). Therefore
the further the distance between actors, the more likely it is that their knowledge bases
differ. This increases the possibility of making novel combinations and thus the creation of
In contrast, having international teams can also hamper diversity creation. Cultural
differences lead to difficulty in transference or decoding of certain types of messages
(Lundvall 1992). Hence, the costs of international teams can exceed the gains of diversity
(Faber et al. 2016; Sirmon and Lane 2004; Williams and O’Reilly 1998), since resources
can be diverted into smoothing cultural differences in the team, which comes at the
expense of innovation and diversity creation.
In addition, Van Rijnsoever et al. (2015) tested this relationship and found that the
effects of geographical distance do not contribute to the creation of technological diversity.
A possible explanation for this is that their study only included Dutch innovation projects.
There might have been too little geographical distance between partners for the knowledge
bases to differ.
This inconclusive evidence strengthens the need for testing the relation of this variable
with the creation of technological diversity. Hence, we hypothesize:
Hypothesis 6 The geographical distance of actors within projects is positively associated
with the creation of technological diversity.
2.3.1 Sample selection and data collection
We tested our hypotheses on the case of nanotechnology as an important emerging
technology and a promising KET. We focussed our research on the healthcare domain due to
the great applicability and growth of nanotechnology in medicine (Gabellieri and Frima
2011), which has been highly prioritized over the past European Framework Programmes
(European Commission 2010; Galsworthy et al. 2012).
For this purpose, we selected health-related projects from the Work Programme LEIT
2014–2015 of H2020 called ‘‘Nanotechnologies, Advanced Materials, Biotechnology and
Advanced Manufacturing and Processing’’, which foster the technological
cross-fertilization of nanotechnologies, biotechnologies and advanced manufacturing systems.
Technological cross-fertilization, as coined by the European Commission, is the process of
combining different KETs resulting in cross-cutting products or services, with enhanced
technological performance (Butter et al. 2014). Therefore, since the combination of
different technologies is being highly prioritized, projects selected under this initiative are
suitable for studying technological diversity. Based on these criteria, 69 projects were
obtained from the Community Research and Development Information Centre (http://
cordis.europa.eu). The projects belong to four types of calls (European Commission
Nanotechnology and advanced materials for more effective healthcare: focusses on the
potential of advanced materials and nanotechnologies to enable effective therapies and
diagnosis. The major innovation challenge in this call is to achieve clinical
applications from pre-clinical laboratory-scale proof-of-concepts.
Exploiting the cross-sector potential of nanotechnologies and advanced materials to
drive competitiveness and sustainability: this call focusses on the break-through
potential of nanotechnology and advanced materials on several applications and
economic sectors by boosting European industry.
Bridging the gap between nanotechnology research and markets: this call addresses
three key nano-enabled industrial value chains (lightweight multifunctional materials
and sustainable composites, structured surfaces, and functional fluids) by taking them
from the laboratory to the industrial scale.
Biotechnology-based industrial processes driving competitiveness and sustainability:
this call focusses on delivering novel products that cannot be produced in the current
industry on the basis of efficient biotechnological methods with less environmental
Within these projects we identified 222 unique actors as co-ordinators and participants.
These are the actors that we use for our actor and network based variables.
2.4 Variable measurements
2.4.1 Technological diversity
Diversity is a multidimensional concept. Stirling (1998) recognized variety, balance and
disparity as the three dimensions of diversity (Stirling 1998). Variety represents the
number of elements or categories in the system. In other words, it represents the counting
or enumeration of the distinctive types of elements or categories in the system of elements
or categories. Balance refers to the distribution of these elements or the evenness of its
distribution. As Stirling argued, this dimension could be equal to asking the following
question: ‘‘how much of each type of thing do we have?’’ (Stirling, 2007, p. 709). Third,
disparity is related to the degree to which these elements are distinct from each other.
In this study we used the first two dimensions to calculate diversity, as there is no
consensus on an appropriate measure for disparity (Rafols and Meyer 2010; Stirling 2007;
Van Rijnsoever et al. 2015; Zhang et al. 2016).
To analyse the creation of technological diversity, the first step was to find all the
technological alternatives present in the system of projects. In the case of publications and
patents this is often done by looking at citation patterns or pre-existing categories
(Boschma et al. 2014; Rafols and Meyer 2010; Yegros-Yegros et al. 2015). Yet these
measures are not applicable to our project data, as we only have access to the abstracts.
Hence, we used topic modelling techniques. Topic Models represent a set of probabilistic
variable models used to evaluate the semantic structure of documents based on a
hierarchical Bayesian method (Blei and Lafferty 2007, 2009) which can be used to identify
topics among documents. The different technological alternatives are based on semantic
clusters, which are usually identified as ‘‘topics’’. Therefore, topics are a set of words that
represent a theme. For example, the words ‘‘nano-capsule’’, ‘‘delivery’’ and ‘‘enzyme’’ can
be classified in one topic because these words are related to each other. The distribution of
topics is the relation that links words in a vocabulary and their occurrence in documents
(mixture of topics). In this study, documents are the abstracts of each project.
To obtain the distribution of topics, we used Latent Dirichlet Allocation (LDA), which
is a common type of topic model that uses discrete probabilistic techniques for information
retrieval, and text and data mining (Blei et al. 2003). LDA assumes that K number of topics
have an association with a collection of documents, and estimates for each document the
probability that it belongs to a topic (Crossno et al. 2011; Gr u¨n and Hornik 2011; Zhang
et al. 2016).
For the LDA analysis we used the lda package (Chang 2015; Ponweiser 2012) of the
R-program. The first step was to pre-process the documents in order to avoid possible
‘‘noise’’. This was done by cleaning the text corpus (e.g. remove punctuation, stop words,
numbers, etc.) and stemming or merging words equivalent in meaning. For that purpose we
used the tm package (Feinerer 2015). Second, an appropriate number of topics needed to be
selected for the LDA analysis. Choosing too many topics will result in the
‘‘over-clustering’’ of a corpus into many small, highly-similar topics, while selecting too few can
produce overly broad results (Zhao et al. 2015). For the estimation of the optimal number
of topics, we used the LDA tunning package (Nikita 2015). This package estimates the
optimal number of topics based on a Bayesian selection model which computes the
likelihood probability distribution of a possible parameter setting by assigning all words of the
corpus w, over a number of topics T expressed as P(w|T) (Griffiths and Steyvers 2004;
Steyvers et al. 2006). The number of topics is therefore the model that leads the highest
posterior probability. Figure 1 plots the posterior probability against the number of topics.
The graph suggests that data are best described by a model with 33 topics.
To visualize the distribution of topics per project, we developed a level plot graph by
using the lattice package in R (Sarkar 2016). Figure 2 shows the LDA graph, where the x
axis shows the projects, and the y axis the 33 topics found in the whole system of projects
the system of projects. The distribution of each topic in each project is defined by the
intensity of colours: more intense blue colours show few topics distributed in a project (so
the colour is concentrated only in one point), while light red colours show a distribution of
more than one topic in a project. To confirm the validity of the result, the lead author, who
Fig. 1 Estimation of the optimal number of topics. Maximum likelihood distribution of all words over a
Fig. 2 Topic distribution per project
is an expert on nanotechnology, verified that the topics assigned to the documents made
sense. As can be seen, most projects were clearly on just one topic. The most common
drug delivery, to give just a few examples.
After estimating the most suitable number of topics and the distribution of each topic in
each project, we calculated how much a project i influences technological diversity in the
Zhao and Shen 2016).
Scaffolds are three-dimensional structures that mimic extracellular matrix, providing an adequate
environment for tissue, bones and organ regeneration and also as a cell delivery platform (O’Brien 2011; Y.
population of N projects (Van Rijnsoever et al. 2015). For that purpose we used Shannon’s
entropy statistic measure (Shannon 1948). This variable measures the randomness of a
distribution or the uncertainty associated with a random variable, and takes into account
variety and balance. Entropy is calculated as follows:
where H is the entropy, and p represents the proportion of projects with a specific design or
topic s. The diversity that a project i creates in the system is obtained through the
difference between the entropy of the population of projects (H0) and a hypothetical
population where the specific project does not exist (H-1):
H0 was obtained through Eq (1) and H-1 was calculated by using the following formula:
where psi represents the proportion of projects with the same design i and psj is the
proportion of any other designs. Both variables are calculated assuming that the focal
project does not exist in the hypothetical population ns. Therefore we have to consider that
there is one project fewer with that design in the population, represented by:
A positive value of dH indicates that diversity is created. A negative value indicates
reduction of diversity in the system of projects. These calculations revealed that there were
four different levels of diversity creation.
2.4.2 Degree of multi-disciplinarity
In line with suggestions by (Rafols and Meyer 2010; Yegros-Yegros et al. 2015), we
measured the degree of multi-disciplinarity by the diversity of topics. Instead of looking at
how often a combination of topics occurs at the system level, we calculated the diversity of
topics within a project, using the probabilities from the LDA and Eq. (1).
2.4.3 Knowledge base
We used the number of patents in a project as an indicator of the size of the knowledge
base. Patents are a very homogeneous measure of technological novelty (Breschi et al.
2000). They reflect creativity (Juanola-Feliu 2009) and the ability to transfer scientific
results into technological applications (Hullmann 2006).
Since we are analysing nano-related projects, we used nano-related patents as the
indicator of the size of prior knowledge base. Nano-related patents of each actor were
retrieved from the European Patent Office—Espacenet Website (EPO) from 1980 to 2015.
This period of time was selected based on the fact that 1980 was the starting year of the
‘‘boom’’ of nanotechnology. However, the first patent retrieved in our database was from
We used the B82 code for nanotechnology standardized by the International Patent
Classification (IPC). This code is widely used as a nano-related patent retrieval in several
studies (Baglieri et al. 2014; Dang et al. 2010; Kumar and Desai 2014; Leitch et al. 2012;
Ozcan and Islam 2014; Porter and Youtie 2008; Scheu et al. 2006). Even though this is a
new classification for nanotechnology-related patents, nano-related inventions granted in
the 80s that weren’t classified as such were re-classified by patent authorities (Ozcan and
Islam 2014). Moreover, the code Y01 N was replaced by B82Y in 2011 in order to have a
uniform nanotechnology related patent classification (European Patent Office 2013).
We selected only European patents, because this enlarges the chances that the
knowledge captured by the patent is present in the project. It is less likely that individuals in a
project will be familiar with knowledge captured by a patent that is registered only in the
USA. In order to select the normalized name of each assignee, the AcclaimIP Patent
Search and Analysis Software was used in parallel, checking the standardized names of the
actors in both sources for more thoroughness. As the number of patents has a skewed
distribution, we used its natural logarithm. This also makes the realistic assumption that
each extra level of the variable results in a decrease in marginal returns for diversity
2.4.4 Number of actors
This variable was obtained by simply counting the number of actors per project. This
variable had a skewed distribution; therefore we used its natural logarithm. The
transformation also makes the realistic assumption that each extra level of the variable results in
a decrease in marginal returns for diversity creation.
2.4.5 Diversity of actors
Based on the standard classification of actors from H2020 (see Sect. 2.2.4), we calculated
the diversity in actor types per project, using the Shannon entropy mentioned in Eq. (1).
2.4.6 Degree of clustering
The degree of clustering was obtained by calculating the local clustering coefficient (CC)
of a project (Wasserman and Faust 1994). The CC is a quantitative way to study the
structure of a network (Ravasz et al. 2002). It represents the probability that two random
neighbours of an actor from a project are connected. It measures the extent of
interconnectivity between the neighbours (Moreira et al. 2006) and is represented as:
where i is the focal project or node, Di is the number of other neighbour projects that have
an actor in common with i, and Li is the number of links that connect the neighbour
projects Di, if they are connected.
Van Rijnsoever et al. (2015) indicate the need to distinguish projects that are not
connected to other projects (isolates) from projects that are connected, but whose
neighbours are unconnected, since both receive a value of 0. Hence, we created an extra dummy
variable for isolates. The number of actors is also correlated by definition on the clustering
coefficient. This is because clustering is conditional on having at least two ties. To separate
the effects of isolates and number of ties, we regressed them both on the clustering
coefficient. The residuals of this regression form an unconfounded measure for clustering,
and this was used as an independent variable in our models.
2.4.7 Geographical distance
The geographical distance variable was obtained by calculating the average distance in
kilometres between the actors’ coordinates (latitude/longitude) from a project and a
calculated geographical centre. The geographical centre was retrieved by using the geosphere
package (Hijmans et al. 2015), and the geographical distances were calculated using the
fossil package, both from the R program (Vavrek 2011). This variable had a skewed
distribution, and we used its natural logarithm. The transformation also makes the realistic
assumption that each extra level of the variable results in a decrease in marginal returns for
As there were only four levels of diversity creation, it would be inappropriate to fit a linear
regression model, as this assumes that a dependent variable has continuous value. Four
values are insufficient to meet this assumption. Hence, we tested our hypothesis using a
cumulative (ordinal) logit regression. This model is more robust against non-normal
distributions or outliers than ordinary least squares regression.
The change in entropy caused by a project was our dependent variable. We added
independent variables as predictors. Moreover, we added the type of call as categorical
control variable with four levels. This might be seen as a limitation of this study, since the
regression model only tested independent variables and names of the calls, and no other
information (such as size of the funding, length of the funding, etc.) was controlled for.
However, these facts are already captured by the name of the programs as specified in the
Two projects were outliers with regards to the dependent variable and the degree of
multi-disciplinarity. As this violates the assumption that there are no outliers, we removed
these two projects from the final model we present below. However, we note that the
models with and without the projects gave very similar results.
Table 1 displays the descriptive statistics and the correlation matrix. As can be seen, the
variable number of actors is strongly correlated with the geographical distance, with a
correlation of 0.72. This correlation makes sense since more actors increase the probability
of establishing large geographical distances between them.
Table 2 shows the results of the cumulative logit model. The McFadden R2 of the model
is 0.11, which is an acceptable fit. The variance inflation factors are all below 10, except
for the number of partners which was at 13. We decided to leave this variable in, as it
controls for other variables that are dependent on project size. Yet, we need to interpret the
estimator of this variable with caution.
Table 2 Results of the cumulative logit model
1 NM: Nanotechnology and advanced materials for more effective healthcare. 2 BN: Exploiting the cross
sector potential of nanotechnologies and advanced materials to drive competitiveness and sustainability;
3 NG: Bridging the gap between nanotechnology research and markets; 4 BIO: Biotechnology-based
industrial processes driving competitiveness and sustainability. Each call had a different proportion of
project participation (NM; 7 projects, BN: 34 projects; NG: 7 projects; BIO: 21 projects). All calls envisaged
applied research or product demonstration stage projects corresponding to levels 5–8 Technological
a p \ 1
Table 2 shows that the degree of multi-disciplinarity has a strong and significant
positive association with the creation of diversity. This supports the idea that a
multidisciplinary environment generates greater diversity and supports Hypothesis 1. Regarding the
knowledge base variable, we observe that the number of nano-related patents also has
significant positive association with the creation of technological diversity, which supports
Hypothesis 2. In this context, the effects of knowledge creation and diffusion measured by
patents contribute to explaining technological diversity creation. Moreover, it demonstrates
that knowledge in nanotechnology is important for the creation of new alternatives in the
system and this ratifies the transversal nature of nanotechnologies.
In contrast, there is a negative association between the number of actors on the creation
of technological diversity, but this is only significant at the 5% level. Moreover, the
variance inflation factor of this variable is rather high. Yet, it ratifies previous literature that
argues that when there are more people involved it is more difficult to manage and more
conflicts between them can emerge (Tatikonda and Rosenthal 2000; Van Rijnsoever et al.
2015). Overall, we interpret this finding as partial support for Hypothesis 3.
The diversity of actors is not significantly related to our dependent variable, which does
not support Hypothesis 4, and casts doubts on the claims made by Van Rijnsoever et al.
(2015). A possible explanation is that in the context of nanotechnology, the content of
technological knowledge is independent of the type of actors. In other words, the content of
the theories and knowledge about nanotechnology is the same for each actor type,
regardless its institutional background. This result also means that the different skills or
points of view that emerge from the different nature of the actors does not contribute to the
creation of technological diversity.
The variable degree of clustering is significantly and negatively associated with the
creation of technological diversity, which is in line with the structural holes arguments and
supports Hypothesis 5. The structural holes argument is related to the degree of clustering.
A lower degree of clustering means that there are more structural holes (since the degree of
clustering is the probability that two nodes that are connected to a third one, are also
connected to each other). Hence, if a project is less embedded, it adds more to diversity.
Finally, our model shows that there is a small positive effect of geographical distance
within projects that is significant at the 10% level. This supports Hypothesis 6 and
corroborates the results obtained from Van Rijnsoever et al. (2015), and is line with the
argument that the knowledge base is geographically bound.
4 Discussion and conclusions
This research suffers from a number of limitations. In the first place, the sample of projects
was relatively small. We only took European nanotechnology healthcare projects into
account as this makes projects more comparable. However, it also limits the
generalizability of our results. It also resulted in limited levels of variation in the dependent variable,
which required us to resort to a more conservative cumulative logit model. Future research
could focus not only in the healthcare domain, but also in other industrial fields where
nanotechnology is applied, such as environmental, energetic, textile, cosmetics,
construction, communication, or other technologies that are not related to nano. Although the
number of topics covered was quite broad, the European focus of the projects also implies
that we possibly missed regional initiatives or priorities that can result in different national
foci for application areas. This could explain regional differences in knowledge bases.
A second limitation is related to the patent data. It is important to consider that not all
innovations are patented, especially in basic science research (Garcia-Vega 2006) and
neither patents nor publications databases always provide complete information about the
names or affiliation of researchers (Bengisu and Nekhili 2006). A possible solution for
future research is to take into account previous participation in funded programmes to
further validate the robustness of the prior knowledge base of actors.
In this paper we explained the creation of technological diversity using the characteristics
of innovation projects. We tested our hypotheses on data from EU-funded nanotechnology
projects belonging to H2020 calls that prioritize the cross-fertilization of emerging
technologies, and applied LDA as a novel method to study the contents of the innovation
Our main addition to the literature is that the degree of multi-disciplinarity of a project
and the size of the joint knowledge base of project partners are strongly predictive for
diversity creation. In this context we find support for the hypothesis that different
disciplines and larger and broader knowledge base increase the chances of recombinant
innovations (Baber et al. 1995; Cohen and Levinthal 1990; Ferna´ndez-Ribas and Shapira 2009;
Rhoten 2004; Schmickl and Kieser 2008).
Second, the results mostly support earlier findings by Van Rijnsoever et al. (2015), and
theoretical expectations with regards to the number of actors in the project (Tatikonda and
Rosenthal 2000; Van Rijnsoever et al. 2015), the clustering coefficient (Burt 2004), and the
effect of geographical distance (Boschma et al. 2014; Frenken and Hoekman 2014).
However, we did not find support for the claim that actor diversity adds to technological
diversity creation. This negative finding could be the result of contextual differences
between nanotechnology projects and bio-gassification projects. Innovation system
research argues that building networks is important for the success of an emerging
technology (Hekkert et al. 2007). Our results verify the claim that it is also important to
consider what the network should look like.
Finally, we also make a methodological contribution. The LDA method (Blei et al.
2003) allowed us to understand the topics of the projects in an efficient and reliable
manner. It allowed us to calculate diversity and the degree of multi-disciplinarity, and can
also aid future researchers with understanding the topics of innovation projects, in addition
to publications or patents (Du et al. 2012).
These contributions allow us to further develop a theory on the creation of technological
diversity, and hence to increase the possibilities of preventing technological lock-in and
increase the chances of recombinant innovation as well as increasing the resilience of the
technology (Van den Bergh 2008).
Our results can serve as guidelines to policy makers, especially at the EU-level, for
fostering the success of emerging technologies on the basis of their cross-fertilization and
technology diversity creation. In order to encourage creation of technological diversity,
emphasis should be placed on subsidizing: (1) projects involving or developing multiple
disciplines, (2) projects with actors that show a strong background in nano knowledge, (3)
projects with partners from different geographical regions, and (4) projects with a limited
number of partners that are not too closely connected with each other. The first three are
already explicit or implicit criteria in Horizon 2020. Yet these projects often involve large
consortia. Our results suggest that it is better for diversity if these consortia are smaller.
Moreover, in some instances, partners are involved in multiple projects. Our results show
that these cases should be handled with care, as this can decrease technological diversity.
Acknowledgements The financial support of SENESCYT - Ecuador National Secretary of Higher
Education, Science, Technology and Innovation (Grant Convocatoria Abierta 2013 - No. AR2Q) is gratefully
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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