R&D collaborations and innovation performance the case of argentinean biotech firms

1. R&D collaborations and innovation performance. The case of
Argentinean biotech firms
Lilia Stubrin1
Abstract
Many emerging countries are encouraging firms to enter into biotechnology, as it is seen as a
window of opportunity to generate a descommoditization of their patterns of product
specialization. We analyze the case of biotechnology in Argentina. We assess what strategies do
firms display to sustain their technological dynamisms and update their knowledge bases in order
to compete in this knowledge-intensive sector. In particular, we study the Argentinean biotech
firms’ network of collaborations in order to evaluate how knowledge diffuses within and to local
firms. Our main results suggest that the knowledge network structure of the Argentinean biotech
firms is different from the ones found in biotech leading regions, but similar to those in other
non-leading ones. The salient features are the scarcity of collaborations among co-located firms,
the key role that local PROs play in knitting the local network together and the striking relevance
of non-local partnerships predominantly forged with partners in leading regions. Collaborations
with local scientific and technological institutions as well as with foreign partners are shown to
be valuable to enhance firms’ innovation performance. Our study contributes to provide new
evidence regarding how-high tech activities develop in emerging countries, and the role of local
and non local knowledge flows to promote firms’ learning and technical change.
PhD Fellow UNU-MERIT
UNU-MERIT
Keizer Kareplain 19
6211 TC Maastricht
The Netherlands
stubrin@merit.unu.edu

2. 1. Introduction
In the last years, emerging countries have been encouraged to foster high-tech sectors, as they are
presented as possible avenues these countries should explore in order to diversify their patterns of
specialization towards more value added and technologically complex activities.
Accordingly, many of these countries are moving forward into activities such as biotechnology,
nanotechnology and ICT. The aim of this paper is to contribute to expand the existent empirical
evidence regarding how high-tech sectors develop in emerging countries, and in particular, what
strategies do firms display in these settings to enhance their technological and productive
capabilities in order to compete in a globalised world. We study the case of Argentinean biotech
firms, and in particular we focus on firms’ networks of collaborations.
To our view the relevance of studying the biotech firms'network relies on several factors. First,
it is known that biotech is an activity which is knowledge-intensive and in which technical
change takes place at a rapid peace. Thus, exploring how knowledge diffuses within and to
Argentinean firms becomes meaningful to comprehend how firms'acquire and build their
technological capabilities. Second, it is a widely held view that the complex and broad
knowledge bases of new technologies encourage firms to become `networked organizations'
looking for complementary knowledge, skills and resources outside their boundaries (Powell,
1992; Barley, 1992; Powell, 1996a; Powell, 2005). Third, networks have been found to be means
that facilitate firms'grow and innovation performance in leading regions (Powell, 1996; Uzzi,
1996; Ahuja, 2000). Thus, we aim at exploring to what extent this is the case for the Argentinean
case.
We are further interested in addressing the composition of the network in terms of the agents with
whom local firms exchange and share knowledge, and to what extent the industry network relies
on local and non-local collaborations. This intends to address the debate regarding the role that
geographical proximity plays in the economics of knowledge transmission as the available
empirical evidence is not conclusive about this matter (Brink, 2007; Bathelt et al, 2004). Non-
local collaborations can be crucial vectors to bring novelty and diversity, and sustain the process
of learning and technical change in relatively laggard knowledge regions.

3. The study is based on original data on Argentinean biotech firms collected in 2008. The firms
considered for the analysis apply at least one modern biotechnology technique to produce goods
and services and/or perform biotechnology R&D (OECD, 2005). These firms are active in
different biotechnology applications: human health, animal health, GM and non-GM agricultural
biotech and industrial processing.
Our study finds that Argentinean biotech firms are networked organizations. Thus, these firms
get actively involved in cooperations particularly with the purpose of sharing, exchanging and
sourcing knowledge from outside the firm. This is a pattern that spans across all firms, regardless
of their main area of biotech application. As regards the knowledge network structure,
knowledge collaborations with local public research organizations2 (PROs) and foreign partners
(mostly located in leading regions) are the most relevant and frequent type of interactions, which
we also find to be valuable to enhance biotech firms’ innovation performance.
The results obtained suggest that the development and sustainability of high tech activities in
emerging countries cannot be explained only focusing on local knowledge interactions.
Collaborations at a distance are not only frequent but also seem to be valuable to improve the
innovation performance of high-tech firms located outside leading regions. In addition, the
development of the biotech activity is highly grounded on the local scientific knowledge based
contained in local PROs. This reveals the relevance of a strong local scientific base for high-
tech activities to spring and further develop in a country.
The paper is organized as follows. Section 2 reviews the literature on collaboration networks,
geography and innovation, in order to address the current debate regarding the role of
geographical proximity and local knowledge flows to enhance learning and innovation. Section 3
describes the methodology and process of data collection used in this study. Section 4 is
concerned to depict the main characteristics of firms’ collaboration activity. In particular, it
focuses on Argentinean biotech firms’ knowledge network, unraveling its main characteristics
and providing plausible explanations for the observed patterns of collaboration. In Section 5 we
PROs refer to universities, research institutions, laboratories and hospitals.

4. assess the value of non local R&D collaborations and cooperations with local PROs for firms’
innovation performance. Finally, in Section 6 we present the conclusions of the study.
2. Literature review
Networks of collaborations: the value of embeddedness
In the last years we have witness an outstanding increase in firms’ engagement in strategic
alliances (Hagedoorn, 2000). These are ‘voluntary arrangements between firms involving
exchange, sharing, or co-development of products, technologies, or services’ (Gulati 1998, page
293). Collaborative arrangements are assumed to be driven by the asymmetric distribution of
technological, organizational, commercial and financial resources within an industry (e.g.
Andrews, 1971). In addition, the expanding knowledge base and complexity of many
technologies further trigger firms to enter into cooperations. That seems to be the case of
biotechnology. The evolution and development of this activity has been found to rely on a
diverse and complex array of cooperations between firms, universities, public research
organizations and venture capitalists (e.g. Bartley et al, 1992, Shan et al, 1994, Koput et al, 1997,
Owen-Smith and Powell, 2004, Powell et al, 1996, Powell et al, 2005). The complexity of the
technology, the high risk that the process of innovation entail as well as the speed at which
technical change takes place, encourage firms to interact and exchange knowledge and resources
with other agents within and outside the industry (Hagedoorn, 1992, Eisenhardt and
Schoonhoven 1996, Mowery et al, 1998).
Social network theory has been applied to study firms’ voluntary cooperation agreements as it
offers a framework to understand how firms came across the opportunity to cooperate with other
organizations, obtain information about potential partners and overcome the uncertainties that
cooperation with others entails. Social network analysis follows the studies of economic
sociology that explain how economic actions can be influenced by the social structure of relations
within which they are embedded (Granovetter, 1985). Thus, the way a firm is embedded in a
collaborative network can provide it with both opportunities and constraints for its behaviour and
performance (Gulati, 1995, 1998; Gulati and Garigulo, 1999). A network of collaborations that
is highly clustered was claimed to positively affect firm’s performance through the nurturing of

5. social capital (Coleman, 1988). Clustering arises as firms keep cooperating with the same
partners over time (‘relational embeddedness’) or collaborations with their partners’ partners
(‘structural embeddedness’). Particularly, firms’ structural embededdness prevents opportunistic
behaviours and enhances trustworthiness which, in turn, favours collaboration and exchange of
information (Coleman, 1988).
Hence, the value of embeddedness was found empirically significant in the biotechnology
industry in which network formation and industry growth are highly influenced by the
development and preservation of social capital (Koput et al, 1997; Powell et al 1996). Also in
other industries embeddedness was found to be significant for network formation3 and to foster
firms’ learning and innovation.4
However, as it was examined by the empirical study of Ahuja (2000), the degree of
embeddedness that can be beneficial to knowledge creation depends on the context and the kind
of links that the network structure facilitates. For instance, a network structure in which
structural embeddedness prevails restricts the potential partners and therefore, ‘put limits to the
inflow of diverse and fresh insights’ (Ahuja, 2000). This can be especially problematic when the
collaborative network is mostly composed by partners that are far from the technological frontier,
as a technological ‘lock-in’ may affect the firms that compose the network. As a matter of fact,
the empirical evidence that supports the value of firms’ embeddedness in networks of
collaborations has been mostly collected in developed countries. Studies are generally based on
samples of firms that are leading technological change in a certain industry, and most of the firms
are already in the frontier or are close to it. We know little if firms’ embeddedness is likely to be
valuable and beneficial for high-tech firms located in more knowledge scarce environments.
Accordingly, firms that are themselves connected to organizations situated outside the local
network may able to diversify their sources of knowledge and also become a bridge for fresh
insights to enter into the local network. Thus, actors that bridge ‘structural holes’ by forging non-
3
In the automobiles industry (Dyer and Nobeoka, 2000) or in new materials and industrial automatation industries
(Gulati and Garigulo, 1999).
4
In textiles (Uzzi, 1996), biotech (Powell et al, 1999) and chemicals (Ahuja, 2000), personal computers (Hagerdoon
and Duysters, 2000).

6. redundant ties between previously unconnected networks may have an information advantage and
a strategic position compared to their local partners (Burt, 1992).
Collaborations and geography: local and non-local collaborations
The embeddedness of firms in dense local networks was also pointed out as being beneficial for
firms’ learning and innovation by the agglomeration and cluster literature. A cluster is a
‘geographically proximate group of inter-connected companies and associated institutions in a
particular field, linked by commonalities and complementarities’ (Porter 2000, p 254). Firms’
clustering and spatial proximity not only can provide advantages in terms of costs as economies
of scale and scope can be achieved, but also facilitates access and circulation of knowledge
(Marshall, 1920). This is specially the case when the knowledge to be transferred is highly tacit,
which requires face-to-face and interpersonal interactions for its better diffusion.5
The benefits of clustering for fostering learning and innovation can be even more important in
those industries in which knowledge creation is the key (Audretsch et al, 1996). Success stories
of high-tech clusters, among which the Sillicon Valley is the most prominent example, fostered
and enhanced the value of clustering.6 Following these successful stories deliberate efforts have
been made to promote the creation of clusters elsewhere. Firms’ were provided incentives and
facilities to locate close to each other, and also nearby universities and scientific institutions, with
the aim that geographical proximity would naturally create room for knowledge diffusion.
5
See the cluster literature based on the seminal work of Marshall (1920). The value of clustering for the
dissemination of ideas in a cluster is addressed in the European literature on industrial districts (e.g. Piore and Sabel,
1984; Becattini, 1990; Schmitz, 1995), Innovative Milleus (e.g. Camagni, 1991), Regional Systems of Innovation
(e.g. Lawson and Lorenz, 1999; Cooke, 2001) and others. Mechanisms highlighted in the literature that facilitate
knowledge transfer among agglomerated organizations are user-producer relationships, formal-informal
collaborations, inter-firm mobility of workers and spin-offs of new firms.
6
Successful clusters in developed countries are the Silicon Valley, Emilia Romana in Italy and Bade-Wuerttembeng
in Germany. Besides other well documented clusters in developing countries are located in Brasil (Schmitz, 1995),
Mexico (Rabelotti 1995), Peru (Visser, 1996) and India (Cawthorne, 1995; Nadvi, 1996).

7. However, empirical evidence that has started to flourish cast doubt on the predominance of
localized knowledge networking and on the idea that learning processes are exclusively local.
Local and non-local knowledge cooperations have found of equal importance by certain studies
(Coenen et al 2004, Lawton-Smith, 2004, Van Geenhuizen, 2007, Mc Kelvey et al 2003, Fontes
2005) and authors started to claim that the value of local links has been very much exaggerated
(Oinas, 1999; Coenen 2004). Coenen (2004) argues that the ‘argument of proximity makes
interaction better, faster, easier and smoother runs the risk of spatial fetichism’ (page 1005). The
space as such may not be of great value if other factors contained in a physical space such as
certain actors, relations, institutions, and shared values are not taken into account.
In a review of the cluster literature Breschi S. and Malerba F. (2001) highlight the importance of
examining the openness of clusters to understand their productive and innovative dynamism.
Explanations mostly based on the benefits of geographical agglomeration lead to a narrow view
of clusters in which they are treated as isolated and self-constrained entities. On the contrary,
external linkages should start to be contemplated as they can be critical to foster and enhance the
dynamism of dense and local network relationships. For instance, they can be very valuable to
avoid technological lock-in and keep aware of technological changes and market opportunities.
Regarding the studies of clusters in developing countries, Bell and Albu (1999) also stress that an
analytical shift towards a more open view of the clusters is needed in order to understand the
bases of their technological dynamism and long-term competitiveness. In fact, external
collaborations may bring novelty and diversity, and thus become a source of competitiveness for
the development of high-tech industries in relatively laggard regions. It has been shown the
value of external alliances to access knowledge in distant contexts. See, among others, Rees
(2005) that analyze the medical biotechnology cluster in Great Vancouver (Canada) and
(Rosenkopf, 2003) who focus on the semiconductor industry
Therefore, the question that underlies here is whether it is the place (the geographical
agglomeration per se) or the network (without any a priori consideration of geographical
boundaries) that matters for encouraging learning and innovation.

8. When thinking about technical change in developing countries - specially in knowledge intensive
industries – a great deal of the sources of knowledge resides outside the local network, and
therefore local densely connected networks by themselves may not be a sufficient condition to
boost learning and generate technical change. In this case, ‘close’, local learning relationships
may fall short to sustain innovation and keep track to the ever changing technological frontier.
The case of biotechnology
In the case of biotechnology the pattern of spatial concentration seems strong. At world level, we
can identify a small number of `nodes of excellence’ constituted by clustered firms and
institutions that lead the industry and the research in the area. Thus, the world-leading biotech
regions are located in two main areas in the US (California and the north-eastern area that goes
from Maasachusetts to North Carolina), in two areas in the UK (Oxford and Cambridge) and a
scatter of small clusters elsewhere in Europe (Carlsson, 2001). However, recent evidence has
shown the emergence and importance of ‘newcomers’ into biotechnology (Heimeriks and
Boschma, 2011). Many developing countries are also trying to move forward into the
development of biotech as it can become a window of opportunity to generate a
‘decommoditization’ in their patterns of specialization. We got intrigued by the following
questions: How does biotech develop outside the world main hubs? Can the emergence and
further development of biotech activities in emergent regions be explained solely based on local
interactions and local knowledge flows?
The evidence from the development of biotechnology activities outside the `nodes of excellence’
shows that even though local collaborations are important, non-local cooperations are more
frequent than expected. As a matter of fact, empirical studies of the biotechnology industry in
non-leading biotech regions reveal that biotech firms in that localities tend to early
internationalize their cooperations (e.g. Fontes, 2005; Rees, 2005; McKelvey, 2003; Gilding,
2008; Belussi, 2008). The internationalism of partnerships in non-leading regions is such that in
some cases non-local partnerships even surpass the rate of local networking activity. For instance,
R&D projects carried on by DBFs in Portugal with foreign partners were more frequent than
those with local partners (Fontes, 2003; 2005). The biomedical firms in the region of Greater

9. Vancouver, a peripheral region of Canada, show to heavily rely upon non-local links as 77% of
the collaborations reported were non-local (Rees, 2005). Similar evidence was found for Swedish
biotech firms specialized in bioscience (McKelvey et al, 2003) and for the Melbourne biomedical
cluster in Australia (Gilding, 2008).
However, non-local collaborations are not exclusive for more laggard or peripheral biotech
regions. On the contrary, Boston Biotechnology Cluster which is a benchmark in the
biotechnology area and it is one of the largest biotechnology clusters in the world, showed a high
local density of connections along with out-of-the-cluster collaborations with organizations in
other US regions and even in other countries (Owen-Smith, 2004). Thus, it seems that the way
the biotechnology activity develops can hardly be explained by closed local interactions. As a
matter of fact, in the biotechnology and network literature the empirical observation that biotech
firms engage in R&D collaborations with foreign organizations is understood as a way firms can
access knowledge, resources and expertise that are not available in their locality (Fontes, 2003;
Rees, 2005; McKelvey, 2005). Thus, a mix between local and non-local knowledge flows can
be ideal to promote firms’ innovation performance particularly in regions that do not lead the
industry.
Accordingly, Gertler and Levitte (2003) in a study of 359 Canadian biotech DBFs show that
those firms that innovated had a more outward looking portfolio of collaborations. In addition,
Cassiman (2006) using data from the Community Innovation Survey on Belgian manufacturing
firms provide econometric evidence showing that those firms that combine internal and external
R&D strategies introduce more and substantially improved products to the market. To our
knowledge there are not much more studies that address the relevance of non-local cooperations
for firms’ learning and innovation in biotech.
In the following sections we address the case of Argentinean biotech firms. Firstly, we provide
new empirical evidence regarding the extent to these firms engage in collaborations for R&D,
manufacturing and marketing purposes. We particularly explore the geographical scope as well
as the organizational composition of their networks of collaborations. Secondly, we assess the

10. value of local and non-local collaborations to enhance firms’ innovation performance. In the last
section, we provide some conclusions.
3. Data and Methodology
This methodological section is organized as follows. First, we present the definitions of
biotechnology and biotechnological firm used in this study. Then, the process of data collection
is described as long as the main characteristics of the data obtained.
3.1. Definitions
As biotechnology is neither an industry in itself nor represents a natural grouping of processes or
products (Miller, 2007) its definition is neither simple nor straightforward. As a matter of fact,
biotechnology embraces several different technologies which can be used for different purposes
in diverse economic activities. For instance, the technology of recombinant DNA can be used to
produce large molecule medicines by the pharmaceutical sector, create new crop varieties by the
agricultural sector, or modify micro-organisms to produce industrial enzymes by the chemical
sector (OECD, 2005). A further concern associated with the term biotechnology is that, apart
from being used to encompass a wide range of technologies and applications, it has been defined
in many different ways (Kennedy, 1991).
In this paper we follow the OECD’s definition of biotechnology as it is broadly accepted by
many countries which follow it to compile statistics on biotechnology activity (see Annex A).
Thus, our study is focused on those firms that apply at least one modern biotechnology technique
to produce goods and services and/or to perform biotechnology R&D.7 Therefore, those firms
that just trade biotechnology products, or use biotechnology inputs without further modifications,
are not subjects of our study.
7
We adopt the OECD definition of biotechnology firm (OECD, 2005; Beuzekom, 2009) in order to obtain
consistent and international comparable data.

11. The study is grounded both on Dedicated Biotechnology Firms (DBFs)8 and on firms involved in
biotechnology activities but which main activity is not the production of biotechnological
products and process. Many empirical studies, particularly in leading biotechnology countries,
analyzed the development of the biotech industry by only focusing on the study of DBFs.9
However; we do not to restrict the study only to Argentinean DBFs as they fall short to represent
all the private efforts that take place in the area of biotech in the country.10
Biotechnology can be applied in many fields such as health (human and animal), agriculture,
food and beverages processing, natural resources, environment and industrial processing
(Orsenigo, 2006). The area of life science, particularly human therapeutics and diagnostic, has
been chosen by many empirical studies to do research about (e.g. Powell et al, 2005; Powell et al,
1999; Deeds and Rothaermel, 2004; Powell et al, 1996; McKelvey et al, 2003). However, our
study covers a larger scope of biotechnology applications as the aim of the study is to picture
biotechnology activity taking place in Argentina, regardless of the area of application.
Therefore, we base our analysis on an empirical material that contemplates an expanding field of
knowledge with multiple application areas with the aim of enriching the empirical evidence and
the analysis of how high-tech activities develop in emerging countries.
8
DBFs are defined by the OECD (OECD,2005) as biotechnology active firms whose predominant activity involves
the application of biotechnology techniques to produce goods or services and/or the performance of biotechnology
R&D.
9
See among others, for the US (Powell et al, 2005; Powell et al, 1996; Deeds and Rothaermel, 2004; Powell et al,
1999; Koput et al, 1997), Australia (Gilding, 2008) and Canada (Niosi, 2003).
10
The same criteria was followed by McKelvey et al (2003), Brink et al (2007), and Dahlander and Mc Kelvey
(2005).

12. 3.2. Database
The fieldwork for data collection took place in Argentina between January 2009 and July 2010.
We had the unique opportunity to participate and cooperate in the design of the questionnaire and
in the process of data collection with the United Nations Economic Commission of Latin
America and the Caribbean (ECLAC) - Buenos Aires office - .
We identified and surveyed those Argentinean firms that suited the adopted definition of
biotechnology firm. The lack of an official and updated database of biotechnology firms in
Argentina led us to the need of building up one. This was not a straightforward task due to the
fact that firms performing biotechnology activities are widespread throughout the productive
spectrum and their products are not easily identified as biotechnologicals at first glance. Thus, we
could not single out those firms solely based on traditional definitions of sectors or even of firms
competing in a certain market. We created a database by searching in secondary sources.11 The
database contained 142 firms which we presumed were active in biotechnology activities in
Argentina. All these firms were approached and invited to participate in the survey.
The main procedure to collect data was to survey firms by sending them a questionnaire12 by post
or by email. Additionally, we further interviewed 33 of these firms. The interviews were semi-
structured and had the purpose of checking and complementing the information given in the
written questionnaire.
In all, out of the 142 firms that composed the original database, 102 enterprises turned out to be
effectively involved in biotechnology activities. 40 companies were discarded as they were
mainly dedicated to market biotechnological products developed by third parties such as
11
The secondary sources consulted were lists of government grants'beneficiaries, membership lists of Technological
Poles, firms incubated in universities, Internet searches on companies` websites, interviews with knowledgeable
people, the business press and published reports on the matter.
12
The questionnaire was pre-tested to control both for the length and the quality of the information gathered.
Accordingly, the pilot survey was performed in the Santa Fe province from December 2008 to February 2009. The
pilot was run in this province due to the existence of a critical mass of active firms in the area of biotechnology as
well as scientific and technological infrastructure dedicated to that scientific field (e.g. two universities that offer
degrees in biotechnology, specialized research institutes, two technological poles with firms'incubators).

13. medicines, vaccines or genetically modified seed varieties. We achieved 59 responses which gave
a response rate of 57, 84%.13 The firms surveyed use biotechnology tools for different
applications such as human health, GM agriculture biotechnology, Non-GM agricultural
biotechnology, veterinary health and industrial processing (see definitions in Annex A).
The number of firms in each of the biotechnology applications considered varies. The largest
share corresponds to those active in health care applications (see Table 1). Thus, firms involved
in all health applications (including human and animal health care) made up 57.84% of the total
firms surveyed.14 The second most important area of application of biotechnology in Argentina
is agriculture representing 34.31% of the firms.15 Then, a smaller number of firms have to do
with industrial processing activities.
As regards the extent to which our sample is representative of the population under study, it
somehow overestimates to a certain extent those firms to do with biotechnology in the human
health activity and underestimates those firms engage in non-GM agriculture biotechnology.
However, the sample bias is small enough to have a trustable and representative sample to
understand and comprehend the development and characteristics of the biotechnology activity in
Argentina.
13
Studies that focus on analyzing the economic dynamics and network structure of biotechnology in the world main
hubs, such as the US or the UK, are based on around 300 firms or so (e.g. Rothaermel et al, 2004; Powell et al, 2005;
Niosi, 2003). However, studies for less advanced regions are generally grounded in a more limited number of firms.
For instance, Gilding (2008) study the biotechnology network in Australia based on 50 DBFs, (Fontes, 2005)
anchored the study of the Portuguese biotechnology network on 33 firms and Galhardi (1994) examined the pattern
of biotechnology development in Brazil out of the study of 12 representative firms. Our empirical evidence is in line
with studies that show a reduced number of firms involved in modern biotechnology in comparison with places that
lead the frontier of the field.
14
The prevalence of firms dedicated to health care was also observed other countries such as Poland (100%),
Sweden (89%), Austria (80%), Canada (58%) and Belgium (53%) (van Beuzekom and Arundel, 2009).
15
This figure is high compared to the share of firms'dedicated to agriculture biotechnology in countries such as
Germany (5%), Sweden (5%), Austria (4%) and Brazil (23%), but it is similar to other countries such as Philippines
(38%) and South Africa (37%) (van Beuzekom and Arundel, 2009).

14. Table 1: Surveyed firms, by biotechnology application
Biotech firms
Biotechnology Application Surveyed firms
in the dataset
Human health 42 27
Veterinary health 17 11
GM Agricultural biotechnology 6 6
Non-GM Agricultural
biotechnology 29 11
Industrial processing 8 4
3.3. Network data
Data about the formal collaborations in which Argentinean firms were involved during the period
2003-2008 was collected in order to unravel how the network of biotech firms'strategic alliances
was constituted.
In the absence of archival records of strategic alliances in the country we gathered data on firms'
collaboration activity by introducing specific questions in the questionnaire used to survey
biotech firms in Argentina.16 Network data were collected using the egocentric network method,
which focuses on a focal actor or object and the relationships in its locality. Thus, the whole
network is discomposed into each objects'egocentric network, so that based on the egocentric
network data the complete network can be built up (Marsden, 2005).
The focal nodes of the network are those firms located in Argentina involved in biotechnology
activities, and the ties of the network are contractual arrangements in which nodes participate to
pool or exchange resources or knowledge. Three types of collaboration ties were considered:
16
Network studies draw extensively on survey and questionnaire data (Knoke and Yang, 2008; Marsden, 1990;
Marsden, 2005). Recent studies that used surveys to collect network data are, among others, (Giuliani and Bell,
2005; Gilding, 2008).

15. knowledge, manufacturing and marketing. We treated each formal agreement as a tie. Thus, an
Argentinean firm is connected to a partner when one more ties exist between them.
To elicit the ties of each focal node we used the name generator method which consists in
asking each ego respondent to name the contacts to whom it has a specific kind of relationship
(e.g. R&D contractual arrangement).17 Therefore, each firm freely generated a list of alters by
writing down the name of the partners with whom it had collaborated during the period 2003-
2008. As the aim of the survey was to unravel both the organizational diversity and the
geographical scope of the network of collaborations, the type of partners to which Argentinean
firms collaborate was not restricted beforehand (see Annex B for details).
As the goal was to picture the formal collaboration network in which Argentinean biotechnology
firms participated in the period 2003-2008, and, in particular, unravel how organizationally
diverse and geographically dispersed the emergent network was, we coded partners by location
and type. Thus partners were classified into locals, when they were located in Argentina, and
external, if they were located in regions outside Argentina. Foreign partners were classified
according to their geographical location into Latin American, European, American and others.
As regards the type of organization, we classified partner into biotechnology firms, other firms
and PROs.
One limitation of our approach is that we do not end up having a complete network, as we lack
collaboration data between actors that are not Argentinean firms engaged in biotechnology. We
ignore if two Argentinean biotechnology firms are indirectly connected through collaboration
partners which are themselves connected. We lack this information as it is hard to collect,
particularly for international partners18. However, we are still able to picture and to explore
17
The name generator method differs from the roster method as the former consists of contacts'recalling whereas
the latter is based on contacts'recognition. The roster method is recommended when the total number of possible
alters is known beforehand, while the name generator method is more appropriate when that it is not the case. As
we mostly ignored all the possible nodes of the network in advanced, and hence one of the main objects of the study
was to unravel which were the nodes that compose the network, we followed the name generator proceudure.
18
Other studies that faced the same difficulty are Powell et al (2005), Powell et al (1996), Koput et al (1997), Gilding
(2008) and McKelvey et al (2003).

16. Argentinean biotech firms'direct partners which allows us to have an accurate approximation of
the structure, the geographical extension and the organizational diversity of the knowledge
network. We acknowledge that both direct and indirect ties can affect firms'knowledge
acquisition and performance (Ahuja, 2000). However, the impact of indirect ties is ultimately
determined by the firms'level of direct ties, which are the ones we were able to trace.
4. The Argentinean biotech network: exploration and analysis
This section explores the extent to which Argentinean biotech firms participated in R&D,
manufacturing and marketing collaborations during the years 2003-2008. Then, the analysis is
narrowed to the R&D network. We explore its organizational composition and geographical
scope. The possible explanations for the main features of the knowledge network structure are
further discussed at the end of the section.
4.1. The knowledge, manufacturing and marketing networks
The network of collaborations in which Argentinean firms got engaged in the period 2003-2008
can be visualized in Figure 1. The network representation contains all cooperations in which
these firms have participated in that period. Nodes are differentiated by their location (shape) and
by type of organization (color), so that agents located in Argentina are represented by circles, and
agents located somewhere else are represented by squares. Then, Argentinean biotech firms are
white circles whereas Argentinean PROs are depicted as black circles, and foreign partners as red
squares. A glimpse to Figure 1 reveals that the Argentinean biotech network is both
organizational diverse and geographically dispersed. Partnerships have been forged with agents
located within and outside the business sphere, and located both in Argentina and abroad.
In addition, ties are differentiated by types of collaborations (color). Knowledge-related ties are
black, manufacturing agreements are shown in red and marketing deals are colored in green.
Directed ties, which represent transfers of technology (e.g. licensing agreements), have arrows
pointed to the agent that receives the technology. The distinct colors of the ties that connect nodes

17. in the graph reveal the differing motives that aimed biotech firms to enter into strategic alliances
with third parties.
The high degree of connectedness that can be observed in the graph is a reflection of a high
degree of firms'participation in cooperations. Accordingly, 51 out of the 59 enterprises surveyed
active in biotechnology in Argentina had engaged in collaborations with other partners either for
R&D, manufacturing or marketing purposes. Thus, we found evidence aligned with the pattern
observed for the development of biotechnology in other regions: biotech firms tend to be
networked organizations.
Although different motives triggered firms to engage in collaborations, the overwhelmingly
superiority of black ties in the graph indicates the predominance of knowledge-related reasons.
We found that 238 out of the 275 cooperations recorded (86%) had to do with knowledge flows
both in the form of R&D collaborations and technology transfers (e.g. licensing). On the
contrary, the number of deals related to manufacturing and marketing are much scarcer as firms
set up 21 and 15 deals of these types of collaborations, respectively.
Some network statistics depicted in Table 2 help to understand further the network structure
pictured in Figure 1. The table shows the firms'average degree and standard deviation, the
maximum and minimum number of cooperations forged by firms, and the number of isolates.
Calculations are shown for R&D, manufacturing and marketing separately, and for all
collaborations together.
Based on the average degree we can state that, on average, each Argentinean firm engaged in
biotechnology had set up around 5 collaborations, the majority of which have been related to
R&D activities. Accordingly, the average degree for the knowledge network is 3.71 whereas the
manufacturing and the marketing ones are 0.15 and 0.03, respectively. The different degree of
firms'participation in each network is also illustrated by the reduced number of isolates in the
knowledge network (11) in comparison to the much larger number of non-connected nodes in the

18. Figure 1 – The complete network of Argentinean biotech firms. Nodes: Argentinean biotech
firms (white circles), Argentinean PROs (black circles), foreign organizations (red squares).
Ties: R&D cooperations and technology transfers (black), manufacturing agreements (red) and
marketing agreements (green).
manufacturing (51) and marketing network (57). Taken all ties together the rate of dispersion of
collaborations is 5.04, as it is indicated by the standard deviation measure. Although the
knowledge network is on average less connected than the complete network, it is more
homogeneous in terms of the number of connections held by each of the firms.

19. Table 2 – Knowledge network statistics, by type of cooperation
Type of Av. St.
Max Min Isolates
cooperation Degree Dev.
R&D 3.71 4.21 23 1 11
Manufacturing 0.15 0.41 2 1 51
Marketing 0.03 0.18 2 1 57
All 4.61 5.04 27 1 8
The analysis so far has not distinguish among different biotech application, as we grouped
together those firms engaged in human health, veterinary health, agriculture and industrial
processing applications. When we do this distinction, we see that for each of the biotechnology
applications, the same pattern than for the aggregate network is observed (see Table 3):
knowledge-related deals account for the bulk of collaborations, whereas manufacturing and
marketing ones are very limited. The area of human health is the one that shows the greatest
number of R&D cooperations (93), accounting for 40% of the total number of R&D
collaborations in the period analyzed. Then, also firms applying biotechnology to human health
are the ones that make most use of manufacturing deals19 whereas firms in veterinary health tend
to engage relatively more than the rest in marketing-related cooperations.
On the whole, even when we distinguish firms by main area of biotech application, we found that
the frequency and patterns of interaction for business purposes (marketing and manufacturing)
largely differ from those that involve knowledge flows.20 These results indicate that Argentinean
biotech firms make more use of strategic alliances to gather knowledge, expertise and
technology, than to manufacture or commercialize goods. Thus, our tentative hypothesis is that
our results support the idea that firms become networked organizations as all the necessary skills
19
This finding is alike the one presented by Thorsteinsdottir (2010) . They find that for health biotech firms located
in developing countries both end-stage commercialization and manufacturing activities are highly important
purposes that trigger collaboration.
20
Similar results were also found by Giuliani (2006, 2007).

20. Table 3 - Number of cooperation agreements by Argentinean biotechnology firms, by types
of cooperation (2003-2008)
Biotechnology Application Number of cooperations
Knowledge Manufacturing Marketing
Human health 93 10 2
Veterinary health 47 2 6
GM Agricultural biotechnology 43 2 3
Non-GM Agricultural
biotechnology 41 6 4
Industrial processing 14 1 0
and organizational capabilities needed to compete in biotechnology are not readily found under a
single roof (Powell and Brantley 1992). In addition, the complexity of the biotechnology
knowledge-base and the rapid evolution of technical change in this area further trigger firms to
become relatively more active in creating knowledge-related alliances with third parties.
Given the relevance of knowledge-related collaborations, and the importance of knowledge flows
to understand the development and evolution of the Argentinean biotechnology industry (Bell
and Albu, 1999), in the next section we will focus on studying the knowledge network in more
detail.
4.2. The knowledge network
The knowledge network is composed by all Argentinean biotech firms that engaged in R&D
cooperations and licensing agreements in the period 2003-2008. We found that all firms
dedicated to veterinary health and GM agricultural biotechnology and the vast majority of firms
involved in the other biotechnology applications considered are actively involved in the
knowledge network. But, with whom do these firms exchange and share knowledge with? Is the
network composed only by local interactions? In order to answer these questions the
composition of the knowledge network, in terms of the type of actors that participate, and their

21. geographical location is pictured in Table 4. For each biotech application it is shown the number
of collaborations that firms forged locally with other biotech firms, local PROs and other firms
(e.g. suppliers), and the number of non-local collaborations with foreign firms and PROs located
in other countries.
We observe that Argentinean biotech firms forged cooperations both with local and non-local
partners. 146 cooperations have been forged with local partners whereas 90 collaborations took
place with foreign organizations. Thus, although, on the whole, biotech firms cooperated more
locally than internationally, non-local cooperations still represent a large share of the total R&D
agreements (40%). These results suggest that when trying to understand how technical change
takes place non-local knowledge flows cannot be ignored.
Table 4 – Number of local and non-local collaborations to firms and PROs, by
biotechnology application area.
Number Number non-local
Biotechnology Local collaborations to collaborations to
application biotech local other
Total firms PROs Total
firms PROs firms
Human health 6 55 0 61 13 19 32
Veterinary health 3 19 1 23 11 12 23
GM Agricultural
biotechnology 0 22 0 22 19 1 20
Non-GM Agricultural
biotechnology 0 28 2 30 3 8 11
Industrial Processing 0 10 0 10 4 0 4
Total 9 134 3 146 50 40 90

22. At local level, the degree to which biotech firms collaborate with peers and with PROs largely
differ. There is an outstanding predominance of collaborations with local PROs and very scarce
inter-firm collaboration. Accordingly, 91% of all local collaborations forged by biotech firms
have been set up with universities and research institutions located in Argentina. And, only 9 out
of 146 local R&D-related collaborations were forged with local firms engaged in biotechnology
activities. In fact, cooperations among biotech firms only occurred between firms engaged in
health applications (human and animal) (see Figure 2).
Figure 2 - The inter-firm R&D collaboration network. Nodes: Argentinean Biotech Firms
(ABF) active in human health (blue), ABF active in veterinary health (yellow), ABF active in
GM agricultural Biotech (grey), ABF active in non-GM agricultural biotech (green), ABF active
in industrial processing (brown), Argentinean PROs (black), foreign organizations (red). Ties:
R&D agreements

23. As regards non-local collaborations, Argentinean biotech firms set up R&D collaborations both
with foreign firms (50) than with foreign PROs (40). The geography of non-local collaborations
can provide a hint of the sort of knowledge firms source and in particular whether non-local
collaborations may be a vehicle to access world-leading research. With that purpose we
classified foreign collaborations according to the region/country in which partners were located.
Thus, we grouped firms’ partners into the following categories: Europe, the US, Latin America
and others.
We found out that North-South collaborations predominate as 54 partnerships (59%) have been
forged with European and American partners. This evidence is coherent with that obtained by
other studies on the biotechnology knowledge network which also makes it visible that foreign
collaborations are not randomly distributed but very much oriented towards the world hubs of
biotechnology. Thus, for the Swedish (McKelvey, 2003) and Australian (Gilding, 2008) cases
partners were first drawn from the US, then the UK, then everywhere else. Assuming that agents
located in the US and Europe possess more advanced knowledge and are closer to the frontier, we
can argue that more than half of the non-local collaborations forged by Argentinean biotech firms
were with agents at the cutting age. These non-local collaborations can actually become a source
of novel and up-to-date technology.
Discussion of results
The Argentinean biotech firms’ knowledge network encompasses both local and non-local
collaborations. On the one hand, knowledge flows at local level mainly through collaborations
with local PROs. Thus, we observe that firms seldom engage in joined R&D activities with their
co-located peers but the bulk of their local R&D cooperations take place with local PROs. On
the other hand, a great deal of firms in all biotech applications actively gets involved in
knowledge collaborations with partners located elsewhere. We analyze these results in detail
below.
The close and intense cooperation between Argentinean biotech firms and local PROs may not
seem that surprising since the synergies between industry and science lye at the very core of the

24. birth and development of the biotechnology industry (e.g. Owen-Smith et al 2002, Zucker et al
1998, Arora and Gambardella 1994). Firms are fed by scientific discoveries, which may be
further developed within the industry and applied to create new products and processes upon
them. Thus, the industry in itself mostly consists of the transformation of academic research into
commercial products. To our view, what this result highlights is the importance of a strong local
scientific base for science-based firms to emerge and further develop.
The very limited the degree of collaboration among Argentinean biotech firms requires some
explanation. The dense inter-firm local network that typically arises in leading regions was
neither observed in the Argentinean case nor in other countries that do not lead the industry.21
One plausible explanation for the scarce local inter-firm interaction relies on firms’ knowledge
specialization. Biotech firms typically manage a reduced number of technologies which
constitute their technological platform, out of which they develop their product portfolio. We can
expect that biotech firms in not leading regions cater specific market segments, and hence, are
specialized in different set of technologies. The heterogeneity of local firms’ knowledge bases
may be an important factor to explain the likelihood of local inter-firm synergies. Thus,
empirical evidence is quite conclusive on the fact that some middle ground between diversity and
similarity in firms'knowledge bases fosters R&D cooperation agreements as firms are more
prone to cooperate with partners who provide them with learning opportunities but with whom
they share some common knowledge so that mutual understanding is possible (Ahuja and Katila,
2001; Mowery et al, 1996; Gulati and Gargiulo, 1999; Duysters and Shoenmakers, 2006).
Therefore, following this argument, it may well be the case that local firms do not share with co-
located peers enough knowledge or research interests so that engaging in partnerships among
them become attractive. Probably, this is accentuated by the fact that the number of firms
engaged in biotech in these regions tends to be relatively reduced.
Although we do not disregard the fact that knowledge diversity within the local industry may be
an important factor preventing local cooperations, it seems that this is not an explanation that
21
See, among others, Fontes (2003) and Fontes (2005) for the Portuguese case, Rees (2005) for the Canadian case,
Gilding (2008) for Australia, McKelvey et al (2003) for Sweeden and Belussi (2008) for Italy.

25. explains it all. Other factors such as the building of trust, reputation as well as competition
should be further studied. Indeed, for the case of Argentinean biotech firms’ we found that some
firms’ knowledge bases are similar enough for potential collaborations to take place.
Accordingly, Argentinean biotech firms'managers provided other reasons, beyond ‘knowledge
fit’, to explain the scarce inter firm collaborations among firms. Many of them claimed that they
acknowledged local peers with whom it could be fruitful to cooperate. Nonetheless, cooperations
did not arise. The most frequent explanation given was related to market rivalry. Thus, local
market competition may be a force that prevents potential cooperations in the Argentinean case,
and may inhibit the possibilities of local cooperations when they do exist.
As regards non-local collaborations, our study finds evidence aligned to other studies of biotech
industries in non-leading regions. Collaborations with foreign partners are frequent, relevant and
not random. In fact, firms mostly cooperate with partners in leading regions. The value of R&D
cooperations with geographically distant partners can be of great importance for high-tech
industries as innovation requires knowledge that is both best global and diverse (Dahlander and
McKelvey, 2005). In particular, non-local collaborations can be a vehicle through which firms
upgrade their technological competences and overcome the relative knowledge disadvantages of
their location (Rees, 2005). The interviews with managers of Argentinean biotech firms rovided
empirical evidence that supports the idea that foreign partners can provide local firms with
knowledge and developments that are not available locally, and also sometimes cheaper.
Accordingly, in the following section we provide empirical evidence regarding the value of
collaborations both with foreign partners and local PROs to enhance Argentinean biotech firms’
innovation performance.
5. R&D alliances and innovation output
This section intends to assess the value of R&D collaborations to enhance firms’ innovation
performance. We focus on local collaborations between firms and PROs, and non-local
collaborations. Both type of collaborations can be valuable to provide novelty and diversity to
the local industry sphere, and in turn, positively contribute to firms’ innovation performance.

26. We study the relation between R&D alliances and innovation output based on a descriptive
analysis of the data collected.22
Table 5 shows the number of firms which introduced new products and processes in the period
2003-2008, given that they have engaged or not in strategic alliances with local PROs and foreign
organizations. Three degrees of innovation’s novelty are considered: those product/process
which constitute an innovation for the firm but that already existed in the local and the
international market; those products/process that are innovations for the local market (and also to
the firm) but that already existed in other foreign markets; and those product/process which are
themselves innovations for the international market as a whole. Clearly, a product that is
`internationally'new is a more relevant innovation, that one that is just new for the firm.
One of the main results displayed in Table 5 is that the majority of firms that introduced products
and processes new to the international market during the period 2003-2008 have also engaged in
strategic alliances during that period. 17 out of the 21 firms that succeeded to produce a product
innovation that was new for the global market have collaborated with local PROs, whereas14 out
of those 21 had set up R&D collaborations with foreign partners. In the case of the 17 firms that
achieved a process innovation at international level, 13 have engaged in joint R&D projects with
local PROs and 10 with foreign partners. Therefore, these results suggest that there is a strong
correlation between firms’ innovation output and the engagement in collaborations with local
PROs23 and non-local partners.
o explain firms’ innovation performance by firms’ engagement in R&D collaborations we face a possible
simultaneity bias. Firms’ innovation performance could cause as well as be caused by R&D cooperations. Thus, in
order to correct for that we needed to build up an econometric model that accounts for it so to have meaningful
results. Several attempts were pursued with that aim, but the limited number of observations and the cross-section
nature of the data prove to be great limitations to achieve that goal. Still, we find strong and clear evidence of the
relation between collaborations and innovation performance.
23
This result is aligned to the study of Mohnen and Hoareau (2003) which shows firms that rely on PROs introduce
more radical product innovations

27. Table 5 - Biotech firms’ innovation performance and R&D alliance activity
Biotech firms that engaged in
collaborations with
Number of biotech firms that
innovated in Local PROs Foreign organizations
No Yes No Yes
Products
New only to the firm 1 2 1 2
New to the local market 4 15 10 9
New to the international market 4 17 7 14
Processes
New only to the firm 2 8 4 6
New to the local market 4 7 5 6
New to the international market 4 13 7 10
In addition, we also observe that the majority of firms that innovated in products and processes,
whatever the degree of product innovation considered, engaged in collaborations with local
PROs. This result provides further support to the idea that universities are one of the most
relevant sources for innovation activity by firms (e.g., Cohen et al 2002; Arundel and Geuna
2004; Kaufmann et al. 2001). Universities and research institutions may help to speed up
innovation (Mansfield, 1991; Klevorick et al 1995) and contribute to reinforce firm´s scientific
capabilities (Arora and Gambardella 1994) by providing knowledge which is not available, or at
least more difficult to obtain, within the industrial sphere. In addition, this result also contributes
to highlight the important role that scientific and research institutions play in the development of
a knowledge-intensive industry.
In the rest of the analysis we just focus on innovations that are new for the international market.
We evaluate the extent that firms that innovated have also engaged in relatively more R&D
collaborations. We consider four type of innovation indicators: whether surveyed firms declared
to have achieved a product innovation or a process innovation, applied for a patent in Argentina
and applied for a patent in the U.S. Table 6 shows the average number of collaborations with

28. local PROs and foreign organizations of firms that succeeded to innovate or not during the period
analyzed.
Table 6 – Biotech firms’ collaborations with local PROs and foreign firms and innovation
performance
Firms that in the period 2003-2008
Average Applied for
number of Innovated in Innovated in Applied patents patents in the
cooperations products processes in Argentina US
Yes No Yes No Yes No Yes No
with local
PROs 2.31 2.2 3.05 1.93 3.76 1.45 3.92 1.74
** *
with foreign
organizations 1.28 1.26 1.64 1.11 2.05 0.87 2.46 0.93
*
*, ** Significance at the 5% and 1% level, respectively.
Having a look at Table 6 we observe that those firms that innovated in products and in processes
new to the international market tend to be on average relatively more involved in R&D
cooperations than firms that did not innovate. However, the differences observed are not large
enough to be statistically significant.
However, firms that applied for patents in Argentina have forged more than the double of R&D
collaborations with local PROs and foreign partners than the ones that did not apply for patents in
the period. These differences prove to be statistically significant. Also, firms that applied for
patents in the US have forged more collaborations with PROs and foreign organizations than the
ones that did not apply for patents in that country. The difference between the number of
collaborations forged with local PROs by those firms that intend to patent in the US in

29. comparison to those that did not apply for intellectual property rights in this country is found to
be statistically significant.
On the whole, this last set of results reveal that firm that collaborated relatively more with local
PROs and foreign organizations show a higher propensity to innovate. These results are
statistically significant when we take patent applications as innovation indicators.
6. Conclusions
Argentinean biotech firms are actively involved in alliances to exchange, share and source
knowledge. Most of these firms are networked organizations, as are biotech firms located in
other leading and non-leading regions. It seems that the characteristic of the industry, the
complexity of the technology and the rapid pace of technical change drives firms to enter into
collaborations. Our results illustrate that even though firms engage in cooperations for
manufacturing and marketing purposes, knowledge is the major factor that stimulates firms to get
involved in collaborations with other partners.
The knowledge network structure of the Argentinean biotech firm is different from the ones
found for the leading regions, but similar to those in other non-leading ones. The salient features
are the scarcity of collaborations among co-located firms, the key role that PROs play in knitting
the local network together and the relevance of non-local partnerships predominantly forged with
partners in leading regions.
The mix between local and non-local cooperation forged by Argentinean biotech firms reveals
that the sustainability and the development of the Argentinean biotech industry cannot be
explained focusing only on local knowledge interactions. Furthermore, we found that non-local
collaborations are valuable for firms’ innovation activity. Most of those firms that introduced
innovations new to the international market have entered into collaborations with foreign
partners. In addition, innovative firms also show a larger number of collaborations with external
organizations than those firms that did not innovate.

30. Another result of our study is that the strength of the local scientific knowledge base, contained in
local PROs, seems to be critical for the development of biotechnology in Argentina, as it is in
every region where a biotechnology industry emerges. We find evidence that suggests that
entering into R&D collaborations with local PROs may have a positive effect on firms’
innovative performance.
Even though we found collaborations with local PROs and non-local collaborations to be
valuable for firms’ innovation, stronger results claim for data available for more years, in order to
apply econometric techniques that permit to address the potential simultaneity bias between
knowledge cooperations and innovation performance.
However, we can still draw some tentative implications of these results for policy
recommendation. The persistent choice of firms to exchange and source knowledge from the
local scientific community and from foreign partners should be acknowledged by policy makers.
In particular, because both types of collaborations seem to be valuable to enhance firms’
innovation performance. Thus, non-local collaborations and cooperations with universities and
scientific organizations should not be ignored but promoted and facilitated. These results also
claim for a more open view of clusters, and an abandonment of a close and geographically
bounded view of knowledge flows.
Future research should address the reasons that lead firms to display the observed patterns of
R&D collaborations. In particular, which are the factors that prevent inter-firm synergies to take
place. We suggest that not only absorptive capacity issues and knowledge related factors should
be addressed, but also factors such as the building of trust, reputation and competition should be
consider in the analysis.
Acknowledgments
Results presented in this article are based on a study joint with ECLAC, Buenos Aires office.
The data collected were drawn from interviews and data from companies, we would like to thank.
We also wish to thank Bernardo Kosacoff for facilitating field research and being supportive and

31. positive about this research, Roberto Bisang for his invaluable insights and guidance during the
fieldwork study, and Robin Cowan for his useful comments and suggestions, and continuous
support on this study.
References
Ahuja, G. (2000) ‘Collaboration networks, structural holes and innovation: A longitudinal study’
Administrative Science Quarterly 45:425-455.
Ahuja, G. and R. Katila (2001) ‘Technological acquisitions and the innovation performance of
acquiring firms: A longitudinal study’ Strategic Management Journal 22:197-220
Andrews, K. (1971) ‘The Concept of Corporate Strategy’ Irwin, Homewood, IL.
Arora and A. Gambardella (1994) ‘Complementarity and external linkages: The strategies of
large firms in biotechnology’ Journal of Industrial Economics 38:361-379
Arundel A. and A.Geuna (2004) ‘Proximity and the use of public science by innovative European
firms’ Economics of Innovation and New Technology 13 (6): 559-580
Audretsch, D.B. and M.P. Feldman (1996) ‘R&D spillovers and the geography of innovation and
production’ American Economic Review 86: 630–640
Bartley, S, Freeman, J. and R. Hybels (1992) ‘Strategic Alliances in Commercial Biotechnology’
in Networks and Organizations: Structure, Form and Action, pages 311-347, President and
Fellows of Harvard College.
Bathelt, H., Malmberg, A. and P. Maskell (2004) ‘Clusters and knowledge: local buzz, global pipelines
and the process of knowledge creation’ Progress in Human Geography 28 (1):31-56
Bell, M. and M. Albu (1999) ‘Knowledge systems and technological dynamism in industrial
clusters in developing countries’ World Development 27 (9): 1715-1734
Belussi, F., Sammarra, A. and S. Sedita (2008) ‘Managing long distance and localized learning in
the Emilia Romagna life science cluster’ European Planning Studies 16(5):665-692.

32. Becattini, G. (1990): ‘The Marshallian industrial district as a socio-economic notion’ in Pyke, F.,
Becattini G. and W. Sengenberger (eds.) Industrial districts and inter-firm cooperation in Italy.
International Institute for Labour Studies, Geneva, 37-51.
Breschi, S. and F. Malerba (2001) ‘The Geography of Innovation and Economic Clustering: some
Introductory Notes’ Industrial and Corporate Change 4 (10):817-833
Breschi, S. and F. Lissonni (2001) ‘Knowledge Spillovers and Local Innovation Systems: A
Critical Survey’ Industrial and Corporate Change 10(4):2001
Brink, J., Dahlander, L. and M McKelvey (2007) ‘Developing capabilities: An analysis of
biotechnology in two regions in Australia and Sweden’ European Planning Studies 15(6):727-
751
Burt, R. S. (1992) ‘Structural Holes: The Social Structure of Competition’ Harvard University
Press, Cambridge, Mass.
Camagni, R. (1991) ‘Introduction: from the local ‘milieu’ to innovation through cooperation
networks’ in Camagni, R. (ed.) Innovation networks: spatial perspectives, Belhaven Press,
London, 1-9.
Carlsson, B. (2001) ‘Technological systems in the bio industries. An international study’ Kluwer
Academic Publishers.
Cassiman, R. and B.Veugelers (2006) ‘In search of complementarity in the innovation strategy:
Internal R&D and external knowledge acquisition’ Management Science 52:68-82.
Cawthorne, P.M. (1995) ‘Of Networks and Markets : The Rise and Rise of a South Indian Town,
the Example of Tiruppur’s Cotton Knitwear Industry’ World Development 23(1): 43-56

33. Coenen, L., Moodysson, J. and B. Asheim (2004); ‘Nodes, networks and proximities: on the
knowledge dynamics of the medicon valley biotech cluster’ European Planning Studies
12(7):1003-1018.
Cohen W.M., R.R. Nelson and J.P. Walsh (2002) ‘Links and Impacts: The Influence of Public
Research on Industrial R&D’ Management Science 48 (1): 1-23
Coleman, J.A. (1988) ‘Social Capital in the Creation of Human Capital’ The American Journal of
Sociology 94:95-120.
Cooke P. (2001) Regional innovation systems, clusters and the knowledge economy, Industrial &
Corporate Change, 10, 945-974
Dahlander, L. and M. McKelvey (2005) ‘The occurrence and spatial distribution of
collaboration: biotech firms in Gothenburg, Sweden’ Technology Analysis & Strategic
Management, 17(4):409-431.
Deeds, D. and F. Rothaermel (2004) ‘Exploration and exploitation alliances in biotechnology: a
system of new product development’ Strategic Management Journal 25(3):201-221.
Duysters, G. and W. Schoenmakers (2006) ‘Learning in Strategic Technology Alliances’
Technology Analysis & Strategic Management 18(2):245–264
Dyer, J.H. and K. Nobeoka (2000) ‘Creating and Managing a High Performance Knowledge
Sharing Network: the Toyota Case’ Strategic Management Journal 21:345-367
Eisenhardt, K.M. and C.B. Schoonhoven (1996) ‘Resource-based view of strategic alliance
formation: Strategic and social effects in entrepreneurial firms’ Organizational Science 7:136-
150.
Fontes, M. (2003) ‘Distant networking? The out-cluster strategies of new biotechnology firms’
Paper presented at ASEAT Conference on Knowledge and Economic and Social Change: New
Challenges to Innovation Studies, Manchester, 79 April., 2003.

34. Fontes, M. (2005) ‘Distant networking: The knowledge acquisition strategies of '
out-cluster'
biotechnology firms’ European Planning Studies 13(6):899-920.
Galhardi, R. (1994) ‘Small high technology firm in developing countries. The case of
biotechnology’, Aldershot, Averbury Press.
Gertler, M. and Levitte, Y. (2003) ‘Local nodes in global networks: The geography of knowledge
flows in biotechnology innovation’ Paper presented at DRUID' Summer 2003 Conference,
s
Helsingore.
Gilding, M. (2008) ‘The tyranny of distance: Biotechnology networks and clusters in the
antipodes’ Research Policy 37(6/7):1132-1144.
Giuliani, E. and M. Bell (2005) ‘The micro-determinants of meso-level learning and innovation:
Evidence from a Chilean wine cluster’ Research Policy 34:47–68
Giuliani E. (2006) ‘The uneven and selective nature of cluster knowledge networks: evidence
from the wine cluster’ CIRCLE Electronic Working Paper Series 2006-11 - CIRCLE (Centre for
Innovation, Research and Competence in the Learning Economy) - Lund University.
Giuliani, E. (2007) ‘Towards an understanding of knowledge spillovers in industrial clusters’
Applied Economics Letters 14:87–90
Granovetter, M. (1985) ‘Economic action and social structure: The problem of embeddedness’
American Journal of Sociology 91:481–510
Gulati, R. (1995) ‘Does familiarity breed trust? The implications of repeated ties for contractual
choice in alliances’ Academy of Management Journal 38: 85–112.
Gulati, R. (1998) ‘Alliances and networks’ Strategic Management Journal 19: 293–317
Gulati R. and M. Gargiulo (1999) ‘Where Do Inter-Organizational Networks Come from?’
American Journal of Sociology 104:1439-1493.

35. Hagedoorn, J. (1992) ‘Strategic Alliances in Information Technology among Firms in Western
Industrialized Nations’ in L.S Peters (ed.) International Issues in the Management of Technology
(10) JAI Series on International Business and Finance.
Hagedoorn, J. and G. Duysters (2000) ‘The effects of Mergers and Acquisitions on the
Technological Performance of companies in High Tech Environment’ Mimeo. University of
Maastricht
Heimeriks, G. and R. Boschma (2011) ‘The Geography of Biotechnology’ Paper presented at the
final DIME Conference, Maastricht 6-8 April.
Kauffman A. and F. Todtling (2001) ‘Science-industry interaction in the process of innovation:
the importance of boundary-crossing between systems’ Research Policy 30: 791-804
Hagedoorn, J. and N. Roijakkers (2006) ‘Inter-firm R&D partnering in pharmaceutical
biotechnology since 1975: trends, patterns, and networks’ Research Policy 35:431-446.
Kennedy, M.J. (1991) ‘The evolution of the word biotechnology’ TRENDS in Biotechnology
9:218-220.
Klevorick A.K., R.C. Levin, R.R. Nelson and S.G. Winter (1993) ‘On the sources and
significance of inter-industry differences in technological opportunities’ Research Policy 24:
185-205
Knoke, D. and Yang, S. (2008) ‘Social Network Analysis’ SAGE Publications.
Kogut, B. (1989) ‘The stability of joint ventures: Reciprocity and competitive rivalry’ Journal of
Industrial Economics 38(2): 183-198
Koput, K.W. and Smith-Doerr, L. and W. W. Powell (1997) ‘Learning and Industry
Structure: The Evolution of Networks in Biotechnology’ in Advances in Strategic Management
Research pages 229-254 Greenwich, CT: JAI Press.

36. Lawson C. and E. Lorenz (1999) ‘Collective Learning, Tacit Knowledge and Regional Innovative
Capacity’ Regional Studies 33(4)305-317
Lawton Smith, H. (2004) ‘The Biotechnology Industry in Oxfordshire: Enterprise and
Innovation’ European Planning Studies 12:985-1001.
Mansfield E. (1991) ‘Academic research and industrial innovation’ Research Policy 20: 1-12
Marsden, P.V. (1990) ‘Network data and measurement’ Annual Review of Sociology 16:435-463.
Marsden, P.V. (2005) ‘Recent Developments in Network Measurements’ in Models and Methods
in Social Network Analysis, pages 8-30 Cambridge University Press.
Marshall, A. (1920) ‘Principles of Economics’, MacMillan, London
McKelvey, M., Alm, H. and M. Riccaboni (2003) ‘Does co-location matter for formal knowledge
collaboration in the Swedish biotechnology pharmaceutical sector?’ Research Policy 32(3):483-
2003.
Miller, H.I. (2007) ‘Biotech' defining moments’ TRENDS in Biotechnology 25:56-59.
s
Mohnen, P. and Hoareau, C. (2003) ‘What type of enterprise forges close links with universities
and government labs? Evidence from CIS 2’ Managerial and Decision Economics 24:133-145.
Mowery, D.C., Oxley, J.E. and B.S. Silverman (1996), ‘Strategic alliances and Inter-firm
knowledge Transfers’ Strategic Management Journal 17:77-91.
Mowery, D.C., Oxley, J.E. and B.S. Silverman (1998) ‘Technological Overlap and Inter-firm
Cooperation: Implications for the Resource-based View of the Firm’ Research Policy 27:507-
523.
Nadvi, K. (1996) ‘Small firms industrial districts in Pakistan’, IDS D.Phill thesis, University of
Sussex

37. Niosi, J. (2003) ‘Alliances are not enough explaining rapid growth in biotechnology firms’
Research Policy 32:737-750.
OECD (2005) ‘A framework for biotechnology statistics’ OECD, Paris
Oinas, P. (1999) ‘Activity-specificity in organizational learning: implications for analyzing the
role of proximity’ GeoJournal 49:363–72.
Orsenigo, L. and M. McKelvey (2006) ‘The economics of biotechnology’ Edward Elgar
Publishers.
Owen-Smith J., M. Riccaboni, F. Pammolli and W.W. Powell (2002) ‘A comparison of US and
European university-industry relations in the life sciences’ Management Science 48 (1): 24-43
Owen-Smith, D. and W.W. Powell (2004) ‘Knowledge networks as channels and conduits: The
effects of spillovers in the Boston biotechnology community’ Organizational Science 15(1) 5-21
Piore, M. J. and C. F. Sabel (1984) ‘The Second Industrial Divide: Possibilities for Prosperity’
New York, Basic Book.
Porter, M. (2000) ‘Locations, clusters and company strategy’ in The Oxford Handbook of
Economic Geography pages 253-274 Oxford University Press.
Powell, W.W. and P. Brantley (1992) ‘Competitive Cooperation in Biotechnology:
Learning Through Networks?’ in Nohria, N. and R. Eccles (Eds.) Networks and organizations,
366–394. Boston, MA: Harvard Business Press.
Powell, W. W., Koput, K. W. and L. Smith-Doerr (1996) ‘Inter-Organizational Collaboration and
the Locus of Innovation: Networks of Learning in Biotechnology’ Administrative Science
Quarterly 41:116-145.
Powell, W. W., Koput K.W., Smith-Doerr L. and J. Owen-Smith (1999) ‘Network Position and
Firm Performance: Organizational Returns to Collaboration in the Biotechnology Industry’ pp.

38. 129-59 in Networks In and Around Organizations. (Special Issue in the series Research in the
Sociology of Organizations), edited by S. Andrews and D. Knoke (eds.) Vol. 16, JAI Press.
Powell W.W., D.R. White, K.W. Koput and J. Owen-Smith (2005) ‘Network Dynamics and Field
Evolution: The Growth of Inter-organizational Collaboration in the Life Science’ American
Journal of Sociology 110:1132-1205
Rabelotti, R. (1995) ‘External economies and cooperation in industrial districts: A comparison of
Italy and Mexico’, IDS D.Phil thesis, University of Sussex
Rees, K (2005) ‘Interregional collaboration and innovation in Vancouver' emerging high-tech
s
cluster’ Tijdschrift voor Economische en Sociale Geografie 96(3):298-312.
Rosenkopf, L. and P. Almeida (2003) ‘Overcoming local search through alliance and mobility’
Management Science 49:751-766.
Rothaermel, F.,Deeds, D. and T. Frank (2004) ‘Exploration and exploitation alliances in
biotechnology: a system of new product development’ Strategic Management Journal 25:201-
221.
Shan W., G. Walker and B. Kogut (1994) ‘Inter-firm cooperation and start-up innovation in the
biotechnology industry’ Strategic Management Journal 15 387–394.
Schmitz, H. (1995) ‘Small Shoemakers and Fordist Giants: Tale of a Super Cluster’ World
Development 23(1): 9-28
Thorsteinsdottir, H., Melon, C., Ray, M., Chakkackal, S., Li, M., Cooper, J., Chadder, J., Saenz,
T., De Souza, M., Ke, W., Li, L, Madkour, M., Aly, S., El-Nikhely, N., Chatuverdi, S., Konde,
V., Daar, A. and P. Singer (2010) ‘South-south entrepreneurial collaboration in health biotech’
Nature Biotechnology 28:407-416.

39. Uzzi, B. (1996) ‘The Sources and Consequences of Embeddedness for the Economic
Performance of Organizations: The Network Effect’ American Sociological Review 61(4):674-
698.
van Beuzekom, B. and A. Arundel (2009) ‘OECD biotechnology statistics 2009’ OECD Paris
Van Geenhuizen, M. (2007) ‘Modeling dynamics of knowledge networks and local
connectedness: a case study of urban high-tech companies in The Netherlands’ Annual Regional
Science 41: 813-833
Visser, E.J. (1996) ‘Local Sources of Competitiveness. Spatial Clustering and Organizational
Dynamics in Small-scale clothing in Lima, Peru’, PhD Thesis, University of Amsterdam.
Zucker L. G.., M. R. Darby and M. B. Brewer (1998) Intellectual Human Capital and the Birth of
US Biotechnology Enterprises American Economic Review 88:290-306
Annex A – Methodology
The OECD definition of biotechnology encompasses both a single definition and a list-based
definition. The single definition defines biotechnology as:
The application of science and technology to living organisms, as well as parts, products and
models thereof, to alter living or non-living materials for the production of knowledge, goods and
services.
This single definition is intentionally broad as it covers not only biotechnological techniques
indentified as traditional biotechnologies but also those labeled as modern biotechnologies. The
single-OECD biotechnology definition is of minimal value to distinguish those firms engaged in
modern biotechnology from those that are only focused on traditional ones (OECD, 2005;
Arundel, 2007; Miller, 2007). The list-based definition functions as an interpretative guide of
the single definition, as it encompasses all technologies that are identified as modern

40. biotechnologies. Thus, the list-based definition narrows the single definition only to (modern
biotechnology) methods as it includes the following biotechnology techniques:
• [DNA/RNA] genomics, pharmacogenomics, gene probes, genetic engineering,
DNA/RNA sequencing/synthesis/amplification, gene expression profiling, and use of
antisense technology.
• [Proteins and other molecules] sequencing/synthesis/engineering of proteins and peptides
(including large molecule hormones); improved delivery methods for large molecule
drugs; proteomics, protein isolation and purification, signaling, identification of cell
receptors.
• [Cell and tissue culture and engineering] cell/tissue culture, tissue engineering (including
tissue scaffolds and biomedical engineering), cellular fusion, vaccine/immune stimulants,
embryo manipulation.
• [Process biotechnology techniques] fermentation using bioreactors, bioprocessing,
bioleaching, biopulping, biobleaching, biodesulphurisation, bioremediation, biofiltration
and phytoremediation.
• [Gene and RNA vectors] gene therapy, viral vectors.
• [Bioinformatics] construction of databases on genomes, protein sequences; modeling
complex biological processes, including systems biology.
• [Nanotechnology] applies the tools and processes of nano/microfabrication to build
devices for studying bio systems and applications in drug delivery, diagnostics, etc.
Biotechnology areas definitions:
• Human health: firms active in the following biotech applications: large molecule
therapeutics and monoclonal antibodies produced using rDNA technology, other
therapeutics, artificial substrates, diagnostics and drug delivery technology.
• GM agriculture biotechnology: firms involved in the production of new varieties of
genetically modified plants, animals and microorganisms for use in agriculture,
aquaculture, silviculuture.
• Non-GM agricultural biotechnology: firms that develop new varieties of non-GM plants,
animals and microorganisms for use in agriculture, aquaculture, silviculuture, biopest

41. control and diagnostics developed using biotechnology techniques (DNA markers, tissue
culture,etc.
• Veterinary health: firms active in all health applications for animals.
• Industrial processing: firms that develop bioreactors to produce new products (chemicals,
food, ethanol, plastics, etc.), biotechnologies to transform inputs (bioleaching, biopulping,
etc.).
Annex B
Questions 13 and 17 elicit data about unidirectional knowledge flows. Respondents are asked to
name those agents from whom they have obtained and transferred biotechnology-related
technologies in the period 2003-2008.
Q 13 - From which firms/institutions did your firm acquire technology (e.g. R&D services,
patent rights) during the period 2003-2008?
Local organizations Foreign organizations
Q 17 - Please specify the name of firms/institutions that your firm has licensed technology to
during the period 2003-2008
Local organizations Foreign organizations

42. Question 20 aims at eliciting collaborative ties for R&D, manufacturing and marketing purposes.
Respondents are asked to name those alliance partners with whom they have set up these types of
cooperations and also provide the number of collaborations forged with each of the named
partners in the period 2003-2008.
Q 20 - With which institutions did your firm set up R&D, manufacturing and marketing
collaboration/cooperation alliances in the period 2003-2008? For each type of alliance, please
specify the names of the partners in Argentina and abroad. Please consider all possible sorts of
partners such as other firms, universities, research institutes, and others.
Purpose of collaborations
Name of partners
R&D Manufacturing Marketing
Argentinean Partners
Foreign Partners