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

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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 [email protected]�

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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.

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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.�

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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

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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).

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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).

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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.

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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

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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

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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.

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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). 10The same criteria was followed by McKelvey et al (2003), Brink et al (2007), and Dahlander and Mc Kelvey

(2005).

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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).

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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).

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Table 1: Surveyed firms, by biotechnology application

Biotechnology Application Biotech firms

in the dataset Surveyed firms

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).

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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).

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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

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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

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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.

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Table 2 – Knowledge network statistics, by type of cooperation

Type of

cooperation

Av.

Degree

St.

Dev. Max Min Isolates

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).��

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Table 3 - Number of cooperation agreements by Argentinean biotechnology firms, by types

of cooperation (2003-2008)

Number of cooperations Biotechnology Application 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

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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

Local collaborations to

Number non-local

collaborations to Biotechnology

application biotech

firms

local

PROs

other

firms Total

firms PROs Total

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

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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

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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

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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.

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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.

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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�

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Table 5 - Biotech firms’ innovation performance and R&D alliance activity

Biotech firms that engaged in

collaborations with

Local PROs Foreign organizations

Number of biotech firms that innovated in�

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

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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

Innovated in

products

Innovated in

processes

Applied patents

in Argentina

Applied for

patents in the

US

Average

number of

cooperations

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

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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.

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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

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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.

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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

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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

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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

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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

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

Technology

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