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Paper to be presented at the
35th DRUID Celebration Conference 2013, Barcelona, Spain, June 17-19
Knowledge networks as the nouvel milieu of biotechnology firms in
peripheral regionsMaria Patrizia Vittoria
National Research Council (CNR)Institute for Services Industry Research (IRAT)
Giuseppe Lubrano LavaderaNational Research Council (CNR)
Institute for Services Industry Research (IRAT)[email protected]
AbstractThe aim of this paper is to increase our understanding of biotechnology knowledge networks in peripheral regionsthrough a detailed case study of the Campania region in Southern Italy. Previous research has linked new developmenttrajectories, learning systems and policy mechanisms that might open opportunities for less-favoured regions. Thepersistent lagging behind that affects the Campania industrial system is fuelling debate on the effectiveness of the localsystem?s exploitation of public aid. Our analysis addresses the question of whether effective biotechnology knowledgenetworks exist in Campania and, if so, whether a better understanding of their structure and modes of formation wouldmake them more aligned to EU policy. We explore the local multiple biotech knowledge networks using experimentalindicators and Social Network Analysis. The empirical bases for our analyses derive from historical documents,statistical data and in-depth interviews with key individuals in public and private organizations. The Campania casehighlights the existence of effective and dense biotechnology knowledge network in the area of public basic researchand a strong centrality of the external academic research groups.
Jelcodes:L65,O33
1
Knowledge Networks as the nouvel milieu of biotechnology firms in peripheral
regions
1. Introduction
There is increased interest among policymakers and academics in the flows of knowledge
between individuals, firms and institutions such as universities and government research
labs, and the role played by these knowledge flows in innovation (Arundel and Constantelou
2006).
At a policy level, since the work of Cooke (2001, 2004) much of the debate has been around
regional innovation systems, learning regions and clusters as policy frameworks for
implementing long term, innovation based regional development strategies. In this analytical
perspective and on the wave of recent economic and political changes, the European
Commission’s DG Research has introduced a comprehensive framework that addresses
current policy needs at the regional level. The Constructing Regional Advantage (CRA)
report (European Commission 2006) collects principles, perspectives and policies related to
this framework.
The CRA rationale for policy interaction is to reduce system failures based on lack of ability
or lack of motivation of individual regional actors to engage in interactive learning with
collaboration partners and absence of critical actors in the region. The idea behind the
construction of regional advantage emphasizes the role of knowledge flows and learning
dynamics between knowledge organizations - such as universities and research institutes,
and regional industries (Cooke 1998).
Given the dynamic and context-based nature of these links, it has been suggested that
knowledge flows should be measured using experimental rather than conventional indicators
(Arundel, Constantelou 2006). Conventional indicators are input indicators such as R&D
expenditure or output indicators, such as number of new firms created; experimental
indicators provide evidence of communication among individuals, firms and institutions
during the innovation process.
This paper examines the emerging structure of three biotechnology KNs among the research
actors in the Campania region in Southern Italy.
The present study considers the Campania biotechnology KNs as given by the map or the
graphical representation of the linkages channelling the knowledge flows originating in local
actors and moving to their local or national and international counterparts. Our method
involves measuring the linkages among internal and external individuals, firms and
institutions such as universities and government research labs, that constitute a basic pillar
2
in the KN architecture, by collecting reciprocal data emerging from the communication flows
among them. Social Network Analysis (SNA) (Scott 2000; Wasserman and Faust 1994;
Carrington, Scott, and Wasserman 2005) is used to explore and fix the critical relations as
vectors of knowledge flows at firm-level, and to describe the entire regional biotech
population on the basis of these critical relations. We are interested in the number of network
configurations involved in well diffused knowledge and learning practices.
We build experimental indicators of knowledge flows for effective regional innovation
potential. In the case of the present research these experimental indicators result from initial
case study research and are considered as dominant relational dimensions for the
subsequent whole network survey.
The empirical research questions addressed in this paper are:
What are the critical relationships for the acquisition of knowledge which involve the local
(few but excellent) research centres, and how many local actors are actually involved? Do
the observed links draw (plot) a significant located KN structure? How critical is the role of
external actors in the whole-network knowledge flow?
The evidence contributes to the idea that the construction of regional advantage includes
dynamic recognition of local and distant KNs to establish local capabilities that contribute to
creating knowledge value.
The paper is structured as follows: Section 2 discusses the CRA analytical framework in
setting the background to and pre-conditions for the regional innovation system. Section 3
describes the methodology, which consists of: 3.1 A two-step analysis; 3.2 Selecting
experimental indicators from case-study evidences; 3.3 Measurement of variables, data
sources and SNA interpretation. Section 4 discusses the complex layout of the local biotech
innovation system through: 4.1 The Campania Biotech Community: two case histories; 4.2
Visualization of the KNs; and 4.3 Results. And, finally, section 5 presents the conclusion.
2. KNs in biotechnology and in peripheral regions: how to establish the
preconditions for CRA at periphery
The CRA recommends that the diversity of regions should be acknowledged and proposes
three ‘ideal type’ of knowledge bases - analytical, synthetic and symbolic – to describe the
respective professional and occupational groups and competences involved in the
production of various types of knowledge.
Biotechnology is categorized as analytical knowledge typical of an industry setting where
scientific knowledge is very important and where knowledge creation often is based on
3
cognitive and rational processes or on formal models (European Commission 2006).
The CRA suggests analysing the biotech local analytical knowledge base to explore the
formal models for codifying knowledge assets (input and output). Inherent in this argument is
a recognition that some institutional features (that are common to the knowledge creation
process in biotechnology) contribute to integrate also the informal relational dimension. They
include:
(1) The centrality of basic research. This is performed in universities or small private firms,
and is critical to the field’s development. In contrast to information and communication
technology (ICT), where new ideas originate in universities or corporate labs and whose
subsequent development is far removed from the initial discovery process, biotech uses
novel science to develop new medicines and therapies (Porter, Whittington, and Powell
2005, p. 287).
(2) The identification of a clear division of labour among the actors in a biotech knowledge
creation process is dogged by functional problems. Both public and private actors can be
considered as fairly involved in the knowledge creation process which has an impact on the
rules related to the division of labour and market competencies1. The flow of knowledge –
especially molecular biology knowledge - for the pursuit of profitable market applications,
tend to follow routes that adhere to country-specific management patterns and can be via
new firms (following the US entrepreneurial model) or large established firms (such as in
Germany or Japan). Thus, the biotech industry includes a variety of actors. This variety is
due also to the nature of biotech innovation, which is characterized by a strong degree of
multidisciplinarity.2
1 More precisely, since the emergence in American research universities in the late 1970s of biotechnology as a field of study,
the classical model describing the relation between basic and applied research as unidirectional, has been changing
(Powell and Owen-Smith 2000). University biology departments have been disrupted by growing numbers of
entrepreneurial biologists engaging in commercialization activities. The biotechnology industry, in large part, was created
as a result of the efforts and critical roles of university professors. According to Kenney (1986), one of the most important
modes of creation of new biotech firms in the US was through spin-off firms set up by academic researchers. These new
firms, which were based on research ideas/projects, initially were dependent on external finance. In the early stages, the
main problem was to establish whether biotechnology would survive as an industry or was merely another tool available
to the traditional, huge, chemical and pharmaceutical companies, which were more able to absorb these biotech start-
ups. However, strategic alliances (especially between small new biotechnology firms and large firms), and collaborations
involving basic and applied research, both public and private, have increased rapidly. A central feature of the new
technological paradigm is the collective nature of the learning process, and cooperation rather than competition as the
dominant market behaviour (Orsenigo 1989; Arora and Gambardella 1994). 2 Research in organizational science examines the phenomenon of biotech alliances (see Nohria and Eccles 1992) and has
produced two important findings: an observed quantitative increase in the number of biotech strategic alliances including
international ones (Barley, Freeman and Hybels 1992), and the need for basic research as the foundation for stable
4
(3) Along the lines of the general features of biotech networks outlined above, we assume
that pursuing scientific discovery and improving the ability to collaborate and communicate
are most critical for biotech organizations. In these processes the role of informal
interactions among the members of scientific and productive communities has been
recognized as a critical vector of information and knowledge (Kreiner and Schultz 1993;
Saxenian 1994), and a strategic innovation resource (Salman and Saives 2005) and as
being directly related to the innovation process (Powell et al. 1996; Powell and Grodal
2006)3.
As a consequence we analyse the biotech analytic local knowledge base to explore the
relative formal and informal models for codifying the knowledge assets (input and output).
The Italian biotech industry has been described in Italian biotech reports and other works
(Bigliardi et al. 2005; Nosella et al. 2005; Passaro and Vittoria 2000; Onetti and Zucchella
2007; Iorio, Labory, and Paci 2012; Simone and Proietti 2012), which outline its salient
features and its dependence on the pharmaceutical industry, based on prolonged periods of
pre-competitive management and difficult-to-develop new products.
Less attention has been paid to the regional distribution and development of the biotech
industry and biotech clusters, although some work has been done on the Lombardia Region
(Orsenigo 2001), which is the most developed part of Italy and closer to the centre of
Europe. The biotech industry in Campania has been mostly ignored since it is on the
periphery4.
biotech research networks (Powell and Brantley 1992). This research acknowledges the utility of research collaborations
and the network configurations among the R&D actors in the biotech field. Work in this area has provoked debate on the
definition and characteristics of biotech networks (size, types of ties, etc.) (Powell and Grodal 2006), and strategic
priorities. E.g. if production/value depends on access to knowledge, which is enabled by dense formal and informal
networks, as well as funding, it is critical to develop the internal organization and strategic capacity required to establish
and ensure access to these networks. Relational ability is a strategic tool in the management of biotech organizations. It
contributes directly to firm (or process) results by performing the function of capturing knowledge (Persico and Vittoria
2010). 3 For example, if production/value depends on access to knowledge, which is enabled by dense formal and informal networks,
as well as funding, it is critical to develop the internal organization and strategic capacity required to establish and
ensure access to these networks. Relational ability is a strategic tool in the management of biotech organizations. It
contributes directly to firm (or process) results by performing the function of capturing knowledge (Persico and Vittoria
2010). The relational ability is quite often based on the absorptive capacity at individual and organizational levels
(Cohen, Levinthal 1999).
4 Some research contributions describe the Campania innovation system. Using a deterministic research approach Castaldi
(2009) notes that some of the most important biotech research centres (10%) are located in Campania, but the number
5
The persistence and unexpected success of some research organizations on the periphery
point to the utility of observing the Campania biotechnological community and its
development model, using an exploratory network approach. On the one hand, the
Campania peripheral innovation system has been considered marginal for external actors,
on the other hand, the links with external partners can be assumed to enrich its regional
innovation potential. Novel opportunities to apply the localized knowledge output in basic
research may emerge in long-distance networks.
Policy related to innovation knowledge flows requires indicators of the production of
knowledge and the extent and magnitude of knowledge-based transactions. The indicators
in the innovation economics literature fall into three categories: input or resource indicators,
including R&D expenditure; output indicators such as patents; and progress indicators
(Grupp 1998). Subsequent research contributions have added the distinction between
conventional and experimental indicators. The former account for every type of
resource/result involved in the innovation process; the latter refer to the economic effects of
innovation at the micro and macro levels. These indicators include a time perspective. There
are several problems related to building experimental indicators of knowledge flows and
more research in this area is needed5 (Arundel and Constantelou 2006).
These arguments raise the specific need to integrate the CRA framework and its capacity to
interpret the pre-conditions (regional endowments) for constructing regional advantage in the
context of biotechnology in peripheral regions.
3. Empirical methodology
3.1 A two-step analysis
The research process consists of two basic steps. In the first step, we use case study
research to explore the most critical nodes in the complex flow of knowledge at firm level.
We conducted the field work in 2008-2010. This informed the second step, which is a whole-
of biotech firms is small (4% of firms). Cannavacciuolo, Capaldo and Rippa (2012) apply SNA to investigate the
entrepreneurial process in molecular biotech in Campania for a sample of 10 private and non-profit actors. The
biotechnology peripheral innovation system has been also analysed for constructing a regional advantage in Tromsø,
Norway (Karlsen, Isaksen, and Spilling 2011) and to explore the development possibilities of the regional innovation
system in La Pocatière, Canada (Doloreux and Dionne 2008). 5 E.g. very few of surveys focus on the different channels available for obtaining information and the mechanisms that apply to
each channel. Most innovation surveys provide no data on the type of information that firms seek from particular
sources. Finally, other measurement problems of knowledge flows can emerge at sector level. It has been claimed that
‘it would be worthwhile to try to open the black box to uncover the particularities of specific sectors’ (Arundel,
Constantelou 2006; p. 63).
6
network survey based on a selected set of experimental indicators. The most critical nodes
connecting the biotechnological actors that emerged as a result of the first step are
considered as relational dimensions for the whole network survey.
The research hypotheses are based on a recognition of the process of production and
circulation of knowledge within the firm, as a key determinant of its capability to innovate
(Kogut and Zander 1992, 1996; Nonaka and Takeuchi 1995; von Krogh, Roos, and Kleine
1998; Choo and Bontis 2002). The knowledge management literature generally refers to how
organizations create, retain and share knowledge (Argote 1999; Huber 1991). The study of
knowledge sharing, which is the means by which organizations obtain access to their
internal knowledge and that of other organizations, emerged from research on technology
transfer and innovation, and from the field of strategic management. Increasingly, research
on knowledge sharing takes an organizational learning perspective (Cummings 2003). In
particular, it is assumed that to be effective, work requires collaboration within and across
functional, physical, and hierarchical boundaries (Cross and Parker 2004). SNA
(Wasserman and Faust 1994; Scott 2000; Carrington, Scott, and Wasserman 2005) has
been used to find and fix critical disconnects in organizations. In making invisible work
visible, SNA has been used to uncover the critical role of informal networks among
individuals (Cross, Borgatti, and Parker 2002).
We applied this technique to collect relational data to map the topography of the critical
nodes around which knowledge flows concentrate for the circulation of information emerging
from problem solving and customer care activities.
We analysed four successful cases based on the collection of empirical data via face-to-face
meetings at company sites. Empirical data were gathered using a data collection guide and
a SNA questionnaire. The units of analysis are the internal and external networks of each
organization, which is in line with Van Wijk, Van den Bosh and Volberda (2003). The cases
(two small private labs and two not-for-profit, research-based organizations, located in
Campania) are success stories, which fixes the performance variable (Peters and Waterman
1982). We consider ‘success’ as the capacity to maintain market stability for at least five
years. The empirical investigation was aimed at identifying the types of relational behaviour
that induce success. The main evidence from the case study analysis is represented by the
most critical relational dimensions of the observed organizations.
In the second step of the study, the relational dimensions identified by the case study
research were applied to the regional biotech community to estimate the existence and
spatial layout of KN. Note that in other studies, mostly conducted within a development
policy perspective, a standard list of characteristics associated with cluster performance is
7
used as the performance measure, and their profile is used to compare a cluster with similar
clusters in other regions, and to identify weaknesses and appropriate actions. In our view,
this process may hide the real potential of local actors. We favour a SNA graphical analysis
because it can be used by policy makers to understand crucial aspects of the Campania
biotech industry. We apply statistical analysis to the case of collaboration, to try to shed light
on some of the elements not evident in the SNA representation.6
Data collection for the SNA is based on observation of nodes and links, and sampling of the
whole network and the egocentric network (Carrington, Scott, and Wasserman 2005, pp.
371-388). Our study is intended to be limited in geographical terms, and is focused not on a
single structure, but on the interactions among different actors. We are interested in the
whole population rather than a sample of it. Data are from AIDA/Bureau Van Dijk and they
cover the period 2004-2010 and are integrated with sector-specific population lists
(RPBiotech Data Base/Irat, CNR and University of Salerno 2005; Biotechnologies in
Italy/BiotechGate Company Data Base, 2009). We use Pajek 3.01 software (De Nooy,
Mrvar, and Batagelj 2005), a program that allows visualization and analysis of large
networks. It processes relational data and provides visual representations (sociograms) in
which the nodes are the actors, individuals or organizations, and the lines are the forms of
affiliation, research collaborations, advisory boards and founding/proprietary teams.
We first identify all the actors that might possibly be implicated in the biotech sector in
Campania7 and compile a complete list of the regional actors involved in the biotech
community. A summary of the actors in our database for Campania in 2004-2010 is
6 SNA is a widely used, differentiated research tool. It can be applied visually to clarify and show the relationships in a limited
system. In the case of relatively large amounts of information (large numbers of nodes and links) descriptive statistics
are more meaningful. In some cases inferential techniques to test some theoretical models are recommended (Kolaczyk,
2009).
7 We used the RP Biotech Database coding system and the system suggested by the OECD (Vittoria and D'Amore 2009), to
establish the unit of observation. We included different biotechnological actors, and among production activities we
collected active, innovative and dedicated biotech firms. A biotechnology active firm (BAF) is defined as a firm engaged
in key biotechnological activities such as application of at least one biotech technique to produce goods or services
and/or performance of biotechnology R&D. A dedicated biotech firm (DBF) is a BAF whose predominant activity involves
the application of biotech techniques to produce goods or services and/or the performance of biotech R&D. An
innovative biotech firm (IBF) is defined as a BAF that applies biotech techniques to implement new products or
processes. Among service activities, we consider R&D, market and other service oriented firms. In particular, a
biotechnology R&D firm with no product sales is categorized by national statistical offices in R&D service industry.
Targeted firms include firms classified as wholesalers, e.g. local operations of large foreign pharmaceutical firms whose
local affiliates perform biotechnology research, but which act mainly as wholesale distributors. Other types of service
firms included are those using biotech techniques to provide a service (e.g. waste management and environmental
remediation firms).
8
presented in Table 1.
A list of 83 Campania-based organizations and their collaborators is the foundation for three
complementary data-sets for the three types of relationship analysis. We cluster actors into
three categories: public, private and non-profit, these last being organizations with no
lucrative purpose, which can include private and public bodies. Firms are the largest group in
the database, but they are mainly firms or plants devoted only to production and services
(diagnostics and distribution), with only around a third also investing in research, 12 of which
are biotech-dedicated.
Table 1. Biotech actors’ distribution in Campania, 2004-2010
(Ownership and productive property) a
Public Private No-profit Total
Production 0 74% 0
Research 100% 36% 17%
Otherb 0 20% 83%
Units (Nr) 27 50 6 83
Source: our data
Legend: a Values expressed as a percentage of the total number of units (see the last row). b Service firms that provide routine services (for example: diagnostics and testing) or consultancy,
biotechnology equipment and other goods suppliers, and firms that distribute biotechnology products.
We include firms that are not focused only on biotech because the sector is not completely
defined within a unique industry and includes exploits sources (D'Amore and Vittoria 2009).
We do not include financial firms with interests in the regional biotech industry; there is a
general lack of availability of financial resources in Campania (Guiso 1998) and the extreme
risks related to biotech do not attract investors (Baeyens, Vanacker, and Manigart 2006).
Public actors are selected on the basis of their research interests and may be completely or
partly focused on biotechnology research. Public actors are not directly involved in biotech
production or services. The third cluster is formed of six non-profit organizations that include
both private and public actors and have a research and technological focus. The main public
actors are listed in the first three rows in Table 28 . During the data collection process we
frequently encountered actors not based in the region, but that interacted with the biotech
8 By collecting the codes related to the local actors involved in our survey this table enables interpretation of the network
visualizations.
9
community. A list of all the involved actors that does not include ‘interacting externals’ and
their relations, would not be representative of the real Campania biotech community. For this
reason, we include those external actors with close relations to the Campania biotech
community, but which are located outside the region. These external actors are grouped
together to indicate their weight in the regional biotech community (see Table 2, rows 3 to 7).
Their activities are relevant for regional companies’ dependence on and capacity to produce
and innovate.
Table 2. Key External and Regional Stakeholders labels of the Campania Biotech
Community
LABELS CATEGORIES
FEDERICO II, IIUNI, UNISA,
UNISANNIO, PARTHENOPE
Local Universities in Campania
IGB, ISA, IEOS, ISPAAM, ICB,
IBP, ISAFoM, IGV Portici, IBAF
Napoli
CNR centres located in Campania
SSR Regional Health System (Campania)
UNITA Other Italian Universities located out of the Campania
PROSTRA Foreign Universities and Research Centres
OUTRIC Research Centres out of the Campania
CNROUT Italian National Research Council located out of Campania
SSN National Health System
PRIVOUT Firms' Headquarter located out of Campania
ASSOUT Italian No-profit Research Centres out of Campania
TTNL Non-local services for Technological Transfer
Source: our whole-network survey
3.2 Selecting experimental indicators from case study evidence
Between the first and second research steps, we selected the most critical nodes in the
knowledge and innovation flow to use as the relational dimension for the subsequent
research phase.
The main results of the case study analysis are presented in Table 3. We distinguish
between internal and external, and formal and informal relations. The four relational
10
dimensions are explored for two classes of organization: public or non-profit, and private.
Focusing on internal procedures, the organizational setting is considered the first kind of
formal link among employees. Informal relations for the public and private spheres differ. In
public organizations there seems to be a wider range of opportunities for informal meetings
that foster knowledge flows, compared to private firms.
The external partners of the observed organizations differ and include local universities;
foreign universities; regional and national science and technology parks; national
professional associations; other private biotech firms; other firms; local public bodies;
international funding agencies; and the international scientific community.
Table 3. Inter and intra-organizational knowledge transfer modes
Public/no-profit Private
Formal Organisational setting Organisational setting
Internal Informal Meetings among the internal researchers
(Data Clubs and seminars);
Occasional meetings among visiting and
internal researchers;
Retreats
Exchanging visits between the CEO and
his previous academic colleagues
Formal Collaboration in public funded research
projects;
Sharing labs and facilities
Collaboration in public funded research
projects;
Sharing labs and facilities;
Equity participations
RE
LA
TIO
NA
L D
IME
NS
ION
S
External
Informal Scientific Director involvement in public
debates;
Meetings among internal and external
researchers;
Individual and organizational commitment
into the Scientific Community;
Board Interlocking
Board Interlocking
Social relations between the CEO and
other experts
Source: our case-studies
Formal relations between private and public organizations are enabled for different types of
links, for instance, collaboration on public funded research, and sharing laboratory facilities
lead to formalized rules to regulate the relationships. Equity participation is more common for
non-public organizations and is another predominant external formal link.
Beyond the roles of the key people in these areas, we identified many external informal
relationships that enable knowledge exchange. Some informal exchanges, such as
11
corporate visits or secondments of researchers, are positively promoted by management.
We identified eight relational dimensions/categories driving firms’ knowledge flows:
reciprocal informal visits of academic researchers; social networks via board interlocks,9
data clubs and seminars; retreats (short-term business visits); participation of scientific
directors in various kinds of public debates (i.e. TV talk shows, magazines, media interviews,
etc.); scientific office support for learning for young researchers; visiting researchers;
spontaneous/indirect social relations.
Among the external links selected for the second step in the analysis, we chose three basic
relational categories: A) research collaborations between private firms and public research
organizations, including universities, government laboratories (CNR) and research hospitals;
B) directorate-/board interlocks; and C) equity participation.
3.3 Measurement of variables, data sources and SNA interpretation
We transformed these three relational categories into measurable links for the local
biotechnological community. Starting from the initial database of 83 Campania-based
organizations we searched, case by case, for research collaborations between private firms
and public research organizations, including universities, government laboratories (CNR)
and research hospitals, on web sites and via telephone interviews, to obtain a complete
scheme of scientific links.
We constructed three relational data subsets. Public Funded Research Projects (PFRP)
includes 871 formal ties (resulting from shared participation in public funded research
projects) involving 83 biotech manufacturing and research organizations for the period 2004-
2010, and the 8 external actors listed in Table 2. The emerging networks explain the biotech
community’s degree of involvement in public funded research and identify individual
positions (centrality) by number (frequency) of links. A graph was constructed starting from a
1-mode matrix in which for each organization there is corresponding cooperation with
another actor; technically, all links are edges. There is a clustering that distinguishes public
(rectangles), private (triangles) and non-profit (circles) organizations, which improves the
capacity of the graph to highlight relationships and bridges between different actors.
The Board Interlocks (BI) data subset builds on board interlocks10 starting with initial data on
9 Board interlock defines a situation where the member of one company board sits on the board of another company.
10 Largely defined as ties among organizations through board membership (Borgatti and Foster 2003). These links are relevant
for success in business, but their human interactions and personal networking contributed to the firms' development
(Johannisson 1998). Many scholars refer to board interlocks to explain behavioural and organizational choices. Several
12
83 regional biotech actors. We searched on directors’ names for each
company/organization, using AIDA data. We found 249 observations (directors’ names)
where only a small proportion (6.8%) was affiliated/connected to ‘other’ organizations. The
emerging networks identified the most central people/organizations in the local biotech
community and the frequency of overlaps among the actors based on this type of informal
connection. We also have an undirected network where all links are edges because there is
a link only if one component is a board member of a private firm or a public manager.
We built a third data set using the same sources, but adding the names of company owners.
This constitutes our Equity Participation (EP) data subset. We found 202 observations
(owners’ names) where 30% were connected to ‘other’ organizations. The emerging
networks allow us to explore whether there is capital participation and the types of actors
involved in this relational choice.
Following the selected theoretical guide-lines (Kogut and Zander 1992, 1996; Nonaka and
Takeuchi 1995; von Krogh, Roos, and Kleine 1998; Choo and Bontis 2002) we analysed the
relational data by scrutinizing and analysing the network diagrams11 of the relationships
among group members. Our analysis of network diagrams identifies types of
individuals/organizations (nodes) in the network. Assessing node positions in formal and
informal networks provides evidence on the roles of the observed actors. A central
connector, for example, recognized by both the formal and informal descriptions, has
multiple connections and is well positioned in the formal hierarchy. Peripheral nodes need to
be examined to understand whether the individual is ‘stuck’ in the periphery or is
‘intentionally peripheral’ to promote change or to try to effect a move to another group. A
bridging node connects groups within the firm or across organizations (Cross and Parker
2004).
4. Tracing the complex Campania Biotech innovation system
4.1 The Campania Biotech Community: two case histories
Before introducing the SNA study we highlight some aspects of the Campania biotech
community. Descriptive statistics from published reports and the information emerging from
studies highlight the benefits of board interlocks to reduce uncertainty in particular environments (Carpenter and
Westphal 2001). 11 A network diagram consists of nodes, lines and arrows. In the analysis of internal networks, nodes are single individuals in
the organization. A continuous line indicates a formal relationship, and a dotted line an informal relationship. Arrows
represent the direction of the relationship (incoming arrows show that the person is a source of information; outgoing
arrows indicate that the team member seeks information from the linked party; bi-directional arrows indicate a reciprocal
communication relationship). In the analysis of external networks, nodes are represented by individual organizations.
13
two case histories provide a list of the main local stakeholders, and the actors’ behavioural
traits.
The local biotech community has a large presence of PROs, including research universities
(5), research hospitals and government laboratories. Among the 31 PRO in southern Italy
that belong to the Italian National Research Council (CNR), 18 (58%) are in Campania, and
7 are involved in research in the Life Sciences and Molecular Design. There are several
other publicly funded projects including three competence centres, two science and
technology parks, a regional agency, and other service organizations, which act as service
suppliers and promote contacts between organizations working in similar research fields,
and collaborations between universities and research institutions, and local manufacturing
firms.
Table 1 shows the distribution of biotech actors in 2004-2010 and the dominance of PROs.
Among private production activities, the operators are distributed in dedicated units (20, 9 of
which focus on research) and innovative firms (21 in total, 19 pharmaceutical and 2
diagnostics). The earliest established firms, from the mid 1990s, cover very specific market
segments. The absence of a venture capital community, and the strong presence of PROs,
have set the Campania biotech community on a specific (and unusual) development
trajectory.
Evidence of both weaknesses and strengths in the regional innovation system emerges
when we examine the histories of two local actors that are examples of the most successful
cases in this area. One is a major Italian non-profit organization that promotes research
aimed at diagnosis, prevention and cure of human genetic diseases. Its business model was
developed using: i) external funds and private donations, and ii) communication
management based on scientific reputation. A Neapolitan scientist working in the US, was
invited to direct the centre. It was established in an important bio-pole in northern Italy and,
in 2000, transferred to southern Italy, and is located in a science and technology research
area that includes several CNR institutes. This organization’s success is due to its
fundamental ability to adhere to its original mission and to increase the number of research
projects in its portfolio, and the number of its external partners and employed researchers. It
has produced various solutions based on the right mix of organization and finance. It is
supported by dynamic capacity at the individual level (e.g. the activities of its Scientific
Director) and throughout the organization as a whole (e.g. the solutions produced, learning,
and the capture of new knowledge via turnover of excellent young researchers).
The second case is an early-stage Italian biotechnology company that started operations in
2004 to develop new and highly specific agricultural products. Its start-up was enabled by an
14
incubation programme provided by a local development agency. It is a small autonomous
company organized according to a simple business model. Similar to many small local firms,
it is based on private and public funds (capital incentives) and the work of a star-scientist,
who embodies both entrepreneurial and scientific skills. She was born in and graduated from
Naples (Italy), then moved to the US to gain experience in the field of plant biology. There
are very few small private biotech firms and those that exist are based on strong individual
motivations and personal passion.
The external environment has a strong welfare tradition. Local manufacturers have not
established efficient communication links to allow the flow and diffusion of innovation. For
this small firm, the search for a market has established links with potential customers which
sometimes are geographically and culturally distant. This firm’s success is built on its ability
to maintain a stable market/profit over time and to increase employee numbers (from 4 to
18) and numbers of patents, through continuous development of technological applications
and links with external partners (other private non-local firms).
Although these two cases are different in size and organization, both demonstrate high
performance (number of scientific publications and an increasing number of formal
partnerships) based on their entrepreneurial ability.
These short case histories show what public incentives can achieve. Although they are
located in this regional area due to agglomeration economies related to monetary
externalities generated by public intervention, their co-location has had limited effects on
their development and success. The advantages of co-location (flexible labour market
opportunities, local employment, transfer of knowledge and technologies to local users) to
the success of these firms is based on their organizational abilities and affiliations to long-
distance networks.
4.2 Visualization of the KNs
We constructed graphical representations of the three network databases. We began with
formal links among organizations in the regional area. We mapped the links based on
participation in publicly funded research projects (Figure 1 (a) and (b)). The corresponding
data source is the PFRP sub-data set. We consider these connections as related to regional
identity. The Campania region has benefited from public incentives since 1950. As a
consequence, the local entrepreneurial culture (involving both public and private managers
and their relational activity) has been strongly influenced by the institutional context.
15
Figure 1(a) - Research collaborations under public funded projects
Source: PFRP sub-data set on our elaboration with Pajek 3.01
This is the biggest dataset related to relational behaviours in this area. It includes
connections among local university departments/institutes, non-profit foundations, and CNR
institutes, with external counterparts. External partners are mostly foreign universities and
research centres. The relational spaces are funded projects where the largest proportion of
the funding is from the EU or some other leading international funder. The public nature of
the majority of the local actors involved in these networks and their core in basic research
reveal that knowledge value is expressed in terms of scientific achievements. The basic
relational choices for the majority of the local biotech organizations is the need for research
collaboration with more advanced international groups, and accessing financial support.
At the centre of the graph, the nodes with more edges highlight the most relevant actors in
relation to the number of links (Figure 1(a)). Most of the actors with high levels of
collaboration are public or non-profit and are engaged in basic research. The most central
position is occupied by the node PROSTRA (foreign universities and research centres). This
collaborative relation with foreign research centres is a strength in the region because it
16
testifies to a pervasive scientific presence. The low level of cooperation with private firms
suggests a lack of interest in investing in new firms with high levels of biotech R&D
capitalization. Some isolated cases seem to be the result of chance rather than a planned
programme. Figure 1(b) is based on the same data subset. The alternative representation
highlights the differences between central and marginal nodes more clearly. By positioning
the more central actors in the upper part of the sociogram we can see the clear-cut division
between the public and private actors in the region.
Figure 1(b) - Research collaborations under public funded projects
Source: PFRP sub-data set on our elaboration with Pajek 3.01
Some descriptive statistics (see Table 4) provide a better understanding of the complex
relational scenario depicted in Figure 1. Table 4 shows the collection of SNA indicators that
emerge from elaboration of the PFRP relational data subset. The statistical indicators are
provided in the rows in the table. The columns distinguish between private and non-private
actors, with and without external actors.
17
Table 4. SNA statistics emerging from the PFRP sub-data set
Index With external Without external
Private No-Private Total Private No-Private Total
Number 51 40 91 50 33 83
Isolated 25 2 27 33 2 35
Average Density 0.026 0.164 0.046 0.015 0.170 0.036
Closeness 0.125 0.394 0.186 0.069 0.216 0.127
Betweenness 0.006 0.033 0.010 0.001 0.016 0.007
Articulation Points Privout 8 IGB 4
Campania INN 3
IGB 4
Ceinge 3
Campania INN 3
Structural Holes Privout 1 CNROUT 2
UNISA 3
UNISA 1
Campania INN 2
Federico II 3
Source: our whole-network survey on PFRP dataset elaborated with Pajek 3.01
This division shows the effect of external actors on the biotech community. The first statistics
show the number of isolated nodes. They are concentrated among private actors (about
50%). Only a half of the local private organizations are involved in research collaborations
with research centres, and public and non-profit firms are engaged in research; only two
actors are not integrated in the network. If we do not consider external actors the number of
private isolated actors increases; in other words, among the private firms there are some
that collaborate only with external partners.
The average density represents the intensity of the relations in nodes.12 The number of links
12 The indexes used in the table are: Average density :
)1(
*2
−=∆
gg
L
(1)
where L is the sum of all links and g represents the number of nodes in the network. Average Closeness :
18
is higher for non-private than for private organizations. This means that knowledge diffusion
is faster and wider in non-private organizations. If we exclude external actors the numbers
are lower, which suggests that the rate of diffusion is slower.
Closeness and betweenness confirm what was highlighted in degree analysis. First public
organizations are closer to other organizations and at the same time are more relevant in the
knowledge transfer process and occupy strategic positions. This is confirmed also if we
exclude external actors. However, if we concentrate on bridging nodes we obtain different
results. First the most important bridge is PRIVOUT, which represents private firms located
outside the Campania region. It links eight firms, which means that without their interventions
more firms would be isolated. PRIVOUT is the most important bridging node in the network.
The strong presence of international research suggests two things. On the one hand,
research is concentrated mainly in some important research centres based in Campania,
which attract and interact with international counterparts. These centres are probably at the
frontier of research in their respective fields and produce most of the innovation, even if
mainly basic research. On the other hand, the intensive interaction with external research
centres compared to regional firms favours the flow of basic biotech innovation across the
region.
Next we consider board interlocks. These kinds of informal connections are rare in the local
biotech community and the graph highlights the prevalence of isolated points. Our research
provides evidence of strong polarization of these connections among local public actors
(Figure 2). These actors are either cases of academic scientific directors who sit on the
boards of non-profit research labs, or university professors who also manage publicly funded
research projects or organizations. The most common reason for their presence is their
scientific reputation. Moreover, excluding some rare cases, the few links are mainly among
public and non-profit organizations. The small number of relations testifies to low levels of
collaborative behaviour among private and public organizations, which means
]),([
1)(
1�
=
−=g
jji
i
nnd
gncl
(2)
where n represent node i and d is the distance between node i and node j. Average Betweenness:
]2/)2)(1[(
)()(
−−=′
gg
nCnC iB
iB
(3)
is the average of all betweenness index (Wasserman and Faust 1994).
19
correspondingly low levels of technology transfer in this region.
Figure 2. Links in Board Interlocking
Source: BI sub-data set on our elaboration with Pajek 3.01
Figure 3 shows the KN based on equity participation. The first step in our study allowed us to
isolate two basic patterns for this kind of participation. There is a virtual cycle driven by a
particular professional figure with both scientific and entrepreneurial abilities. These star-
scientists are able to establish their own enterprises (a few cases here), to engender the
trust of external experts (often technician or engineers in large established firms) and
establish stable external R&D commitment and cooperation based on capital sharing. The
presence of external investors (see node PRIVOUT in Figure 3) is based on monetary
externalities created by public incentives. This kind of territorial attractiveness, more diffused
in the observed cases, is able to capture only the localization of autonomous units that
remain isolated in terms of transferring knowledge. Also, in this case, we observe many
isolated points which indicate low levels of collaboration among actors. The property
20
clustering is of particular relevance in interpreting this graph. On the one hand, public and
non-profit organizations seem to be more dense, which implies a more collaborative
environment and, as a consequence, higher capacity and higher likelihood of research
results circulating. On the other hand, we find that private organizations are completely
separate from public ones, with little collaboration between the two networks. The most
important bridge between the two networks is due to the node PRIVOUT (firms' headquarter
located outside Campania). Again, an external actor acts as a unique bridge between public
and private organizations and a technology transfer star. The key role assumed by external
capital means that there is an interest in technology transfer from basic research produced in
Campania biotech to production, but also a problem of appropriation of research results, and
a risk that acquisition of R&D from an external actor takes away investment from Campania
to the benefit of more prosperous regions.
Figure 3. Formal connections for equity participation
Source: EP sub-data set on our elaboration with Pajek 3.01
Comparing the EP and PFRP networks we can deduce that the central role played by
21
PRIVOUT is not limited to knowledge flows; it serves also to lock in knowledge to the firm.
Many firms that collaborate with external actors are bound to them by equity agreements.
This allows external actors to benefit from part of the local, financial and R&D results.
4.3 Results
The relative higher portion of PROs focused on biotech basic research is a structural
element that differentiates the Campania innovation system from the innovation systems in
the rest of Southern Italy. The inherent knowledge creation process is distributed on a
regional, national and international basis.
The critical relationships for the acquisition of knowledge, which involve the local PROs, are
given by participation in public funded research projects (PFRP). This relational mode is the
inherited relational dimension of a publicly supported area such as the Campania region.
The PFRP network structure shows the higher degree of variety of local actors involved and
a strong centrality of overseas academic institutions (PROSTRA). This high dynamism has
been addressed by EU policies published since 1990. The leadership positions achieved by
some local research centres are evidence of their capacity on their own to follow an
endogenous development path.
An equally higher central position for public actors is registered in the other two networks
built on board interlocks (BI) and equity participation (EP). The expected greater involvement
of private firms in this field does not emerge. Local private firms are mostly in marginal
network positions. In this respect Figure 2 showing that the informal relations (through the
board interlocking) involve mainly public research actors is interesting. In sharing the
informal relational dimensions the public actors appear more interconnected. The absence of
private actors in these informal communities can be considered a system-failure. Also figure
3 showing the clear connecting position covered by external private actors (PRIVOUT) is
interesting. The network based on EPs is weak because of the critical bridging role played
by the external private firms. Thus the PRIVOUT is decisive in the whole regional innovation
system in connecting the two communities of actors involved in basic and applied
research.13
13 The potential gains from bridging different parts of a network were important in the early work of Granovetter (1974) and are
central to the notion of structural holes developed by Burt (1992). In recent years, a number of empirical studies show
that individuals or organizations who bridge `structural holes' in networks gain significant payoffs advantages (Goyal and
Vega-Redondo 2005).
22
5. Conclusions
The present study used and examined the relevance of the Constructing Regional
Advantage (CRA) framework and its and prescriptions for constructing regional advantage
with specific reference to the case of knowledge intensive industries in peripheral regions.
By analysing the capacity of the CRA framework to investigate the specific case of the
biotechnology in a peripheral region of Southern Italy, we found that:
To analyse the local biotechnology development process an industrial knowledge base
approach is useful;
The above mentioned research approach for measuring knowledge flows requires the
construction of experimental indicators;
Relational main categories as experimental indicators of knowledge flows must be derived
from in-depth case study research. In these case studies a longitudinal approach must be
followed. Data collection and analysis must consider both the formal and informal relational
dimensions.
A whole-network survey based on experimental indicators can reveal the extent of the local
distributed KN.
The evidence on Campania’s distributed KN is as follows:
The forces driving the local KN structure are constituted by the public research actors. A
wider innovation potential is polarized around the local PROs and close to their regional,
national and international communities.
The local private enterprises are weakly inter-connected. A crucial role is played by external
private firms in acting as a unique link between local firms and the core knowledge base in
the region.
We argued that in the Campania biotechnology community the effective forces at the base of
the local KN structure are represented by the links created by public funded research
collaborations. These collaborations primarily involve the local basic research actors and
many international research centres. A large proportion of the basic research conducted
locally is exploited by the international community. In this wider environment, local, basic
research organizations can find the real opportunities for the application of their scientific
discoveries.
Our research proposes some methodological guide-lines for achieving more effective KNs
involving links between specific local actors and international organizations, and allowing a
redefinition of the local biotech community through an analysis of the actors’ relational
behaviour. There is a belief that clustering should be encouraged and that such behaviour
will result in a good mix of local and specific advantage (e.g. institutional environment,
23
dominant management culture, local or situated knowledge flows). Since, according to
organization studies, geographical proximity is the critical variable for successful clustering,
the spatial dimension has received much attention. However, our findings show a strong role
of public incentives in the initial localization choices of biotech actors. While weak relations
are established over time, the private biotech community remains mainly isolated from the
bulk of public basic research actors. Rather, the external actor (often a large private pharma)
is the critical bridge between local firms and the basic research centres.
The few excellent research centres in Campania (mainly not-for-profit organizations)
collaborate with the international scientific community. The local community specialized in
basic research produces publications and scientific discoveries rather than being
manufacturers/appliers of this new knowledge. Physical proximity can be both a strong and a
weak environmental condition for knowledge creation in a biotech community. It plays a part
in the initial phases of a biotech organization’s life cycle; but strong specialization in basic
research among the local actors can promote a network structure that includes external and
more distant counterparts.
Finally, more research is needed into network governance in peripheral biotechnology
communities supported by empirical evidence on the different role played by short and long
distance networks. In our specific setting, for example, we need a deeper analysis of the
hidden power (or potential) of KN related to effective interaction among local scientists and
international counterparts, through differentiation among specific research fields.
References
AIDA/Bureau Van Dijk (Company accounts, ratios, activities, ownership, management and
ratios for 1,000,000 Italian companies; accessed December 5, 2012). http://aida.bvdep.com/
Amin, A. and Cohendet, P. 2003. Architectures of Knowledge. Oxford: Oxford University
Press.
Argote, L. 1999. Organizational learning: Creating, retaining and transferring knowledge.
Norwell, MA: Kluwer.
Arundel, A., and A. Constantelou. 2006. “Conventional and experimental indicators of
knowledge flows”. In Knowledge Flows in European Industry, edited by Y. Caloghirou, A.
Constantelou, and Nicholas S. Vonortas, 45-66. New York: Routledge.
24
Arora, A., and A. Gambardella. 1994. “The Changing Technology of Technical Change:
General and Abstract Knowledge and the Division of Innovative Labor.” Research Policy 23:
523-532.
Baeyens, K., Vanacker, T., and S. Manigart. 2006. “Venture Capitalists' Selection Process:
The Case of Biotechnology Proposals.” International Journal of Technology Management
34(1/2), 28–46.
Barley, S. R., Freeman, J., and R. C. Hybels. 1992. “Strategic alliances in commercial
biotechnology.” In Networks and Organizations: Structure, Form and Action, edited by N.
Nohria, and R. Eccles, 311-347. Boston, MA: Harvard Business School Press.
Bigliardi, B., Nosella, A., and C. Verbano. 2005. “Business models in Italian biotechnology
industry: a quantitative analysis.” Technovation 25(11): 1299-1306.
BiotechGate Company Data Base/Biotech in Italy/Assobiotec (Biotechnology/Life Sciences
companies located within a country; accessed December 5, 2012).
http://www.italianbiotech.com/it/db/
Borgatti, S. P., and P.C. Foster. 2003. “The Network Paradigm in Operational Research: A
Review of Topology.” Journal of Management 26: 991-1013.
Boshma, R.A. and ter Wal, A.L.J. 2005. “Knowledge networks in an industrial district in the
South of Italy. The case of the footwear district of Barletta.” Paper presented at workshop on
“Regional Innovativiness”, Max Plank Institute for Research into Economic System, Jena,
September 2005.
Burt, R.S. 1992. Structural Holes the Social Structure of Competition. Cambridge MA:
Harvard University Press.
Cannavacciuolo, L., Capaldo, G., and P. Rippa. 2012. “Social Networks and the
entrepreneurial process in molecular biotechnology in Southern Italy.” In Social Networks,
Innovation and the Knowledge Economy, edited by I. Salavisa and M. Fontes, 163-84.
London: Routledge.
Carpenter, M., and J. Westphal. 2001. “The Strategic Context of External Network Ties.”
Academy of Management Journal 4: 639-660.
Carrington P.J., Scott J., and S. Wasserman. 2005. Models and Methods in Social Network
Analysis. New York: Cambridge University Press.
Castaldi, L. 2009. “L'industria del biotech.” In Le imprese biotech Italiane, strategie e
performance [The Italian Biotechnological Firms, Strategies and Performance]. Edited by M.
Sorrentino, Bologna (Italy): Il Mulino, 45-81.
Choo, C.W., and N. Bontis. 2002. The Strategic Management of Intellectual Capital and
Organizational Knowledge. New York: Oxford University Press.
25
Cooke, P. 1998. “Introduction: Origins of the Concept.” In Regional Innovation System,
edited by Braczyk, H., Cooke, P. and Heidenreich, M. 2-25. London, UCL Press.
Cooke, P. 2001. “Regional Innovation Systems, Clusters, and Knowledge Economy.”
Industrial and Corporate Change 10 (4): 945-974.
Cooke, P. 2004. “The role of research in regional innovation systems: new models meeting
knowledge economy demands.” International Journal of Technology Management 28 (3-6):
507-533.
Cross, R., Borgatti, P.S., and A. Parker. 2002. “Making Invisible work visible: using social
network analysis to support strategic collaboration.” California Management Review 44 (2):
25 – 46.
Cross, R., and A. Parker. 2004. The hidden power of social networks: understanding how
work really gets done in organizations. Boston MA: Harvard Business School Press.
Cummings, J. 2003. “Knowledge Sharing: A Review of the Literature.” The World Bank OED,
Washington, D.C.
D’Amore, R., and M.P. Vittoria. 2009. “Assessing Statistical Standards for Emerging
Industries. Applying OECD Statistical Codes to Italian Biotech Population Lists.” World
Review of Science, Technology and Sustainable Development 6 (2-4): 233-243.
De Nooy, W., Mrvar, A., and V. Batagelj. 2005. Exploratory Network Analysis with Pajek.
Cambridge: Cambridge University Press.
Doloreux, D., and S. Dionne. 2008. “Is regional innovation system development possible in
peripheral regions? Some evidence from the case of La Pocatière, Canada.”
Entrepreneurship & Regional Development 20: 259-283.? Not in text
European Commission 2006. “Constructing regional advantage: principles – perspectives –
policies.” www.dime-eu.org/files/active/0/regional_advantage_FINAL.pdf (23/11/2008)
Gallaud, D., and A. Torre. 2005. “Geographical proximity and the diffusion of knowledge.
The case of SME’s in biotechnology.” In Rethinking Regional Innovation and Change: Path
Dependency or Regional breakthrough, edited by G. Fuchs and P. Shapira, 127-146.
Boston: Springer.
Gertler, S., M., and M.Y. Levitte. 2005. “Local Nodes in Global Networks: The Geography of
Knowledge Flows in Biotechnology Innovation.” Industry and Innovation 12:487-502.
Goyal, S. and Vega-Redondo, F. 2005. “Network formation and social coordination.” Games
and Economic Behaviour 50 (2), 178-207.
Granovetter, M. 1974. Getting a Job: A Study of Contacts and Careers. Cambridge MA,
Harvard University Press.
26
Grupp, H. 1998. Foundations of the Economics of Innovation: Theory, Mesurement and
Practice. Cheltenham: Edward Elgar.
Guiso, L. 1998. “High-tech firms and credit rationing.” Journal of Economic Behaviour &
Organization 35: 39-59.
Huber, G.P. 1991. “Organizational learning: The contributing processes and literatures.”
Organization Science 2(1): 88-115.
Iorio, R., Labory, S., and D. Paci. 2012. “Research Collaboration and Publication's Citation
Impact: Evidence in Italian Biotech Sector.” Rivista Italiana degli Economisti 1: 75-100.
IPI, Istituto per la Promozione Industriale. 2007. Il Settore delle Biotecnologie in Italia [The
Biotechnology Sector in Italy]. Accessed January 20.
http://www.eurotransbio.eu/lw_resource/datapool/__pages/pdp_38/Biotecnologie_in_Italia.p
df.
Johannisson, B. 1998. “Personal networks in emerging knowledge-based firms: spatial and
functional patterns.” Entrepreneurship & Regional Development 10(4): 297-312.
Karlsen, J., Isaksen, A., and O.R. Spilling. 2011. “The challenge of constructing regional
advantages in peripheral areas: The case of marine biotechnology in Tromsø, Norway.”
Entrepreneurship & Regional Development 23: 235-257.
Kenney, M. 1986. Biotechnology: the University – Industrial Complex. New Haven: Yale
University Press.
Kingsley, G., and E.J. Malecki. 2004. “Networking for Competitiveness.” Small Business
Economics 23(1): 71-84.
Kogut, B. 2000. “The network as knowledge: generative rules and the emergence of
structure.” Strategic Management Journal 21(3): 405-425.
Kogut, B., and U. Zander. 1992. “Knowledge of the firm, combinative capabilities, and the
replication of technology.” Organization Science 3(3): 383-397.
Kogut, B., and U. Zander. 1996. “What firms do? Coordination, Identity and Learning.”
Organization Science 7: 502-518.
Kolaczyk, E.D. 2009. Statistical Analysis of Network Data. New York. Springer.
Kreiner, K., and M.Schultz. 1993. “Informal collaboration in R&D: the formation of networks
across organizations.” Organization Studies 14(2): 189-209.
Loane, S. 2006. “The role of the internet in the internationalization of small and medium-
sized companies.” Journal of International Entrepreneurship 3: 263-277.
Maleki, E. 2002. The internet age: not the end of geography, in Promoting Local Growth:
Process, Practice and Policy, edited by D. Felsenstein, and M.J. Taylor, 227-253. Aldershot:
Acshgate.
27
Nohria, N., and R. Eccles. 1992 Networks and Organizations: Structure, Form, and Action.
Boston: Harvard Business School Press.
Nonaka, I., and H. Takeuchi. 1995. The Knowledge-creating company. New York: Oxford
University Press.
Nosella, A., Petroni, G., and C. Verbano. 2005. “Characteristics of the Italian biotechnology
industry and new business models: the initial results of an empirical study.” Technovation
25(8): 841-855.
OECD, 2005. Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data. 3rd
ed. Paris OECD.
Onetti, A., and A. Zucchella. 2007. “Le biotecnologie in Italia: dimensioni del settore e
prospettive di evoluzione.” [Biotechnologies in Italy: Industry and Evolution] Economia e
Politica Industriale 3: 215-233.
Orsenigo, L. 1989. The Emergence of Biotechnology: Institutions and Markets in Industrial
Innovation. New York: St. Martin’s Press.
Orsenigo, L. 2001. “The (Failed) Development of a Biotechnology Cluster: The Case of
Lombardy.” Small Business Economics 17(1): 77-92.
Passaro, R., and M. P. Vittoria. 2000. “Modalità di nascita e di evoluzione delle imprese di
biotecnologia in Italia.” [Biotechnological Firms in Italy: Birth and Evolutionary Modes]
Economia e Politica Industriale 108: 69-96.
Persico, P., and M. P. Vittoria. 2010. “Social Enterprise in Dynamic Capabilities
management model.” paper presented at the annual Satter Conference on Social
Entrepreneurship, New York University, Stern School of Business, November 3-5.
Peters, T. J., and R. H. Waterman. 1982 In Search of Excellence. Lessons from America’s
Best Run Companies. New York: Harper & Row.
Porter K., Whittington K.B., and W.W. Powell. 2005. The Institutional Embeddedness of
High-Tech Regions: Relational Foundations of the Boston Biotechnology Community, in
Clusters, Networks, and Innovation, edited by S. Breschi, and F. Malerba, 261-296. New
York: Oxford University Press.
Powell, W. W. 1996. “Inter – organizational collaboration in the biotechnology industry.”
Journal of Institutional and Theoretical Economics 120(1): 197-215.
Powell, W. W. 1998. “Learning from collaboration: knowledge and networks in the
biotechnology industry and pharmaceutical industries.” California Management Review
40(3), 228-240.
28
Powell, W.W., and P. Brantley. 1992. “Competitive cooperation in biotechnology: learning
through networks?” In Networks and Organizations, edited by R. Eccles, and N. Nohria, 366-
394. Boston: Harvard University Press.
Powell, W.W., and S. Grodal. 2006. “Networks of Innovators.” in The Oxford Handbook of
Innovation, edited by J. Fagerberg, D. Mowery, and R.R. Nelson, 56-85. Oxford New York:
Oxford University Press.
Powell, W. W., Koput, K. W., and L. Smith-Doerr. 1996. “Interorganizational collaboration
and the locus of innovation: networks of learning in biotechnology.” Administrative Science
Quarterly 41(1), 116-145.
Powell, W. W., and J. Owen-Smith. 1998. “Universities and the Market for Intellectual
Property in the Life Sciences.” Journal of Policy Analysis and Management 17(2): 253-277.
Powell, W.W., and J. Owen-Smith. 2000. “Universities as creators and retailers of intellectual
property: life–sciences research and commercial development.” In To Profit or Not to Profit,
edited by B.A. Weisbrod, 169-194. Cambridge UK: Cambridge University Press.
Salman, N., and A. L. Saives. 2005. “Indirect networks: an intangible resource for
biotechnology innovation.” R&D Management 35(2): 203–215.
Saxenian A. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route
128. Cambridge, Mass.: Harvard University Press.
Scott, J. 2000. Social Network Analysis (2nd ed.). Thousand Oaks, CA: Sage.
Simone, C., and L. Proietti. 2012. “Wholly proprietary versus wholly open knowledge
strategies: some empirical evidences from Italian biotech firms.” Journal of Management &
Governance 16(3): 425-447.
Tödtling, F. and Trippl, M. 2005. “One size fits all? Towards a differentiated regional
innovation policy approach.” Research Policy 34, 1203-1219.
Van Geenhuizen, M. 2005. ICT, Location Dynamics and the Future of Cities (Delft/The
Hague: Faculty of Technology, Policy and Management, Delft University of
Technology/Netherlands Science Foundation (www.nwo.nl/nwohome.nsf).
Van Geenhuizen, M. 2008. “Knowledge Networks of Young Innovators in the Urban
Economy: Biotechnology as a Case Study.” Entrepreneurship & Regional Development 20
(2): 161-183.
Van Wijk, R., Van den Bosh, A.J., and H. W. Volberda. 2003. “Knowledge and Networks.” In
The Blackwell Handbook of Organizational Learning and Knowledge Management, edited by
M. Easterby-Smith, and M.A. Lyles, 428-453. Oxford: Blackwell Publishers.
29
von Krogh, G., Roos, J. and D. Kleine, eds. 1998. Knowing in Firms. London:
SageWasserman, S., and K. Faust. 1994. Social Network Analysis: Methods and
Applications. Cambridge: Cambridge University Press.
Yin, R. K. 2003. Case Study Research: Design and Methods. (3rd ed.). Thousand Oaks, CA:
Sage.
