Paper to be presented at the DRUID Summer Conference 2006 on
KNOWLEDGE, INNOVATION AND COMPETITIVENESS: DYNAMICS OF
FIRMS, NETWORKS, REGIONS AND INSTITUTIONS
Copenhagen, Denmark, June 18-20, 2006
Track A: Networks and Knowledge Creation, Accumulation and Exchange
THE DETERMINANTS OF ORGANISATIONAL KNOWLEDGE CREATION IN THE CONTEXT OF R&D COOPERATION.
THE ROLE OF ABSORPTIVE CAPACITY
Ioanna Kastelli
Laboratory of Industrial and Energy Economics, National Technical University of Athens
9, Heroon Polytechniou, 15780 Athens, Greece. Tel. +30210 772 3209, Fax +30210 772 3155
e-mail: [email protected]
February 22, 2006
Abstract
This paper considers R&D cooperation as an organisational form that amplifies and crystallises knowledge to higher levels through interaction. It is shown that absorptive capacity is the main condition for the firm to benefit in this context. Absorptive capacity is studied in terms of its antecedents and constituent elements aiming to avoid tautological definitions and is measured not with the conventional way based on R&D expenditure but on experience and processes for acquiring, assimilating, transforming and exploiting knowledge. These processes encompass R&D and organisational practices for knowledge diffusion as well as interacting and networking activities.
Keywords: absorptive capacity, R&D cooperation, knowledge creation.
JEL – codes: O33, O31, O32, L24.
Acknowledgements
This paper draws on the research work that has been carried out in the context of the STEP
TO RJVs project under the supervision of Professor Yannis Caloghirou at the National
Technical University of Athens. Financial contribution from the EC is gratefully acknowledged.
A previous version of the paper was presented at the DRUID Winter Conference 2006. I am
grateful to Brian Loasby, Ina Drejer, Line Gry Knudsen and other DRUID participants for
important and constructive comments.
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1. Introduction
Interaction is today emphasized in economic literature as the key element for
accumulation and creation of knowledge (Teece, Pisano, 1994, Nonaka, 1991, 1994,
Nonaka, Takeushi, 1995, Nonaka, Konno, 1998, Nonaka, Toyama, Nagata, 2000). It
is suggested that firms consider learning through a diversity of contacts as a way to
generate positive returns in a long-term perspective (Foss, 1999, Hagedoorn,
Duysters, 1997).
An important body of the economic and managerial literature that can be grouped
under the name of knowledge-based approach, considers cooperation both inside
and outside the boundaries of the firm as one interactive mechanism and a field
where knowledge flows take place. This approach is relating cooperation to learning,
development of capabilities giving a prominent place to knowledge, and opposing to
“neoclassical” perspectives (industrial organisation and transaction cost) that are
interested in market structure or cost minimisation (for a literature review Caloghirou
et al. 2003, Hagedoorn et al., 2000, Combs, Ketchen, 1999, Vonortas, 1997, Kogut,
1988).
In this context the paper argues that it is not self-evident that all firms benefit to a
considerable extent from their involvement in R&D cooperation. In fact those firms
that possess the capabilities to assimilate and exploit knowledge outside their
boundaries in order to transform it into new products, processes or services benefit
most from cooperation in terms of organisational learning and knowledge creation. In
this respect, our argument points to the role of absorptive capacity and to actions and
mechanisms improving that particular capability. As a consequence of this
proposition, the paper goes beyond the cost minimisation approach of research
cooperation; it argues that the dynamic development of interactive practices and
processes within the cooperation improves assimilation and exploitation of
knowledge and enables a positive outcome of cooperation.
The contribution of this paper is three fold: a) it undertakes a micro-economic
analysis of the effects of R&D cooperation, relating organisational knowledge
creation to firm’s capabilities, b) it studies absorptive capacity in terms of its
antecedents and constituent elements, enhancing the “traditional” definition and the
way of measuring it, c)it attempts to avoid tautological definitions according to which
success stories rely on capabilities and vice versa and to avoid as well, bias in terms
2
of benefits from cooperation – by taking into consideration the dark side of
cooperations.
The structure of the paper is the following. The next section undertakes a literature
review and synthesis on the determinants of organisational knowledge creation in the
context of cooperation. The emphasis is given to setting the theoretical basis for
studying the role of absorptive capacity and factors that enable or constrain
knowledge flows. The third section presents the econometric analysis on the data set
of European subsidised research cooperations and the results. The final section
summarises the main conclusions and points to some policy implications.
2. Determinants of organisational knowledge creation in the context of research cooperation
By organisational knowledge creation we mean the capability of a firm (the firm is the
object of analysis here) to create new knowledge, disseminate it throughout the
organisation and embody it in products, services and systems (Nonaka, Takeushi,
1995).
The study of organisational knowledge creation in the context of research
cooperation, relies on the following general assumptions, that stem from an
evolutionary and organisational learning perspective:
a) the firm is conceived as an organisation that creates new knowledge in order to
redefine both problems and solutions re-creating its environment– and not as a
mechanism of information processing in order to adapt to new circumstances.
This assumption helps to explain innovation.
b) the process of organisational knowledge creation is a path-dependent and
interactive process. Interaction concerns the types of knowledge –tacit and
codified- and the entities involved –individuals, groups, organisations.
c) cooperation can be considered an organisational form where interactions are
formed (communities of interactions) and knowledge flows are shaped,
constituting the basis for knowledge transfer, creation of knowledge,
enhancement of the knowledge base and crystallisation to higher levels of entities
(Nonaka, 1994, Escribá-Esteve, Urra-Urbieta, 2002).
3
An interesting question concerning the relation of R&D cooperation to technological
development, knowledge creation, diffusion and learning is about the conditions for
organisational knowledge creation.
Evolutionary theories underline the role of organisational learning processes and
capabilities in the evolution and dynamics of firms. Cooperation may play an
important role for the firm in acquiring or developing new knowledge with critical
implications for its competitive position, but this depends crucially on firm’s
idiosyncratic characteristics, that is on organisational capabilities and processes.
It is important however to consider that cooperation may involve competitive
behaviour and thus result to one firm’s attempt to use its partners’ know-how for
private benefit. Assumptions stemming from more “neoclassical” perspectives on
research cooperation, relate this organizational form with opportunistic behaviour,
conflict of interests and inappropriabilities leading to failure situations. These
perspectives do not offer I think, a fertile ground for studying cooperation as a means
for organizational knowledge creation as they conceive the firm as an incentive
structure and are not interested in productive capabilities and the potential
exploitation of different sources of knowledge for developing and applying firm’ s
knowledge base and specific capabilities. But this is not to deny that opportunism
and competitive conflicts may be of importance and so, in this paper, my intention is
to take into account the “dark side” of cooperation as perceived especially in
transaction cost theory, in an attempt to avoid bias in terms of benefits from
cooperation.
A first hypothesis according to the above can be formulated as follows:
H1: The dynamic capabilities of the firm are the main determinants for organisational knowledge creation in the context of cooperation. Although opportunistic and competitive behaviour may have a negative impact they do not constitute the critical determinant.
The cornerstone of organisational capabilities that determines the extent to which a
firm will benefit from its involvement in R&D cooperation is absorptive capacity. Other
elements relevant for organisational knowledge creation and knowledge flows in the
context of cooperation, relate to partners’ behaviour and relationships, to the type of
interaction and to the business environment.
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Absorptive capacity: opening the black box.
A number of empirical studies, investigating the effects from R&D cooperation in a
knowledge-based perspective, related them to the capabilities of the participating firm
and more specifically to absorptive capacity.
Many economists had suggested that most innovations are based more on the
access to external sources of knowledge than to invention (March, Simon, 1958,
Lundvall, 1985, 1988, 1993, Von Hippel, 1988).
Cohen and Levinthal (1989, 1990) studied the ability to exploit external knowledge as
being critical to innovative performance, introducing the concept of absorptive
capacity at the level of the firm. Thus, absorptive capacity refers not only to the
acquisition and assimilation of information by an organisation but also to the
organisation’s ability to exploit it. Its main attributes are that it is path dependent and
cumulative (Cohen, Levinthal, 1990). According to both authors, absorptive capacity
relies on two important elements: the existing knowledge base and the intensity of
efforts made for the development of technological capabilities (ibid). The existing
knowledge base increases the ability to search, recognise and represent a problem
as well as assimilate and use new knowledge for problem solving. The intensity of
effort or commitment in problem solving refers to the amount of energy that
organisational members devote to solve problems (Kim, 1999). The effectiveness of
cooperation depends on the firm’s ability to recognise the value of new external
information, assimilate it and apply it to new commercial ends (Cohen, Levinthal,
1990). It follows that firms that participate in cooperative R&D agreements, need to
be prepared to invest internally for improvement of their absorptive capacity in order
to be able to effectively exploit the knowledge and information diffused or created
within the research network (ibid). Undoubtedly, when a partner in a R&D consortium
possesses the skills and knowledge needed and establishes mechanisms of
knowledge management, then the knowledge transfer that may occur in the context
of this network may be exploited easier by the specific partner. The capability of a
firm to absorb knowledge and information from external sources is one of the pillars
in the process of transformation of knowledge and information into new knowledge
and its conversion into new value.
Since Cohen and Levinthal’s seminal work, there have been a lot of theoretical and
empirical research on that issue. These works subscribe either to mainstream
(Veugelers, 1997, Katsoulacos, Ulph, 1998, Kamien, Zang, 2000, Grünfeld, 2003) or
to knowledge-based approaches (Mowery et al., 1996, Cockburn,Henderson, 1998,
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Lane, Lubatkin, 1998, Lane, Salk, Lyles, 2001). However, definitions and
operationalisations of the concept vary and as Zahra and George pointed out “…it is
unclear if the measures adopted converge to capture similar attributes of the same
construct” (italics are ours) (2002: 186). The two authors build upon the work of
Eisenhart and Martin (2000) referring to dynamic capabilities and define absorptive
capacity as a set of organisational routines and processes by which firms acquire,
assimilate, transform and exploit knowledge to produce a dynamic organisational
capability (Zahra, George, 2002: 186). Their contribution is two fold: first they
analysed absorptive capacity in its constituent dimensions and elements and second
they tried to overcome the critic made to dynamic capabilities theory as being
conceptually vague and tautological (Williamson, 1999, Priem, Butler, 2000).
However, what is still lacking in their work is the application of their conceptualisation
to real evidence.
In order to empirically investigate the role of absorptive capacity on organisational
knowledge creation in the context of cooperation I will build on Zahra and George
definition of absorptive capacity and try to identify its determinants. I will then
propose a way to measure it capturing its different constituents.
The main rationale behind this effort is to identify the elements and processes that
help a firm acquire, assimilate, transform and exploit knowledge, especially in the
context of cooperation and try to measure them. I am interested not only in internal
processes of the firm such as R&D activity but also in organisational practices
supporting knowledge diffusion within the firm and in interactions of the firm with
other actors. This approach goes beyond the “traditional” way of defining and
measuring absorptive capacity.
Determinants can thus be grouped under four factors; prior knowledge and
experience, learning and creative mechanisms, diffusion mechanisms and interactive
mechanisms.
Prior knowledge and experience influence the development of future capabilities and
facilitate the use of new knowledge. Pre-existing knowledge determines to some
extend the new opportunities arising from the interaction with external knowledge.
The technical background of the firm and its existing range of knowledge and skills
strongly condition what the firm is able to do in the future. Technical change is a
cumulative and path-dependent process and what the firm can do technically is
conditioned by what it has been able to do technically in the past (Pavitt, 1984).
6
Absorptive capacity has also to be related to limits to cognition, as it is domain-limited
and shaped by experience (Loasby, 2006, 2005). If external sources of knowledge
supplement our own limited cognitive abilities, new knowledge to be created depends
on the connection between what is accessed and what is already possessed by the
firm.
Additionally, previous experience from collaborating may complement firm’s
capability to take advantage from its participation in cooperation. It forms a capability
to negotiate, communicate and manage external relationships and an establishment
of a culture to cooperate and share knowledge and experience (Gulati, 1995, Gulati
et al., 2000, Anand, Khanna, 2000, Kastelli et al., 2001, 2004).
Learning and creative mechanisms: Knowledge from external sources requires
specific efforts for assimilation and internalisation. These efforts relate to R&D
processes, to organisational practices for information and knowledge storage,
processing and exploitation, to managerial practices that trigger new ideas, to
practices that capture and deploy information from clients, suppliers (e.g. formal or
informal communication) or even competitors. Any efforts to exploit sources of
knowledge such as books, manuals and equipment require, apart from a technical
background, processes that will support them (Kim, 1998). More precisely, imitation
efforts or reverse engineering rely on capabilities to assimilate and transform external
knowledge to something valuable for the firm. Finally education and training efforts
enhance the firm’s knowledge base by combining existing and new knowledge
(Nonaka, Takeushi, 1995).
Diffusion mechanisms: Processes of internal diffusion are at the origin of the
internalisation of knowledge and information. They constitute one of the main
processes for conversion of knowledge and creation of new knowledge (Nonaka,
1994, Nonaka, Takeushi, 1995, Nonaka, Konno, 1998) and can be formal or informal.
They facilitate collection and diffusion of information within the firm, increase
interaction of employees and motivate them for active participation in problems’
solution. Functional integration through TQM system, job rotation, development of
databases and manuals, intranet and development of communication channels
among employees facilitate access to knowledge and information and enable their
exploitation within the firm.
Interactive mechanisms: Efforts for interaction and sharing of experience with other
organisations is indicative of firm’s knowledge openness. They develop the capability
of negotiating with other actors, managing complex situations and acting strategically
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in a context of complex relationships (Tsang, 2002, Scott, 2003, Liyanage, Barnard,
2003). Interactions with other organisations (e.g. firms, Universities, Public Research
Institutions) complement internal R&D efforts and expand the firm’s absorptive
capacity (Tsang, 2002, Scott, 2003, Caloghirou et al., 2004). This happens because
the firm gains experience related to the management, coordination and
implementation of cooperative projects, and expands its knowledge base through the
access to external knowledge and other organisations’ in-house R&D.
According to the above a firm should invest in developing specific learning, diffusing
and interacting processes in order to be able to use efficiently external sources of
knowledge. These efforts should not subscribe to the rationale of cost minimisation
as redundancy of efforts is identified as one of the conditions for organisational
knowledge creation, learning and technological development (Nonaka, Takeushi,
1995, Hagedoorn, Duysters, 1997).
The following hypotheses are then formulated:
H2: Absorptive capacity, defined as a set of organisational routines and processes that enable the firm to acquire, assimilate, transform and exploit external knowledge is a critical condition for organisational knowledge creation.
The above hypothesis can be analysed to more specific propositions regarding the
role of absorptive capability and absorptive mechanisms.
H3: The intensity of R&D efforts, efforts for knowledge diffusion and processing and for networking is expected to have a positive impact on organisational knowledge creation within R&D cooperation.
H4: The technical background and existing knowledge base and experience determine organisational knowledge creation because what the firm has cumulatively learned and chosen to do in the past constitutes the boundaries of its cognition.
H5: Redundancy of efforts to develop learning, diffusion and interacting mechanisms within a firm will underpin positive effects of cooperation.
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The partners’ relation
a)The role of complementarity
The relation between two partners can be complementary or competitive.
Complementarity can refer either to distinctive strategic motivations or to
complementary activities within the cooperation. Cooperation between users-
producers is one such type of interaction that creates conditions for combination of
the needs of the former with the technical solutions of the latter resulting to the
creation of new solutions (Håkansson, 1987, Von Hippel, 1988). Another type of
complementary interaction is the cooperation between firms and Universities or
research centres. Connection of basic to applied research is considered to exploit
synergies with positive effects for the cooperative outcome (Baldwin, Link, 1998,
Mora-Valentin et al., 2003). The “triple helix” concept emphasises the role of public
research in the firms’ innovative performance through the dynamic relations of
networking (Etzkowitz, Leydesdorff, 2000, Leydesdorff, Etzkowitz, 1998).
Accordingly, relations with public and private research actors are emphasised as
beneficial for innovation by the National System of Innovation approach (Lundvall,
1992). Cockburn and Henderson (1998) have empirically confirmed the positive role
of the cooperation between firms and public research intsitutions (PRIs), underlining
that they facilitate knowledge flows as they improve firm’s capability to recognise and
use scientific progress and change the way research is implemented within the firm.
It is also argued that cooperation with Universities and PRIs gives the opportunity to
firms to access a critical mass of R&D resources that could be very expensive for the
firm to develop on its own (Vavakova, 1995).
The above underpin the following hypothesis:
H6: Complementary relationships are expected to involve more knowledge flows and positively affect organisational knowledge creation.
b)The question of understanding and coordination
Diffusion and effective exploitation of knowledge in the context of cooperation may be
hindered by differences in partners’ strategic objectives, experience and
organisational culture, elements which are at the origins of knowledge ambiguity
(Simonin, 1999), and result to conflicts and problems of coordination.
Problems of communication and coordination may also result from rigidities and path
dependency in partners’ behaviour (Bruner, Spekman, 1998) constraining knowledge
flows and sharing.
9
H7: Differences in the way partners interpret information because of their experience, culture and interests may lead to communication and coordination problems with negative effects to cooperation.
The partners behaviour: opportunism versus trust
The type of interaction is also related to the partners’ behaviour that apart of common
goals can encompass competitive practices (Khanna et al., 1998). Cooperative
agreements for R&D are considered to reflect simultaneously competitive and
cooperative behaviour by participating firms (ibid). The competitive aspect is a
consequence of each firm’s attempt to use its partners’ know-how for private benefit
(ibid, p. 194). In some cases the absence of common purpose in the cooperation
agreement and the prevalence of private interests may result to a racing behaviour
(ibid). Of relevance here is the assumption of opportunistic behaviour, one of the
pillars of transaction cost theory. Individuals are assumed to behave opportunistically
by pursuing their self-interest with guile (Williamson, 1985). Also, a conflict between
individual and collective interests –as is invariably the case with inappropriabilities in
the context of technological spillovers- may lead to failure situations. Of course, this
implies that the problem of spillovers is more acute the closer to the market the
object of the research project lies. Commitments differ according to the relative
position of the participating firms. Thus, competitive behaviour and conflicts of
strategic interests may result to failure situations. Differences in the partners strategic
objectives, corporate culture and organisational differences may lead to additional
cost and efforts (Williamson, 1985, Khanna et al., 1998, Bruner, Spekman, 1998).
The above situations relate to constraints for knowledge and information flows as
firms may hesitate to share knowledge and experience.
When the partnership includes competitors it is likely that there will be a more
competitive attitude between them with more opportunistic or reserved attitude,
unless the research is at a very primary and pre-competitive stage. The development
of efficient cooperation depends on a well-founded common ground, where harmony
of interest and value exists. This applies particularly among not competing firms that
share common goals and obligations (Hu, Korneliussen, 1997, Inkpen, 1998).
Cooperation with users or suppliers usually leaves more space for information and
knowledge flows as firms do not have competitive strategic interests (Sørensen,
Reve, 1998). Trust may be easier established between complementary partners than
10
between rivals, pointing to the relationship that exists between the type of partners
relation and behaviour and the effects of cooperation.
Trust can be established through repeated cooperation and organisational learning.
One firm may believe through experience (which is gained at the organisational level)
in the other partners’ integrity and good will, when margins for opportunistic
behaviour exist or new conditions for which no commitment has been established
arise (Zaheer et al., 1998, Montoro, 1999). Experience from previous collaboration
and trust create stable relationships that facilitate knowledge and information flows.
The above arguments lead to the following hypothesis:
H8: Rivalry and problems of opportunism although not expected to play the critical role are expected to affect negatively organisational knowledge creation.
The role of uncertainty
Uncertainty relates to variety and variability of technology and markets that result to
unpredictable complexities in the development processes.
Uncertainty may play a twin role for cooperation. It increases the value of information
and induces firms to use inter-firm agreements for accessing external sources of
knowledge and complementary assets. On the other hand, it enhances opportunistic
behaviour, because of incomplete information and partners’ reservations (Arrow,
1962, Bureth et al., 1997), and produces higher levels of ambiguity when due to high
interdependence of technologies, routines, individuals and resources linked to a
particular knowledge or asset (Simonin, 1999), with opposite effects on cooperation
and organisational learning.
H9: Uncertainty may increase the cost and risk of the research activity with negative impact on the outcome of research cooperation.
3. Subsidised cooperative R&D as a means for knowledge creation and/or acquisition
In the present section I will investigate empirically the following two questions:
a) what are the conditions for organizational knowledge creation within the
context of R&D cooperation and
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b) what is the impact of specific absorptive mechanisms on the performance of
specific research projects.
Two econometric models are used to investigate the above questions.
The analysis builds on empirical information extracted from the STEP TO RJVs
Databank1. The population of the research is the funded research cooperations in the
context of the EU FPs in seven European countries, namely France, Greece, Ireland,
Italy, Spain, Sweden and UK. The empirical data were collected from an extensive
survey that has been carried out in 317 firms from the seven European countries
mentioned. These firms have participated in 530 research projects funded in the
context of EU FPs.
The field research has been carried out during the period from February 1999 to
September 1999. The questionnaire included general information about the firm and
its general strategic orientation, information about specific research partnerships the
firm had recently participated in (going back to 5 years) and information about the
involvement of the firm’s business units in R&D cooperation in general (competitive
strategy, reasons for cooperating, benefits from cooperation, problems during the
cooperation). The questionnaires were initially sent to company R&D managers. If
this was not possible or the R&D manager could not give detailed information about
specific projects, the questionnaire was forwarded to the appropriate executives in
the responsible business unit. This procedure was considered appropriate for
increasing the degree of accuracy of the information provided.
The variables that have been used for the econometric analysis were constructed
using sets of specific statements included in the questionnaire. A Likert scale 1-5
allowed respondents to indicate the importance of each statement to their firm in
relation to the partnerships they have been involved in2. Using principal components
analysis the number of variables was reduced and meaningful factors representative
of the whole set of sub-questions were created.
1This Databank which has been recently developed in the context of the STEP TO RJVs project, contains all research
projects that have been funded by the European Commission and other national sources and includes at least one
firm. These projects were initiated during the period 1984-1998. 2 All variables have been tested for their reliability. The Crombach-Alfa index was in satisfactory levels.
12
a) Conditions for organizational knowledge creation in the context of research
cooperation.
The first econometric model investigates the factors that influence the benefits for the
firm from its participation in R&D cooperation in terms of organizational knowledge
creation.
The dependent variable
Benefits obtained from R&D undertaken through cooperation (BEN): This variable
was constructed from the question that tried to measure the benefits from the
participation in R&D cooperation in terms of knowledge acquisition and creation and
in terms of development of products/services or processes. Ten possible benefits for
firms involved in cooperative R&D have been listed and the factor analysis gave the
results summarised in the following table3:
Table 1: Results from factor analysis for the variable ΒΕΝiDevelopment of new products (3,18) Improvement of existing products (3,02) Increase of profitability (2,54) Increase of market share (2,56)
Commercial benefits
(ΒΕΝ1)
Continuation/acceleration of existing research (3,2) Exploitation of complementary resources (2,8) Acquisition/ creation of new knowledge (3,9) Development of technological and organizational capabilities (3,4)
Development of resources and capabilities (ΒΕΝ2)
Development of new processes (3) Improvement of existing processes (3)
Development / improvement of processes (ΒΕΝ3)
Rotation method: Varimax with Kaiser Normalisation, rotation converged in 5 iterations. MSA-values exceed 0.5. Eigenvalues > 1. Variance explained 65%. Source: Data processing with SPSS programme.
The first factor represents benefits from creation of new or improved products that
can be commercially exploited. The second regroups benefits that relate to the
development of firm’s knowledge base and capabilities. Finally the third corresponds
to benefits from process development.
Independent variables
Two variables were used for absorptive capacity: the capabilities of the firm and the
firm’s technological activities. Both variables are describing what the firm has
3 The parenthesis includes the mean of the answer.
13
cumulatively learned to do and what is she able to do concerning its technological
development.
The capabilities of the firm (CAPi): the relevant question included the importance for
the firm of a number of processes for acquiring and/or creating new knowledge.
Three factors were obtained from the factor analysis. The first represents the
intensity of R&D efforts of the firm and is thus a proxy to the capability of the firm to
undertake research activities. It corresponds to the traditional way of measuring
absorptive capacity. The factor regroups efforts for internal diffusion, improvement
and targeting of firm’s resources, which is a proxy for organisational capabilities.
Finally the third is a proxy for firm’s capability to interact formally or informally with
users or suppliers.
Table 2: Results from factor analysis on capabilities of the firms involved in R&D cooperative agreements. Conducting basic research internally (2,5) Conducting applied research internally (3,7) Conducting development (4) Conducting design engineering (3,2) Patenting (2,4)
Internal R&D capability (CAP1)
Organising and exploiting scientific and technical information (3,5) Education and training (3,4) Long-term forecasting and product planning (3,1) Specific procedures exploiting individual initiatives and ideas within the business unit (3,1)
Organisational capability for knowledge diffusion and processing
(CAP2)
developing formal relationships with users or suppliers (3,6) developing informal relationships with users or suppliers (3,5)
Interacting capability
(CAP3)
Rotation method: Varimax with Kaiser Normalisation, rotation converged in 5 iterations. MSA-values exceed 0.5. Eigenvalues > 1. Variance explained 56,4%. Source: Data processing with SPSS programme.
The technological activities of the firm (TECH): The firm has a number of
technological activities and decides about technology in a way determining its
technological orientation and strategy. Firms answered about the degree to which
they adopted one or more of the activities, presented in table 3, for technological
development. The principal components analysis extracted three factors.
The first factor represents an orientation of the firm towards transfer of know-how and
use of external sources of knowledge, the second to an internal development of
productive processes, whereas the third one to internal product development.
In accordance with hypotheses 2, 3 and 4, the intensity of research efforts, efforts for
knowledge diffusion and processing and efforts for networking is expected to have a
14
positive impact on organisational knowledge creation within R&D cooperation and
that the technological orientation and accumulated know-how will determine the type
and level of benefits.
Table 3: Results from factor analysis on variable TECHiCooperation in R&D (3,6) Extramural R&D (2,3) Technology acquisition from third parties (2,2) Modification/improvement/adaptation of foreign technology (2,7)
Technological development based on external
resources (TECH1)
Introduction of new processes (3,3) Improvement/ modification of existing processes (3,5)
Internal development of new productive
processes (TECH2) Introduction of new products (3,5) Improvement/ modification of existing products (3,6)
Internal development of new products
(TECH3) Rotation method: Varimax with Kaiser Normalisation, rotation converged in 5 iterations. MSA-values exceed 0.5. Eigenvalues > 1. Variance explained: 61,1%. Source: Data processing with SPSS programme.
The appropriability problems (APR): Appropriability problems are a way to measure
opportunistic and competitive behaviour. The question used was problems that the
firm faced during the cooperation and more specifically the problems of
appropriability of research results among partners. The answers were given at a
Likert scale 1-5 and the average of the answers was 2,5, indicating the weak
presence of problems of appropriability. According to hypothesis 8 a negative
influence of appropriability problems is expected on organisational knowledge
creation as they constitute a constrain to knowledge flows within cooperation.
The type of partners (PARTNi): A proxy for partners’ relation and interaction is
constructed from the question “to what extent your business unit is involved in
research cooperation with the following type of organisations”. The principal
components analysis extracted three factors that are used as three independent
variables (see table 4).
The variable then represents complementary or competitive relationships in terms of
incentives or capabilities. It is expected that complementary relationships (PARTN2
and 3) will positively affect and competitive relationships (PARTN1) negatively affect
the benefits from cooperation.
15
Table 4: Results from factor analysis on variable PARTNi
Competitor firm in the same geographic market (1,8) Competitor firm in different geographic markets (2,2)
Competitors
(PARTN1) Public research institution (2,8) University (3,4)
Higher education and research institutions (PARTN2)
Supplier firm (2,4) Client / user firm (2,8)
Suppliers/users (PARTN3)
Rotation method: Varimax with Kaiser Normalisation, rotation converged in 5 iterations. MSA-values exceed 0.5. Eigenvalues > 1. Variance explained: 72%. Source: Data processing with SPSS programme.
Constraints to knowledge flows in the context of research cooperation may result
from coordination and communication problems among partners.
Problems of coordination (COORD): Problems of coordination occur when there are
differences among the partners in terms of objectives, organisational culture or
understanding. The same question as in the case of appropriability problems was
used to construct a variable for coordination problems. As in the case of APR the
average is low (3) indicating the weak presence of problems during cooperations that
have been funded in the context of European FPs. Following hypothesis 7, a
negative sign is expected for this variable too.
Business environment (ENV): A variable was constructed measuring the
predictability of the business environment, from the question “future demand and
competitor’s movements easily predicted”, again answered at a Likert scale. In fact,
the more the future demand and competitors’ movements are easily predicted the
less the uncertainty. Hypothesis 9 is then tested in an adverse way as it is expected
that the predictability of the business environment will positively affect the benefits
from cooperation.
Two control variables were also used:
The size of the firm (SIZE) measured by the average of firm’s employees for the
period 1992-1997. The logarithm was calculated because the variable doesn’t follow
normal distribution.
The country effect (CNTRi) with dummy variables indicating the country origin for
each firm of the sample4.
4 FR (France), IT (Italy), SE (Sweden), IE (Ireland), ES (Spain), UK (United Kingdom), GR (Greece). Greece is
represented by the intercept.
16
17
The following equation is then estimated using the OLS method5:
BENi = f{ TECHi, CAPi, COORD, APR, PARTNi, ENV, SIZE, CNTRi} (1)
where BENi, the benefits in terms of organisational knowledge creation.
Using SPSS statistical package we obtained the results that are presented in table 6.
All necessary tests are effectuated for heteroskedasticity and multicollinearity.
5 The canonical correlation analysis effectuated to two sets of variables, namely BENi as composite of dependent
variables and CAPi, APR, COORD as composite of independent variables, was a first attempt to study the
relationship we estimate in equation (1) and to test for the existence of endogeneity. The results confirmed the
existence of strong correlation between BENi and CAPi and the lack of endogeneity as the redundancy index showed
that the independent variables explain a bigger proportion of variance of the dependent variables than the other way
round.
18
Table 5: Benefits from R&D undertaken through cooperation. Variables BEN1 (269) BEN2 (270) BEN3 (267)
model 1 model 2 model 3 model 1 model 2 model 3 model 1 model 2 model 3 C 0,180
(0,522) 0,322 (0,986)
0,496 (1,535)
0,853*** (2,794)
0,939*** (3,250)
1,003*** (3,476)
0,367 (0,919)
0,320 (0,851)
0,348 (0,956)
SIZE -0,050 (-1,030)
-0,04 (-1,008)
-0,081 (-1,504)
-0,072* (-1,691)
-0,079* (-1,884)
-0,096** (-1,985)
0,014 (0,245)
0,037 (0,067)
0,049 (0,814)
TECH1 -0,011 (-0,146)
-0,014 (-0,180)
-0,035 (-0,456)
0,155** (2,281)
0,159** (2,346)
0,150** (2,193)
-0,053 (-0,594)
-0,048 (-0,546)
-0,086 (-0,991)
TECH2 -0,130*** (-2,490)
-0,131*** (-2,556)
-0,131*** (-2,514)
0,018 (0,394)
0,026 (0,576)
0,009 (0,192)
0,594*** (9,900)
0,603*** (10,244)
0,567*** (9,670)
TECH3 0,280*** (4,909)
0,295*** (5,253)
0,267*** (4,783)
0,061 (1,203)
0,066 (1,328)
0,065 (1,315)
-0,031 (-0,476)
-0,040 (-0,621)
-0,031 (-0,500)
CAP1 0,142** (1,894)
0,153** (2,111)
0,165** (2,237)
0,202*** (3,075)
0,224*** (3,514)
0,215*** (3,303)
-0,001 (-0,017)
0,011 (0,131)
0,046 (0,547)
CAP2 0,088 (1,199)
0,095 (1,448)
0,028 (0,324)
CAP3 0,106* (1,833)
0,120** (2,113)
0,118** (1,877)
0,075 (1,460)
0,088* (1,745)
0,089* (1,726)
0,023 (0,344)
0,025 (0,382)
0,071 (1,095)
COORD 0,045 (0,950)
-0,015 (-0,370)
-0,064 (-1,189)
APR -0,049 (-1,012)
0,002 (0,043)
0,038 (0,671)
PARTN1 0,072 (1,302)
0,068 (1,235)
0,048 (0,869)
0,080 (1,639)
0,076 (1,572)
0,070 (1,443)
0,046 (0,717)
0,046 (0,718)
0,030 (0,486)
PARTN2 0,120** (2,360)
0,125*** (2,465)
0,130*** (2,519)
0,147*** (3,259)
0,155*** (3,481)
0,166*** (3,630)
0,162*** (2,740)
0,168*** (2,815)
0,165*** (2,848)
PARTN3 0,170*** (3,041)
0,170*** (3,087)
0,163*** (2,991)
0,044 (0,896)
0,044 (0,897)
0,051 (1,058)
0,062 (0,951)
0,058 (0,915)
0,063 (1,019)
ENV 0,079* (1,633)
0,087* (1,809)
0,085* (1,734)
0,038 (0,875)
0,049 (1,139)
0,011 (0,204)
0,012 (0,216)
0,010 (0,189)
IT 0,250 (1,237)
0,173 (0,964)
-0,171 (-0,755)
FR -0,312 (-1,346)
-0,117 (-0,568)
-0,733*** (-2,821)
SE 0,447* (1,867)
-0,061 (-0,288)
-0,510* (-1,897)
ES 0,271 (1,370)
0,114 (0,651)
0,111 (0,497)
IE -0,030 (-0,118)
-0,159 (-0,710)
-0,776*** (-2,740)
GB 0,178 (0,988)
0,078 (0,484)
-0,292 (-1,431)
R²=0,31 R²=0,27 F=8,790***
R²=0,30 R²=0,28 F=11,144***
R²=0,31 R²=0,27 F=7,429***
R²=0,29 R²=0,26 F=8,185***
R²=0,29 R²=0,26 F=10,449***
R²=0,30 R²=0,26 F=6,920***
R²=0,37 R²=0,34 F=11,306***
R²=0,36 R²=0,34 F=14,612***
R²=0,40 R²=0,36 F=10,734***
The introduction of blocks of variables to the model, namely one with firm specific
variables (TECHi, CAPi, SIZE) and another two with research cooperation specific
variables (APR, COORD and PARTNi) and business environment characteristics
(ENV), showed that the largest part of the variance is explained by firm specific
variables, confirming the first hypothesis about the critical role of dynamic
capabilities.
I then proceed to the estimation of model 1 with the full set of variables, as it appears
in table 5. Model 2 is an improved version where CAP 2 is abstracted because of
collinearity and COORD and APR because of no statistical significance. Model 3
includes country dummies.
From the results presented in table 5, the following points are important:
1. There is clear and strong influence of accumulated know-how on organisational
knowledge creation as it results from the impact of TECHi on BENi. There is path
dependence in the sense that what the firm has done in the past, its existing
range of knowledge and its technological background determine organisational
knowledge creation. More precisely technological orientation to the development
of products or processes affects positively the exploitation of R&D cooperation to
the same direction. Accordingly, the more the firm relies on external sources of
knowledge for its technological development the more are the benefits in terms of
deployment of resources and capabilities through R&D cooperation. It seems that
experience from knowledge transfer can play an important role and when firms
build on the capability of exploiting external resources of technological
development they can support the creation of competitive advantage. This point
is not in line with the view that the source of competitive advantage lies on non-
transferable and inimitable resources (Barney, 1991) and supports that firms
should aim to the storage and exploitation of transferred knowledge, either at the
level of negotiations and terms of contracts or at the level of internal processes.
However, it has to be noted that the negative relationship between different types
of technological backgrounds points to an opportunity cost when developing
absorptive capacity, which is related to the accumulated experience of the firm in
some type of know-how and not to other.
2. The results showed the positive role of internal R&D capability and interacting
capability on the benefits from cooperation. Cumulative efforts in acquiring and
creating technological and organisational capabilities and especially R&D efforts
and interactive efforts determine positively the extent to which a firm will exploit
19
its involvement in R&D cooperation. It is then important that the firm invests in
upgrading its existing knowledge base and capabilities (especially in conducting
R&D and developing relationships with other organisations).
Hypotheses 3 and 4 are thus confirmed.
3. The results related to appropriability problems and coordination problems show
no significance of the relevant variables. This is probably because the sample
includes only subsidised R&D cooperations and thus subsidies may outweigh the
dark side of cooperations. Baldwin and Link (1998:910) pointed in their study on
research cooperations that appropriability problems are counterbalanced by “the
advantage of obtaining information more cost effectively and sooner than if each
participant undertook the research individually”. The same goes with competitors’
interaction. Probably these cooperations did not concern projects resulting to
commercial exploitation of the research outcome (pre-competitive research),
assumption which is in line with the general objective of EU FPs; to help firms
develop a common knowledge and technological know-how that they will use
independently when competing to the market (Vonortas, 1997). Hypotheses 7
and 8 are thus rejected and hypothesis 1 is more strongly supported.
4. Results about complementarity support the sixth hypothesis. Cooperation with
Universities and PRIs seems beneficial to all types of benefits, especially to
BEN2. This result becomes more important if we take into consideration that 60%
of the projects funded under the European Framework initiative include a
University or PRIs (Caloghirou et al., 2004:44) and their participation increases
over time (Caloghirou et al., 2001). Universities and PRIs can play an important
role in diffusing basic and sometimes applied research to firms through
cooperations and not only through publications. Other empirical studies have
concluded to similar results pointing to the benefits for the firms when
participating in cooperations with Universities and PRIs. These benefits concern
access to complementary resources and capabilities, to scientific expertise and
establishment of trust ensuring channels of knowledge flows (Etzkowitz,
Leydersdorff, 1995, 2000, Leydersdorff, Etzkowitz, 1998, Cockburn, Henderson,
1998, Baldwin, Link, 1998, Lundvall, 1992, etc.). The same positive effect of
complementarity is observed when referring to interaction with suppliers and
users, on commercial benefits, a result which was expected and pointed in other
studies as well (von Hippel, 1988, Slaughter, 1993, Hakansson, 1987).
20
5. Predictability of demand and competition has a slightly positive effect (p<10%)
only for commercial benefits. Reversely, uncertainty may prove negative for
benefits related to product development and commercialisation, but this relation is
not strong and thus hypothesis 9 is partially and not strongly confirmed.
6. The size of the firm has a negative sign for all types of benefits but is significant
only in the case of BEN2. The smaller the firm is the bigger the benefits in terms
of resources and capabilities development. However it should be taken into
account that our sample includes mainly SMEs (60% of the firms have less than
250 employees).
7. The introduction of the country dummies (model 3) does not affect the
significance of the other independent variables but slightly diminishes the value of
adjusted R2, which means that the addition of country dummies diminished the
predictive power of the model. However, there is some geographical
differentiation in the extent of benefits among the different countries, which points
to the role of country specificities in terms of institutional set-up, socio-economic
structure and interaction of economic actors in the process of learning and
innovation (Lundvall, 1992).
b) The role of absorptive mechanisms
In this section I estimate the impact of absorptive mechanisms on the performance of
R&D cooperation, using a logistic regression. The sample used includes 530
research projects.
The importance of absorptive mechanisms on the possibility of success or failure of
R&D cooperation is then evaluated, using a measure of performance which is
independent from knowledge creation.
The dependent variable
A subjective measure is used in order to evaluate the success or failure of the
project, the achievement of firm’s objectives. This measure was proposed by
Brockhoff and Teichert (1995) and applied by Caloghirou et al. (2003) and Mora-
Valentin et al. (2003). The constructed variable measures the extent to which the
21
firm’s objectives when entering the cooperation have been met6. The variable PERF
is a dichotomous variable taking the value 1 for success and 0 for failure.
Table 6a presents the firm’s objectives for undertaking R&D cooperation that have
been evaluated for their degree of expectation and fulfilment:
Table 6a: Objectives for undertaking R&D cooperation
R&D cost sharing Risk sharing-reduce uncertainty
Access to complementary resources and skills Reduce loss of information to competitors
Research synergies Technological learning
Keeping up with major technological developments Improve speed to market
Achieving critical mass in R&D Joint creation and promotion of technical standards
Promote user/producer interactions Control future market developments
Create new investment options Obtain public funding
Establish new relationships Access external resources
Then a second variable was constructed, representing the achievement of
technological objectives using the same methodology and isolating two of the above
objectives. The new variable PERFKNOW is again a dichotomous one, taking value
1 for success and 0 for failure (see table 6b).
Table 6b: Technological objectives Technological learning
Keeping up with major technological developments
6 We used the question “Please estimate to what extent your business unit initially had the following objectives for
undertaking the specific R&D project and to what extent these objectives have been achieved”. The question had two
evaluation scales, one for the initial objectives and one for the fulfilled. We calculated the average of the initial
objectives and then the average of the objectives fulfilled. Then we set the following conditions for deciding if one
project succeed or failed: when the initial objectives had an average higher or equal to three we considered them
important. If the same went for the fulfilled objectives we considered that the project succeed. The project failed when
the firm’s objectives were not met (the average of fulfilled objectives less than 3) and when the initial objectives were
evaluated not important (with average less than 3).
22
Independent variables
Absorptive mechanisms (AB-MECH): I used the information from the questionnaire
concerning the extent to which the firm used any learning mechanisms and
processes for internalisation and assimilation of knowledge during the research
cooperation. The principal components analysis extracted three factors (see table 7).
The first factor represents joint learning efforts as it regroups common research
efforts and observation of others’ efforts. The second factor represents internal
efforts for assimilation and diffusion, what the firm does alone to internalise
knowledge. The third one corresponds to socialisation efforts during the project.
Following the fifth hypothesis stated previously, a positive sign is expected for the
activation of absorptive mechanisms during R&D cooperation.
Table 7: Results from factor analysis on AB-MECHi Implementing joint research tasks (3) Observing other partners’ research facilities and practices (2,7)
Joint learning efforts (JOINT)
Undertaking on your own similar R&D (2,3) Training related to the specific cooperative R&D activity (2,4) Codification of related information and database construction (2,4)
Internalisation efforts made in parallel with the
project (INTERN)
Project meetings (4) Informal communication among partners (3,8)
Socialisation and interacting efforts (COLLAB)
Rotation method: Varimax with Kaiser Normalisation, rotation converged in 5 iterations. MSA-values exceed 0.5. Eigenvalues > 1. Variance explained: 60,2%.
Source: Data processing with SPSS.
Firm’s previous experience (PREXP): The information used was whether the specific
project was a continuation or extension of previous research efforts. A variable was
created, taking value 1 for research projects which were a continuation or extension
of previous efforts and 0 if not. It is expected that previous experience will positively
affect the probability of success of the research project.
Problems during the research cooperation (PRBi): As in the previous model two
types of problems were used, but this time related to specific research projects. The
two sub-questions used concerned the coordination and communication problems
among partners (COORD) and appropriability problems for research outcome (APR)
and a negative sign is expected for both variables.
23
The following equation is estimated, using a binary logistic regression model. Size
and country dummies are the control variables:
PERF = f { PREXP, AB-MECHi, PRBi, SIZE, CNTRi} (2a)
The log-likelihood statistic is decreasing when introducing the independent variables
in the equation from 624,88 to 555,96 confirming the increase in the predictive power
of the model. The model is statistically significant at p<1% as well as the increase of
its predictability.
The introduction of country dummies slightly improves the model’s predictive power
but the addition of the six dummies is significant only at a 10% level.
The Hosmer – Lemeshow tests were not significant, which means that the observed
data are not significantly different from the predicted values and the model’s
predictability is good . The model correctly classifies 70% of cases.
The results of the logistic regression are summarised in table 8.
The statistically significant variables at p<1% level of significance are JOINT,
INTERN and APR, while COLLAB is unstable and becomes not significant with the
introduction of country dummies. Internalisation efforts (INTERN) is the most
important variable (Wald statistic 20,30) followed by joint efforts for R&D (JOINT)
(Wald statistic 11,16). A unit change of INTERN results to 1,81 times increase of the
probability of PERF to take value 1 and a unit change of JOINT increases this
probability 1,45 times. Appropriability problems present less importance (Wald
statistic 6,01 and expb 1,29).
The positive sign of APR, which may surprise at a first glance, can be explained by
the fact that appropriability problems occur when there is a research outcome that
relates to a success of the research cooperation. A new variable was introduced into
the regression, measuring research outcome (RO)7 as well as the interaction
between the appropriability problems and the research outcome (APR-RO). The
results support this explanation as appropriability problems became not significant
with a negative sign and the research outcome presented a positive and significant
sign.
7 RO was measured by the extent to which the business unit achieved to develop or improve a specific
product.
24
25
There is also a geographical differentiation with italian, irish and UK firms related to
higher probabilities of fulfilment of their objectives.
26
Table 8: Absorptive mechanisms and achievement of firm’s objectives Model 1 without country effect Model 2 with country effect Variables Βº Exp β Confidence
intervals Β Exp β Confidence intervals
C -4,377***(40,18) 0,013 -4,398*** (38,35) 0,012 SIZE 0,075 (0,66) 1,078 (0,90) (1,29) -0,030 (0,08) 0,970 (0,79) (1,20) PREXP 0,291 (2,00) 1,338 (0,89) (2,00) 0,251 (1,39) 1,285 (0,85) (1,95) JOINT 0,346*** (10,41) 1,414 (1,15) (1,75) 0,369*** (11,16) 1,446 (1,17) (1,80) INTERN 0,526*** (17,23) 1,692 (1,32) (2,17) 0,593*** (20,30) 1,810 (1,40) (2,34) COLLAB 0,235* (3,10) 1,265 (0,97) (1,64) 0,187 (1,82) 1,206 (0,92) (1,58) COORD -0,018 (0,05) 0,982 (0,84) (1,15) -0,040 (0,22) 0,961 (0,81) (1,14) APR 0,263*** (6,97) 1,301 (1,07) (1,58) 0,252*** (6,01) 1,287 (1,05) (1,57) IT 0,733** (4,67) 2,081 (1,07) (4,04) FR 0,542 (1,49) 1,719 (0,72) (4,10) IE 0,847** (4,51) 2,332 (1,07) (5,10) SE -0,003 (0,00) 0,997 (0,46) (2,16) ES -0,013 (0,00) 0,988 (0,47) (2,09) GB 0,823** (4,76) 2,277 (1,09) (4,77) ▫:Wald statistics appear in parenthesis.
27
The impact of the same independent variables on the probability of achievement of
technological objectives, was estimated by the following equation.
PERFKNOW = f { PREXP, AB-MECHi, PRBi, SIZE, CNTRi} (2b)
The dependent variable takes the value 1 when the firm meets its objectives
concerning learning and know-how acquisition and 0 when it doesn’t.
The Log-likelihood coefficient decreases with the introduction of the independent
variables (from 537,05 to 496,83) and the model’s predictive power increases. The
model is statistical significant at p<1% and the same goes for the improvement of its
predictive capability. The Hosmer-Lemeshow test takes the value 3,96 in model 1,
which is not statistically significant and thus our model is representative of the real
world. Model 1 correctly classifies 77% of the cases.
The introduction of country dummies is not statistically significant, although the model
remains significant.
In the case of achievement of technological objectives, the same clear influence of
absorptive mechanisms was obtained. The significant variables are JOINT and
INTERN. A unit change of these two variables doubles the probability of achievement
of technological objectives.
Table 9 summarises the results for equation (2b).
Results of equations 2a and 2b confirm hypothesis 5, strengthening the results about
the positive role of absorptive capacity. Joint learning efforts and internalisation
efforts affect positively the performance of the cooperation. According to the cost-
minimisation approach firms participating in R&D cooperation should avoid parallel
internal efforts for learning and assimilation. This approach however does not prove
to be the best when aiming at the maximum of benefits from participation in R&D
cooperation. An approach based only on the efficient management of resources does
not recognise the opportunity for diffusion and development of new knowledge
through such practices as parallel research efforts of the participants in a research
cooperation or storage and learning efforts.
28
Table 9: Absorptive mechanisms and achievement of firm’s technological objectives Model without country effect Model with country effect Variables Βº Exp β Conficence intervals Β Exp β Conficence intervals C -1,728***(7,23) 0,178 -1,845*** (7,92) 0,158 SIZE 0,032 (0,10) 1,033 (0,84) (1,26) 0,087 (0,58) 1,091 (0,87) (1,37) PREXP -0,074 (0,11) 0,929 (0,60) (1,45) -0,032 (0,02) 0,968 (0,62) (1,52) JOINT 0,435*** (13,62) 1,545 (1,23) (1,95) 0,461*** (14,49) 1,586 (1,25) (2,01) INTERN 0,395*** (7,89) 1,484 (1,13) (1,95) 0,394*** (7,69) 1,483 (1,12) (1,96) COLAB 0,127 (0,93) 1,135 (0,88) (1,47) 0,121 (0,81) 1,129 (0,87) (1,47) COORD 0,101 (1,37) 1,107 (0,93) (1,31) 0,113 (1,59) 1,119 (0,94) (1,33) APR -0,065 (0,37) 0,937 (0,76) (1,16) -0,063 (0,33) 0,939 (0,76) (1,16) IT 0,091 (0,06) 1,095 (0,54) (2,22) FR -0,084 (0,03) 0,919 (0,37) (2,29) IE -0,207 (0,23) 0,813 (0,35) (1,90) SE -0,149 (0,13) 0,862 (0,38) (1,93) ES -0,207 (0,25) 0,813 (0,36) (1,82) GB -0,546 (1,89) 0,579 (0,27) (1,26) ▫:Wald statistics appear in parenthesis.
General discussion
From the above presentation two important issues arise:
a) Firms need a sufficient technological and knowledge background and to possess
absorptive capacity in order to obtain the best from their involvement in R&D
cooperation. This condition is not however independent of the type of
technological and knowledge background built over time.
b) The more active the firm is in the context of a cooperative project in terms of
learning, diffusing and assimilating knowledge efforts, the more is the possibility
to succeed its goals.
The firm today should rely more and more on external sources of knowledge in order
to face intense competition. What appears a pre-condition to that is the development
of its own capabilities, in order to better exploit mechanisms of knowledge flows and
creation such as research cooperation.
European Research and Technology Policy should take into consideration these two
aspects if aiming not only to European Research Area of excellence but also to
cohesion. The objective of cohesion depends on diffusion of knowledge and
technology in a way that an increasing number of actors are involved in this area. If
policy neglects this aspect then a big part of economic actors will fall behind.
This issue is getting more importance if we take into consideration that the
technological gap between a small group of leaders and those that are in the
“average” is gradually increasing. “Those which are the strongest profit most, those
which are somewhat weaker profit less” (Clarysse, Muldur, 2001: 293). This reflects a
weakness of the EU Framework Programmes in promoting and sustaining catching-
up processes. It is clear that European S&T policy should shift its interest towards the
development of absorptive capacity for those actors lagging behind, with specific
actions promoting learning mechanisms, R&D activities, training or processes for
knowledge management.
29
References
Arrow K., 1962, “Economic welfare and the allocation of resources for invention” in R. R.
Nelson (ed.) The Rate and Direction of Inventive Activity: Economic and Social Factors,
Princeton University Press for the NBER.
Baldwin W., A. Link, 1998, “Universities as research joint ventures partners: does size of the
venture matter?”, International Journal of Technology Management 15(8): 895-912.
Barney J. B., 1991, “Firm resources and sustained competitive advantage”, Journal of
Management 17: 99-120.
Brockhoff K., T. Teichert, 1995, “Cooperative R&D and partners’ measures of success”,
International Journal of Technology Management 10(1): 111-123.
Bruner R., R. Spekman, 1998, “The Dark Side of Alliances: Lessons from Volvo-Renault”,
European Management Journal 16(2): 136-150.
Bureth A., S. Wolff, A. Zanfei, “The two faces of learning by cooperating: The evolution and
stability of inetr-firm agreements in the European electronics industry”, Journal of Economic
Behavior and Organisation 32: 519-537.
Caloghirou Y., G. Hondroyannis, N. Vonortas, 2003, “The Performance of Research
Partnerships”, Managerial and Decision Economics.
Caloghirou Y., I. Kastelli, A. Tsakanikas, 2004, “Internal capabilities and external knowledge
sources: complements or substitutes for innovative performance?”, Technovation 24(1): 29-
39.
Caloghirou Y., N. Vonortas, S. Ioannides, 2004, “European Collaboration in Research and
Development. Business Strategy and Public Policy”, Edward Elgar.
Caloghirou Y., S. Ioannides, N. Vonortas, 2003, “Research Joint Ventures”, Journal of
Economic Surveys 17(4), pp. 541-570.
Caloghirou Y., Vonortas N., 2000, “Science and Technology Policies Towards Research Joint
Ventures” final report of the project STEP TO RJVs, European Commission (April).
Clarysse B., U. Muldur, 2001, “Regional cohesion in Europe? An analysis of how EU public
RTD support influences the techno-economic regional landscape”, Research Policy 30: 275-
296.
30
Cockburn I., R. Henderson, 1998, “Absorptive Capacity, Coauthoring Behavior and the
Organization of Research in Drug Discovery”, The Journal of Industrial Economics XLVI(2):
157-182.
Cohen W., D. Levinthal, 1989, “Innovation and Learning: The two faces of R&D”, Economic
Journal 99: 569-596.
Cohen W., D. Levinthal, 1990, “Absorptive capacity. A new perspective on Learning and
Innovation”, Administrative Science Quarterly, 35, pp. 128-152.
Combs J., D. Ketchen, 1999, “Explaining Interfirm Cooperation and Performance: Towards a
reconciliation of Predictions from the Resource-Based View and Organisational Economics”,
Strategic Management Journal 20: 867-888.
Eisenhardt K., J. Martin, 2000, “Dynamic Capabilities: What Are They?”, Strategic
Management Journal 21: 1105-1121.
Escribá-Esteve A., J. A. Urra-Urbieta, 2002, “An analysis of co-operative agreements from a
knowledge-based perspective: an integrative conceptual framework”, Journal of Knowledge
Management 6(4): 330-346.
Etzkowitz H., L. Leydesdorff, 2000, “The dynamics of innovation: from national systems and
“Mode 2” to a Triple Helix of university-industry-government relations”, Research Policy 29(2):
109-123.
Foss N., 1999, “Networks, capabilities and competitive advantage”, Scandinavian Journal of
Management 15: 1-15.
Grünfeld L., 2003, “Meet me halfway but don’t rush: absorptive capacity and strategic R&D
investment revisited”, International Journal of Industrial Organization 21: 1091-1109.
Gulati R., 1995, “Does Familiarity Breed Trust? The Implications of Repeated Ties for
Contractual Choice in Alliances”, Academy of Management Journal 38(1): 85-112.
Gulati R., N. Nohria, A. Zaheer, 2000, “Strategic Networks”, Strategic Management Journal
21: 203-215.
Hagedoorn J., A. Link, N. Vonortas, 2000, “Research Partnerships”, Research Policy 29, p.
567-586.
Hagedoorn J., G. Duysters, 1997, “Satisficing Strategies in Dynamic Inter-Firm Networks. The
efficacy of quasi-redundant contacts”, Working Paper 2/97-016, MERIT.
Håkansson H., “Industrial Technological Development. A network approach”, Croom Helm
1987.
31
Hu Y., T. Korneliussen, 1997, “The Effects of Personal Ties and Reciprocity on the
Performance os Small Firms in Horizontal Strategic Alliances”, Scand. J. of Management
13(2): 159-173.
Kamien M., I. Zang, 2000, “Meet me halfway: research joint ventures and absorptive capacity”
International Journal of Industrial Organization 18: 995-1012.
Kastelli I., Y. Caloghirou, S. Ioannides, 2004, “Cooperative R&D as a means for knowledge
creation. Experience from European publicly funded partnerships”, International Journal of
Technology Management, vol. 27(8):712-730.
Katsoulakos Y., D. Ulph, 1998, “Endogenous Spillovers and the Performance of Research
Joint Ventures”, XLVI (3): 333-357.
Khanna T., Gulati R., Nohria N., 1998, “The Dynamics of Learning Alliances: Competition,
Cooperation and Relative Scope”, Strategic Management Journal vol. 19, p. 193-210.
Khanna T., R. Gulati, N. Nohria, 1998, “The Dynamics of learning alliances: competition,
cooperation and relative scope”, Strategic Management Journal vol. 19, pp. 193-210.
Kim L., 1998, “Crisis Construction and Organizational Learning: Capability Building in
Catching-up at Hyundai Motor”, Organization Science 9: 506-521.
Kim L., 1999, “Building Technological Capability for Industrialisation: Analytical frameworks
and Korea’s experience”, Industrial and Corporate Change vol. 8(1) (March): 111-136.
Kogut B., 1988, “Joint Ventures: Theoretical and Empirical Perspectives”, Strategic
Management Journal 9: 319-332.
Lane P., J. Salk, M. Lyles, 2001, “Absorptive Capacity, Learning and Performance in
International Joint Ventures”, Strategic Management Journal 22: 1139-1161.
Lane P., M. Lubatkin, 1998, “Relative Absorptive Capacity and Interorganizational Learning”,
Strategic Management Journal 19: 461-477.
Leydesdorff L., H. Etzkowitz, 1998, “The Triple Helix as a model for innovation studies”,
Science and Public Policy 25(3): 195-203.
Liyanage S., R. Barnard, 2003, “Valuing of Firms’ Prior Knowledge: A Measure of Knowledge
Distance”, Knowledge and Process Management 10(2): 85-98.
Loasby B., 2005, “Making Connections”, Economic Journal Watch 2(1): 56-65.
Loasby B., 2006, “The Social Science of Economics”, 9th ESHET Conference 2005.
Lundvall B-A., 1985, “Product Innovation and User-Producer Interaction”, Aalborg, Aalborg
University Press.
32
Lundvall B-A., 1988, “Innovation as an Interactive Process –from User-Producer Interaction to
the National System of Innovation” in Dosi et al. (eds), Technical Change and Economic
Theory, London, Pinter Publishers.
Lundvall, B.-Å., 1993, “User-producer relationships, national systems of innovation and
internationalization”, in Foray, D. and Freeman, C. (eds.), Technology and the Wealth of
Nations, Pinter Publishers.
Lundvall B-A., 1992, “National Systems of Innovation: Towards a Theory of Innovation and
Interactive Learning”, Pinter Publishers, London.
March J., H. Simon, 1958, “Organisations”, NY Wiley.
Montoro M. A., 1999, “Factores organizativos determinantes del exito de la cooperacion entre
empresas. Un analisis de los acuerdos internacionales en investigacion y desarrollo”,
unpublished PhD dissertation, Universidad Complutense de Madrid.
Mora-Valentin E. M., A. Montoro-Sanchez, L. A. Guerras-Martin, “Determining factors in the
success of R&D cooperative agreements between firms and research organizations”,
Research Policy 33: 17-40.
Mowery D., J. Oxley, B. Silverman, 1996, “Strategic Alliances and Interfirm Knowledge
Transfer”, Strategic Management Journal 17(Winter Special Issue): 77-91.
Nonaka I., 1991, “The Knowledge-Creating Company”, Harvard Business Review 69(6): 96-
104.
Nonaka I., N. Konno, 1998, “The Concept of Ba: Building a Foundation for Knowledge
Creation”, California Management Review, vol 40(3), p. 40-54.
Nonaka I., R. Toyama, A. Nagata, 2000, “A Firm as a Knowledge-creating Entity: A New
Perspective on the Theory of the Firm”, Industrial and Corporate Change: 1-20.
Nonaka Ι., 1994, "A dynamic theory of organisational knowledge creation", Organisation
Science, vol. 5(1), p. 14-37.
Nonaka Ι., Takeushi, 1995, "The knowledge-creating company", Oxford University Press: New
York.
Pavitt K., 1984, “Sectoral patterns of technical change: Towards a taxonomy and a theory”,
Research Policy 13: 343-373.
Priem R., J. Butler, 2001, “Is the Resource-Based View a Useful Perspective for Strategic
Management Research?”, Academy of Management Review 26(1): 22-40.
33
Scott J,. 2003, “Absorptive Capacity and the Efficiency of Research Partnerships”,
Technology Analysis & Strategic Management 15(2): 247-253.
Simonin B., 1997, “The Importance of Collaborative Know-How: An Empirical Test of the
Learning Organization”, Academy of Management Journal 40(5): 1150-1174.
Simonin B., 1999, “Ambiguity and the Process of Knowledge Transfer in Strategic Alliances”,
Strategic Management Journal 20: 595-623.
Slaughter S., 1993, “Innovation and learning during implementation: a comparison of user and
manufacturer innovations” Research Policy 22: 81-95.
Sørensen H., T. Reve, 1997, “Forming Strategic Alliances for Asset Development”,
Scandinavian Journal of Management 14(3): 151-165.
Teece D., G. Pisano, 1994, “The dynamic capabilities of the firms: An introduction”, Industrial
and Corporate Change 3(3).
Tsang E., 2002, “Acquiring Knowledge by Foreign Partners from International Joint Ventures
in a Transition Economy: Learning-by-doing and Learning Myopia”, Strategic Management
Journal 23: 835-854.
Vavakova B., 1995, “Building research-industry partnerships through European R&D
programmes”, International Journal of Technology Management 10(4-6): 567-585.
Veugelers R., 1997, “Internal R&D expenditures and external technology sourcing”, Research
Policy 26: 303-315.
Von Hippel E., 1988, “The Sources of Innovation” OUP.
Vonortas N., 1997, “Cooperation in Research and Developement”, Kluwer Academic
Publishers.
Williamson O., 1985, “The Economic Institutions of Capitalism”, The Free Press.
Williamson O., 1999, “Strategy Research: Governance and Competence Perspectives”,
Strategic Management Journal 20: 1087-1108.
Zaheer A., B. Mcevily, V. Perrone, 1998, “Does trust matter? Exploring the effects of
interorganizational and interpersonal trust on performance", Organization Science 9(2): 142-
159.
Zahra S., G. George, 2002, “Absorptive Capacity: A Review, Reconceptualization and
Extension”, Academy of Management Review 27(2): 185-203.
34
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