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: iokast@chemeng.ntua.gr

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).

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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.

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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

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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.

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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

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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.

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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

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