An Approach to Measure Knowledge Transfer in Open-Innovation
António Abreu
1,2
and Paula Urze
3,4
1
ISEL/IPL, Instituto Politécnico de Lisboa, Lisboa, Portugal
2
CTS – Uninova, Almada, Portugal
3
FCT/UNL, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Lisboa, Portugal
4
CIHCT - Centro Interuniversitário de História das Ciências e da Tecnologia, Pólo Universidade Nova de Lisboa,
Lisboa, Portugal
Keywords: Knowledge Transfer, Open-Innovation, Collaborative Networks, Social Network Analysis.
Abstract: Recent studies show that a growing number of innovations that are introduced in the market come from
networks of enterprises that are created based on core competencies of each enterprise. In this context, the
characterization and assessment of the knowledge transfer among members within a network is an important
element for the wide adoption of the networked organizations paradigm. However, models for
understanding the knowledge transfer and indicators related to knowledge transfer in a collaborative
environment are lacking. Starting with some discussion on mechanisms of production and circulation of
knowledge that might operate in a collaborative environment, this paper introduces an approach for
assessing knowledge circulation in a co-innovation network. Finally, based on experimental results from a
Portuguese collaborative network, BRISA network, a discussion on the benefits, challenges and difficulties
found are presented and discussed.
1 INTRODUCTION
In order to be competitive, enterprises must develop
capabilities that will enable them to respond quickly
to market needs. According to several authors, one
of the most relevant sources of competitive
advantage is the innovation capacity (Tidd, 2005);
(Argote, 2000). However, innovation capacity
requires access to new knowledge that enterprises do
not usually hold. As a result, enterprises can
improve their knowledge either from their own
assets, making sometimes high investments, or from
the knowledge that may be mobilized through other
enterprises based on a collaborative process. In fact,
there is an intuitive assumption that, when an
enterprise is a member of a long-term networked
structure, the existence of a collaborative
environment enables the increase of knowledge
production as well as the transfer of knowledge, and
thus the enterprises may operate more effectively in
pursuit of their goals (Abreu, 2010).
However, in spite of this assumption, it has been
difficult to prove its relevance due to the lack of
models that support mechanisms that explain the
production and transfer of knowledge in
collaborative environment. Furthermore, the absence
of indicators related to knowledge transfer – clearly
showing the amount of knowledge transferred and
the impact of this knowledge at a member level, for
instance, in terms of capacity for generating new
ideas, processes and products, organizational
improvement through the combination of the
existent resources, and diversity of cultures and
experiences of other enterprises – might be an
additional obstacle for a wider acceptance of this
paradigm.
In this context, the definition and application of a
set of indicators can be a useful instrument to the
network manager, and also to network members.
This work aims at contributing to answer the
following main questions:
• How is knowledge transferred from one network
member to another?
• How can competences circulation be analyzed in
a collaborative context based on an inter-
organizational perspective in order to support
decision-making processes?
2 SOME BACKGROUND
Upon reviewing the international literature, we find
183
Abreu A. and Urze P..
An Approach to Measure Knowledge Transfer in Open-Innovation.
DOI: 10.5220/0004811801830189
In Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems (ICORES-2014), pages 183-189
ISBN: 978-989-758-017-8
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

many studies highlighting the societal importance of
innovation and knowledge within modern economies
(Castells, 2005); (Soete, 2006). "Knowledge
Economy" are highly regarded concepts, but we
could mention other interesting works from Toffler
(2003), Bell (1974), or Giddens (1990).
Knowledge always played an important role in
the economy. But only over the last few years has its
relative importance been recognised, just as that
importance is growing. However, the stock of
knowledge upon which economic activity is based
today is definitely much larger than in previous eras.
In the emergent economy and society, the
accumulation of knowledge becomes the main
motivational strength towards growth and
development (Gosman, 1991); (Maskel, 1999) and
(Urze, 2011).
Actually, the last decades have shown a
generalised concern about the study on how
companies create knowledge and, particularly, on
how they operate this transference. Knowledge is
recognised as a principal source of economic rent,
and the effective management of organizational
knowledge has increasingly been linked to
competitive advantage and is considered critical to
the success of the business firm. One of the
distinctive features of the knowledge-based
economy is the recognition that the diffusion of
knowledge is just as significant as its production,
leading to increased attention to "knowledge
distribution networks" and “national systems of
innovation”. These are the agents and structures
which support the advance and use of knowledge in
the economy and the linkages between them.
In this line of thought, Gibbons (1994) introduce
a distinction between Mode 1 knowledge
production, which has always existed, and Mode 2
knowledge production, a new mode that is emerging
alongside it and which is becoming more and more
relevant. While knowledge production used to be
located primarily at scientific institutions
(universities, government institutes and industrial
research labs) and structured by scientific
disciplines, its new locations, practices and
principles are becoming much more heterogeneous.
Mode 2 knowledge is produced in different
organizations, resulting in a heterogeneous practice.
The potential sites for knowledge production include
not only the traditional universities, institutes and
industrial labs, but also research centres, government
agencies, think-tanks, and high-tech spin-offs.
Mode 2 refers to a production of knowledge
which is not exclusively reserved for qualified
academic research but focuses on the different actors
integrated in a contextualised problem-solving
oriented process. The importance of knowledge is
then assessed by its social value and interest to
stakeholders engaged in the process of production.
Five main features of Mode 2 summarise how it
differs from Mode 1. First, Mode 2 knowledge is
generated in a context of application; Mode 1
knowledge can also result in practical applications,
but these are always separated from the actual
knowledge production in space and time. A second
characteristic of Mode 2 is transdisciplinarity, which
refers to the mobilisation of a range of theoretical
perspectives and practical methodologies to solve
problems. Transdisciplinarity goes further than
interdisciplinarity in the sense that the interaction of
scientific disciplines is much more dynamic.
Theoretical consensus cannot easily be reduced to
specific scientific parts. Thirdly, Mode 2 knowledge
is produced in a diverse variety of organisations,
resulting in a very heterogeneous practice. The
potential sites for knowledge generation include not
only the traditional universities, institutes and
industrial labs, but also research centres, government
agencies, think-tanks, high-tech spin-off companies
and consultancies. These sites are linked through
networks of communication, and research is
conducted in dynamic interaction. The fourth feature
is reflexivity. It means that researchers become more
aware of the societal consequences of their work
(‘social accountability’). Sensitivity to the impact of
the research is built in from the start. Novel forms of
quality control constitute the fifth characteristic of
the new production of knowledge. Traditional
discipline-based peer review systems are replaced by
additional criteria of economic, political, social or
cultural nature.
Figure 1: Production of knowledge environment 1A)
Mode I and 2B) Mode I.
In Mode 2, research is carried out in the context
of application in which there is a continuing
dialogue between interested parties – including
producers and users of knowledge – from the
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beginning. Thus, the concept of knowledge transfer
has to be reconsidered. It cannot be understood as a
simple transmission of knowledge from the
university to the receiver. The participants may
include business people, venture capital, industry,
research centres and many others in addition to the
university. In short, all need to become actively
engaged in the process of knowledge production and
its transfer.
Figure 1 illustrates the two modes (I, II) of
knowledge production and its transfer taking as
environment the collaborative networks.
3 A MODEL TO ANALYSE
KNOWLEDGE TRANSFER
Based on the literature (Gibbons, 1994); (Forzi,
2004); (Abreu, 2008); (Camarinha-Matos, 2008);
(Urze, 2012), and taking into account the context of
collaborative networks, to analyse and understand
the processes and mechanisms of knowledge transfer
in a collaborative network, it is necessary to develop
a model that supports, as a first approach, the
following perspectives:
• Transfer Mechanisms – This perspective
focuses on the identification and characterisation
of distinct ways of “physical” interrelationship
that support the process of knowledge transfer
between enterprises within a network, such as
internal publications, external publications,
reports, patents, exchange of resources between
organizations, videoconferencing, infrastructure
to support collaborative processes (e.g.
workgroup tool), telephone / mobile phone,
informal meetings, and periodic meetings.
• Competences Management - This perspective
addresses the principles, policies, and
governance rules that may facilitate or constrain
the processes of creating the competence and
searching for competences by the members of
the network. Therefore, general issues such as
definition of accessibility levels (e.g. public,
internal to network members or private),
definition of policies in terms of competence
dissemination among members of the network,
definition of principles to assure the transparency
and traceability of the competences in the
network), and definition of rules in terms of
Intellectual Property rights (IPR) (e.g.
confidential or non-confidential) are considered
here.
• Nature of the Relationships - The nature of the
relationships determines the way collaborative
space enables or facilitates the flow of
knowledge among enterprises. Thus, this
perspective focuses on the identification and
characterisation of the various types of
relationships that enterprises may have with
other enterprises within the network: the
relationships with new enterprises created from
existing enterprises that belong to the network
(e.g. spin-offs and start-ups) and also the
relationships between the network as a whole
and external entities (e.g. suppliers, customers,
end-users, competitors, external institutions, and
potential new partners).
Figure 2 illustrates the proposed model for the
analysis of knowledge transfer in the context of
network organizations.
Figure 2: Knowledge transfer model.
In order to analyse the processes of knowledge
transfer in a collaborative network, it is necessary to
develop a model that supports the analysis of
knowledge transfer among enterprises.
In an attempt to contribute to such need, we start
with the assumption that the processes of knowledge
transfer in a collaborative network can be
represented graphically through a graph.
Therefore, as a first approach, using concepts
from Social Network Analysis it is possible to apply
several graph properties and relating them to
circulation of knowledge.
To illustrate the potential application of graph
properties let us consider some simple examples
“archetypes” in this discussion. Assuming the degree
of a node is a measure of the “involvement” of the
enterprise in the network, it may be relevant to
analyze the knowledge transfer process based on this
perspective. According this approach, a network can
be classified as decentralized or centralized. A
network is decentralized when all enterprises have
equal value of nodal degree (in-degree and out-
degree), otherwise the network is centralized.
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185

Figure 3A illustrates an example of decentralized
knowledge transfer network supported by a
mechanism of indirect reciprocity and Figure 3B)
shows an example of centralized knowledge transfer
network supported by a mechanism of direct
reciprocity. However, comparing these two types of
network, a knowledge transfer process supported on
a decentralized network might be more attractive,
since the number of provide/receive new
competences is identical for all enterprises.
Figure 3: Decentralized vs. centralized knowledge transfer
network.
Based on analyse of network connectivity Figure
4A) shows an example of acyclic network. This type
of network is characterized by a weak connectivity
among enterprises.
According to this approach the existence of
acyclic knowledge transfer network means that there
are enterprises that provide/receive competences
to/from someone and do not receive/provide none
from/to others. As a result, for some enterprises (in
this case, enterprise E
i
and E
m
) the participation in a
collaborative process supported by acyclic
knowledge transfer network might not be
advantageous, unless one of the following
assumptions is verified:
 The enterprises believe that its actions can be
perceived as an investment and later on, they can
get some competences or benefits from others.
 The enterprises that receive new competences
recognize a “debit” as a result of contributions
received in the past.
On the other hand, Figure 4B) shows an example of
cyclic network. A cycle is a closed walk of at least
three nodes in which all links are distinct, and all
enterprises except the beginning and ending
enterprises are distinct. Consequently, the
development of a knowledge circulation process
based on a cyclic transfer network assumes that
enterprises provide/receive new competences
to/from someone and simultaneously
receive/provide new competences from/to others. As
a result, the participation in a knowledge transfer
process supported by cyclic or closed walk
knowledge transfer network is usually more
attractive.
Figure 4: Acyclic vs. Cyclic network of knowledge
transfer.
Table 1: Indicators for competences production and
circulation analysis.
Indicator Potential Use Expression
Total of
Competences
(C)
This indicator measures the level of
versatility/polyvalence of the
network.
C – Number of
distinct competences
involved in the
network
Total of
enterprise
Owned
Competences
(TOC)
This indicator measures the level of
expertise and the potential capacity
of an enterprise in terms of
knowledge transfer.
TOC = Number of
competences held by
an enterprise.
Apparent
Owned
Competence
Index (AOCI)
An enterprise with an AOCI close to
one means that this enterprise is the
owner of nearly all competences
available within the network.
M
TOC
AOCI 
M – Number of
competences held by
the network
Owned
Competences
Index
(OCI
i
)
Normalization of the number of
competences held by an enterprise
in relation to other members of the
network.
Benchmarking with enterprises
involved in other networks.



N
j
j
i
i
TOC
TOC
OCI
1
N – Number of
enterprises involved
in the network
Owned
Competences
Progress Ratio
(OCPR
i
)
The aim of this ratio is to measure
the progress of competences held by
an enterprise over a period of time.
If:









decreasedOCPR
increasedOCPR
changenoisthere
OCPR
i
i
tt
i
1
1
1
21
,
Benchmarking with enterprises
involved in other networks




1
2
21
,
t
i
t
i
tt
i
OCI
OCI
OCPR 
12
tt 
Competences
Abundance
(CA
i
)
This indicator measures the level of
abundance of a competence inside
the network. A competence with a
CA near to zero means that it is
exclusive because it is owned by
few enterprises of the network.
CA
i
= Number of
ownership relations
connected to
competence i.
Apparent
Competences
Exclusivity
Index
(ACEI
i
)
This index gives a simple to
compute measure of exclusivity of a
competence. A competence with an
ACEI near to zero means that such
competence belongs to few
enterprises. On the other hand, a
competence with an ACEI close to
one means that such competence is
owned by all enterprises in the
network.
N
CA
ACEI
i
i

N –Number of
enterprises involved
in the network
Competences
Exclusivity
Index
(CEI
i
)
Normalization of the level of
exclusivity of a competence in the
network.
Benchmarking with other networks.



M
j
j
i
i
CA
CA
CEI
1
M – Number of assets
held by the network
Competences
Exclusivity
Progress Ratio
(CEPR
i
)
The aim of this ratio is to measure
the variation of exclusivity of a
competence over a period of time.
If:









decreasedCEPR
increasedCEPR
changenoisthere
CEPR
i
i
tt
i
1
1
1
21
,
Benchmarking with other networks




1
2
21
,
t
i
t
i
tt
i
CEI
CEI
CEPR 
12
tt 

B
Ei
Ej
KT(j)
KT(m)
Em
KT(i)
KT(i)
Ei Ej
KT(j)
KT(i)
Ek
KT(k)
A
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Since, the most favourable network for
promotion of knowledge transfer is dependent on the
existence of cycles or close walk processes, it is
useful to analyse in detail the conditions that drive
the emergence of this type of structure. Therefore, in
order to establish a close walk process it is necessary
to satisfy the following three conditions:
 Provide Condition – Enterprises must provide
new competences. For each enterprise E
j
, there is
at least another enterprise E
k
to which E
j
provides a new competence.
 Receive Condition – Enterprises have to receive
new competences. For each enterprise E
k
there is
at least another enterprise E
j
from which E
k
receives a new competence.
 Identity Condition – Enterprise E
k
≠ E
j
.
Taking into account the context of collaborative
networks, and combining concepts borrowed from
the Social Networks Analysis (SNA) area. Table 1
shows a number of basic indicators that can
contribute to evaluate the level of expertise of an
enterprise and how production and circulation of
knowledge is done within the network. Furthermore,
these indicators can be determined for a particular
collaboration process or over a period of time
(average values) and can be used in decision-making
processes, such as the planning of a new
collaborative network.
However, the use of graphs implies a partial
view and consequently, a limitation of this approach.
In order to have a full description it is important to
combine other tools (such as: game theory, causal
models, fuzzy tools, belief networks, etc.) to analyse
in detail the impact of the three dimension proposed
in a knowledge transfer process.
4 BRISA CASE STUDY
The paper’s empirical section is based on one case
study pointed to the largest Portuguese motorway
operator. Brisa - Auto-estradas de Portugal, founded
in 1972, currently operates, on a concession basis, a
network of 11 motorways, with a total length of
around 1096 km, constituting the main Portuguese
road links. The Brisa co-innovation network is a
long-term collaborative network that has more than
30 members from several domains and business
activities (e.g. researches institutions, universities,
associations, governmental entities, start-ups,
business angels, and suppliers).
The empirical work is grounded on two main
projects developed by BRISA, namely E_TOLL –
Electronic Tolling System a self-service toll lane
where it is possible to pay by a bank card, money
and ALPR – Advanced License Plat Recognition an
enforcement system based on the automatic license
plate recognition for situation where the vehicle is
not equipped with an on-board-unit (OBU) or the
OBU fails to electronically identify the vehicle. In
the case study three techniques were combined to
carry out the empirical research: in-locu observation
of the work processes, semi-directive interviews and
questionnaires addressed to actors belonging to
different organizations that take part of E_TOLL and
ALPR.
Taking into account the data collected, Table 2A
shows the types of competences used by each
partner in the collaborative projects, and Table 2B
identifies the types of competences held by each
partner in the end of the collaborative projects.
Applying the equations defined in Table 1, Table
3A evaluates the production of new knowledge
based on the number of distinct competences held by
network in the end of the project E_TOLL and
ALPR, and the number of different competences
used by the network when the projects started. Based
on these data, it is possible to verify that 6 new
competences were produced (C19, C20, C21, C22,
C23, and C24).
Table 2: Record of the competences.
Entity C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 Total
O1 111100000000000000000000 4
E1 010100000000000000000000 2
E2 000000000000000001000000 1
E3 000010000000000000000000 1
E4 010101000000000000000000 3
E5 000000100000000000000000 1
E6 000000000001111100000000 5
E7 100000000000001010000000 3
O2 000000011110000000000000 4
Total 231311111111112111000000 18
Competences
Entity C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 Total
O1 111100000000000000111110 9
E1 010100000000000000000000 2
E2 001100000000000001000000 3
E3 000010000000000000000000 1
E4 111101000000000000000000 5
E5 000000100000000000000000 1
E6 000000000001111100000000 5
E7 100000000000001010000000 3
O2 000000011110000000000001 5
Total 333411111111112111111111 34
Competences
Table 3B shows indicators to analyse, for
instance, how the competences are held by network
members, and the benefits of the entities’
participation in a collaborative process. Assuming
that the benefits of an entity can be viewed as the
capacity of involvement in a collaborative process;
in this case, we are not particularly concerned with
whether this benefit is due to the development of
exclusive competences, but rather in analysing how
many distinct competences might be performed by a
member. According to Owned Competences
AnApproachtoMeasureKnowledgeTransferinOpen-Innovation
187

Progress Ratio (OCPRI), at the end of those two
projects, there are three members, O1, E2, and E4
that had a significant increase in terms of acquiring
new competences that might be used in the future,
and consequently, they have more opportunities to
participate in collaborative processes than those who
have a low ratio.
Table 3C illustrates some examples of indicators
to evaluate, for instance, the level of exclusivity of
each competence and the circulation of competences
among members. Based on these data, it is possible
to verify, for example, that according to
Competences Exclusivity Progress Ratio (CEPR),
the highest value belongs to competence C3
(infrared illumination) that had a great proliferation
among members of the network.
Table 3: Indicators for Knowledge production and
circulation analysis.
Start Finish
C 18 24
A
Entity
TOC AOCI OCI TOC AOCI OCI OCPR
O1 4 0,22 0,17 9 0,38 0,27 1,64
E1 2 0,11 0,08 2 0,08 0,06 0,73
E2 1 0,06 0,04 3 0,13 0,09 2,18
E3 1 0,06 0,04 1 0,04 0,03 0,73
E4 3 0,17 0,13 5 0,21 0,15 1,21
E5 1 0,06 0,04 1 0,04 0,03 0,73
E6 5 0,28 0,21 5 0,21 0,15 0,73
E7 3 0,17 0,13 3 0,13 0,09 0,73
O2 4 0,22 0,17 5 0,21 0,15 0,91
Start Finish
B
Competences CA ACEI CEI CA ACEI CEI CEPR
C1 Computervision 2 0,22 0,11 3 0,33 0,09 0,79
C2 SoftwareEngi nee ring 3 0,33 0,17 3 0, 33 0,09 0,53
C3 Infraredillumination 1 0,11 0,06 3 0,33 0,09 1,59
C4 Automaticpatternrecogniti on 3 0,33 0,17 4 0, 44 0,12 0,71
C5 Tollsystems 1 0,11 0,06 1 0, 11 0,03 0,53
C6 InformationSystemsArchi te cture , 1 0,11 0,06 1 0, 11 0,03 0,53
C7 IndustrialDesign 1 0,11 0,06 1 0,11 0,03 0,53
C8 Modellingofproducts 1 0,11 0,06 1 0,11 0,03 0,53
C9 Rapidprototyping 1 0,11 0,06 1 0, 11 0,03 0,53
C10 Developmentofmolds 1 0,11 0,06 1 0, 11 0,03 0,53
C11 Plasticinje ction 1 0,11 0,06 1 0, 11 0,03 0,53
C12 FunctionalTe sts 1 0,11 0,06 1 0, 11 0,03 0,53
C13 SoftwareDeve lopment 1 0,11 0,06 1 0, 11 0,03 0,53
C14 SoftwareArchitecture 1 0,11 0,06 1 0,11 0,03 0,53
C15 ProjectManagement 2 0,22 0,11 2 0,22 0,06 0,53
C16 FunctionalAnalysis 1 0,11 0,06 1 0,11 0,03 0,53
C17 Remotemonitoring 1 0,11 0,06 1 0,11 0,03 0,53
C18 Supplierofequipmentforimagecapture 1 0,11 0,06 1 0, 11 0,03 0,53
C19 ElectronicTollCollection(ETC)systems 0 0,00 0,00 1 0, 11 0,03 ‐‐‐‐
C20 InformationSystemsopentomulti‐vendor 0 0,00 0,00 1 0,11 0,03 ‐‐‐‐
c21 Automati cvehicleidentificationsyste ms 0 0,00 0, 00 1 0,11 0,03 ‐‐‐
C22 Communicationsystemsbetweenvehicles 0 0,00 0,00 1 0,11 0,03 ‐‐‐
C23 Classificationsystemsofvehicles 0 0,00 0,00 1 0,11 0,03 ‐‐‐‐
C24 Shortrunproduction 0 0,00 0,00 1 0,11 0,03 ‐‐‐
FinishStart
5 CONCLUSIONS
Reaching a better characterization and understanding
of the mechanisms of production and circulation of
knowledge in collaborative networks is an important
element for a better understanding of the behavioral
aspects, and also to improve the sustainability of this
organizational form.
The development of a set of indicators to capture
and measure the circulation and production of
knowledge can be a useful instrument to the
manager of this network, as a way to support the
promotion of collaborative behaviors, and for a
member as a way to extract the advantages of
belonging to a network. Using simple calculations as
illustrated above, it is possible to extract some
indicators. Some preliminary steps in this direction
were presented. However, the development of
indicators to measure the potential impacts and
worth related to production and circulation of
knowledge, for instance, at a member level, in terms
of capacity of generating new ideas, development of
new processes, new products or services,
organizational improvement through the
combination of the existent resources and diversity
of cultures and experiences of other enterprises is
not yet well understood and requires further research
and development.
ACKNOWLEDGEMENTS
This work was partially supported by BRISA Innovation
and Technology (BIT) through a research and
development project.
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