Goals and Scientific Approach
Summary
During the last decades the amount of studies related to innovation systems has been huge, originating a great interest for policy makers, aiming for scientific background and technical support to choose the more adequate strategies for development. In general, the different approaches are consensual on the fact that knowledge creation,
at the long run, and innovation, at a shorter term, are the drivers of change and growth for sectors and territories. But such a consensus is broken as soon as the complexity of innovation and knowledge are addressed; Innovation goes much beyond new product or process development due to its interactive nature, and knowledge surpasses the firm attribute because it is also a spatial endogenous characteristic.
From this perspective, three levels of innovation analysis may be used to improve the conceptual dimension of innovation and knowledge creation: The micro, meso and the macro levels of the system as pointed out in the next Figure 1.
Figure 1
Knowledge circuit in the process of sustainable growth
Source:
Vaz, M.T.N. and Nijkamp, P., (2009) "Knowledge and innovation: the strings between global and
local dimensions of sustainable growth", in Entrepreneurship and Regional Development.
Within the above described framing context there are still several unsolved problems
requiring attention as it is the case of rules, norms, conventions or shared practices
that form national or regional patterns of interactions among institutions. They provide
those bases for the dissemination of knowledge which promote innovations further.
The present project is a contribution to the earlier discussion and represents an effort
to develop a methodology able to answer to the following two questions: 1) How are
institutions relating to each other and how do they create networks of innovation?; 2)
How can such inter-relationships be detected at spatial level?
The two questions have been at the core of innovation literature, addressed from
various angles and scientific fields such as: the economic (e.g. Antonelli & Calderini,
1999), the sociological (e.g. Granovetter, 1994), the strategic management (e.g. Porter,
1998), and the psychological (e.g. Pounder & John, 1996) – perspectives that offer
different but complementary views on the subject.
The suggested research method aims to better understand the issue of "networks of
innovation": First, by identifying networks of innovation and then evolving into a deeper
understanding of such networks by mapping the existent relationships and theirbackward motivations. The application of such methodology to Portugal is ideal. The
country has concentrated most of its development efforts into focused innovation policy
in the last four years. Therefore, our proposal foresees the creation of a large database
by gathering data from a sample of more than one thousand private companies and
one hundred public institutions – all of them analysed through the content of the
respective WebPages. Data shall also be gathered from already existent primary data
(GLEARI, MCTES - CIS 6 (6805 firms), COTEC (100 most innovative firms), and
IAPMEI (DB of 100 to 1500 PMEs and data on Patent registers) ). We should note that,
the amount of information provided nowadays in the internet about the relational flows
among firms is quite interesting, and can also be used as a basis for further primary
data construction. The database will allow the use of multivariate statistical methods to
detect group performances of innovative firms by location in terms of inter-firm
relationship. Also this data, hence spatially identified, will permit graph-flows design to
understand where and how strong are the links for innovation in Portugal and the
respective level of concentration or dispersion. If possible, a parallel evaluation of
innovation policy in the country and the detected innovation flows can supply the
arguments to sustain the level of effectiveness of innovation systems in the country –,
may they be national or regional innovation systems. GIS and SNA could provide
methods to approach such concerns.
In summary, the general goals of this project are:
• To contribute for a better understanding of innovation in Portugal. By mapping
the relational structure of innovative firms and public institutions in the country
advantages and fragilities in the firms' capacity to cooperate may be detected;
• To clearly define cooperative forms within the existent networking innovative
systems in order to design performing governance structures at meso-economic
level and, eventually, dynamic changes of such structures;
• To identify public/private partnerships and judge their added value for
innovativeness;
• To conclude on the concentration or dispersion level of innovation flows,
evaluating in terms of policy effectiveness.
Innovation and change
Innovation has been an element of human intelligence from its earliest stages of
development, although, only recently, has it been recognized as a significant driver of
social and economic change. Schumpeter (1934; 1954), Freeman (1987), and
Fagerberg (2003) are among the most important contributors to this view.
One of the ways to induce the process of change is to contemplate continuous
production of new products or processes while, at the same time, encouraging
adaptation of the society to absorb them. This represents a very accurate attempt to
combine knowledge and consumption, in an interactive model for innovation, which
requires a capacity of the organizations to coordinate and manage the knowledge
assets (Hall, 2004 and Lundvall, 1988 and1992).
Innovation and knowledge
Posner (1961), Krugmann (1979), and Fagerberg (1987; 1988) maintain that, at crosscountry
level analyses, the presence or lack of innovation may "affect differential
growth rates". An imitative or innovative modus operandi may explain different levels of
development among countries or regions, the so called "technology gap" or even the "north-south" asymmetry. Thus, Schumpeter's concern with the tendency of
innovations to cluster, in spite of the closed link between innovation and economic
growth, suggests that its use as an instrument of public policy, to promote fast
development, may require more detailed attention. Fagerberg (2004) helps in supplying
the epistemological limitations of this research field: cross-disciplinarity, undetermined
causality; path dependency, pluralistic-leadership, systemic approach.
The multiple efforts to better perceive drivers of innovation took research to the resource-based view of the firms, accepting their heterogeneous character and unique choices related to strategic behavior (Knudsen, 1995; Kaleka, 2002). In this context, knowledge is recognized as a key resource for firms and other economic agents and, both codified knowledge and tacit knowledge are pertinent aspects of innovativeness. (Albino et al., 1999; Nooteboom, 1999).
Networks of innovation
It is still under discussion whether the co-operation between research institutes and
industrial firms enhances creativity and innovation, as argued by Antonelli and
Calderini (1999) or, contrarily, that such links are of minor importance, as defended by
Diederen et al. (2000). In any case, it seems to be of common agreement that the
impact of the co-operation with research institutes is sector-related. In general, high
tech firms tend to co-operate more often with research institutes than firms producing in
low technology areas.
Additionally, some authors have stressed the key role of 'good communication'
between industry and research institutes for the successful transfer of technological
knowledge (Kaiser, 2002).
Another body of the literature has taken a different approach to the problem of
networks. In effect, the writings on clustering have contributed to describe how – yet
not so much to explain why – organizations and institutions get together to face
competitive challenges (e.g. Porter, 1998). A few attempts, however, exist to explain
why people from different entities join efforts to collaborate. Porter and Sölvell (1998),
for example, explain that a cluster offers an environment for the development of a
common language, social bounds, norms, and values, i.e. a social capital. Pounder and
John (1996) take a deeper view, and try to understand the cognitive reasons behind
the existence of a cluster. They explain that in a cluster, managers and decisionmakers
share a wide number of values, cognitive references, perceptions, and
experiences (called normative isomorphism), hence they tend to establish connections
and follow the same patterns of organizational behaviour (e.g. competing,
collaborating, and so on). Furthermore, there may also exist negative consequences of
such isomorphism; since they all share a particular culture and a set of beliefs, there is
a risk of strategic myopia, which reinforces imitator and non-innovative behaviours.
In spite of much literature on this issue, there is much confusion on the terms
networking and clustering. According to Teigland et al. (2005) while the term cluster is
widespread, no one universal definition exists, however. "We define a cluster as a
spatial agglomeration of similar and related economic and knowledge creating
activities". In turn, the concept of Regional Innovation Systems (RIS) has been defined
as "a network of organizations, institutions and individuals, within which, the creation,
dissemination, and exploitation of new knowledge and innovation occurs" (Cook et al.
2004). The RIS concept has been introduced to describe how industrial and
institutional structure of a given national or regional economy tends to steer
technological and industrial development into certain trajectories. The link between "clusters" and "regional innovation systems" is that within these spatial systems, groups
of similar and related firms (e.g large and small firms, suppliers, service providers,
customers, rivals, etc) comprise the core of the cluster, while academic and research
organisations, policy institutions, government authorities, financial actors and various
institutions for collaboration and networks make up the innovation system of which the
cluster is a part (Teigland and Schenkel, 2006).
Mapping networks of innovation to focus on creativity
Due to recent information systems applications it is possible to identify and geographically locate the different patterns of networking systems within a certain national or regional context. So far, innovation has been investigated mainly from amicro and a macro perspective, not so much from a meso-economic approach comprehending the way how firms and other agents co-operate (intensity, flow continuity, and type of exchanges). Or, no attention at all has been due to geographic design that such networks may create. Information system managers have developed tools that are able to map such kind of relationships, even to measure the amount of spillovers that can result from links established among economic agents. Fisher, 2006, supplies much information on such techniques and our goal is to use them in favour of analysing Innovation Networks in Portugal. Based in Csikszentmihalyi, 1996, by which creativity and innovation should be associated and can be either fostered or blocked by the degree to which interact and cooperate we expect further to identify geographic niches of creative behaviours.
Justification
This project shall be a contribution to a better understanding of innovation flows in
Portugal. By mapping the relational structure of innovative firms and public institutions
in the country, many advantages and fragilities in the firms' capacity to cooperate may
be detected; also their cooperative patterns will be identified. Such major goal
represents a step further into governance structures. Seem from a meso-economic
level, eventual dynamic changes of such structures can be traced for the future and
help in tailor made policy making, without much additional costing. Agent partnerships
may be evaluated and judge their added value for innovativeness. Also bottlenecks can
be identified when approaching the concentration or dispersion level of innovation
flows, also helping in the evaluating of innovation policy effectiveness.
This project comes at the right time, when the Government, in power since March
2005, has as its main flag a Technological Plan and as part of its commitment to fulfil
the Lisbon strategy, renewed an Integrated Plan, the PNACES – Plano Nacional de
Acção para o Crescimento e Emprego 2005-8. This Plan had as one of its main goals "to increase the competitiveness of Portuguese economy", through the implementation
of the Technological Plan. The main question is now: How did it succeeded? A further
justification is provided by the soon announced Strategies for Collective Efficiency
based on Clusters and Economical Valorisation of Endogenous Resources (http://www.pofc.qren.pt/PresentationLayer/conteudo.aspx?menuid=457).
In these circumstances, the management of knowledge transfer concerns academic
and research organizations, policy institutions, financial actors, and large and small
firms existing in the country and becomes crucial for innovation promotion. It is hoped
that an improved understanding of how this knowledge transfer takes place will enable,
the innovation actors to overcome these challenges while facilitating their ability to
create and sustain a knowledge – based competitive advantage.
Specific objectives
It is expected to address this issue using methods of multidisciplinary research.
Therefore, the research aims the following methodological goals:
• To provide a data base composition on the actors of innovation. This
relates an extended number of organizations, respective historical
background and own goals, kind of evolving cooperation and exchanges. A
description of patterns of linkages and relationships.
• To develop a complete set of indicators that can be used to describe
innovation agents and respective cooperation forms; identification and
characterization of organizational networks of innovation; selection of certain networks (criteria – relationships intensity); game of power and
dominance; detection of creative environments and promoting factors.
• To identify alternative research methodologies, instruments and
techniques to carry out the main study. Factors determining specific
behavioural patterns will be selected using methods, among others related
to multivariate statistics and non linear regressions.
• To map the detected relations by flow-graphs techniques and by
Geographic Information Systems.
• To characterize and evaluate the innovation networking system and its
architecture by case study approach. A note for the fact that only those
more intensive relationships shall be analyses from a micro perspective.
Such specific goals may be methodologically linked reflecting management tasks of the
project. They are the following, as in Figure 2.
Methods by task
• Task I – Advances in the literature revision
Methods: Choice and adequacy of different theoretical views as a positive
contribution to a multidisciplinary approach. This task will produce two or more
research reports which will be first presented in scientific and industrial national
and international meetings, and then subject to the international articlesubmission
process.
• Task II – a complete map of all potential agents involved in creativity and
innovation in the country
Methods: Data base composition on the actors of innovation. This relates an
extended number of organizations, respective historical background and own
goals, kind of evolving cooperation and exchanges. A description of patterns of
linkages and relational flow –graphs will be supplied. This shall be mapped in a
GIS application or Social Network Analyses
• Task III – Identification and characterization of innovative networking systems
and building a set of related indicators.
Methods: a complete set of indicators that can be used to describe innovation
agents and respective cooperation forms; identification and characterization of
organizational networks of innovation (Please, see detailed variables in Table 1,
in the Attachment file); selection of certain networks (criteria – relationships
intensity); game of power and dominance; detection of creative environments
and promoting factors.
• Task IV – Evaluation of innovative and creative behaviours within the
organizations
Methods: Identification of alternative research methodologies, instruments and
techniques to carry out the main study. Factors determining specific behavioural
patterns will be selected using methods, among others related to multivariate
statistics and non linear regressions.
• Task V – Case study research
Methods: Identification and characterization of the main constructs to be used in
the research
Tasks
Task I – State of the Art expansion
There are five sets of tasks in the current project. The first one is an expansion of the
literature review; the next two are based on secondary data, collected via Internet and
other existing search engines. The fourth task is preparatory to the fifth task, and both
are based on primary data, for which multiple techniques within a case study strategy
will be used. Figure 1 depicts the sequence and interdependence of all tasks.
Notwithstanding a thorough literature survey in preparation for the current project, there
is a need for an expansion of such a review, so that a preliminary research model is
produced and diffused throughout the scientific and practitioner communities. This
research model will expand on the need to carry out the current project, from both a
theoretical and practical perspective. Moreover, a more comprehensive literature
review will allow identifying alternative research methodologies as well as a set of
research instruments to be used in later tasks. These impacts are depicted in figure 1,
and can be summarized as follows:
Task I - will produce two or more research reports which will be first presented in
scientific and industrial national and international meetings, and then subject to the
international article-submission process.
Task II – a complete map of all potential agents involved in creativity and innovation in
the country;
Task III – a complete set of indicators that can be used to describe innovation agents
behaviour patterns;
Task IV – identification of alternative research methodologies, instruments and
techniques to carry out the main study;
Task V – identification and characterization of the main constructs to be used in the
research. Micro analyses by case-study identification.
Task II – Data Base composition on the actors of innovation: mapping the
activity of innovation agents
Innovation agents are of various types and categories. From an initial literature review
and our own experience of previous projects, the following innovation agents can be
proposed:
• Governmental entities: all entities pertaining to the sphere of governmental
power and which exercise regulator roles in political terms as far as innovation
is concerned. Furthermore, they play an important role in the promotion,
administration, financing, and evaluation of creativity and innovation processes
in the country.
• Associations: this category includes all entities with a legal status which, depending on the interests of their associates, influence creativity and innovation. Examples of activities of such associative entities include:
o Sector or regional cooperation
o Knowledge transfer management
o Support to value creation (e.g. certification)
o Partnerships
• Technological parks and centers: in this category one can find institutions which
give technical, technological or other type of support to organizations in the
same economical or industrial sector. These entities contribute to creativity and
innovation processes through numerous ways: technology transfer,
partnerships, and certification.
• R&D organizations: organizations which direct their main activities to R&D, and
which concentrate on large economical and industrial application (this category
does not include private and public companies whose main activity is not R&D,
though they may have large investments in R&D activities).
• Entrepreneurship support entities: it refers to structures or organizations which
aim to stimulate creative and entrepreneurship activity.
• Technological schools: these are concerned with entities which aim to provide
technological and professional training and education in innovation-related
areas.
• University interfaces: it includes structures, units, or university associations,
operating in a particular university, and which aim to act as an interface
between the university and private and public companies.
• Companies: public and private organizations involved in innovation and/or with
investments in innovation activity.
• Other: other entities with a role in creativity and innovation and which have not
been included in any of the previous categories.
Task III – Identification and characterization of innovative networking systems
and building a set of innovation-flow
indicators
All innovation agents identified in Task II will be characterized in terms of several
indicators created within the current project. Since Task II is largely based on Internet
search, the following set of indicators portrays intentionality more than factual data:
• Purpose: the goal of the particular entity as far as innovation is concerned.
• Promotion: it indicates whether the particular entity aims to promote innovation
though process improvement, product innovation, new organizational forms,
and so on.
• Research: it indicates if the entity aims to study and research on innovation.
• Management: it indicates if the entity aims to manage knowledge transfer and
innovation.
• R&D investments: it indicates whether or not the entity carries out and/or
finances R&D activities.
• Knowledge transfer: to classify entities as far as information diffusion, and
knowledge transfer is concerned.
• Support to entrepreneurship: if the entity promotes company-creation and
sustenance of entrepreneurship activities.
• New product development: if the entity looks for developing new products,
processes, systems, services, organizational form and so on.
• Fostering partnership/cooperation: it indicates if the entity promotes cooperation
processes, communication between public and private companies and other
entities related with innovation activity.
• External technologies use: it indicates if the entity acquires innovative
technologies elsewhere, to use in its production processes.
• Company orientation: goals of innovation processes: commercial, industrial, or
organizational
In addition to this information, it is also important to collect data regarding:
• Entity history: when was it created and how it evolved?
• Geographical location of the headquarters and how is the entity operating at a
regional level?
• Target: who is the entity aiming at? (Universities, consumers, other businesses,
and so on).
• Financial and other performance data.
• Links with other entities: if the entity holds links to other entities, either through
real links in the website or expressed in the information made public by the
entity.
• Other: other activities which may favour innovation processes (training,
certification, information supply/provision and so on).
We also expect to be able to access other already existent sources of primary data
such as: GLEARI, MCTES - CIS 6 (6805 firms), COTEC (100 most innovative firms),
and IAPMEI (DB of 100 to 1500 PMEs and data on Patent registers.
Task IV – Evaluation of innovative and creative behavior within the organization: Development and testing of data-
collection instruments
This task actually comprises three sub-tasks, with the overall purpose of preparing data
collection for the fifth and final task of the current project. The three sub-tasks are:
• Contacting and securing commitment to the project: tasks II and III should
deliver a list of innovation agents organized according to various criteria, such
as industry sector and region. In order to collect data for task V, it is first needed
to initiate personal contacts with a selected number of agents, so that
commitment to the project is established and, in a later phase, primary data can
be collected from such agents. Furthermore, a key deliverable of this sub-task
is identification of key informants for task V. Key informants are individuals
and/or primary sources of information where data can be assessed and
collected. For example, a typical key informant is a top manager.
• Selection and/or development of instruments: task I should inform on available
instruments which can be used in task V to collect primary data from innovation
agents. Typical instruments include questionnaire surveys, interviews,
observations, and company documents and reports. From available
instruments, it should be possible to adapt, build, and prepare a full set of
instruments during the case study phase.
• Testing of instruments and of the case study strategy: this sub-task is of
paramount relevance since it will serve to test the case study strategy and
associated instruments to be used in task V. It will typically involve a pilot case
study (Yin, 1984), and its results will lead to refinement of the research method
and of the data-collection instruments. Additionally, it will produce a first picture
of the factors and reasons underlying a creativity and innovation network.
Task V – Case study research
Tasks II and III are the central descriptive activities in the current project. Its key
deliverable is a comprehensive picture of innovation networks in Portugal, involving
various types of innovation agents and according to numerous criteria.
Task V takes a step further, in that it goes deeper into the reasons which explain the
existence of a selected number of networks. Such reasons are both derived from the
literature, and from the empirical study, which denotes a combination of deductive and
inductive approaches.
Methodologically, this combination is achieved via a case study strategy. Hence, task V
depends on primary data collected in the process of a restricted number of case
studies. Furthermore, this method enhances the possibility to identify respectivesuccess cases and describe best practices for creativity and innovation within the
networking systems.
Attributes of the current case study strategy are:
o Protocol: the case study protocol will be the main guiding document linking
theory and practice. Specification of the concepts to be observed during data
collection is informed both by theory and practice, especially the pilot case
study.
o Type: according to Yin (1984), the current research method falls within a Type
IV, i.e. embedded multiple cases.
o Unit of analysis: the main unit of analysis is the cluster, or the innovation
network, but other embedded units are: the entity (organization) and the
individual.
o The innovation network will be partially identified during tasks II and III, through
secondary data, but it may be needed to take a more direct approach to identify
other entities of the network. For example, further entities of the network can be
identified after primary data of the first entity of the network is collected.
o Entities of the network can be geographically co-located or dispersed by the
country. For practical reasons, only entities in the country will be investigated.
o Given the fact that information needed for the current study lies at different
levels of analysis and with distinct informants, and also given that research
validity needs to be secured, a triangulation approach will be followed, i.e. data
will be collected through various means and from various sources. Table 1
shows some of the data-collection and analysis techniques that may be used to
address the issues under analysis.
o Whenever possible, it is advantageous that data collection is carried out in
tandem, i.e. by more than one researcher. Data analysis will also gain if it is
done by multiple researchers. These measures are intended to provide
reliability and research validity, as advised by several authors (e.g. Yin, 1984,
Miles and Huberman, 1994).
o Data analysis will start shortly after data collection, and will produce interim
reports which will be presented to participating entities for cross-validation and
further knowledge generation. Additionally, the reports will generate two or
more articles; these will be first presented in scientific and industrial national
and international meetings and then subjected to the international articlesubmission
process.
