What we want AEGIS to do: firms, knowledge and systems. 5. Dimensions and ..... the individuals whose function is to car
Deliverable 1.1.1: “Conceptualizing knowledge-intensive entrepreneurship: Concepts and models” (UB and IMIT) –
Franco Malerba and Maureen McKelvey
ATHENS April 2010
DRAFT DO NOT QUOTE
CONTENT
1. An initial definition of KIE and the main concepts 2. What the literature has said about KIE 2.1 Economics 2.2 Entry and industrial dynamics 2.3 Innovation management 3. Evidence from KEINS 4. What we want AEGIS to do: firms, knowledge and systems 5. Dimensions and indicators of KIE 6. Case studies 7. Survey
INTRODUCTION
This paper examines knowledge intensive entrepreneurship in various ways and represents a broad framework within which the AEGIS project will take place. Knowledge-intensive entrepreneurship is considered a key socio-economic phenomenon that drives innovation, and economic growth, and is at the base of the competitiveness of countries.
After an initial definition of knowledge intensive entrepreneurship and the main concepts in Section 1, we will concentrate on what the literature has said about entrepreneurship: we will divide the brief survey into economics (section 2.1), industrial dynamics (section 2.2) and innovation management (2.3). We will then turn into what has already been researched about knowledge intensive entrepreneurship by examining KEINS, a EU exploratory project carried out in the last years (section 3). Following a presentation of the main results from KEINS, we will introduce the main points of AEGIS, centered on firms, knowledge and systems (section 4). Finally, in the last sections we will look at the various empirical dimensions of AEGIS in its attempts to explore, identify and measure knowledge intensive entrepreneurship: the main company and meso indicators (section 5), case studies (section 6) and survey (section 7).
1. AN INITIAL DEFINITION OF KNOWLEDGE INTENSIVE ENTREPRENRUSHIP AND THE MAIN CONCEPTS
1.1 A definition of knowledge intensive entrepreneuship
Knowledge intensive entrepreneurship (KIE) helps renew the economy, giving rise to new industries but also to entrepreneurial-driven renewal within existing industries. Hence, KIE is not confined only to one sector. KIE refers to venture creation, in particular types of firms. These types of firms are particularly important mechanisms to translate knowledge into innovation, which in turn helps cause economic growth and societal well-being.
An initial definition of KIE takes into account the four basic characteristics of KIE:
• KIE are new firms • That are innovative • Have a significant knowledge intensity in their activity • And develop innovative opportunities in diverse sectors The definition of KIE stresses new firms, as venture creation. They must be innovative in economic terms and have significant dimensions of knowledge intensive in their activity. It also implies that these new firms have significant elements of relevant knowledge integrated into the development of innovative opportunities and related activities which create value and growth at the firm level. Hence, this definition implies that KIEs are involved in the economy as mechanisms to translate knowledge into innovation (and further on, into growth and societal well-being)..
Let us differentiate this from a few other dimensions. KIE is much richer and also is rather different from a general definition of start up firms as found in the general statistics and in the GEM survey [because they stress primarily the first characteristic new ventures]. This definition also excludes corporate entrepreneurship from KIE.
1.2 The key dimensions of knowledge intensive entrepreneuship
From this definition that proposes that KIE are new firms that are innovative and knowledge intensive, some key dimensions can be identified which are important for the theoretical conceptual and empirical analysis carried out in AEGIS.
The first dimension is new firm and innovation. It relates to the fact that entrepreneurship is considered as dealing with new activities and innovation involving economic value. This follows a Schumpeterian approach.Joseph Schumpeter’s (Hanusch 1999) key insight was that change is endogenous to economic systems: It is not imposed from without, but rather is generated within, and it is that insight that is followed here. Schumpeter argued that fundamental change in existing activities as well as the introduction of entirely novel activities would keep providing the ‘fuel’ to the capitalist engine (Nelson 1996).
The second dimension relates to knowledge, which we primarily define in relation to scientific and engineering and design knowledge. With knowledge we refer to systematic, problem solving knowledge, which is very different from a primary emphasis on experience and skills, which is
present in the sport, tourism, creative industries. This has implications for our view of the firm and entrepreneurs. Entrepreneurs are considered knowledge operators, dedicated to the utilization of existing knowledge, the integration and coordination of different knowledge assets, and the creation of new knowledge, and engaged in the development of new products and technologies.
Thus knowledge intensive entrepreneurship (KIE) regards the launch of new activities and organizations that intensively use existing scientific and technological knowledge or that intensively create new scientific and technological knowledge for commercial purposes or for bringing products to markets. The increased importance of KIE is related to the central role of knowledge in innovative activities in the last decades. (Foray, 2004). Related to the increasing role of knowledge, also science has become key for innovation (Nelson and Rosenberg, 1987). As a consequence, absorptive capabilities of firms have become more and more relevant and a share of scientifically educated work force has risen continuously over the past three decades in almost all European countries. In addition to the classical qualifications in applied sciences in the various engineering disciplines, the last decades have also
witnessed rapidly expanding opportunities for applied
scientific research in new sub-disciplines in physics and chemistry (e.g. material sciences), in biology (e.g. gene technology), in pharmacology, medicine, and, last but not least, information sciences. However, a very important more recent dimension is knowledge about uses and applications.
Thus for many of highly specialized applications KIE
needs to be capable of
absorbing the new technological opportunities into their commercial R&D outcomes.
The third dimension refers to innovation systems. Networks and also actors other than firms are key for entrepreneurship, interpreted from an innovation system perspective. Networks are essential because they are links for the KIE to potential sources of knowledge, new capital, new employees, strategic alliance partners, and service providers (lawyers, accountants, consultants). Networks also allow entrepreneurs to share information and assessments on markets and technologies as well as lessons learned form their own entrepreneurial activities. Thriving regions generally boast a wide array of both informal and formal networking structures. Taking this broader perspective on innovation systems implies that in addition to business firms, we must also analyze how universities and research organizations are involved in generating KIEs.
In particular, in academic
organizations knowledge from scientific research may generate new technologies and often reach commercial exploitation. This takes place through a person (“the academic entrepreneur”) that embodies the necessary scientific and technological knowledge, or through licensing and other transfer mechanisms. Similarly, users and suppliers may be a source of entrepreneurship and
innovation in various ways and intensity, generally through knowledge related to market opportunities and customer demands.
An innovation system view has three consequences for the analysis of KIE. One consequence deals with the role of institutions as a key factor shaping entrepreneurship. Institutions include norms, routines, common habits,
established practices, rules, laws, standards and so on, that shape
entrepreneurship cognition and action and affect their interactions with other agents. Institutions and the related organizations differ greatly in terms of types of impacts upon the behaviour of entrepreneurs. These impacts can range from the ones that bind or impose enforcements on KIE to the ones that are created by the interaction among agents (such as contracts); from more binding to less binding; from formal to informal (such as patent laws or specific regulations vs. traditions and conventions). Many institutions are national (such as the patent system), while others are specific to sectoral systems related to industries, such as sectoral labor markets or sector specific financial institutions. The other consequence is the tremendous importance of the context. Knowledge intensive entrepreneurs are active in quite different contexts, that affect KIE but are also affected by them. One could identify three of them: -
national innovation systems (Nelson, 1993; Lundvall, 1993;
-
sectoral systems (Malerba,2002)
-
regional and local systems (Cooke, 2003), each of which affecting entrepreneurship in different ways and through different mechanisms.
In our conceptual view of KIE, the role of the national innovation systems and sectoral systems will be stressed as a factor shaping the type and intensity of KIE in different ways.
The fourth dimension is the notion of innovative opportunity. We recognize that entrepreneurs in KIEs must mobilize their understanding of knowledge and innovation systems, in order to create value. An innovation opportunity is here defined as ‘the possibility to realize a economic value inherent in a new combination of resources and market needs, emerging from changes in the scientific or technological knowledge base, customer preferences, or the inter-relationships between economic actors’. The concept of ‘innovative opportunities’ comprises both aspects related to a market as well as aspects related to the scientific and technological knowledge needed to serve this specific market. ‘Innovative opportunities’ is a somewhat more complex concept than the ones which exist in the literature, including entrepreneurial, technological and productive opportunities. In our view, the conceptualization of innovative opportunities has to be more complex than the existing literature would suggest, because innovation is more than a known technology or an
individual perception or an internal bundle of resources in the firm. The firm must put together these elements. Hence, the notion is useful to broadly grasp the type of actions and decision-making which diverse actors must engage in to identify and exploit such an opportunity in the innovation system. Still, definitions are often only the beginning of the research process. It is necessary to go further in order to define conceptual elements which are derived from the definition and yet also more directly and practically useful for research and for decision-making. Consequently, an innovation opportunity must consist of at least the following three conceptual elements in order for actors to have the possibility to observe and act upon the potential inherent in an idea: (1) a perceived economic value for someone, (2) a perceived possibility that the resources needed to realize the opportunity can be mobilized, 3) a perceived possibility that at least some part of the generated economic value can be appropriated by the actor pursuing the opportunity Hence, innovative opportunities are a way of capturing the three key elements of the business model. The firm must identify the value to a customer, mobilize the resources, and capture the economic benefits from innovating.
1.3 Differentiating knowledge intensive entreprenruship form other existing concepts
As will be developed in subsequent sections, we argue that the concept of KIE is quite different from two other view of entrepreneurship: new technology based firms (NTBF) and entrepreneurship in high technology industries
As far as New Technology-based firms (NTBF), the model suggested by Autio (Figure 1.1) illustrates the functional role that new, technology-based firms in the process of technology articulation or “the process by which generic scientific knowledge is transformed to application specific technological knowledge” (Autio, 1997, p. 266). The model shows that scientific knowledge can be transformed into application specific technologies in two principal ways. First, generic knowledge may be used to develop a basic technology or “a set of physical insights, heuristic principles, and manipulative skills which enables one to control and exploit the properties of natural objects and processes” (Stankiewicz, 1990, p. 18). This basic technology can subsequently be transformed into application specific technologies. It is also possible that scientific knowledge can be directly transformed into application specific technologies. Consequently, there are three potential niches in the process of knowledge transfer that can be exploited by NTBFs:
− the utilization of generic research to develop basic technologies; − the utilization of generic research to develop application specific technologies; − the application of basic technologies to specific needs and tasks. Based on the niche orientation of the firm, Autio (1997) proposed classifying NTBFs into two groups: − Science-based firms, those which utilize the results of generic research by transforming them into basic technologies or application specific technologies, by developing very sophisticated products or services with a broad scope of application. − Engineering-based firms, those which apply basic technologies to the development of new products or services addressing specific customer needs. What is different in KIE?
NTBF literature focuses upon the technical assets (scientific,
engineering, OK), while KIE focuses upon the translation of S/T assets into economic value creation (Relationship to incentives and opportunities) and on innovation system
As far as Entrepreneurship in high-tech sectors is concerned, there is now a lot of literature on this topics. What is different in KIE? The high-tech literature focuses upon only specific sectors, very often it is ICT, Biotechnology, Nano and possibly a few other science based. KIE is not limited to these few sectors. Translation of knowledge to innovation through KIE is more complex than that, can occur in any venture creation and any sector. But the sectoral conditions greatly affect the opportunites (through knowledge, market, institutions, etc of the sectoral system of innovation) and also thereby affect the possibilities for growth. That is why we study high tech, low tech. and also services.
2.
KNOWLEDGE INTENSIVE ENTREPRENRUSHIP AND THE EXISTING
LITERATURE
We group the literature on entrepreneurship in three major strands: economics, industrial dynamics and innovation management. In what follows we identify the main elements of each strands, and address who is interested to more detailed surveys done within the KEINS and the AEGIS projects (Garavaglia and Grieco,2005; Audtrestch, 2002 and 2007, Larsen and McKelvey 2010
2.1
Economics: entrepreneurs as the equilibrating or disequilibrating factor
Garavaglia and Grieco (2005) in KEINS survey the main streams of economic literature that have dealt with entrepreneurship. They group them in neoclassical, Austrian/Schumpeter and evolutionary.
In the neoclassical approach the role of entrepreneur is demand driven, is related to the profit opportunities of the market and has an equilibrating function.
Garavaglia and Grieco call it
“ordinary entrepreneur” because it combines different pieces of knowledge that already exist. From Knight (1921, a clear distinction between business managers and entrepreneurs is made. In this approach there is no scope for a very active entrepreneur. No explicit function to knowledge is given.
In the Austrian school, the focus is on the supply side of entrepreneurship, related to the ability to identify speculative opportunities, the personality and attitude of the entrepreneurs and the creative behaviour of the individuals. Knowledge plays an active role: entrepreneurs create new opportunities and exploit new ideas, bring new knowledge into the economic system. Entrepreneurial behaviour is related to disequilibrium and the understanding the process of change and adjustment is crucial (Von Mises; Kirzner)
Schumpeter considers entrepreneurship as dealing with new activities and innovation. This follows a Schumpeterian approach, as Schumpeter’s 1936 statement claims: “The carrying out of new combination we call ‘enterprise’; […] the individuals whose function is to carry them out we call ‘entrepreneurs’”. In this perspective entrepreneurship deals with the setting up and the owning of a new business. However it often happens that, even if a new firm is per se ‘something new’, it has no characteristics of novelty or change (just think about the simple break-up of a firm in two subsidiaries). . As a consequence, entrepreneurship is an activity that faces uncertainty and has the goals of creating something new: a technology, a product, an organization, a market. In Capitalism, Socialism and Democracy, Schumpeter makes a very specific argument about the role of innovations in economic transformation. In ‘The Vanishing of Investment Opportunities’, Schumpeter reacts to contemporary arguments about why capitalism would ‘stop’ working.
‘The main reasons for holding that opportunities for private enterprise and investment are vanishing are these: saturation [of wants/demand], [decline of] population, [no more] new lands, [end of] technological possibilities, and…investment opportunities belong to the sphere of public rather than private investment’ (1942:113).
In contrast to these pessimistic views, Schumpeter’s argument is that capitalism will indeed keep functioning. ‘There is what may be described as the “material” the capitalist engine feeds on, i.e., the opportunities open to new enterprise and investment’ (ibid). A longer quote, which is also fundamental to our thinking about economic transformation and opportunities, is reproduced in full (Schumpeter 1942: 82-82):
‘Capitalism, then, is by nature a form or method of economic change and not only never is but never can be stationary. And this evolutionary character of the capitalist process is not merely due to the fact that economic life goes on in a social and natural environment which changes and by its change alters the data of economic action; this fact is important and these changes (wars, revolutions and so on) often condition industrial change, but they are not its prime movers. Nor is this evolutionary character due to a quasi-automatic increase in population and capital or to the vagaries of monetary systems of which exactly the same thing holds true. The fundamental impulse that sets and keeps the capitalist engine in motion comes from the new consumers’ goods, the new methods of production or transportation, the new markets, the new forms of industrial organization that capitalist enterprise creates.’
Schumpeter puts forward the thesis that fundamental change in existing activities – as linked to creative destruction and various forms of innovation – would keep providing ‘fuel’ to the capitalist engine.
Finally, in the evolutionary approach (Nelson and Winter, 1982) entrepreneurship is at the base of the search processes of the new routines that leads an organization to exploit new opportunities and ideas, and trigger change. Entrepreneurs introduce variety in to the economic system through innovation. Most of the AEGIS contributions are in this line of reasoning
In addition, other social sciences have discussed entrepreneurship. Sociology has inserted entrepreneurship into social groups and in social and local environments. Psicology on the other hand has focussed more on the motivation and cognitive aspects of individual entrepreneurs. Finally, a more “generalist” approach has recognized the importance of the multi-disciplinary determinants of entrepreneurial activity and explained the practical nature of entrepreneurship.
2.2 Industrial dynamics: entrepreneurs as new entrants into an industry
Entrepreneurship as been studied as the key driving force of industrial dynamics (entry in an industry) and industry evolution (how existing industries change): the surveys by David Audretchs in 2002 and in 2007 for KEINS discuss these aspects.
Analysis has related entry into an industry to various aspects of a sector: the extent and intensity of innovation (radical and incremental); the amount of productivity increases; the contribution to the growth of employment: the relationship with exports and international specialization; the effects on industrial clustering in specific areas or regions. In most of these studies entrepreneurship has been found to have a positive and dynamic effect.
A particular aspect has been the analysis of the relationship between entry, survival and growth. This is a quite relevant theme because it is at the base of one of Europe problems: the limited capacity of new firms to increase in size and become large and develop international operations. The economic and institutional barriers to growth have been examined at both the quantitative and quantitative levels.
One recent strand of literature examines spin-offs and the pre-entry experience of the founders. In particular, in his work Klepper examines the affects that initial resources and entrepreneurial background (in terms of originating companies, technology and knowledge) have on the entry, survival and economic performance of spinoff. In general it has been found that progressive and capable firms originate progressive and capable spin-offs, who survive longer than other types of entrants.
Some
contributions examine the external context in which new firms operate. Some work
examines entry in terms of technological regimes, defined in terms of opportunity, appropriability and cumulativeness conditions (Malerba and Orsenigo, 1999). Others relate entry to the specificity of the sectors. Other relate entry to the role of universities, which is quite prominent in some science based sectors such as biotechnology.
Finally, more specific contributions identify key factors that affect entrepreneurship. The main factors that affect the supply and performance of entrepreneurship are finance (different types of
finance, useful at different phases of the entrepreneurial process), cultural and social capital; institutions (role of regulations, …); immigration; and public policy
2.3
Innovation Management and Entrepreneurship Literature
Larssen and McKelvey (AEGIS 2010) review a large part of the entrepreneurship literature within innovation management and entrepreneurship that addresses issues of direct relevance to KIE. Key words based upon related concepts and conceptual aspects identified in this paper were used to identify papers.
The review identifies three main phases related to innovation management, strategy and business models of KIEs. These three phases are: -
Inputs to the knowledge-intensive venture,
-
Managing the knowledge-intensive venture/process,
-
Output of the knowledge-intensive venture.
See Appendix 1 for the main variables identified in each phase, as well as the relative frequency of specific research topics in publications. This overview is therefore helpful to identify empirical indicators for quantitative research and research designs for case studies, as well as the conceptual contributions. It will be published as a separate additional AEGIS deliverable in Workpackage 1.
Literature on the Inputs to the knowledge-intensive venture primarily focuses upon four factors. Two factors related to financing and also the characteristics (and intentions) of specific entrepreneurs are well addressed in the literature, but are not central in the AEGIS project. Two factors do figure importantly in AEGIS – namely sources of KIE and Institutional impacts. In terms of sources of KIE, most studies focus on the spin-offs either from academia or from corporations, and thus to some extent overlap with the literature in industrial dynamics, but here primarily from a firm perspective. Therefore, the key impacts upon venture creation in KIE of relevance to AEGIS include the relationships between the individual/team as sets of complementary dynamic capabilities as well as how the firm relates to the context of the innovation system, specifically institutions, organizations and innovative opportunities.
Literature on Managing the knowledge-intensive venture/process has been categorized as focusing upont these seven dimensions -
Human resources Network/social capital
-
Growth patterns Incubators/ CVC units Relationship between knowledge, innovation and entrepreneurship From R&D to market Dynamics of the KI venture
These seven dimensions relate to how the firm designs and manages the internal processes, through their specific business model which links the firm to the innovation system context.. They provide very detailed information about specific variables, often related to the growth of the firm per se ….
Note that a key area of research (in terms of frequency of publications) in the topic of the relationship between knowledge, innovation and growth . However, most of these papers address very specific sub-topics related to specific firms, and few address growth and well-being more broadly as the economists discussed above do.
Our review of the Output of the knowledge-intensive venture shows that the four main topics are: patent, new firm formation, growth performance, knowledge creation. Most of this work is quantitative and many studies use statistics at national and industrial levels. It is interesting to note that a wide variety of definitions of ‘growth and performance’ are used, as this has impact on synthesizing the results into a more coherent whole.
3. Evidence from KEINS
A previous STREP project KEINS (Knowledge base entrepreneurship: institutions, networks and systems, EU project n.CT2-CT-2004-506022) supported by European DG research and carried out by seven European research centers. CESPRI- now KITeS (Bocconi University), Max Plank Institute Jena, IMIT and Chalmers University Sweden, Beta - University Louis Pasteur, University College London, Cisep University of Lisbon and Case Research Center Warsaw, has started to shed light on these issues.
People interested in the results of the project could go to
www.kites.unibocconi.it and go to the KEINS project for specific papers. Some papers from the KEINS project have been published in the book
Knowledge intensive entreprenrship and
innovation systems. Evidence from Europe edited by Franco Malerba (Routledge, 2010).
The main questions that KEINS attempted to answer have been the following. What are the building blocks of knowledge intensive entrepreneurship? What are the factors affecting it? What is its
impact on economic growth? How relevant is it in advanced European countries? What are the main aspects of KIE in transition economies?
KEINS examined entrepreneurship as dealing with innovation and change. In addition, entrepreneurs have been considered as knowledge operators, dedicated to the utilization of existing knowledge, the integration and coordination of different knowledge assets, and the creation of new knowledge, and engaged in the development of new products and technologies. Finally, knowledge intensive entrepreneurs have been examined as part of innovation systems. KEINS has examined KIE in a variety of ways: theoretical and conceptual frameworks, quantitative analyses (using either existing micro data or ad-hoc surveys) and case studies.
At the empirical level, KEINS has considered KIE also as those companies that have a high level of skills or high expenditures in R&D and that are active in a variety of sectors. As a consequence, not necessarily KIE are present in in high-tech sectors. More specifically, three complementary definitions of KIE have been used for quantitative analyses using large sets of micro data. The first one relates to KIE as new firms in sectors that are highly knowledge intensive. Here KIE is represented by new firms in high technology sectors, or in the ICT sector or in sectors with a high content of human capital.
In this case KIE is related to knowledge generated by investments in
R&D, embodied in high level of human capital and skills, and related to information and communication technologies. The second relates to KIE as new innovators in a technology/sector, resulting in corporate entrepreneurship or de novo entry. The first one refers to new innovators in a technology/sector, that are established firms diversifying technologically in that technology/sector. It can be measured through the patenting of established innovators into new fields. The second refers to new innovators that are also new firms. This can be measured by identifying firms patenting for the first time in a technology/sector. The third related to academic entrepreneurship. This refers to academic start-ups as well as to academic patenting in various technological classes.
KEINS has developed a conceptual framework in which KIE is active in specific learning environments and in different opportunity conditions that the entrepreneurs have to identify and capture. A variety of conceptual frameworks are presented. One of these is McKelvey and Brink 2010 (in Malerba,2010) who provide a conceptual framework, which relates differing market and technological opportunities to the trajectories of firms in the biotechnology sector. KEINS has also discussed how in a system perspective, the features, behaviour and performance of KIE is the result
of the interaction with several actors, among which established producers, users, universities and public research institutes and venture capitalists. This has led KEINS to examine the different innovation systems in which KIE are active in: sectoral systems, national systems and regional systems
In particular, KEINS has examined KIE in Europe along four different perspectives. The first one refers to the quantitative relationship between KIE, industrial dynamics, economic growth and regional development. The second one looks at KIE as the creation of new firms in knowledge intensive sectors in Western Europe and in Central and Eastern Europe as inserted in various types of innovation systems. The third analyses KIE as new innovators across countries, technologies and specific industries. The fourth one looks at KIE in terms academic entrepreneurship and patenting.
KEINS found that KIE affects economic growth and regional development and is characterized by a specific industrial dynamics.
For example, for 440 counties in Germany in 2000, Audretsch and
Keilbach (in Malerba, 2010) show that KIE entrepreneurship in general, and
also high tech
entrepreneurship (entry in sectors that have an R&D intensity greater than 2.5%) entrepreneurship (entry in ICT sectors), have
and ICT
a positive role in German region economic
performance. High knowledge intensity (i.e. high technology sectors or ICT) affects entrepreneurship by providing more entrepreneurial opportunities and entrepreneurship affects economic performance through increase in competition and increase in diversity. For these 440 German counties high tech entrepreneurship covers 15% of all entrepreneurial activity. As Audretsch and Keilbach (2007) show GDP growth (but not GDP stock) and R&D intensity have a positive effects on high tech start up but only the second one has an effect on ICT start ups (may be due to the high growth of ICT start ups in the 1990s). Similarly, population density (i.e. cities) is a major factor for high tech and ICT start ups. Unemployment rate only affects low tech star ups, while social diversity (Florida,2002) (measured in terms of entropy index of voting behaviour) affects high tech start ups. Industry diversity affects all types of entrepreneurship, implying that an environment open to the acceptance of new ideas is conducive to new firm formation. In addition, external effects of the Marshall-Arrow-Romer type related to industry concentration have
a
positive affect on all entrepreneurship
What are the main features of KIE in industries? Mamede, Mota and Godinho (in Malerba, 2010) examine the dynamics of KIE in Portugal between 1995 and 2000, using two classifications, the OECD one that considers high and medium technology industries plus knowledge based services,
and the other one that considers industries that are in the 10% of industries with the highest average proportion of employees holding a university degree. Entrepreneurship in industries that are knowledge based has higher survival rates and higher growth rates that entrepreneurship in other industries. In these industries entry is less responsive to incentives (in terms of price cost margins or industry growth), and more to other factors such as the behaviour of incumbents and competition (low concentration) and limited economies of scale.
What are the characteristics of KIE in Western Europe and Central and Eastern Europe when we consider a system perspective? Two specific firm surveys have been administered by the KEINS project to a large number of firms in Western and Central and eastern Europe.
For Western Europe Lenzi et al (in Malerba, 2010) in KEINS explore the characteristics of KIE in Germany, France, UK, Italy, Sweden and Portugal. KIE have been selected as a group of new firms that innovate within a very short time after their establishment and are both knowledge-based (i.e. active in science-based and science-driven sectors) and technology-based (i.e. patenting in sophisticated and dynamic technological contexts). The KEINS database has been
collected
through a long and complex process of sample identification, data collection, questionnaire elaboration, and survey administration. Special attention has been dedicated to the background of their founder/s and the relationships of the new firm with the parent organisation. The database has been inspired by contributions on human capital theory and on the parent firm knowledge and technological inheritance. The former concentrates on founder’s individual characteristics and intellectual capital as principal determinants of start-up success. The latter stresses how new entrants’ performance is significantly influenced by founders pre-entry background and experience as well as their technology and knowledge endowments. 99 companies have been examined. They have been divided according to three broad regions: Germany, Northern Europe and Latin Countries (France and Italy).
They have been also divided according to three sectors: biotechnology,
electronics and medical devices. The major findings regard the role of venture capital, which is very important, in Germany and Northern countries, and in biotechnology. Banks on the contrary are more relevant for medical devices and local and regional authorities for biotechnology. In general, IPR (licensing and patent) are important for founding a new firm. All firms found specialized labor key for their survival and success. Networks are considered always important for a large part of firms, but more than 40% does not consider important links with universities. Collaborations with previous employers is more frequent in Nordic Countries: in biotechnology, and R&D is the main area of collaboration. The success of knowledge based entrepreneurs is based on the uniqueness of
their products. In addition, marketing skills and customer services are important in electronics and medical devices. Interestingly, from the previous employer, biotechnology spin offs have received knowledge about products, while electronics and medical devices spin offs knowledge about customers. Most of the firms offer a product for a specialized market which are unrelated to the founder’s previous employer. And the level of human capital is confirmed high: most founders have a PhD or a master. The main motivation of the founder is independence and commercial exploitation of your own idea, and 50% have become serial entrepreneurs. Differences across countries (Germany, Nordic Countries and France/Italy) are
related to two the degrees of
development of, and the easy of access to, financial markets, and the different functions and effectiveness of the university system. The first one affects the probability of entry into entrepreneurship. The second affects the educational profile of founders, the frequency of collaborations with the private sector and the rate of university spin-offs. Differences across sectors are related to the type of knowledge and competences necessary in order to be successful on the market. Also major differences across sectoral systems refer to the competences, the knowledge endowment, the inheritance from, and the relationship with the previous employer, and the types of customers. Ultimately, the combination and interplay of both country and technology specific elements strongly affect and shape the initial decision of entry into entrepreneurship as well as the early evolution of new innovative firms.
For Central and Eastern Europe, Radosevic, Savic and Woodward (in Malerba, 2010) examine Knowledge Intensive Entrepreneurship in Hungary, Lithuania, the Czech Republic, Croatia, Poland and Romania. The chapter represents an attempt, based on evidence from a survey of 304 firms in six countries of the region, to separate myth from reality and identify both the strengths and weaknesses of Knowledge Intensive Entrepreneurship (KIE) in the CEEC countries in the light of the factors that affect the performance of firms. These factors can be broadly divided into those referring to the entrepreneur, to the firm, and to the environment within which firms and entrepreneurs operate. The results of the survey show that knowledge-based entrepreneurs usually start their careers in the business sector rather than in the scientific sector, and start knowledge intensive firms in order to take advantage of market (i.e., commercial and financial) opportunities. These entrepreneurs bring knowledge about products and technology from their previous employment and then develop new markets with their new firms. In that respect, KIE in CEECs can be considered as primarily a market repositioning activity. Technological opportunities are frequently mentioned as a key rationale for establishing companies only in the Hungarian sample, where more entrepreneurs come from the science sector.
KIEs in CEEC are not ‘gazelles’ (i.e.,
fast growing new technology based firms which have the potential to reshape the industrial landscape). They consist of distinct types of companies, of which NTBFs are only one. The key factor in KIE firms’ growth is most often firm specific capabilities which do not always involve R&D. Based on what the firms have identified as their success factors, we have grouped firms in three: new technology based firms, ‘networkers’ and companies whose success is based on ‘customer-oriented organisational capabilities’.
The most common developmental barrier in
domestic markets is the low level of demand on those markets. This is followed by high labour costs, increased competition, and lack of public support. Firms fall into two groups with respect to the kinds of barriers that were most important for them. The first group is one where the main barriers concern skills shortages and high labour costs. For the second group, the major barriers are related to finance (lack of access to finance and of public support). Compared to
standard
companies in CEEC , which tend to limit their strategic interactions for innovation to value chain partners such as buyers and suppliers, the networks of KIEs are broader and more frequently involve innovation system actors (research institutes, universities), including professional networks (fairs and exhibitions). Indeed, here again Radosevic et al. identify a number of distinct types of firms (with respect to the sources of knowledge that are most important for their innovation processes): those where value chain partners are the key source of knowledge for innovation; those where formalised R&D like patents and journals and research organisations are the key source, and those where in-house or firm specific innovation activities are a key source of knowledge for innovation. Another grouping of companies with respect to the intensity of their links with external organisations distinguishes four types of firms: network dependent, public research system oriented, foreign and domestic value chain dependent firms. This clearly illustrates that for different types of KIEs different networks are important. In general, these are either vertical (foreign and domestic value chains) or horizontal (links with the domestic public research system).
How relevant are the new innovators in a technology or a sector? A first answer may examine firms and other organizations entering for the first time as innovator in a technology/sector. This can be done by looking at firms patenting for the first time in that technology or sector.
Camerani and
Malerba (in Malerba, 2010) have examined new innovators in terms of companies and other organizations that patent for the first time in 12 technologies with a high rate of change, ranging from ICT, to semiconductors, to pharmaceuticals and biotechnology, to machine tools They have used patents applications at the EPO in the period 1990-2003 in Europe, the Unites States and Japan. New innovators in a technology field can be de novo technological entrant if they patent for the first time ever, or technological diversifiers if the entrant has already patented in another technology.
They find that innovative entry is a very frequent phenomenon: on average more than 40% of the patenting firms in every period did not patent before in that class. More than 25% of new innovators in a technology never patented before. The importance of
new innovators differs across
technologies: de novo innovators are particularly high in ICT, medical engineering and measurement instruments, and low in semiconductors and chemicals. However the relevance of new innovators in terms of total number of patents is much less relevant. This means that innovative entrants start innovating with very few patents and are smaller than incumbent innovators: this is similar across technologies and across countries.
If technological entry is quite common,
persistence is more difficult: around half of the firms have only one patent. Those firm that enter and become persistent innovators keep patenting in the same technological field in which they entered. Their initial patent is usually more cited than the ones of the occasional innovators. Again, no major differences in this respect across countries and sectors seems relevant.
What could we say about the features and the micro dynamics of entrants and new innovators in specific sectors and technologies? Evidence have been gathered by the KEINS project for lasers, switches, biotechnology, telecommunications and semiconductors, using different methodologies and databases.
Buenstorf
(in Malerba, 2010) examines KIE in the German laser industry, characterized by
heterogeneity of product submarkets, limited economies of scope, continuous innovation and developments exploitation of new laser applications. He identifies four types of entrants: de novo entry based on academic research, diversifying entry based on large scale industry research or on small scale industry, Spinoffs and entry from distribution activities. The similarities between the German case and the American case (examined by Klepper and Sleeper, 2007) are striking. Spin offs play a major role in industrial dynamics, and there is a major role of the background of the founder on performance, where the academic background had the least impressive performance of all entrants. In terms of companies both laser spin offs – which have technological experience - and integrating distributors – which had knowledge of applications- have the best performance
Breschi, Malerba and Mancusi (in Malerba, 2010) analyse new innovators in two knowledge based industries: switches and lasers. New innovators are firms that apply for their first patent within a relatively short after their establishment. Their work examines if the performance of new innovators in terms of persistence of survival and persistence of innovations is affected by the type of founder
(the type of
founder can be the inventor or not), the inventor experience in patenting, the
technological scope and the relevance of the fist patent and the link of patent to science
Bureth, Penin and Wolff (KEINS, 2007) examine biotechnology start ups in the Rhine Valley Biovelley. They find a major relevance of academic spin off. Here the main motivations is not just money. In this sector public research and science greatly affects start ups. Links to public research increase firms reputation and credibility and provide access to new relevant scientific knowledge. In this respect not only networking, but also the scientific capability and personality of the founder plays a key role in the start up and growth phases. However projects that are too far from the market do not survive long enough. Patents play a role in the creation of new firms in drug development, but less so in non drug products and services. In general, entrepreneurship in biotech is a collective process involving many heterogeneous actors.
Finally, Malerba and Zirulia (KEINS,2007) examine the relationship between new innovators and R&D alliances. They analyze the relationship between new innovators in terms of patents and R&D alliances in semiconductors, telecommunications and pharmaceuticals. They find that new innovators may enter a network of R&D alliances soon after entry, but they do not form another alliance after the first one. Established networks are beneficial to new innovators because they provide new innovators with the complementary assets. Similarly, new innovators bring new capabilities into existing networks. Both features can be illustrated by the high technological distance (in terms of patents) between the new innovator and the other partners in the network. However, initially new innovators are in a marginal position in established networks. Over time, however, as a result of the mutual learning process they become close to the other partners in terms of technological capabilities. Those new innovators that continue to form alliances become more central to the network. Differences across sectors emerge from the analysis: in semiconductors firms are more technologically similar since the beginning, while in telecom those new innovators that continue to form alliances continue to remain less central.
As mentioned previously, academic entrepreneurship can be examined ether through academic start ups, or through the contributions that academics provide to innovative activity. In this second view, academic patenting represents a key dimension and is part of the larger phenomenon of university-industry technology transfer. Patents represent a tool for protecting innovation in a number of science based industries (pharmaceuticals, biotechnology, chemicals and electronics)
when academic scientists generate advancements that may have industrial applications and could be protected. Using these perspectives, some very interesting results emerge from KEINS research.
Lissoni, Llerena, McKelvey and Sanditov (KEINS, 2007) have examined academic patenting in France, Italy, and Sweden. They show that in those countries academic scientists have signed many more patents than previously estimated in other studies. This re-evaluation of academic patenting in Europe comes by considering all patents signed by academic scientists active in 2004, both those assigned to universities and the many more held by business companies, governmental organizations, and public laboratories. Academic patenting is a growing phenomenon in Europe: from 2% of domestic EPO patents in 1985 to 4% in 2000. So, from these new KEINS data, universities’ contribution to total domestic EPO patent applications in France, Sweden and Italy appears not to be much less intense than that of their US counterparts: 4% in EU and 6 % in US. Specific institutional features of the university and research systems in the three countries (IPR arrangements, the institutional profile of national academic systems and the research contracts) contribute to explain these different ownership patterns between the US and Europe. Professors that are inventors are 4% of total professors, concentrated in chemicals, engineering (electronics) biology and medical sciences.
In Europe between 60 and 80% of academic were owned by
business companies, 14% by individuals and only 10% by universities in France and Italy. In the US 69% of academic
patents are owned by universities.
In Europe in the various sectors,
businesses own 57% of academic patents in pharmaceuticals
and 85% in electronics. This
difference depends on the funding of research and on the exploitation strategies of
patents.
However university ownership of patents, albeit small, has increased in all the three European countries considered. So one may conclude that there is a similar propensity to patent in Europe and in the United States, although not necessarily a similar scientific or technological level.
Looking at the networks of academic inventors in France, Italy and Sweden, Lissoni and Sanditov (KEINS, 2007) show that academic inventors occupy central positions in the small world networks of inventors (i.e. where small world properties come from social network analysis and mean high knowledge variety but also a high speed of information diffusion). Academic inventors stand in between otherwise unconnected inventors and teams of inventors so that they can control the information between otherwise unconnected inventors and teams. However only few of these academic inventors occupy a position of “brokers” i.e. they stand in between two industrial researches. An analysis of brokers in Italy show that academic inventors can play a brokerage
position in case they have strong publication record, large number of patents and high propensity to keep continuously in touch with industrial business.
Lissoni (in Malerba, 2010) combine a relational analysis on inventors’ data with the results of a short questionnaire submitted to a subset of Italian academic inventors, and with data on their scientific publication record and CVs in order to explore in greater depth the personal relationships that academic inventors entertain with co-inventors with a different background and/or professional status. Lissoni finds that that brokerage and gatekeeping positions are very few, and they are held by scientists with both a large number of patents and a strong publication record. These scientists are not especially better than colleagues at further co-operating on research with co-inventors from industry after the patent experience. However, they do better when it comes to keeping in contact with industrial researchers. While brokerage and gatekeeping positions are not correlated to academic inventors’ propensity to entertain stable research collaborations with their co-inventors from business companies, they are correlated to their propensity to keep in touch after patents, for information exchanges of all sorts. Network ties between academic and industrial researchers may be short-lived as far as knowledge exchanges are concerned, but may serve well other purposes. In particular, the joint reading of our quantitative evidence and the top brokers’ biographical notes suggest that the latter manage actively their relationships outside university. Some of them, especially those who have signed patents only for one or two different assignees, are likely to keep in touch mainly for research or research funding purposes. Others, such as those academic inventors with many different assignees and/or many assignees such as public consortia and the likes, may nurture their personal links outside universities for more strategic purposes. The existing literature on university patenting has focussed almost exclusively on academic inventors’ monetary incentives. Here Lissoni finds that the social contacts gained through collaboration with industry may be part of the reward, as they help boosting the academic inventors’ reputation and career both inside and outside the university.
As mentioned previously, the extent and type of KIE is affected by national innovation systems. We have seen that KEINS has examined the cases of CEEC. Most of these countries are transition countries, in that they all shared the move from central planning to a more market oriented one. However transition countries have had very different initial conditions in terms of industrial structure, institutions, norms and practices, which greatly affected the type of entrepreneurship during transition (Aidis, KEINS, 2006). These conditions can explain the divergence between entrepreneurship in the Baltic countries (CEEB – Central and Eastern Europe Baltic countries),
and the one of the previous Commonwealth of Independent States (CIS),. In the CEEB countries the economy is similar to the one of the EU and the role of the state is not so pervasive. In the CIS countries on the contrary the role of the state is still relevant, and networks of various types are still strong. In both these countries, entrepreneurship is manly in retail trade and low tech sectors, and KIE is extremely limited.
Radosevic (KEINS, 2007) identifies some key features of KIE in new member countries (NMS) of the European Union. In NMS there is a limited domestic demand for knowledge based products and activities, including public sector demand. This is even more son for knowledge intensive services.
Local networks do not play a major role. However customers have an increasing
relevance, particularly in software. This is so because customization is quite important for the success and growth of firms. Knowledge is usually developed in-house, except in internet based business. Interestingly enough, in previous Commonwealth of Independent States tow featuresare quite common: research institutes try to commercialize their activities and large firms are a substitute mechanism for venture capital.
Some CEEC country cases studies conducted by KEINS researchers illustrate some of the previous points. Bishop (KEINS, 2006) examines the cases of KIE in Czech Republic and Hungary. She brings evidence that access to finance remains key for firms’ start up and survival but that there is reluctance of entrepreneurs to seek and accept external finance. Instead, personal networks and family networks remain relevant for finance. Two other main problems are the training for potential entrepreneurs and the difficulties of entering international markets. However the impact of privatization and the removal of regulation are opportunities for BKE in these countries. In some cases the government provides the initial demand as in the case of environmental services. In these cases, also users’ networks are important. Woodward (KEINS, 2006) examines KIE in Poland and Estonia and finds differences in the national contexts. In Poland demand for knowledge products is weak, while in Estonia university and transfer offices are much more active and foreign demand relevant for KIE. In general, however, there is low relevance of networking in the generation of knowledge: knowledge resources are developed largely in house. Instead networks play a role in the distribution phase and in the search for new customers. In both these contexts, the public sector is unable to create demand for high tech products.
4. FROM KEINS TO AEGIS: THE AIM OF AEGIS
AEGIS focuses upon some key themes of KEINS and at the same time has expand the range of issues concerning KIE. The AEGIS project focuses on factors which influence the birth and expansion of business and its dynamic organizational capabilities. Starting from the KEINS framework, the AEGIS project specifically introduces a series of new research topics to advance the state-of-the-art: •
An organization-centered and network-centered view of entrepreneurship.
•
Emphasis on the link between micro and macro phenomena.
•
Attention to cultural and organizational issues.
•
Concentration on both high- and low-technology sectors, including manufacturing, services, and agriculture.
•
Greater focus on activities exploiting non scientific, research-based innovations.
•
Extension of the analysis beyond supply to the demand side of the entrepreneurial phenomenon.
•
Analysis in the context of various socio-economic models and systems of innovation in Europe.
•
Comparison of European patterns with those of other successful economies such as the United States, China, India and Russia.
•
A systemic view of the phenomenon and, consequently, of the associated policy implications.
•
A focus on factors that affect dynamic organizational capabilities which are key to knowledge-intensive entrepreneurship.
Among the new key issues examined by AEGIS with respect to KEINS, one regards the link between knowledge-Intensive Entrepreneurship, Innovation and Economic Growth. In this respect, AEGIS intends to examine how entrepreneurship is able to foster innovation and economic growth by breaking barriers of various types. The role of entrepreneurship in this process is twofold. On the one hand, the establishing of new firms is often based on new knowledge and on ideas on how to apply it. This means that entrepreneurship, in particular knowledge-intensive entrepreneurship, is a direct source of new knowledge and innovation, and thereby it stimulates economic growth. However, an important part of the role of entrepreneurship is also played by its indirect spillover effect. New firms may serve the important role of challenging existing firms and the established market structure, because they have either particular incentives to innovate, or they can introduce into an industry new competencies, new products and new ways of doing. Furthermore, the project
will specifically address the issue of measuring the commercial propensity to create knowledgeintensive new ventures by looking at the commercialization of academic research and by identifying which factors are conducive to or inhibit scientist commercialization.
A second issue relates to effects of entrepreneurship and innovation on economic growth mediated by sectoral specificities in terms of entry of new firms, growth of incumbent firms, and a mixture of both processes. These differences reflect the ways the knowledge base, as well as other actors such as universities, the public sector and financial organizations come to play different roles in different contexts
A third issue refers to the link between clusters and entrepreneurship. In AEGIS the bi-directional relationship between clustering and entrepreneurship is investigated. Are clusters more efficient in attracting entrepreneurship, and is entrepreneurship a more efficient way of promoting economic growth in clusters? A fourth issue refers to the relationship between Knowledge-Intensive Entrepreneurship and Social Well-being. AEGIS examines the effects of newly created knowledge converted by knowledgeintensive entrepreneurship into commercially useful applications on economic growth and social well-being. New technologies, products, and services can help to make the production processes more efficient, potentially leading to increased capital intensity, labour-productivity, and per-capita income. They can also result in new consumption opportunities in the form of new goods and services offered for final consumption. But the effects of increased knowledge-intensive entrepreneurship on social well-being are quite relevant to examine. The concept of well-being encompasses both market-mediated improvements in citizens' quality of life (via higher earnings and spending) and improvements that are not mediated by the markets. These latter improvements are not well-reflected in quantitative measures of economic growth. Knowledge-intensive entrepreneurship can foster innovations with complex distributive effects that may influence wellbeing, contributing to improved quality of life in some dimensions, and negative impacts on wellbeing in other dimensions. A broad assessment of the impact of more intensive knowledge-related entrepreneurship aimed at commercially useful applications requires an account of the qualitative effects on social well-being. Another major theme that will be addressed by AEGIS concerns how building a more entrepreneurial society is associated with empowerment. Knowledge-intensive entrepreneurship is likely to diffuse the ability to take such initiatives to a broader group of actors. In large
organisations, variety is needed to stimulate those initiatives with the greatest promise and, instead of 'business as usual' and the repetition of routines, the need to foster a culture where change and novelty are priorities. This applies to smaller organisations, as well. To assess these consequences and opportunities associated with a more knowledge-intensive entrepreneurial culture, it is necessary to examine these developments and their relationship to the distribution of employment creation over time and across different spaces. AEGIS also identifies some of the dangers associated with the emerging dominance of a knowledge-intensive entrepreneurial culture, recognizing that all citizens are
not be able to
participate and that the vision of a greater participation in knowledge-based activities throughout society results in new classes of consumption and consumer behaviour as well as new social modalities. The focus is on how education and training process need to evolve to meet the challenge of potential exclusion. The emphasis is on whether enhanced knowledge-intensive entrepreneurship can stimulate innovative products and services which, through their consumption, enhances the social well-being of citizen/consumers; and whether new models of education and technology use in a-vocational contexts gives rise to new opportunities for inclusion that increase social well-being for citizens otherwise excluded due to the absence of the cognitive capabilities required for entrepreneurial activities leading to participation in knowledge-intensive societies..
Finally, a broad discussion of public policy and governance characterize AEGIS. Following on the footsteps of KEINS , but in a broader, more interrelated and more ambitious way, AEGIS focuses on policy at the system level, especially as it relates to knowledge-intensive ventures. As a start, one can consider the sets of policies that enhance market opportunities, technological opportunities and institutional opportunities for knowledge-intensive entrepreneurship . AEGIS wants to integrate these partial policy perspectives into a systemic policy perspective. Going beyond KEINS, AEGIS pays attention to demand as well as to supply conditions, focusse on high-tech, low-tech and service sectors, and
expand the analysis to non European large fast-growing
economies in order to asses their impact European knowledge-based competitiveness, international specialization and institutions. In addition, AEGIS links entrepreneurship policy to various strands of capitalism and innovation systems appearing in the European Union. AEGIS aims to arrive at a much more comprehensive policy approach than is currently the norm which takes into account the non-technological aspects as well as the systemic nature of knowledge-intensive entrepreneurship. The aim is to improve both horizontal and vertical coherence of direct and indirect policies that affect knowledge-intensive entrepreneurship.
5. DIMENSIONS
AND
INDICATORS
OF
KNOWLEDGE
INTENSIVE
ENTREPRENEURSHIP
5.1 The company level
From the previous definition provided in Section 1 and literature reviews, some key dimensions for the analysis of KIE at the firm level emerge. They can be grouped in six categories,.and the discussion of KIE, start ups have to be examined along some key dimensions: a. the origin of the company b. the evolution and the life cycle of the company c. the type of knowledge produced and used by new firms d. performance and innovation e. system dimensions f.
the business models used
The firm level is related to our specific interest in the firm’s relationship to the innovation system, and to the pattern of development/performance/growth. We can identify the main variables in each dimension, and the link with the relevant literature, in the following table
TABLE: COMPANY INDICATORS Origin and Evolution inputs Indicators
Knowledge
Performance
System
Business
and life cycle of High
Spin-off or Growth sales
not
model tech Number
of Knowledge
capabilities
from users
innovations
and sector
for innovation
employment Low
Founder background
Origin
sector
of
the idea
products and
products that
service
innovated
in Knowledge
advantage
on degrees of last 5 years - from CIS)
innovation introduced
competitors Analysis
Strategies for growth
Skills
of
(f.e. % new suppliers
Skills/educati
entrepreneur
Types
from
Competitive
Resources at the start
and Relevance of Knowledge
Life cycle of medium tech innovations the firm
Dynamic
of Patents
Type
of profitable
of
Financing
cooperation
employees
(VC, banks, Gazelles
services
Market share
corporate
(special
Access
to
VC)
definitions)
knowledge
Role Profits
products and
of
university
Customer
/
stakeholder Links
to Market
large
competition
companies Cooperation Role
of
Sources
Other links
of
focus
knowledge
(including
inputs
market
(from
knowledge)
university, consumers
public
Internal
and users…)
organization
policy
of knowledge Role
productive
of
resources
demand
Opportunity
Conceptu
Literature
al link to
on spin-off conditions
Role
of Success
knowledge
and Innovation
failure
and start ups and identification Role
of
large
Mobilization
knowledge
Firm survival
(see KEINS and growth survey)
strategy
and
organization
between
realization
national
literature
knowledge
innovative
market
sectoral, regional
providing
like patents, literature
models,
Opportunity
and
Strategy
system:
Relationship
company in of resources
resources
Business
activity
5.2 The sectoral, regional and country levels The analysis of the relevance of KIE at the sectoral, regional and country level requires quantitative indicators. These indicators are not easily available, nor does a comprehensive one exists and therefore a portfolio of company indicators that are also relevant to large databases/datasets must be used.
In contrast , GEM identifies new companies and entrepreneurship in general, but not finer grained indicators that link start ups with innovation, knowledge and systems So, AEGIS will resort to use various indicators, each of which present strengths and weaknesses,
A first indicator is new firms that are also innovative. In particular, a first one refers to all new firms that can be identified with new patenting activities. This measure is quite general because patents cover all the sectors and technologies. But a lot of new innovative ventures do not patent. So, while having
the advantage of
examining a whole population of patenting companies, this
indicator has the disadvantage of
being confined to a small fraction of the innovative firms that
decide to use IPR for their innovation.
A second indicator refers to new firms in sectors that are highly knowledge intensive. This indicator assumes that all new firms that enter a high technology sector have to be innovative or are knowledge intensive. This assumption is quite strong. In addition, it confines the analysis only to high technology sectors, while it is evident that KIE are active also in low and medium tech sectors.
The third indicator refers to new firms that use highly skilled human capital, irrespective of the sector. Here KIE is defined in terms of individual firm content of its human capital, irrespective of the sector. Human capital can be measured in terms of the education of the entrepreneur, the skills of the labor force, and so on. In this way, also new firms active in the so called traditional sectors may be considered KIE. However, the information on the skills of the members of the new ventures are usually not available.
A fourth indicator is related to new ventures that have an academic entrepreneurship. Here the assumption is that entrepreneurship that has scientific skills generate innovation (if coupled with market and application knowledge).
. A final set of indicators refers to the innovation system dimension of sectors, regions and countries. It may include R&D cooperation, formal alliances and licences. These indicators may be available for specific sectors, regions or countries.
6. CASE STUDIES RESEARCH DESIGN
The AEGIS case studies address several of the conceptual issues which related KIE to innovation systems, growth and performance as identified in this paper. Still, many of the case studies focus upon strategy, business models, mobilization of resources and other internal processes of the venture creation, as related to a temporal dimension.
The AEGIS case studies are primarily theory-driven, based upon an explicit research design to address very specific questions/puzzles that are not answered through qualitative data. Case studies may primarily focus upon qualitative data but may also combine qualitative and quantitative data in multiple case study design. A few case studies are chosen upon a sampling strategy, which implies that the projects have first identified (often surveyed or otherwise gather quantitative data) a total population and then selected relevant case studies.
The cases of KIE take their primary unit of analysis as the firm (new venture creation). In general, though, the process of following KIEs could include these levels of analysis: •
Individual entrepreneur
•
Venture creation / firm
•
Network between individual/firm and innovation sytsem
The AEGIS case studies can be categorized into the three phases of KIE identified in the AEGIS review of literature on innovation management and entrepreneurship. •
Inputs to the knowledge-intensive venture,
•
Managing the knowledge-intensive venture/process,
•
Output of the knowledge-intensive venture.
The case studies use the following theoretical concepts and explanations as well as empirical variables and indicators, as detailed in the Table below (see Middel and McKelvey AEGIS 2010).
Theoretical explanations Inputs to the knowledgeintensive venture
concepts
and Types of issues addressed through empirical variables and indicators
• Uncertainty • Opportunity creation • Recognition
Managing the knowledgeintensive venture/process
• Decision making logic • Absorptive Capacity • Emergent strategies • Exploration/exploitation
• Effectual decision making • Causal decision making
• Design
• Stakeholder relationships
• Entrepreneurial Orientation
• Type of partners • Type of relationships • Networking strategy
Output of the knowledgeintensive venture
• Inter-organizational networks
• Development stages
• Market creation • Increasing understanding of how the companies' networking during the first 5-10 years affects innovation and growth
By definition, case studies follow a process of development, and therefore they are able to address very specific questions about how variables are related to processes of growth and development. The following effects were mentioned by the partners, as analyzed in the case studies: • Knowledge based entrepreneurship is a process of both causal and effectual reasoning, markets are created through a swing between causal and effectual logics, the emphasis depends on critical challenges facing the company. • Effects on innovation capability and growth • Strategic insight into how high-tech ventures are best supported and used as a vehicle to explore and exploit new possibilities
The case studies will likely also identify a number of key areas where the theoretical predictions do not hold (special cases) or where empirical indicators at the company and database level must be called into question. These special cases and questioning of indicators arises due to the complexity enabled in case studies.
7. THE AEGIS SURVEY
The AEGIS survey tries to address several of the issues raised in this paper and attempts to measure some of the company level variables discussed above in a detailed and precise way.
The reason for the survey is that GEM and CIS do not address KIE. In fact the GEM survey addresses entrepreneurship in general, while the CIS survey addresses innovation, and not new firms.
So, a survey expressly focused on KIE is quite appropriate for understanding and measuring KIE, and this is the aim of the survey. The AEGIS survey tackles most of the features of KIE.
First, it examines the main characteristics and its knowledge assets and skills.
Second, it addresses the origin of the company in terms of characteristics of the founders of the company in terms of experience, skills
and training; determinants of the start up;
type of
formation the company had; type and amount of funding needed; obstacles that the company faced
Third, the survey focuses on the performance of the company, its innovativeness, its success factors and its relevant capabilities.
Fourth, the AEGIS survey goes in detail to examine the system factors that affect the origin, growth and performance of KIE: the role of customers, the sources of knowledge such a universities, PRO. suppliers, and so on; the links and networking the company has; the types of alliances that KIE has established.
Fifth, survey examines the institutional and market environment that surrounds KIE and that can be a source of growth as well as a block to innovation.
Finally, the strategy, the sensing and seizing of opportunities and the business models are analysed
APPENDIX 1: Literature review on Innovation Management & Enterpreneurship Literature (Larssen and McKelvey 2010 AEGIS) Figure 1: Distribution of KIE research foci
APPENDIX 1 con’t: Literature review on Innovation Management & Enterpreneurship Literature (Larssen and McKelvey 2010 AEGIS)
Input to the knowledgeintensive venture
Managing the knowledgeintensive venture/process
Outp entre
Financing KIE
Human resources
Paten
Characteristics of the KI entrepreneur/ Intentions to start
Network/social capital
New
Growth patterns
Grow
Sources of KI (academia/industry) Institutional influences Counselling Business planning Triple Helix KIS Training
Incubators/ CVC units
Relationship between knowledge, innovation and entrepreneurship From R&D to market Dynamics of the KI venture
• • • •
Know
