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206 Seiten, Note: 1.0
List of Figures
List of Tables
1 Theory: Concepts & Definitions
1.1.1 Definition of Innovation
1.1.2 What is Innovation About
1.1.3 Innovation and Economic Performance .
1.2.1 Definition of Clusters
1.2.2 Innovative Clusters and Typologies
1.2.3 Clusters and Economic Performance
1.3 High-Tech Start-Ups
1.3.1 Definition of High-Tech Start-Ups or New Technology Based Firms (NTBFs)
1.3.2 Steps to an High-Tech Start-Up
1.4 Information and Communication Technologies .
1.4.1 Definition of ICT
1.4.2 Evolution of the ICT sector
2 The Venture Capital Market
2.1 Venture Capital
2.1.1 Historical Overview
2.1.2 Definition of Venture Capital
2.1.3 Sources of Venture Capital: Type and Role of Investors
2.1.4 Benefits of Venture Capital
2.1.5 Phases of Venture Capital Financing
2.1.6 Venture Capital in Innovative High-Tech Sectors
2.2 The Venture Capital Market in Germany and Italy
2.3 The German Venture Capital Market
2.3.1 The Distribution of Venture Capital Investments in Germany
2.3.2 Venture Capital for High-Tech Start-Ups in Germany
2.4 Italian Venture Capital Market
2.4.1 The Distribution of Venture Capital Investments in Italy
2.4.2 Venture Capital for High-Tech Start-Ups in Italy
3 Determinants of Start-ups
3.1 Factors Fostering the Creation of High-Tech Start-Ups
3.1.1 Local Context Factors
3.1.2 Government Support Factors
3.1.3 University Level Support Factors
3.1.4 Individual Level Related Factors
3.2 The German Innovation System
3.2.1 The German Cluster Strategy
3.2.2 The German System of Coordination of Innovation Policies
3.2.3 The German High-Tech Strategy and the Direct Public Support to High Tech Start-Ups
3.2.4 The Regional Distribution of Businesses and High-Tech Start-Ups in Ger many
3.2.5 The Regional Characteristics and Locational Determinants of High-Tech Start-Ups in Germany
3.3 The Italian Innovation System
3.3.1 The Italian Cluster Strategy
3.3.2 The Italian System of Coordination of Innovation Policies
3.3.3 The Italian High-Tech Strategy and the Direct Public Support to High-Tech Start-Ups
3.3.4 The Regional Distribution of Businesses and High-Tech Start-Ups in Italy .
3.3.5 The Regional Characteristics and Locational Determinants of High-Tech Start-Ups in Italy
4 Two ICT Clusters in Comparison
4.1 The German ICT Sector
4.1.1 German ICT Industry Segments
4.2 The Italian ICT Sector
4.3 German and Italian ICT Clusters in Comparison
4.4 BICC NET: a German ICT cluster
4.4.1 Bavaria as Attractive Location for New Business and High-Tech Start-Ups
4.4.2 ICT in Bavaria: an Overview
4.4.3 BICC NET: the Bavarian ICT cluster
4.4.4 The Role of the Bavarian Government in Supporting Innovation
4.4.5 Effective Support in Bavaria for High-Tech Start-Ups
4.4.6 Public and Venture Capital for High-Tech Start-Ups in the Bavarian ICT Sector
4.5 Torino Wireless: an Italian ICT cluster
4.5.1 Piedmont as Attractive Location for New Business and High-Tech Start-Ups
4.5.2 ICT in Piedmont: an Overview
4.5.3 Torino Wireless: the Piedmontese ICT cluster
4.5.4 Actors Supporting Innovation and ICT in Piedmont
4.5.5 Effective Support in Piedmont to Innovation and High-Tech Start-Ups
4.5.6 Public and Venture Capital for High-Tech Start-Ups in the Piedmontese ICT Sector
5.1 Main Findings
5.2 Concluding Remarks
1.1 Example of Vertical/Horizontal Cluster
1.2 Example of Planned Cluster
1.3 The Porter Diamond Framework
2.1 Private equity segments
2.2 VC fundraising and investments in Europe, EUR bn
2.3 Allocation of European VC investments by segment, 2006 %
2.4 Exits via initial public offering in Europe
2.5 Use and Source of Venture Capital in Business Development
2.6 Venture capital investment amounts in European countries: 1996-2006 (C millions)
2.7 Italian venture capital investment amounts by stage: 1996-2006 (C millions)
2.8 German venture capital investment amounts by stage: 1996-2006 (C millions)
2.9 Level of investment on venture capital initiatives (number of deals) in Europe (ex- pressed in C Millions)
2.10 The spatial distribution of VC firms in Germany
2.11 The geographical distribution of VC investments in Germany
2.12 Proportion of start-ups (%) with VC financing after year of launch
2.13 History of the Italian venture capital market
3.1 The main activities in the German government’s cluster strategy
3.2 Federal ICT funding by Federal State (1991-2005)
3.3 Start-ups in the technology-intensive branch of the manufacturing sector at district level (average for the years 2001 to 2004)
3.4 Settlement intensity in the technology-intensive branch of the manufacturing sector at district level (average for the years 2001 to 2004)
3.5 Regional Distribution of Start-Up Rates - High-Tech Firms 1998-2001
3.6 ICT oriented Entrepreneurship Capital in German counties expressed as startups of new firms in ICT industries - 2000 to 2002 - relative to the counties’ population.
3.7 The Italian Innovation System
3.8 Geographical distribution - ICT Hotspots
3.9 The Localization of Local Labor Supply (LLS) specialized in ICT activities in Italy, 2001
4.1 EU-25 ICT Industry Market Volumes 2008 (in EUR billion)
4.2 German ICT Industry Market Volume* (in EUR billion)
4.3 Number of Employees in Germanys ICT Industry (in thousands)
4.4 Map of German ICT Companies, Clusters, and Competence Networks
4.5 Germany’s ICT Market by Segment 2009 (in EUR billion)*
4.6 % European ICT market by country
4.7 % The Italian ICT market (2005-2007) - Values in million of Euros
4.8 Bavaria’s Incubation Centers
5.1 The Blue Banana
1.1 Classification of technology-intensive service sector
1.2 European ICT sector (OECD definition)
2.1 Seed and Start-up Investments of VC Funds in Italy - year 2000
3.1 The German Innovation Policy System at a glance
3.2 Influencing factors for the location choice of innovative firm founders
A.1 Definition of High-Tech Industries
A.2 Classification of High-Tech-Fields in manufacturing
A.3 Techology-Intensive Services
A.4 R&D-Intensity and Innovation-Intensity of Technology-Intensive Industries
A.5 Sector distribution of Italian venture capital investments (%)
A.6 Sector distribution of German venture capital investments (%)
Abbildung in dieser Leseprobe nicht enthalten
Firstly, I would like to express my immense gratitude to my family for all the support given to me
- both moral and financial - during my period abroad, allowing me to study in a country such as Germany to which I am particularly affectionate.
A particular thanks goes to Prof. Dr. Wiebke Störmann which allowed me to write about this topic with her and also for her precious insights and advices during the course of the drafting of this work.
Lastly, I would like to thank all the professors, friends, especially international students and master colleagues which I had the opportunity to meet during the wonderful time I spent studying in Schmalkalden, which has allowed me, thanks to its international flavor and atmosphere, to ap- preciate and enjoy other cultures and their languages. In particular, I would like to thank my former master colleague, and friend, José Abraham Chavarría Castillo, for his advices in writing this work with LATEX, but also for his precious explanations regarding technicalities with this typesetting software.
The choice to write this document derives from my personal interests on technology and innovation and the ways on how they are promoted and financed; interests that I recently matured during an internship period at an innovative German start-up company.
I had the luck to be born in Germany and raised up in Italy, and from here comes also the interest to make a cross-country analysis on these two countries which gave me - and will still do so for a while - a lot both at personal level and as my future professional career is concerned.
I am really proud of having lived in this two countries which throughout their history have been (are and will be) home to famous innovators which have made precious contributions to the mankind.
To conclude, I would like to leave the reader by quoting one of these innovators that particularly fascinates me and according to which. . .
“ All our knowledge has its origins in our perceptions. ”
Leonardo da Vinci
Fabrizio Milio Schmalkalden, Germany
Now-a-days fundamental changes are taking place in the world as well as in the global economy, where events such as globalization, financial crises, wars, hurricanes and earthquakes - just to new a few of them - are shaping the daily life of million of people. However, to be aware of these events is not possible without another phenomenon that makes information about these extraordinary events available to each of us, i.e. innovation, since without it would not be possible to know about these events.
However, one of the fields where innovation occurs really fast is the field of information and communication technologies (ICT). Indeed, incredible progress in the field of information and communication technologies (ICT) has made things possible today which would have been un- thinkable some decades ago, and as ICT continued to spread into all sectors of social and economic life, it became a vital instrument for creating knowledge based society, building and sustaining human development, as well as transforming our world from the industrial society into an informa- tional one.
In this context, by the opening of borders to trade and foreign investment, globalization brings opportunities and pressures for domestic firms in global market economies to innovate and improve their competitive position, and thus contribute to foster the economic growth of a country.
Aware of this, and even though the Lisbon Strategy, set out by the European Council in Lisbon in March 2000, with the aim to make the EU “the most competitive and dynamic knowledge based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion”, by 2010, did not fully accomplished most of its initial goals, many countries are now starting policies with the aim to develop their strategic innovative sectors in order to compete in the international markets.
Among these countries we can find also Germany and Italy, which aware of their growth po- tentials in some high-tech sectors such as ICT, are seizing the moment by reconsidering new policy actions to cope with the international competitive pressures that the globalization as unleashed in the last few years.
Bearing this in mind, aim of this work is to present the current trends, as well as growth potentials, in the German and Italian ICT sectors by focusing the attention on venture capital and high-tech start-ups.
The structure of this thesis is as follows. The first chapter introduces concepts and defini- tions which will constitute the needed theoretical foundation to understand the content of this work throughout the whole document. In this chapter important concepts such as innovation, cluster, high-tech start-ups and ICT are introduced. However, a missing piece of the theoretical founda- tion, is analyzed and exposed in more detail in the second chapter, which explains the role that venture capital plays for high-tech start-ups, i.e. by defining first what venture capital is, what its main elements are and then reviewing which financing alternatives (phases) for innovative firms exist. Afterwards, a comparison between the venture capital markets both in Germany and Italy is presented.
The first two chapters have to be seen as constituting the “theoretical foundation” of this work, while the “political” foundation is introduced in the third chapter.
Indeed, the third chapter deals with the determinants of start-ups at local, governmental, uni- versity, and individual level, but introduces also the innovation systems of Germany and Italy, respectively. This is of fundamental importance since it reveals the role of the state with its public policies in support of innovation, with particular attention on incentives and programs targeting the establishment of new start-ups, especially in the ICT sector.
Next follows the fourth and most important chapter of this work, since it is here that the effective role of venture capital and high-tech start-ups is analyzed in the context of two ICT clusters: the two ICT clusters at issue are, for Germany, BICC NET which is the Bavarian Information and Communication Technology Cluster, and for Italy, Torino Wireless, the ICT Technology District in Piedmont. However, before to introduce the two ICT clusters, the fourth chapter opens with an overview of the current trends in the German and Italian ICT sectors.
Finally, a last chapter reporting the main findings and some concluding remarks is presented.
Innovation is very important since it acts as stimulus to both economic development and competi- tiveness. Moreover, innovation is not a new phenomenon. Arguably, it is as old as mankind itself, since there seems to be something inherently “human” about the tendency to think about new and better ways of doing things and try them out in practice (Fagerberg, 2003). Without innovation the world in which we live today would have looked very, very different. Let’s try for a moment to think about a world without automobiles, airplanes, telecommunications and refrigerators, just to mention a few important innovations from the not too distant past. Or - in an even longer perspec- tive - how could we think to live nowadays without fundamental innovations such as agriculture, the wheel, the alphabet, printing etc.?
Innovation and innovative ideas come from different sources, and the success of local firms depends (also) on their capacity to interact, appropriate and exploit resources and ideas that spin off of the market interactions (Teece, 1986).
The following sections are going to introduce the concept of innovation by defining what it is 5 and by presenting the role that innovation plays for economic performance.
There are various definitions of the term “innovation”, which derives from the latin “ innovatio ” which means the creation of something new. But due to the existence of many definitions of the term “innovation”, probably the most famous one is the definition provided by Schumpeter (Schumpeter, 1997) who distinguishes five areas in which companies can introduce innovation:
1. Generation of new or improved products.
2. Introduction of new production processes.
3. Development of new sales markets.
4. Development of new supply markets.
5. Reorganization and/or restructuring of the company.
The above definition clearly distinguishes innovation from minor changes in the make up and/or delivery of products in forms of extension of product lines, adding service components or product differentiation. Successful innovation, e.g. innovation that is also profitable to the Information and Communications Technology sector - ICT - (for example) in a competitive market, must increase the value of the product or ICT experience. Since the value is customers’ perceived quality divided by the price (cost) of this quality successful innovation must increase value by improving quality or by lowering price (cost) (Heskett, 1986).
Innovation combines invention and discovery with practical application, either by bringing the invention to the market or to the workplace. The Oslo Manual (OECD/Eurostat, 1997) outlines proposed guidelines for collecting and interpreting innovation data and allows the production of internationally comparable, meaningful indicators of innovation. The manual identifies two types of technological innovation - product innovation and process innovation.
“An innovative firm is one that has introduced a new or significantly improved prod uct onto the market or introduced a new or significantly improved process into the production process during the previous three years.”
In the case of product innovation, the product must be new to the establishment and it must have been introduced to the market, and not simply be ready for introduction to the market. The term product includes both goods and services. Complex products may be innovative as a result of changes to one of the components or subsystems. Changes to a firms existing products that are purely aesthetic, or that involve only minor modifications, are not considered to be innovations.
A process innovation must have been actually used within the production process. New or significantly improved processes are those that are new to the firm. The outcome of process in- novation should be significant with respect to the level of output, quality of products (goods or services) or costs of production and distribution. Minor or routine changes to processes are not to be included. The term “process” also includes improved ways of delivering goods or services.
Another important distinction is that made between invention and innovation.1 Invention is the first occurrence of an idea for a new product or process. Innovation is the first commercial- ization of the idea. Sometimes invention and innovation are closely linked, to the extent that it is hard to distinguish one from another (biotechnology for instance). In many cases, however, there is a considerable time lag between the two. In fact a lag of several decades or more is not un common (Rogers, 1995). Such lags reflect the different requirements for working out ideas and carrying them out in practice. First of all, while inventions may be carried out anywhere such as, for instance, in universities, innovations occur mostly in firms in the commercial sphere. To be able to turn an invention into an innovation a firm normally needs to combine several different types of knowledge, capabilities, skills and resources. For instance the firm may require production knowledge, skills and facilities, market knowledge, a well-functioning distribution system, suffi- cient financial resources and so on. It follows that the role of the innovator,2 e.g., the person or organizational unit responsible for combining the factors necessary (what the innovation-theorist Joseph Schumpeter called the “entrepreneur”), may be quite different from that of the inventor. Long lags between invention and innovation may also have to do with the fact that in many cases, some or all of the conditions for commercialization may be lacking. There may not be a sufficient need or it may be impossible to produce and/or market because some vital inputs or complementary factors are not available. For instance, although Leonardo da Vinci is reported have had some quite advanced ideas for an airplane, these were impossible to carry out in practice due to lack of ade- quate materials, production skills and - above all - a power source. In fact the realization of these ideas had to wait for the invention and subsequent commercialization (and improvement) of the in- ternal combustion engine.3 Hence, as this example shows, many inventions require complementary inventions and innovations to succeed at the innovation stage.
Another complicating factor is that invention and innovation is a continuous process. For instance, the car as we know it today is radically improved compared to its first commercialization, through the incorporation of a very large number of different inventions/innovations. In fact, the first versions of virtually all significant innovations, from the steam engine to the airplane, were crude, unreliable versions of the devices that eventually diffused widely. Kline and Rosenberg (1986), in an influential paper, point out: “it is a serious mistake to treat an innovation as if it were a well-defined, homogenous thing that could be identified as entering the economy at a precise date - or becoming available at a precise point in time (...). The fact is that most important innovations go through drastic changes in their lifetimes - changes that may, and often do, totally transform their economic significance. The subsequent improvements in an invention after its first introduction may be vastly more important, economically, than the initial availability of the invention in its original form. ” (Kline and Rosenberg 1986, p. 283).
Thus, what we think of as a single innovation is often the result of a lengthy process involving many interrelated innovations. This is one of the reasons why many students of technology and innovation find it natural to apply a system’s perspective rather than to focus exclusively on individual inventions/innovations. Innovations may be classified according to “type”. Schumpeter distinguished between five different types: a) new products, b) new methods of production, c) new sources of supply, d) exploitation of new markets, and e) new ways to organize business. However, in economics most of the focus has been on the two first of these.
Now, leaving definitions aside, the fundamental question for innovation itself is of course to explain how innovations occur. One of the reasons why innovation was ignored in mainstream social science for so long was that this was seen as impossible to do. The best one could do, it was commonly assumed, was to look at it as a random phenomenon (or “manna from heaven” as some scholars used to phrase it). Schumpeter, in his early works, was one of the first to object to this practice. His own account of these processes emphasized three main aspects. The first was the fundamental uncertainty inherent in all innovation projects. The second was the need to move quickly before somebody else did (and reaped the potential economic reward). In practice, Schumpeter argued, these two aspects meant that the standard behavioural rules economists used to assume, e.g., surveying all information, assessing it and finding the “optimal choice”, wouldn’t work. Other, quicker ways had to be found. This did in his view involve leadership and vision, two qualities he associated with entrepreneurship. The third was the prevalence of “resistance to new ways” - or inertia - at all levels of society, which threatened to destroy all novel initiatives, and forced entrepreneurs to fight hard to succeed in their innovation projects. Or as he put it: “In the breast of one who wishes to do something new, the forces of habit raise up and bear witness against the embryonic project” (Schumpeter 1934, p. 86).
It is probably fair to say that most sectors of economic activity in Western economies have undergone strong technological changes moving towards IT-based flexible manufacturing with global outsourcing, creating the “.com” or “new economy”, which very much fits with what Schumpeter described as the creative destruction of existing institutional arrangements and patterns of exchange in order to create new wealth through innovation. His vision also included an increased willingness to take calculated risks by new or real entrepreneurs (Schumpeter, 1934).
The Marx-Schumpeter model was not, however, primarily intended as a model of industrial dynam- ics (and differences in such across industries, sectors and time). Its primary purpose was to explain long run economic change, what Schumpeter called “development”. The core of the argument was (1) that technological competition is the major form of competition under capitalism (and firms not responding to these demands would fail), and (2) that innovations, e.g., “new combination” of existing knowledge and resources, open up possibilities for new business opportunities, and inno- vations, in the future, and in this way set the stage for continuing change. This perspective, while convincing, had little influence on the economics discipline at the time of its publication, maybe because it did not led itself easily to formal, mathematical modeling of the type that had become increasingly popular in that field. More recently, however, economists (Romer, 1990), drawing on new tools for mathematical modeling, have attempted to introduce some of the above ideas in formal growth models (so-called “new growth theory” or “endogenous growth theory”).4
In applying this perspective, Schumpeter (1939) was, as noted, particularly concerned with the tendency of innovation to “cluster” in certain contexts, and the resulting structural changes in production, organization, demand etc. Although these ideas were not well received by the eco- nomic community at the time, the big slump in economic activity world-wide during the 1970s lead a to renewed attention, and several contributions emerged, viewing long run economic and social change from this perspective. For instance, both Mensch (1979) and Perez (1983, 1985) argued that major technological changes, such as, for instance, the ICT revolution today, or electricity a cen- tury ago, require extensive organizational and institutional change to run its course. Such change, however, is difficult because of the continuing influence of existing organizational and institutional patterns. They saw this inertia as a major growth-impeding factor in periods of rapid technological change, possibly explaining some of the variation of growth over time (e.g., booms and slumps) in capitalist economies. While the latter proposition remains controversial (Freeman and Louçã, 2001), the relationship between technological, organizational and institutional change continues to be an important research issue (Freeman and Louçã, 2001), with important implications both for the analysis of the diffusion of new technologies and the policy discourse.
Although neither Marx nor Schumpeter applied their dynamic perspective to the analysis of cross-national differences in growth performance, from the early 1960s onwards several contri- butions have emerged that explore the potential of this perspective for explaining differences in cross-country growth. In what came to be a very influential contribution, Posner (1961) explained the difference in economic growth between two countries, at different levels of economic and tech nological development, as resulting from two sources; innovation, which enhanced the difference, and imitation, which tended to reduce it. This set the stage for a long series of contributions, often labelled “technology gap” or “north-south” models (or approaches), focusing on explaining such differences in economic growth across countries at different levels of development (see Fagerberg, 1996, for details). As for the lessons, one of the theoretical contributors in this area summed it up well when he concluded that: “Like Alice and the Red Queen, the developed region has to keep running to stay in the same place” (Krugman, 1979, p. 262).
A weakness of much of this work was that it was based on a very stylized representation of the global distribution of innovation, in which innovation was assumed to be concentrated in the developed world, mainly in the USA. However, as shown by Fagerberg, successful catch up in technology and income is normally not based only on imitation, but also involves innovation to a significant extent. Arguably, this is also what one should expect from the Schumpeterian per- spective, in which innovation is assumed to be a pervasive phenomenon. Fagerberg (1987, 1988) identified three factors affecting “why growth-rates differ”; innovation, imitation and other efforts related to the commercial exploitation of technology, as driving forces of growth. The inclusion of innovation in the explanatory framework, alongside the more conventional variables, signifi- cantly increased the model’s explanatory power. For instance, the analysis presented in Fagerberg (1988) suggested that superior innovative activity was the prime factor behind the huge difference in performance between Asian and Latin-American NIC-countries5 in the 1970s and early 1980s. Fagerberg and Verspagen (2002) likewise found that the continuing rapid growth of the Asian NICs relative to other country groupings in the decade that followed was primarily caused by the rapid growth in the innovative performance of this region. Moreover, it has been shown (Fagerberg, 1987, Fagerberg and Verspagen, 2002) that while imitation has become more demanding over time (and hence more difficult and/or costly to undertake), innovation has gradually become a more powerful factor in explaining differences across countries in economic growth.
Now after having given some definition of what is innovation, I will go on talking about what “clusters” are, i.e. another fundamental concept which will help the reader to better understand the developments that will be shaping the next chapters where I will speak about ICT clusters both in Germany and Italy.
Conceptually, industry clusters have become the sine qua non of economic development policy in many parts of the world (Rosenfeld, 2002). And due to this it can be said that successful economies are, to varying degrees, specialized. Even the most diversified regions are home to industries that, because of historical reasons or geographic peculiarities, are found in higher concentrations than in other places. Competitive advantage of place can be best understood in terms of the comparative advantages of specific industries within that place’s borders. No nation, and certainly no region, can be outstanding at producing everything. Therefore successful places develop strengths and focus innovative capacities on certain types of industries, or clusters. Clustering provides firms with access to more suppliers and specialized support services, experienced and skilled labour pools and the inevitable knowledge leakage that occurs where people meet and talk about business, as for example in the ICT sector. The advantages of place draw not only similar but also complementary enterprises and, as a result, clusters become a breeding ground for new clusters.
Why are some regions better able to develop and support innovative and competitive clusters and become more prosperous than others? In Germany for example, the federal state of Baden Württemberg has some outstanding research universities with its best graduate greatly contributing to the economic growth of the place. On the contrary, the south of Italy has a milieux that attracts tourists but lacks the skilled labour force to attract technology-dependent firms. However, on this last point I will return later on when comparing two ICT clusters.6 Now after this brief introduction I proceed by explaining what clusters are, which typologies exist, and expose the linkage between innovation and clusters by introducing the concept of “innovation clusters”. At the end the role that clusters play for economic performance is introduced.
To define a cluster is not as simple as one might think. The concept is widely used for in order to express a variety of different business structures: national-regional-cross-border clusters, clusters of competence, industrial or production systems and innovation systems. It is also used for dif- ferent purposes: to increase the competitiveness of SMEs, support collective research, rationalize a whole industry, implement environment management system. Even though there is a multitude of definitions most of them share the idea of proximity, networking and specialization. Let’s have now a look at the most diffused definitions of cluster present in the economic literature.
Following the research of Malmberg et al. (1996), regional clusters are limited geographical areas with a relatively large number of firms and employees within a small number of related industrial sectors. Thus, the clusters are specialized in a small number of industries. This reflects the more general point that economic, entrepreneurial and technological activities in specific industrial sectors tend to agglomerate at certain places.
One of the most used definitions of a cluster is the one of Michael Porter:
“ Clusters are geographically close groups of interconnected companies and associ- ated institutions in a particular field, linked by common technologies and skills. They normally exist within a geographic area where ease of communication, logistics and personal interaction is possible. Clusters are normally concentrated in regions and sometimes in a single town ” .
Another definition given by Porter is the one according to which clusters are groups of companies and institutions co-located in a specific geographic region and linked by interdependencies in pro- viding a related group of products and/or services (Porter, 1990). The definition of clusters builds on three pillars (Ketels and Memedovic, 2008): geography, creating value and business environ- ment. Geography refers to the fact that clusters are driven by proximity and are often concentrated in a region within a larger nation, and sometimes in one town. Creating value refers to the fact that clusters include companies in different industries that are related to each other in the production of goods and services valued by customers. The third pillar, business environment is related to the effects of cluster-specific business environment conditions resulting from individual actions as well as cooperation of companies, government agencies, universities and other institutions on economic performance of clusters.
Clusters differ in many dimensions and we can say that there are different types of clusters appropriate for every stage of development. Christian Ketels (Ketels, 2003) mentions the example of Claas van der Linde from Harvard Business School: in the industry of footwear, Northern Italy is home to a very successful, high wage cluster, serving the world market and focusing on design, brand building and high value production. Portugal is home to another cluster, focused on footwear manufacturing and short production runs serving fashion-conscious markets in Europe.
The links between Innovation and District / Cluster theory can be traced back to 1977, when Bagnasco published his study on the Third Italy, describing small cities and communities of central Italy flourishing on the basis company clusters sustaining flexibility and continuous product innovation (Bagnasco, 1977).
Michael Porter popularized the concept of industry clusters is his book The Competitive Ad- vantage of Nations (1990). Porter recognized that the majority of economic activity takes place at the regional level and his ideas are commonly applied to cities and regions. Clusters are geographic concentrations of interconnected companies and institutions with systematic relationships to one another based on complementarities or similarities in particular fields that co-operate and establish close linkages and working alliances to improve their collective competitiveness. Clusters have different origins: many (Italian districts) have grown by the volunteer decision of manufacturing SMEs, while others have been influenced by large manufacturing companies (Bayer in the Rhine region), and others are by-products of universities and research institutes, in the case of planned science and technology parks.
Abbildung in dieser Leseprobe nicht enthalten
Figure 1.1: Example of Vertical/Horizontal Cluster.
Source: Komninos (2006).
Diversity: Vertical - Horizontal clusters. Vertical are the clusters with strong inter-firm linkages; the companies are specialized in different phases of the production process, and linked along the supply chain with supplier-producer relationships; characteristic case, the Italian industrial districts. Horizontal are the clusters with weak inter-linkages; the organizations composing the cluster act as a whole to achieve a common objective, i.e. to open a new market, to use an infrastructure, to cover subcontracting needs of a large company, to cooperate with a strong R&D institution. The structure of Vertical - Horizontal clusters is visible in Figure 1.1 here above.
Diversity: Planned clusters. Here the complexity of networks within the district makes ‘tech nology districts planning’ extremely difficult. The nearest application of the district concept to regional planning comes through science and technology parks.
Abbildung in dieser Leseprobe nicht enthalten
Figure 1.2: Example of Planned Cluster.
Source: Komninos (2006).
We have 400 cases in Europe. Moreover, the main four constituting elements are: (a) land + infrastructure, (b) R&D, (c) technology intermediaries, (d) innovative companies. The structure of a planned cluster is shown in Figure 1.2 here above.
Cluster-based innovation mechanism. Becattini (1989) described the innovation mechanism within the cluster / district with respect to the agglomeration of skills:
- The concentration of many and diverse skills in the cluster or district covering various fields of knowledge and production. Even in cases where the whole cluster focuses on a single industrial sector, the multiplicity of skills comes from specialization in different stages of the production process.
- The cooperation networks between the members of the cluster. Cooperation produce inno- vation, as the later stems from the combination of skills, knowledge, and qualities that are put together.
- The presence of “catalysts” that facilitate combinations among the many and diverse skills and units. The Venture Capital (VC) functions as catalyst in high-tech clusters.7 The central administration and liaison offices in the case of technology parks.
However, it is also at least conceptually possible to begin to construct a taxonomy of clusters by beginning to identify key types of innovative local areas based on a number of performance characteristics, or dimensions.
The most basic, common characteristic of all of these areas is that particular types of firms are located in a relatively close physical proximity to each other, i.e. they form agglomerative economic clusters, or spatial concentrations. But once one has said this one has not said a good deal - there are different types of agglomerative clusters. In some cases - but not all - these clusters are innovative in terms of producing goods or services, or both. The generic title for these areas are agglomeration economies. Another way of describing these areas are that there are flexible local production systems which employ different forms of social capital, including information and communication linkages, to create highly-articulated producer and supplier market networks.
One way of building a taxonomy is by using basic Set Theory. Within the overall agglomeration economy, or LPNP - Local Production Network Paradigm - (Simmie and Hart, 1999) main set, it is possible to theoretically identify at least three sub-sets which have been widely discussed in the literature. They are:
- Type A - Cohesive Clusters
- Type B - New Industrial Districts - Type C - Innovative Milieux
The Set Theory notation for this is: Types A, B, C (are contained within) the Agglomeration Economies/Local Production Network Paradigm Set - that is, the three sub-sets share common el- ements of the main set. Now I briefly present the operating characteristics of each of these sub-set types and give examples of industries and areas where they operate for illustrative purposes.
Type A - Cohesive Clusters
The analysis of clusters relates, unsurprisingly, to the period of time when they were identified and the type of industries which were prevalent at the time. What might be termed Cohesive Clusters are the oldest types of areas under examination here. The operational characteristics of these agglomerative economies were mentioned by (Weber, 1909) and (Marshall, 1925). Cohesive clusters are groups of firms which initially located together to reduce costs. Weber’s logic was that entrepreneurs would locate in areas of least cost with regard to factors such as transport and labour and therefore benefit from economies of scale. He assumed that transport costs are a function of weight and distance. The concern was to keep the costs of movement associated with material assembly, and subsequent distribution to the market, to a minimum.
Cohesive Clusters were often located in urban, including inner city, locations, such as the Jewelry Quarter in Birmingham, or the Hackney area in London. Their method of dealing with the threats posed by innovation were too be extremely flexible in terms of rapidly responding to change in the production of new products and they drew on the abilities of a highly-skilled local labor force. They tended to specialize in industries such as fashion items, reproduction furniture, and printing - all of which required the capacity for quick change production. The main economic advantage has traditionally been described as the reduction of ‘transaction costs’ particularly trans- port costs. But there is another reason forming this type of cluster as well which relates to the risks and uncertainty associated with the innovative process itself. By working together in a flexibly inter-active way firms in this cluster could reduce risk by spreading it between and among them - in effect, by syndicating it.
Type B - New Industrial Districts
New Industrial Districts differ from the previous example in several ways but they share the fact that their description relates to the period of time when they were identified and the type of industries which were prevalent at the time within them. New Industrial Districts tend be knowledge-based, that is they often have a high proportion of companies in high-tech sectors such as computing, In- formation Technology (IT) and micro-electronics.8 They rely extensively on R&D for the creation of new products. They tend to be located on the fringe of urban areas or even at some distance from them - examples include Silicon Valley in California and the Etna Valley in Sicily, Italy.
In contrast with Type A clusters, New Industrial Districts produce goods with are relatively small and light in weight and therefore have a high value-to-bulk ratio and, as consequence, trans- port costs are not a major concern for entrepreneurs in locational decision terms. Transports costs are not a major concern but transport speed - and reliability of delivery - are. The type of goods produced in these clusters are urgently required throughout the world by customers and they need to be rapidly produced and shipped - often by air to global markets. Speed, in general, is an im- portant concern in the New Industrial Districts and there is constant concern about being overtaken by innovations produced by competitors so the pace of fostering innovation is brisk. The em- ployees in these firms are not simply highly-skilled, a substantial proportion are highly-educated scientifically and technologically. Thus in terms of transaction costs information and dependable high-speed transport links are key elements.
Again in contrast with Type A clusters, Type B clusters are composed of a range of different size firms, from Trans-Nationals to SMEs. The large firms form, often, long-standing relations with their smaller suppliers and they work jointly on projects - in some cases with time horizons of decades. These relatively stable supply chains allow firms to deal with the threats posed to them by the innovation process by seeking to control change through established long-term planning and production arrangements in what might be described as a ‘closed club’. Finally, although they are called new industrial districts many have been in existence for 30 years and more and are now better described as mature rather than recent.
Type C - Innovative Milieux
The description of the third type of cluster is largely based on the work of the group of researchers called GREMI (Groupe de recherch é europeen sur les milieux innovateurs) which emphasised the importance of social capital in promoting innovation (Aydalot, 1986, Camagni, 1991, Maillat, 1995). In the Innovative Milieux social networks were established between individuals within firms and between individuals in different firms. These networks were based on experience of working together in the past and therefore trust bonds within the network were created. This type of clus- ter tends to be located in urban areas where established relations between firms and individuals have existed for some time. As Capello has noted, ‘Cumulative and collective learning processes enhance local creativity and innovative output, through the informal exchange of information and specialised knowledge’ (Capello, 1999). Learning takes place in a variety of ways with individuals in different firms exchanging information or individuals moving from one firm to another. Exam- ples of innovative milieux clusters include Emilia-Romagna and parts of Northeast Milan in Italy. Firms in this type of cluster are willing to jointly pursue common goals on innovative projects which may involve risk.
There are many parallels between the innovative milieux cluster and the Cohesive Cluster which was mentioned earlier. Both are largely based on small and medium sized firms within urban areas who rely heavily on the skills and knowledge of a common workforce which, in turn, means the firms are deeply ‘embedded’ in their local place. There are also importance differences as well. The Type C clusters actively seek to promote innovation rather than simply rapidly responding to it and actively work together to promote common, medium and long-term innovative goals. The firms in the Type C cluster respond to the threats posed by the innovative process, once again, by seeking to spread the risk through active and continuing syndication of their production arrangements.
However, a fourth type of innovative cluster which displays characteristics which are different from the previous clusters, can be mentioned. This type of cluster is the most recently described in the literature and its characteristics raises questions both about conventional agglomeration eco- nomics, per se, and about current national and European Union policies for promoting innovation.
Type D - Proximity Clusters
Compared to the three types of cluster mentioned earlier Proximity Clusters, work in a completely different way. They exhibit a great degree of internal heterogeneity in terms of their production organisational arrangements, rather than cohesiveness (Hart and Simmie, 1997, Capello, 1999). On the basis of a number of growing number of publications, it has been discovered that within overall innovative areas such as the county of Hertfordshire immediately to the north of Greater London, there are innovative clusters which are not agglomerations in the way the term is used conventionally. That is, innovative clusters have been identified and examined empirically which have extremely limited linkages of any type within the cluster area but often have extensively linkages outside of it. These proximity clusters are so-called because they are located in a relatively close spatial relationships with each other but do not form the kind of Local Production Network which the previous three clusters exhibited in different ways. They are not so much embedded in an area but weakly attached to it.
Proximity clusters typically occur outside major conurbations and at least in the Hertfordshire example contain a number of very small ‘micro-firms’. In these micro-firms the importance of the individual innovator has begun to re-assert itself as it did in the 19th century. The firms are highly innovative and develop specialist products which they sell all over the world. Often it is the continuing client of the firm - in many cases intermediate buyers such as health services, or defense organizations - who seek to promote innovation rather than simply the firm on its own. In this case the innovative process is more influenced by ‘ demand-pull ’ rather than ‘ technology-push ’.
Clusters develop and are important because they create economic benefits. The benefits of a cluster come in three dimensions:9 First, companies can operate with a higher level of efficiency, drawing on more specialized assets and suppliers with shorter reaction times than they could in isolation. Second, companies and research institutions can achieve higher levels of innovation.10 Knowledge spillovers and the close interaction with customers and other companies create more new ideas and provide intense pressure to innovate while the cluster environment lowers the cost of experi- menting. Third, the level of business formation tends to be higher in clusters. Start-ups are more reliant on external suppliers and partners, all of which they find in a cluster. Clusters also reduce the cost of failure, as entrepreneurs can fall back on local employment opportunities in the many other companies in the same field.
These benefits are important both for cluster participants and for public policy. For companies, they create additional value that outweighs the often-higher costs of more intense competition for specialized real estate, skills, and customers at the location.11 They are thus the reasons that clusters emerge naturally from profit-maximizing decisions. For public policy, higher productivity and innovation in clusters are critical because they are the factors that in the long term define the sustainable level of prosperity in a region. Note, however, that the interests of these groups are not identical: public policy is not concerned about the distribution of the cluster benefits among companies, employees, and owners of critical assets such as real estate, while company owners clearly are.
The performance of a cluster at a specific location is driven by the business environment that the cluster is operating in. “Business environment” is a broad and naturally vague term: almost everything - from the quality of the schools to the strategies of local competitors - matters for the level of productivity and innovation that companies in the cluster reach at this specific location.
To organize this complexity, Michael Porter (1990) has introduced the so-called “diamond” as an analytical tool to assess business environments. The diamond model is depicted in Figure 1.3 here under.
Abbildung in dieser Leseprobe nicht enthalten
Figure 1.3: The Porter Diamond Framework.
Source: Michael Porter, Institute for Strategy and Competitiveness, Harvard Business School. See Porter (1998b).
The diamond includes the four elements factor conditions (e.g., physical infrastructure, skills, etc.), demand conditions (e.g., sophistication of local customers, product and consumer regulation), the context for strategy and rivalry (e.g. taxation structure, competition laws, and the strategies of com- peting local companies), and the presence of related and supporting industries (e.g., the breadth and depth of the cluster). These elements interact in their impact on specific companies and clusters; they exhibit system-effects where the weakest element often tends to have the strongest impact on the overall quality.
The diamond can be used to analyze the general quality of the business environment at the national or regional level. But it can also be applied at the regional cluster level, looking at the specific conditions relevant for the cluster in the four categories defined.12 Note that the impact of different aspects of the business environment depend on the position that the cluster aims to take in the field.
Government policy has an impact on all elements of the cluster-specific diamond. It often has responsibilities for large parts of the infrastructure, it sets key rules and regulations affecting com- petition and demand, and it affects the cluster presence through, for example, recruiting companies from other locations to make investments. More recent research has emphasized the need to look at government policies in a more differentiated way, separating the role of government at different geographic levels - from the cross-national, such as the EU or Baltic Rim region,13 to the national to the regional and local14 - and of different, often quite autonomous government agencies. All these influences culminate at the regional cluster level. Now after having broadly exposed the con- cept of cluster with its taxonomy in the next sections I am going to expose the other two pieces of our puzzle in order to understand the conceptual framework which constitutes the theoretical foundation of this Master Thesis.
Many policy makers and entrepreneurship scholars regard high-tech start-ups as driving forces in making contributions toward economic growth, employment and structural change based on their innovations. They can enter new markets with more flexibility compared with established firms and full of new ideas. Additional public subsidies and an infrastructure oriented toward foundation and innovation provide advantageous founding and location factors.
New firms in high-tech industries take over the position of future leadership especially in advanced economies, such as Italy and Germany. They are considered to be crucial elements in closing the productivity gap and the gap within existing firm structure and guidelining the transformation process in knowledge-driven economies.
Since high-tech firms are one of the main contributors to a nation growth rate, here a definition of High-Tech Start-Ups and of the ICT sector is presented, concepts these which are necessary in order to understand the chapters to come.
1.3.1 Definition of High-Tech Start-Ups or New Technology Based Firms (NTBFs) A high-tech start-up is defined as being a legally independent company which is no older than ten years and operates in a high-technology sector(s) (see Table A.1 in Appendix, p. 169).
However, despite the growing interest on High-Tech Start-Ups there is no clear-cut definition of what is meant by the term High-Tech Start-Ups or New Technology-Based Firm, as well as very different opinions and different definitions, depending often on the availability of data. The famous study conducted by the Arthur D. Little Group (1977) defined a NTBF as an independently owned firm not older than 25 years and whose main aim is to exploit a technological invention or innovation. Other authors apply the word “new” to the technology used, or to both, the technology and the firm (see for a discussion of different definitions Storey and Tether, 1998a).
The definition of firm foundation types (e.g. differentiation with respect to prior structural change as well as independence) plays also crucial role regarding regional differences in the frequency of foundation (see Geroski 1995).
A definition of high-tech start-ups can be derived possible according to the classification of the ‘technology- intensive’ goods based on innovation input as defined by OECD which uses a top down high technology definition (see Nerlinger and Berger 1995). This classification is based on the average R&D intensity of five-digit industry groups (so called WZ79). If the R&D intensity (R&D expenditures in percent of turnover) of a five-digit group is 8,5 % or more, the authors classify this group as “top technology”, with a R&D intensity in the range from 3,5 to 8,5 % they are classified as “high technology”. Five-digit groups with a R&D intensity below 3,5 % are classified as “low and medium technology” (see Table A.2 in Appendix, p. 170).
Recent empirical studies (see Harhoff et al. 1996, Nerlinger 1998) show that a lot of firms in services carry out R&D and innovation activities in a big amount (see Table A.3, Table A.4 in Appendix, p. 171). In analogy, those industries in the service sector are considered as hightech service sector comprising industries. Of course, such an industry specific definition of “high technology” has several drawbacks.
Once industries or product groups are categorized as “high-tech”, every firm operating in such industries are considered as being a high-tech firm, independent of whether the firm is really in- novative or not. A second disadvantage associated with such a definition is the fact, that there surely are firms which are very innovative and perhaps on the technological forefront which are not considered as high-tech, simply because they do not belong to the top down defined high-tech industries.
The increasing importance of the service sector with regard to innovation and technological change is being recognized more and more. Indeed, some services are at the forefront of innovation: “. . . new IT-based services, such as software and telematics, are triggers to innovation across the economy, rather than passive recipients of innovation from the manufacturing industry.” (Miles, 1994, 252). Thus, especially the emergence of an autonomous software sector with tight links to the computer industry and the recognition of constantly growing R&D expenditures by the service sector make it necessary to integrate the service sector into the definition of technology intensive sectors (Malecki, 1991, Nerlinger and Berger, 1995). Under the heading of “technology intensive services” we understand services with a high complexity and/or knowledge intensity. Table 1.1 shows the classification of technology-intensive service sectors.
Table 1.1: Classification of technology-intensive service sector.
Abbildung in dieser Leseprobe nicht enthalten
Source: Nerlinger and Berger 1995.
Typically, high-tech start-ups follow a series of common steps. Here are shown the 4 steps which describe the sometimes-torturous path from concept to success of an High-Tech Start-Up. Normally a start-up goes through four phases:15
1. Preseed phase: Finding and formulation of business idea, initial draft of business plan.
2. Seed phase: Business plan, technology development, proof of concept. Here the objective is the creation of a prototype, and this phase might last up to one year.
3. Growth phase: Securing of key customers, optimization of technologies, increasing of mar- ket shares.
4. Exit: IPO, sale or trading of equity, buy-back by founders.
Information Society has been one of the key terms used to describe today’s world, as ICT have brought revolutionary changes impacting every aspect of our society - connecting cultures, creating new opportunities for education, restructuring employment, generating new economies, and changing citizens’ relation to government. Recognizing these and other paradigm shifts are typically seen as the natural development of the modern liberal tradition. Information and Communication Technologies (ICTs) represent to today’s world what industrial machines represented during the industrial revolution; they have revolutionized ways of working, transformed the economy, had an irreversible impact on the way people live, and have shaped a new “information society”. It is about a second industry revolution but this time based in the information and communication arena. But let see what ICT is and how it is defined in more detail.
Information and Communication Technologies broadly refer to set of activities that facilitate - by electronic means - the capturing, storage, processing, transmission, and display of information.16 Information and Communications Technology is “an umbrella term that includes computer hard- ware and software; digital broadcast and telecommunications technologies as well as electronic information repositories such as the World Wide Web or those found on CD-ROMs (Selwyn 2002). It represents a broad and continually evolving range of elements that further includes television (TV), radio, mobile phones, and the policies and laws that govern these media and devices.”17
Information and communication technologies are the tools that underpin the emerging “information society.” While no universally accepted definition for Information Society exists, it can be de- fined as “society in which the creation, distribution, and manipulation of information has become the most significant economic and cultural activity. An Information Society may be contrasted with societies in which the economic underpinning is primarily Industrial or Agrarian (TechTar- get 1999).18 “ Information ” exchange between people and through networks of people has always taken place. The ICT enablement of information exchange, however, has radically changed the magnitude of this exchange, and thus, factors such as timeliness of information and information dissemination patterns have become more important than ever.”19 As “information” is “the data that has been organized and communicated”20 while “knowledge” has been described as “the set of statements, facts or ideas; presenting a reasoned judgment or an experimental result, which is transmitted to others through some communication medium in some system systematic form.”21 In addition, both of the “information” and “knowledge” both considered being main pillars in achiev- ing the “socialization of knowledge” in order to build “information society.”
And the term of “socialization of knowledge” is used to “express the idea of transforming the private and individual knowledge to public and collective knowledge.”22 Another term commonly used to describe the changes produced by information technology is the “digital divide” term which refers to the gap between those who benefit from digital technology and those who do not. Instead, the term digital economy is used to refer to the new opportunities created by transforming information into a binary digital code. The digital economy refers to more than the boom and bust cycle of many new ventures that aim at tapping the potential of the Internet for commercial purposes. The more profound effect of ICT is likely to be in improving the efficiency and reach of the mainstream production of goods and services, in both the public and private sectors of the economy. Based on this we can define the “global digital divide” as a term used to describe “great disparities in opportunity to access the Internet and the information and educational/business opportunities tied to this access. . . between developed and developing countries” (Lu, 2001, p.1). Unlike the traditional notion of the ”digital divide” between social classes, the ”global digital divide” is essentially a geographical division.
Basically, the ICT sector includes the manufacturing and services activities which rely on the use of Integrated Circuits (ICs) and more generally electronic components, for the purpose of communi- cations and information processing. This definition seems fairly reasonable but still raises borders issues. Is, for example, the medical instrument industry (scanners, IRM, radiography apparatus, etc.), which largely relies on ICs, part of the ICT sector? We can argue that its main purpose is information processing, but we can also say that the objective is to cure patients, as much as the purpose of the motor vehicle (which by the way uses more and more electronic devices) is to transport persons or goods.23 Seen from an “electronic perspective”, the ICT sector includes, in a broad sense, “assemblers” and “integrators” which manufacture systems used by end users, or other manufacturers or service providers. To give an example, the German car equipment manu- facturer Bosch, can also be considered as an “electronic integrator”, which relies heavily on ICs for its production. The pervasiveness of electronic components in general, suggests that there are many of these “electronic integrators and assemblers” in various areas of the economic activity (car and aircraft manufacturing, medical instruments, toys production, etc.).
1 A consistent use of the terms invention and innovation might be to reserve these for the first time of occurrence of the idea/concept and commercialization, respectively. In practice it may not always be so simple. For instance people may very well conceive the same idea independently of each other. Historically there are many examples of this; writing for instance was clearly invented several times (and in different cultural settings) throughout history (Diamond, 1998). Arguably, this phenomenon may have been reduced in importance over time, as communication across the globe has progressed.
2 In the sociological literature on diffusion (i.e. spread of innovations) it is common to characterize any adopter of a new technology, product or service an innovator. This then leads to a distinction between different types of innovators, depending on how quick they are in adopting the innovation, and a discussion of which factors might possibly explain such differences (Rogers, 1995). While this use of the terminology may be a useful one in the chosen context, it clearly differs from the one adopted elsewhere, and to use both definitions simultaneously may easily lead to confusion. It might therefore be preferable to use terms such “imitator” or “adopter” for such cases.
3 Similarly for automobiles: while the idea of a power driven vehicle had been around for a long time, and several early attempts to commercialize cars driven by steam, electricity and other sources had been made, it was the incorporation of an internal combustion engine driven by low-cost, easily available petrol that made the product a real hit in the market (Mowery and Rosenberg, 1998).
4 For an overview, see (Aghion and Howitt, 1998).
5 Newly Industrialized Countries, they are: Brazil, China, India, Malaysia, Mexico, Philippines, South Africa, Thailand, Turkey. Sometimes of this category make also part countries like Egypt, Indonesia, Russia.
6 See chapter 4 of this Masterarbeit.
7 As we will also see in Chapter 4 when the German and Italian ICT clusters are compared.
8 This is the typology of cluster which will be further discussed in chapter 4.
9 For a more extensive discussion see Michael Porter (1998a).
10 Because of the critical importance of innovation for advanced economies ‘innovation clusters’ have become a par- ticularly popular topic. See OECD (2001) and Monitor Company, Council on Competitiveness, and Michael Porter (2001).
11 For the implications of clusters on company strategy see Porter (2000) and Ketels (2002).
12 For the implications of clusters on company strategy see Porter (2000) and Ketels (2002).
13 See Ketels (2002).
14 Council for Competitiveness/Monitor Company/Porter (2001).
15 However, since High-Tech Start-ups are normally venture-capital backed, a more extensive analysis on their financing phases is presented in Chapter 2, section 2.1.5, p. 44 of this thesis.
16 OECD definition cited by Cynthia De Hewitt (2001): The Development Divide in a Digital Age: An Issues Pa per, UNRISD, Technology, Business and Society Programme Paper Number 4, August 2001, United Nations Research Institute for Social Development, Geneva , p. 3.
17 See Vosloo et al. (2005).
18 Cited in Sobeih (2007).
19 See Vosloo et al. (2005).
20 See Romer (1990).
21 Daniel Bell (1973:175).
22 See Vosloo et al. (2005).
23 It is estimated that cars, which included 1700 $ of electronics per vehicle in 2001, would have seen this number raise to 2700 $ by the year 2008. See Dominique Lemoine (2004).
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