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The current international business literature has often underlined the role that location has on a firms' competitiveness. Many different researchers have shown that multinational enterprises selecting the location of their foreign investments to tap knowledge linked to a specific local context (Almeida, 1996; Frost, Birkinshaw and Ensign, 2002), to benefit the possibility of growing markets (Brouthers, Werner and Wilkinson, 1996) or to get access to valuable resources (Almeida and Kogut, 1997; Dunning, 1996; Frost, 2001). An important economic geography research done by Fujita, Krugman, and Vanables (1999), focuses on the influence of industry agglomeration and spatial clustering on the location decisions of multinationals. This paper shows the evidence that a significant concentration of related firms in proximity of eqach other may strongly reinforce co-location by other firms (Maskell and Malmberg, 1999; Storper, 1997). Krugman (1991) suggested that location decisions by multinationals can be explained by agglomeration economies (Cantwell & Iammarino, 2000).
Despite a lack of ageement between the different definitions of the cluster phenomenon given in the literature (Feser and Bergman 2000), three basic dimensions can be identified in any cluster: geographical proximity, networks between companies and networks with organisms and institutions (Rocha 2004). Keeping this in mind, the most recent and wide accepted definition of a cluster is that of Porter (1998): “a cluster is a geographically proximate group of interconnected firms and associated institutions in related industries”.
A cluster can play different roles in economic activity: on the one hand, to intensify the competitiveness of companies by availing themselves of the advantages generated by business cooperation and agglomeration economies and, on the other hand, to stimulate areas where companies formerly drove local development have gradually lost competitiveness (Carlsson 2002). In my thesis my focus will be on the existing clusters in the Amsterdam region.
The supposed increase of competition in a cluster should leads to a higher productivity, innovation and finally to more new firms. This can be achieved because of the possible acces to employees and suppliers. There also will be a better flow of information between individuals and firms. This flow of information gives the new start-ups a better insight into market needs which gives the entrepreneurs better possibilities to come up with new innovative ideas to meet the changes quicker than outside a cluster. Important factors are also a specialized infrastructure, a good access to public goods and institutions and finally closeness to investors (Porter, 1998).
As mentioned above, the basic assumption concerning clusters is that they contribute with more new firms than other geographical areas. This indicates that the entrepreneurial process of identifying and developing business opportunities is easier in a cluster environment. An explanation of this is that a cluster is characterized by a high movement of people between organizations; the workforce in these areas has access to a lot of information and networks which can lead to more ideas (Power and Lundmark, 2004). Informal contacts and information flows between people and companies are important for innovation and entrepreneurship (Porter, 1998). This statement of Porter is in line with other findings that suggest that earlier knowledge about markets like customer needs, which the entrepreneur learns in other organizations, stimulates the entrepreneurial process (Scott, 2000). This cluster environment can act as a supportive environment for entrepreneurs because the cluster network provides the entrepreneur with better financial support and it is easier to have access to skilled employees. These factors in total supposed to lower the barriers to entry on the market for new individual entrepreneurs that are trying to develop their ideas into new businesses (Porter, 1998).
Besides these positive externalities concerning cluster the question remains whether proximity and location really are important factors for innovation and entrepreneurial activity in geographical regions such as cluster. Research about cluster environments has shown that cluster environments give firms opportunities as well as literature has shown that more new firms are created in cluster areas compared with other areas. It can therefore be assumed that cluster environments offer entrepreneurs better possibilities to identify and develop business ideas.
My thesis will focus on the Amsterdam region. Therefore I will first try to identify if there are any clusters located in Amsterdam and if so, where these are located and what the type of industry is of these areas. The focus of my research will be on the factors of this cluster environment that influence the entrepreneurial attitude of potential and active entrepreneurs on starting up a new business.
2. Problem definition, Objective & Research questions (main and sub).
2.2 Research Problem
In which way do clusters encourage entrepreneurship and how do clusters stimulating entrepreneurs in their process to start up new op new business?
2.3 Research Objectives
The main research objective of my thesis is to understand how clusters help entrepreneurs to start up new firms. My ambition is to gain an insights and knowledge that can contribute in the creation of an understanding about why a how individual entrepreneurs chose a specific cluster environment as a valuable area to start up a new business. The aim with the analysis is to see what kind of impact a cluster has on the individual entrepreneurs within a cluster. First I will point out the characteristics of clusters and as a result I will identify the clusters in the Amsterdam region. The impact from cluster environments on start ups will be gained by interviewing entrepreneurs that have started a new venture within a cluster in Amsterdam. The intention is to contribute with results that can be useful for policy makers, future entrepreneurs and academics in order to better understand entrepreneurial activities in cluster environments.
2.4 Research Questions
- What are the characteristics of clusters?
- Which clusters can be identified in the Amsterdam region?
- In which way did the cluster help entrepreneurs to develop new start ups?
2.5 Conceptual Framework
3. Literature review
Agglomerations of economic activity in general, and clusters in particular, are natural economic and social phenomena. Local clusters with a worldwide reach are easy to identify in all kind of industries, like financial services (inner London, Wall Street, Zurich), movies (Hollywood and Bollywood), cars (Detroit, Modena, Toyota City, Southern Germany and West Sweden), watches (Switzerland and Japan), optical equipment (Tokyo), flowers (The Netherlands), computer software (Silicon Valley, Bangalore), mobile telecommunications (Helsinki), wine (Southern Chile, and California), biotech and medical instruments (Boston), music (Nashville), gambling (Monte Carlo and Las Vegas), chocolate (Switzerland and Belgium), and so on and so on.
In this paragraph I will go deeper into the theory of clustering. I will explain the different definitions from the literature and will give the various theoretical backgrounds of cluster theory.
Furthermore I will explain how to identify clusters in general and via various databases I will calculate which clusters can be found in the Amsterdam region. Because the main objective of my thesis is to found out what the influence of this cluster phenomenon is on entrepreneurship. I will describe what the current literature tells me about the influence of clustering on entrepreneurial activity.
3.2 Clusters: Definition and Characteristics
One of the first things I found on this subject, after a general review of the existing literature, is the amount of differences that exists concerning the definition of what is understood by a cluster and the lack of specificity regarding its key factors, characteristics and effects. Therefore I will try to explain what is understood by a cluster in this thesis and which key factors I identified.
3.2.1 What is a Cluster?
The word “cluster” is differently interpreted by different researchers in the academic world. However, the evolution of the cluster phenomenon shows that clusters have three basic dimensions: geographical proximity, networks between companies and networks with organisms and institutions (Rocha 2004).
With this in mind, the most widely accepted definition in recent times is that of Porter (1998): “a cluster is a geographically proximate group of interconnected firms and associated institutions in related industries”.
The geographical dimension refers to the proximity of companies' location and is key in defining a cluster. The network dimension between companies refers to the relations that are established between the companies located in the cluster. Inter-firm networks refer to both market-based transaction and untraded or informal relationships (Storper 1997) between firms within a cluster.
The third dimension, institutional networks, refers to the relationships between firms, nongovernmental, and governmental organizations within the cluster (Aydalot 1986; Becattini 1979; Saxenian 1994). The institutional network dimension of clusters includes both formal and informal relationships.
In this way, clusters are characterized as a set of tangible assets (companies and infrastructures) and intangible (knowledge, technologies, know-how); and institutional elements such as public administrations and training and research centers, which act interconnected in proximity.
3.2.2 Clusters Key Factors
A review of the current literature shows me a long list of key factors in the emergence of clusters: economies of scale and of scope, transport costs, transaction and sourcing costs, availability of production factors and/or components in a specific location, knowledge, information and technological spillovers, innovation development, cooperation between companies or between suppliers and buyers and the reduction in uncertainty (Baptista and Swann 1998; Krugman 1991; Muizer and Hospers 2000; Nelson 1999; Porter 1990).
Porter (1998) highlights the positive effect of agglomeration economies as an explanatory factor for the existence of clusters. Specifically, he proposes as key factors: sharing infrastructures, communication technologies and access to input and to output market.
Clusters may also produce high levels of technological spillovers and innovation (Krugman 1991). This is because geographical proximity facilitates the flow of information. All the previously mentioned cluster characteristics play an important role in promoting technological innovation within the clusters. The highly confident environment and the easy access to specialist suppliers increase the number of transactions which, in turn, leads to an increase in the exchange of technical and technological know-how among the companies of the cluster.
Khan and Ghani (2004) highlight the existence of trust in clusters as a concept related to social capital. They define social capital as the “sum of the actual and potential resources embedded within, and available through, and derived from the network and relationships possessed by an individual or social unit”. Adler and Kwon (2002) summarize a series of studies indicating how social capital facilitates resource exchange between firms, product innovation, entrepreneurship, creation of intellectual capital, supplier relationships, and regional production networks (clusters). Nahapiet and Ghoshal (1998) discuss how networks with high levels of social capital encourage firms to combine and exchange knowledge. This reduces the amount of time and investment required to gather information.
So, although clusters have many key factors, there are four especially important ones: the availability of a specialised workforce, the existence of knowledge spillovers (for example, because of the proximity of universities), the degree of competition and cooperation between companies and institutions.
3.3 Cluster in the Amsterdam region
This thesis tries to identify the relationship between the spatial concentration of industries in Amsterdam and the effects on entrepreneurship. Before going further with this research question it is needed to identify the clusters in Amsterdam. To do this, I first have to select a methodology to identify clusters. Secondly I will use this tool and use an appropriate dataset.
3.3.1 How can cluster be identified?
There is no common way to identify a cluster, either in terms of the key variables that should be measured or the method the geographical boundaries of clusters should be determined (Martin & Sunley, 2002).
In the current theory (Krugman, 1991) concentration is often described by measurements which specify the degree of spatial distribution of labor. The most used tool is the coefficient of localization which is based on the location quotient (LQ).
The LQ is calculated as the industry's share of total employment in a given region relative to the industry's share of total employment in the whole geographic area in question. A LQ equal to one means that the given region is not specialized in the given industry. A LQ equal to 1.5 means that the given industry is represented by a 50% bigger share of employment in the given region than the industry's share of employment on the level of all regions. This indicates that the region is specialized in the industry.
But if I look at the definition of a cluster the spatial dimension is ignored by measurements of specialization. There is no value that gives any indication of the size of the analyzed region or the proximity between the companies in the region. Not only the employment should be measured, also the region as a value should be measured. Using the above measures of specialization as measures of industrial concentration implies that the location of industries depends on the distribution of the total employment and that the location of industry and the location of employment and of the inhabitants are not interdependent. The LQ is an appropriate tool to measure concentration but it has to be weighted with the size of the regions. The LQ, weighted with the area of the region is proposed as a measurement of spatial industrial concentration.
3.3.2 Clusters in Amsterdam
Info from O+S Amsterdam
3.4 Entrepreneurship in Clusters
3.4.1 Entrepreneurship in the cluster
The performance of companies is no longer explained only on the basis of determinants related to the firm itself or to the entrepreneur. Environmental factors like spatial proximity are starting to become more important and interesting. Many studies are revealing that entrepreneurship is a social and collective phenomenon that cannot be described only through the dimension of individuals. Entrepreneurship should be explained with recourse to the “entrepreneurial social infrastructure” (Butler, Flora & Flora, 1993) regarding the social capital concept and the individual networks associated to it, which create positive externalities. This makes the relationship between regional development and entrepreneurship and evident.
Arguments for the assumption that clusters have a positive influence on entrepreneurial activities are outlined on the following:
The probability that a person will start a new firm within a certain region increases as a function of the number and size of incubator organizations within the region whose fertility is sufficient for the emergence of new firms. The development of already existing start-ups also profits from a positive regional environment, which, in addition to the incubators hinges necessarily on an equally positive entrepreneurial climate. Within the scope of a self-augmenting process, e.g. via role model effects of successful start-ups, and their interregional networking (see Fornahl, 2003), regional clusters of start-ups may form regions, in which the creation and development of start-ups is economically more favorable than outside these clusters. In general, the favorable climate on start-ups is a result of agglomeration economies and other positive external effects associated with spatial proximity.
A primarily demand- or environmental-oriented approach, whereat an individual's decision to start a new enterprise is the result of influences from the macro- and the microsocial environments. These two influences are sensed differently depending on person-related factors. This is represented in the figure below (Figure #).
The macrosocial environmental factors include both factors that improve mainly on a supraregional level (in other regions belonging to the country or outside the country) and factors that develop within the regions of interest. The importance of the supraregional and regional determinants varies from factor to factor. Most important determinants among these are named here are the cultural, social, political, and financial conditions of a area, as well as the system of education, the infrastructure, and the economic structure (Bruno & Tyebjee, 1982).
The microsocial environmental factors include the social and professional backgrounds and the individual networks of the potential entrepreneurs. These elements can also be shaped by primarily regional (private networks) or primarily supraregional (a large number of professional networks) forces. Personal factors are entrepreneurial motivation (push vs. pull factors), demographic factors (age and sex), as well as personality traits (the willingness to take risks).
3.4.2 The influence of clusters on entrepreneurship
As discussed in the previous paragraph, the relevant entrepreneurship and new firm formation literature distinguishes between person-related and environment-related determinants as a basis to search for theoretical explanations for an individual's decision to start a new firm.
Environment-related determinants include all determinants of the potential entrepreneur's decision that are external to the person. This includes regional factors, i.e. determinants, whose manner of influence varies from region to region (e.g. the so-called 'entrepreneurial climate'). The way the potential or actual entrepreneur perceives these external factors affects his or her decision-making and the success of a possible start-up.
Stuk personal influences
Reviewing the current theory on the establishment processes of new startups, I can develop the following hypothesizes of cluster characteristics for entrepreneurial activities and entrepreneurial attitudes:
- Cluster characteristics may reduce entry barriers for new start-ups.
- These lower entry barriers make it easier for future entrepreneurs to take the risk from being a potential founder to being a real founder of a new business (since some individuals compare lower entry barriers with better chances of success, which generally is a mistaken belief).
- The local/regional market and customers are crucial for many start-ups during the first period. If the regional economy has cluster characteristics it is easier for those entrepreneurs to find useful customers and suppliers, because there are more options within the region.
- Social-networks are helpful to start-ups or wannabe entrepreneurs. Empirical studies reveal that the social environment (frequently connected with the home area of the entrepreneur) and the individual networks can have a crucial impact on the determination to start a new firm.
4. Research methodology and data
4.1 Research method
To reach the main objective of this thesis and to contribute to the understanding of how cluster environments encourage entrepreneurs to start up new ventures, I will use a qualitative research method. The reason I choose for a qualitative method instead of a quantitative method is because of the focus on the individual level of entrepreneurship instead of the aggregate level as quantitative methods intent to do. By means of interviews (qualitative), the individual can express clearer what their intentions were to establish their venture in the cluster and they can express what their most important factors are in starting a new business.
4.2. Operational definitions
Dependent Variable = Entrepreneurship
Consistent with the definition of entrepreneurship as creation of a new business, I measure it in terms of nascent and new firms.
Acknowledging that new businesses do emerge from both people and established firms, the present study chooses the labour market approach for two reasons. First, the present study is interested in independent start-ups rather than in corporate entrepreneurship. Therefore it is not appropriate to use a measure theoretically based on the assumption that established businesses start new ones. Also, the labour market approach assumes that entrepreneurs start their business in the same labour market where the new business operates, which is supported by previous research (Reynolds and White, 1997).
Independent variables = cluster
A review of the literature on clusters shows that both quantitative and qualitative techniques should be employed to identify clusters accurately. This thesis uses LQ's and existing studies from the research and statistics department of the municipality of Amsterdam (O&S Amsterdam).
4.3 Focus, data and sources
7. Limitations & further recommendations
1 type of industry and one dimension of cluster.