Empowering Europe s future: a future-oriented approach to exploiting clusters as drivers of innovation and growth

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1 Articles Empowering Europe s future: a future-oriented approach to exploiting clusters as drivers of innovation and growth To innovate is not to reform. Edmund Burke, Irish political philosopher Source: A Letter to a Noble Lord (1796) Medical Valley, Bio Valley, Consulting Hill...these are all buzz names for the clusters that are regional agglomerations of industries driving regional and national competitiveness. Since the management guru Michael Porter proclaimed, in the 1990s, that location and clustering play important roles in competitive strategy, the term clusters has spread throughout the globe and made its way onto the agendas of managers, policymakers and academics everywhere. Prominent examples of highly competitive clusters are Silicon Valley (Information Technology) and the San Francisco Bay Area (Biotechnology). 1 These high-tech clusters have become role models for successful modern industries, and policymakers around the world are trying to emulate them. Indeed, the buzz around clusters as key drivers of productivity and innovation triggered a range of cluster initiatives to drive their development and to increase regional competitiveness and innovation. Innovation is a key driver of profitable growth and, at the same time, clusters as regional agglomerations of an industry potentially provide important sources of future innovation. That said, innovation has become more interactive, suggesting that organizations need to interact with a variety of external actors often beyond their location and that the relevance of innovation networks has increased. 2 This forces us to revisit and extend existing approaches to mapping and managing clusters. In this article, we explore these challenges and provide some new insights into how to identify and build the innovation hubs and lighthouses that will drive Europe s future. The cluster phenomenon revisited From the regional specialized cluster towards an innovative interactive network As the spread of globalization continues, clusters have been hailed as a way of surviving in today s turbulent economic environment. Indeed, the cluster concept has been used in many different settings and as a substitute for other concepts, such as networks. 3 Only a sound understanding of the cluster concept, its history and evolution can provide a better understanding of the myth. Long before Michael Porter proclaimed the relevance of clusters, economists and geographers started the discussion on the localization of economies in the 19 th century. e.g., the British economist Alfred Marshall coined the expression, Marshallian industrial district and referred to certain localities with a concentration of specialized industries in terms of natural resources or industrial production capacity. 4 However, in 1990, when Porter published, The Competitive Advantage of Nations, the cluster concept and its impact on competitiveness entered the lexicon among managers, strategic thinkers and management gurus. 5 1 Lee Chesbrough Oerlemans et al Oerlemans et al Porter Performance

2 Table 1 gives an overview of different concepts of cluster, localized innovation network and cluster initiative. A cluster, in its original meaning, represents a geographical agglomeration of interconnected companies and associated organizations linked to each other in a value-adding production system, with certain commonalities and complementarities. 6 Thus, there are two defining factors for a cluster: local and sectoral concentration. Moreover, a cluster need not subsume the active management of conjoined activities among its members, nor is it necessarily related to collaborative innovation activities. A localized innovation network, however, implies collaboration among companies and associated organizations (usually based on trust, formal collaboration agreements, or other conventions) in a geographical space, resulting in the higher innovation performance of all the actors involved. Indeed, specialization in terms of industry is not a must. Table 1: Overview on different concepts Concept Cluster Local innovation network Cluster initiative Description A cluster initiative implies the active management of a cluster often in a networked sense in order to further develop a cluster and to improve the regional competitiveness. Key actors of historically grown and actively managed clusters A cluster whether indigenous or driven by cluster initiatives encompasses a range of different actors, such as enterprises of different sizes and related organizations, including research organizations, universities, media and financial investors. Multinationals companies (MNCs) usually play a driving and shaping role in specialized clusters. But small and mediumsized enterprises (SMEs) and start-ups are crucial to the development of clusters and the overall innovation performance. In many cases, they are more important than MNCs. Cluster constituents are not limited to profit-oriented enterprises. A range of supporting institutions of a value-adding production system, such as universities and A geographical agglomeration of interconnected companies and associated organizations linked to each other in a value-adding production system Network of companies and associated organizations in a geographical space with strong collaboration for innovation purposes Program initiated by regional government aiming for actively managing and developing clusters in a geographical space (usually from a specific industry) public and private research institutions, are key sources of productivity and innovation. In addition to providing knowledge and human capital, which are useful to businesses, they also provide a platform for interaction and collaboration between the scientific and research communities and the enterprises. Finally, financial actors, media, standardization institutes, regional policymakers and customers are important players in clusters that drive the interaction between the key actors and establish an active innovation network. Clusters and their impact on competitiveness and innovation Recent feedback from managers, researchers and policy makers offers some interesting insights into the competition and innovation benefits of an industrial agglomeration in a geographical space. Indeed, there are major profitability advantages from engaging in clusters of a specific industry. Clusters offer companies superior and lower-cost access to specialized inputs such as machinery, business service and human capital and, as a result, they increase the effectiveness and efficiency in collecting input. Clustering also facilitates the provision of complementary products and assets; it provides access to public goods and services (or quasi-public goods and services) such as specialized infrastructure, and also creates incentives due to local competition and the ease of constant comparison. As a result, profitability is positively impacted by greater knowledge flow and knowledge spill-overs. 7 6 Porter Porter 2000 Performance 5

3 A cluster as an agglomeration of key actors of a specific industry in a geographical space is perceived as a driver of innovation. Clusters also have a positive impact on new business formations: the creation of start-ups, ventures and spin-offs is facilitated by the ready availability of many of the required assets, skills and inputs and lower barriers to entry. Clusters can provide significant opportunities for partnerships and, overall, the cost of failure tends to be lower. Indeed, it is argued that high levels of competition, demanding customers and closer interaction with the market, enhance the innovative capabilities and innovation performance of companies. 8 Where clusters have strong internal networks, companies benefit from the exposure to new technology and tend to invest in it more quickly as a result. They are also more attuned to the needs of their buyers, and have an edge in learning about evolving technologies and machinery availability, thanks to strong relationships and frequent face-to-face contacts. 9 However, there are contrasting views on whether and how the clustering of enterprises in a particular value chain drives innovation performance. 10 As well as the positive effects of clusters on innovation, it is important to bear in mind that the impact on innovation depends also on other variables such as a firm s openness to engage in R&D collaborations and innovation. The sharing of knowledge requires the forming of strong human relationships and it is essential that there is an atmosphere of collaboration with the company next door. New technologies and innovations often emerge at the interface of different scientific domains and industry sectors. Consequently, a pure sectoral agglomeration might actually reduce the potential of breakthrough innovations. 11 Clusters, networks and innovation beyond a pure regional perspective of established clusters Our discussion of the benefits shows that clusters can impact the innovation and competitiveness of both the individual firm and the regional innovation system. In light of recent trends and new approaches towards innovation, we would like to consider some new perspectives on clusters and link these with an innovation network perspective. Nowadays, innovation has become very interactive in its nature, suggesting that organizations have to interact with a variety of external actors to generate a steady flow of innovations. 12 Clusters were originally defined as a geographically proximate group of interconnected companies, specialized suppliers, service providers, firms in related industries and associated institutions (for example, universities, standards agencies and trade associations) from a specific value-adding production system. 13 But with the increasing need to open up and collaborate with other clusters from the same or a different sector, within the same or a different region, it is crucial to drive innovation by exploiting innovation networks. Indeed, results of recent studies show that interaction among actors within a cluster can positively impact the innovation outcome. 14 A key issue for clusters is how to combine exploration (invention, new technology, new products and processes) and exploitation (efficient use, improvement of products, efficiency and organization). Local interaction within a cluster is needed to set things in motion, in both exploration and exploitation, then external linkages with other clusters are needed to expand sales in exploitation and to obtain novel ideas for novel combinations in ongoing exploration. 15 Thus, mega clusters are becoming ever more important. They encompass cross-sectoral interaction (within and among clusters) and crossnational interaction among clusters. In terms of innovation and performance, the development stage of a cluster is crucial. There are various development phases that must be completed before a high agglomeration of a specific industry can be measured. From an innovation standpoint, potential and emerging clusters are highly relevant for investors and policymakers alike (see figure 1). 8 Porter 2000; OECD Oerlemans et al OECD 1999; Lee et al Oerlemans et al. 2001; Lee 2008; Gassmann et al Chesbrough 2003c; Laursen et al Porter Oerlemans et al Nooteboom Performance

4 Figure 1: Development stages of a cluster Potential clusters Emerging clusters Slow growth High growth Indeed, clusters may have different development stages. A cluster may be able to continue the combination of exploration and exploitation; however, it may also proceed from being an exploratory to an exploitative cluster. In addition, the cluster may stagnate and may need to be broken up for re-configuration, for the sake of renewed exploration. In that case, different network structures and different cluster policies will be needed at different stages. e.g., in the early stage, ties among the actors are characterized by a high frequency of interaction, intensive trust and also covering various different technological dimensions. Later, in the exploitation stage, ties may become less dense (often with central Existing/established Re-inventing Declining Potential Emerging Growing Existing/established Re-inventing/declining hub players) and characterized by formal contracts and financial investments. 16 Cluster initiatives and organizations challenges and opportunities in the light of networked innovation The hype around clusters, cluster initiatives and cluster organizations has steadily increased in recent years. This has led regional policymakers to launch policy programs to develop clusters including development strategies, information services, networking and marketing support, and funding programs. Across Europe, there is variability in the performance and proficiency of cluster organizations in actively managing clusters and increasing their impact. Professional cluster management requires at least five fields of action. These include: (1) information and communication; (2) training and qualification; (3) cooperation; (4) marketing and PR; and (5) internationalization. The Automotive Cluster (AC), a cluster initiative from Clusterland in Oberösterreich is a practical example of professional cluster management. The cluster initiative has 231 partner companies, of which 60% exemplifies the largest cluster initiative in the automotive area. A strongly customeroriented, non-profit organization, it offers services in all five areas in order to actively drive performance and innovation. In Germany, the CARS initiative is a good example of building cross-national bridges and cooperating with other clusters in the European Car Subject Strategy Network (EASN) and clusters in the US. Apart from these examples of good practice, in general, cluster initiatives often fail to impact the performance of SMEs and there is a lack of transparency about the service offerings. Often, SMEs are not aware of such programs. Cluster organizations as innovation brokers Innovation has become more interactive: innovation brokers can play an important role by acting as catalysts to accelerate the combination of the complementary knowledge and skills necessary for further business opportunities. Cluster organizations can capture the role of brokers within and between clusters supporting enterprises to exploit sources for innovation beyond the boundaries of the firm. They can act as intelligent 16 Nooteboom et al Performance 7

5 Figure 2: Cluster organization as broker among different actors SME Business idea University Basic research technologies SME New product agents of change that allow companies to increase the number of external exchanges in a focused way, stimulating innovation and creating momentum of action within and across clusters. 17 The type of value exchanges via a broker varies in terms of the fuzziness of the information. Offerings range from a raw idea to a concrete product or technology. Recent practices show that cluster organizations can link various actors in a networked cluster. However, they need to build on solid value propositions and then adapt them to the target audience (see figure 2). Involving SMEs and the need for more transparency SMEs the key drivers of Europe s growth often do not know what the service offerings of cluster organizations and Cluster organization Investors Investment Global players Complementary product Cross cluster organization Technologies other supporting constituents (such as research organizations) are and how they can benefit from them. Thus, a big concern for many regions and countries has been the low involvement of SMEs in clusters and networks. For more than 15 years, since the introduction of the regional clustering model, many regions have seen mainly large companies attracted to cluster initiatives. Spain s Basque region is an example of how regional policies have somewhat overlooked the importance of SMEs over the years, affecting the capacity of innovation and competitiveness of many firms. However, SMEs are the key source of innovation in Europe. Rapid technological changes, market volatility and exposure to competition from distant corners of the world force small and medium-sized enterprises (SMEs) to rethink their growth strategies and exploit innovation as a key value driver. 18 Indeed, their participation in clusters offers clear benefits in two ways. On one hand, for SMEs to belong to a well-known cluster can bring many benefits, from corporate reputation, innovation, competitiveness and access to internationalization to external financing. On the other hand, clusters also benefit from internationalization by attracting more local and foreign entities and investors. 19 Mapping and measuring clusters a review of existing approaches Recently, serious attention has been paid to geographical clustering as there is a widely held view, among policymakers, academics and practitioners alike, that clustering can foster innovation and regional growth. However, the indicators and methods used also tend to vary widely. The evaluation of cluster initiatives is based on minimal information, as data on the impact of cluster organizations on their performance and the performance of their actors is rarely available and, when it is, it is usually restricted to selected case studies. Consequently, the mapping and benchmarking of clusters across regions is usually driven by initiatives at a European level. Evaluation and benchmarking of regional clusters Regional studies and data on clusters range from analysis at the mega level 17 Sousa 2008; Nambisan et al. 2007a 18 Engel et al Venkataramanaiah et al Performance

6 through to the micro level. 20 This can be used to identify existing clusters, map emerging clusters and analyze the interrelation of the actors (e.g., transactions and communication). There are only limited data sources that enable the comparison of regional clusters. The Regional Innovation Scoreboard (RIS), developed by the European Commission, provides a certain number of cluster indicators which enable the benchmarking of regional clusters in terms of limited input and output data. The RIS for 2006 had seven indicators, such as human resources in science and technology (% of population); participation in life-long learning per 100 population aged 25-64; or public R&D expenditure (% of GDP); and employment in medium-highand high-tech manufacturing (% of total workforce). However, these indicators provided only limited understanding of the innovation efforts in relation to overall economic performance. Mapping activities and information services at a European level The most sophisticated service platform for measuring and benchmarking clusters at a European level is the European Cluster Observatory (ECO) (www. clusterobservatory.eu). It was established as part of the Europe INNOVA initiative (funded by the European Commission) and provides information services about clusters in Europe for policymakers and researchers around the world. More specifically, it provides a sound insight into clusters and identifies those regions with high agglomerations of SMEs across 38 sectors in the EU s 27 countries (plus Iceland, Israel, Norway, Switzerland and Turkey). It serves as a strategic tool for policymaking and is used to identify important wealth spots in Europe. Using sound mapping methodology and holistic data, the ECO has mapped more than 2,000 regional clusters and provides insight into the degree of specialization and level of expenditure associated with innovation. Recently, it has started to include information on cluster organizations and cluster initiatives to inform cluster practitioners and SMEs about relevant cluster programs. Challenges and opportunities for enriching services on cluster at a European level The ECO provides sound information on the location of enterprise agglomerations and whether these agglomerations are linked to industry specializations and expenditure on innovation. However, in today s globalized economy, innovation strategies have changed and innovation has become even more networked. Eco-innovations, for example, emerge at the interfaces of different domains and industries. Moreover, innovation spots with high-growth potential impacting the future of Europe also depend on the capabilities of actors involved. Clusters are not static phenomena, but evolve over time and, so, require a more evolutionary approach. In this regard, cluster mapping is a challenge as it does not provide sufficient depth of information on a number of features, including: the clusters capability, or collaboration within the cluster and among clusters in mega clusters, the evolution and development of clusters. A second major challenge is that current services do not provide any insight into the content and impact of cluster initiatives. Beyond these two major challenges, additional aspects need to be addressed in terms of future developments: Cluster organizations lack the capability to successfully develop and implement an effective strategy and to exploit the innovation potential of actors involved. Thus, their competency needs to be increased. SMEs lack the resources and capability to build transnational partnerships and the capability to internationalize successfully. There is a lack of usable information and benchmarking services designed for SMEs. The phenomenon of clusters has been intensively discussed in theory and at the policymaking level. However, only value-adding services that meet the needs of key users will enable SMEs to exploit clusters for growth and innovation. European clusters need to open up and foster collaboration at the national level and across clusters at an international level, and also link with lighthouses outside Europe in emerging economies. Investors need transparency on the hubs that have high-growth potential. 20 Sautter 2004 Performance 9

7 Figure 3: Towards a future-oriented service platform on clusters Research organizations and universities Policy makers Cluster organization Good practice transfer Cooperation Cooperation Capital Investors Growth and innovation Media Established companies Good practice transfer Start-ups Consultants Small and medium enterprises European cluster observatory service platform: enabling innovation and collaboration Automotive Finance Biopharma Transnational collaboration in the same sector Collaboration within clusters in the different sectors Bringing good practices from international lighthouse clusters 10 Performance

8 Towards innovation an interactive, impact-oriented approach to cluster mapping and evaluation: In order to increase their value, the next generation of cluster service platforms needs to have an innovative, interactive and impact-oriented approach to developing and implementing information and evaluation services. A service platform with an excellent level of service (see figure 3) is required to empower enterprises to identify, engage in and exploit clusters. At the same time, it needs to provide sound evaluation of clusters based on an enhanced cluster mapping methodology that takes a more dynamic and holistic approach towards cluster analysis and evaluation. Only a truly international approach will allow enterprises to exploit clusters for growth and innovation. Key success factors for the next generation service platform are to: in a threefold way and also to identify emerging and world-class clusters. Increase the transparency on activities of cluster organizations and strengthen their competency in managing and implementing cluster strategies and offering value-added services to their clients; this includes the development of a good practice repository. Open up clusters, foster greater collaboration within and across them by linking and building world class cluster organizations that support transnational activities and projects. We explored some new and futureoriented approaches to extending the current mapping methodologies and increasing the value of cluster mapping in three ways: (1) an holistic and multidimensional analysis of clusters and their actors; (2) specifically addressing the different development stages of clusters; and (3) mapping mega clusters. A multi-dimensional perspective: inside-out versus outside in Future cluster mapping analysis needs to take a holistic approach. Firstly, it is crucial to combine an analysis at cluster level (outside-in) with an analysis at the individual actor level (e.g., the company or the cluster organization) and then to investigate their interrelationship. Secondly, it is necessary to extend the purely input and economic performancedriven approach by looking into the competencies and capabilities of the various actors (technological competency, absorptive capacity, managerial capabilities). Beyond that, a set of innovation performance indicators needs to be implemented to measure the overall value creation. Design and implement an extended and innovation-focused framework that links network and cluster analysis and addresses the need for networking within and across clusters. This includes analysis at both the cluster and actor levels and the mapping of mega clusters including cross-sector, cross-national and emerging clusters. Take a user-oriented perspective to develop information services for SMEs in order to increase the interaction within and across clusters and to exploit clusters for innovation and growth. Figure 4: Towards a holistic perspective towards mapping and evaluating clusters Outside-in Cluster Actor interrelations Input Policy makers Cluster organization Analysis of value creation Capability Innovation Performance SME SME SME SME SME Investor Research organization Develop cluster intelligence services to assess the performance of clusters and to identify cluster opportunities Inside-out Actors Media Performance 11

9 Identifying emerging clusters Potential and emerging clusters are of high importance in the drive for innovation and future growth. However, identifying those slightly fuzzy clusters requires new mapping approaches. These approaches build on classical statistical methods and also borrow from foresight activities (e.g., Delphi-Panels, scenario development, scouting). The first stage of a cluster development often involves new firm spin-offs leading to a geographical concentration of firms. An empirical method for identifying a spatial cluster could be developed based on regression analysis, offering researchers the opportunity to categorize regions, municipalities, or cities with respect to the over- or underrepresentation of establishments and employment for any given industry. Some approaches suggest the existence of a cluster may be identified by a significant over-representation in the number of establishments as well as in employment for any given sector in a region. Furthermore, this over-representation should be related to the size of the region. Research programs that are related to specific technology domains or that specifically aim to build localized innovation hubs (such as the German high excellence initiative on clusters) provide useful insight into the identification of potential clusters. Data analysis needs to concentrate on efforts that encourage relevant research. Together with information on research programs, experts and large research organizations involved in future-oriented R&D activities can give access to key information on the most relevant clusters and networks. Patent data provides insight into the emergence of new technological fields and the identification of high-impact technology companies. Four parameters have been identified and can be measured at the time the patents issue: Public sector presence involvement of companies, universities and government laboratories. Science index emerging, leadingedge technologies tend to reference scientific articles as prior art. Originality index the originality metric measures the extent to which a patent combines disparate ideas. Patents that combine several technologies to create a new technology are deemed more original, and enabling for generating broader downstream commercial applications than patents that build on a single technology area. Reference index patents that build on many ideas will tend to have more prior art references than simple, incremental improvement patents. Interestingly, these parameters have higher scores for clusters that contain patents for early-stage, high-risk technologies. This suggests that a range of other parameters, that we have defined, may be useful in identifying emerging, highrisk technologies. Examples are: venture capital (as % of GDP), non-r&d innovation expenditures (% of turnover), community trademarks per million population, SMEs introducing product or process innovations (% of SMEs) just to name a few. In order to measure the development stage and the lifecycle, the balance between exploration (new technologies, patents, etc.) and exploitation (efficiency) needs to be investigated. The density and the strength of ties also vary across the life-cycle of a cluster. Network analysis, combined with expert interviews and surveys, provides an effective means to investigate the current development stage over time.

10 Figure 5: Types of mega clusters Sectors MC* C Regions Mapping mega clusters MC*** MC** As discussed above, interaction within and among clusters is crucial and, as such, mega clusters should be the focus of future mapping and evaluation activities. We have defined a set of clusters with agglomeration of innovativeness and economic performance along the two dimensions of concentration: (1) the same or different sectors; and (2) the same or different regions. In addition, the context and the strategic fit (e.g., innovation search field) are taken into account. As a result, mega clusters can be classified into three categories based on the interconnection on two dimensions of sectors and the region, MC* (Type1): with agglomeration of innovativeness and economic performance in one region in multiple sectors MC** (Type2): with agglomeration of innovativeness and economic performance in one sector in multiple regions MC*** (Type3): with agglomeration of innovativeness and economic performance in different sectors in multiple regions The detailed steps for applying the new methodology are as following: Filter 1: The first step is a filtering process to make cluster sets where MC* is selected from the same region (same NUTS code); MC** from the same sector, but different region (NUTS x, y, z ); MC*** from different sectors (S1, S2, S3.) within different region (NUTS x, y, z ). Filter 2: The next step is to filter cluster sets based on key economic and innovation performance indicators (KPIs). A threshold value is calculated by simulation and addressing all factors related to economic and innovation KPIs. All cluster sets are then mapped on a 2D plot with their dispersion values of innovation and profitability. The cluster sets with dispersion values crossing the threshold for both innovation and economic KPIs are selected. For MC** and MC***, different combination sets of cluster as doublets, triplets, quadruplets, etc., are plotted to find the set with the highest number of cluster participants which are less than or equal to five. All other subsets with a lower number of participants are discarded as redundant. Filter 3: The next step of filtering by elimination is to check the contextual fit (e.g., innovation search field or technology field) of these cluster sets in order to qualify as mega clusters. This is done using expert interviews, industry and sector specialists, qualitative assessment and analyzing their value positioning. A decision tree is plotted as shown in figure 6, which is used to eliminate cluster sets based on factors such as: development stage, public investment, complementarities of actors, age/size of clusters, diversity of actors, collaborative projects. Filter 4: The last elimination/selection check is done to match the strategic fit of the cluster sets. Examples of factors considered in the analysis are technology performance, network capability, cluster organization involvement and performance. Performance 13

11 Figure 6: Qualifying steps of mega clusters* (Type 1) Filter 1 Filter 2 Filter 3 Filter 4 Process Selection of clusters with same NUTS codes (NUTS x) Filtering clusters within one region but different sectors based on economic and innovation KPIs Contextual fit check Strategic fit check Analysis step Analysis performed for each participating cluster with same regional/ location vectors but different sectors(s1, S2...) Calculate simulated threshold value Calculate dispersion of each set of clusters w.r.t. threshold Select cluster sets crossing threshold with minimum dispersion Value chain positioning Expert interviews Qualitative assessment Technology performance Cluster organization involvement and performance Network capability Required set of clusters Decision tree Graphical presentation Sectors MC* MC*** C MC** Regions Economic KPIs dispersion decrease Innovation KPIs dispersion decrease >90% <90% >80% <80% >80% <80% Decision factors involved: Development stage Public investment Complementarity of actors Age/size of clusters Diversity of actors Collaborative projects Network capability Technology performance Output Clusters in one region in multiple sectors C1(S1, S2, S3...) C2(S2, S4, S5 ) Identified set of clusters with agglomeration of innovativeness and economic performance in one region in multiple sectors Sets of clusters with agglomeration of innovativeness and economic performance in one region in multiple sectors with a contextual match Mega clusters*(type 1) identified: Agglomeration of innovativeness and economic performance in one region in multiple sectors in the right context and with the right strategic fit 14 Performance

12 Figure 7: Qualifying steps of mega clusters** (Type 2) Filter 1 Filter 2 Filter 3 Filter 4 Process Selection of clusters within same sector but different regions NUTS codes (NUTS x, y, z..) Filtering cluster sets within one sector but different regions based on economic and innovation KPIs Contextual fit check Strategic fit check Analysis step Analysis performed for a combination of participants in sets of two to five cluster within same sector but different regions(nuts x, NUTS y...) Calculate simulated threshold value Calculate dispersion of each set of clusters w.r.t. threshold Select cluster sets crossing threshold with minimum dispersion Value chain positioning Expert interviews Qualitative assessment Technology performance Cluster organization involvement and performance Network capability Required set of clusters Decision tree Graphical presentation Sectors MC* MC*** C MC** Regions Economic KPIs dispersion decrease Innovation KPIs dispersion decrease >90% <90% >80% <80% >80% <80% Decision factors involved: Development stage Public investment Complementarity of actors Age/size of clusters Diversity of actors Collaborative projects Network capability Technology performance Output Clusters in one sector but with multiple regions S1(C1C2, C1C2C3; C1C2C4...) S2(C2C3, C2C4C7C8...) Identified set of clusters with agglomeration of innovativeness and economic performance in one sector in multiple regions Sets of clusters with agglomeration of innovativeness and economic performance in one sector in multiple regions with a contextual match Mega clusters**(type 2) identified: Agglomeration of innovativeness and economic performance in one sector in multiple regions in the right context and with the right strategic fit Performance 15

13 Figure 8: Qualifying steps of mega clusters*** (Type 3) Filter 1 Filter 2 Filter 3 Filter 4 Process Selection of clusters within different sector(s1 S2 S3.. And within different regions NUTS codes (NUTS x, y, z..) Filtering cluster sets within different sectors and different regions based on economic and innovation KPIs Contextual fit check Strategic fit check Analysis step Analysis performed for a combination of participants in sets of two to five cluster within different sectors different regions(nuts x, NUTS y...) Calculate simulated threshold value Calculate dispersion of each set of clusters w.r.t. threshold Select cluster sets crossing threshold with minimum dispersion Value chain positioning Expert interviews Qualitative assessment Technology performance Cluster organization involvement and performance Network capability Required set of clusters Decision tree Graphical presentation Sectors MC* MC*** C MC** Regions Economic KPIs dispersion decrease Innovation KPIs dispersion decrease >90% <90% >80% <80% >80% <80% Decision factors involved: Development stage Public investment Complementarity of actors Age/size of clusters Diversity of actors Collaborative projects Network capability Technology performance Output Clusters in one sector but with multiple regions (S1C1S2C2, S1C1S2C2S4C3;...) Identified set of clusters with agglomeration of innovativeness and economic performance in different sectors in multiple regions Sets of clusters with agglomeration of innovativeness and economic performance in different sectors in multiple regions with a contextual match Mega clusters***(type 3) identified: Agglomeration of innovativeness and economic performance in different sectors in multiple regions in the right context and with the right strategic fit 16 Performance

14 This new approach extends the existing approach to cluster mapping, providing insight to three types of mega cluster which provides new insight into the sources of future innovation and growth. Conclusion empowering Europe s future We believe that only an impact-focused approach will enable cluster organizations, policymakers, investors and other stakeholders alike to benefit from a European cluster service platform. Thus, future development should build on a vision towards an innovation-oriented and network-driven methodology for mapping and evaluating clusters. Impact is only generated through a comprehensive performance evaluation and management framework that encompasses not only an outside-in perspective of clusters, but also an inside-out perspective that analyzes the actors in the cluster and especially the evaluation of cluster organizations. Beyond this, only usable services that address the needs of the key stakeholders will generate a high impact. Finally, impact implies sustainability and, so, future service offerings need to develop an evolutionary approach to cluster mapping and evaluation. They need to remain relevant to enterprises and investors alike in a changing context: identifying new and emerging clusters is key to avoid focusing only on a fixed set of sectors that are currently present, rather, keeping a constant eye on what is new and emerging. The same goes also for mega clusters where there needs to be a collaborative network of SMEs within different clusters to broker the new technologies that stand at the intersection of different technological capabilities. A global perspective, in terms of emerging markets, is also necessary; if we do not take into account the changing dynamics of this globalized world, we cannot empower Europe for the future. Authors Sabine Brunswicker is a Senior Researcher at Fraunhofer Institute for Industrial Engineering, Germany Gunjan Bhardwaj is a Manager in the Advisory Practice, Ernst & Young Germany Ankit Garg is a Consultant in the Advisory Practice, Ernst & Young Germany Daniel Gonzalez is a Consultant in the Advisory Practice, Ernst & Young Germany Performance 17