Cluster Existence and Cluster Characteristics of the
Dutch Aerospace Industry
Author: Rudolf, Renwick, Nok
Student number: 9054901 / 10113126
Date of submission final version: April 14th, 2014 Qualification: MSc. In Business Studies, Strategy Track
Name of Institution: Amsterdam Business School, University of Amsterdam, Netherlands Name First Supervisor: Prof. Jaap de Wit
Table of Contents
Tables and Figures overview 3
Abstract 4
Introduction 5
1. Aerospace industry
1.1. Overall characteristics of the industry 6
1.2. Clusters in Aerospace 11
2. Theoretical basis
2.1. Micro level 14
2.2. Meso level 17
3. Empirical issues of cluster theory: frameworks, constructs and methodology
3.1. Cluster frameworks and conceptualisation 20
3.2. Cluster identification methods 24
4. Research Question 26 5. Methodology 29 6. Results 32 7. Discussion 44 8. Conclusion 48 References 50 Appendices
1a Strategic Schools of thought 58
1b RBV and DC 59
1c Cluster Critique 63
2 Regional Distribution EU Aerospace & World Fleet 64
3a Interview Questions (Dutch) 65
3b Interview Coding Scheme 67
4a Cluster Questionnaire 69
4b Flow chart Cluster Questionnaire 72
5 Connection of research questions to mini survey questions 75
6 Brief Cluster description 76
Tables and Figures overview
Figure Description Page
1 Breakdown EU Aerospace Turnover by sub-sector 6
2 Aircraft Manufacture Pyramid 7
3 Global end market geographical distribution of projected new airplane market 8
4 Elements of National Innovation Capacity (NIC) 21
5 Porter’s Diamond 21
6a Industry sub-sector distribution of total cluster, and of companies outside the cluster
35 6b Descriptives: industry sub-sector distribution by sub-cluster, compared to total
cluster sector distribution
36
7 Important business interactions 38
8 Type of relationships 40
9 Business advantages 42
10 Key drivers 43
11 Internal versus external analysis 59
12 From VRIN resources to SCA 60
Table Description Page
1 Top 4 aircraft integrators 2009 figures: balance sheet, employees, revenue (civil aircraft, business jets, and helicopters), aircraft fleet (i.e. commercial aircraft produced and currently in service), and market share of aircraft fleet
6
2 a, b Overview of Dutch aerospace industry 10
3 Aerospace clusters in North America and Europe 13
4 Resource types 14
5 Cluster ideal types 22
6 Knowledge based cluster framework 23
7 Classification of various cluster research methods according to their purpose 25
8 Descriptives: SME, Independent, International parent 37
9 Important interactions 38
10 Most strategic interactions 39
11 Type of business relationships 39
12 Business advantages of sub-cluster presence 42
Abstract
Academic interest of spatial concentrations of economic activity within country regions, also referred to as clusters, industrial clusters, or regional clusters has recently grown. Clusters tend to be related to growth in employment, productivity, and innovation and its drivers tend to be associated with interdependence among companies and institutions. This thesis investigates whether the Dutch Aerospace industry consists of such clusters, using three semi-structured interviews, and a survey among 122 aerospace related firms associated with three regional areas in the Netherlands, grouped in firms inside and firms outside the cluster. Second aim is to uncover the characteristics of possible connections among cluster members, and whether these connections involve Strategic Alliances, in which firms collaborate to share and combine their resources towards a common goal. The results of this research provide evidence that Dutch aerospace indeed consists of three (sub) clusters, in which important interactions among firms exist. These connections to firms within these (sub) clusters tend to be of higher strategic value to firms inside the same (sub) cluster, than firms outside the (sub) cluster. Concerning Strategic Alliances (SA), the majority of respondents has not been involved in SA in the past five years. However twice as many firms within the (sub) cluster indicate to be involved in SA compared to firms outside of the (sub) cluster.
Introduction
Academic interest in concentrations of economic activity within country regions, such as IT software and hardware industry around Santa Clara in the Silicon Valley California, automotive engineering around Toyota City Japan, or (on a larger scale) general industry in the region of Emilia-Romagna Italy, has recently grown. These regional concentrations can be referred to as clusters, industrial clusters, or regional clusters. Recent search of Ketels (2003) on cluster literature yields 300 articles on clusters between 2000 and 2003. This interest in clusters is picked up by governments and supra national bodies such as the European Commission, resulting in dedicated websites such as ‘US Cluster Mapping’ (www.clustermapping.us), and the ‘EU Cluster Observatory’
(www.clusterobservatory.eu). Some of these initiatives even have resulted in concrete policy actions in the EU in the form of the EU CLUNET project (European Cluster Alliance, 2010). In the case of US some of these initiatives have led to a multitude of federal multi-million dollar investments in clusters, such as recently the Energy Regional Innovation Cluster in Philadelphia, with funding of up to $ 122 million, and of which the effects are questionable (Chatterji, Glaeser, & Kerr, 2013, p. 23-27). Nonetheless the interest in the cluster phenomenon from the academic field, as well as in policy is established.
Regional differences in labor productivity and level of innovation can be prominent across countries and continents. Within European countries spatial differences in average labor productivity tend to be large, while little research is done in this phenomenon comparative to the US, whilst much data is available in Europe at a fine level of geographic detail that corresponds roughly with county level in the US (Ciccone, 2002). The dynamics causing the superior productivity performance within these areas are attributed to various forces at play, of which some tend to be associated with linkages among companies and institutions. “The prevailing opinion is that such interdependence significantly improves the individual and collective competitiveness of the constituent firms” (Feser & Sweeney, 2000). The phenomenon of so called ‘clustering’ or agglomeration is by many seen as fundamental causes of enhanced local development, creating so called spatial ‘externalities’ that cause firms to grow faster and larger than otherwise (Fingleton, Igliori, & Moore, 2005).
Dutch aerospace industry tends to be organized (among other forms) through three self-proclaimed clusters. This thesis investigates whether this is actually the case and how it affects the industry’s competitiveness. One of the advantages of clustering is the interactions between firms to share knowledge and other resources (e.g. Simmie, Sennett, Wood, & Hart, 2002). Especially in R&D intensive industries such as aerospace this sharing or combining of resources is present through R&D partnerships and alliances (Hagedoorn, 2002). Hence, it is important to discover whether the regional concentrations of Dutch aerospace firms can be seen as industrial cluster(s), and interesting to investigate whether within the assumed cluster(s) alliances exist, and in what form. In the following chapters we will first discuss the aerospace industry (Chapter 1), second the theoretical basis of clusters and Strategic Alliances (Chapter 2), to be followed by the research question of this paper (Chapter 3). Consecutively we will address the conceptual model (Chapter 4), and the methodology (Chapter 5). Finally we will present the results (Chapter 6), conduct the discussion of these results (Chapter 7), and address the concluding remarks on the subject (Chapter 8).
1. Aerospace industry
1.1. Overall characteristics of the industry Industry structure
The aerospace industry in general can be divided into 7 sub-sectors namely Aircraft integration (large civil-, regional aircraft, business jets, military aircraft, and helicopters –civil & military-),
Aerostructures, Aircraft engines, Aircraft equipment, Aircraft maintenance (Maintenance, Repair and Overhaul: MRO), Missiles, and Space (INNOVA, 2008; ASD, 2006). Figure 1 below represents the proportional distribution in terms of turnover by aerospace sub-sectors in 2006 in the European Union (EU), clearly indicating a majority of market share of final products, involving aircraft integrators.
Distribution EU Aerospace Turnover
Figure 1 Breakdown EU Aerospace Turnover by sub-sector (as percentage of total EU aerospace turnover)
Source: ASD, Annual Reports 2006
In the aerospace industry the market of civil commercial aircraft manufacture can be viewed as indicative for the industry structure, when compared to other parts of the industry, such as helicopter and military aircraft manufacture. The value chain of the end product of commercial aircraft tends to be highly convergent, with an oligopolistic situation involving just two leading manufacturers of airplanes, so called integrators. Boeing and Airbus, of which customers are mostly airliners and aircraft lease-companies, hold approximately 66 % of the market in terms of revenues (AW Source book, 2009, in ECORYS, 2009). Table 1 below shows the main figures in terms of business variables of the four main western aerospace integrators, providing a comparison in sizes, and emphasising the dominance of Airbus and Boeing.
Key Figures 2009 Top 4 Aircraft Integrators Company Balance sheet
total € m
Employees Civil Aerospace Revenue € m Aircraft Fleet Share (%) Airbus 80,304 119,506 30,016 5,647 22.7 Boeing 43,092 157,100 23,646 10,923 43.9 Bombardier 14,795 66,700 6,920 2,603 10.5 Embraer 6,164 16,853 3,722 1,767 7.1 Table 1 2009 figures: balance sheet, employees, revenue (civil aircraft, business jets, and
helicopters), aircraft fleet (i.e. commercial aircraft produced and currently in service), and market share of aircraft fleet.
Below this top, market shares of suppliers to these integrators are much more dispersed, except for engine manufacturers, of which 5 companies hold 95 % of the global market in terms of in service and ordered engines in 2009 (ECORYS, 2009). The aircraft industry tends to be hierarchically
organised into “tiers”, related to the previously mentioned convergent value chain of the aerospace industry. Niosi & Zhegu (2005, p. 7-8) provide the following tiers in their “pyramid”, with on top the integrators and at the bottom the parts manufacturers (the later not visible in the pyramid):
1. Airframe assemblers (i.e. prime contractors, integrators, or OEMs) 2. Sub assemblers consisting of manufacturers of:
a. Propulsion systems b. Avionics systems
c. Airframe structures and subassemblies
3. Producers of electronic subassemblies, engine components, hydraulics systems and fuselage parts
4. Suppliers of components and parts; also offering products and services to a large range of other industries outside of aerospace (not visible in the pyramid).
Figure 2 below provides an image of the pyramid shaped aerospace industry divided by the tier levels 1 to 3.
Aircraft Manufacture Pyramid
Figure 2 Aircraft Manufacture Pyramid by tier level Source (Niosi & Zhegu, 2005, p. 8)
The pyramid reflects the dispersion of the industry in terms of the number of producers within each tier. Tier 1 consists of just a few major players such as: Boeing, Airbus, Bell Helicopter, Bombardier, and Embraer, at the bottom the tier 4 (not visible in the figure), parts and small component
Most aircraft are produced in a so called ‘family’ of aircraft types based on variations of one aircraft design, with variations in passenger capacity, aircraft flight range, or novel developments such as economic fuel consumption, and customer experience. These family types range (from large to small) from very large (e.g. Boeing 747; Airbus A380), twin aisle or wide body (e.g. Boeing 777, 787; Airbus A340, A350), single aisle (e.g. Boeing 737; Airbus A320), to regional aircraft (Bombardier, CRJ and C series; Embraer ERJ 145 and E jets).
The global end market roughly consists of a market of about 15.000 (20.000) commercial airplanes in 2010 (2011), rising since 1977, and expected to grow with approximately 31.000 (34.000) of new passenger aircraft needed till 2030 (2031), (first figure Airbus GMF, 2011; figure between parenthesis Boeing, CMO, 2012). The main drivers of this growth are according to Airbus and Boeing, growth in global GDP and population (especially in emerging markets) related to growth in air travel , increase in global trade related to air travel and air transport growth, replacement of aircraft in service, growth in Low Cost Carriers, market liberalisations, and oil prices (as a negative effect). Figure 3 below shows the prospected growth in terms of airplane deliveries, and in terms of market value by geographic region between 2012 and 2031, according to Boeing .
Projected New Aircraft End Market
Figure 3 Global end market geographical distribution of projected new airplane market Source: Boeing, CMO, 2012
With regard to the geographical distribution of the total aircraft equipment manufacture industry in the EU, top 3 aerospace manufacturing countries (United Kingdom, France, and Germany) hold 85% of total industry turnover in 2002, with a high consolidation rate in the last decades (EU major players 1990-2003, from 30 to 11), (Eurostat in INNOVA, 2008). Concerning size distribution within the EU in 2001, 91% of the total of 2248 companies is a Small or Medium Enterprise (SME), holding just 7% of total (EU) market turnover, and consisting of just 10% of the total employment in (EU) aerospace in 2001 (Eurostat in INNOVA, 2008). In comparison 3% of all enterprises consisting of 1000 employees or more, hold 83% of total market turnover.
Specificities
Firstly the aerospace industry is historically intertwined with defence industry through state defence sponsored R&D (research and development) activities (e.g. Francis and Prevzner, 2006), and because OEM’s usually also are defence contractors. Second the aerospace industry is an industry of high national interest, international prestige and national pride because of importance to citizen mobility and national security (e.g. Fligstein, 2006). This leads to government involvement in terms of: -financial- support, security and defence issues, and government as purchaser.
Third, this government involvement is also apparent in compensation / offset orders, which refers to compensatory trade arrangements in which the exporting nation grants concessions (e.g. production sharing arrangements with domestic manufacturers) to the importing nation (Pritchard &
MacPherson, 2007). Other specific industry traits are: a long lead time, which is the time of
placement of customer order to delivery of production and R&D, large R&D investments compared to manufacturing industry in general, long product lifecycles of aircraft, architectural complexities, and strict standards.
Important market developments are an aging fleet of in service aircraft, airliners (incl. low cost carriers) with lower profit margins putting pressure on profit margins of aircraft assemblers (Niosi & Zhegu, 2005, p.2), disintermediation (Rossetti and Choi, 2005), which involves direct supply of parts from maintenance companies (MRO) to airliners instead of supply by OEM’s, and a growing shortage in skilled labour. Another important development due to the rising oil prices, and the rising
environmental concerns regarding air pollution, is demand for lighter, more fuel efficient aircraft (resulting in the European Research initiative, Clean Sky).
Other market developments per aircraft industry category (e.g. ECORYS, 2009) are the following. Aircraft assembly: more foreign components. Structures: innovative materials such as composites (a compound of –carbon- fibre and resin). Engines: innovative highly efficient engines with less fuel burn, less pollution, and noise reduction. Equipment: more integration of avionics systems, and innovative aircraft sensors. MRO: retrofit (use of new technology in aged in service aircraft), innovative diagnostics of airplanes systems health, and a tendency of the OEMS to attract and provide more MRO activities themselves. Another development is the shifting of the industry architecture towards fewer suppliers to the aircraft integrators in the higher tiers (Airbus AerVico, 2007, in ECORYS, 2009).
Dutch situation
The Netherlands is one of few countries in the world with knowledge of total aircraft manufacture, design, and R&D (incl. testing validation, qualification, and certification). Although Dutch aerospace only accounts for about 0.9 % of total EU aerospace manufacture production value (Eurostat 2006, in ECORYS, 2009), it holds the 8th position in the EU. Dutch aerospace was with Fokker airplanes still in the top 7 (with 541 units) western manufacturers of the world’s airplanes fleet in service (or on order) in 2009 (Flight Global ACAS database, World Airline Census 2010). It was Fokker at the end of the 1920’s that made Holland the top producer of airplanes in the world, however it stopped airplane integration after bankruptcy in 1996 (Flight Global, 1996). The aging Fokker airplane fleet results in less activity for Dutch aerospace in Fokker related business. However the Netherlands still has a high level of aerospace science, technology, R&D and manufacturing knowledge (know what and know how). For instance 40% of innovations in the Airbus A380 are from Dutch origin (reference: the Dutch aerospace industry association – NAG-). Examples of Dutch innovations and R&D
contributions involve: Glare, embedded systems, composites (TAPAS), wind tunnel testing, future European aircraft development (CleanSky), and integration of European airspace (SESAR). Current Dutch aerospace industry consists for the bulk part of Fokker former but still viable
departments combined with MRO activities of Air France-KLM in the Netherlands, for the remainder of SME companies (some of them owned by large foreign component manufacturers) also active in MRO, and in manufacturing.
The Dutch aerospace consists of 15.000 employees (of which 60% in MRO), activities in military and civil aerospace, and 2.5 billion euro’s in turnover in 2008 (NIVR, in Bartels, 2010). Airliner KLM (with its MRO department), manufacturer Fokker, research organisation NLR (the Dutch ‘National
Aerospace Laboratory’), and Technical University of Delft (with Europe’s largest aerospace
engineering faculty), are some of the major players in the industry (reference: NAG). Table 2a and 2b below provide an overview of Dutch aerospace industry by sub-sector in terms of employment, and revenue development.
Employment aerospace industry sub-sectors, 2008
Sub -sector
Number of employees (fulltime equivalent)
Manufacture 4,924 MRO 8,800 Engineering Consultancy 90 Knowlegde Infrastructure 1,047 Trade 105 Total 14,966
Table 2a Overview of Dutch aerospace industry in terms of employment by sub-sector Source NIVR in Bartels, 2010
Revenue development aerospace industry sub-sectors (in million Euro's)
Sub -sector 2006 2007 2008 Index (2006 = 100) Manufacture 563 646 636 130 MRO 1,529 1,527 1,626 106 Engineering Consultancy 9 9 10 111 Knowlegde Infrastructure 85 121 116 136 Trade 54 74 89 165 Total 2,235 2,375 2,475 111
Table 2b Overview of Dutch aerospace industry in terms of revenue development by sub-sector Source NIVR in Bartels, 2010
See appendix 2 for regional distribution of EU 27 Aerospace industry 2006, and world fleet by western manufacturer 2009, for the position of Dutch aerospace and Fokker.
In general the most important characteristics with respect to this thesis are the highly convergent industry structure (with on top oligopoly of the integrators, large suppliers in the middle and a large amount of SME’s providing parts and services), the high complexity of certain components, the high R&D expenses combined with the high R&D level in aerospace, the high involvement of government support to and protection of domestic aerospace (and related) firms, and the geographical
distribution of the industry. Also important is the shifting of the OEM’s to work with fewer suppliers with higher Tier levels.
1.2. Clusters in Aerospace
Next we will discuss four recent studies on aerospace clusters by Beaudry (2001), Lublinski (2003) Bönte (2004), and Niosi & Zhegu (2005), to better focus on the specific characteristics of aerospace clusters in various countries. Some of the findings of those four studies will be used to confront the results of the research of this paper, and will therefore be integrated into the discussion part. Beaudry (2001) studied industrial clusters in the UK aerospace industry on the variables of growth (using employment growth as proxy), patent growth, and entry of firms from outside the clusters. Only the industry and not the R&D or government institutions were studied. Four findings were prominent. First aerospace companies co-located with other aerospace companies within the same sub sectors grow faster than average in terms of employment. Second, aerospace companies co-located with many firms from other sub-sectors (outside aerospace) do not benefit from clustering in terms of employment growth. Third, the strongest effect on patent growth is within company
employment, but cluster employment within the aerospace sector (especially innovative firms) also had a strong, but smaller effect. Finally, mechanical engineering, avionics (or electrical engineering), and engine manufacture sub-sectors are the centre entry attraction for the clusters. Beaudry (2001) concludes with respect to clustering that it attracts new entrants, benefits growth of firms and their innovativeness. Furthermore avionics is leading relative to the other aerospace sub-sectors through its strongest positive effect on own sub-sector and other sector employment growth, patenting growth, and cluster entry. In comparison MRO suffers from negative cluster effects of local competition in output and congestion and competition in input markets (such as labour market), partly because of their presence near airfields and densely populated areas (Beaudry, 2001 p. 420). Study of Lublinski (2003) and Bönte (2004) focusses on the German Aerospace clusters in terms of geographic proximity, employment growth and employment, comparing clustered and non-clustered firms and institutions (e.g. Universities of applied sciences and technical colleges). Lublinski (2003) finds that cluster firms’ proximity to institutions and competing firms, tends to be more important, than for non-clustered firms (p. 460, 461), in the supply of specialised labour. Cluster firms believe that proximate customers are more important for knowledge spillovers, than distant customers, however fear exists of leaking valuable knowledge to proximate customers (p. 461, 462). Demanding local customers tend to be more significant for cluster firms than for non-cluster firms, with
customer pressure as negative effect, but proximity of other firms as leveraging effect (462). The majority of cluster and non-cluster firms find geographic proximity to customers and suppliers important, when considering transportation and transaction costs (p. 463). In conclusion local demanding customers tend to be important in clustering, exert pressure on the cluster firms, and are the cause of knowledge spillovers between clustering firms and institutions.
Bönte (2004, by the way using the same Lublinski, 2003 data set of cluster and non-cluster firms) finds that however proximity to other firms and institutions such as universities has a positive effect on a firm’s probability to innovate this effect is not cluster specific. Furthermore the found positive effect of labour market pooling on employment growth is also not cluster specific. Labour market pooling concerns recruitment of employees that have been dismissed by other firms (p. 275). Finally only the effect of demanding customers on innovation tends to be cluster specific. The weak findings of both Lublinski (2003) and Bönte (2004) may be due to the nature of the Northern German
aerospace industry, which is mostly cabin manufacturing and MRO, which is less prone to cluster than for instance avionics (Bönte, 2004, p. 276; Beaudry, 2001).
Large prominent firms tend to be at the centre of the cluster (as found by Nijdam & Langen, 2003) surrounded by large groups of small and medium-sized suppliers of parts and components to prime contractors or OEMs (Niosi & Zhegu, 2005). These suppliers tend to diversify to reduce their
dependence on one major client. Through this diversification the nature of the cluster can be different from the specialisation effects theorised by (McCann & Folta, 2008). Next four other characteristics are drawn from Niosi & Zhegu (2005). First is the separation of design and production spanning globally. Added to this the nature of the business in the end market, in which compensation orders made in the country of delivery are common. Both effects result in regional and international knowledge spillovers. Second, the role of local universities and government laboratories as main providers of knowledge within clusters is secondary, compared to the magnitude of international knowledge spillovers by the fewer but much larger tier 1 and tier 2 manufacturers in the cluster. Third, clusters around large manufacturing plants tend to be long term phenomena, and as the consolidation in the aerospace industry progressed from the late 1980’s (see Fligstein, 2006) a lot of them converted from design and assembly of entire aircraft to producers of subassemblies to the more successful tier 1 producers. Fourth, regional knowledge spillovers can vary depending on the local importance of the supply chain.
Furthermore aerospace clusters display large international connections instead of local ones; materials exchange within the cluster, involving a large group of tier 4 components and parts suppliers, tends to be of lower strategic value than the supply of large subassemblies, due to international outsourcing within the aircraft industry (Niosi & Zhegu, 2005). Evidence is found of large scale international collaborations in aerospace at the highest tier levels (Dussauge & Garette, 1995), involving for instance the development and manufacture of the Airbus A-300 to A-340 types, and the CFM-56 aircraft engine (used in Airbus A-320, and Boeing 737). It is through these types of alliances that technical aerospace knowledge is exchanged between alliance partners active
sometimes at very long distances from each other. As much of the technical knowledge tends to be highly codified, alliance partners do not need proximity to transfer this knowledge effectively. This is contrary to tacit knowledge which requires shorter distances (Jaffe, Trajtenberg, Henderson, 1993 in Beaudry, 2001; Baptista, 2000; Dahl, Pederson, 2004, Bathelt, Malmberg, and Maskell, 2004), and face to face interaction to transfer (Storper & Venebles, 2004).
Concluding we can note the following general findings of four aerospace related cluster studies, relevant to this paper. Beaudry (2001) found evidence of cluster benefits to employment growth, innovativeness and attraction of new entrants. Lublinski (2003) and Bönte (2004) however found weak evidence of cluster benefits, arguing that the importance of proximity of firms and institutions is not cluster specific, and that only the effect of proximal demanding customers on the
innovativeness of firms tends to be cluster-specific. Niosi & Zhegu (2005) on their part describe the nature of aerospace industry related to clusters, and find characteristics of aerospace clusters, such as large prominent firms at the centre, large scale international connections, and a secondary role of universities and research institutes as main providers of knowledge.
Top North American aerospace metropolitan areas
European aerospace clusters
Table 3 Top Aerospace metropolitan areas in North America, and aerospace clusters in Europe in 2000
2. Theoretical basis 2.1. Micro Level
The phenomenon of clusters can be viewed at meso level (i.e. industry level) at which spatial
elements tend to be prominent (e.g. McCann & Folta, 2008; Crawley, Beynon, & Munday, 2012), and at micro level (i.e. firm level) at which inter-firm connections tend to be salient (e.g. Gordon & McCann, 2000; Schmitz, 2000). Both levels seem to be applicable in the attempt to uncover cluster existence and cluster characteristics. At micro level one of the reasons why firms tend to engage in inter-firm connections within clusters, will be explained using Strategic Alliances theory.
Strategic Alliances (SA) theory addresses knowledge and other resources generation and sharing, within a group of alliance partners. Acquiring and sharing resources are viewed as important reasons for firms to collaborate on a strategic level (e.g. Eisenhardt & Schoonhoven, 1996; Das & Teng, 2000; Reid, Bussiere, Greenaway 2001; Hagedoorn & Duysters, 2002; Grant & Baden-Fuller, 2004).
However some alternative theories are power (or game) theory (Katz, 1986; Schwartz, 1987),
resource-dependency, and transaction cost theory (Oxley, 1997). Alliance formation can be seen as a quest for resources (Eisenhardt and Schoonhoven, 1996; Gulati, 1999, Das and Teng, 2000). Das and Teng (2000, p. 42) provide examples of resource types grouped by their dominant resource
characteristics.
Characteristics of Resource Types
Table 4 Resource types and their characteristics Source: Das & Teng 2000
Table 4 above assumes that resources such as technological and managerial resources tend to be hard to substitute, patents and registered designs tend to be hard to imitate, and human resources tend to be immobile to a certain extent. It is these types of resources firms are looking for to
complement or strengthen theirs to enhance their competitiveness, when alliancing with other firms. Contextually firms in vulnerable strategic positions in highly uncertain situations (i.e. emerging markets, innovative technology and high competition) seek additional resources (e.g. technological know-how, cash, legitimacy), leading to the formation of SA’s, in which strategic and social factors tend to dominate (Eisenhardt and Schoonhoven, 1996).
In a broad sense Strategic Alliances are referred to as: ‘agreements characterized by the
commitment of two or more firms to reach a common goal entailing the pooling of their resources and activities’ (Teece, 1992, p. 19), in which ‘the parties ... maintain autonomy but are bilaterally dependent to a non-trivial degree’ (Williamson, 1991, p.271). This puts the governance structure of SA between (the two extremes of) the formal contractual agreement (market) and ownership (hierarchy: i.e. Merger & Acquisition). In a narrow sense SA can be regarded on a more project basis, with attributes existing for a set time and task and typically involving formal agreements between two partners (Parmigiani and Rivera-Santos, 2011).
Firm’s preference for Strategic Alliances over M&A’s is a matter of flexibility versus control. In high tech industry the most common collaborative form is the alliance, due to its flexibility, and
opportunities for learning through loosely structured agreements (Hagedoorn and Duysters, 2002). This preference for alliance forming is especially apparent in R&D intensive industries. “This flexibility in R&D partnerships ties into the more general demand for flexibility in many industries, where inter-firm competition is affected by increased technological development, innovation races and the constant need to generate new products” ( Hagedoorn, 2002). Hagedoorn and Duysters (2002) however argue that the closer the external sourcing of innovative capabilities comes to the firm’s core business, the more important integration becomes, towards the preference for M&A, giving them more control. Strategic Alliances can take the form of supplier-buyer partnerships, outsourcing agreements, technical collaborations, joint research projects, franchising, or arrangements on shared new product development, manufacturing, distribution, and cross-selling.
The growth and concentration of alliances in industrial or R&D intensive sectors suggests a key role for technology in alliance formation in recent decades (see Hagedoorn, 2002). In a broader sense, and originated from the learning perspective (process school), the goal is acquiring know-how and organisational capability of an alliance partner, resulting in a ‘competition for learning’ (Hamel, 1991). Moreover, Grant and Baden-Fuller (2004) identify two conceptually distinct dimensions in this process, namely increasing the firms ‘stock of knowledge’ (i.e. knowledge generation), and of
application of existing knowledge (i.e. knowledge sharing). In knowledge generation, or acquisition partners use the alliance to transform and ‘absorb’ (or internalise) the partners knowledge base. But to the authors it is the knowledge sharing or knowledge accessing (instead of acquisition), and the ‘desire to create value through combining their separate existing knowledge base’, that primarily motivates knowledge based alliances. The authors point out that knowledge generation requires specialisation, while application requires diversity of knowledge. Given the limited transferability of (tacit) knowledge, knowledge application can be difficult, because of the difficulty to access
knowledge external to the firm. Alliance forming can leverage that.
Although the purposes of alliances and the modes of exchange can be (relatively) clear, the dynamics involved can be complex. “It is important to recognise that although Strategic Alliances are
essentially dyadic exchanges, key precursors, processes, and outcomes associated with them can be defined and shaped by social networks within which most firms are embedded” (Gulati, 1998), pointing out to a strand of economic sociology literature that devoted itself to explaining how economic actions may be influenced by the social structure of ties within which they are embedded (e.g., Granovetter, 1985). A social network can be defined as ‘a set of nodes (e.g., persons,
organisations) linked by a set of social relationships (e.g., friendships, transfer of funds, overlapping membership) of a specified type’ (Laumann, Galaskiewicz, and Marsden, 1978, p. 458). So instead of neo-classical behaviour assumptions of the economic actor as ‘homo economicus’ (e.g. rationality of actions), actions should be seen in the social context in which a person or organisation is currently in
(referred to as ‘social embeddedness’). For instance intra-firm networks, political coalitions, and individual search for approval, status and power (Granovetter, 1985).
The theory of Strategic Alliances described above is to emphasize the importance of collaboration to especially high tech industries, such as aerospace. We assume the proposed linkage between firms in cluster theory and research can be viewed as a proxy to resources sharing in the SA theory. We furthermore assume that Strategic Alliances are more extensive linkages between firms and of more strategic value, hence more important for the competitiveness of firms, as they allow firms to innovate products, processes and management, in a complex industry such as aerospace. The SA theory sheds light on the social aspect of collaboration (e.g. Granovetter, 1985; Gulati, 1998), indicating that research should be done at a qualitative level to find evidence of valuable resources being shared among firms. This is in contrast to the quantitative research conducted in the form of cluster identification that does not uncover linkages among firms in which for instance knowledge sharing is involved. So the SA theory can be seen as complementary to qualitative research that aims to find cluster characteristics and dynamics. Consequently it will be used in this research to address the nature of linkage and inter-firm sharing within clusters in more detail, and as such will provide a richer view of the extent of the linkages within the cluster dynamics. See appendix 1b for the underlying theories to SA, of Resourced based Theory (including cautionary remarks on the limited practical and managerial use of RBV), and Dynamic Capabilities View.
2.2. Meso Level Origins of cluster theory
To better understand at meso level what stimulates growth within clusters we will next briefly discuss prior literature on cluster theory. Cluster or agglomeration literature (these tend to be used disorderly by various authors) can be subdivided in three main streams of thought referred to as: Jacobs externalities, Marshall-Arrow-Romer approach, and Porter approach. The differences revolve around the question whether diversity, or specialisation of economic activity causes the in the introduction mentioned spatial externalities (McCann & Folta, 2008), in relation to knowledge exchange among firms. By diverse is meant non-similar firms, or firms in different industry sectors, and by specialisation is meant similar firms, or firms active in the same industry.
The first stream of thought according to Bishop & Gripaios (2010) state that proponents of diversity, associated with work of Jacobs (1969), argue that new innovations in diversified cities and regions tend to encourage growth as it better facilitates knowledge flows across industries.
The second stream of thought associated with work of Marshal-Arrow-Romer also known as MAR (e.g. Marshall, 1920; Arrow, 1983; Romer, 1987), proposes specialisation, arguing knowledge as primarily sector specific; hence specialisation enhances growth, as concentration of specialised firms facilitates knowledge flows.
The third stream associated with work of Porter (1998, 2000), likewise proposing specialisation, also argues knowledge flows as sector specific, but in addition that competition stimulates innovation and growth, as firms are pressed to innovate to survive.
The phenomenon of clusters in economics, strategic management, economic geography and regional science, dates back to earlier work of Marshall (1920) on agglomerations. Marshall proposed reasons why firms localise within a same area, referred to as ‘economic externalities’. Economic externalities, external scale economies, or ‘spatial externalities’, are spatial economic side-effects of proximity between economic actors that cause growth. Marshall (1920) defines ‘external scale economies’ as cost savings accruing to the firm, because of size or growth of output of the industry in general. Such economies contrast with internal scale economies, which are the source of increasing returns from growth in the size of plants. Weber (1929) defines ‘agglomeration economies’ as cost savings firms enjoy as a result of increased spatial concentration; i.e. suggesting it to be an external variation of (internal) economies of –i.e. increasing returns to, red.- scale (Bergman & Feser 1999, p.11). Hoover (1937), added the distinction between ‘localisation economies’, which are externalities within a certain group of local firms within a certain sector, and ‘urbanisation economies’ which are externalities available to all local firms, irrespective the sector (Gordon & McCann, 2000).
Attractiveness of clusters tends to be economic in nature, is assumed to be related to geographic proximity among firms (Gordon, McCann, 2000, p. 518, 519), and traditionally related to the local production factors available and to the local demand conditions (Porter, 2000, p. 20). Based on the Diamond model of Porter (2000, p. 21-23) geographic proximity of partly competing and partly co-operating firms, the following five competitive advantages of a regional (state, industrial- or metropolitan area) cluster are:
1. Access to specialized inputs and employees 2. Access to information
3. Complementarities (complementary products for buyers, marketing complementarities, complementarities due to better alignment of activities), and
4. Access to institutions and public goods 5. Incentives and performance measurement (competitive- and peer pressure).
The term clusters (as a widely used and accepted term) dates back to the early 1990’s. Michael Porter was among the first and probably one of the more notable authors on this matter. Porter (1990) with his “Competitive Advantage of Nations” is one of the most pronounced proponents of emphasising national comparative contexts, to identify relative competitive advantages of nations. This national context he later also applied in regional contexts. The essence of his approach was to explicate comparative differences in performance of certain spatial areas of economic activity, within regions around the world. There is an array of different cluster definitions, but in general they encompass a regional concentration of related economic activity of firms and other institutions, which in some cases have certain relationships with each other, that (in turn) are located in relative regional proximity of each other. However key linkages to firms farther from a location or region may not be ignored (e.g. Bergman & Feser, 1999, p.6; McCann and Folta, 2008, p. 541).
The diversity of cluster definitions seems useful in characterising the general phenomenon, but can cause conceptual and empirical ambiguity (Bathelt, 2008) and practical problems, when applied to research, in-depth study, and measurement. Despite these ambiguities and practical problems many articles refer to, cite, and use Michael Porter (2000) when discussing the conceptualisation of a cluster (e.g. Bergman & Feser, 1999; Ketels, 2003; Ketelhöhn, 2006; McCann and Folta, 2008). According to Porter (2000, p.28) clusters are: “geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (e.g. universities, standards agencies, trade associations) in a particular field that
compete but also co-operate.” With this definition Porter (2000) addresses the aspects of proximity, the existence of institutions besides industry, and the existence of linkages between cluster
members. See appendix 1c for existing academic critique on cluster related theory.
A more elaborate and more recent definition of a cluster comes from Morosini (2004, p. 307), adding to it: “a significant part of both the social community and the economic agents work together in economically linked activities, sharing and nurturing a common stock of product, technology and organizational knowledge in order to generate superior products and service.” In this definition the socio-economic element of ‘linkage’ in the cluster is emphasised, which is also done by other researchers (Dahl & Pederson, 2004; Gordon & McCann 2000; Granovetter, 1985, 1991, 1992). This viewpoint is, related to social- and socio-economic sciences, and part of recent interest in cluster theory (e.g. Storper & Venables, 2004; Glückler, J., 2007; Maskell & Malmberg, 2007; Crespo, Suire, & Vicente, 2013).
To sum up the above: cluster literature tends to be originated with the work of Marshall of agglomeration or reasons why firms tend to concentrate in a given region. The cause of growth within clusters typifies the three means streams of thought, dividing theory as question of diversity or specialisation. The attractiveness for firms to cluster according to Porter (2000) encompasses access to: specialised inputs, information, complementarities, institutions and public goods. Cluster definitions recently made popular by Porter (1990) can differ (e.g. Bergman & Feser, 1999, p. 7 and 8), can cause conceptual and empirical ambiguity, but tend to be useful for characterisation of the general phenomenon (Bathelt, 2008). Recent literature has focussed on the socio-economic aspects of linkage among firms within and among clusters (Dahl & Pederson. 2004; Gordon & McCann, 2000), through which firms share and exchange knowledge and other resources. See appendix 1b for the reason why firms need to access valuable resources according to the Resourced Based Theory and its derivative theory of the Dynamic Capabilities View.
3. Empirical issues of cluster theory: frameworks, constructs and methodology
3.1. Cluster frameworks and conceptualisation
In order to characterise the nature of clusters or regional concentrations various models and frameworks are used, ranging from regional science oriented such as ‘core periphery’, ‘urban systems’, and ‘industrial concentration & trade’ (e.g. Krugman & Fujita, 2005), to more economic science oriented models and frameworks such as National Innovative Capacity Framework (Porter & Stern, 2001a, 2001b). To make distinction among frameworks, models and theory, we assume that: a framework describes the variables of a phenomenon, the model adds to it the connections among the variables, and the theory proposes (adding to that) the direction (i.e. causal relationships) and the moderating and mediating effects of the connected variables. We will address three applicable frameworks, first two oriented towards economic science and management, the third oriented toward regional science and urban studies: National Innovative Capacity Framework (Porter, 1990; Porter & Stern, 2001a, 2001b), Cluster Ideal Types (Gordon & McCann, 2000), and the Knowledge Based Cluster Framework (Morosini, 2004). We address these three frameworks because they best relate to the subject of this research namely identification of cluster constructs, the applicability of the cluster concept to Dutch aerospace Industry, and the identification of the cluster characteristics. The first framework provides a clear description of the elements, the infrastructure and the
conditions of the cluster environment. Building on Porter (1990) according to Porter & Stern (2001a) a cluster is modelled in a National Innovative Capacity framework (NIC), in which on the one hand there is the Common Innovation Infrastructure. This Common Innovative Infrastructure sets the basic conditions for innovation, including overall human and financial resources, and (long term) public policies that stimulate and support innovation, like Intellectual Property Rights (IPR), and tax reliefs. Also part of the common innovation infrastructure is the openness of the economy to trade and investment. On the other hand there is the Cluster-Specific Environment (for innovation), set by interconnected companies in the competitive field. The Cluster-Specific environment of the
framework is related to the Porter’s Diamond (Porter 1990), in which the competitiveness of a nation is explained, but now applied to regional level. It consists of a number of interconnected Porter Diamonds. Between the common innovative infrastructure and the cluster environment are Qualitative linkages: “…strong clusters feed the common infrastructure and also benefit from it. A variety of formal and informal organizations and networks -which we call institutions for
collaboration-, can link the two areas” (p. 29). Figure 4 below provides a schematic image of NIC, with clearly the connecting element of the ‘institutions for collaboration’ represented by the ‘quality of linkages’. Within NIC the ‘cluster-specific conditions’ are build up by Porter’s Diamonds shown in figure 5, involving the elements influencing competitiveness of industries within countries and regions, but now reflecting the cluster context.
National Innovative Capacity framework Porter’s Diamond
Figure 4 Elements of NIC Figure 5 Porter’s Diamond in context of cluster theory
Source Porter & Stern, 2001b (both figures)
Where the NIC emphasises national level and cluster environment, the next model focusses on cluster characteristics at firm level. As we will address the firm level in this research a firm level framework is applicable, one giving a clear distinction of the dynamics within these regional concentrations. Gordon & McCann (2000) identify three ‘ideal types’ of cluster appearances, and argue that each cluster can have characteristics of one or more of these ideal types, each to more or less extent. According to this model the three Dutch sub-clusters therefore, to a certain extent, bear within them these ideal types. The three ideal types are:
1. Agglomeration, 2. Industrial-Complex, 3. Social Network.
1. Agglomeration (p. 516-518) by some called ‘Marshallian Theory’ (Marshall, 1920) identifies three reasons for cluster emergence and growth: A. Local pool of specialised labour, B. Non-traded inputs, and C. Flow of information across cluster members. These advantages are called “external
economics” or “externalities”. The absence of formal structures or strong long-term relations between businesses in this model means that the local system (or cluster) has essentially ‘open memberships’ (Gordon, McCann, 2000, p. 517). This form of cluster basically concerns
concentrations of businesses found in urban areas. “The pure form of agglomeration depicted here is evidently the model underlying modern urban economic theory...” (Fujita, 1989).
2. Industrial-complex Model (p. 518,519) is characterised by sets of identifiable and stable relations, primarily in terms of trading links, which principally determine location behaviour. It recognizes the construct of ‘spatial transaction costs’, which is about the relationship between optimal location of the firm, the level of transport costs, and the price of local production factors (Weber, 1909/1929). These models can involve oil refineries, chemical plants and pharmaceutical complexes. Also examples are known of automotive engineering, such as ‘Toyota City’ in Japan (Gordon, McCann, 2000).
3. Social-network Model (p. 518-521) is based on sociological literature (Granovetter, 1985, 1991, 1992) and is a response to the institutional school (Williamson, 1975, 1985). Social-network theory argues that there is more order in interactions between firms than within firms. It usually involves relationships (informal in nature) of top management and decision makers of the firms (Gordon & McCann, 2000). Crucial in these interpersonal relationships is trust. “The strength of these
relationships is described as the level of ’embeddedness’ of the social network” (Gordon & McCann, 2000, p. 520). Social networks differ from agglomerations in that the relationships are not just based on economic responses, but also on an unusual level of ‘embeddedness’ and social integration (Gordon & McCann, 2000). “There is nothing spatial about the social-network model although it has explicit spatial applications.” Examples of social-networks: application can be found in the spatial industrial cluster of Emilia-Romagna in Italy (Scott, 1988) and Santa Clara County in California USA (Larsen and Rogers, 1984; Saxenian, 1994). Open for question is Silicon Valley (Suarez-Villa and Walrod, 1997; Arita and McCann, 1998). Table 5 below demonstrates the three ideal types and their basic characteristics from a transaction cost perspective.
Cluster Ideal Types
Agglomeration Industrial Complex Social Network Business rational Specialised labour pool
Non-traded inputs Flow of information Transaction costs reduction Knowledge diffusion within network Spatial Element Near urban areas Trading links
determine locational behaviour
Not spatial in nature, but consequences of proximal location Connection
Characteristics
No long term relations No formal structures
Stable relationships within value chain
Social Embeddedness Analytical approach Models of pure
agglomeration
Location- production theory Input-Output analysis
Social network theory (Granovetter)
Table 5 Cluster ideal types by basic characteristics
Source Gordon and McCann 2000, and McCann, Arita & Gordon, 2002
As knowledge-based elements of clusters have recently gained widespread attention from science and practice, Morosini (2004) incorporates them in his framework for cluster strength and dynamics. “Knowledge-based elements as key determinants of a cluster’s strength and performance do receive a considerable amount of attention within qualitative and case-based research studies (Meyer-Stamer, 1998; Rabellotti, 1999).” Based on the existing scientific approaches the author summarises a series of key variables to be included in a knowledge-based framework. This framework has use in empirical research of clusters. It ought to help researchers to assess what variables bind and drive the cluster. So called ´key constructs´ involve four main variables: Institutional fabric (social aspects and economic determinants), Geographic closeness (advantages related to proximity of cluster
members to each other), Economic linkages (such as common customers), and Common Glue (elements such as leadership, communication rituals, and knowledge interactions).
Of these main variables a number of items are suggested for measurement and comparison of cluster characteristics and dynamics. Some main variables are sub divided. Academic references are given to each of the main variables, or its subdivided elements. See table 6 below: it is these key constructs that will be used in this research, to provide a scientific basis for cluster characteristics and comparison among the Dutch sub-clusters.
Knowledge Based Cluster framework
Key constructs Example of measurement items Example of main references
I - Institutional fabric
Social community homogeneousness of value systems Gordon and McCann (2000) Economic agents number of skilled individuals Feser and Bergman (2000) II Geographic closeness internal economies of scale Porter (1998)
III Economic linkages common customers Chesire and Gordon (1995)
IV "Common Glue"
Leadership explicit cluster leadership Rosenberg (2002)
Building blocks industrial culture Simmie and Sennett (1999) Communication rituals regular communication events Porter (1998)
Knowledge interactions
role of research centres and
universities Christensen (1997)
Professional rotations Inter-firm mobility Lorenz (1996)
Table 6 Knowledge based cluster framework and related key constructs for use in empirical cluster research
Source Morosini, 2004
In summary the NIC framework (National Innovative Capacity) of Porter & Stern (2001a, 2001b) provides the national elements of a cluster, its surrounding environment, and the specific conditions present. This clarifies the phenomenon underlying the Porter definition of a cluster, used in this research to uncover applicability of the cluster definition to Dutch Aerospace industry. On the other hand the model of the three ideal cluster types of Gordon & McCann (2000) provides insight at firm level in the different characteristics and their subsequent dynamics within a cluster. It also alleviates confusion caused by the mixed use of terms ‘agglomeration’ and ‘cluster’ in cluster literature, in the fields of both regional science and economics. This model is only used for additional clarification of the phenomenon and will not be used any further in this research. Finally the knowledge based framework of Morosini (2004) identifies key constructs to be used for empirical research, and will be used in this research for comparison of the three sub-clusters of the Dutch aerospace cluster.
3.2. Cluster identification methods
Cluster research can be conducted at various levels of analysis using different methodologies: micro level involving firms and firm interactions, meso level concerning inter and intra industry linkages, and macro level viewing industry groups in the broader economic structure, or at regional level (OECD Proceedings, 1999). However, here the methods will be addressed by their academic purpose, and not their economic level, in attempt to provide a clearer classification of methods being used. The following purposes to cluster research are argued: identification, testing and proofing of a certain method, characteristics, dynamics, and policy formulation. Only the methods used for
identification purposes will be addressed, because methods used in the other purposes, are common in business research (e.g. Diamond, SWOT, Case Study, Survey), or are already discussed in this paper (Porters’ NIC model), or encompass a combination of methods . Finally a table will be provided to categorise the purpose of the cluster research, the method used, the resulting data it produces, and the level of analysis it can be applied to. The produced table will place the methodology of this research in perspective with other empirical research methods, and supplements the justification of the chosen method in the methodology part.
In identifying cluster existence including broad overview of industry, industry groups and regional concentration, ‘cluster mapping’, Input / Output tables (IO) and Location Quotients (LQ) are used methods. These methods are largely quantitative, compared to qualitative research aimed at the characteristics and dynamics of clusters.
Cluster mapping uses aggregated national or regional industry employment data based on SIC and NAICS codes (US), or NACE codes (EU), to uncover the presence of certain businesses and their spread across continents, countries and regional areas. Examples of large scale international cluster mapping initiatives are the EU Cluster Observatory (www.clusterobservatory.eu) and the US Cluster Mapping (clustermapping.us) web sites. In some cases related interlinked industries are added to the original industry of interest to provide a more complete picture of the cluster size.
Input / Output tables (IO) are used to assess these inter industry linkages. IO uses industry trade linkage data available at national level, which indicates whether industry x trades products or services to industry y. Alternative linkages can be found in research at EU level in the form of
innovation linkages (OECD Proceedings, 1999). A load factor (L1 to L3) can be added to the IO linkage, to indicate the relative strength of the linkage of the industry to a certain industry or factor (e.g. Bergman & Feser, 1999, p. 4). This can be done with a principle component factor analysis formula (PCA), indicating the correlation between different industries and production factors.
Location Quotients (LQ) uses a comparison of industry employment data at regional level divided by regional manufacturing employment, with industry employment at national level divided by
manufacturing employment at national level (Crawley, Beynon, & Munday, 2012). This statistical method provides a relative employment density at regional level of a given industry.
Although a wide variety of quantitative research in identification of clustering is done at macro, meso and regional level, more qualitative methods are being used at micro level. In these qualitative researches the aim is to find data of how firms operate in clusters and spatial concentrations. Because this research is aimed at exploring the dynamics (involving qualitative data, e.g. social patterns) within and among clusters at firm level, the method of expert interviews and (mini) survey will be used. The Methodology section provides further explanation of the data collection method of choice.
Table 7 below provides a classification of the various methods according to their purpose. The first column represents the purpose of the empirical method represented in the second column. When considering ‘test and proof of methodology’ on an existing and currently used ‘identification’ method, “Mapping”, ‘IO’ and ‘LQ’ are likewise used (hence: ‘like above’). Same with ‘policy formulation’, in which also various methods tend to be used. The third column provides a
description of the type of results yielding from the method used in the second column. Finally the third column provides de economic levels applicable to the purpose of the research method in column one.
Cluster Research Methods
Purpose Method Results Level
Identification Industry concentration i.e. “Mapping”.
Regional industry employment levels Geographical map of spread.
Meso (-inter/intra- industry) Macro (industry groups) Regional
Input /Output tables (IO) e.g. factor analysis.
SIC / Load table.
Graphical presentation of linkages (direction and size).
Meso Macro Regional Location Quotients (LQ). Statistical estimation of
relative regional industry employment levels LQ (incl. SIC) tables and graphs.
Meso Macro Regional
Test and proof of methodology
Like above. Modified and new methods.
Meso Macro Regional Characteristics Case study e.g.
Diamond and NIS analysis,
SWOT analysis.
Factor conditions, Supply and demand conditions, Competitive environment, Innovation system.
Macro Regional
Internal strength & weaknesses / External opportunities & treads.
Meso Regional Dynamics Expert interviews,
Surveys.
Qualitative data e.g. social patterns.
Micro (-inter- firm level) Policy
formulation
Variation of above. Insight national and local situation, economic capabilities, resource and organisational needs, and innovation gaps.
Guidance for policy
Micro Meso Macro Regional
4. Research Question
Dutch aerospace industry is organized (among other forms) through three self-proclaimed clusters. This thesis investigates whether this is the actually the case and how it affects the industry’s competitiveness. One of the advantages of clustering is the interactions between firms to share resources. Especially in R&D intensive industries such as aerospace this sharing or combining of resources is present through R&D partnerships and alliances (Hagedoorn, 2002). According to the theory of Resourced Based View (RBV), in order for a firm to gain competitive advantage resources matter, and these resources need to be valuable, rare, inimitable, non-substitutable –VRIN- (e.g. Barney, 1991). A further development of RBV is Dynamic Capabilities (DC), arguing that capabilities to utilise these VRIN resources are of importance and not the resources by themselves (e.g. Newbert, 2007). In this sense broad attention is given to
knowledge and capabilities aspects, in which a company should be accessing, recombining and reconfiguring knowledge, internal and external to the firm (e.g. Teece, 2007). One way for firms to acquire external knowledge is through forming Strategic Alliances (SA), which are defined as ‘agreements characterized by the commitment of two or more firms to reach a common goal entailing the pooling of their resources and activities’ (Teece, 1992, p. 19), in which ‘the parties ... maintain autonomy but are bilaterally dependent to a non-trivial degree’ (Williamson, 1991, p.271). Hence important to discover whether the regional concentrations of Dutch aerospace firms can be seen as industrial cluster(s), and interesting to investigate whether within the assumed cluster(s) Strategic Alliances exist, and in what form.
Can the regional concentrations be viewed as clusters, do the interconnections involve Strategic Alliances, and if so in what form?
Research Questions (partials)
Clusters
1. Can the regional concentrations be viewed as clusters?
a. Do the firms perceive themselves as part of an -industrial- cluster? b. And if so are interconnections present between the firms?
c. What is the key driver: proximity to, inter firm connections with certain firms within the area, or the regional location itself?
d. What Spatial Externalities is the firm benefiting from?
e. Is there a Leader Firm present, and if so what are its merits (i.e. benefit to self)? Alliances
2. Do the interconnections involve Strategic Alliances, and if so in what form? a. What is the Rationale behind its formation?
b. What types of Governance Arrangements are predominant?
c. Do the firms have a Dedicated Alliance Management function present? d. What External Sources of Alliance support are being utilised?
Clusters
Clusters are geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (e.g., universities, standards agencies, trade associations) in a particular field that compete but also cooperate (Porter, 2000, p. 15).
1. a and 1. b Do the firms perceive themselves as part of an -industrial- cluster? And if so are interconnections present between the firms?
Proximity among firms and to local institutions improves effectiveness both of the individual firms and of the region as a whole. An advanced telecommunications infrastructure that connects remote firms is no substitute for face-to-face interaction when it comes to building trust
(Rosenfeld, 1995, p. 20). Regions nowadays offer cultural and community cohesion (Rycroft and Kash, 1994), arguably, if industrial patterns are adequately concentrated, regions also offer sufficient scale of production (Padmore and Gibson, 1998), and can be tied to certain (natural) resource endowments. The cluster can span a metropolitan area (Ellison and Gleaser, 1994), is sometimes linked to one or more major centres by history or a particular commercial advantage, or can comprise from middle-level jurisdictions (states, provinces), which are politically defined sub-national regions often derived from industrial history and geography (Padmore and Gibson, 1998). Inter-firm connections can stretch from static trading, or production links within industrial complexes, to more dynamic personal interactions within a social network were trust is an important element and the strength is dependent on the social embeddedness of the participants (Gordon and McCann, 2000). Hence it is interesting to discover the key driver for presence in a certain cluster.
1. c What is the key driver: proximity, inter-firm connections, or regional location?
A number of benefits or spatial externalities can emerge from local concentrations within clusters (briefly summed up by Fingleton, et al., 2005, p.285, 286): first by the existence of thick markets for specialised labour, knowledge and technology spillovers, and subsidiary trades (Marshall, 1920), second by internal or external increasing returns (Krugman, 1991, 1995; Fujita and Thisse, 1996; Fujita, Krugman, & Venables 1999), third because of access to information, or institutions and public goods, complementarities (products, services, marketing, or due to alignment of activities), competitive and peer pressure (Porter, 2000, p. 21 - 23), fourth boosting innovation and producing spin-offs of new firms within and among innovative milieus (Camagni, 1991) comprising of SME’s, universities, research centres, associations and government agencies, through a process of interactions and learning (Fingleton et al. 2005). Hence question is which externality is relevant to the members of the assumed cluster.
1. d. What Spatial Externalities is the firm benefiting from?
Leader firms are firms with the ability and incentive to make investments with benefits for other companies in the cluster, these benefits are created in three ways: by encouraging innovation, by enabling internationalisation and by enhancing labour pool quality (Nijdam and De Langen, 2003). 1. e Is there a Leader Firm present, and if so which one and what are its merits (i.e. benefit to self)?
Alliances
Alliances often improve the market power of the firm, because the partner is a customer or has distribution channels, because buying power can be combined, (Eisenhardt and Schoonhoven, 1996, p. 139), or specific knowledge-based resources as manufacturing or customer information is shared (Hamel et al., 1989; Shan, 1990; Teece, 1987). Technically innovative strategy demand a high level of competence in basic technology, require substantial resources and time, and require a level of legitimacy of the pioneering technologies, which a –customer –alliance partner (or its customers) can provide (Eisenhardt and Schoonhoven, 1996, p. 140)). A R&D partnership can pursue both cost-sharing and / or skill-sharing motives, which depend on the degree of capability heterogeneity, and minimum efficient scale in the market (Sakakibara, 1997, p. 147). Hence interesting to discover what rationale underpins the alliance forming.
1. a What is the Rationale behind the alliance formation?
Following governance structure literature (Ness & Haugland, 2005; Poppo and Zenger, 2002; Roath, Miller, & Cavusgil, 2002), two dimensions: contractual-based which emphasizes formalised legally binding agreement or contract, and relational-based which encompasses mutual trust and commitment (Lee and Cavusgil, 2006). Hence it is interesting to discover which governance arrangements are in place.
1. b What types of Governance Arrangements are predominant?
A dedicated alliance function acts as a focal point for learning and for leveraging lessons and feedback (Dyer, Kale, & Singh, 2001). Second, alliance management is a potential source of competitive advantage and value creation (Ireland, Hitt, & Vaidyanath, 2002). Furthermore, research suggest that as the number of alliances a firm is engaged in increases, a dedicated alliance function enhances the firm’s alliance success and value creation through alliances (Kale, Dyer, & Singh, 2002).
1. c Do the firms have a Dedicated Alliance Management function present?
Firms can use different third parties to mediate the lack knowledge of (small) firms to initiate or manage alliances, such as financial and legal experts (Heimeriks, 2004, p. 90).
