CAIRN-INT.INFO : International Edition

1 The concept of the Regional Innovation System (RIS) builds on an integrated perspective of innovation and acknowledges the contribution of the knowledge-production subsystem, the regulatory context, and the enterprises, to a region’s innovative performance. The regional approach stresses the importance of proximity to maximize synergies and spillovers and stresses the need to deepen collaboration and networking for innovation. The importance of creating mechanisms that facilitate technology transfer, resulting from the designs of R&D activities, into the production system, materializing in patents, appears to be a political priority.

2 The importance of turning designs/blueprints into profitable products points to the need to create platforms that promote interactions between R&D activities and the economy. It becomes a priority to create institutions and modify existing ones, due to the required interactions between universities, industries, governments, civil society, and the environment. In turn, Science and Technology Parks (STPs) emerge as infrastructures designed to co-locate university research centers and highly innovative firms to create an innovative milieu (Vásquez-Urriago et al., 2014; Vásquez et al., 2016; Díez-Vial, Fernández-Olmos, 2017; Hobbs et al., 2017). The appealing conceptual contours, along with the demonstrable effects of successful cases such as Silicon Valley, Cambridge or Grenoble, elevated STPs to the status of “panacea” and led to a boom in STPs across Europe, promoted by both universities and regional development agencies. These policy tools became a key element in operationalizing regional innovation policy (Vásquez-Urriago et al., 2016; Guadix et al., 2016).

3 The proliferation of STPs has assumed different models with associated different results that have raised doubts concerning the actual value added to these infrastructures. Hobbs et al. (2017) provide an extensive literature review that highlights the different angles of approach regarding STP, but also uncovers the need to clearly understand the definition, goals and key elements necessary for success (Guadix et al., 2016). Hence, despite the proliferation of STPs, the formula of STPs and their functions within an RIS remain unclear in practice, as well as how different mixes of functions affect performance (e.g. Albahari et al., 2013, study the difference between Technology Parks and Science Parks to understand if there are also differences in performance). Furthermore, from Hobbs et al. (2017), the interest in these types of infrastructures is geographically biased. A strand of literature uses UK and US examples because of data availability, whereas China and Spain have been the focus. Science parks are nowadays more appealing to emerging economies, which use these policy tools to artificially create a more favorable landscape for knowledge transfer and innovation.

4 We aim to contribute to the literature on three levels. The first level addresses the vagueness of definition and, specifically, the lack of depth in the literature discussing the key elements that assure the STP’s effectiveness (Guadix et al., 2016). We attempt to contribute to the literature by highlighting the different approaches to the definition of STPs, as well as compiling a set of functional definitions that includes infrastructural and location features, as well as the availability of advanced support services and the involvement and amount of resources allocated to the project. The second level focuses on the contribution of STPs to the RIS and addresses the case of follower regions. This link is not explored in the literature in an explicit way, although the different analysis on the Spanish case provides an interesting approach. Finally, we apply our functional definition to a set of 55 STPs across Portugal, Spain and the UK. This categorization of parks can be helpful in guiding policymakers and, hence, it was relevant to determine how different categories and functions are correlated with effectiveness.

5 Usually, empirical literature focuses on the UK and Spain or on firms’ performance, due to data availability. There are some examples targeting Spain, but most follow a qualitative approach (Hobbs et al., 2017). Thus, a broad comparison between a leading region and two countries whose innovation systems are under consolidation provides a clear contribution to understanding how functions vary across the maturity of the RIS. We construct a database and use cluster analysis to uncover patterns that reveal features correlated with a greater effectiveness of the STP, which may be particularly relevant to guide follower regions in their policy-making decisions and to consider their structural fragilities.

6 The paper is structured as follows. In Section 2 we review the literature on STPs, which highlights the profusion and vagueness of concepts. In Section 3 we discuss the functions of an STP and distinguish between the role and features of STPs within a consolidated RIS of a frontier region and a developing RIS in a follower region. In Section 4, preceding the conclusions, we use a two-step cluster on a 55 STP dataset to perform cluster analysis on 55 STPs located in Portugal, Spain and the UK.

STP: Literature Review

STP: A Still Ambiguous Concept

7 The first STP dates back to 1950 and was established at Stanford University in the United States. The Cambridge University STP, established in the 1960s, was the first European example. It was only in the 1980s that this concept became popular as a policy instrument designed to promote technological transfer between universities and other research facilities and firms. Storey and Tether (1998) accounted for 310 STPs in 15 European Union (EU) countries. This boom aimed to promote reindustrialization, regional development and synergies (Castells, Hall, 1994). Even though this policy instrument enjoyed increasing popularity, its concept is still blurred (Hansson et al., 2005) and creates confusion with concepts like technopole, technology park, innovation center, or even business park (Stockport, 1989). Nowadays, the geographical distribution of new STPs favors emerging economies (Huang et al., 2012) where potential impacts and innovation policy focus on accelerating structural change. A better understanding of STP is required and a better discussion towards a clearer definition is necessary. Table 1 summarizes the definitions, which can be grouped in four categories in relation to the main focus.

Table 1 – Different definitions of STP in the literature

Author Definition Main focus
International Association of STPs STP is an organization managed by specialized professionals whose main aim is to increase the wealth of its community by promoting the culture of innovation. Management organization and cluster linkages
UKSPA (1996) STP is a cluster of knowledge-based businesses, associated with a center of technology such as a university or research institute.
Link and Scott (2006) STP is a cluster of technology-based organizations that locate on or near a university campus to benefit from the university’s knowledge base.
Hansson et al. (2005) STP provides proximity between academic organizations and firms and to promote interactions and formal and informal links.
UNESCO (2011) STP is an economic and technological development complex that aims to develop and foster the application of high technology to industry. Planned intervention of regional development policy
Asheim and Coenen (2005) STP is a planned innovative milieu comprising firms with a high level of competences.
Stockport (1989) STP provides a comprehensive range of services to support NTBFs and accommodate firms with a high R&D level and low level of in-Park manufacturing. Services provided to NTBFs
Bakouros et al. (2002) STP is, as an infrastructure, close to universities, providing a range of services and, most importantly, conveys a technology transfer function.
United States Association of University STPs STP conveys planned land, buildings and a range of support services designed for R&D activities by public and private organizations and high technology firms.
Monck et al. (1998) STP is a property-based infrastructure with close links to a university and is designed to promote knowledge-based firms. Infrastructure
Link et al. (2003) STP is an infrastructural mechanism for transferring technologies from universities to firms.
Phan et al. (2005) STP is a property-based organization with an administrative center whose goal is to promote knowledge production and interactions that promote NTBFs.

Table 1 – Different definitions of STP in the literature

8 As a result of different perspectives on an STP’s definition and categories and intended functions, different defining focuses arise from the literature. A first group of definitions targets links, clustering and networking as main purposes for STPs and, hence, focuses on facilitating knowledge transaction between university and firms, but also fosters increasing spillovers. For example, Link and Scott (2006, p. 44) use the definition of the National Science Board that acknowledges STPs as a “cluster of technology-based organizations that locate on or near a university campus to benefit from the university’s knowledge base…”.

9 A second group of definitions arises from policymakers and attempts to be a recipe for regional development aimed at inducing or accelerating processes of knowledge intensification and, in follower regions, to catalyze structural change processes. Within this strand we find, for example, UNESCO’s (2011) definition that states that an STP is an economic and technological development complex that aims to develop and foster the application of high technology to industry. Also, Asheim and Coenen (2005) defined STPs as planned innovative milieus comprising firms with a high competence level. The role of these infrastructures is to provide proximity between academic organizations and firms, and to promote interactions and formal and informal links (Hansson et al., 2005).

10 A third group focuses on support for NTBFs and the role of STPs as services’ providers. In this line, Stockport (1989) highlights the infrastructural aspect of an STP, namely the close geographical proximity to universities, and the low ratio of buildings with high quality design and landscaping. In the “software” aspect, Stockport (1989) states that an STP must provide a comprehensive range of services to support NTBFs and accommodate firms with a high level of R&D and a low level of in-Park manufacturing. The support for NTBFs lies in the Bakouros et al. (2002) definition that describes STPs as an infrastructure to provide a range of administrative, logistic and technical services and convey a technology transfer function.

11 Another group of authors emphasizes the real estate perspective. Monck et al. (1998) defined an STP as a property-based infrastructure with close links to a university and designed to promote knowledge-based firms through the provision of technology transfer and business support services to firms. The US Association of University STPs (AURP) also emphasizes the property dimension, stating that an STP (in this case university owned) conveys planned land, buildings, and a range of support services designed for R&D activities by public and private organizations and high technology firms. Link et al. (2003, p. 1221) defined an STP as “an infrastructural mechanism for transferring technologies from universities to firms”. Also focusing on the infra-structural dimension, Phan et al. (2005) define STPs as property-based organizations with an administrative center whose goal is to promote knowledge production and interactions that promote NTBFs.

12 Clearly, there is not a consensual definition on STPs (Fukugawa, 2006; Hobbs et al., 2017), nor a clear perception of what the role of an STP is within an RIS and in the setting of a follower region. Mixed results also lead to doubts on actual effectiveness, namely, regarding the features associated with better performance (Guadix et al., 2016). Hence, we attempt to contribute to analyzing the role of STPs in follower regions using RIS concepts and from there we propose a functional definition and a categorization that can help policymakers.

The Doubts on Effectiveness

13 The use of an STP as one of the tools to structure RISs has been tested with very different outcomes. Founded on a somewhat linear perspective of innovation, STPs aim to promote an artificial innovative milieu that can increase knowledge spillovers, foster the emergence of NTBFs, and attract qualified FDIs, with expectations to play a role in the structural change of follower regions. Nevertheless, the literature presents us with different analyses of relative success without a clear conclusion. For examples with favorable perceptions of STPs, we find Jaffe et al. (1993), Maurseth and Verspagen (2002), Löfsten and Lindelöf (2002), Fukugawa (2006), Squicciarini (2008, 2009), Huang et al. (2012) and Albahari et al. (2013). However, one strand of literature is very critical about STPs, questioning their efficacy as a policy tool – e.g. Massey and Wield (2003), Castells and Hall (1994), Westhead (1997) and Bakouros et al. (2002).

14 Starting with the favorable articles, Jaffe et al. (1993), using patent citations, tried to analyze through an econometric study the importance of the geographical proximity of universities and firms, concluding that agglomeration plays a role in knowledge spillovers. In a world of open innovation and globalization, local and global seem to be complementary, with proximity still being relevant. Fukugawa (2006) states that NTBFs located on an STP have a higher propensity to participate in joint research with other institutions. Similarly, Löfsten and Lindelöf (2002), comparing the performance of tenants with off-site firms, positively assessed the performance of Swedish STPs and stated that the parks milieu had a positive impact on sales and employment. Squicciarini (2008, 2009) also acknowledges a superior performance of firms located in STPs. Huang et al. (2012) analyzed innovation performance in firms located in Taiwan Science parks and concluded that the effects are positive. Albahari et al. (2013), in their analysis of 849 firms located in 25 Spanish STPs, conclude that: (i) firms located in very new or longer established STPs show better innovative performance; (ii) the size of the STP and its management company positively affects the innovative performance of tenants, while services provision has no effect on firms’ achieving better results; and (iii) firms in less technologically-developed regions benefit more from location in an STP.

15 However, Massey and Wield (2003) characterized STPs as high-tech fantasies that actually had a small effect on promoting technology transfer, linking universities to industry or enhancing the performance and growth of NTBFs. Westhead’s (1997) survey on NTBFs on and off an STP concluded that there was no significant difference in terms of R&D intensity. Bakouros et al. (2002), in a rare analysis of STPs effectiveness in a follower country, assessed though a study on the link levels and networking induced in tenants, concluded that STPs in Greece presented poor cooperation and networking results. Hansson et al. (2005) attribute these poor results to the misconception of the innovation process presiding over the STP and which leads to neglecting support in terms of managerial skills to university spin-offs.

16 Different studies have challenged the actual level of impact that a STP can have in inducing and accelerating structural change in a region, especially in low-density territories. The proliferation of STPs without an appropriate strategy beyond the simplistic linear perception of innovation and without guaranteeing relevant R&D resources may explain the failure of several STPs. Furthermore, the overly optimistic expectations on the contribution of an STP, along with a low economic density, may also contribute to justifying the assessment of low performance in those STPs (Castells, Hall, 1994). Defining the functions of an STP and categorizing their different shapes may help to shed light on how to realistically set goals and define evaluation parameters. Hence, the next section attempts to systematize STPs’ possible functions within the RIS contribution and to identify key success factors.

An STP in RIS: A Functional Definition

Connecting RIS Features to STP Conceptual Building Blocks

17 Following Saviotti (1997), an innovation system can be defined as a set of actors and interactions that have as their main objective the generation and adoption of innovations. This definition recognizes that innovations are not generated only by individuals, organizations and institutions, but by complex patterns of interactions between them. Thus, within an innovation system, we can define their elements, the interactions, the environment and the frontier. The concept of an innovation system was conceived under the analysis of the National Innovation System (NIS) – e.g. Freeman (1987, 1995), Lundvall (1992).

18 The RIS concept is more recent and in large part derived from the former concept of the NIS. As referred to by Cooke (2001), the recent idea of RIS results from some convergence between the works of regional scientists, economic geographers, and analysts of NISs. The relevance of the RIS relates to the fact that proximity plays a major role in networks and interactions density; this is in general attributed to the tacit nature of a relevant part of knowledge. Tacit knowledge “is best shared through face-to-face interactions between partners who already share some basic commonalities: the same language, common ‘codes’ of communication and shared conventions and norms” (Asheim, Gertler, 2005, p. 293). The regional dimension also generates a more “focused” knowledge basis as a cumulative result of the clustering of economic and innovation-oriented activities. Asheim and Gertler (2005, p. 291) develop analogous arguments and do not hesitate to stress that “the more knowledge-intensive the economic activity, the more geographically clustered it tends to be”. An STP by definition implies a co-location of firms and of firms and other knowledge organizations.

19 Thus, if effective, an STP can be at the center of an RIS building process and play a major role in the provision of certain functions that an innovation system must ensure. Edquist (2005), in his attempt to systematize functions and activities that an innovation system is expected to ensure, considers a list of 10 functions covering the fields of knowledge inputs provision, demand side factors, provision of constituents (e.g. organizations and institutions) and of SI, and support services for innovating firms. Adapting Edquist’s (2005) list of 10 functions of the innovation system (R&D, competence building, formation of new product markets, articulation of user needs, creation and change of organizations, networking around knowledge, creating and changing institutions, incubating activities, financial resources, and consultancy services), we can consider central to the scope of functions of an STP their grouping in the order mentioned in the following subheadings.

Provision of R&D and Competence Building

20 Formal R&D activities are the main source of new knowledge creation and also of competence building. In an STP this function relies both on University R&D and Business R&D.

21 (i) Knowledge Creation: University R&D. The presence of university research centers in STPs is an extension of academic research, but at the same time this is potentially more applied because the co-location of university facilities and firms generates a closer perception of firms’ technology needs. Universities have been recognized for the potential to function as a major input for innovation and STPs have become the policy tool to bridge science to enterprises, strengthening linkages and accelerating knowledge transfer and diffusion as well as the economic exploitation of academic research and competences (Mowery, Sampat, 2004).

22 (ii) Knowledge Creation: Business R&D. Firms are the central organizations of the innovation system. An STP stimulates R&D activities led by firms through demonstration and collaborative effects and by facilitating access to technological inputs such as researchers and specialized equipment. On the other side, the presence of firms potentially generates a demand-pull rationale for academic research.

Networking Around Knowledge and Articulation of User Needs

23 Networking is what distinguishes an innovation system from a simple collection of elements. In a broad sense networking can include several mechanisms.

24 (i) Technology transfer. In the absence of market failures, technology transfer would be a market transaction and inappropriate to classify as networking. STPs frequently include organizations called technology transfer offices (TTO). TTOs are meant to favor knowledge transfer from universities or other research centers to firms. The co-location of academic research facilities and firms and the existence of brokerage entities such as TTOs inside the campus of an STP favor technology transfer by reducing transaction costs. In the long run TTOs and similar organizations contribute to building a market for knowledge. We use their presence as a proxy for the capacity and potential/ease of access to technology transfer measurement.

25 (ii) Networking (stricto sensus). In innovation processes networking corresponds to a process by which knowledge is transferred over collaboration, cooperation and long-run arrangements (OECD 2002, quoted by Edquist, 2005). The relevance of networking for innovation is usually associated with the reduction of uncertainty and the transmission of tacit knowledge. In an STP, co-location of firms, university or other research facilities favors interactions such as knowledge spillovers, informal networking, and formal networking. This perception of STPs as promoters of systemic industry-university cooperation and NTBFs (Asheim, Coenen, 2005) have put this type of infrastructure on the political agenda of regional innovation policies and contributes to explaining the proliferation of STPs across developed countries, despite increasing doubts regarding their effectiveness and added value. An STP can also enlarge networks by clustering external initiatives. Asheim and Coenen (2005) refer to the cases of innovative activities based on analytical knowledge, the clustering of R&D laboratories of large firms and governmental research institutes in planned STPs, normally located near the universities, which, combined, can all be seen as an example of a planned innovative milieu.

Creating and Changing Organizations, Incubating Activities, Financial Resources, and the Formation of New Product Markets

26 As pointed out by Edquist (2005), an innovation system must contain procedures for creating and changing organizations needed for the development of new fields of innovation, and which enhance entre- and intrapreneurship, which create new research organizations and policy agencies. An STP is an example of a complex organization devoted to the management of innovation, and often induces the creation of other non-profit organizations such as applied research centers and technology transfer offices. However, what distinguishes an STP from university or other public-owned facilities is its role in creating, attracting and clustering firms.

27 (i) Creation of New Firms and Formation of New Product Markets. An STP includes incubating activities through structured programs containing facilities, administrative and legal support. Incubation of NTBF is favored by this formal promotion and by demonstration and collaborative effects. An STP is perceived as a milieu favoring the perception of new technical opportunities and its transformation into economic opportunities. Squicciarini’s (2009) findings support the existence of spillovers and the positive role of incubators over those firms.

28 (ii) Clustering/Attraction of External Initiatives. An STP functions as an attractor for consolidated foreign/external firms that seek technological inputs for their R&D activities. An STP can attract external non-profit R&D activities, which can promote the consolidation of an RIS. An STP will increase the external visibility of the region and signal the scientific and technological potential. In successful cases this process is typically marked by increasing returns and becomes cumulative. According to Druilhe and Garnsey (2000), the Cambridge STP and the Grenoble infrastructure first succeeded in creating an innovative milieu by providing incentives to entrepreneurs to stay in the region and thereby develop their NTBFs. Afterwards, multinationals perceived the excellence of regional research centers and further established high-tech industries’ R&D to augment their knowledge base and capabilities (Druilhe, Garnsey, 2000). Since the 1990s, R&D FDI flows have increased significantly and changed their scope (e.g. Serapio, Dalton, 1999; Meyer-Krahmer, Reger, 1999; Kuemmerle, 1999; Gerybadze, Reger, 1999; Hedge, Hicks, 2008). The globalization of R&D activities conducted by the world’s leading firms is potentially increasing the role of STPs as attractors of foreign initiatives. More moderately public or non-profit R&D institutions are beginning to exploit the advantages of outward locations and follow the same principle of home-based augmenting and exploiting opportunities generated by high-skilled human capital reservoirs in other countries and regions.

Creating and Changing Institutions

29 The process raises the need to create new institutions and modify existing ones due to the required interactions between universities, industries, governments, civil society and the media, and the natural environment. As interactions increase, each component evolves to adopt some characteristics of the other institution.

Provision of Consultancy Services

30 The provision of business support services within an STP fosters business sophistication, especially for newly created firms. Business consultants that act inside an organization (e.g. an STP) will be more aware of technological dimensions and develop capabilities oriented to a specific set of firms, such as those in their early stages.

The Case of Follower Regions

31 The literature on STPs that addresses the case of follower regions is quite scarce, which stands in sharp contrast to the increasing popularity of this instrument among policy makers and the proliferation of STPs across Europe. Follower regions are those where the lower level of per capita GDP translates the structural deficiencies in systemic value creation through innovation, and they have low levels of technological activities. Moreover, these regions have a relative bias towards public R&D due to the weakness of business R&D and low technological intensity of economic activities. This structural situation creates something of a paradox: follower regions need a public push to increase technological levels and to break with “lock-in” barriers  [1] generated by economic activities that do not induce the development of technological capabilities. However, at the same time, the risk of low effectiveness of public efforts and academic research is higher than in frontier regions.

32 Hence, the implementation of STPs in follower regions will have some additional difficulties/specificity at this level.  [2] Here, an STP is a part of a necessary “public push” (Huang et al., 2012; Albahari et al., 2013) for R&D activities to break the inertia of the “lock-ins”. Nevertheless, this “public push” must not follow a university-driven perspective, but instead a systemic approach that aims to catalyze the different territorial dynamics, namely regional demand for technological inputs. An STP following a systemic approach will also contribute to focusing resources on a reduced number of scientific domains and economic sectors. This need is more pressing in follower regions where resources are far more limited than in a frontier region. The scarcity of scientific resources, human capital and other technology-intensive activities leads to lower attractiveness, which has implications for the importance of the STP instrument capable of effectively promoting startups in new activities.

33 Follower regions not only have the challenge of fostering innovation, but they also have more severe structural change needs. In frontier regions, an STP can expect to attract external firms and simultaneously stimulate start-ups and spin-offs. Follower regions have a scarcer presence of high-tech firms; entrepreneurial resources can be concentrated in sectors that generate a low demand for technological services and for knowledge. Thus, concerning entrepreneurial resources, follower regions have a severe challenge: a need to ensure structural change and the emergence of new and more technology-intensive sectors; however, at the same time, proximity demand for new activities and other impulses to new entrepreneurship (e.g. intrapreneurship) are weaker than in frontier regions. New entrepreneurship through the creation of NTBFs must be a central target for STPs in follower regions and is crucial to the STP’s effectiveness. Both the low-managerial skills of universities regarding technology transfer and the NTBFs support each other with a flawed and linear conception of the innovation process (Albahari et al., 2013; Bakouros et al., 2002; Quintas et al., 1992), and may account for a lack of effectiveness in creating NTBFs. An STP in follower regions must establish structured programs supporting NTBFs to follow successful international methodologies.

34 Moreover, follower regions’ structural deficiencies imply that the success of STPs in creating NTBFs depends on demand pull policies creating the technological market for these. Proximity demand for new activities must include opportunities generated by public demand and implies good coordination with the public sector;  [3] this is valid for frontier regions and is crucial for regions where a private demand for new goods and services is weaker. Finally, the effort to aid the development of emerging sectors should lead to a concentration of resources rather than a profusion of initiatives of a wide sectoral spectrum. Hence, a well-defined focus on a knowledge basis is needed due to the scarcity of technological inputs.

35 STPs may also have an important role in the clustering of external initiatives and could be a major scope for RIS implementation in follower regions. Frontier regions have built RIS in an international context where R&D activities largely relied on endogenous initiatives. Even though multinationals’ global R&D investments are still mostly focused on developed countries (Meyer-Krahmer, Reger, 1999), these flows are now being extended to less developed regions (e.g. Indian ICT cluster in Bangalore – Kumar, 1996). Follower regions, due to the lower level of income and the lower technology level, face a problem of lack of visibility and attractiveness, even though publicly-driven R&D and the investment in higher education have allowed some follower regions to develop important human-capital stocks and excellence in several scientific domains. In a context in which follower regions often have poor external visibility, an STP can signal the scientific potential of a follower region and contribute to the increase in external visibility of a region’s potential and to the attraction of multinationals’ R&D and technology centers. The assessment on the effectiveness of STPs as instruments for fostering innovation and structural change is far from finished.

36 Many STPs, namely in European countries, are of recent creation, and two main sets of considerations must be taken into account. The first relates to the vagueness of the STP concept. The second relies on the different economic and social contexts in which the STP is implemented. We have attempted to refine the concept of STP by discussing its functions and its potential effectiveness in assuring these functions. In its minimal definition an STP follows a science push perspective and assumes that knowledge production access leads to innovation and its economic exploitation. That is, an STP would be a platform from which the knowledge and basic research outputs of universities would be tapped by firms that would undertake applied and experimental research and innovate (Quintas et al., 2002). Even when considering the importance of networking, STPs are still implemented following a science push approach. Löfsten and Lindelöf (2005) state that it is assumed that providing the STP infrastructure and the knowledge base is enough to enable firms to establish the necessary networks and develop. Westhead (1997) synthesized this perspective, claiming that STPs were based on the assumption that innovation is a result of scientific research and that parks are the perfect “habitat” to catalyze the transformation of pure research into innovation and production. Even though the literature is focused on frontier and fast catching-up regions, the poor results of different STPs have highlighted the need to balance the science push perspective with demand pull considerations (Watkins-Mathys, Foster, 2006). If the return on R&D must be maximized, especially with public R&D, Watkins-Mathys and Foster (2006) argue that policy makers and STP managers need to pay more attention to entrepreneurship.

37 Hence, facilities oriented to the creation of NTBF and the ability to attract external firms must be underlined. Many European follower regions are making strong advances in their endowments of technological inputs, but they still have a lack of real innovation systems because interactions between higher education and academic research outputs and the technological activities of existing firms are weak. However, STPs in follower regions can be seen as a major contribution to the consolidation of an RIS and act as a major impulse to structural change. To be successful in that perspective, STPs should integrate in their conception the functions of promoting university technological spin-offs and attracting and clustering external R&D initiatives from multinationals, but also from public and nonprofit institutions.

38 In follower regions demand pull mechanisms are weaker since regional economies’ specialization is characterized by industries locked in paths with limited absorptive capacity. Thus, STP activities should include some public support to create and attract new economic activities. Furthermore, in follower regions STPs may convey a larger role in interlinking and articulating regional infrastructures. Quintas et al. (1992) have previously pointed out the flaws in the conception of such parks in terms of both the linear conception of innovation and the closed perspective of this infrastructure. This “enclave” perspective neglected the meaning of articulating STPs with other infrastructures and firms off-park and in the RIS in general.

39 Table 2 summarizes the discussion of this section.

Table 2 – The functional interpretation of an STP in the context of a follower region

Functions / Characteristics Contribution to the (Regional) Innovation System Specificities for “follower” regions
Knowledge creation: University R&D Creation of technological opportunities following a technology push rationale; closer perception of firms’ technology needs. Because of the weakness of the demand pull rationale, academic R&D is carried out under scientists’ bottom up agendas. STPs contribute to the need for a “push” for R&D activities, and to a more strategically-oriented and more applied effort for academic R&D.
Knowledge creation: Business R&D STPs stimulate R&D activities led by firms by demonstration and collaborative effects and by facilitating the access to technological inputs. Firms’ access to technological inputs is often limited by an information and assessment gap. The STP offers information and access to scarce technological inputs.
Technology transfer STPs favor technology transfer and interactive learning. STPs can promote a market for knowledge and reduce transaction costs. The knowledge market and technological services market are barely existent in follower regions. STPs can be a major impulse to fill those gaps, bridging science and knowledge creation to firms’ technological needs.
Networking Co-location of firms and of firms and the university favors interactions. STPs function as brokers to overcome low cooperation levels. The technological relatedness of co-located firms may assume importance. We use this assumption to identify a proxy with available data.
Creation of new firms STPs are usually seen as a milieu that favors the perception of new technological opportunities and its transformation into economic opportunities. Creation of NTBF is a main impulse to structural change. Through a structured and publicly-supported program for incubating new technological firms, STPs can provide an emergent entrepreneurial basis to new sectors and overcome “lock-in” effects coming from former entrepreneurial resources.
Clustering/attraction of external initiatives An STP functions as an attractor for foreign firms that seek technological inputs for their R&D activities. STPs can attract external nonprofit R&D activities. STPs can signal the scientific potential of the region, in a global context, follower regions often have poor external visibility. Besides attracting companies and other external players, the STP can actively seek to cluster firms and resources around an external anchor.
Business support services The provision of business support services within an STP fosters business sophistication for newly created firms. Business consultants are more aware of technological aspects. The incidence of services provided by the STP or public agencies has to be larger since the business services market is less organized and extended in follower regions.
Common infrastructures STPs generate some agglomeration economies by the existence of common infrastructures and amenities. High quality, low building construction ratio. No specificity for follower regions
Land for business location STPs provide land for R&D centers of firms and for NTBF in its early stages. Besides R&D centers and NTBFs, STPs in follower regions may also agglomerate medium high- and high- tech production facilities.
Restricted access / focus Restricted to knowledge-intensive activities. Some sectoral focus or scientific domain focus generates a degree of specialization or related diversity, favoring interactions. STPs can present a more hybrid set of sectoral or scientific priorities. STPs should promote selectivity to concentrate the few existing resources around a related variety of activities.
Community involvement The STP’s contribution to RIS will be increased by the involvement of other players. Local or regional governments and external nonprofit agencies make the STP a node of the RIS. Given the low level of demand and fewer scientific resources, the divide between universities and the economy is greater. STPs are promoted by regional authorities hoping to accelerate structural change.

Table 2 – The functional interpretation of an STP in the context of a follower region

Uncovering STP patterns: Correlating performance, functions and regions

40 Here, we apply cluster analysis to a dataset of 55 STPs located in Portugal, Spain, and the UK. Our sample was built based on the information published by an STP’s national associations regarding its affiliations (TEC Parques, APTE and UKSPA). We retrieved information on a set of proxies for each of the functional features, as well as locational and performance proxies that we match in Table 3. We use occupancy rate as a proxy for performance in the sense that the success can be measured by the capacity to attract and breed NTBFs. It is a rough approximation, but meaningful since in many STPs in follower regions we observe either difficulties in attracting/generating NTBFs or a degradation of standards to fill in the space.

Table 3 – Identifying proxies to the functions of an STP and to other location/infra-structural features

Functions/Characteristics Variable for cluster analysis Comments
Knowledge creation: University R&D Number of academic R&D units located in the park Number of researchers not available in many cases
Knowledge creation: Business R&D Presence of R&D centers of private companies Number of researchers and of firms not available in many cases
Technology transfer Co-location of TTO and/or formal program for transferring technology. Commercialization of University R&D
Networking Scientific/sectoral domain focus. Number of sectors with 20 or more firms Focus on specific scientific or sectoral domain favors interactions
Creation of new firms Existence of incubators with technological entrepreneurship support programs
Clustering/attraction of external initiatives Number of well-known FDI/Foreign agencies A well-established STP functions as an attractor for other companies wishing to tap that knowledge/innovation reservoir.
Business support services Patent offices. Venture capital Advanced services
Land for business location Total area Area Park
Micro location Proximity to the university.  [4] Urban location
Community involvement Main promoter. Number of co-promoters Different kind of promoters. Universities, Local governments, Public Agencies, others.
Period of operation Time period (years) since creation STPs have long maturation periods relating to the firm’s presence
Region Type of Region. % R&D Expenditures in GDP We consider three categories based on the developmental level: convergence, phasing out/phasing in, competitiveness.
Country Country Characteristic “country” will be relevant for clusters composition if the National Innovation System effect is strong.
Performance Occupancy rate. Total number of firms No standardized and widely available measure of innovative output is available. Nevertheless, the quality of tenants can be inferred from their economic activity.

Table 3 – Identifying proxies to the functions of an STP and to other location/infra-structural features

Source: Own elaboration based on reports and publications by APTE (SP), TECPARQUES (PT) and UKSPA (UK) and web search

Methodological Considerations – Cluster Analysis

41 Cluster analysis seeks to identify a set of homogeneous groups that minimize within-group variation and maximize between-group variation. We aim at grouping a set of STPs to identify distinctive features that may help to refine the concept and pinpoint features that are either associated with a higher success or a potential dynamo role within an RIS. Our sample comprises a total of 55 STPs located in Spain (24), Portugal (8), and in the UK (23) – see Table 4. For each one we retrieved and constructed a set of categorical variables based on information collected from the reports and publications by APTE (SP), TECPARQUES (PT) and UKSPA (UK), as well as from the websites of each of the parks. The data set comprises information regarding the indicators in Table 3, following, as much as possible, the functional characterization we have proposed for STPs. There is a wide range of methods for cluster analysis. We opted to use the SPSS Two Step cluster procedure, which more adequately handles categorical data and simpler binary data (see the appendices).

Table 4 – Main descriptive statistics

Variable Categories N Share (%)
Country Portugal 8 14.5%
Spain 24 43.6%
UK 23 41.8%
Type of location Urban 13 23.6%
Outskirts 42 76.4%
Proximity to university (distant) Yes
Date of creation Before 1980 3 5.5%
1981-1985 4 7.3%
1986-1990 5 9.1%
1991-1995 12 21.8%
1996-2000 12 21.8%
2001-2005 16 29.1%
2005 - onwards 3 5.5%
Area Less than 10ha 16 29.1%
10ha-20ha 5 9.1%
21ha-30ha 4 7.3%
31ha-40ha 3 5.5%
More than 40ha 27 49.1%
Incubation (no) Yes
Business Park area Yes
Univ. R&D units Less than 5 17 30.1%
5-10 26 47.3%
More than 10 12 21.8%
Private R&D units Yes
Scientific domain Physics/ICT 6 10.9%
Health/Biotech 8 14.5%
Energy/environment 4 7.3%
Other 2 3.6%
Miscellaneous 34 61.8%
Design 1 1.8%
Explicit R&D commercialization Yes
Patent office Yes
Venture Capital Yes
Occupancy rate Less than 25% 7 12.7%
25%-50% 9 16.4%
51%-75% 12 21.8%
More than 75% 27 49.1%

Table 4 – Main descriptive statistics

Step 1 – Pre-Cluster

42 Pre-cluster consists of a sequential clustering approach in which records are individually analyzed and a decision to merge with a previously formed cluster or to start a new cluster is based on compliance with a threshold distance. The algorithm forms pre-clusters and constructs a modified cluster feature (CF) tree, which summarizes information on a given cluster, and the cluster feature tree consists of nodes further decomposed into a number of leaf nodes and leaf entries. A leaf entry represents a final sub-cluster. Each entry is recursively guided by the closest entry in the node to find the closest child node and descends along the CF tree. Upon reaching a leaf node, it finds the closest leaf entry in the node. If the record is within a threshold distance of the closest leaf entry, it is absorbed into the leaf entry and the CF of that leaf entry is updated. Otherwise it starts its own leaf entry in the leaf node.

Step 2 – Cluster

43 The algorithm used the pre-clustering information resulting from step 1 and groups the set of pre-clusters using an agglomerative hierarchical clustering method in a number of clusters compatible with the information of Akaike Information Criterion (AIC). Then, we validated our analysis following three criteria: (i) cluster size; (ii) meaningfulness; (iii) criterion validity. To provide certainty about the robustness of our results we applied the Kruskall-Wallis Chi-square test to assess the significance of the differences between the clusters retrieved (see Appendix 3, available on request).

Cluster Membership Results: Descriptive Analysis

44 The Akaike Information Criterion reaches its lowest level for a set of six clusters indicating this to be the best solution in statistical terms for our cluster analysis (see Appendix 1, available on request). Hence, our cluster analysis retrieves the following six clusters (see Table 5):

Table 5 – Cluster membership

Cluster 1 - small, urban and university-oriented Cluster 2 - medium, company and university- oriented
- Aston STP (UK)
- Ciudad Politecnica de la Innovacion (ES)
- Liverpool STP (UK)
- Madan Park (PT)
- Parc Cientific Barcelona (ES)
- Parc d’innovació La Salle (ES)
- Parque Cientifico de Madrid (ES)
- TecMaia (PT)
- Begbroke STP (UK)
- Cambridge STP (UK)
- Oxford STP (UK)
- Parc Cientifico Alicante (ES)
- Parque Cientifico y Tecnologico de Leganes (ES)
- Parque Tecnologico de Ciencias de la Salud de Granada (ES)
- TagusPark (PT)
- University of Cambridge - West Cambridge Site (UK)
Cluster 3 - high-tech and medium high-tech company oriented Cluster 4 - distant from universities and city centers
- Avepark (PT)
- Biocant (PT)
- Coventry University Technology Park (UK)
- Loughborough Science and Enterprise Park (UK)
- Parque tecnologico de Asturias (ES)
- Parque Tecnologico y Logistico de Vigo (ES)
- Southampton STP (UK)
- Tecnoalcalá (ES)
- University of Warwick STP (UK)
- Wolverhampton STP (UK)
- York STP (UK)
- Cambridge Research Park (UK)
- Kent STP (UK)
- Liverpool Innovation Park (UK)
- Longbridge Technology Park (UK)
- Madeira Tecnopolo (PT)
- Parc Cientifico-tecnologico de Gijon (ES)
- Parc Tecnologic del Vallés (ES)
- Parkurbis (PT)
- Parque Balear de Innovacion e Tecnologia (ES)
- Parque Cientifico e Tecnologico Albacete (ES)
- Parque Tecnologico Castilla y Leon (ES)
- Parque Tecnologico Walqa (ES)
- Parque Tecnoloxico Galicia (ES)
Cluster 5 - large, periphery-oriented and co-promoted by university Cluster 6 - large, periphery-oriented and co-promoted by university
- Aberdeen Science and Energy Park (UK)
- Aberdeen Science and Technology Park (UK)
- Manchester STP (UK)
- Cartuja 93 (ES)
- Chesterford Research Park Cambridge (UK)
- Colworth STP (UK)
- Cranfield Technology Park (UK)
- Edinburgh Technopole (UK)
- Parque Tecnologico de San Sebastian (ES)
- 22@barcelona (ES)
- Parque Tecnologico de Álava (ES)
- Parque Tecnologico de Andalucia (ES)
- Parque Tecnologico de Bizkaia (ES)
- Valencia Parc Tecnologic (ES)

Table 5 – Cluster membership

45 Using this segmentation of our sample, we apply descriptive statistics to identify the main distinctive features between clusters. In Appendix 2 (available on request) we present the cross-tabulation results, while presenting here a short summary and our analysis.

Cluster 1 – Small, Urban, and University-Oriented

46 These parks comprise relatively small infrastructures (eight out of nine cases are below a 10ha area) and are all located in proximity to the university. With the university as the main promoter in six out of nine cases and as a co-promoter on the remaining three, they are a small-scale operation mostly restricted to NTBF. A stronger focus is placed on a model that functions as an extension to the university and where the presence of companies is, overall, restricted to start-ups in incubation. Seven out of nine of these parks have no area for enterprise location, apart from start-ups. The proximity to the university provides a reasonable deployment of university R&D units or shared access to R&D laboratories. The model of these parks, focusing more on the university perspective than on technology transfer, has repercussions for the functional features provided. Technology transfer offices are available in less than half of these nine parks and the commercialization of R&D is absent in seven, a number identical to the absence of patent offices. Venture capital is not available on site in any of these nine parks. Occupancy rate is relatively high, and the type of tenants is, in the vast majority, operating in medium- or high-technology sectors. This conception follows a university-centric perspective that places a lower emphasis on technology transfer and on the linkages to private firms, hence diminishing the technology-pivoting role of the STP. Though the scale may be adequate on different stages across regions with a good university, the economic valorization of scientific inputs and, thus, the actual impact of these parks within the RIS is limited. These parks follow a university-driven perspective lacking the systemic approach that is of utmost relevance to a contribution to the consolidation of an RIS in a follower region setting. This can be a good starting point for follower regions.

Cluster 2 – Medium, Company and University-Oriented

47 These parks constitute a reference in terms of an STP (e.g. Cambridge STP, Oxford STP). The majority of these eight parks are near the university, but outside the urban perimeter, and comprise an area larger than 40 ha in six out of eight cases. These parks combine an area of university R&D units with a space for companies’ installations capable of accommodating both incubating companies and large companies’ R&D centers or high-tech small production units. These parks are characterized by a higher degree of specialization in terms of scientific domain, the highest occupancy rates, the highest concentration of both university R&D resources, and private companies’ R&D resources. All of the eight STPs have technology transfer programs and offices and some have instituted patent offices. Six out of eight cases provide direct commercialization of R&D, meaning that the university sells its expertise to private companies in line with one of the features of the successful models of Stanford and MIT in the US. Unlike these two examples, most parks in our sample have no on-site operating venture capital provider and this severely constrains technological entrepreneurship and start-up growth. These parks are located in regions with strong R&D investment levels (the NUT2 average is 2.4% of GDP, with Cambridge reaching 4.25%). These capabilities and the awareness allowed by, for instance, the Cambridge University’s STP led to the attraction of several multinationals R&D centers, which, in turn, created a cumulative effect on the consolidation of the RIS. This cluster of parks is the one that has attracted more and more significant FDI. These are also parks located in frontier regions or fast catching-up followers that have opted for concentrating resources around a narrow set of scientific fields and in close association with private companies. Hence, the STP of Cluster 2 gathers the best examples of STPs across Europe, both in functional terms and in effectiveness terms.

Cluster 3 – High-Tech and Medium High-Tech Company-Oriented

48 These parks constitute a more heterogeneous group. They tend to be outside the urban perimeter and in seven out of 11 cases are also distant to the university. Again, the university is one of the main promoters, but now municipalities are also a major player in supporting and creating these places. With different sizes ranging from the less than 10 ha to above the 40 ha thresholds, the occupancy rate is generally high (above 75%). These parks have a large accommodation area for enterprises and an onsite incubator in more than 60% of the 11 parks. The smaller scale of the university R&D resources deployed, combined with the higher distance to a university, indicates a smaller flow of scientific inputs to the park’s activities. This is also associated with a smaller relative presence of private R&D units. Most of these parks have neither an explicit technology transfer program nor a patent office, and R&D services are only available in a more technological, rather than a scientific, sense. In what concerns risk capital, three of these parks have on-site providers. These characteristics are closer to a model of a technological park with some science, but whose focus is on accommodating high- and medium high-tech companies in an excellence infrastructure, rather than on promoting the connection of a university’s resources to private companies. Some of these STPs provide common infrastructures and act to maximize synergies among tenants. These facilities are closer to the concept of a technological park, though in some cases they are aiming to evolve into a STP.

49 The role of these parks within an RIS may be enhanced through a closer connection with universities and a stronger emphasis on technology transfer. This cluster is an example of the attempt to use STP to structure RIS in follower regions with weak technological capabilities and which are undergoing structural change processes. This is the case of the Norte and Centro regions of Portugal or Galicia in Spain, where STPs have been used with moderate success. These STPs lack a strong and effective commitment of universities in deploying R&D resources. The focus on a university-driven perspective instead of a systemic approach has led to low attractiveness of both local and foreign firms. Unlike the STP of Cluster 1, the approach here was based on a more extensive concept with the deployment of these parks in a large area of terrain. Despite the scientific quality of some research units (e.g. Avepark in Portugal has the European Excellence Institute for Tissue Engineering and Regenerative Medicine with 200 researchers from 13 countries and state-of-the-art facilities), in the context of follower regions with a thin layer of more knowledge intensive activities and with a low demand for technology this approach may be less adequate than the approach followed in Cluster 1. The pressure to occupy land and justify a public push has led some of these STPs to downgrade and relax their focus to increase occupancy. The STP of Cluster 3 constitutes an example of how a public push disregarding a systemic conception may, in a context of a follower region with scarce scientific resources and a low percentage of high-tech firms, be inadequate as a first stage of a public push. These parks should function as second or third stage interventions following the consolidation and need to expand of the type of STP of Cluster 1.

Cluster 4 – Distant from Universities and City Centers

50 These parks present important distinguishing features in relation to the previous clusters. The different model is perceivable in the dropping of the term “science” in almost all the labeling, but it is evident when analyzing the characteristics. These parks are developed relatively distant from universities and city centers and occupy an area that is either small (four cases below 10 ha) or very large (eight cases above the 40 ha threshold). The concept underlying these facilities seems closer to a somewhat selective business park that aims to attract high-tech companies and is mostly in territories where local economic activity is scarce in that typology. This, associated with an emphasis on technology, may account for the low occupancy rates registered in most of these parks. These parks are also mainly promoted by others, rather than by universities, as they are rooted where scientific capabilities are scarce. The dispersion of resources through a miscellaneous focus, the absence of incubation facilities in ten out of 13 parks, a reduced number of university R&D units and a small and questionable number of private R&D units contribute to a possibly illusory label of business parks. This also creates a distraction in terms of focus that, instead of inducing innovation, leads to a set of vacant business parks that detract from the location of less knowledge-intensive businesses and are not sufficiently attractive for knowledge-intensive firms. Hence, we observe a combination of a weak local R&D basis with functional gaps in the parks. Many of these parks are situated in follower regions that are attempting to transform their structural profile in favor of a more knowledge-intensive and thus innovation-prone economy. Nevertheless, these parks are not only located in regions with weak RIS, particularly with low technological capabilities, but they are also detached from universities and diffuse in focus. This scattering of resources and the non-involvement of the community has led, in most cases, to “white elephants” with zero contribution to the RIS and with no effect upon the visibility or the attractiveness of the region. These parks are very weak in functional terms and quite distinguishable from the parks in Cluster 3 for their lack of effective support from the university, which adds other problems to their success in follower regions.

Cluster 5 – Large, Periphery-Oriented and Co-Promoted by University

51 This cluster includes nine large parks, many of them labeled “science”, but with little science present. This comprises parks of relatively large areas (six above 40 ha and none below 10 ha), which have been built in peripheries and at some distance to a university. The university does not appear to be the main promoter but, unlike in Cluster 4, the university is now a co-promoter in many of the cases. These parks were created earlier and generally have no scientific/economic activity focus but they register a high occupancy level. We observe an intermediate level of university R&D resources being deployed and private R&D performed by tenant firms. Nevertheless, these infrastructures appear not to perform technology transfer (observed in eight out of the nine parks), nor to stimulate the commercial linking of a university’s R&D resources to private firms (eight out of nine have no explicit program for R&D service commercialization), nor did any of the parks have a patent office or a privileged access to risk capital. Thus, despite the upgrade in relation to the parks in Cluster 4, these parks’ current model still lags behind the one in Cluster 2. In relation to Cluster 3, there are some similarities of model with these parks differing in terms of area (usually larger), proximity to a university, promoter (the university is not the main promoter) and R&D resources. Cluster 5 parks have a higher concentration level of R&D resources (also in regional terms, the average is the second highest at 1.9%) and constitute technology parks with more knowledge-intensive activities, also partially justified by the context of being inserted in a region with an economic structural profile that is richer in knowledge-intensive activities. This minimizes the weaknesses of a follower region RIS, though the need for a systemic approach is vital to raise the STP to a status of an actual beacon of excellence.

Cluster 6 – Promoted by Municipalities and Co-Promoted by University

52 If we reduce the number of clusters to five, this cluster would be merged with Cluster 5. Members are parks that have a higher rate of R&D transfer programs and an intermediate level of R&D resources but have a lower occupancy area and are inserted in convergence regions. Yet the functional similarities to the previous cluster are significant, but the distance to the university, the high importance of municipalities as the main promoter, the lower specialization level, and the urban location of 40% of the parks, were sufficient for Akaike’s information criterion to indicate the presence of six clusters. The lower performance in terms of occupancy may be related to the deployment of only an intermediate level of R&D resources. The concentration of resources in scientific fields has created a critical mass and obtained visibility potentiated by the use of an STP as an attractor to R&D FDI and as a clustering driver for knowledge- intensive activities – e.g. the Parque Tecnologico de Bizkaia has several pharmaceutical firms on site.


53 STPs have been presented as the panacea for follower regions seeking to catch up and accelerate costly structural change processes. The effect of Cambridge’s success has led many policymakers to invest in STPs to foster innovation vis-à-vis respective productive implementation. However, in territories with deficient R&D capabilities, these investments have proven to be highly controversial. The strong focus on science in follower regions where the link of firms to universities is weak and where the technological market is shallow has led to poor results. There are many potential reasons, but we focus our discussion on three topics.

54 Firstly, the concept of STP remains blurry and narrow in the sense that the focus is on the infrastructure and not on the functions. From a systemic perception of innovation, we try to contribute by adapting Edquist’s function of an RIS to devise the functions of a STP in an RIS. An STP can be a privileged tool to structure and rationalize an RIS, contribute to the concentration and accumulation of resources, and function as a beacon that, on the one hand, signals technological capabilities and, on the other hand, attracts multinational R&D centers.

55 Secondly, we address the particular case of follower regions. Follower regions face the challenge of conducting structural change processes that break technological “lock-ins” and build new competitive advantages around knowledge and innovation. Additionally, many follower regions not only endure the harsh processes of structural change, but also depart from a low regional level of scientific resources and technological demand. The weak technology push and the limited and often diffuse scientific push translate into an unstructured and ineffective RIS. We believe that an STP can be an effective tool within the implementation of the necessary public push and work as a focal point in the RIS and thereby contribute to overcoming the scattering of resources. An STP can function as a structuring and rationalizing element of the RIS, not only focusing resources, but also signaling capabilities and hence directly contributing to overcoming the poor visibility of follower regions. This function is of the utmost importance and transforms the STP into an attractor for technology-intensive FDI, which may lever the structural change and the construction of the RIS.

56 Finally, we used cluster analysis on a set of 55 STPs to try to identify patterns that could shed some light on more suitable approaches to STPs in follower regions. Results seem to indicate that in follower regions there are thin or non-existent high-tech clusters of firms and limited scientific inputs. In terms of policy recommendation, we consider that starting from a more moderate approach and in close association with universities, as with the parks of Cluster 1, might be a better solution in a first stage. To have a significant role in the RIS, an STP must widen its density and evolve to a layout similar to an STP in Clusters 3 and 5. However, as observed in the STP of Cluster 3, if the regional economic profile is scarce in terms of technology-intensive activities, as happens in most follower regions, the approach that consists of a vast area reserved for technology firms creates pressure to increase visible results (e.g. occupancy rate) and leads to the loss of focus and the downgrade of tenant requirements. The STP of Cluster 5, located in regions with a considerably more technology- intensive profile, presents good occupancy rates, and a higher proportion of medium- and high-technology firms attract R&D FDI. It is also important to highlight Cluster 4. The popularity of the STP concept has also led to the proliferation of functionally poor parks labeled as STPs. These parks, mostly in Cluster 4, are basically “premium or good land sites”, lack critical mass in terms of technology inputs and local demand of more technology intensive firms, fail to attract activities, and present low levels of effectiveness (occupancy rate and technological profile of tenants).

57 We conclude that an STP is a valid and useful policy tool in a public push to build RIS in follower regions, so policymakers need to be aware of this fact. The STP may have significant impacts in concentrating and focusing resources and, hence, create critical mass and cumulative processes of clustering that can enhance the effects of the public science push with a demand pull (possibly created through the orientation of public demand for technology, for instance, e-government). In follower regions where the RIS is too thin, the over-ambitious extensive conception present in the STP of Cluster 3 may be inadequate since it develops a large area and creates political pressure for large-scale results. This has led to those STPs losing focus and limiting their role and effectiveness as a structuring element of a follower region’s RIS.

58 The success and structural change impact of STP requires a systemic approach that also creates the setting for the STP to function as an attractor of R&D FDI and to explore significant cost advantages and the increased tendency towards R&D globalization. This may be an important catching-up opportunity for follower regions which are also interested in increasing the return on public-led R&D, but which have tended to disperse resources and pursue dreams that are unmatched by internal capabilities. Thus, STPs can be important tools in developing RISs in follower regions, and policymakers need to be aware of this fact, but a lot more effort is needed, as it is crucial to increase scientific resources and networking, as well as define the functional features in accordance with the local context and RIS limitations.


59 In this paper, we discuss the definition of STPs and their proliferation throughout Europe, and we also analyze their role in RIS. More specifically, we question the effectiveness of STPs as policy instruments in the case of follower European regions compared to leading regions, since STPs have been a key policy instrument in promoting the clustering of science and high-tech firms in a particular territory, and have been perceived as the answer to accelerating structural change and innovation performance within a region. Indeed, this policy instrument, despite the variety of conceptual approaches underlying its use, has gained much popularity and has benefited from relevant investments across follower European regions.

60 We start by providing a literature review on the known topic of science parks, insisting on the blurriness of definitions and on the doubts concerning their effectiveness. We then try to establish a functional definition based on the concept of RIS and of a follower region, and we offer an empirical analysis based on a clustering method and which leads to a typology of STPs, considering a set of 55 science parks in the UK, Spain and Portugal to highlight key functions commonly associated with better performing parks.

61 STPs have been presented as the panacea for follower (European) regions seeking to catch up and accelerate costly structural change processes. Yet, in territories with scarce R&D abilities, these investments have proven to be highly controversial. An STP can be an effective tool within the operation of the needed public push and can work as a crucial point within the RIS. These may also contribute to, for example, overcoming the poor visibility of follower regions, being an attractor for technology-intensive FDI. Results of the empirical analysis point towards showing that in follower regions there are thin or non-existent high-tech clusters of firms and limited scientific inputs. In terms of policy recommendations, we suggest starting with a more moderate approach by small, urban and university-oriented parks. Then, in a second phase, STPs should move in the direction of high- and medium high-tech company-oriented parks and in the direction of large, periphery-oriented co-promoted by university parks.

62 It results from the analysis that there is a clear need for an assessment of public policy effectiveness and that biased results may result from the mismatch between the objectives underlying public policy intervention, the design of the instruments to address it (in this case, STPs), and the functions of the STPs which can be modeled according to the different objectives (more relevant in terms of an innovation system build-up, where there is a lack of critical mass or as an instrument to attract and foster the clusterization of high-tech firms, including FDI, as an attractor of talent and a more urban planning approach).

63 To sum up, STPs can be vital tools in developing RIS in follower regions throughout Europe, and policymakers need to be aware of this fact, but a lot more effort is needed, crucial for increasing scientific resources and networking as well as defining the functional features in line with the local context and RIS limitations.

Appendix 1 – Determination of optimal number of clusters (AIC’s results)

tableau im1

Appendix 2 – Some descriptive statistics (partial)

TwoStep Cluster Number Total
1 2 3 4 5 6
Country 0 3 1 2 2 0 0 8
1 4 3 3 7 2 5 24
2 2 4 6 4 7 0 23
Total 9 8 11 13 9 5 55
Note: 0- Portugal; 1- Spain, 2- UK
TwoStep Cluster Number Total
1 2 3 4 5 6
Location 0 8 1 0 0 2 2 13
1 1 7 11 13 7 3 42
Total 9 8 11 13 9 5 55
Note: 0- urban location; 1- outskirts location
TwoStep Cluster Number Total
1 2 3 4 5 6
Proximity to University 0 9 7 4 1 9 1 31
1 0 1 7 12 0 4 24
Total 9 8 11 13 9 5 55
Note: 0- proximate to a University; 1- distant to the University
TwoStep Cluster Number Total
1 2 3 4 5 6
Date of creation 0 0 1 0 0 2 0 3
1 1 0 1 0 1 1 4
2 0 0 0 1 3 1 5
3 1 1 5 1 2 2 12
4 1 6 1 2 1 1 12
5 5 0 4 7 0 0 16
6 1 0 0 2 0 0 3
Total 9 8 11 13 9 5 55
Note: 0- before 1980, 1- between 1981 and 1985; 2- between 1986 and 1990, 3- between 1991 and 1995, 4- between 1996 and 2000; 5- between 2001 and 2005, 6- after 2005.
TwoStep Cluster Number Total
1 2 3 4 5 6
Main promotor 0 6 6 9 0 0 0 21
1 1 0 2 0 5 4 12
2 1 1 0 5 1 1 9
3 1 1 0 8 3 0 13
Total 9 8 11 13 9 5 55
Note: 0- university, 1- municipality, 2- other public agency, 3- others
TwoStep Cluster Number Total
1 2 3 4 5 6
Number of promoters 0 1 6 2 7 0 0 16
1 1 1 5 0 4 1 12
2 3 1 0 1 0 4 9
3 4 0 4 5 5 0 18
Total 9 8 11 13 9 5 55
Note: 0- none, 1- one, 2- two, 3- three or more.


TwoStep Cluster Number Total
1 2 3 4 5 6
area 0 8 1 4 3 0 0 16
1 1 0 2 1 1 0 5
2 0 0 1 1 2 0 4
3 0 1 2 0 0 0 3
4 0 6 2 8 6 5 27
Total 9 8 11 13 9 5 55
Note: 0- less than 10ha, 1- between 10ha and 20ha, 2- between 20ha and 30ha, 3- between 30has and 40ha, 4- above 40ha.
TwoStep Cluster Number Total
1 2 3 4 5 6
Incubation 0 7 6 7 3 3 5 31
1 2 2 4 10 6 0 24
Total 9 8 11 13 9 5 55
Note: 0- presence of incubation facility, 1- absence of incubation facility.
TwoStep Cluster Number Total
1 2 3 4 5 6
Business park 0 2 8 11 13 9 4 47
1 7 0 0 0 0 1 8
Total 9 8 11 13 9 5 55
Note: 0- includes business park area, 1- absence of business park area.
TwoStep Cluster Number Total
1 2 3 4 5 6
University R&D units 0 2 0 6 7 0 2 17
1 3 2 5 6 9 1 26
2 4 6 0 0 0 2 12
Total 9 8 11 13 9 5 55
Note: 0- less than 5, 1- between 5 and 10, 2- above 10.
TwoStep Cluster Number Total
1 2 3 4 5 6
Private R&D units 0 6 8 8 8 9 4 43
1 3 0 3 5 0 1 12
Total 9 8 11 13 9 5 55
Note: 0- presence of private companies R&D laboratories, 1- absence of private companies R&D laboratories.
TwoStep Cluster Number Total
1 2 3 4 5 6
Scientific Domain 0 2 3 1 0 0 0 6
1 1 1 3 1 2 0 8
2 1 0 2 0 1 0 4
3 0 0 2 0 0 0 2
4 5 4 2 12 6 5 34
5 0 0 1 0 0 0 1
Total 9 8 11 13 9 5 55
Note: 0- physics/ICT, 1- Health/Biotech, 2- Energy/Environmental Sciences, 3- Other, 4- Miscellaneous, 5-Design.
TwoStep Cluster Number Total
1 2 3 4 5 6
Explicit R&D commercialization 0 2 6 4 1 0 0 13
1 7 2 7 12 9 5 42
Total 9 8 11 13 9 5 55
Note: 0- explicit sale of R&D services by the university, 1- absence of indications regarding explicit sale of R&D services by the university.
TwoStep Cluster Number Total
1 2 3 4 5 6
TTO 0 4 8 3 1 1 2 19
1 5 0 8 12 8 3 36
Total 9 8 11 13 9 5 55
Note: 0- presence of a technology transfer office or a similar program/office, 1- absence of technology transfer function.
TwoStep Cluster Number Total
1 2 3 4 5 6
Pat Office 0 2 2 1 0 0 0 5
1 7 6 10 13 9 5 50
Total 9 8 11 13 9 5 55
Note: 0- presence of a patent office or a similar program/office to manage IPR, 1- absence of a patent office.
TwoStep Cluster Number Total
1 2 3 4 5 6
Venture Capital 0 0 0 3 0 0 1 4
1 9 8 8 13 9 4 51
Total 9 8 11 13 9 5 55
Note: 0- presence of a risk capital office or a similar program/office, 1- absence of risk capital institution.
TwoStep Cluster Number Total
1 2 3 4 5 6
Occupancy rate 0 1 0 2 4 0 0 7
1 0 1 0 5 1 2 9
2 4 1 1 3 2 1 12
3 4 6 8 1 6 2 27
Total 9 8 11 13 9 5 55
Note: 0- less than 25%, 1- between 25% and 50%, 2- between 50% and 75%, 3 – above 75%.

Appendix 3 – Kruskall-wallis Chi-Square Test results

Test statistics a, b

Proximity to university Occupancy rate Year of creation Main promoter Number of promoters
Asymp. Sig.

Test statistics a, b

a. Kruskal Wallis Test
b. Grouping variables: Ward method.

Test statistics a, b

Area Incubation AAE Units of I&amp: D Univ Private R&amp:D Unit Scientific domain
Asymp. Sig.

Test statistics a, b

a. Kruskal Wallis Test
b. Grouping variables: Ward method.

Test statistics a, b

Explicit provision of services I&amp:D TTO Pat office Venture capital Ward method
Asymp. Sig.

Test statistics a, b

a. Kruskal Wallis Test
b. Grouping variables: Ward method.

Test statistics a, b

Country Localization
Asymp. Sig.

Test statistics a, b

a. Kruskal Wallis Test
b. Grouping variables: Ward method.


  • [1]
    In many follower regions, the challenge of structural change is closely linked to the development of the innovation system. Although followers, these regions sometimes face the need to change their pattern of specialization and end up trapped between the need to foster NTBFs and the mismatch of these with the existing economy.
  • [2]
    For a discussion on the specificities of follower regions about the implementation of an RIS see Almeida et al. (2011).
  • [3]
    For instance, e-government and both the health and the educational sectors make a strong demand for ICT.
  • [4]
    Proximity is a constructed binary variable that measures if the university and STP distance is more or less than 5k.

Science and Technology Parks (STPs) have been a key policy instrument in promoting the clustering of science and high-tech firms in a particular territory and have been perceived as the solution to accelerating structural change and innovation performance within a region. Hence this policy instrument, despite the variety of conceptual approaches underlying its use, gained much popularity and benefited from significant investments across European regions and especially follower regions, which were attempting to catch up. This paper addresses these issues by discussing the many interpretations of a science park and attempting to contribute to a unified definition by studying the role of these policy instruments within the innovation system framework, and by analyzing a set of 55 science parks in the UK, Spain and Portugal to highlight key functions commonly associated with better performing parks.
JEL Codes: O30

  • Science and Technology Parks
  • Regional Innovation Systems
  • Innovation System Framework
  • Cluster Analysis
  • European Regions
  • Follower Regions.


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Alexandre Almeida
Faculdade de Economia
Universidade do Porto (Portugal)
Óscar Afonso
Faculdade de Economia
Universidade do Porto and CEFUP(Portugal)
Mário Rui Silva
Faculdade de Economia
Universidade do Porto (Portugal)
This is the latest publication of the author on cairn.
This is the latest publication of the author on cairn.
This is the latest publication of the author on cairn.
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