Public Policies and Private
Innovations:
assessing geographical biases in public R&D incentive
acts
University of Groningen
Faculty of Economics and Business
MSc. Thesis
Thijs van Werven – S2173980
MSc. BA Strategic Innovation Management
Supervisor: Dr. F. Noseleit Co-Assessor: Prof. Dr. P.M.M. de Faria
2
Abstract
Many countries adopt a public incentive program in order to stimulate firms to perform R&D to remain competitive. This study focuses on the WBSO, a Dutch incentive program, and examines the relation between project innovativeness and allocated public funding. Additionally, the moderating effects of the regional aspects urbanization and university are taken into account. The data originates from a Dutch consultancy company which intermediates in WBSO applications. Furthermore, this study applied a text-analysis method provided by experts in order to assess the innovativeness of project descriptions. Hence, this study adds to extant research on innovativeness by generating a more refined measurement. The results of this study show that innovativeness is positively related to the amount of funding firms receive and that a low degree of urbanization strengthens this affect. In addition, this study also finds that being proximate to a university also increases the amount of funding firms receive when applying for the WBSO. Additionally, the results of this study provide research implications as well as practical recommendations for intermediates.
3
1. Introduction
Recently, Dutch state secretaries Keijzer and Snel claimed that the Netherlands are the European innovation leader due to a policy that incentivizes firms to perform innovative projects (Rijksoverheid, 2019): the WBSO act 1. The secretaries state that the WBSO is successful in fulfilling its purpose - providing public financial incentives to stimulate corporate R&D activities - and strengthens the competitive position of the entrepreneurial climate. The Netherlands, as a member state of the OECD, implemented this research & development (R&D) stimulating act to support and redirect firms towards innovative behavior. Public policies aimed at stimulating private R&D are currently attracting increased attention from governments, public institutions and academic researchers (Edler & Fagerberg, 2017). Concerning the importance of governmental innovation support, the OECD (2018, p. 23) stresses the following:
“In line with Sustainable Development Goals, governments are seeking to redirect technological change from existing trajectories towards more economically, socially and environmentally beneficial technologies, and to spur private science, technology and innovation investments along these lines.”
The purpose of these policies is increasing positive externalities and creating societal benefits through corporate innovations. Following Schneider & Veugelers (2010), it appears that many European countries aim to support their young, innovative SMEs with fiscal policies to amplify national economic growth and the diffusion of positive externalities. The foundational trait of innovation related public policies is to allocate public funding towards innovative private projects in order to overcome market failures (Castellacci & Lie, 2015; Marino, Lhuillery, Parotta & Sala, 2016; OECD, 2018). Traditionally, innovation policies held a national focus, which often favored the developed regions in countries (Freeman, 1995). As a result, core regions increased their competitive position while other regions scarcely received attention and support of the implemented policies. Currently, the focus has shifted to a regional approach in many OECD countries, including the Netherlands (OECD, 2014). Through the adoption of a regional approach, governments preserve and strengthen the position of core regions and enable the development of lagging regions. The vast differences between these regions affect policy choices, due to the fact that each region is unique and has distinct needs. Policies are now used as a tool to reinforce regional characteristics and to convey benefits to a national level. Accordingly, the OECD (2014) suggests countries to actively promote connections between innovative peripheral firms and clusters in order to connect innovative firms with innovation networks. Furthermore, Revest & Sapio (2012) suggest that a regional policy focus is better suited to address market failures and regional coordination. Hence, innovative projects in peripheral regions are increasingly important for aiming to leverage positive spillovers. Furthermore, contemporary research on innovation policies mainly focuses on: (i) effectiveness of tax subsidy in order to determine the effect on private R&D spending by firms
4
(Brouwer, Den Hertog, Poot & Segers, 2002; Lokshin & Mohnen, 2010; Poot, Den Hertog, Grosfeld & Brouwer, 2003; Rao, 2016) and (ii) the possibility of the crowding-out effect due to government market intervention, which affects the balance in the economy (Almus & Czarnitzki, 2003; Czarnitzki, Hanel & Rosa, 2011; Mazzucato & Semieniuk, 2017).
Currently, literature on public innovation incentives is indecisive on the role of regions. On the one hand, Lee & Drever (2014) state that it is a matter of perception that companies in peripheral regions feel that public funding gaps exist. On the other hand, such gaps do exist in private capital markets, which results in a dependence on public funding for peripheral firms (Martin, Berndt, Klagge & Sunley, 2005). Additionally, Oughton, Landabaso & Morgan (2002) argue that firms in peripheral locations are unable to absorb public funds in a similar intensity compared to agglomerated regions. Hence, a regional innovation paradox emerges, in which these regions require more funding to remain competitive, but are not able to utilize the funding efficiently (Oughton et al., 2002). Since the Dutch Ministry of Economic Affairs stated that the WBSO act is successful (Rijksoverheid, 2019), it may be of interest to examine if this act truly is unbiased in allocating funding towards innovative firms, despite their geographical location.
However, determining the innovativeness of these projects through their descriptions can be difficult due to an absence of a theoretically supported parameter. Current literature places innovativeness on a continuum with two extremes, but does not offer a more nuanced perspective (Assink, 2006). In order to analyze and deduce the innovativeness of R&D projects, one needs to be familiar with the industries, the technologies used and current trends. Therefore, experts of Consultants
Firm provide a professional assessment of descriptions of innovative projects, written by their peers for
WBSO applications, upon which this research will be based. By means of the expert-based assessment an objective perspective on innovativeness can be given based upon experience, rationality and an analytical approach (Weible, 2008).
The aim of this research is therefore twofold. By means of text-analysis, an innovativeness score will be generated as a result of an expert-based approach which will function as a parameter. Furthermore, the distribution of the WBSO act will be assessed, in which potential regional biases will be examined as well as their effect on the relationship between firm innovativeness and project performance. Therefore, this study posits the following two research questions:
(1) How can text-analysis help in defining an innovation score as parameter for R&D projects? (2) To what extent does firm location affect public subsidizing for innovative firms?
5
location on the accessibility of public finance by means of an expert-based text-analysis. The other objective of this study is to verify if the WBSO act performs as satisfactory as the Dutch government states it does, by examining the effect of the degree of innovativeness on allocated funding, as well as the interaction with geographic location to analyze public bias in allocation of subsidies.
The remainder of this study is structured as follows. In section two I delve deeper into the theoretical foundations of this study. Section three elaborates on the WBSO and the institutional setting in the Netherlands. In section four I explain the rationale behind the hypotheses, followed by the methodology of this study and an explanation of the data in section five. In section six, I present the findings of this research. Section seven continues with the discussion and conclusion of the main findings of this study, followed by the implications, the assessment of this study’s limitations and an outlook on future research.
2. Theoretical framework
Investments in R&D projects are distinct from the more generic investments made by firms due to two major factors: (i) wage costs (Czarnitzki, 2006) and (ii) uncertainty (Mazzucato & Semieniuk, 2017). Firstly, the wage costs of the personnel working on R&D projects are a major expense. R&D projects require high-skilled workers and the associated higher wages. Moreover, these costs rise even more as a result of relatively long project durations (Czarnitzki, 2006). The tacit knowledge incorporated in these employees functions as the foundation of R&D projects and directly influences project success (Hall, 2002). Therefore, fluctuations in the R&D department’s budget have a direct effect on the knowledge base of the company. When organizations do not retain their investment pace, they might be forced to lay off employees, reducing their expenses while negatively affecting their knowledge base and project success (Hall, 2002). Secondly, R&D project outcomes are inherently uncertain for firms that venture into the unexplored. Resultantly, high failure rates for R&D projects are common across all industries (Mazzucato & Semieniuk, 2017). Svensson (2008) argues that especially the early stages in R&D projects are characterized by high uncertainty. Uncertainties often lead to higher costs, which are a major determinant for managerial decisions whether to invest in R&D (Lokshin & Mohnen, 2012). Companies can be deterred to privately invest in their R&D projects due to the outcome uncertainty (Czarnitzki, 2006; Hall, 2002), and the amount of capital needed to start such projects (Czarnitzki, 2006). These risks increase when accounted for firm size (Lee, Sameen & Cowling, 2015). Project risks are higher for SMEs, due to their lack of resources and scale, which stops these ventures from investing in multiple projects (Lee et al., 2015; Nishimura & Okamuro, 2011).
6
upon existing capabilities, they are often more familiar to the firm and consumer. Hence, such innovations frequently perform well when released in the market (Assink, 2006; Kleinschmidt & Cooper, 1991). Contrarily, if a project could be seen as radical on the newness continuum, it will be more difficult to forecast potential market success (Assink, 2006). According to Avlonitis & Salavou (2007), the majority of research focuses on the relationship between the degree of innovativeness and market performance. This does not correspond with the intended goals of innovation subsidies, where emphasis is placed on stimulating R&D efforts and company development, instead of project performance (Brancati, 2015; Czarnitzki, 2006; Poot et al., 2003). Resultantly, all projects new to the firm are encouraged by subsidy providers, but the assessment of the degree of novelty remains underwhelming. Hence, to assess the degree of innovativeness of projects, an innovation parameter has to be created in order to rate projects on a newness continuum. Through an expert-based approach, novelty might be categorized in a more nuanced manner instead of being placed on either end of the continuum (Assink, 2006; Danneels & Kleinschmidt, 2001). Moreover, experts frequently fulfill an intermediate role in the application process for subsidies (Martin et al., 2005) and are therefore well-experienced in describing and assessing the innovativeness of projects. This can be of help with analyzing if and how project innovativeness might be intertwined with public subsidy allocations.
2.1 External financing of Research and Development
Financing of innovative R&D projects can roughly be divided in two streams: (i) internal and (ii) external. Internal financing is the use of private financial resources, such as retained revenues, in order to perform R&D projects. It is often difficult to achieve internal financing for SMEs due to a lack of private capital (Revest & Sapio, 2012). Additionally, maturity of the firm also impacts the possibility of internal financing. Younger companies are often characterized by their innovativeness, but do lack a track record as well as the corresponding retained earnings on top of their small scale (Schneider & Veugelers, 2010). Due to the scarcity of internal financial resources, SMEs are often dependent upon external parties in order to acquire the necessary funding (Schneider & Veugelers, 2010). External capital can be acquired through capital markets or public subsidies. However, market failures are frequent, due to a mismatch between the investors and investees (Lee, Sameen & Cowling, 2015; Revest & Sapio, 2012). Typically, the firms that encounter more difficulties while trying to obtain external funding through capital markets are start-ups. This can be attributed to their lack of a track record and absence of collateral value, which subsequently increases the costs of external capital for these firms and depreciates their chances to perform innovative projects (Hall, 2002).
7
(Martin et al., 2004). Secondly, the outcomes of innovative R&D projects can be distinguished as codified knowledge (Tödtling & Kaufmann, 2001), which reduces the investor’s potential for monopolistic appropriability (Hall, 2002). If the appropriability of an investment appears uncertain, an investor’s impetus to be exposed to financial risks and invest drastically declines. Thirdly, information asymmetry plays a role in the alteration of capital market conditions. Investors lack in-depth project or sector knowledge, making it highly difficult to assess the quality and potential return on investment (ROI) of an innovative project (Brancati, 2015; Lee et al., 2015). Informational opaqueness increases with the duration of R&D projects, as a result of a higher possibility of unforeseen circumstances which could alter the ROI adversely. However, if the duration of the project is short-term, external financiers assess it as relatively safer to invest in (Hall, 2002; Takalo & Tanayama, 2008). Although this often implies that the project is more of an incremental nature. Finally, many small firms trying to obtain external funding often find that venture capital investments are too large for their projects which further increases their risks (Takalo & Tanayama, 2010). Additionally, venture capitalists tend to invest in ongoing R&D projects (Mazzucato & Semieniuk, 2017). This creates another barrier in attracting capital during the crucial early stages (Svensson, 2008). Early stage funding entails more risks compared to later stages, when uncertainty decreases and risks can be soundly assessed. However, the decline of uncertainty in the later stages does not solve the issue of the financial constraints in the crucial early stages (Mazzucato & Semieniuk, 2017). The costs of capital market financing are typically too high for firms lacking private capital due to the distinct perspectives on potentials risks (Hall, 2002). Resultantly, many small firms encounter a multitude of disadvantages and difficulties while trying to obtain external financing, which eventually makes these ventures dependent on public policies to overcome the finance gap they encounter (Martin et al, 2004).
8
González, Jaumandreu & Pazó, 2005). Additionally, the societal ROI is often higher compared to the private gains for firms and compared to the costs of such policies (OECD, 2018).
Many OECD countries adopted similar innovation policies in order to stimulate R&D projects by means of financial incentives, such as tax reductions and subsidies (Dechezleprêtre et al., 2016; Rao, 2016; Morgan, 2017). Indirect public support measures are widespread due to their applicability, non-discriminatory character and their success rate: they reach the desired effect of incentivizing private investments in R&D (Rao, 2016). The disadvantage of indirect support is that the opportunities of governmental steering are limited. Contrarily, direct subsidies enable governments to select their preferred innovative projects and steer the development of societal benefits (Dechezleprêtre et al., 2016). However, policies based upon direct subsidies might contain flaws. Direct subsidies might be exposed to a selection bias due to the fact that the subsidies are manually assigned to projects (González et al., 2005; Jaffe & Le, 2015). According to Jaffe & Le (2015), this bias may result in prematurely picking winners of fiscal benefits, on the basis of certain characteristics in their subsidy application such as location or industry. This parallels to the location bias argument, which poses that certain core regions receive significantly more attention of policy makers compared to peripheral or lagging regions concerning the incentivizing of R&D projects (Doloreux & Dionne, 2008; Jaffe & Le, 2015).
2.2 Regional Innovation Systems
Lagging geographical regions are frequently analyzed through a regional innovation system (RIS) perspective (De Bruijn & Lagendijk, 2005; Doloreux & Dionne, 2008; Morgan, 2017; Oughton et al., 2002). Research on innovation systems aims to analyze innovations as the outcome of an interdependent system. According to this stream, public policy incentive structures and externalities, such as knowledge spillovers, determine the rate and direction of innovation trajectories (Patel & Pavitt, 1994). The innovation system approach attempts to explain differences in innovativeness between different countries in terms of rate and direction. However, international differences are less significant compared to regional differences (Oughton et al., 2002). The RIS approach considers regional characteristics as important determinants which might influence the rate and direction of innovations in a region. Asheim & Coenen (2005, p. 1177) described it as: “The regional innovation system can be thought of as the
institutional infrastructure supporting innovation within the production structure of a region”. Due to
the regional perspective, policies can focus on the focal region’s needs, strengthening its position by means of tailored policies and incentives (Freeman, 1995). Moreover, the implementation of a specifically determined regional focus aids lagging peripheric regions to receive public support enabling these regions to catch up with core regions, which effectively reduces disparities between regions (De Bruijn & Lagendijk, 2005).
9
Drever, 2014; Oughton et al., 2002). Oughton et al. (2002) state that firms in lesser developed often lack absorptive capacity for public financial support due to the low average baseline of investments. Subsequently, additional public funding does not yield a higher innovative output. This results in under-utilization of dedicated public funds by firms in these regions. Lee & Drever (2014) support this by emphasizing the lack of social networks and infrastructure needed to successfully put such external capital to use. Next to that, the authors are critical about public support due to the fact that not all enterprises are created equal. Some firms in lagging regions will not put public support to good use, or as intended, no matter the incentive provided by governments. Lee & Drever also pose that direct subsidies are the preferred incentive, because of its controlled approach. Furthermore, lesser developed regions are often characterized by a relatively high number of SMEs, scarcity of specialized and financial services and difficulties in attracting high-skilled or educated workers (Doloreux & Dionne, 2008; Tödtling & Kaufmann, 2002; Tödtling & Trippl, 2005). All the aforementioned characteristics fulfill a major role in determining the ability of the lesser developed regions and the corresponding firms to innovate.
Geographical location also influences what the collaborative opportunities are for SMEs. SMEs seldom cooperate with scientific partners in innovative projects (Tödtling & Kaufmann, 2001). On the contrary, SMEs deem interfirm relations as more important or fitting for their activities, and therefore miss out on the expertise of dedicated centers for science. Larger firms rely less on their region and do interact more frequently with scientific partners (Tödtling & Kaufmann, 2001). Moreover, regional differences with respect to the presence of venture capitalists (VC) affects possibilities of getting external funding for innovative projects (Lee & Drever, 2014; Martin et al., 2005; Nauwelaers & Wintjes, 2002). The presence of VC’s is often more common in clustered environments, due to the interconnectivity in such networks (Doloreux & Dionne, 2008). Clusters often receive extra recognition by public policies as a result of their potential benefits of interfirm learning, knowledge spillovers and emerging complementarities (Oughton et al., 2002). Engel (2015) debates that the rate of innovation is generally higher in clusters and firms located in clusters have increased accessibility to capital, knowledge and high-skilled personnel. Thus, close proximity to external financiers enables clustered firms to more easily direct the information flow towards risk capital providers. This is also known as the ‘spatial proximity effect’, which suggests that venture capitalists have a certain reach in which they are active (Martin et al., 2005). Peripheral regions are often strongly affected by this effect, due to the scarcity of venture capitalists in these regions. Resultantly, public policies fulfill an important role due to their non-discriminative nature concerning location and in which industry firms are active.
10
provided by the Ministry of Economic Affairs and Climate (Rijksdienst voor Ondernemend Nederland, 2019) and provided by the Netherlands Enterprise Agency (RVO). The main benefit of the WBSO for companies is the reduction of wage costs for R&D personnel, incentivizing firms to actively invest in R&D activities in order to reach a socially desirable level of investment in R&D (Czarnitzki et al., 2011; Poot, et al., 2003). The WBSO is characterized by a low threshold for companies to apply and a broad range of fiscal incentives (Brouwer et al., 2002). All Dutch firms performing R&D activities within Europe can apply for the WBSO subsidy for future R&D activities, on the condition that their innovative activities are aimed at the development of processes, software or products new to the company or perform technical-scientific research (Poot et al., 2003; Rijksdienst voor Ondernemend Nederland, 2019).
According to research, the WBSO act is effective in incentivizing firms to invest in R&D projects (Brouwer et al., 2002; Lokshin & Mohnen, 2010; Poot et al., 2002). Therefore, the main goal of the WBSO to counter underinvestment in R&D projects and increasing opportunities of positive externalities seems to be achieved (Brouwer et al., 2002; Lokshin & Mohnen, 2010; Poot et al., 2002). Currently, the use of the tax credit measure is decreasing compared to previous years (Rijksdienst voor Ondernemend Nederland, 2018). According to the RVO (2018) this could be ascribed to the current state of the Dutch economy. In order to encourage the use of the WBSO subsidy, the RVO starts to provide additional fiscal benefits for start-ups, lowering the costs for R&D even further. The majority of WBSO-requests are development projects (96%) while only 4% is scientific research (Rijksdienst voor Ondernemend Nederland, 2018). All firms in the Netherlands can apply for the WBSO, regardless of their size or budget. However, 97% of the WBSO’s utilizers were SMEs in 2017, showing the importance of public support for innovativeness in SMEs (Rijksdienst voor Ondernemend Nederland, 2018). Enterprises which do perform R&D but do not apply for the WBSO often state that it is the result of the definition for R&D wielded by the RVO (Dialogic, 2019).
11
4. Hypotheses development
This section explains and discusses the relationships between the concepts which were discussed in the theoretical framework. Firstly, the relationship between project description innovativeness and the success in applying for the WBSO subsidy will be discussed. Afterwards, the focus shifts to the degree of urbanization of the region and the potential interaction it might have on the relationship between project description innovativeness and project funding. Thirdly, the effect of proximity towards universities and the first relation will be examined.
4.1 Project innovativeness
The WBSO act is implemented by the Dutch government with the purpose to stimulate companies to perform R&D activities. By means of the WBSO, the Dutch government aims to tackle impeding factors in the development of innovations, such as financial constraints (Brancati, 2015; Czarnitzki, 2006; Revest & Sapio, 2012; Schneider & Veugelers, 2010). The projects that apply for the WBSO differ in their degree of innovativeness, since firms need to work on R&D projects which are new to the firm in order to be applicable (Poot et al., 2003; Rijksdienst voor Ondernemend Nederland, 2019). Consequently, the degree of innovativeness varies severely among the projects; some are regarded as
12
incremental whereas others are radically changing what is familiar. Incremental projects might be performed by firms without governmental support since they build on already existing firm capabilities and knowledge. Moreover, incremental innovations are more familiar and often perform well in the market (Kleinschmidt & Cooper, 1991). On the contrary, more radical innovations often require the creative destruction of owned capabilities, as famously argued by Schumpeter, but do offer an opening to a new dominant position in the market or the creation of a new market (Danneels & Kleinschmidt, 2001) and aim to fulfill the needs of the future (Jansen, Van den Bosch & Volberda, 2006). Additionally, highly innovative projects are linked to new technological trajectories, unique benefits and to strong managerial support (Kleinschmidt & Cooper, 1991). Especially the latter can be regarded as important, since this could help to convince others to invest in the project, despite the uncertainties and financiers short-termism (Hall, 2002; Mazzucato & Semieniuk, 2017; Takalo & Tanayama, 2008). Research by Kleinschmidt & Cooper (1991) claims that either end of the innovativeness continuum can be linked to market success, whereas the moderately novel innovations are less successful. However, in the capital market this might not be widely believed, as: “The more radical the innovation, the more difficult it is
to estimate its market acceptance and potential” (Assink, 2006, p. 217). As mentioned before in section
2.1, uncertainty might deter investors (Martin et al., 2004) and their short-termism perspective to invest in the more radical innovations (Mazzucato & Semieniuk, 2017). Subsequently, this would lead to a finance gap and possibly even impede firms to develop more radical innovations (Brancati, 2015; Czarnitzki 2006; Revest & Sapio, 2012). Nonetheless, since these financial constraints are a main concern for governments and their interventions (Schneider & Veugelers, 2010), it would seem that especially the more radical innovations would be supported by innovation policies, due to the increased uncertainty, risk-taking and costs (Avlonitis & Salavou, 2017; Czarnitzki, 2006; Hall, 2002; Mazzucato & Semieniuk, 2017). Furthermore, radical innovations offer new technological trajectories and future markets (Danneels & Kleinschmidt, 2001), so by supporting such endeavors governments can shape the future they aspire. Hence, the assessment of project innovativeness through experts could enable governments to tailor their policies better as well as to be more future oriented. Through the rating provided by experts, a legitimate assessment of innovativeness will be the result, which is based upon experience, objective perspectives and public interests (Weible, 2008). This leads to the following hypothesis:
H1: The expert rating on project innovativeness has a positive effect on project funding
4.2 Region
13
78) describes clusters as “geographic concentrations of interconnected companies and institutions in a
particular field” as well as “critical masses of unusual competitive success in particular fields”. The
benefits of being located in or near a cluster include connections with linked industries, an increased availability of knowledge, access to external resources and finance as well as the availability of skilled employees (Engel, 2015; Porter, 1998). Many governmental institutions are also actively partaking in these agglomerations. Governmental institutions often fulfill an important role in shaping clusters through policies aimed at stimulating collaborative behavior because of the benefits that clusters bring to a region (Porter, 1998). Clusters often stimulate the competitive strength of a region, generate employment opportunities and bring multiple organizations together which increases the chances on discovering groundbreaking innovations (Engel, 2015; Porter, 1998; Van der Panne, 2004) and resultantly the amount of allocated funding. Eventually, agglomerations of organizations bring forth positive externalities such as knowledge spillovers. This can result in increased productivity and the emergence of novel ideas.
In the literature, there are two contrasting dominant views on externalities: (i) the Marshall-Arrow-Romer specialization model and (ii) the Jacobs diversification model (Van der Panne, 2004). On the one hand, the Marshallian perspective argues that knowledge is specific to a certain sector and can therefore only be used appropriately by firms active in the same sector. Thus, the collocation of companies specialized in a specific sector will result in more innovative behavior due to relevant knowledge spillovers (Glaeser et al., 1992). On the other hand, there is Jacobs, who states that knowledge can spillover between complementary industries and still be applied even if the firm itself is not active in that particular sector (Van der Panne, 2004). Whereas the Marshallian perspective focuses on knowledge depth, Jacobs emphasizes the importance of diversity and knowledge breadth for innovations. According to Laursen & Salter (2006), openness for dissimilar external sources benefits firms in the search for novel opportunities. However, both depth (Marshall-Arrow-Romer) and breadth (Jacobs) are equally important for clusters nowadays, since both externalities can be found in practice and have a positive effect on the innovative performance of regions (Van der Panne, 2004). Hence, one could argue that being located near both similar and dissimilar organizations benefits the innovative performance of firms and hence the amount of allocated project funding. According to this perspective, derived from Porter’s (1998) seminal piece on clusters, proximity to other (chain-)organizations fulfills a key role in shaping the innovative capabilities of companies. The majority of research on clusters emphasizes the benefits of unintended spillovers such as tacit knowledge for the shaping of innovations (Asheim & Isaksen, 2002; Engel, 2015; Tödtling, Asheim & Boschma, 2013). These knowledge spillovers are generally geographically bounded to the agglomeration, since tacit knowledge does not transfer well (Van der Panne, 2004). Due to the bounded nature of these spillovers it firms need to be locally present in order to capture these benefits.
14
play a major role in the creation and direction of innovations, underlining the importance of regional characteristics. However, not all regions are equally endowed. Whereas clusters are characterized by urbanization, abundance of resources and their importance as core regions for a nation, in many countries there are also lesser developed regions which encounter multiple issues (Doloreux & Dionne, 2008; Porter, 1998). Core regions such as clusters and highly urbanized areas often have a comparative advantage, which makes them economically interesting for governments. Densely populated areas such as metropolitan regions benefit from scale, density, diversity and the proximity of universities and research institutes (Tödtling et al., 2013). In the vicinity of metropolitan regions there often is a strong infrastructure, which supports the innovative endeavors of firms (Cooke, 2001). According to Oughton et al. (2002), clusters do obtain more attention from policy makers, increasing their chances on obtaining public funding. Thus, companies in metropolitan areas or clusters are more likely to create radical innovations, have an increased access to resources and obtain more governmental attention. Contrarily, low urbanized regions are often less endowed, lack complementary enterprises, experience difficulties in attracting human capital and are therefore unlikely to experience considerable growth (Doloreux & Dionne, 2008). Thus, proximity to or participation in clusters helps firms to be noticed by policy makers as well as other firms and institutions, positively affects the innovativeness and hence the possibility to attract funding. This leads to the following hypothesis:
H2: The effect of project innovativeness on project funding will be positively affected by the degree of urbanization
4.3 University proximity
15
Moreover, knowledge institutions such as universities are able to attract highly-educated human capital to the region and offer the possibility of an industry-university collaboration (Du et al., 2014; Laursen et al., 2011). Such highly-educated personnel can eventually decide to work for a private organization such as a firm, bringing their expertise and strengthening the knowledge base of a firm.
In order to establish a fruitful collaboration with universities, the location of the focal firm plays an important role. Because tacit knowledge is ‘sticky’, thus remains local, proximity to such a knowledge source is important (Asheim & Isaksen, 2002; Morgan, 2004). For a firm to access such knowledge, it is a prerequisite to establish interaction with universities. Face-to-face interaction between the parties creates a bond which allows for information exchange, trust and sympathy of each other’s goals and culture (Laursen et al., 2011; Morgan, 2004). This type of contact differs from IT-aided communication in the sense that it allows for the assimilation of such context-dependent knowledge, which in turn leads to a better adoption of tacit knowledge (Laursen et al., 2011). On top of that, it is similarly important for firms to establish a good relationship with universities which is aided by being proximate. Because of geographical proximity, firms tend to be proximate in other aspects as well, such as being socially alike. According to Boschma (2005), a major aspect which showcases the importance of social proximity is an open attitude by the organizations involved, which benefits knowledge exchange. Additionally, this reduces opportunistic behavior from both sides, facilitates learning and increases opportunities for longstanding relationships. By means of long-standing relationships with universities, companies might gain a competitive advantage over other organizations due to the access of novel research outcomes.
However, firms with a high R&D intensity are not as inclined to cooperate with universities as compared to firms with a low intensity (Laursen et al., 2011). Hence, firms that do have a well-developed R&D department might rely more upon their own skills and knowledge. Resultantly, SMEs in the proximity of universities might benefit relatively more of the resources offered by universities compared to more mature large corporations.
Subsequently, such areas are seen as innovative hubs by policy makers. On the contrary, peripheral regions are characterized by a lack of knowledge institutions, lowering the available knowledge which would result in more incremental innovations. In the research of Nishimura & Okamuro (2011) it is suggested that firms which actively consort with research institutions and universities have a higher tendency to apply for public subsidies. Thus, enterprises engaged in collaborative activities with such institutions will more likely apply to public support programs as well as perform more innovative research due to the spillovers. Therefore it can be argued that:
H3: The effect of project innovativeness on project funding will be positively affected by firms’ proximity to universities
16
4.4 Conceptual Model
The following conceptual model as depicted in figure 2 arises from the hypotheses development.
Figure 2: Conceptual model
5. Methodology
5.1 Data description
The data for this research is provided by a Dutch consultancy and intermediate firm that supports SMEs during their application for the WBSO incentive. Due to privacy reasons they are referred to as
Consultants Firm. This research examines the most recent data available, since it supports the idea of
forecasting novel trends and reduces time-related biases. The dataset contains the descriptions used in the WBSO requests in the period of 1/1/2018 – 6/5/2019 from 6536 different projects. Furthermore, the descriptions of the projects are written by Consultants Firm for the application of the WBSO subsidy. Although Consultants Firm’ database consists of Dutch projects, the dataset is characterized by the use of both Dutch and English in the project descriptions. A sample of project descriptions has been extracted and subsequently the corresponding firm characteristics and other variables were matched in a sample dataset.
Codebook approach
17
In this codebook multiple trends from different industry sectors of the past seven years were incorporated in order to find innovative project descriptions. This approach was unsuccessful and will be elaborated upon in section 6.
Expert-based approach
A second approach was necessary in order to analyze the data. For this approach, a random number generator was used to randomly select 120 project descriptions from a total of 6536, which were to be graded on the innovativeness of the description by the experts provided by Consultants Firm. The experts did not receive any firm characteristics or background information and had to use their experience and expertise in order to assess the innovativeness. In order to tackle any language barriers these experts might experience, only Dutch project descriptions are used in the sample. If the randomly generated number matches with an English project description, the subsequent Dutch project is selected. Eight experts from Consultants Firm were requested to evaluate 15 project descriptions each. In order to tackle possible bias due to the passing of time, only projects from 2018 on have been selected in the dataset. The experts rated the project innovativeness on a Likert scale, ranging from 1 to 5 (table 1). By providing a five-point Likert scale, the experts face less indistinctness and are offered to express a degree of agreement with the question (Matell & Jacoby, 1972). Moreover, by using an odd numbered scale they are permitted to give a neutral response.
For the statistical analysis, this study applies the Poisson regression analysis. Since there is no upper value for the dependent variable that is a count variable which approximates a continuous variable, the Poisson regression suits the analysis of the dependent variable Approved funding better, compared to the ordinary least squares (OLS) regression (see 5.2 for further explanation). Furthermore, the basic assumptions of OLS are violated, which supports the choice for the Poisson regression. The Poisson regression facilitates the analysis of the effect of a categorical variable on a count variable. Next to that, the Poisson regression supports the analysis of a smaller sample size and deals with the skewness such samples often have. The Poisson regression approximates a normal distribution, which aids the interpretation of the outcomes of the regression analysis. Additionally, robust standard errors are used in the analysis to tackle heteroscedasticity.
5.2 Dependent variable
In this empirical study, Approved funding is the dependent variable. This variable can be regarded as a success measurement and is measured by the amount of allocated project subsidy in euros according to
Consultants Firm. This variable can be regarded as a continuous due to the wide range between
minimum and maximum values.
5.3 Independent variable
18
which provides new insights in text-based analysis (Gupta & Lehal, 2009; Kobayashi, Mol, Berkers, Kismihók & Den Hartog, 2017). In this study, project innovativeness is measured through the ratings of the innovativeness of project descriptions, which were sent to the experts of Consultants Firm. This study experiments with text-based expert ratings as potential input for computer-aided text analysis. The experts rated the project descriptions on a scale of 1 to 5, which corresponds with the degrees of innovativeness as depicted in table 1. Consultants Firm’ experts rated the description of an innovative project generated by one of their colleagues, while other firm characteristics remained unknown for them to ensure an objective assessment of the project description. This resulted in the categorical variable Innovativeness.
Degree of Innovativeness
1 2 3 4 5
Not innovative Moderately innovative
Neutral
Innovative Highly innovative
Table 1: Likert scale of project description innovativeness.
5.4 Moderator variables
Degree of urbanization
The type of region will be measured by the number of inhabitants of the geographical location where a firm is located. The postal codes of all companies included in the sample are known, hence the degree of urbanization could be calculated, named Urbanization. In order to determine the degree of urbanization of the firms’ location, the definition of the OECD and EC will be used (Dijkstra & Poelman, 2012), albeit slightly adjusted. In order to fit the sample, two degrees of urbanization will be used in the analysis as depicted in table 2. All projects and their corresponding geographical location of the firms are analyzed and categorized into one of the three degrees.
Degree of urbanization Population
Low < 100 000
Medium and high > 100 000
Table 2: Degree of urbanization (Dijkstra & Poelman, 2012)
Proximity to universities
In this study, only academic universities will be included in the analysis, as: “the universities of applied
science perform relatively little and mostly applied research” (OECD, 2014, p. 21). Proximity to
19
which approximates 80 kilometers. Adopting this radius in his study this does not make sense, since Orlando focuses on the United States of America, which is much vaster compared to the Netherlands where this study takes place. Moreover, such a radius would increase the number of companies near a university substantially and therefore diminish the purpose of the variable. Hence, for this research a radius of 20 kilometers has been chosen. The distance between the firms’ postal codes and universities was calculated in order to assess the proximity. Firms within the radius can be regarded as in close proximity to a university.
5.5 Control variables
The aforementioned hypotheses will also be corrected for control variables, which are based upon firm characteristics. Such characteristics can affect the outcomes when not controlled for, hence this will be a constant variable in the analysis. The characteristics chosen are firm size, firm age, industry sector and project type. These determinants are described in the subsequent paragraphs.
Firm size
Firm size is a factor of influence which is often mentioned in research. According to the European Commission (2017), one could distinguish two categories: (i) SMEs and (ii) larger corporations. According to Nishimura & Okamuro (2011), small-sized companies in high-tech sectors experience stronger effects of allocated public support, due to their relatively small amount of private capital. Subsequently, smaller enterprises lack the resources to split their risks over multiple projects (Nishimura & Okamuro, 2011), leading to a higher risk exposure and increasing importance of public financial support. Almus & Czarnitzki (2003) found that firm size influences the chances of receiving public funds. An increase in firm size simultaneously increases the probability of collecting public support, due to information advantages and increased R&D intensity, capacities and credibility (Almus & Czarnitzki, 2003; Schneider & Veugelers, 2010). Nishimura & Okamuro (2011), Jaffe & Le (2015) as well as Schneider & Veugelers (2010) all find that larger corporations are more prone to receive and utilize public support compared to SMEs. The natural logarithm of this variable (Firm Size) will be used, in order to counter the skewness of the variable (see Appendix A).
Firm age
20
compared to older companies. Whereas older companies are not as discouraged in applying compared to younger peers. The control variable Firm age is measured in years and the natural logarithm +1 will be used to counter skewness and to correct for firms which are less than one year old (see Appendix A).
Industry sector
Certain industries rely more on R&D, such as high-tech sectors and science-based sectors. Pavitt (1984) emphasizes the importance of industry structure on the innovative behavior and strategy of incumbents. Castellacci & Lie (2015) follow Pavitt (1984) and argue that different industries require different strategies for firms to be successful in their innovative activities. The amount of R&D performed in sectors is partially determined by sector characteristics such as competition, technology reliance, positive spillovers, technology and market opportunities (Castellacci & Lie, 2015; Freitas, Castellacci, Malerba & Vezzulli, 2016). Thus, it could be argued that industry-level differences determine the amount of R&D performed in a sector. Particularly firms located in high-tech and science-based industries are prone to apply for public support in order to decrease their private expenditures (Freitas et al., 2016). On the other end of the spectrum there are companies active in more traditional sectors, where R&D plays a reduced role (Castellacci & Lie, 2015). Subsequently, sectors affect the impact of public incentives aimed at stimulating private R&D. Research has shown that low-tech industries face stronger effects by such policies compared to high-tech industries, in which R&D already holds a key role (Castellacci & Lie, 2015). According to Castellacci & Lie (2015), manufacturing firms tend to perform more R&D and apply more frequent for subsidies compared to other sectors. Industry sectors have SBI-codes2 in this dataset, which refer to a specific industry. These codes were introduced by the Centraal Bureau voor de Statistiek (CBS), are very specific to the sector they represent, are hierarchically mapped and related to international equivalents (Centraal Bureau voor de Statistiek, 2019). In this research, the industry sectors will be categorized into manufacturing industry and others as the variable Manufacturing in order to control for industry specific effects.
Project type
There are three possible project types for which firms can apply to receive WBSO support, namely: development of processes, products and software (Rijksdienst voor Ondernemend Nederland, 2019). Each project type is distinct from the others. Product development often are market driven, while process innovations are intended to increase firm productivity and hence are driven by the organization itself (Utterback & Abernathy, 1975), whereas software innovation can be described as both. Hence, the categorical variable Project type is created consisting of process (1), product (2) and software (3).
21
6. Results
This section will be divided in two parts. The codebook approach results will shortly be discussed, followed by the second expert-based approach of this study’s analysis. The latter will involve the statistical analysis from which this study will draw its conclusions.
6.1 Codebook approach
The codebook approach did not provide a reliable analysis of the data due to outdated and crude measurements used to separate highly innovative projects from lesser innovative projects. Many sources focused on similar technological trends and innovations. Moreover, as a result of the diversity in projects carried out by Consultants Firm, it rapidly became clear that it would be a near impossible task to create an inclusive codebook with as many technologies as possible used in a multitude of fields. After compiling a trial codebook, the first analysis could be conducted. The result was that trends observed by our sources were applied by firms years before sources would write about it. Additionally, the database of Consultants Firm was too diverse, incorporated innovations of all degrees of newness and some of the technologies were only mentioned by a single company. Concludingly, the codebook was based upon outdated trends, which found their way into publicity and were adopted by the masses now. Hence, it was only possible to conduct a codebook upon already established trends, which resulted in the opposite of the purpose of this analysis. The idea of the codebook was to discover novel trends, unfortunately it revealed trends which were already in the latter part of the adoption curve and hence cannot be described as novel. Additionally, the measure itself was too crude to filter out non-innovative projects and resultantly would filter out the majority of projects.
6.2 Expert-based approach
Descriptive statistics
22
Descriptive statistics
Variables Obs Mean Std.Dev. Min Max
Approved funding 106 10200 209000 0 1030000 Innovativeness 106 3.113 1.054 1 5 Urbanization 106 .425 .497 0 1 University 106 .66 .476 0 1 Firm size 106 220.160 513.675 1 3336 Firm age 106 23.028 20.727 0 94 Manufacturing (1 = Yes) 106 .321 .469 0 1 Project type 106 2.123 .597 1 3
Table 3: Descriptive statistics of all variables included in the analysis
23
there are more universities. When looking at the Netherlands, this does seem to make sense, since universities often settle in cities.
Regression analysis
Table 6 shows the Poisson regression analysis. Each model adds variables gradually in order to analyze and test the different hypotheses. Model 1 contains the analysis of the first hypothesis, the baseline of the analysis, and consists of the independent variable (Innovativeness) and the control variables. Model 2 and 3 show the results of the direct effects of Urbanization and University on the Approved funding. There are no significant relations between respectively Urbanization and University with Approved funding. However, in both models the coefficient of Innovativeness remains still highly significant and even increases. Hypothesis 1 states a positive relationship between Innovativeness and Approved funding, which has a highly significant positive effects in all models. Hence, it can be concluded that a higher degree of innovativeness will result in higher approved funding for firms. Furthermore, firms receive on average 10.200 euros, and an increase of one point on the scale of innovativeness results in an additional 18.521,49 euro in project funding (table 5). Model 2 and 3 measure the direct effects of respectively Urbanization and University on Approved funding. In both cases there is no significant direct effect of these variables.
The interaction term between Urbanization and Innovativeness is introduced in model 4, in order to test hypothesis 2. The results show a positive significant moderating effect for low urbanization and a non-significant effect for high urbanization. Thus, the relationship between Innovativeness and
Correlation matrix
Variables Approved
funding Innovativeness Urbanization University
24
Approved funding is strengthened when firms are located in low urbanized areas. This is not in line with the argued hypothesis and the hypothesis is therefore not supported. Hypothesis 3 proposes a positive moderating effect of University on the relationship between Innovativeness and Approved funding. Model 5 introduces the interaction term of University in the equation and depicts two positive significant relations between being proximate or distant to a university. However, the coefficient of being in the proximity of a university (0.429) is stronger positive compared with distant (0.259). Hence, the hypothesis is supported.
Model 6 is not directly related to any of the hypotheses, but does offer interesting insights. In this model both the interaction terms of hypothesis 2 and 3 are simultaneously introduced, in order to provide a more nuanced image of the relationships. Proximity to universities does not have a significant moderating effect anymore, while the effect of low urbanization has increased. Hence, it might be that the effect of universities is similar to the effect that urbanization has on the baseline hypothesis. Moreover, model 6 shows the absence of an interaction effect between Urbanization and Innovativeness due to collinearity. The data cannot clearly explain the emergence of the collinearity. However, one possible explanation might be the missing of six cases, as can be seen in Appendix E (tabulation 3 and 4). These cases might be explained by other coefficients in the analysis, hence the collinearity.
As for the control variables, they are included in each of the models. Firm size remains highly significant in each of the models and seems to positively affect the amount of funding firms receive. Additionally, the sector in which firms are active seems to have an effect as well. Throughout all the models Manufacturing remains highly significant which indicates that firms active in the manufacturing industry receive significantly more funding. Furthermore, an increase in Firm size has results in higher allocated funding, which is in line with Almus & Czarnitzki (2003). Additionally, being active in the Manufacturing industry results in a higher allocated funding, as can be seen in table 5.
Marginal analysis of model (1)
Dy/dx Std. Err 95% C.I.
Variables Innovativeness 18521.49*** 6409.33 5959.44 31083.54 Firm size 25062.67*** 5537.94 14208.5 35916.84 Firm age -2432.05 8569.02 -19227.02 14362.93 Manufacturing (1 = yes) 59643.1** 25826.53 9024.04 110262.2 Projecttype (0 = process; 1 = product) 14220.69 12660.41 -10593.26 39034.65 (0 = process; 1 = software) 21420.87 32042.36 -41381.01 84222.75 Significance: *** p<0.01, ** p<0.05, * p<0.1
Note: dy/dx for factor levels is the discrete change from the base level
25
Poisson regression analysis
(1) (2) (3) (4) (5) (6)
Variables
Innovativeness 0.339*** 0.349*** 0.350***
(0.114) (0.116) (0.115)
Urbanization (0 = low; 1 = high) 0.462 1.960** 2.949***
(0.341) (0.771) (0.858) Urbanization (0) x Innovativeness 0.477*** 0.713*** (0.126) (0.268) Urbanization (1) x Innovativeness 0.040 0.000 (0.189) (0.000) University (1 = yes) 0.104 -0.480 -1.793** (0.356) (0.735) (0.754) University (0) x Innovativeness 0.259** -0.420 (0.121) (0.300) University (1) x Innovativeness 0.429** 0.077 (0.178) (0.188) Firm age (ln) -0.045 -0.006 -0.028 0.021 -0.027 0.040 (0.158) (0.170) (0.162) (0.156) (0.159) (0.138) Firm size (ln) 0.459*** 0.439*** 0.459*** 0.416*** 0.465*** 0.427*** (0.081) (0.081) (0.083) (0.079) (0.088) (0.087) Manufacturing (1 = yes) 0.899*** 1.150*** 0.927*** 1.134*** 0.962*** 1.178*** (0.330) (0.353) (0.324) (0.340) (0.330) (0.341)
Project type (0 = process; 1 = product) 0.299 0.379 0.323 0.356 0.360 0.410
(0.293) (0.321) (0.327) (0.328) (0.341) (0.319)
Project type (1 = software) 0.422 0.530 0.464 0.447 0.554 0.578
(0.581) (0.613) (0.662) (0.622) (0.688) (0.657)
Constant 7.697*** 7.241*** 7.505*** 6.842*** 7.733*** 7.327***
(0.520) (0.703) (0.999) (0.718) (0.900) (0.824)
Observations 106 106 106 106 106 106
Pseudo R2 0.539 0.554 0.54 0.573 0.543 0.595
Significance: *** p<0.01, ** p<0.05, * p<0.1, robust standard errors in parentheses
26
7. Discussion and Conclusion
Discussion
Firstly, this study analyzed the effect of project description innovativeness on the receival of public funding for companies. The results show a positive relation between the degree of innovativeness of a project and funding firms receive. Hence, the baseline hypothesis of this study is supported. On average, firms receive an additional 18.521,49 euro on governmental subsidy with an increase in innovativeness (table 5). Thus, firms performing highly innovative projects and who encounter higher risks, costs and uncertainty of the more radical innovations (Hall, 2002), do receive more governmental support compared to their more incremental counterparts. This is not the main function of the WBSO, which aims to support all types of innovations new to the firm and does not on distinguish the basis of innovativeness (Poot et al., 2003). It does seem to be a positive side-effect, since it tackles the lack of incentive to pursue long-term innovations (Edler & Fagerberg, 2017). However, it could also be argued that companies with highly innovative projects request significantly higher funding compared to low innovative projects (see Appendix D).
Secondly, this study investigated the effects of the geographical location of a company and their prospect on public funding. The results showed an opposite effect, which suggests that firms located in low urbanized regions receive on average more funding compared to high urbanized areas (see Appendix D). This might be due to the higher number of SMEs located in low urbanized regions (Tödtling & Kaufmann, 2002; Tödtling & Trippl, 2005) and the fact that SMEs experience more risks in R&D projects (Lee et al., 2015; Nishimura & Okamuro, 2011). Moreover, firms located in low urbanized regions are strongly dependent on governmental financial support due to a scarcity of VC’s (Doloreux & Dionne, 2008) and hence might apply more frequent to such incentive programs. The RVO (2018) argued that regional discrepancies might be attributed to the industry sector. Hence, it could be that certain industries which on average receive more funding are more frequent in such regions.
27
Conclusions
Since the importance of innovations as well as their externalities increases (OECD, 2018) and more countries adopt innovation policies in order to strengthen their competitive position (Schneider & Veugelers, 2010), it becomes progressively interesting to analyze these public. As mentioned in the introduction, Keijzer and Snel (Rijksoverheid, 2019) claim that the Netherlands is a leading innovator as a result of the WBSO act. This study aimed to explain the relationship between the innovativeness of projects applying for the WBSO and their receival of WBSO funding. Assink (2006) argued that extant research ranks innovativeness on a continuum, but only specifies two ends which does not provide a grounded parameter. This research took a proactive role in nuancing this continuum by means of an expert-based approach. Expert-rated project descriptions were used to examine the relation between project innovativeness and the amount of WBSO funding firms received. Concludingly, this initial study shows promising results concerning the adoption of a more refined definition and measurement of innovativeness and it can be concluded that text-analysis does help in defining a new innovation parameter. As elaborated upon in the results section, the degree of project description innovativeness as rated by the experts showed a positive effect on the funding firms receive.
Furthermore, this study tried to explain differences in obtained public funding by examining the role of geographical regions. Funding differences were examined by the degree of urbanization and proximity to universities. Proximity to universities positively moderates the amount of funding firms receive. Thus, the geographical location of a firm does affect the amount of WBSO subsidy they might receive. Being located in a low urbanized area increases the payout firms receive. This remains significant throughout all regression models, also when included the moderating effects of proximity to a university.
Implications
This study adds to the extant literature by assessing the possibility of creating a parameter for innovativeness. In contemporary research the demarcation of innovativeness does not offer a refined assessment. By taking this initial step in developing a parameter, I intended to bring a more refined view on the effect of project innovativeness on firm performance. Experienced consultants rated project descriptions in this sample on their innovativeness. Hence, this study confirms that text-analysis can be used to establish a new innovativeness parameter. Discovering why experts deem certain projects more innovative than others will be beneficial for the improvement and further construction of the parameter. Herein lies a new challenge for scholars, which might bridge the gap between theoretical and practical use of such a measure. Once these factors are known, the implementation of a text-mining approach could deliver valuable insights due to the ability to delve deep into unstructured texts.
28
the application for subsidies. An important implication from this study is the fact that enterprises in low urbanized, peripheral regions receive on average more funding compared to their metropolitan equivalents. An important realization for intermediates might be that innovation does not solely take place in highly urbanized regions. Hence, consultancy and intermediate firms could review their client base and possibly extend their services into rural areas. Furthermore, proximity to a university positively affects the amount of funding ventures receive. If firms are not located near such institutions, consultancy firms might encourage collaborative efforts between universities and companies. This does not only affect the payout, but could also result in more innovative projects (Laursen & Salter, 2006) which leads to higher funding as investigated in this study.
Limitations and future research
This study draws upon the database of Consultants Firm, which affects the type of firms and projects used in this analysis. Companies which did not use an intermediate are not included in this study, which might have influenced the outcomes. Hence, these outcomes are mainly generalizable for intermediates. Another significant limitation is that the size of the Netherlands has not been taken into account. The Netherlands can be characterized by a high density and is rather small. Additionally, with the rise of IT and industries becoming increasingly more digital, geographical distance might not matter as much (Lee & Drever, 2014). Furthermore, not all SMEs are created equal (Lee & Drever, 2014). Some SMEs are excellent in R&D and do not experience restrictions based upon their characteristics (Nauwelaers & Wintjes, 2002). Moreover, research shows that companies applying for public support often have a lower baseline of innovative activities compared to other firms who do not apply (González et al., 2005). Therefore, the most innovative firms in this database might not be the most innovative in the Netherlands. Moreover, the measure of proximity towards universities was not based upon prior research, since a similar way of measuring is unknown to the author. Hence, a well-considered distance has been selected to function as a demarcation in the analysis. A more grounded manner would add to the generalizability of this study. Finally, this research only utilized a small number of experts and a small sample, which affects the generalizability.
29
References
Almus, M., & Czarnitzki, D. (2003). The effects of public R&D subsidies on firms' innovation activities: the case of Eastern Germany. Journal of Business & Economic Statistics, 21(2), 226-236. Asheim, B. T., & Coenen, L. (2005). Knowledge bases and regional innovation systems: Comparing
Nordic clusters. Research policy, 34(8), 1173-1190.
Asheim, B. T., & Isaksen, A. (2002). Regional innovation systems: the integration of local ‘sticky’ and global ‘ubiquitous’ knowledge. The Journal of Technology Transfer, 27(1), 77-86.
Assink, M. (2006). Inhibitors of disruptive innovation capability: a conceptual model. European Journal
of Innovation Management, 9(2), 215-233.
Avlonitis, G. J., & Salavou, H. E. (2007). Entrepreneurial orientation of SMEs, product innovativeness, and performance. Journal of Business Research, 60(5), 566-575.
Boschma, R. (2005). Proximity and innovation: a critical assessment. Regional studies, 39(1), 61-74. Brancati, E. (2015). Innovation financing and the role of relationship lending for SMEs. Small Business
Economics, 44(2), 449-473.
Brouwer, E., den Hertog, P., Poot, T., & Segers, J. (2002). WBSO nader beschouwd. Onderzoek naar
de effectiviteit van de WBSO.
Brown, J. R., Martinsson, G., & Petersen, B. C. (2012). Do financing constraints matter for R&D?.
European Economic Review, 56(8), 1512-1529.
Carlino, G. A. (2001). Knowledge spillovers: cities’ role in the new economy. Business Review Q, 4(1), 17-24.
Castellacci, F., & Lie, C. M. (2015). Do the effects of R&D tax credits vary across industries? A meta-regression analysis. Research Policy, 44(4), 819-832.
Centraal Bureau voor de Statistiek. (2019, April 4). SBI 2008 - Standaard Bedrijfsindeling 2008. Retrieved June 16, 2019, from https://www.cbs.nl/nl-nl/onze-diensten/methoden/classificaties/activiteiten/sbi-2008-standaard-bedrijfsindeling-2008
Cooke, P. (2001). Regional innovation systems, clusters, and the knowledge economy. Industrial and
corporate change, 10(4), 945-974.
Czarnitzki, D. (2006). Research and development in small and medium‐sized enterprises: The role of financial constraints and public funding. Scottish journal of political economy, 53(3), 335-357. Czarnitzki, D., Hanel, P., & Rosa, J. M. (2011). Evaluating the impact of R&D tax credits on innovation:
A microeconometric study on Canadian firms. Research Policy, 40(2), 217-229.
Danneels, E., & Kleinschmidtb, E. J. (2001). Product innovativeness from the firm's perspective: its dimensions and their relation with project selection and performance. Journal of Product
Innovation Management: An International Publication of the Product Development & Management Association, 18(6), 357-373.
De Bruijn, P., & Lagendijk, A. (2005). Regional innovation systems in the Lisbon strategy. European
