Submission to the 11th Asia Association of Learning, Innovation and Coevolution Studies International Conference (ASIALICS), 25-27 September 2014, DGIST, Daegu, Republic of Korea
Exploring Three Conditions for Innovative Cluster Development
through Co-Patenting Relationships
Pieter E. Stek1 and Marina S. van Geenhuizen
Faculty of Technology, Policy and Management, Delft University of Technology Jaffalaan 5, 2628 BX Delft, The Netherlands
19 August 2014
Keywords Open Innovation, Triple Helix, international, co-patenting, clusters
Abstract The paper explores the innovative performance of 9 high and medium-high technology manufacturing industries across 29 countries based on patent and R&D expenditure data. The purpose of this study is to evaluate whether the benefits of particular forms of collaboration have a significant positive effect on innovative performance at the national level. The study considers three perspectives connected to innovative cluster performance: open innovation, the Triple Helix model of university-industry-government relations, and the presence of global pipelines (international ties) as being of influence on innovation performance. The three perspectives are evaluated using co-patenting analysis. The findings of the study suggest that patents and R&D expenditure are highly correlated and that the three perspectives appear to have varying significance depending on the industry being considered. In science-based industries international ties tend to have a positive effect on innovative performance, while in most engineering-based industries such ties tend to weaken innovative performance. Open innovation and the Triple Helix perspective do not show as clear a distinction, and so the differentiation across industries merits further research.
1 INTRODUCTION
Innovation is a process that is deeply influenced by the social network of individual inventors (Nonaka and Takeuchi 1995), entrepreneurs (Davidsson and Honig 2003), and firms (Chesbrough 2006), and by the dynamic interaction between different groups of actors, such as academia, industry and government: so-called 'Triple Helix' relations (Etzkowitz and Leydesdorff 2000). As a result of manifold uncertainty (Mohr, Sengupta, and Slater 2010) innovative firms tend to form networks through which missing resources can be accessed, particularly missing knowledge that contributes to successful innovation (Ponds, Van Oort, and Frenken 2007; Lavie 2006; Malerba and Orsenigo 2000; Barney 1991; Wernerfelt 1995; Tether 2002).
However, countries have different business cultures that influence the networking and trust building necessary for open innovation (Hofstede, Hofstede, and Minkov 2010; Edquist 1997; Nelson 1993; Lundvall 1992) This differentiation holds true not only for relationships between companies but also between companies and universities and government (OECD 2007; Veugelers and Rey 2014). Thus, some countries have developed intermediary institutions that cross the gaps between the Triple Helix actors while others have not or to a smaller extent (Bruneel, d’Este, and Salter 2010; Todeva 2013). Also, the international orientation in research at universities and in innovative
companies may be different between countries, due to differences in size of the domestic market and also different levels of specialization in R&D (Mcdougall, Shane, and Oviatt 1994; Nelson 1993; Guellec and Van Pottelsberghe de la Potterie 2001). Thus small economies with high levels of specialization have developed a strong global orientation in knowledge creation and commercialization of knowledge. However, employing international relations is also subject to barriers among actors pursuing this strategy, as it requires an efficient search process, selection and establishing and maintaining of relationships, which call for sufficient financial, time and managerial resources among research groups and companies. Particularly, when the latter are young and small, they may face various barriers, like language and cultural barriers, and legal and regulatory barriers in international knowledge relationships (OECD 2009; BIS 2010; Freeman et al. 2010; De Clercq et al. 2012).
The above three circumstances in countries, concerning open innovation, Triple Helix networking and international collaboration, indicate a different presence in countries of conditions under which regional clusters of innovative activity develop, including sufficient networks abroad, as global pipelines (Bathelt, Malmberg, and Maskell 2004). However, there is not much knowledge on the presence of ‘spatial cluster conditions’ on the national level.
The national level in innovative cluster development is particularly salient as national government continue to extend significant influence over higher education, scientific research and economic activity, both as regulator and by providing funding. Government support ranges from scholarships to research grants, to subsidies, loan guarantees and tax breaks to knowledge intensive firms. Various institutions, including capital markets and universities, are also national because of their sensitivity to national policies, such as in the recognition of the ‘third mission’ of universities, namely, to bring their knowledge to market (commercialization), in many EU countries (Geuna and Muscio 2009; van Geenhuizen 2013). As national governments around the world have increasingly placed innovation at the centre of their economic policy agenda, understanding the extent to which national collaboration patterns affect innovative performance has become a matter of national urgency and importance (APEC 2014; OECD 2012; OECD 2013).
The aim of this study is therefore to explore to what extent particular patterns of collaboration, as requirements for innovative cluster formation, are present at the national level and part of the national innovation system. We expect a strong presence of such requirements and of strong innovation performance at the national level in some countries but not in other ones.
Bibliographic data such as patents and scientific publications have been used extensively to measure both innovation performance and knowledge exchanges (Griliches 1998; Tijssen, Van Leeuwen, and Van Wijk 2009; Guellec and Van Pottelsberghe de la Potterie 2001; de Rassenfosse and van Pottelsberghe de la Potterie 2009). This is perhaps because bibliographic data has a number of important advantages: it is easily accessible through public and private databases, long time series are available, detailed information about authors, including their affiliations and address is usually provided, and there is broad coverage of almost all major fields of science and technology. In this paper the versatility of bibliometric indicators is utilized to explore how different research collaboration patterns affect the innovative performance of different knowledge-intensive sectors. In total 9 high- and medium-high technology industries (as defined by Eurostat) are compared across 29 countries in Europe, North America and Asia-Pacific. The collaboration patterns are revealed by analysing co-patenting relationships, while innovative performance is defined as patent output
relative to R&D expenditure.
The research addresses two gaps in the current literature. First, there are a number of partially overlapping perspectives on collaboration in innovation of which it is not clear how strong (and different in strength) they are in contributing to an explanation of innovation. This study explores which perspective offers the best explanation, keeping in mind that certain types of collaboration may depend on the specific characteristics of an industry. Second, the research aims to provide a multi-country and multi-industry overview to complement the many country, cluster or industry-level studies that have been conducted in recent years. Both the scope of the research (covering 29 countries and 9 industries) and the fact that the three perspectives that are tested simultaneously contribute to the novelty of this study. In addition there is a novelty in using R&D collaboration networks and coupling them to innovation inputs and innovation outputs (R&D expenditure and patent numbers, respectively).
Taking the above into consideration, the following research questions are addressed in this paper: 1. What differences can be observed in innovation performance between countries for
particular sectors, and what differences can be observed concerning the three cluster conditions in collaboration between the sectors in these countries?
2. To what extent is there a correlation between innovative performance and the level of collaboration as evidenced by any of the three cluster conditions?
3. To what extent does the correlation between innovative performance and collaboration, as evidenced by the three cluster conditions, vary by type of industry?
The remainder of this paper consists of a literature review with a focus on three collaboration conditions in cluster development and sector differentiation in this context (section 2). This is followed by a description of the research model and hypotheses (section 3), data and methodology (section 4), results and analysis (section 5) and the paper concludes with implications and indication of future research lines (section 6).
2 CLUSTER DEVELOPMENT AND INDUSTRY DIFFERENTIATION
2.1 Cluster Development
Though the paper does not deal with relationships or networks in regional clusters in themselves, it is concerned with relationships that are important in cluster development and may vary between different industry sectors. Therefore we first discuss the concept of regional clusters. A regional cluster can be defined as a geographically proximate group of interconnected firms (in vertical and horizontal relationships) and associated localized enterprise support infrastructure (institutions), linked by commonalities and complementaries (Nooteboom 2006; Porter 1998). Most of the existing theory on innovative activity in clusters has a focus on agglomeration economies and externalities (Audretsch and Feldman 1996), also described as local mechanisms of sharing, matching and learning on the micro-level of firms (Duranton and Puga 2004).
Knowledge spillovers, especially tacit ones, and difficult or risk-sharing activities require a large degree of trust, which is more likely to be found inside a cluster where common conventions and norms and reliability and trustworthiness of individual actors support the flow of knowledge (Cooke, Heidenreich, and Braczyk 2004; Keeble et al. 1999; Leamer and Storper 2001; Storper and Venables 2004). Provided there is relatively strong collaboration at the national level, and given shared cultural norms, many conditions for the development of innovative regional clusters in a
country clearly appear to be present.
2.2 Three Perspectives on Innovative Cluster Development
The presence of social networks appears to be a crucial factor in the innovative performance of many high technology clusters across different industries (Powell, Koput, and Smith-Doerr 1996; Nooteboom 2006; Gertler and Wolfe 2006; Ponds, Van Oort, and Frenken 2007). As a result, different conceptual models have been proposed to explain this phenomenon, three of which are discussed here.
The broadest concept is perhaps the Open Innovation model, which “assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology” (Chesbrough 2006, 1). Open innovation is based first of all on the notion that technology is so advanced and complex that a single organization cannot independently develop all the knowledge that it needs, creating an imperative to acquire knowledge from outside sources. Similarly, as innovation is often serendipitous, technology developed inside an organization may not fit a firm's business model. It can therefore be advantageous to transfer the technology to another firm that is in a position to take full advantage of it. In doing so both parties can maximize the technology's value in order to recover the R&D investment made. In general, open innovation can be undertaken for a variety of purposes, including to enhance technological innovation, to enhance market introduction, or to enhance both (Enkel, Gassmann, and Chesbrough 2009).
Organizations also collaborate to solve a common problem, either by pooling resources and sharing research outcomes, thus benefiting from economies of scale, or by making the results freely accessible, as is the case with open source software (Chesbrough 2006). Collaboration with users is also an increasingly prevalent form of open innovation, giving rise to user co-creation and living labs (Von Hippel 1986; Dahlander and Gann 2010; Barge-Gil 2010).
The Triple Helix and global pipelines perspectives are related to open innovation in the sense that they both involve inter-organizational or international collaboration, which can coincide with open innovation. However neither concept necessarily involves open innovation. Collaboration between Triple Helix actors in the sphere of defence research is very likely to be closed, and much international research occurs within multinational corporations (MNCs) and is thus also closed. But there are of course instances where the three perspectives overlap.
The Triple Helix concept offers both an evolutionary and institutional perspective on the dynamic relationship between university, industry and government (Etzkowitz and Leydesdorff 2000; Ranga and Etzkowitz 2011). In an ideal model, the role of universities as creators of knowledge has been extended to include the commercialization of knowledge as well. In fact, universities and the basic research they produce fulfil a range of economically important roles (Salter and Martin 2001). Conversely, large industries have adopted the role of educational institutes by establishing their own campuses where small and large firms, researchers and entrepreneurs meet, learn and collaborate. Accordingly, knowledge intensive industries interact with universities, and hybrid institutions which are able to bridge the cognitive distance between the two, are needed for successful collaboration (Bruneel, d’Este, and Salter 2010; Meyer et al. 2014). And the third Triple Helix actor, government, influences industry and academia through regulation and by funding basic research and is often responsive to university and industry needs (Etzkowitz and Leydesdorff 2000; Bekkers and Bodas Freitas 2008). Relations between the three parties are therefore important, and so significant
research collaboration between the three parties is therefore seen as a prerequisite and indicator of high innovation performance.
Like the Triple Helix perspective, the global pipeline perspective places an emphasis on a particular feature of the innovation system, in this case international knowledge-exchange ties. A cluster which is strongly connected globally in terms of knowledge exchanges is able to access knowledge worldwide, thus enhancing its innovative performance (Bathelt, Malmberg, and Maskell 2004; Sassen 2002; Fritsch 2004). The creation of such pipelines requires a shared cultural context that enables the formation of trust allowing exchanges of tacit knowledge to happen (Gertler 2003; Polanyi 1967). Rather than being just a matter of communications or transportation technology, international ties are thus a reflection of an industry's ability to bridge cognitive distance and enabling knowledge exchanges with researchers in different parts of the world (Nooteboom 2013; Gertler and Wolfe 2006; Bathelt 2007).
All three perspectives are thus based on strong theoretical foundations and are partially overlapping. While the Open Innovation model essentially posits that “certain types of collaboration are good”, the other perspectives are more specific by addressing the importance of particular relationships, such as international ties or relations with government or university. However it must be noted that open innovation and research collaboration is not a panacea. Under certain circumstances it can also be detrimental to innovation in organizations, especially when the relationship is an unequal one or when the relations are too tight or too much information is transferred which makes attraction and enjoying the benefits of new information increasingly difficult (Fritsch 2003; Liu and Buck 2007; Gaillard 1994; Leiponen and Byma 2009).
2.3 Research Collaboration Patterns Across Industries
Research collaboration patterns vary across industries, depending in part on their knowledge base, and also on competitive dynamics (Tidd and Bessant 2013). In science-based industries patents and academic papers – as formal channels – are more important as knowledge transfer channels, while in engineering and creative industries tacit knowledge plays a much more important role (Asheim, Coenen, and Vang 2007; Gilsing et al. 2011; Jensen et al. 2007). Other differences include the source of innovation, which in science-based industries is from the discovery of new knowledge, requiring close interaction with universities, while in engineering innovation is based on applying existing knowledge to meet design requirements from users (Asheim, Coenen, and Vang 2007; Stankiewicz 2002; Jensen et al. 2007). Note that Asheim et al. (2007) refer to science/engineering industries as industries with analytic/synthetic knowledge bases, while Stankiewicz (2002) refers to science/engineer industries as discovery/design-driven industries, and Jensen et al. (2007) as 'science, technology and innovation'/'doing, using and interacting'-mode industries. Biotechnology and semiconductor engineering are typical examples of science-based industries, whereas software and mechanical engineering can be considered design-driven technologies (Carlsson 2013; Stankiewicz 2002).
Research on the differences in knowledge networking between industries has mainly focused on channels rather than on collaboration patterns, such as the three perspectives mentioned above. These studies have shown that science-based industries rely more on patents and codified knowledge, although informal knowledge transfers play an important role in all types of industries, including science-based industries (Gilsing et al. 2011; Asheim, Coenen, and Vang 2007; Arundel and Geuna 2004). The varying importance of patents and the varying propensity to patent in different industries means that comparisons between countries should be made at the industry level
(Kleinknecht, Montfort, and Brouwer 2002). The need for industry-level comparisons is confirmed by the fact that patent output has been shown to correlate to innovation activity in a broad range of different industries (Acs, Anselin, and Varga 2002). Furthermore, the literature suggests that differences and similarities between industries should be studied carefully: small differences can have a widely varying impact on different industries while many industries share similarities in networking patterns and channels (Asheim, Coenen, and Vang 2007; Gilsing et al. 2011). The present study therefore considers 9 different industries with a view of identifying possible differences between them.
3 RESEARCH MODEL AND HYPOTHESES
To empirically study the phenomena outlined in the previous section, some very simple assumptions are proposed. First, innovation productivity is the relative amount of innovation inputs needed to yield a particular innovation output. The presence of certain features of the innovation network (e.g. open innovation, Triple Helix, international ties) is assumed to increase innovation productivity, which is considered to be a positive outcome. Based on these general assumptions a more detailed and formalized description of the research model is presented (section 3.1) and a number of hypotheses are developed (section 3.2).
3.1 Research Model
In this study, innovation productivity is dependent on the industry type and the industry's collaboration network. In the research model both innovation output and innovation productivity can be considered as dependent variables, while innovation inputs and collaboration network are independent variables. The industry type is a moderating variable.
In this model, because the number of patents P and R&D expenditure E (in US$) are used as model outputs and inputs respectively, innovation productivity is in effect the patenting efficiency, i.e. how many dollars of research expenditure yields one patent. However we shall continue to refer to innovation productivity so as to not to cause confusion.
Following earlier studies of innovation productivity, the following model of expected innovation output P relative to innovation input E is proposed (de Rassenfosse and van Pottelsberghe de la Potterie 2009):
Pic = Eicλ (1)
Here, λ represents innovation productvity. Innovation output P and innovation input E vary depending on the country, c (c = 1 … 29), and industry, i (i = 1 … 9). Equation 1 can also be re-written with natural logarithms (ln) as:
lnPic = λlnEic + εic (2)
Here, εic is an error term which varies depending on each country-industry, thus there are 29∙9 = 261 such error terms. The innovation productivity λ can be subdivided into two parts: a constant (minimum) innovation productivity depending on the industry, λi, and a variable innovation productivity that depends on various industry and country-specific factors Xm, in this case the presence of certain kinds of innovation networks, λic. Therefore:
λ=λi+
∑
mXmλic (3)
Equation 3 can be combined with equation 2 to yield equation 4, a model that is evaluated using the methodology and hypotheses described below. The complete research model is therefore as follows:
ln Pic=λiln Eic+
∑
mXmλicln Eic+εic (4)
The model presented in equation 4 allows the patenting efficiency in each country to vary, while assuming that patenting efficiency within a particular industry is constant. Thus, patent output (Pic) is dependent on the industries' patenting propensity (λi), R&D expenditure (Eic), specific characteristics of the innovation network (Xm), including network-related patenting propensity (λic), and an error term with other factors that are not included in the model. This allows the effect of the innovation network Xm in a particular country and industry to be compared to its innovation output. 3.2 Hypotheses
Based on the relations assumed in the model, it is first explored to what extent innovation input (research expenditure) and innovation output (patents) are related to each other. If this relationship is found to be confirmed, then further analysis about the innovative performance becomes possible. Therefore the first hypothesis is:
H1: R&D expenditure positively correlates to patent output at the industry level.
Based on this relationship it is possible to evaluate the effect of innovation networks (the three perspectives) on patent productivity. Patent productivity is expressed as research expenditure per patent at the national level. Thus hypothesis 2 through 4 are as follows:
H2: The frequency of co-assigned patents positively correlates to patent productivity at the industry level.
H3: The frequency of co-assigned triple helix patents positively correlates to patent productivity at the industry level.
H4: The frequency of international co-invented patents positively correlates to patent productivity at the industry level.
The hypotheses are evaluated according to the methodology described in section 4.
4 DATA AND METHODOLOGY
Although novel, the bibliometric methods applied in this paper borrow from previous bibliometric studies that have been applied to explore many different aspects of science, technology and innovation. These include: changes in Triple Helix relations in national innovation systems (Kwon et al. 2012; Guan and He 2007; Huallacháin and Lee 2014; Stek and van Geenhuizen 2014), comparing science and technology relations between developed and developing countries (Choi, Yang, and Park 2014), the role of technological relatedness in university-industry collaboration (Petruzzelli 2011), the effectiveness of R&D alliances (Kim and Song 2007), differentiating between different technologies' innovation patterns (Lee and Lee 2013) and measuring research productivity (de Rassenfosse and van Pottelsberghe de la Potterie 2009; Griliches 1998; Acs, Anselin, and Varga 2002).
4.1 Data
The empirical study is based on the assumption that innovation output is influenced by innovation inputs and certain other factors such as the aforementioned three types of research collaboration. Innovation output is typically defined as the number of new ideas that are brought to market (Tidd and Bessant 2013). Therefore from a conceptual perspective, patents can serve as a proxy (partial indicator) as they are filed to protect new ideas which the inventor expects will have some commercial value. Although not all new ideas are patented, and not all patents hold significant commercial value, patents could serve as a partial indicator of research output.
In this study patents are used as an indicator of innovation output. Although patents are scientific documents, the decision to apply for a patent is subject to commercial considerations. In order to enforce patents they must be applied for in the relevant jurisdiction, and to reduce expenses applicants often choose to register patents only in a number of key jurisdictions in which they have a large commercial interest, i.e. a large market. The United States Patents and Trademarks Office (USPTO) therefore has one of the world's largest patent collections, which can also be downloaded in bulk and free of charge from Google (http://www.google.com/googlebooks/uspto-patents-applications-biblio.html)
For this study USPTO bibliographic patent application data published between 1 January 2010 and 31 December 2013 is used. All patent applications with priority dates between 1 January and 31 December 2010 with inventors or assignees from 29 countries are selected (total: 118,240 patents). Typically a patent application is filed in the year of invention or shortly after, and therefore a the 2010-2013 period (4 years) contains the bulk of applications with priority dates in 2010. For instance, in 2013, patents with a priority date in 2010 accounted for only 4.1% of all applications. However it is likely that in 2014 a small number of patents with 2010 priority dates will trickle in due to various administrative delays. The dataset used is therefore a somewhat incomplete sample that covers approximately 90% of all 2010 priority dated patents. It is not known whether delayed patents have a higher frequency of collaboration, but there is no reason to assume such bias in the dataset.
Industry R&D expenditure data is taken from the Structural Analysis (STAN) database published by the OECD (http://stats.oecd.org). Industry R&D expenditure includes both public and private R&D expenditure in industry. Although R&D expenditure in industry is a partial indicator of total R&D expenditure, most R&D expenditure takes place in industry, and industry tends to file the majority of patents, with industry's share of R&D expenditure and patents typically accounting for 70 to 80% of the total (NCSES 2014). For the year 2010 industry research expenditure data from 30 countries is available, including the United States. But as patent application data from the USPTO is used, the United States has a 'home bias', and the country is therefore dropped from the analysis. Expenditure statistics in the STAN database are categorized by industry, following the International Standard Industrial Classification (ISIC), revision 4.2, which is broadly equivalent to and compatible with the European Union's Statistical Classification of Economic Activity (NACE) revision 2.
It is important to note that the STAN database is incomplete: data is not published for every industry and for every country as becomes clear from the number of observations noted in Table 3 and Table 4. In this study industries classified as high and medium-high technology are selected, see Table 1. The distinctions between high and medium-high technology are made by Eurostat. The
distinction is based on the share of tertiary-educated employees in the respective industry's workforce (Eurostat 2014).
Technology Level Manufacturing Industry
High 21: Pharmaceuticals, medicinal chemicals and botanical products 26: Computer, electronic and optical products
30.3: Air and spacecraft and related machinery Medium-high 20: Chemicals and chemical products
27: Electrical equipment
28: Machinery and equipment n.e.c.
29: Motor vehicles, trailers and semi-trailers 30: Other transport equipment
32.5: Medical and dental instruments and supplies.
Table 1: Selected manufacturing industries based on Eurostat high-tech aggregation (high and
medium-high) classified according to NACE Rev. 2.
Country Code Country Name Region
AT AU BE CA CN CZ DE DK EE ES FI FR GB HU IL IT JP KR MX NL NO PL PT RO SG SI SK TR TW Austria Australia Belgium Canada China Czech Republic Germany Denmark Estonia Spain Finland France United Kingdom Hungary Israel Italy Japan Korea (South) Mexico Netherlands Norway Poland Portugal Romania Singapore Slovenia Slovakia Turkey Taiwan Europe Asia-Pacific Europe North America Asia-Pacific Europe Europe Europe Europe Europe Europe Europe Europe Europe Middle East Europe Asia-Pacific Asia-Pacific North America Europe Europe Europe Europe Europe Asia-Pacific Europe Europe Europe Asia-Pacific
Table 2: Selected countries
The data for R&D expenditure match the priority date in the patent applications, i.e. both cover the year 2010. However several studies have shown that a time lag between research expenditure and
the patent application exists, which varies between half a year to two years (Rapoport 1971; Wagner 1968; Pakes and Griliches 1980; Mansfield 1968). Although a time lag may exist, R&D expenditure does not vary significantly between years, for instance when using R&D expenditure data for 2011 (which is available for almost the same countries as 2010) with 2010 patent data, no significant change in the results is found. The 29 countries included in the analysis are listed in Table 2. They are selected based on the availability of data.
Although the countries included in the analysis (Table 2) are selected based on data availability, according to the IMF (http://www.imf.org/external/data.htm), the sample includes 14 of the world's 20 largest national economies and more than half of global GDP in nominal terms. Although mainly consisting of European countries, the sample also has countries from the Asia-Pacific region, the Middle East and North America. Although some countries with significant innovation activity are excluded from the analysis (such as the United States, Russia and Switzerland), the present research aims to draw general conclusions and its outcomes are therefore less likely to be affected by the inclusion or exclusion of individual countries.
4.2 Data Processing
USPTO bulk bibliographic data does not contain ISIC codes, nor indicators of whether the author is a university or non-profit foundation. Instead, these entities are listed as either US or foreign corporations. However the distinction between a for-profit corporation, non-profit foundation or university is an important distinction for the Triple Helix model. Therefore all assignees are evaluated using a simple algorithm which detects words such as university, college, school, academy, foundation and institution in their names, both in English and other national languages, from the countries being studied. A full list of search terms is provided in the appendix. Based on verifying 100 randomly selected samples by hand, we estimate an error rate of 3%.
ISIC codes are also not part of the USPTO bibliographic dataset. The translation of patents to industries uses the concordance tables created with the 'algorithmic links with probabilities' approach (Lybbert and Zolas 2014), which can be used to assign ISIC codes based on patents' international patent classification (IPC) code. The IPC code is included in the USPTO bibliographic dataset. The concordance tables are developed using a probabilistic approach, and therefore a patent can be partially assigned to multiple industry categories. It must be noted that patents sometimes carry multiple classifications, which leads to double-counting. But since the number of patents that are double-classified is small (less than 0.1%), and some authors have suggested that multiple classification increases their value (Deng 2007), this is not corrected.
During the data analysis process, all correlations are also visually scanned for outliers: countries whose research input and output clearly diverges from the average are removed so as not to affect the result. Typically the divergence is around 10 standard deviations and there is only a single country that is an outlier. Because of the extremely large and exceptional deviation, the countries are removed. The reasons for the deviation are not immediately clear.
4.3 Patterns of correlation
Below in Table 3 and Table 4 is an overview of the correlations relevant to the four hypotheses outlined in section 3.2. Here R is the Pearson correlation, p is the confidence level of the correlation and n is the number of observations (countries) available for each industry.
ISIC Innovation Input and Output R p n 21 26 30.3 0.686 0.934 0.837 1.000 1.000 1.000 28 29 17 20 27 28 29 30 32.5 0.918 0.776 0.853 0.724 0.818 0.851 1.000 1.000 1.000 1.000 1.000 1.000 25 26 28 25 22 21
Table 3: Correlations between innovation input (R&D expenditure) and innovation output (patents) by
industry.
ISIC Open Innovation Triple Helix International ties
R p n R p n R p n 21 26 30.3 -0.283 0.438 ** 0.787* 0.960 ** 21 22 ** -0.074 0.619 ** 0.256* 0.993 ** 22 17 (-BE) ** 0.578 0.709 -0.599 0.999 1.000 0.990 26 (-SK, SI) 28 (-SK) 15 (-NL, SG) 20 27 28 29 30 32.5 0.579 0.731 0.211 ** 0.037 0.643 0.986 0.992 0.628* ** 0.091* 0.989 17 11 (-CN) 20 ** 12 14 0.424 -0.419 0.541 ** ** 0.655 0.901 0.829* 0.981 ** ** 0.987 16 (-MX) 12 18 (-MX) ** ** 13 -0.066 0.321 -0.466 -0.371 -0.125 -0.533 0.246* 0.891* 0.988 0.933 0.423* 0.988 25 26 28 24 (-DK) 22 20 (-NO)
Table 4: Correlations between frequency of collaboration type and innovation productivity by industry, *
correlation not statistically significant, ** sample size too small.
Note that the number of observations for some perspectives, especially Triple Helix, is very low. This is because in many countries' industries no Triple-Helix relations were observed in 2010 as the overall percentage of Triple Helix patents is only about 2.7%. Typically the sectors where zero Triple Helix patents are detected only produce 10 to 20 patents and so to avoid skewing the results, zero Triple Helix clusters are removed from the result.
5 RESULTS AND ANALYSIS
A summary of the correlations listed in Table 3 and Table 4 is given in , below:
The results first of all confirm hypothesis 1, that R&D expenditure (innovation input) is positively correlated to patenting activity (innovation output). Given the diverse sample of countries (see Table 2), this is a significant result in itself, suggesting that innovation productivity at the industry level is broadly equal across countries. (de Rassenfosse and van Pottelsberghe de la Potterie 2009; Furman, Porter, and Stern 2013). It also means that this important underlying assumption of the research model holds, making it possible to evaluate hypotheses 2, 3 and 4.
However hypotheses 2, 3 and 4 are not confirmed by the results, in fact, correlations vary significantly depending on the industry being analysed. This variation is significant because it qualifies the three collaboration types: they are not a positive influence on innovation performance
in every industry.
ISIC Innovation Input
and Output Open Innovation Triple Helix Internationalties
21 (Pharmaceutical) 26 (Electronics) 30.3 (Aerospace) + + + none + ** none + ** + + – 20 (Chemicals) 27 (Electrical equip.) 28 (Machinery) 29 (Automotive) 30 (Other transport) 32.5 (Medical/dental instruments) + + + + + + + + none ** none + + none + ** ** + none none – – – none
Table 5: Summary of correlations, ** sample size too small.
The variation of innovation networks between industries is something that has been recognized in the technology management and technology policy literature (Tidd and Bessant 2013; Asheim, Coenen, and Vang 2007). The distinction between science-based and engineering-based industries is primarily connected to whether codified knowledge is used, and what the primary source of innovation is. In science-based industries codified knowledge created at universities is an important source of competitive advantage, while in engineering-based industries tacit knowledge gained by interaction with other market participants is the main source of a firm's competitive advantage (Asheim, Coenen, and Vang 2007; Jensen et al. 2007).
Existing empirical research also confirms variations in collaboration patterns between industries. Industries with above-average levels of partnering include pharmaceuticals (21), electronics (26), and dental/medical instruments (32.5). Industries with above-average levels of international partnering include chemicals (20), pharmaceuticals (21), electrical equipment (27) and aerospace (30.3), while those with below-average levels of partnering include electronics (26), automotive (29), and dental/medical instruments (32.5) (Tidd and Bessant 2013; Hagedoorn 2002). The study by Hagedoorn (2002) is based on the Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT)'s MERIT-CATI database which contains records of R&D collaboration between firms worldwide as published in the specialised business and industry publications, company annual reports, academic papers, books, case studies and certain commercial databases. The data on which Hagedoorn's (2002) result is based is from 1960 to 1998. This large time gap of at least 12 years is one potential source of the differences in outcome, as R&D collaboration has rapidly expanded during the past decade (Chesbrough 2006).
Taking this prior research as a starting point for an interpretation of the analysis, the second hypothesis, which broadly reflects the open innovation perspective, is confirmed in a number of different industries, including electronics (26), chemicals (20), electrical equipment (27), motor vehicles (29) and medical equipment (32.5). But it is not found in pharmaceuticals (21), aerospace (30.3), machinery (28) and other transport equipment (30) (note that 30.3 is also part of 30, so the similarity in outcomes is not surprising). This suggests that in these industries research collaboration by itself does not lead to improvements in innovation performance. The results, except for dental/medical instruments (32.5), also conflict with the findings of Hagedoorn (2002). This suggests that collaboration networks extracted from patent data may vary significantly from
collaboration networks extracted from other sources. Thus using patent networks as a proxy for other forms of innovation collaboration should be critically evaluated.
Collaboration that yields co-assigned patents often involves a significant amount of risk and knowledge sharing. Depending on the competitive conditions of the industry, research collaboration at the patenting stage may be more difficult, especially if patents are a key source of a firm's competitive advantage. The idea that patents play an important role in securing competitive advantage in the pharmaceuticals industry seems very plausible, given the industry's vocal opinion on intellectual property protection, whereas in the electronics industry (26) research consortia and patent cross-licensing is more common (Lehman 2003). This would explain why hypothesis 2 is confirmed in the electronics industry (26), but rejected for pharmaceuticals (21). Another reason is the tendency of large pharmaceuticals companies to buy out smaller start-up firms, thus 'internalizing' much of the research collaboration that takes place, and removing it from co-patenting indicators.
The third hypothesis, which reflects triple-helix collaboration, especially between university and industry is confirmed in most industries, except in pharmaceuticals (21), electrical equipment (27) and motor vehicles (29). The lack of correlation between the triple helix model and pharmaceuticals (21) is somewhat surprising, as biotechnology is often viewed as a sector that depends heavily on university research. But this difference can perhaps be explained by differences in collaboration mechanism: electronics, chemicals, machinery and medical equipment may be sectors where there is active research collaboration between both industry and universities, whereas in biotechnology spin-offs are a frequently used form of technology commercialization which is not captured by the data. Electrical equipment and motor vehicles are clearly very engineering-based industries in which the contribution of university-based research may contribute much less to innovative performance.
The fourth hypothesis, which reflects the existence of global pipelines/international ties offers the most interesting result as there are positive and negative correlations. In more high technology and science based industries such as pharmaceuticals (21) and electronics (26), international collaboration raises innovative performance. But in medium-high and more engineering focussed technologies such as machinery (28), motor vehicles (29) and transportation equipment (30), international ties are indicative of lower innovation output. These results conflict with those by Hagedoorn (2002), again suggesting that collaboration networks vary significantly depending on the indicator used.
A possible explanation for the results from hypotheses 4 is provided by Asheim et al. (2007). Engineering-based industries, if equated to the medium-high technology category and aerospace (30.3) tend to be more market focused, relying more on local “buzz” for their competitive advantage. Therefore high levels of local collaboration should be expected. However for science-based industries knowledge is more codified. Pharmaceuticals (21) and electronics (26), especially semiconductors, are science-based industries in which knowledge is highly codified and this makes international collaboration more viable, although such firms nevertheless tend to cluster around the top universities, as is the case in California's Silicon Valley, Taiwan's Hsinchu High Technology Park and Israel's Silicon Wadi (Asheim, Coenen, and Vang 2007; Tidd and Bessant 2013; Ponds, Van Oort, and Frenken 2010; Anselin, Varga, and Acs 1997).
machinery (28) and other transport (30). The second couple's (28 & 30) similarity may be due to the fact that both are engineering intensive, whereas the first couple (20 & 32.5) are mixed in that the chemical and medical parts may originate from the science base, but the chemical processes and equipment are engineered around this knowledge. Hence their correlation to international ties is neutral, but not negative, as both engineering (involving the transfer of tacit knowledge) and science (involving codified knowledge) play an important part. And because of the scientific base, correlation with Triple Helix ties is positive.
However the establishment of collaborative ties is not exclusively the result of scientific and economic factors: cultural factors may also play a role. At the institutional level, the cultural gap between university and industry can form an important barrier to collaboration (Bjerregaard 2010). At the same time, collaboration which is too close has the potential to limit innovative performance by creating situations of technological lock-in and closing of the network to potentially beneficial outside influences (Tödtling and Trippl 2005).
At the national level, a number of distinct cultural characteristics have been identified (Hofstede, Hofstede, and Minkov 2010). From that perspective certain cultural characteristics may be more conducive to establishing collaborative research relationships, such as a common language, similar cultural norms, comparable educational background and shared social network (Gertler 1995; Gilsing and Nooteboom 2005). Thus cultural factors may ultimately be an important determinant of innovative performance, and this is clearly another area of potential future research.
In summary, while hypothesis 1 is accepted, the other three hypotheses can only be partially accepted as their level of correlation varies depending on the industry. Although the exact drivers of this differentiation ought to be the subject of further research, there appears to be a distinction between science-based and engineering-based industries, including hybrid industries which draw on both modes of innovation.
6 DISCUSSION AND CONCLUSION
The analysis presented here provides a number of interesting insights in relation to the research questions formulated in the introduction, and raises several questions for further research.
For the first research question, 'What differences can be observed in innovation performance between countries for particular sectors, and what differences can be observed concerning the three cluster conditions in collaboration between the sectors in these countries?' the results suggest that innovation output is positively correlated to innovation inputs, and that innovation productivity is relatively constant within the same industry, but varies between industries. The three cluster conditions vary significantly both between and within industries.
For the second research question, 'To what extent is there a correlation between innovative performance and the level of collaboration as evidenced by any of the three cluster conditions?' the results suggest that the influence of the three cluster conditions correlate to innovation performance, but that this correlation is not uniform; in the case of international ties, correlation with innovation performance is in fact negative for some industries, and positive for others.
For the third reseach question, 'To what extent does the correlation between innovative performance and collaboration, as evidenced by the three cluster conditions, vary by type of industry?' the results suggest that there are significant differences between industries which cannot be easily explained by
a distinction between science-based or engineering-based industries.
These insights raise a number of methodological issues. First, the validity of the collaboration networks that are extracted from patents should be carefully evaluated, because they diverge significantly from the study by Hagedoorn (2002). This either means that collaboration networks change significantly over time, or that collaboration networks extracted from patent data differ significantly from collaboration networks extracted from other data sources.
Second, the results selectively support the three different perspectives on innovative cluster performance, with their significance varying between industries. This is very intriguing as it suggests that different industries may benefit from different collaboration patterns, and that changes in collaboration patterns, or policies directed thereto, may not always enhance innovative performance. Of particular interest is the fact that international co-invention of patents is a sign of weak innovation performance in medium high-tech industries that are more engineering-based. This finding points towards a more nuanced view of the role of internationalization in innovation.
Finally, the fact that a number of 'outlier' countries were selectively removed during the data processing phase raises interesting questions about these countries. While some outliers could be explained by a limited presence of their science and technology sectors, the removal of the Netherlands (NL) and Singapore (SG) from international co-invented patents for aerospace (30.3) is very interesting and could suggest these countries are facing a unique situation in terms of their innovative cluster development. Similar exceptions may exist in other countries, such as the United States, that are not included in the present study.
The findings in this paper are also subject to a number of limitations and offer several directions for future research. First, only patent data is used, and so similar research could be carried out using academic publications to attempt further validation of the findings. Second, only data from 2010 is used, and data from different years could help ascertain whether the collaboration network features observed vary with time. Third, the framework of science-based and engineering-based industries should be further explored to provide a deeper theoretical understanding of the differentiation in collaboration patterns. Fourth, the propensity to collaborate may be influenced by cultural and institutional factors was not taken into account in this study and may prove to be fruitful areas of future research.
APPENDIX
The identification occurred in series. Only the final identification was used to classify an assignee.
Triple Helix group Identifiers (comma separated)
Industry Ltd., limited, inc., incorporated, corp., corporation, gmbh, gesellschaft, ag, s.p.a., b.v., n.v., bv, nv, kaisha, compagnie, company, llc, s.a., s.r.o., co., kft., a/s, ab, g.k., sas
Government Foundation, stichting, stiftung, funda, fonda, e.v., institut, national, nacional, centre, center, zentrum, centrum, organization, organisation, centro
University Akadem, academ, universi, universid, college, technion, georgia tech, institute of technology, purdue, institute of science, uniwersytet, universzita
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