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Paper to be presented at the DRUID Tenth Anniversary Summer Conference 2005 on

DYNAMICS OF INDUSTRY AND INNOVATION: ORGANIZATIONS, NETWORKS AND SYSTEMS Copenhagen, Denmark, June 27-29, 2005

THE FRUIT FLIES OF INNOVATION: A TAXONOMY OF INNOVATIVE SMALL FIRMS Jeroen P.J. de Jong EIM Small Business Research E-mail: [email protected] Orietta Marsili* RSM Erasmus University E-mail: [email protected] *Corresponding author Erasmus University Rotterdam, Burg. Oudlaan 50, 3000 DR Rotterdam, The Netherlands Tel.: +31 (0)10 408 9508 Fax: +31 (0)10 408 9015. February 2005 Abstract Taxonomies of patterns of innovation give a dominant role to large firms, and are often based on empirical studies that exclude micro firms. This paper proposes an empirical taxonomy of the innovative firms at the bottom of the size distribution, based on a new survey of 1,234 small firms and micro firms in the Netherlands, in both manufacturing and services. These firms differ not only in their innovative activities, but also in their business practices and strategies – such as management attitude, planning and external orientation – that they use to achieve innovation. The taxonomy identifies four categories of small innovative firms: science-based, specialised suppliers, supplier-dominated and resource-intensive. It suggests a more diverse pattern of innovation of small firms than in Pavitt’s (1984) taxonomy, a pattern that is shared by both manufacturing and services firms. Finally, the research shows that taxonomies can be effectively used to map differences in the rates, sources and nature of innovation, with the differences in the business strategies of innovative firms. Keywords: Innovation; Taxonomy; SMEs; Business strategies; Services.

1. Introduction Fruit flies are a favourite object of study in evolutionary biology as things happen fast for them and their evolution can be observed over short time periods (Maynard Smith, 1996)1. In analogy, the firms at the fringe of the business population may take a similar role in the study of technological change. Innovation creates a lot of “room” at the bottom of the size distribution, as new small firms continue to enter the market with new ideas for new products and processes (Audretsch, 1995). Often these firms are short-lived, as they exit the market within few years after their entry, thus leading to high turnover of firms at the margin (Caves, 1998). However, those that do innovate successfully, can increase considerably their chances of survival (Cefis and Marsili, 2003; De Jong et al., 2004). Despite the short appearance of some, the new firms who survive contribute to a large proportion of growth at the economy wide level (Foster et al., 1998). Yet, their behaviour can vary substantially. Some new firms survive by competing in a market niche, while others may pursue more radical innovations and become themselves market leaders. Pavitt (1998) argued that the diversity of innovative behaviour in reality, for small as for large firms, cannot be reduced to the few invariant laws of a general model. Instead, in order to understand this diversity, one needs to draw on the descriptive tools and techniques of biology sciences (more than of physics). In Pavitt’s words: “…[T]ruths about the real innovating firm will never be elegant, simple or easy to replicate. Certainly, it is just as wrong to criticise formal models in evolutionary economics, because they don’t help managers, as to criticise formal biological models, because they don’t help us to survive in the forest. But in biology, there are also the empirical and often descriptive disciplines of botany and zoology, which offer insights that are mostly conditional and contingent, but quite useful in coping with the real world” (Pavitt, 1998). Taxonomy, as the science of classification of organisms, has largely diffused to social sciences, and among these to the study of technological change. As exemplified by Pavitt taxonomy (1984), empirical classifications are a useful tool that helps understanding the diversity of innovative patterns in firms and sectors (Archibugi, 2001). Taxonomies are systems of classification that organize and label many different items into groups that share common traits. A useful taxonomy is one that reduces the complexity of empirical phenomena to few and easy to remember categories. Taxonomies provide scholars with a framework that help to build theories. As well, practitioners and policy makers can use taxonomies to shape firm strategies and policy decisions (Pavitt, 1984; Archibugi, 2001). The use of taxonomies of innovation finds a theoretical precedent in the concept of “technological regime” (Nelson and Winter, 1977; Dosi, 1982). Nelson and Winter (1977) propose that a “useful theory of innovation” needs to be built on the empirical observation of the diversity of innovative behaviour of firms; what they call “appreciative theorising”. In particular, firm behaviour is shaped and constrained by the nature of technology, whose varieties of forms can be categorised into a few broad categories, or “technological regimes”. They reflect differences across technologies in (i) the sources and principles of knowledge at the origin of innovation (Dosi, 1982); (ii) the boundaries that can be achieved in the process of innovation (Nelson and Winter, 1977); and (iii) the directions, or “natural trajectories”, along which incremental innovations take place (Nelson and Winter, 1977; Dosi, 1982). Previous empirical research has shown that the sources, rates and directions of technological change vary significantly across sectors, both in manufacturing and in services (Pavitt, 1984; Klevorick et al., 1995; Evangelista, 2000). These patterns however have been 1

SPRU Conference on “Evolutionary Economics”, University of Sussex, UK, March 11 – 12, 1996.

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explored empirically mainly for relatively large firms, while sectoral variations in innovative behaviour of small and medium sized firms (SMEs) are yet overlooked. Many studies have examined the success factors of innovation in SMEs, looking at the characteristics of products, firms, markets and innovation processes. Yet, most of the studies on SMEs did not investigate in a systematic way the differences across industries in the patterns of innovation (Brouwer and Kleinknecht, 1996; Hadjimanolis, 2000; Bougrain and Haudeville, 2002). This often reflects a lack of feasible data, especially for micro-firms with less than 10 employees. This paper aims at classifying empirically groups of SMEs with similar innovation patterns. For this purpose it examines the innovative behaviour of a sample of 1,234 Dutch firms with less than 100 employees. In doing so, it extends the current literature on innovation patterns in several directions. First, the classification is built using some new indicators, which are especially relevant to innovation in SMEs. Second, it captures manufacturing and service firms at once, thus enabling a direct comparison between the two. Also, it selects the firm as its unit of classification, while most previous taxonomies are formulated at the sector level. Finally, it includes micro-enterprises, with less than 10 employees. This adds to taxonomies based on the data from the Community Innovation Survey, with is carried out for firms with at least 20 employees in most countries, and for firms with at least 10 employees in few countries. The results show that the innovation behaviour of small firms is highly differentiated; small firms cannot be regarded as a homogenous group in their innovative activities. We find that a classification into four categories well represents this diversity, with a profile that resembles Pavitt’s taxonomy. This suggests that small firms display innovative patterns more diverse than expected with Pavitt’s taxonomy, in which small firms fall within the two categories of specialised suppliers and supplier dominated firms. In addition, firms in manufacturing and services sectors share common patterns, as firms from both sectors belong to the all range of categories. The paper is organised as follows. In the Section 2 we present an overview of the relevant taxonomies of innovation in the literature and discuss some methodological issues in the building of taxonomy. Section 3 describes the sample used in the analysis, the data collection process, and the variables which we employ to construct and validate the taxonomy. Section 4 illustrates the method of classification of the innovative firms. In section 5 we present the results of the analysis and describe the profile of the taxonomy obtained into four clusters of firms. Section 6 is the conclusions. 2. Taxonomies of innovation In his pioneering article, Pavitt (1984) proposed a taxonomy that distinguishes different categories of innovative firms based on their structural characteristics and organisation of innovative activities. The aim of the taxonomy was to provide an empirically based framework as a basis for developing a theory of innovation as well as guiding S&T policies. Based on the data from the SPRU innovation survey and review of cases studies, Pavitt identified four groups of firms: science-based, specialised suppliers, supplier dominated and scale intensive. The Pavitt taxonomy has become a heavy-cited framework for innovation researchers to explore deviations across industries. Indeed, the 1984 article is the most cited of Keith Pavitt’s articles (Meyer et al., 2004). Pavitt taxonomy has been used as a predictive tool of various dimensions of firms’ behaviour and performance, such as: strategy related variables (Souitaris, 2002); the sources of international competitiveness (Laursen and Meliciani, 2000); and innovative performance (Laursen and Foss, 2003). Although Pavitt taxonomy was in principle formulated at the firm level, the various categories are often regarded, as also suggested in the 1984 paper, as representative of interindustries differences in technological conditions (Archibugi, 2001). Other taxonomies can be

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found that focus on the organization of the innovation process in particular industries. A classification inspired to the views of the innovative firm expressed by Schumpeter (1934; 1942), distinguishes between two categories: “Schumpeter Mark I” (SM-I) and “Schumpeter Mark II” (SM-II) (Malerba and Orsenigo, 1995). This classification is directly linked to the concept of “technological regime” (Nelson and Winter, 1977) and the typology suggested by Winter (1984). This identifies an “entrepreneurial regime” (or SM-I) as one in which the nature of technology favours innovation by new and small firms. Opposite to that, a “routinised regime” (or SM-II) is one in which the nature of technology is such that established firms are the major innovators. The emergence in a sector of one or the other of the two Schumpeterian patterns of innovation is to be interpreted on the ground of the specific combination of the level and sources of technological opportunity; appropriability conditions; cumulativeness of innovation; and the nature of knowledge bases (Dosi, 1988; Malerba and Orsenigo, 1990; Breschi et al., 2000). Taxonomies of innovation built on the concept of technological regime have a cognitive basis; they reflect the diversity in the nature of the technological competencies that shape and constraint what “firms can and cannot do” (Pavitt, 1998). As pointed out by Nelson and Winter (1977), “[their] concept is more cognitive, relating to technicians’ believes about what is feasible or at least worth attempting” (p. 57). Other taxonomies of innovation, which are based on cognitive mechanisms, focus on the effects that major improvements in technology have on the firm-specific competencies (Pavitt, 1998). These effects consist of either enhancing the competencies accumulated by established firms, or of destroying them, and thus of threatening the position of the established firms in the industry (Tushman and Anderson, 1986). Based on this distinction, Abernathy and Clark (1985) elaborate a typology of innovations into four categories: incremental, component, architectural and revolutionary. Some taxonomies rest on the differences in the nature of technology as new technologies evolve along the stages of the Product Life Cycle (Utterback and Abernathy, 1975; Klepper, 1997). In the PLC model, industries are classified according to their stage of evolution, in relation to the changing nature of product and process innovation. Although with variations in the number of stages among later studies, the basic classification, going back to the work of Abernathy and Utterback (1978), distinguishes industries that are at a ‘fluid’ stage, a transition stage and a ‘specific’ stage. Recently, McGahan (2004) has proposed a revisited version of the PLC model, which account for the type of effects that technological change has on the core activities and core assets of an industry. To the extent that these effects may threaten industries of obsolescence, four trajectories of industry evolution are identified: radical, progressive, creative and intermediating. Classifications based on technology sectors were also developed under the auspices of the OECD, which contrast high-tech and low-tech industries. This type of classification is based on the intensity of technology production, as measured by the intensity of R&D expenditure in a sector, and, in a revised version, on the intensity of technology use and the process of diffusion across sectors (Hatzichronoglou, 1997). While the OECD classification is technology-based, recent attempts are made to also include non-technological dimensions (intangible investments and human capital) among the factors of production (Peneder, 2002). In sum, the biology analogy in the use of descriptive tools of analysis like taxonomies, advocated by Pavitt, is widely diffused in the literature on innovation studies. Table 1 provides an overview of the empirical taxonomies of innovation patterns that, in the past twenty years, have been elaborated in the line of Pavitt’s effort.2 ----- Insert Table 1 about here ----2

A review of industry classifications, which highlights the aim, scope and techniques of major taxonomies that have been developed in the broader context of applied economic studies, can be found in Peneder (2003).

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2.1. Methodology issues Pavitt’s taxonomy is not immune from criticisms and limitations. Archibugi (2001) points out two main issues: (a) the sectoral composition of the classification and (b) its unit of analysis. With regard to the sectoral composition, the main issue concerns the treatment of the services sector in the taxonomy. The 1984 version of the Pavitt taxonomy classifies the whole services sector within the category of supplier-dominated firms, while the revised version adds a fifth category of information intensive firms (Tidd et al., 2001). Nevertheless, studies of innovation in services highlight that there is more diversity of configurations within the sector than assumed in Pavitt’s only category of information-intensive firms (Miles, 1993; Evangelista, 2000; Miozzo and Soete, 2001). These studies often use more robust measures of innovation in services that are now available from the Community Innovation Survey (Evangelista, 2000; Kleinknecht, 2000). The new classifications display close resemblance to Pavitt taxonomy, suggesting that the patterns of innovation in the service firms can be as differentiated as in the manufacturing firms (Evangelista, 2000; Miozzo and Soete, 2001). Because of this similarity of results, as well as the difficulty to identify the actual activity of firms, who increasingly bundle manufacturing and services (Miozzo and Soete, 2001), Archibugi (2001) suggests that taxonomies should be developed jointly for the two sectors. With regard to the level of observation, the issue is here in the choice between the firm level and the industry level of analysis (Archibugi, 2001). Pavitt taxonomy, as well as most of its successors, is based on industry-level indicators of innovative activities (Archibugi et al., 1991; Hatzichronoglou, 1997; Evangelista, 2000). While the sector level may better respond to the requirement of policy making (Raymond et al., 2004), it does not account for the diversity of innovative performance and strategies within a sector (Dosi, 1988; Marsili and Salter, 2004). Archibugi (2001) argues that to account for this heterogeneity, taxonomies of innovation need to be developed directly at the firm level, before aggregating the firms into the standard system of industrial classification. Indeed, Pavitt taxonomy is in principle a taxonomy of firms, although its empirical validation relies on industry data (Archibugi, 2001). The few empirical studies that classify firms directly (Cesaratto and Mangano, 1993; Arvanitis and Hollenstein, 1998) find that firms within the same industry are often dispersed across several different groups of the constructed taxonomy. Following the lines of research discussed above, this paper extends previous empirical work in four respects. First of all it focuses on small and medium sized firms, and especially on micro enterprises (with less than 10 employees). Pavitt taxonomy is based on a sample of innovations that under represents the contribution of small firms; these, in turn, are grouped into two categories: supplier dominated and specialised suppliers. Successive empirical work has often overlooked the innovative behaviour of small firms, and most often that of micro firms (see Table 1). Samples are usually limited to firms with a number of employees above the threshold of twenty (Archibugi et al., 1991), and in few cases above ten (Raymond et al., 2004) or five (Arvanitis and Hollenstein, 1998). Yet, firm size appears to matter for the presence and nature of innovative practices (Welsh and White, 1981; Vossen, 1998; Bodewes and de Jong, 2003). Accordingly, this study aims at a more fine grained representation of patterns of innovation in small firms, as compared to Pavitt taxonomy. Second, we use some new variables to build a taxonomy, which have relevance to small firms, since indicators of R&D and of other innovation costs do not account for the more informal type of innovative activities that are typical of small firms (Brouwer and Kleinknecht, 1996; De Jong, 2002). In addition, we extend the set of variable used for the classification to include variables related to firm strategy. In this respect, the taxonomy is seen as an “integrative tool” (Souitaris, 2002), which links differences in the patterns of

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innovation, as expressed by the rates, sources and directions of innovation, with differences in firm strategies (Malerba and Orsenigo, 1993; Kaniovski and Peneder, 2002; Souitaris, 2002). Third, as suggested by Archibigi (2001), we consider manufacturing and service firms at once, thus enabling a comparison in the main patterns of innovative behaviour between the two sectors. More taxonomic exercises have been carried out for manufacturing than for services (Table 1), also because of lack of statistical data on innovation in the latter. Most often taxonomies are developed separately for manufacturing and services industries or whenever joined, services fall within one or two broad classes. We expect that a similar distribution of patterns of innovation emerges for manufacturing and services firms. Finally, while most taxonomies are based on sector-level data, we use firm-level data to classify firms directly according to their innovative behaviour. This allows testing for the assumption that firms within an industry share common innovative patterns (Archibugi, 2001). 3. Data and Variables 3.1. Data collection The data were collected as part of a survey performed by EIM Small business research. This survey was meant to collect innovation statistics for Dutch policy makers. Its sample was stratified across 18 industries and two size classes, covering all parts of the Dutch commercial business society with the exclusion of agriculture. The sample was randomly drawn from the population of all small and medium-sized firms in the Netherlands, following the Dutch definition of SMEs as firms with no more than 100 employees (Bangma and Peeters, 2003). The population was derived from a database of the Chambers of Commerce, containing data on all Dutch firms. The data were collected in April 2003, over a period of three weeks, by means of computer assisted telephone interviewing (CATI). All respondents were managers responsible for day-to-day business processes – usually the owner/entrepreneur, and otherwise a general manager. Attempts to contact the reference person were made five times before considering the company as a non-respondent. 3.2. Sample The initial sample consisted of 2,985 firms. Of these, 1,631 firms were willing to participate in the enquiry, with a response rate of 55 per cent. To check for non-response bias, the distribution of respondents and non-respondents across type of industry and size class was compared. The χ2-tests contrasting the two groups revealed no significant differences at the 5% level (p = 0.07 for type of industry and p = 0.65 for size class), indicating that nonresponse bias was not a serious problem. As the focus of the survey was on innovative firms, only respondents who had implemented at least one (product or process) innovation in the previous three years were asked to respond to the full questionnaire. Of the 1,631 respondents, 1,234 innovative firms completed the full questionnaire (76 percent). Another 383 firms did not implement any innovation recently, while 14 firms were discarded because of incomplete answers. Table 2 shows how the innovative firms are distributed according to their sector of activity and size class. Of the 1,234 innovative firms, 776 firms are microfirms, with less than 10 employees (63 per cent). This percentage under estimates, to a certain extent, the vast presence of micro firms in the entire population, for which it is 90 per cent of the total (Bangma and Peeters, 2003). Yet, our sample gives a much larger representation than other innovation surveys (the CIS excludes firms below the threshold of 10 employees). --- Insert Table 2 about here ---

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3.3. Clustering variables For the construction of the taxonomy, we rely on cluster analysis techniques. These are sensitive to the selection of the variables used, since the addition of irrelevant variables can have a serious effect on the results of the clustering (Milligan and Cooper, 1987). Cluster variables should also be representative for the typology one wants to present (Everitt, 1993). For this reason, we employ a combination of “core variables” that have been applied several times before and of new variables, because of their relevance to innovation in SMEs. Table 3 shows the dimensions and variables that formed the basis of our taxonomy. Some of them (‘managerial attitude’ and ‘product innovation’) are constructed by summing up the responses to different statements, for which the Cronbach’s alpha is also reported. --- Insert Table 3 about here --Core variables that have been largely used in building taxonomies of innovation are the innovativeness of a firm (input and output), also in relation to the different nature of product and process innovation, and the sources of innovation (Pavitt, 1984; Archibugi et al., 1991; De Marchi et al., 1996; Arvanitis and Hollenstein, 1998; Evangelista, 2000; Marsili, 2001); the latter often influence how various patterns of innovation are labelled. Innovative output. The definition and measurement of product and process innovation in the EIM survey are similar to those employed in the Community Innovation Survey (OECD, 1997). To account for both the presence and degree of novelty of product innovation, occurred in the three years previous to the survey, we construct an indicator that combines two dichotomic variables (Cronbach’s alpha = 0.74). One indicates whether or not a firm has introduced a product new to the firm, including minor product improvements or mere imitation. The other indicates whether or not a firm has introduced a product new to the industry, thus covering those product innovations with a higher degree of novelty. For process innovation, we use a dichotomic variable of whether the firm has introduced at least one new work process in the past three years. Innovative input. It is well known that input measures that rely on indicators based on R&D expenditure or R&D-personnel do not adequately capture the innovative effort by small firms (Brouwer and Kleinknecht, 1996; Roper, 1997; Rogers, 2004). For example, Sundbo (1996) argues that as an alternative to R&D, many small firms empower their workforce to contribute to the innovation process. Most SMEs are not involved in R&D themselves or, not being the best bookkeeper, they may fail to record their R&D-related expenses in their accounting systems. For the above reasons, the EIM survey collected information on alternative input indicators: the reservation of an annual budget (money) and of capacity (time) to implement new products or processes, and the presence of innovation specialists. The first variable reflects the financial constraints that especially within SMEs can act as bottleneck to realise something new (Acs and Audretsch, 1990; Hyvärinen, 1990). The investment of time is also regarded as a success factor (Brouwer and Kleinknecht, 1996; Roper, 1997), not only in terms of the time that one needs for developing innovations, but also the time that is devoted to the commercialisation stage and customer relationships (Hyvärinen, 1990). The third variable, presence of innovation specialists, is defined by the employees occupied with innovation as part of their daily work. In SMEs, this measure has been identified as one that predicts innovation success (Hoffman et al., 1998). Sources of innovation. Our dataset provides information on three alternative sources of innovation: suppliers, customers, and scientific developments. This selection is somehow limited with respect to the broad range of sources of innovation that are considered in the CIS questionnaire. However, customers, suppliers and knowledge/education institutes are generally found to be significant sources for innovation in SMEs (Hyvärinen, 1990; Brouwer

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and Kleinknecht, 1996; Roper, 1997; Appiah-Adu and Singh, 1998; Oerlemans et al., 1998). In particular, a strong customer orientation appears to be closely linked to the success of small firms in developing innovative products and services (Appiah-Adu and Singh, 1998). A second set of variables that we use in the construction of the taxonomy is composed of variables that are related to the strategy of the firms (Table 3). Only few studies have used similar variables in combination with taxonomies of innovation patterns (Evangelista, 2000; Souitaris, 2002). We consider three dimensions: 1) managerial attitude, 2) innovation planning and 3) external orientation. Managerial attitude. In SMEs the owner/entrepreneur has a larger direct influence on employees compared to managers of large organizations (Bodewes and de Jong, 2003). Leaders in small firms can successfully instil an ‘entrepreneurial dynamism’ (Davenport and D., 1999) in the behaviour of others in the organization. A positive attitude towards innovation correlates with a continuous attention for innovative opportunities and provides employees with support for innovative behaviour. This, in turn, strongly affects the decision to innovate and the way innovation is carried out in small firms (Kim et al., 1993; Hoffman et al., 1998; Hadjimanolis, 2000). Managerial attitude is measured by combination of three variables (Cronbach’s alpha = 0.67). They reflect a positive attitude of the SME towards investing resources (time) in innovation and the expected results from innovation, in relation to customers and competitors. Innovation planning. Planning is generally recognized as a factor for success especially in new firms (Delmar and Shane, 2003). This is one of the factors that distinguishes innovate firms from their less innovative counterparts (Hadjimanolis, 2000). In our dataset, this variable is measured by the presence of a documented innovation plan, implying that explicit ambitions, targets and milestones were defined and written down. External orientation. Empirical evidence supports that SMEs that are aware of and use external information perform significantly better in terms of innovation success (Hoffman et al., 1998; Romijn and Albaladejo, 2002; Freel, 2003). To represent this dimension, we use two variables. One is defined as the number of external parties that a firm consulted for information and advice, and, although these may not be directly related to the innovation process (see Table 3), they can be assumed to extend a firm’s knowledge base. The index is similar to the one introduced by Laursen and Salter (2004), to measure the degree of “openness” of a firm in the search for innovation ideas (Chesbrough, 2003; Laursen and Salter, 2004). The second variable refers to the participation in inter-firm cooperation, which is believed to favour innovation, particularly in SMEs, for a variety of reasons, such as the ability to overcome a lack of resources, to spread risks, and to acquire complementary assets (Brouwer and Kleinknecht, 1996; Hadjimanolis, 2000; Hanna and Walsh, 2002). 3.4. Variables used for validation In order to assess the validity of the taxonomy derived from the above variables we relay on a new set of variables that are not used to construct the taxonomy but are known or expected to vary across its clusters (Milligan and Cooper, 1987; Hair et al., 1998). For this purpose we use the variables indicative of whether the firm is a first mover, whether it has an explicit search strategy for new knowledge, and whether the firm use innovation subsidies (Table 4). --- Insert Table 4 about here --These variables can be assumed to vary across (groups of) innovative firms. If a cluster is highly innovative, we expect that its firms are more likely to regard themselves as firstmovers, to implement search strategies for new knowledge, and to use innovation subsidies.

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Finally, we investigate differences across type of industry and size class to establish whether some industries are more represented in some clusters of firms than others and to assess whether different patterns emerge across size classes. 4. Method Our analysis consists of three steps. We begin with a principal component analysis to reduce the number of dimensions in our dataset. Next, we apply cluster analysis techniques to build a taxonomy of innovative firms. Finally, we use analysis of variance and χ2-test to validate the taxonomy. 4.1. Principal component analysis Several studies that perform taxonomic exercises of innovation patterns use Principal Component Analysis (PCA), as a way to reduce the number of dimensions to be used in the clustering. However, due to the focus on the sector level of most studies, the component analysis may suffer from too few observations (Evangelista, 2000; Peneder, 2002), while at the firm level a greater number of observations can provide more robust results for it. In general, the PCA reduces the risk that single indicators dominate a cluster solution, and helps to prevent that irrelevant (non-discriminative) variables are included (Everitt, 1993; Hair et al., 1998). Another advantage is that the factors obtained from a PCA are uncorrelated and therefore no variable would implicitly be weighted more heavily in the clustering and thus dominate the cluster solution (Hair et al., 1998, p. 491). Since most of our variables have significant correlations, often exceeding absolute values of 0.20, they seem to be appropriate for a PCA. Furthermore, we test if our data are suitable for a component analysis, by calculating Measures of Sampling Adequacy (MSA) for the individual variables (Hair et al., 1998). All the variables, with the exception of process innovation, have satisfactory MSA values (> 0.60), and they are suitable candidates for a PPA. In addition, KMO and Bartlett’s test of sphericity met common standards (KMO = 0.80 and p(Bartlett) < 0.001) (Hair et al., 1998). With regard to the process innovation variable, the value of the MSA test suggests that this is not adequate for a PCA (MSA = 0.46 < 0.50). The great majority of firms, 92 per cent, carry out process innovations, leaving a small group of only 8 per cent of the cases to dominate any solution. Therefore we omit this variable from the taxonomy construction phase, and use it to validate and describe the profile of the clusters. In performing the principal component analysis, we use the extraction technique with varimax rotation and, for the selection of the number of factors, we apply the latent root criterion, requiring that the eigenvalues are greater than one. As result, we obtain a threedimensional solution explaining 46% of the variance. Since we use the PCA with the aim of reducing the number of dimensions for building the taxonomy, whose groups will be validated and profiled on the basis of the original variables, the output is not presented here.3 4.2. Cluster analysis Because cluster analysis is sensitive to outliers, we first assess for outlying observations in the scores of the three principal components. Using Hair et al.’s (1998) method, two extreme cases are detected: their scores on the first component deviate more than three standard deviations from the mean. Both cases are then eliminated from the analysis. In the cluster analysis, we follow a two-step procedure, which combines hierarchical and non-hierarchical techniques, to obtain more stable and robust taxonomy (Milligan and Sokol, 1980; Punj and Stewart, 1983). As a first step, we carry out a hierarchical analysis to group the firms into homogeneous clusters, by using the Ward’s method based on squared 3

This and other results of the cluster analysis that could not be reported for reason of space are available from the authors on request.

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Euclidian distances.4 Homogeneous groups are built so as to minimise the distance in scores of firms within a single cluster and to maximise the distance in scores between companies from the various clusters. This analysis gives the initial solutions for the taxonomy. The second step consists of a non-hierarchical cluster analysis to improve on the initial solutions and to select the number of clusters for the taxonomy. At first, a visual inspection of the dendogram, plotting the initial solutions of the hierarchical analysis, suggests a taxonomy with four clusters. For a better assessment of robustness, we then consider the all range of initial solutions from the hierarchical analysis, going from two up to six clusters. For each number of clusters (k), we perform a k-means ‘non-hierarchical’ cluster analysis, in which the firms are iteratively divided into clusters based on their distance to some initial starting points of dimension k. While some k-means methods use randomly selected starting points, we employ the centroids of our initial hierarchical solutions for this purpose (Milligan and Sokol, 1980; Punj and Stewart, 1983). Finally, to assess robustness we compute Kappa, the chance corrected coefficient of agreement (Singh, 1990), between each initial and final solution. The four-cluster solution appears to have the highest value of Kappa (k = 0.75, while k < 0.72 for the other solutions) and is thus selected as our final taxonomy. 5. Results 5.1. Descriptive statistics Table 5 reports the means for all variables used in the analysis. With regard to the output, process innovation is more widespread than product innovation. While 92 per cent of firms have implemented process innovations, 45 per cent has not carried out any product innovation. In input, a larger proportion of firms dedicate time to innovation (69 per cent), than those who reserve a budget for innovation (50 per cent). Lower is the percentage of those who write down a formal plan for innovation (37 per cent). As well, the presence of innovation specialists is fairly low (16 per cent). Customers are considered to be a source of innovation more important than suppliers and scientific developments. Small firms appear to be fairly ‘open’ as on average they tend to draw on more than three relevant sources of knowledge,5 55 per cent of them has an explicit search strategy for new knowledge, and 57 per cent participate in formal collaborations for innovation. In contrast, most firms do not consider themselves as first mover innovators (76 per cent), and a small proportion uses innovation subsidies (18 per cent). Finally, respondents display a positive attitude towards innovation, with a mean score of 4.06 close to the maximum. This is not surprising as the sample consists of innovative firms only. Yet, we do recognize some variance in the extent to which respondents agree on this summated scale.6 --- Insert Table 5 about here --5.2. Empirical taxonomy Table 6 shows the descriptive statistics of the four clusters of small innovative firms. On the basis of the scores of the clustering variables, we label the four groups, to stress the similarity with Pavitt taxonomy, as supplier-dominated (23 per cent), specialised suppliers (24 per cent), science-based (26 per cent) and resource-intensive firms (27 per cent).

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The Ward’s method generally provides good results compared to other clustering methods (Milligan and Cooper, 1987). 5 In particular, 4 per cent of firms consult with 6 or more external sources; 13 per cent with five sources; 27 per cent with four; 25 per cent with three; 18 per cent with two; 9 per cent with one source and 4 per cent with none. 6 A closer inspection of the data shows that the mean score falls in the range (4; 5] for 36 per cent of the firms; in (3; 4] for 58 per cent; in (2; 3] for 5 per cent of the cases, and in [1; 2] for the remaining one percent.

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-- Insert Table 6 about here -Supplier-dominated firms. Among the four groups, this displays the lowest score in most variables and below the average in all but process innovation, the role of suppliers as sources of innovation (for which it ranks first), and the number of external sources of knowledge (for which it ranks second). Innovativeness is low in all dimensions: in all forms of input (financial, time and employment), in formal planning and managerial attitude. Innovation mainly consists of process innovation, and essentially responds to the proposal of new applications from suppliers. Firms are relatively “open” as, on average, they consult with more than three external parties. However, this orientation seems to be more related to the normal solution of business problems than to formal partnership aimed at innovation. Specialised suppliers. Innovativeness among these firms is fairly high. It is almost at the highest level (comparable to science based firms) in product innovation, while it is lowest in process innovation, implying a distinctive prevalence of product on process innovations. The innovation process is based on a more diffused use of specialised labour when compared with financial and time resources. Firms are customer driven, as they heavily rely on understanding customers’ needs as a source of innovation. Together with the fact that the relevance of the other sources of innovation, suppliers and scientific development, is the lowest in this group, it suggests a distinctive customer focus. This is also consistent with the low degree of “openness” of these firms, given the number of sources they consult is the lowest (about two), although they frequently participate in formal collaboration (likely with their customers). Science-based firms. This cluster displays the highest score on most variables with the exception of the relevance of suppliers as a source of innovations (the only one for which it ranks third). Innovativeness is high, both in products and processes, and innovation specialists are most often employed within these firms. Firms are distinguished for using knowledge from universities and research institutes as a source of innovation, but they also draw heavily on customers needs (the latter is a common feature with specialised suppliers). Managers have a strong positive attitude towards innovation, which is most frequently accompanied by a written-down plan (66 per cent versus the average of 37 per cent). Science-based firms are also the most “open”; they tend to consult with more than four external parties on average, and very often participate in innovation collaborations (90 per cent of firms). Resource-intensive firms. This group shares some characteristics with supplierdominated firms, although the degree of innovativeness is relatively higher (not far from the middle), and the prevalence of process on product innovations, as well as the role of suppliers as a source of innovation, is less pronounced. The most distinctive feature consists in the high shares of firms who reserve budgets and time for innovative activities, 92% and 95% respectively, which are the highest across the four clusters. The external orientation is below average, in terms of both the consultation of external parties and formal partnership for innovation. These firms thus appear to allocate financial and time resources to innovation, although with a limited use of dedicated personnel and external networks. While the similarities of the first three clusters with Pavitt taxonomy are manifest, and hence the use of the same labels, the last cluster departs from it. Although it shares some traits with Pavitt category of scale intensive firms, such as the combination of in-house resources and reliance on suppliers as sources of (mainly) process innovation, yet, the similarity is the smallest, because our classification is not based on firm size. 5.3. Validation and industry composition As a basic requirement for validity, one needs to find significant differences on the variables that have been used to develop a taxonomy (Milligan and Cooper, 1987). A multivariate analysis of variance test reveals significant differences on all clustering variables (Pillai’s

10

Trace F-value = 143.0 and p < 0.001). In addition, one-way analyses of variance for each individual variable confirm this finding (see Table 6). In particular, significant differences are also found for the indicator of process innovation, which has not been used for cluster development. These differences are consistent with the clusters profile that emerges from the variables actually used in the clustering. Besides, validity is supported in the similarity with the Pavitt categories (1984); with process innovation in supplier dominated firms, a mix of product and process innovation in science based and product innovation in specialised suppliers. These results can be considered as a first indication of validity. A further assessment of the validity of clusters exploits variables not used to form the clusters, but known or expected to vary across them (Milligan and Cooper, 1987; Hair et al., 1998, p. 501). Table 6 also reports the descriptive statistics of the three external variables used for this purpose: whether the firm consider itself as first-mover in innovation, the presence of a formal policy to collect new knowledge, and the use of innovation subsidies. The analysis of variance shows significant differences across the four groups on all external variables. The nature of these differences is consistent with the characterisation of clusters in our taxonomy. As expected, science-based firms who are highly innovative both in relation to their internal practices and external networks, rank also highest on all external variables. Indeed these firms are often the first ones to introduce the innovations, have a well-defined search strategy for new knowledge (70 per cent of firms), and take the lead in the use of innovation subsidies. Supplier-dominated firms, with an almost opposite profile, score the lowest also in all the external variables. In particular, it is here supported that the external orientation that was observed in terms of consultation of various external parties, is not part of a search strategy for new knowledge. For the specialised suppliers, who are characterised for product innovation and close relationship with customers, it is not surprising that speed to the market is important; indeed, the number of firms that consider themselves as first movers is just below the maximum value (33% versus 24% average). Finally, the resource intensive firms again score slightly below or never far from the average of all innovative SMEs. Some more evidence on the taxonomy validity is found when comparing the distribution of clusters across industries and size classes (Table 7). The chi-square test shows that within the industries, the distribution of firms across the clusters is not uniform (χ2=142.6, df=51, p < 0.001). In some industries particular patterns appear as more represented than others. Science-based firms are well present in the chemicals, machinery, office and electrical equipment, as well as in economic services (e.g. consultancy) and engineering and architectural services. In the Netherlands, these industries are characterised by a highly educated workforce and the frequent application of new technologies (Bangma and Peeters, 2003). Specialised suppliers are most often found in wholesale and computer and related services; while supplier-dominated firms are the prevailing type in the metals industry, transport and construction, three industries that in the Netherlands have been recognized for their lack of initiative in innovation. Resources intensive firms are especially represented in hotels and restaurants, and personal services. At first glance, the industry composition of clusters suggests that firms from the various clusters can be found both in manufacturing and services. To establish whether there are systematic differences between the two sectors, we examine the distribution of firms in the four clusters within each one broad sector, leaving aside the construction firms (figures printed in italics in Table 7). A chi-square test shows that overall the differences are statistically significant at the 1 per cent level (χ2=14.5, df=3, p < 0.01), with a concentration of service firms in the resource intensive cluster. However, one should notice that – although one cannot deny variance across industries – the different patterns are observed in each individual industry (e.g. in each industry at there is at least 8 percent of the firms that are

11

supplier-dominated). This may reflect the high degree of aggregation of the industrial classification used in the analysis, which may average the patters of individual industries. As for size class, we observe significant differences in the distribution of clusters between micro firms (below 10 employees) and small-medium firms (between 10 and 100 employees). The chi-square test indicates that they are highly significant (χ2=45.8, df=3, p < 0.001), as science based firms tend to be relatively larger in size than other types of firms. 6. Conclusions Empirical taxonomies have proven to be a useful tool for understanding the diversity of innovative behaviour that can be observed across firms. In this paper, we have attempted at extending this approach to small firms (below 100 employees), which are often excluded or under-represented in taxonomies of innovation. In particular, within this class of firms, our research covers a dominant proportion of micro firms, with less than 10 employees. The class of micro firms finds very little part in earlier empirical taxonomies of innovation. Drawing upon a sample of 1,234 SMEs in the Netherlands, we derive a taxonomy of four types of innovative firms, with a profile that supports but also qualifies and extends Pavitt’s taxonomy to small firms, in both manufacturing and services. This leads to conclude that, first, the innovation patterns in small firms are more diverse than generally believed, as for example in Pavitt taxonomy, in which they are represented mainly by two categories. The similarity between our categories of small and new firms, and the Pavitt taxonomy, suggests that these firms can in fact play the role of the fruit flies of innovation. They manifest on a fast changing setting, some underlying properties of technological change that are likely to be found on the more stable and less selective setting of large and established firms. Second, our research shows that the diversity of patterns in services firms is as at least as broad as that observed in manufacturing firms, and the two sectors share, to a large extent, common patterns. This is consistent with the results of previous studies that have focused on classifications a la Pavitt of the service sector, and that have revealed highly differentiated patterns of innovation in the services firms (Evangelista, 2000). Our findings are also in line with Archibugi (2001) who proposes that the two sectors share some fundamentals in the process of innovation, such as the interaction with suppliers and the innovation intensity. This also consistent with the observation that the boundaries between manufacturing and services have blurred as services and manufacturing activities are often closely bundled within organizations (Miles, 1993; Schmoch, 2003). Finally, our results confirm that taxonomies of innovation based on differences in the patterns of innovation – as defined by the rates, sources and directions of innovation – can usefully be extended to map differences in business practices and strategies. These variables are especially important to capture the more informal aspects of the innovation process that are typical of small firms, and cannot be measured by traditional indicators of input and output. As in Souitaris (2002), we find that the differences in a number of strategy related variables – such as innovation budget, business strategy, and management attitude – can be systematically linked to differences in the rates, sources and nature of innovation. This supports Souitaris’s view of the Pavitt taxonomy as an “integrative tool” of the management perspective and the economic perspective in the study of the determinants of innovation. In this paper, we focused on some elements of a firm’s strategy that are related to its innovative activities. A question for further research is whether the taxonomic approach can be extended to link innovation to the broader set of competitive strategies of firms. For example, Laursen and Salter (2004) conjecture that the strategies firms pursue in their search for innovative ideas, could find a match with the way firms position themselves along the value chain. Taxonomies are a valid instruments to organise the diversity of patterns observed in reality, which can be used for policy making and theory building, to the extent that they are

12

invariant across specific contexts. In this research we have looked at the invariance with respect to firm size, as our taxonomy of small firms resembles others built on samples of predominantly large firms, and to the distinction between manufacturing and services. Another important issue is whether taxonomies are invariant across institutional contexts, in particular across countries and over time. Across countries, the specific institutional setting may lead not only to a different distribution of common patterns but also to the emergence of more idiosyncratic patterns. Over time, new industries may be created per effect of the introduction of new technologies as well as existing industries may change their profile (Archibugi, 2001); taxonomies are then to be seen not as static but rather as dynamic tools.

13

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Table 1 Author

Overview of empirical taxonomies of patterns of innovation Relevant dimensions and variables Data source and sample

Paviit (1984), • extended in Tidd et al. (2001) •

Sources of technology: R&D, design, suppliers, users, public science Type of user: price or quality sensitive

• Means of appropriation: patents, IPR, secrecy, etc. • Objective: cost-cutting or product design • Nature of innovation: ratio of product on process innovation • Firm size

Industry classification

Method

• SPRU Innovation Manufacturing and services: (1) • Sector-level science-based, (2) scale survey • Quantitative and intensive, (3) specialised qualitative • 2,000 significant suppliers, (4) supplier innovations in analysis dominated. Great Britain Extended in Tidd et al (2001) (1945-1983) with a fifth category: (5) • Dominance of information intensive large firms (53% with more than 10,000 employees, 25% with less than 1000)

• Rate and direction of technological diversification Archibugi et • Innovation intensity: share of • CNR-ISTAT al (1991) innovators; share innovation sales; innovation survey ratio of internal on external 1987 sources of knowledge • 16,700 Italian firms, with more • Nature of innovation: ratio product on process innovation than 20 employees • Sources of knowledge: design;

Manufacturing: (1) traditional • Sector-level consumer goods; (2) traditional • Cut-off points in intermediate goods; (3) ratios of industry specialised intermediate goods; level indicators to (4) assembled massthe mean across production; (5) R&D based industries

R&D; patents; capital embodied.

• Firm size: average size and concentration index of innovators De Marchi et • Innovation intensity: R&D, design, • CNR-ISTAT Manufacturing: Pavitt’s (1984) al (1996) patents innovation survey taxonomy 1987 • Nature of innovation: ratio of product on process innovation

Malerba & Orsenigo (1996)

• 16,700 Italian firms, with more than 20 employees

• Patent activities in Manufacturing: ‘Schumpeter Mark I’ (entrepreneurial) and 7 industrialised countries ‘Schumpeter Mark II’ • Persistence of innovation (routinised) • Institutions and • Technological entry and exit (firms firms excluding patenting for the first or last time) individual inventors

• Sector-level • Test of Pavitt’s taxonomy based on predicted rankings across pre-assigned groups and ANOVA

• Firm size of patenting firms

• Technology-level

• Concentration

• Factor analysis and cut-off points on factor scores

Hatzichronog • Technology intensity: intensity of lou (1997) direct and indirect (embodied) R&D

Manufacturing: (1) high tech, (2) • Sector-level medium-high tech, (3) medium• Cut-off points of low tech, (4) low tech • Sampling of small technology firms, varying indicators across countries

Arvanitis & Hollenstein (1998)

• Swiss KOF-ETH Manufacturing: 5 clusters innovation survey 1996

• Innovation intensity: inputs (R&D, design) and outputs (innovations’ value and shares of innovative sales) • Sources of knowledge: other firms, institutions, universally accessible information and other inputs (machinery, licences, personnel)

• ANBERD STAN dataset

• 516 firms with more than 5 employees

• Firm level • Factor analysis and clustering

Evangelista • Innovation intensity: innovation Services: (1) technology users, • Sector-level • ISTAT-CNR (2000) costs per employees, % innovators innovation survey (2) S&T based, (3) interactive • Factor analysis and IT based; (4) technical 1997 and clustering • Nature of innovation: ratio of consultancy. product on process innovation • 19,000 firms with more than 20 • Type of innovation inputs: R&D, employees design, software, training,

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Author

Relevant dimensions and variables Data source and sample machinery, marketing

Industry classification

Method

Manufacturing: (1) science based, (2) fundamental processes, (3) complex systems, (4) product engineering, (5) continuous processes

• Qualitative and quantitative analysis

• Sources of information: internal (R&D lab) and external (other firms, institutions, etc) • Innovation strategies: objectives of innovation (market driven, efficiency, etc) Marsili (2001)

• Technological intensity • Technological entry barriers (share of innovative activity in large firms) • Persistence of innovation

• SPRU databases on innovative activities of large firms

• Inter-firm diversity

• Sector-level

• Technological diversification • Sources of knowledge OECD (2001)

• Knowledge intensity: direct and indirect R&D expenditure; skill levels.

Manufacturing and services: (1) • Sector-level high-tech manufacturing, (2) • Cut-off points of low-tech manufacturing, (3) indicators • Sampling of small knowledge intensive services, firms, varying (4) traditional services across countries

Peneder (2002)

• Input intensity: labour; capital; • Expenditure by advertising sales ratio; R&D sales investment ratio. category in US firms

• ANBERD STAN dataset

•

Manufacturing: (1) technology- • Sector-level (3 driven, (2) capital intensive, (3) digit) marketing driven, (4) labour • Factor analysis intensive, (5) mainstream and clustering manufacturing.

Raymond et • Model of innovative behaviour: Manufacturing: (1) high-tech, • Econometric • Manufacturing al (2004) (2) low-tech, (3) wood industry. Estimated effects of firm-level and firms with more model at the firm industry-level characteristics on than 10 level with industrythe decision to innovate and the employees in the specific coefficients returns to innovation. Netherlands • CIS-2, CIS-2.5 and CIS-3

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Table 2

Distribution of innovative SMEs by industry and size class Size class

1-9

10-99

Total

%

employees employees

Industry Manufacturing: Food, beverages and tobacco

51

29

80

6

Textiles, leather and paper

39

25

64

5

Wood, construction materials and furniture

45

29

74

6

Metals

48

23

71

6

Chemicals, rubber and plastic products

49

31

80

6

Machinery, motor vehicles and transport equipment

40

24

64

5

Office, electrical, communication and medical instruments

42

29

71

6

Retail and repairs

35

25

60

5

Hotels and restaurants

41

18

59

5

Personal services

35

26

61

5

Transport

35

23

58

5

Financial services

48

26

74

6

Business services (cleaning, surveillance, etc)

42

23

65

5

Wholesale

48

25

73

6

Computer and related services

51

29

80

7

Economic services (accountancy, consultancy, etc)

45

26

71

6

Engineering and architecture

48

28

76

6

34

19

53

4

Total

776

458

1,234

100

%

63

37

100

Services:

Other: Construction

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Table 3

Variables used to develop the taxonomy of firms

Dimension (1) Innovative output

Variable Product innovation

Process innovation (2)

Innovative input

Innovation budget Innovation capacity Innovation specialists

(3)

Sources of innovation

Suppliers Customers

(4)

Managerial attitude

(5)

Innovation planning External orientation

(6)

Scientific development Innovative orientation

Documented plans Consultation of external sources

Inter-firm cooperation

Description and response code± Mean score of two items (Cronbach’s alpha = 0.74): 1. Firm introduced any product new to the firm in the past three years (yes/no) 2. Firm introduced any product new to the industry in the past three years (yes/no) Firm implemented at least one new work process in the past three years (yes/no) Firm reserved an annual budget (money) to implement new products or processes (yes/no) Firm reserved capacity (time) to implement new products or processes (yes/no) Firm employed people who were occupied with innovation in their daily work - e.g., specialised staff members, new product developers, etc. (yes/no) Firm innovates when suppliers propose new applications (5-point Likert scale) Firm innovates when customers express new desires/needs (5-point Likert scale) Firm innovates to commercialise universities/knowledge institutes’ new technologies or findings (5-point Likert scale) Mean score of three items (Cronbach’s alpha = 0.67) 1. It is worth to spend my time on innovation (5-point Likert scale). 2. Innovation enables my firm to better serve its customers (5-point Likert scale). 3. Innovation is needed to keep up with our competitors (5-point Likert scale). Firm had a documented plan describing renewal ambitions, targets and milestones (yes/no) Number of sources consulted for information or advice on any business problem in the past three years (e.g., suppliers, colleague firms, commercial consultants, sector organisations). Measure based on respondents’ indication of consulted parties (min = 0, max= 6 sources or more) Firm formally co-operated with other firms or institutes to initiate or develop renewal activities - based on a formal agreement (yes/no).

Note: ± Dichotomic responses are coded as ‘yes’ = 1 and ‘no’ = 0. Responses on a 5-point Likert scale are coded as ‘totally agree’ = 5, ‘agree’ = 4, ‘neither agree nor disagree’ = 3, ‘disagree’ = 2, ‘totally disagree’ =1.

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Table 4

Variables used to validate the taxonomy of firms

Description and response code± Firm is among the first to introduce new products, services or techniques (self-rated) (yes/no) Policy to collect new Firm has explicit policy to collect new knowledge (yes/no) knowledge Use of innovation Firm used innovation subsidies in the past three years (yes/no) subsidies Type of industry 18 industries (see Table 2) ‘1-9 employees’ and ‘10-99 employees’ Size class Variable First-mover

Note: ±

Dichotomic responses are coded as ‘yes’ = 1 and ‘no’ = 0.

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Table 5

Descriptive statistics of innovative SMEs (n=1,234)

Dimension

Variable

Mean score

Clustering variables Innovative output Innovative input

Sources of innovation

Managerial attitude Innovation planning External orientation

Product innovation Process innovation Innovation budget Innovation capacity Innovation specialists Suppliers Customers Scientific development Innovative orientation Documented plans Consultation of external sources (no. of sources) Inter-firm cooperation

0.42 0.92 0.50 0.69 0.16 2.64 3.19 2.19 4.06 0.37 3.15 0.57

First-mover in innovation Policy to collect new knowledge Use of innovation subsidies

0.24 0.55 0.18

Variables for validation

Note: For the frequencies of firms by industry and size class, also used for the validation and profiling of the taxonomy, see Table 2.

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Table 6

Profile of clusters of firms and analysis of variance tests Cluster

Dimension

Variable

Supplier Specialised dominated suppliers

Science based

Resource F-value intensive

Clustering variables Innovative output

Product innovation Process innovation± Innovative input Presence of budgets Presence of capacity Innovation specialists Sources of innovation

Managerial attitude Innovation planning External orientation

Suppliers Customers Scientific development Innovative orientation

0.13 0.93 0.14 0.25 0.01 3.33 2.56 1.65 3.72

0.59 0.84 0.18 0.57 0.19 1.91 3.56 1.14 4.08

0.60 0.96 0.67 0.91 0.31 2.47 3.58 4.61 4.33

0.36 0.94 0.92 0.95 0.11 2.83 3.06 1.27 4.10

103.5** 10.3** 323.2** 238.9** 41.3** 110.7** 58.9** 668.2** 69.2**

Documented plans

0.10

0.36

0.66

0.36

81.3**

Number of sources Inter-firm cooperation

3.22 0.39

2.24 0.57

4.47 0.90

2.64 0.43

223.3** 81.1**

0.06

0.33

0.34

0.21

30.5**

0.44

0.48

0.70

0.57

16.2**

0.07

0.15

0.39

0.11

48.6**

291

293

317

331

Variables for validation First-mover in innovation (self-rated) Policy to collect new knowledge Use of innovation subsidies N obs

Notes: ** p < 0.001, * p < 0.01, ^ p < 0.05, ± This variable was not used to construct the typology.

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Table 7

Distribution of firms across clusters within industries and size classes Cluster

Variable Industry Manufacturing: Food, beverages and tobacco Textiles, leather and paper Wood, construction materials and furniture Metals Chemicals, rubber and plastic products Machinery, motor vehicles and transport equipment Office, electrical, communication and medical instruments Services: Retail and repairs Hotels and restaurants Personal services Transport Financial services Business services (cleaning, surveillance etc) Wholesale Computer and related services Economic services (accountancy, consultancy) Engineering and architecture Other: Construction Totals: Manufacturing Services Size class: 1-9 employees 10-99 employees Total

N

Supplier Specialised Science dominated suppliers based

Resource intensive

80 64 74 70 80 64

28% 31% 24% 39% 16% 22%

24% 22% 32% 24% 31% 27%

26% 19% 18% 19% 35% 36%

23% 28% 26% 19% 18% 16%

71

21%

21%

38%

20%

60 59 61 58 74 65 73 79 71 76

32% 32% 25% 41% 20% 14% 18% 8% 15% 13%

17% 17% 10% 17% 28% 28% 33% 34% 24% 17%

22% 10% 15% 16% 27% 29% 22% 24% 41% 41%

30% 41% 51% 26% 24% 29% 27% 34% 20% 29%

53

40%

11%

17%

32%

503 676

26% 21%

26% 23%

27% 25%

21% 31%

774 458 1232

26% 20% 24%

26% 20% 24%

19% 37% 26%

29% 23% 27%

26