PATTERNS OF NETWORKS AND INNOVATION IN SPANISH SERVICE FIRMS

Transcription

1 Paper to be presented at the Summer Conference 2009 on CBS - Copenhagen Business School Solbjerg Plads 3 DK2000 Frederiksberg DENMARK, June 17-19, 2009 PATTERNS OF NETWORKS AND INNOVATION IN SPANISH SERVICE FIRMS Alexandre Trigo De Campos Applied Economics, University of Santiago de Compostela, Spa alexandretrigo@hotmail.com Abstract: The diverse context in which innovation take place in services leads us to wonder which inputs are the best drivers of innovation in services. This paper aims to analyse the real scope of the diversity of innovation patterns in services in terms of networks. The range of variables analysed includes formal alliances and cooperation, the significance of information sources as well as the innovation outputs. The empirical analysis is based on the Fourth Community Innovation Survey for Spain. In order to identify clusters with the same behaviour as far innovation and networks are concerned, we use the latent class analysis as alternative method of clustering. Firm-level analyses have permitted to scrutinize in-depth innovation patterns, beyond the boundary of the traditional standard industrial classification largely applied so far. The results of the empirical analysis led to create a network-related typology for services, taking into account the cooperation agreements and the valuation of the different sources of information, composed of thee profiles: Intensive in techno-scientific flows of information, intensive in interactions with clients and, finally, a profile of low intensity in interactions, called lonely innovators. From the standpoint of public innovation policies, this open and systemic perception of innovation may be manifested through actions whose aims are to foster, fortify and develop networks, alliances and partnerships between different economic actors, given the existence of complementarities between the different modules of knowledge as well as innovation spillovers. JEL - codes: O39, L80, -

2 Paper for the DRUID Summer Conference on INNOVATION, STRATEGY and KNOWLEDGE June 17-19, 2009 Copenhagen, Denmark Patterns of networks and innovation in Spanish service firms ABSTRACT The diverse context in which innovation take place in services leads us to wonder which inputs are the best drivers of innovation in services. This paper aims to analyse the real scope of the diversity of innovation patterns in services in terms of networks. The range of variables analysed includes formal alliances and cooperation, the significance of information sources as well as the innovation outputs. The empirical analysis is based on the Fourth Community Innovation Survey for Spain. In order to identify clusters with the same behaviour as far innovation and networks are concerned, we use the latent class analysis as alternative method of clustering. Firm-level analyses have permitted to scrutinize in-depth innovation patterns, beyond the boundary of the traditional standard industrial classification largely applied so far. The results of the empirical analysis led to create a network-related typology for services, taking into account the cooperation agreements and the valuation of the different sources of information, composed of thee profiles: Intensive in techno-scientific flows of information, intensive in interactions with clients and, finally, a profile of low intensity in interactions, called lonely innovators. From the standpoint of public innovation policies, this open and systemic perception of innovation may be manifested through actions whose aims are to foster, fortify and develop networks, alliances and partnerships between different economic actors, given the existence of complementarities between the different modules of knowledge as well as innovation spillovers. Keywords: Innovation patterns, service sector, networks, cooperation, source of information. 1

3 1. INTRODUCTION Most of the studies on services have pointed KIBS (Knowledge Intensive Business Services) in the vanguard with regard to innovation. This prominent performance is noticed not only by the high proportion of innovating firms but also mainly by the high number of innovating firms engaged in most of innovation activities and innovation cooperation. In contrast, others services such as distributive services (transport, wholesale and retail, etc.) and HORECA (hotels, restaurants and catering) present a very low innovative performance. The literature also stressed that innovation has a wide range of determinants apart from the traditional R&D activities. That is even more evident in the context of services. Therefore, the appropriate framework to the analysis of the information flows must embrace a dynamic, open, interactive and systemic perspective of the innovation processes (Rothwell, 1992, 1994; Lundvall, 1988, 1992; Nelson, 1993; Vence, 1998). It is also important to notice that the necessary knowledge to make innovation attainable is distributed throughout the society (Hayek, 1945; Andersen, Metcalfe & Tether, 2000; Coombs, Harvey & Tether, 2003) what means that a great number of information sources needed for innovation are located beyond the firm s boundary. In the sense that the higher the capacity of interaction with other economic actors, the higher the innovation capacity of an enterprise. Indeed, the analysis of networks is often an effective approach to grasp how enterprises create value and knowledge to innovate. The diverse context in which innovation take place in services leads us to wonder which inputs are the best drivers of innovation in services. This paper aims to analyse the real scope of the diversity of innovation patterns in services in terms of networks. The range of variables analysed includes formal alliances and cooperation, the significance of information sources as well as the innovation outputs. The empirical analysis is based on the Fourth Community Innovation Survey for Spain. The recent debate whether innovation patterns rely on the sectoraldeterminism or strategic-choice is also discuss here. Indeed, the use of latent class analysis as statistical methods to identify clusters permits an approach at firm-level to the traditional sectoral perspective of studies with this nature. 2. THE INTERACTIVE AND SYSTEMIC NATURE OF INNOVATION Innovation can be defined as the application of a new knowledge. Therefore, the efficient management of both creation and diffusion of knowledge are crucial to foster innovation. Hence, the analysis of the innovative capacity of the enterprises ought to take into account the set of mechanisms used to generate and incorporate new knowledge. Another important issue to 2

4 consider is that innovation is not a linear model, but a systemic process composed by a vast number of determinants and many actors involved apart from the enterprise. From a systemic perspective, innovation consists of a dynamic and interactive process of creation of a new goods or services. Innovation is an interactive process because the relationship with others economic agents, and the knowledge exchange that this interaction promote, is the heart of any kind of innovation. In the same way that the importance of innovation in economic growth has changed over time, how the innovation is developed from the viewpoint of the company has also experienced changes. Generations of innovation models have shown that innovation today is an interactive, multidimensional, integrated and systemic process (Rothwell, 1992, 1994; Lundvall, 1988, 1992; Nelson, 1993; Vence, 1998), unlike known as models of technology-push and demand-pull, where the interaction was represented in a linear scale. Empirical studies have shown that enterprises rarely innovate in isolation of the economic system (Christensen & Lundvall, 2004). In this regard, networks have become the basis of the innovation process in many companies. The traditional research and development (R&D) is complementary to many other sources of information as well as internal and external partnerships and other types of collaboration with a wide range of actors (suppliers, customers, universities, technology institutes, government, etc.) and with multiple functions that include a regulator, facilitator, seller and financier (Georghiou, 2006). This perspective has taken a significant shift towards a new way of understanding the complex process of innovation from the recognition of a number of factors such as market failures, the size and dispersion of the agents of the system, as well as the decreasing cost of R&D activities from fiscal policies. In academia, a new wave of studies has revealed new dimensions, including: (a) the importance of tacit knowledge (Polanyi, 1967, Collins, 1974), (b) a new generation of dynamic models of innovation (Kline & Rosenberg 1986, Rothwell, 1992, 1994), (c) the concept of techno-economics networks (Michael Callon, 1991), (d) the role of lead users (Von Hippel, 1988) and (e) the development of the concept of core competence (Prahalad & Hamel, 1990; Leonard-Barton, 1992). As result of this new step in studies of innovation, new elements have emerged, including (1) the existence of multiple agents, (2) access to new knowledge through collaboration, (3) the important role of users and (4) providers as codevelopers. This new configuration has conditioned the design of new theoretical frameworks for economic and innovation policies such as the evolutionary perspective (Nelson & Winter, 1982), system failures (Metcalfe, 1995, 2003), National Systems of Innovation (Freeman, 1987a, b, 1995, 3

5 Lundvall 1992, Nelson 1993, Edquist, 1997) and the three-dimensional model of knowledge creation well known as the Triple Helix model (Etzkowitz & Leydesdorff, 1997). The model proposed by Rothwell (1992, 1994) to describe the innovation process in businesses is an example that illustrates the importance of interactions between agents at a multidimensional level. At the same time, the model emphasizes integration between the internal functions of the company, considering the nature of the business and the organisational culture. Rothwell's model has been identified in the literature as the fifth generation after linear models, the mixed model of Kline and Rosenberg (1986) known as Chain-linked model, and the model characterized by the combination of various business functions as a parallel and integrated process. The latter has been developed from the boom of Japanese models of management and coordination of innovation. In this regard, the sources for innovation not only found at the poles of the linear perspective of innovation defined as R&D and marketing, but widely distributed through the various activities carried out within the company. Moreover, it highlights the importance of learning processes based on experience in the use and implementation - learning by doing and learning by using. The success of the model of Japanese production and innovation has also highlighted the distributed nature of knowledge (Tether, Mina, Consoli et al., 2005). This has been an important step for the next generation of innovation models, based on a systemic level and an interactive multidimensional, as discussed above. P. A. David and D. Foray (2002) consider the opening of businesses to "knowledge communities" as result of the arrival of an economy that is going to value the knowledge and human capital as fundamental to innovation processes. In addition to the fifth generation proposed by Rothwell (1992, 1994) for the process of knowledge creation, other variants have also suggested such an interactive nature at multidimensional level. One holds the localized nature of the creation and dissemination of knowledge from the effects of spillovers of knowledge, known in the literature as knowledge spillovers (Mansfield, 1985; Schrader, 1991; Vence, 1995, 1998; Beschi & Lissoni 2001; Foray, 2004; Antonelli, 2006). Given that knowledge is characterized by its cumulative and progressive character, externalities play the role of reinforcing not only the welfare of consumers but also, and mainly, the accumulation of knowledge and collective progress (Foray, 2004). X. Vence (1998) noted that the externalities and spillovers are present in the collective production of knowledge in the sense that any progress in knowledge in a given field causes progress in other related fields. The dissemination of knowledge, in other words, the exchange of ideas between individuals reflects the importance of the geographic aspect in the creation of knowledge. The 4

6 geographical concentration of enterprises benefits directly information flows and knowledge and, therefore, innovation also in services, principally in technology-intensive segments (high-tech services). Vence and Gonzalez (2002) state that both geographical and institutional proximity, in a diverse environment, creates the best conditions for innovation The flow of knowledge is facilitated by the existence of these clusters and the constant interaction between companies. Therefore, the approach that supports knowledge spillovers underscores the importance of absorptive capacity of the enterprises (Cohen & Levinthal, 1990). The heterogeneity of skills and experiences of individuals compose a collective, distributed and localized production of knowledge (Antonelli, 1998, 2005; Andersen, Tether & Metcalfe, 2000; Coombs et al., 2003). However, M. Callon (1991) adds an alternative perspective to describe the knowledge production as a social and cultural process, based on the continuous construction of techno-economic networks. His contribution added a social notion to the models of innovation developed by economists, expressed by the role of different actors and the society in the processes of innovation. The sociological models have provided the understanding of innovation on a scale beyond a simple open process of exchange and accumulation of technological knowledge. Technological change is unpredictable and multilateral; it rarely takes place in a stable social and industrial context (Callon, 1991, 1994, Foray, 2004). In the same vein, X. Vence (1986, 1995) suggests that innovation is not solely the result of the search for profit maximization, but a result of social context. In this sense, innovation is not only as a result of economic growth, reflected by the maximization of profits or gains, but also as a consequence of social dynamics, that is manifested by the class struggle. This observation highlights the efforts not only individual to innovate but collective, that was appointed by K. Marx ( ) as "collective work", where the product is represented by the joint work of individuals. Another alternative model that attempts to explain the current way of knowledge creation from the perspective of the research system in a social context and is known as Triple-Helix model, whose vertices are represented by universities, companies and government (Etzkowitz & Leydesdorff, 1997, 2000 Leydesdorff & Meyer, 2006). This thesis supports the assumption that these three players have a growing interdependence, and therefore should be studied together. The triple helix model focuses on the importance of interaction and communication among these institutions, highlighting the growing role of universities in innovation processes in modern economies. H. Etzkowitz and L. Leydesdorff (2000) assert that the main peculiarity of the Triple- Helix model is therefore the importance of universities in the process of knowledge creation, unlike other analytical approaches such as innovation systems (Freeman, 1987a,b, 1995; Lundvall, 1988, 1992; Nelson, 1993; Metcalfe, 2003) whose role is undertook by enterprises, or the 5

7 triangular model of Sábato (Sabato, 1975; Sábato & MacKenzie, 1982) where the government seems to play the lead role. However, such differentiation may not be entirely clear because of authors that support the perspective of innovation systems have highlighted in different occasions the importance of universities as centres of scientific and technological knowledge. 3. EMPIRICAL EVIDENCE: NETWORKS AND INNOVATION The data used in the empirical analysis derive from the Technological Innovation Panel (PITEC) created from the Community Innovation Survey that was realized in Spain in The number of service enterprises is among them are innovating firms. We consider that innovating firm is one that has implemented any type of innovation during the last two years, as recommended in the Oslo Manual (OECD, 2005b). The classification of the services branches is found in table 1. FIGURE 1 ABOUT HERE The external environment plays an increasing role in the processes of innovation. The external mechanisms can be expressed in various ways such as through public financing of entrepreneurial innovation, as well as external contacts and collaborations with other companies or entities. Among these, cooperation is considered a very important mechanism to fortify the innovative capacity of many services. Therefore, organisational learning is increasingly generated through networks in a synergistic atmosphere of production and value creation (Metcalfe, 2003; OECD, 2005a; Goussevskaia, 2007). These alliances are strategically key in open innovation processes, since they enable organizations to gain access to knowledge, technology and knowhow dissipated among other economic actors. The figure 2 shows that the correlation between innovation and cooperation is direct. It is almost inexistent highly innovative industries with low propensity to cooperate as well as branches with low innovative firms with high propensity to cooperate. Therefore, we classify service activities into two groups: one composed of those branches with high proportion of innovative firms with high propensity to cooperate. This cluster is constituted essentially by knowledge-intensive services, software and telecommunications. Another cluster is composed of activities with low tendency to innovate and cooperate. The branches of retail trade, hotels and restaurants, Motion picture and video activities are some of the branches that make up this class. Reports published by the OECD also highlighted the low propensity to cooperate on the part of the distributive services (OECD, 2001). 6

8 FIGURE 2 ABOUT HERE However, the propensity to cooperate should not be restricted merely to the nature of the activities. The experience of the company in previous innovation initiatives is extremely important when it comes to carrying out an innovation project with other partners. Indeed, it appears that companies with failed innovation projects in the past are more likely to cooperate than those organizations without this kind of experience. In this sense, past experiences of failure seem to lead to a higher readiness of firms to look for outside help and competence for new innovation projects in order to reduce the risk of such projects (Tödtling et al., 2008, 10).The hypothesis of a higher proportion of enterprises with cooperation with unsuccessful innovation projects has also been confirmed in the Spanish CIS4 (Figure 3). According to the chart, innovative companies with ongoing or abandoned innovation activities are more prone to cooperate with all actors 1. FIGURE 3 ABOUT HERE Many theoretical and empirical studies that attempt to highlight the importance of alliances to the innovation. Indeed, enterprises engaged in networks throughout the innovation process, apart from being more innovative, are more prone to comes to innovations "new to market" (see Tether, 2002). This evidence has also been confirmed in the sample of Spanish companies (Figure 4). The 67% of the innovating firms (product innovation) and with cooperation were able to introduce new products to market. The branches with higher proportion of new-to-the-market innovators are KIBS, all of them above the service sector average. Apart from the knowledge-intensive business services, transport, telecommunications, radio and television activities and repair of motor vehicles also show a high proportion of enterprises with cooperation and new-to-the-market innovation 2. FIGURE 4 ABOUT HERE 1 The same variable collects the data on firms with ongoing or abandoned innovation activities. We eliminated from the sample of innovating firms that answered positively to this question, those firms with innovation activity. In other words, we aimed to identify those innovative companies with abandoned innovation activities but without ongoing ones. In this case, the approximation with the above hypothesis would be more accurate. 2 The high percentage showed in some cases such as real estate activities and hotels and restaurants is basically due to the low number of product innovating firms. 7

9 On the other hand, there is a group of activities with a high proportion of innovating firms with cooperation, but with low level of novelty on their new products. This is the case of financial intermediation and supporting transport activities and travel agencies. To some extent, this result can be justified by the profile of these companies. For both, but especially to the second branch, product innovation is not very usual, so alliances are carried out in the context of innovation projects in other business areas. Moreover, the branches with significant relationships with science and technology-related actors are fundamentally activities with a high propensity to engage internal R&D and to be composed by highly skilled human capital. This finding reinforces the argument on the necessity of the existence of an absorption capacity of information and external knowledge, that was suggested by Cohen and Levinthal (1990). In addition, it highlights the importance of highly skilled human capital as the main connection between the company and these institutions of science and technology. The value of human capital in entrepreneurial innovation becomes clear once again Differences in partnerships The diversity of information flows in services is also manifested in the choice of partners in cooperation agreements. In general, clients and universities are the most frequent partner in alliances in services. It is worth to notice the important weight of suppliers to some branches and of consulting services for others. Firms whose productive and innovative activities are related to technological progress tend to cooperate with suppliers, universities and technology institutes. Moreover, their innovations (product innovations) tend to be breakthroughs, not only new to the company but also to the market. In this sense, the types of interactions along the innovation process would influence the scope of the novelty of the innovations in some way. Recent studies support this result, noticing that science and technology-related alliances enhance the ability of firms to introduce advanced innovations. Therefore, universities are more important for highly innovative industries with high propensity to engage in R&D - KIBS and telecommunications. Meanwhile, cooperation with suppliers does not seem to have great weight within the overall of cooperation in services, although the value of this relationship is more evident for KIBS and telecommunications. 8

10 Cooperation with customers is an important part of the total weight of the partnerships, mainly to activities such as supporting transport activities and travel agencies, financial intermediation, other business activities and radio and television activities. In reality, these companies, as well as telecommunications and research and development, are the ones with the highest tendency to cooperate with customers. On the other hand, firms that cooperate with actors closer to the productive system such as customers, consultants, etc., with more practical than scientific-knowledge, tend to develop new-to-the-market innovation in a lesser extent (Kaufmann & Tödtling, 2001; Tödtling et al., 2008). In general, consultants, commercial labs, or private R&D institutes are most valued within the set of activities with low innovative capacity, and at the same time with reduced propensity to cooperate. TABLE 1 ABOUT HERE 4. PATTERNS OF INFORMATION FLOWS AND INNOVATION IN SERVICES 4.1. Methodology and data analysis: data set and the determination of the model s dimension Although the study of innovation patterns in services is not a novel approach, the analysis proposed here is unique in two aspects. The first of these relates to the variables chosen to determine the patterns: the knowledge-related networks of innovation. These inputs are classified into two sets of variables: partners for cooperation and sources of information for innovation. It was also taken into account a set of output-related variables composed by the types of innovation developed. The idea behind this analysis is also to verify whether different types of innovation rely on specific sort of network arrangement. The second dimension of the originality of this analysis is the use of Latent Class Analysis (LCA) as alternative to traditional Cluster Analysis 3. Latent class analysis (LCA) is a multivariate technique based on conditional probabilistic analyses. The objective of this statistical method is to verify whether the association between a set of observed categorical variables could be explained through a latent typology that is composed by different classes. The variables used in each analysis are summarized in the follow table. This statistical technique has many advantages compared with other tools. We can say that one important advantage is the probability distribution of the clusters identified. Elements 3 More details of Latent Class Analysis in Heinen (1996) and Hagenaars & McCutcheon (2003). 9

11 (in the case of this work, the branches) present different probabilities of belonging to each of the cluster model. One of the outputs offered by this statistical technique, therefore, is a classification by groups based on probabilities. Another significant advantage is in determining the numbers of clusters. This is due to the existence of rigorous statistical tests that support the choice of the dimension of the model (choice of the dimension of the best solution to data what means the number of clusters in the model). Another important attribute is the possibility of using both variables categorical variables. The sample of Spanish enterprises employed in the analysis is composed of innovating firms. TABLE 2 ABOUT HERE The results of Latent Class Analysis display different solutions, each one with different numbers of classes. The criterion for selecting the most accurate model that fit with the data set was the Bayesian Information Criterion (BIC) due to their consistency in comparison with other criteria. Most of the empirical analysis carried out through Latent Class Analysis has chosen such statistical criterion for model selection. According to this criterion, the accurate model is the one with the lowest value for BIC (see Kashyap, 1977; Schwartz, 1978). Therefore, the accurate model of network arrangements and innovation output consists of six clusters. TABLE 3 ABOUT HERE 4.2. Innovation patterns based on networks and sources of information In addition to innovation activities, information flows can also be measured from the internal relations within the company, as well as externally, between different companies or institutions. Many authors have highlighted the importance of networks and partnerships for the development of new products or processes in companies as an alternative way for incorporation of external knowledge for innovation (Gerlach, 1992; C. Freeman, 1994; Gulati, 1998; Tether, 2002; Gomes-Casseres, 2003, Lundvall, 2007). Indeed, customers are considered important sources of information - lead users can provide new ideas that help firms to develop and refine their innovations (von Hippel, 1978, 1986, 1988). In this way, the analysis of the nature and dynamics of these relationships complements the study of entrepreneurial efforts towards innovation started with the study of innovation activities in the previous section. 10

12 TABLE 4 ABOUT HERE Intensive in techno-scientific flows of information According to the results, two of the six clusters (4 and 5) have a similar profile, based on techno-scientific networks (similarity in the nature), but with some difference in intensity (difference in the scope). The probability of cooperating with universities, technology centers or with their suppliers is higher than collaborating with any other partner. This disparity in relation to the intensity is also reflected in the significance of information sources and in the innovative intensity. It is interesting to notice that the less dynamic (with a lower probability of positive responses), cluster 5, presents a higher propensity to product innovations than any other (53% for innovative goods and services 52%). Indeed, the probability of organisational innovations is the lowest among the others groups (innovations related to knowledge management systems: 39%, and related to the work organization: 33 %). On the other hand, the more dynamic (with a higher probability of positive responses), cluster 4, presents a very high probability of organisational innovations, especially those related to knowledge management systems (84%), although they have a high propensity also for service innovations (79%). With regard to the significance of information sources, as mentioned before, the main difference is in the magnitude of significances, not so much in nature (differences in the choice of partners to cooperate). However, we should mention that only the most dynamic between those two clusters and presents positive rates (above 1,5 - Likert scale of 0-3). The probability that an innovating service firm of the Spanish sample belonging to these clusters is 28%. Although almost all branches have a certain probability of belonging to these two clusters, the service activities with higher tendency o be intensive in techno-scientific flows of information are, architectural and engineering activities, telecommunications, research and development and technical testing and analysis. Many others taxonomies on services have indentified this profile for instance in Hollenstein (2003) who named it as Science-based high-tech firms with full network integration Intensive in interactions with clients Another profile of interactions identified from LCA has the client as the main partner. Therefore, the performance of these collaborations is highly determinate by the needs of consumers and users of the service. The probability of an innovating firm with this profile cooperate with clients is approximately 60% (highest of the entire set of clusters identified). In this sense, it is logical that the customer receives the highest rating among all possible external 11

13 sources. Clients-led firms are very innovative, especially for process and organisational (e.g., probability of 81% to be support processes innovators and 76% knowledge systems innovators). It is also worth noting the high probability of developing service innovations (67%). Among all analyzed branches, financial intermediation is the service activities that best fits to this profile. According to the generation of knowledge, those innovating firms with clients networks are intensive both in internal R&D activities and training, as well as in acquisition of machinery and software. The probability of an innovating Spanish firm belonging to this profile is only 13%. Other taxonomies based on networks have highlighted the high propensity of cooperating with customers in some service activities. Those typologies also stressed the high tendency of those firms to be also R&D-intensive. Client-led innovation (Bilderbeek & Den Hertog, 1999), Information Network (Soete & Miozzo, 1989; Miozzo & Soete, 2001), Network Patten (Sundbo & Gallouj, 2000), Cost-oriented process innovators with strong external links along the value chair (Hollenstein, 2003) and Network basis (Hipp & Grupp, 2005) are some of the labels used by other authors to describe such a profile Lonely innovators The third identified profile is composed of three of the six LCA clusters (clusters 1, 2 and 3). The firms that fit in this profile show very low probability of cooperating. This result leads us to the assumption that those innovating firms are lonely innovators due to the minor involvement with other economics actors, through formal cooperation, along their innovation processes. However, although those tree clusters display very little tendencies to cooperate in innovation, the significance of information sources used is quite dissimilar. Two of the three lonely innovator clusters (cluster 1 and 3), consider relatively important the information that come from customers and suppliers. These firms have low propensity to cooperate formally but simultaneously they present high valuation of their relationship with actors along the value chain. However, that interaction seems to be result of the business relationship instead of common efforts to achieve innovation. Indeed, the significance of information sources is higher in cluster 3 than any other cluster, except cluster 5. With reference to innovation outputs, lonely innovators present similar tendencies. In general, those clusters are more likely to innovate in the organization aspect than in others, even beyond the cluster 1, intensive in techno-scientific flow of information. However, there is a considerable difference in the propensity to innovate. Taking into account the three lonely innovators clusters, the cluster 2 is the least dynamic. Its probability of innovating in product is the lowest in the whole model (27% for product innovation and 33% for service innovation). On the other hand, clusters 1 and 3 are as prone to be product innovator as cluster 5 (intensive in 12

14 techno-scientific flow of information), and even more prone in terms of organisational innovation. The probability that a Spanish firm belongs to this profile is almost 60%. This scenery is even worse when we take into account the employment by economic activities in Spain. Almost 65% of the employment in Spain is assigned to activities with high probability of having this profile. As noted by Molero (2006), the growth of technology-intensive sectors has been significantly lower in Spain compared to other European economies. In other words, the specialization of the Spanish economy has been concentrated mainly in areas with low or medium technological intensity. The branches that have a higher probability of this profile are retail trade, post and courier activities, repair of motor vehicles, motorcycles, hotels and restaurants among others. Indeed, most of those activities have a considerable participation in the Spanish economic structure because, among other reasons, the existence of important weather and natural advantages (Gordo & Jareño Urtasun, 2006). Other authors also highlighted the existence of this profile. Hollenstein (2003) presents a taxonomy of innovation patterns, in which two of five groups do not prone to establish external linkages: Market-oriented incremental innovators with weak external links y Low profile innovators with hardly any external link. The following chart shows the Triplot 4 representation of this typology. FIGURE 5 ABOUT HERE TABLE 4 ABOUT HERE 5. CONCLUSIONS AND POLICY IMPLICATIONS The analyses developed in this work lead to the conclusion that services neither incorporate nor create new knowledge similarly. Furthermore, the assumption of plurality has been confirmed, as far as the nature and the dynamics of networks are concerned, as demonstrated by other authors. This evidence objects the growingly untenable hypothesis of a unique pattern of innovation and networks in services. Firm-level analysis has permitted to scrutinize in-depth innovation patterns, beyond the boundary of the traditional standard industrial classification largely applied so far. 4 We used the software for Microsoft Excel Triplot developed by David Graham (Loughborough University) and Nicholas Midgley (Liverpool John Moores University), and distributed free of charge. See D. J. Graham & N. G. Midgley (2000). For details, see additional documentation in 13

15 The results of the empirical analysis led to create a network-related typology for services, taking into account the cooperation agreements and the valuation of the different sources of information, composed of thee profiles: Intensive in techno-scientific flows of information, intensive in interactions with clients and, finally, a profile of low intensity in interactions, called lonely innovators. The innovating firms intensive in techno-scientific flows of information are characterized by the high probability of cooperating with agents such as technology institutes, universities and suppliers. Lonely innovators, on the other hand, show low probabilities of carrying out any type of innovation project with other partners. The probability that a service firm innovates solitarily is sixty percent. It seems to be reasonable to affirm that the innovation cooperation is not a common practice in Spanish service enterprises. If one takes into account the composition of the Spanish business sector and its passive profile as far as innovation initiatives are concerned the result is more comprehensible. This evidence supports the assumption that formal networks in Spain are much less present in the innovative dynamics of the companies than in other countries, as indicated by European Commission in many occasions. The empirical evidence also permit to conclude that the nature of the activities affects both the nature and the intensity of the flows of information for the innovation. That nature of the activities can be expressed by the technological capacity, in other words, the intensity of the use of technologies, the technological opportunities, the growth of the demand, life cycle of the services, which describe the evolution of the subsector, as well as the degree of standardization or customization of the service activity. While knowledge and technological intensive business services demonstrate a very dynamic behaviour as far as the information flows analyzed are concerned, innovating distributive services (transport, wholesale and retail, etc.) and innovating HORECA (hotels, restaurants and catering) present a very low innovative performance. However, the results underline the co-existence of different innovation pattern within the same industry. Although the existence of the increasing debate on which one between the sectoraldeterminism and strategic-choice is the most significant factor to shape innovation patterns, the results lead to the conclusion that there exist clear association between the information flows used throughout the innovation processes and the nature of the activity. However, it is important to stress that, in the same way that the decision that leads one firm to innovate is an strategicchoice, the inclination to use certain information flows is also a choice in terms of managerial and innovation strategy. The empirical evidence confirmed that the firm trajectory is crucial to the innovation capacity. Innovating companies with past experiences of failure exhibit major propensity to cooperate. It was also observed that technological and scientific networks strengthen the firms 14

16 capacity to develop innovations new to the market in a greater extent than any other type of interaction. From the standpoint of public innovation policies, this open and systemic perception of innovation may be manifested through actions whose aims are to foster, fortify and develop networks, alliances and partnerships between different economic actors, given the existence of complementarities between the different modules of knowledge as well as innovation spillovers. These structures support the main arguments embraced by both evolutionary and institutional economics, especially the localized, specific and cumulative tacit character assigned to knowledge. It is worth mentioning here that public policy should absorb the implications of a sectoral integration process (so-called synthesis approach to the analysis of innovation in services) justified from interplay between economic sectors as well as the existence of different patterns of innovation within the economic branches. This evidence, what is specially highlighted in the recent literature on services, endorses the creation of specific policies to foster organisational innovations, which in turn affect the productive and innovative dynamics of sectors such as manufacturing. This requires an effort on the incorporation of the non-technological aspect in the public innovation policies. References: ANDERSEN, B., METCALFE, J. S., & TETHER, B. (2000): Distributed Innovation Systems and Instituted Economic Process in Metcalfe J. S. & Miles I. (eds.) Innovation systems in the service economy. Measurements and case study analysis. Boston: Kluwer, pp ANTONELLI, C. (1998): Localized Technological Change, New Information Technology and the Knowledge-Based Economy: The European Evidence, Journal of Evolutionary Economics, 8, pp ANTONELLI, C. (2005): Models of Knowledge and Systems of Governance, Journal of Institutional Economics, 1, 1, pp ANTONELLI, C. (2006): The embedded generation of knowledge: contextual spillovers and localized appropriability, Journée d étude «Déterminants et impacts économiques du management des connaissances» GEMO-ESDES (UCL) et LEFI (Université Lumière Lyon 2), Jeudi 23 novembre BARRAS, R. (1986): Towards a theory of innovation in services, Research Policy, 15, 4, pp BESCHI, S. & LISSONI, F. (2001): Knowledge spillovers and local innovation systems, Industrial and Corporate Change, 10, 4, pp CALLON, M. (1991): Techno-economic networks and irreversibility in Law, J. (eds.), A sociology of monsters: essays on power, technology and domination. London: Routledge, pp CALLON, M. (1994): Is Science a Public Good?, Science, Technology and Human Values, 19, pp CHRISTENSEN, J. L. & LUNDVALL, B.-Å. (eds.) (2004): Product Innovation, Interactive Learning and Economic Performance. Amsterdam: Elsevier. COHEN, W. M. & LEVINTHAL, D. A. (1990): Absorptive capacity: a new perspective on learning and innovation, Administrative Science Quarterly, 35, 1, pp

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