Le rôle de la gouvernance pour l’innovation des KIBS dans les technopôles : le cas de Savoie Technolac

Rachel Bocquet, Sébastien Brion, Caroline Mothe
IREGE, Université de Savoie

Résumé

La question de savoir si la proximité organisationnelle peut être encouragée au sein des pôles, en se concentrant sur les KIBS (Knowledge-Intensive Business Services), a été peu traitée dans la littérature. Nous abordons la question des structures de gouvernance locale de technopôles en identifiant comment la gouvernance peut améliorer les performances d'innovation des KIBS. La recherche empirique s'appuie sur un échantillon représentatif de 53 KIBS au sein de Savoie Technolac dans la région Rhône-Alpes. Nos résultats suggèrent que, outre les déterminants traditionnels de la performance de l'innovation, la gouvernance (comme prestataire de services) a un impact direct sur ​​l'innovation des KIBS du technopôle. La gouvernance joue également un rôle indirect de tiers pour les KIBS engagés dans des partenariats de R&D. Elle a un rôle décisif dans la création de structures de communication et d’interface entre KIBS, essentiels pour leur innovation. Cette recherche a des implications importantes pour les modes de gouvernance, non seulement des technopôles, mais, plus largement, dans les formes de cluster. Elle tend également à réhabiliter la politique volontariste des pôles de compétitivité français en montrant le rôle clé que peut jouer la gouvernance dans le renforcement des capacités d’innovation des firmes membres.

Mots-cles: Cluster, Gouvernance, Innovation, KIBS, Proximité, Technopôle

The role of governance for KIBS’ innovation in technopoles: The case of Savoie Technolac

Le rôle de la gouvernance pour l’innovation des KIBS dans les technopôles : le cas de Savoie Technolac

INTRODUCTION 
The objective of this paper is to study the innovation performance of cluster members of a particular type of industry, the Knowledge Intensive Business Services (KIBS). Strangely enough, scarce empirical investigations are related to clustering and innovation performance for KIBS, although the literature has emphasized the increased importance of such type of firms. Most studies focusing explicitly on innovation performance have been made in the context of technological-based manufacturing industries (Geenhuizen and Gonzales-Reyes, 2007; Gilbert et al., 2008) - and not on services or KIBS per se. The clustering of KIBS firms thus remains largely unstudied, although their importance has been acknowledged (Corrocher et al., 2009; Rodriguez and Camacho, 2010). Some studies provide a better understanding of the specificities of KIBS and of their vital role for economic growth. However, quite surprisingly, we still know very little about their clustering behaviour (Zhao et al., 2010) and on the way it can affect their innovation performance. This pre-occupation remains of crucial importance for both academic research and government policy initiatives and programs on clusters and regional innovation systems as tools for economic development (OECD, 2009; Doloreux et al., 2010; Uyarra, 2010; Uyarra and Flanagan, 2010; Cooke et al., 2011). 
The established typology of territorial innovation makes it possible to identify the characteristics of specific KIBS clusters such as technopoles and technology parks (Longhi and Quéré, 1993; Levesque, Fontan and Klein, 1998; Moulaert and Sekia, 2003; Carluer, 2006). Despite the importance of their research activities, innovation technopoles, especially French ones, are often characterized as having weak inter-firm relationships (Cooke, 2001; Asheim, 2007), thereby limiting their innovation potential on a collective level. Among the factors that contribute to this process, the development of non-spatial forms of proximity represents a key explanatory factor (Markusen, 1996; Angel, 2002; Rallet and Torre, 2007). The various forms of non-spatial proximity can be assimilated to organizational proximity with respect to the interactions between actors, regardless of their nature (Bouba-Olga and Grossetti, 2009). Pecqueur and Zimmermann (2004) proposed a more nuanced characterization that distinguishes between coordination processes based on direct interaction among actors (organizational proximity) and those with no direct interaction (institutional proximity). This distinction seemed particularly relevant to introduce the role that governance may play in order to help develop a local and stable environment, conducive to KIBS’ innovation (Longhi and Quéré, 1993; Levesque et al., 1998). 
Research has long focused on the impact of the structural properties of clusters on their performance and evolution (Brezis et al. 1993; Suire et al. 2006). More recently, Bocquet and Mothe (2009) have shown that cluster characteristics also affect the type of governance mode, in line with studies underlying the central role of institutions in the creation of non spatial forms of proximity in interactive agglomeration forms (Grossetti, 2004; Pecqueur and Zimmermann, 2004; Leloup et al. 2005; Asheim, 2007; Rallet and Torre, 2007; Carrincazeaux, Grosseti and Talbot, 2008). The French School of Proximity (Torre and Gilly, 2000; Torre, 2006) explicitly introduces the notion of “territorial governance” to designate "the institutional and organizational process of bringing together different modes of coordination between geographically close actors" (Colletis et al., 1999: 34). Territorial (or local) governance (Leloup et al. 2005; Ehlinger et al. 2007; Bocquet and Mothe, 2009) has been shown to play a role in the creation of favourable conditions for collaborative innovation. It seems to be particularly the case in technopoles, where firms do not necessarily have the material and cognitive resources to perceive technological opportunities and/or to enter into formal R&D partnerships. The governance structure can thus play an active role in developing these resources as well as organizing certain types of interactive forms to make them more innovative. 
Empirical research dedicated to the governance practices for innovation implemented by local governance is scarce. The original approach adopted in this paper consists in identifying such practices within a technopole and in assessing their impact on its KIBS members’ innovation performance. The first contribution of this paper is to fill a void in the literature on the question as to whether organizational proximity can be fostered by taking into account the local governance structures of technopoles, a dimension that has been given low consideration. The second contribution of this article is to provide a better understanding of the determinants of KIBS’ innovation performance. Studying a specific type of KIBS’ cluster, we distinguish the role of governance as a potential new local source of innovation (the governance) from more ‘traditional’ ones. 
The empirical research is based on a representative sample of 53 KIBS affiliated to the French technopole Savoie Technolac in the Rhone-Alpes region. It yields two types of results. First, we demonstrate a direct effect of governance on KIBS’ product/service innovation by implementing two types of practices: services dedicated to business creation and a ‘networking’ activity aimed to put firms into relation with another organization. Second, results show an indirect effect of governance on KIBS’ innovation. More precisely, KIBS cannot benefit from their cooperation with universities and research labs without the mediation of governance. 
The paper is organized as follows. We first identify the different sources of innovation sources in KIBS’ clusters. Following the proximity approach, we then analyse the specificities of French technopoles considering the possible influence of governance practices on the innovation performance of KIBS member firms. Finally, we analyze empirically the impact of such practices on innovation through the study of Savoie Technolac, discuss results and conclude by providing the limitations and some avenues of future research. 

I. KIBS’ Innovation and cluster GOVERNANCE 
Over the last decade, the literature on service innovation has represented an increasingly important field of research. In this stream, Knowledge Intensive Business Services (KIBS) have received particular attention (e.g. Flikkema et al., 2007; Shunzhong, 2009). The on-going debate  on whether services can be treated like manufacturing with respect to innovation has not been resolved yet and three distinct views on service innovation co-exist (Coombs and Miles, 2000). For the demarcation approach, service innovation is different from manufacturing and therefore requires specific theories. The assimilation approach considers that services are similar to manufacturing and attaches little importance to non-technological innovation. The synthesis approach is an integrative view that allows innovation to take place in manufacturing and in services (Gallouj and Weinstein, 1997; Love and Mansury, 2007). We here used a “demarcation” research strategy designed to concentrate on the specificities of service firms and, more specifically, on KIBS in order to test the specificity of the triptych innovation-cluster-governance in these activities. Indeed, the literature positioned in the “particularist or differentiation principle” (Djellal and Gallouj, 2010, 90) has agreed on the specificities of services, especially in terms of innovation. Since Gallouj and Wenstein (1997), the importance of the soft side of innovation in service activities has been recognised. This may entail consequences in terms of clustering activities and governance practices to foster cluster firms’ innovation.

KIBS and clustering 
Miles et al. (1995) highlighted the main features of KIBS: they rely heavily upon professional knowledge, are themselves primary sources of information and knowledge (reports, training, consultancy, etc.) or use their knowledge to produce intermediary services for their clients´ production processes, and are of competitive importance and supplied primarily to business. However, despite those common characteristics and the recent trend to put aside KIBS within services in terms of innovation activity especially, important differences exist within this group. “There is still much to do in order to adequately characterise how KIBS firms innovate” (Rodriguez and Ballesta, 2010, 62). And most empirical studies on innovation in KIBS (e.g. Tether & Hipp, 2002; Drejer, 2004; Leiponen, 2005; Wong & He, 2005; Freel, 2006; Amara et al., 2009 ; Corrocher et al., 2009), conclude that research should differentiate patterns of innovation in KIBS. Our purpose is to partially fill this gap by looking at KIBS innovation within a cluster.
Explaining geographical clustering in KIBS is still a difficult task. Indeed, it is widely accepted that the supply-side focused ‘agglomeration economy’ literature (Illeris 1989; Moulaert and Gallouj, 1993; Gordon and McCann, 2000) may not be appropriate to study KIBS clusters. The strongly interactive or client-related character of services (Czarnitizi and Spielkamp, 2000) leads to explicitly introduce the demand-side considerations for understanding the clustering at the heart of global cities (Kleeble and Nachum, 2002). Other studies demonstrate that, alongside traditional supply and demand factors, networking, collaboration and skilled labor flows as collective learning processes are additional explanatory factors. These factors would explain the high concentration of KIBS in some regions located at the top of the spatial hierarchy, i.e. in the global metropolis or capital cities. In this vein, Kleeble and Nachum (2002) confirm that clustering of professional and business service consultancies in Central London is strongly influenced by the need for and benefits of localized processes of knowledge acquisition, development and networking. Moreover, these firms strongly benefit of proximity and accessibility in clients in London, and of the accessibility to national and global communication nodality. In contrast, they show that similar firms in non-clustered locations have achieved poorer growth than their counterparts in London. The only two determinants reported as important by these firms are founders’ personal considerations (quality of life, proximity to home) and the lower costs of premises, rates and staff, thus suggesting a poor effect of location influences. 
Other studies detail the organization features and the role of localized collective learning processes in the service industry. Studying ICT clusters in Bavaria and Shanghai, Zhao et al. (2010) focus on the vital role of networking mechanisms in ICT clusters in Bavaria and Shanghai. They found that German firms’ innovation depends more on the soft environment (including local human resources, social and cultural environment, on technological potential and funding) than the hard one (infrastructure, local management system, regulation and assessment system) – the situation being reverse in the case of China. Since Chinese firms rely more on infrastructure and tax policies than on a dense network of collaborations, they are not able to develop the high value-added services of their German counterparts. In the same vein, Segelod and Jordan (2004) analyze the importance of external sources of knowledge in 92 computer software development projects. They show that linkages with the scientific system and public authorities (universities, other research institutes, patent offices, or financial promoters) are less important than those with mediating parties and markets (customers, suppliers, competitors, fairs/conferences, technical consultants, the press, affiliated companies, and market consultants) for computer software firms. For these authors, this result suggests that firm size matters. Small firms suffer from a lack of resources and a deficit of absorptive capacity (Cohen and Levinthal, 1990) that hampers them from entering into R&D and scientific partnerships. 
Trippl and Tödtling (2010) provide another characterization of firms’ knowledge sources in the Vienna software cluster, showing that software firms adopt a large variety of external channels to gain access to local external knowledge; but informal modes of knowledge transmission (such as informal contacts, recruitment of specialists, fairs/conferences, scientific literature, patent citations) are of crucial importance. Apart from R&D, cooperation and contract research undertaken by firms oriented towards radical innovation, formal networks and market relations are never associated with innovation. 
Studying the determinants of innovation capability in small UK electronics and software firms, Romijn and Alabaladejo (2002) provide a different picture. They provide evidence that interactions with scientific institutions and service providers (including public assistance) are major drivers of small firms’ innovation performance. Linkages with other local actors, especially clients, do not matter. They also demonstrate that the interactions between firms and universities/science laboratories came from previous employment of the owner/managers in these institutions - and not from colocation on parks. 
Other authors propose additional findings focusing on the geography of knowledge interactions. Nachum and Keeble (2003) examine the geographical scale of the linkages of media firms in Central London: “the networks of linkages of Soho firms embed them simultaneously in process operating at different geographical scales. These include the immediate environment of Soho and London, as well as other key media centers worldwide (largely Hollywood, but also New York, Paris, etc.)” (ibid: 472). They also stressed the interdependence between the dynamics taking place at the different geographical scales: local linkages for the provision of services and labor and global networks for obtaining knowledge acquisition, learning, specialized skills and expertise. Bunker et al. (2009) examined the joint effects of geographical proximity and network position on firms’ innovation. Results indicate that innovation benefits do not derive from simple proximity to sources of spillovers in biotechnology but depend on the degree to which an organization is centrally connected, both within and beyond a home region.
In sum, there is little consensus about the different knowledge sources in KIBS clusters (and their geography), although the contradictory results might be partly due to the heterogeneous nature of KIBS sectors (and sub-sectors) (Trippl and Tödtling, 2010). Besides, and quite surprisingly, these studies do not explicitly capture the influence of practices implemented by the governance of clusters on KIBS' innovation. When they are taken into account in empirical studies, it is not possible to isolate their specific effect since they are mixed in a wider category (such as suppliers). However, recent studies show that they might play a significant role, especially for small firms, which leads us to include governance practices as an explanatory factor of cluster members’ innovation.

Governance Practices for Innovation in Technopoles 
The technopolitan phenomenon and its associated technology parks or technopoles have experienced strong growth in many developed countries since the late seventies (Longhi and Quéré, 1991, 1993). Technopoles are a tool for the strategic development of regional innovation systems (Doloreux, 1999). Far from presenting a homogeneous model of territorial innovation, Cooke (2001) has identified two types of technopoles, linear and interactive ones, with distinct properties and performances. We interpret this distinction following the proximity view and identify the role of the practices adopted by a technopole’s governance on its members’ innovation performance, focusing on KIBS. 
Within linear technopoles, “pure” geographical proximity has been shown to influence innovation performance (Amin and Cohendet, 2005). However, geographical proximity alone cannot account for knowledge flows, learning and innovation (for a review, see Ozman, 2009): other forms of proximity may be essential in order to foster interactive learning and innovation capabilities (Boschma, 2005; Rallet and Torre, 2005). Since the founding distinction between geographical proximity and organizational proximity, different types of proximity were added to refine the concept of organizational proximity (Bouba-Olga and Grossetti, 2008). For Rallet and Torre (2005), “organized proximity” designates two types of logics: similarity and belonging. Similarity includes actors who are alike, who share the same reference space and knowledge, whereas belonging refers to actors who interact. Other authors, such as Suire et al. (2006), adopt a similar view by proposing the concepts of cognitive proximity and relational proximity. Cognitive proximity suggests that agents are cognitively close, i.e. share conventions, values and representations. Relational proximity refers to actors sharing a same interaction structure to make transactions or to exchange. As mentioned above, Pecqueur and Zimmermann (2004) differentiate the nature of interactions between actors depending on whether they are direct (organizational proximity) or indirect (institutional proximity). This distinction enables us to introduce the idea that actors can rely on coordination arrangements without having to rely on personal channels such as norms, standards, directories, human intermediaries. As suggested by Bouba-Olga and Grossetti (2008), these devices or mediation resources are no longer considered at the individual level (cognitive or relational proximity) but rather as collective coordination resources. Such a perspective seems particularly relevant when establishing the role that governance may play when it has to face a lack of cognitive and relational proximities. This governance acts as a focal actor that plays the strategic role of a “lead organization” (Provan and Kenis, 2008) which acts as a decision centre that maintains the network (Alberti, 2001). Collective actions and major decisions for the network are supported by a unique referent actor (Provan and Kenis, 2008).
More precisely, we propose that cluster local governance may contribute to foster organizational proximity which is lacking in linear technopoles and considered as a main characteristics of interactive technopoles. Governance may enhance cognitive proximity when it is low due to high inter-firm heterogeneity (in terms of size, activity, markets, status, etc.) and/or create incentives for relational proximity. Cognitive and relational proximities become possible without physical proximity (Amin and Cohendet, 2005) and “the social architecture of learning in firms cannot be reduced to territorial ties” (ibid: 469) as knowledge is not linked to particular geographical sites. 
When geographical proximity is observed without any form of organizational proximity, the economic actors have little chance of maintaining direct relations (Torre, 2006). This is especially true when the technopole brings together a high proportion of small businesses (Leloup et al. 2005; Ehlinger et al. 2007; Bocquet and Mothe, 2009). Studying clusters with a strong concentration of SMEs, Bocquet and Mothe (2010) showed the difficulties that arise when trying to establish synergies between SMEs due to their highly individualistic behaviour and difficulties to perceive innovation opportunities. Although cooperation and the use of external sources are key dimensions to their innovation activity (De Jong and Marsili, 2006; Freel and Harrison, 2006; Huet and Lazaric, 2008), not all SMEs have the ability to absorb external resources and/or are reluctant to establish such relationships. As cross-fertilization is one of the main functions of the technopole, it is clearly challenging for the governance to create mediation resources (institutional proximity) when dealing with weak organizational proximity (both, cognitive and relational proximities). "Anchoring appears when the territorial organization (geographic proximity) is capable of generating organizational and institutional proximity effects based on the interaction and cooperation between units operating in the same geographical proximity" (Zimmerman, 2008: 115). Through its actions, governance might help to develop innovative networks. 
The emergence of an interactive technopole can therefore stem from the establishment of a local governance structure aimed at developing a cognitive proximity so as to create a sense of common belonging that will bind actors together, as well as a relational proximity designed to favour the emergence of innovative collaborations. Following Gilly and Wallet (2001), we propose that the governance of technopoles should play a significant role in stimulating innovation through the quality of the local anchoring and via networking.
Territorial anchoring 	
Beyond the services and infrastructure developed to host firms (generic resources), the governance of the technopole should adopt a strategic approach (Simmie et al., 2004) to generate diversity and complementarity of activities. The combination of a common strategy, of the actors’ involvement in social, personal or professional networks and of geographic proximity makes it possible to generate specific resources and services to cluster firms (Grossetti, 2000). The governance should therefore develop incentive schemes designed to encourage opportunities for formal or informal meetings between actors. This can be achieved through thematic meetings or seminars and the exchange of information between members. The governance must also ensure consistency and continuity of the local labour market. Firms require an adequate stock of technically qualified manpower, particularly scientists and engineers to absorb new technologies, to modify them, to create and transfer new technological information (Hoffman et al., 1998; Wignaraja, 1998). The ability to recruit high quality technical staff can allow firms to exchange expertise and to benefit from localized learning spillovers (Longhi and Quéré, 1991). 
Networking 
The governance plays a major role in stakeholder networking in order to develop synergies. This networking dimension has a formal nature here, as opposed to informal relationships that characterize territorial anchoring. Technological incubators also contribute to the promotion of innovation (Doloreux, 1999). The governance can sustain the development of a common "knowledge architecture" among members (Tallman et al., 2004), seen as a translator or a knowledge hub between different communities (Bocquet and Mothe, 2010). Lazaric et al. (2008) highlighted the need for specific coordination mechanisms (as a knowledge platform) to create places where to exchange and confront ideas. 
Access to new skills and resources is a key motive in research partnerships (Mowery et al., 1998). University-industry relations are considered to be a determinant of innovation performance, particularly in high-technology sectors (Arvanitis et al., 2008). Romijn and Alabaladejo (2002) also provide evidence that interactions with scientific institutions and service providers (including public assistance) increase small firms’ innovation performance. But they show that the historical origin matters, and that this positive effect is valid only when the owners/managers came from these institutions. This idea is consistent with the literature on governance which shows the importance of relational networks and that cooperation is not spontaneous. And although personal contacts and social ties between actors are seen as necessary for innovation, some consider they are not sufficient to gain access to knowledge often embodied in joint scientific and industrial research teams (Breschi and Lissoni, 2001; Balconi and Laboranti, 2006). 
The cluster governance can play an active role in supporting innovation by facilitating spin-offs and patents or license registrations, which are essential channels of technology transfer (Arvanitis et al., 2008). As mentioned by Spithoven et al (2011), little attention has been paid to how small firms, which often dispose of no, or atmost a relatively low level of absorptive capacity, engage in searching for technology and knowledge activities. The authors underline the role of a specific type of technology intermediaries, namely collective research centres, in building their absorptive capacity. They highlight the multiple activities of which absorptive capacity in intermediaries is made up and demonstrate how firms lacking absorptive capacity collectively cope with distributed knowledge and innovation. In this paper, we focus on the role of another type of intermediary, the cluster governance.
Overall, the ambition of governance should focus on building an "innovative network” through appropriate mediation resources (material and cognitive) in order to create and sustain the actors’ interest and motivation to collaborate. The following empirical section is based on a recent study of the French technopole Savoie Technolac. It presents the first, to our knowledge, quantitative analysis of the effects of governance practices on firms’ innovation performance. 

II. EMPIRICAL STUDY of sAVOIE TECHNOLAC 
The study of Savoie Technolac, conducted in June 2009, is based on a survey of the 88 KIBS firms located in the technopole. Internet questionnaires were sent to all CEOs. After two follow-up mails, we received 53 valid questionnaires, thus leading to a final response rate of 62%. This exceptionally high response rate is due to the institutional support provided by the Savoie Technolac governance, who took in charge two follow-up phone calls for the on-line questionnaire. The final sample is representative of the business population in terms of sub-sectors affiliation and size. 

Background and History 
Savoie Technolac was created in 1987 after the closure of the military air base at the Bourget-du-Lac and is the result of a territorial restructuring. Following the Silicon Valley model, the technopole emerged from a joint local political and economic motivation to develop a new territory combining university, research and high-technology services. Savoie Technolac is currently made up of around 180 companies, 19 research laboratories and 69 scientific and technical higher education programmes covering four main activity sectors: 1) Solar energy and environmentally sound technologies; 2) Information, electronic and communication technologies; 3) Design, development and prototyping of industrial equipment; and 4) Plastics and composite materials. Savoie Technolac has centered its development around environmental concerns by offering a pleasant natural environment. In 2005, this environmental dimension was explicitly reinforced with the establishment of the National Institute for Solar Energy (INES). Savoie Technolac also hosts firms that belong to the Tenerrdis cluster focused on renewable energies. The resulting economic conversion has turned the “Solar Valley” into a reference for solar industry in France.

Descriptive Data (see Appendices 1.1 and 1.2)
Savoie Technolac is characterized by a large proportion of mostly independent very small firms with less than 10 employees. Most firms operate in service activities, especially in knowledge-intensive business services (KIBS): consulting, engineering or R&D. We here concentrated on these KIBS and on their product innovation performance. Only half of the 53 KIBS of our sample are engaged in innovation (58,5 % in product innovation, 45,3 % in service innovation) even though the studied KIBS are essentially engaged in high tech activities (defined as firms who invest more than 7% of their turnover in R&D). 
Savoie Technolac has a public governance structure made up of a joint syndicate of local authorities including the department of Savoie, Chambery Métropole and the Community of Municipalities of the Bourget Lake. This public governance expresses the motivation for local public institutions to remain independent from regional or national public policies. From its creation, the local institutions decided to set up a permanent team that would be in charge of managing and developing the technopole. The executive team of Savoie Technolac has “twelve members who work closely with a network of distinct partners to serve the best interests of the territory”. These partners are endowed with complementary areas of expertise such as innovation and technological transfer (CRITT), the financing of innovation (ARDI, OSEO), industrial and intellectual property (INPI), internationalization and economic development (consular chambers, local and regional councils). Alongside a CEO who in charge of the overall strategy and an administrative and financial director, the team fulfils the three below-mentioned governance missions: 
Territorial anchoring	
Building on three major resources – large available space in a highly attractive environment, proximity of university and academic facilities, and funding sources - Savoie Technolac succeeded in attracting SMEs as well as scientific departments of the University of Savoie. It assists firms seeking to relocate in the technopole and helps innovative start-ups through an incubator. 50,1% were incubated, attesting to the dynamism of the incubator, also to the spin-off role played by the technopole. Two thirds of KIBS (69,8%) rely on the local labour market. In order to anchor firms on the territory, Savoie Technolac offers a wide range of business services such as incubators, personal counselling, help for financial support, or other events (theme breakfasts, conferences, open days...) to disseminate information, facilitate meetings between firms and create a dynamic territorial anchoring. These services are used by 41,5% of KIBS. 
Networking 	
Savoie Technolac tries to develop relationships with on-site and/or off-site partners for innovation. The governance team put 24,5% of KIBS in relation with a potential innovation partner, 18,9% with customers, and 13,2% with a supplier. This networking has been set up on an individual level. These are not collective actions but specific relationships between two actors that are initiated by the governance structure. Developing industry-research cooperation through enhanced networking incentives in order to develop the technology transfer opportunities is a priority for the governance of Savoie Technolac. 
Savoie Technolac has taken a number of strategic initiatives to build innovation networks at the local and global levels. It is a member of an innovation network dedicated to French technopoles and incubators. It is also involved in two “competitiveness clusters” in the Rhône-Alpes region: Tenerrdis, dedicated to renewable energies, and Plastipolis, specialized in plastics and composites. It has also developed links with other science parks at the international level such as the Metropolitan Technopark (Quebec) and Techno Park (Montreal). However, as shown above in the descriptive statistics on innovation collaborations, innovation networking remains to be developed.

METHODS AND RESULTS 
In this section, we present our methodological approach. In line with structural equation modelling analysis, we first present the variables and their latent constructs and show their empirical validation. Second, we present the results of the regression model.
PLS Modelling 
We used the PLS (Partial Least Square) structural equation modelling to process the data. PLS is relevant when it comes to evaluating predictive relationships among variables and for analyses aimed at building theory (Wold, 1985). PLS is well suited to the exploratory nature of our approach, bearing in mind the limited research on governance and determinants of innovation in technopoles. Analysis using PLS follows two steps. The first aims at validating the relevance of the latent construct from theoretical literature (see table 1), the second seeks to evaluate the explanatory and predictive dimensions of the structural model. To highlight the impact of the technopole governance on innovation, we built two successive models, which correspond to two modes of governance intervention. 
Following the literature on KIBS, the first model (PLS1) considers the governance as a service provider. It captures the direct impact of governance on KIBS’ innovation through the provision of different services. The second model (PLS2) is inherited from the literature on cluster governance that conceives governance as an intermediary, a third party, which is able to support a partnership. It highlights the moderating effect of governance networking on KIBS’ innovation performance. 
Table 1: Description of variables
Independent Variables	Items	Measures	
Territorial Anchoring (ServiceCrea)
(Cooke et al., 1997 ; Grossetti, 2000 ; Carluer, 2006)	During the last 3 years, did your firm use the business services offered by Savoie Technolac?
Services dedicated to business creation (incubators, personalized counselling, providing help to request funds…)	
Binary	
Networking activity by the governance (NetworkingST)
(De Jong and Marsili, 2006 ; Favoreu et al.2008)	During the last 3 years, did the Savoie Technolac team put you in relation with another organization, located on or off-site:
1.	An innovation partner (RelaPART)
2.	A customer (RelaCLST)
3.	A supplier (RelaFOST)	

Binary
Binary
Binary	
Use of local labour market (EmploiST)
(Longhi and Quéré, 1991)	
During the last 3 years, did your company use the local labour market ?

 =1 if the firm uses local labour (0 if not)	

Binary	
Research cooperation as innovation source (CollUniST)

(Arvanitis et al., 2008; Romijn and Alabaladejo, 2002)	
During the last 3 years, did your company use the following sources to innovate?

=1 if the firm did collaborate with co-located university or other research institutes to innovate (0 if not)	
Binary	
Other innovation sources (SourcesInno)
(Tether, 2003 ; De Jong and Marsili, 2006 ; CIS 2006)	During the last 3 years, did your company use the following sources to innovate?
1.	Collaboration with customers (SourcCLSrv)
2.	Collaboration with suppliers (SourcForSrv)	

Binary
Binary	
Dependent Variable			
Product/service Innovation (InnoPdtsvr)
(CIS 2006)	During the last 3 years, did your company introduce 
1.	new products (SourcCLSrv)
2.	new services?	
Binary
Binary	

Variables 
Dependent variables 
Following the Oslo Manual and the approach adopted in the CIS surveys, we focused on the subject approach to innovation (Archibugi and Pianta, 1996; Mairesse and Mohnen, 2010). Data were collected at the company level. Respondents were asked to declare whether they had introduced an innovation in the three years preceding the survey (2006-2008) and, if so, what type of innovation, product or service (InnoPdtsrv). The perceptions of participants, even if they entail some bias, were considered to be the best measure as patent data do not provide a good objective measure of innovation nor for KIBS firms nor for small firms (Romijn and Alabaladejo, 2002). We here study a sample of mostly small KIBS firms.
Independent variables
Our independent variables, extracted from the literature, are of three types. 
First, some measures are intended to measure the quality of territorial anchoring. A variable illustrates the collective services used by firms on site (Servcrea). Another variable indicates whether the firms used local labour (EmploiST). 
Second, a networking measure has been introduced. This latent variable captures the general (both on site and off site) networking activity induced by the governance structure (NetworkingST). This variable consists of three items describing collaborations with innovation actors, customers and suppliers. 
Third, following the literature on innovation on KIBS and small firms, we identified the role of innovation sources – independently from governance actions. The first measures the collaboration between universities and companies within the technopole (CollUniST). The second (latent) variable (SourcesInno) measures the level of firms’ vertical cooperation for innovation by combining upstream (supplier) and downstream (customer) collaboration. This construct (SourcesInno) does not consider competitors as an innovation source because the item “collaboration with competitors” did not reach the minimum value of 0.5. In line with most empirical studies, competitors are therefore not a crucial determinant for KIBS’ innovation.  

Validation of the Latent Constructs 	
Before considering latent constructs for structural equation modelling, it is necessary to examine the convergent and divergent validity of the confirmatory factor analysis (Gefen and Straub, 2005). Convergent validity is demonstrated when items measuring a latent variable reach a significant value on the axis of this variable (t-value). It also requires that all latent constructs reach a minimum average variance extracted (AVE) score of 0.5 (Fornell, 1987). This criterion can be consolidated by checking the values of composite reliability for each construct (see Table 2). To investigate the discriminating validity of latent constructs, we used the matrix of factor loadings of the full model. All items exceed the value of 0.55 and each item obtained a higher score on its corresponding factorial axis – comparatively to values obtained in other areas (see Appendices 2.1 and 2.2). Discriminating validity is demonstrated when the square root of the AVE for each construct is greater than any correlation between that construct and each of the other constructs (see Appendices 3.1 and 3.2).

Table 2: Indicators of convergent validity 
	Model PLS1	Model PLS2	
 	AVE	Composite Reliability	AVE	Composite Reliability	
NetworkingST	0,615	0,823	0,615	0,823	
SourcesInno	0,603	0,752	0,603	0,752	
InnoPdtsrv	0,521	0,726	0,521	0,726	
CollUniST * Networking		0,750	0,900	

Results and Discussion
Results for both models, PLS1 and PLS2, are presented in Table 3. They indicate that, beyond the traditional determinants for firm innovation performance, governance plays a significant role. Given the relatively high R-square values in each model, we can conclude that the variables capturing the governance practices of innovation have a significant impact on business innovation performance. The slight increase of the R-square value of the second model leads us to consider that it is better than the first one.
Table 3: Results of the two PLS models
	PLS1	PLS2	
	InnoPdtsrv	InnoPdtsrv	
		T	 	t 	
Independent Variables					
CollUniST	-0,195	1,979*	-0,286	2,267*	
EmploiSt	0,334	2,565**	0,339	2,614**	
NetworkingST	0,322	2,940**	0,242	1,892*	
Servcrea	0,262	2,593**	0,240	2,233*	
SourcesInno
	0,147
	1,255
	0,178
	1,671*
	
Moderating effect
CollUniSTxNetworkingST		
	0,190	1,657*	
					
R²	0,367	0,386	
* p < .05 (One tailed test: 1.645, df = 499) ; ** p < .01; (2.326, df = 499) ; *** p < .001 (3.090, df = 499).

Results show the impact of cluster governance practices on the innovation performance of co-located KIBS firms.
Direct effects: PLS1
The first PLS model highlights the impact of the quality of the local anchoring on business innovation. Indeed, both variables have a significant positive impact on product/service innovation: EmploiSt for the local labour market (model 1: β=0,334, t=2,565, p <0.01), and Servcrea for the specific business services offered by the governance (model 1: β=0,262, t=2,593). This result is consistent with previous findings on KIBS’ innovation. It confirms that a soft environment (Zhao et al., 2010), informal modes of knowledge transmission (Trippl and Tödling, 20109) and a dynamic labor market (Segelod and Jordan, 2004) are major determinants of KIBS’ innovation. Let us precise that we here capture the specific role played by the governance in creating this territorial anchoring – approach which greatly differs from previous studies on KIBS which do not clarify the origin of this dimension.
Territorial anchoring, through its two dimensions - a local labour market and dedicated business services, is positively linked to innovation. In line with previous literature (Hoffman et al., 1998; Wignaraja, 1998), we demonstrated that, for KIBS, having access to qualified employees and high quality scientific and technical staff, is essential to absorb and create knowledge, to benefit from localized learning spillovers (Longhi and Quéré, 1991), and consequently to develop product/service innovation. Our result for the second dimension also confirms the previously underlined importance of specific business services (start-up support, themed breakfasts, conferences, entrepreneur club, seminars, etc.) are transverse enough to enable the different actors to meet, to get to know each other and to create a common language. These specific services appear to be a prerequisite for the creation of organizational proximity (Grossetti, 2000). This result suggests that the governance of Savoie Technolac has succeeded in creating mediation resources and incentive schemes which, in fine, enhance the KIBS’ innovation performance. The recent focus on the solar industry should further improve this aspect, leading to the emergence of a dominant activity and a critical threshold (Levesque et al., 1998), essential for local anchoring and its effectiveness in terms of innovation.	
Second, results confirm the significant positive impact of the networking practices implemented by the governance on firm’s innovation (model 1: β=0.322, t=2,940, p <0.01). Whereas the empirical literature on KIBS has shown that customers, suppliers and other partners can be crucial sources of innovation, the role of governance networking had not been explicitly studied yet. We therefore refer to recent studies on cluster governance that emphasize the moderating role of governance, especially for small firms that do not necessarily have the material and cognitive resources to perceive business and technological opportunities. 
Networking practices for innovation implemented by the governance are effective for KIBS with partners (innovation partners, clients and suppliers) located both within and outside of Savoie Technolac. In line with previous literature (e.g. Tallman et al., 2004; Lazaric et al., 2008; Bocquet and Mothe, 2010), having (mostly informal) relations with different types of actors is an essential dimension of innovation processes.
Third, results do not confirm the expected positive impact of innovation sources - independently from governance. While there is no significant impact of sources stemming from collaborations with customers and clients, results show a significant negative direct effect of CollUniSt, which measures collaborations between firms and universities/research institutes within the technopole, on product innovation (model 1: β=-0,195 t= 1,979, p <0.05). This result may suggest that KIBS suffer from a deficit of absorptive capabilities (Spithoven et al., 2011) and cannot benefit from their formal R&D partnerships. It is somewhat in opposition with the usual statement that KIBS are often associated with higher cognitive capabilities compared to firms from other sectors. However, the characteristics of KIBS in our sample (large sectoral heterogeneity and very small-sized firms) can explain this difference. 

Moderating effect: PLS2
In the second PLS model, we wanted to assess the moderating effect of governance through its networking practices, as these are considered as key in cluster activities for innovation performance. Results show a very interesting effect. Indeed, while all the effects identified in PLS1 are confirmed here, results clearly indicate that technology transfers stemming from collaboration with research institutes indeed exist within the technopole. When this type of research collaboration is combined with networking practices implemented by the governance (NetworkingSTxCollUniST), the impact becomes significant and positive (β=0,190, t=1,657, p <0.05). This result means that when research collaborations are stemming from governance intermediation, the impact of these collaborations on product innovation is positive whereas it is negative when such collaborations do not stand against networking practices from the technopole governance. This confirms that governance can play a vital role as ‘third party’ for firms that lack resources for pursuing R&D (Spithoven et al., 2011). Besides, this second model indicates that firms’ collaborations with upstream and downstream actors (clients and suppliers) during their innovation processes stimulated product/service innovation (SourcesInno: β= 0.178, t =1,671, p <0.05). Thus, introducing the moderating effect of governance networking practices also increases the impact of these two types of innovation sources, which here becomes significant. In sum, networking boosts the effect of all types of innovation sources and collaborations.
This is the most interesting result of this study: when considering networking as a moderating variable, this link becomes not only stays significant, but becomes positive. Governance thus boosts collaboration, while collaboration with universities without the support of governance induces negative effects on product innovation. Indeed, the governance acts as an intermediary by putting the small firm in relation with other institutions that can help in terms of technical support, financial and property right advice, financial resources, partner search, international collaboration, etc. Here, governance has not only a monitoring role but also provides strategic additional resources in order to make R&D projects effective. Since governance has a global and strategic view of the KIBS in the technopole, and on the collaboration project, it can find the right partner which can fill the resource gap. This result has been identified by previous empirical studies. For instance, Romijn and Alabaladejo (2002) showed that interactions with scientific institutions increased small firms’ innovation performance only when the CEOs originated from these institutions thus had created previous relations with such research labs. This suggests that, when the small firm (here the KIBS) does not have a previous experience with the scientific institution, it looses too much energy, resources and time to be able to benefit from this collaboration. If you also consider the fact that most of these small firms lack absorptive capacity (Spithoven et al., 2011), the role of a technological intermediary, here the local governance, clearly appears as vital. Governance helps to increase absorptive capacity through previous networking that provide crucial resources for R&D projects. When networking is introduced as a moderating variable, the impact of innovation sources becomes positive for collaboration with universities and research centers and significant for collaboration with clients and suppliers.

CONCLUSION 	
This first quantitative analysis of the role of governance in a French technopole not only confirms the fact that "traditional" variables effectively foster innovation performance for co-localized firms, but also emphasizes the role played by the technopole governance. Local territorial anchoring and networking have a positive significant impact on KIBS product innovation. Our results are thus twofold: on one hand, KIBS have the capacity to absorb knowledge on their own (direct effect of local anchoring and networking practices); on the other hand, the KIBS in our sample are probably too small to benefit from R&D collaboration without the intermediation of the technopole governance (indirect effect). The KIBS’ absorptive capacity is thus at a medium level as an effect of the combination of KIBS “natural” high absorptive capacity and their very small size. More generally, these two (direct and indirect) effects of governance show that firms can benefit from the (French) cluster policies based on voluntary and active strategies for knowledge creation.
After 20 years of existence, Savoie Technolac relies on a dynamic local labour market and has succeeded in implementing networking incentives. The technopole and KIBS firms are still in an emerging phase, and industry-research collaborations boost firms’ product innovation only when these are induced by the governance. This fact is not singular as most studies show that a certain lapse of time is required in order to establish an effective network of relationships between industry, research and education (Levesque et al., 1998). Time is indeed a key variable for the construction of a common history (Torre, 2006). Examples of successful technopoles suggest a maturation period of 15 to 20 years and a long initial starting process that enables the development of a sufficiently stable context of inter-organizational relationships (Levesque et al., 1998). 
This research is not without limitations, in particular due to the specificities of our sample. Even though it is representative of Savoie Technolac KIBS, it has the drawback of being relatively small and subject to great heterogeneity between sub-sectors of KIBS – which is a general problem for all research on KIBS in general. As the sample was relatively small (though quasi-exhaustive), it was not possible to distinguish between various categories of KIBS - although we expect that governance practices to foster members’ innovation should vary according to KIBS’ specificities. The measures used to assess the role and action of the governance could also be enriched. Future research could be undertaken in order to enrich empirical knowledge pertaining to organizational proximity and to the role of governance practices aimed at boosting firms’ innovation performance. More specifically, such research could benefit from a thorough qualitative study on the choice of the “right” governance practices according to the stage of development of the KIBS, or to its maturity and positioning towards technological (product/service) innovation.

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Appendix 1.1: Descriptive Statistics (n=53)
Economic Characteristics of firms implanted within Savoie Technolac	n (%)	
Variables 	Definition	0	1		
INCUB	=1 if the firm was incubated in the technopole (0 if not)	26 (49,1)	27 (50,1)		
INDEP	=1 if the firm belongs to a group (0 if not)	 14 (26,4)	39 (73,6)		
		1	2	3	
SIZE	=1 if less than 10 employees ; = 2 if 10 to 20 employees ; =3 if more than 20 employees. 	37 (69,8)	9 (17)	7 (13,2)	
COMPET	During the last three years (2006-2008), how would you describe the competitive environment of your company? 
(=1 if weak to medium competitive intensity ; =2 if intense competition ; =3 if very intense competition	22 (41,5)	20 (37,7)	11 (20,8)	
EXPORT	How much of your turnover (in %) did your export sales represent in 2008? 
(1= if export % < 2% of total turnover; =2 if export % between 2 and 10%; =3 if export % > 10%)	33 (62,3)	6 (7,5)	16 (30,2)	
MARKET	On which geographic market did your firm sell its goods or services between 2006 and 2008?
(1=Regional; 2=France; 3=International)*	24 (45,3)	17 (32,1)	12 (22,6)	
AGE	Since how many years does your company exist?
(1= if firm age < 5 years ; =2 if age between 5 and 10 years ; =3 if firm older than 10 years)	12 (22,6)	18 (34)	23 (43,4)	
ANCIEN	Since how many years is your company established in Savoie Technolac?
(1= if establishment < 2 years; =2 if establishment between 2 to 10 years ; =3 if more than 10 years)	6 (11,3)	34 (64,1)	13 (24,6)	
* Note that the total percentage is superior to 100% as multiple responses were permitted

Innovation Profile of firms located in Savoie Technolac	0	1	
InnoPDT	=1 if the firm introduced goods innovation during the last 3 years 
(0 if no product innovation)	22 (41,5)	31 (58,5)	
InnoSRV	=1 if the firm introduced service innovation during the last 3 years 
(0 if no service innovation)	29 (54,7)	24 (45,3)	
SourcCLSrv	=1 if the firm did collaborate with co-located customers in order to innovate (0 if not)	27 (50,9)	26 (49,1)	
SourcForSrv	=1 if the firm did collaborate with co-located suppliers in order to innovate (0 if not)	30 (56,6)	23 (43,4)	
CollUniST	=1 if the firm did collaborate with co-located university/research centre or other higher education institutes to innovate (0 if not)	44 (83)	9 (17)	

Territorial Anchoring and Networking on Savoie Technolac	0	1	
Servcrea	=1 if the firm uses offered services to business creation / 
0 if not 	31 (58,5)	22 (41,5)	
EmploiST	=1 if the firm uses local labour (0 if not) 	16 (30,2)	37 (69,8)	
RelaPart	=1 if the governance team did put the firm in relation with a potential innovation partner / 0 if not 	40 (75,5)	13 (24,5)	
RelaCLST	= 1 if the governance team did put the firm in relation with an organization that became a customer afterwards / 0 if not 	43 (81,1)	10 (18,9)	
RelaFOST	= 1 if the governance team did put the firm in relation with an organization that became a supplier afterwards / 0 if not 	46 (86,8)	7 (13,2)	
Appendix 1.2: High vs. Lowtech distribution of firms (n=53)

 	HTECH (a)	LMTECH (b)		
n	42	11	53	
%	79,2	20,8	100	
High-Tech = R&D intensity> 7% ; (b) Low-Medium Tech = R&D intensity ≤ 7% (cf. OECD, 2005) 

Appendix 2.1: Items loading on each factor – PLS1

 	CollUniST	EmploiST	InnoPdtserv	Networking	Servcrea	SourcesInno	
COLLUNI	1,000	0,297	-0,043	0,123	0,027	0,042	
EMPLST	0,297	1,000	0,241	0,085	-0,197	-0,075	
INNOPDT	0,075	0,030	0,564	0,338	0,243	0,309	
INNOSERV	-0,109	0,268	0,778	0,295	0,234	0,093	
RELACLST	-0,090	0,107	0,288	0,584	0,279	0,183	
RELAFOST	0,120	0,014	0,377	0,854	0,237	0,199	
RELAPART	0,209	0,088	0,401	0,879	0,321	0,228	
Servcrea	0,027	-0,197	0,347	0,354	1,000	0,284	
SourcCLSrv	0,059	0,070	0,197	0,049	0,169	0,743	
SourcForSrv	0,010	-0,171	0,224	0,337	0,267	0,809	

Appendix 2.2: Items loading on each factor – PLS2
 	CollUniST	CollUniST
x
Networking	EmploiST	InnoPdtserv	Networking	Servcrea	SourcesInno	
     CollUniST	1,000	0,511	0,297	-0,044	0,122	0,027	0,042	
COLLUNIxRELACLST	0,307	0,838	0,091	0,200	0,373	0,165	-0,165	
COLLUNIxRELAFOST	0,438	0,945	0,130	0,285	0,446	0,235	0,020	
COLLUNIxRELAPART	0,632	0,810	0,188	0,172	0,392	0,194	0,115	
     EMPLOIST	0,297	0,152	1,000	0,243	0,085	-0,197	-0,075	
     INNOPDT	0,075	0,195	0,030	0,552	0,337	0,243	0,309	
    INNOSERV	-0,109	0,169	0,268	0,787	0,296	0,234	0,094	
    RELACLST	-0,090	0,191	0,107	0,290	0,588	0,279	0,183	
    RELAFOST	0,120	0,467	0,014	0,375	0,853	0,237	0,200	
    RELAPART	0,209	0,406	0,088	0,398	0,878	0,321	0,229	
    Servcrea	0,027	0,231	-0,197	0,346	0,354	1,000	0,284	
   SourcCLSrv	0,059	-0,001	0,070	0,194	0,049	0,169	0,740	
   SourcForSrv	0,010	-0,018	-0,171	0,223	0,337	0,267	0,811	

Appendix 3.1: Correlations and AVE square roots – PLS1
 	CollUniST	EmploiST	InnoPdtserv	Networking	Servcrea	SourcesInno	
   CollUniST	1,000	    	        	     	     	      	
    EmploiST	0,297	1,000	        	     	     	      	
InnoPdtserv	-0,043	0,241	0,722(1)	     	     	      	
   Networking	0,123	0,085	0,458	0,784	     	      	
   Servcrea	0,027	-0,197	0,347	0,354	1,000	      	
  SourcesInno	0,042	-0,075	0,272	0,260	0,284	0,777	
(1) AVE square root

Appendix 3.2: Correlations and AVE square roots – PLS2
 	CollUniST	CollUniST x Networking	EmploiST	InnoPdtserv	Networking	Servcrea	SourcesInno	
CollUniST	1,000	 	 	 	 	 	 	
CollUniST x
 Networking	0,511	0,866(1)	 	 	 	 	 	
EmploiST	0,297	0,152	1,000	 	 	 	 	
InnoPdtserv	-0,044	0,262	0,243	0,722	 	 	 	
Networking	0,122	0,467	0,085	0,456	0,784	 	 	
Servcrea	0,027	0,231	-0,197	0,346	0,354	1,000	 	
SourcesInno	0,042	-0,013	-0,075	0,270	0,261	0,284	0,776	
(1) AVE square root