The themes of technological innovation, entrepreneurship, and organizing

Institutional Innovation Practices in Technopoles: An Example in France

Anne Berthinier-Poncet

Universite de Savoie, France

Rachel Bocquet

Universite de Savoie, France

Sebastien Brion

Universite de Savoie, France

Caroline Mothe

Universite de Savoie, France

ABSTRACT

This chapter aims atfilling a void in the literature on the question as to whether organizational proximity can be fostered within clusters. We address a dimension that has received little attention until recently, namely the local governance structures of technopoles. The objective was to gain an insight into such institutional practices and to evaluate their effects on firms ’ innovation performance. By identifying how geographical and organizational (cognitive and relational) proximity interrelate in the analysis of clusterforms we sought to contribute to the burgeoning literature on the different types of proximity. The empirical research relies on a representative sample of 88 firms implanted within the Savoie Technolac technopole, in the French Rhone-Alpes region. Our results suggest that, even though local governance contributes to territorial anchoring, only the local labor market had a direct significant impact on the firms’ innovation performance. In addition, territorial anchoring combined with the roles played by governance in terms of ‘matchmaking ’ and support for technology transfer significantly increased the number of innovation projects. These results suggest that governance has a decisive role in the creation of communication and interaction structures between firms, which are essential for firm innovation. This research may have important implications for governance modes, in not only technopoles but also more generally in clusters.

DOI: 10.4018/978-1-61350-165-8.ch024

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

INTRODUCTION

The established typology of territorial innovation makes it possible to identify the characteristics of specific clusters such as technopoles and technol­ogy parks (Longhi & Quere, 1993; Levesque, Fontan & Klein, 1998; Moulaert & Sekia, 2003; Carluer, 2006). Despite the importance of their research activities, innovation technopoles 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 & 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 & Gros - setti, 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 collective innovation (Longhi & Quere, 1993; Levesque et al., 1998).

Focusing on the dynamics of innovation, two types oftechnopoles can be distinguished (Cooke, 2001). A “linear” type, which is typical of science parks “a la frangaise” and related to a simple localized agglomeration of productive activities that have no real relationship with each other, and by contrast, an ‘interactive’ type, which is based on organizational and institutional networking between firms, promoting their ability to innovate. 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. This is in line with many studies that underline the central role of institutions in the creation of non spatial forms of proximity in interactive agglomeration forms (Grossetti, 2004; Pecqueur & Zimmermann, 2004; Leloup et al. 2005; Asheim, 2007; Rallet & Torre, 2007; Carrincazeaux, Grosseti & Talbot,

2008) . The French School of Proximity (Torre & 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 & Mothe,

2009) has been shown to play a role in the cre­ation of favourable conditions for collaborative innovation. In interactive technopoles, where firms already share material and cognitive resources, one could think that the coordination challenge associ­ated with governance would be lower. However, this is far from being the case when the cognitive distance between firms is weak, leading to higher appropriation defaults (Nooteboom, 2009). The governance structure can thus play an active role in maintaining and developing these resources within linear types of technopoles as well as in certain types of interactive forms.

Empirical research dedicated to the institu­tional innovation practices implemented by local governance is scarce. The original approach ad­opted in this chapter consists in identifying such practices within a technopole and in assessing their impact on its members’ innovation performance. We seek here to fill a void in the literature on the question as to whether organizational proximity can be fostered by taking into account a dimension that has been given low consideration, namely the local governance structures of technopoles.

The empirical research concerned a representa­tive sample of 88 companies affiliated to the French technopole Savoie Technolac in the Rhone-Alpes

region and yielded two types of results. Firstly, although the governance was involved in the ter­ritorial anchoring of firms, we showed that calling upon a local labor market had a direct impact on the firms’ innovation performance. Secondly, together with the inter-firm matchmaking role of governance and its facilitating role in technology transfer between research and industry, this ter­ritorial anchoring also contributed significantly to the implementation of innovation projects within the technopole. As these innovation projects were ongoing at the time of the study, their effects on innovation performance were difficult to observe.

This chapter is organized as follows: We first characterize the concept of “technopole” by fo­cusing on the specificities of French technopoles. Then, following the proximity approach, we con­sider the possible influence of institutional prac­tices on the innovation performance of member firms, and analyze the impact of the institutional practices implemented by the local governance on innovation through the study of Savoie Technolac.

TECHNOPOLES AND DYNAMICS OF INNOVATION

The technopolitan phenomenon and its associated technology parks or technopoles have experi­enced strong growth in many developed coun­tries since the late seventies (Longhi & Quere, 1991, 1993; Doloreux, 1999). Technopoles are a tool for the strategic development of regional innovation systems (Doloreux 1999; Longhi & Quere, 1993). Technopoles are public initiatives designed to promote technological innovation. They are also viewed as local zones created in order to coordinate existing resources and create new ones. Far from presenting a homogeneous model ofterritorial innovation, recent research has identified two types of technopoles with distinct properties and performances (Cooke, 2001). We interpret this distinction following the proxim­ity view and identify the role of the institutional practices adopted by a technopole’s governance on its members’ innovation performance.

Within technopoles, “natural” geographical proximity has been shown to influence innovation performance (Amin & Cohendet, 2005). However, geographical proximity alone cannot account for knowledge flows, learning and innovation (for a review, see Ozman, 2009): other forms ofproxim - ity may be essential in order to foster interactive learning and innovation capabilities (Boschma, 2005; Rallet & 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 & 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.

More precisely, we propose that cluster local governance may contribute to foster organizational proximity in linear 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 & 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. The extent to which organizational proxim­ity can be fostered remains an open question. We contribute to fill this void in the literature by analyzing whether governance structures might help to develop innovative networks.

Technopoles: “Cathedrals in the Desert” or Networks for Innovation?

The concept of technopole refers to various forms of industrial organization. In the United States, the Silicon Valley is often cited as a typical example ofa technopole, viewed as a new techno-industrial complex of high-tech firms in which government and universities play a crucial role for its develop­ment (Castells & Hall, 1994). In Great Britain, “science parks” have helped to “reinforce the power of universities in the face of an important disengagement of the State” (Longhi & Quere, 1991). An economic logic of innovation diffu­sion and technology transfer to small local firms is predominant in German “Technologieparks”.

In France, technopoles such as SophiaAntipolis and ZIRST Meylan have emerged as the result of public policies for economic development oriented towards high technology activities (Longhi & Quere, 1993). These are organized around three main functions (Faberon, 1990): (1) company enrolment and anchoring; (2) cross-fertilization and the development of local synergies between scientific, industrial and financial representa­tives; and (3) technology transfer, as the scientific component is inseparable from the concept of technopole (Doloreux, 2002).Although this model of technopolitan development has been very suc­cessful in developed countries, empirical studies show a strong heterogeneity in terms of their in­novative performance. Despite significant invest­ments in innovation services and infrastructure, French technopoles seem to be the locus of weak relationships between co-localized firms (Carluer, 2006) and of a lack of synergy between research and industry (Cooke, 2001; Rallet & Torre, 1998).

French technopoles tend to correspond to the linear innovation policy (Cooke, 2001). They result from large-scale political investment in public infrastructure. Agglomeration is induced and no institutional effort is made to create links between co-localized actors. This lack of interaction is mainly due to the explicit motiva­tion to provide hosting facilities for high-tech companies instead of facilitating links between scientific, academic and industrial potentials (Quere, 1996; Doloreux 1999; Longhi & Quere,

1991) . French technopoles share a set of specific properties extensively identified in the literature on local development. Levesque et al. (1998) observed that technopoles are characterized by a high number of innovative firms from high-tech sectors, mainly SMEs. However, they are seldom involved in innovation networks, particularly with local private or public partners. In their longitudi­nal study of Sophia Antipolis, Longhi and Quere (1993) showed that when inter-firm relationships existed, these remained essentially vertical in na­ture. Often isolated from their parent company or due to their small size, firms are not encouraged to collaborate with research laboratories and/or neighbouring partners. The main reason is the fear of losing specific expertise. Levesque et al. (1998) confirmed the weakness of contractual relations within linear technopoles: only the largest SMEs were successful in finding their place within a permanent network of business relations.

The lack of trust within the technopole also hinders the establishment ofinformal relationships and of a local labor market, both key dimensions for territorial anchoring. De Bernardy (1999) showed that when these dimensions exist, they remain highly vulnerable to competitive pressure. Fierce competition between firms acts against the creation of informal relationships. Carluer (2006) explained the absence of genuine local anchoring of firms by the fact that geographical proximity does not lead to relational embeddedness. Indeed, companies have access to generic, readily avail­able and transferable resources without having to engage in non-market relationships. Altogether, these findings echo recent studies that question the premise that geographical proximity would be sufficient for the creation and dissemination of innovation at a collective level.

Conversely, “interactive” technopoles (Cooke,

2001) reject the linear view of innovation and fo­cus on the dynamic learning process taking place in a local “milieu” (Camagni, 1991; Maskell & Malmberg, 1999; Antonelli, 2000) where institu­tions are expected to play a central role (Amin, 1999; Cooke & Morgan, 1998). The success of this second type of technopole no longer relies on the existence of infrastructures, generic resources and skilled workforce. It depends on strong territo­rial anchoring and on active networking between firms, as well as on closer ties between research and industry. For Longhi and Quere (1991), the “innovative network” depends on both the comple­mentarity of activities/skills available on site and on the dynamics of the local labor market. These two central dimensions, which form the basis for local learning, require a strong synergy between firms and local institutions (Boekholt & Van der Weele, 1998, Levesque et al., 1998).

Although geographic proximity plays a sig­nificant role, approaching innovation from an interactive perspective is insufficient to explain innovation; organizational proximity must also be considered. Doloreux and Parto (2005) showed the importance of “social relations” that support the activities of production, consumption and trade: “[they] are made up of symbolic elements, social activities, and material resources that define the structure of the interaction among hu­mans based on rules, norms, and values” (ibid: 146). This dimension is inherent to the concept of organizational proximity between actors that are geographically close (Gilly et al., 2004). It guar­antees the viability and stability ofthe technopole in the long run (Dupuy & Gilly, 1999). 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. 2003;Ehlinger et al. 2007,Bocquet & 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 & Marsili, 2006; Freel & Harrison, 2006; Huet & 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 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).

Institutional Practices for Innovation in Technopoles

The emergence of a technopole as an innovative network can 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. Gilly and Wallet (2001) identified four modes of territorial governance: (1) private governance structured around a broker; (2) collective private governance, where the broker is a formal insti­tution that brings together private operators; (3) public governance, where the public actors that manage the network and the private actors who benefit from these resources are distinct; and (4) joint governance where dominant players are from both public and private sectors. Following this approach, the governance of technopoles should play a significant role in stimulating innovation through the quality of the local anchoring, and via networks and cooperation between research and industry.

Territorial Anchoring

Beyond the services and infrastructure developed to host firms (generic resources), the governance ofthe 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 (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 labor market in order to enable firms to exchange expertise and to benefit from localized learning spillovers (Longhi & Quere, 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). Hosting knowledge-intensive business services (KIBS) has a strategic impact in this cross-fertilization to the extent that KIB S play a “key interface role between a bunch of generic know ledge and a variety of unique and specific ap­plications” (Antonelli & Quere, 2002: 1060). The governance can also 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 & 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.

Cooperation between Research and Industry

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). Although personal contacts and social ties between actors are necessary for innovation, they are not sufficient to gain access to knowledge often embodied in joint scientific and industrial research teams (Breschi & Lissoni, 2001; Balconi & Laboranti, 2006). The technopole 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).

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 ofthe French technopole Savoie Technolac. It presents a first quantitative analysis of the ef­fects of the governance institutional practices on firms’ innovation performance.

CASE STUDY OF SAVOIE TECHNOLAC

The study of Savoie Technolac, conducted in June 2009, is based on a survey of 125 companies located in the technopole. Internet questionnaires were sent to all CEOs. After two follow-up mails, we received 88 valid questionnaires, thus leading to a final response rate of 70.5%. The final sample is representative of the business population in terms of sector affiliation and size.

Background and History

Savoie Technolac was created in 1987 after the closure ofthe 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 Appendix 1.1)

A large proportion of mostly independent very small firms with less than 10 employees char­acterizes Savoie Technolac. Most firms (87.5%) operate in service activities, especially in knowledge-intensive business services (KIBS): consulting, engineering or R & D. Most firms are engaged in technological innovation: 55.7% in product innovation, 52.3% in service innova­tion and 39.8% in process innovation. Their main markets in 2008 were local or regional (69.4%). 47.8% export their goods or services. 28.8% are engaged in other innovative clusters at the regional or national level. Descriptive data show that 45.5% of firms were engaged in innovation projects with co-located partners - but only 15.9% with the local university and less than 5% with nearby customers and suppliers.

Savoie Technolac: A “Linear” or “Interactive” Model of Technopole?

Savoie Technolac has a public governance mode made up of a joint syndicate of local authorities including the department of Savoie, Chambery Metropole 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. Alongside a CEO in charge of the overall strategy and an admin­istrative and financial director, the team fulfils the three above-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. Among the 70% of firms that were created on the site, 47% were incubated, attesting to the dynamism of the incubator, also to the spin-off role played by the technopole. A vast majority of firms (68.2%) rely on the local labor market. In order to anchor firms on the territory, Savoie Technolac offers a wide range of generic services and infrastructure, which are widely used (by 90.9% of firms). 40% of firms use more specific business services such as events (theme breakfasts, conferences, open days...) to disseminate information, facilitate meetings between firms and create a dynamic territorial anchoring.

Networking

Savoie Technolac has taken a number of strategic initiatives to build innovation networks. It is a member of an innovation network dedicated to French technopoles and incubators. It is also involved in two “competitiveness clusters” in the Rhone-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. The low involvement of firms within on-site innovation networks, either hori­zontal or vertical, is typical of a ‘linear’ type of technopole. However, the firms do acknowledge that the governance structure of Savoie Technolac plays a significant role in developing relationships with on-site and/or off-site partners for innovation.

Cooperation between Research and Industry

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. It endeavors to reinforce the circulation of infor­mation through specific newsletters or thematic meetings that will encourage researchers to “come out of their laboratories”. The creation of spin­offs is also sustained.

METHOD AND RESULTS PLS Modelling

We used the PLS (Partial Least Square) structural equation modeling to process the data1. 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 corresponding to the interactive model of
the technopoles (Cooke, 2001). The first (PLS1) shows the direct effects ofgovernance on business innovation. The second (PLS2) highlights the ef­fects of governance on collaborative projects for innovation within the technopole.

Variables

Dependent Variables

Table 1. Composition of latent variables

Independent Variables

Items

Measures

Networking activity per­formed by the technopole

(NetworkingST)

(De Jong et Marsili, 2006;Favoreu et al.2008)

Did the Savoie Technolac team put you in relation with another organization, located on or off-site, that became:

1. An innovation partner (RelaPART)

2. A customer (RelaCLST)

3. A supplier (RelaFOST)

Binary

Binary

Binary

Quality of Territorial An­choring (ServiceEnt)

(Cooke et al., 1997; Gros­setti, 2000;Carluer, 2006)

Does your firm use the business services offered by Savoie Technolac?

1. Specific business services (themed business breakfasts, conferences, symposium, E-entrepreneur Club, open-day visits...) (Servent)

2. Services dedicated to business creation (incubators, personalized counselling, providing help to request funds.) (Servcrea)

Binary

Binary

Control Variables

Innovation Expenses (Ex- penseInno)

(Freel, 2006;De Jong & Marsili, 2006)

1. What percentage of your firm’s turnover was dedicated to R&D in 2008?

(DepRD)

2. How much working time (in %) did your firm dedicate to innovation in 2008?

(LaborIn)

Continuous (Value Ln) Binary

Clusters’ affiliation

(Cluster)

CIS 2006

Is your firm member of another cluster:

1. In the Rhone-Alpes region l(CluRegR)

2. Outside the region? (ClusHreg)

Binary

Binary

Innovation sources within branch of activity

(Source)

(Tether, 2003;De Jong et Marsili, 2006;CIS 2006)

Over the last three years, did your firm use the following sources to innovate?

1. Collaboration with customers (SourcCLSrv)

2. Collaboration with suppliers (SourcForSrv)

Binary

Binary

Following the Oslo Manual and the approach adopted in the CIS surveys, we focused on the subjective approach to innovation (Archibugi & Pianta, 1996; Mairesse & Mohnen, 2010). Data were collected at company level. Respondents were asked to declare whetherthey had introduced an innovation in the three years preceding the survey (2006-2008), and, if so, what type of in­novation. In the first model (PLS1), the following three binary variables corresponded to our three dependent variables: product innovation, process innovation and service innovation. In the second model (PLS2), a fourth binary dependent variable was introduced to clarify whether companies had used innovation projects in collaboration with other partners. In this second model (PLS2), this variable was interposed between governance variables and the dependent variables of the first model (PLS1).

Independent Variables

Two latent variables were constructed to illustrate the institutional practices serving innovation in the technopole. The first latent variable illustrated the collective services used by firms on site. A second variable indicated whether the firms used local labor. Together, these two variables captured the quality of local anchoring. A third variable measured the networking activity within the technopole. This variable consists of three items describing collaborations with customers and
suppliers. A fourth and final explanatory variable assessed the level of technology transfer between universities and companies within the technopole.

Control Variables

Table 2. Indicators of convergent validity

Model 1 (PLS1)

Model 2 (PLS2)

AVE

Composite Reliability

AVE

Composite Reliability

Cluster

0,688

0,813

0,699

0,823

Expenselnno

0,910

0,953

0,910

0,953

NetworkingST

0,630

0,835

0,638

0,840

Source

0,661

0,796

0,662

0,797

ServiceEnt

0,683

0,812

0,683

0,812

Among the five control variables, three were latent. The first (innovation expenditure) is composed of two items: the percentage of turnover devoted to R&D and the working time dedicated to in­novation. This latent construct sought to capture the internal effort made by the company to in­novate in a broader manner than the traditional measures of input exclusively focused on R & D expenses and number of patents. This construct was particularly suited to capture the innovative efforts of smaller companies, especially services, which are largely dominant within Savoie Tech - nolac. The second latent variable stated whether the company also belonged to another regional, national or international cluster. The third latent variable measured the level of the company’s vertical cooperation for innovation by combining upstream (supplier) and downstream (customer) collaboration. The two other control variables were the independent status of the company and its size (less than 10 employees, 10-20 employees, more than 20 employees).

Validation of the Latent Constructs

Before retaining latent constructs in structural equation modeling, it is necessary to examine the convergent and divergent validity of the confir­matory factor analysis (Gefen & 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 require s 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 0.7 and each item obtained a higher score on its corresponding factorial axis - comparatively to values obtained in other areas (see Appendix 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 Appendix 3.1 and 3.2).

Results

Results for both models, PLS1 and PLS2, are presented in Table 3. They indicate that, beyond the traditional determinants for firm innovation performance, governance also plays a significant role. Given the relatively high R-square values
in each model, we can first conclude that the variables capturing the institutional practices of innovation implemented by the governance have a significant impact on business innovation performance in PLS1 and / or on innovative col­laborative projects in PLS2.

Model PLS1: The Effect of Governance on Innovation Performance

According to the three institutional practices previ­ously identified, three key results show the impact ofthe technopole’s governance on the innovation performance of co-located firms:

The first result concerns the quality of the lo­cal anchoring, which does not have a full impact on business innovation. The results show that although the local labor market has a significant impact on product innovation (P=0169, t=2,027, p <0.05), this is not the case for the specific services offered by the governance, regardless of the type of innovation concerned.

The second result shows the significant effect of technology transfer between universities and firms within the technopole on product innovation (P=0174, t= 2,071, p <0.05). The effect of this covariate (CollUniST) is positive and indicates that governance plays a role in supporting scien­tific collaboration.

The third result concerns the networking prac­tices implemented by the governance. No direct effect of these institutional practices has been measured on the different types of innovation.

Moreover, we noted that the traditional de­terminants of innovation also had a significant influence on the innovation performance of firms within the technopole. This was particularly pronounced for product innovative firms. These were rather small (P=- 0348, t = 3.683, p <0.001) and belonged to a group (Indepr: p = - 0455, t= 4.819, p <0.001). Thus, the internal resources at their disposal for innovation (ExpenseInno: P=0,447, t = 5.282, p <0.001) positively affected their firm’s innovation performance. In addition to these internal resources, these firms also col­laborated upstream and downstream as part of their innovation process, probably through the group to which they belonged (Source: P= 0.226, t =2.246, p <0.05). However, given the risk of potential leakage of specific expertise, belonging to another cluster represents a barrier to product innovation (cluster: p=- 0.229 t = 2.536, p <0.01).

Firms engaged in process or services innova­tion presented a distinct innovation profile. The results show that only internal expenses (Expen - seInno: P= 0.282, t= 2.581, p <0.01) represent a key determinant with respect to process innova­tion. By contrast, firms that innovated in services benefited only from external sources related to their affiliation with another cluster (cluster: p =

0. 276, t = 3.538, p <0.01).

Model PLS2: The Role of Governance for Collaborative Innovation Projects

The second structural model aimed to highlight the explanatory power of the cluster governance on the firms’ collaborative innovation projects. Un­like the previous model (PLS1), specific business services offered by the governance contributed positively to collaborative innovation projects (P =0.231, t = 2.204, p <0.05) whereas the local labor market had no influence. The results of this second model are particularly interesting for the institutional practices of networking. They confirm an impact of these practices on collab­orative innovation projects (P=0.314, t=3.631, p <0.001). F inally, collaboration with the university remains significant (P=0.243, t=2.841, p<0.01), attesting to the persistent nature of technology transfers between universities and companies as part of innovative projects. Thus, we note that the institutional practices implemented by the governance have an impact on the innovation projects rather than on innovation itself. This result is not surprising since the projects are still

PLS1

PLS2

Dependent Variables

Dependent Variables

Product

Innovation

Process

Innovation

Service

Innovation

Product

Innovation

Process Innovation

Service

Innovation

Innovative Collaborative Projects ST

InnoPDT

InnoPROC

InnoSRV

InnoPDT

InnoPROC

InnoSRV

ProjetlnnoC ollabST

T

t

t

t

t

t

t

Independent

Variables

CollUniST

0,174

2,071*

0,133

1,337

-0,039

0,407

0,243

2,841**

WorkST

0,169

2,027*

0,112

1,114

0,152

1,440

0,017

0,187

NetworkingST

-0,046

0,354

0,012

0,104

0,176

1,291

0,314

3,631***

ServiceEnt

0,154

1,581

0,165

1,407

0,127

1,035

0,231

2,204*

Control Variables

Cluster

-0,229

2,536**

-0,013

0,103

0,188

1,767*

-0,134

1,409

-0,013

0,865

0,276

3,538***

Expenselnno

0,447

5,282***

0,282

2,581**

0,079

0,670

0,416

4 914***

0,298

2,826**

0,079

0,635

Indep

-0,455

4 819***

0,038

0,344

-0,048

0,423

-0,410

4,358***

0,097

0,943

-0,048

0,425

Source

0,226

2,246*

0,098

0,916

0,150

0,916

0,234

2,465**

0,132

1,321

0,231

2,284*

Size

-0,348

3,683***

-0,074

0,675

-0,027

0,233

-0,307

3,613***

-0,059

0,641

-0,066

0,689

R2

0,423

0,206

0,230

0,362

0,161

0,148

0,264

* p <.05 (One tailed test: 1.645, df = 499); ** p <.01; (2.326, df = 499); *** p <.001 (3.090, df = 499).

Institutional Innovation Practices in Technopoles

461

ongoing. The traditional determinants of innova­tion represented by the control variables showed a close proximity with the results of PLS1. Rare differences remained, particularly in terms of sources of innovation for vertical firms engaged in innovation in the services sector (P=0.231, t = 2.284, p <0.01).

DISCUSSION AND 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 it also emphasizes the role played by the technopole governance. Ofthe three identified dimensions - technology transfer, local anchor­ing and networking - only technology transfer between research and industry had an impact on both product innovation and collaborative innovation projects. Territorial anchoring had a more ambiguous effect: recourse to local labor market had a positive effect on product innova­tion, whereas providing business services only promoted inter-firm collaboration for innovation. A similar result was found with the networking practices implemented by the governance: the latter affected collaboration for innovation but no direct effect was evidenced on innovation.

Following Longhi and Quere (1991), we con­firmed the importance of territorial anchoring on product innovation through the existence of a local labor market. This suggests that the governance of Savoie Technolac has succeeded in creating mediation resources and incentive schemes for collaboration, which, in fine, enhance the firms’ innovation performance. By contrast, the other dimensions (territorial anchoring through business services, networking and technology transfer) had no direct effect on firm innovation. This can be explained by the fact that:

Specific business services (start-up support, themed breakfasts, conferences, I-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 services appear to be a prerequisite for the creation of organizational proximity. However, proposing such services does not compensate for inter-firm heterogeneity. The focus on the solar industry should improve this aspect soon, 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.

Networking practices for innovation are ef­fective with partners within Savoie Technolac. However, the technopole is “linear” in its form, as these relationships are limited to local partners on one hand, and have no direct effect on product innovation on the other hand. This result can also be explained by the specific nature of our sample. Indeed, firms engaged in product innovation are mainly subsidiaries. They largely depend on resources held by their parent company in order to innovate. Their business opportunities are gov­erned and often enhanced by the group to which they belong, thus limiting the use of calling upon co-located actors.

After 20 years of existence, Savoie Technolac relies on a dynamic local labor market and has partly succeeded in implementing networking incentives. Although industry-research collabo­ration boosts firms’ product innovation thanks to the presence of university laboratories on site, inter-firm networking is still not very satisfactory due to its restricted perimeter and its low direct effect on the firms’ innovation performance. This fact is not singular as most studies show that a certain lapse of time is required in order to estab­lish a 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).

In parallel to the development observed with Sophia Antipolis (Lazaric et al., 2008), which evolved from a satellite platform toward a high - technology cluster thanks to the emergence of specific local skills rooted in global innovation networks, the question remains as to whether Savoie Technolac will be able to evolve gradually from a “linear” form to an “interactive” technopole (Cooke, 2001). In order to accelerate the current evolution of the technopole towards an interac­tive model, the public institutions in charge ofthe governance of Savoie Technolac need to reinforce their commitment toward the solar industry and to adopt an implementation strategy designed to increase the impact of synergies and territorial anchoring. However, this should not be assimilated to a “quantitative” approach, i. e. an increase in the number of member companies, which could play against the development of cognitive proximity. In the Laval (Canada) technopole, firms were selected only on the basis of the nature of their activities, which did not leave room for comple - mentaries and synergies nor for the identification to a collective project (Doloreux, 1999). As noted by Nooteboom (2009), the “right degree of cog­nitive proximity” in accordance with geographic proximity should thus be neither too high nor too low. As the primary objective for Savoie Technolac is to reach a critical threshold, firms will need to be recruited with careful attention. Generic, transversal activities and services for businesses could be developed to the detriment of more specific forms of support for innovative enterprises (Longhi & Quere, 1993). The strategic approach adopted by the technopole governance with the development of excellence hubs in the solar energy industry (with the Tenerrdis cluster and the INES) therefore seems justified.

This research is not without limitations, in particular due to the specificities of our sample. Although representative of Savoie Technolac firms, it mainly focuses on SMEs in the service industry. 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 institutional practices aimed at boosting firms’ innovation performance. It could be of particular interest to compare the actions taken by the governances of several technopoles or, more broadly, of clusters such as the recent French competitiveness clusters. Such a comparative approach could provide a more nuanced characterization of the role played by governances by controlling the distinct structural dimensions of the clusters involved.

The themes of technological innovation, entrepreneurship, and organizing

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