Paper to be presented at the DRUID Summer Conference 2004 on
INDUSTRIAL DYNAMICS, INNOVATION AND DEVELOPMENT. Elsinore (Helsingør), Denmark, June 14-16, 2004 Theme F:
Networks, Clusters and other inter-firm relations as Vehicles for Knowledge Building and Transfer
NETWORK POSITIONS AND RADICAL INNOVATION: A SOCIAL NETWORK ANALYSIS OF THE QUEBEC OPTICS AND PHOTONICS CLUSTER Mathieu Ouimet, Réjean Landry and Nabil Amara Department of Management, Faculty of Business Université Laval, Québec, QC, Canada, G1K 7P4 Phone: 418-656-2131 ext. 4388; Fax: 418-656-2624
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[email protected]; Corresponding author: Mathieu Ouimet Date of submission: April 30/2004 Abstract This paper explores the relationship between the network positions of firms within an industrial cluster and radical innovation. Our emphasis in this paper differs from much of the previous work on clusters in that we adopt a systematic social network analysis approach that allows us to construct sociometric variables measuring the network positions of firms within the Quebec optics and photonics cluster. The following three network measures are used in this paper: degree, betweenness and effective size. Degree and betweenness were developed by Freeman (1977, 1979), while effective size was created by Burt (1992). These network measures are used to assess whether or not network positions matter with regard to radical innovation within a small industrial cluster. The results show that degree and effective size are positively correlated with radical innovation. However, the results show no significant correlation between radicalness of innovation and betweenness. Keywords: Industrial clusters, social network analysis, radical innovation
Network positions and radical innovation: a social network analysis of the Quebec optics/photonics cluster By Mathieu Ouimet, Réjean Landry and Nabil Amara Department of Management, Université Laval, Québec, QC, Canada, G1K 7P4
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1. Introduction This paper explores the relationship between the network positions of firms within an industrial cluster and radical innovation. The importance of innovating to stay competitive in an environment in constant movement has become more and more critical for firms, especially for those in the science-based industries such as optics and photonics. Even though the innovation capacity of a firm depends first of all on its own internal capabilities (Von Hippel, 1988; Lundvall, 1988; Hakansson, 1989), networking and knowledge exchange between clients, suppliers, universities, etc. have a key role to play. A firm’s competitive position is increasingly based on the existence of networks where exchange of codified and tacit knowledge occurs. These networks directly refer to the notion of clusters discussed in this paper. The literature on clusters frequently points out the importance of social networks without looking at them in a systematic way. Our emphasis in this paper differs from much of the previous work on clusters in that we adopt a systematic social network analysis approach that allows us to construct sociometric variables measuring the network position of firms within the Quebec optics and photonics cluster. Over the years, social network analysts have created various measures reflecting the network positions occupied by actors within social networks. In this paper, we concentrate on three of these measures: degree, betweenness and effective size. Degree and betweenness were developed by Freeman (1977, 1979), while effective size was created by Burt (1992). Degree is simply the number of direct ties that an actor has within a network. As for betweenness, it is a measure that captures the extent to which an actor is in a position to play an intermediary (or broker) role within a network. Finally, effective size captures the diversity or the nonredundancy of an actor’s network. The idea here is that novel information that leads to higher performance (i.e. innovation) is accessible through diversified ties rather than through redundant ones. The disadvantages of network redundancy have been pointed out by network analysts like Granovetter (1973) and Burt (1992). These network measures will be used to assess whether or not network positions matter with regard to radical innovation within a small industrial cluster. This paper builds on the existing literature to examine empirically radical innovation in the Quebec optics/photonics cluster by measuring the variable radicalness through an index capturing different levels of risk encountered by firms in developing new products or manufacturing processes (Ettlie, Bridges and O’Keefe, 1984; Green, Gavin and AimanSmith., 1995). These levels of risk can be interpreted in terms of efforts undertaken by firms in developing new products or manufacturing processes such as investments in
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R&D, investments in equipment, replacement of suppliers, use of new production technologies, etc. The remainder of this paper is structured as follows: In the next section, we define the concept of industrial cluster and briefly introduce the reader to those in optics and photonics. In the third section, an attempt is made to link network theories with the concept of radical innovation. As for section four, it is dedicated to data and method. The fifth section reports the results of the study. Finally, the last section presents the conclusion, implications and main limitations of our study. 2. Optics & photonics clusters The concept of ‘clusters’ is more and more used by economic geographers, economists and decision-makers who are interested in local industrial agglomeration and specialization. However, it is now recognized that this popular concept is suffering from ‘conceptual and empirical confusion’ (Martin and Sunley, 2003). As pointed out by Martin and Sunley (2003: 10), “a major source of ambiguity is that of definition. Because Porter’s definitions are so vague, in terms of geographical scale and internal socioeconomic dynamics, this has allowed different analysts to use the idea (of cluster) in different ways to suit their own purpose”. Feser and Bergman (2000: 3) also pointed out that “the term ‘cluster’ means different things to different researchers and policy makers”. However, Feser and Bergman (2000) brilliantly summarized the different dimensions of clusters that are included in the various definitions of the concept: “Various definitions of industrial clusters as groups of related firms encompass one or more of the following dimensions: formal input-output or buyer-supplier linkages; geographic co-location; shared business-related local institutions; and evidence of informal co-operative competition.” (Feser and Bergman, 2000: 3) Only a few studies have been conducted on optics/photonics clusters. Optics, also known as opto-electronic, photonics and optical science and engineering, is an important and fast growing industry with applications in a large variety of markets. Optics/photonics technologies involve the “production, manipulation, transmission, and detection of photons, fundamental components of light composed of waves and energy particles.”1 Until recently the information and telecommunications industries were the largest markets for optical technologies, but optical applications are being introduced in other sectors such as aerospace, automobile, biomedical, defence, environment, forestry, industrial process, safety, transport, retailing, etc. The International Society for Optical Engineering (SPIE), which was funded in 1955 and became a large non-profit society that works at facilitating the exchange and dissemination of knowledge in optics, photonics and imaging all around the world, has built over the years a cluster database including thirty optics and photonics clusters.
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Quebec Optic City’s Web site: http://www.quebecciteoptique.com/en/definition.asp. Consulted in May 26th 2003.
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Following Dr. Robert Breault, a recognized photonics cluster expert and co-chairman of the Arizona Optics Industry Association (AOIA), the SPIE defines a cluster as follows: “a concentration of firms across several industries that creates quality jobs, exports goods and services, shares common economic foundational needs, and unites the public sectors of economic development, legislatures at all levels, universities, community colleges, the K-12 educational community, workforce development, support foundations, and all community economic stakeholders.” Studies on optics and photonics clusters most frequently describe the cluster’s development, composition, and functioning and make little attempt to understand the linkages that sustain it (Catts, 1999; USF Office of Economic Development, 1999). Hendry, Brown and Defillippi (2000) are among the few researchers who have carried out studies directly related to optics/photonics clusters. In their study, they investigated the extent and significance of localized inter-firm trading and network relationships in six optics/photonics clusters located in Wales and East Anglia (UK), Arizona and Massachusetts (LISA), and Bavaria (primarily Munich) and East Thuringia (Germany). The main finding of their study is that national and international relationships are found to be much stronger than local ones in the optic/photonic industries. As mentioned by Hendry, Brown and Defillippi (2000), studies of optics/photonics clusters are conspicuously lacking and more are needed to help us better understand their particular characteristics and dynamics. This study focuses on one of the thirty optics and photonics clusters that are included in the SPIE cluster database, more precisely the one that is located in the Quebec City area (Canada). This specific cluster did not fit very well with Porter’s definition of industrial cluster (1990: 761), which did not “take geographic proximity as a defining characteristic of clusters (McCormick, 1999: 1532). As we will show later in the paper, the Quebec optics and photonics cluster is better represented through a Marshallian definition such as the one of Schmitz (1992) for whom a cluster is “a geographic and sectoral agglomeration of enterprises”. Furthermore, as is shown later in the paper, this specific “geographic and sectoral agglomeration of enterprises” is not solely composed of firms, but also of various other important organizations such as research institutes, educational institutions, financial institutions, local development support organizations, and governmental organizations. Are the social interactions that the firms have with this large variety of organizations related to radicalness of innovation? This is the question that we will try to answer in this paper. 3. Clusters, networking and radicalness of innovation The fact that networking and knowledge exchange are key characteristics of industrial clusters renders the utilization of social network concepts and methods highly relevant. As pointed out by Bergman and Feser (1999), “qualitative analysis of industry clusters using techniques perfected in the social network analysis literature is promising though has not been attempted to our knowledge.” As was mentioned in the preceding section, the Marshallian perspective on cluster is largely based on the idea of geographical 3
proximity or co-location. The promotion of proximity and co-location is based on the assumption that such physical proximity between firms and other parts of the innovation system increases networking opportunities. Thus, proximity is not an end in itself. Rather, it is a means to achieve greater interactions between the various actors of the innovation system. The social interactions that firms have with clients, providers, research institutes, and so on, is what constitute the social capital that allows firms to achieve greater innovation capacities (Landry, Amara and Lamari, 2002). The importance of social capital with regard to radical innovation can explain why some authors have stressed the importance of learning from social network concepts and theories in order to better understand the knowledge exchange between firms (Landry and Amara, 2003; Raider, 1998; Angel, 2002; Beckman and Haunschild, 2002). The two most popular network theories are the one of Granovetter (1973) and the one of Burt (1992). These authors have implicitly based their theory on the work of Freeman (1979) regarding the advantage for social actors of being intermediaries or gatekeepers within a social structure. Granovetter’s ‘strength of weak ties theory’ can be summarized as follows: weak ties engender more information benefits than strong ties, because they are more likely to bridge otherwise disconnected clusters of actors than strong ties. Burt’s structural hole theory extends the Granovetter theory by arguing that ‘tie weakness is a correlate, not a cause’ of information benefits (Burt, 1992: 27). For Burt, it is the ties that bridge disconnected elements that create the information benefits, whether or not these ties are weak or strong. Using Burt’s theory to study interindustry differences in innovation activity, Raider (1998) brilliantly summarizes the main arguments behind this theory : “The network competition model (…) is from structural hole theory. A structural hole is a gap in a social structure, a disconnection among actors. Actors are favourably positioned when they span a structural hole, meaning they are connected to other actors who themselves are not connected. This brokerage or gatekeeping location in the social structure is a position of competitive advantage because it offers the opportunity to access diverse information, to control the transfer of information between disconnected parties, and to identify and broker transactions between otherwise disconnected parties” (Raider, 1998: 5). Summing up, Burt’s social theory of competition is based on the importance of ‘network diversity’. For Burt (1992:17), increasing network size without considering diversity can cripple a network in significant ways. What matters is the number of nonredundant contacts. Contacts are redundant to the extent that they lead to the same people, and so provide the same information benefits”. Accepting the idea that an industrial cluster can be considered as a certain type of ‘social structure’, then, studying the relationships within such a structure becomes highly relevant. The aim of this study is thus to examine if network positions such as degree,
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betweenness and effective size are related to radicalness of innovation within a small optics and photonics cluster. 4. Data and method The data used in this study were collected through a research project entitled Innovation Systems and Economic Development: The Role of Local and Regional Clusters in Canada. This five-year project is managed by the Innovations Systems Research Network (ISRN) and is funded by the Social Sciences and Humanities Research Council (Canada). This project aims at examining the impact and importance of cluster-driven innovation in Canada. In total, 19 industrial clusters are studied all across Canada. One of them is the Quebec optics and photonics cluster. The data collected on this specific cluster are the ones that were used in this study. The population of this study was identified in December 2002 through the Quebec Optics City’s official directory, which included a list of 58 organizations. At the time the study was conducted, the Quebec Optics City was the main intermediary organization responsible for the creation and maintenance of linkages within the cluster. The Quebec Optics City is now under the management of the new Quebec Chaudières-Appalaches Pole. The organizations identified through the Quebec Optics City’s official directory were divided into the following six categories: firms (n=22), research institutes (n=5), educational institutions (n=6), government organizations (n=6), financial institutions (n=7), and local development support organizations (n=12). The chief executive officer for each organization was identified and sent a letter explaining the project. All participants were contacted by phone to schedule an appointment. A total of 47 face-toface interviews were held from January to May 2003. The response rate for the study was as follows: 18 out of 22 firms, 4 out of 5 research institutes, 4 out of 6 educational institutions, 5 out of 6 government organizations, 5 out of 7 financial institutions, and 11 out of 12 local development support organizations. The overall participation rate was 81%. All the interviews were recorded and transcribed. The average length of the interviews was approximately one hour. The interviews were conducted with the Innovations Systems Research Network (ISRN) questionnaire, which was administered in the 19 Canadian cluster case studies. The questionnaire is based on the cluster literature and the Oslo Manual methodological guidelines (OECD, 1997). The main topics covered by the questionnaire were the company’s background, research and innovation strategy, locational infrastructure factors, role of research institutes and technology transfer centres, local cluster characteristics, and the future of the cluster (cluster’s strengths and weaknesses). The questionnaire was suitably modified for use with non-firm organizations. At the end of the interviews, respondents were asked to fill out a supplementary questionnaire in order to measure the degree of novelty of their innovations, to identify their main sources of new employees, the factors contributing to the emergence of the industry in Quebec, and to qualify the links that their organization has with other
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organizations within the cluster. The question aiming at measuring the links which firms have within the cluster has rendered possible the collection of network data. Network Data Network data describing the relational ties linking different organizations within the cluster were collected through a special section of the supplementary questionnaire in which all the actors of the Quebec optics industry were listed. The list’s content of the cluster was selected by following a positional approach (Scott, 1991: 55-58), which is done by selecting all actors that appeared in the directory of the Quebec Optics City. The sociometric section of the supplementary questionnaire included the following two questions: how frequently does your organization have contact with the following organizations (1=never, 2=rarely, 3=sometimes, 4=often and 5=very often)? During these contacts, did you discuss: market development, financing, personnel training, production processes, research and development, services provided by support organizations, market situation, and/or other, specify? The first question allowed us to measure the frequency or strength of all direct ties. According to Granovetter “the strength of a tie is a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie” (Granovetter, 1973: 1361). In this study, tie strength was measured by using frequency criteria. Strong ties refer to the “often” and “very often” responses and weak ties to the “rarely” and “sometimes” responses. Our operationalization of strong ties assumes that frequent or very frequent interactions take more time and generate more emotional intensity and more intimacy than less frequent ties. As for the second question, it allowed us to identify the topics that were discussed between the cluster’s members. Measures Radicalness of innovation The firms that are part of the Quebec optics and photonics cluster are highly innovative. Hence, all responding firms have developed or significantly improved products and/or production processes in the last three years. Furthermore, all innovations were qualified by the respondents as the world’s first innovations! The fact that all firms are highly innovative renders essential the utilization of an additional measure that will permit capturing a certain amount of variability regarding the degree of innovativeness. As McDermott and O’Connor (2002) point out, several measures have been proposed in order to qualify the radicalness of innovation. In this study, a measure inspired from the works of Ettlie, Bridges and O’Keefe (1984) and Green, Gavin and Aiman-Smith (1995) was used. The measure is based on the assumption that innovation is essentially a question of risk undertaken by firms. Once we accept this assumption, then it is possible to qualify innovativeness by measuring the extent to which firms have taken risks to develop new products or new production processes. Here risks are considered as
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significant investments made by firms in order to implement the most important changes in their products or production processes. In this study, the index that was used in order to qualify the degree of radicalness has been created in the following way: first, each firm was asked to rank on a five point Likert scale of agreement (1= strongly disagree to 5= strongly agree) different types of risk that they may have taken to implement the most important changes in their products or production processes: investments in R&D, investments in equipment, replacement of suppliers, use of new production technologies. Overall, a list of six types of potential risk was provided. The exhaustive list of these potential risks is reported in Table 1. Table 1 – about here Second, the index of radicalness of innovation was created by taking the sum of the score of all items. The index can range from 6 to 30. The reliability of the index was calculated by using the Cronbach Alpha. This index had a Cronbach Alpha of .59. Considering that this construct is in an exploratory phase, this result is satisfactory. Indeed, as pointed out by Ahire and Devaraij (2001), “Nunally recommended two thresholds of .5 for early exploratory work on constructs and .7 for maturing constructs” (Nunally, 1967, 1978). As pointed out in the data section, the population under study included 58 organizations. Inside this population, there were 22 firms and 18 of them participated in this study. However, it must be noted that 2 out of the 18 firms that participated in the study did not answer the special question related to radicalness of innovation. Thus, 16 firms out of a population of 22 firms have qualified their degree of innovativeness. Even if we were not able to get the entire population of 22 firms, key firms such as ABB Bomen, Gentec and Exfo have participated in the study. Network measures In this study, an attempt was made to test the level of relationship between the index of radicalness of innovation described above and various network measures. The network measures that were tested for association with the index of radicalness of innovation are the degree (Freeman, 1977), the betweenness (Freeman, 1979) and the effective size of all firms’ network (Burt, 1992). The degree (Freeman, 1977) is simply the total number of direct ties that an actor has in a network. As for the betweenness (Freeman, 1979), it is a measure of the number of times an organization occurs on a geodesic, where a geodesic is the shortest path linking a pair of actors. In other words, the betweenness score of an organization reflects the extent to which this organization is in a position of acting as an intermediary within a social network. For its part, Burt’s measure of effective size of firm i network is the number of alters (other organizations with which firm i has contacts) minus the average number of direct
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contacts of alters within the firm i network, not counting ties with firm i (Burt, 1992: 52 – equation 2.2). Burt also developed another measure called the constraint. Burt (1992: 56) admitted that “the constraint and redundancy (effective size) measures are closely related” and that “it might be seen appropriate to use just one or the other”. In this study, we chose to utilize the effective size measure. Borgatti (1997) skilfully ‘unpacked’ Burt’s effective size measure by showing that it is highly correlated with the degree centrality measure developed by Freeman (1975). Indeed, Borgatti found a correlation of .95 between these two measures. This finding caused Borgatti (1997) to write that “in practice the humble degree (centrality) measure can substitute for effective size”. One reason that may explain why Burt has created a measure that is highly correlated with the degree centrality is the fact that he was looking for a measure of network diversity when the basic measure of such diversity is simply the number of direct ties in a network (or the degree centrality, see Freeman, 1977). However, in this study both the effective size and the degree centrality measures were used. Analyses Network measures were calculated by using UCINET software (Borgatti, Everett and Freeman (2002). Graph drawing, which was used to visualize the density of the Quebec optics and photonics cluster, was performed by using Netdraw software which is included in the UCINET package. All network measures for which we have tested their level of relationship with the index of radicalness of innovation were calculated three times. Indeed, we have calculated the network measures from three adjacency (actor by actor) matrices: one matrix including all ties (strong and weak), one matrix including strong ties only and one matrix including weak ties only. This procedure will allow us to examine if the weakness of ties makes a difference such as suggested by Granovetter (1973). All data matrices included binary data for which a score of 1 was given when there was a tie between two actors, and a score of 0 when two actors were not tied together. An illustration of an adjacency matrix is presented in Figure 1. This matrix includes the network data reflecting the ties within a network of 4 organizations. As shown in Figure 1, all four organizations are connected. In our case, the three adjacency matrices (all ties, strong ties only and weak ties only) included 58 actors (thus, 58 rows and 58 columns). The adjacency matrix including all ties was coded as follows: a score of 1 was given to all ties that were ranked rarely, sometimes, often or very often, while a score of 0 was given to all ties that were ranked never on the five-point Likert scale of frequency. As for the adjacency matrix including the strong ties only, it was coded as follows: a score of 1 was given to all ties that were ranked often or very often, while a score of 0 was given to all ties that were ranked never, rarely or sometimes. Finally, the adjacency matrix including weak ties only was coded as follows: a score of 1 was given to all ties that were ranked rarely or sometimes, while a score of 0 was given to all ties that were ranked never, often or very often.
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The calculation of centrality measures such as the degree requires symmetric data matrices. A symmetric actor by actor (or adjacency) matrix is one in which all ties are reciprocal. An example of symmetry is when the respondent of organization i declares that his organization enters frequently into contact with organization j and the respondent of organization j confirms that his organization enters frequently into contact with organization i. The fact that we are dealing with organizations that include many individuals – or potential respondents – may explain why the condition of symmetry rarely holds. The transformation of asymmetric data into symmetric ones was performed by using the following approach: for each pair of answers, we took the highest frequency score (never, rarely, sometimes, often or very often), except for the relations between firms and non-firms, for which we selected the firm’s response. We hypothesized that non-firms will tend to overestimate the frequency of interactions that they have with firms because of their mission, which is frequently based on knowledge exchange with firms. Finally, organizations that did not participate in the study were nonetheless included in the network analysis, because participating firms could mention interacting with them. Keeping non-participating organizations in the network analysis allows for the production of more realistic results. The only ties that were missed are the ones between non-participating organizations. It must be recalled that 4 out of 22 firms have not answered the sociometric questionnaire. Finally, parametric correlation tests (Pearson product-moment correlation) and nonparametric measures of association (Spearman rank correlation) were performed in order to examine the level and statistical significance of the relationships between the index of radicalness of innovation and the various network measures that were derived from the social network analysis. In order to decide which test to utilize, parametric or nonparametric, we have used the Shapiro-Wilk test of univariate normality on all measures. The Shapiro-Wilk statistic (W) is used for sample sizes below 200. The results of the W statistic are presented in the last column of Table 2, which also presents the descriptive statistics of all measures. Table 2 – about here When the P-value of the W statistic is significant, the hypothesis that the variable follows a normal distribution has to be rejected. As we can see in Table 2, the index of radicalness of innovation had a non-significant W, which means that it would be inappropriate to reject the hypothesis of normality for this variable. Thus, parametric correlation was performed with network measures for which the W statistic was not significant. The final selection of accurate correlation tests is presented in Table 3. Table 3 – about here As we can observe in Table 3, 10 parametric correlations and 5 nonparametric correlations were performed. 9
5. Results The results of this study are presented in three parts. Firstly, an overview of the Quebec optics and photonics cluster is presented. Secondly, descriptive statistics derived from the social network analysis are reported. Finally, results of the parametric and nonparametric correlations between the index of radicalness of innovation and the various network measures are presented. Overview of the Quebec optics and photonics cluster As has already been mentioned, the existence of the Quebec optics and photonics cluster is officially recognized by the International Society of Photonics Engineering, a non-profit society that was founded in 1955 and that has become an important international forum for the exchange, collection and dissemination of knowledge in optics, photonics and imaging. Indeed, the Quebec optics and photonics cluster is indexed on the web site of this organization (see, http://photonicsclusters.com/clusterdatabase.html). Let us now start with the geographical location of this specific industrial cluster. The Quebec optics and photonics cluster is located in the Quebec City area, approximately 280 Km from Montreal. Quebec City is the Capital of the Province of Quebec, the second biggest Canadian province after Ontario. According to Canada Economic Development for Quebec Regions, “in 2001, the region counted 640,000 habitants, 8.9% of Quebec's total population. Eighty percent of the region's population lives in Quebec City”. Regarding employment, Canada Economic Development for Quebec Regions stressed the fact that “the region is second in (the Province of) Quebec in terms of educational level (post-secondary and university education completed). A critical mass of enterprises and institutions in promising knowledge-based fields has developed over the past few years”. Being the Capital of the Province of Quebec, the Quebec region, and especially Quebec City, has an important tertiary sector which is mainly focused on government agencies and tourism. Now, let’s turn to the characteristic of the Quebec optics and photonics cluster. At the time the survey was undertaken, the Quebec optics industry and supporting institutions were primarily composed of 22 firms, 5 research institutes, 6 educational institutions, 6 government organizations (ministries, etc.), 7 financial institutions, and 12 local development support organizations. The number of firms has increased from 9 in 1999. Sales increased from $150 million in 1999 to $300 million in 2001. They have since decreased to $214 million in 2003. A similar trend can be observed in the R&D expenditures, which grew from $17 million in 1999 to $31 million in 2001 and then fell to $21 million in 2003. The firms in the industry are mostly SMEs. The average number of employees per firm is 56 with 64% of firms having less than 50 employees. Overall the industry increased its employees from 769 in 1999 to a ceiling of 2095 in 2000. Employment stands at 1548 in 2003. Key firms in the industry are EXFO (66th in the top 100 Canadian R&D expenditures), ABB Bomem, and Gentec. The firms show a high level of diversification. They manufacture products dedicated to many different market
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niches, notably telecommunications, biomedical, retailing, pharmaceutical, and the wood industry. Table 4 presents examples of the industry’s product range. Table 4 – about here The firms are export-oriented. The majority of them make more than 80% of their sales abroad and are usually small players compared to their international competitors. This high level of export suggests that the firms benefit from ideas, information and knowledge from clients located outside the region. Such openness to the world is a necessary condition to insure the long-term development of clusters (Cowan and Jonard, 2001). To better understand the characteristics of these firms it is useful to compare the Quebec City industry with other clusters. We have done so by using data gathered by Hendry, Brown and Defillippi (2000: 135). As we can see from Table 5, the firms located in the Quebec City region tend to be smaller and younger than the firms operating in the other optics clusters (except for the Thuringia cluster). In terms of products, firms in Quebec do not produce generic products as is the case in the other clusters. Contrary to the other clusters considered in Table 5, a higher proportion of Quebec firms have been spun off from research organizations rather than from other firms. Quebec firms also rely much more extensively on national investments than in the other clusters where foreign investments are much more significant. Finally, as can be seen in Table 5, the number of firms operating in the Quebec area is much smaller than in many other clusters. Table 5 – about here The science-based firms of the Quebec region are supported by an impressive research infrastructure made up of three major organizations: the Institut National d’Optique (INO) with 240 researchers, the Centre d’optique, photonique et laser (COPL) at Laval University with 130 researchers and the Centre de recherche pour la défense of the Department of Defence at Valcartier with 350 researchers. The mission of these organizations is to support the development of applications for the private sector and each has a long track record of working productively with private partners. This research infrastructure has played a key role in the emergence of the industry and is likely to play a key role in insuring the industry’s capability to evolve. The firms are also supported by a training infrastructure at the doctoral and postdoctoral level at Laval University and other research organizations. Technical training is offered by two community colleges: the Collège de la Pocatière and the Collège de Limoilou. Laval University and the community colleges work in partnership with the clustering organizations to insure that graduates have the skills necessary to meet the industry’s needs. This training infrastructure represents another necessary condition to insure the development and sustainability of a science-based cluster. However, during the interviews, many CEOs pointed out that it is sometimes difficult to attract highly
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qualified personnel capable of managing science-based firms and marketing sciencebased products in the region. A great amount of venture capital is active in the Québec City area, especially Innovatech Québec-Chaudière-Appalaches, which is especially active in the optics/photonics industry. Firms have access to R&D tax incentives offered both by the Canadian and the Quebec governments. The interviews also indicate that while start-up capital is abundant, the region suffers from the lack of venture capital firms with the level of capital required to insure the development of firms. Clients and suppliers Prior studies on innovation (Lundvall, 1992; Von Hippel, 1988) have noted the importance of clients as sources of knowledge that help a firm develop or improve products or processes. The cluster literature, especially that in the Porter tradition, claims that vibrant clusters require a broad base of demanding sophisticated local clients. This is not the case for the Quebec optics industry. These firms have very few clients located in the Quebec City area. This agrees with other findings that opics/photonics clusters in general show a high level of international activities (Hendry and Brown, 1998; Hendry, Brown and Defillippi, 2000). Two thirds of the firms interviewed had less than 1% of their clients located in Quebec City and two thirds had, on average, 95% of their clients located outside the country. We should not conclude from these findings that the exchange of ideas, information and knowledge with clients is made more difficult because there are very few sophisticated local clients. It must be pointed out that one of the optic/photonic industry’s characteristics is the long sale cycle. Furthermore, in the small niches occupied by Quebec firms, one can expect to have very few sophisticated local clients. To develop products these firms have to work closely with potential clients to understand their needs and expectations. It is common for a firm to spend from 6 to 12 months working with a client. Since products have to achieve precise functional properties in conjunction with other components and systems, their production depends on close interactions, learning and feedback between manufacturers and clients. Our findings clearly do not support Porter’s hypothesis that a large base of sophisticated local clients is a necessary characteristic of a cluster. As for the suppliers’ localization, 63% of the firms buy more than 50% of their supplies in the Quebec City area. However the interviews indicate that firms do not consider their local suppliers as important sources of ideas, information, and knowledge to develop or improve products or manufacturing processes. The strong links, however, suggest that suppliers could play a significant role in fostering innovation in the industry. Further investigation will be needed to shed light regarding their distinctive contributions in the matter of product/process development. Labour mobility The innovation literature frequently claims that labour mobility is the major driver of knowledge flows between firms. Power and Lundmark (2003) found that the most
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successful industrial clusters in Stockholm were the ones with the highest rates of interfirm labour mobility. Another recent study (Malmberg and Power, 2003) arrives at similar results. Overall the results of our interviews show that inter-firm labour mobility is significant: the Quebec firms hired 47% of their production personnel, 57% of their sales/marketing personnel, 31% of their scientists/engineers, and 57% of their managers from other firms in the region. Approximately 33% of the sales/marketing personnel were hired from outside Québec City, compared to 14 % of the managers, and 6% of the scientific and engineering personnel. Nearly 66% of the scientists and engineers have been hired from local research organizations or educational institutions. Overall, these findings suggest that knowledge flows are facilitated and fostered by these high rates of inter-organization/firm labour mobility. Competitors The data collected in the interviews show that 7% of the direct competitors of the Quebec firms are located in the Québec City area, 5% in the rest of Canada, 58% in the United States, and 30% elsewhere in the world. Overall, 81% of the firms interviewed indicated that the proximity to competitors did not influence the development and improvement of products and manufacturing processes. As noted earlier, the high degree of complexity and customization of the products developed in the Quebec industry requires sales representatives and engineers to spend a significant amount of time with their clients. Our interviews indicated that these interactions also provided opportunities to learn about their competitor’s products. When asked about the global market shares of their products, most firms were unable to provide a precise answer. This lack of precise information about competitors is likely related to the fact that most Quebec firms occupy small, specialized niches where the number of clients and competitors is small and to the fact that Quebec firms may lack marketing expertise. Other studies on innovation show that small firms are less able than large firms to identify their competitors. Overall, these results suggest that the ‘interdependence’ (Feser and Luger, 2003) within the Quebec optics and photonics cluster did not mainly happen through conventional value chain ties, but rather through labour pools, and joint linkages with R&D institutions. We will now turn our attention to the interactions and exchange of knowledge that exist within the cluster. Descriptive statistics: the social network analysis A graphical visualization of the interactions within the Quebec optics and photonics cluster is presented in Figure 2. We recall that this graphical visualization was drawn by using the Netdraw software. The standard multi dimensional scaling drawing procedure (MDS) was used in order to localise central organizations at the center and less central organizations at the periphery. The shape of the nodes (organizations) was designed in a way that renders possible the distinction between the following six types of organizations: firms, educational institutions, government agencies, financial institutions,
13
local development support organizations, and research institutes. It also must be noted that the graph presented in Figure 2 represents all ties (strong and weak). Figure 2 – about here Three remarks must be made on this graph. First, there is no isolate in the cluster, which means that all organizations are directly connected to at least one organization. Second, the density of the graph is .48, which means that 48% of the possible direct ties within the cluster are effective. Finally, three out of the five research institutes are located at the center of the cluster, which in part confirms the fact that we are dealing with a science-based cluster. Table 6 presents the percentage of strong and weak ties within the cluster as a whole (all ties), between firms, between non-firms, and finally, between firms and non-firms. The operational definitions of weak and strong ties are presented in the preceding section concerning data and method. Table 6 – about here As we can observe from Table 6, 62.6% of the ties within the cluster are weak and 37.4% are strong. The proportion of weak ties increases when we look at ties between firms. Hence, 78.5% of the relations between firms are weak and 21.5 % are strong. In proportion, non-firms are more strongly connected than firms. Indeed, 44.7% of the ties between non-firms are strong and 55.3% are weak. Table 7 presents descriptive results regarding the centrality issue. More precisely, this table shows the top five scores on two different measures of centrality: degree centrality and betweenness centrality. We recall that degree centrality is simply the number of direct ties that an organization has in a network. As for the betweenness centrality (Freeman, 1979), it is a measure that reflects the extent to which an organization is in a position of acting as an intermediary within a social network. As we can see in Table 7, it is a research institute that has the highest number of direct contacts within the cluster. On a possibility of 57 direct ties (n (58) minus 1), this research institute has 54 direct contacts within the cluster. The same research institute is also the most central on the betweenness centrality measure. Once more, this result confirms that the Quebec optics and photonics cluster is highly science-based. The results presented in Table 7 also show that only one firm is part of the top five centrality scores. Indeed, four out of the five organizations that are included in the top five centrality scores are non-firms. This suggests that in terms of networking, non-firms such as research institutes, financial institutions and local development support organizations are important actors in the Quebec optics and photonics cluster. Table 7 – about here Now, what is the most frequent discussion topic within the cluster? The answer to this important question is presented in Table 8. Table 8 presents the percentage of ties that are linked to different discussion topics according to: all ties within the cluster, ties between
14
firms, ties between non-firms, and finally, ties between firms and non-firms. We must recall that the discussion topics were captured through the following question: During these contacts, did you discuss: market development, financing, personnel training, production processes, research and development, services provided by support organizations, market situation, and/or other, specify? The discussion topics presented in Table 8 are not mutually exclusive, which means that more than one topic can be discussed between two organizations. Table 8 - about here As we can see in Table 8, R&D is the most frequent topic discussed within the cluster as a whole. Moreover, R&D is also the most frequent topic discussed between firms as well as between firms and non-firms. The only category of ties for which R&D is not the most frequent discussion topic is between non-firms. Indeed, when considering the ties between non-firms, financing as well as services offered by support organizations are the two most frequent discussion topics. After R&D, market development and market situation are respectively the second and third most frequent topics discussed between firms. As for the relations between firms and non-firms, labour training and financing are respectively the second and third most frequent topics discussed, after R&D. Once more, the fact that R&D is the most frequent topic discussed within the cluster confirms that the Quebec optics and photonics cluster is highly science-based. Network positions and radical innovation Let us now turn to the relationships between the index of radicalness of innovation and the various network measures. The results of the parametric and nonparametric measures of association are presented in Table 9. We must recall that the decision to utilize a parametric or a nonparametric correlation test was based on the results of the ShapiroWilk test of univariate normality that are presented in Table 2. A two-tailed criterion was used in order to assess the significance level of all tested correlations. This means that no direction was postulated regarding the relationship between the index of radicalness of innovation and the various network measures. Table 9 - about here We must recall that all network measures were calculated three times. More precisely, the network measures were calculated from the following three data matrices: the one including the data on all ties (weak and strong), the one including the data on weak ties only, and finally, the one including the data on strong ties only. This procedure allowed us to examine which types of ties counted the most with regard to radical innovation. Let’s start with the number of ties within the cluster (degree). The results presented in Table 9 show a significant positive correlation between the degree and the radicalness of innovation. When calculating the degree from the strong ties and weak ties data matrices, the results show that the number of weak ties is significantly and positively correlated with the index of radicalness of innovation, while the number of strong ties is not significantly associated with the radicalness of innovation.
15
When calculating the degree by counting the direct ties that firms have with other firms, the results show that the number of direct ties with firms is significantly and positively correlated with the index of radicalness of innovation. However, when calculating the number of ties with firms from the strong ties and weak ties matrices, it seems that tie-strength did not make a difference with regard to radicalness of innovation. As for the number of direct ties with non-firms, it is significantly and positively correlated with the index of radicalness of innovation. When distinguishing between the number of strong ties and the number of weak ties with non-firms, the results show that the number of weak ties is significantly and positively correlated with the index of radicalness of innovation, while the number of strong ties with non-firms is not significantly associated with the radicalness of innovation. Furthermore, the results of the correlation tests show that there is no significant association between the betweenness measure and the index of radicalness, whether betweenness is calculated from the data matrix including all ties, weak ties or strong ties. Finally, let’s look at the results for Burt’s measure of network effective size. The operational definition of this network measure is presented in the data and method section. For now, let’s simply recall that the higher the score of a firm on this measure is, the less redundant its network is. In the data and method section, we stressed the fact that Borgatti (1997) showed that the effective size measure is highly correlated with the degree centrality measure, which is simply the total number of direct ties of an actor in a network. Indeed, Borgatti found a correlation of .95 between these two measures. This finding made Borgatti (1997) write that “in practice the humble degree (centrality) measure can substitute for effective size”. In our case, we found a correlation (not shown in Table 9) of .92 between the effective size measure and the degree centrality. In the data and method section, we suggested that one reason that may explain why Burt has created a measure that is highly correlated with the degree centrality is the fact that he was searching for a measure of network diversity when the basic measure of such diversity is simply the number of direct ties in a network (or the degree centrality, see Freeman, 1977). The high correlation we found between the number of direct ties within the cluster (degree centrality) and the effective size explained why these two network measures are similarly correlated with the index of radicalness of innovation. Indeed, as was found for the number of ties in the cluster (degree centrality), the results presented in Table 9 show a significant positive correlation between the effective size and the index of radicalness of innovation. When calculating the effective size measure from the strong ties and weak ties data matrices, we found that the effective size calculated from the weak ties matrix was significantly and positively correlated with the index of radicalness of innovation, while the effective size calculated from the strong ties matrix was not significantly associated with the radicalness of innovation. This result suggests that in the Quebec optics and photonics cluster, firms with the highest level of innovativeness tend to have a 16
more effective network, which is a diversified network that allows access to nonredundant information. Conclusion and implications The objective of this study was to examine the relationship between the network positions of the Quebec optics and photonics firms and their propensity to develop radical innovations. The utilization of methodological techniques derived from SNA (social network analysis) allowed us to calculate various network measures at the cluster’s and the firms’ levels. At the cluster level, SNA techniques allowed us to highlight the fact that the cluster is quite dense with almost half of the possible ties that are effective. The use of SNA has also permitted confirming that the Quebec optics and photonics cluster is highly science-based such as indicated by the central position occupied by research institutes within the social structure of the cluster as well as by the frequency with which R&D is discussed between members of the cluster. At the firm level, SNA allowed us to count the number of direct ties that firms have with different types of organization as well as to calculate the betweenness and the effective size measures. Inspired from the works of Ettlie, Bridges and O’Keefe (1984) and Green, Gavin and Aiman-Smith (1995), the index that we used to measure the radicalness of innovation allowed us to capture variability between firms that are all highly innovative. This variability allowed us to test the association between the various network measures and the index of radicalness of innovation. Overall, the findings suggest that the Quebec optics and photonics firms with the highest degree of innovation have a highly diversified network which is mainly based on weak ties, except in the case of ties with firms for which tie-strength did not seem to make a difference. Our study contributes to show that ties that the Quebec optics and photonics firms have not only with other firms, but also with the other actors of the “regional innovation system”, seem to be important for the development of the innovative capacity of firms in this industry. In addition, contrary to the popular assumption that the stronger the ties are, the better it is, our study shows that in certain case such as in the Quebec optics and photonics one, an effective network is mainly composed of non-frequent and non-redundant ties. However, our findings suggest that the propensity of firms to be intermediaries or gatekeepers in the cluster, such as captured by the betweenness measure, is not significantly associated with radical innovation. This suggests that in the Quebec optics and photonics cluster, network diversity and weak ties matter, but not being a network intermediary. Every study has its own limits and our study is not different from the others on that point. Being a case study, our study has not been able to produce results that can be generalized to all optics and photonics clusters. Indeed, the population targeted in this study was the firms and the others actors that are part of the Quebec optics and photonics cluster. Thus, the results of this study may not be applicable to other optics and photonics clusters in the world. Another limit is the small number of observations that we had at our disposition to conduct the analyses. However, this situation did not really constitute a limit, because it is directly linked with the real composition of the Quebec optics and photonics cluster. Indeed, this industrial cluster is very small such as indicated by the
17
small number of firms in it (N=22). This situation impeded us from conducting more sophisticated types of data analysis such as multiple regressions. Finally, another limitation of our study is the fact that it did not take into account variations of networking over time. In future research, it would be interesting to examine how the social structure of the Quebec optics and photonics cluster has evolved over time.
18
References Ahire, S. L. et S. Devaraj (2001), «An empirical comparison of statistical construct validation approaches», IEEE Transactions on Engineering Management, Vol. 48, p. 319-329. Angel, D. P. (2002), “Inter-Firm Collaboration and Technology Development Partnerships within US Manufacturing Industries”, Regional Studies, 36(4): 333344. Beckman, C. M. and P. R. Haunschild (2002), “Network Learning: The Effects of Partners’ Heterogeneity of Experience on Corporate Acquisitions”, Administrative Science Quarterly, 47: 92-124. Bergman, E. M. and E. J. Feser (1999), Industrial and Regional Clusters: Concepts and Comparative Applications. Regional Research Institute, West Virginia University, http://www.rri.wvu.edu/WebBook/Bergman-Feser/contents.htm
Borgatti, S. P. (1997), “Structural Holes: Unpacking Burt’s Redundancy Measures”, Connections 20(1): 35-38. Borgatti, S.P., Everett, M.G. and Freeman, L.C. (2002), Ucinet for Windows: Software for Social Network Analysis. Harvard: Analytic Technologies. Burt, R. S. (1992), Structural Holes: The Social Structure of Competition. Cambridge (MA) and London (UK): Harvard University Press. Catts, B.C. (1999), “Arizona Optics: a Targeted Industry Summary Report”, Report prepared for the Business Development Division Arizona Department of Commerce, 20p. Ettlie, E., W. P. Bridges and R. D. O’Keefe (1984), “Organization Strategy and Structural Differences for Radical versus Incremental Innovation”, Management Science 30: 682-695. Feser, E. J. and M. I. Luger (2003), “Cluster Analysis as a Mode of Inquiry: Its Use in Science and Technology Policymaking in North Carolina”, European Planning Studies 11(1): 11-24. Feser, E. J. and E. M. Bergman (2000), “National Industry Cluster Templates: A Framework for Applied Regional Cluster Analysis”, Regional Studies, 34: 1-19. Freeman, L. C. (1977), “Centrality in Social Networks: Conceptual Clarification,” Social Networks 1: 215-239. Freeman, L. C. (1979), “A Set of Measures of Centrality Based Upon Betweenness,” Sociometry 40: 35-41. Granovetter, M. (1973), “The Strength of Weak Ties,” American Journal of Sociology 78 (6): 1360-1380. Green, S. G., M. B. Gavin and L. Aiman-Smith (1995), “Assessing a Multidimensional Measure of Radical Technological Innovation”, IEEE Transactions on Engineering Management 42(3): 203-214. Hakansson, H. (1989), Corporate Technological Behaviour: Co-operation and Networks. London: Routledge. Henry, C., J. Brown and R. Defillippi (2000), “Regional Clustering of High TechnologyBased Firms: Opto-Electronics in Three Countries”, Regional Studies 34(2): 129144.
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Hendry, C. and J. Brown (1998), Clustering and Performance in the UK OptoElectronics Industry, Conference on Regional Advantage and Innovation, Conference Universidade Catolica Portugese, Porto, October 23-24. Landry, R. and N. Amara (2003), Effects of Sources of Information Novelty of Innovation in Canadian Manufacturing Firms”, Understanding Innovation in Canadian Industry, Gault (eds), Montreal & Kingston: McGill-Queen’s University Press: 67-110. Landry, R., N. Amara and M. Lamari (2002), “Does Social Capital Determine Innovation? To What Extent?”, Technological Forecasting and Social Change, 69 (7): 681-701. Lundvall, B.-A. (1988), “Innovation as an Interactive Process-Form User-Producer Interaction to the National System of Innovation,” in Technical Change and Economic Theory, Dosi, G. et al. (eds), London: Pinter Publishers. Malmberg, A. and D. Power (2003), “How Do Firms in Clusters Create Knowledge,” Paper presented at the DRUID summer conference 2003 on Creating, Sharing and Transferring Knowledge. The Role of Geography, Institutions and Organizations, Copenhagen, June 12-14: 1-15. Martin, R. and P. Sunley (2003), “Deconstructiong Clusters: Chaotic Concept or Policy Panacea?”, Journal of Economic Geography 3: 5-35. McCormick, D. (1999), “African Enterprise Clusters and Industrialization: Theory and Reality”, World Development, 27(9): 1531-1551. McDermott, C. M. and G. C. O’Connor (2002), “Managing Radical Innovation: An Overview of Emergent Strategy Issues”, Journal of Product Innovation Management, 19: 424-438. Nunnally, J. C. (1967), Psychometric theory, 1st Edition, New York, McGraw-Hill. Nunnally, J. C. (1978), Psychometric theory, 2nd Edition, New York, McGraw-Hill. OECD (1997), “Oslo Manual: Proposed Guidelines for Collecting and Interpreting Technological Data.” Available at this address: http://www.oecd.org/LongAbstract/0,2546,en_2649_33703_2367514_119669_1_1_1,00.html
Porter, M. (1990), The Competitive Advantage of Nations, New York: The Free Press. Power, D. and M. Lundmark (2003), “Working Through Knowledge Pools: Labour Market Dynamics, the Transference of Knowledge and Ideas, and Industrial Clusters,” Presented at DRUID Summer Conference 2003 on Creating, Sharing and Transferring Knowledge. The role of Geography, Institutions and Organizations, Copenhagen. Raider, H. J. (1998), “Market Structure and Innovation”, Social Science Research, 27: 121. Schmitz, H. (1992), “On the Clustering of Small Firms”, IDS Bulletin 23(3): 64-69. Scott, J. (1991), Social Network Analysis: A Handbook, London: Sage. USF Office of Economic Development (1999), “Report on Florida Laser and Optic’s Cluster,” Report prepared for the Florida High Tech Corridor Council. 48p. Von Hippel, E. (1988), The Sources of Innovation. New York: Oxford University Press.
20
Table 1: Operational definition of radicalness of innovation Radicalness of innovation
The sum of the number of six types of risk taken by the firms in order to implement the most important changes in their products or production processes (from 1= strongly disagree to 5= strongly agree). ⇒ ⇒ ⇒ ⇒ ⇒
Important investments in research and development (R&D); Investments in equipment that was very important for the firm; Important changes in the marketing strategies of the firm; Replacement of old suppliers by new ones; Hiring of workers with qualifications that were nonexistent in the firm before; ⇒ Using production technologies that the firm has not used before. Mean
20.19
Std
4.56
Minimum
8
Maximum
27
Cronbach Alpha
.59
21
Figure 1: Adjacency matrix Organization A Organization B Organization C Organization D
A 1 1 1
B 1 1 1
C 1 1 1
D 1 1 -
22
Table 2: Descriptive statistics and Shapiro-Wilk test of normality for the different measures Innovation measure Radicalness of innovation Network measures Calculated on All ties Number of direct ties in the cluster (degree) Number of direct ties with firms (degree firms) Number of direct ties with nonfirms (degree non-firms) Betweenness Effective size Calculated on Strong ties Number of direct ties in the cluster (degree) Number of direct ties with firms (degree firms) Number of direct ties with nonfirms (degree non-firms) Betweenness Effective size Calculated on Weak ties Number of direct ties in the cluster (degree) Number of direct ties with firms (degree firms) Number of direct ties with nonfirms (degree non-firms) Betweenness Effective size †
Minimum
Maximum
Mean
Standard deviation
Shapiro-Wilk Statistic†, ‡
8
27
20.19
4.56
.912
5
46
27.94
10.25
.979
2
18
10.75
5.09
.934
6
28
17.38
5.56
.974
.07 1.40
46.12 20.09
13.43 9.84
11.59 4.65
.870** .975
1
17
7.88
5.15
.943
0
7
2.06
2.26
.839***
1
14
5.81
3.64
.949
0 1
201.05 12.29
29.54 4.61
53.54 3.30
.584*** .899*
4
33
20.13
7.37
.967
2
14
8.69
3.48
.960
4
21
11.56
4.49
.970
2.82 3.50
154.25 22.27
49.15 12.51
37.38 4.92
.887** .982
When the Shapiro-Wilk statistic is significant, the hypothesis of univariate normality has to be rejected. ** *** p ≤ 0.1, p ≤ 0.05, p ≤ 0.01 (Two tailed)
‡*
23
Table 3: Identification of the accurate tests to use in order to test the relationship between radicalness of innovation and the various network measures Measures
Number of direct ties in the cluster (degree) Number of direct ties with firms (degree firms) Number of direct ties with non-firms (degree non-firms) Betweenness Effective size
Index of radicalness of innovation Matrix 1: Matrix 2: Matrix 3: All ties Strong ties Weak ties Accurate test†, ‡ Accurate test†, ‡ Accurate test†, ‡ Parametric Parametric Parametric Parametric Nonparametric Parametric Parametric Parametric Parametric Nonparametric Nonparametric Nonparametric Parametric Nonparametric Parametric
† Parametric test is accurate when the hypothesis of normality is true. When both the index of radicalness of innovation and the network measure were normally distributed, the Pearson correlation was used. ‡ Nonparametric test is accurate when the hypothesis of normality is rejected. When the network measure was not normally distributed, the Spearman rank correlation was used.
24
l
Figure 2: Visualisation of the networking within the Quebec optics/photonics cluster
FI GO FM
LD
FM
RI
FM
FM FM
FI
FM FM
FM
FM
FM
FM
FI
LD
FM
GO
FM
FM
RI
LD
LD FI
FM RI
EI
RI
FM
EI
FI
FM
LD LD
GO
EI LD
EI EI
FI
RI
EI
FM
GO
FI LD
LD LD
FM GO
LD
FM
FM
GO
FM
Legend
LD
FM = Firm EI = Educational institution GO = Government organization FI = Financial institution LD = Local development support organization RI = Research institute
25
Table 4: Examples of optics and photonics products produced within the cluster Companies
Product description
ABB Bomem
Analytical solutions (i.e. Oil and fat analyses) Erbium doped optical fiber (i.e. Boosting the transmission speed of information in telecommunication system) Medical device to improve diabetes control (i.e. Watch with integrated glucometer) Optical testing and measurement (i.e.. Fiber installation and maintenance) Fiber Optics Sensors (i.e. Optic in-vivo pressure transducer) Counting Systems (i.e. Counting people electronically (bus, Mall, etc.)) 3D full body digitizer (i.e. Movies and Plastic Surgery) Machine vision inspection systems for high-speed packaging lines. (i.e. Pharmaceutical Industry)
CorActive Cybiocare Exfo Fiso Technologies Infodev InSpeck Optel Vision
26
Table 5: The Quebec cluster compared with other optics and photonics clusters
* The first column is from our data set. The other columns are from Hendry, Brown and Defillippi (2000). ). "Regional clustering of high technology-based firms: Opto-electronics in three countries." Regional Studies 34(2): 129-144.
27
Table 6: Percentage of strong and weak ties Weak
Strong In % of ties
Total
All ties
62.6
37.4
100.0
Ties between firms
78.5
21.5
100.0
Ties between non-firms
55.3
44.7
100.0
Ties between firms and non-firms
63.5
36.5
100.0
Table 7: Top five centrality scores† Degree centrality Type
‡
Betweenness centrality
Score
Type
Score
RI
54
RI
110.41
FI
48
LD
64.10
LD
47
FM
45.22
FM
46
FI
42.23
LD
44
LD
37.57
†
Total number of actors is 58. ‡ FM= Firm, RI= Research institute, EI= Educational institution, GO= Government organization, FI= Financial institution, LD= Local development support organization.
28
Table 8: Percentage of ties for different discussion topics according to the cluster as a whole and to three sub-networks All ties Discussion topics†
Inter-firms ties
Inter-nonfirms ties
Between firms and non-firms
In % of ties (Rank on 7 topics)
Market development
17.8 % (4)
30.5 % (2)
19.0 % (6)
12.5 % (5)
Financing
20.5 % (2)
22.3 % (4)
27.3 % (1)
14.2 % (3)
Personnel training
16.8 % (6)
10.7 % (7)
20.6 % (5)
15.5 % (2)
Production processes
5.0 % (7)
16.5 % (5)
2.2 % (7)
3.5 % (7)
Research and development
22.8 % (1)
38.8 % (1)
24.5 % (3)
16.1 % (1)
Services offered by support organizations
18.3 % (3)
15.7 % (6)
25.7 % (2)
12.8 % (4)
Market situation
17.5 % (5)
24.0 % (3)
23.8 % (4)
10.1 % (6)
†
Categories of topics are not mutually exclusive.
29
Table 9: Correlation between the index of radicalness of innovation and various network measures† Index of radicalness of innovation Parametric correlations Network measures calculated on 3 adjacency matrices Number of direct ties in the cluster Number of direct ties with firms Number of direct ties with non-firms Betweenness Effective size † *
**
p ≤ 0.1, p ≤ 0.05,
***
Nonparametric correlations
Matrix 1: All ties
Matrix 2: Strong ties
Matrix 3: Weak ties
Matrix 1: All ties
Matrix 2: Strong ties
Matrix 3: Weak ties
.59***
.37
.56**
—
—
—
.42*
—
.41
—
.37
—
.61***
.32
.49**
—
—
—
—
—
—
.28
.18
.14
.47*
—
.45*
—
.25
—
p ≤ 0.01 (Two tailed)
30
