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Working Papers WP 2014-06 Centre for German and European Studies (CGES) Bielefeld / St. Petersburg 2014 6 Nikita Basov, Vera Minina Science, Education and Business Collaboration in the Maritime Cluster of Algarve: The Impact of Personal Networks WP 2014-06

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Page 1: Collaboration in the Maritime Cluster of Algarve: The

Working Papers WP 2014-06 Centre for German and European Studies (CGES)

Bielefeld / St. Petersburg

2014

№ 6

Nikita Basov, Vera Minina

Science, Education and Business

Collaboration in the Maritime Cluster of

Algarve: The Impact of Personal Networks

WP 2014-06

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Bielefeld University

St. Petersburg State University

Centre for German and European Studies (CGES)

CGES Working Papers series includes publication of materials prepared within different activities of

the Center for German and European Studies both in St. Petersburg and in Germany: The CGES

supports educational programmes, research and scientific dialogues. In accordance with the CGES

mission, the Working Papers are dedicated to the interdisciplinary studies of different aspects of

German and European societies.

This paper results from the project Personal networks and Formal Science, Education and Business

Collaboration Neworks in Regional Innovation Clusters run by the CGES (St. Petersburg State

University - University of Bielefeld). The project has benefited from the financial support by DAAD

with input from the German Foreign Office and funding from the European Commission within

Erasmus Mundus Programme (Action 2). We are most thankful to Antti Ainamo for his comments on

the project, to Maria Cabral, Ray Pinto, Luis Rodrigues, Hugo Pinto, Ivete Silva, and Neuza Costa for

their valuable help during data collection, to Larissa Potkonen and Bassim Boutemine for their help in

data processing, to John Kuti for proof-reading, and to Aleksandra Koltcova for her help in processing

the manuscript. We are very grateful for comments received on earlier drafts of this paper from the

participants of the Triple Helix conference in 2014, International Conference Science, Education and

Business Cooperation: The Innovation Landscapes of Europe and Russia in 2013, and at the XXXIII

Sunbelt Social Networks Conference of the International Network for Social Network Analysis in

2013.

Dr. Nikita Basov is Scientific Manager of the Center for German and

European Studies, St. Petersburg State University.

Contact: [email protected]

Dr. Vera Minina is Professor at the Chair of Sociology of Culture and

Communication, St. Petersburg State University.

Contact: [email protected]

ISSN 1860-5680

© Centre for German and European Studies, 2015

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Table of Contents

Introduction ........................................................................................................... 3

Personal networks and their impact on organizational collaboration..................... 4

The maritime cluster of Algarve, Portugal ............................................................. 9

Data and method ................................................................................................ 10

Findings .............................................................................................................. 14

Discussion and conclusion .................................................................................. 18

Limitations and future prospects ......................................................................... 19

References ......................................................................................................... 20

Annex I ................................................................................................................ 25

Annex II ............................................................................................................... 26

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Introduction

It is widely recognized that interaction between the fields of science, education and business provides benefits, both to each of the sectors and to society as a whole (see e.g.: Bruneel et al. 2010). Building globally competitive innovation systems that integrate science, education and business sectors is a common policy task for many countries striving to complete transition to the knowledge-based economy. For example, the EU is now working to create and encourage knowledge transfer and cooperation between scientific, educational and business organizations and trying to provide more coordination of the European and National policies. This task, among others, involves creating regional network structures for organizational collaboration across the sectoral boundaries of science, education and business to stimulate innovation. Most of the studies on science, education and business innovation-oriented collaboration focus on generalized science-industry or formal organizational links in narrowly defined fields of research and technology (mostly, so-called ‘high-tech’ industries (Bania et al. 1993; Meyer-Krahmer & Schmoch 1998), the aggregate effect of university research on knowledge production in firms (Jaffe 1989; Anselin et al. 1997), or certain types of knowledge interactions such as citations of university research in firm patents (Jaffe et al. 1993), personnel mobility (Bania et al. 1992), joint publications (Hicks et al. 1993; Hicks et al. 1996) and spin-off formations of new firms by university members (OECD 2000b; Parker & Zilberman 1993). Meanwhile, the value of personal networks for the emergence and development of relations between organizations has been recognized as comprising the basis for trust (Newell & Swan 2000), information exchange (Grandori & Soda 1995) and knowledge creation (Easterby-Smith et al. 2008), so the links between the development of personal networks across the sectors of science, education and business and the trilateral collaboration of organisations representing the three sectors deserve attention. Revealing these links may allow a deeper understanding of the process of integration between science, education and business and help to find new opportunities to stimulate innovative growth, developing certain configurations of cross-boundary personal relational structures. So we put the question: In which ways may personal networks impact the state of organizational collaboration across sectors?

This article applies network analysis to the case of an innovation-oriented science-driven maritime cluster located in the Algarve region in Portugal composed of 25 units: university departments, research centers and companies. The authors depart from studying the background of what is suggested in the literature about the relations between personal structures and organizational structures, including those of science education and business (including university-industry) in general. They also consider the literature on the properties of personal relational structures which are more likely to be linked to the development of organizational level relations. Proceeding with a description of the main features of the empirical context and the data delivered in 2012, they present the network mapping procedures used in the study and network analysis techniques applied. Further, using QAP correlation procedure

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the authors check if there is a direct correspondence between personal and organizational networks in the cluster. Based on the analysis of the organizational network, and on the surveys, they get insights on the level, and particular features of organizational integration, in the cluster. Then the personal network is analysed along a set of measures corresponding to structural properties.Based on the literature review these properties may be considered important for stimulating innovation and the integration of science, education and business. The results are discussed with regard to the level of integration observed in the cluster. In the conclusion, possible future research avenues are outlined which would proceed with network analysis of relations between personal networks and organizational science, education and business networks.

Personal networks and their impact on organizational collaboration

Formal organizational collaboration networks, where nodes are organizations and lines are collaborations between them, have been thoroughly investigated, especially in the context of innovation (see e.g., Hargadon & Sutton 1997; de Man 2008; Malerba 2009). These networks are usually seen as structures of various relations between organizations: collaborations, alliances, resources exchanges, etc., both formal and informal (Di Maggio 1986). Networking in all forms – from friendship between members of different organizations to formal collaborations – is considered to be a predictor of the very survival of an organization (Baum & Oliver 1991). It has been studied, how networks are typically structured and how their structure influences relationships between particular organizations (Powell et al. 1996; Inkpen & Tsang 2005).

The central problem in organizational networks is “striking the right balance between differentiation and integration” (Grandori & Soda 1995: 184). Personal networks across units or organizational boundaries play significant role in achieving this balance (see Brass et al. 2004).

As shown by organizational sociology, and particularly M. Granovetter (1983; 1985), social relationships are the pre-existing structures in which all organizational networks are embedded, a basis for elaboration of more complex organizational collaboration relational structures. Sometimes, embeddedness in the informal personal structures is strong enough to motivate organizations to pursue goals not linked to immediate economic revenue, but to long-term network strengthening instead. Informal relationships appear to be the basis for the development of more formal organizational-level relations (Cooke 1996; Hanna and Walsh 2002). Personal ties are ‘capable of generating other, more institutionalized forms of coordination’ (Grandori & Soda 1995: 199) and it is shown empirically, that in personal networks organizations search for trustworthy partners to collaborate with (Aldrich & Glinov 1990) and what is considered in the first place is reputation, ‘information from one’s own past dealings with that person’ (Granovetter 1985: 490). It is ‘personal rapport and

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chemistry among the individuals’ that make organizational alliances emerge and work, as ‘personal trust is also built up when people are prepared to be open to new ideas, to listen to each other and to accept that there is something to learn from the other alliance partners’ (Taylor 2005: 481), and this entails going beyond formal relations (Gilsing et al. 2008). Soda (1992) has shown that, in the case of Italian industrial firms, 90 % of consortia were formed based on prior personal relations and the same is true of 50 % of joint ventures. Inkpen & Tsang (2005) argue that social capital within an organizational network in an industrial district consisting of a set of independent co-located organizations is also increased through personal relations, as opposed to alliances, where formal agreements seem to determine existence of personal ties. Quantitative analysis by Ceci and Iubatti (2012) revealed that various types of collaborative economic activities between organizations are strongly linked to the personal dimensions of relations between them. Those may be not only relations between different individuals, but also interlocks in personnel, like in the case of interlocking directorates (Burt 1979, 1980; Mizruchi 1996). Inter-personal ties in terms of bringing partners together and binding the network may thus be an alternative to brokerage, which has been referred to as an ambiguous tool, as brokers often tend to disconnect the network and impede collaboration instead of stimulating it (Stark 2011).

According to research on science-industry collaborations, informal personal relationships are considered to provide an impetus for cooperation between universities and industry (Kaufmann and Tödtling 2001; Verspagen 1999). Similarly, personal networks, as normally used by scientific partners, are thought to contribute to innovation networks (Bower and Keogh 1996). Research on university-industry knowledge interactions revealed that personal, informal relations are the most widespread form of interaction between universities and industry (Arundel and Geuna 2004), and that the individual characteristics of researchers have a stronger impact on this interaction than the characteristics of their departments or universities (D’Este & Patel 2007). At least 50% of university–industry interactions are personal contractual arrangements with individual researchers, not with universities, especially by small firms using open innovation model (Bodas Freitas et al. 2013). Research also shows that knowledge dissemination between university and industry in clusters is through informal contacts (Østergaard 2009) and that the main channels of knowledge transfer are publications and reports, accompanied by informal communication, public events, and consulting (Cohen et al. 2002). Personal, non-formalized contacts are also reported to be most valuable for academic researchers (Meyer-Krahmer and Schmoch 1998). This is because in personal ties, trust gets coupled to the sharing of common values and allowing cognitive coordination (Lorenzen 2001), personal ties across university-industry boundaries support the exchange of tacit knowledge between organizations and comprise the basis for joint ‘language’ and common research culture and thus form social capital (see e.g., Bonaccorsi and Piccaluga 1994; Fischer 2006). University-industry technology transfer is reported to run through informal contacts, and informal interaction between university scientists and managers/entrepreneurs in the private sector is particularly encouraged by

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researchers (Siegel et al. 2003). This is also the reason suggested in the literature why spatial proximity of research institutions and firms within clusters appears to be important (see: Arundel and Geuna 2004: 561; Sammarra and Biggiero 2008).

Consequently, the existence of personal ties between science, education and business organizations at dyadic level as such may be expected to directly stimulate the establishment and sustaining of their organizational collaboration, thus working for growth of integration between science, education and business. It is thus feasible to expect a direct relation between the two networks.

Less direct linkages – between the ego- and whole-network properties of the two networks – are also possible and it could be questioned, what are the particular features of personal networks that accompany broader science, education and business cooperation at an organizational level.

It has been pointed out that multidimensionality of network relations particularly contributes to knowledge sharing and to the establishment of new relations (Padgett & Powell 2011). Most studies of personal relations in this area collaborative innovation and organizational cooperation focus on ego-networks which organizations maintain with their external environment. They also tend to study the positive or negative effects of particular network positions (e.g., Granovetter 1973; Burt 1980, 1992; Hansen 1995; Coe 2000; O’Donnell et al. 2001; Inkpen and Tsang 2005) or consider the problem on a dyadic level, particularly on how personal ties influence how interorganizatinal relations are governed (Alter & Hage 1993; Coleman 1990). Besides, as pointed by Provan at al. (2007), at the whole network level of analysis, it has not yet been adequately addressed, which network structures are more effective (by any means) than others, not to speak of which structural configurations of personal network affect organizational networks. However, from some of the existing studies – which will be considered below – we can derive a set of key structural properties of personal networks that are likely to be linked to emergence and development of innovation-oriented networks.1 It is to be expected that these are also positive for the development of organizational collaboration in trilateral science, education and business relations.

Studies applying social network analysis of knowledge and innovation collaborations point out that the problem of weak and strong network ties in personal networks certainly deserves attention. On the one hand, sufficient number of weak ties – involving infrequent interactions with low emotionality – provides information redundancy (Granovetter 1973; Nonaka and Takeuchi 1995: 80) and allows novel ideas to occur. A high density of weak ties is thus preferable to generate innovations (Swan et al. 2003, p. 685) and knowledge transfers in networks of spatially co-located organizations, such as industrial districts (Inkpen & Tsang 2005) and clusters. On the other hand however, empirical research shows that knowledge transfer requires strong ties –

1 Organizational relations also are affected by other dimensions. Our purpose is not to develop an exhaustive list but rather to focus the most important characteristics of personal networks which may be derived from literature.

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involving frequent interactions with sufficient emotional entrainment – between the partners (Inkpen & Dinur 1998; Reagans, McEvily 2003). As demonstrated by M. Hansen (1999), a more successful search for knowledge is made through weak network ties, while complex knowledge transfer requires strong ties providing trust (Newell & Swan 2000) and mutual understanding, preferably with face-to-face interaction (de Man 2008; Storper & Venables 2005; Maskell 2005; Schartinger et al. 2002). It can be concluded that a balance between the shares of weak and strong ties would be present in science, education and business networks with a higher level of innovation-oriented integration.

Another relevant line of social network analysis studies is focusing on the issues of brokerage and structural holes. As Inkpen and Tsang (2005) state, boundary spanners linking various cliques are particularly valuable in facilitating knowledge transfers among co-located organizations. This is particularly relevant to the case of science, education and business cooperation in clusters, as the sectors tend to form strong cliques within sectors and use single ties to span the cross-sectoral network space. In light of this R. Burt's studies (2005; 1992) are very important as they focus on the nodes that appear to be brokers across structural holes – the empty spaces in network structure – to connect otherwise disconnected nodes. Burt introduced a constraint index showing the extent to which the relationship between ego and each of the ego's contacts "constrain" the ego (Burt 1992). i is considered to be more constrained by its relationship with j to the extent that i has few other ties except j, and i's other alternative ties also – indirectly – lead to j. A sum of actors’ constraints shows to what extent brokerage is possible in the network in general. And if a personal network contains plenty of structural holes it provides opportunities for actors to establish new cooperation with actors from different domains and get diverse information to innovate. Thus lower levels of average constraint in a personal network may be expected to accompany innovation-oriented collaboration across science, education and business sectors.

Literature on alliances argues that the support and involvement of leaders is a very important factor in the formation of inter-unit (Knoke 2001) and organizational collaboration links (examples of empirical studies: Volkoff et al. 1999; Taylor 2005). Thus, creating plenty of personal contacts between the leaders of units or organizations is extremely important for organizational collaboration as this helps by “ironing out any differences of view between the partners and laying down broad plans for the future, they very importantly set an example and establish a climate of cooperation for the people working further down the alliance” (Child et al. 2005: 65). Put into network terms, higher density of personal network between top managers of science, education and business organizations should be expected to be present in cases of higher integration between these organizations.

Additionally, a specific feature of personal networks that may benefit science, education and business cooperation at organizational level may be derived from the Triple Helix approach. According to it, science, education and business are more likely to sustainably collaborate when the sectors collaborate as equal partners with intersecting interests (Etzkowitz & Leydesdorff 2000; Leydesdorff

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& Meyer 2003). Thus, higher levels of cross-sectoral collaboration between science, education and business should be expected when none of the sectors dominate the relationship. In structural terms this means that either none of sectors is to have its actors in dominating positions, or representatives of different sectors should be in such positions as to balance relations. Taking actor centrality as the measure normally used in network analysis to compare dominance, we can expect that for science, education and business integration at organizational level it would be beneficial if actors representing different sectors have highest levels in the key types of personal network centralities.

The influence of various structural properties of personal relations networks on organizational collaboration is represented by the model reported in Fig. 1. These personal network properties probably play a role science, education and business integration in a cluster.

Figure 1 – Analytical model of linkage between personal network properties and science, education and business collaboration

Source: Figure by N.B., V.M.

To study the relationship between personal and organizational networks in science, education and business collaboration, and also to get a deeper understanding of which structural properties of personal networks may impact innovation-focused science, education and business integration networks, it is useful to look at these in particular regional innovation structures for which the level of cross-sectorial innovative integration has been evaluated. In these cases we hope to see how personal networks are structured across the boundaries of science, education and business organizations.

Balance of weak and

strong ties Average constraint

Amount of direct ties between

organizational leaders

Organizational

collaboration of science,

education and business

Diversity of central actors

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The maritime cluster of Algarve, Portugal

Algarve is the most southern region of mainland Portugal, which is part of the European Union. The region has 4.5 mln inhabitants. The regional economy is rather in a stage of decline; regional GDP is rather small – 8.841 mln PPS (2010), GDP per capita – 16.800 Euro (2010). The maritime economy is at the basis of the regional economy: tourism and related activities combined with industries driven by marine sciences. The region has a very rich natural environment and includes unique ecosystems (e.g. Ria Formosa), which provides many unique natural tourist attractions; it also gives numerous opportunities for maritime research not available anywhere else in the world, putting Algarve research and education in marine studies (primarily disciplines are biology, chemistry and physics) at the top of the global leaders in the field.

To develop the economy in the region, drawing on its natural potential, both state and local administration are attempting to perform a transformation of Algarve from the state of economic periphery - driven by agriculture and fisheries, to knowledge-based innovative industries with state support of science, education and business integration in the region. Innovations as well as integration of science, education and business are promoted, supported and nurtured both with direct and indirect funding; numerous innovation intermediaries (e.g., CRIA – Regional Centre for Innovation of the Algarve; NERA – Business Association of the Algarve Region; ANJE – National Association of Young Entrepreneurs and others) and initiatives are present. It is also worth mentioning that there are 2.8 times more brokers supporting cooperation between science, education and business organizations than science, education and business actors themselves. Thus, it can be said that the political and economic environment in general is rather positive for innovations as well as science, education and business integration in the Algarve cluster.

The maritime cluster spread all over Algarve (Figure 2) is relatively young; it is formed around the University of Algarve, founded in 1994. Activities in the cluster include services (research in biology, physics, chemistry, education, logistics, flag registration) and tourism plus related activities (hotels, restaurants, rides, golf fields, etc.). In coastal tourism, innovation is done mainly in existing firms through collaborative R&D projects focusing mainly the reduction of environmental impacts and energetic rationalization in tourist activities. In maritime services innovation is a cumulative process, essentially vertical, going from university actors to related companies, thus it is a university-based subcluster.

Communication activities (at conferences, meetings, as well as informal communication, etc.) are reported as crucial for the activities in the cluster (Maritime clusters… 2011).

The culture of the Algarve region, as Portugal in general, has catholic values as central, and the support networks based on kinship are extremely important in both rural and urban areas. During long time such values as God, family, and work have been cultivated in the minds of the Portuguese. Catholic faith has

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promoted humility and respect for authority as guiding principles of social life. These features make us suppose (no specific research has been previously done on that, to our knowledge) that Portuguese culture should be closer to a so-called ‘high context culture’ which according to Hall (1976) is relational, collectivist, intuitive, and contemplative. Communication style in high-context culture is known to be influenced by the closeness of human relationships (Kim et al. 1998: 512) that is important for analysis of the link between personal and organizational collaboration across science, education and business, as well as the relative impacts of such aspects of personal relations as communication frequency, emotional and intellectual influence.

Figure 2 – Spatial distribution of Algarve maritime cluster (Cruz et al. 2011) Source: Figure by N.B., V.M.

Data and method

The study included: (1) mapping of organizational science, education and business collaboration networks in the cluster; (2) mapping of personal networks between the members of science, education and business organizations; (3) estimation of the level of science, education and business integration in the cluster; (4) analysis of direct relations between personal and organizational networks; (5) analysis of the personal network.

Organizational collaboration network mapping

Based on 45 semi-structured expert interviews with Algarve maritime cluster actors, A. Cruz and other colleagues (see: Cruz et al. 2011) have mapped a network of science, education and business actors: companies, university

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departments, research units as well as innovation intermediaries: development agencies, public regional authorities, and support organizations of the cluster. The question asked was: Who does your organization collaborate with? The resulting network included 154 nodes.2 We supplemented and verified the node set using open-source data on organizational partnerships, and filtered out actors not actually present in Algarve, but only linked with Algarve actors, who were in the initial set of Cruz et al. The resulting set of actors is presented in Table 1.

Table 1 – Organizations of Algarve maritime cluster Source: Table by N.B., V.M.

2 The data was kindly provided by R. Pinto, University of Algarve.

Full title Short name (used further)

Industry

Waters of Algarve Aguas Algarve Water supply and sanitation services

Aqualvor - Activities in Aquaculture Ltd AQUALVOR Food

Big Game Fishing Big Game Tourism

Centre of Marine Sciences, University of Algarve CCMAR Science

Coastal and Marine Environments Research Centre, University of Algarve

CIACOMAR Science

Centre for Marine and Enviromental Research, University of Algarve

CIMA Science

Technological Research Centre of the Algarve, University of Algarve

CINTAL Science

Centre for Studies in Travel and Leisure, University of Algarve CITel Science

Company of Fisheries of Algarve ComPes Food

Ecoceanus, Unipessoal, Lda ECOCEANUS Tourism

School of Management, Hospitality and Tourism, University of Algarve

ESGHT Education

Faculty of Sciences and Technology, University of Algarve FCT Science

Faculty of Economics, University of Algarve FE Education

International Centre for Coastal Ecohydrology, University of Algarve

ICCE Science

Inovsea, Lda INOVSEA Food

Research Institute of Fisheries and Sea, University of Algarve IPIMAR Science

Marsensing, Lda Marsensing Maritime

Natura Natura Tourism

Necton, Portuguese Culture Marine Company, SA NECTON Food

Portuguese Company of Sanitized Salt, SA SALEXPOR Food

Information Processing Laboratory, University of Algarve SIPLAB Science

Sparos, Lda SPAROS Maritime

Sunquays Sunquays Tourism

Vitacress VITACRESS Food

Zoomarine Zoomarine Entertainments

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Further, as we are interested in studying science, education and business collaborations, non-cross-sectoral ties were removed.

Two matrices were produced as a result: (1) a binary matrix representing direct ties between science, education and business actors only, without considering ties through intermediaries; (2) a valued matrix representing direct ties as 1 and indirect ties through intermediaries as 0.5.

Personal network mapping

In order to map cross-sectoral networks between the members of science, education and business organizations of the Algarve maritime cluster, we ran an e-mail survey of university faculty, researchers and employees of companies belonging to the cluster, asking about their individual collaborators in organizations within the cluster belonging to one of the other two sectors. Personal tie strength was captured, scaled from 0 to 4. Each unique combination of organization and individual’s name was mapped as a separate vertex, thus, when the same individual was a member of several organizations, e.g., for a business and an education organization, it was one vertex for a business organization and one for an education organization. The resulting matrix included 155 vertices. Relations referring to the same individuals who are members of different organizations were mapped as ties of maximum strength, following the interlocking directorate’s tradition (Burt 1979; Mizruchi 1996). Further, two types of matrices were produced based on the initial one. For one matrix we selected strong ties only: the initial matrix was dichotomized by 2, which means that ties with strength 2 and lower were taken as 0 and those with strength higher than 2 – as 1. A binary matrix of personal ties between science, education and business employees was produced as a result. For another matrix, personal ties were aggregated, thus forming vertices of valued personal ties between the nodes representing organizations ranging from 0 to 103.3.

Level of science, education and business integration evaluation

To collect data on science, education and business integration level we used an e-mail survey of university faculty, researchers, and employees of companies belonging to the maritime cluster and an expert survey of the representatives of different intermediaries, supporting cooperation between science, education and business actors. To estimate the level of science, education and business integration we also conducted network analysis of organizational collaboration networks between science, education and business organizations along such metrics as density, fragmentation and average path length, of which the latter two were then compared to average metrics of 50 random networks of same density and number of nodes generated using the Erdos-Renyi model which sets a link between each pair of nodes with equal probability, independently of other links.

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Relations between personal and organizational networks

To check for relations between organizational network and personal network structures in the Algarve maritime cluster, we applied the Quadratic Assignment

Procedure (QAP) (Hubert & Schultz 1976).

. A conventional proportion of .05 or

less is taken to suggest a non-chance relationship. Statistical significance in our tests is computed by UCINet using permutation trials (10.000 per run).

Personal network properties

Personal network metrics indicated as linked to organizational collaboration based on the literature review were calculated with UCINet (Borgatti et al. 2002).

The level of each actor’s constraint was calculated, following Burt (2005), as:

Cij = (pij + ∑qpiqpqj)2, q≠i,j,

where pij = zij/∑qziq,

and z is tie strength between i and j.

Then network constraint was calculated as a sum of all Cij; in line with average actor constraint.

The proportion of strong and weak ties in the personal network calculated by dividing the number of ties of strength 1 by total number of ties to get the proportion of weak ties, and dividing those of strength 4 by total number of ties – to get the proportion of strong ties.

The density of ties between the top managers of organizations is the amount of observed relations between top managers multiplied by the total amount of possible relations between top managers.

Different types of centrality measures were calculated.

Degree centrality was calculated as the number of ties a node has:

di = ∑j xij

Closeness centrality calculated as the inverse sum of distances to all other nodes:

ci (ni) = [∑gj=1 d(ni,nj)]

-1

Betweenness centrality calculated as the number of times a node acts as a bridge along the shortest path between two other nodes:

bi = ∑i<k gijk / gik

All centrality measures were then normalized by network size.

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Findings

Science, education and business integration level and organizational network properties

The results of the expert survey of the representatives of different intermediaries, supporting cooperation between science, education and business actors (see Annex I) as well as results of an e-mail survey of university faculty, researchers and employees of companies belonging to the cluster (see Annex II) show that the level of integration between science, education and business is at average considered as very modest, almost never getting evaluations higher than medium, but more likely – low.

Network analysis of organizational collaboration between science, education and business across sector boundaries (see also Figure 3) reveals rather low density of organizational network of cross sectorial collaborations: 13% in the combined matrix and 5% in the matrix of direct ties, which exposes scarcity of collaborations and signifies low integration.

Figure 3 – Organizational network across science, education and business boundaries in Algarve maritime cluster

Source: Figure by N.B., V.M.

A valued matrix representing direct links as thick and those through intermediaries as thin is visualized. Size of a node represents its betweenness centrality.

The difference in density between the two networks also shows that intermediaries play a crucial role in it binding it together: they provide a larger part of total density: 8% of the 13%. Intermediaries also make average paths between entities almost one third (one edge) shorter: average distance in the matrix of direct ties is 2.99 (that is rather high compared to 2.67 in random

- education

- science

- business

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generated networks), while in the combined matrix it is 2.05 (rather low compared to 2.95 in random generated networks). This means that when intermediaries are in play, to reach one node from another normally requires negotiating with only one node in between while without intermediaries it is normally two nodes, one by one. Of course, searching for a partner in the network can be much faster with intermediaries. It should be noted, however, that this search quite often requires using the services of intermediaries, which may be expensive and time-consuming.

Besides, intermediaries seem to reduce fragmentation by a factor of more than 2: from 55% in the network of direct ties to 23% in the combined network. This means that only 23% of bodies are not connected to the network at all, when intermediaries provide their connections. Otherwise, more than half of the nodes would be completely disconnected. However, this is tricky, because 55% in the direct network is not too high for a network of such density (76% is the average in random generated networks), while 23% fragmentation in the combined network is rather high compared to average 6% in random networks with 13% density. Besides, quite often, in order to reach other partners across sectors, the entities lacking connections would have to use the services of intermediaries, which may induce costs.

Summarizing, the analysis shows that integration between science, education and business organizations in the Algarve maritime cluster is rather low.

Network analysis results

During QAP correlation analysis of binary graphs, Jaccard coefficient was considered as recommended by Hanneman and Riddle (2005) for binary matrices. 31.82%3 correlation was found between the existence of direct cross-sectoral collaboration ties between science, education and business organizations, and the existence of personal connections across these organizations. This implies that there definitely is some non-random relation between the presence of organizational collaboration ties and personal connections between the organizations there.

As for correlation between the existence of cross-sectoral collaboration ties between science, education and business organizations through intermediaries exclusively, and the existence of personal connections across these organizations, only 12.70%4 correlation occurred, and it was insignificant.

Pearson coefficient was considered throughout QAP correlation of the valued matrices. The results have shown a 32% relation between existence and strength (direct or via an intermediary) of organizational ties, and existence and cumulative strength of personal ties between the members of respective organizations.5

3 Significance: 0.0001, average result: 0.05%. 4 Significance: 0.11, average result: 0.07%. 5 Significance: 0.0002, average result: -0.0009.

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Personal network properties

The network structure of personal-level relations between science, education and business organizations in Algarve Maritime cluster is visualized in Figure 4.

Figure 4 – Personal network across science, education and business boundaries in Algarve maritime cluster

Source: Figure by N.B., V.M.

The valued matrix is visualized. Line color and width represent tie strength, size of a node represents its betweenness centrality.

The two types of ties are unequally spread: only 36% of ties are strong. This does not correspond to the condition of balance between weak and strong ties constraint in personal network. In Algarve most of the personal cross-sectoral relations are not configured to provide knowledge-sharing or collaboration; these are more contacts, novel information and ideas.

Average constraint is 44.9 % with 4 nodes of 100% constraint (Table 2), which considering Burt’s studies (1992; 2005) appears to be a medium level of constraint. This does not correspond to the condition of low constraint in personal network stimulating science, education and business integration.

As for the density of ties between the leaders of organizations across sectors, analysis shows that personal contacts between the leaders are not present at all (except overlaps in nodes, when one individual heads two collaborating organizations simultaneously), completely mismatching what is expected to stimulate organizational collaboration networks in the literature.

- education

- science

- business

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Table 2. Personal network constraint Source: Table by N.B., V.M.

Actor Constraint

Aguas Algarve 0.413

AQUALVOR 0.441

Big Game 0.587

CCMAR 0.213

CIACOMAR 0.000

CIMA 0.148

CINTAL 0.398

CITel 1.000

ComPes 0.336

ECOCEANUS 0.500

ESGHT 0.500

FCT 0.179

FE 0.500

ICCE 1.000

INOVSEA 1.000

IPIMAR 0.311

Marsensing 0.321

Natura 0.587

NECTON 0.333

SALEXPOR 0.000

SIPLAB 0.486

SPAROS 0.380

Sunquays 0.000

VITACRESS 1.000

Zoomarine 0.587

AVERAGE 44.9

The balance between representatives of different sectors in different types of centrality is present in the network. The dominating actors in the personal contacts network (Table ) are Faculty of Science and Technology (education) with betweenness centrality of 35% and high degree centrality of 59%, and ComPes (business) when we look at closeness centrality (21%). This means that most network paths are going through the faculty, that they have most connections, they are most powerful actors in the personal network while business seems to be more successful in establishing paths of further reach. However, it is quite clear that there is an imbalance in centrality of science, education and business actors in the cluster, and it is expected that the upshot of this would not be too favourable for the emergence of organizational networks.

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Table 3 – Centralities in personal network Source: Table by N.B., V.M.

Metric Avg. Min/Max Min/Max Nodes

Betweenness 0.03288 0.00000 12 nodes (46%) have this value

0.35230 FCT

Closeness 0.15034 0.03846 4 nodes (15%) have this value

0.21008 ComPes

Total Degree 0.12745 0.00000 CIACOMAR, SALEXPOR, Sunquays

0.58824 FCT

Discussion and conclusion

Other research on regional innovation clusters shows that high levels of integration between research establishments and business sector, and ‘strong’ regional networks increase the innovative capacity of a region (see, e.g., Krätke 2011: 92-127). Our results show that the level of integration between science, education and business in the Algarve maritime cluster is quite low. Probably, innovative capacity of the cluster could then develop through strengthening links between University of Algarve and business sector.

In line with what is suggested in the network analysis literature, and the works on university-industry links, our evidence also shows that personal and organizational networks between science, education and business entities are correlated. Taking into account that strong links between science, education and business are important for regional innovation, this means that innovation in the region could benefit from developing personal networks between companies and the university.

However, in our case we see that personal network does not have the features normally seen as stimulating for inter-organizational collaborations across sectors and for innovation. We found that there are far less strong personal ties across sectors to allow knowledge sharing, and we encountered an imbalance in centrality of science, education and business actors in the interpersonal network which should allow balanced trilateral partnerships. We observed a level of constraint higher than that which would be desirable for innovating, and located insufficient amount of ties between the leaders of organizations across the sectors. Thus, the configuration of inter-personal network may be hindering innovation and integration of science, education and business in the Algarve maritime cluster.

One may also notice, that no significant correlation was found between personal network and organizational network including intermediaries. This is probably as one would expect, as while personal relations between employees of two organizations intuitively, and from the literature, tend to be aligned with direct collaborations between these two organizations, they do not need to

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correspond to connections through some brokers. Yet, innovation intermediaries seem to play a crucial role in it - binding the organizational collaboration structure in Algarve together. Looking at larger density and shorter average path length when intermediaries were considered, one could argue that development of personal networks is not desirable as they hinder the work of intermediaries who are important for binding the network together. However, together with larger density when intermediaries were considered, we observed higher fragmentation. This means that though intermediaries link science, education and business, they also tend to break the network into pieces. Besides, obviously, using paths going through intermediaries also involves additional costs. This finding supports Stark’s argumentation on the problem of brokerage (2011): as the brokers make profits from connecting organizations, they are not interested in arranging direct collaborations between them, but prefer instead to keep them otherwise disconnected. According to our findings also, the role of brokers in science, education and business integration is at least controversial and development of more direct relations between the top managers of science, education and business organizations may be advisable to stimulate science, education and business integration in the region.

Limitations and future prospects

The limitations of our research stem from the case study methodology. The research involves a single case which limits the possibility for generalizations. To make the results general, comparisons between different clusters are needed with extreme cases, particularly with highly different clusters which were purposefully created, with longer-existing clusters and with science, education and business integration referred to as well-developed. This would allow researchers to determine whether the results are due to peculiarities of this particular case or are general to different contexts. Considering methodological extensions, more general quantitative analysis could be performed using data on multiple clusters. This, however, may result in loss of the analytical depth provided by case study methodology.

Future research avenues are broad. As the Algarve maritime cluster is not very well developed in terms of science, education and business integration, we can expect, as a very preliminary supposition, that similar properties of personal network that may be expected to hinder emergence of collaboration between science, education and business organizations in other clusters as well:

high or medium constraint;

dominance of one sector actors in the network (repeating highest in centrality);

lower amount of strong ties;

absence of ties between the managers of science, education and business organizations;

30% or less correlation of binary and/or valued personal network with organizational collaboration network.

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Annex I

Expert Survey on Integration Level

Question

Average

result,

quantified

Average result,

qualitative

1. How often in Algarve companies fund researchers/professors, research projects? (scale

0-4) 1.11 Sometimes

1. How often in Algarve publications are prepared in joint trilateral effort by employees

of companies, research centers and university faculties/schools simultaneously? (scale 0-

4)

0.78 Sometimes

2. How often in Algarve curriculums, educational programs and courses are formed in

joint trilateral collaboration of people from companies, research centers and university

faculties/schools simultaneously? (scale 0-4)

0.67 Sometimes

3. In your opinion, to what extent long-term alliances simultaneously involving

companies, research centers and university faculties/schools are spread in Agarve? 1.11 Few alliances

4. How often in Algarve employees switch jobs at research centers for companies, at

research centers for university faculties/schools, at university faculties/schools for

companies and vice versa? (scale 0-4)

1.00 Sometimes

5. How often employees in Algarve occupy positions simultaneously in research centers,

companies and university faculties/schools? (scale 0-4) 1.00 Sometimes

6. How often in Algarve co-patenting and cross-licensing are done in joint trilateral effort

of research centers, companies, and university faculties/schools? (scale 0-4) 0.88 Sometimes

7. How often in Algarve joint venture agreements made simultaneously involve research

centers, companies and university faculties/schools? 1.10 Sometimes

8. How often collaborative projects involving research centers, companies and university

faculties/schools simultaneously are run in Algarve? 1.27 Sometimes

9. How often in Algarve the same people are in executive and/or advisory boards of

research centers, companies and university faculties/schools simultaneously? 1.14 Sometimes

10. Please evaluate the quality of structures coordinating actions of research centers,

companies and university faculties/schools of Algarve. 2.20 Medium

11. How often is the personnel of research centers, companies and university

faculties/schools of Algarve jointly educated (e.g., courses on innovation management,

R&D management, university-industry knowledge transfer management)?

0.71 Sometimes

12. How often joint events (conferences, workshops, symposia, seminars, etc.) involving

simultaneously representatives of research centers, companies and university

faculties/schools are held in Algarve?

1.78 Often

13. How intense in Algarve is the exchange of ideas and developments between research

centers, companies and university faculties/schools? 1.33 Low intensity

14. How strong in Algarve is mutual understanding between representatives of research

centers, companies and university faculties/schools? 1.38 Slight

15. To what extent, in your opinion, research centers, companies and university

faculties/schools of Algarve influence each other (strategies, structures and practices)? 1.11 Slight influence

16. In your opinion, to what extent research centers, companies and university

faculties/schools in Algarve form an integral cluster? 1.36 Poor integration

17. Do you know of any consulting and/or benchmarking projects in Algarve dealing

with the problems of trilateral collaboration between research centers, companies and

university faculties/schools?

0.50

Half of the

experts know of

such projects

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Annex II

E-mail Survey on Integration Level

Level of integration (regular survey esteems,

0-4)

Average

result,

quantified

Medium result, qualitative

Business 1.33 Poorly developed

Science 1.61 Developed to some extent

Education 1.65 Developed to some extent

Average across sectors 1.62 Developed to some extent

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Bielefeld / St. Petersburg

ZDES Working Papers

Arbeitspapiere des Zentrums für Deutschland- und Europastudien

Рабочие тетради Центра изучения Германии и Европы

Universität Bielefeld – Fakultät für Soziologie Postfach 100131 – 33501 Bielefeld – Deutschland

Staatliche Universität St. Petersburg – 7/9 Universitetskaja Nab.

199034 St. Petersburg – Russland

http://zdes.spbu.ru/

[email protected]