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Abstract
A new definition of regional milieu is emerging from the recent innovation policy framework inspired by the notion of a ‘knowledge economy’. It is grounded in a theoretical context where the emphasis is on the interactive character of innovation, involving the sharing and exchange of different forms of knowledge among the actors. Identifying regional positioning within the global knowledge value chain is a current preoccupation of both policy and empirical research. This study tries to measure the degree of involvement of a (follower) regional community of biotechnology actors in the global knowledge value chain. It applies inductive research and exploratory case studies to analyse local relational behaviour within the knowledge network (KN) structure. Our description of a regional bio-community highlights the distinctiveness of regional knowledge in relation to the distribution of KN capabilities. The critical nodes in the KN structure are the intra-regional actors, represented by public basic research organizations. These actors bridge between local basic research groups and the international scientific community, although the ability of local actors to collaborate can affect the strength of the links among them. This aspect, which is not addressed by regional strategies, should be the focus of new regional policies.
Acknowledgements
We are really thankful to Pasquale Persico for his crucial suggestions during the elaboration of this article. Thanks go also to Maria Prosperina Vitale for her support in data analysis and visualizations. This article has also benefited from comments and insights from participants in the 2012 CCSBE (Canadian Council for Small Business and Entrepreneurship) International Conference, the 35th DRUID Celebration Conference 2013, and the 4th International Workshop on Social Network Analysis. The usual disclaimers apply.
Notes
1. These analyses propose two ways of modelling successful and potentially successful regional clusters, emphasizing ‘market’ (Zucker, Darby, and Armstrong Citation1999) and ‘social’ (Owen-Smith and Powell Citation2004) perspectives.
2. It is well known that SNA can be applied to uncover the latent social structure (industry, local system, open innovation group, etc.) underlying a network and its formation. This structure initially is not completely known and needs to be constructed from what is observed. The structure underlying a social network can be modelled in various ways. In our case, the starting network nodes are the actors located in the region. During the research process, additional nodes will come from external formal and/or informal linkages with these local actors (see Section 4.3).
3. Here, the relational-based definition of ‘milieu’ follows work in the philosophy of science and is considered to result from one or more codes that determine its structure and the ordering among its components. It generally can be considered ‘unit space-time’ where different relationships provide different meanings (Deleuze and Guattari Citation1980).
4. In particular, local knowledge spillovers and proximity in clusters are influenced by modern high-speed transport and telecommunications. Telecommunication can substitute partly for face-to-face communication, search operations, meetings and working on particular tasks, through the mediums of email, web-assisted search, teleconferencing and virtual teams (Malecki Citation2002; Van Geenhuizen Citation2005).
5. In general, at the individual and intra-organizational (micro) levels, different kinds of formal and informal learning behaviours are analysed along with their role in fostering inter-organizational learning involving substantive knowledge (Janowicz-Panjaitan and Noorderhaven Citation2008). Beyond the broad range of contractual forms (from arm's length contracts to equity joint ventures) (Gulati and Gargiulo Citation1999), special attention is paid to informal learning behaviours. These kinds of relationships can take the shape of informal social contacts among boundary spanners. Spontaneous organizational learning is characterized by informal interactions based on collegiality.
6. Following work on communities of practice (Wenger Citation1998; Wenger and Snyder Citation2000) and the dynamics of innovation and knowledge creation, some researchers highlight that different kinds of ties and spatial forms distinguish professional from epistemic and virtual communities. Ties within epistemic communities (common among molecular biologists) tend to be structured closely around common projects and problem-driven cooperation (including in online communities), while professionals, such as the health workers, tend to rely on training histories and institutional affiliations (Amin and Roberts Citation2008). In all cases, it seems that the high level of independence of individual participants, together with their distributed contact networks, results in collaborative practices that cross organizational boundaries.
7. Social network studies use either a ‘whole-network’ or ‘ego-centric’ design. Whole-network studies examine sets of interrelated objects or actors, which, for analytical purposes, are considered bounded social collectives, although, in practice, network boundaries are often permeable and/or ambiguous. Egocentric studies focus on a focal actor or object and the relationships in its locality (Marsden Citation2005).
8. We used the RP Biotech Database coding system and the system suggested by the Organisation for Economic Co-operation and Development (D'Amore and Vittoria Citation2009) to establish the unit of observation. We include different biotechnology actors and, among production activities, we collected active, innovative and dedicated biotech firms. A biotechnology active firm (BAF) is defined as a firm engaged in key biotechnological activities, such as application of at least one biotech technique to produce goods or services, and/or performance of biotechnology R&D. A dedicated biotech firm is a BAF whose predominant activity involves the application of biotech techniques to produce goods or services and/or the performance of biotech R&D. An innovative biotech firm is defined as a BAF that applies biotech techniques to implement new products or processes. Among service activities, we consider R&D, market and other service-oriented firms. In particular, a biotechnology R&D firm with no product sales is categorized by national statistics offices as belonging to the R&D service industry. Targeted firms include firms classified as wholesalers, e.g. local operations of large foreign pharmaceutical firms whose local affiliates perform biotechnology research, but which act mainly as wholesale distributors. Other types of service firms included are those using biotech techniques to provide a service (e.g. waste management and environmental remediation firms).
9. We use Pajek 3.01 software (De Nooy, Mrvar, and Batagelj Citation2005), which allows visualization and analysis of large networks. It processes relational data and provides visual representations (sociograms) in which the nodes are the actors, individuals or organizations, and the lines are the forms of affiliation, research collaborations, advisory boards and founding/proprietary teams.
10. They include national and international university academic groups, other national PROs (CNR), national health system research units, EXPRIVs, national non-profit research centres and extra-regional technology transfer services units.
11. The programs used to estimate these indicators are R igraph and SNA packages.
12. A recent Banca d'Italia survey (Citation2013) shows that southern Italy's manufacturing value added per capita in 2010 was less than one-fifth of the value for northern regions and a quarter of that achieved by underdeveloped German and Spanish regions.
13. During 2010, 90% of Campania's production facilities had fewer than 10 employees (Banca d'Italia Citation2013). The percentage is higher than the percentage of very small firms in underdeveloped German and Spanish regions (respectively, 10% and 27%).
14. The rationale for these policies was to create a European Research Area to integrate European research tools and make the EU the most competitive research area in the world.
15. The policy intervention supports the ‘creation of ten Competence Centres’ at a cost of Euro 20 million each, co-financed by the ERDF, during the period 2002–2006. These centres operate in seven scientific fields reflecting local potential such as information and communication technologies, environment, food production, transport, industrial technologies, biotechnology (three centres), arts and cultural heritage (two centres); see (http://www.sito.regione.campania.it/ricerca_scientifica/centri_competenza/CRdC.htm).
16. The policy initiative is to support the ‘development of research and innovation networks in the biotechnological sector through the financing of R&D Projects’ at a cost of Euro 2–4 million each, financed by the ERDF during the period 2007–2013. The projects must be related to one of six research fields (biomedicine, biomedical and molecular diagnostics, biotechnology for human health, industrial biotechnology, biotechnology for human welfare, biotechnology for the development of new drugs) and to specific technological areas. The related policy actions are currently being implemented; see (http://www.innovazione.regione.campania.it/content/bando-rete-delle-biotecnologie-campania-0).
17. Data are from the (annual) ISTAT Statistical Survey on R&D in Italy.
18. In other words, depending on the external ‘selection’ environment, evolutionary-fitness-relational-abilities enable the organization to capture knowledge and information and, thus, to survive in the marketplace. The extent of evolutionary fitness depends on how well the DC (relational abilities in our study) of an organization match the context in which the organization operates (Helfat et al. Citation2007).
19.
The degree of a node is the number of the node's links, which is the cardinality of the node's neighbourhood. (…) The density of a network keeps track of the relative fraction of links that are present, and is simply the average degree divided by n–1. (Jackson Citation2008, 9)
20.
The distance between two nodes is the length of (number of links in) the shortest path or geodesic between them. (…). The diameter of a network is the largest distance between any two nodes in the network. (…). Average path length (also referred to as characteristic path length) between nodes is another measure which captures related properties. The average is taken over geodesics, or shortest paths. Clearly, the average path length is bounded above by the diameter; in some cases it can be much shorter than the diameter. (Jackson Citation2008, 33)
21. The homophily of a social network refers to the fact that people are more prone to maintain relationships with people who are similar to themselves (Lazarsfeld and Merton Citation1954).