Knowledge management in local and regional innovation systems

re-gions was first stressed in the concept of the learning region (Florida, 1995; Morgan, 1997). The concept of the learning region highlights the increasing need for regions to adapt to the same criteria of competitiveness and innovativeness in the learning economy as knowledge-based companies; new ideas, knowledge creation and continuous organiza-tional learning. Hence, regions’ competitive advantage in the knowledge-based economy is defined by “their ability to mobilize and to harness knowledge and ideas” (Florida, 1995:

532).⁹¹ As a response to these challenges, learning regions typically promote organization-al learning in high-technology and knowledge-based industries, as well as cross-sectororganization-al and cross-institutional learning through the implementation of diverse innovation govern-ance mechanisms and the coordination of flexible networks of a manifold set of regional innovation actors, namely companies, higher education institutions, public and private re-search organizations, public administration, as well as business associations and cham-bers (OECD, 2001; Hassink, 2005).

However, the applicability of knowledge management systems and instruments to the con-text of geographically defined innovation systems and networks such as knowledge re-gions, knowledge cities and science parks has only recently gained attention in the aca-demic discussion. Kujath (2008) has defined territorial knowledge management as a sup-porting instrument to organize spatially defined “knowledge networks between different firms, universities and other knowledge carriers in order to support the development of a locality of learning” (Kujath, 2008: 17).⁹² In this respect, the management of territorial knowledge networks is considered a part of national, regional or local economic, industrial, research and innovation policy (Kujath, 2008).⁹³

91 Florida (1995) has also argued that learning regions provide the fundamental inputs to cope successfully with the challenges posed by the learning economy. In addition to a manufacturing infrastructure, a talent infrastructure to provide and continuously train skilled talent, a physical and communication infrastructure enabling the global connectedness of goods and information, as well as allocated capital allowing the growth of technology and knowledge-oriented industries, specific governance mechanisms must be in-stalled that cater to the needs of knowledge organizations.

92 Kujath and Schmidt (2010) have indicated knowledge management in the geographical context as spatial and relational platforms and transfer channels, which coordinate and harness knowledge networks and the combination of existing expertise of diverse knowledge organizations in distinct territorial areas of innova-tion and localities of learning.

93 Sternberg (1995) has defined technology policy as a comprehensive set of public measures and actions that support and promote the development of new technologies, as well as the commercial exploitation and use of existing and new technologies. Technology policy is considered as intersection between innovation, research and industrial policy.

Several empirical studies have examined the effect of individual knowledge management tools in different contexts of TIS and localities of learning, respectively, including science parks (e.g. Lazaric et al., 2004; Fukugawa, 2006), regional innovation networks (e.g.

Harmaakorpi & Melkas, 2005), knowledge cities and regions (e.g. Kostiainen, 2002; Mal-ecki, 2010), as well as large-scale industrial clusterings (e.g. Dahl & Petersen, 2004;

Cooke & Morgan, 1993).

In case of Silicon Valley, Dahl and Pedersen (2004) have highlighted the supporting role of social networking services including trade fairs, conferences, seminars and social activities provided by intermediaries in order to promote informal personal contacts and encourage the sharing of market and technical information. Cooke and Morgan (1993) have under-lined the importance of specific regional public institutions and government programmes, for example, Steinbeis Foundation’s technology centres and the Chamber of Commerce and Industry, in the Baden-Württemberg innovation system that act as intermediaries and knowledge brokers in terms of enabling and mediating the transfer of state of the art tech-nologies from research institutions to industry in order to sustain the SMEs’ global competi-tiveness.⁹⁴ In the example of loose multi-actor innovation networks in the Lahti region, Harmaakorpi and Melkas (2005) have described the creation of a complex knowledge management system, which closely corresponds to the SECI model of Nonaka (1991), to support collective learning.⁹⁵ The model refers to the application of specific knowledge management tools responding to the specific stages of knowledge creation and required types of ba (see Table 4). The Lathi-based model particularly stresses installed instruments to facilitate personal interaction and to increase trust in order to foster knowledge creation and learning within the regional innovation network. Also, the definition of a mutual knowledge vision is emphasized to set the direction for the knowledge-creating process and to help the diverse multi-actor regional innovation network “in creating and obtaining

94 Intermediaries, also referred to as knowledge brokers and boundary-spanners, facilitate transactions be-tween previously unrelated actors lacking trust or access to each other (Hargadon & Sutton, 1997). The concept is derived from transaction cost economics (Nooteboom, 2001). Third parties, who act as interme-diaries in inter-organizational knowledge exchange, take over a very complex function. They extend an or-ganization’s internal resources regarding the identification and validation of suitable external knowledge sources holding knowledge searched and required, in initiating and mediating the development of function-al, trustful relations, as well as in coordinating the actual knowledge interaction process. Thus, intermediar-ies significantly reduce transaction costs and uncertainty. As indicated earlier, in particular public and uni-versity-based technology transfer offices (TTO), Chambers of Industry and Commerce, as well as economic development agencies are underlined as such boundary-spanning organizations (Howells, 2006; Cantner et al. 2011; Battistella et al., 2016).

95 Harmaakorpi and Melkas (2005) have added self-transcending knowledge, which is defined as tacit knowledge prior to its embodiment (i.e. sensing the presence of a certain potential), as an additional type of knowledge in their model (in addition to explicit and tacit knowledge).

knowledge in the right amount, at the right moment and in the right form” (Harmaakorpi &

Melkas, 2005: 656).⁹⁶

Knowledge management sytem in Lahti regional innovation network based on SECI model

Phases of ba Knowledge types Knowledge management instruments

1. Visualization /

Source: Author (based on Harmaakorpi & Melkas (2005))

96 Harmaakorpi and Melkas (2005), however, have not provided any empirical results about the model’s effects, sustainability and replicability.

Finally, Lazaric et al. (2004, 2008) have found evidence for enhanced trust, reduced cogni-tive distance and jointly created knowledge about markets and potential innovation oppor-tunities in the ICT cluster of firms and scientific institutions in the Sophia-Antipolis tech-nopole that have been actively stimulated by IT-based knowledge management. Firstly, a semantic web service mapped and codified the competencies across actors in the local cluster to facilitate the identification of demanded knowledge.⁹⁷ Secondly, the active inte-gration of the heterogeneous actors in the transparent presentation of technological, scien-tific and entrepreneurial resources in conjunction with an increasing awareness of potential knowledge combinations has enabled the development of a shared language and, thus, the reduction of cognitive distance. Reduced cognitive distance, in turn, has allowed local clus-ter members to benefit “from both Marshallian exclus-ternalities (exploitation of the same tech-nological trajectory) and Jacobian externalities (exploration of new combinations)” (Lazaric et al., 2008: 849). Furthermore, intense interaction around prototype design and codifica-tion of actors’ competencies has also led to the emergence of epistemic communities and the creation of certain knowledge externalities that set potential for further collaborative activities, for example, the identification of potential knowledge combinations and for inter-active innovation opportunities. As a result, IT-based knowledge management has contrib-uted to “organize proximities” (Lazaric et al., 2004: 22) and to generate a shared space for enhanced knowledge interaction and interactive learning in two ways; by mapping the local ICT value added chain and by integrating all relevant local ICT stakeholders into this pro-cess (Lazaric et al., 2004, 2008). Also for the Sophia-Antipolis technopole, Longhi (1999) and Lazaric et al. (2004) have stressed the positive effects of specifically aligned business networks and clubs on localized knowledge relations. While specific business associations aim to link actors in complementary markets, others are designed to connect resident or-ganizations and knowledge carriers in specific areas of technology and knowledge (see Figure 10).

97 In order to make the ICT cluster’s firms’ and research institutions’ heterogeneous knowledge and compe-tencies comprehensible and comparable, a standard set of information covering various topics, namely key resources, deliverables, business activity, patents, publications, as well as R&D and industrial collabora-tions, was recorded (Lazaric et al., 2008).

Figure 10: Specialized business networks in Sophia-Antipolis’ ICT cluster

Source: Lazaric et al. (2004, p. 21) (modified by author)

Overall, selected studies have primarily focused on individual support instruments linked to specific categories of knowledge management instruments; information management (e.g.

Lazaric et al., 2004; Lazaric et al., 2008), people management (e.g. Dahl & Pedersen, 2004; Lazaric et al., 2004) and external structures (e.g. Cooke & Morgan, 1993).⁹⁸ The work of Harmaakorpi and Melkas (2005) represents one of the few studies of a comprehensive knowledge management system being applied to a regional multi-actor knowledge network. However, to date a comprehensive (quantitative and qualitative) evaluation of the effects and underlying mechanisms of comprehensive knowledge management systems in distinct localities of learning such as STPs has not been carried out (see Box 5).

In conclusion, the central question for designated seedbeds of innovation such as STPs is how knowledge management systems have to be orchestrated to organize proximities ef-fectively, that is, beyond merely geographical co-location in order to tap knowledge sources

98 Most of the mentioned tools (e.g. awareness raising, networking and brokerage) are considered universally in the academic discussions of knowledge management, micro-level network governance, as well as cluster and technology policies (Boekholt & Thuriaux, 1999).

and coordinate knowledge interaction.⁹⁹ As highlighted in Chapters 2.1 and 2.2, this is im-portant to promote local and non-local knowledge interaction and learning among knowledge organizations in general and between firms and academia in particular in order to add to STPs’ evolution towards active knowledge-creating and knowledge-coordinating entities in the knowledge-based economy.

Box 5: A working definition of knowledge network management in STPs

In this thesis, I utilize the term knowledge network management (KNM) to describe specialized support mechanisms applied by third actors that aim to facilitate and promote the initiation and cultivation of businesses’ egocentric knowledge networks with scientific institutions in the context of local innovation systems such as STPs. The term knowledge network management has been coined by Seufert et al. (1999). It refers to a “proactive, systematic approach to the planning and design of intentional, formalized networks for knowledge creation and transfer, and the establishment of conditions to cultivate emergent, informal networks, widening their scope, guiding them towards high performance, and transferring best practices to other application contexts” (Seufert et al., 1999: 187). Thus in the context of STPs, knowledge network management underlines the active furtherance and organization of cross-institutional knowledge networks (e.g. firms, scientific institutions and other knowledge organizations) in order to promote the development of such planned localities of learning systematically, as also highlighted by Kujath (2008) as well as Kujath and Schmidt (2010).

2.4 Combination of the theoretical concepts and formulation of the