The Dynamics of Local Innovation Systems

Structures, Networks and Processes

Eva Panetti

• Publisher: Routledge
• Year: 2019
• Print ISBN: 9780367194437
• eBook ISBN: 9780429514449
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

This book offers a comprehensive overview of the dynamics underpinning the successful performance of local innovation systems (LIS), that is, spatial concentration of innovation activities in specific geographical areas, characterized by the synergetic co-localization of research centers, innovation-driven enterprises, large corporations and capital providers.

The reader will gain a deeper knowledge of LIS theory and learn about the theoretical and empirical challenges of studying the LIS from a relational perspective. The book also provides an analytical framework to explore the level of connectivity among LIS actors through the use of social network analysis (network architecture) and second, to assess the variety of different types of relationships that local actors put in place to produce innovation within the LIS (network portfolio). More specifically, this book explores which network configuration is associated with a successful LIS by deriving evidence from the empirical study of the biopharma LIS in the Greater Boston Area (GBA), which has been exemplified as a benchmark case in terms of successful LIS performance.

This book also contributes to the theoretical debate about the optimal configuration of network structure (e.g. network closure vs. network openness). In capturing the heterogeneous nature of the LIS demography, it addresses the challenges brought about by the adoption of a holistic approach. Finally, the study provides insights into the network portfolio composition, which has been underexplored by extant literature. Besides addressing the scientific community in the field, this book will also be a valuable resource with practical implications for policymakers and those actors willing to undertake an active role in the development of an LIS in their own regions.

1
Local innovation systems

An overview

1.1 The impact of geography on innovation

Learning is considered as a key concept within innovation system literature. In the late 1980s, Lundvall (1985; Lundvall, Dosi, & Freeman, 1988) and Johnson (Johnson & Lundvall, 1994) introduced the notion of learning by interacting to emphasize the role of geographic proximity in providing a more direct and easy access to information within user-producer interactions (Lundvall, 1985). More specifically, the authors consider learning “a socially embedded process which cannot be understood without taking into consideration its institutional and cultural context” (Lundvall, 1992, p. 1). This is mainly explained by the fact that innovation generation represents a process characterized by low levels of predictability where learning plays a central role in such uncertain process, which in turn explains why complex and frequent communication between the parties involved is highly required, with specific regard to the exchange of tacit knowledge (Nonaka, Takeuchi, & Umemoto, 1996). The importance of geographic proximity in knowledge transfer processes is further emphasized with the introduction of the notion of learning region (Storper, 2005). In this regard, learning is conceived as a territorially and socially embedded and interactive process (Asheim, 1996), able to drive the successful growth and the innovation performance of regions (Cooke, 1992) thanks to the catalyst role of proximity (Coenen, Moodysson, & Asheim, 2004). Networking with other firms and organizations is therefore considered as a “learning capability” (Lundvall & Johnson, 1994), and different kinds of “learning relationships” (e.g. customer-supplier; cross-sectorial interactions) are deemed to be at the core of the innovation process (Johnson & Andersen, 2012).

Another important aspect is that the impact of geographic proximity on innovation-driven learning dynamics varies according to the nature of knowledge and innovation modes. Lundvall and Johnson (1994) grouped knowledge into four economically relevant knowledge categories:

• Know-what, i.e. knowledge about facts;
• Know-why, i.e. knowledge of scientific principles;
• Know-who, i.e. specific and selective social relations;
• Know-how, i.e. practical skills.

(p. 129)

This taxonomy is useful to understand the different channels through which learning takes place. Indeed, while know-what and know-why can be learned through codified information (e.g. by reading books or lectures), the other two forms of knowledge are more difficult to codify and may be required to be transferred through practical experience. Consequently, while know-why and know-what are more typically produced through STE-based innovation (science, technology and engineering), know-how and know-who are generally associated with DUI-based innovation (doing, using and interacting). Following Jensen, Johnson, Lorenz and Lundvall (2007), the STE mode is “based on the production and use of codified scientific and technical knowledge”, whereas the DUI mode “relies on informal processes of learning and experience-based know-how” (p. 680). Main differences between the two modes of learning are shown in Table 1.1.

Asheim and Gertler (2005), building on the concept of learning as an interactive process, introduce a new dimension analytic dimension to the study of innovation processes (i.e. knowledge base Laestadius, 1998), which can be alternatively analytical or synthetic. The analytical knowledge base refers to industrial settings, “where scientific knowledge is highly important, and where knowledge creation is often based on formal models, codified science and rational processes” (Asheim and Gertler, 2005, p. 296), as in the case of biotechnology, information and communication technologies (ICT) and genetics. University-industry networks turn out to be particularly important, as companies tend to frequently rely on results from research institutions for the development of their innovations. The type of exchanged and produced knowledge tends to be codified, and its application gives origin to radical innovation more frequently. Indeed, radical innovation is typically produced when knowledge is exchanged among actors of different nature through inter-organizational relationships and cooperative mechanisms capable of stimulating reciprocal learning and thereby processes of innovation (Capaldo, 2004). Hence, the presence of actors of different nature (i.e. universities, firms, government institutions), presenting different skills and capabilities and diverse backgrounds can boost the creation of radical innovation as far as they exchange non-redundant information.

Table 1.1 STE mode vs. DUI mode

STE mode (science driven)	DUI mode (user driven)
Aim: Increase the R&D capacity of the actors in the system and increase cooperation between firms and R&D organizations	Aim: Foster inter-organizational learning and increase cooperation between in particular producers and users
Typical innovation policy: Increase the R&D capacity of organizations Support joint R&D projects between firms and universities Support higher education programs Subsidies for R&D infrastructure (laboratories, research and technologies centers, research groups, etc.) Support (financial) for increasing mobility between academia and industry Support for commercialization of research results	Typical innovation policy: Support on-the-job learning and organizational innovations Matchmaking activities and building and sustaining existing networks Stimulate trust building andjoint innovation projects between actors in the value chain (producers-suppliers, users-consumers) Stimulate joint projects between competing and auxiliary businesses

Source: Isaksen and Nilsson (2011)

Table 1.2 Analytic vs. synthetic knowledge bases

Synthetic knowledge base	Analytic knowledge base
Innovation by application or novel combination of existing knowledge	Innovation by creation of new knowledge
Importance of applied, problem-related knowledge (engineering), often through inductive processes	Importance of scientific knowledge often based on deductive processes and formal models
Interactive learning with clients and suppliers	Research collaboration between firms (R&D department) and research organizations
Dominance of tacit knowledge due to more concrete know-how, craft and practical skill	Dominance of codified knowledge due to documentation in patents and publications
Mainly incremental innovation	More radical innovation

Source: Asheim and Gertler (2005)

On the other hand, the synthetic knowledge base refers to “industrial settings, where the innovation takes place mainly through the application of existing knowledge” (Asheim and Gertler, 2005, p. 295) or through new combinations of knowledge. It is the case of incremental innovations, which are developed to solve specific problems as, for example, in the field of industrial machinery or shipbuilding, where products are generally manufactured on a small scale. Research and development (R&D) and university-industry links tend to be less important compared to the analytic knowledge base, and knowledge is often produced as a result of experimenting, testing and practical processes presenting a low level of codification. Main characteristics and differences of the two knowledge bases are summarized in Table 1.2...

Table of contents

• Cover
• Half Title
• Series Page
• Title
• Copyright
• Contents
• List of figures
• List of tables
• Introduction
• 1 Local innovation systems: an overview
• 2 Local innovation systems as networks of relationships
• 3 Exploring the relational dimension of LIS: an empirical case study
• 4 Results from the empirical study
• Index