Innovation Networks – Social Networks and the Generation of Innovations

Innovation and Networks

Key findings of empirical research in the 1960s focused on the role of external sources for the generation of innovations and thus, on the importance of boundary-spanning networks. As Freeman (1991: 499) emphasizes, they started to demonstrate the “vital importance of external information networks and of collaboration with users during the development of new products and processes”. And, as he continues to explain, until that time most innovation studies were only “anecdotal and biographical or purely technical. [. . . ] Even those economists, such as Schumpeter, [. . . ] did not study the specific features of actual innovations in any depth”.
The SAPPHO project (see Rothwell 1974; Rothwell et al. 1974) was one of the most comprehensive empirical studies during the late 1960s which is representative of this time’s research on innovations, although it concentrated on only two branches of manufacturing industry, chemicals and scientific instruments. The most important characteristics that play an essential role for the success and failure of innovations as identified in this project are (as outlined by Freeman 1991: 500):

  • user needs and networks,
  • coupling of development, production, and marketing activities,
  • linkage with external sources of scientific and technical information and advice,
  • concentration of high quality R & D resources on the innovative project,
  • high status, wide experience and seniority of the “business innovator”,
  • basic research.

These characteristics show the primary importance of networks and external resources as critical factors for the success and failure of innovations. Moreover, the results of the project already stressed the importance of both, formal and informal networks. During the 1950s, Carter and Williams (1957, 1959) had shown the basic character of multiple links for what they called the “progressive” firm. Piore and Sabel (1984) provide many examples on the role and importance of externalities that are generated by regional networks. They have been historically important
since the early days of the industrial revolution. As Freeman (1991: 510) summarizes, “networking is in itself an old phenomenon and networks of suppliers are as old as industrialized economies”. Nevertheless, a major upsurge of formal and informal networks can be realized in research and literature of the 1980s of both changes in quantitative and qualitative character. “In quantitative terms there is abundant evidence of a strong upsurge of various forms of research collaboration especially in the new generic technologies [. . . ], involving extensive international collaboration as well as national and regional networks. There is also ample evidence of a qualitative change in the nature of the older networking relationships which have existed for a long time” (Freeman 1991: 507). The latter includes sub-contracting networks, research associations, government research and development projects (R&D-projects) and programs, computerized data banks, and value added networks.
A new upsurge of all kinds of networks takes place with the spread of new information and communication technologies based on internet technologies since the late 1980s until today. This constitutes their primary importance in what is known as the knowledge society. It is the various kinds of information technologies that affect, through their convergence with the telecommunication systems, the network of communications within and between organizations, including the firm and its supplier networks, technology networks, customer networks, etc. And not the information technology industry itself is characterized “by
intensive technological networking for the development of its own products, but its diffusion throughout the economy to new sectors of application depends on the development of new networks in every sector [. . . ]. Finally, it provides the technical means for improving communication networks everywhere and for making them feasible in areas where they could hardly have been introduced before. It is a networking technology par excellence” (Freeman 1991: 509).

Social Networks in R&D-Environments

According to Jain and Triandis (1990: 21-43), R&D management should always be guided through its business, technology, and innovation strategies that are realized by manifold enabling mechanisms like people, ideas, communication networks, funds, and cultural elements. Here, we will put our focus on knowledge communication in R&D environments through social networks in a very narrow sense.
Following the distinction of the different phases of research, Tushman (1982: 351-352) characterizes the key features of research projects, development projects, technical service projects and their corresponding communication networks (as cited by Jain and Triandis 1990: 29-30). “[D]ifferent R&D activities require different communication networks” (Jain and Triandis 1990: 31). As shown by experience, there is an “evolution of language, concepts, values unique to the
types of projects undertaken and, at times, unique to the organization itself.” And while this common understanding facilitates communication with a project team (or within a densely connected network), “[t]his local language and other characteristics make communication with the outside—that is, beyond the organization project boundary—difficult and prone to bias and misunderstanding” (Jain and Triandis 1990: 30; with reference to Tushman 1982: 357). Furthermore, following the types of relationships on the different levels of collaboration, organizations, and individuals (as outlined by Liyanage et al. 1999: 387-388), the networks in R&D can be distinguished according to their level of individual, organizational, or institutional (societal) knowledge management processes as follows (see also Liyanage et al. 1999: 387-388):

Network Levels
R&D Process
R&D Objective
Collaborations
Integration
Resource complementary, reduce risks, pool resources
Institutional / Organizational
Linkages
Value creation, long range planning
Individual
Creations
Knowledge creation, creativity and inquiry

Following Collinson and Gregson, the “[i]nitial contacts from social networks evolve into business-focused networks, and then into strategic networks, which allow firms to innovate and to thrive by their links to other organizations” (Collinson and Gregson 2003: 192; with reference to Aldrich and Zimmer 1986; Butler and Hansen 1991; Dubini and Aldrich 1991; Falemo 1989; Flynn 1993; Johannisson 2000).

Knowledge Management and Innovation Networks

During the last two decades, the linear model of subsequent phases of research has been abandoned in favor of recursive models (see Kline and Rosenberg 1986). These models do not assume sequential phases anymore (Schmoch et al. 2000: 5-7), but recursively interconnected phases that are passed through multiple times (“multiple cycling”). This shift of perception of the technology development process is significantly further extended through approaches from a network perspective (Reinhard 2001: 15). A network perspective on the technology development process is primarily based on the Coase Williamson theory of markets and hierarchies. Following this theory, networks differ from other types of organizational interactions in that they achieve co-ordination neither through market mechanisms nor through hierarchy. Rather, social networks reach co-ordination on the normative basis of the partners’ mutual objectively and subjectively felt advantages (see Hakansson 1989: 15-26; Freeman 1991: 506-510). Thus, they provide a distinct type of co-ordination mechanisms, which are especially useful for the efficient organization of innovation activities (Reinhard 2001: 15).
This network perspective on knowledge transfer as a type of co-ordination that is distinct from market mechanisms and hierarchical co-ordination proves the fundamental misconception of knowledge sharing as so-called knowledge markets. Other authors who criticize the linear model of research phases speak of an “interactive” or “coupling” model of innovation (see Asheim and Cooke 1999). Collinson and Gregson (2003: 191) speak of “distributed innovation” which means “collective action amongst firms in a distributed innovation network which cannot be reduced to market transactions and formal contracts”. Although not all of these authors speak of networks, the various concepts of social or organizational interaction, collaboration, and co-ordination in innovation management imply the network perspective more or less focused.
Looking at knowledge communication in innovation management, we can borrow some of the insights on the knowledge management model for knowledge flow in the R&D process as outlined by Armbrecht et al. (2001). They share the conception of a central position of human beings in the processes of knowledge creation and transfer. Their knowledge flow models conceive of the complexity of interaction and suggest a model of “a highly interpersonal and iterative process of filtering, focusing and expanding in which the creative process takes place” (Armbrecht et al. 2001: 32). They study aspects of knowledge management that are unique or especially important for the process of R&D through interviews with R&D managers and derive therefrom a catalog of best practices. The highest priority issues for knowledge management as mentioned in these interviews are (Armbrecht et al. 2001: 33):

  • “What kind of culture facilitates knowledge flow and how can it best be designed, incorporated and managed.”
  • “How can the knowledge of experts and people leaving the organization be captured.”
  • “What can be done to accelerate the R&D process.”
  • “How can the creativity envelope withing the R&D organization be expanded.”

Social network analysis provides not only a conceptual framework for research on innovation networks, but also an analytical tool for the empirical study and support of networks within and between organizations.

* A comprehensive version of this text and all references can be found in the book presented here.