Overview

Editors:

John Steen and Tim Kastelle
University of Queensland Business School
Queensland, Australia

The innovation literature has a long-held tradition of using networks to understand processes of idea generation, opportunity recognition and the diffusion of knowledge. This dates back at least to Schumpeter (1912/1983), who talked about the importance of creating new combinations in the innovation process. However, the most dominant use of the network construct in the innovation research context to date is in its qualitative or metaphorical sense. For example, a study might interview a manager and ask them how important their professional network is for generating new ideas.

While this has been a productive line of enquiry, new analytical techniques in graph theory (the quantitative analysis of networks) are only just starting to be applied to innovation research. When used to analyse social relationships, graph theory is generally referred to as network or social network analysis. The roots of this approach date back to the studies by Morello in psychology in the 1930s (Freeman, 2004).

As network analysis has moved forward, sophisticated techniques in probabilistic network methods, weighted network and longitudinal network analysis have created further possibilities for understanding the interactions between network structures, agents and innovation across multiple levels of analysis. These techniques have been adopted from the physical sciences, and social network analysis has become complex network analysis (Newman, Barabasi and Watts, 2006). When the technical advances are combined with the recent increases in computing power, it has become much more feasible to use complex network analysis more broadly within the social sciences in general, and in innovation studies in particular.

From this research we have begun to understand the importance of network structures and the relationship between agents and these structures in the process of innovation. Initial work in this area has focused on specifying the structure of business networks. For example, there have been several papers identifying networks with a ‘small world’ structure (short average distance through the network combined with high levels of clustering) (Verspagen and Duysters, 2004). More recent work has started to link structural characteristics of networks to innovation performance (Uzzi and Spiro, 2005; Schilling and Phelps, 2007).

This special issue of Innovation: Management, Policy & Practice titled ‘New Network Perspectives on the Innovation Process’ (ISBN 978-1-921348- 32-7) looks at some of the state-of-the-art research incorporating complex network analysis in the study of the innovation process.  It is a highly relevant read for anyone involved in innovation research, policy analysis and best practice in large and small enterprises, public and private sector service organizations, state and national government, and local and regional societies and economies. Topics covered include:

• Using network analysis to understand innovation
• The use of social network analysis in innovation studies
• Networking, entrepreneurship and productivity in universities
• Investigating the structure of regional innovation system research
• Networks for generating and for validating ideas
• Dynamics of a technological innovator network and its impact on technological performance
• Are small world networks always best for innovation?
• Inter-technology networks to support innovation strategy
• Social capital and individual innovativeness in university research networks

Table of Contents

Introduction: Using network analysis to understand innovation
Tim Kastelle, John Steen

The use of social network analysis in innovation studies: Mapping actors and technologies
Tessa van der Valk, Govert Gijsbers

Networking, entrepreneurship and productivity in universities
Alex Maritz

Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis
Pei-Chun Lee, Hsin-Ning Su

Networks for generating and for validating ideas: The social side of creativity
Sandra Ohly, Robert Kase, Miha Škerlavaj

Dynamics of a technological innovator network and its impact on technological performance
Ju Liu, Cristina Chaminade

Are small world networks always best for innovation?
Tim Kastelle, John Steen

Inter-technology networks to support innovation strategy: An analysis of Korea's new growth engines
Sungjoo Lee, Moon-Soo Kim

Social capital and individual innovativeness in university research networks
Cristóbal Casanueva, Ángeles Gallego