Introduction

Using network analysis to understand innovation

Tim Kastelle
School of Business, University of Queensland, St Lucia QLD

John Steen
UQ Business School, University of Queensland, St Lucia QLD

PP: 02 - 04

Article Text

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.

The first paper by van der Valk and Gijbers (2010) provides an excellent overview of the use of social network analysis in innovation studies, reviewing all 49 papers using network analysis which have been published in the top 10 innovation journals. They then use social network analysis to identify the key issues that these techniques have been used to study: interpersonal and interorganisational collaboration networks, communication networks and technology and sectoral structures. Citation network analysis is one area of wide application for network analysis techniques. This paper provides a good overview of the use of social network analysis within innovation studies, which provides a useful context for the remaining papers in the special issue.

The next paper by Maritz (2010) investigates the interactions between networks and entrepreneurial productivity in universities. He shows that academics with larger networks and with more frequent communication within these networks are both more entrepreneurial and more productive. This is an excellent example of the non-structural network papers. It makes extensive use of network concepts and ideas, and it demonstrates the importance of connections in generating novel ideas.

Lee and Su (2010) use techniques that are similar to those of van der Valk and Gijsbers, but in this case their focus is on the research literature on regional innovation systems. Using data from 432 papers on the topic, they track keyword co-occurrence to map regions, institutions and authors that share a particular research focus, as well as creating a keyword network. The results are interesting for two reasons. The first is that Lee and Su demonstrate where this research is taking place, and how the dominant themes vary geographically. Second, they provide a good picture of the key themes in this stream of literature.

The influence of network structures on creativity is the topic of the paper by Ohly, Kase and Skerlavaj (2010). They map networks within a Slovenian software company to determine the impact of network structure on idea generation and idea validation. The authors use exponential random graph modeling to determine which factors drive the evolution of these two types of networks, and to show that the two processes are indeed supported by different network structures. These results, based on the latest analytical techniques, make a valuable contribution by illustrating how to manage the creative part of the innovation process more effectively.

Liu and Chaminade (2010) also study an intra-firm network, while also investigating the inter-firm network supporting the focal organisation. They look at the impacts of network structure on both product and process innovation within a Chinese textile firm over a 10-year period. This paper makes several important contributions. The authors show how the structure of an innovation network evolves over time, and they identify the network characteristics that appear to be most important as the innovation goals of the firm change over that time. Finally, they identify differences between network structures that successfully support product innovation and those that support process innovation.

The next paper also analyses an intra-firm problem-solving network. Kastelle and Steen (2010) study the structure of an engineering team within a project-based firm. They look at several different types of network structures to determine their impact on innovation within the project team. The authors find that there are different possible generative mechanisms for small-world network structures, and that consequently a deeper understanding of the network's history and evolution is necessary.

Kim and Lee (2010) look at the influence of network structures based on patent data on the innovation performance of seventeen technologies identified by the Korean government as key drivers of economic growth. The authors use the results of this analysis to construct recommendations that will prove useful to those in charge of creating policy to support industrial and competitive development.

The final paper in the special issue, by Casanueva and Gallego (2010) revisits the issue addressed by Maritz - the impact of networks on university innovation and productivity. This paper adds network structural analysis, and builds on the earlier paper by demonstrating the specific network characteristics that drive higher productivity.

Taken together, the papers in this special issue provide valuable resources to both managers and policy makers. They also amply demonstrate the value of using complex network analysis within innovation studies. The papers by Lee & Su (2010) and Kim & Lee (2010) provide important insights into how to effectively manage regions and industry to improve innovation performance. Network analysis has not been widely used to date in setting policy, and this work provides models for how it might be productively applied in these contexts.

The remaining papers (with the exception of the first scene-setting article) all investigate the impact of intra-firm networks on the innovation process and innovation outcomes. They move well beyond simply mapping networks - all make some effort to explicitly tie differences in structures to differences in performance. This represents a significant advance in the use of these analytical tools. We hope to see this research built upon, and we look forward to participating in a more widespread application of network analysis to the key questions within innovation studies.

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