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- 1. New Tools to Map and Manage Innovation Networks John Steen Tim Kastelle
- 2. What is Network Analysis <ul><li>Analytical tool for measuring network structure consisting of actors (people, firms, etc.) and the connections between them (social, financial exchange, technical collaboration, etc.) </li></ul><ul><li>Draws from Social Network Analysis (sociology, psychology) and Complex Network Analysis (physics, economics) </li></ul><ul><li>Theoretical justification in evolutionary economics </li></ul>
- 3. Formal Structure
- 4. Actual Structure
- 5. Network Issues <ul><li>Up to a point, the more connections you have, the better flows are (of information, for example) </li></ul><ul><li>However, connections are expensive to maintain </li></ul><ul><li>So managing a network involves trading off the cost of building links against performance </li></ul><ul><li>Most network analyses focus on structure rather than quality of ties </li></ul>
- 6. Example – International Trade Network <ul><li>Data taken from my PhD research </li></ul><ul><li>Analysing changes in the International Trade Network from 1938 to 2003 </li></ul><ul><li>Looking for evidence of globalisation </li></ul>
- 7. 1938 Network
- 8. 1968 Network
- 9. 1998 Network
- 10. Network Metrics <ul><li>In the same way that a normal distribution is characterised by its mean and standard deviation, a network is characterised by four primary measures: </li></ul><ul><ul><li>Degree distribution </li></ul></ul><ul><ul><li>Density </li></ul></ul><ul><ul><li>Clustering </li></ul></ul><ul><ul><li>Average Path Length </li></ul></ul>
- 11. Degree Distribution <ul><li>The degree of a node is the number of edges that connect to it. An important characteristic of most graphs is the mean degree k , the average number of connections per node. The degree distribution of a graph is the measure of degree frequencies, in other words, it is a count of how many nodes have degree = k for each possible value of k . </li></ul>
- 12. Degree David, degree = 7 Sarah, degree = 1
- 13. In-Degree Distribution
- 14. Density <ul><li>The density of a graph is a number between 0 and 1 which indicated the actual number of edges as a proportion of the possible number of edges: 2 L / n ( n -1); a graph is fully connected if its density = 1. Graphs with relatively low densities are referred to as sparsely connected . </li></ul>
- 15. Density 15 people 15x14 = 210 possible connections 41 actual connections 41/210 = density = 19%
- 16. Clustering <ul><li>The clustering coefficient CC is a number between 0 and 1, which expresses the likelihood that two nodes which are both connected to node i will also be connected to each other. </li></ul>
- 17. High vs. Low Clustering High Low
- 18. Path Length <ul><li>A geodesic path is the shortest path through the network which connects two nodes. The mean path length of a graph is the average geodesic length between all pairs of nodes. </li></ul>
- 19. High Average Path vs Low High Low
- 20. Sample Networks
- 21. Clustering and average path <ul><li>Random – clustering low, average path low </li></ul><ul><li>Regular – clustering high, average path high </li></ul><ul><li>Small-world – clustering high, average path low </li></ul><ul><li>Many well functioning real-world networks are small worlds </li></ul>
- 22. Increased Interdependence? <ul><li>The number of links has increased, but the density is the same </li></ul>
- 23. Implication <ul><li>The increased number of connections and average degree supports the idea that the international economy is more interconnected </li></ul><ul><li>However, the stability of the density measure suggests that this more due to growth in the size of the network than to substantially higher numbers of connections </li></ul>
- 24. Regionalisation or Globalisation?
- 25. Implications <ul><li>Increasing interdependence should result in a decrease in clustering </li></ul><ul><li>This change occurred between 1938 and 1948, but the measure has been very stable since then </li></ul>
- 26. Fundamental Change in Structure?
- 27. In-Degree Distributions
- 28. Implication <ul><li>The overall structure of the network has been remarkably stable over the 65 year period </li></ul><ul><li>The power law distribution demonstrates that the decision making of the agents is highly interconnected </li></ul>
- 29. New Analytical Tools <ul><li>Hypothesis testing with PNet </li></ul><ul><ul><li>Simulates random graphs with similar statistics to determine whether certain structures could occur by chance </li></ul></ul><ul><li>Longitudinal analysis with SIENA </li></ul><ul><ul><li>Measures the contribution of network and actor attributes to the formation of new ties </li></ul></ul>
- 30. SIENA Analysis <ul><li>Tracking changes from 1998 to 2003 looking for generative mechanisms </li></ul><ul><li>Tie statistics: </li></ul><ul><ul><li>No Tie both times: 26642 </li></ul></ul><ul><ul><li>No Tie (1998) -> Tie (2003): 881 </li></ul></ul><ul><ul><li>Tie (1998) -> No Tie (2003): 917 </li></ul></ul><ul><ul><li>Tie both times: 1798 </li></ul></ul>
- 31. World Trade Network SIENA Analysis 1
- 32. World Trade Network SIENA Analysis 2
- 33. Interpretation <ul><li>Wealth effect: Although this is a very restricted version of the wealth effect demonstrated in [8], it is still both relatively large and statistically significant. This suggests that at least at the OECD level, the wealth of the countries involved has a strong impact on the formation of new ties. </li></ul><ul><li>Innovation effect: While the overall fit of Model 6 is good, it is not significantly better than that of the baseline Model 2. Furthermore, the relative size of β for the innovation variable is small. This shows that adding the innovation variable to the model does not improve it sufficiently to justify its inclusion. </li></ul>
- 34. More Interpretation <ul><li>Regional effect: The two models that include the regional clustering variables have the best goodness-of-fit out of all of those estimated, which suggests that the regional clustering effect is in fact quite important in the formation of new ties. The only region to have a statistically significant β is the Middle East, the rest of the β s are relatively small. Nevertheless, the extremely good fit of the models including clustering shows that this does in fact have an important impact on the evolution of the WTW. </li></ul><ul><li>Gravity effect: The main difference between Models 7 and 8 is that they use different variables to account for wealth. Model 7 controls for the propensity of the OECD countries to trade with each other, while Model 8 uses the multiplication of economic variables suggested by the Gravity Model. The goodness-of-fit is better for Model 7, which suggests that OECD wealth effect is stronger than the gravitational effect of the sizes of the trading partners. </li></ul>
- 35. Firm Level Analysis
- 36. Collaborative Networks <ul><li>Schilling and Phelps study 11 collaborative R&D networks </li></ul><ul><li>Collaborative networks with a small world structure are more innovative (both in terms of number of innovations and success of new innovations) </li></ul>
- 37. Implications <ul><li>The high level of clustering (densely connected local networks) leads to high levels of trust, which encourages innovation </li></ul><ul><li>A small number of links outside of this dense cluster leads to the acquisition of new ideas and knowledge </li></ul>
- 38. New innovation models and competitive strategy <ul><li>The shift towards open innovation </li></ul><ul><li>Changing of mindset about ‘innovative’ and ‘non-innovative’ industries (e.g. CSL vs. Rio Tinto). </li></ul><ul><li>What’s the value of SNA for developing leading indicators for open innovation? </li></ul>
- 39. Networks as leading indicators of innovation performance <ul><li>Firm networks </li></ul><ul><ul><li>gatekeepers and boundary spanners = origins of radical innovation </li></ul></ul><ul><ul><li>specialisation and integration = firm performance </li></ul></ul>
- 40. Diagnosing unhealthy innovation networks <ul><li>Firm networks </li></ul><ul><ul><li>segregation = failure to leverage synergy </li></ul></ul><ul><ul><li>overload = burn out </li></ul></ul>
- 41. Tracking search, problem-solving and connections <ul><li>Rio: problem-solving </li></ul><ul><li>Vestas: Search </li></ul><ul><li>Hatch: collaboration and recombinant innovation </li></ul>

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