Using Social Media to Leverage Triple Helix Insights in Innovation Ecosystems

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Using Social Media to Leverage Triple Helix Insights in Innovation Ecosystems

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  • Our research questions center on innovation ecosystems.A dynamic innovation ecosystem is characterized by a continual realignment of synergistic relationships of people, knowledge, and resources that promote harmonious growth of the system in agile responsiveness to changing internal and external forces.Around the world, policy makers, program leaders, researchers and entrepreneurs want to optimize the impact of their investments.We believe value co-created through a cycle of shared vision and transformations. The people who participate in events create coalitions and networks whose impacts can be measured and tracked with data-driven visualizations, revealing changes. The interaction of key people, using feedback about the transformation, serves to evolve – to co-create – their shared vision.-----------------A dynamic innovation ecosystem is characterized by a continual realignment of synergistic relationships of people, knowledge, and resources that promote harmonious growth of the system in agile responsiveness to changing internal and external forces.Innovation Ecosystems refer to the inter-organizational, political, economic, environmental, and technological systems through which a milieu conducive to business growth is catalyzed, sustained, and supported. Value is co-created for the innovation ecosystem through events, impacts and coalitions/networks that emerge from a shared vision of the desired transformations. Data-driven metrics measure, track and visualise the transformation, empowering interaction with feedback for the shared vision.
  • The Innovation Ecosystems Network has been studying co-creation from several perspectives.We look for insights that can be used to:Communicate complexity to co-create visionIdentify and empower influential individuals for critical actionsConnect components to catalyze the evolution of the ecosystemDevelop and implement programs (meetings, funding, initiatives) to foster co-creator networks Measure and transform an innovation ecosystemFor example, a recent paper in the Journal of Networks reported our study on “A Network Analysis of Investment Firms as Resource Routers in the Chinese Innovation Ecosystem.” In this workshop, we will present some preliminary results of early work – in order to invite collaboration.
  • I suggest going into detail with this slide. It will be the basis of people agreeing that IEN knows the fundamental concepts of TH, establishing trust that we appreciate the mental models used by the conference group, and establish a basis of how questions are asked.I suggest not saying we have new answers – rather establishing that we appreciate the questions the TH community has been asking.I removed the eClusters. I would imply (rather than show) the question mark.What about wrapping threads of the triple helix around this arrow – might match to some of the seminal references – and the start date of the Triple Helix concept.
  • Relationships provide the infrastructure for resource flows. This is especially important as information technology and globalization have changed the way we think about organizations.These resources might be financial; they might be informational; they might be access to markets or materials. Among executives and key employees, relationships are the basis for the transfer of technologies and knowledge, professional networks, business culture, value-chain resources, and mental models.Corporate governance is embedded and filtered through social structures in the relationships among Directors. These relationships influence co-creation of things such as: executive compensation, strategies for takeovers, defending against takeovers. Through relationships with investors and service providers, businesses co-create an awareness of external forces, of competitive insights, and they are able to leverage resources. Relationship interlocks provide a social relationship “filter” for governance, for information flow & norms. Relationships are the vehicle for co-creating and transferring mental models, as well as implicit and explicit know-how.Using social network analysis we can visualize the patterning of social connections and relationships.
  • Showing the live Vizter is REALLY seductive because of the interactivity of it.Is the live version still available?
  • Add after vizster slide #15
  • These are models used by the Dachis Group to describe new business models of resource flows.They may apply across the relationships of busienss, education and government – as well.
  • It is rare that the data is simply brought to us on a silver platterWe have to try hard to actively acquire it
  • There is a lot of nice data on innovation but it is not so recent. In traditional data gathering, data is often gathered over a period of time. Then it goes through various processes within organization, gets analyzed; some reports are released; and then the data is released. This process may take several years.
  • Innovation happens very fast. If you are too slow – you loose.To react fast, we need the current data.
  • IEN Dataset is derived from English language resources. Some caveats needed for generalizing results to non-english speaking countries.
  • It is surprising how many similarities there are between the fields of journalism and data miningWe both try to make sense of data and facts and to communicate it to others.We looking for trends and providing an explanation for them.Looking for outliers (something that does not follow the trend). This could be a break through innovation that may become a trend in the future; or a politician behaving inappropriately (that hopefully does not become a trend).
  • Please think of several patterns and outliers in bicicles picture.ASK AUDIENCE---So let me just mention a few:Color is one of the patters that jumps out right awayFor example there is a lot of aluminum colorsYellow bike jumps out as an outlierIf we look closer we may also notice that there is only one bike where the handles are greenOnly a few bikes have their seat covered with plasticBikes are more or less lined upThere is a bike that is facing the wrong way though----------Even in these small dataset there are so many patterns and outliersBut how many of them are interesting; that really depends.We try to find patterns that are novel; since telling people that bicycles tend to have two wheels is perhaps not so interesting.What is interesting also depends on the purpose;A person checking whether bicycles have permit for parking – is looking for a specific outliersWhen I look for my own bike; I have a different outlier in mindSo ability to spot things that are interesting is extremely important.Outliers are normally discarded in data mining …Because you are often trying to find a pattern, and outliers screw up things.In business, some outliers have become very successful as described in the following book.So we thing it is interesting to look not only for patterns but also for outliers
  • Can’t do data mining without the data; so we need data and the more the better – since then we can see patterns more clearly
  • Also when we have more dimensions it is easier to spot patterns
  • Just like journalists in order to get a complete story we try to get data from different sources.For example, the usefulness of map may increase significantly as we add information about trafic, points of interest, etc.
  • The problem is that people could not deal well with large number dimensions, and big amounts of data.So that’s where computers and data-miners come in handy.
  • Here also, I suggest some detail on these concepts in order to establish credibility.Emphasize that we appreciate the concepts being developed by TH – rather than identify new concepts we’re planning to introduce.
  • It would be good to update the numbers.And it will be really important to identify a thread that will link these charts to tell a story – perhaps tie the story to Silicon Valley and Finland – for continuity.
  • We can also look at the companies by sector
  • We can try to analyze relations between sectors; here are the advertising and web sectorsA lot of things going on in Silicon Vaelly; but also in the North East and other parts
  • Here is the biotech and cleantech
  • We can also at specific cities and regionsSV looks very interesting
  • This is seattle
  • DC
  • And NY
  • So as you can see the patters are very different from city to city
  • So far I have shown analysis based on the spatial distance;However the aspects of distance is changing;We don’t know where these people are physically located but they seem to be in the same space.
  • So the new maps may be based on the connections; rather than on distance.For this analysis we have utilized an open source tool called NodeXL
  • The red edge begs an explanation.
  • I’ll scan the article on EU and email Monday.Need to build a story – starting with sectors and moving to people.Need to “so what’s from the sector view and at least two “so what’s” from the people view.Expect to get the question of how female networks differ from male networks. Neil, do the network statistics reveal differences?
  • And the reason this is interesting is - - - - How would this be different from the male graph?
  • Networks may reside with technical colleagues rather than at the executive level – or is this particular to female STO’s?
  • She’s running for governor of CA.
  • By utilizing various AI techniques we can further enhance the data that we have gathered.For example the gender of many of the executives is not specified.The standard approach of dictionary lookup does not work well; since many of the names are of the foreign origin
  • Example view to IEN dataset in Gephi. Companies are selected with keyword search “Norway + Norwegian;” the funding organizations associated with those companies are added Nodes represent companies and their investors; edges indicate resource flows. The network layout is created with Yifan Hu Multilevel algorithm and nodes are inflated according to their indegree, i.e. the number of the connected investors. Companies leverage value co-creation opportunities through relationships with multiple investors. Some investors are international.Investors leverage co-creation opportunities with investments in multiple companies. Not shown here are international companies linked through relationships with the same investors.Timeline analysis of investment events reveals patterns of co-investment – an indication of intention to co-create value.What do we see in Knowledge-based Norway:Norwegian industry Most investment firms have invested in a few investments. Investments from Norway and from elsewhere? Is collaboration Norwegian or international?PRINCIPLES AND CONCEPTSNetwork structure: random, small world or scale free? (Barabási, 2003)Network properties:density, cohesionPhenomena driving network evolution:homophily, reciprocity and transitivity (cf. Giuliani and Bell, 2008)Actor roles: hubs and connectors (Barabási, 2003; Heer, 2005)peripheral, central connector, broker (Hansen, Schneiderman and Smith, 2010)SNA metrics (Wasserman and Faust, 1994):centrality: betweenness centrality, actor degree centralityprestige: actor degree prestige, actor proximity prestige, rank prestige - also rage rank (cite{pagerank1999})
  • PRINCIPLES AND CONCEPTSNetwork structure: random, small world or scale free? (Barabási, 2003)Network properties:density, cohesion () Phenomena driving network evolution:homophily, reciprocity and transitivity (cf. Giuliani and Bell, 2008)Actor roles: hubs and connectors (Barabási, 2003; Heer, 2005)peripheral, central connector, broker (Hansen, Schneiderman and Smith, 2010)SNA metrics (Wasserman and Faust, 1994):centrality: betweenness centrality, actor degree centralityprestige: actor degree prestige, actor proximity prestige, rank prestige - also rage rank (cite{pagerank1999})
  • What do we see in Funding in Finland:Finnish Industry Investment, Nexit Ventures, Veraventures, Most investment firms have invested in a few investments. Investments from Finland and from elsewhere? Is collaboration Finnish or international?PRINCIPLES AND CONCEPTSNetwork structure: random, small world or scale free? (Barabási, 2003)Network properties:density, cohesion () Phenomena driving network evolution:homophily, reciprocity and transitivity (cf. Giuliani and Bell, 2008)Actor roles: hubs and connectors (Barabási, 2003; Heer, 2005)peripheral, central connector, broker (Hansen, Schneiderman and Smith, 2010)SNA metrics (Wasserman and Faust, 1994):centrality: betweenness centrality, actor degree centralityprestige: actor degree prestige, actor proximity prestige, rank prestige - also rage rank (cite{pagerank1999})
  • PRINCIPLES AND CONCEPTSNetwork structure: random, small world or scale free? (Barabási, 2003)Network properties:density, cohesion () Phenomena driving network evolution:homophily, reciprocity and transitivity (cf. Giuliani and Bell, 2008)Actor roles: hubs and connectors (Barabási, 2003; Heer, 2005)peripheral, central connector, broker (Hansen, Schneiderman and Smith, 2010)SNA metrics (Wasserman and Faust, 1994):centrality: betweenness centrality, actor degree centralityprestige: actor degree prestige, actor proximity prestige, rank prestige - also rage rank (cite{pagerank1999})
  • Geographical distribution and of Stanford and Berkeley is very similar within the united states
  • As we look closer we begin to see the differences;Stanford has a stronger presence in the Bay Area, and San DiegoBerkeley has a stronger presense in the LA Area
  • As we look even closer we can see more differencesMost of Berkley graduates are concentrated in SF (a bridge across from UC Berkley)While Stanford’s graduates are present throughout the valleyAlso it is interesting to note that East Bay has very little presence of graduates (according to our dataset).It is particularly interesting since UC Berkeley is in the East bay; however graduates do not tend to stay in that area.
  • Using Social Media to Leverage Triple Helix Insights in Innovation Ecosystems

    1. 1. http://www.flickr.com/photos/angelinux/3034564360/ Using Social Media to Leverage Triple Helix Insights in Innovation Ecosystems Innovation Ecosystems Network Stanford Media X Led Research Initiative
    2. 2. The Innovation Ecosystems Network (IEN) brings together an international interdisciplinary team that seeks to develop and diffuse novel data and tools for understanding the catalytic impact of regional ICT experiments. http://www.innovation-ecosystems.org
    3. 3. Structure of the presentation • Background of Analysis of Innovation • Proposed Framework – Data-driven – Data Mining and Analysis – Visualization • Cases – Regional – Sectoral – Gender – University • Discussion
    4. 4. Innovation takes at least two. Team skills are required. There are winners and loosers. Although people can communicate anywhere, anytime, it’s difficult for anyone to have all the insights necessary at any one time for major decisions on the complex global issues
    5. 5. Events Impacts Coalitions & Networks Shared Vision Transformation Measure, Analyze & Visualize DATA  ANALYSIS  MEANING Interaction & Feedback Value Co-Creation IEN Transformation Framework Translate, measure and transform an innovation ecosystem
    6. 6. Research Problem/Questions Theme: How can data-driven visual social network analysis provide insights to catalyze innovation ecosystems? We look for insights that can be used to: • Identify and empower influential activate the evolution of the ecosystem • Develop and implement programs (meetings, funding, initiatives) to foster co-creator networks • Communicate complexity to co-create vision • Measure the transformation of an innovation ecosystem How do co-creation networks enable local/regional ROI on innovation investments made for globalization? Work in progress! Collaboration is invited! www.innovation-ecosystems.org
    7. 7. Models of innovation Economic Innovation (producer innovation)- Schumpeter 1934 End-user Innovation- von Hippel 1986 Strategic Innovation- Hamel and Prahalad 1994 Open innovation- Chesbrough 2003 Collaborative Innovation Networks- Gloor 2005
    8. 8. The Way We USED to Think About Organizations New Organizational Chart Based on Relationships Relationship-Focused Co-Creation Infrastructure (Visual) Social Network Analysis “. . . allows investigators to gain new insights into the patterning of social connections, and it helps investigators to communicate their results to others.“ (Freeman, 2009) Infrastructure for Resource Flows - - - Relationships (Companies are interlocked through key people – information flow, norms, mental models.(Davis,1996)
    9. 9. Relationship Interlocks • Executives and key employees – Transfer of technologies and knowledge, professional networks, business culture, value-chain resources • Directors – US Fortune 500 firms interlocked (shared directors) with average 7 other firms • Corporate governance embedded and filtered through social structures – Executive compensation, strategies for takeovers, defending against takeovers • Gerald F. Davis, “The Significance of Board Interlocks for Corporate Governance,” Corporate Governance 4:3, 1996 • Investors and service providers – Awareness of external forces, competitive insights, resource leverage • Relationship interlocks provide – Social relationship “filter” for governance, information flow & norms – Transfer of implicit and explicit know-how – Mental models
    10. 10. Through personal relationships companies have interconnects
    11. 11. Social Roles in Social Media Journal of Social Structure “Visualizing the Signatures of Social Roles in Online Discussion Groups” http://www.cmu.edu/joss/content/articles/volume8/Welser/ • Answer person – Outward ties to local isolates – Relative absence of triangles – Few intense ties • Reply Magnet – Ties from local isolates often inward only – Sparse, few triangles – Few intense ties  Discussion person  Ties from local isolates often inward only  Dense, many triangles  Numerous intense ties
    12. 12. Imagesource:InternetPioneers:PaulBaranatibiblio.org Network Structures
    13. 13. Archetypes of Social Business Design • Ecosystem – a community of connections • HiveMind – a socially calibrated mindset of individuals • Dynamic Signal - the constant multi-faceted means of collaboration • Metafilter- a method of finding signals in vast amounts of noise
    14. 14. http://www.fabcats.org/owners/feeding/info.html http://www.flickr.com/photos/manpsing/2618332693/ Passive Learning Active Learning
    15. 15. http://www.flickr.com/photos/tomatoskin/1339929731/ Traditional Data Gathering Methods
    16. 16. http://www.flickr.com/photos/clydeorama/3495284608/ Have to react QUICKLY
    17. 17. Data to measure/track innovation?
    18. 18. IEN Dataset Martha Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010. Updated quarterly with rapid growth each quarter
    19. 19. Making Relationships Visible Wille Framework, OPAALS, 2010.
    20. 20. New Data & New Tools Accessing Data Streams about Innovation Building a Dataset on Innovation Crystallisation Through Visualisation The Card-Mackinlay-Shneiderman visualisation reference model:(Card et al., 1999; Miksch, 2005)
    21. 21. Trends & Innovation Patterns & Outliers
    22. 22. http://www.flickr.com/photos/ritavitafinzi/2192500407/
    23. 23. Tan,Steinbach,Kumar;2004 “There is no data like more data” (Mercer at Arden. House, 1985) 2,000 points 500 Points8,000 points “There is no data like more data” (Mercer at Arden. House, 1985)
    24. 24. http://www.iro.umontreal.ca/~bengioy/yoshua_en/research_files/CurseDimensionality.jpg Could be easier to find patterns More Data / More Dimensions http://wissrech.ins.uni- bonn.de/research/projects/engel/engelpr2/pr2_thumb.jp g
    25. 25. Easy, fast integration, using APIs and data sources to produce results that were not the original reason for producing the raw source data [wiki]. 25 Mashup
    26. 26. http://www.stat.columbia.edu/~cook/movabletype/archives/2006/04/a_wallful_of_da.html Information Overload
    27. 27. Place of innovation Localized concentrations - Marshall 1890 Menlo Park (Research Park) 1948, Stanford Industrial Park 1951, Research Triangle Park 1959 Clusters- Porter 1998, Saxenian 1994 Regional Innovation Systems- Metcalfe 1995 Innovation Cluster- Yim 2008, 2002
    28. 28. . Technology-based companies - worldwide, Dec 2009 35,000 companies include: Sectors: Advertising, biotech, cleantech, consulting, ecommerce, enterprise, games_video, hardware, legal, mobile, network_hosting, public relations, search, security, semiconductor, software, web, other firms serving these. Investment profiles from Ltd to public, financing rounds identified Merger & Acquisition profiles Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    29. 29. . . Employees in technology-based companies - worldwide, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    30. 30. . Technology-based companies – worldwide by employee size and sector, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    31. 31. Number of European Technology-based companies Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    32. 32. . Number of European Technology-based companies by employee size, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    33. 33. Number of European Technology-based companies By sector, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    34. 34. European Technology-based companies By sector and number of employees, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    35. 35. # of Companies # of People Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    36. 36. . Number of US Technology-based companies By sector, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    37. 37. . Number of US Technology-based companies Advertising & Web, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business. Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    38. 38. . Number of US Technology-based companies Biotech & Cleantech, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
    39. 39. . Number of Technology-based companies In Silicon Valley by sector, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
    40. 40. . Number of Technology-based companies In Seattle by sector, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
    41. 41. . Number of Technology-based companies In DC by sector, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
    42. 42. . Number of Technology-based companies In New York City by sector, Dec 2009 Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Behind the Innovation Curtain: Mobile Players and Their Moves.” Submitted to the International Conference on Mobile Business,” Intl Conf on Mobile Business.Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves”
    43. 43. NYCSilicon Valley BostonSeattle NeilRubens,KaisaStill,JukkaHuhtamaki,MarthaG.Russell“BehindtheInnovationCurtain:MobilePlayersandTheir Moves.” Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.
    44. 44. /Users/neil/Dropbox/2010.06.InJo.IEN/timeline Dynamics
    45. 45. Distance Old New
    46. 46. The new maps may be based on the connections - rather than on distance.
    47. 47. Networks of Female and Male Executives in Companies in the Clean Tech Sector Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    48. 48. Networks of Female and Male Executives in Companies in the Biotech Sector Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    49. 49. Networks of Female and Male Executives in Companies in the Public Relations Sector Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    50. 50. Networks of Female and Male Executives in Companies in the Web Services Sector Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    51. 51. Context of Investments into/from China Insights into Innovation Social Network Analysis Socially Constructed Data Socially constructed dataset, in English, openly available– all challenges in China Innovation Ecosystems Dataset: •323 technology-based companies with one or more locations in China •42 Chinese, 77 foreign investment firm •Investment into China US$ 5.4 B •Investment originating from China US$ 3.1 B Insights explored: The flow of financial resources into and out of China More illustrative than descriptive/prescriptive results Innovation Ecosystem Network Example: Chinese International Investments Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell, A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal of Networks, Fall, 2010.
    52. 52. Initial Data Analysis: 53% (113) of the Chinese companies from eCIS business sector 50 % (66) of the foreign companies are from the eCIS business sector Toward Insights about: Patterns and differences in the characteristics of investment flows into and from China More Specific: Context of eCIS sector eCommerce and electronic security= eCommerce, software search, network hosting, mobile, games &video, enterprise Insights into Innovation Social Network Analysis Socially Constructed Data Innovation Ecosystem Network Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell, A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal of Networks, Fall, 2010.
    53. 53. Network Metrics
    54. 54. HARVEST Investments from Chinese (making investments) Innovation Ecosystem Network Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell, A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal of Networks, Fall, 2010.
    55. 55. CULTIVATION Investments into China (receiving investments) Innovation Ecosystem Network Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal of Networks, Fall, 2010.
    56. 56. Emerging Chinese business clusters linked by firms’ relationships Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell A Network Analysis of Investment Firms as Resource Routers in Chinese Innovation Ecosystem, Journal of Networks, Fall, 2010.
    57. 57. • Cultivation / Harvesting modes - value co- creation • Chinese interlocks at the investment firm level – Government-led investment firms – Knowledge of government guarantees – Investments in firms that return benefits to China • Global interlocks at both investment firm and enterprise levels • Opportunity network & value co-creation http://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg Topline Findings
    58. 58. http://www.flickr.com/photos/arena_provietnam/3544601667/sizes/m/in/photostream/
    59. 59. Executive Women in Technology-Based Companies • We asked: – What interventions can attract more women to leadership positions in technology-based companies? • Important because – National dependence on science, technology, math for competitiveness – Shortage of women entering, staying, leading – In tech-based companies, • roughly 8 % executives are women • Companies in our sample showed: – Corroborated recent EU reports – Cleantech sector lowest • Fewer women in: enterprise, semiconductor and mobile – Biotech sector highest • More women in public relations, legal, consulting
    60. 60. Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report Networks of Female Executives in Companies – All Sectors
    61. 61. Networks of Female and Male Executives in Companies in the Web Services Sector Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    62. 62. CTOs the CTO of doctr.com CTO at Hemarina Co-founder of HEMARINA Vice President Engineering at Survey Monkey Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    63. 63. R&D Execs Principal Research Scientist at Yahoo! Chief Software Editor at Yandex Advisor at PlaceBlogger Was a VP at Netscape and AOL, a senior director of Product Development at Yahoo Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    64. 64. Creative CMOs – Biz Dev Product Marketing Manager at Google Worked in Public Relations at Apple, Marketing at Tiny Pictures, VP, Marketing at Loopt Was VP Marketing at Project Playlist Director of User Experienceof Kosmix Was Principal UI Designer at Bebo Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    65. 65. CEOs CEO of NUS Enterprise at the National University of Singapore Was Managing Director, Investments, of Bio*One Capital Pte Ltd CEO of SeedCamp was part of the Venture team at 3i CEO of Piazzza worked at Facebook on their News Feed Team Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    66. 66. Board Members Co-Chair Disney Media Networks and President, Disney-ABC Television Group CEO of FON USA and president of the US Advisory Board Board of Directors of Posit Science, Sabrix, AccountNow, Danger, QuinStreet, The Threshold Group and the Board of Directors of the NVCA Advisor at Betwyx Was VP, Western Sales at Glam Media. Was VP of sales at MySapce Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    67. 67. Founders Founder/CEO SmartWork Network Co-founded Flickr Ran Yhoo Tech Dev group Co-founded Brickhouse Now product Officer at Hunch Founder of TinyMassive Formerly COO Wireless @ Realhome.com Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    68. 68. FoundersFounder of Google Webmaster Central Now works for Ignition Partners as an ‘entrepreneur in residence’ CEO and Founder of LiveHit Was Vice President of Products and Marketing at Piczo Co-founder at 23andMe Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    69. 69. Investors Investor at InnerRewards Was executive digital strategist at Johnson & Johnson Invested in Fluidinfo; The Extrodinaries; Factual; Vizu;Square; Vurve; Fluidinfo; ChallengePost; Airship Ventures; Joobili; Dopplr; Wee Web A Partner with Accel Partners Board at Wetpaint; Trulia; Kosmix; Jaser Design; Imperva; Forescout; Glam Media Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
    70. 70. Figure 1: Point-to-Point Figure 2: Tree Figure 3: Complex Tree Figure 4: Simple Star Figure 5: Complex Star Figure 6: Mesh Figure 7: Complex Mesh Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report Type of Networks in Technology-Based Companies Company Executives, Investors and Board Members
    71. 71. NETWORKS MATTER
    72. 72. http://fe-male.appspot.com/ In collaboration with: Under active development, algorithm may change without notice
    73. 73. http://www.slowtrav.com/blog/chiocciola/Geirangerfjord.jpg
    74. 74. The Norwegian Puzzle • Norway is a wealthy country with high standard of living and almost NO unemployment • Yet, low rate of technology-based innovation • What is the future of Norway after oil reserves have been extracted? • Given technology targets: – How can innovation be catalyzed? – How can establishing global relationships be accelerated?
    75. 75. Example: Norwegian Tech-based Companies Their Branch Offices and Their Financial Orgs Links show relationships PRELIMINARY Example view to IEN dataset in Gephi. Companies are selected with keyword search “Norway + Norwegian;” the funding organizations associated with those companies are added Nodes represent companies and their investors; edges indicate resource flows. The network layout is created with Yifan Hu Multilevel algorithm and nodes are inflated according to their indegree, i.e. the number of the connected investors.
    76. 76. Advisors & Investors Expand Access Investors leverage co-creation opportunities with investments in multiple companies. Intl companies not shown. Companies leverage value co-creation opportunities through relationships with multiple investors. Some investors are international. Timeline analysis of investment events reveals patterns of co-investment – an indication of intention to co-create value and, perhaps, stimulus programs. IEN Dataset, July 2010
    77. 77. International Relationships for Value Co-Creation Huge opportunities for international relationships lie 2 & 3 degrees out from Norwegian companies Example view to IEN dataset for keyword search. Nodes represent companies and their previous and current employees. The network layout is created with Fruchterman Reingold algorithm and nodes are inflated according to their outdegree. Protocols for anonymity are evolving. IEN Dataset, July 2010
    78. 78. Globalization of Innovation Ecosystems IEN Dataset, July 2010 Norwegian tech-based companies with financing are more likely to have networked relationships. Norwegian tech-based companies have access to global relationships through current board members, investors, and key personnel.
    79. 79. Dynamics IEN Dataset, July 2010 /Users/neil/Documents/neil/Research/Innovation_Ecosystems/proj/Norway/g1-norway/movie/n1
    80. 80. Insights About Norway • Dual offices: regional and Oslo • In sectors we studied • Business locations parallel technical university programs • Investor relationships have strong local links – Some investing organizations are governmental programs – Expands to Oslo when offices are in Oslo – International relationships linked to small set of personal relationships at executive level • International investors drawn through executive relationships – Relationships through execs at Google and AOL provide channels for global network expansion
    81. 81. FINLAND
    82. 82. Case Example: Funding for Finland xample view to IEN dataset in Gephi. Nodes represent companies and their investors; companies are selected with eyword search “Finland + Finnish”. The network layout is created with Yifan Hu Multilevel algorithm and nodes are flated according to their indegree, i.e. the number of the connected investors.
    83. 83. Case Example: Funding for Finland xample view to IEN dataset in NodeXL. Nodes represent companies and their investors; companies are selected with eyword search “Finland + Finnish”. Nodes are inflated according to their indegree, i.e. the number of investors of a ompany. Finnish Industry Investment is the main investor with outdegree 17 (betweenness centrality 1965). Degree distribution
    84. 84. Case Example: People & Tampere Example view to IEN dataset for keyword search “Tampere”. Nodes represent companies and their previous and current employees. The network layout is created with Fruchterman Reingold algorithm and nodes are inflated according to their outdegree.
    85. 85. University - Industry PRELIMINARY
    86. 86. Future Work: Untangling the Web, Looking for Patterns
    87. 87. http://4.bp.blogspot.com/_qFju91K89HM/SxRpABd1DTI/AAAAAAAABjw/6LaSJfjfk-I/s1600/Unexpected_Guests.jpg What do we know to ask? What can we learn that we don’t know to ask?

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