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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

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.

Transcript

  • 1. Innovation Ecosystems Network
    Stanford Media X led Research Initiative
    Using Social Media to Leverage Triple Helix Insights in Innovation Ecosystems
    Innovation Ecosystems Network
    Stanford Media X Led Research Initiative
    http://www.flickr.com/photos/angelinux/3034564360/
    Innovation Ecosystems Networkinitiative lead by
  • 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. 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. 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
    Innovation is Extreme Sport
    Innovation is Social
    Innovation takes at least two.Team skills are required.There are winners and loosers.
  • 5. IEN Transformation Framework Translate, measure and transform an innovation ecosystem
    Measure, Analyze & Visualize
    DATA  ANALYSIS  MEANING
    Value Co-Creation
    Impacts
    Shared Vision
    Transformation
    Events
    Coalitions & Networks
    Interaction & Feedback
  • 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
    • 7. Develop and implement programs (meetings, funding, initiatives) to foster co-creator networks
    • 8. Communicate complexity to co-create vision
    • 9. 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
  • 10. Models of innovationFrom organizations to single users to networked individuals
  • 11. Infrastructure for Resource Flows
    - - - Relationships
    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)
    (Companies are interlocked through key people – information flow, norms, mental models.(Davis,1996)
  • 12. 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
  • 13. Through personal relationships companies have interconnects
  • 14. Social Roles in Social Media
    • Answer person
    • 15. Outward ties to local isolates
    • 16. Relative absence of triangles
    • 17. Few intense ties
    • 18. Reply Magnet
    • 19. Ties from local isolates often inward only
    • 20. Sparse, few triangles
    • 21. Few intense ties
    • 22. Discussion person
    • 23. Ties from local isolates often inward only
    • 24. Dense, many triangles
    • 25. Numerous intense ties
    Journal of Social Structure “Visualizing the Signatures of Social Roles in Online Discussion Groups” http://www.cmu.edu/joss/content/articles/volume8/Welser/
  • 26. Network Structures
    Image source: Internet Pioneers: Paul Baran at ibiblio.org
  • 27. 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
  • 28. http://www.flickr.com/photos/manpsing/2618332693/
    http://www.fabcats.org/owners/feeding/info.html
    Passive Learning
    Active Learning
  • 29. Traditional Data Gathering Methods
    http://www.flickr.com/photos/tomatoskin/1339929731/
  • 30. Have to react QUICKLY
    http://www.flickr.com/photos/clydeorama/3495284608/
  • 31. Data to measure/track innovation?
    Analysis
    Mining
    Visualization
    News
    Organizations
    Social
    Media
    Federation
    WILLE Framework
    Active
    Intelligence
    Private
    Data
  • 32. IEN Dataset
    Updated quarterly with rapid growth each quarter
    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.
    Martha
  • 33. Making Relationships Visible
    WilleFramework, OPAALS, 2010.
  • 34. 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)
  • 35. Patterns & Outliers
    Trends & Innovation
    http://mathworld.wolfram.com/Outlier.html
    http://www.disrupt-this.com/2008/09/disruptive-conf.html
  • 36. http://www.flickr.com/photos/ritavitafinzi/2192500407/
  • 37. “There is no data like more data”
    (Mercer at Arden. House, 1985)
    “There is no data like more data”
    (Mercer at Arden. House, 1985)
    Tan, Steinbach, Kumar; 2004
    2,000 points
    500 Points
    8,000 points
  • 38. More Data /
    More Dimensions
    http://wissrech.ins.uni-bonn.de/research/projects/engel/engelpr2/pr2_thumb.jpg
    Could be easier to find patterns
    http://www.iro.umontreal.ca/~bengioy/yoshua_en/research_files/CurseDimensionality.jpg
  • 39. Easy, fast integration, using APIs and data sources to produce results that were not the original reason for producing the raw source data [wiki].
    Mashup
    25
    Google Maps
  • 40. Information Overload
    http://www.stat.columbia.edu/~cook/movabletype/archives/2006/04/a_wallful_of_da.html
  • 41. Place of innovationFrom localized to regional to virtual shared spacesFrom Places to Spaces (Etzkovitz & Ranga)
  • 42. .
    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.
  • 43. .
    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.
  • 44. .
    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.
  • 45. 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.
  • 46. .
    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.
  • 47. 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.
  • 48. 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.
  • 49. # 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.
  • 50. .
    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.
  • 51. .
    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.
  • 52. .
    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”
    Technical Report. Media X, Stanford University, Feb.2010.
  • 53. .
    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”
    Technical Report. Media X, Stanford University, Feb.2010.
  • 54. .
    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”
    Technical Report. Media X, Stanford University, Feb.2010.
  • 55. .
    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”
    Technical Report. Media X, Stanford University, Feb.2010.
  • 56. .
    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”
    Technical Report. Media X, Stanford University, Feb.2010.
  • 57. NYC
    Silicon Valley
    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.
    Boston
    Seattle
    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.
  • 58. Dynamics
    /Users/neil/Dropbox/2010.06.InJo.IEN/timeline
  • 59. Distance
    Old
    New
  • 60. The new maps may be based on the connections - rather than on distance.
  • 61. 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
  • 62. 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
  • 63. 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
  • 64. 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
  • 65.
  • 66. Example: Chinese International Investments
    Context of Investments into/from China
    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
    • 67. 42 Chinese, 77 foreign investment firm
    • 68. Investment into China US$ 5.4 B
    • 69. Investment originating from ChinaUS$ 3.1 B
    Insights explored:
    The flow of financial resources into and out of China
    More illustrative than descriptive/prescriptive results
    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.
    Innovation Ecosystem Network
  • 70. 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 sectoreCommerce and electronic security=eCommerce, software search, network hosting, mobile, games &video, enterprise
    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.
    Innovation Ecosystem Network
  • 71. Network Metrics
  • 72. HARVESTInvestments from Chinese (making 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.
    Innovation Ecosystem Network
  • 73. CULTIVATIONInvestments into China (receiving 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.
    Innovation Ecosystem Network
  • 74. 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.
  • 75.
    • Cultivation / Harvesting modes - value co-creation
    • 76. Chinese interlocks at the investment firm level
    • 77. Government-led investment firms
    • 78. Knowledge of government guarantees
    • 79. Investments in firms that return benefits to China
    • 80. Global interlocks at both investment firm and enterprise levels
    • 81. Opportunity network & value co-creation
    http://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg
    Topline Findings
  • 82. http://www.flickr.com/photos/arena_provietnam/3544601667/sizes/m/in/photostream/
  • 83. 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
  • 84. Networks of FemaleExecutives
    in Companies – All Sectors
    Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
  • 85. 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
  • 86. CTOs
    CTO at Hemarina
    Co-founder of HEMARINA
    Vice President Engineering at Survey Monkey
    the CTO of doctr.com
    Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
  • 87. R&D Execs
    Advisor at PlaceBlogger
    Was a VP at Netscape and AOL, a senior director of Product Development at Yahoo
    Principal Research Scientist at Yahoo!
    Chief Software Editor at Yandex
    Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
  • 88. 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 ExperienceofKosmix
    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
  • 89. CEOs
    CEO of NUS Enterprise at the National University of Singapore
    Was Managing Director, Investments, of Bio*One Capital Pte Ltd
    CEO of Piazzza
    worked at Facebook on their News Feed Team
    CEO of SeedCamp
    was part of the Venture team at 3i
    Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
  • 90. Board Members
    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
    Co-Chair Disney Media Networks and President, Disney-ABC Television Group
    Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
  • 91. Founders
    Founder/CEO SmartWork Network
    Founder of TinyMassive
    Formerly COO Wireless @ Realhome.com
    Co-founded Flickr
    Ran Yhoo Tech Dev group
    Co-founded Brickhouse
    Now product Officer at Hunch
    Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
  • 92. Founders
    CEO and Founder of LiveHit
    Was Vice President of Products and Marketing at Piczo
    Founder of Google Webmaster Central
    Now works for Ignition Partners as an ‘entrepreneur in residence’
    Co-founder at 23andMe
    Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report
  • 93. Investors
    Invested in Fluidinfo; The Extrodinaries; Factual; Vizu;Square; Vurve; Fluidinfo; ChallengePost; Airship Ventures; Joobili; Dopplr; Wee Web
    Investor at InnerRewards
    Was executive digital strategist at Johnson & Johnson
    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
  • 94. Type of Networks in Technology-Based Companies
    CompanyExecutives, Investors and Board Members
    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
  • 95. NETWORKS MATTER
  • 96. In collaboration with:
    http://fe-male.appspot.com/
    Under active development, algorithm may change without notice
  • 97. http://www.slowtrav.com/blog/chiocciola/Geirangerfjord.jpg
  • 98. 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?
  • 99.
  • 100. Example: Norwegian Tech-based CompaniesTheir Branch Offices and Their Financial Orgs
    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 YifanHu Multilevel algorithm and nodes are inflated according to their indegree, i.e. the number of the connected investors.
    Links show relationships
    PRELIMINARY
  • 101. 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.
    PRELIMINARY
    IEN Dataset, July 2010
  • 102. International Relationships for Value Co-Creation
    Huge opportunities for international relationships lie 2 & 3 degrees out from Norwegian companies
    PRELIMINARY
    IEN Dataset, July 2010
    Example view to IEN dataset for keyword search. Nodes represent companies and their previous and current employees. The network layout is created with FruchtermanReingold algorithm and nodes are inflated according to their outdegree. Protocols for anonymity are evolving.
  • 103. Globalization of Innovation Ecosystems
    PRELIMINARY
    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.
    IEN Dataset, July 2010
  • 104. Dynamics
    IEN Dataset, July 2010
    /Users/neil/Documents/neil/Research/Innovation_Ecosystems/proj/Norway/g1-norway/movie/n1/media
  • 105. 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
  • 106. FINLAND
  • 107. Case Example: Funding for Finland
    ILLUSTRATIVE
    Example view to IEN dataset in Gephi. Nodes represent companies and their investors; companies are selected with keyword search “Finland + Finnish”. The network layout is created with YifanHu Multilevel algorithm and nodes are inflated according to their indegree, i.e. the number of the connected investors.
  • 108. Case Example: Funding for Finland
    ILLUSTRATIVE
    Degree distribution
    Example view to IEN dataset in NodeXL. Nodes represent companies and their investors; companies are selected with keyword search “Finland + Finnish”. Nodes are inflated according to their indegree, i.e. the number of investors of a company. Finnish Industry Investment is the main investor with outdegree 17 (betweenness centrality 1965).
  • 109. Case Example: People & Tampere
    ILLUSTRATIVE
    Example view to IEN dataset for keyword search “Tampere”. Nodes represent companies and their previous and current employees. The network layout is created with FruchtermanReingold algorithm and nodes are inflated according to their outdegree.
  • 110. University - Industry
    PRELIMINARY
  • 111.
  • 112.
  • 113.
  • 114.
  • 115. Future Work:
    Untangling the Web, Looking for Patterns
  • 116. What do we know to ask?
    What can we learn that we don’t know to ask?
    http://4.bp.blogspot.com/_qFju91K89HM/SxRpABd1DTI/AAAAAAAABjw/6LaSJfjfk-I/s1600/Unexpected_Guests.jpg
    http://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg