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Value Co-Creation in Innovation Ecosystems (Presentation @ Tokyo Institute of Technology)
 

Value Co-Creation in Innovation Ecosystems (Presentation @ Tokyo Institute of Technology)

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Value Co-Creation in Innovation Ecosystems (Presentation @ Tokyo Institute of Technology)

Value Co-Creation in Innovation Ecosystems (Presentation @ Tokyo Institute of Technology)

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  • 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
  • So we try to get data from different source types.Social Media produces very current data, but may not always be as reliable (biased towards the public consensus)News data tends to be accurate but coverage is often limited (biased by authors views)Data from government organizations, is often of high quality, but takes years to produceWe then federate this data, and iterate between analysis and visualization
  • Now let me briefly describe a case of how we utilized the above mentioned principles.In our project we try to understand innovation, so have gathered the data on companies, people and money.What makes this data set different, besides its timeliness is the majority of data (thanks to social media) is about small companies having between 1 – 5 employees.A lot of innovation happens there so it is important to track.
  • This shows how the models of innovations have evolved reflecting the changes
  • This shows how we have evolved from the local/regional activities
  • We can also look at the companies by sector
  • 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
  • At the core of this research we have what initially were called “regional technology-based economic development”– however each of the three parts has experienced changes, which calls for updating the whole concept
  • It is rare that the data is simply brought to us on a silver platterWe have to try hard to actively acquire it
  • This map indicates the location of the companies. Size of circle indicates number of companies.For this part of analysis we have used Tableau Software.
  • -------------------------http://www.bbsservicesinc.com/sitebuildercontent/sitebuilderpictures/world-map.gifPartners: Government agenciesEducational institutionsSME’s Services & consultanciesVenture groupsLarge organizations   Data points:PatentsLicensesJobsPublicationsCitationsResource flows – investments, sales, valuations-----------------ChinaJapan – JSTNYC – NYC MediaLabAustin – MCC, SematechMpls/St.P – Finland – TEKKES, FINNODEAbuDhabi

Value Co-Creation in Innovation Ecosystems (Presentation @ Tokyo Institute of Technology) Value Co-Creation in Innovation Ecosystems (Presentation @ Tokyo Institute of Technology) Presentation Transcript

  • Identifying Value Co-creation in Innovation Ecosystems Using Social Network AnalysisInnovation Ecosystems NetworkMartha G RussellAugust 5, 2010
  • 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
    Innovation is Social
  • The Knowledge Revolution is here.
    What can we learn to improve our play?
  • http://www.innovation-ecosystems.org
    Innovation Ecosystems Network
    • Martha G Russell, PhD, martha.russell@stanford.edu
    • Sr. Research Scholar, HSTAR Institute
    • Associate Director, Media X at Stanford University
    • Neil Rubens, PhD, neil@hrstc.org
    • Assistant Professor, Graduate School of Information Systems
    • University of Electro-Communications, Tokyo
    • Jukka Huhtamäki, jukka.huhtamaki@tut.fi
    • Researcher, Lecturer
    • Hypermedia Laboratory (HLab) of Tampere University of Technology (TUT).
    • Kaisa Still, PhD, kaisastill@yahoo.com
    • Knowledge Management Specialist
    • Beijing DT Electronic Technology Co., Ltd
    • Mario Gastel, mariogastel@zeelandnet.nl
    • Graduate student, Texas Advertising, UT Austin
    • Fulbright Scholar (2009-11)
    • Jiafeng (Camilla) Yu, camillayu@gmail.com
    • M.A. in Advertising in Planning Track
    • The University of Texas at Austin
  • Innovation Ecosystems Network
    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.
    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.
    Optimizing the impact of investments made by stimulus programs and public and private stakeholders is a quest shared by developers around the world.
    A clear understanding of how to invest local resources for global participation that will accrue benefits to the local area has yet to be fully articulated, and metrics to measure interim progress are greatly needed. IEN aims to fill this void.
  • ImplicationsInnovation Vital Signs
    Utility of Indicator
    Significance
    Policy Relevance
    Clarity
    Acceptance
    • Quality of indicator
    • Accuracy
    • Timeliness
    • Comparability
    • Accessibility
    EglisMilbergs, “Innovation Vital Signs: Framework Report and Update” June 2007.
  • Context and Consequence
    Changing context
    Rampant uncertainty
    Transparency
    Co-opetition
    Atomization
    Flattened hierarchies
    Self-organizing systems
    • Consequence
    • Boundarylessorg
    • Global/local
    • Self-organizing systems
    • Open leadership
    • The power of pull
  • The Power of PullJohn Hagel, John Seely Brown, Lang Davison
    Sources of economic value creation have shifted to flows of knowledge and insights
    Pull creates platforms that enable responses to situations when they arise
    The network effect, the learning rate
    Just-in-time information
    Trust-based relationships
    CSR enhances the power of pull
  • http://www.flickr.com/photos/ritavitafinzi/2192500407/
  • “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
  • Higher Dimensions: Double Edged Sword
    More Data is Need
    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
  • News
    Organizations
    Social
    Media
    Federation
    WILLE Framework
    Active
    Intelligence
    Analysis
    Mining
    Visualization
    Private
    Data
  • .
    Innovation Ecosystems
    Dataset
    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.
  • Models of Innovation
    From organizations to single users to networked individuals
    eClusters
    ?
  • The Place for Innovation
    From localized to regional to virtual shared spaces
    Innovation Acceleration
    Networks
    !
  • .
    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.
  • # 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.
  • The Way We USED to Think About Organizations
  • The New Organizational Chart
  • Distance
    Old
    New
  • The new maps may be based on the connections - rather than on distance.
  • Need for Updating
    Regional technology-based economic development
    “The global map of businesses is increasingly dominated by geographically concentrated groups of companies and related economic actors and institutions”
    The Use of Data and Analysis as a tool for cluster policy, Green Paper on international best practices and perspectives prepared for the European Commission, November 2008
    “Members of a cluster can be sometimes located worldwide, but linked through information and communication technologies… the term e-cluster is used”
    Danese, Filippini, Romano, Vinelli 2009
    “Technological trends are causing a change in the way innovation gets done in advanced market economies”Baldwin & von Hippel November 2009, Harvard Business School Working Paper 10-038
    “Recognizing that a capacity to innovate and commercialize new high-technology products is increasingly a part of the international competition for economic leadership, governments around the world are taking active steps to strengthen their national innovation systems”Understanding Research, Science and Technology Parks: Global Best Practices, National Research Council of the National Academies, Report 2009
  • 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
    http://fusionenterprises.ca/Business_Training.php
  • CleanTech
    Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell ,
    “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
  • BioTech
    Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell ,
    “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
  • PR
    Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell ,
    “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
  • Web
    Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell ,
    “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
  • Roles
    CTOs
    Investors
    CMOs
    Founders
    Kaisa Still, Neil Rubens, JukkaHuhtamäki, and Martha G. Russell ,
    “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.
  • Question?
    • What interlock patterns characterize investments into technology-based companies being made by Chinese investment firms?
    • How are these patterns similar or different to those made by the rest of the world into China?
    http://4.bp.blogspot.com/_qFju91K89HM/SxRpABd1DTI/AAAAAAAABjw/6LaSJfjfk-I/s1600/Unexpected_Guests.jpg
    http://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg
  • Context of Investments into/out of China
    Socially constructed dataset
    in English language, openly available info
    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
    NodeXL, Tableau
    Innovation Ecosystem Network
  • 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
    Innovation Ecosystem Network
  • HARVESTInvestments from Chinese (making investments)
    Innovation Ecosystem Network
  • CULTIVATIONInvestments into China (receiving investments)
    Innovation Ecosystem Network
  • Network metrics
    Innovation Ecosystem Network
  • Emerging Chinese business clusters linked by investment firms
    Innovation Ecosystem Network
  • Topline Findings
    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
  • http://www.flickr.com/photos/manpsing/2618332693/
    http://www.fabcats.org/owners/feeding/info.html
    Passive Learning
    Active Learning
    FURTHER RESEARCH
    Personal relationships/opportunity networks
    Time series analysis
    Expansion of data
    Chinese language press releases
    Chinese business registries
  • Innovation Ecosystems Network Regional Studies with Global Perspective
    China, Norway, Finland
  • .
    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.
  • INNOVATION ECOSYSTEMS INITIATIVE
    Applied Research Initiative on Data-driven Visualization of Innovation Ecosystems for Local and Global Innovation Accelerators
    Neil Rubens, neil@hrstc.org
    Jukka Huhtamäki, jukka.huhtamaki@tut.fi
    Kaisa Still, kaisastill@yahoo.com
    Martha Russell, martha.russell@stanford.edu
    Data and Analysis
    Hypothesis
    Formation of alliances is a catalyst for success.
    Success factors can be identified.
    Analyze & compare intl alliance formation across different countries and their effects.
    [USA, China, Japan, Finland, etc.]
    Federated datasets of companies, people, resource flows, and deals.
    Network analysis, pattern recognition, and stakeholder interviews.
    Data partners, analysis partners, and community-of-practice partners.
    Information dissemination FTF and virtual.
    Goal
    Established initiatives
    New initiatives
    [Deighton, Quelch, 2009]
    1990
    2000
    1980
    government
    industry
    academia
    Triple Helix
    [Russell 2008]
    [Smith, Powell, 2004]
    [Tekes]
  • Discussion
    Data, Tools, Questions
    www.innovation-ecosystems.org
    Innovation Ecosystem Network
  • Horizons
    Truth, trust and privacy in online communications
    Collaboration at scale – esp virtual worlds and video streaming
    Experimentation and big data – visualization for shared meaning and decision environments
    Imagining anything as a service – personalization and persuasion
    Innovating from the bottom of the pyramid
    Making the network the organization – the communication element of social media