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)
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Value Co-Creation in Innovation Ecosystems (Presentation @ Tokyo Institute of Technology)
- 1. Identifying Value Co-creation in Innovation Ecosystems Using Social Network AnalysisInnovation Ecosystems NetworkMartha G RussellAugust 5, 2010
- 6. 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
- 7. The Knowledge Revolution is here. What can we learn to improve our play?
- 9. Sr. Research Scholar, HSTAR Institute
- 10. Associate Director, Media X at Stanford University
- 11. Neil Rubens, PhD, neil@hrstc.org
- 12. Assistant Professor, Graduate School of Information Systems
- 13. University of Electro-Communications, Tokyo
- 16. Hypermedia Laboratory (HLab) of Tampere University of Technology (TUT).
- 17. Kaisa Still, PhD, kaisastill@yahoo.com
- 19. Beijing DT Electronic Technology Co., Ltd
- 21. Graduate student, Texas Advertising, UT Austin
- 23. Jiafeng (Camilla) Yu, camillayu@gmail.com
- 24. M.A. in Advertising in Planning Track
- 27. Accuracy
- 28. Timeliness
- 29. Comparability
- 30. AccessibilityEglisMilbergs, “Innovation Vital Signs: Framework Report and Update” June 2007.
- 32. Boundarylessorg
- 33. Global/local
- 35. Open leadership
- 38. “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
- 39. 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
- 40. News Organizations Social Media Federation WILLE Framework Active Intelligence Analysis Mining Visualization Private Data
- 41. . 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.
- 42. Models of Innovation From organizations to single users to networked individuals eClusters ?
- 43. The Place for Innovation From localized to regional to virtual shared spaces Innovation Acceleration Networks !
- 44. . 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.
- 45. # 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.
- 46. The Way We USED to Think About Organizations
- 47. The New Organizational Chart
- 48. Distance Old New
- 49. The new maps may be based on the connections - rather than on distance.
- 50. 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
- 51. 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
- 52. 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.
- 53. 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.
- 54. 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.
- 55. 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.
- 56. 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.
- 58. 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
- 60. 42 Chinese, 77 foreign investment firm
- 61. Investment into China US$ 5.4 B
- 62. Investment originating from China US$ 3.1 BInsights explored: The flow of financial resources into and out of China More illustrative than descriptive/prescriptive NodeXL, Tableau Innovation Ecosystem Network
- 63. 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
- 64. HARVESTInvestments from Chinese (making investments) Innovation Ecosystem Network
- 65. CULTIVATIONInvestments into China (receiving investments) Innovation Ecosystem Network
- 66. Network metrics Innovation Ecosystem Network
- 67. Emerging Chinese business clusters linked by investment firms Innovation Ecosystem Network
- 68. 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
- 69. 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
- 70. Innovation Ecosystems Network Regional Studies with Global Perspective China, Norway, Finland
- 71. . 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.
- 72. 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]
- 73. Discussion Data, Tools, Questions www.innovation-ecosystems.org Innovation Ecosystem Network
- 75. 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
Editor's Notes
- 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