Using socially constructed data, parsed from data retrieved from online English-language press releases, network analysis shows patterns of organizational infrastructure. The cultivation approach to global investments into Chinese technology-based companies is contrasted with the harvesting approach of Chinese investments into the rest of the world. Critical implications for board interlocks and flows of information are discussed. Research conducted at Media X at Stanford University, by Martha G. Russell, Neil Rubens, Kaisa Still, Jukka Huhtamaki. Presented at Hong Kong University of Science and Technology, August 2, 2010.
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1. Identifying Value Co-creation in Innovation Ecosystems Using Social Network AnalysisInnovation Ecosystems Network,Martha G Russell, Neil RubensAugust 2, 2010
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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
26. “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
27. 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
28. 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.
29. . 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.
30. # 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.
31. Models of Innovation From organizations to single users to networked individuals eClusters ?
32. The Place for Innovation From localized to regional to virtual shared spaces Innovation Acceleration Networks ?
33. . 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.
34. 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
37. 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
38. The new maps may be based on the connections - rather than on distance.
39. 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.
40. 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.
41. 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.
42. 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.
43. 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.
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45. 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
49. Investment originating from ChinaUS$ 3.1 BInsights explored: The flow of financial resources into and out of China More illustrative than descriptive/prescriptive results NodeXL, Tableau Innovation Ecosystem Network
50. 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
55. 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. . 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.
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
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
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
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
My name is Neil Rubens, I am not a journalist; I am a data miner – but I think in essense it is not so different.
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.
