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Identifying Value Co-creation in Innovation Ecosystems Using Social Network Analysis

Innovation Ecosystems Network,Martha G Russell, Neil Rubens

August 2, 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?

• 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

http://www.innovation-ecosystems.orgInnovation Ecosystems Network

• 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

http://www.flickr.com/photos/ritavitafinzi/2192500407/

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)

http://www.iro.umontreal.ca/~bengioy/yoshua_en/research_files/CurseDimensionality.jpg

Could be easier to find patterns

More Data is Need

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Higher Dimensions: Double Edged Sword

Innovation Ecosystems NetworkInnovation 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.

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 EcosystemsDataset

# 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.

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

From organizations to single users to networked individuals

?eClusters

The Place for 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

From localized to regional to virtual shared spaces

?Innovation Acceleration

Networks

.

Number of US Technology-based companiesBy 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.

Need for Updating

“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

Regional technology-based economic development

Distance

Old

New

The New Organizational Chart

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

The new maps may be based on the connections - rather than on distance.

Kaisa Still, Neil Rubens, Jukka Huhtamäki, and Martha G. Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.

CleanTech

Kaisa Still, Neil Rubens, Jukka Huhtamäki, 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, Jukka Huhtamäki, 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, Jukka Huhtamäki, 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, Jukka Huhtamäki, and Martha G. Russell , “Networks of Executive Women in Technology-Based Innovation Ecosystems,” Technical Report , Media X, Stanford University, May.2010.

CTOs

CMOs

Investors

Founders

Roles

http://4.bp.blogspot.com/_qFju91K89HM/SxRpABd1DTI/AAAAAAAABjw/6LaSJfjfk-I/s1600/Unexpected_Guests.jpg

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?

Context of Investments in/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 ChinaUS$ 3.1 B

Insights explored:

The flow of financial resources into and out of China

More illustrative than descriptive/prescriptive results

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

Insights into Innovation

Social Network Analysis

Socially

Constructed

Data

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

• 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-creationhttp://successbeginstoday.org/wordpress/wp-content/unexpected2.jpg

Topline Findings

http://www.fabcats.org/owners/feeding/info.html http://www.flickr.com/photos/manpsing/2618332693/

Passive Learning Active LearningFURTHER 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.

Discussion

Data, Tools, Questionswww.innovation-ecosystems.org

Innovation Ecosystem Network

Insights into Innovation

Social Network Analysis

Socially

Constructed

Data