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

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

# of Companies
# of People

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

PR

Web

Roles
CTOs
Investors
CMOs
Founders

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

HARVESTInvestments from Chinese (making investments)

CULTIVATIONInvestments into China (receiving investments)

Network metrics

Emerging Chinese business clusters linked by investment firms

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

.

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

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

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

by Neil Rubens

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