HBS - MIT User and Open Innovation Workshop 2008 August 4-6,
2008 Harvard Business School, Boston, MA Short Presentation Slides
Track 1 2 3 4 5 6 7 8 9 10 11 Title Lead User and User Innovation
Policy and User Entrepreneurship Communities Communities Open
Innovation Intellectual Property Open Source Lead User and User
Innovation Open Innovation Intellectual Property Communities &
Open Source Research Update 1 Research Update 2 Research Update 3
Research Update 4 List of Attendees Page 2 43 74 99 128 153 183 213
251 295 326 372 398 423 461 493
Track 1: Lead User and User Innovation (Hawes 101) Monday Aug. 4
2:00 - 3:30 "Learning at the boundary of the firm: What Happens
between Learning-by-Doing and Learning-by-Using" (Sung Joo Bae,
MIT) "User Innovation in the Medical Device Industry" (Aaron
Chatterji, Duke University) "User Innovation: Incidence and
Transfer to Producers" (Jereon de Jong, Erasmus University)
"Founder identity and variation in opportunity recognition &
exploitation" (Emmanuelle Fauchart, University of Lausanne)
"Harnessing "lead user" Innovation : From Collaborative User
Communities to Mass Market" (Salah Hassan, George Washington
University) "The Dynamics of User Innovation - Drivers and
Impediments" (Christina Raasch, Hamburg University of
Technology)
Learning at the boundary of the firm Sung Joo Bae MIT
[email protected]
Language difference as the consequence of learningManufacturer
SideManufacturer Learning
User SideUser Learning
Language A
Language B
Learning in Joint Product Development Projects
The influence of language difference on joint product
developmentManufacturer Learning User Learning
Language A
Language B
Learning in Joint Product Development Projects
Empirical Evidence Field study with a Canadian manufacturer of
custom-made enclosures Joint product development between users and
the manufacturer
Main customers Research labs and new product development teams
& low-volume manufacturers E.g. Boeing, IBM, three divisions of
NASA, UCLA, Stanford University and MIT, etc.
Interviews at the manufacturing site (Sales support, Tech
support, etc.) Interviews with representative users Analysis of
archival data Customer Relationship Management (CRM) & Order
Management System (OMS) 899 projects 8400+ emails and call logs
(5%)
Joint Development ProjectsType of InteractionInitial Contacts
Orders 2.74 hrs Design Iteration CAD Drawing Confirmation
Avg. 82 hrs
ManufacturingShipping 185 hrs
Templating N = 899 projects
Duration
Communication Pattern700 642
Number of Communication Instances
600
500
400
396 368
300
Frequency
200
104 100
0 Initial contacts prior to official start of projects Design
iteration Manufacturing After the delivery of the product
Project Phases
n = 1510 74 call logs (5%)
Local learning Language acquisitionCustomer RepresentativeAt
06:07 AM 4/26/2005, you wrote: Dear Sam, We have received your
order by phone for the following part: 1 x 1U 19" rackmount,
consisting of front panel, rear panel, chassis, cover and 2 hub
mounts, 11 gage plain steel front panel, 18 gage plain steel for
remaining parts, powder coated matte black You will receive an
e-drawing of your order within 24 hours for your approval. Your
Order Number is: A042605001. Once approved your order will enter
production. Best Regards, Paul Simon Business Development Protocase
Inc. Ph 1-866-849-xxxx Fax (902) 567-xxxx Email:
[email protected] www.protocase.com
User
what is the e-drawing?... and how will it differ from what i
sent you?... e-drawing required because of the material thickness
change? sean
Customer Representative
Hello Sam, The e-drawing is a 3d model of your rackmount. The
e-drawing will give you the opportunity to evaluate your part
before we take it into production. You can view the e-drawing with
your regular Internet browser. Feel free to give me a call or send
me an e-mail if you have another question.
Best Regards, Simon
Content AnalysisCommunication Pattern during the Joint
Development Projects500 450 400
Number of Cases
350 300 250 200 150 100 50 0 Initial contacts prior to official
start of projects Design iteration Manufacturing After the delivery
of the product PRICE DESIGN SHIPPING LANGUAGE COMMUNICATION
FEEDBACK
Project Phases
The Role of Physician Innovation, Collaboration and
Entrepreneurship in the Medical Device Industry
Aaron K. Chatterji Kira FabrizioDuke University Fuqua School of
Business
This work is part of a research agenda on the knowledge sources
for innovation and entrepreneurship
Dissertation work
Spawning Several cases of doctors inventing devices and starting
companies
How important is user innovation in the medical device
industry?
Extent and impact (under review) Conflicts of interest (under
review)
How do corporations access and exploit user knowledge?
Exploration vs. Exploitation (preliminary results)
Under what conditions do user innovators start their own
firms?
Networks, geography, specialty
How did the Fogarty catheter become the industry standard in
medicine?
Who is Thomas Fogarty? Physician Professor of Surgery at
Stanford Medical School Inventor Over 70 surgical patents
Entrepreneur Founded over 30 companies Revolutionized minimally
invasive surgery and helped 15 million patients Catheter is now
marketed by Edwards Lifesciences
We investigate the nature of user innovation in the medical
device industry
What is the extent of user innovation in the medical device
industry?How, if at all, are user innovations different from
industry innovations?
What are the implications of this result for understanding the
trajectory of medical device innovation? How do firms collaborate
with users to access valuable knowledge?
We match 2 sources of data to create the unique dataset used for
this paper
Secondary data
NBER Patent Data AMA database-2006 Snapshot
Demographic and workplace data on all (currently 819,443)
licensed physicians (e.g. practice type, specialty, location,
history of state licenses, school, year graduation, group vs. solo
practice) Match names to patent database to identify innovations
patented by doctors. Use data to know whether they are in practice
or work at companies
Plans for a potential survey of physician inventors
Of the over 26,000 patents filed for medical devices between
1990-1996, over 5,000 were filed by physiciansTable 1 Sample
Summary Statistics: Means and Test for Difference of Means N =
26,158 (full sample); 5053 (doctor); 21005 (non-doctor) Variable #
claims # Nonpatent Cites # Cites Made # Industry Cites Made # Cites
Recd # Industry Cites Recd Generality # Distant Cites Recd Full
Sample 17.11 2.71 16.68 10.41 13.16 10.88 0.39 5.35 Doctor
Inventions 17.71 3.84 15.70 9.12 15.23 12.55 0.41 6.46 Non-Doctor
Inventions 16.97 2.44 16.91 10.72 12.66 10.47 0.39 5.09 Difference
(Doc-NonDoc) 0.74** 1.40** -1.22** -1.61** 2.57** 2.07** 0.02**
1.37**
We also have some preliminary insights into the various
motivations driving manufacturer-user collaborations
The largest, least research intensive device firms appear to be
working with doctors for purposes of exploitation
Patent based measure using repeat and self citations
Smaller, more research intensive firms appear to be working with
doctors for purposes of exploration
Patent based measure using new citations
More results to come....
Thank You!
User Innovation:Incidence and Transfer to Producers
Jeroen de JongRSM Erasmus University & EIM Business and
Policy Research The Netherlands
Eric von HippelMIT Sloan School of Management
August 4 2008
Industrial products Printed Circuit CAD Urban and von Hippel
(1988) Pipe Hanger Hardware Herstatt and von Hippel (1992) Library
IT Systems Morrison et al (2000) Software security features Franke
and von Hippel (2003) Surgical Equipment Lthje (2003) Consumer
products Outdoor Products Lthje (2004) Extreme sports equipment
Franke and Shah (2003) Mountain biking equipment Lthje et al
(2002)Source: Von Hippel (2005, p. 20)
n 136 74 102 131
% innovating 24.3% 36% 26% 19.1%
261 n 153 197 291
22% % innovating 9.8% 37.8% 19.2%
Research objectivesIncidence of user innovation in broad
surveys?Develop indicators Apply to a broad sample of SMEs
Comparison with traditional innovation indicators? Transfer to
producer firms? Exploratory study drawing on two surveysBroad
survey of 2 416 SMEs in the Netherlands Detailed survey of 498
technology-based SMEs
ConclusionsUser innovation is out there21% of all SMEs
Everywhere, no just manufacturing
Current innovation indicators record only part of it User
innovations spill overMost user innovators do not protect their
innovations25% of user innovators are aware of adopting producer
firms Compensation is none or at best informal
This implies that...Current innovation surveys (CIS) can be
improved, i.e. should be further detailed It is legitimate to
develop policies for user innovation
Founder identity and variation in opportunity recognition &
exploitationE. Fauchart, M. Gruber, S. ShahAugust 2008 HBS-MIT user
innovation workshop
Entrepreneurship / Prior literature Why individuals recognize
different opportunities and exploit them differently? Literature
says: prior knowledge, social networks and cognitive aptitudes We
add another factor: the entrepreneurs identity
Identity theory We draw upon identity theory to frame our
argument that the motives and sentiments (Turner) of a firm founder
affects the opportunity he recognizes and the early strategic
decisions he makes to exploit it If an identity is salient for a
given role, it affects behaviors / actions Individuals undertake
actions that are consistent with their motives and sentiments
Founder identities From our interviews we were able to extract 4
dimensions along which there was great variance regarding the
interviewees motives and sentiments for starting a firm in their
field And we derived two extreme salient identities: - business
oriented identity - community oriented identity
Founder identity affects entrepreneurial actions Founders with
different identities differ systematically along : - the type of
opportunity they recognize / what they perceive is worth bringing
to the market, to whom and how - the early strategic decisions they
make to exploit that opportunity (IP policy, marketing)
Implications Better understanding of the factors shaping
opportunity recognition and exploitation / sources of variance
among firms Contributions of different types of entrepreneurs to
industry development & consumer welfare Opens numerous research
questions
Harnessing "lead user" Innovation: From Collaborative User
Communities to Mass Market (Brief Presentation)Salah S. Hassan,
Ph.D. Chair & Professor of Marketing GW School of Business The
George Washington University E-mail: [email protected] User and Open
Innovation Workshop August 4-6, 2008 Harvard Business School
RESEARCH MOTIVATIONS & OBJECTIVES
The high failure rates of substantial number of innovations in
the marketplace.Consequently a better understanding of the factors
influencing innovation diffusion is becoming a top priority for
marketing researchers and managers, particularly those in high-tech
firms. The objectives of this paper are:1) to evaluate the
influence of lead users and opinion leaders on accelerating the
diffusion rate, 2) to evaluate the degree of fit between the
perceived innovation attributes of lead users, lead users with
opinion leadership qualities and that of the perceived innovation
attributes of non-lead users, and 3) to report on research
findings/ testing hypotheses that would provide directions for
future research.
AN INTEGRATIVE RESEARCH MODEL
The proposed research model posits that both lead users and
opinion leaders affect the evaluation of innovation attributes,
which subsequently affect the rate of innovation diffusion.
AN INTEGRATIVE RESEARCH MODELLead Users Characteristics Need
Dissatisfaction w/ existing products Value/ benefit seekers
Capabilities Motivation Experience P 3a, b P 4a, b Opinion Leaders
Characteristics Knowledge Social Influence Community Active
Innovativeness Information Sharing Creativity Perception of the
Innovations Current Attributes P 1a, b Relative Advantage
Compatibility Complexity Trialability Observability Usability
Communicability
Diffusion RateIntent to PurchaseIntent to Communicate WOM
Ideal Innovations Expected Attributes Relative Advantage
Compatibility Complexity Trialability Observability Usability
Communicability
P 2a, b
Control VariablesSocio-Economic, Demographic, and Marketing Mix
VariablesCopyright 2007, Salah S. Hassan, Ph.D. All rights
reserved
Operationalization of the Research ModelLead Users
Characteristics Need Dissatisfaction w/ existing products Value/
benefit seekers Capabilities Motivation Experience
1st Stage
Participation in a TIC*Opinion Leaders Characteristics Knowledge
Social Influence Community Active Innovativeness Information
Sharing Creativity
Radical Ideas / Ideal Innovation
Control VariablesSocio-Economic, Demographic, and Marketing Mix
Variables
* TIC, Tookit for Idea Competition, see Piller and Walcher,
2006Copyright 2007, Salah S. Hassan, Ph.D. All rights reserved
Clustering Ideas
Using Experts the original 34 ideas where clustered into a
finished product form to allow for a better evaluation/adoption
measure. Agreement between expert was high (ICC) on the
clustering.
34 most innovative
8 clustered Laptop
76 ideas collectedExpert Panel Ideas above the mean
Operationalization of the Research ModelLead Users
Characteristics Need Dissatisfaction w/ existing products Value/
benefit seekers Capabilities Motivation Experience
2nd stage
TIC ideas
Opinion Leaders Characteristics Knowledge Social Influence
Community Active Innovativeness Information Sharing Creativity
Evaluation of Existing versus Ideal Innovation
Intent to Purchase Intent to Communicate WOM H3 and H4
H1 and H2Control Variables
Socio-Economic, Demographic, and Marketing Mix Variables
* TIC, Tookit for Idea Competition, see Piller and Walcher,
2006Copyright 2007, Salah S. Hassan, Ph.D. All rights reserved
Innovation Adopters & Diffusion PatternsLead UsersFrom
Collaborative User communities To Mass Market
% of Adopters
Opinion Leaders
Bell-shaped Frequency curve
0
_ x - 2sd
_ x - sd
_ x
_ x + sd
Time
THANK YOU!Salah S. Hassan, Ph.D. Chair & Professor of
Marketing School of Business The George Washington University
E-mail: [email protected]
Cornelius Herstatt, Christina Raasch
Hamburg University of Technology
The dynamics of user innovation Drivers and impedimentsUser and
Open Innovation Workshop
HBS-MIT, Boston, August 4th 6th, 2008
The Flying Dinghy ProjectStudy focus How does the level of user
innovation activity evolve over time? What drivers and impediments
affect activity levels?
Methodology Longitudinal case study based on secondary data,
in-depth interviews, and survey
Research field International Moth sailboat Characteristics:
Development class of performance sailboats with high innovation
activity historically driven by users
Source: C. Herstatt, C. Raasch
- 1-
User innovation activity in the Moth class does not wane
Cyclical pattern in the pace of design progressPhase 1 Phase 2
Phase 3
10 9
Age of winning design in years
8 7 6 5 4 3 2 1 0 1955 1960 1965 1970 1975 1980 1985 1990 1995
2000 2005 Year of championship
: International championships (world or European) : National
championships (Australian or UK)
Co-existence of standardisation and user innovation activities
at any point in time No evidence of users being supplanted
permanently by manufacturersSource: C. Herstatt, C. Raasch
- 2-
Instead, users consecutively open up new design spaces
Focus of activity
Hull
Foils
Rigging
1960
1970 Proliferation of glass fibre reinforced plastic, later
carbon
1980
1990
2000 Decreasing benefits to incremental improvements
2010 ?
User activity declined due to
Standardisation rules
Manufacturer forcing one-design
Drivers/ User satisfaction impediments of user Technology
complexity innovation activity Barriers to user innovation
Market structure
Technology maturitySource: C. Herstatt, C. Raasch
- 3-
Implications for dynamically expandable design spaces
Technology maturityHigh
In our case study we find A re-focusing of user activity after
exogenous or endogenous changes in the innovation environment No
mining-out of the entire design spaceInnovation barriersHigh
Technology complexityHigh
Market concentrationHigh
User Innovation ActivityCustomer satisfactionHigh
High
Low
This suggests that, given a supportive environment, users may
not withdraw, but simply move on!
Source: C. Herstatt, C. Raasch
- 4-
For further information
Please attend our session:Track 3, Hawes Hall 201, today, 2-3.30
p.m.
Please refer toRaasch, C., Herstatt, C. (2008) The dynamics of
user innovation: Drivers and impediments of innovation activities,
International Journal of Innovation Management, forthcoming
THANKS!
- 5-
Track 2: Policy & User Entrepreneurship (Hawes 102) Monday
Aug. 4 2:00 - 3:30 "Conditions under which collaborative user
innovation dominates producer innovation" (Carliss Baldwin, Harvard
Business School) "Drawing User Innovation into Policy: The UK
Experience" (Steve Flowers, University of Brighton) "The Accidental
Entrenpreur: The Emergent and Collective Process of User
Entrenpreneurship" (Mary Tripsas, Harvard Business School)
Corporate Venture Capital and User Entreprenuership in Medical
Device Industry (Sheryl Winston Smith, Temple University)
"Professional-User Innovation Commercialization and
Entrepreneurship" (Jennifer Woolley, Santa Clara University)
Where Will Op en Develop m ent Com m u nities Prevail?Carliss Y.
Baldwin Eric von Hippel HBS-MIT User and Open Innovation Workshop
Boston, MA August 8, 2008
Background For
a long time (1750-1990) it appeared to most people that producer
innovation was the only economic way to realize large, complex
designs Free, open innovation driven by collaborative users is a
newly important way to realize large, complex designs Is this a
contest? Who will win?
Slide 2
Carliss Y. Baldwin and Eric von Hippel 2008
Answers Is
it a contest? No, in the large Yes, in the small Who will win?
It depends (this is a contingent theory) On what? The technological
profile of an artifact At a given time Within a given
institution
Slide 3
Carliss Y. Baldwin and Eric von Hippel 2008
Who wins for different combinations of design cost and
communication costB A A B C D E F G H I No Innovation Singleton
User Innovation Only Producer Innovation Only SUI and Producer
Innovation Coexist SUI OR Producer Innovation Collaborative User
Innovation Only CUI and Producer Innovation Coexist CUI and SUI
Coexist All Three Forms Coexist
Communication cost, b
E D C G I H F
Design cost, dSlide 4 Carliss Y. Baldwin and Eric von Hippel
2008
This demonstrates the limits of modelingCome to our session to
see what we plan to do instead!
User Innovation in the UK The New InventorsWorking to change the
linear view of innovation
Steve Flowers CENTRIM University of BrightonCENTRIM/SPRU
Overview Inform academic & policy community Linear model
hangover
Explore user innovation in UK context Case studies Metrics and
indicators Questions: value, measurement, relevance,
significanceetcCENTRIM/SPRU
Policy recommendationsRe-frame regulation to promote user-led
innovation
Establish a User Innovation ForumExtend R&D tax
creditsCENTRIM/SPRU
The New Inventors The New InventorsHow users are changing the
rules of innovation
Steve Flowers CENTRIM University of BrightonCENTRIM/SPRU
MY RESEARCH ON INNOVATION AND USERS: THE 5-MINUTE VERSION
Mary Tripsas Harvard Business School
Customer Preference Discontinuities: A Trigger for Radical
Technological Change (Managerial and Decision Economics, 2008) What
drives the timing of technological discontinuities in an industry?
Existing research: limits of old technology, technological progress
driven by firms This paper: Users!!
Preference Discontinuities -- radical changes in what users
value make radical technology from other industries relevant
Analog Phototypesetter Machine Speed, 1949-1982100.00
80.00
60.00
cps40.00 20.00 0.00 1945 1950 1955 1960 1965 1970 1975 1980
Next-generation CRT machine introduced (1965)
User entrepreneurs were the first to introduce new technology to
the industry Photon (first electro-mechanical analog
phototypesetter) We were asked to publish a French patent gazette
in the most economical manner. Mr. Higonnet was told that in order
to prepare a plate for offset printing it was necessary to cast
lines of type, lock them in chases, set them up on the press and
then produce only one good repro proofhis reaction was immediate:
there should be a market for a photographic type composing machine.
Photon inventor
Alphanumeric (first CRT phototypesetter) Alphanumerics potential
market is the portion of the $1.5 billion typesetting market that
produces non-creative and repetitive information for printing and
publishing. It is anticipated that this unit connected to a general
purpose computer will provide the necessary hardware for the
company to initiate a photocomposition service. 1964 offering
brochure
The Accidental Entrepreneur: The Emergent and Collective Process
of User Entrepreneurship (Strategic Entrep Journal, 2007 with S.
Shah) Where do firm founders come from? Existing research:
spin-offs from existing manufacturers, university-based technology
transfer This paper: users!
84% of juvenile products firms founded from 1980-2007 (and alive
in 2007) were founded by users Process was often accidental
innovated for own use, others saw product and requested it,
demonstrating demand Collective members of user communities
provided feedback and improvement ideas
Thinking about Technology: applying a cognitive lens to
technical change Research Policy, 2008 (with S. Kaplan) When/ why
do users innovate? Existing research: economic incentives This
paper: different cognitive framing enables users to view problems
in a fundamentally different way.
Innovation, corporate venture capital, and entrepreneurial
clinicians:Returns to CVC investment in the medical device
industrySheryl Winston Smith, Ph.D.Fox School of Business Temple
University HBS-MIT User and Open Innovation Workshop
Intro
Methods
Model & Data
Results
Conclusions
Extra
Motivation
Why do firms make direct equity investment in entrepreneurial
companies?
Some possibilities:Harvest external ideas and capabilities
Synergy Strategic goals Financial returns
Mutually exclusive? Time horizon?
collaborative ecosystem to invests in companies with innovation,
driven As a strategic investor, JJDChelp accelerate the pace
oftechnologies that by The whole idea hybrid internal and external
research model to identify, was Leveragingaddressto get aunmet
medical needs. JJDC will seek to maximize its ever-advancing
customer needs (IBM VP corporate strategy, group potentially our is
major pulse of the industry.the investmentClaudia Fan the eyes in
commercialize promising new technologies and the National nurture
onand ears of Medtronic.any other VC. Munce, investment, similar to
(Michael Ellwein, former return and The MoneyTree,
PricewaterhouseCoopers Chief Development Officer) Venture Capital
Association, 2006)
8/4/2008
CVC and Entrepreneurial Clinicians
2
Intro
Methods
Model & Data
Results
Conclusions
Extra
Research overview: CVC and Entrepreneurial Innovation
Setting:
Medical device industry, 1978-2007 CVC investment by medical
device firms in 134 entrepreneurial startups
Methods:
Grounded research + theory testable hypotheses Novel
project-level data on CVC and patenting performance of CVC
investment
CVC and innovation strategy
Production of knowledge that is directly relevant to
investorFounder attributes of startup matter
Project level dynamics and staging of investment Competitive
investment by rivals
Diminished innovation performance Other goals matterCVC and
Entrepreneurial Clinicians 3
8/4/2008
Intro
Methods
Model & Data
Results
Conclusions
Extra
CVC and Entrepreneurial Clinicians Clinicians
can be entrepreneurial users
Physician innovators who come up with innovation based on
experience in the clinical settingWho it is not: not patient, not
engineer, not serial entrepreneur
Lead
users matter
Ties strongly cultivated Medically/commercially significant
breakthrough advancesDr.Lillihei with external pacemaker. Circa
1957 Dr. DeBakey sewing Dacron aortic grafts on his wifes sewing
machine. Circa 1953
8/4/2008
CVC and Entrepreneurial Clinicians
4
Intro
Methods
Model & Data
Results
Conclusions
Extra
Research strategy
Grounded research
Semi-structured interviews (Medtronic, University of Minnesota,
Georgia Tech)
Theory + GR testable hypotheses
Empirical analysis
Construct dataset: micro-level project data
Analytically test relationship between entrepreneurial
innovation and firm performance
Model: E[cij|Xij] = exp(Xij +Zj )cij = number of citations by
incumbent device firm j to a patent filed by PCi Xi j = vector of
project-specific characteristicsZj = vector of firm specific
attributes of incumbent device company j
unit of analysis : CVC investment-project level
8/4/2008
CVC and Entrepreneurial Clinicians
5
Intro
Methods
Model & Data
Results
Conclusions
Extra
Hypotheses
CVC and Innovation
H 1: CVC investment is associated with innovation performance
directly relevant to the investing firm H 2: CVC investment in
entrepreneurial user founded companies should perform better than
companies founded by other types of entrepreneurs
Incomplete contracting
H 3: As the total level of CVC investment increases in a given
project, innovation performance is expected to be U-shaped H 4: As
the number of rounds of CVC investment in a given project
increases, diminishing returns to innovation performance are
expected
Competitive coinvestment
H 5: CVC investments made for competitive strategic goals will
have lower innovation performance relative to other CVC
investment
8/4/2008
CVC and Entrepreneurial Clinicians
6
Intro
Methods
Model & Data
Results
Conclusions
Extra
Sample selectionAvg Per Comp (USD Mil) 3.03 3.62 5.54 7.37 Rank
(all VC in industry) 20 46 70 78 Rank (device comp. CVC) 1 2 3
4
Firm Name Johnson & Johnson Development Corporation
Medtronic, Inc. Boston Scientific Corporation Guidant
Corporation*1987-2007
No. of No. of Avg Per Deal Deals Comp (USD Mil) 58 33 22 20 37
22 14 10 1.93 2.41 3.52 3.68
Avg Per Firm (USD Mil) 112.13 79.56 77.55 73.70
Sum Inv. (USD Mil) 112.13 79.56 77.55 73.70
Four largest medical device companies engaged in CVC Period:
1978-2007
SDC/VentureXpert database 134 portfolio companies, 144
projects
Source: SDC VentureXpert, author calculations8/4/2008 CVC and
Entrepreneurial Clinicians 7
Intro
Methods
Model & Data
Results
Conclusions
Extra
Regression results (full sample)Table 7. Negative binomial
regression results, full sampleDependent variable: c_ij (1)
0.048413 (5.02)*** 0.939814 (3.21)*** ---------------Yes 0.23686
(0.64) (2) 0.051628 (5.32)*** 0.847496 (2.88)*** 0.570657 (2.16)**
----------Yes -0.13544 (-0.40) (3) 0.047933 (4.64)*** 0.631986
(2.05)** 0.570582 (2.17)** 1.486823 (2.87)*** -----Yes -0.0583
(-0.17)8 (4) 0.0472726 (4.52)*** 0.6757906 (2.10)** 0.53324 (1.89)*
1.459434 (2.79)*** 0.3367666 (1.00) Yes -0.0786359 (-0.22)
H1: CVC investment is associated with innovation performance
directly relevant to investing firm
pat_j invest user acquire_cvc acquire_nocvc firm dummies
cons
no. obs. Log psuedolikelihood Wald chi2
449 -853.14356 62.97
449 -850.3077 71.68
449 -847.01525. 69.36
449 -846.71006 71.75
H2: CVC investment in entrepreneurial user founded companies
should perform better than investment in non-user founded
companies
Negative binomial regression estimators with
heteroskedasticity-consistent standard errors (t-statistics in
parentheses)* ** ***
p < 0.10. p < 0.05 p < 0.01
8/4/2008
CVC and Entrepreneurial Clinicians
8
Intro
Methods
Model & Data
Results
Conclusions
Extra
Conclusions and implicationsCVC is important part of firm level
innovation strategy
Strategic venturing associated with enhanced innovation
performance
Startup IP directly incorporated by investing firm
User founded firms (entrepreneurial clinicians) outperform
othersRobust across specifications Entrepreneurial clinician
generated innovation is the complementary asset of the medical
device industries
8/4/2008
CVC and Entrepreneurial Clinicians
9
Intro
Methods
Model & Data
Results
Conclusions
Extra
Conclusions and implicationsIncomplete contracting for
innovation
Level and staging of CVC investment matters
Strategic venturing may involve riskiest ventures Biggest return
in first rounds, but have to stick around enough rounds to benefit
(-) sign on ln(cvc), (+) sign on ln(cvc)2
U-shaped relationship Invest in most exploratory research, least
like existing internal body of knowledge? Have knowledge to build
on now from prior investment?
As invest further, likelihood of citation increases
CVC investment by rivals
Decreased innovation performance Competitive strategic
investment
8/4/2008
CVC and Entrepreneurial Clinicians
10
Professional-User Innovation Commercialization and
EntrepreneurshipJennifer L. Woolley Santa Clara University
User innovators End-user:
individual uses product in daily life
Employee:
Embedded in organization Creates innovation in same industry as
organizationEmbedded in organization Uses product in professional
life Create innovation in different industry as organization.
Professional-user:
Summary: Process of Professional-User Innovation
Commercialization and EntrepreneurshipFirm internalizes production
of innovation No additional innovation needed Firm partners with
another to produce innovation
Professional- user creates innovation to meet need
Innovation solves problem
Internal demand remains
Firm sells IP of innovation to firm to produce Firm sells IP of
innovation to professionaluser to spin-off
PropositionsFirm internalizes production of innovation No
additional innovation needed Firm partners with another to produce
innovation
Professional- user creates innovation to meet need
Innovation solves problem
Internal demand remains
Firm sells IP of innovation to firm to produce Firm sells IP of
innovation to professionaluser to spin-off
Implications Finds that professional-user innovators are
sources
of technological development, intrapreneurship, and
entrepreneurship. Explores the options that a firm has with
professional-user innovations Provides insight into processes that
occur prior to the founding of a firm.
Track 3: Communities (Hawes 202) Monday Aug. 4 2:00 - 3:30
"Revisiting Generalized Exchange: Extending Theory to Understand
Wikipedia, Open Source & Other Collaborative Communities"
(David Gomulya, University of Washington) "Status Effects in
Technological Communities" (Lee Fleming, Harvard Business School) *
"How are users membership in brand communities influencing them as
innovators?" (Yun Mi Antorini, Aarhus School of Business) "The
Challenge of Knowledge Novelty and Reuse in Distributed Innovation"
(Karim Lakhani, Harvard Business School) "The Emergence of
Architecture: Coordination across Boundaries at ATLAS, CERN"
(Philipp Tuertscher, Vienna University of Economics and Business
Administration)
*no slides available
Revisiting Generalized Exchange:Extending Theory to Understand
Wikipedia, Open Source & Other Collaborative Communities
Sonali K. Shah & David Gomulya University of Washington
GENERALIZED EXCHANGE: A BRIEF OVERVIEWC ACommon pool
D
B
E
THE PUZZLE Observation: exchange patterns in Wikipedia, open
source & other collaborative communities look like generalized
exchange Current Theory: However, theory posits that one or more of
the following mechanisms must be in effect for generalized exchange
systems to function: Altruism Group norms Rational action and
enforcement Theoretical puzzle: But, these mechanisms are not
present or appear relatively weak in many collaborative
communities
OUR RESEARCH
What are the mechanisms and structures supporting generalized
exchange in collaborative communities?A theory paper with
illustrative data from Wikipedia
Stay tuned! Come to our talk! Track 3, Hawes 202, 2pm
YUN MI ANTORINIAssistant Professor Department of Language and
Business Communication Aarhus School of Business Denmark
MY PROJECTHow are users membership in brand communities
influencing them as innovators?
MY CASEThe Adult fan of LEGO community There [at the LEGO Group]
it's a job, they have to do it. Here its passion
In 1998 the LEGO Group launched LEGO Mindstorms Robotics
Invention System
80,000 Robotics Invention Systems were sold within the first
three months. Many sets were sold not to children, but to students
at MIT, Stanford, and other universities around the world.
>250 setsCastle
>250 sets>850 setsTown Trains
Space
>4.000 productsYahoo! Group Yahoo! Group EJTC FGLTC Robotics
Group message LEGO set database LEGO set reference
>350 sets
Official LEGO sitesTrain Clubs
Group message
Recent 7 days Recommended group messages
LEGO sets rankedGroup message
Peeron
Yahoo! Group
Guide to LEGO products
>6.000 sets inventoried >12.000 unique parts listed
> 500 links
Parts reference
SpotlightLinksLEGO listings
Shopping guide
Jeff Hall
Auctions
Marketplace Members
>3.500 member profilesJeff Block
Bricklink
Online shops Dear LEGO Admin.
>3.000 shopsOff-topic
Andy Blau
BrickFest
LEGO Ambassadors
Events
64 different forums74 different Local User groups DK
ForumsBrickWorld 1000 Steineland
Space Trains
CAD
Help/FAQ
Israel Chile
USA Acronym guide History of LEGO
Lugnet FAQ
MY MOST IMPORTANT FINDINGSBRANDING LITERATURE: Brand meanings
define a playground within which the innovator expresses his or
hers ideas. Brand meanings help innovators distinguish between
great creations and old trash, pure and poor innovations, useful
and non-useful product improvements. BRAND COMMUNITY LITERATURE
User communities go through stages of development. Some stages
foster a more innovation-friendly environment than others. Shared
brand meanings rather than shared consumption practices hold the
community together. User community membership provides an important
learning ground for users.
MY MOST IMPORTANT FINDINGSUSER INNOVATION LITERATURE:
Innovations do more than satisfy needs for functional and
performance related needs. They satisfy important social and
identity related needs as well. Innovation in brand communities can
be described via four interacting key factors: individual, mood,
brand community, and external environment factors.
METHODOLOGY:
A multi-method/multi-sample approach offers substantial
benefits, when investigating social and dynamic phenomena, such as
user communities.
The Dynamics of Collaborative Innovation:Exploring the tension
between knowledge novelty and reuse
Work in Progress
Ned Gulley (The MathWorks) Karim R. Lakhani (Harvard Business
School & Berkman Center)
Overview of findings Collaborative innovation involves taking
pre-existing (old) knowledge/designs and combining them with new
knowledge/designs Re-use of old knowledge/designs by others is a
function of: Increasing visibility of contribution
Understanding/cognition of contribution by others: Inverse-U
relationship with novelty in contribution U-relationship with
reused code of others in contribution Technical complexity of
contribution
Technical performance of contribution is function of: Increasing
borrowing of code from others Quality of contributor Less frequent
participation
Broader question for discussion: How do we resolve tension
between novelty/reuse?
2
MATLAB Programming Contest is a Unique Setting to Explore and
Inform Collaborative Innovation Theory
3
A One Week Wiki-like Programming Contest rules standings1
Carliss 2 Stefan 3 Eric
view entryCarliss fcn f(x) ...
standings1 2 3 4 Joachim Carliss Stefan Eric
Joachim fcn f(x) ...
new entry
4
Nathan saysWell, this is my first MATLAB contest and it is
giving me far too much enjoyment. It's one of the most addictive
and compulsive things I have tried... Also, I have experienced
physical trembling while making the final preparations to submit
code. Is that normal?5
Contest Consists of Three Phases: Darkness, Twilight and
Daylight
Better
Darkness Twilight
Daylight
6
111 Authors - 3914 Entries
Dramatic Improvement in Performance
Better
7
Time
Reuse of Code Dominant Feature of ContestNumber of Different
People in Leading Entry % of Borrowed Code in Leading Entry
Leaders borrow from average 19 other people
Average 89% of Leader code is borrowed
8
The Emergence of Architecture: Coordination across Boundaries at
ATLAS, CERN
Philipp Tuertscher (WU-Wien) Raghu Garud (Penn State University)
Markus Nordberg (CERN)HBS-MIT User and Open Innovation Workshop
Cambridge, MA, August 2008
Coordination of complex technological systemsThe role of
architecture and modularity
Architecture determines path for distributed development of
technological systems Coordination is embedded in architectures by
pre-specifying modules and interfaces Coordination cost is reduced
as long as architecture is clearly understood and stable over time
Yet, there is very sparse literature as to how architectures
emerge
Where do architectures come from?
The ATLAS Experiment at CERNA complex technological system with
an emergent modular design
Largest experiment ever in high energy physics (HEP) 25 m in
diameter, 45 m long, weight of 7.000 tons 2000 scientists and
engineers From more than 165 institutions in 34 countries
Collaborate to design, build and run a detector In the absence of
traditional organizing principles In a decentralized setting
One-of-a-kind technological system Involving various expertise
areas(HEP, electronics, semi-conductor technology, material
science, cryogenics, optoelectronics, electrical engineering,
mechanical engineering, computer science, )
Impossible to extrapolate from a dominant design Uncertainties
and conflicting requirements complicate specification
Interactive Emergence of ATLAS through ongoing negotiation
between component groups
Architecture did not emerge in a deterministic way Deviations
from baseline as the design emerged Changes in one module had
impact on other modules These changes caused controversies about
previously agreed upon specifications
As the design was unfolding, ongoing negotiation took place
within and across groups Renegotiation of the interfaces eventually
changed the architecture itself Interlaced knowledge emerged at
interfaces: local knowledge bases of interdependent groups
overlapped
Conclusion
Classical view on modularity: Assumes that architecture is
pre-specified No justification if specifications are
taken-for-granted Very efficient from information processing
perspective, development lock in on pre-specified path The case of
ATLAS: Architecture remained ambiguous and continued to change
Instead of blackboxing and information hiding, continuous
questioning of interfaces preserved rich context Better
understanding of each others context and requirements Enabled to
interrelate heedfully as unforeseen changes occurred
Track 4: Communities (Hawes 101) Tuesday August 5 2008 2:00 -
3:30 "Community-Based Knowledge Production: Team Composition and
Task Conflict in Wikipedia" (Ofer Arazy, University of Alberta) "Do
Lead Users Appreciate the Community Around Product Co-Design?
Evidence from Stated Preferences for a Mobile Gaming Portal"
(Christoph Ihl, RWTH Aachen University) "Organizing for
Collaborative Innovation: The Community of Firms Model"
(Christopher Lettl, Aarhus School of Business) "Explaining
Progression Without Hierarchy: Lateral Authority in Context"
(Siobhan O'Mahony, UC Davis) "Complex Innovation Projects Without
Managers" (Eric von Hippel, MIT)
Community-Based Knowledge Production: Team Composition and Task
Conflict in Wikipedia
Ofer Arazy* Oded Nov** Ray Patterson* Lisa Yeo* * = The
University of Alberta ** = NYU Polytechnic
Team Composition in Open Innovation Projects
Insiders Middle
Outsiders
Group Composition Functional Diversity & Typical
FunctionFunctional Diversity High 25% 35% 40% 40% 35% Insider
Middle Outsider
25%
45% 5% 55% Low
55% 5% 45%
Outsider
Insider
Typical Member Function
Research ModelFunctional Diversity
H3: +
H1: +H5a: +
TeamFunctional
Task Conflict
Product Quality
CompositionH4: H2: -
H5b: -
Typical Function
Research Method Two samples of Wikipedia articles (100 and 50
articles each) Each article viewed as a team project
Operationalization Product Quality (dependent variable):
information quality perceptions Different method for the 2
different samples
Typical Function and Functional Diversity: metrics extracted
from Wikipedia Task Conflict: text analysis of articles discussion
pages (3 independent raters), adapting Jehn & Mannix (2001)
instrument
Results (Sample1 / Sample2)** = P