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 Sloansjbae@mit.edu

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: simon@protocase.com 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: hassan@gwu.edu 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: hassan@gwu.edu

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

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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

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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

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CVC and Entrepreneurial Clinicians

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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

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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

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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

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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