“Research on Academic Entrepreneurship in the U.S. and

Europe: Lessons Learned and a Research Agenda”

Professor Donald Siegel

Dean-School of Business

University at Albany, SUNY

President, Technology Transfer Society

Editor-Journal of Technology Transfer

Co-Editor-Academy of Management Perspectives

InterTradeIreland All-Island Innovation Conference

NUI Galway

June 12, 2012

Outline

Shameless Self-Promotion: Plugs For Technology

Transfer Society/Journal of Technology Transfer/Academy

of Management Perspectives

Background Information on University Technology

Transfer/Academic Entrepreneurship

Summary of Key Research Quantitative and

Qualitative Results From the Burgeoning

Literature on University Technology Commercialization

/Academic Entrepreneurship

Agenda for Additional Research

Economics of Innovation and Entrepreneurship

Joseph Schumpeter (1939)-

Stresses the Importance of Technological Competition,

Not Price Competition-The Role of the Entrepreneur

Technological Change is a Force of “Creative

Destruction”

Zvi Griliches/Edwin Mansfield/Richard Nelson-Diffusion

of Innovations/R&D is a Key Source of Economic Growth

(Especially Basic Research)/Technology Transfer/Role of

Universities

Technology (Primary) Industry Period Developed University Created Electronic University of 1940s Calculator Pennsylvania Computers Fiber 1960s Optics MIT Telecommunications Stanford, 1970s rDNA California Biotechnology 1980s Supercomputing Illinois Internet Sequencing of DNA/ Human Genome Cal Tech, 1990s Project Johns Hopkins Pharmacogenomics 2000s Nanotechnology UAlbany ?????

Universities, “GPTs”, and The Creation of New Industries

Background Information on

University Technology Commercialization

U.S.-1960’s, 1970’s Decline in Competitiveness

(“Japanese Challenge,” Productivity Slowdown)

Dramatic Changes in U.S. National Innovation Policy

Expansion of Programs to Support Public-Private

Partnerships (e.g., Advanced Technology Program-ATP,

NSF-ERC, IUCRC)

Relaxation of Antitrust Enforcement to Promote

Collaborative Research (e.g., NCRA)

Policies Promoting More Rapid Diffusion of Federally-

Funded Technologies From Universities and Federal Labs

to Firms (e.g., Bayh-Dole, Stevenson-Wydler, SBIR )

Legacy of the Bayh-Dole Act

Bayh-Dole Act of 1980: Universities Own the Rights to

Technologies That Arise from Federal Research Grants

Purpose: Accelerate the Rate of Technological

Diffusion, Promote Economic Development

Almost All Universities Have Established a Technology

Transfer or Licensing Office

Rapid Growth in Commercialization of University

Technologies:

U.S. Universities

1980 2010

University Patents 300 4469

Licensing Agreements 276 4284

Startups 35 651

Interdisciplinary Research on Institutions and Agents

Involved in Academic Entrepreneurship

Agents and Institutions

University Scientists

Industry Scientists

Entrepreneurs

Industry-University Cooperative Research Centers

University Technology Transfer Offices

Science Parks

Incubators/Accelerators

Firms That Interact With Universities

Venture Capital Firms

Selected Research Questions

How Does the Process of University Technology

Commercialization/Academic Entrepreneurship Work?

Which Universities “Perform” Best?

What is the Role of the TTO?

How Should We Measure Performance?

Which Factors “Explain” Variation in Relative

Performance? (e.g., Incentives, Organizational, and

Environmental Factors)

Do Incubators/Accelerators and Science Parks Add

Value?

Interdisciplinary Research on Institutions and Agents

Involved in Academic Entrepreneurship

Indicators of Output/Performance

Invention Disclosures

Patents

Number of Licensing Agreements

Licensing Revenue

Research Productivity of Industry Scientists/Firms

Research Productivity of University Scientists

“Productivity” of Universities in Technology Transfer

Start-Up Formation

Survival

Employment Growth

Changes in Stock Prices

NBER/Alfred P. Sloan Foundation Project on

Industrial Technology and Productivity

Theme: Economists Need to Supplement Statistical

Analysis of Productivity and Technology

With “Pin-Factory” Visits:

Inside the “Black Box”:

Organizational Structure-Milgrom & Roberts (1992)

HRM-Lazear (1995)

Strategy-Jensen (1998)

Technology Transfer Offices-Siegel et al. (1999)

Goals of My Original NBER/Sloan Study

Specify a UTT “Production Function”

“Explain” Relative Productivity in UTT

(Assess the Relative Importance of Organizational

Factors in Explaining Variation in UTT Performance)

Tactics of the NBER/Sloan Study

Quantitative Methods-Constructed Estimates of the

Relative Productivity of 113 U.S. Universities with

Regard to Licensing

Qualitative Methods-Inductive Analysis to Explore

Organizational Issues, Based on Structured Interviews of

Academic and Industry Scientists, University

Administrators, and Firms/Entrepreneurs

Scientific

Discovery

Invention

Disclosure

Evaluation of

Invention for

Patenting

Patent

Negotiation

of

License

Marketing of

Technology to

Firms

License

to Firm

(an existing firm

or startup)

FIGURE 1

How A Technology is Transferred from a

University to a Firm or Entrepreneur

(According to Theory)

University

Scientist

University

Scientist

and TTO

University

Scientist

and TTO

University

Scientist

and TTO

University

Scientist, TTO,

and

Firm/

Entrepreneur

University

Scientist,

TTO, and

Firm/

Entrepreneur

University

Scientist,

TTO, and

Firm/

Entrepreneur

Key Stylized Facts From My Qualitative Research

(Relevant to the Measurement and Analysis of the

Effectiveness of Technology Transfer)-

(Siegel et al., 2003a, 2003b, 2004)

Patents Are Not that Important for Certain

Technologies/Industries

Many Scientists do not Disclose Inventions

Faculty Involvement/Engagement is Critical

Universities Often Hire Outside Lawyers to Negotiate

with Firms

Multiple “Outputs” (e.g., licensing, startups, sponsored

research )

UTT Production Function

LICENSE & STARTUP=f (INVDISC, STAFF, LEGAL )

where LICENSE = licensing agreements or revenue

STARTUP = start-up activity (counts)

INVDISC = invention disclosure

STAFF = TTO staff

LEGAL = (external) legal expenditures

We also need to account for environmental,

institutional, and organizational factors that are not

typically included in a production function.

Choices: Parametric or Nonparametric Estimation?

Single vs. Multiple Outputs?

UTT Production Function

LICENSE & STARTUP=f (RESEARCH, STAFF, LEGAL )

where LICENSE = licensing agreements or revenue

STARTUP = start-up activity (counts)

RESEARCH = research expenditure

STAFF = TTO staff

LEGAL = (external) legal expenditures

We also need to account for environmental,

institutional, and organizational factors that are not

typically included in a production function.

Choices: Parametric or Nonparametric estimation?

Single vs. Multiple Outputs?

Frontier Production Function

(Single Technology Transfer Output and Input)

Regression

Line

(parametric)

Technology

Transfer

Output

(e.g.

Licensing)

Technology Transfer

Input (e.g., Research)

Best Practice

Frontier

(non-parametric)

Non-Parametric:

Data Envelopment Analysis (DEA)

s m

Max Ek = S urkYrk / S vikXik

r =1 i =1

subject to s m

S urkYrj / S vikXij 1; j=1,..., n

r=1 i=1

All urk > 0; vik > 0

where i=inputs (m inputs)

r=outputs (s outputs)

n=# of DMUs (Decision Making Units)

Data Envelopment Analysis (DEA)

-Two-Stage Analysis

1st Stage: DEA generates efficiency “score”

(0 < E<1)

2nd Stage: E = b0 + b1Z1 + b2Z2 ... +bkZk

where Z1 through Zk are environmental, institutional,

and organizational factors)

Stochastic Frontier Estimation (SFE)-Single Output

yi = xi + I

where

i is an error term with two components:

i = Vi - Ui

where

Ui represents technical inefficiency

Ui i.i.d. N+(0,2u ), ui 0

Vi i.i.d. N(0,2v )

Stochastic Frontier Estimation (continued)

SFE with environmental variables:

Assume that Ui are independently distributed as

truncations at zero of the N( mi,2u ) distribution

with:

mi = zi

where z is a vector of environmental/

institutional/ organizational factors

Multiple Outputs: Distance Function approach

(can be estimated with Parametric

or Nonparametric Methods)

Multiple Output Distance Function

STARTUP

LICENSE

B

A

LICENSEA

STARTUPA

P( x)

Do(x,y) = min{: (y/ P(x)}

=0A/0B

0

Stochastic Frontier Production Function Model

Stochastic Production Function (Paper-CD, Also TL)

ln(LICENSEi) and ln(STARTUP)

=0+1 ln(RESEARCHi)+2 ln(STAFFi)

+3 ln(LEGALi) + γ11 ln(RESEARCHi)2

+ γ22 ln(STAFFi)2 + γ33 Lln(LEGALi)

2

+ γ12 ln(RESEARCHi)ln(STAFFi)

+ γ23 ln(STAFFi)ln(LEGALi)

+ γ31 ln(LEGALi)ln(RESEARCHi) + Vi - Ui

Determinants of Inefficiency

Ui = 0 + M MEDSCHi + INC INCUBij+ SCI SCIij + AAGE i

+ INDPERCINDi + i

Selected Empirical Studies of

University Technology Licensing

Author(s) Methodology Results

Siegel et al.

(2003)

Productivity of

Licensing-SFA

Organizational and

Environmental Factors Have

Considerable Explanatory Power

Thursby and

Kemp (2002)

Productivity of

Licensing-DEA

Private Universities More Efficient

Thursby and

Thursby (2002)

Productivity of

Licensing-DEA

Growth in Licensing/Patenting

Due to an Increase in the

Willingness of Professors to Patent

and License and Firm Outsourcing

of R&D

Siegel et al.

(2005)

Productivity of

Licensing-SFA

Higher Royalty Shares For Faculty

Associated With Greater Licensing

Income; Land Grant Universities

Are More Efficient

Selected Empirical Studies of

University Technology Licensing

Author(s) Methodology Results

Siegel et al.,

(2003)

Quantitative

Analysis of

Qualitative

Data

Three Key Impediments:

Informational and Cultural

Barriers Between Universities and

Firms (Especially for Small Firms)

Insufficient Rewards for Faculty

Involvement in UITT

TTO Staffing and Compensation

Practices (e.g., High Rate of

Turnover, Insufficient Business/

Marketing Experience)

Siegel et al.

(2006)

Productivity

of Licensing

and Startups

–Distance

Function

U.S, Universities More Productive

Than U.K. Universities;

Universities With Medical Schools

and Incubators More Efficient

Selected Studies of University Science Parks

Author(s) Unit of Analysis Results

Siegel,

Westhead, and

Wright (2003)

Firms Located

on Science

Parks (U.K.)

Firms Located on University Science

Parks Have Higher Research

Productivity Than Comparable

Firms

Westhead and

Storey (1995)

Firms Located

on Science

Parks (U.K.)

Science Park Firms With a Link to

the University Have a Higher

Survival Rate Than Science Park

Firms Without Such a Link

Link and Scott

(2003)

Science Parks

(U.S.)-Based on

Self-Reported

Qualitative

Data

Proximity to a University and

Availability of Venture Capital

Enhance Growth; Science Parks

Enable Universities to Generate

More Publications and Patents,

More Easily Place Graduates, and

Hire Preeminent Scholars

Selected Empirical Studies of University-Based Start-ups and Entrepreneurial Activity at Universities

Author(s) Unit of Analysis Results

Louis,

Blumenthal,

Gluck, and Stoto

(1989)

Faculty

Members in the

Life Sciences

Key Determinant of Faculty-

Based Entrepreneurship: Local

Group Norms; University

Policies and Structures Have

Little Effect

DiGregorio and

Shane (2003)

University-

Based Startups

Two Key Determinants of Start-

up Formation: Faculty Quality

and Equity-Friendly University

Policies

Markman, Phan,

Balkin, and

Gianiodis (2005)

TTOs and

University

Startups

The Most Attractive Licensing

Strategies For Entrepreneurship

Are Least Likely to Favored by

the University (Due to Risk

Aversion and Short-Run Revenue

Maximization)

Selected Empirical Studies of University-Based Start-ups (cont.)

Author(s) Unit of Analysis Results

Zucker, Darby,

and Brewer

(1998)

Relationships

Involving “Star”

Scientists and

U.S. Biotech

Firms

Location of Star Scientists

Predicts Firm Entry in

Biotechnology

Markman, Phan,

Balkin, and

Gianiodis (2004a)

TTOs and

University-Based

Startups

Equity Licensing and Startup

Formation Are Positively

Correlated With TTO Wages;

Siegel et al. (2003) TTOs and Firms TTOs Serve the Needs of Large

Firms More Effectively Than

Those of Small, Entrepreneurial

Companies

Key Quantitative Results

Production Function Models Provide a Good Fit

Results Are Fairly Robust to Single or Multiple Outputs

Staff in the Technology Transfer Office Add Significant Value to the Commercialization Process

No Strong Consensus on Returns to Scale

Bayh-Dole Type Legislation Appears to Have Been “Effective”

Private Universities and Those With Medical Schools

Appear to Be Somewhat More Productive

Key Quantitative Results (cont.)

Property-based Institutions (Incubators and Science

Parks) Appear to Enhance Technology

Commercialization

Incentives Matter (e.g., Royalty Distribution Formula),

But So Do Organizational Practices and Other

Institutional Policies

Universities Are Becoming More “Strategic” in

Technology Transfer (More On That Later) –More

Heterogeneity and Application of Management Theories

to Practice

Key Quantitative Results (cont.)

Universities Increasingly Focusing on the Entrepreneurial

Dimension (Evidence Mixed on Success of University Based

Startups)

Academic Entrepreneurs Are Not Less Productive in Their

Academic Research After Commercialization

Foreign-Born Scientists Are More Likely to Become

Academic Entrepreneurs

Social Networks of Star Scientists Key for New Firm

Creation

Key Stylized Facts From Qualitative Research

Major Impediments to University Technology Transfer:

Informational and Cultural Barriers Between

Universities and Firms (Especially for Small Firms)

Insufficient Rewards for Faculty Involvement in Technology Transfer at Some Institutions, Especially w.r.t. Entrepreneurial Activity

Technology Transfer Office Staffing and Compensation Practices (High Rate of Turnover, Insufficient Business/ Marketing Experience, Possible Need for Incentive Compensation)

Education/Training is Needed for Faculty Members, Post-Docs, and Graduate Students in the Specifics of the Entrepreneurial Process, the Role of Entrepreneurs, and How to Interact with the Business/Entrepreneurial Community

Key Stylized Facts From Qualitative Research (cont.)

A Failure to Address These Barriers Will

Induce More Faculty Members and Firms to

Circumvent the TTO and Engage in

“Informal” UITT

University Technology

Transfer/Commercialization/Entrepreneurship

Should be Considered From a Strategic Perspective

Strategic Implications of University Technology Transfer

/Academic Entrepreneurship-Formulation Issues

Setting Institutional Goals/Priorities

Resources Devoted to University Technology Transfer

Choices Regarding Technological Emphasis

Strategic Choices Regarding Modes of University

Technology Transfer:

Licensing

Startups

Sponsored Research

Other Technology Transfer Mechanisms That are

Focused More Directly on Stimulating Economic

Development (e.g., Incubators and Science Parks)

Strategic Implications of University Technology Transfer

/Academic Entrepreneurship-Implementation Issues

Improving Information Flows

Organizational Design/Structure

HRM Practices-Staffing/Compensation of TTO

Personnel

Reward Systems for Faculty Involvement in University Technology Transfer (perhaps including P&T- e.g., 6/-06-Texas A&M)

Implementation Issues Regarding Modes of University Technology Transfer

Different Ways of Structuring Licensing Agreements

Academic vs. Surrogate Entrepreneurs

Different Ways to Manage University-Based

Incubators and Science Parks

Unanswered Research Questions (Mainly in the Domains of Management and Economics)

Which Organizational Practices Enhance Performance?

What is the Role of Department/Organizational Culture?

What is the Role of Organizational Structure?

What is the Role of Leadership (“Entrepreneurial Leadership”)?

“Nature vs. Nurture” (e.g., Role of Genetics vs. Environment)

What are the Tradeoffs Associated With Involvement in Academic Entrepreneurship (e.g., Educational, Basic Research)?

Unanswered Research Questions (Mainly in the Domains of Management and Economics)-cont.

Additional Benchmarking Issues (e.g., International Comparisons)

Evaluation of Entrepreneurial Initiatives and Programs

National Level-(e.g., SBIR)

State Level-(e.g., Ben Franklin Technology Partners)

What is the Relationship Between Technology Commercialization and Ethics/Corporate Social Responsibility?

Better Measures of the Private and Social Returns to University Technology Commercialization/ Entrepreneurship (e.g., need better data-most data at the institutional level)

Personal Reflections Based on

Studies of Academic Entrepreneurship

We Need More Detailed Exploration of the Nature of the Connection Between Entrepreneurial Firms and the University, Including the Role of Property-Based Institutions (i.e., Incubators/Accelerators & Science/Technology Parks

What is The Relationship Between Academic Entrepreneurship and Federal/National Labs (The “Last Frontier” of Technology Transfer)

We Need More Detailed Analysis of Technology Transfer Strategy Implementation

Personal Reflections Based on

Studies of Academic Entrepreneurship (cont.)

Strong Need to Enhance Incentives for Faculty Members to Be Engaged in Entrepreneurial Activity (and Perhaps For Successful Ones to Serve As Mentors)

Important to Increase Participation/Success of Women & Minorities in Academic Entrepreneurship (as we found in the NRC Evaluation of SBIR)

Entrepreneurship Research, Education, and Community-Based Initiatives Are Key Complements

Entrepreneurship As An Academic Field

Entrepreneurship (2007) vs. Strategy (1989)

Returns to Studying This Topic Are High (e.g., NSF-IGERT, Kauffman, development)