Academic Entrepreneurs: Social Learning and Participation in University Technology Transfer

1. Academic Entrepreneurs:Social Learning and Participation inUniversity Technology Transfer Janet Bercovitz University of Illinois Maryann Feldman University of Georgia

2. Changing Environment for University-Industry Relationships • Universities Have Long Served as a Source of Scientific and Technical Knowledge • Recent Environmental Changes. . . • Emergence of New Technology Platforms • Greater Knowledge-Based Competition • Legislative Mandate -- Bayh-Dole Act of 1980 • Greater Budgetary Uncertainty • Have Catalyzed a Shift in Emphasis • Open Dissemination of Knowledge • Commercialization of Academic Discoveries

4. Inventor is a Faculty Member Eureka Moment! Faculty Files Invention Disclosure Federal requirement Low cost procedure, 0n-line forms Technology Transfer Office Evaluates Is it new? Useful? Non-obvious? If yes, then patent If Patent, then the Desired Outcomes Licenses Licensing revenues Start-up companies We care about outcomes, but they are predicated on faculty disclosing inventions University Technology-Transfer Process

5. Results are Not Uniform • Overall, a significant increase in the level and formalization of knowledge transfer activities at the university-industry interface • However, there remains great variation in technology transfer activity across and within universities • Why do some entities perform better than others? • Not resources • Not organizational initiatives • Not incentives

6. Fundamental Question • How Do Organizations (Places) Change? • Change as an emergent rather than calculated phenomenon • Collective rather than individual process • Individual in context • Localized learning? • non-pecuniary sharing of information • Groups of individual agents as conduits for organizational change • Social Actors in the Geography of Innovation

7. Seemingly Straightforward It’s the law Articulated university goal Just about anything can be disclosed But, In Practice, Has Proven Difficult Only a subset of research with commercial potential is disclosed Perceived Barriers Basic research is not amenable (wrong) Risk of publication delays (wrong) Just not appropriate == older norms of science Invention Disclosure Measures Adoption of Change to Entrepreneurial Behavior Getting Faculty Invention Disclosures

8. Disclosures are Differentially Concentrated within Medical School Department

9. Within Department Variation in Disclosure

10. Central Research Question • What factors influence an individual faculty member’s disclosure decision? • Technical Opportunity? • Financial Incentives? • Social Imprinting? • Social Learning? • What happens when individuals face dissonant situations? • Lack of alignment • Symbolic behavior • Academic Entrepreneurship to study organizational change • Understand Individual decision making in context

11. Imprinting & Entrepreneurial Activity • Training Effects • An Individual is Shaped by the Norms and Values Prevalent: • In Key Social Institutions (Schein, 1985; DiMaggio and Powell, 1983) • During Formative Stages of Career Development (Ryder, 1965) Training Institution Active in Tech-Transfer H1 (+) Likelihood of Disclosure Completed Training Recently H2 (+)

12. Social Learning & Entrepreneurial Activity • Individuals Learn How to Behave in Organizations by Observing the Behavior of Referent Others (Bandura, 1986) • Leaders • Build/Define Culture • Act as Role-Model • Peers • Information Source • Influence Decisions Leader is Active in Tech-Transfer H3 (+) Likelihood of Disclosure Peers are Active In Tech-Transfer H4 (+)

13. Data • Observation – Individual Faculty Member • Duke University and Johns Hopkins University • Both late entrants in technology transfer • Strong Medical Schools • Same financial incentives at time under consideration • Fifteen Matched Medical School Departments • Basic, Nexus, and Clinical Departments • Departmental fixed effects • Research is expected from all faculty members • 1779 Individuals • Administrative Records • Technology Transfer Office Database • ISI Publications

14. PROBIT Model • Two Period Model • Dependent Variable • Three-Year Window: Academic Years 1996-1998 • Disclosure Activity Dummy Variable • Independent Variables • Independent variable = individual characteristics and local context • Activity in Previous Five-Year Window: Academic Years 1991 – 1995 • Controls

15. Control Variables • Quality • Individual NIH Awards • Departmental NIH Awards • Number of Prior Disclosures • Inventive Capacity • Boundary Spanning • Dual Degree • Number of ISI publications • Non-US Degree • Type of Department (clinical omitted) • Nexus Service Department • Basic Science Department • Academic Rank (Associate Professor omitted) • Full Professor • Assistant Professor • University dummy variable

16. The Likelihood of DisclosingIncreases • Each additional publication + 0.1%. • Strong Local Peer Effects • 1% increase in the percentage of faculty disclosing within the relevant cohort increases the probability of an individual disclosing by 12%. • Training Matters • Pro Tech Transfer Institution +4% for every 10 patents • Stanford + 27% • Dual Training (MD/PhD) +4% • Chairman influence weakest • Chair active +4% (weakly significant)

17. Selection or Socialization? • Department Chairs with a History of Disclosing were No More Likely to Hire Individuals “Predisposed” to Disclosing than Non-Active Chairs • Robustness Checks • Departmental Fixed Effects • Number of Disclosures

18. Dissonant Situations What happens when training and current work environment provide mixed signals? H5: When individuals are faced with a situation where their individual training norms are not congruent with the localized social norms in their work environment, they conform to local norms.

19. Figure 1: Alignment between training norms and localized social norms

20. Localized Learning Trumps Training • Individuals are most responsive to local cohort pressures • If not trained with entrepreneurial expectations, local cohort can catalyze • If trained with entrepreneurial expectations, local cohort can suppress • If neither training nor local pressure then entrepreneurship is a rare event • Localized learning is a knowledge source for entrepreneurship

21. Symbolic versus Substantive Adoption Just enough to seem to be in compliance but not as much as might be done, ceteris paribus N = 169 Symbolic Individuals N = 136 Individuals H6: Symbolic compliers will respond to different influences than substantive adopters.

22. Symbolic vs. Substantive Adoption Participants • Probit Model • Dependent Variable = Disclosure Filed (0, 1) • Same Basic Specification • Substantive Adoption Disclosures • Local Peer Effect is Stronger • Symbolic Disclosures • Stronger Chair Effect • NIH is positive and statistically significant

23. How to Change an Organization • Creating Entrepreneurial Organizations & Promoting Organizational Change • Requires Understanding and Management of both Individual Motivations and Departmental Composition • Individual decisions influenced by relevant others • Sub-unit composition and dynamics are key • Not just about leaders • Not about hiring individuals with • Appropriate training • Prior experience • Critical mass of symbolic participants • Enforcement of rules and incentives • Traction for creating local cohort • Keep these individuals together then culture changes

24. Organizing for Entrepreneurial Success • Academic Entrepreneurship is a team sport • 40% Individual Efforts; 60% Team Efforts • Compared to linked academic publications the number of inventors on a disclosure is less than half the number of authors on a paper. • Ave. publication team size is 5.33 (sd = 1.81) • Ave. disclosure team is size 2.11 (sd = 1.31) • Solo efforts • Publications = 3% of all inventors’ papers • Disclosures = 40% of disclosures

25. Broader Use of Disclosure Data • Studying Disclosure Teams • Same 2 Prominent East Coast Universities with Medical Schools • From 1988 to 1998 – July 1, 1988 to June 30, 1999 • Data from Tech Transfer Offices • 2340 Disclosures Filed • 4942 Unique Individual participated, all academic departments plus outsiders • Configurations change • Augmented with • Web of Science/ISI Publication data • Patent data • Probit Model • Dependent variables = relevant outcomes = patent, license, Royalty $

26. All in One: Hypotheses & Results • Technical Diversity: Two Competing Influences • Diversity in Knowledge is Key for Innovation (+) • But Diversity Raises Coordination Costs (-) • We find Diverse Teams are Less Productive • But Team Experience Matters: The Negative effect is Reduced as the Team Gains Experience Together • Organizational Diversity • Diverse Networks Gives Access to Resources (+) • Having an Industry Team Member Matters • Leadership Effect • The Experience of the Leader Matters Directly (+) • Learning Effects beyond specific team configuration

27. What we are doing now • Power Relationship • Stars (Scientist) and their Constellations • The Great Person or the Great Team • Apprenticeship System • Reconfigurations of teams • Over trials, do teams become • Larger or smaller • More homogenous or more diverse • More successful • Stay tuned

28. Questions?