The Evolution of Biotechnology as a Knowledge Industry: Network Movies and Dynamic Analyses of Emergent Structure

1. The Evolution of Biotechnology as a Knowledge Industry: Network Movies and Dynamic Analyses of Emergent Structure • Walter W. Powell Douglas R. White Kenneth W. KoputStanford University UC Irvine University of Arizona Slides by Douglas R. White • Biotechnology as a knowledge-based industry involves extensive reliance on organizational learning. This occurs through networks of dense collaborative ties among organizations. We model the emergence of the industry networkof contractual collaborations from 1988-99 in relation to both firm-level organizational and financial changes.

2. Network Movie: 1988-99 • The following twelve slides show the evolution of collaborative contracts within the biotech industry • Green ties =Finance • Red ties =Res&Dev • Grey =Finance • LiteBlue =Biotech • Yellow =Pharmaceuticals • Orange =Res.Labs and Universities • DarkBrown =Government (e.g., NIH) • Pink =Miscellaneous

3. 1988: Lots of Finance, little R&D

4. 1989: Nucleus of R&D attracts more Finance

5. 1990: Massive investment in R&D

6. 1991: The phase transition is complete

7. 1992: …and then it stops

8. 1993:

9. 1994:

10. 1995:

11. 1996:

12. 1997:

13. 1998:

14. 1999:

15. Figure 1: Levels of Network Cohesion: Tricomponents (within dark/red circles) embedded in Bicomponents (circled in medium/green) within Components (in light/yellow circles)

16. Figure 2: Slow Transitions to Giant Components,1990-1994

17. Figure 3: Rapid Phase Transition in Bicomponent Growth

18. Figure 4: Degree(+1) Distributions for the 1-Mode Data

19. Figure 5 a: Degree(+1) Distributions for the 2-Mode Data

20. Figure 5 b : Degree(+1) Distributions for the 2-Mode Data(removing zero frequency counts)

21. Figure 5 c: Exponential for DBFs' degree choosing partners; power-law for partners degree chosen by DBFs

22. Figure 6: Scatterplot of Biotech degree as chosen by other Biotechs (1-Mode) and as chosers of 2-Mode Partners (v2log=.55+0.52v1og; R-Square=0.15)

23. Figure 7: The Sexta-component of R&D and Finance Contracts in the Biotech Industry Blue=Biotech, Size of nodes reflects number of finance ties. Brown=government Orange=Research Institutes and Universities Yellow=Pharmaceuticals (f=DBF p=partners)

24. Figure 7: The Sexta-component of R&D and Finance Contracts in the Biotech Industry Blue=Biotech, Size of nodes reflects number of finance ties. Brown=government Orange=Research Institutes and UniversitiesYellow=Pharmaceuticals (f=DBF p=partners)

25. Figure 8: 1988

26. Figure 8: 1989

27. Figure 8: 1990

28. Figure 8: 1991

29. Figure G1: Time Series of Growth Parameters: Mean Growth by Tie Activity, Separately for Form of Partners.

30. Figure G2: Time Series of Growth Parameters: Mean Growth by Partner Form, Separately for Type of Activity.

31. Figure G2: Time Series of Growth Parameters: Mean Growth by Partner Form, Separately for Type of Activity.

32. Figure G1: Time Series of Growth Parameters: Mean Growth by Tie Activity, Separately for Form of Partners. • Left Figure Plots Coefficient of Variation over time for Modal Combinations: Magenta = R&D to Universities and NonProfits, Red = R&D to Government, Green= Finance to Financial, Cyan = Commercial to Biotech, Blue = Commercial to Pharmaceutical and Other For- Profit. • Right Figure Plots Coefficient of Variation for all remaining combinations with no color legend.

33. Figure 9: Cycles of Learning and Organizational Returns (Powell et al. 1999) Legend 1. Ovals represent network properties, rectangles are performance and outcome measures, while rounded rectangles can be treated as either firm characteristics or outcomes.2. Shadowed components carry over from Powell et al. (1996).

34. Figure 10 a: Finance and R&D networks of 2219 organizations 1988-1991

35. Figure 10 b: Finance and R&D networks of 2219 organizations 1992-1995

36. Figure 10 c: Finance and R&D networks of 2219 organizations 1996-1999

37. Figure 11: Distance from NIH: R&D and Finance