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Innovation Networks in the Biotechnology-Based Sectors

Technological progress in the biological sciences is now advancing across such a wide range and at such a pace, that, irrespective of size, no firm can hope to keep up in all the different areas. Participating in innovation networks, bundling of competencies and capabilities, therefore, offers an alternative to extremely expensive go-it-alone strategies, whether carried out by acquisition and mergers or by isolated R&D. This imbalance between the rate of growth of the biotechnology knowledge base and the capability of individual firms to access it can explain the persistence of cooperative R&D in the biotechnology-based sectors at the end of the 90s. Such imbalance is not due any more only to the lack of absorptive capacity of existing firms, because the large pharmaceutical firms have meanwhile developed considerable competencies in that field. This previous competence-gap was considered to be the reason for cooperative behaviour in the early phases of these industries in the end of the 70s and early 80s. To the extent that this was considered to be the only knowledge gap innovation networks were considered as a temporary phenomenon, which could not persist beyond the period required by large firms to catch up with the new technology. We are then proposing that a new role, that of explorers scanning parts of the knowledge space that LDFs (Large Diversified Firms) are capable of exploring but unwilling to commit themselves in an irreversible way, can be played by DBFs (Dedicated Biotechnology Firms) in innovation networks. Our simulation approach attempts to represent the emergence of these two roles as endogenous changes in the motivation for participating in innovation networks, allowing them to become an important and long-lasting organizational device for industrial R&D. Drawing on a history friendly modeling approach the decisive mechanisms responsible for the emergence of innovation networks in these industries are figured out and compared to real developments.

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Paper provided by Universitaet Augsburg, Institute for Economics in its series Discussion Paper Series with number 205.

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Length: pages
Date of creation: Jul 2001
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Handle: RePEc:aug:augsbe:0205
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  1. Saviotti, P P & Mani, G S, 1995. "Competition, Variety and Technological Evolution: A Replicator Dynamics Model," Journal of Evolutionary Economics, Springer, vol. 5(4), pages 369-92, December.
  2. Saviotti, P. P. & Metcalfe, J. S., 1984. "A theoretical approach to the construction of technological output indicators," Research Policy, Elsevier, vol. 13(3), pages 141-151, June.
  3. Teece, David J., 1993. "Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy," Research Policy, Elsevier, vol. 22(2), pages 112-113, April.
  4. Orsenigo, L. & Pammolli, F. & Riccaboni, Massimo, 2001. "Technological change and network dynamics: Lessons from the pharmaceutical industry," Research Policy, Elsevier, vol. 30(3), pages 485-508, March.
  5. Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press, vol. 8(1), pages 3-40, March.
  6. Klepper, Steven & Simons, Kenneth L, 1997. "Technological Extinctions of Industrial Firms: An Inquiry into Their Nature and Causes," Industrial and Corporate Change, Oxford University Press, vol. 6(2), pages 379-460, March.
  7. Cantner, Uwe & Pyka, Andreas, 1998. "Absorbing Technological Spillovers: Simulations in an Evolutionary Framework," Industrial and Corporate Change, Oxford University Press, vol. 7(2), pages 369-97, June.
  8. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, vol. 99(397), pages 569-96, September.
  9. Fagerberg, Jan, 2000. "Technological progress, structural change and productivity growth: a comparative study," Structural Change and Economic Dynamics, Elsevier, vol. 11(4), pages 393-411, December.
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