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Cited by 265

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Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model

Malerba, F.·2002·Industrial and Corporate Change

Abstract

This paper is a first attempt at modelling the long‐term dynamics of market structure and innovation in the pharmaceutical industry in a history‐friendly way. The model examines the relationships between the nature of the search space, demand, the patterns of competition, and industry evolution in the age of random screening and in the age of molecular biology, and shows that concentration in the pharmaceutical industry is shaped by lack of cumulativeness in innovative activities and market fragmentation. The model conforms to our appreciative understanding and responds to changes in parameters concerning demand, costs, economies of scale, opportunity conditions and the relative advantages of new biotechnology firms (NBFs) vis‐à‐vis incumbents. With the exception of cost increases, the model is quite robust to these changes in its essential features: it is quite difficult to raise substantially concentration and to have NBFs displacing incumbents.

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Crossref · 10.1093/icc/11.4.667

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Metadata

Authors
Malerba, F.
Venue
Industrial and Corporate Change
Published
2002-08-01
Volume
11
Issue
4
Pages
667-703
ISSN
  • 0960-6491 (ISSN-L)
  • 1464-3650
OpenAlex
W2121194385
Cited by
265
References
0
FWCI
19.14
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model. https://app.cochat.ai/papers/10.1093/icc/11.4.667
The paper
Malerba, F. (2002). Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model. Industrial and Corporate Change. https://doi.org/10.1093/icc/11.4.667

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.