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Open accessJournal ArticleDOI: 10.1080/09537325.2019.1631449

Policy support for disruptive innovation in the life sciences

04 Mar 2021-Technology Analysis & Strategic Management (Routledge)-Vol. 33, Iss: 3, pp 307-319
Abstract: This paper focuses on business models and value chains to analyse sectoral innovation systems involving synthetic biology and gene editing, as potentially disruptive platform technologies in the life sciences. In the context of industrial biotechnology, we propose that the extent to which an innovation is expected to be disruptive, and the location of that disruption in existing value chains, are relevant to policy decision making on how to govern innovative technologies. Policy decisions on how to regulate or support an innovative technology will be among the most important factors determining the extent to which it is able to deliver its disruptive potential, leading to sectoral transformations through new business models and value chains. This mode of thinking about disruption and innovation could be incorporated in standard procedures for policy makers to guide decisions designed to support translation from basic scientific research to societally useful products and processes.

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Topics: Disruptive innovation (65%), Business model (54%)

7 results found

Open accessJournal ArticleDOI: 10.1016/J.EIST.2020.12.001
Paula Kivimaa1, Paula Kivimaa2, Senja Laakso3, Annika Lonkila2  +1 moreInstitutions (3)
Abstract: Because of the urgency of accelerating transitions, we examine the emerging understanding of the concept of ‘disruption’ in the context of sustainability transition studies to critically assess its value, pitfalls and potentials. By conducting a qualitative systematic review of 47 articles, we analyse how disruption is seen in this literature and what is being disrupted. We identify four non-technical dimensions of disruption, adding ‘behaviour, practices and cultural models’ to previously suggested dimensions, i.e., markets and business models, regulations and policy, and actors and networks. We summarise what the literature identifies as disruption in transitions and draw on other literatures (e.g. social practice theory and institutional theory) to elaborate the dimensions of disruption. We provide a new definition of disruption in sociotechnical transitions, with focus on both speed and magnitude of change. We end by highlighting the importance of disruptive practices and low-tech solutions alongside disruptive technologies and policies.

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Topics: Disruptive innovation (52%)

13 Citations

Open accessJournal ArticleDOI: 10.1080/09537325.2019.1685087
Geoffrey Banda1, James Mittra1, Joyce Tait1, Andrew Watkins1  +1 moreInstitutions (2)
Abstract: This paper identifies, characterises and analyses six evolving regenerative business (RM) models in the UK based on 10 cases we studied. Our conceptual framework extracts four key elements from con...

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Topics: Business model (56%), Conceptual framework (53%)

3 Citations

Journal ArticleDOI: 10.1080/18366503.2021.1904602
Abstract: Studies around the world have identified a multitude of innovations that have the potential to create an economic impact and disrupt various facets of life in the coming years. Most of these innova...

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2 Citations

Open accessJournal ArticleDOI: 10.1049/ENB2.12005
Joyce Tait1, Alex Brown2, Isabela Cabrera Lalinde1, Daniel Barlow  +2 moreInstitutions (3)
01 Mar 2021-

2 Citations


20 results found

Open accessBook
07 May 2009-
Abstract: Introduction Part I. Sectoral Sytems: Concepts and Issues: 1. Sectoral systems of innovation: basic concepts Franco Malerba 2. Sectoral dynamics and structural change: stylised facts and 'system of innovation' approaches Fabio Montobbio Part II. Six Sectoral Systems: 3. Pharmaceuticals analysed through the lens of a sectoral innovation system Maureen McKelvey, Luigi Orsenigo and Fabio Pammolli 4. The chemical sectoral system: firms, markets, institutions and the process of knowledge creation and diffusion Fabrizio Cesaroni, Alfonso Gambardella, Walter Garcia-Fontes and Myriam Mariani 5. The fixed internet and mobile telecommunications sectoral system of innovation equipment production, access provision and content provision Charles Edquist 6. The European software sectoral system of innovation W. Edward Steinmueller 7. Machine tools: the remaking of a traditional sectoral innovation system Jurgen Wengel and Philip Shapira 8. Services and systems of innovation Bruce S. Tether and J. Stan Metcalfe Part III. Sectoral Systems and National Systems: International Performance and Public Policy: 9. National institutional frameworks, institutional complementarities and sectoral systems of innovation Benjamin Corait, Olivier Weinstein 10. Sectoral systems of innovation and varieties of capitalism: explaining the development of high-technology entrepreneurship in Europe Steven Casper and David Soskice 11. The international performance of European sectoral systems Benjamin Coriat, Franco Malerba and Fabio Montobbio 12. Sectoral systems: implications for European technology policy Charles Edquist, Franco Malerba, Stan Metcalfe, Fabio Montobbio and Ed Steinmueller Part IV. Conclusions: 13. Summing up and conclusions Franco Malerba.

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Topics: Innovation system (60%)

590 Citations

Journal ArticleDOI: 10.1080/713698467
Abstract: We revisit the Schumpeterian model of competition driven by the perennial gale of creative destruction. Not every innovation must necessarily lead to the destruction of incumbent firms. In many high-technology industries we observe a symbiotic coexistence between new entrant and incumbent firms. This phenomenon warrants more attention. We build upon the innovation and strategic alliance literature to develop the notion of ’complementary innovation.’ We advance propositions with respect to the following questions: What impact will a complementary innovation have on firm entry, interfirm cooperation, and the nature of competition? Based on the propositions advanced, we develop a cyclical model of industry dynamics initiated by a complementary innovation. The propositions and the cyclical model of industry dynamics are illustrated in a case study of the biopharmaceutical industry.

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100 Citations

Journal ArticleDOI: 10.1016/0016-3287(92)90032-B
Joyce Tait1, Les Levidow2Institutions (2)
01 Apr 1992-Futures
Abstract: The evolution of regulatory systems to control the development and use of products containing live genetically manipulated organisms (GMOs) is raising important questions about the nature and desirability of proactive approaches to risk regulation and their impact on industrial innovation This article attempts to elucidate some of the complex issues underlying pressures for so-called product-based (as opposed to process-based) approaches to the regulation of GMOs, to relate the product/process argument to the more general objectives of promoting reactive or proactive approaches to risk regulation and to compare the situation of GMOs with broader issues of precautionary risk regulation In conclusion the implications of these issues for the future development of the biotechnology-based industries and for risk regulation in general are discussed

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81 Citations

Journal ArticleDOI: 10.2217/PME-2016-0057
Tamar Sharon1Institutions (1)
Abstract: Consumer-oriented mobile technologies offer new ways of capturing multidimensional health data, and are increasingly seen as facilitators of medical research. This has opened the way for large consumer tech companies, like Apple, Google, Amazon and Facebook, to enter the space of health research, offering new methods for collecting, storing and analyzing health data. While these developments are often portrayed as ‘disrupting’ research in beneficial ways, they also raise many ethical issues. These can be organized into three clusters: questions concerning the quality of research; privacy/informed consent; and new power asymmetries based on access to data and control over technological infrastructures. I argue that this last cluster, insofar as it may affect future research agendas, deserves more critical attention.

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Topics: Disruptive innovation (55%), Big data (50%)

63 Citations

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