Postgraduate Course: Current Trends in Life Science Innovation II (PGSP11332)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Science and technology innovation has complex dynamics with respect to the rate, complexity, and impact of innovation. There is no simple way to predict which bioscience will become world-leading, which biotechnology will become a frontrunner, and how to make the most of targeted investments. No rule of the market, and no policy of government, is a reliable predictor of innovation. Furthermore, factors exogenous to the domain of science and technology innovation can become significant determinants of how the bioeconomy performs. In situations where there is rapid public response to emerging technologies, it can be enough to stifle innovation.
Despite these caveats, it is also true that without targeted investments and the coordination of academe, the private and public sectors, bioscience and biotechnology innovation would flourish less, making universities, firms, regions and countries less competitive and citizens less prosperous. Given the scale of science and technology infrastructure and associated investment costs, strategic planning in the bioeconomy is a high risk, but potentially high reward, endeavour. Government, academe and the private sector have to make decisions, separately or jointly, that will foster innovation and minimize the risk of failure. The evidence they amass the reasons behind the decisions must be better than educated guesses about the future.
Science and technology foresight exercises generally have three phases involving the collection of evidence, analysis of the most likely drivers of change, and a phase in which avenues to preferable futures are explored while minimising the likelihood of dystopias. There are many methods used to support foresight exercises, including Delphi studies, financial trend analysis, focus groups, computer modelling and so forth. A subset of methods is deployed when there is a need for more empirical information, and a different subset is used when a decision requiring judgments of experts is needed.
Foresight exercises provide a structured response to the problem of facing uncertainties about the future of science and technology innovation. They are widely used in the private sector, but also by governments who use them to set science and technology policy, and innovation policy. |
| Course description |
WEEK TOPIC
1) Foresight methodology
2) Scenario planning methods
3) Scenarios as a basis for innovation or business strategy
4) Presentations of foresight analysis and strategy recommendations
5) Presentations of foresight analysis and strategy recommendations
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- ¿ Be able to apply core concepts and theories learned to develop a case study of bioscience and biotechnology innovation in the bioeconomy using established techniques for case study development.
- Have the knowledge and understanding of the trends in life science innovation, allowing them to situate the case study they are developing within the context of current trends in life science innovation discussed in Current Trends in Life Science Innovation.
- ¿ Have critical knowledge of the methodology of science and technology foresight analysis, and will be able to offer practical analyses of the role that case study development plays in foresight.
- ¿ Be able to apply their knowledge of theories, concepts and practices associated with science and technology foresight analysis to identify and asses the drivers of change and the potential routes to desirable futures, while avoiding dystopias.
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Reading List
Bishop, P., Hines, A., and T. Collins. 2007. The Current State of Scenario Development: An Overview of Techniques. Foresight 9:5-25.
Coates, J.F. 1985. Foresight in federal government policy making. Futures Research Quarterly. 1:29-53.
Popper, R. 2008. How are foresight methods selected? Foresight 10:62-89.
Ellet, W. 2007. Case Study Handbook: How to Read, Discuss, and Write Persuasively About Cases. Harvard: Harvard Business Press Books.
Georghiou, L. 2001. Third generation foresight ¿ integrating the socio-economic dimension. Proceedings of the International Conference on Technology Foresight. The Approach to and Potential for New Technology Foresight, NISTEP Research Material No. 77, March.
Georghiou, L. and M. Keenan 2005. Evaluation of national foresight activities, assessing rationale, process and impact. Technological Forecasting and Social Change. 73:761-77.
Gerring, J. 2007. Case Study Research: Principles and Practices. Cambridge: Cambridge University Press.
Jefferson R. 2012. Shell scenarios: What really happened in the 1970s and what may be learned for current world prospects. Technological Forecasting and Social Change 79(1): 186-97.
Ramirez, R. 2008. Forty Years of Scenarios: Retrospect and Prospect. In Mapping the Management Journey. Oxford: Oxford University Press.
Ramírez R, Österman R and D Grönquist. 2012. Scenarios and early warnings as dynamic capabilities to frame managerial attention. Technological Forecasting and Social Change. Online preprint.
Ramírez R, Roodhart L and W Manders. 2011. How Shell¿s domains link innovation and strategy. Long Range Planning 44:250-70.
Trochim, W. 1989. An Introduction to Concept Mapping For Planning and Evaluation. Evaluation and Program Planning 12:1-16.
Wack P. 1985. Scenarios: Uncharted Waters. Harvard Business Review, September-October, pp 73-89.
Wack P. 1985. Scenarios: shooting the rapids. Harvard Business Review, November-December, pp 2-14.
Weber, E., Eriksson, A. and K. Matthias. 2008. Adaptive Foresight: Navigating the complex landscape of policy strategies. Technological Forecasting and Social Change. 75:462-482.
Wilkinson, A. 2009. Scenarios Practices: In Search of Theory. Journal of Futures Studies. 13:107-114.
Yin, R. K. (1984). Case study research: Design and methods. Newbury Park, CA: Sage. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr James Mittra
Tel: (0131 6)50 2453
Email: |
Course secretary | Miss Jade Birkin
Tel: (0131 6)51 1659
Email: |
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