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 |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Postgrad (School of Social and Political Studies) |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | 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. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | No |
Course Delivery Information
|
| Delivery period: 2013/14 Semester 2, Available to all students (SV1)
|
Learn enabled: Yes |
Quota: 16 |
|
Web Timetable |
Web Timetable |
| Course Start Date |
13/01/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
1. This course has two specific aims. The first is to have students develop an in-depth case study of a developing field of bioscience or innovative new biotechnology. Relevant case studies are bioscience and biotechnology in a research and development phase, which eliminates cases in which the science is well developed and a stable line of products and services is already commercialised. Case studies involving science and technology false starts might be considered in special circumstances, but generally the best cases are those where the science and technology pathway is relatively well-defined. This could mean early stage, targeted research investments, it could suggest translational research and early stage technology development, or it could mean later stage technology development, possibly at or nearing commercialisation. In all cases the case study must be directly relevant to bioeconomy innovation and governance.
The second aim of the course is for students to engage with the uncertain future of innovation in the bioeconomy by framing and interpreting the case study using the tools of science and technology foresight methodology. The case study method is often used in foresighting exercises, but other methods can be brought into the foresight exercise where empirical information or judgments must be made. This approach entails that case study development cannot be based solely on the written record. Instead, finding and using other sources of information to build the case study and undertake the foresight analysis is a critical component of course.
2. 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.
3. 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 I.
4. 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.
5. 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. Students learn to communicate effectively about the foresight exercise in the format and language of a strategic plan. |
Assessment Information
| Assessment will be a final essay of 3000 words presenting a foresight scenario and associated strategic recommendations. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
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 |
| Transferable skills |
Not entered |
| 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. |
| Study Abroad |
Not entered |
| Study Pattern |
The course is a 10 credit option delivered over ten weeks, with bi-weekly meetings of two hours each. The first class introduces the course and explains the case study method and criteria for selecting case studies in CTLSI-II. The second meeting is a workshop on student-selected case study topics. The third class introduces the foresight methodology developed through a case study. The fourth class is a workshop on foresight methodology and student-selected case studies. The fifth meeting is devoted to presentations of the strategic plans arising from the foresight exercise. |
| Keywords | Not entered |
Contacts
| Course organiser | Prof David Castle
Tel: (0131 6)50 2449
Email: David.Castle@ed.ac.uk |
Course secretary | Miss Jade Birkin
Tel: (0131 6)51 1569
Email: Jade.Birkin@ed.ac.uk |
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