Skip to main content
SpringerLink
Log in
Book cover

Recent Developments in Foresight Methodologies pp 27–52Cite as

Classification of Tools and Approaches Applicable in Foresight Studies

  • Chapter
  • First Online:

Part of the book series: Complex Networks and Dynamic Systems ((CNDS,volume 1))

Abstract

Are predictive quantitative methods too limited to serve as tools in foresight studies? This concern has recently been met by the emerging application of qualitative methods as a means to complement and compensate for the perceived weaknesses of quantitative methods. It is particularly in terms of reflecting sudden changes or detecting incremental and weak signals of change in real societies that quantitative methods are deemed too static. A productive foresight analysis will need a more differentiated sense-making and robust repertoire (Rossel 2010, 2012). Krawczyk and Slaughter (2010: p. 75) state:

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    All candidate tools are described in this book. According to a general template, some common characteristics such as scope, first appearance, mathematical/formal/logical representation, type of mobilization, existing software, sector/scale of application, type of output, strengths and weaknesses, and, finally, a bibliography are all used to describe the characteristics of the foresight tools.

  2. 2.

    All experts were members of the COST A22, work group 2, which focused on the methodological integration of narratives and numbers in foresight exercises.

  3. 3.

    The reader should be cautioned: We probably cannot take the second law of thermodynamics (increasing disorder with time in closed systems) to define a more psychological time arrow: Arguably, we experience a rather cyclical development, alternating between increasing and decreasing order (birth-death, destruction-construction of institutions, etc.) which is typical for open systems (life is the export of entropy, overcoming the increasing disorder trend). The only closed system is the cosmos which we definitely do not experience as a whole.

  4. 4.

    In this chapter, we use the terms typology, taxonomy, and classification interchangeably.

References

  • Bell, W. (2002). A community of futurists and the state of the futures field. Futures, 34(3/4), 235–247.

    Article  Google Scholar 

  • Cunha, M. P. (2002). Time travelling: Organisational foresight as temporal reflexivity. Paper presented at Probing the Future Conference, Glasgow, July 11–13, 2002.

    Google Scholar 

  • Fuller, T., & Loogma, K. (2009). Constructing futures: A social constructionist perspective on foresight methodology. Futures, 41, 71–79.

    Article  Google Scholar 

  • Gordon, T. J. (1994). Integration of forecasting methods and the frontiers of futures research. AC/UNU Millennium Project Paper.

    Google Scholar 

  • Hatch, M. J., & Yanow, D. (2008). Methodology by metaphor: Ways of seeing in painting and research. Organization Studies, 29(1), 23–44.

    Article  Google Scholar 

  • Hawking, S. (1988). A brief history of time. London: Bantam Books.

    Google Scholar 

  • Holden, C. (2005). Time’s up on time travel. Science, 308, 1110.

    Google Scholar 

  • Jacobsen, D. I. (2005). How to conduct research (Hvordan gjennomføre undersøkelser?). Kristiansand: Norwegian Academic Press.

    Google Scholar 

  • Karlsen, J. E. (2002). The self-fulfilling prophecy. In S. U. Larsen (Ed.), Theory and methods in the social sciences. New York: Columbia University Press.

    Google Scholar 

  • Karlsen, J. E., & Karlsen, H. (2007). Expert groups as production units for shared knowledge in energy foresights. Foresight, 9(1), 37–49.

    Article  Google Scholar 

  • Karlsen, J. E., Oeverland, E. F., & Karlsen, H. (2010). Sociological contributions to futures’ theory building. Foresight, 12(3), 59–72.

    Article  Google Scholar 

  • Keenan, M., Butter, M., Sainz, G., & Popper, R. (2006). Mapping foresight in Europe and other regions of the world: The 2006 annual mapping report of the EFMN (Report to the European Commission). Delft: TNO.

    Google Scholar 

  • Krawczyk, E., & Slaughter, R. (2010). New generations of futures methods. Futures, 42, 75–82.

    Article  Google Scholar 

  • Kreuger, R. A. (1988). Focus groups: A practical guide for applied research. London: Sage.

    Google Scholar 

  • Massé, R. (2000). Qualitative and quantitative analyses of psychological distress: Methodological complementarity and ontological incommensurability. Qualitative Health Research, 10, 411–423.

    Article  Google Scholar 

  • May, G. H. (1996). The future is ours: Foreseeing, managing and creating the future. Westport/London: Praeger/Adamantine.

    Google Scholar 

  • Miles, I., & Keenan, M. (2003). Overview of methods used in foresight. Paper presented at the Technology Foresight for Organizers Training Course, Ankara, December 8–12, 2003.

    Google Scholar 

  • Miller, R. (2007). Futures literacy: A hybrid strategic scenario method. Futures, 39, 341–362.

    Article  Google Scholar 

  • Morgan, D. L. (1988). Focus groups as qualitative research. London: Sage.

    Google Scholar 

  • Morgan, G., & Smircich, L. (1980). The case for qualitative research. Academy of Management Review, 5(4), 491–500.

    Google Scholar 

  • Patton, M. Q. (1994). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage.

    Google Scholar 

  • Popper, K. (1957). Propensities, probabilities, and the quantum theory. In D. Miller (Ed.) (1985), Popper selections. Princeton: Princeton University Press

    Google Scholar 

  • Popper, K. (1990). A world of propensities. Bristol: Thoemmes Press.

    Google Scholar 

  • Popper, R. (2008). How are foresight methods selected? Foresight, 10(6), 62–89.

    Article  Google Scholar 

  • Popper, R., Keenan, M., Miles, I., Butter, M., & Sainz, G. (2007). Global foresight outlook 2007: Mapping foresight in Europe and the rest of the world (Annual Mapping Report). European Commission, EFMN, pp. 66. Available at http://prest.mbs.ac.uk/efmn/gfo_2007.pdf

  • Rescher, N. (1998). Predicting the future: An introduction to the theory of forecasting. New York: State University of New York Press.

    Google Scholar 

  • Rossel, P. (2010). Making anticipatory systems more robust. Foresight, 12(3), 73–86.

    Article  Google Scholar 

  • Rossel, P. (2012). Early detection, warnings, weak signals and seeds of change: A turbulent domain of futures studies. Futures, 44(3), 229–239.

    Article  Google Scholar 

  • Suddendorf, T., & Corballis, M. C. (1997). Mental time travel and the evolution of the human mind. Genetic Social and General Psychology Monographs, 123, 133–167. http://cogprints.org/725/.

    Google Scholar 

  • Suddendorf, T., & Corballis, M. C. (2007). The evolution of foresight. What is mental time travel and is it unique to humans? Behavioral and Brain Sciences, 30, 299–351.

    Google Scholar 

  • Tsoukas, M., & Hatch, M. J. (2001). Complex thinking, complex practice: The case for a narrative approach to organizational complexity. Human Relations, 54, 979–1013.

    Article  Google Scholar 

  • UNIDO Technology Foresight Manual (Module 3) (2005). Foresight Brief No 45.

    Google Scholar 

  • Weick, K. E., & Sutcliffe, K. M. (2001). Managing the unexpected. San Francisco: Jossey Bass.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Social Sciences University of Stavanger, Stavanger, 4036, Norway

    Jan Erik Karlsen

  2. Department of Leadership and Organizational Management at BI, Norwegian School of Management, Nydalsveien 37, 0484, Oslo, Norway

    Hanne Karlsen M.Phil.

Authors
  1. Jan Erik Karlsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanne Karlsen M.Phil.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jan Erik Karlsen .

Editor information

Editors and Affiliations

  1. , Department of Geography and Regional Pla, National Technical University Athens, Iroon Polytechniou 9, Athens, 15 780, Greece

    Maria Giaoutzi

  2. Fondazione Ugo Bordoni, Viale del Policlinico 147, Roma, 00161, Italy

    Bartolomeo Sapio

Appendix A

Appendix A

1.1 A.1 Tools/Approaches: Towards a Classification

On a scale of 1–10, evaluate the following aspects regarding the tools/approaches with which you are familiar.

1.2 A.2 Qualitative/Quantitative

Evaluates the presence of narratives/metrics in the representation of information (1  =  purely qualitative, 10  =  highly quantitative)

1.3 A.3 Mobilization

Evaluates the number of experts required for the application of the tool (1  =  lowest number of experts, 10  =  highest number of experts)

1.4 A.4 Scope

Evaluates the number of domains to which the tool is applied (1  =  lowest number of domains, 10  =  highest number of domains)

1.5 A.5 Complexity: Input

Evaluates the complexity (numerosity, interrelations, sources) of input data (1  =  lowest complexity, 10  =  highest complexity)

1.6 A.6 Complexity: Process

Evaluates the complexity (mathematical, philosophical, emergence, stakeholders) of the process (1  =  lowest complexity, 10  =  highest complexity)

1.7 A.7 Complexity: Output

Evaluates the complexity (representation, communication, transparency) of the output (1  =  lowest complexity, 10  =  highest complexity)

1.8 Appendix B

1.9 Table B1 Tools and approaches assessed by the experts

 1. Analogy analysis

12. Game theory

23. Expert panels

 2. Backcasting

13. Gap analysis

24. Qualitative dynamic modelling

 3. Brainstorming

14. Input-output analysis

25. Relevance trees

 4. Cost-benefit analysis

15. Integrated assessment

26. Risk assessment

 5. Cross impact analysis

16. Inter-temporal utility optimization

27. Roadmapping

 6. Decision matrices

17. Key technologies

28. Scenario analysis

 7. Delphi

18. Lateral thinking

29. Subjective probability assessment

 8. Evolutionary modelling

19. Micro-simulations

30. Surveying

 9. Focus groups

20. Multi-agent simulations

31. SWOT analysis

10. Forecasting

21. Multi-criteria analysis

32. System dynamics

11. Fuzzy logic

22. Neural networks

33. Trend spotting

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Karlsen, J.E., Karlsen, H. (2013). Classification of Tools and Approaches Applicable in Foresight Studies. In: Giaoutzi, M., Sapio, B. (eds) Recent Developments in Foresight Methodologies. Complex Networks and Dynamic Systems, vol 1. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-5215-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation