Knowledge Generating Decision Support Systems: Managing the Trade-Off Between Generating Knowledge and Supporting Decisions

  • Carlo Caserio
Conference paper


The aim of the paper is to analyze the effectiveness of Decision Support Systems (DSS) in the business planning area, especially with regard to the trade-off between supporting decisions and generating knowledge.

The research hypothesis is that different conditions and approaches in managing the planning DSS may strengthen either decisional support or the knowledge generation capability.

Assuming that a DSS is generally composed of a database, a model and an user interface (Power D.J. (2002) Decision Support Systems: Concepts and Resources for Managers, Quorum Books, Westport), and considering that it can be classified as open or closed, the following main conditions for effectively manage an open DSS are discussed:
  • The IT tools used;

  • The formalization criteria which defines relationships between the variables;

  • The interaction between the decision maker and the model;

  • The interaction between the decision maker and organizational context, according to Nonaka’s framework (Nonaka I. (1991) The Knowledge-Creating Company, Harvard Business Review, Nov/Dec, Vol. 69(6):96–104).

The paper also discusses the reasons why a closed model is more suitable for supporting decisions, and an open model is more appropriate for generating knowledge. In addition, the research hypothesis will be empirically tested with a panel of users.


Decision Maker Open Model Expert System Decision Support System Tacit Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Beemer B.A. and Gregg D.G (2010) Dynamic Interaction in Knowledge Based Systems: an Exploratory Investigation and Empirical Evaluation, Decision Support Systems, 49(4): 386–395.CrossRefGoogle Scholar
  2. 2.
    McGraw K. And Harbison-Briggs K.A (1989) Knowledge Acquisition: Principles and Guidelines, Prentice-Hall, NJ.Google Scholar
  3. 3.
    Kim C.N., Yang K.H. and Kim J (2008) Human decision-making behaviour and modelling effects, Decision Support Systems, 45(1):517–527.CrossRefGoogle Scholar
  4. 4.
    Ayed M.B., Ltifi H., Kolski C. And Alimi A.M. (2010) A User-Centered Approach for the Design and Implementation of KDD-based DSS: a Case Study in the Healthcare Domain, Decision Support Systems, Vol. 50(1):64–78.CrossRefGoogle Scholar
  5. 5.
    Mintzberg H., Raisinghani D. and Theoret A (1976) The structure of “unstructured” decision processes, Administrative Science Quarterly 21(2):246–275.CrossRefGoogle Scholar
  6. 6.
    Simon H.A. (1960) The New Science of Management Decision, Harper and Row, Prentice Hall.Google Scholar
  7. 7.
    Power D.J. (2002) Decision Support Systems: Concepts and Resources for Managers, Quorum Books, Westport.Google Scholar
  8. 8.
    Beynon M., Rasmequan S. And Russ S. (2002) A New Paradigm for Computer-Based Decision Support, Decision Support Systems, 33(2):127–142.CrossRefGoogle Scholar
  9. 9.
    Larman C. (2007) Agile and Iterative Development: A Manager’s Guide, Addison-Wesley, Pearson Education.Google Scholar
  10. 10.
    Kent B. (2000) Extreme Programming Explained: Embrace Change, Addison-Wesley.Google Scholar
  11. 11.
    Drucker P. (1995) The Post-Capitalist Executive: Managing in a Time of Great Change, Penguin, New York.Google Scholar
  12. 12.
    Sanin C., Szczerbicki E. (2004) Knowledge Supply Chain System: A Conceptual Model, Knowledge Management: Selected Issues, Szuwarzynski A. (Ed), Gdansk University Press/Gdansk, 79–97.Google Scholar
  13. 13.
    Sanin C., Szczerbicki E., Toro C. (2007) An OWL Ontology of Set of Experience Knowledge Structure, Journal of Universal Computer Science, vol. 13(2):209–223.Google Scholar
  14. 14.
    Caserio C., Marchi L. (2010) Generating Knowledge by Combining Prediction Models with Information Technology, D'Atri A., De Marco M., Braccini A.M., Cabiddu F. (Ed) Management of the Interconnected World, 1st edition, Springer, 2010, 307–314.Google Scholar
  15. 15.
    Kontio J., Bragge J., Lenthola L. (2008) The Focus Group Method as an Empirical Tool in Software Engineering, Shull F., Singer J., Sjøberg D.I.K. (Ed) Guide to Advanced Empirical Software Engineering, Springer.Google Scholar
  16. 16.
    Morgan D.L. (1997) Focus Group as Qualitative Research, Sage Publications, Thousand Oaks, CA.Google Scholar
  17. 17.
    Nonaka I. (1991) The Knowledge-Creating Company, Harvard Business Review, Nov/Dec, Vol. 69(6):96–104.Google Scholar
  18. 18.
    De Geus A.P. (1992) Modelling to predict or to learn?, European Journal of Operational Research, Vol. 59(1):1–5.CrossRefGoogle Scholar
  19. 19.
    - (1988) Planning for learning, Harvard Business Review, Mar/Apr, Vol. 66(2):70–74.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Business AdministrationUniversity of PisaPisaItaly

Personalised recommendations