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A Theory of Decision Support System Design for User Calibration

Author

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  • George M. Kasper

    (Department of Information Systems and Quantitative Sciences, College of Business Administration, Texas Tech University, Lubbock, Texas 79409-2101)

Abstract

A theory is proposed for designing decision support systems (DSS) so that the confidence a decision maker has in a decision made using the aid equals the quality of that decision. The DSS design theory for user calibration prescribes properties of a DSS needed for users to achieve perfect calibration. Relevant calibration, decision making, and DSS literatures are synthesized; and related behavioral theories are borrowed to identify the properties of expressiveness, visibility, and inquirability as requisite components of the DSS design theory for user calibration.

Suggested Citation

  • George M. Kasper, 1996. "A Theory of Decision Support System Design for User Calibration," Information Systems Research, INFORMS, vol. 7(2), pages 215-232, June.
    • Handle: RePEc:inm:orisre:v:7:y:1996:i:2:p:215-232
    DOI: 10.1287/isre.7.2.215
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    Cited by:

    1. Kjell Hausken, 2017. "Information Sharing Among Cyber Hackers in Successive Attacks," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 1-33, June.
    2. Liying Wei, 2016. "Decision-making Behaviours toward Online Shopping," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 8(3), pages 111-121, June.
    3. Patrick Krieger & Carsten Lausberg, 2021. "Entscheidungen, Entscheidungsfindung und Entscheidungsunterstützung in der Immobilienwirtschaft: Eine systematische Literaturübersicht [Decisions, decision-making and decisions support systems in r," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 7(1), pages 1-33, April.
    4. Hausken, Kjell, 2017. "Defense and attack for interdependent systems," European Journal of Operational Research, Elsevier, vol. 256(2), pages 582-591.
    5. Richard Baskerville & Jan Pries-Heje, 2010. "Explanatory Design Theory," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(5), pages 271-282, October.
    6. Robinson, Stewart, 2002. "General concepts of quality for discrete-event simulation," European Journal of Operational Research, Elsevier, vol. 138(1), pages 103-117, April.
    7. Lurie, Nicholas H. & Wen, Na, 2014. "Simple Decision Aids and Consumer Decision Making," Journal of Retailing, Elsevier, vol. 90(4), pages 511-523.
    8. T R Willemain & W A Wallace & K R Fleischmann & L B Waisel & S N Ganaway, 2003. "Bad numbers: coping with flawed decision support," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(9), pages 949-957, September.
    9. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    10. Claudia Schütze & Catherine Cleophas & Monideepa Tarafdar, 2020. "Revenue management systems as symbiotic analytics systems: insights from a field study," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 1007-1031, November.
    11. Almilia Luciana S. & Dewi Nurul H. U. & Wulanditya Putri, 2019. "The effect of visualization and complexity tasks in investment decision making," HOLISTICA – Journal of Business and Public Administration, Sciendo, vol. 10(1), pages 68-77, April.
    12. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.

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