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Influence of user participation on DSS use and decision accuracy

Listed author(s):
  • Lawrence, Michael
  • Goodwin, Paul
  • Fildes, Robert
Registered author(s):

    The paper reports a study of the impact on user satisfaction and forecast accuracy of user involvement in the design of a forecasting decision support system (FDSS). Two versions of an FDSS were tested via a laboratory study. Version 1, allowed the user control over all aspects of the system including the "look" of various screen elements and, most importantly, the model to be used could be selected (and tested) from a number of alternative forecasting models provided within the FDSS. In contrast, Version 2 did not allow the user to modify the "look" of the screen, and also provided no opportunity for model selection: this feature was carried out optimally by the FDSS. The user was told the advantage of optimal model selection. Both versions finished by asking the user to either accept the forecast (displayed as a point on the time-series graph) or to modify it via the mouse if unhappy with it. Results showed a much greater satisfaction with the forecasts provided by Version 1, confirming the importance of user involvement. Users of Version 1, in about half the cases, selected poor models with high forecast error. Where a model close to optimal was selected, the accuracy of Version 1 users greatly outperformed low involvement Version 2 users. Overall, however, the accuracy of the final forecasts for users of Version 1 was slightly inferior to that of users of Version 2. Measurements of ease of use and usefulness showed no real differences between the two versions.

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    Article provided by Elsevier in its journal Omega.

    Volume (Year): 30 (2002)
    Issue (Month): 5 (October)
    Pages: 381-392

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    Handle: RePEc:eee:jomega:v:30:y:2002:i:5:p:381-392
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    1. James E. Hunton & Kenneth H. Price, 1997. "Effects of the User Participation Process and Task Meaningfulness on Key Information System Outcomes," Management Science, INFORMS, vol. 43(6), pages 797-812, June.
    2. Goodwin, Paul & Wright, George, 1993. "Improving judgmental time series forecasting: A review of the guidance provided by research," International Journal of Forecasting, Elsevier, vol. 9(2), pages 147-161, August.
    3. Igbaria, M & Guimaraes, T, 1994. "Empirically testing the outcomes of user involvement in DSS development," Omega, Elsevier, vol. 22(2), pages 157-172, March.
    4. Watson, Moira C., 1996. "Forecasting in the Scottish electronics industry," International Journal of Forecasting, Elsevier, vol. 12(3), pages 361-371, September.
    5. Arkes, Hal R. & Dawes, Robyn M. & Christensen, Caryn, 1986. "Factors influencing the use of a decision rule in a probabilistic task," Organizational Behavior and Human Decision Processes, Elsevier, vol. 37(1), pages 93-110, February.
    6. Davis, Fred D. & Kottemann, Jeffrey E., 1995. "Determinants of Decision Rule Use in a Production Planning Task," Organizational Behavior and Human Decision Processes, Elsevier, vol. 63(2), pages 145-157, August.
    7. Lawrence, Michael & Sim, William, 1999. "Prototyping a financial DSS," Omega, Elsevier, vol. 27(4), pages 445-450, August.
    8. Lawrence, Michael & O'Connor, Marcus & Edmundson, Bob, 2000. "A field study of sales forecasting accuracy and processes," European Journal of Operational Research, Elsevier, vol. 122(1), pages 151-160, April.
    9. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
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