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What drives decision makers to follow or ignore forecasting tools - A game based analysis

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  • Lackes, Richard
  • Siepermann, Markus
  • Vetter, Georg

Abstract

Using forecasts is a prerequisite for good decision making but often decision makers ignore the forecasts' outcomes and rely on their personal assessment. This usually leads to worse decisions. But forecasts can also be defective. Thus, it is crucial that decision makers do not rely blindly on forecasts but scrutinize critically the results. The question is under which circumstances decision makers follow or ignore forecasts. Therefore, we conducted a laboratory experiment where decision makers have the choice between two alternatives. The forecast provided gives an advice. But it is manipulated so that it is only partly reliable. Results show that participants do not act optimally. Many decision makers follow the manipulated forecast if for example their performance is dominated by the forecast or if they perceive competition. Hence, gamifying such research may impact the results that can be to some extent reinforced. However, the general meaning is not distorted.

Suggested Citation

  • Lackes, Richard & Siepermann, Markus & Vetter, Georg, 2020. "What drives decision makers to follow or ignore forecasting tools - A game based analysis," Journal of Business Research, Elsevier, vol. 106(C), pages 315-322.
    • Handle: RePEc:eee:jbrese:v:106:y:2020:i:c:p:315-322
    DOI: 10.1016/j.jbusres.2019.02.036
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    1. Andrew W. Lo & Dmitry V. Repin & Brett N. Steenbarger, 2005. "Fear and Greed in Financial Markets: A Clinical Study of Day-Traders," American Economic Review, American Economic Association, vol. 95(2), pages 352-359, May.
    2. Caterina Lucarelli & Pierpaolo Uberti & Gianni Brighetti, 2015. "Misclassifications in financial risk tolerance," Journal of Risk Research, Taylor & Francis Journals, vol. 18(4), pages 467-482, April.
    3. Moon, Mark A. & Mentzer, John T. & Smith, Carlo D., 2003. "Conducting a sales forecasting audit," International Journal of Forecasting, Elsevier, vol. 19(1), pages 5-25.
    4. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    5. Kai Huotari & Juho Hamari, 2017. "A definition for gamification: anchoring gamification in the service marketing literature," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(1), pages 21-31, February.
    6. Marie Juanchich & Miroslav Sirota, 2016. "How to improve people's interpretation of probabilities of precipitation," Journal of Risk Research, Taylor & Francis Journals, vol. 19(3), pages 388-404, March.
    7. Huang, Wei, 2016. "The use of management forecasts to dampen analysts' expectations by Chinese listed firms," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 263-272.
    8. Remus, William, 1986. "Graduate students as surrogates for managers in experiments on business decision making," Journal of Business Research, Elsevier, vol. 14(1), pages 19-25, February.
    9. Kathy Rupar, 2017. "Significance of Forecast Precision: The Importance of Investors’ Expectations," Contemporary Accounting Research, John Wiley & Sons, vol. 34(2), pages 849-870, June.
    10. Gerrit Sarens & Giuseppe D’Onza, 2017. "The perception of financial analysts on risk, risk management, and internal control disclosure: Evidence from Belgium and Italy," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 14(2), pages 118-138, May.
    11. Lisa M. Gaynor & Andrea S. Kelton, 2014. "The effects of analyst forecasts and earnings trends on perceptions of management forecast credibility," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 54(1), pages 189-210, March.
    12. Robert E. O'Connor & Brent Yarnal & Kirstin Dow & Christine L. Jocoy & Gregory J. Carbone, 2005. "Feeling at Risk Matters: Water Managers and the Decision to Use Forecasts," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1265-1275, October.
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