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The effect of relative performance feedback on judgmental forecasting accuracy

The effect of relative performance feedback on judgmental forecasting accuracy Forecasting processes in organizational settings largely rely on human judgment, which makes it important to examine ways to improve the accuracy of these judgmental forecasts. The purpose of this paper is to test the effect of providing relative performance feedback on judgmental forecasting accuracy.Design/methodology/approachThis paper is based on a controlled laboratory experiment.FindingsThe authors show that feedback that ranks the forecasting performance of participants improves their accuracy compared with the forecasting accuracy of participants who do not get such feedback. The authors also find that the effectiveness of such relative performance feedback depends on the content of the feedback information as well as on whether accurate forecasting performance is linked to additional financial rewards. Relative performance feedback becomes more effective when subjects are told they rank behind other participants than when they are told they rank higher than other participants. This finding is consistent with loss aversion: low-ranked individuals view their performance as a loss and work harder to avoid it. By contrast, top performers tend to slack off. Finally, the authors find that the addition of monetary rewards for top performers reduces the effectiveness of relative performance feedback, particularly for individuals whose performance ranks near the bottom.Originality/valueOne way to improve forecasting accuracy when forecasts rely on human judgment is to design an effective incentive system. Despite the crucial role of judgmental forecasts in organizations, little attention has been devoted to this topic. The aim of this study is to add to the literature in this field. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management Decision Emerald Publishing

The effect of relative performance feedback on judgmental forecasting accuracy

Kim, Hyo Young; Lee, Yun Shin; Jun, Duk Bin
Management Decision , Volume 57 (7): 17 – Jul 5, 2019
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References (77)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0025-1747
DOI
10.1108/md-06-2017-0549
Publisher site
See Article on Publisher Site

Abstract

Forecasting processes in organizational settings largely rely on human judgment, which makes it important to examine ways to improve the accuracy of these judgmental forecasts. The purpose of this paper is to test the effect of providing relative performance feedback on judgmental forecasting accuracy.Design/methodology/approachThis paper is based on a controlled laboratory experiment.FindingsThe authors show that feedback that ranks the forecasting performance of participants improves their accuracy compared with the forecasting accuracy of participants who do not get such feedback. The authors also find that the effectiveness of such relative performance feedback depends on the content of the feedback information as well as on whether accurate forecasting performance is linked to additional financial rewards. Relative performance feedback becomes more effective when subjects are told they rank behind other participants than when they are told they rank higher than other participants. This finding is consistent with loss aversion: low-ranked individuals view their performance as a loss and work harder to avoid it. By contrast, top performers tend to slack off. Finally, the authors find that the addition of monetary rewards for top performers reduces the effectiveness of relative performance feedback, particularly for individuals whose performance ranks near the bottom.Originality/valueOne way to improve forecasting accuracy when forecasts rely on human judgment is to design an effective incentive system. Despite the crucial role of judgmental forecasts in organizations, little attention has been devoted to this topic. The aim of this study is to add to the literature in this field.

Journal

Management Decision – Emerald Publishing

Published: Jul 5, 2019

Keywords: Decision making; Laboratory experiment; Judgemental forecasting; Nonfinancial incentives; Rank feedback

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