Improving judgmental forecasts with judgmental bootstrapping and task feedback support

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)62_Review of books or of software (or similar publications/items)Not applicablepeer-review

14 Scopus Citations
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Author(s)

  • Marcus O'Connor
  • William Remus
  • Kai Lim

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)247-260
Journal / PublicationJournal of Behavioral Decision Making
Volume18
Issue number4
Publication statusPublished - Oct 2005

Abstract

This study examines the utility of two widely advocated methods for supporting judgmental forecasts - providing task feedback and providing judgmental bootstrapping support. In a simulated laboratory based experiment that focused on producing composite sales forecasts from three individual components, we compared the effectiveness of these two methods in improving final judgmental forecasts. In the presence of cognitive feedback task, feedback led to better forecasts than providing judgmental bootstrap forecasts. Simply providing bootstrap forecasts was of no additional benefit over a control condition. This was true in terms of the Brunswik Lens model measures of achievement, knowledge, and consistency, and in terms of forecast accuracy. This occurred both in stable environments and when special events (unusual one-time events requiring adjustments to the forecasts) arose. Copyright © 2005 John Wiley & Sons, Ltd.

Research Area(s)

  • Brunswik lens model, Feedback intervention theory, Judgmental bootstrapping, Judgmental forecasts, Social judgment theory, Statistical forecasts, Task feedback

Citation Format(s)