Effective Judgmental Forecasting in the Context of Fashion Products (Reprint)

Matthias Seifert (), Enno Siemsen, Allègre L. Hadida and Andreas E. Eisingerich
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Matthias Seifert: IE Business School – IE University
Enno Siemsen: University of Wisconsin
Allègre L. Hadida: Cambridge University
Andreas E. Eisingerich: Imperial College Business School

Chapter Chapter 4 in Judgment in Predictive Analytics, 2023, pp 85-114 from Springer

Abstract: Abstract We study the conditions that influence judgmental forecasting effectiveness when predicting demand in the context of fashion products. Human judgment is of practical importance in this setting. Our goal is to investigate what type of decision support, in particular historical and/or contextual predictors, should be provided to human forecasters to improve their ability to detect and exploit linear and nonlinear cue-criterion relationships in the task environment. Using a field experiment on new product forecasts in the music industry, our analysis reveals that when forecasters are concerned with predictive accuracy and only managerial judgments are employed, providing both types of decision support data is beneficial. However, if judgmental forecasts are combined with a statistical forecast, restricting the decision support provided to human judges to contextual anchors is beneficial. We identify two novel interactions demonstrating that the exploitation of nonlinearities is easiest for human judgment if contextual data are present but historical data are absent. Thus, if the role of human judgment is to detect these nonlinearities (and the linearities are taken care of by some statistical model with which judgments are combined), then a restriction of the decision support provided would make sense. Implications for the theory and practice of building decision support models are discussed.

Keywords: Judgmental forecasting; Fashion products; Lens model design; Demand uncertainty; Music industry; New product forecasting (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-30085-1_4

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DOI: 10.1007/978-3-031-30085-1_4

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