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• W2039968229 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 makes sense. Implications for the theory and practice of building decision support models are discussed." @default.
• W2039968229 created "2016-06-24" @default.
• W2039968229 creator A5005954794 @default.
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• W2039968229 creator A5079674709 @default.
• W2039968229 creator A5086167634 @default.
• W2039968229 date "2015-02-28" @default.
• W2039968229 modified "2023-09-29" @default.
• W2039968229 title "Effective judgmental forecasting in the context of fashion products⋆" @default.
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• W2039968229 doi "https://doi.org/10.1016/j.jom.2015.02.001" @default.
• W2039968229 hasPublicationYear "2015" @default.
• W2039968229 type Work @default.

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