Sales forecasters in industries like fast–fashion face challenges posed by short sales time series that are highly volatile. Computers can produce statistical forecasts, but these are often judgmentally adjusted to take into account factors such as market intelligence. We explore the role of two potential influences on these adjustments: the forecaster’s involvement with the product category and their emotional reaction to particular products. Two forecasting experiments were conducted using data from a major Italian leather fashion goods producer. The participants’ judgmental adjustments tended to lower the accuracy of forecasts but this was exacerbated when participants had strong preferences for particular products. This appeared to result from a false consensus effect. The most accurate forecasts were made when participants had no knowledge of the product and only received time series information High involvement with the product category also led to greater accuracy.
- Sales Forecasting; Judgmental Forecasting; New Products; Emotions; Product Involvement; False Consensus.