Advice Utilization From Predictive Analytics Tools: The Trend is Your Friend

Abstract

ABSTRACT Management decision-making is increasingly supported by new data types and advanced predictive analytics tools. Prior research suggests that the inclusion of new data types – such as social media data – in forecasting models can improve forecasting. We explore whether managers’ operational decisions differ depending on the data type used by a predictive analytics tool and the consistency of the trend with prior developments. Experimental results show that the extent to which managers use predictions from analytics tools is a joint function of the data type utilized and trend consistency. If a trend predicted by an analytics tool reveals a downward break from prior positive developments (i.e., an unexpected negative trend), managers utilize predictions less if they are mainly based on social media data rather than on traditional accounting data. If a trend predicted by an analytics tool continues a prior positive trend, we do not find such a difference. In supplemental analyses, we explore managers’ comfort level and related attitude concerning the data types and find that only in the trend-breaking condition mediation effects are observed. Together, our findings have important implications for the management accounting function that needs to embed knowledge about managers’ information utilization to facilitate decision-making.