Advice Utilization From Predictive Analytics Tools: The Trend is Your Friend
实验发现，管理者对预测分析工具建议的利用程度取决于数据类型和趋势一致性：当预测出现意外负面趋势时，基于社交媒体数据的建议比基于传统会计数据的建议更少被采纳。
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.