The Adverse Influence of Data Analytic Attributes and Impact of Interventions on Improving Reliance

To improve performance, organizations are increasingly emphasizing fact-based decision-making supported by data analytics. While the recent focus on data analytics is transforming how managers make decisions, analytics alone do not generate increased performance; the synergy between data analytics and user judgments is a vital component of realizing value. To this end, two experiments investigate the relationship between data analytics and decision-making in accounting. Experiment 1 investigates how data analytic attributes affect individuals’ reliance on the analytics. Through the lens of cognitive fit, a 2×2 between-subjects experiment is conducted to examine the effects of input and process attributes on users’ reliance. Experiment 2 investigates the impact of management interventions (i.e., actions influencing adoption) on improving reliance on data analytics. From an organizational perspective, an important concern for management is promoting greater employee acceptance and utilization of data analytics. As such, Experiment 2 examines the effect of management support and consensus of multiple analytics on increasing reliance. Results from the two experiments collectively support several conclusions. Analytics using input data with more uncertainty (i.e., unstructured data) will decrease user reliance due to a lack of fit; however, this effect can be overcome by explicit management support of a data analytic. Findings also suggest that process-related attributes may not affect user judgements. Results from both experiments fail to yield any evidence that either the transparency of the data analytics process (low vs. high) or consensus between two low-transparency-process analytics affected users’ decision-making. These findings have important theoretical and practical implications