Barriers to Predictive Analytics Use for Policy Decision-Making Effectiveness in Turbulent Times: A Case Study of Fukushima Nuclear Accident

Abstract

Predictive analytics are data-driven software tools that draw on confirmed relationships between variables to predict future outcomes. Hence they may provide government with new analytical capabilities for enhancing policy decision-making effectiveness in turbulent environments. However, predictive analytics system use research is still lacking. Therefore, this study adapts the existing model of strategic decision-making effectiveness to examine government use of predictive analytics in turbulent times and to identify barriers to using information effectively in enhancing policy decision making effectiveness. We use a case study research to address two research questions in the context of the 2011 Fukushima nuclear accident. Our study found varying levels of proactive use of SPEEDI predictive analytics system during the escalating nuclear reactor meltdowns between Japan’s central government agencies and between the central and the state government levels. Using the model, we argue that procedural rationality and political behavior can be used to explain some observed variations.