Policy learning and crisis policy-making: quadruple-loop learning and COVID-19 responses in South Korea

Abstract

This study aims to analyze how the Korean government has been effective in taming COVID-19 without forced interruptions (i.e. lockdowns) of citizens' daily lives. Extending the theory of organizational learning, we propose the quadruple-loop learning model, through which we examine how a government can find solutions to a wicked policy problem like COVID-19. The quadruple-loop learning model is applied to explain how the Korean government could effectively tame COVID-19 in the initial stage through its agile as well as adaptive approach based on effective interactions of backstage (time, target, and context) and frontstage of policy processes mainly focusing on the initial stage until the highest alert level was announced. Based on the Korean case, this study also examines critical factors to effective learning organizations such as leadership, information and transparency, as well as citizen participation and governance.