Crisis Learning in Local Governments: Organizational Adaptation and Policy Change During the COVID-19 Crisis

Abstract

Crises can trigger multi-dimensional transformations in public administration systems. This study focuses on the integrated mechanism of learning during crisis to enhance our understanding of emergency management optimization in complex crisis environments. This study constructs an integrated analytical framework that integrates organizational adaptation and policy change to explain crisis learning in local governments and examines the sequential adaptive evolution of Xi’an City’s responses to the Delta and Omicron COVID-19 variants using network analysis and policy text analysis. The findings reveal that learning during crisis in Chinese local governments follows a practice emergence-policy institutionalization feedback loop: ad-hoc special work teams emerged to break rigid bureaucratic labor divisions, and their proven successful practices were institutionalized as permanent emergency working groups in revised emergency plans. This transition from a pure hierarchical structure to a function-led hybrid governance structure shifts local crisis governance from passive reactive response to proactive risk preparedness.