JOURNAL ARTICLE

Using a Hybrid Collaborative Crisis Management Framework to Foster Long‐Term Growth in Post‐Disaster Reconstruction: Findings From the Chinese Paired Assistance Policy.

  • Published In: Systems Research & Behavioral Science, 2026, v. 43, n. 1. P. 146 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Wang, Linlin; Bivona, Enzo; Yan, Haiyan; Qi, Jiayin 3 of 3

Abstract

This study applies the causal loop analysis grounded in the system dynamics methodology to examine the underlying drivers of the Paired Assistance Policy (PAP) in post‐disaster reconstruction after the Chinese Wenchuan earthquake. The findings reveal that the PAP fosters coordination among diverse stakeholders operating at multiple governmental tiers and across various sectors. This coordination promotes immediate recovery and introduces an innovative mechanism that supports the long‐term development of both the assisting and assisted entities. The study also identifies the potential risks associated with the competitive performance system among the assisting parties. Furthermore, this study also examines the conditions necessary for the effective implementation of the PAP in non‐centralized governance contexts. By exploring capacity building of local authorities, public–private partnerships and adaptation to cultural and social contexts, the findings contribute to a broader understanding of how the PAP can be adapted across different institutional and policy environments worldwide. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Systems Research & Behavioral Science. 2026/01, Vol. 43, Issue 1, p146
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:1092-7026
  • DOI:10.1002/sres.3158
  • Accession Number:191614918
  • Copyright Statement:Copyright of Systems Research & Behavioral Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.