Evolution of Collaborative Governance in the 2015, 2016, and 2018 Myanmar Flood Disaster Responses: A Longitudinal Approach to a Network Analysis

Abstract

In disaster response, collaboration facilitates interactions among actors, such as the government, the military, nongovernmental organizations, and civil society organizations. This study examined the longitudinal changes in collaborative governance in Myanmar’s disaster responses based on cases of flooding in 2015, 2016, and 2018. To examine the mechanisms underlying this dynamic network formation, the collaborative ties of the actors involved in search and rescue activities were converted into longitudinal relational data sets, and the evolution of collaborative governance was analyzed by relying on the assumptions of social capital, transaction cost, homophily, and resource dependency theories and using a longitudinal social network analysis method. The findings show that the collaborative networks of search and rescue processes in disaster response evolved and changed over time according to the hypothesized patterns of strong, weak, and preferential tie formations. The study also revealed that the collaborative governance system assumes the form of a hierarchy rather than a generalized exchange, and the actors’ reliance on military organizations is not obvious due to the emerging alternative non-military actors and diverse local actors observed in the cases.

Emergence and Development of Health Risk Communication Networks Among Street-Level Health Bureaucrats During the COVID-19 Pandemic Crisis in Myanmar

“…With respect to methodology, we employed stochastic actor-oriented modeling (SAOM) using a statistical toolthe simulation investigation for empirical network analysis (SIENA) (Ripley et al 2022)-to analyze the evolution of health risk communication among the 50 frontline healthcare workers over time. Stochastic actor-oriented modeling is appropriate for analyzing longitudinal network dynamics in terms of individual actors' decisions to change their tie formation status, including forging, terminating, or maintaining ties (Lim and Nakazato 2020;Aung and Lim 2021;Lim 2021). In particular, SAOM helps us model the longitudinal changes in risk communication networks as emergent properties from the aggregates of individual actors' choices of communication partners, which will mutually limit and enable each other's choices across time periods within the networks (Contractor et al 2012;Lim and Nakazato 2020).…”

The Multiplexity of Collaborative Networks in Post-Disaster Recovery: Testing Intra-Sector and Cross-Sector Network Contexts

“…Network analysis work is highly valuable in mapping out the overall collaborative networking structures in disaster management (Kapucu & Hu, 2020). Aung and Lim (2021) illustrate this point, comparing the emergency collaborative networks formed by the 2015, 2016, and 2018 Myanmar floods by using a longitudinal social network analysis method, they offer evidence that how collaborative networks of search and relief processes in disaster response formed and evolved during these multiple floods. Furthermore, their analysis shows that the reciprocity of joint actions and collaborative relationships among multiple organizations will affect disaster response efforts.…”

Analisis Bibliometrik Tentang Mitigasi Bencana dan Pembangunan Berkelanjutan: Inisisasi Kebijakan Untuk Indonesia

“…Program-program pendidikan dan pelatihan yang meluas, yang mengintegrasikan literasi bencana, kesiapsiagaan, dan pengetahuan tentang tindakan mitigasi yang tepat, akan membantu meningkatkan kesadaran masyarakat tentang risiko bencana dan memperkuat kapasitas mereka dalam menghadapi situasi darurat. Kolaborasi antara lembaga pendidikan, pemerintah, dan organisasi masyarakat akan menjadi kunci dalam menyusun dan melaksanakan program-program ini secara efektif (Aung & Lim, 2021;Juhadi et al, 2021;Logayah et al, 2022).…”