Health and well-being are key focuses for international organizations and governments around the world as one of the United Nations' (UN) Sustainable Developmental Goals (SDGs). The emergence of COVID-19 pandemic since late 2019 has led to not only substantial life losses, but also negative social and economic impacts that are relatively comparable to the "Spanish Flu" pandemic. The insights from public administration literature suggested that emergency and crisis management require collaborative efforts from various stakeholders across sectors and levels. However, the existing literature that studied the COVID-19 pandemic response under network perspective is still scarce in developing countries, especially in Thailand in particular. Understanding the structures of the COVID-19 pandemic response networks and their effectiveness can provide insights for not only scholarly theoretical development, but also lessons learned for practitioners in developing future pandemic response system. The current study builds upon the network governance literature by providing multiple case studies of Thailand's provincial COVID-19 pandemic response networks. Specifically, it is aimed to explore the characteristics of the network structures in COVID-19 pandemic response networks in Samut Sakhon province, and the relationship between network structures and network effectiveness of this initiative. Furthermore, the current study is also aimed at comparing the network structures and network effectiveness between different geographical-administrative structures, as well as between different policy processes, to examine the influence of the regional administration and the local municipality administration on the provincial COVID-19 pandemic response networks in Thailand. This study applies a Multi-Theory, Multi-Level Network Governance (MTML) framework as key conceptual framework for studying Thailand's provincial COVID-19 pandemic response networks. It adopts a comparative multiple-case case studies research design with the purposive sampling and snowball sampling strategies. The semi-closed-ended roster of the organization list and interviews were used for social network data collection. Descriptive social network statistics, network visualization, and MR-QAP regression are key data analyses methods for this study. The descriptive findings suggest that Thailand's provincial COVID-19 response networks in Samut Sakhon province demonstrate the centralized network structures that the provincial administration actors and the district administration actors are the key policy actors in several network relationships in all districts and at the provincial level. The explanatory analyses suggest that policy institutional rules networks and the actual policy ties are key predictors for the resources allocation networks in all districts and the provincial networks, whereas there is no consensus for network characteristics as predictors for perceived network effectiveness. Study's theoretical contributions, its limitations, and implications for research and practice are also discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2681 |
| Date | 01 January 2023 |
| Creators | Trairatananusorn, Ruechagorn |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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