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Journal and Book Chapter
RDI eBook series, No.1 (DCR) 20230702
This contributing paper delves into the impact of a tsunami and the COVID-19 pandemic on the coastal tourism sector in Pandeglang Regency (Banten Province) in Indonesia. The sector is a significant contributor to regional income and one of its coastal areas is designated as a Special Economic Zone in 2018. The tourism sector in Pandeglang Regency is supported by many components, one of them is the hotel industry which has a wider economic impact because of its links to other industries, creating a supply chain. Unfortunately, most of those accommodation facilities are located in area prone to tsunamis. This resulted in the large impact that the tsunami had on Pandeglang’s tourism hospitality supply chain. One year after the disaster, tourism was still unable to recover, then two years after the tsunami, the COVID-19 outbreak appears, making the recovery measures more difficult. Based on that, this study aims to understand the indirect impacts of Sunda Strait Tsunami and COVID-19 pandemic and how it affects the resilience of tourism hospitality supply chain in Pandeglang. In order to quantify the indirect losses, this research adopts the Support Analysis Framework (SAF) with several relevant modifications. There are three indicators included in SAF analysis: (1) Losses of Financial Function; (2) Business Disruption Between Tourism Business Actors; (3) Operational Changes. This paper finds, based on the SAF calculation, that the index of indirect losses from the Sunda Strait Tsunami and COVID-19 is in the third class which is high losses. The two incidents had caused huge losses on the Pandeglang tourism sector, affected suppliers, distributors, and customers, also forced the tourism businesses to stop operating for several months. The analysis of these indirect impacts can be useful for governments and private sectors to develop strategies and interventions in tourism sector to increase disaster preparedness and the resilience of tourism and hospitality supply chains in Pandeglang based on resilient supply chain determinants.