Abstract
Understanding the dynamic impacts of multiple Covid-19 pandemic waves on operational challenges is crucial for mall and micro enterprises (SMEs) to enhance resilience and reduce vulnerability. This study employs a Threshold Vector Autoregression (TVAR) model to analyze operational challenges faced by SMEs during the COVID-19 pandemic, based on time-series data collected from 592 SMEs in Dongguan, China, between June 2021 to December 2022. The findings reveal that COVID-19 pandemic growth has nonlinear impacts on customer loss, business interruptions, and supply chain issues, while its influence on cost increases is linear. These effects vary by lag periods and risk regimes. Customer loss and supply chain challenges show greater fluctuations in high-risk regimes but moderate responses in low-risk regimes. Conversely, business interruptions exhibit milder fluctuations in high-risk regimes and pronounced shifts in low-risk ones. This study offers a scientific basis for SMEs owners and policymakers to design adaptive, stage-specific measures. Insights from these findings can strengthen SMEs’ crisis management strategies, contributing to the stability and sustainable development of the economy and society.
| Original language | English |
|---|---|
| Article number | 105737 |
| Number of pages | 21 |
| Journal | International Journal of Disaster Risk Reduction |
| Volume | 128 |
| Early online date | 30 Jul 2025 |
| DOIs | |
| Publication status | Published - 1 Oct 2025 |
Bibliographical note
Copyright © 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license(https://creativecommons.org/licenses/by/4.0/ ).
Data Access Statement
Data will be made available on request.Funding
This research work is supported by the National Natural Science Foundation of China 933 (No. 72304064), Guangdong Basic and Applied Basic Research Foundation (No. 934 2022A1515110339) and Guangdong Provincial Key Laboratory of Intelligent Disaster 935 Prevention and Emergency Technologies for Urban Lifeline Engineering (2022) (Grant 936 No. 2022B1212010016).
| Funders | Funder number |
|---|---|
| National Natural Science Foundation of China | 72304064 |
| Guangdong Basic and Applied Basic Research Foundation | 2022A1515110339 |
| Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for urban Lifeline Engineering (2022) | 2022B1212010016 |
Keywords
- COVID-19 pandemic shock
- Nonlinear impact
- Operational challenges
- Small and micro enterprises (SMEs)
- Threshold vector autoregression (TVAR) model
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