ABSTRACT
Purpose Logistics capability is an important enabler of supply chain resilience (SCR). However, few studies have analyzed the underlying influence mechanism of logistics capability on SCR in extreme conditions, such as those of the COVID-19 pandemic. The purpose of this study is to increase understanding of the role of logistics capabilities in constituting a resilient supply chain. Design/methodology/approach Drawing upon the dynamic capability perspective and contingency theory, the proposed conceptual framework aims to demonstrate the relationship between a firm's logistics capabilities and SCR. Furthermore, the conceptual framework is illustrated by empirical evidence from a case study of a Chinese manufacturing company, which focuses on extracting practical lessons from the COVID-19 pandemic. Findings The findings suggest that digitalization, innovativeness, and modularization comprise potential mediating pathways for firm logistics capability to affect SCR and government policies, risk management culture, trust and cooperation moderate the effect positively. The potential associations are identified and elucidated by detecting the corresponding strategies and practices of a Chinese manufacturer that performed well amid the COVID-19 pandemic. Practical implications This study provides specific guidelines for logistics managers to enhance SCR during the COVID-19 pandemic. Seeing SCR as a dynamic capability, the framework is also instructive for manufacturers, supply chain members, and policymakers to achieve the sustained competitive advantage of supply chains. Originality/value The findings expand the understanding of enhancing SCR in a logistics approach. The empirical validation of propositions in the case study reveals a new vista for research on SCR.
ABSTRACT
Objective: To understand the epidemiological characteristics of imported COVID-19 cases in Guangzhou and provide scientific basis for the prevention and control of the disease. Methods: The data of imported COVID-19 in Guangzhou reported as of April 1, 2020 were collected from National Notifiable Disease Report System of China. The software Excel 2010 and SPSS 19.0 were applied for data cleaning and statistical analysis. Results: As of April 1, 2020, a total of 103 imported COVID-19 cases had been reported in Guangzhou, in which 92 were confirmed cases and 11 were asymptomatic infection cases. The number of the confirmed imported cases accounted for 11.4% (92/806) in of the total in China at the same time. The male to female ratio of the cases was 1.58â¶1 (63â¶40). The median age of the cases was 31 years (P(25)-P(75):22-40 years), range of age was 11-63 years. The main occupational distributions of the cases were business services (41/103, 39.8%) and students (36/103, 35.0%). The imported cases whose destinations were 19 provinces and municipalities rather than Guangdong after entering the country accounted for 43.7%. The main source countries of infections were the United Kingdom (27/103, 26.2%), the Philippines (13/103, 12.6%), the United States (13/103, 12.6%) and Nigeria (7/103, 6.8%). There were 34 inbound flights from which the imported COVID-19 cases were detected, in which 10 flights (10/34, 29.4%) were found to carry more than 3 cases, with an average voyage time of (11.14±0.53) hours. A total of 29 imported cases(28.2%) showed symptoms before entering the country, and 65 cases (63.1%) had been isolated before the onset of the disease. The mean free activity time of the isolated cases after the onset was (6.76±0.79) days. The average number of the imported cases' close contacts was 53. There were 13 clusters of COVID-19 caused by the imported cases, involving 36 cases (including 1 imported associated case). Conclusions: The sources of the imported COVID-19 cases in Guangzhou were widely distributed, and no cases had been found to be infected on the flights. In the early stage of the imported epidemic, there was high risk for the spread of the epidemic. Strengthened prevention and control of imported COVID-19 effectively reduced the of transmission risk of COVID-19 in communities.