Content area
Purpose
This research aims to uncover coronavirus disease 2019’s (COVID-19's) impact on shipping and logistics using Internet articles as the source.
Design/methodology/approach
This research applies web mining to collect information on COVID-19's impact on shipping and logistics from Internet articles. The information extracted is then analyzed through machine learning algorithms for useful insights.
Findings
The research results indicate that the recovery of the global supply chain in China could potentially drive the global supply chain to return to normalcy. In addition, researchers and policymakers should prioritize two aspects: (1) Ease of cross-border trade and logistics. Digitization of the supply chain and applying breakthrough technologies like blockchain and IoT are needed more than ever before. (2) Supply chain resilience. The high dependency of the global supply chain on China sounds like an alarm of supply chain resilience. It calls for a framework to increase global supply chain resilience that enables quick recovery from disruptions in the long term.
Originality/value
Differing from other studies taking the natural language processing (NLP) approach, this research uses Internet articles as the data source. The findings reveal significant components of COVID-19's impact on shipping and logistics, highlighting crucial agendas for scholars to research.
Details
COVID-19;
Shipping;
Economic crisis;
Artificial intelligence;
Data mining;
Business indicators;
Pandemics;
Social networks;
Epidemics;
Data processing;
Supply chains;
Natural language processing;
Algorithms;
Logistics;
Recovery;
Coronaviruses;
Trade restrictions;
Shipping industry;
Research;
Language;
Computers;
Internet resources;
Machine learning
; Matsuda, Takuma 2
1 Center for Mathematical and Data Sciences, Kobe University, Kobe, Japan
2 Takushoku University, Tokyo, Japan