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The coronavirus pandemic is an unprecedented event, putting global supply chains (SCs) into the focus of a wider public. Yet it is unclear what is communicated about this and how and what consequences SC management (SCM) would take away. This research aims at analyzing how text mining can provide insights on the impact of the coronavirus pandemic on SCs, focusing on the implications of the pandemic for the SC constructs related to risk, resilience, and sustainability. A method applies text mining of general newspapers as well as SC and logistic newspaper articles employing the open-source software R. This paper shows that certain SC topics like risk, resilience, disruption, and sustainability vary in their news coverage on the type of newspaper and the number of coronavirus disease 2019 (COVID-19) infections. It reassures trends and observations from individual experiences on a broader and global picture and discusses the limitations and possibilities of using text mining in this field. The time period was split into three phases regarding the course of the number of infections and differences in the news coverage of the phases that can be distinguished already: (1) the onset of the crisis, (2) the peak and lockdown, and (3) managing SCs during the crisis. As this pandemic is highly dynamic, is new, and has not yet ended, research implications are too early to make. This research rather serves as a base for further, more detailed research into certain topics and identifies limitations and improvements of the applied method. Due to the method chosen and the timeliness of data, this empirical research is unique and can be of great help to see trends and patterns to further deep dive into specific areas.
Meyer et al. (Thu,) studied this question.