Since the outbreak of the COVID-19 pandemic in December 2019, the world has gradually appeared markedly different compared with the pre-crisis era. There is no doubt that the COVID-19 pandemic has triggered dynamic changes, but the long-lasting turbulence across the business world was not merely caused by this global crisis. In the first two months of the outbreak, regional shutdowns led to a dramatic supply shock, followed by a demand shortage due to the increasing need for pharmaceuticals and critical medical products (Sherman, 2020; Shih, 2020). In addition, panic buying behaviors of essential living supplies, together with the demand shortage, increase the difficulty of handling supply chain disruptions. These sudden changes caused by COVID-19 are extremely challenging for companies to react appropriately, especially in such a short period of time. However, at the time of writing, it has been more than ten months since the first confirmed case, and the business world is still affected by supply chain disruptions. So, risk management needs breadth and depth investigation to identify potential risk sources beyond the initial disruptions, to assess the risk impacts of subsequent vulnerabilities, to mitigate and predict unnecessary risks, and to improve resilience capabilities from a dynamic view (DuHadway et al., 2019; Ivanov et al., 2017; Ivanov & Dolgui, 2020).
This dissertation aims to identify the accelerated risk forces that occur during and after a significant disruption event (i.e., disruption event itself is not the main focus of this investigation), examine how those risks drive the advancement of firm resilience capabilities through strategic and operational practices, and then evaluate the associated performance outcomes.
This study first explored the trending topics and business issues from major business newspapers and media sites to obtain research relevant. It results in 1,660 news articles from 11 different sources (e.g., Financial Times, Wall Street Journal, The New York Times, CNN, etc.) being analyzed. And a further statistical text analysis method (Latent Semantic Analysis of text mining) is used to detect the research themes. Next, A set of semi-structured interview questions are derived from the text mining results for a qualitative case study. The insights and findings from both text mining and case studies contribute to the theoretical development phase. Furthermore, the research model generated from the theoretical formulation stages is then validated by the large-scale survey method (Hong et al., 2019; Singh & Hong, 2011).
This dissertation contributes to both the academic and practitioner communities. From the practitioner's perspective, this research synthesizes business issues, trends, response mechanisms, consumer reactions, and future directions that are related to COVID-19 through the text mining technique. By analyzing the entire event's changing pathway, this study generalizes a risk management guideline for practitioners. This approach helps practitioners to anticipate, respond to, and overcome disruptions in the long run. From an academic point of view, this dissertation (1) presents a research framework that tackles the dynamic idea of risk management and resilience, (2) incorporates the qualitative (case interview) and quantitative (text mining and large scale survey) methods to validate the dynamic risk management framework, (3) implement a systematical procedure to conduct research which consists of exploration (i.e., topic exploration)- confirmation (i.e., topic confirmation)- formulation (i.e., theoretical framework formulation) - validation (research model validation) stages.