Bibliometric and Text Analytics Approaches to Review COVID-19 Impacts on Supply Chains

Nishant Saravanan, Jessica Olivares-Aguila () and Alejandro Vital-Soto
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Nishant Saravanan: Department of Production Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India
Jessica Olivares-Aguila: Shannon School of Business, Cape Breton University, 1250 Grand Lake Road, Sydney, NS B1M 1A2, Canada
Alejandro Vital-Soto: Shannon School of Business, Cape Breton University, 1250 Grand Lake Road, Sydney, NS B1M 1A2, Canada

Sustainability, 2022, vol. 14, issue 23, 1-33

Abstract: The current COVID-19 pandemic has virtually disrupted supply chains worldwide. Thus, supply chain research has received significant attention. While the impacts have been immeasurable, organizations have realized the need to design strategies to overcome such unexpected events. Therefore, the supply chain research landscape has evolved to address the challenges during the pandemic. However, available literature surveys have not explored the power of text analytics. Hence, in this review, an analysis of the supply chain literature related to the impacts of COVID-19 is performed to identify the current research trends and future research avenues. To discover the frequent topics discussed in the literature, bibliometric analysis (i.e., keyword co-occurrence network) and text mining tools (i.e., N-gram analysis and topic modeling) are employed for the whole corpus and the top-three contributing journals (i.e., Sustainability, International Journal of Logistics Management, Operations Management Research). Moreover, text analytics (i.e., Term Frequency-Inverse Document Frequency: TF-IDF) is utilized to discover the distinctive topics in the corpus and per journals. A total of 574 papers published up to the first semester of 2022 were collected from the Scopus database to determine the research trends and opportunities. The keyword network identified four clusters considering the implementation of digitalization to achieve resilience and sustainability, the usage of additive manufacturing during the pandemic, the study of food supply chains, and the development of supply chain decision models to tackle the pandemic. Moreover, the segmented keyword network analysis and topic modeling were performed for the top three contributors. Although both analyses draw the research concentrations per journal, the keyword network tends to provide a more general scope, while the topic modeling gives more specific topics. Furthermore, TF-IDF scores unveiled topics rarely studied, such as the implications of the pandemic on plasma supply chains, cattle supply chains, and reshoring decisions, to mention a few. Additionally, it was observed how the different methodologies implemented allowed to complement the information provided by each method. Based on the findings, future research avenues are discussed. Therefore, this research will help supply chain practitioners and researchers to identify supply chain advancements, gaps in the literature and future research streams.

Keywords: supply chain; COVID-19; bibliometric analysis; text analytics; text mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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