Asia Pacific Journal of Marketing and Logistics
School of Business and Law
Edith Cowan University
Purpose: This study applied the herd mentality theory to explore local and global social media users’ responses to panic buying across the USA, UK and Australia during the COVID-19 crisis to understand the implications on operations and supply chains. Design/methodology/approach: A total of 208,806 social media user-generated content (UGC) pieces were collected from Twitter in three countries – the USA, UK and Australia. The analysis of this big qualitative data was performed using machine learning–based software – Leximancer. Findings: Positive and negative sentiment towards panic buying during the COVID-19 crisis was observed in the UGC. No significant differences in social media UGC sentiment between the three countries were found; however, differences did exist in key themes. This suggests that the focus, not the sentiment, of consumers’ responses to panic buying differed across countries. Social media users follow their location-based and topic-consonant social “herd”, rather than the global “herd”. Research limitations/implications: This study was the first to show that social media users’ herd mentality differs in a crisis. The herd mentality of social networks is dependent on factors such as the geographic location of the social network (herd), which can differ from the global herd’s reaction, specifically in terms of topics evident in UGC. Practical implications: Operations and supply chain managers need to include social media UGC analysis in their strategies in crisis management responses. The topics, not the sentiment, of consumers’ responses to panic buying require managerial actions. Originality/value: This is the first study to show that herd mentality during a crisis, such as COVID-19, is not unidimensional and varies according to the location of the social media network with profound implications for operations and supply chain managers.
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Securing Digital Futures
Digital citizenship and human behaviour