An area of consumer behaviour that caught retailers and supply chains unprepared during the initial stages of the COVID-19 pandemic was the increased prevalence of the purchase of utilitarian goods – referred to in the media as “panic buying.” In this study, we take a novel approach to understanding such panic buying during the pandemic using compensatory control theory (CCT), text analytics, and advanced data modelling. Using a big data set over 14 days from 24,153 Twitter users in Italy, we create dictionaries to capture CCT constructs and note the dates of two government announcements. We measure constructs in the longitudinal data and test the CCT model using generalized linear mixed models for both fixed effects and random variation across individuals and time. The results support CCT, with anxiety driving a lack of perceived control, moderated by effective government announcements, and a lack of perceived control leading to purchasing, negatively moderated by utilitarian qualities. The study demonstrates the benefit of the methods for studying social phenomena and for early warning of potential demand issues via social media.

在COVID-19大流行初期，令零售商和供应链措手不及的消费者行为领域是购买功利主义商品的普遍性增加-在媒体上称为“恐慌性购买”。在这项研究中，我们采用一种新颖的方法来使用补偿控制理论（CCT），文本分析和高级数据建模来了解大流行期间的此类恐慌购买行为。我们使用来自意大利24,153个Twitter用户的为期14天的大数据集，创建了字典来捕获CCT结构并记录了两个政府公告的日期。我们测量纵向数据中的结构，并使用广义线性混合模型测试CCT模型的固定效果以及个体和时间之间的随机变化。结果支持CCT，焦虑导致缺乏控制感，由有效的政府公告来缓和，以及缺乏导致购买的控制感，而由功利主义品质消极地缓和。这项研究证明了通过社交媒体研究社会现象和对潜在需求问题进行预警的方法的好处。