Lash Michael T, Sajeesh S, Araz Ozgur M
School of Business, University of Kansas, Lawrence, KS 66045, United States.
College of Business, University of Nebraska - Lincoln, Lincoln, NE 68588, United States.
J Bus Res. 2023 Mar;157:113413. doi: 10.1016/j.jbusres.2022.113413. Epub 2023 Jan 6.
The COVID-19 pandemic has changed consumer behavior substantially. In this study, we explore the drivers of consumer mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We capture multiple dimensions of perceived risk using local and national cases and death counts of COVID-19, along with real-time Google Trends data for personal protective equipment (PPE). While Google Trends data are popular inputs in many studies, the risk of multicollinearity escalates with the addition of more relevant terms. Therefore, multicollinearity-alleviating methods are needed to appropriately leverage information provided by Google Trends data. We develop and utilize a novel optimization scheme to induce linear models containing strictly significant covariates and minimal multicollinearity. We find that there are a variety of unique factors that drive mobility in different geographic locations, as well as several factors that are common to all locations.
新冠疫情极大地改变了消费者行为。在本研究中，我们探讨了在美国几个大都市地区，在新冠疫情可感知风险下消费者流动性的驱动因素。我们利用新冠疫情的本地和全国病例及死亡人数，以及个人防护装备（PPE）的实时谷歌趋势数据，来捕捉可感知风险的多个维度。虽然谷歌趋势数据在许多研究中是常用的输入数据，但随着添加更多相关术语，多重共线性风险会加剧。因此，需要采用减轻多重共线性的方法来恰当地利用谷歌趋势数据提供的信息。我们开发并运用了一种新颖的优化方案，以构建包含严格显著协变量且多重共线性最小的线性模型。我们发现，有多种独特因素推动着不同地理位置的流动性，也有一些因素是所有地点共有的。
