Exploring the Impact of the COVID-19 Pandemic on Automobile Sales: A Confirmatory Composite Analysis Using Google Trends Data to Develop a Structural Equation Model

Abstract

Confirmatory factor analysis (CFA) analyzes latent variables in structural equation models representing unmeasurable attitudes or behaviors. In contrast, confirmatory composite analysis (CCA) uses emergent variables such as capacities, values, and indices. When significant secondary and Google trend data are used, CCA may be preferred over CFA. This study employs CCA and partial least squares to develop a structural equation model. It examines COVID-19’s impact on vehicle sales in Thailand. The study uses data from March 2020 to September 2021, including COVID-19 infection rates, newly registered cars, and Google Trends data on COVID-19 and vehicle sales. The results indicate that the overall model fit, and measurement model parameters are outstanding. This suggests a significant negative influence of the COVID-19 pandemic on car sales in Thailand. The study concluded that CCA and partial least squares were used to analyze the impact of COVID -19 on vehicle sales in Thailand. Analysis of secondary data and Google trend data revealed a significant negative impact of the pandemic on car sales, indicating a negative impact on the automotive industry in Thailand during the indicated period.