FEAR APPEAL ANALYSIS OF CONSUMER BEHAVIOR DURING THE COVID-19 PANDEMIC USING MULTIPLE LINEAR REGRESSION AND AGENT-BASED MODELLING

• Main Author: Syania, Jihan
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/67147
• Institution: Institut Teknologi Bandung

Physics is a natural science that studies matter, its motion and behaviour through space and time. Currently, the scope of the study of physics has expanded to include systems related to objects that can organize themselves (self-organization), called complex systems. At the beginning of the COVID-19 pandemic, almost all governments in the world implemented new policies such as lockdowns and social distancing that caused public fear or panic. This study aims to analyze whether fear or panic affects people's shopping decisions during the COVID-19 pandemic and measure how much influence it has. In addition, an impulsive buying simulation will also be made based on data from a supermarket in the city of Bukittinggi. This study will use one of the machine learning algorithms, namely multiple linear regression, to see the effect of fear on decisions. A simulation of impulsive buying will be made using ABM, which the El Farol Model Library inspires on Netlogo. From this study, it was found that fear significantly affects humans in making shopping decisions. Generation Y and Generation Z each feel the effects of fear differently. The simulation of impulsive buying using ABM shows that the level of panic will affect the agent's behaviour. This impulsive buying behaviour affects the satisfaction level of agents who shop at supermarkets.