Abstract
Consumer behavior is a key factor that affects the profitability of a business. It is altered significantly in disaster times, which affects the businesses and their supply chains. The media reports during the recent COVID19 pandemic has provided adequate proof of consumer stockpiling and its consequential effects on the supply chain, also in fuelling additional stockpiling. We have developed an agent-based tool to understand consumer purchase behavioral changes in a disaster scenario. The quantitative consumer decision-making model is based on a logistic transformation model using a regression analysis of a questionnaire survey. The significant factors in the scenario have been assessed and employed in the model. The agent-based model evaluates the purchase intention probability, given the personal and situational factors. Such tools give a quantitative perspective to evaluate consumer behavior for future disaster mitigation and management to help the government and the industries.
1. Introduction
Disasters have lasting effects on all sectors, from people to economic growth. Disaster mitigation and management have risen to a priority with the increasing number of large-scale disasters in the past few decades [1]. Crises such as the Great East Japan earthquake, the Christchurch earthquake, hurricane Katrina, and more recently the COVID-19 pandemic had a severe impact on global economies and also on the lives of millions of people, with drastic changes in their lifestyles. The occurrence of such events has shown the importance of mitigation measures in order to alleviate the resulting human and economic loss. People have been tirelessly trying to reduce the impact and damage by developing mitigation measures and methods which enable a speedy post-disaster recovery.
6. Conclusions
An agent-based simulation model has been developed to understand the changes in the purchase behavior of a consumer when a disaster occurs. A quantitative decision-making model has been implemented to calculate the stockpiling probability of the consumers, given the personal and situational factors, using a logistic transformation model based on a multiple regression analysis of a questionnaire survey. The changes in consumer behavior during a disaster can be observed, using this model, by setting various situations, such as a few stores employing restrictions, a lot of shortage reports, etc. This model can be implemented in the supply chain simulation model to calculate the response and effects of consumer stockpiling on the supply chain and test various mitigation methods to reduce or avoid disruptions.
