Modelling, Simulation and Analysis of Deshopping Using A Multi-Agent Based Method

Deshopping has been highlighted in recent media and academic research for its role in today’s retail supply chain losses. There is much interest in studying the unintended consequences of deshopping in a modern-day retail operation.
Simulation can be useful tool for modelling different aspects of retail operations, particularly sales and returns operations with consumer and retailers. This thesis is an exploratory attempt at using Agent-based modelling and simulation (ABMS) to show how deshopper behave in a retail environment. There exists a gap in modelling of deshopping in retail landscapes. This thesis is first of its kind in an effort to bring rational choice modelling, shop choice, return leniency, opinion dynamics and churn using agent-based modelling and MoHuB framework into focus. It also combines questionnaire survey, ABM modelling and validation interview.
The thesis focuses on the use of agent-based modelling and simulation to demonstrate how to model deshopper behaviour withing a retail environment and how this behaviour is related to utility, return leniency and churn. A discussion of the limitations of such a study is also included.
The findings of the research demonstrate that ABMS is suited to simulate consumer behaviour in a retail environment.
The main contributions of the thesis are:
1. An Approach to explain deshopping using Rational choice theory
2. A model to study deshopper behaviour and retail performance.
3. How return leniency drives deshopping behaviour