Investigation on interest of internet shopping via Bayesian network

Abstract

Nowadays, there is a very popular kind of electronic commerce service which allows consumers to buy goods or services from a seller over the internet which is called online shopping. Online shopping is getting more and more popular throughout the world as there are a growing number of people who prefer to shop online over traditional shopping. It has been recorded that over 30 billion USD worth of orders has been garnered in a one-day event called Double 11 in the year 2018. The reason behind the growing popularity of online shopping is still unknown. Therefore, this research aims to investigate the factors affecting people to do online shopping. Online questionnaires are used for data collection in this study. The model is constructed by utilizing Bayesian network. Bayesian network is a graphical model that represents knowledge about an uncertain domain. It represents the causal probabilistic relationship among a set of random variables and it provides a compact representation of a joint probability distribution. The main findings of this study revealed that the frequency of online shopping, web design, preferred lifestyle at work and memberships are the key factors that influence people to do online shopping over conventional shopping. This research has its significance as retailers are able to have a better understanding on the association between the dominating factors and consumer’s purchase intention, allowing them to develop and execute better strategies that may improve customer engagement.