Online Shopping Adoption Factors in Kuwait Market based on Data Mining Rough Set Approach

Abstract

This study applied data mining process to provide an insight about factors which led to the adoption of online shopping in medium and Large-sized shopping centers (MLShCs) in Kuwait. This research is focused on proposing the high quality environmental factors which affect the success of online shopping among (MLShCs) in Kuwait and ignoring the low impact factors that may cost a lot of money. It also researches the behavior of the customers and their desire to buy directly from the physical store, the online shopping website, or both. This was achieved by distributing a questionnaire, collecting the data in dataset, cleaning the data, minimizing the dimension of the dataset vertically by applying the rough set feature selection technique, and building a classification model. The result of the previous process is the key to examine the success of online shopping in MLShCs. This study could work as the decision maker for those new investors in Kuwait who are thinking of establishing new shopping business and advise them to go for physical store, online shopping website, or both. It also advices current online shopping business to improve their infrastructure, website's design and availability, and security issues. The proposed approach performs effectively and generates interested results.

Comparative Study of Data Mining Classification Techniques for Detection and Prediction of Phishing Websites

“…Classification data mining methods were used in the literature to build a satisfied classification model for specific problems such as in Learning (Al-Shalabi, 2016), online shopping (Al-Shalabi, 2018), crime (Al-Shalabi, 2017), medicine (Al-Shalabi, 2009) and so on. In this article, nine data mining classification methods were used for the problem of fishing websites.…”