Prediction of online perceived service quality using spider monkey optimisation

Abstract

With the evolution of technology, the attention of customers for the shopping has triggered to online platforms in a way that can never be thought of thus, giving a huge competition to the traditional methods but with this there arises a case of doubt in the perceived service quality of the products/services. For attracting the customers towards it, a website should always have some characteristics through which a customer can evaluate its quality easily. This study is one of a kind endeavour aiming to predict online perceived service quality by focusing on the characteristics of user interface, security and customer service of an e-commerce website. A swarm-based intelligent optimisation algorithm, SMO which is known for having good capacities for providing the best solution in the sufficient time has been used for the purpose of feature selection in the study. Along with SMO, many classification models like rpart, decision trees (C5.0), support vector machine and general linear model are used for prediction.