Magesh Kumar R, Pradeep E and Sunil Vakayil
Purpose – The current study aims to identify the foremost inevitable factors that end up in on-line shopping behavior towards customer repurchase. Explicitly a model based on the Multilayer Perceptron Neural Network was trained to predict customer repurchase on an online shopping environment. Design/methodology/approach- The conceptual framework of the study empirically examined the influence of e-service quality dimensions (web design, reliability, responsiveness, assurance, and ease of use), customer satisfaction, trust; and loyalty constructs which affects the customers repurchase of Gen Z customers who have prior experience in purchasing their products in online. Findings- The result of the study was analyzed using Artificial Neural Networks. A multilayer neural perceptron network was equipped with a back-propagation algorithm to determine whether customers will buy again in online. The proposed model using a neural network technique has strongly predicted with 84.6 percent of e-shoppers as either repeat purchase or will not repeat purchase. Practical implications- Gen Z as digital natives are unique as without digital technologies such as smartphones and social media, they have never experienced life. This shift changed the world's fastest-growing consumer markets. By recognizing the consumer behavior of this community through the present study, the marketers will have the ability to bring out more successful marketing campaigns. Originality/value- A neural network model is a significant predictive analytics tool for analyzing the non-compensatory decision making of online customers. The study is unique to predict what draws the web visitors to repurchase in e-shopping. Also, from the independent variable of importance by using a neural network, it is apparent that e-satisfaction and responsiveness of the e-commerce companies are the strong predictors of customer repurchase. The study will highly benefit the online marketers to reach out to Gen Z consumers who are more dependent on smart gadgets and the internet