Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

Transactions through the web are now a progressive mechanism to access an ever-increasing range of services over more and more diverse environments. The internet provides many opportunities for companies to provide personalized online services to their customers, but the quality and novelty of some web services may adversely affect the appeal and user gratification. In the future, prediction of the consumer intention needs to be a main focus for selecting the web services for an application. The aim of this study is to predict online consumer repurchase intentions; to accomplish this objective a hybrid approach is chosen with a combination of machine learning techniques and artificial bee colony (ABC) algorithm being used. The study starts with identification of consumer characteristics for repurchase intention, followed by determining the feature selection of consumer characteristics and shopping mall attributes (with >0.1 threshold value) for the prediction model using ABC. Finally, validation using k-fold cross has been employed to measure the best classification model robustness. The classification models, viz. decision trees (C5.0), AdaBoost, random forest, support vector machine and neural network, are utilized to predict consumer purchase intention. Performance evaluation of identified models on training–testing partitions (70–30%) of the data set shows that the AdaBoost method outperforms other classification models, with sensitivity and accuracy of 0.95 and 97.58%, respectively, on testing the data set. Examining the consumer repurchase intentions by considering both shopping mall and consumer characteristics makes this study unique.