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

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. 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 (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

E-commerce ethics and its impact on buyer repurchase intentions and loyalty: an empirical study of small and medium Egyptian businesses

The theoretical understanding of e-commerce has received much attention over the years; however, relatively little focus has been directed towards e-commerce ethics, especially the SMEs B2B e-commerce aspect. Therefore, the purpose of this paper is to develop and empirically test a framework that explains the impact of SMEs B2B e-commerce ethics on buyer repurchase intentions and loyalty. Using SEM to analyse the data collected from a sample of SME e-commerce firms in Egypt, the results indicate that buyers’ perceptions of supplier ethics construct is composed of six dimensions (security, non-deception, fulfilment/reliability, service recovery, shared value, and communication) and strongly predictive of online buyer repurchase intentions and loyalty. Furthermore, our results also show that reliability/fulfilment and non-deception are the most effective relationship-building dimensions. In addition, relationship quality has a positive effect on buyer repurchase intentions and loyalty. The results offer important implications for B2B e-commerce and are likely to stimulate further research in the area of relationship marketing

From retail innovation and image to loyalty: moderating effects of product type

This study aims to analyse value-satisfaction-loyalty relationships in retailing by examining the contribution of image and innovation and understanding value as a multidimensional construct. Furthermore, to identify possible differences in these relationships the moderating effect of the type of product marketed in the store is examined. On a sample of 820 customers from four types of stores, SEM methodology and multigroup analysis were applied. The results confirm that image has more influence than innovation on the dimensions of value and that entertainment and excellence are the main antecedents of satisfaction. Some relationships have also been found in which the type of product marketed in the store has a moderating effect