This study aims to investigate the influence of e-quality and online trust on customer engagement and e-word of mouth. In particular, this study explored and analyzed a relatively new relationship, the impact of customer engagement on e-word of mouth. The measurement model and conceptual model describing the relationships hypothesized in the study was evaluated, based on responses from 370 online purchasing customers who are students or office workers in Ho Chi Minh City. E-quality has a direct impact on online trust, which impacts online customer engagement of customers and e-word of mouth. Online trust has a direct effect on customer engagement and e-word-of-mouth. In particular, online engagement impacts on e-word of mouth. This study provides not only theoretical and practical meaning, and enables companies to realize the importance of customer engagement and e-word of mouth but also a number of solutions to help businesses build and increase their customer engagement and positive e-word of mouth.

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.