With the rapid development of the Internet and the widespread adoption of mobile devices, live streaming has become a new social medium in the last few years. Hence, the continuous usage of live streaming platforms has become an important issue. Based on social exchange theory, this study investigates how different kinds of interactions affect viewers’ sense of virtual community (SOVC) and in turn facilitate their continuous watching intention. Empirical results (N = 423) reveal that both viewer-broadcaster interaction and viewer-viewer interaction positively affect viewers’ SOVC, which further enhances their stickiness. Furthermore, viewer-platform interaction positively moderates the effects of interactions between viewers and broadcasters on their SOVC, whereas the moderating role of gift-giving behaviour is insignificant. Finally, the effects of viewer-broadcaster interaction and viewer-viewer interaction on stickiness are partially mediated by SOVC. This study contributes to in-depth research on the relationships between different specific interactions and viewers’ continuous watching intention, which enriches the existing study on live streaming and deepens our understanding of viewers’ stickiness. The empirical findings of this study suggest that social media developers and managers take actions to encourage interactions that increase viewers’ stickiness.

Educational informatisation (e.g. e-learning, m-learning, massive open online courses (MOOCs)) has actively increased, leading to a greater focus on the design and development of course management systems. In this study, a research model based on cognitive fit theory and scanpath theory is proposed to investigate how information presentation formats (flow diagram navigation versus menu navigation) of a course management system influence user experience and intention. Performance load (cognitive load and kinematic load) and user perception (perceived usefulness and ease of use) are considered to evaluate user experience. The results of an eye tracking experiment utilised in this research reveal the following. First, information presentation formats can significantly influence user experience of course management systems. Second, flow diagram navigation fits students’ tasks better and leads to lower performance load and better use perception. Third, performance load and user perception shows a significant effect on user satisfaction and thereby affect the intention. These findings deepen our understanding of the importance of information presentation and enrich its theoretical foundation for course management systems development. Practically, these findings provide designers with guidelines on how to improve user experience and increase use intention by varying information presentation formats.

User-generated reviews on the e-commerce site reflect consumers' sentiment about products, which can further direct consumers' purchasing behaviors and sellers' marketing strategies. In this paper, we propose a semi-supervised approach to mine the aspects of product discussed in Chinese online reviews and also the sentiments expressed in different aspects. We first apply the Latent Dirichlet Allocation model to discover multiaspect global topics of the product reviews, then extract the opinion short sentences based on sliding windows and pattern matching from context over the review text. The polarity of the associated sentiment is classified by the domain lexicon-based method. Finally the results are collected as features for the feedback for the machine learning method and applied in semantic orientation classification. The experiment results show that the novel method we proposed could help to discover multi-aspect fine-grained topics and associated sentiment, which helps to improve semantic orientation classification simultaneously.

The partner selection in virtual enterprises organization is one of the key issues corporate enterprises experience nowadays. Based on the model of Ant Colony Optimization Algorithm (ACA) in virtual enterprise partner selection, in this paper, we fuse the genetic algorithm into ACA, called fusion algorithm, in order to improve the effect of the partner selection. The fusion algorithm has two steps: 1) it uses the GA to optimize the model of partner selection and takes advantages of rapid convergence of GA in initial search periods. 2) When GA search speed has become slow, the ACA takes over the search process, in which it uses the candidates produced by the GA as the seeds of pheromone used by ACA. By experimental comparison with GA optimization and ACA optimization, it shows that the fusion algorithm has performed better than the GA and ACA optimization, respectively, in both speed and accuracy under our selected numerical case. The fusion algorithm presented in this study may be applicable to similar business problems.