China is currently the world’s largest cross-border e-commerce purchaser and destination country. Therefore, how to promote consumer online shopping is the most important goal for cross-border e-commerce sustainability. Meanwhile, the previous research has not empirically verified the precise effect of online shopping context and perceived value on consumers’ cross-border online purchase intention. To address this gap, this study analyzes the online shopping context that determines consumers’ purchase intention and innovatively identifies four cues that promote this consumption behavior in cross-border e-commerce, such as online promotion cues, content marketing cues, personalized recommendation cues, and social review cues. It proposes a theoretical model based on cue utilization theory and stimulus-organism-response model, which introduces this four cues and brand familiarity in analyzing the effects on consumers’ purchase intention in cross-border online shopping (CBOS). In addition, the paper examines the mediating role of perceived functional value and perceived emotional value. Survey data collected 372 cross-border online consumers from China and the PLS-SEM method was used to empirically test the proposed model. The results show that these four cross-border online shopping context cues have a significantly positive impact on consumers’ purchase intention. Brand familiarity has significantly negative moderating effects between the four cues and the perceived functional value, while brand familiarity also negatively moderates the relationship between online promotion cues, social review cues, and perceived emotional value, respectively.

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With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.

The existing mobile personalized service (MPS) gives little consideration to users’ privacy. In order to address this issue and some other shortcomings, the paper proposes a MPS recommender model for item recommendation based on sentiment analysis and privacy concern. First, the paper puts forward sentiment analysis algorithm based on sentiment vocabulary ontology and then clusters the users based on sentiment tendency. Second, the paper proposes a measurement algorithm, which integrates personality traits with privacy preference intensity, and then clusters the users based on personality traits. Third, this paper achieves a hybrid collaborative filtering recommendation by combining sentiment analysis with privacy concern. Experiments show that this model can effectively solve the problem of MPS data sparseness and cold start. More importantly, a combination of subjective privacy concern and objective recommendation technology can reduce the influence of users’ privacy concerns on their acceptance of MPS.