Mingyang Zhang

An increasing number of college students have taken out campus loans. This trend has had a negative impact on their learning and development. Using survey data about Chinese college students, this study explores the influence of a conspicuous identity and conformity on campus loan behavior and usage intention. Identify economics posits that identity is a key factor affecting individual behavior and decision-making. Differentiated identify is linked to differentiated social groups and constituted through specific value orientations, social norms and codes of conduct. This study shows that college students with a conspicuous identity are more likely to take out campus loans and more willing to take out such loans in the following year. The discussion on heterogeneity shows that a conspicuous identity has significantly positive effects on campus loan behavior for students in groups with higher consumption and grade, while conformity has significantly positive effects on campus loan behavior when students are in lower-grade groups. Compared with students who have never used campus loans, students who have taken out are more willing to take them out again in the future. Finally, avoidance strategies of campus loan debt are provided from two perspectives, namely, identity construction and the classification of education.

With the development of the digital economy, the emerging marketing strategy of the e-commerce flash sales has been changing the traditional purchasing habits of customers. This imposes new decision-making challenges for companies involved in flash sales. It is important for companies to build the accurate product demand forecast analysis focusing on the characteristics of the flash sales and customer behaviors. In this paper, VIPS is taken as a case study with the key focus on how sentiment factors in customer reviews affect product demand in flash sale platforms. The paper adopts two sentiment analysis methods based on emotional dictionaries. The method with a higher evaluation index is adopted to integrate the emotional factors into the autoregressive model for product demand and assessment. The experiments prove that the autoregressive model for integrating the sentiment factors demonstrates better forecasting performances than the models without sentiment factors. The experiments further confirm that when product demand for the previous two weeks and customer review sentiment factors in the previous week are taken into consideration, demand forecast effects are most accurate.