No abstract

No abstract

With the rapid development of the Internet, the online shopping market expands constantly. Inspired by¯erce competition and complex and diverse consumer demand, personalized recommendation has become an e®ective marketing tool for e-commerce enterprises. However, the existing recommendation methods based on online consumer behavior or preferences are characterized by poor accuracy and low e±ciency. The paper mainly conducts three studies, the study1 proves that seven online lifestyles, which are \Comfortable, Entertainment, Luxury, Tradition & Conservation, Rational, Fashion Sense, and Social Activities", a®ect Chinese consumers' purchase. However, the di®erent online lifestyles have di®erent e®ects on purchase, thus the response rates of recommending. The study2 proposes a new personalized recommendation method \online lifestyle tagging (OLT)" based on online lifestyle and user behavior tags to identify online lifestyles. In the study3, the e±ciency of OLT is tested and veri¯ed using data collected from enterprises, it suggests that OLT has a signi¯cantly higher response rate than traditional behavior-based methods. This study demonstrates that OLT improves the accuracy and e±ciency of personalized recommendation, and thus contributes to the theory of personalized recommendation and marketing methods based on lifestyle.

Abstract:With the advent of mass tourism, tourism-related environmental problems are often reported in the news media. Tourists' environmentally responsible behaviors (TERB) are critical for solving tourism environmental problems. This study argues that college students are a critical source of collective impact for tourism sustainability, and examines chained relationships that might determine college student TERB. Five hundred and twenty-five (525) college tourists were surveyed. Structural equation modeling was used to determine the relationships among the variables and the mediating effects. Results confirmed our proposed relationships of chained influences from tourism destination image (as key information) to tourist expectation (as cognition), to perceived quality and value (as experiences), to tourist satisfaction, loyalty, and complaints (as emotional reflection), and finally to TERB. Such results shed light on TERB education and construction, as well as on the collective impact for sustainable tourism.