网上消费者购物动机、点击流行为及其之间的关系

Online shopping has been a growing phenomenon all over the world as well as China in the recent years. Studies on online shopping with clickstream data have become a new research stream. But it is a pity that the online conversion rate is low. Accordingly, we can study on online consumers focusing on their shopping motivation, and put their shopping motivation and clickstream behavior into an integrative frame, study on the both construction and their relationship, and then we can get insight in chinese online consumers. This study has two processes. First, this study will use the questionnaire to explore all kinds of consumers’ online shopping motivation, and then emend the questionnaire and form the ultimate one for the second process. Second, we will simulate a shopping site to get clickstream data, participants need to complete the ultimate questionnaire at the same time. We will analyse the integrated data from two measures, cluster analysis separately, and explore the correspondence between the two cluster methods. Results show that, first, Chinese online shoppers contain five steady motivation factors: usefulness, fashion involvement, ease of use on searching, ease of use on alternative evaluation, ease of use after trade. Fashion involvement is comparatively independent, while the other have correlations between each two. Second, Chinese consumers can be clustered into five steady clusters according to online shopping motivation: functional shoppers, following shoppers, surfing shoppers, conflicting shoppers, e-laggard. The five clusters have significant differences on job, monthly income and online shopping experience of late six months, while have no significant differences on gender, age and education. Third, Chinese consumers can be clustered into five steady clusters according to clickstream data: functional browsers, hedonic browsers, impulsive shoppers, comparative shoppers and knowledge building browsers. The five clusters have significant differences only on age, while have no significant differences on other demographic variables. Fourth, the cluster methods according to motivation and according to clickstream data are two comparatively independent cluster frame, but they have limit correspondence.

消费者特征对其网络购物态度和行为意向的影响

In recent years, global online shopping grows rapidly, China's growth rate is far greater than the average level of the world. Online shopping as a new type of shopping patterns gradually drew the researcher's attention. There were so many existing researches on the relationship between consumer characteristic and online shopping attitude and intention, but few togethered the different abstract levels of consumer characteristic in one research. In this study, 3M Model was introduced as theory guide of whole research work, the Chinese consumers who knew about the online shopping was the research object, questionnaire survey was used to collect the data, different abstract levels of consumer characteristic were togethered in a hierarchical model, tried to establish a model to explain the relationship between different abstract levels of consumer characteristic and online shopping attitude and intention. In addition, the study also compared the models posed by data from different consumer groups. The results showed that: First, consumers’ openness, need for arousal, assessment of online shopping experience, perceived risk of online shopping would affect their online shopping attitude and online shopping intention. Second, openness, need for arousal indirectly influenced the online shopping intention through the perceived risk of online shopping. Third, the perceived risk of online shopping indirectly influenced the online shopping intention through online shopping attitude. Fourth, assessment of online shopping experience indirectly influenced the online shopping intention through the perceived risk of online shopping and online shopping attitude. Fifth, only online shopping attitude would directly affect online shopping intention. It also worked as a mediator variable in the final model. Sixth, network age, risk propensity did not significantly affect the online shopping attitude and online shopping intention. Seventh, freight fluctuation can affect student more than in-service on online shopping intention.