The Research Of The Online Reviews' Effects On Internet Consumer Behavior In Big Data Environment

With the development of E-commerce,shopping online has become an essential consumption patterns among people's daily life.As it is shown in CNNIC,the number of China's Internet users had reached to 713 million until December 2016,42.99 million of them are new Internet users.The number of Internet users who are shopping online is 467 million.All the things shown that shopping online had become a trend.With the change of consumption patterns,the study of online consumer behavior has become a hot topic.When the consumers are shopping online,they are used to browse online reviews.So online reviews become the best materials to research the Internet consumer behavior.Online reviews can record the consumers' perception of the goods in a real way.The characteristics of the online reviews and the characteristics of the reviewers all have major impacts to the browsers of online reviews,so the study of online reviews is very important.The development of big data technology also provides a tool for the study of online reviews.In order to affect the consumers' purchase decisions,the network vendors can take targeted measures to guide the reviewers emotional tendencies through the study of online review.The main contents of this paper are shown as follows:First of all,it provide a directional guidance for the study and explicit research purpose and research significance.Through the reading of the literatures,we sort out the research status and the related theory about the online reviews and Internet consumer behavior in order to lay a solid foundation for buliding the research model.Secondly,we constructed the theoretical model.Through the study of relevant theories,we found that the characteristics of online reviews can not only effect the Internet consumer behavior directly,but also affect consumer behavior through the perceived risk of consumer,so we constructed the model with perceived risk as an intermediary.In the model,the characteristics of online reviews are measured by the tone of online reviews,the depth of online comments and the description richness of online review.The perceived risk is measured by the quality risk,logistics risk and service risk.Again,we use LocoySpider to extract the required comments from massive online reviews.And the data were sorted by the Likert 5 scale,so the reliability of the data was analyzed by SPSS17.0,and the path analysis of the model was carried out with AMOS17.0 in order to reveal the influencing factors and degree of online comment on Internet consumer behavior.Finally,we put forward the corresponding strategies according to the research results.We can guide the reviewers from the aspects of the characteristics of online reviews and the perceived risk,so as to get more positive online comments,thereby increasing sales.