Explore The Influencing Factors Of Reviews Usefulness From Information Processing Perspective

Online review site becomes a powerful decision assisting tool,which provides consumers with rich product information and personal shopping experience.Based on the research results,we find that the comments information's effectiveness doesn't improve with the volume growth.It becomes harder for consumers to identify useful information when information volume getting larger.In the internet environment where emphasizing user experience is emphasized,identifying consumers' preference and providing consumers with higher perceived usefulness comments are becoming key successful factors become mature in the mature online review systems.In this paper,we conduct the research on the key influencing factors of review usefulness and choose Yelp.com as the research subject.Based on the theory of Information Processing Theory and Multipath Anchor Adjustment Model,we propose that three characteristics of online reviews that affect the assessment of review helpfulness: attractiveness,representational sufficiency and functional sufficiency.In view of Information Overload theory,we try to find the effect of overloaded information on the customers' behavior,and explore the moderating effect of information volume on the relationships amongst each characteristics and usefulness.This article adopts the method of econometrics to build zero poisson model.We choose a dataset containing 4 million comments on the Yelp.com and randomly selected 41,369 reviews as samples.Text analysis method is used to extract data features and stata data analysis tool is applied to verify the models.Results indicate that attractiveness,representational sufficiency and functional sufficiency decide the perceived usefulness of the review simultaneously,and the effects of these features are moderated by information volume.